SOIL SPATIAL VARIABILITY ANALYSIS, FERTILITY MAPPING
AND SOIL PLANT NUTRIENT RELATIONS IN WOLAITA ZONE,
SOUTHERN ETHIOPIA
PhD DISSERTATION
FANUEL LAEKEMARIAM AEMERO
NOVEMBER 2015
HARAMAYA UNIVERSITY, HARAMAYA
SOIL SPATIAL VARIABILITY ANALYSIS, FERTILITY MAPPING
AND SOIL PLANT NUTRIENT RELATIONS IN WOLAITA ZONE,
SOUTHER ETHIOPIA
A PhD Dissertation submitted to the Postgraduate Program Directorate
(School of Natural Resources Management and Environmental Sciences)
HARAMAYA UNIVERSITY
In Partial Fulfillment of the Requirements for the Degree of
DOCTOR OF PHILOSOPHY IN SOIL SCIENCE
By
Fanuel Laekemariam Aemero
November 2015
Haramaya University
HARAMAYA UNIVERSITY
Postgraduate Program Directorate
We hereby certify that we have read and evaluated the dissertation titled “Soil Spatial
Variability Analysis, Fertility Mapping and Soil Plant Nutrient Relations in
Wolaita Zone, Southern Ethiopia ” prepared under our guidance by Fanuel Laekemariam
Aemero. Werecommend that it be submitted as fulfilling the PhD dissertation requirement.
1. Kibebew Kibret (PhD)
Chairman of Advisory Committee
Signature
Date
2. Prof. Tekalign Mamo (PhD)
Member of Advisory Committee
Signature
Date
3. Prof. Heluf Gebrekidan (PhD)
Member of Advisory Committee
Signature
Date
As a member of the Board of Examiners of the PhD Dissertation Open Defense Examination,
we certify that we have read and evaluated the Dissertation prepared by Fanuel Laekemariam
Aemero and examined the candidate. We recommend that the Dissertation be accepted as
fulfilling the Dissertation requirements for the degree of Doctor of Philosophy (PhD) in Soil
Science.
1. _______________________
Chairperson
2. ________________________
Internal Examiner
3. ________________________
External Examiner
______________
Signature
_______________
Signature
______________
Signature
ii
____________
Date
_____________
Date
______________
Date
DEDICATION
This Dissertation is dedicated to"Aleka (Merigeta)" Aemero Haddis; "Ababye" Ato
Laekemarim Aemero and "Etye" W/ro Melkam Baynesagn (whom I lost while doing my PhD
study) who brought me up with love and affection, inspired and made me reach this stage.
iii
STATEMENT OF THE AUTHOR
By my signature below, I declare and affirm that this dissertation is my own work. I have
followed all ethical and technical principles of scholarship in the preparation, data collection,
data analysis and compilation of this Dissertation. Any scholarly matter that is included in the
Dissertation has been given recognition through citation.
This Dissertation is submitted in partial fulfillment of the requirements for a PhD degree at
Haramaya University. The Dissertation is deposited in the Haramaya University Library and is
made available to borrowers under the rules of the Library. I solemnly declare that this
Dissertation has not been submitted to any other institutions anywhere for the award of any
academic degree, diploma or certificate.
Brief quotations from this dissertation may be made without special permission provided that
accurate and complete acknowledgement of the source is made. Requests for permission for
extended quotations from or reproduction of this dissertation in whole or in part may be
granted by the head of the school or department when in his or her judgment the proposed use
of the the material is in the interest of scholarship. In all other instances, however, permission
must be obtained from the author of the Dissertation.
Name: Fanuel Laekemariam Aemero
Signature: ________________
Place: Haramaya University, Haramaya
Date of Submission: __________________
iv
BIOGRAPHICAL SKETCH
The author was born on 16 August 1978. He attended his primary school at Motta Elementary
School; junior school at Nigus Tekele Haimanot Junior Secondary School and high school at
Debre Markos Senior Secondary School, Debre Markos, East Gojjam of the Amhara National
Regional State. The time from elementary to high school was 1984 to 1996. In 1996, he joined
Mekelle University and graduated in 2000 with Bachelor of Science Degree in Dryland Crop
Science. After his graduation, he was employed by Ministry of Agriculture (MoA) in GendeWoin woreda, East Gojjam Zone. He served there for two years (August 2000-August 2002)
as Irrigation Agronomist as well as Horticultural Crop Production Expert. Then, he was
employed by MoA, in Mertule Mariam Agricultural Technical Vocational Education Training
(ATVET) College, East Gojjam Zone, as an instructor in the Department of Plant Sciences. He
severed for two and half years (August 2002-February 2005) and also college practical
coordinator. In February2005, he joined the graduate school of Hawassa University to pursue
his MSc studiesin agronomy and graduated in 2007G.C. He was employed in Self Help
Development International, Bora project office, Alem Tena, Oromia region as agricultural
expert and served between May 2006 to December 2007. In January 2008, he joined Wolaita
Sodo University, Ethiopia where he served as head of the Department of Plant Sciences until
he joined the School of Graduate Studies at Haramaya University in 2011 to pursue his PhD in
Soil Science.
v
ACKNOWLEDGEMENTS
First and foremost, I thank God and his mother for the endless support. I would like to thank
my advisors Dr. Kibebew Kibret, H.E. Prof Tekalign Mamo (State Minister & Advisor to the
Minister of Agriculture) and the late Prof. Heluf Gebrekidan who have made immense
contribution indirecting and commenting on my work fromits commencement till today. My
deepest gratitude also goes to H.E. Prof Tekalign Mamo for accepting and making me part of
the historical soil fertility mapping initative of the country and co-funding my field survey
work. I acknowledge Dr. Erik Karltun for his contribution. I would also like to extend my
great appreciation to Hailu Shiferaw (EthioSIS project) for giving me GIS training and his
invaluable contribution to my study. I would also like to thank those who have been with me
in the major and challenging part of the the study, which is field data collection. This includes
Ermiyas Eilka, Simon Yohannes and the late Daninel Milkyas, who all deserve appreciation.
You arenot easily forgettable. I am very grateful for all the assistance, knowledge and
experiences I have received from the farmers' in Damot Gale, Damot Sore and Sodo Zuria
woredas or districts.
I would like to thank Ministry of Education (MoE) for the scholarship, and the Ethiopian Soil
Information System (EthioSIS) at the Agricultural Transformation Agency (ATA) for
financial support. In addition, the contributions of Haramaya University, Wolaita Sodo
University (Plant Science Department, Finance Section and logistics), Wolaita Zone soil
laboratory, Wolaita Zone agriculture and input offices, Wolaita Zone finance (statistics
section) are immense and I extend my gratitude to them.
My special thanks go toDr. Habtamu Admas, Dr. Biruk, Kehali Jembere and Dereje Tesgaye
for their excellent contribution to my papers. I also appreciate Dr. Dawit Alemu, Dr. Hiranmi
Yadav, Hailu Gebru, Yalew Bizu for their inputs. My friend Gifole Gidago you deserve many
thanks and a special place in this study. Tegbaru Belete, Ephrem, Ato Yimer Ali, Ato Bekele,
W/ro Alemtsehay, W/ro Mulu, W/rt Sinknesh, Tigist, Rahel, Dr.Zebene and other laboratory
staffs of National Soil Testing Centerare acknowledged for their support during sample
preparation, wet chemistry and spectral analysis. My gratitude also goes to Lakew Getaneh
and Abera Habtee for their unreserved encouragement.
I am deeply grateful to my father Laekemariam Aemero and the late mother (Melkam
Baynesagn)who brought me up, inspired and made me reach this stage. Inaddition, Anteneh
Aemero, Askale Aemero, Family of Yemistrach Laekemariam, (Selome, Yohannes and
Tihitina Laekemariam), Addisu, Melesech and Gebeyanesh Amsalu, deserve my deepest
gratitude and special respectfor theirencouragementthroughout my study.
Last but not least, thanks go to my beloved wife Sophia Liyew Teferi and my beautiful
daughters (Maedot Fanuel and Meklit Fanuel)withouttheir love, fun, supportand
encouragement this study would not have reached this final stage. Meselech Ayele(my family
member), your contributionsin managing kids have made me concentrate on my study. I thank
you with a deep respect.
vi
ACRONYMS AND ABBREVIATIONS
AS
ATA
BD
CEC
CSA
CV
DAP
EA
EthioSIS
FAO
FYM
G
GDP
GIS
GPS
ha
ICP
LUT
m.a.s.l
MIR
MnAI
MoA
MoE
MSE
NSTC
OC
OK
OM
PBS
PCA
PSD
RMSE
RMSE
RMSSE
RMSSE
SNNPRS
SPSS
TN
UTM
WADU
WZFEDD
Acid Saturation
Agricultural Transformation Agency
Bulk Density
Cation Exchange Capacity
Central Statistical Agency
Coefficient of Variation
Di-amonium Phosphate
ExchangeableAcidity
Ethiopian Soil Information System
Food and Agricultural Organization
Farm Yard Manure
Goodness of prediction
Gross Domestic Products
Geographical Information System
Geographical Positioning System
hectare
Inductively Coupled Plasma Spectrometer
Land Use Type
meter above sea level
Mid Infrared diffused Reflectance
Manganese Activity Index
Ministry of Agriculture
Ministry of Education
Mean Standard Error
National Soil Testing Center
Organic Carbon
Ordinary Kriging
Organic Matter
Percentage Base Saturation
Principal Component Analysis
Particle Size Distribution
Root Mean Square Error
Root Mean Square Error
Root Mean Square Standardized Error
Root Mean Square Standardized Error
Southern Nations’, Nationalities and Peoples’ Regional State
Statistical Package for Social Sciences
Total Nitrogen
Universal Transverse Mercator
Wolaita Agricultural Development Unit
Wolaita Zone Finance and Economic Development Department
vii
TABLE OF CONTENTS
DEDICATION
STATEMENT OF THE AUTHOR
BIOGRAPHICAL SKETCH
ACKNOWLEDGMENTS
ACRONYMS AND ABBREVIATIONS
LIST OF TABLES
LIST OF FIGURES
LIST OF TABLES IN THE APPENDIX
LIST OF MANUSCRIPTS
1 GENERAL INTRODUCTION
1.1 DESCRIPTION OF THE STUDY AREA
1.2 REFERENCES
2 LANDSCAPE CHARACTERISTICS OF AGRICULTURAL LANDS AND
FARMERS SOIL FERTILITY MANAGEMENT PRACTICES IN WOLAITA
ZONE, SOUTHERN ETHIOPIA
2.1 INTRODUCTION
2.2 MATERIALS AND METHODS
2.2.1 Description of the Study Area
2.2.2 Data Collection
2.2.3 Soil Sampling and Analysis
2.2.4 Data Analysis
2.3 RESULTS AND DISCUSSIONS
2.3.1 Topography of Agricultural Lands
2.3.2 Ethnopedology / Local Soil Classification /
2.3.3 Land Use and Soil Fertility Management Practices
2.4 CONCLUSIONS
2.5 ACKNOWLEDGEMENTS
2.6 REFERENCES
3 SOIL FERTILITY STATUS OF AGRICULTURAL LANDS IN WOLAITA
ZONE, SOUTHERN ETHIOPIA
3.1 INTRODUCTION
3.2 MATERIALS AND METHODS
3.2.1 Description of the Study Area
3.2.2 Soil Sampling Procedure and Laboratory Analysis
3.2.3 Statistical Analyses
3.3 RESULTS AND DISCUSSION
3.3.1 Variability of Soil Properties
3.3.2 Soil Physical and Chemical Properties
3.4 CONCLUSION
3.5 ACKNOWLEDGEMENTS
3.6 REFERENCES
viii
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Cont...
4 SOIL FERTILITY SPATIAL VARIABILITY ANALYSIS AND MAPPING IN
WOLAITA ZONE, SOUTHERN ETHIOPIA
4.1 INTRODUCTION
4.2 MATERIALS AND METHODS
4.2.1
Description of the Study Area
4.2.2
Soil Sampling Procedure and Laboratory Analysis
4.2.3
Geostatistical Analysis and Soil Fertility Mapping
4.3 RESULTS AND DISCUSSION
4.3.1
Spatial Variability of Soil Properties
4.3.2
Soil Fertility Status Map
4.3.3
Fertilizer Type Recommendation
4.4 CONCLUSION
4.5 ACKNOWLEDGEMENTS
4.6 REFERENCES
5 SOIL-PLANT NUTRIENT STATUS AND THEIR RELATIONS IN MAIZE
GROWING FIELDS OF WOLAITA ZONE, SOUTHERN ETHIOPIA
5.1 INTRODUCTION
5.2 MATERIALS AND METHODS
5.2.1 Description of the Study Area
5.2.2 Soil and Plant Sampling and Analysis
5.2.3 Statistical Analysis
5.3 RESULTS AND DISCUSSION
5.3.1 Maize Field Management and Soil Characteristics
5.3.2 Leaf Nutrient Content and Relationship with Soil Nutrient
5.4 CONCLUSION
5.5 ACKNOWLEDGEMENTS
5.6 REFERENCES
6 GENERAL SUMMARY AND CONCLUSIONS
7 APPENDIX
ix
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LIST OF TABLES
Table
2.1
2.2
2.3
2.4
2.5
2.6
2.7
2.8
3.1
3.2
3.3
3.4
3.5
3.6
4.1
5.1
5.2
5.3
5.4
5.5
Page
Soil properties, fertilizer application and crop yield as influenced by topographic
position in studied districts of Wolaita Zone, Southern Ethiopia
Farmers' soil types and perceived soil characteristicsin studied districts of Wolaita
Zone, Southern Ethiopia
Soil properties, fertilizer rates and crop yield as affected by farmers' soil type classes
in studied districts of Wolaita Zone, Southern Ethiopia
Farmers' soil management practices at different topographical categories and soil
types on sampled agricultural fields of Wolaita Zone, Southern Ethiopia
Average application rate of fertilizers on total cultivated and fertilized cultivated
fields of studied districts of Wolaita Zone, Southern Ethiopia during 2013
Fertilizer application rates (kgha-1) of studied districts of Wolaita Zone, Southern
Ethiopia during 2009 - 2013
Fertilizer type and application rates on crops grown in the studied districts of Wolaita
Zone, Southern Ethiopia during 2013
Farmers' estimated mean yield on sampled cultivated fields in the absence and
presence of fertilizer in the studied districts of Wolaita Zone, Southern Ethiopia
during 2013
Eigen value and explained variances of PCA for the study districts in Wolaita Zone
during 2013
Particle size distribution,silt: clay ratio and bulk density of surface soil samples
collected from study districts in Ethiopia during 2013
Descriptive statistics of soil pH, exchangeable acidity and acid saturation of soil
samples collected from study districts in Ethiopia during 2013
Descriptive statistics of soil OC, TN, Av. P and SO42--S in the study districts in
Ethiopia
Descriptive statistics of soil exchangeable bases, CEC and PBS in the study districts
Descriptive statistics of soil micronutrients (B, Cu, Fe, Mn and Zn) in the study
districts
Model performance and semivariogram characteristics of soil properties of the study
area
Maize production environment, fertilizer rates and farmers estimated yield in
surveyed area of Woliata Zone, southern Ethiopia
Soil particle size distribution and bulk density of maize growing fields in Wolaita
Zone, southern Ethiopia
Selected soil chemical properties of maize growing fields in Wolaita Zone, southern
Ethiopia
Leaf tissue analysis of samples collected from maize growing fields in Wolaita Zone,
southern Ethiopia
Pearson correlation matrix for relationship between soil and maize leaf nutrients in
Wolaita Zone, Southern Ethiopia
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LIST OF FIGURES
Figure
1.1
Location map of SNNPRS in Ethiopia and Wolaita Zone in SNNPRS (A), study districts
in Wolaita Zone (B) and soil sampling points in the study areas (C)
1.2
Ten years (2003-2013) monthly average rainfall and temperature of the study area.
1.3
Agro-ecology of Damot Gale, Damot Sore and Sodo Zuria in Wolaita, Southern Ethiopia
2.1
Spatial variability of slope in the study districts A) Slope and B) Contour lines
2.2
Farmers soil types for samples collected from studied districts
2.3
LUT of of areas represented by soil samples collected from studied districts
2.4
Spatial fertilizer application type and rates in the studied districts during 2013
2.5
Fertilizer distribution quantity (ton) on the studied districts during 2009-2013
3.1
Component plot of soil properties of Damot Gale (A), Damot Sore (B), Sodo Zuria (C)
and Total (D)
3.2
Soil color value and chroma indices of samples collected from Damot Gale, Damot Sore
and Sodo Zuria districts
3.3
Soil textural classes of the study districts in Ethiopia
4.1
Soil pH map of Damot Gale, Damot Sore and Sodo Zuria Districts
4.2
Soil OC (%) map of Damot Gale, Damot Sore and Sodo Zuria Districts in Ethiopia
4.3
Total nitrogen (%) A) Status and B) Management based map of Damot Gale, Damot Sore
and Sodo Zuria Districts
4.4
Soil available P A) Status and B) Management based map of Damot Gale, Damot Sore
and Sodo Zuria districts
4.5
Soil exchangeable K A) Status and B) Management based map of Damot Gale, Damot
Sore and Sodo Zuria districts
4.6
Soil exchangeable K:Mg ratio map of Damot Gale, Damot Sore and Sodo Zuria districts
4.7
Soil exchangeable Ca A) Status and B) Management based map of Damot Gale, Damot
Sore and Sodo Zuria districts
4.8
Soil exchangeable Mg A) Status and B) Management based map of Damot Gale, Damot
Sore and Sodo Zuria Districts
4.9
Soil sulfur A) Status and B) Management based map of Damot Gale, Damot Sore and
Sodo Zuria Districts.
4.10
Soil CEC map of Damot Gale, Damot Sore and Sodo Zuria districts
4.11
Soil boron A) Status and B) Management based map of Damot Gale, Damot Sore and
Sodo Zuria districts
4.12
Soil copper A) Status and B) Management based map of Damot Gale, Damot Sore and
Sodo Zuria districts
4.13
Soil Fe A) Status and B) Management based map of Damot Gale, Damot Sore and Sodo
Zuria districts in Ethiopia
4.14
Soil Mn map of Damot Gale, Damot Sore and Sodo Zuria Districts
4.15
Soil Zinc A) Status and B) Management based map of Damot Gale, Damot Sore and
Sodo Zuria districts
4.16
Blended fertilizer type map of Damot Gale, Damot Sore and Sodo Zuria districts in
Ethiopia
5.1
Relationship between soil and maize leaf macronutreints (N, P, K, Ca and Mg)
5.2
Relationship between soil and maize leaf micronutrients (Cu, Fe, Mn and Zn).
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LIST OF TABLES IN THE APPENDIX
Appendix
Page
Table
1
Topography and slope category of sampled agricultural fields in the studied
districts.
152
2
Farmers' soil management practices (%) on sampled agricultural fields in the
studied districts.
152
3
Correlation between fertilizer use, slope and crop yield in the studied districts.
153
4
Summary of descriptive statistics of soil properties in the maps in the studied
districts.
153
5
EthioSIS ratings used for Ethiopian soils.
154
6
Pearson correlation matrix of selected soil properties in the studied districts
155
xii
LIST OF MANUSCRIPTS
The dissertation consists of the following manuscripts presented as chapters:
i.
Landscape characteristics of agricultural lands and farmers soil fertility management
practices in Wolaita zone, southern Ethiopia (Submitted to Agriculture and Food
Security Journal and being reviewed).
ii.
Soil fertility status of agricultural lands in Wolaita zone, southern Ethiopia (Part of the
manuscript is submitted to Journal of Soil Use and Management).
iii.
Soil fertility spatial variability analysis, mapping and fertilizer type recommendation
inWolaita zone, southernEthiopia (Part of the manuscript is submitted to Journal of
Land Degradation and Development).
iv.
Soil-plant nutrient status and their relations in maize growing fields of Wolaita zone,
southern Ethiopia (Accepted by the Journal of Communications in Soil Science and
Plant Analysis).
xiii
Soil Spatial Variability Analysis, Fertility Mapping and Soil-Plant Nutrient
Relations in Wolaita Zone, Southern Ethiopia
ABSTRACT
Timely knowledge about soil fertility status and soil-plant nutrient relation is important for adopting
efficient and economic corrective soil management measuresand contributeto the increase in crop
productivity.However, until recently, adequate information was lacking in Wolaita area of
Ethiopia.The purpose of this study was to (1)evaluate landscape characteristics of agricultural lands
and soil fertility management practices (2) evaluate soil fertility status (3) analyze soil spatial
variability, produce fertility atlasand recommend fertilizer type;and (4) investigate soil-plant nutrient
status in Damot Gale, Damot Sore and Sodo Zuria districts,Wolaita zone, southern Ethiopia. About
789 soil samples and maize leaves from 50 fields were collected.Soil nutrients except N were extracted
with Mehlich-IIIand measured with inductively coupled plasmaspectrometer.Geostatistical analysis
was performed using ordinary kriging.The result showed that continuous cultivation without fallowing
has been taking place on diverse slopes (1 - 58%). Majority of the farmers'(96%) removed crop
residues from theiragricultural fields. Fertilizer use was not adequate and the average rates of diammonum phosphate and urea were 30 and 9 kgha-1, respectively which is below the national
recommended rate. Farmers' yield estimation (tha-1) showed 1.1 ± 0.32 (haricot bean), 2.1 ± 0.8
(maize), 11.1± 3.7 (sweet potato) and 13.3±4.7 (taro), which are lower than the national average and
potential yieldof the crops. The low levels of soil OC, TN, available P, S, B and Cu in the area might be
among the reasons for lower yields recorded. Low levels of exchangeable Ca, Mg and K were
recordedon 22, 34 and 15%, respectively, of the samples. The study areas have silt loam and clay soil
textures.The soil pH varied from 4.5 to 8.0. The range of soil properties varied as0.2-6.9% (OC), 0.010.7% (TN), 0.1-238 mg kg-1 (ava. P), 4-30 mg kg-1(S), 0.01-6.9 mg kg-1(B), 0.01-5.0 mg kg-1(Cu), 22392 mg kg-1(Fe), 50-1138 mg kg-1(MnAI), 0.3-117 mg kg-1(Zn) and 3-52 cmolkg-1(CEC). Based on
principal component analysis, 52% of soil variability across districts was explained by differences in
soil pH, exchangeable bases, CEC, available P, Cu, B and particle size distribution. The spatial autocorrelation of soil properties varied from 276 m(Cu) to 15,118m (Mn).Except S, Cu and Zn, the other
soil parametersshowed range values > 512m (average sampling distance).This implies that sampling
interval in this study was adequate to capture the variability. The spatial dependence was strong
(<25%) for Cu, Fe and Zn; moderate (25-50%) for pH, OC, TN, S, Ca, Mg, B, Mn and CEC and weak
(>75%) for P, K and PBS. This indicates that the soil variability was caused by the natural and
anthropogenic factors. The predictive efficiency for almost all the parameters revealed RMSSE values
close to one and nearest values of RMSE toMSE. Goodness of prediction varied from -2 to 100%. All
these confirm a good prediction performance.Thus, the generated map is recommended for soil fertility
interventions.Maize leaf analysis reflected 100, 84, 54 and 28% deficiency of N, P, K and Cu,
respectively. Furthermore, soil-plant relation showed significant and positive correlation (r = 0.70*,
0.40*, 0.50*) for P, Ca and Cu. Therefore, gradual building-up of soil OM, application of three blended
fertilizer types (NPKSBCu, NPSBCu and NPKSB)and liming on strongly acidic soils (3.3% of the
studied area)are recommended. Further studies on the application rates of fertilizers are also
suggested.
Keywords:Geostatistics, Kriging, Macro and micronutrients, Slope, Soil Management, Soil Fertility,
Spatial Dependence,Goodness of Prediction,Plant Tissue analysis
xiv
1. GENERAL INTRODUCTION
Soil fertility is a dynamic processcomprising physical, chemical and biological properties of
the soil. Soil fertility constraints to crop production in sub-Saharan Africa (SSA) are
recognized as the major obstacles to food security in the region (Sanchez et al., 2000).Soil
fertility showsa declining trend particularly in densely populated and hilly countries of the Rift
Valley areas. Ethiopia, Kenya, Rwanda and Malawi had the most negative nutrient balances
(Roy et al., 2003). According to Bationo et al. (2006), the average estimated soil fertility
depletion rate of cultivated lands in 37 African countries including Ethiopia on 30 years has
been 660 kg N ha-1, 75 kg P ha-1 and 450 kg K ha-1.
Ethiopia, one of the fastest growing non-oil economy countries in Africa, is heavily reliant on
agriculture as a main source of employment, income and food security for a vast majority of
its population (IFDC, 2012). The sector generates 40% of gross domestic products (GDP)
(UNDP, 2014) and accounts for 85 and 90% of total employment and exports, respectively
(IFDC, 2012). Considerable growth in crop production in Ethiopia in terms of cultivated area
and volume of production has been observed (Alemayehu et al., 2011). As indicated by IFPRI
(2010) and Alemayehu et al. (2011), the growth in production was largely attained through an
increase of cultivated lands. Yet, the national crop productivity for major cereal crops is as low
as 2.05 tons per hectare (tha-1) (CSA, 2013). Several natural and anthropogenic factors have
been responsible for the low crop productivity. Among the factors, a decline in soil fertility is
considered as serious limitation (IFPRI, 2010).
The primary causes of soil fertility decline include loss of organic matter (OM), macro and
micronutrient depletion, soil acidity, topsoil erosion and deterioration of physical soil
properties (IFPRI, 2010).In addition, salinity is also a major problem in the country. The
problem is also common for SSA countries (Bationo et al., 2006; Sommer et al., 2013) in
which soil fertility is constrained by soil erosion, inherent fertility problem, continuous and
long term cultivation and inadequate fertilizer applications.
2
In Ethiopia, population is increasing with an estimated growth rate of 2.6% per annum
(Ringheim et al., 2009). This has resulted in decline of cultivated land sizes. According to
Spielman et al. (2011), the per capita land area of Ethiopian highlands has fallen from 0.5
hectare (ha) in the 1960s to only 0.2 ha by 2008. As a consequence, pressure on landscape
stability has become extremely high (Thiemann et al., 2005; Wassie and Shiferaw, 2011).
According to Pound and Jonfa (2005) and IFPRI (2010), the small land size has
compelledmost farmers to practice continuous and multiple cropping systems with subsequent
removal of crop residues. Increasingly growing population has also caused intensive land
utilization and forest clearing for cultivation even in areas that are not practical for agriculture
(e.g. steep hill slopes or marginal land) (Simane, 2003; Thiemann et al., 2005). This condition
accelerated soil erosion and has brought disturbances to the ecosystems particularly on soils
(Yengoh, 2012). In addition, it aggravates soil degradation (Gajic et al., 2006) and derives for
change in the landscape characteristics (Alemu, 2015; Gebreselassie et al., 2015). Therefore,
understanding landscape characteristics of agricultural lands and soil fertility management
practices is very pertinent to verify the potential and limitations of the soils and for devising
effective agricultural land management strategies.
Various researches aimed at evaluating soil fertility status in various areas have been
conducted in Ethiopia. The results have demonstrated low levels of nitrogen (N) and
phosphorous (P); deficiency of potassium (K) (Abiye et al., 2004; Wassie and Shiferaw, 2011;
Abdena et al., 2013; Alemayehu and Sheleme, 2013), sulfur (S) (Itanna, 2005; EthioSIS, 2014
and 2015; Habtamu, 2015) and micronutrients such as boron (B), copper (Cu) and zinc (Zn)
(Haque et al., 2000; Wakene and Heluf, 2003; Teklu, 2004; Wondwosen and Sheleme, 2011;
EthioSIS, 2015). In addition, Fe deficiency due to high pH in northern Ethiopia was also
reported by EthioSIS (2014). It is very likely that the range of deficient nutrients will increase
as the survey work addresses more areas. This calls for detailed investigation of soil fertility
status for a specific area before taking any fertility intervention programs.
Based onthe differences in soil management, topography, soil types, agro-ecologies and
inherent soil properties such as acidity, soil spatial variability is predicted (Tittonell et al.,
2005; Haileslassie et al., 2007; Masvaya et al., 2010; Nourzadeh et al., 2012). Soil variability
3
generally shows spatial dependence (Cambardella et al., 1994; Ozgoz et al., 2013; Costa et al.,
2015) and it cannot be analyzed using classical statistical approach. Rather, the complex
spatial relationships between soil fertility factors is possibly measured and mapped better
using geostatistical approaches (Cambardella et al. 1994; Patil et al., 2011).
Soil map helps to understand the spatial patterns of soil fertility parameters. It willbe used for
site-specific fertilization and environmental monitoring (Singh et al., 2010). However,
Ethiopian soil maps are chronically outdated and not detailed enough to provide functional soil
fertility information for soil fertility management interventions. This led to decisions that are
constrained by a lack of systematic soil information (Url. 1). As a consequence, until 2014,
blanket fertilizer recommendations have been implemented across the country (EthioSIS,
2015). Uniform soil management under spatially variable conditions affects fertilizer use
efficiency of the commonly applied fertilizers. This would also make farmers not to get the
optimum return from their investment in fertilizers. Furthermore, in Ethiopia, though macro
and micronutrients have been reported as limiting nutrients, their spatial distributions are not
well identified for devising location specific fertilizer and other soil management
recommendations. Thus, this gap should be filled by investigating the soil spatial variability
and mapping this variability of the soil properties.
Plant analysis is usually considered to be an important method for monitoring the nutrient
status as it measures the amount of nutrients a plant has absorbed from the soil. It is used to
identify imbalances, insufficiencies and excesses nutrients (Høgh-Jensen et al., 2009; Ramulu
and Raj, 2012). Combined use of soil and plant analyses results prior to fertilizer interventions
is helpful for taking corrective measures and thereby improving yield. However, extensive
study dealing with soil-plant nutrient relationship of cultivated fields is lacking.
Therefore, in order to fill those mentioned information gaps and support the country’s effort in
combating soil fertility problem, an investigation that assesses farmers' soil management
practices, current soil fertility status and the spatial variation of soil physico-chemical
properties including its mapping and evaluating soil-crop interaction has a paramount
importance. Furthermore, it is hoped that the generated data will enrich the national soil
4
information database being established through the soil fertility mapping initiative, named
Ethiopian Soil Information system (EthioSIS), which waslaunched in 2011 to establish a
national soil information system and conduct soil fertility survey of Ethiopian soils.
In view of this, the study involved the following four sets of hypotheses.
1. Farmers' regardless of proper soil management practices perform agricultural activities
in all landscape features. This affects soil fertility and yield of crops.
2. Farmers’ soil fertility management interventions are not sufficient to maintain soil
organic matter and the required plant nutrients.
3. Spatial variability of major soil properties is expected in the study areas and hence
soils require site specific soil management practices.
4. Combined use of soil and plant analysis will improve fertilizer recommendation.
To test the above hypotheses, this study was conducted with thegeneral objective of evaluating
local farmers' soil management practices, soil fertility status, analyzing soil spatial variability
and mapping; and investigating soil-crop nutrient status of maize crop in three districts of
Wolaita zone, Southern Ethiopia.
The specific objectives of the study were:
1. To investigate the effects of landscape characteristics and farmers soil fertility
management practices on soil fertility and crop productivity.
2. To investigate the current soil fertility status.
3. To investigate spatial variability of soil fertility, produce soil fertility mapand
recommend fertilizer types.
4. To evaluate soil and plant nutrient status and investigate any soil-plant relationships
under low-to-no input soil conditions.
1.1. DESCRIPTION OF THE STUDY AREA
The study was conducted in Damot Gale, Damot Sore and Sodo Zuria districts, Wolaita zone,
SNNPRS of Ethiopia (Figure 1.1) during 2013. The study districts or woredaswere
purposefully selected as they are among the districts where crop production has largely been
5
undertaken. The study area is located between 037°35'30" - 037°58'36"E and 06°57'20" 07°04'31"N. A total of 82 kebeles (peasant associations) were surveyed,of which 31 were from
Damot Gale, 18 from Damot Sore and 33 from Sodo Zuria district. In total, the study area
covers about 84,000 hectares (ha) of land.
The recent ten years (2003 – 2013) mean annual precipitation of the study area was about
1355 mm. The area has a bimodal rainfall pattern (Figure 1.2) and about 31 and 39% fall
during autumn (March - May) and summer (June - August) seasons, respectively. The mean
monthly temperature for the last ten years (2003-2013) ranges from 17.7 – 21.7 °C with an
average of 19.7 °C (NMA, 2013).
A
B
C
Figure 1.1.Location map of SNNPRS in Ethiopia and Wolaita Zone in SNNPRS (A), study
districtsin Wolaita Zone (B) and soil sampling points in the study areas (C)
6
The elevation of the study districts ranges between 1473 to 2873 meters above sea level
(m.a.s.l) (Figure 1.3). As per traditional agro-ecological zone classification of Ethiopia, the
area is predominantly characterized by mid highland agro-ecology. Besides, small portion of
highlands in Damot Gale and Sodo Zuria districts and very small pocket lowland areas in
Damot Sore districts are identified. Spatially, out of the total study area, about 0.003, 96 and
3.7% are found under lowland, mid highland and highland agro-ecologies, respectively.
250
25
200
20
150
15
100
10
50
5
0
Temperature (°C)
Rain fall (mm)
RF (mm)
Temp (°C)
0
Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec
Month
Figure 1.2. Ten years (2003-2013) monthly average rainfall and temperature of the study area
(National Meteorological Agency, 2013).
Eutric Nitisols associated with Humic Nitisols are the most prevalent soils in Wolaita Zone
(Tesfaye, 2003). These are dark reddish brown soils with deep profiles. Agriculture in the
study area is predominantly smallholder mixed subsistence farming and is dominantly rainfed.
Continuous cultivation without any fallow periods coupled with complete removal of crop
residues is a common practice on cultivated fields. Farmers in the study area use DAP, urea
and farmyard manure (FYM) as sources of fertilizers.
The major crops grown in the study area include tef(Eragrostis tef (Zucc.)Trotter), maize (Zea
mays L.), bread wheat (Triticum aestivum L.), haricot bean (Phaseolus vulgaris L.), field pea
(Pisum sativum L.), potato (Solanum tuberosum), sweet potato (Ipomea batatas), taro
(Colocasia esculenta), enset (Ensete ventricosum) and coffee (Coffea arabica). The vegetation
is dominated by eucalyptus trees (Camaldulensis spp.). Remnants of indigenous tree species
7
such as croton(Croton macrostachyus Hochst. ex Rich.), cordia (Cordia africana Lam.),
Erythrinaspp., podocarpus (Podocarpus falcatus) andJuniperus(Juniperus procera) are also
present.
Figure 1.3. Agro-ecology of Damot Gale, Damot Sore and Sodo Zuria in Wolaita, Southern
Ethiopia (Source: Interpolated result, 2013)
8
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Masvaya, E. N., Nyamangara, J., Natasha, R.W., Zingore, S., Delve, R.J. and Giller, K.E.
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prediction spatial variability in chemical properties of agricultural soil using
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spatial variability assessment of land use effects in a Typic Haplustoll. Land Degradation
and Development, 24: 277–286
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Surface Soils.WorldApplied Sciences Journal, 14(7): 1020-1024
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survey in three districts of Andhra Pradesh. Journal of research ANGRAU, 40(1): 16-20
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and Development. Policy brief, Population Reference Bureau, Washington DC, USA.
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Balance: Approaches and Methodologies. FAO Fertilizer and Plant Nutrition Bulletin 14,
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intensification and the environment in Africa. In D.R. Lee and C.B. Barrett (eds).
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(Experience of SARDP in East Gojjam and South Wollo), 127-136.In: Tilahun Amede.
(ed.), Proceedings of the Conference on the Natural Resource Degradation and
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Ethiopia.
Singh, K.N. Rathore, A., Tripathi, A.K., Rao A.S. and Khan S. 2010. Soil Fertility Mapping
and its Validation using Spatial Predication Technique. Journal of the Indian Sociaety of
Agricultural Statistics, 64(3): 359-365
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Thierfelder, C. and Winowiecki, L. 2013. Profitable and Sustainable Nutrient
Management Systems for East and Southern African Smallholder Farming Systems –
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terms of past experiences, present and future opportunities in promoting nutrients use in
Africa
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Spielman, D.J., Dawit Kelemwork and Dawit Alemu. 2011. Seed, Fertilizer, and Agricultural
Extension in Ethiopia. Development Strategy and Governance Division, International
Food Policy Research Institute, Ethiopia Strategy Support Program II, working paper 020,
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selected areas of Ethiopia for maize production. PhD dissertation. Graduate School,
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Konso, Wolaita, and Wollo, Ethiopia. PhD Thesis, Wageningen University and Research
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– a case study from the northern Ethiopian highland. Topics of Integrated
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diversity in soil fertility management of smallholder farms in western Kenya II. Withinfarm variability in resource allocation, nutrient flows and soil fertility status. Agri, Ecosys
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under different land-use systems of Alfisols in Bako area of Ethiopia.Ethiopian
Journal of Natural Resources, 5:17-37.
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Application of Potassium at Acidic Soils of Chencha, Southern Ethiopia.International
Journal of Agriculture and Biology, 13: 595-598.
Wondwosen Tenaand Sheleme Beyene. 2011. Identification of growth limiting nutrient(s) in
Alfisols: Soil physico-chemical properties, nutrient concentrations and biomass yield of
maize. American Journal of Plant Nutrition and Fertilization Technology, 1: 23-35.
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systems in Cameroon. Agriculture and Food Security, 1:19
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12
2. LANDSCAPE CHARACTERISTICS OF AGRICULTURAL LANDS
AND FARMERS SOIL FERTILITY MANAGEMENT PRACTICES
IN WOLAITA ZONE, SOUTHERN ETHIOPIA
13
2. LANDSCAPE CHARACTERISTICS OF AGRICULTURAL LANDS
AND FARMERS SOIL FERTILITY MANAGEMENT PRACTICES IN
WOLAITA ZONE, SOUTHERN ETHIOPIA
Fanuel Laekemariam1, Kibebew Kibret1, Tekalign Mamo2, Erik Karltun3 and Heluf
Gebrekidan1
1.
Haramaya University, School of Natural Resources Management and Environmental Science,
Ethiopia.2.Ministry of Agriculture, Ethiopia.3.Swedish University of Agricultural Sciences,
Department of Soil and Environment, Uppsala, Sweden.
ABSTRACT
Understanding of the landscape features of agricultural lands and farmers' soil management
practices is pertinent to verify the potential and limitations of the soil resources; and for
devising relevant soil management strategies. Thus, the present study was conducted in Damot
Gale, Damot Sore and Sodo Zuria districts in Wolaita Zone, southern Ethiopia to investigate
the effects of landscape characteristics and farmers' soil fertility management practices on soil
fertility and crop production. The survey was done in 2013 on 789 randomly sampled
agricultural lands. The result showed that agriculture has been practiced on diverse slopes (1
- 58%). This practice significantly influenced most of soil properties. Soils on flat slopes had
silty clay texture and lowest bulk density; and highest content of available P, exchangeable
Ca, extractable B, Cu, Fe and Zn. The clay texture and vice versa of soil parameters occurred
on steep slopes. Farmers in the studied area named seven soil types that accounted to describe
99% of sampled fields. The soil types significantly varied (p < 0.001)in their properties,
fertilizer use and crop yield. Among the soil types, Arrada bita (fertile) and infertile groups
(Lada, Zoo and Gobo bita) showed observable relation withsoil chemical characteristics,
management differences and yield. The soil management practice evaluation indicated the
presence of continuous cultivation without fallowing. Majority of farmers (96%) completely
removed crop residues and fertilizer use was not sufficient. The average rate was 30 and 9
kgha-1 for di-ammonium phosphate and urea, respectively; and about 89% of studied area
received manure below 1.5 tha-1. All these limitations would have made farmers to experience
lower crop yield than the national average estimates. Therefore, diversified nutrient
management interventions such as soil conservation, application of sufficient organic and
inorganic fertilizers to restore the soil fertility and improve crop productivity are
recommended.
Key words: Farmerssoil type, Fertilizer, Slope, Soil management, Yield
14
2.1. INTRODUCTION
Ethiopia is one of the fastest growing non-oil economy countries in Africa. The country is
heavily reliant on agriculture as a main source of employment, income and food security for a
vast majority of its population (IFDC, 2012). Agriculture generates 40% of gross domestic
products (GDP) (UNDP, 2014), and accounts for 85 and 90% of total employment and
exports, respectively (IFDC, 2012).This sector is, however, beset by several natural and
anthropogenic factors that affect its productivity.
In Ethiopia, pressure on landscape stability is extremely high due to the sharp increase in the
population density (Thiemann et al., 2005; Wassie and Shiferaw, 2011). Increasingly growing
population causes intensive land utilization and forest clearing for cultivation even in areas
that are not practical for agriculture (e.g. steep hill slopes or marginal land) (Belay, 2003;
Thiemann et al., 2005) and this accelerates soil erosion. Regardless of this, smallholder
farmers on densely populated highlands of the country produce everything from the soil and
very little remains to re-invest in soil fertility replenishment for the following year (IFPRI,
2010). All these farming practices could have brought disturbances to the ecosystems
particularly on soils by disrupting the stable natural biogeochemical processes of nutrient
cycling, causing rapid depletion of plant nutrients (Yengoh, 2012) and derives for change in
the landscape characteristics (Binyam, 2015; Yihenewet al., 2015). Accordingly,
understanding the landscape features of agricultural lands and soil management practices at
local level are helpful to verify the potential and limitations of the soils.
Landscape character assessment is a means by which distinct, recognizable and consistent
elements which form landscapes can be identified. It is intended to inform the development of
policies regarding the sustainable management of change affecting landscape (Anonymous,
2012). The attributes used to define landscape characteristics include physiography
(topography, slope, and altitude), geology and soils, land cover (land use/type) and cultural
patterns (settlement and agricultural land holdings) (www.eastdevonaonb.org.uk, 2008 and
Anonymous, 2012).
15
Soils are naturally variable and their properties are changing across landscape. The prevalence
of differences in soil properties along the landscape affects not only patterns of plant
production, but also litter production and decomposition (Shimeles, 2012). Soil properties
such as texture, pH and soil organic matter correlate highly with landscape position. For
instance, a research report by Shimeles(2012) in Wollo, Ethiopia, showed an increasing trend
of soil pH and exchangeable bases with decreasing slope. In northwestern Ethiopia, Yihenewet
al. (2015) reported higher mean values of total nitrogen (TN), organic matter (OM) and cation
exchange capacity (CEC) on lower than upper slope. Fantaw and Abdu (2011) also indicated
significant variations in soil organic carbon (SOC), total N, exchangeable cations, CEC and
percentage base saturation (PBS) on varied altitudinal ranges of Bale Mountains, Ethiopia.
Soil properties can also vary with soil management systems and land use types (Awdenegestet
al., 2013; Karltun et al., 2013; Teshomeet al., 2013; Yihenewet al., 2015), in which the
involvement of farmers through different soil management practices such as crop rotation,
fallowing,conservation works, fertilizer application and cropping systems impact the soil
productivity. Belay (2003) and Mesfin (1998) indicated that shortage of land for crop
production has reduced the practice of fallowing, crop rotation, recycling of dung and crop
residues to the soil. These could have detrimental effects on the soil physicochemical
properties. Similarly, very low return of crop residue, lack of crop rotation, low rate of mineral
fertilizer application and absence of long term fallowing on wheat growing highlands of
southeast Ethiopia was reported by Taye and Yifru (2010). Awdenegest et al. (2013)
alsoshowed lower SOC content on farm lands than grazing and protected forest lands due to
continuous cultivation, absence of fallowing and erosion. Furthermore, the positive effects of
fallowing as compared to continuous cultivation (Fantaw and Abdu, 2011), soil bund
construction and manure application (Yihenewet al., 2015) for soil fertility improvement and
restoring soil productivity were reported. The limited maintenance of soil chemical and
physical health, in general, aggravates nutrient related plant stresses and stagnation of yields
for most food crops (Shimeles, 2012; Yengoh, 2012).
Wolaita, located in South Nations’, Nationalities and Peoples’ Regional State (SNNPRS) of
Ethiopia is densely populated zone (385 population per km-2) (CSA, 2010) where the
16
livelihood of farmers relies heavily on agriculture, even though, farm lands are small in size.
About 57% of households in the zone possess less than 0.25 ha of land (WZFEDD, 2012). The
situation is forcingmost farmers to practice continuous and multiple cropping systems with
subsequent removal of plant residues (Pound and Jonfa, 2005). The practice results in decline
of soil OM, plant nutrients and also brings poor aggregate stability and thereby aggravates soil
degradation (Gajic et al., 2006). Hence, having a detailed knowledge about the landscape
characteristics of agricultural lands and farmers' soil fertility management practices of Wolaita
area are very pertinent to verify the potential and limitations of the soils, devising land
management strategies, and planning to attain food security needs of the community in
question.
Nevertheless, very little information is currently available in this regard. Hence, the present
study hypothesized that farmers' in Wolaita regardless of proper soil management practices
perform agricultural activities in all landscape features. This negatively influences the soil
fertility and level of crop productivity. Therefore, this work was initiated to investigate the
effects of landscape characteristics and farmers' soil fertility management practices on soil
fertility and crop productivity. Furthermore, it wasalso hoped that the generated data enrich the
database being established through the soil fertility mapping initiative, named as Ethiopian
Soil Information system (EthioSIS), which was launched in 2011 to establish a national soil
information system and fertility survey of Ethiopian soils.
17
2.2. MATERIALS AND METHODS
2.2.1. Description of the Study Area
The study was conducted in Damot Gale, Damot Sore and Sodo Zuria districts, Wolaita zone,
Southern Nations’, Nationalities’ and Peoples’ Regional State (SNNPRS)Ethiopia (Figure 1.1)
during 2013. The study districts from Wolaita zone were purposely selected because they have
good potential for agriculture. The sites are located between 037°35'30" - 037°58'36"E and
06°57'20" - 07°04'31"N. The study area covers about 84,000 ha. The area has a bimodal
rainfall pattern with mean annual precipitation of 1355 mm (Figure 1.2). The mean annual
temperature ranges between 17.7 to 21.7 °C with an average of 19.7 °C (NMA, 2013). The
elevation in studied districts varied from 1473 to 2873 m.a.s.l (Figure 1.3). The area is
predominantly characterized by mid highland agro-ecology. Eutric Nitisols associated with
Humic Nitisols are the most prevalent soil types (Tesfaye, 2003). Agriculture in the study area
is predominantly small-scale mixed subsistence farming. The farming system is mainly based
on continuous cultivation without any fallow periods. Brief descriptions about the area are
indicated under section 1.1.
2.2.2. Data Collection
Geographical information system (GIS) was employed to randomly assign sample collection
points.A total of 789 sampling points (243 on Damot Gale, 216 on Damot Sore and 330 on
Sodo Zuria) covering all representative land use types were generated for sample
collection.The samples were randomly distributed at an average distance of 512 meters.
During survey work, the pre-defined sample locations were visited in the field and the location
was recorded using the GPS (geographical positioning system) receiver (model Garmin
GPSMAP 60Cx).
For describing each data collection point, a short structured questionnaire was used to record
the following variables: topography, soil color, traditional soil classification, dominant land
use types, crop type grown, crop rotation practices, fallowing, cropping intensity, crop residue
18
management, fertilizer use (types and rates), and farmers estimated crop yield level. Data
collection was undertaken from April to August of 2013.
Slope was measured using a clinometer and the soil air-dry color was described using Munsell
soil color chart (KIC, 2000). The existing land use and crop type were recorded.
Farmers'owning the fields were interviewed for local soil name, fertilizer use and list of the
crops grown during the previous cropping season. The types and rates of fertilizers used were
also recorded for the existing crops, which were sown during sampling time in 2013.
Additionally, cultivated area and fertilizer (types and amount) distributed per year in the study
districts were collected from secondary sources.
2.2.3. Soil Sampling and Analysis
Disturbed and undisturbed soil samples were taken from the field using augur and core
sampler, respectively. In order to form, one kg composite sample10 to 15 sub-samples from
each field were collected. The sampling depth was 20 cm for tef, haricot bean, wheat, maize,
etc, while it extends up to 50 cm for perennial crops such as enset and coffee growing fields.
From the composited sample, one kilogram (kg) of soil was taken with a labeled soil sample
bag.
After soil processing (drying, grinding and sieving), soil physicochemical properties like
texture, bulk density (BD), pH, soil organic carbon (OC), macro and micronutrient contents
and cation exchange capacity (CEC) were analyzed. Particle size distribution (PSD) was
analyzed by laser diffraction method using laser scattering particle size distribution analyzer
(Horiba- Partica LA-950V2) (Stefano et al., 2010). Soil BD was determined using the core
method as described by Anderson and Ingram (1993). Soil pH (1:2 soil: water suspension) was
measured with a glass electrode (model CP-501) (Mylavarapu, 2009). Available P, available
S, exchangeable basic cations (Ca, Mg and K) and extractable micronutrients (Fe, Mn, Zn, Cu
and B) were determined using Mehlich-III multi-nutrient extraction method (Mehlich, 1984).
The concentration of elements in the supernatant was measured using inductively coupled
plasma (ICP) spectrometer. Mid-infrared diffused reflectance spectral analysis was also used
to determine the amount of soil OC, total N and CEC. The available soil Mn content was
determined using manganese activity index (MnAI) as described by Karltun et al. (2013).
19
Particle size distribution, pH, OC, TN and CEC were analyzed at the National Soil Testing
Center (NSTC), Addis Ababa, Ethiopia while Ca, Mg, K, B, Cu, Fe, Mn and Zn were analyzed
in Altic B.V., Dronten, The Netherlands.
2.2.4. Data Analysis
Descriptivestatistics was employed for data analysis. Mean, standard deviation, range and
percentage were computed for different variables. In addition, Pearson correlations, chisquare, t and F tests were also computed. Data analysis was carried out using Microsoft excel
and statistical package for social sciences (SPSS) software version 20. Additionally, maps
showing spatial variability and area coverage of slopes and fertilizer application were
produced using GIS software (Arc Map version 10) with spatial analyst function.
20
2.3. RESULTS AND DISCUSSIONS
2.3.1. Topography of Agricultural Lands
The study area is characterized by marked topographic variations and agriculture is being
practiced on flat to very hilly sloping topographic lands. The results showed that out of the
total sampled fields (n = 789), 20% were located on flat topography, 49% on gentle slope,
20% on strongly slopping and 11% on hilly topographic lands (Appendix 1). Considering
districts, 36% of sampled agricultural fields in Damot Gale, 31% in Damot Sore and 30% in
Sodo Zuria districts were situated on strong to hilly sloping topographic lands. The maximum
slope recorded on hilly topographic cultivated lands was 58, 31 and 58% in Damot Gale,
Damot Sore and Sodo Zuria districts, respectively. Spatially, 17.6% of agricultural fields out
of the study area were found on flat topographic lands,whereas 48% on gentle slope, 26.1% on
strongly slopping lands and 8.3% on hilly topographic lands (Figure 2.1). This indicates that
agricultural activities on about 34% are practiced on strong to hilly slopping topographic
lands. This could be related with rapidly growing population (CSA, 2010) and very small land
size in the area (WZFEDD, 2012). Correspondingly, adjustment and change in agricultural
practices by farmers to sustain the livelihood due to growing population and shortage of land
was reported by Pound and Jonfa (2005), Karltun et al. (2013) and Amanuel(2014).
A
B
Figure 2.1. Spatial variability of slope in the study districts A) Slope and B) Contour lines
(Source: Survey result,2013)
21
Significant variability of soil properties with respect to physiographic categories was observed
(Table 2.1). Soil texture varied across landscape positions in which the upper and lower slope
positions have clay and silty clay textural classes, respectively. Although upper slopes are
prone to quick degradation (Erkossa et al., 2015); the clay texture in upper slopes could
probably be explained due to a recent cultivation history. On the other hand, the deposition of
soil particles at lower slope positions may be attributed to silty clay textural class. Topography
has an influence on pattern of soil distribution over landscape (Taichi, 2012; Lawal et al.,
2014). According to Lawal et al. (2014), surface erosion and depositional processes by runoff
influenced by topography might have been responsible for the differences observed in textural
classes in the study area. Moreover, an increase in soil bulk density (BD) with slope was
recorded. This was in agreement with Lawal et al. (2014) and Yihenew et al. (2015).
Furthermore, a decreasing tendency of available P, exchangeable Ca, extractable soil
micronutrients (B, Cu, Fe and Zn) on steep slopes compared to gentle and flat topography
were observed (Table 2.1). Overall, lower slope positions were found to be better in most of
soil properties than steep slopes. Steep slopes are ecologically fragile areas and farming
practices on steep slopes usually result in soil and nutrient losses (FAO, 1998;Shimeles, 2012;
Erkossa et al., 2015). Similarly, a decrease in soil nutrients of upper than lower slope position
soils (Khan et al., 2007; Shimeles, 2012; Khan et al., 2013 and Yihenew et al., 2015) that was
presumed to be due to soil erosion was reported.Meanwhile, the result of fertilizer application
rates with respect topographic position and farmers estimated crop yield was not statistically
significant. However, the least application of DAP and manure; and minimum crop yield were
recorded on slopping landscapes (Table 2.1).
22
Table 2.1. Soil properties, fertilizer application and crop yield as influenced by topographic
position in studied districts of Wolaita Zone, Southern Ethiopia
Parameters
Physiographic category / Slope (%)/
<2
Sand (%)
F
15.98
2-4
AF
15.77
Silt (%)
41.93
44.01
31.89
28.10
31.02
15.07 0.000
Clay (%)
42.09
40.22
54.04
60.21
57.25
16.17 0.000
Silty Clay
Silty Clay
Clay
Clay
Clay
BD (g cm-3)
1.12
1.05
1.17
1.20
1.24
pH-H2O
6.15
6.07
6.11
6.08
6.08
13.1 0.000
0.51 0.731
OC (%)
TN (%)
P (mg kg-1)
S (mg kg-1)
Ca (Cmolc kg-1)
K (Cmolc kg-1)
Mg (Cmolc kg-1)
TEB (Cmolc kg-1)
B (mg kg-1)
Cu (mg kg-1)
Fe (mg kg-1)
Mn (mg kg-1)
MnAI (mg kg-1)
Zn (mg kg-1)
DAP (kgha-1)
Urea (kgha-1)
FYM (tha-1)
Maize (W/O) (tha-1)
Maize (With) (tha-1)
Haricot bean (W/O) (tha-1)
Haricot bean (With) (tha-1)
Sweet potato (W/O) (tha-1)
Sweet potato (With) (tha-1)
2.06
0.13
15.25
10.54
8.84
1.33
1.85
12.83
0.61
0.65
142.3
133.6
509.3
10.7
35
9.8
1.0
0.6
2.3
0.4
1.2
3.9
9.7
2.0
0.13
13.32
11.88
8.34
1.15
1.77
12.11
0.52
0.52
182.9
109.9
421.8
9.4
32
2.9
1.5
0.6
2.2
0.4
1.2
5.0
10.3
2.04
0.14
9.92
10.73
7.87
1.33
1.92
11.89
0.56
0.58
122.3
153.9
586.1
9.1
35
7.6
0.9
0.6
2.1
0.4
1.2
4.7
11.1
1.99
0.15
11.03
11.15
7.99
1.3
2.18
12.19
0.53
0.54
118.9
150.4
573.2
7.8
33
5.2
0.6
0.5
2.0
0.3
1.0
5.0
12.7
1.8
0.15
4.74
10.98
7.3
1.03
2.18
11.34
0.46
0.31
120.2
121.4
464.6
6.4
31
6.4
0.4
0.2
1.3
0.3
1.1
3.0
6.0
1.33
1.02
2.49
1.42
2.28
3.17
3.43
1.16
2.01
12.17
22.37
13.90
14.42
5.48
0.31
1.31
2.31
1.84
1.83
2.01
2.90
0.28
1.12
Textural class
4-8
GS
14.07
8 - 16
SS
11.69
> 16
HI
11.73
Fvalue
Sig.
13.84 0.000
0.256
0.400
0.042
0.225
0.060
0.013
0.009
0.328
0.091
0.000
0.000
0.000
0.000
0.000
0.90
0.27
0.06
0.12
0.12
0.09
0.02
0.89
0.36
W/O = Without fertilizer application
With = with fertilizer application AF= Almost Flat,
F=Flat, GS=Gentle Slope, SS=Strongly Sloping, HI=Hilly Slope
23
2.3.2. Ethnopedology / Local Soil Classification /
In the study area, farmers have a tradition of associating soil variability with different local
soil nomenclatures. Farmers used soil color, water permeability, water holding capacity,
workability and soil fertility (i.e. response to soil management and yield level), to judge and
give soil names (Figure 2.2 and Table 2.2). The classification indicators across districts were
more or less similar. Farmers use the word bita as suffix, an expression in Wolaitia language,
that literally means soil.
Farmers in the area characterized and named 12 soil types (Table 2.2) of which the first seven
are common across all districts and accounted for nearly 99% of the soils (Figure 2.2). Among
soil types, Arrada bita predominates in proportion followed by Lada bita > Gobo bita >Talla
bita > Zoo bita (Figure 2.2). Soil color was among the indicators used by farmers for soil type
classification. Accordingly, the dominant soil colors in the area are brown, dark reddish brown
and reddish brown colors. Farmers locally regarded reddish brown and darker soils as low and
good fertile soils, respectively. A similar perception of farmers was also reflected by Desbiez
et al. (2004); Pound and Jonfa (2005) and Hailesilase et al. (2006) who reported that reddish
soil colors are perceived to be less fertile than the darker ones.
The soil types occur on different slope positions. Arrada, Gobo and Keretabita are dominant
in gentle slopes. Chere bita is common on flat slope positions. Lada, Talla and Zoobita which
are prominently regarded as low fertile soils are found on gentle to hilly slope lands.
According to farmers classification, about 58% of the sampled fields were considered to be in
the dominantly low fertility category. In confirmation with this study, the use of soil color,
permeability, water holding capacity, workability and soil fertility by farmers in different parts
of Ethiopia (Tilahunet al., 2001; Haileslassie et al., 2006 and Yifruand Taye, 2011), western
Kenya (Tittonell et al., 2005) and Nepal ( Desbiez et al., 2004) have been reported.
Additionally, previous study in the neighboring district (Kindo Koyisha), Wolaita zone by
Pound and Jonfa (2005) reported similar soil type names such as Kereta, Gobo, Talla, and
Chere bita.
24
Soil types
8
7
6
3
2
6
1
5
11
5
3
11
16
4
22
8
7
3
3
28
16
2
21
11
23
1
43
33
26
0
Damot Gale
Damot Sore
Soil types
1. Arrada bita
2. Lada bita
3. Gobo bita
4. Talla bita
5. Zo'o bita
6. Chere bita
7. Kereta bita
Sodo Zuria
Figure 2.2. Farmers soil types for samples collected from studied districts.
The size and number of each bubble indicate proportion and percentage considering the number of samples at the
district. Sample size for Damot Gale = 243, Damot Sore=216, Sodo Zuria=330 and total = 789.
Soil properties, fertilizer rates and yield of some crops were significantly varied by farmers
soil types (Table 2.3). The variation in soil properties for each soil type did not show clear
demarcation. However, differences in the soil properties between fertile (Arrada bita) and
infertile soil groups (Lada, Gobo, Zo'obita) were observable. Soils perceived to be fertile (e.g.
Arradabita) and infertile (e.g. Lada bita)by the farmers in their orders showed soil bulk
density (1.1, 1.2g cm-3), pH (6.4 , 6.0), OC ( 2.2 , 1.7%), TN ( 0.2 , 0.1%), available P ( 21 ,
3.3 mg kg-1), total exchangeable bases (14.7, 8.9 Cmolc kg-1soil). In addition, higher
micronutrient (B, Cu, Fe, Mn and Zn) and CEC values for Arrada bita than Lada / Zoo bita
were recorded (Table 2.3). Similarly, a good agreement between farmers’ assessment of soil
fertility and measured soil chemical characteristics in Ethiopia (Haileslassie et al., 2006);
Karltun et al., 2013) and Nepal (Desbiez et al., 2004) has also been reported.
25
Table 2.2. Farmers' soil types and perceived soil characteristics in studied districts of Wolaita
Zone, Southern Ethiopia
Indicators
Local soil
classification
Munsel color (dry)
Fertility
Workability
Permeability
Brown (42%) > reddish brown (20%) > dark
reddish brown (17%) > Gray (7%), other
brownish
Brown=Dark reddish brown (30% each),
Reddish brown (24%), Yellowish brown(4 %)
Medium-high
Easy to
Moderate
Optimum
Low
Moderate
Moderate – High
Gobo Bita
Dark Reddish brown (42%) > Reddish brown
(32%) > brown (20%)
Low > Medium
Moderate
High
Talla Bita
Brown=Dark reddish brown (34% each),
Reddish brown (11%), Gray (8%)
Low - Medium
Strong,
Sticky when
wet, hard
when dry
Low –Moderate
Zo'o Bita
Dark reddish brown (45%) > Reddish brown
(36%) > Brown (11%)
Low > Medium
Moderate
Moderate–High
Chere Bita
Gray (55%), Grayish brown (17%), Brown
(15%), other Gray to brownish
Brown (44%) > Reddish brown (19%), Grayish
brown = Gray (11%), Dark gray=Dark Reddish
brown (7.4%)
Medium
Difficult
(when wet)
Difficult
(dry and wet)
Low (Water
retaining soil)
Moderate
Brown > Pinkish Brown=Very Dark gray
Low
Moderate
High
Brown
Medium
Moderate
High
Grayish Brown
Low
Moderate
High
Brown
Low
Moderate
High
Brown
Very Low
Moderate
Moderate
Arrada Bita
Lada Bita
Kereta Bita
Barta Bita
Akiaka Bita
Alo Bita
Dubule Bita
Goshe Bita
Medium-high
Numbers in parenthesis indicate the sample size. "Bita" literally mean soil. (Source: Survey result, 2013)
26
Table 2.3.Soil properties, fertilizer rates and crop yield as affected by farmers' soil type classes
in studied districts of Wolaita Zone, Southern Ethiopia
Kereta
bita
17.4
47.2
35.4
Bulk density (g cm-3)
pH-H2O
Arrada
bita
15.1
40.0
44.9
Silty
Clay
1.1
6.4
1.1
6.2
Farmers soil types
Chere
Talla
Gobo
bita
bita
bita
14.7
13.5
11.5
40.5
36.1
22.4
44.8
50.4
66.1
Silty
Clay
Clay
Clay
1.1
1.2
1.2
5.8
6.2
5.9
OC (%)
TN (%)
P (mg kg-1)
Ca (Cmolc kg-1)
K (Cmolc kg-1)
Mg (Cmolc kg-1)
S (mg kg-1)
B (mg kg-1)
Cu (mg kg-1)
Fe (mg kg-1)
Mn (mg kg-1)
MnAI (mg kg-1)
Zn (mg kg-1)
TEB (Cmolc kg-1)
CEC (Cmolc kg-1)
DAP (kg ha -1)
Urea (kg ha -1)
FYM (t ha -1)
Maize (W/O) (t ha -1)
Maize (With) (t ha -1)
Haricot bean (W/O) (t ha -1)
Haricot bean (With) (t ha -1)
Tef (W/O) (t ha -1)
Tef (With) (t ha -1)
Sweet potato (W/O) (t ha -1)
Sweet potato (With) (t ha -1)
2.2
0.2
21.0
9.9
1.9
2.2
10.4
0.7
0.7
121.1
159.7
602.7
13.3
14.7
23.0
23.3
7.02
1.7
0.66
2.28
0.42
1.28
0.19
0.73
4.9
11.1
1.9
0.1
7.6
8.7
1.0
1.6
10.5
0.4
0.6
140.5
125.9
480.0
9.8
12.1
19.6
43.0
4.6
0.7
0.42
1.88
0.34
1.15
0.23
0.65
-
1.8
0.1
7.3
7.8
0.6
1.6
10.0
0.4
0.6
234.7
89.9
351.2
4.1
10.8
17.8
50.6
3.8
0.6
0.10
1.33
0.30
1.10
0.13
0.70
-
Parameters
Sand (%)
Silt (%)
Clay (%)
Textural class
Silty Clay
Loam
W/O = Without fertilizer application
1.6
0.1
5.3
8.9
1.2
2.3
9.9
0.5
0.4
134.9
141.6
538.1
6.5
13.2
22.1
33.0
8.4
0.23
0.56
2.02
0.29
0.97
0.15
0.66
5.5
15.5
2.2
0.2
5.7
6.5
1.1
1.9
11.5
0.5
0.6
114.7
157.0
601.2
7.8
10.3
20.3
43.2
8.9
0.5
0.46
1.91
0.36
1.21
0.16
0.74
4.5
10.1
Zo'o
bita
9.7
19.2
71.1
Clay
Lada
bita
14.1
30.7
55.2
Clay
1.2
5.6
2.2
0.2
3.0
5.5
0.9
1.7
13.5
0.5
0.4
106.6
137.5
532.4
5.8
8.9
19.6
45.3
6.5
0.3
0.34
1.82
0.24
0.98
0.13
0.63
4.5
9.3
With = With fertilizer application
Fvalue
Sig.
15.1
31.8
31.0
0.000
0.000
0.000
1.2
6.0
17.1
28.8
0.000
0.000
1.7
0.1
3.3
6.3
0.9
1.7
10.8
0.4
0.4
118.3
136.4
522.5
6.3
9.7
19.1
39.2
7.08
0.09
0.55
1.99
0.28
1.02
0.13
0.70
4.0
10.1
12.3
15.1
15.6
31.4
44.9
14.8
11.5
14.8
16.9
86.4
17.0
16.3
33.8
32.3
24.3
9.6
0.4
19.4
5.7
2.2
10.2
7.4
1.2
0.8
0.32
3.0
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.880
0.000
0.000
0.040
0.000
0.000
0.320
0.56
0.90
0.03
27
Application rate of DAP and FYM; and crop yield (maize and haricot bean) were significantly
variedon farmers soil types (Table 2.3). Farmers applied the least (23 kg ha-1) and highest
(50.6 kg ha-1) amount of DAP fertilizer on fertile soil (Arrada bita) and waterlogged (Chere
bita), respectively. The use of inorganic fertilizer was higher on less fertile soil types than
fertile soils. The opposite trend holds true for FYM. Farmers grow permanent and vegetable
crops on fertile and homestead fields, whereas the distant fields are used mostlyfor cereals.
Hence, due to proximity to home, scarcity of FYM and the nature of crop types; farmers
allocate more of FYM to fertile soils. Conversely, they use more of the inorganic fertilizers on
distant and less fertile soils. Similar trend in Ethiopia was reported by Haileslassie et al.
(2006).
Data regarding grain yield indicated that soils perceived as fertile type (Arrada bita) revealed
comparative yield advantage than infertile soils. Meanwhile, remarkably low yield was
observed in Chere bita. This was related to high water logging problem of the soil. As per
farmers’ estimation, maize yield in Arrada bita without fertilizer application varied from 0.2
to 1.6 tha-1, whereas it was between 2.0 to 5.0 t ha-1 when fertilized. The maize yield in Lada
bita, without fertilizer ranged from 0.2 to 1.2 t ha-1 and it varied from 1.0 to 3.0 t ha-1 if
fertilized. Similar trend holds true for the other crop types. The better yield advantage on
fertile soil type might be attributed to better soil management, better nutrient reserves and
supply capacity of soils. In agreement with this, the positive impacts of soil organic matter in
storing and supplying of nutrients were reported by Morris et al. (2007); Buyinza and
Nabalegwa (2011) and Ailincăi et al.(2012).
Generally, among each soil types a clear trend of differences in termssoil chemical parameters
was not observed. This limitation needs further clarification and investigation. Nonetheless,
this study reveals that farmers soil type classification in groups (fertile vs infertile) had
observable relations with soil fertility parameters, management and yield. In order to facilitate
easy communication and share the information widely among the local farmers, systematic
integration of the scientific approach with traditional soil classification should be followed
during soil fertility management interventions.
28
2.3.3. Land Use and Soil Fertility Management Practices
2.3.3.1. Land use types
The results of land use types (LUT) indicated that rainfed agricultural activities are prevalent
in the study area (Figure 2.3). A chi-square statistic (χ2=21.9, p < 0.05) also revealed
significant differences in LUT (data not shown). Overall, 60% of the sampled areas were used
for cereal and pulse crops production followed byroot and tuber crops,grass land, coffee
plantation, enset plantation andfallow lands in their order. Furthermore, a culture of keeping
small patchy grass fields for livestock "Maatta Gadya" (literally means grassland) has been
observed. Grass lands from sampled agricultural fields had considerable share (13%)
indicating livestock is an integral component of livelihoods. Generally, different LUTs' were
observed at different topographic positions. Nevertheless, cereals and pulses, coffee, enset and
root crop land uses were dominant under gentle slope topographic positions. The grass lands
were reasonably distributed from flat to hilly topographic features.
Land use type (LUT)
7
6
11
19
5
11
12.5
20
15
4
2
0.5
1
3
2
2
2
2
7
1
67
0
Damot Gale
10.5
LUT
1=Cereal & pulse
2=Coffee
3=Enset
4=Fallow
5=Grass
6=Root & tuber
5
55.5
Damot Sore
57
Sodo Zuria
Figure 2.3. LUT of of areas represented by soil samples collected from studied districts.The size
and number of each bubble indicate proportion and percentage considering the number of samples at the district.
Sample size for Damot Gale = 243, Damot Sore=216, Sodo Zuria=330 and total = 789 .
29
2.3.3.2. Soil fertility management practices
Fallowing
Despite the fact that farmers have knowledge about the benefits of fallowing, it has rarely been
practiced in almost all of the sampled agricultural fields. The practice of fallowing
significantly varied with topographical categories (χ2 = 32.6, p < 0.001) and farmers soil types
(χ2 = 20.2, p < 0.01) (Table 2.4). In this finding, only about 2.5% (Damot Gale), 0.5% (Damot
Sore), 1.2% (Sodo Zuria) and 1.4 (overall) of the sampled agricultural fields were under
fallowing. To a certain extent, the practice of seasonal fallowing such as leaving the land
under fallow for one season has been observed. Among the very few farmers who practice
fallowing, larger proportions (64%) of fallow lands were located on hilly topographic
positions and infertile soil type (Lada bita). This could indicate that nutrients in the hilly slope
positions can be washed by runoff than flat topographic positions (Buyinza and Nabalegwa,
2011) which in turn forces farmers to change into fallow lands. Rice and Emanuel (2014) also
stated that landscape position exerts a strong influence on the distribution of different forms of
land use and the likeliness of a given area undergoing land use change.
As noticed from the discussion, farmers were forced to practice fallowing not deliberately for
restoring soil fertility; rather it was due to very low return from it i.e. when the cultivable land
has reached the point of no return in crop yield. The small size of farmland in the study area
(WZFEDD, 2012) is one of the main motivations for the practice of limited fallowing for soil
fertility management. In agreement with the present finding, limited practice of fallowing for
managing soil fertility due to small landholdings in different parts Ethiopia were reported by
Pound and Jonfa (2005); Yifruand Taye (2011) and Negasiet al. (2013). The continuous
cultivation without fallowing might change soil characteristics and soil productivity. Besides,
the positive effects of fallowing reported by different researchers (Fantaw and Abdu, 2011;
Tematio et al., 2012; Oriola and Bamidele, 2012) such as an improvement in macro
aggregates, soil OC, TN, exchangeable cations , CEC and restoring soil productivity will not
be maintained.
30
Table 2.4.Farmers' soil management practices at different topographical categories and soil
types on sampled agricultural fields of Wolaita Zone, Southern Ethiopia.
Soil Fertility
Management Practices
Fallowing (N=789)
Yes
No
Crop intensity (N=674)
One
Two
Total
users
χ2
Topographical Soil type
category
χ2
χ2
3.5NS
32.6***
20.2**
20.5 ***
14.7 NS
14.9 NS
5.3 NS
4.7 NS
21.6**
2.9NS
5.4 NS
32.9***
0.13NS
1.3 NS
67.3***
11.4**
10.0 NS
132.3***
Three
Rotation (N=674)
No
Yes
Residue Management (N=789)
Clear
Retain
Inorganic Fertilizer (N=674)
Yes
No
Organic Fertilizer (N=674)
Yes
No
"N" indicates number of sampled lands used to compute the proportions (Source: Survey result, 2013)
** = 0.05, ***= 0.01 NS=Not significant
Cropping intensity
Cropping intensity refers to growing of a number of crops in the same field during one
agriculture year. The practice of continuous cropping revealed significant differences (χ2 =
20.5, p < 0.01) among sampled fields, however, it was not significantly influenced by
topographical categories and farmers soil types (Table 2.4). About 62% of sampled cultivated
fields in Damot Gale, 51% from Damot Sore, 61% from Sodo Zuria and 59% across districts,
had grown two successive crops in a year in the same field (Appendix 2). Discussion with the
farmers indicated that poor soil fertility, water logging on flat topographic position, lack of
oxen to plough the land and/or lack of capital to purchase input were reasons compelling them
for cultivating one crop per year in a field. Similar to the field observation, a study in Gununo,
Wolaita by Tilahunet al. (2001) indicated a practice of growing up to six different crops in
mixtures. This would lead to over exploitation of the land through nutrient mining and nutrient
31
depletion (Hartemink, 2006). Likewise, plant nutrient deficiencies under intensive cropping
systems due to lower dose of fertilizer application were reported in Bangladesh (Dey and Haq,
2009).
The status of the soil is the primary factor to support higher cropping intensity in which the
soil need to be fertile, well drained and responsive to inputs (URL 1). Exposure of the field to
intensive cropping through double, relay or inter-cropping implies that the demand on soil for
plant nutrients is becoming more in the growing year. In this regard, soil fertility of sampled
fields are assumed not have been managed properly using adequate fertilizer application,
fallowing and residue maintenance. Therefore, cropping intensity might lead to heavy nutrient
removal unless complemented by proper soil management practices.
Crop rotation
Crop rotation is considered as one of the soil management practices that increase soil
workability and nutrient recovery. In the present study, a chi-square statistic revealed a non
significant difference among rotation practicing farmers and the topographical categories
(Table 2.4). Rather, crop rotation revealed significant variation (χ2 =21.6, p < 0.01) with
farmers soil types (Table 2.4).
Among the soil types, larger proportion (88%) of crop rotation was recorded on Kereta bita
followed by Lada (73%) >Zo'o (63%) > Talla (58%) >Arrada (56%) >Chere (53%) >Gobo
bita (52%). Overall, about 66, 57, 58 and 61% of sampled cultivated fieldsof Damot Gale,
Damot Sore, Sodo Zuria and across districts, respectively practiced crop rotation (Appendix
2). Cereals are often rotated with legumes and root crops. Haricot bean is a major legume crop
used in the rotation cycle. The rotation in the present study is implemented in the following
patterns such as maize, haricot bean, teff; root crops, haricot bean, teff or cereals with root
crops. Farmers during survey indicated that they practice crop rotation for replenishing soil
fertility, better growth and yield advantage of the succeeding crop.
In line with the findings of this study, Tilahunet al.(2001) and Karltun et al. (2013) indicated
that farmers practiced crop rotation to maintain the positive effects of fertilizers for better
growth and yield of crops. In addition, Bationo et al. (2012) described the positive effects of
32
rotation from the added N from legumes, improvement of soil biological and physical
properties, solubling occluded P and insoluble calcium (Ca) bound P by legume root exudates.
Moreover, the authors mentioned soil conservation, soil OM restoration and pest and disease
control advantages.
Crop residue management
Crop residues in almost all of the surveyed fields have been removed for varied purposes
(feed, fuel and construction material). Return of crop residue into the soil among total sampled
fields and different topographical categories did not show significant differences (Table 2.4).
However, the use of crop residue indicated a significant variation (χ2 =32.9, p < 0.001) among
soil types (Table 2.4). Overall, crop residues were removed from 98% of Damot Gale, 94% of
Damot Sore and 96% of sampled agricultural fields on Sodo Zuria districts (Appendix 2).
Among farmers,whoreturned crop residue, majority (80%) applied to Arrada bita. In the study
area, crops like haricot bean, field pea, sweet potato and potato have been harvested by
complete uprooting. In the case of maize, teff, wheat and sorghum, harvesting has been done
by mowing close to the soil surface and then the farm was again subjected to grazing. Besides
these, uprooting of roots (e.g. maize and sorghum) for fuel purposes was also observed.
During the survey work, it was also noticed that the leaves of coffee trees were picked out in a
piecemeal fashion to make locally made coffee “Haita tukya” and also leaves are serving
assource of income.
Residue management through incorporation of the plant material into the soil for fertilizing is
an important process in cases where small-scale farmers have limited economic potential to
acquire synthetic fertilizers (Yengoh, 2012). However, the result in the present finding
indicated that limited amount is remained in the soil system that could replenish the soil. This
implies a negative impact on the building up of soil OM and plant nutrient restoration
processes, as residues are important for recycling of plant nutrients into the soil system (Gajic
et al., 2006; Buyinza and Nabalegwa, 2011). The low soil OC contents than adequate level in
the study area (Table 2.1 and 2.3) also confirms this speculation.
33
In annual crops, considerable portion of total nutrient uptake is found in straw / vine compared
to grain/tuber (Hartemink et al., 2006). The finding of Hartemink et al. (2006) in Papua New
Guinea indicated that more than 75% of nutrient uptake was found in the above ground part
(sweet potato vines). Surendran et al. (2010) also indicated that 51, 68 and 46 % of N, P and K
uptake, respectively, were found in maize residue. This implies that if crop residues are
incorporated to soil, and as it decomposes, plant nutrients would become available for the
subsequent crops. Quite the opposite, farmers in the study areas removed almost all residues
from the field, which would result in declining of soil fertility. Correspondingly, research
conducted in different parts of Ethiopia (Tilahunet al., 2001; Yifruand Taye, 2011;
Mohammed, 2014; Tegbaru, 2014) have also indicated a small return of crop residues to crop
fields.
Fertilizer use and application rates
In the study area, di-amonium phosphate (DAP), urea and farm yard manure (FYM) were
fertilizer sources used by farmers. The use of inorganic fertilizers among farmers and
topographical categories did not show statistical differences, whereas it was significantly
influenced (χ2 = 67.3, p < 0.001) with farmers soil types (Table 2.4). The analysis result
regarding organic fertilizer revealed significant differences among the users (χ2 =11.4, p <
0.01) and soil types (χ2 =132.3, p < 0.001), however, statistically variation was not seen at
topographical categories (Table 2.4). Overall, about 58 and 25% of sampled cultivated fields
were managed with inorganic and organic fertilizer types, respectively. Among inorganic
fertilizer users', the proportion applied on water logged soil (Chere bita) was 82% followed by
Zo'o (78%) >Lada (71%)= Kereta (71%) > Gobo (70%) > Talla (56%) >Arrada (39%). From
those farmers who used FYM, majority of them (49%) applied to Arrada bita compared to 4 10% on infertile soil types (data not indicated).
Generally, the application rate of DAP on total cultivated fields ranged between 0 to 200 kg
ha-1. The maximum was observed on one irrigated field; otherwise the range for the remaining
fields did not cross 100 kg ha-1. Among cultivated fields using DAP fertilizers, the rate varied
from 16 to 200 with a mean rate of 59 + 19 kg ha-1(Table 2.5). The application rate of urea
fertilizer on cultivated fields ranged from 0 to 100, with mean rate of 7 + 18 kgha-1. On urea
fertilized cultivated fields, the use varied from 18 - 100 with a mean value of 47.7+ 17 kgha-1.
34
Data regarding FYM application indicated that on total cultivated fields, the amount ranges
from 0 to 10 with a mean rate of 0.82 + 1.8 tha-1, whereas on FYM fertilized cultivated fields,
the application varied from 0.5 to 10 with mean amount of 3.3 + 2.1 tha-1 (Table 2.5).
Furthermore, spatial analysis result (Figure 2.4) further indicated that out of total study area,
about 36, 61 and 3% received DAP fertilizer at rate of 5 - 25, 26 - 50 and 51 - 75 kg ha-1,
respectively, whereas about 97% of study area received urea at a rate below 25 kg ha-1. In
addition, 89% of study area received FYM at a rate below 1.5 t ha-1 and 9% received at 1.6 - 3
t ha-1.
Figure 2.4. Spatial fertilizer application type and rates in the studied districts during 2013
(Source: Survey result)
Table 2.5. Average application rate of fertilizers on total cultivated and fertilized cultivated
fields of studied districts of Wolaita Zone, Southern Ethiopia during 2013.
Districts
Damot Gale
Damot Sore
Sodo Zuria
F-test
Grand Total
DAP (kg ha-1)
Cultivated Fertilized
field
field
33+ 30
56+ 15
36+33
62+ 16
34+33
58+ 22
0.4 NS
3.3*
34+32
59+ 19
Urea (kg ha-1)
Cultivated Fertilized
field
field
7+17
42.4+13
4+14
52.5+15
9+21
50.3+18
4.9**
3.2*
7+18
47.7+17
FYM (t ha-1)
Cultivated Fertilized
field
field
0.98+2.0
4.2+2.2
0.99+2.0
3.0+2.2
0.57+1.4
2.9+1.6
4.0*
5.8**
0.82+1.8
3.3+2.1
Cultivated = fields managed with + without fertilizer. Fertilized fields = fields managed with fertilizer *,**
significant at p<0.05,0.01, respectively. NS=Non significant difference (Source: Survey result, 2013)
35
Time framed inorganic fertilizer application rates (2009 - 2013) were also computed from the
ratio of distributed fertilizer and cultivated areas (Table 2.6). The average use of DAP ranged
from 27 to 35 kg ha-1 and urea varied from 7 to 13 kg ha-1. The amount is almost comparable
with the survey findings presented in Table 2.5. Meanwhile, the absence of credit and low
ability of farmers to purchase fertilizers with cash in hand system caused low use of fertilizer
in 2012 (Table 2.6 and Figure 2.5). Similarly, low financial capacity of smallholder farmers
and increasing price were reported as a cause for no to lowinorganic fertilizer application rates
in Ethiopia (Karltun et al., 2013) and Cameron (Yengoh, 2012).
Table 2.6. Fertilizer application rates (kgha-1) of studied districts of Wolaita Zone, Southern
Ethiopia during 2009 - 2013. (Calculated from the input supply in Wolaita Zone agricultural
and input office data)
Fertilizer application rate (kg ha-1)
Damot Gale Damot Sore
Sodo Zuria
Average
DAP Urea DAP Urea DAP Urea DAP Urea
26.2
3.4
16.2
2.5
21.4
1.4
21.9
2.5
29.5 10.8 22.6
4.5
24.8
3.6
26.0
6.6
46.0 17.3 50.2 15.2 34.0 10.5 42.5 14.1
13.7
3.1
21.4
4.1
16.6
2.5
16.5
3.1
32.1 13.6 63.0 37.6 36.4 16.1 40.9 20.1
30.0 10.0 35.0 13.0 27.0
7.0
30.0
9.0
Year
2009
2010
2011
2012
2013
Average
2700
Quantity distributed (ton)
2400
2100
DAP
Urea
1800
1500
1200
900
600
300
0
2009
2010
2011
Year
2012
2013
Figure 2.5. Fertilizer distribution quantity (ton) on the studied districts during 2009-2013.
Source: Wolaita Zone Input Office
36
In general, the amount of inorganic fertilizer used on cultivated fields of the study area was a
bit higher than the 2012 World Bank (URL 2) and Sommer et al. (2013) reports, who
indicated 23.8 and 13 - 16 kg ha-1 inorganic fertilizer on arable lands of Ethiopia, respectively.
However, the rate was not sufficient to meet the needs of growing crops; and was lower than
blanket dose (100 kg ha-1 of each DAP and urea fertilizer) used in Ethiopia (Admasu 2009;
Sime and Aune, 2014, Fanuel and Gifole, 2013). Similar report on the wheat growing areas of
southeastern Ethiopia (Yifruand Taye, 2011) was indicated. Regarding FYM, the amount was
very small compared to research findings such as 12 -18 ton ha-1of FYM to be as effective as
100 kg ha-1 DAP (Taye, 1993) and 5 ton ha-1 compost with inorganic fertilizer for maize
(Fanuel and Gifole, 2013). This suggests that the lower application rate of chemical fertilizers
have not been compensated with FYM or integrated use of organic and inorganic fertilizers.
This might be associated with scarcity, priority to home stead crops such asenset, and
transportation difficulty to distant fields.
Fertilizer uses and application rates on crop types
The analysis of variance regarding fertilizer application rate in the study area indicated
significant variations (p < 0.001)among the crop types (Table 2.7). The rate of inorganic
fertilizer was higher for cereals (maize, teff, wheat) and haricot bean. On the other hand, fields
growing taro, enset and coffee received higher amount of FYM. It is generally noted that
inorganic and organic fertilizer types seem to have an inverse relationship depending on crop
types, in which application of inorganic fertilizers were higher for cereals and very small for
perennial crops and vice versa for FYM. This is supported with a significant (p < 0.001) and
negative (r = -0.42) relationship between inorganic and organic fertilizer application. The
higher use of inorganic fertilizer for maize, teff, wheat, potato and haricot bean probably
reflects that 1) the crops are among staple food crops in the area, 2) they are grown in distant
fields where soil fertility is low, 3) wider yield responses in the absence and presence of
fertilizer and 4) better market demand. Farmers in the study area indicated that the reasonable
yield obtained from root crops make them to grow in the absence of fertilizers by giving
priority for cereals. The use of more fertilizer for maize, teff and wheat in Ethiopia was
reported by FAO (2002) and Admasu (2009). Besides, Morris et al.(2007) also reported that
37
the relatively large share of fertilizer for maize is due to its response to fertilizer and higher
market demands.
The application rate of DAP (kg ha-1) was highest for teff (61.3±20.3) followed by wheat
(57.5±24.3), potato (52.7±24.5), maize (46.7±30.1) and haricot bean (45.5±25.8), while the
amount was small on other crops (Table 2.7). On the other hand, an application rate of urea
fertilizer was very small ranging from nil to 24 kg ha-1 (Table 2.7). The use of FYM (tha-1)
was highest on enset (3.8 ± 2.3) followed by coffee (3.5±2.7), taro (2.4±2.1) and sweet potato
(1.1±2.0). As a whole, the use of small and non balanced nutrient management would lead to
depletion of other nutrients (Mesfin, 1998; Romheld and Kirkby, 2010 and MoA and ATA,
2012), as uptake without replenishment occurs in the soil and this consequently results in
reduction of crop yields.
Table 2.7. Fertilizer type and application rates on crops grown in the studied districts of
Wolaita Zone, Southern Ethiopia during 2013.
Crop
Maize
Teff
Wheat
Sorghum
Haricot bean
Field Pea
Potato
Sweet potato
Taro
Cassava
Enset
Coffee
F-test
Sample
Size
213
20
14
10
188
21
29
36
46
12
15
55
% fields receiving
DAP
Urea
FYM
Average fertilizer rate
DAP
Urea
FYM
(kg ha-1)
(kg ha-1)
(ton ha-1)
46.7± 30.1 17.5±25.9
0.5±1.3
61.3±20.3
18.0±21.5
0.0±0.0
57.5±24.3
24.1±23.7
0.0±0.0
0.0±0.0
0.0±0.0
0.0±0.0
45.5±25.8
0.4±4.1
0.1±0.4
5.2±16.6
0.9±3.9
0.2±0.8
52.7±24.5
2.6±10.2
0.3±1.0
1.7±10.0
0.9±5.3
1.1±2.0
3.0±14.6
1.0±7.1
2.4±2.1
13.3±25.7
2.5±8.7
0.1±0.2
0.0±0.0
0.0±0.0
3.8±2.3
0.0±0.0
0.0±0.0
3.5±2.7
45***
16.3***
40.2***
76
35
17
95
45
0
93
57
0
0
0
0
82
1
4
10
5
10
90
7
10
3
3
33
4
2
78
25
8
8
0
0
100
0
0
91
(Source: Survey result, 2013) *** significant at p < 0.001.
2.3.3.3. Farmers’ Estimate of Crop Productivity Based on Fertilizer Use
Crop yield in the present study showed statistically significant differences due to the
interventions made on soil fertility improvements. In all crop types and districts, fertilizer
application resulted in an increase ofthe harvest (Table 2.8). The correlation analysis between
yield with and without fertilizer application also strengthens this observation. The result
38
showed a significant (P < 0.001) and strong correlation for maize (r = 0.73), tef (r = 0.41),
wheat (r = 0.80), potato (r = 0.71), sweet potato (r = 0.52) and taro (r = 0.74) (Appendix 3).
Table 2.8. Farmers' estimated mean yield on sampled cultivated fields in the absence and
presence of fertilizerin the studied districts of Wolaita Zone, Southern Ethiopia during 2013.
Major Crop
Fertilizer
application
Maize (195)
0.76±0.3
2.54±0.9
***
0.17±0.09
0.73±0.23
***
0.50±0.3
1.9± 0.65
***
0.52±0.2
1.95±0.64
***
0.16±0.09
0.69±0.15
***
0.46±0.25
1.93±0.84
***
0.26±0.16
1.54±0.50
***
0.08±0.11
0.65±0.19
***
0.18±0.16
1.05±0.48
**
Overall
Mean ±SD
0.55±0.31
2.08±0.81
***
0.15±0.10
0.70±0.19
***
0.45±0.28
1.81±0.74
***
No
Yes
0.37±0.14
1.14±0.32
***
0.35±0.13
1.21±0.33
***
0.22±0.13
0.88±0.15
***
0.34±0.14
1.12±0.32
***
19.6***
16.2***
No
2.56±0.87
1.89±1.09
0.47±0.42
2.14±1.09
7.4**
Yes
7.30±2.32
7.63±3.80
2.67±0.61
7.11±3.16
3.7*
***
***
**
***
No
Yes
t-test
Teff (105)
No
Yes
t-test
Wheat (62)
No
Yes
t-test
Haricot bean
(244)
t-test
Potato (42)
t-test
Sweet potato
(46)
63.9***
27.2***
7.4***
1.3NS
3.4*
3.9*
No
4.11±1.68
4.59±2.34
5.52±2.55
4.69±2.28
0.9NS
9.88±3.04
11.13±3.76
12.23±4.12
11.13±3.71
0.8NS
***
***
***
***
No
4.43±1.96
5.89±2.43
8.20±6.34
5.73±2.96
3.4*
Yes
13.41±7.09
13.03±3.28
15.20±3.35
13.33±4.68
0.5NS
***
***
*
***
0.56±0.16
0.42±0.10
0.49±0.25
4.9±1.5
t-test
Coffee (37)
F-test
Yes
t-test
Taro (58)
Damot Gale
Mean yield (t ha-1)
Damot Sore
Sodo Zuria
Yes (FYM)
4.3*
Numbers in parenthesis indicate number of samples; *,**,*** significant at
p<0.05,0.01,0.001, respectively; NS=Not significant (Source: Survey result,2013)
In the present finding, the yield of maize (t ha-1) from fertilized fields was 2.1 ± 0.8 followed
by wheat (1.8±0.74). Haricot bean gave about 1.1 ± 0.32 t ha-1. The yields of root crops (t ha-1)
such as potato, sweet potato and taro using fertilizer were 7.1±3.2, 11.1±3.7, 13.3±4.7,
respectively (Table 2.8). Nevertheless, farmers in the study area are experiencing lower yield
than the national average productivity (t ha-1) reported by CSA (2013) such as maize (3.06),
teff (1.4), wheat (2.11), haricot bean (1.26), sweet potato (28.46), potato (11.52) and Taro
(27.04). Scarcity of arable lands, intensive exploitation of crop fields at diversified slope
ranges, abandoned fallowing, very poor crop residue maintenance, possibly low nutrient status
and inadequate compensation of plant nutrients into the soil system could explain the lower
39
crop yields in study area. Hence, proper investment in practices leading to soil fertility
enhancement is mandatory to generate better yield.
2.4. CONCLUSIONS
Soils are naturally variable and their properties are changing across landscape. In the study
area, land-use for agriculture has grown to extent of ecologically fragile steep slope
topographic positions. This has shown negative consequences on soil fertility and yield of
crops. Farmers traditional soil classification was found to have observable relation with
analysed soil chemistry and also yield. In general, the soil management interventions by
farmers were very insufficient to bring soil fertility improvement and good yield returns.
Therefore, by integrating the indigenous soil knowledge;diversified interventions such as soil
conservation, organic material application, use of bio-fertilizers and balanced inorganic
fertilizer application to restore the soil fertility and productivity should be implemented.
2.5. ACKNOWLEDGEMENTS
We would like to thank Ministry of Education (MOE) for the scholarship, the Ethiopian Soil
Information System (EthioSIS) at the Agricultural Transformation Agency (ATA) for
financial support. We are very grateful for all assistances, knowledge and experiences we have
received from the farmers in Damot Gale, Damot Sore and Sodo Zuria districts.
40
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45
3. SOIL FERTILITY STATUS OF AGRICULTURAL LANDS IN
WOLAITA ZONE, SOUTHERN ETHIOPIA
46
3. SOIL FERTILITY STATUS OF AGRICULTURAL LANDS IN
WOLAITA ZONE, SOUTHERN ETHIOPIA
Fanuel Laekemariam1, Kibebew Kibret1, Tekalign Mamo2and Heluf Gebrekidan1
1.
Haramaya University, School of Natural Resource Management and Environmental Science, Ethiopia. 2.Ministry
of Agriculture, Ethiopia
ABSTRACT
Timely knowledge on soil fertility status is important for soil management intervention and
also to increase crop productivity. However, until recently, adequate information on soil
fertility status was lacking in Wolaita area of Ethiopia. Hence, this investigation was
conducted in Damot Gale, Damot Sore and Sodo Zuria districts of Wolaita zone, southern
Ethiopiato evaluate soil fertility status of agricultural lands and also to contribute information
to the national soil fertility mapping initiative of Ethiopia. About 789 surface soil samples
were collected during 2013 and soil physical (color, particle size and bulk density) and
chemical properties (pH, OC, N, P, K, Ca, Mg, B, Cu, Fe, Mn, Zn, PBS and CEC) were
analyzed. Laser diffraction method for soil particles and mid infrared diffused reflectance
(MIR) spectral analysis for OC, TN and CEC determination were employed. The other
elements were extracted using Mehlich-III and measured using inductively coupled plasma
spectrometer. Based on principal component analysis (PCA), the results across districts
showed that 52% of the total variations were explained by exchangeable bases, CEC, pH,
available P, Cu, B and particle size distribution. The soils have silt loam and clay textures
with moderately acidic soil reaction. The soil properties with the following ranges were found
at low status: soil OC (0.2-6.9%), total N (0.01-0.7%), available P (0.1-238 ppm), S (4-30
ppm), B (0.01-6.9 ppm) and Cu (0.01-5.0 ppm).Besides this, low level of exchangeable Ca, Mg
and K (Mg induced K deficiency) on 22, 34 and 54% soil samples, respectively were recorded.
In the area, Fe, Zn and Mn contents were sufficient. Continuous cropping, low return of crop
residues, low and/or no fertilizer application /N, P, S, B and Cu/ might have caused the low
fertility statusin the area. In order to sustain soil fertility and improve crop productivity,
diversified nutrient management interventions like gradual buildup of soil OM, liming of
acidic soils and application of N, P, K, S, B and Cu fertilizers are recommended.
Key words: Chemical Property, Management, Mehlich-III, PCA, Physical Property,.
47
3.1. INTRODUCTION
Declining soil fertility is a serious limitation to crop production in Ethiopia. The primary
causes are loss of organic matter (OM), macro and micro-nutrient depletion, acidity, salinity,
topsoil erosion and deterioration of physical soil properties (IFPRI, 2010). Different studies
conducted in Ethiopia in the past few years by various researchers have demonstrated the
deficiency of nitrogen (N) and phosphorous (P) (Tekalign et al., 2002; Hailesilase et al.; 2005;
Lalisa et al., 2010; Wondwosen and Sheleme, 2011; Abreha et al., 2012; Fanuel and Gifole,
2012). Apart from these, the low levels of potassium (K), sulfur (S), zinc (Zn) and boron (B)
in the country have also been reported (Abiye et al., 2004; Wassie and Shiferaw, 2011;
EthioSIS, 2015).
Recent studies conducted in cultivated fields of the country also indicated the deficiency of
sulfur (S) (Abreha et al., 2012; Eyob, 2014; Mohamed, 2014; Tegbaru, 2014; Habtamu et al.,
2015), B (Teklu, 2004; Tegbaru, 2014) and Copper (Cu) (Abebe and Endalkachew, 2012a;
Girma and Endalkachew, 2013). Moreover, results from the soil fertility mapping initiative
started by the Agricultural Transformation Agency (ATA) in partnership with the Ministry of
Agriculture (MoA) have persistently shown the deficiency of seven soil nutrients (N, P, K, S,
Zn, B and iron (Fe)) across the entire surveyed areas of Tigray region (EthioSIS, 2014) and in
many other districts of Amhara, Oromia and southern regions of Ethiopia (EthioSIS, 2015).
Additionally, deficiencies of magnesium (Mg), manganese (Mn), Fe and Cu in a few locations
were also observed (Tekalign Mamo, Personal communication). It is very likely that the range
of deficient nutrients will increase when the survey work addresses more areas, particularly
those with either high or low pH.
Wolaita, located in Southern Nations’, Nationalities’ and Peoples’ Regional State (SNNPRS)
of Ethiopia, is densely populated zone (385 population per km-2) (CSA, 2010). Land is a
scarce resource in the area and arable land per household is declining as a result of high
population pressure. According to WZFEDD (2012), about 57% of households in the zone
possess less than 0.25 hectare (ha) of land. This has compelled most farmers to practice
continuous and multiple cropping system with subsequent removal of plant residues (Pound
48
and Jonfa, 2005) and ultimately causing a decline in soil OM and plant nutrients of cultivated
lands (Mulugeta, 2006; Wondwosen and Sheleme, 2011; Fanuel and Gifole, 2012; Alemayehu
and Sheleme, 2013). Natural factors and their interaction with anthropogenic factors would
also be important reasons for declining soil fertility. In line with this, some studies conducted
in some districts of Wolita zone have shown the deficiency of available P, Fe, Cu, Mn and Zn
(Mulugeta, 2006; Alemayehu and Sheleme, 2013). In addition, Wondwosen and Sheleme
(2011) reported below critical levels of N, P, Cu, S and K indicating that these are among the
yield limiting nutrients in the soil. Furthermore, lower OM, N and P contents in cultivated
lands was also reported by Fanuel and Gifole (2012).
In areas like Wolaita, owing to differences in topography, soil management practices and
inherent soil properties such as acidity, inadequacy of essential elements in vast areas of
agricultural lands are expected. This could be among the reasons for the apparent lower yield
in the area. Nevertheless, a detailed knowledge describing the concentration and spatial
distribution of essential soil nutrients is lacking. Hence, assessing the soil fertility status is
imperative to identify yield limiting nutrients, design better soil management practices and
thus to solve the problem of low productivity. Therefore, this study was conducted to
investigate the current soil fertility status and determine nutrients that are in short supply and
limit crop yield in Wolaita zone, Southern Ethiopia. Furthermore, it is hoped that the generated
data will enrich the national soil information database being established through the soil
fertility mapping initiative, named Ethiopian Soil Information system (EthioSIS), which is
launched in 2011 to establish a national soil information system and conduct fertility survey of
Ethiopian soils.
49
3.2. MATERIALS AND METHODS
3.2.1. Description of the Study Area
The study was conducted in Damot Gale, Damot Sore and Sodo Zuria districts, Wolaita zone,
Southern Nations’, Nationalities’ and Peoples’ Regional State (SNNPRS)of Ethiopia (Figure
1.1) during 2013. The study districts from Wolaita zone were purposely selected because they
have good potential for agriculture. The sites are located from 037°35'30" - 037°58'36"E and
06°57'20" - 07°04'31"N. The study area covers about 84,000 ha. The area has a bimodal
rainfall pattern with mean annual precipitation of 1355 mm (Figure 1.2). The temperature
ranges between 17.7 to 21.7 °C with an average of 19.7 °C (NMA, 2013). The elevation in
studied districts varied from 1473 to 2873 m.a.s.l (Figure 1.3). The area is predominantly
characterized by mid highland agro-ecology. Eutric Nitisols associated with Humic Nitisols
are the most prevalent soils (Tesfaye, 2003). Agriculture in the study area is predominantly
small-scale mixed subsistence farming. The farming system is mainly based on continuous
cultivation without any fallow periods. Brief descriptions about the area are indicated under
section 1.1.
3.2.2. Soil Sampling Procedure and Laboratory Analysis
3.2.2.1. Soil sampling procedure
Geographical information system (GIS) was employed to randomly assign a predefined
sampling locations following EthioSIS sample distribution procedure (EthioSIS, 2014).
Accordingly, 789 sampling points (243 on Damot Gale, 216 on Damot Sore and 330 on Sodo
Zuria) were generated for sample collection.These samples were randomly distributed at an
average separation distance of 512 meters. During the survey work, the pre-defined sample
locations were navigated using geographical positioning system receiver (model Garmin
GPSMAP 60Cx).
Once the sampling point was navigated and reached in the dominant land use types, 10 to 15
sub-samples were taken based on the complexity of topography and heterogeneity of the soil
type. Samples were collected using soil auger and then composited. During sample collection,
50
hot spot areas (manure and crop threshing sites) were excluded. Surface soil samples were
taken to a depth of 0-20 cm for tef, haricot bean, wheat, maize, etc, while the sampling depth
was extended to 50 cm for perennial crops such as enset and coffee fields. From the
composited sample, one kilogram (kg) of soil was taken with a labeled soil sample bag. To
reduce the potential for cross-sample contamination among samples, the soil auger and other
sampling tools were cleaned before taking the next sample at different locations. For soil bulk
density determination undisturbed soil samples using core sampler were collected.
3.2.2.2.Sample Preparation andSoil analysis
Soil samples were air-dried at room temperature, ground by mortar and pestle, and sieved
through a 2 mm mesh sieve and 0.5 mm mesh sieve (for mid infrared diffused reflectance
(MIR) spectral analysis). Soil samples were processed at the National Soil Testing Center
(NSTC), Addis Ababa, Ethiopia.
Soil samples were analyzed for soil color, bulk density, particle size distribution (PSD), pH,
OC, total N, available P and S, exchangeable bases (Ca, Mg, Na and K), extractable
micronutrients (Fe, Cu, Mn, Zn and B), CEC, exchangeable acidityand acid saturation. Particle
size distribution (PSD), pH, OC, TN, CEC and exchangeable acidity analyses were done at
NSTC, whereas available P and S, exchangeable bases (Ca, Mg, K and Na) and extractable
micronutrients (Fe, Cu, Mn, Zn and B) were conducted in Altic B.V., Dronten, The
Netherlands using Mehlich-3 (M-3) multi-nutrient soil extraction method (Mehlich, 1984).
The soil color (dry) was described with Munsell soil color chart during the noon hours (KIC,
2000). Soil bulk density was determined using core method as described by Anderson and
Ingram (1993). The PSD was analyzed by laser diffraction method using laser scattering
particle size distribution analyzer (model: Horiba- Partica LA-950V2). A teaspoon of soil
(approximately 10 g) sieved through 2 mm was introduced into the dispersion unit of the laser
particle analyzer for measurement. The soil sample was run in a wet mode using deionized
water and 1% sodium hexametaphosphate (Calgon) solution as dispersing agent. To maintain
the random orientation of particles in suspension, automatic ultrasonication was applied. All
the required operations were controlled by a personal computer. The LA-950 software version
51
7.01 for Windows (Horiba Ltd, NextGen®, 2010) was used to run the analysis. For each
sample, four consecutive readings were taken within 15 minutes duration. The readings were
converted to % sand, silt and clay, using the appropriate script on the R language and
environment for statistical computing (R Core team, Vienna, Austria, 2013). The fourth
reading that was taken after continuous agitation of the particles was considered as a final data
for the particle size distribution.
Soil pH (1:2 soil: water suspension) was determined by standard methods (Mylavarapu, 2009)
with a glass electrode (model CP-501). For soils having pH < 5.5, exchangeable acidity was
measured using the method described in Sahlemedhin and Taye (2000). Mehlich-III multinutrient extraction method using 1:10 soil: solution ratio was used to extract available P,
available S, exchangeable basic cations (Ca, K, Mg and Na) and extractable micronutrients
(Fe, Mn, Zn, Cu, and B). The concentration of elements in the solution was measured using
inductively coupled plasma (ICP) spectrometer. Soil Mn content was evaluated using
manganese activity index (MnAI) (Karltun et al., 2013).
MnAI=101.7-(15.2*pH) +3.75*Mn_soil
where: pH is pH(H2O) and Mnsoil is the concentration of Mehlich-III extracted manganese.
The same samples used for wet chemistry were also subjected to MIR spectral analysis. The
study adapted the EthioSIS protocol used by Eyob (2014), Mohammod (2014) and Tegbaru
(2014). For MIR spectral analysis, soil samples were ground using Retsch mortar grinder RM
200 to size smaller than 0.5 mm. Soil samples weighing 0.035 g were loaded in a single well
and one sample was loaded in four consecutive wells of an aluminum microplate having 96
wells. The sample surface was gently pressed, leveled and smoothed using a micro-spatula (a
rounded, smooth surface glass rod). The absorbance of diffused reflectance spectra was
scanned using the HTS-XT accessory of a Bruker-TENSOR 27 spectrometer. The background
(i.e. soil sample free well) was scanned using roughened surface well of the aluminum
microplate. Absorbance spectra of the entire soil samples were measured using OPUS version
7.0 software (Bruker® Optic GmbH, 2011) with 32 scans and spectral range of 7400 – 600
cm-1 (wave numbers) including part of near infra red (NIR) region. The spectrum acquisition
took an hour per plate. The MIR region spectra, in the wave number range of 4000 – 600 cm-
52
1
(2500 – 16667 ηm) were used to predict soil properties. Quantitative analysis of the spectra
was done using Quant 2 evaluation function of OPUS (software version 7) to predict
concentration of OC, TN and CEC. The average of four consecutive scans of each parameter
was taken as a predicted data for the report.
3.2.3. Statistical Analyses
To group related soil properties and explain most of the variance with a small set of variables,
the principal component analysis (PCA) was applied. The Kaiser-Meyer-Olkin measure
(KMO) of sampling adequacy was performed prior to PCA. In addition, descriptivestatistics
like mean ± standard deviation, median, minimum and maximum values, ratio and percentage
were used for data analysis. Furthermore, analyses of variance test, Pearson’s correlation
analyses were performed to evaluate the soil properties. Variation in soil properties was
determined using the coefficient of variation (CV) and rated as low ( < 20%), moderate (20 50%) and highly variable (> 50%) according to Aweto (1982) cited in Amuyou et al. (2013).
For the management purposes, interpretation of the results was given using proper ratings. All
data analysis were performed using Microsoft excel and statistical package for social sciences
(SPSS) software version 20.
53
3.3. RESULTS AND DISCUSSION
3.3.1. Variability of Soil Properties
The overall variability soil properties were evaluated using principal component analysis
(PCA). The KMO valuesof PCA were 0.75 for Damot Gale, 0.69 for Damot Sore and 0.69 for
Sodo Zuria and 0.70 (across districts). In all cases, the value was above 0.6 indicating the
sample number was adequate /acceptable/ to put the data into PCA analysis. Accordingly,
PCA was computed and the result is presented in Table 3.1.
Table 3.1. Eigen value and explained variances of PCA for the study districts in Wolaita
Zoneduring 2013.
Location
Damot Gale
Damot Sore
Sodo Zuria
Total
PCA
Eigen Var Cum Eigen Var Cum Eigen Var Cum Eigen Var Cum
value (%)
%
value (%)
%
value (%)
%
value (%)
%
1
7
41
41
6
33
33
5
30
30
5
32
32
2
3
16
57
3
20
53
4
21
51
3
20
52
3
2
14
71
2
14
67
2
14
65
2
13
65
4
1
7
78
1
8
75
1
6
72
1
7
72
Var = Explained variance
Cum = Cumulative variance
In Damot Gale district, PCA 1 and PCA 2 were able to explain 57% of the total variation
(Table 3.1). Among measured soil variables of PCA 1, B followed by CEC > Ca > K >
available P > Zn > Mg > pH > Mn have shown higher contribution to variability (loading
factor > 0.6). Their contributions to the variability were found to be positive (Figure 3.1a). In
PCA 2, higher loading factor was observed on clay particles with negative contribution
followed by silt which had a positive contribution to the variability (Figure 3.1a).
In Damot Sore district, PCA 1 and PCA 2 together were able to explain 53% of the total
variation (Table 3.1). The loading factor in PCA 1 was highest for Ca followed by CEC > K >
Mg > pH > available P > B > Cu with positive contribution to the variability (Figure 3.1b). In
PCA 2, the higher loading factor was observed on clay particles with a negative contribution
followed by silt and sand having a positive contribution to the variability (Figure 3.1b).
54
In Sodo Zuria district, PCA 1 and PCA 2 in combination were able to explain 51% of the total
variation (Table 3.1). The loading factor in PCA 1 was highest for Mg followed by CEC > Ca
> pH > K > Cu with positive contribution to the variability (Figure 3.1c). In PCA 2, higher
loading factor was observed on clay particles with negative contribution followed by silt, Fe
and sand having a positive contribution to the variability (Figure 3.1c).
Across districts (n=789), PCA 1 explains 32% while PCA 2 explains 20% and both
components together were able to explain 52% of the total variation (Table 3.1). The loading
factor in PCA 1 was highest for Ca followed by CEC > K > Mg > pH > available P > Cu with
positive contribution to the PCA1 variability (Figure 3.1d). In PCA 2, higher loading factor
was observed on clay followed by silt and sand particles (Figure 3.1d).
As expected, PCA has shown to be a useful technique in the reduction and summarization of
soil variables. In general, the most important variables in all districts that accounted for soil
variability were exchangeable bases (Ca, Mg, K), CEC, soil pH, available P, Cu and soil
particle size. In the study areas, the variation in topography, management, land use types and
inherent soil property could explain the observed variability. In line with this observation,
inherent soil spatial variability, soil management differences, clay mineralogy (Okubay et al.,
2015) and variation in mineralogy of parent rock (US.EPA, 2006; Kabata- Pendias and
Murkhejee, 2007; Kibet, 2013) were reported to be causes for soil variation.
In the study area, Nitisols are dominant (Tesfaye, 2003) in which their clay assemblage is
dominated by kaolinite (FAO, 2014). These clay minerals have pH dependent charges (Havlin
et al., 2009). In kaolinte, as soil pH increases, some of H ions on the Si-OH and Al-OH groups
are removed, neutralized and thus increases the (-) edge charges /CEC values/. In the present
study, the association between soil pH and increase in the soil CEC might be linked with the
presence of pH dependent charges, probably on kaolinite clay mineralogy (Havlin et al.,
2009). The soil pH in the present study also co-varied and influenced CEC, exchangeable
bases (Ca, Mg, K), available P and Cu. This is evidenced with the significant (P ≤ 0.001) and
positive correlation between soil pH and available P (r=0.44), Ca (r=0.66), Mg (r=0.52),
K(r=0.65), Cu (r=0.27), B (r=0.33) and CEC (r = 0.53). Similarly, Maria and Yost (2006) and
55
Joao et al. (2009) observed variation in the soil CEC along with soil pH. In addition, a positive
correlation between pH with available P and exchangeable bases under different land uses and
soil depths of Alfisols was reported by Alemayehu and Sheleme (2013). Moir and Moot
(2010) from New Zealand also reported the lower level of Ca, Mg and K, base saturation and
CEC at a pH below value of 5.8 (Islabão et al., 2012).
Concerning soil particles, variation among districts were observed. Silty loam textured soils
are found in Damot Gale while clay textured soils are found in the other two districts. In this
study, difference in soil particle sizes might be the reason for its appearance as an important
source of variability. Clay content was found to show a significant and inverse relationship
with sand (r =-0.8), silt (r= -0.98), pH (r=-0.3), available P (r=-0.3) and Ca (r=-0.3). This
would imply that the higher clay content might be associated with gradual accumulation of
acidic cation such as exchangeable Al, H, and oxides of Al and Fe. This results in P fixation
and reduces its availability (Abreha et al., 2012). Besides this, the negative correlation
between clay and Ca, according to Tabu et al. (2005) indicates the dominance of low activity
clay minerals that predispose to leaching of exchangeable bases.
56
B
A
C
Figure 3.1. Component plot of soil properties of Damot Gale (A), Damot Sore (B), Sodo Zuria (C) and Total (D)
D
57
3.3.2. Soil Physical and Chemical Properties
3.3.2.1. Selected soil physical properties
The hue index of the study area was 2.5YR, 5YR, 7.5YR and 10YR. The value and chroma of
sampled soils are indicated in Figure 3.2. Combining the indices, brown, dark reddish brown,
reddish brown and gray soil colors were observed. Damot Gale district has dominantly brown
color, whereas samples in Damot Sore and Sodo Zuria have shown brown, dark reddish brown
and reddish brown colors. Light reflection tends to increase on brown and reddish soil colors,
and resulted an increase of color value. Similarly, purity of spectral colors increases with
increase in reflection resulting with an increase of the chroma.
The soil colors in the present finding might reflect the low status of soil OM and influence of
oxidized Fe. As indicated in FAO (2014), the higher content of Fe in Nitisols would result in
red or reddish-brown soil colors. Moreover, bright colors due to oxidized Fe were also
reported by Teshome (2013). Analogously, Desbiez et al. (2004) found higher value and
chroma for red and least fertile soils; and lower value and chroma for darker and fertile soils.
Though caution is needed, the perception to consider reddish colors as less fertile soil by
farmers than the darker soil colors have also been stated by Desbiez et al. (2004),Pound and
Darker <------Chroma------->Lighter
Jonfa (2005) and Hailesilase et al. (2006).
7
6
5
4
3
2
1
0
0
1
2
3
4
5
Darker <----------Value----------> Lighter
6
7
Figure 3.2. Soil color value and chroma indices of samples collected from Damot Gale, Damot
Sore and Sodo Zuria districts in Ethiopia.The size of each bubble is proportional to the number of
samples at the corresponding value/chroma combination.
58
Statistically significant differences in particle size distribution and bulk density (BD) (p <
0.001) among sampled soils were recorded (Table 3.2). The mean particle size distribution in
Damot Gale district is in the order of silt > clay > sand, whereas the distribution in Damot Sore
and Sodo Zuria districts are in the order of clay > silt > sand. The textural class of Damot Gale
district varied from silty loam to silty clay loam; where silt loam texture is dominant (Figure
3.3). Soil samples in Damot Sore and Sodo Zuria districts are clay in texture (Table 3.2 and
Figure 3.3).
The median and range value of silt /clay ratiowas 2.8 (0.2-10.7), 0.4 (0.1-1.5) and 0.3 (0.0310.7) for the soil samples collected from Damot Gale, Damot Sore and Sodo Zuria districts,
respectively (Table 3.2). According to Young (1976), a ratio of silt /clay greater than 0.15
indicated as young soil, not highly weathered and having easily weatherable minerals.
However, Nitisols which are known to have relatively advanced weathering (FAO, 2014) are
dominant in the study area. This implies that the clay in the upper surface might have been
translocated or removed by erosion. In agreement with this, the report of Alemayehu and
Sheleme (2013) in Sodo Zuria district revealed a high silt/clay ratio with a relative reduction
with depth i.e. (2.6, 1.9), (2.7, 1.2) and (2.3, 1.6) on 0 - 15 and 15 - 30 cm of enset, grass and
maize land use types, respectively. In addition, Wondewosen and Sheleme (2011) reported
high silt/clay ratio (1.5) on surface soils of Sodo Zuria district, however, these authors
associated with non advanced stage of soil development.
59
Table 3.2.Particle size distribution, silt: clay ratio and bulk density of surface soil samples
collected from study districts in Ethiopia during 2013.
District
Descriptive
Sand
Silt
Clay
Silt :
*BD
Soil
-3
statistics
Clay
(g cm )
textural
ratio
classes
Damot Gale
Mean
18.3
56.2 25.4
3.2
1.14
(N=243)
Std.Dev
6.6
10.6 13.7
2.1
0.12
Median
17.2
58.0 21.7
2.8
1.13
Silt Loam
Minimum
0.5
17.0
6.1
0.2
0.76
Maximum
39.5
78.6 79.9
10.7
1.48
CV (%)
36
19
54
66
11
Mean
12.0
24.7 63.3
0.4
1.23
Damot Sore
Std.Dev
4.1
7.8
11.4
0.2
0.13
Clay
(N = 216)
Median
12.3
24.7 62.1
0.4
1.22
Minimum
2.8
7.3
32.5
0.1
1.04
Maximum
23.3
47.9 89.7
1.5
1.52
CV (%)
34
32
18
50
11
Sodo Zuria
Mean
11.3
21.6 67.0
0.4
1.20
Clay
(N=331)
Std.Dev
4.3
10.0 13.0
0.3
0.13
Median
11.5
19.6 69.2
0.3
1.19
Minimum
1.3
3.0
28.6
0.03
0.76
Maximum
30.8
51.5 95.6
1.8
1.50
CV (%)
38
46
19
75
11
Total
Mean
13.7
33.1 53.2
1.3
1.18
Clay
(N=789)
Std.Dev
5.9
18.2 22.6
1.8
0.13
Median
13.1
27.5 58.8
0.5
1.17
Minimum
0.5
3.0
6.1
0.03
0.76
Maximum
39.5
78.6 95.6
10.7
1.52
CV (%)
43
55
42
138
11
Fvalue
150
1015 827
503.5
18.4
P value
0.000 0.000 0.000
0.000
0.000
*Sample size for BD (bulk density) of Damot Gale = 197, Damot Sore=42, Sodo Zuria=193 and total = 432
The soil bulk density (BD) values varied with soil textural classes. Soils with silty loam
textural classes had relatively lower BD values than the clay textural classes. This is evidenced
by significant (p < 0.01) correlation of BD with silt (r = -0.44) and clay (0.43) particles. Soil
BD in the present finding had also shown significantly (p < 0.01) inverse relation with soil
OM (r = -0.33*). In line with this finding, an inverse relationship between soil BD and OM
was reported by Oguike and Mbagwu (2009) and Gajic et al. (2006).
60
Bulk density influences soil physical properties, particularly soil-water movement, aeration
and root proliferation. The soil BD values of the silty loam textured Damot Gale soils varied
from 0.76 - 1.48 gcm-3, whereas the BD on clay textured soils was between 1.04 to 1.52 gcm-3
(Damot Sore) and 0.76 to 1.52 gcm-3 (Sodo Zuria). The effect of soil BD for plant root growth
depends on soil texture. Hazelton and Murphy (2007) indicated 1.6 and 1.4 g cm-3 as critical
BD values for loam/clay loam and clay texture soils, respectively. Based on these values,
except 1.7% of soil samples from clay textured soils of Damot Sore and Sodo Zuria districts,
soils are considered satisfactory for plant growthand root penetration would less likely be
restricted by the soil.
61
Damot Sore
Damot Gale
Sodo Zuria
Figure 3.3. Soil textural classes of the study districts
62
3.3.2.2. Soil pH and exchangeable acidity
Soil pH within and across the study areas showed low variability (CV < 20%). It ranged
between 4.5 to 8 (Table 3.3). Most nutrients for field crops are available at pH values between
5.5 and 8.0 (Landon, 2014). On the bases of this, about 21% of total samples had pH < 5.5
(strongly acidic reaction). This pH range is where hydrolysis of Al and a sharp increase in
exchangeable Al is expected (Moir and Moot, 2010). This process releases H+ ions that further
lower the pH of the soil to a level that seriously affects the availability of certain nutrient
elements such as P and increases the toxicity of Al and Fe. This is also confirmed with
significant (P ≤ 0.001) and positive correlation between pH and available P (r = 0.4).
Overall, acidic soil reaction was found to be prevalent in the study areas. In terms of
proportion, about 3.3, 60, 31.3 and 5.3% of the samples in the Damot Gale district are
categorized as strongly acidic (pH < 5.5), moderately acidic (5.6 – 6.5), neutral (6.6 – 7.3) and
moderately alkaline (7.4 – 8.4), respectively, as per the ratings of EthioSIS (2014). In Damot
Sore district, about 33, 42, 22 and 3% of the samples are rated as strongly acidic, moderately
acidic, neutral and moderately alkaline soils, respectively, whereas, in Sodo Zuria district,
about 26.7, 58.0, 14.5 and 0.9% of the samples were found to be strongly acidic, moderately
acidic, neutral and moderately alkaline, respectively.
Removal of bases by crop harvest,leaching of basic cations and Al hydrolysis, in the present
study, could be the most important acid forming factors and processes. In agreement with this,
various research results showed that removal of basic cations through crop harvest
(Hartemink, 2006, Abreha et al., 2012; Yihenew et al., 2015), leaching due to excessive
precipitation, steepness of the topography, application of inorganic fertilizer (Khan et al.,
2007, Yihenew et al., 2015), mineralization and formation of humic substances (Islabão et al.,
2012)have reported as causes for the soil acidiy formation. Though the use of inorganic
fertilizers in the study area are small,Cardelli et al. (2012) and Alexandra et al. (2013)
explained that H+ ion released through nitrification of NH4+ sourced fertilizers on cultivated
lands might contribute to the development of lower pH.
63
The exchangeableacidity (EA) and acid saturation (AS)on entire districts varied from 0 to
5.1Cmol (+)kg-1and 0 to 21%, respectively; and their median was nil(Table 3.3). Relatively,
EA and AS levels werehigher in fine textured soils of Damot Sore and Sodo Zuria.Overall, 7%
of samples which entirely from fine textured areas showed exchangeableacidity 1.9-5.1 Cmol
(+) kg-1 andAS> 10%. In Ethiopia, AS level of 10% wasused for lime rate determination (Eyob,
2014; Mohammed, 2014; Tegbaru, 2014). Hence, to revert the adverse effects of soil pH and
make it permissible for crops, liming and organic material application should be considered
for soils having pH > 5.5.
Table 3.3. Descriptive statistics of soil pH, exchangeable acidity and acid saturation of soil
samples collected from study districts in Ethiopia during 2013.
Districts
Descriptive
pH
Exc. acidity
Acid saturation
statistics
(H2O)
(Cmol(+kg-1)
(%)
Mean
6.4
0.00
0.10
Damot Gale
StdDev
0.5
0.10
0.60
(N=243)
Median
6.3
0.00
0.00
Minimum
5.2
0.00
0.00
Maximum
8.0
1.30
7.10
CV (%)
8.0
Damot Sore
Mean
6.0
0.50
2.30
(N=216)
StdDev
0.7
1.00
4.70
Median
5.9
0.00
0.00
Minimum
4.5
0.00
0.00
Maximum
7.8
5.10
20.80
CV (%)
12.0
Sodo Zuria
Mean
5.9
0.40
2.20
(N=330)
StdDev
0.6
0.90
4.40
Median
5.9
0.00
0.00
Minimum
4.7
0.00
0.00
Maximum
7.4
4.10
19.50
CV (%)
10.0
Total (N=789) Mean
6.1
0.30
1.60
StdDev
0.6
0.80
3.90
Median
6.1
0.00
0.00
Minimum
4.5
0.00
0.00
Maximum
8.0
5.10
20.80
CV (%)
10.0
Fvalue
49.0
26.0
27.0
P
0.000
0.000
0.000
Numbers in brackets refer to sample size
64
3.3.2.3. Soil OC, TN, Available P and S
The soil parameters of agricultural soils (Table 3.4) within and across districts showed
variability. The variability for most of the parameters was found to be moderate (CV=20-50%)
to high (CV > 50%), according to Aweto (1982) cited in Amuyou et al. (2013). In study areas,
this was ascribed to (1) random pick up of large number of soil samples and (2) remarkably
high variation of topography, management, land use types and inherent properties like texture
and pH. These resulted variation in measured values (minimum and maximum). Hence,
moderate to higher CV values became apparent. Similarly, in soil fertility survey researches
like this study, higher CV values ranging from 13 (bulk density and pH) to 585%
(mineralizable N) (Cambardella et al. 1994), 0.3 (exchangeable K) to 118.64% (Zn)
(Nourzadeh et al., 2012), 5.37 (pH) to 100% (exchangeable K) (Behera and Shukla, 2015) and
0.6 (pH) to 69.7% (exchangeable K) (Mohammed, 2014) were reported.
The OC content of agricultural soils in the study areas showed moderate variability (Table
3.4). The content across the entire districts ranged from 0.2 to 6.9% and the median was 2%.
The maximum OC (6.9%) was obtained from the grass field. The median value of OC was
highest (2.5%) in Damot Sore districts,whereas, the other two districts were comparable
(Table 3.4).This figures highlighted the lowlevel of soil organic matter in the majority of
sampled fields. In the study area,complete removal of crop residues without adding external
organic inputs such as compost or manure were common practices.As per the ratings of
Landon (2014), about 48, 51 and 1% of soil samples across the study area qualified as very
low (< 2%), low (2 - 4%) and moderate (4 - 10%) levels of OC, respectively.
In general, soils in the present studywerefound to be low in soil OC. Several authors reported
the lower levels of soil OC in cultivated lands. But, it is clear from the present finding that
differences in soil OC status were not found between samples collected from the so called
fertile land use types “ homestead fields" (fields growing enset and coffee plants) and other
fields (cultivated and grazing). The finding is contrary to earlier observations (e.g. Elias, 2000;
Pound and Jonfa, 2005; Hailesilase et al., 2005; Lalisa et al., 2010; Abebe and Endalkachew,
2012b) who reported higher soil fertility status in homestead areas /enset/ and coffee agroecosystem/ than other cultivated lands in Ethiopia.
65
In conformity to the present observation, complete removal of aboveground biomass
(Gebeyaw, 2007; Sheleme, 2011; Teshome et al., 2013; Tegbaru, 2014; Okubay et al., 2015),
intensive cultivation (Teshome et al., 2013;Tegbaru, 2014), insufficient application of organic
inputs (Gebeyaw, 2007; Okubay et al., 2015) and heavy grazing (Sheleme, 2011) were
reported for encouraging lower soil OC in Ethiopian soils. The lower soil OC could result in
poor aggregate stability and thereby aggravate soil degradation (Gajic et al., 2006); and also
influence soil macro and micro-nutrient reserves (Habtamu, 2015). Using simple aggregate
stability estimation equation (% OM x 100/% clay ≤ 7) (FAO, 1998), the soil OM on about
62% of the sampled fields does not contribute to soil aggregate stability. This implies that soil
particles are more likely to be detached with erosion. Lower aggregate stability due to lower
OC resulted due to continuous cultivation and rapid oxidation of soil OM was also reported by
Girma and Endalkachew (2013) and Ozgozet al. (2013).
66
Table 3.4. Descriptive statistics of soil OC, TN, Av. P and SO42--S in the study districts in
Ethiopia.
Districts
Descriptive
OC
TN
C : N ratio
Av. P
SO42--S
statistics
%
%
mg kg-1
mg kg-1
Damot Gale
(N=243)
Damot Sore
(N=216)
Sodo Zuria
(N=330)
Total (N=789)
Mean
Std Dev
Median
Minimum
Maximum
CV (%)
Mean
Std Dev
Median
Minimum
Maximum
CV (%)
Mean
Std Dev
Median
Minimum
Maximum
CV (%)
Mean
Std Dev
Median
Minimum
Maximum
CV (%)
Fval
1.89
0.69
1.80
0.2
4.2
37.0
2.52
0.68
2.5
1.0
4.4
27.0
1.9
0.69
1.9
0.3
6.9
36.0
2.08
0.74
2.0
0.2
6.9
36.0
60.4***
0.12
0.06
0.12
0.01
0.35
50.0
0.18
0.06
0.18
0.04
0.35
33.0
0.14
0.07
0.14
0.02
0.68
50.0
0.15
0.07
0.15
0.01
0.68
47.0
42.6***
17.2
5.6
15.5
8.6
40.6
33.0
14.4
2.4
14.0
10.1
30.9
17.0
14.2
3.3
13.4
7.4
35.0
23.0
15.1
4.1
14.0
7.4
40.6
27.0
42.9***
18.3
34.8
6.3
0.1
215.0
190.0
9.4
25.2
3.1
0.1
238.1
268.0
5.6
10.0
2.9
0.1
99.4
179.0
10.5
24.8
3.4
0.1
238.1
236
19.0***
10.2
2.9
9.7
4.2
20.3
28.0
11.0
3.9
10.2
3.7
28.2
35.0
11.3
3.7
10.9
4.2
30.3
33.0
10.9
3.6
10.4
3.7
30.3
33.0
6.4**
Numbers in the bracket refers to sample size, **, *** significant at p <0.01, 0.001, respectively.
The total nitrogen (TN) content of surface soils on the other hand showed moderate variability
within and among districts. It followed the trend of soil OC. This is best described by
significantly (P ≤ 0.001) and positive correlation between TN and OC (r =0.95). In general,
clay textured soils of Damot Sore and Sodo Zuria districts were found to have higher TN
values compared to the silty loam textured soils of Damot Gale district. This is also supported
by significant correlation between TN and clay (r=0.3) as well as silt (r =-0.2) contents.
67
Overall, TN varied between 0.01 to 0.7%, with mean values 0.12 ± 0·1 Damot Gale, 0.18 ±
0.1 Damot Sore and 0.14 ± 0.1 Sodo Zuria (Table 3.4). The maximum OC (0.7%) was
recorded from the grass field. Similar to soil OC, most of the soils showed low to very low
TN. Based on the rating of EthioSIS, (2014), about 27.6, 31.6, 38.7, 2.0 and 0.1% of the soil
samples across the study area were very low (< 0.1), low (0.1 -0.15), optimum (0.15 - 0.3),
high (0.3 - 0.5) and very high (> 0.5%) in TN levels, respectively. In addition, considering the
ratings of Landon (2014) for tropical soils, 61% and almost 39% were under very low to low
(< 0.2%) and medium (0.2-0.5) in soil TN, respectively. The C to N ratio of soil samples
varied from 7 to 41 (Table 3.4), indicating variation in microbial activity and CO2 evolution.
This implies that soil OM decomposition proceeds at a maximum rate (if C/N < 25) (Hazelton
and Murphy, 2007).
Considering aforementioned TN ratings (EthioSIS, 2014 and Landon, 2014), lower to
moderate TN including areas which are thought to have high and very high soil fertility levels
were noticed. The result is an alarm indicating that soil fertility including fertile land use types
(i.e. enset/coffee fields) is going to the worsen state. The apparent TN status in fertile land use
types of the present study (low and medium)was in disparity from the reports of Elias (2000),
Pound and Jonfa (2005), Hailesilase et al.(2005) and Lalisa et al.(2010) who noted higher TN
content at homestead, enset and coffee growing farms. Practically, total nitrogen is not
guaranty of available forms of N (NH4-N and NO3-N) as it comprises them only 2–3 % of the
total soil nitrogen. Thus, the recent fertilizer program of Ethiopia included N as a common
element in blended fertilizer types (EthioSIS, 2014) which is irrespective of TN values.
Hence, gradual build up of soil OM and application of N containing fertilizers in the soils of
the study area need to be considered for ensuring sustainable productivity.
Organic matter is the main supplier of soil N, S and P in low input farming systems (Gebeyaw,
2007). Tiejun et al. (2007) and Alexandra et al. (2013) reported that changes in soil OM could
lead to changes in total N. The long term cultivation without organic fertilizers leads to a
decrease in soil OC and total N contents because organic forms generally account for more
than 95% of soil N (Tiejun et al., 2007; Alexandra et al., 2013). The lower TN in the study
districts may be ascribed to complete removal of crop residues, less organic input application
68
and more intensive cultivation. Frequent cultivation would accelerate the higher oxidation rate
of soil OM. In general, the existing input use practices could not compensate the observed
mineralization of OM and N losses. In line with this finding, Abreha et al. (2012); Girma and
Endalkachew (2013) and Tsehaye and Mohammed (2013) reported lower soil TN due to
intensive cultivation, less input application and higher mineralization rate in Ethiopian soils.
The available P content showed high variability. The values in theentire districtsranged from
0.1 and 238.1 mg kg-1and the median was 3.41 mg kg-1. The maximum values (Table 3.4)
were recorded on maize, coffee and carrot fields of Damot Gale, Damot Sore and Sodo Zuria
districts, respectively. It was found that about 76, 10, 8, 4.5 and 1.6% of the samples in Damot
Gale district were found to have very low (< 15), low (15 – 30), optimum (30 – 80), high (80 150 ) and very high (> 150 mg kg-1) available P, respectively when compared to the values
proposed by EthioSIS (2014) whereas about (89, 86.7), (6, 6), (3, 4.7), (1, 1.9) and (1, 0.8%)
of soil samples in Damot Sore and Sodo Zuria districts were qualified to be very low, low,
optimum, high and very high available P contents, respectively.
The existence of low contents of available P as a common characteristic in most of the
cultivated soils of Ethiopia were indicated by (Tekalign et al., 2002; Wondwosen and
Sheleme, 2011; Abreha et al., 2012; Fanuel and Gifole, 2012; Abebe and Endalkachew,
2012a). The low available P status seems to be related mainly to the presence of low pH and
high exchangeable acidity. It was also noted that there existed significant (P ≤ 0.001) and
positive correlation (r = 0.44) between available P and soil pH. It is obvious that low pH (<
5.5) soils often have high P fixing capacity due to their high Al and Fe oxide concentrations.
In this situation, the result agrees with those of Álvarez-Solís et al. (2007) and Abreha et
al.(2012) who reported low available P on strongly acidic soils. However, P deficiency due to
fixation (pH < 5.5) in the present study could likely occur on 21% of the total soil samples,
which were strongly acidic. Hence, P fixation could likely be very small for majority (79%) of
the soil samples having slightly acidic to neutral pH. Thus, the low available P status could
also be ascribed to low P fertilizer application rate, massive nutrient depletion through
continuous cropping, low return of crop residues and surface soil erosion (Teshome et al.,
2013; Girma and Endalkachew, 2013).
69
In this study though small, significant (P ≤ 0.001) and positive correlation (r = 0.14) was
observed between available P and OC. Organic materials can be used as soil conditioners due
to chelation of Fe and Al (hydr) oxides and corresponding release of OH-. Apart from this,
organic matter is also one of the pools of P in the soil. Its mineralization can contribute to
available P (Álvarez-Solís et al., 2007). Low soil OM may therefore imply low available P if
other sources are not there. In addition, the available P was found to significantly correlate
with Ca ( r = 0.5), Mg (r = 0.4), K (r = 0.6), B (r = 0.4), Cu (r = 0.4), Mn (r = 0.2) and Zn (r =
0.5 ). This implies that they have no antagonistic effect on this nutrient. The low P status in
this finding indicates the need for application of P fertilizer for soils of the study areas.
The agricultural lands across districts of the present study areas revealed relatively moderate
variability of available S (Table 3.4). It varied from 3.7 to 30 mg kg-1with mean values of 10 ±
3 (Damot Gale), 11 ± 4 (Damot Sore) and 11 ± 4 (Sodo Zuria). In terms of proportion, about
49, 47 and 4% of samples in Damot Sore district are categorized as very low (< 10), low (10 20) and optimum (20 - 80 mg kg-1) in available S, respectively, as per the ratings of EthioSIS
(2014). Regarding Sodo Zuria district, about 40 and 60% the samples were qualified to very
low and low level of S, respectively whereasthe proportion in Damot Gale district was 52%
(very low) and 47% (low).
Available sulfur content in the study areas irrespective of field and land use types were found
at low level. The amount is below the critical level (20 mg kg-1) suggested for Ethiopian soils
(EthioSIS, 2014). The correlation analysis, further indicated significantly (p< 0.001) negative
(r = -0.35) and positive (r = 0.25) relationships between available S with pH and OC,
respectively. Maintaining adequate levels of soil OM and external S application is required for
sustainable crop production in the study areas. In line with this finding, deficiency of Son the
cultivated fields of different parts of Ethiopia were reported by many authors (Itanna, 2005;
Abreha et al., 2012; EthioSIS, 2014; Eyob, 2014; Mohamed, 2014; Tegbaru, 2014; Habtamu
et al., 2015).
Different authors associated the lower S content with lower OM, as it is the major source of
total S in surface soils (Tekalign and Haque, 1987; Solomonet al., 2001; Itanna, 2005; Nand et
al., 2011; Habtamu, 2015). In addition, SO42– adsorption to Al and Fe oxides at lower pH
70
(Eyob, 2014; Mohamed, 2014; Tegbaru, 2014; Habtamu, 2015), increasing cropping intensity
(Rao, nd; Arshad et al., 2010) and large S uptake by crops (Itanna, 2005) were indicated.
Furthermore, nonuse of S fertilizers (until recently), removal of crop residues, leaching losses
and lower application of organic fertilizers (Itanna, 2005, Rao, nd, Eyob, 2014, Habtamu,
2015, Mohamed, 2014; Tegbaru, 2014) were also reported to be causes for low level of S for
Ethiopian agricultural lands.
3.3.2.4. Exchangeable bases, CEC and PBS
There existed significant variability (p< 0.001) of exchangeable bases across and within
districts (Table 3.5). The dominant exchangeable base was Ca followed by Mg. Overall, the
distribution in the exchange complex has been characterized in the order of Ca > Mg > K >
Na. This could be related to the charge density where the divalent cations (Ca and Mg) have
higher affinity towards the colloidal sites than monovalent cations (K and Na).Similar
arrangements of cations were also reported by Tsehaye and Mohammed (2013), Teshome et
al. (2013) and Okubay et al. (2015).
Exchangeable Ca (Cmol(+)kg-1) across the study area varied from 1 to 31, exchangeable Mg
ranged between 0.2 - 9.5 Cmol(+)kg-1, whereas exchangeable K and Na varied from 0.1 to 6.2
and 0.1 to 3.1 Cmol(+)kg-1, respectively (Table 3.5). Using the ratings suggested by Landon
(2014) about 1, 21, 54, 23 and 1% of the total samples exhibited very low (< 2), low (2 - 5),
medium (5 - 10), high (10 - 20) and very high (> 20) exchangeable Ca (Cmol (+) kg-1),
respectively. Data regarding exchangeable Mg (Cmol(+)kg-1) indicated that 0.5, 33.6, 60.8, 4.8
and 0.3% of samples across the study areas qualified to be very low (< 0.3), low (0.3 - 1.0),
medium (1.0 - 3.0), high (3.0 - 8.0) and very high (> 8.0), respectively as per the ratings of
Landon (2014). Based on the suggestion made to Ethiopian soil (EthioSIS, 2014), about 0.1,
14.7, 57.7, 14.8 and 12.7% of soil samples across districts were categorized as very low (<
0.2), low (0.2 - 0.5), optimum (0.51 - 1.5), high (1.51 - 2.3) and very high (> 2.31Cmol (+) kg1
) in soil exchangeable K, respectively. Additionally, exchangeable sodium percentage values
across districts were found below the critical level (15%) for sodicity.
71
Antagonistic effects could exist when disproportionate quantities of exchangeable cations are
present in the soil. The Ca/Mg ratio across studied districts using the rating of Eckert (1987)
have shown the low level of Ca (1 - 4) on 35%, balanced (4 - 6) on 60% and low Mg (6 - 10)
on 5% of the samples. The K/Mg ratio has been evaluated into consideration of soil textures,
in which it is 1:1 for loamy soils and 0.7:1 for clay soils (Loide, 2004). In silty loam textured
soils of Damot Gale, the K/Mg ratio varied from 0.2 to 1.6, while the ratio ranged between
0.1-1.5. in clay textured soils of Damot Sore and Sodo Zuria district. Accordingly, 47, 57 and
54% of the silty loam soils, clay soils and total soil samples have shown Mg induced K
deficiency. The observed order of cation in the exchange complex (Ca > Mg > K > Na) could
also support the existence of Mg induced K deficiencyHence, K containing fertilizer should be
considered for soils of the study areas.
In the present study, exchangeable bases showed non-significant and very weak correlation
with OC, implying that the contribution of soil OM is low to these elements. Hence, the
medium to high contents of exchangeable bases of sampled soils could be associated with the
variation of parent materials mineralogy (US.EPA, 2006; Kabata- Pendias and Murkhejee,
2007; Kibet, 2013). Kibet (2013) explained that the variation in Ca and K concentrations are
largely related to the variation in mineralogy of parent rock with high source from K-feldspars
followed by kaolinite/1:1 clays, then quartz and plagioclase. In addition, Ca and K are
presumed to be originating primarily from parent material i.e. feldspar as reported by US.EPA
(2006) and Kabata- Pendias and Murkhejee (2007).
On the other hand, continuous removal with crop harvest, low soil OM, soil erosion, acidic
nature of the soil and lack of K containing fertilizer could explain the low level of
exchangeable Ca, Mg and K in some of the soil samples. In addition, moderate to strong
leaching on 32% of soil samples according to leaching criterion of Hazelton and Murphy
(2007) would also contributing. In line with this finding, Adesodun et al. (2007) reported that
continuous cultivation led to reduction, uptake and leaching of exchangeable cations,
especially in acidic tropical soils. Furthermore, breakdown of primary minerals, particularly
K-feldspars and plagioclase (Acosta et al., 2011) or depletion in well-drained soils over long
periods of pedogenic weathering (Marques et al., 2004) favors lower Ca and K.
72
Table 3.5. Descriptive statistics of soil exchangeable bases, CEC and PBS in the study districts.
District
Descriptive
Ca
Mg
K
Na
Statistics
-1
Cmol(+)kg
Damot Gale
(N=243)
Damot Sore
(N=216)
Sodo Zuria
(N=330)
Total
(N=789)
Mean
Std Dev
Median
Minimum
Maximum
CV (%)
Mean
Std Dev
Median
Minimum
Maximum
CV (%)
Mean
Std Dev
Median
Minimum
Maximum
CV (%)
Mean
Std Dev
Median
Minimum
Maximum
CV (%)
Fvalue
9.1
3.1
8.4
1.5
19.6
34
8.3
4.9
7.1
2.0
31.2
59
7.0
3.6
6.4
1.1
31.3
51
8.0
4.0
7.4
1.1
31.3
50
20.4***
Number in brackets refer to sample size, *** significant at p < 0.001
1.9
0.6
1.9
0.2
4.2
32
2.3
1.4
2.0
0.5
9.5
61
1.8
0.7
1.8
0.2
4.6
39
2.0
0.9
1.9
0.2
9.5
45
15.2***
1.4
0.8
1.1
0.1
3.9
57
1.4
1.0
1.0
0.2
6.2
71
1.1
0.7
0.9
0.2
4.5
64
1.3
0.9
1.0
0.1
6.2
69
8.5***
0.8
0.3
0.7
0.1
2.1
38
0.8
0.3
0.7
0.4
3.1
38
0.7
0.3
0.6
0.2
2.3
43
0.7
0.3
0.6
0.1
3.1
43
13.4***
CEC
PBS
21.1
4.0
20.3
3.3
34.3
19
22.6
6.1
20.9
13.8
50.5
27
20.0
3.7
19.4
12.4
43.8
19
21.0
4.7
20.0
3.3
50.5
22
20.7***
%
61.3
8.4
61.4
30.5
80.1
14
53.8
14.9
54.4
19.5
87.8
28
51.8
13.8
53.2
8.8
86.5
27
55.3
13.4
56.9
8.8
87.8
24
40.5***
73
Owing to the differences in soil environment and soil OM contents, moderate variability of
cation exchange capacity (CEC) within and among districts were recorded (Table 3.5).The CEC
ranged from 3.3 cmol(+) kg-1 in the soils of Damot Gale to 50.5 cmol(+) kg-1 in Damot Sore
district; and their median value was 20.0 cmol(+) kg-1which is comparable with median values of
each district. In Damot Sore district, based on the rating of Landon (2014), the proportion of soil
samples which fall in the
low (5-15), moderate (15 - 25),
high (25-40) and very high
(>40cmol(+) kg-1) CEC categories were 2, 75, 19 and 4%, respectively. Furthermore, about (1,
6%), (84, 88%) and (14, 6%) of sampled soils in Damot Gale and Sodo Zuria district were
categorized at low, moderate and high CEC levels, respectively. Overall, majority (83%) of
sampled soils across districts had medium CEC values.
Cation exchange capacity of the soil is highly influenced by soil pH, OM and clay particles as
both of the latter are colloidal sites. Different researchers reported an increase in the CEC of soils
due to high OM and clay contents (Abebe and Endalkachew, 2012b; Yihenew et al., 2015).
Furthermore, the CEC of soil could vary with soil pH, if soil has pH dependent charge edges
(Havlin et al., 2009). The correlation analysis in the present finding also proved the existence of
significant (P < 0.001) and positive correlation between CEC with soil pH (r = 0.5) and OC ( r =
0.2). The CEC value also significantly (P ≤ 0.001) correlated with Ca (r = 0.9), Mg (r = 0.9) and
K (r = 0.7). Differences in the type of clay mineralogy and soil OM might have been the factors
that contribute to the CEC values of studied soils. In consistent with this finding, Maria and Yost
(2006) and Joao et al. (2009) reported the reduction in soil CEC as soil pH became lower.
Overall, the moderate CEC values implied that the soil has moderate buffering capacity against
the induced changes.
The percentage base saturation (PBS) followed the trend of exchangeable bases. It varied from 9
to 88% (Table 3.5). As per the ratings proposed by Maria and Yost (2006), about 0.4, 14.7, 44.7,
39 and 1.1% qualified to be very low (< 20), low (20 - 40), medium (40 - 60), high (60 - 80) and
very high (80-100%) PBS, respectively. Considering PBS as acriterionofleaching (Hazelton and
Murphy, 2007), about 5%, 27%, 58% and 10% of sampled soils were strongly leached (1530%PBS), moderately leached (30-50% PBS), weakly leached (50-70% PBS) and very weakly
leached soils (70-100% PBS), respectively
74
3.3.2.5. Micronutrients (B, Cu, Fe Mn and Zn)
The micronutrientcontents of sampled soils in the present studywithin and among districts
showed observable variations (Table 3.6). According to Aweto (1982) cited in Amuyou et al.
(2013), Fe and Mn were found to be moderate (CV=20-50%) in their variability, whereas, B, Cu
and Zn showed high variability (CV > 50%). Variations in terms of topography, management,
land use types and inherent properties like texture and pH; and random sampling large samples
from vast areas could result moderate to high soil variability. Considering the median values, the
concentration of B and Cu, and Mn were comparably higher in fine textured soils of Damot Sore
and Sodo Zuria, districts, respectively. Iron and Zn contents revealed higher median values in the
silty loam soils soils of Damot Gale district (Table 3.6).These variations could be linked with
differences in soil textures, soil pH, OM and soil management. For instance, soil pH has an
influence on the solubility and availability of soil micro nutrients. This was evident from a
significant (p ≤ 0.01) correlation between soil pH and B (r = 0.3), Cu (r = 0.3), Fe (r = -0.2), Mn
(r = 0.4) and Zn (r = 0.2). The soil OC content also indicated a significant (p ≤ 0.01) correlation
with B (r = 0.2), Cu (r = 0.3), Mn (r = - 0.1) and Zn (r = 0.3).
The range and median (mg kg-1) values of micronutrients for the entire districtsin their order is
indicated as follows: B (0.01 to 6.9, 0.46), Cu (0.01 to 5, 0.47), Fe (22 to 392,119), Mn (50
to1138,545.0) and Zn (0.3 to 117,7.20). Considering the ratings proposed for Ethiopian soils by
EthioSIS (2014) about 57, 30 and 13% samples across districtshad very low (< 0.5), low (0.5 –
0.8) and optimum (0.8 – 2.0 ppm) B content, respectively. The result regarding Cu content also
revealed that about 53, 37 and 10% of the entire soil samples qualified to be very low (< 0.5),
low (0.5 – 0.9) and optimum (1 – 20 ppm) Cu content, respectively. The content of Fe was
optimum except for some localized deficiencies. Overall, about 7, 91 and 2% of the soil samples
had low (60 – 80), optimum (80 – 300) and high Fe (300 – 400 mg kg-1), respectively. Soil Mn
(mg kg-1) in all samples was above 25, which is optimum according to the EthioSIS ratings
criteria (EthioSIS, 2014). Furthermore, the result of the present finding indicated the sufficiency
of Zn. In terms of proportion, 3, 65, 26 and 6% of the soil samples across districts have low (1 1.5), optimum (1.5 – 10), high (10 – 20) and very high (> 20 mg kg-1) Zn levels, respectively.
Generally, B and Cu contents in all soil samples of studied districts were found to be yield
limiting nutrients, whereas Fe, Mn and Zn levels were sufficient for crop production.
75
Acidic pH, loss through leaching, low B absorbing capacity of soil and low B containing parent
material were reported to cause low B status (Oyinlola and Chude, 2010). Under highly
weathered soils and acid soil conditions, B is more water soluble and can therefore be leached
below the root zone. Crops on these soils may suffer from B deficiency (Chesworth, 2007; Part
of Rio Tinto, 2012). Soils with low OM content are also susceptible to boron deficiency (Part of
Rio Tinto, 2012). Furthermore, intensive cultivation of soils, lower application rate of manure
and non use of B containing fertilizer could also aggravated the B deficiency (Gebeyaw, 2007;
Abebe and Endalkachew, 2012a; Bitondo et al., 2013).
Table 3.6. Descriptive statistics of soil micronutrients (B, Cu, Fe, Mn and Zn) in the study
districts.
DistrictDescriptive
B
Cu
Fe
Mn
Zn
-1
statistics
mg kg
Damot Gale
Mean
0.56
0.47
133.0
521.0
10.59
(N=243)
Std Dev
0.30
0.41
29.0
162.0
7.13
Median
0.48
0.40
133.0
523.0
8.80
Minimum
0.01
0.01
22.0
84.0
1.10
Maximum
1.82
5.00
259.0
950.0
51.00
CV (%)
54.0
87.0
22.0
31.0
67.0
Damot Sore
Mean
0.60
0.78
131.0
510.0
9.03
(N=216)
Std Dev
0.41
0.44
42.0
160.0
5.73
Median
0.51
0.68
124.0
490.0
7.60
Minimum
0.20
0.01
61.0
61.0
0.30
Maximum
4.97
2.66
392.0
912.0
36.80
CV (%)
68
56
32
33
63
Sodo Zuria
Mean
0.50
0.46
120.0
599.0
7.29
(N=330)
Std Dev
0.41
0.27
55.0
223.0
8.13
Median
0.42
0.41
104.0
616.0
5.40
Minimum
0.18
0.01
45.0
50.0
0.70
Maximum
6.90
1.42
384.0
1138
117.40
CV (%)
82
59
46
37
112
Total
Mean
0.55
0.55
127.0
551.0
8.78
(N=789)
Std Dev
0.38
0.39
45.0
193.0
7.36
Median
0.46
0.47
119.0
545.0
7.20
Minimum
0.01
0.01
22.0
50.0
0.30
Maximum
6.90
5.00
392.0
1138.0 117.40
CV (%)
69
71
35
35
84
Fvalue
4.7** 56.1***
7.3** 18.8*** 14.8***
Numbers in the brackets refers to sample size; **, *** significant at p < 0.01, 0.001, respectively.
76
The Cu deficiency in the study areas could be linked with low soil OM, practice of intensive
cropping systems, acidic nature of the soil and non use of Cu containing fertilizer, which could
result in high Cu mining. Chesworth (2007) and Bitondo et al. (2013) reported that organic
matter complexes can retain substantial proportion of micronutrients. This is significantly (p <
0.001) supported by the positive correlation of OC with B (r = 0.24) and Cu (r = 0.34). Hence,
this suggests that the low level of soil OM would contribute to the low level of these elements.
Correspondingly, the research under intensive cropping systems of Venezuela by Rodríguez and
Ramírez (2005) reported Cu deficiency on acid soils (pH < 6.5). It was accounted to low level of
soil OM. In line with this finding, the study in some Nitisols of Ethiopia indicated Cu deficiency
(Teklu, 2004; Abebe and Endalkachew, 2012a; Girma and Endalkachew, 2013; Tegbaru, 2014).
Furthermore, the research by Alemayehu and Sheleme (2013) in soils of the present the study
area indentified the deficiency of available Cu in enset and maize land uses types.
The results regarding Fe and Mn levels obtained from this study are similar to previous finding
by various researchers (Haque et al., 2000; Teklu, 2004; Abebe and Endalkachew, 2012a; Eyob,
2014; Mohamed, 2014; Tegbaru, 2014) who reported sufficient levels of Fe and Mn in different
parts of Ethiopia. The sufficient levels of these elements could be linked with acidic nature of the
soils. In agreement to the current findings, Oyinlola and Chude (2010) and Habtamu (2015)
indicated higher solubility and availability of micronutrients like Fe, Mn and Zn under acidic
conditions (pH of 5.0 to 6.5). Furthermore, the higher Fe content might also be explained with
higher content of raw mineralogy (Kibet, 2013). In addition, the higher Mn levels are often found
in soils rich in Fe (Kibet, 2013). Zinc is reported to be generally associated with Al- and Fecontaining minerals such as feldspars, micas, pyroxenes and amphiboles (Acosta et al., 2011).
77
3.4. CONCLUSION
The study districts have silty loam to clay textural classes. Acidic soil reaction was prevalent in
the area. Soil OC, total N, available P, K, S and micronutrient (B and Cu) contents are likely to
constrain crop production in the study areas. The problems have been observed in almost all of
the agricultural lands including land use types, which the farmers thought were fertile soils. The
turning point to solve the problems should be restoring, maintaining and increasing the fertility
status of the soils. In this regard, gradual build-up of soil OM and application of N, P, K, S, B
and Cu fertilizers should be implemented to improve soil fertility and increase crop productivity
in the areas.
3.5. ACKNOWLEDGEMENTS
We acknowledge Ministry of Education (MOE) for the scholarship, the Ethiopian Soil
Information System (EthioSIS) at the Agricultural Transformation Agency (ATA) for financial
support. We are very grateful for all assistances, knowledge and experiences we have got from
the farmers in Damot Gale, Damot Sore and Sodo Zuria districts. The authors would also like to
acknowledge technical assistants of Plant Science department, Wolaita Sodo University
(Ermiyas Elka, Simon Yohannes, and the late Daniel Milkyas) for their tremendous support
during the field work.
78
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4. SOIL FERTILITY SPATIAL VARIABILTY ANALYSIS AND MAPPING
IN WOLAITA ZONE, SOUTHERN ETIIOPIA
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4. SOIL FERTILITY SPATIAL VARIABILITY ANALYSIS, MAPPING
AND FERTILIZER TYPE RECOMMENDATION IN WOLAITA ZONE,
SOUTHERN ETHIOPIA
Fanuel Laekemariam1, Kibebew Kibret1, Tekalign Mamo2, Hailu Shiferaw3and Heluf Gebrekidan1
1.
Haramaya University, School of Natural Resources Management and Environmental Science, Ethiopia. 2.Ministry
of Agriculture, Ethiopia.3. Agricultural Transformation Agency (ATA) and International Food Policy research
Institute (IFPRI)
ABSTRACT
Spatial variability of soil properties and mapping of soil variations are very useful to plan site
specific soil and crop management practices. Yet, no comprehensive soil fertility survey exists in
Wolaita area, southern Ethiopia. This study was conducted to investigate soil fertility spatial
variability, develop fertility map and recommend fertilizer types for Damot Gale, Damot Sore
and Sodo Zuria districts, Wolaita Zone, Southern Ethiopia. About 789 soil samples were
collected, analyzed and subjected to geostatistical analysis using ordinary kriging technique.
The result revealed that linear kriging model cannot be recommended for all soil parameters,
instead fitted with either Spherical, Exponential or Gaussian models. The spatial autocorrelation varied from 276 m (Cu) to 15,118 m (Mn) and all measured soil parameters showed
range > 512 m (average sampling distance) except for S, Cu and Zn.This implies that sampling
interval was adequate to capture the variability. The spatial dependence was strong (< 25%) for
Cu, Fe and Zn; moderate (25-50%) for pH, OC, TN, S, Ca, Mg, B, Mn and CEC and weak
(>75%) for P, K and percent base saturation. This explains that the soil variability was resulted
by the natural and anthropogenic factors. The predictive performances revealed RMSSE values
close to one except for P (0.71) and Zn (1.4); nearest values of RMSE to MSE except P and Fe,
and Goodness of prediction (G) varied from-2 to 100%. These confirm a good prediction
performances and fertility maps are recommended for soil fertility intervention programs. The
fertility map displayed OC, TN, available P, K, S, B and Cu as major soil fertility constraints and
also indicated three major types of blended fertilizers(NPKSBCu, NPSBCu and NPKSB) for
interventions. Therefore, building up of soil OM and application of N, P, S, B and Cu containing
blended fertilizers have to be considered. In addition, further studies on application rates of
limiting nutrients are suggested.
Keywords: Blended Fertilizers, Kriging, Prediction Performance, Spatial Dependence
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4.1. INTRODUCTION
Soils are not homogeneous and vary spatially and temporally. Their variability is the outcome of
many processes acting and interacting on spatial and temporal scales (Cambardella et al. 1994).
Soil properties exhibit spatial dependency. Spatial variation can be attributed to differences in
soil management (Tittonell et al., 2005; Haileslassie et al., 2007; Masvaya et al., 2010) and
inherent soil properties (Nourzadeh et al., 2012).
Understanding soil spatial variability in agricultural lands is imperative for assessing the effects
of agriculture on soil properties, plan soil fertility management practices and develops site
specific farming. This improves efficient and balanced utilization of agricultural inputs, achieves
better soil health and sustains production with respect to soil spatial variation (Singh et al., 2010;
Patil et al., 2011). Under smallholder farming systems of Ethiopia, spatial soil fertility variability
has largely been ignored when designing technological interventions. This would bring low
nutrient use efficiency and fails to allocate scarce fertilizer resources (Masvaya et al., 2010).
Soil properties can be investigated using laboratory measurements. However, it is very expensive
to address soils collected from vast areas. Specifically, it is challenging for countries like
Ethiopia with lack of well equipped laboratories and limited financial and human resources.
Different methods have been employed to investigate the variability of soil properties in vast
areas. Accordingly, classical statistic is among methods to assess the soil variability. It assumes
soils to have a random variability (Cemek et al., 2007). However, according to Cambardella et
al.(1994), Ozgoz et al. (2013) and Costa et al. (2015) soil characteristics generally show spatial
dependence. That is, samples close to each other have similar properties than those far away
from each other. As a result, classical statistics is not capable of analyzing the spatial
dependency of the variables since the data assumed to be measured independently (Vieira et al.,
1983); and it is possibly evaluated using geostatistical approach (Cambardella et al. 1994; Patil
et al., 2011).
Geostatistics is a set of statistical tools that can be used for studying and predicting the spatial
structure of georeferenced variables and generating soil properties map (Patil et al.,
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2011).Geostatistically generated maps are highly welcomed as means for a site-specific
fertilization and environmental monitoring activities (Singh et al., 2010). Researchers in different
parts of the globe have been using geostatistics as a tool to predict values of soil physical and
chemical properties for larger areas where the soil samples are not actually collected and
measured (Cambardella et al. 1994; Kravchenkoet al., 2006; Ike, 2010; Tesfahunegn et al., 2011;
Ewis, 2012;Nourzadeh et al., 2012; Xu et al., 2013;Costa et al., 2015; Behera and Shukla, 2015).
In an attempt to produce soil map of Ethiopia, FAO (1984) has conducted soil survey at
1:2,000,000 scale. However, the map is chronically outdated and not detailed enough to provide
functional soil fertility information for policy makers to design fertility management
interventions and is leading to decisions that are constrained by a lack of systematic soil
information (Url 1).
Wolaita zone is among zones in South Ethiopia where majority of farmers are practicing
agriculture on less than 0.25 hectare of land (WZFEDD, 2012). This compel farmers exploit their
land as much as possible. Different researches conducted in Wolaita area have shown a clue of
macro and micro nutrient deficiencies (Elias, 2000; Pound and Jonfa, 2005; Mulugeta, 2006;
Alemayehu, 2007; Sheleme, 2011; Wondwosen and Sheleme, 2011).In Wolaita, where there is
topographic variations, soil management differences and inherent soil variation; soil spatial
variability is predictable. Under spatially variable conditions, uniform soil management in the
area using blanket fertilizer recommendation approach would not address limiting nutrients and
enhance productivity. To date in Wolaita area, no comprehensive soil nutrient spatial variability
analysis and mapping of soil parameters exists to be used for a site-specific management
practices.
Hence, knowledge on soil spatial variability and mapping of the soil properties is important to
manage the soil and sustaining crop productivity. Thus, the present study hypothesized that
spatial variability of major soil properties and other soil nutrients than the most known (N and P)
is expected in the study area and hence soils require site specific soil management
practices.Therefore, the objectives of this study were to investigate the soil fertility spatial
variability, produce soil fertility map and recommend fertilizer types.
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4. 2. MATERIALS AND METHODS
4.2.1. Description of the Study Area
The study was conducted in Damot Gale, Damot Sore and Sodo Zuria districts, Wolaita zone,
Southern Nations’, Nationalities’ and Peoples’ Regional State (SNNPRS)of Ethiopia (Figure 1.1)
during 2013. The sites are located between 037°35'30" - 037°58'36"E and 06°57'20" 07°04'31"N. The study area covers about 84,000 ha. The area has a bimodal rainfall pattern with
mean annual precipitation of 1355 mm (Figure 1.2). The temperature ranges from 17.7 to 21.7
°C with an average of 19.7 °C (NMA, 2013). The elevation in studied districts varied from 1473
to 2873 m.a.s.l (Figure 1.3). The area is predominantly characterized by mid highland agroecology. Eutric Nitisols associated with Humic Nitisols are the most prevalent soils (Tesfaye,
2003). Agriculture in the study area is predominantly small-scale mixed subsistence farming.
The farming system is mainly based on continuous cultivation without any fallow periods. Brief
descriptions about the area are indicated under section 1.1.
4.2.2. Soil Sampling Procedure and Laboratory Analysis
4.2.2.1. Soil sampling procedure
Geographical information system (GIS) was employed to randomly assign a predefined sampling
locations following EthioSIS sample distribution procedure (EthioSIS, 2014). Accordingly, 789
sampling points (243 on Damot Gale, 216 on Damot Sore and 330 on Sodo Zuria) were
generated for sample collection.These samples were randomly distributed at an average
separation distance of 512 meters. During the survey work, the pre-defined sample locations
were navigated using geographical positioning system receiver (model Garmin GPSMAP 60Cx).
Once the sampling point was navigated and reached in the dominant land use types, 10 to 15 subsamples were taken based on the complexity of topography and heterogeneity of the soil type.
Samples were collected using soil auger and then composited. During sample collection, hot spot
areas (manure and threshing sites) were excluded. Soil samples were taken to a depth of 0-20 cm
for tef, haricot bean, wheat, maize, etc, while it extends up to 50 cm for perennial crops such as
enset and coffee fields. From the composited sample, one kilogram (kg) of soil was taken with a
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labeled soil sample bag. To reduce the potential for cross-sample contamination among samples,
the soil auger and other sampling tools were cleaned before taking the next sample at different
locations.
4.2.2.2. Sample Preparation and Soil analysis
Soil samples were air-dried at room temperature, ground by mortar and pestle, and sieved
through a 2 mm mesh sieve and 0.5 mm mesh sieve (for mid infrared diffused reflectance (MIR)
spectral analysis). Soil samples were processed at the National Soil Testing Center (NSTC),
Addis Ababa, Ethiopia.
Soil samples were subjected for analysis of pH, organic carbon (OC), total nitrogen (TN),
available P and S, exchangeable bases (Ca, Mg, Na and K), soil micronutrients (Fe, Cu, Mn, Zn
and B) and CEC. Soil pH, OC, TN and CEC analyses were done at NSTC, whereas available P
and S, exchangeable bases (Ca, Mg, K and Na) and extractable micronutrients (Fe, Cu, Mn, Zn
and B) were conducted in Altic B.V., Dronten, The Netherlands.
Soil pH (1:2 soil: water suspension) with a glass electrode (model CP-501) was determined by
standard methods (Mylavarapu, 2009). Melich-III multi-nutrient extraction method was used to
extract available P, available S, exchangeable basic cations (Ca, K, Mg and Na) and extractable
micronutrients (Fe, Mn, Zn, Cu and B) (Mehlich, 1984). The concentrations of extracted
elements were measured using inductively coupled plasma (ICP) spectrometer. Soil Mn content
was evaluated using manganese activity index (MnAI) (Karltun et al., 2013). In addition, MIR
diffused reflectance spectral analysis was used to determine the amount of OC, TN and CEC.
4.2.3. Geostatistical Analysis and Soil Fertility Mapping
The spatial variability of selected soil properties was analyzed using geostatistics to determine
the degree and range of spatial dependence. An optimal interpolation method, which is ordinary
kriging (OK), was performed to interpolate the values of un-sampled locations and produce maps
of soil properties (Singh et al., 2010). Semivariogram was constructed from the scatter point set
to be interpolated and the spatial variation was quantified from the input point dataset. The idea
underlying this approach is that records found in closer points are more similar than those
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points’farther apart (Costa et al., 2015). Theoretically, the value of semivariogram for a
separation distance of gamma h (referred to as the lag distance) is the average squared difference
in Z-value between sample points separated by h (Mohammadi, 2002; Costa et al., 2015). The
semivariogram is represented in equation (Eq.) 1.
1
γ(h) = 2𝑛 ∑𝑛𝑛=1[𝑍(𝑋𝑖 ) − 𝑍(𝑋𝑖 + ℎ)]2 .....................................................................................Eq.1
where: n is the number of pairs of sample points separated by the distance h and Z(xi)'s are the
value of the characteristic under study at ith location (i = 1, 2, 3, ..., n).
Estimations were made for all h’s and then the lag values were plotted against h to obtain
empirical semivariogram. In the present study, the lag distance varied between 28 and 2429 m
and the total number of lags for all models was 12. Then, a theoretical model was fitted on the
empirical semivariogram. The semivariogram models provide information about the spatial
structure as well as input parameters for kriging interpolation (Costa et al., 2015).
Kriging works best for normal distribution data (Goovaerts, 1997). Hence, normal distribution
tests were performed for all soil variables. Variables without normal distributions were subjected
to log transformation. Prior to geostatistical analysis, three semivariogram models (Spherical,
Gaussian and Exponential models) were tested for each soil parameters to select the best fitted
model. The models provide information about the spatial structure as well as the input
parameters for interpolation. Predictive performances of the fitted models were checked on the
basis of error values computed from the entire data sets (Ike, 2010; Ewis, 2012; Gorai and
Kumar, 2013). In this regard, the values of root mean square standardized error (RMSSE) (Eq.2),
mean standard error (MSE) (Eq.3) and root mean square error (RMSE) (Eq.4) were estimated to
ascertain the fitted model. Accordingly, the model showing RMSE close to the MSE; and
RMSSE value close to one is considered as best fitting model. After conducting error
evaluations, the best fitted model was selected for prediction. Then after, kriged maps showing
the values of un-sampled locations were generated. The maps provided a visual representation of
the distribution of the soil parameters.
1
2
RMSSE = √n ∑ni=1({Z(𝑋𝑖) − Ž(𝑋𝑖)}/σ(Xi )) ...................................................... Eq.2
1
MSE = √n ∑ni=1 σ2 (Xi )........................................................................................... Eq.3
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2
1
RMSE = √n ∑ni=1 (Z(𝑋𝑖) − Ž(𝑋𝑖)) ...................................................................... Eq. 4
where: Z(Xi) is the value of the variable Z at location of Xi, Ž(Xi) is the predicted value at
location i, n is the sample size and σ2 (Xi ) is the kriging variance for location Xi.
As indicated by Gorai and Kumar (2013), if the RMSE is close to the MSE, the prediction errors
were correctly assessed. If the RMSE is smaller than the MSE, then the variability of the
predictions is overestimated; conversely, if the RMSE is greater than the MSE, then the
variability of the predictions is underestimated. The same could be deduced from the RMSSE
statistic. It should be close to one. If the RMSSE is greater than one, the variability of the
predictions is underestimated; likewise if it is less than one, the variability is overestimated.
The performance /effectiveness/ of interpolation was evaluated based on Goodness-of-Prediction
Estimate (G) (Eq.5) (Krivoruchko and Gotay, 2003; Karydas et al., 2009). A G value equal to
100% indicates a perfect prediction, positive values (i.e. from 0 to 100%) indicate that the
predictions are more reliable than the use of the sample mean, and negative values indicate that
the predictions are less reliable than the use of the sample mean instead.
2
𝐺 =1−
∑𝑛
𝑖=1(𝑍(𝑋𝑖 )−Ž(Xi )
∑𝑛
𝑖=1(𝑍(𝑋𝑖 )−Ŷ)
2
∗ 100.................................................................Eq.5
where Z(xi) is the observed value at location i, Ž(xi) is the predicted value at location i, n is the
sample size and Ŷis the sample mean.
The corresponding nugget (C0), sill (C0 + partial sill (C)) and range values of the best fitted
theoretical models were used to evaluate spatial distribution of soil variables. Nugget represents
random field and experimental variability that is not detectable at the sampling scale than the
sampling interval (Costa et al., 2015). Range is the lag distance between measurements at which
the values of variables become spatially independent of another. The magnitude of spatial
dependence of soil variables was estimated from the ratio of nugget to sill (C0/(C + C0))
(Cambardella et al., 1994). If the ratio is less than 25%, the variable is characterized by strong
spatial dependence; if the ratio is between 25 and 75%, it indicates moderate spatial dependence
and if it is greater than 75%, a variable shows weak spatial dependence (Cambardella et al.,
1994).
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For the management purposes, the spatial variations of soil parameters were categorized on the
basis of standard ratings of EthioSIS (2014), Landon (2014) and Maria and Yost (2006). Soil pH,
TN, available P, S, K, B, Cu, Fe, Mn and Zn were rated based on EthioSIS (2014). Landon
(2014) was used to rate soil OC and CEC, whereas Maria and Yost (2006) was used to rate
exchangeable Ca and Mg. As an output, depending on the parameter, two maps such as fertility
status map and binary map (sufficient and deficient areas requiring management) are presented.
Universal Transverse Mercator (UTM), Zone 37N projection and Datum of WGS_1984 were
employed for map projection. All the tasks were done using GIS software (Arc Map version 10).
4.3. RESULTS AND DISCUSSION
4.3.1. Spatial Variability of Soil Properties
It is clear from the results that a uniform kriging model cannot be recommended for all the soil
parameters (Table 4.1). Exponential model provided the best fit for the semivariogram of OC,
TN, available P, exchangeable Mg, B, Fe, Zn and CEC. On the other hand, the spherical model
described the variation of the semivariogram of soil pH, exchangeable Ca, Cu and Mn better than
the exponential and Gaussian models, while the variations of the semivariogram of available S,
exchangeable K and PBS were described best by the Gaussian model. In line with the present
findings, published papers regarding models reported a spherical model for soil pH (Cambardella
et al., 1994; Nourzadeh et al., 2012; Tesfahunegn et al., 2011) and Ca (Behera and Shukla,
2015); exponential model for soil OC and extractable Zn (Xu et al., 2013) and exchangeable Mg
(Tesfahunegn et al., 2011; Behera and Shukla, 2015); and Guassian model for K (Behera and
Shukla, 2015).
The nugget values were found to be variable among the measured soil parameters and relatively
it was high for P, Mn, PBS, and CEC. This according to Costa et al., (2015) indicated that there
are sources of variations within distances smaller than the shorter sampling interval. The range
values, which indicate auto-correlation, showed considerable variability among the parameters.
The range of different soil properties varied from about 276 m (Cu) to 15,118 m (Mn). The
measured soil parameters showed range values > 512m (average sampling distance) except S, Cu
and Zn, implying that sampling interval in this study was adequate to capture the variability.
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Research papers on variogram analysis also showed varied ranges of different soil parameters
(Cambardella et al.1994; Ike, 2010; Nourzadeh et al., 2012; Xu et al., 2013). The variation could
be attributed to the size of the study area and sampling intensities. For instance, the study based
on less than 1.2 ha (Kravchenko, 2006;Costa et al., 2015) reported a range values between 83 200 m. While, the study on 12,000 ha area reported the range of 255 - 620 m (Ike, 2010).
Working in approximately 3500 km2 watershed in north Florida, Vasquez et al. (2010) reported a
major range of 5 – 11 km. Nourzadeh et al. (2012) in the northwest of Iran with an area ~19 500
km2 found a major range of 30 -79 km. In addition, Behera and Shukla (2015) measured different
ranges (44 - 11,936 m) in acid soils of India which was due to the combined effects of parent
material, climatic conditions and land management practices adopted in different soil series.
Overall, estimates of range tend to be landscape dependent and may be interpreted to indicate the
distance across distinct soil types (Cambardella et al., 1994; Behera and Shukla, 2015).
Spatial dependence is not only a function of the range, but also relies heavily on the spatial
structure as defined by the nugget: sill ratio. Among the soil properties, as shown by the low
nugget : sill ratio the spatial structure proved to be stronger for extractable Cu, Fe and Zn
contents; of which Cu and Fe had zero spatial dependence. Strong spatial dependence indicates
that random factors have less influence on soil contents, while internal factors associated with
inherent variations of soil characteristics have more influences. In the present study, Cu and Zn
are among the principal components contributing to variations in the soils of the study area. They
have approximately a loading factor of 0.6 in the domain of principal components analysis (data
not shown). In addition, the variation of micronutrients could also be associated with soil pH as
there is significant (p ≤ 0.001) correlation (r = 0.3, -0.2 and 0.4) between pH and Cu, Fe and Zn,
respectively. Different authors reported that strongly dependent variables may be controlled by
intrinsic variations in soil characteristics such as soil parent material, texture and mineralogy
(Cambardella et al., 1994; Xu et al., 2013; Costa et al., 2015; Behera and Shukla, 2015).
96
Table 4.1. Model performance and semivariogram characteristics of soil properties of the study area
Soil
property
Model
pH
Spherical
Data
Trans
forma
tion
No
Nugget
(m)(Co)
OC
Exponential
No
0.17
0.24
0.41
104.47
777.0
41
Moderate
0.691
1.053
0.653
22.0
TN
Exponential
No
0.00
0.002
0.00
113.29
813.5
50
Moderate
0.067
1.068
0.062
38.0
P
Exponential
Log
1.93
0.51
2.44
883.87
10606.0
79
Weak
24.477
0.713
46.60
3.0
S
Gaussian
Log
0.05
0.03
0.08
33.93
301.9
63
Moderate
3.556
1.068
3.397
0.0
Ca
Spherical
Log
0.17
0.06
0.23
674.52
6313.0
74
Moderate
3.659
1.00
3.867
14.0
Mg
Exponential
Log
0.11
0.07
0.18
266.17
1833.8
61
Moderate
0.813
0.947
0.908
23.0
K
Gaussian
Log
0.37
0.04
0.41
372.16
4465.9
90
Weak
0.860
1.025
0.945
99.0
B
Exponential
Log
0.13
0.15
0.28
77.18
926.0
46
Moderate
0.389
1.171
0.337
-2.0
Cu
Spherical
No
0.00
0.14
0.14
28.39
276.0
0
Strong
0.364
0.937
0.388
14.0
Fe
Exponential
Log
0.00
0.07
0.07
44.71
536.0
0
Strong
40.555
1.084
36.59
19.0
Mn
Spherical
No
1986.9
867.04
2853.9
2203.83
15118.0
70
Moderate
47.251
1.007
46.85
19.0
Zn
Exponential
Log
0.03
0.36
0.39
65.98
301.0
8
Strong
7.131
1.381
7.306
6.0
PBS
Gaussian
No
144.7
29.32
174.0
2429.07
7996.8
83
Weak
12.19
0.982
12.42
93.0
CEC
Exponential
No
6.85
12.56
19.4
119.59
1293.0
35
Moderate
4.358
1.007
4.314
14.0
K:Mg
Exponential
No
0.004
0.06
0.064
124.19
843.4
6.0
Strong
0.252
1.007
0.249
100
0.20
Partial
Sill
(m)(C)
Sill(m)
(Co+C))
Lag size
0.16
0.36
52.58
Range
(m)
Spatial
dependence
status
RMSE
RMSSE
523.7
Spatial
dependence
C0/(C + C0)
(%)
56
MSE
G(%)
Moderate
0.603
0.958
0.629
10.0
RMSE = root mean square error, RMSSE = root mean square standardize error and MSE = Mean Standard Error, G = Goodness-ofPrediction
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The results presented on the other hand demonstrated that pH, OC, TN, available S,
exchangeable Ca, Mg, extractable B, Mn and CEC exhibited moderate spatial dependence
(nugget ratio 25-75%)whereas available P, exchangeable K and percent base saturation (PBS)
had weak spatial dependence (nugget ratio >75%). Moderate spatial dependence is owing to both
intrinsic and extrinsic factors (Behera and Shukla, 2015) and they indicate non-randomness in
the distribution of nutrient concentrations along the studied area (Costa et al., 2015). Weak
spatial dependence shows a more random distribution (Costa et al., 2015).
It seems that soil management practices have a strong influence on the spatial dependence of soil
properties measured on agricultural lands. They exert several effects on soils. In the study area,
an intensive cultivation to the extent of ecologically fragile areas, depletion of basic cations
through continuous crop harvest, low return of crop residue, leachingand lower rate of fertilizer
application are predominant. Overall, the current soil management interventions by farmers are
very insufficient. As a result, over exploitation of agricultural lands and a decline in soil fertility
could be inevitable on the entire fields of the study area. Consequently, these factors are
expected to alter the soil properties. This may be the reason for the moderate to weaker spatial
dependence in almost all of the soil parameters in the study area. In consistent with this, Ozgoz
et al.(2013) indicated that soil management practices in a farmland through tillage, fertilization,
crop rotation and water management affect the soil quality. Similarly, Cambardella et al. (1994)
and Ozgoz et al. (2013) indicated that extrinsic variations, such as fertilizer application and
tillage, may control the variability of the weakly spatially dependent parameters.
The weaker spatial dependence of soil available P could be related with the frequent soil tillage
(Ozgoz et al., 2013) and influence of non predictable extrinsic factor like runoff (Costa et al.,
2015). In addition, the negative effect that tillage has on the decomposition rate of soil OM,
inadequate application of organic materials and soil erosion have contributed to the low level of
soil OM on cultivated soils. This might have been the reason for medium spatial dependence of
soil OC (Ozgoz et al., 2013).
In accord with the findings of this study, strong spatial dependence on extractable Cu (Xu et al.
(2013), moderate spatial dependence on pH (Nourzadeh et al., 2012; Behera and Shukla, 2015),
98
TN (Weindorf and Zhu, 2010; Costa et al., 2015), OC (Kavianpoor et al., 2012; Ozgoz et al.,
2013; Xu et al. 2013; Costa et al., 2015; Behera and Shukla, 2015), Ca and Mg (Behera and
Shukla, 2015) and B (Nourzadeh et al., 2012) were reported. Furthermore, weak spatial
dependence on soil available P (Weindorf and Zhu, 2010; Kavianpoor et al., 2012; Nourzadeh et
al., 2012; Xu et al., 2013; Ozgoz et al., 2013; Costa et al., 2015) and K (Kavianpoor et al.,
2012;Weindorf and Zhu, 2010; Xu et al. 2013) were observed. In general, the range and spatial
dependence values (moderate to weak) indicate that the sampling scheme used in this study was
adequate to quantify spatial dependence of the soil properties; while continuous
measurementmay be required for the proper characterization of variability on strongly spatially
dependent variables (Sivarajan et al., 2013).On moderate spatial dependence variable,
continuous measurement may not be required for the proper characterization of variability.
Ideally, the predicted values should be the same as the measured ones, but in reality data points
would scatter due to natural variations and uncertainties. Data regarding the predictive
performances in the present study gave an indication of good predictions. The RMSSE values for
almost all variables except available P (0.71) and Zn (1.4) were close to one (Table 4.1) which is
an indication of a good prediction. Close values of RMSE and MSE for soil parameters except
available P and Fe also showed a good agreement of prediction (Ike, 2010; Ewis, 2012; Gorai
and Kumar, 2013). The smaller RMSE than MSE and RMSSE less than one for available P
revealed the presence of overestimatedpredictions variability of the element (Gorai and Kumar,
2013). This might be associated with very high variability of available P in the study area which
ranged between 0.1 to 238.1 mg kg-1 soil.
The Goodness of prediction (G) coefficients of the present study varied from -2 to 100% (Table
4.1). The coefficients for all soil variables except S and B were positive. Available S had zero
value while it was slightly a negative (-2) for B. Coefficients such as zero and positive signify
that interpolation technique and predictions are more reliable than using the sample mean
(Karydas et al., 2009). Though small, the negative value for B indicate that the prediction would
also be reliable if the sample mean have been used instead. Overall, the G coefficients confirmed
good prediction performances. In line with this finding, Grunwald et al. (2005)in
Floridaconcluded high prediction performances for the G coefficients ranging from 0.3 to 33.2%.
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Hence, the generated soil fertility maps for the studied area using identified models are
recommended as a helpful tool for soil fertility intervention programs.
4.3.2. Soil Fertility Status Map
4.3.2.1. Soil pH, organic carbon and macronutrients
Statistical summary of interpolationand soil fertility maps are presented in Appendix Table 4 and
Figures 4.1 - 4.15, respectively. The results of the spatially interpolated maps indicated that there
exists variation in the distribution of macro and micro nutrients in the soils of the studied
districts.
Soil pH of the predicted map showed variation among districts (Figure 4.1). Soils in the Damot
Gale district were moderately acidic to neutral, whereas soils in Damot Sore and Sodo Zuria
districts were dominated by moderately acidic pH. The soil pH in the map ranged from strongly
acidic (5.2) to neutral (7.3) with mean values of 6.13 ± 0.39. Spatially, about 3.3, 78 and 18.7%
of the studied areaswere categorized as strongly acidic (< 5.5), moderately acidic (5.6 - 6.5) and
neutral (6.6 - 7.3), respectively, as per the rating of EthioSIS (2014). The prevalence of acidic
soil reaction could be linked to uptake of basic cations with no return into the soil system. In
addition, leaching of basic cations and continuous fertilization with NH4+ sourced fertilizers
which is though samll, might have contributed to acidity. Themoderate spatial dependence of soil
pH also proves the influence of both intrinsic (like parent material)and extrinsic factors (like
precipitation)on pH status of the studied soils. Similarly, Tesfahunegn et al. (2011) and Behera
and Shukla (2015) pointed out the moderate spatial dependence of soil pH is due to the intrinsic
and extrinsic factors. Studies by Alexandra et al. (2013) and Yihenew et al. (2015) also indicated
the influence of management practices (fertilizer types and crop removal) and environment
(precipitation, leaching and runoff) as sources of soil acidity. Overall, the pH of the soil is
suitable for the crops growing in the area except in some places of Damot Sore and Sodo Zuria
districts having pH < 5.5 which requires reclamation using lime or organic material application.
100
Figure 4.1. Soil pH map of Damot Gale, Damot Sore and Sodo Zuria Districts.
Organic carbon (OC)
Soil OC (%), which is an indicator of soil quality and productivity, showed less variability
among districts. The soil OC (%) contents of interpolated map (Figure 4.2) varied from 0.72 to
5.0% with the mean value of 1.94 ± 0.47. The spatial variability of OC further showed that about
60.36, 39.6 and 0.04% of the study area is rated under very low (2 - 4%), low (2 - 4%) and
medium (4 - 10%) category, respectively (Landon, 2014). Soil OC influences the physical,
chemical and biological properties of soils such as structure, water retention, nutrient contents
and retention as well as soil microbiological activities.However, the soils of the studied districts
were found to be low in soil OC, which has a negative implication on soil fertility and crop
productivity. Similar reports elsewhere on smallholder farmshave also indicated the low soil OC
contents (Patil et al., 2011; Ezeaku and Eze, 2014). Complete removal of crop residue, lack of
addition of adequate organic manures, runoff, continuous cultivation that exposes organic matter
101
for accelerated mineralization, and lack of return of crop residues may be accountable for the low
contents of soil OC. The result suggested that previous farmers management was not adequate.
Hence, maintenance of adequate levels of soil OM through integrated soil management practices
is an essential component of soil-fertility management as it helps to stabilize soil structure,
prevents soil erosion and helps the soil function properly.
Total nitrogen (TN)
There existed a moderate variability of TN. It varied from 0.02 to 0.47% with a mean value of
0.14 ±0.05%. About 22.2, 42.6, 34.5 and 0.7% of the studied area exhibitedvery low (< 0.1%),
low (0.1-0.15%), optimum (0.15 - 0.3) and high (0.3-0.5) TN as per the ratings of EthioSIS
(2014) (Figure 4.3). Overall, the lower value of TN could be associated with lower soil OC
contents. The use of inadequate fertilizer, intensive cropping and losses of N through leaching
might have resulted in low level of TN (Patil et al., 2011). Similarly, intensive cropping system,
imbalanced use of fertilizers, nutrient mining and low level of OM application could cause lower
TN (Akhter et al., 2010). In general, total soil N includes all forms of inorganic and organic soil
N. Hence, the optimum TN level in the present study could not be taken as a guarantee for the
available N. It is suggested that for high crop production, N in the form of organic and inorganic
fertilizers have to be applied so as to supplement the N levels of the soils.
102
Figure 4.2. Soil OC (%) map of Damot Gale, Damot Sore and Sodo Zuria Districts.
A
B
Figure 4.3. Total nitrogen (%) A) Status and B) Management based map of Damot Gale, Damot
Sore and Sodo Zuria Districts.
Available phosphorus
The soil available P contents of the study area (Figure 4.4) were found below the critical limits
(P ≤ 30 mg kg-1) as adopted for Ethiopian soils (EthioSIS, 2014) and would not satisfy the P
103
demand of crops. It varied from 0.78 to 26.22 mg kg-1with a mean value of 4.79 ± 3.56mg kg-1.
The spatial variability in terms of area coverage revealed that about 97.5% was very low (< 15
mg kg-1) and the rest 2.5% of the total area was found to be low (15 - 30 mg kg-1) in available P,
according to EthioSIS (2014) (Figure 4.4). Low level of P fertilizer application, loss of P through
crop uptake due to continuous crop production, low return of crop residues and soil erosion
would be ascribed for the loss of P from the soils of the study area (Akhter et al., 2010). In
addition, the availability of P is a function of soil pH, and hence, its availability may be
negatively affected on strongly acidic soils due to fixation. The present finding is in line with
those of Mulugeta (2006) and Alemayehu (2007) who reported P deficiency in soils where the
present research has been conducted. The result indicates the need for applying P containing
fertilizers in all places of the studied districts. Hence, to secure high crop production, P
application in the form of fertilizers, manure, crop residues together with lime (on strongly acidic
soils) have to be considered in order to supplement the deficient levels of P in the soils.
A
B
Figure 4.4. Soil available P A) Status and B) Management based map of Damot Gale, Damot
Sore and Sodo Zuria districts.
Exchangeable potassium (K)
The soils in the study districts showed higher contents of soil exchangeable K (Figure 4.5). The
content (Cmol (+) kg-1) ranges from 0.46 to 2.55 with a mean value of 1.09 ± 0.32. Spatially,
104
about 0.3, 92, 6.7 and 1% from the total area were found to have low (0.2 - 0.5), optimum (0.5 1.5), high (1.5 - 2.3) and very high (> 2.31) levels of soil K, according to (EthioSIS, 2014). The
observed exchangeable K values were above the critical limits (K >0.5 Cmol(+) kg-1) adopted for
Ethiopian soils (EthioSIS, 2014) and seems to satisfy the K demand by crops with the exception
of very small areas in Sodo Zuria district that showed K deficiency. In consent with this finding,
sufficient level of K in Nitisols of southwestern Ethiopia was reported by Abebe and
Endalkachew (2012).
A
B
Figure 4.5. Soil exchangeable K A) Status and B) Management based map of Damot Gale,
Damot Sore and Sodo Zuria districts.
However, for better agronomic interpretation, understanding the saturation levels of the basic
cations in the soil system is key to avoid nutrient imbalances. Exchangeable K uptake would be
reduced and K becomes deficient as Ca and Mg contents in soils are increased. The predicted
map of this study showed that K:Mg ratio varied between 0.14 to 1.48 with mean value of 0.65 ±
0.16. About 68% of the study areas showed the K:Mg ratio < 0.7 (Figure 4.6). This according to
Loide (2004) indicates that the soil Mg was dominant in the exchange site than K and cause Mg
induced K deficiency. The K deficiency due to high Mg in the study areas should be considered
as a serious obstacle for achieving higher yields. Therefore, application of K fertilizer is needed.
105
Figure 4.6. Soil exchangeable K:Mg ratio map of Damot Gale, Damot Sore and Sodo Zuria
districts.
Exchangeable calcium (Ca)
There exists less variability of exchangeable Ca content of the predicted map.The map displayed
that the majority of soils in all studied districts except in some areas of Sodo Zuria district, were
sufficient in exchangeable Ca (Figure 4.7). Exchangeable Ca contents (Cmol (+) kg-1) ranged
from 3.65 to 14.9 with a mean value of 7.45 ± 1.74. As rated by Maria and Yost (2006), about 6,
87 and 7% of soils from total study area were found to have low (2 - 5), medium (5 - 10) and
high (10 - 20) levels of exchangeable Ca, respectively. The medium to high level of
exchangeable Ca may be attributed to high percentage of clay particles (inorganic colloids)
contained in the soil. According to Hazeloton and Murphy (2007), about 11% and 64% of total
soil samples (n = 789) were having high (40 - 50%) and very high clay (>50%) content,
respectively. Hence, as the percentage of soil colloids increases, the capacity of the soils to
contain exchangeable Ca might increase as well (Loide, 2004). The low exchangeable Ca content
may be associated with high level of leaching and lower soil pH. Hence, application of Ca
containing fertilizers should be considered in the deficient areas. Comparative results were
reported by Eyob (2014) who indicated that there was sufficient exchangeable Ca in soils of
Alicho Woriro district in south Ethiopia. In contrast, the study by Mohammed (2014) on acidic
soils of Cheha district, south Ethiopia revealed low contents of exchangeable Ca.
106
A
B
Figure 4.7. Soil exchangeable Ca A) Status and B) Management based map of Damot Gale,
Damot Sore and Sodo Zuria districts.
Exchangeable magnesium (Mg)
Soil exchangeable Mg content of interpolated map varied between low (0.7) and high (6) with a
mean value of 1.88 ± 0.43. In terms of area coverage, about 14, 84 and 1% of total study area
were having low (0.5 - 1.5), medium (1.5 - 3.3) and high (3.3 - 8.3) Mg contents, as rated by
Landon (2014) (Figure 4.8). The interpolated result indicated that exchangeable Mg was not a
limiting nutrient to crop production, but moderate status seems to justify that Mg could be a
potential problem in the future. For soils having low Mg contents particularly in Damot Sore and
Sodo Zuria districts, addition of Mg containing fertilizer is needed. However, extensive soil
survey works in two districts of southern Ethiopia such as Alicho Woriro (Eyob, 2014) and
Cheha (Mohammed, 2014) also indicated that exchangeable Mg is not a limiting nutrient for
crop production.
Available sulfur
Available S content of spatially interpolated map for the study area revealed similarities within
and among districts (Figure 4.9). The result showed that all districts had <20 mg kg-1S content,
which is the critical limit adopted for Ethiopian soils (EthioSIS, 2014). Overall, the S content
(mg kg-1) varied between very low (6.45) and low (17.50), with a mean value of 10.56 ± 1.57.
The low level of available Sin soils may be associated with low soil OM content, non use of S
containing fertilizer and continuous removal of S by crops (Solomon et al., 2001). In addition,
107
the use of N and P containing fertilizers could lead to shortage of nutrients that plants need
(Nourzadeh et al., 2012). The recent soil survey works in southern Ethiopia (Eyob, 2014;
Mohammed, 2014), western Ethiopia (Tegbaru, 2014) and 35 districts in Tigray, northern
Ethiopia (EthioSIS, 2014) indicated S contents below the critical level set for Ethiopian soils. In
order to rectify these shortages, use of S containing fertilizers is suggested. Besides, application
of OM and maintenance of crop residues should be integrated.
A
B
Figure 4.8. Soil exchangeable Mg A) Status and B) Management based map of Damot Gale,
Damot Sore and Sodo Zuria Districts.
A
B
Figure 4.9. Soil sulfur A) Status and B) Management based map of Damot Gale, Damot Sore and
Sodo Zuria Districts.
108
4.3.2.2. Cation exchange capacity (CEC)
There exists less variability in the CEC of soils in the study area (Figure 4.10). The CEC (Cmol
(+) kg-1) in the map ranged between 11.18 and 45.13 with a mean value of 20.97 ± 2.44.
Spatially, about 0.1, 94.8, 5 and 0.1% of soils from the total area, had low (5 - 15), medium (15 25), high (25 - 40) and very high (> 40) CEC based on the rating of Landon (2014). In soils
where soil OM content is low like the study area, the CEC mightbe controlled by the nature and
amount of clay minerals present in the soil (Okubay et al., 2015;Havlin et al., 2009; Fasil and
Charles, 2009). Relatively, the moderate to higher percentage of clay particles in the study area
might have resulted in the medium level of CEC.
Figure 4.10. Soil CEC map of Damot Gale, Damot Sore and Sodo Zuria districts.
4.3.2.3. Soil micronutrients
Boron (B)
Soil content of B in the studied districts varied between very low (0.05) and optimum (1.83),
with mean value of 0.52 ± 0.11 mg kg-1. According to the classification proposed by EthioSIS
(2014), about 48, 50 and 2% of the studied areasare categorized as very low (< 0.5 mg kg-1), low
(0.5 – 0.8 mg kg-1) and optimum (0.8 – 2.0 mg kg-1) B contents, respectively (Figure 4.11). The
109
results imply that about 98% of the total area had ≤ 0.8 mg kg-1, which is the critical limit
adopted for Ethiopian soils (EthioSIS, 2014). Nutrient uptake, non use of B containing fertilizers,
low soil OC, and low organic fertilizer application might be the reasons for the low level of B
recorded in most agricultural soils of the study districts. Similar to S, the recent extensive soil
survey works in different parts of the country such as southern Ethiopia (Eyob, 2014;
Mohammed, 2014), Abay Chomen district, western Ethiopia (Tegbaru, 2014) and 35 districts in
Tigray, northern Ethiopia (EthioSIS, 2014) also reported that B was found to be deficient. Hence,
addition of B containing fertilizer is needed for cultivated soils of studied districts.
A
B
Figure 4.11. Soil boron A) Status and B) Management based map of Damot Gale, Damot Sore
and Sodo Zuria districts.
Copper (Cu)
Soil Cu showed moderate variability (Figure 4.12)and is observed as a limiting nutrient in the
study area. It ranged between 0.05and 2.64 mg kg-1with a mean value of 0.52 ± 0.19 mg kg-1.
About 50, 48 and 2% of the study areasare categorized as very low (< 0.5 mg kg-1), low (0.5 –
0.9 mg kg-1) and optimum (1 – 20mg kg-1) in their Cu contents, as per the ratings of EthioSIS
(2014). Overall, 98% of the total area showed Cu content ≤ 0.9 mg kg-1, which is the critical limit
adopted for Ethiopian soils (EthioSIS, 2014). The practices on the studied districts such as
nutrient removal through continuous cropping, non use of Cu containing fertilizer, low soil OC,
and low organic fertilizer application might have caused the low level of Cu in the
110
soils.Correspondingly, Abebe and Endalkachew (2012) reported Cu deficiency in the Nitisols of
southwestern Ethiopia. However, the result in the present finding was incontrast with the recent
findings (Eyob, 2014; Mohammed, 2014)who reported sufficient levels of Cu in soils of southern
Ethiopia.
A
B
Figure 4.12. Soil copper A) Status and B) Management based map of Damot Gale, Damot Sore
and Sodo Zuria districts.
Iron (Fe)
Extractable Fe content map of the study area revealed similarities within and among districts
(Figure 4.13). Soil Fe content (mg kg-1) ranged between very low (42.85) and high (296.23) with
mean value of 126.86± 25.95. In terms of spatial distribution, about 0.04, 0.46, 99.5% of the
study area had very low ( < 60mg kg-1), low (60-80mg kg-1) and optimum (80 - 300 mg kg-1)
extractable Fe contents, respectively (Figure 4.13) following the ratings set by EthioSIS (2014).
Overall, almost all soils of the studied areas had > 80 mg kg-1Fe content, which is found to be a
sufficient level for crop production for Ethiopian soils (EthioSIS, 2014). The presence of Fe rich
parent materials and high dissociation of Fe in acidic soils might be responsible for optimum soil
Fe contents of the study area. Analogously, Kibet (2013) notified that the higher Fe content
might be linked with parent materials (hematite). Furthermore, Fe deficiency is very unlikely in
acid soils (Oyinlola and Chude, 2010) as it is known to be soluble under relatively acidic and
reducing conditions. Hence, addition of Fe containing fertilizers is not needed for soils of the
studied districts.
111
A
B
Figure 4.13. Soil Fe A) Status and B) Management based map of Damot Gale, Damot Sore and
Sodo Zuria districts.
Manganese (Mn)
Soil map of Mn based on Mn activity index (MnAI) revealed that the soils in all districts had >
25 mg kg-1(Figure 4.14), which is the critical limit adopted for Ethiopian soils (EthioSIS, 2014).
This suggests that soil Mn is not a limiting nutrient for crops growing in the area. The most
probable reason for the high Mn content in the present study could be related to the acidic nature
of the soils, as the bioavailability of Mn is affected by pH and redox conditions (Karltun et al.,
2013). This finding is also in agreement with that of Haque et al. (2000), Eyob (2014), Tegbaru
(2014) and EthioSIS (2014) who reported adequate level of Mn in soils of different parts of
Ethiopia. Based on the current study,addition of Mn containing fertilizers is not needed for soils
of the study area.
Zinc (Zn)
The soil map regarding available Zn content indicated that Zn (mg kg-1) varied between 1.02 and
35.47 with the mean value of 8.35 ± 3.65 (Figure 4.15). In terms of area coverage, about 0.005 (4
ha), 72.5, 26.7 and 0.8% of soils from the total area had low (1-1.5), optimum (1.5-10), high (1020) and very high (> 20 mg kg-1) Zn content, based on the rating for Ethiopian soils (EthioSIS,
2014). This suggests that soils in all studied districts were sufficient in Zn (> 1.5 mg kg-1)
contents based on the critical level set for Ethiopian soils (EthioSIS, 2014).
112
Figure 4.14. Soil manganese map of Damot Gale, Damot Sore and Sodo Zuria Districts.
A
B
Figure 4.15. Soil zinc A) Status and B) Management based map of Damot Gale, Damot Sore and
Sodo Zuria districts.
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4.3.3. Fertilizer Type Recommendation
Soils in the study areas were found to be deficient in six soil nutrients (N, P, K, S, B and Cu).
The deficiencies for some of the nutrients were area specific. Hence, an overlay analysis for
deficient nutrients was performed to identify blended fertilizer types for site specific
intervention. Blended fertilizer is a fertilizer product made by physically blending three or more
fertilizer ingredients in a factory to provide several nutrients (EthioSIS, 2014). The result showed
that soils in the study areas require eight blended fertilizer types (Figure 4.16) of which Blend-1
(N, P, K, S, B and Cu), Blend-2 (N, P, S, B and Cu) and Blend-3 (N, P, K, S and B) were the
most dominant. In terms of proportion, 64.5% of the studied areas require blended fertilizer type1, followed by 29.73% (blend-2) and 2.59% (blend-3). Other five blends cover about 3.18%.
Figure 4.16. Blended fertilizer type map of Damot Gale, Damot Sore and Sodo Zuria.
So far, in Ethiopia based on soil analysis conducted in over 180 districts,12 blended fertilizer
types have been identified and their formulae also developed (EthioSIS, 2014). Though Cu (a
limiting nutreint) was not found in the list of EthioSIS(2014), other identified blendedfertilizers
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types in the presnent study are among the 12 formulated lists in the country. Hence, farmers in
the studyareacould be addressed easily from the existing fertilizer types. As indicated by
EthioSIS (2014), the nutrient formulation of NPKSB and NPSB fertilizer blend are 13.7 N – 27.4
P2O5 – 14.4 K2O + 5.1S + 0.54B and 18 N – 36 P2O5 + 7S + 0.71B, respectively. Different
researchers in Ethiopia concluded the significance of blended fertilization for higher nutrient
uptake, grain yield and maximum economic return (Brhan Abayu, 2012; Fayera et al., 2014). For
instance, the research on tef indicated that plots treated with blended fertilizer showed 30-35%
yield increase when compared to conventional fertilizer application of DAP and Urea (Brhan
Abayu, 2012).Therefore, current fertilizer use approach in the study area needs to be corrected to
attain higher crop yield and optimum economic return
4.4. CONCLUSION
It is axiomatic from the present finding that soils are found to show considerable spatial
variability. This was evidenced by a wider range and spatial structures of soil properties. The
nature variation was explained by the influences of natural and anthropogenic factors. In general,
the range and spatial dependence values (moderate to weak) indicated that the sampling scheme
used in this study was adequate to quantify spatial variability of the soil properties. In addition,
the predicted values based on fitted theoretical models were also found dependable. Hence, the
generated soil fertility maps are recommended as a helpful tool for soil fertility intervention
programs of the study area. Soil OC, TN, available P, K, S, B and Cu were found important soil
fertility constraints seeking immediate attention for sustained crop production. Overall, this
finding overcomes the philosophy of promoting only N and P sourced fertilizers and uniform soil
management approaches in the area. Therefore, building up of soil OM and use of N, P, K, S, B
and Cu containing blended fertilizers are recommended. Further refinement of fertilizer rates
targeting for the major crops grown in the study area should be thought in the future studies.
4.5. ACKNOWLEDGEMENTS
We would like to thank Ministry of Education (MOE) for the scholarship, the Ethiopian Soil
Information System (EthioSIS) at the Agricultural Transformation Agency (ATA) for financial
support. We are very grateful for all assistances, knowledge and experiences we have got from
the farmers in Damot Gale, Damot Sore and Sodo Zuria districts.
115
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5. SOIL-PLANT NUTRIENT STATUS AND THEIR RELATIONS ON
MAIZE (Zea mays L.) CROP FIELDS OF WOLAITA ZONE,
SOUTHERN ETHIOPIA
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5. SOIL-PLANT NUTRIENT STATUS AND THEIR RELATIONS IN
MAIZE (Zea mays L.)GROWING FIELDS OF WOLAITA ZONE,
SOUTHERN ETHIOPIA
Fanuel Laekemariam1, Kibebew Kibret1, Tekalign Mamo2 and Heluf Gebrekidan1
1.
Haramaya University, School of Natural Resource Management and Environmental Science, Ethiopia. 2.Ministry of
Agriculture, Ethiopia.
ABSTRACT
Integrating plant analysis with soil analysis is necessary to get an accurate picture of fertilizer
requirements. This study was aimed at investigating the soil and plant nutrient status and their
relationships under low-to-no input farming systems of Wolaita Zone, southern Ethiopia. Maize
(Zea mays L.)ear leaves, soil samples and field management history from 50 fields were
collected and analyzed. The result indicated that continuous cultivation without fallowing,
complete removal of crop residues and low replenishment of nutrients lossfrom external sources
were common practices in all fields. The soilswere strongly acidic to neutral in their reaction,
low in soil organic carbon (OC), total nitrogen (N), available phosphorus (P), sulfur (S), boron
(B) and copper (Cu).Magnesium induced potassium (K) deficiency was also observed. Tissue
analysisrevealed that 100, 84, 54 and 28% of the investigated fields were deficient in N, P, K and
Cu, respectively. Significant (P < 0.05) and positive correlations (r = 0.70, 0.40 and 0.50,
respectively) of soil available P, Ca and Cu with tissue content of the respective elements were
observed. Tissue P (r = 0.4, P < 0.01), iron (Fe) (r = - 0.3, P < 0.05) and manganese (Mn) (r =
-0.7, P <0.01)also correlated with soil pH. Generally, the results indicated that the levels of Ca,
Mg, Fe, Mn and zinc (Zn) except in some fields (8%) were found to be adequate for maize
production in the study area. However, total N, P K and Cu nutrients appeared to be less than
the optimum. Therefore, it is suggested that fertilizer management practices addressing N, P, K
and Cu deficiencies are recommended in order to ensure increased maize productivity in the
study area.
Keywords: Ear leaf, Macro and micronutrients, Plant Tissue analysis
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5.1. INTRODUCTION
Agriculture is an important development engine in Ethiopia. It generates 40% of gross domestic
products (GDP) (UNDP, 2014) and also accounts for 85 and 90% of employment and exports,
respectively (IFDC, 2012). Maize (Zea mays L.) is one of the major food crops in the country
leading in volume of production and productivity (3.25 ton per hectare (t ha-1)) (CSA, 2014).
Yet, the national crop productivity remained low compared to the 4.7 t ha-1 reported from on
farm trials (IFPRI, 2010) and6.0 - 7.5 t ha-1 in trial plots of Wolaita, Ethiopia (Fanuel and Gifole,
2013). The productivity, however, beset by different factors.
Soil erosion, intensive crop cultivation, complete crop residue removal and high nutrient
depletion in the country (Gruhn et al., 2000; Haileselassie et al., 2005; Tadele et al., 2013;
Fanuel, 2015) have been cited as a pressing challenges that aggravates soil degradation and
thereby low crop productivity. To counterbalance the soil nutrient problems, the intervention in
the country have been based on the research efforts made during 1950-1960s. The study
underlined that the need for macronutrient application particularly nitrogen (N) and phosphorous
(P) elements. Accordingly, N and P are added to the soil in the form of di-ammonium phosphate
(DAP) and urea fertilizers, whereas, other macro and micronutrients until recently have been
neglected from being used for crop production. This has led to imbalanced nutrient management.
Research activities in the country demonstrated higher depletion rate of macronutrients (122, 13
and 82 kg N, P and potassium (K) ha-1 yr-1, respectively (Haileselassie et al., 2005). In addition,
deficiencies of K (Abiye et al., 2004; Wassie and Shiferaw, 2011; Abdena et al., 2013;
Alemayehu and Sheleme, 2013), sulfur (S) (Itanna, 2005; EthioSIS, 2014 and 2015; Habtamu,
2015) and micronutrients such as boron (B), copper (Cu) and zinc (Zn) (Haque et al., 2000;
Wakene and Heluf, 2003; Teklu, 2004; Wondwosen and Sheleme, 2011; EthioSIS, 2015) and
iron (Fe) (EthioSIS , 2014) were reported. On the basis of the foregoing, the decline in soil
fertility could take the lead for the apparent lower yield in the country. This demands the need to
investigate the soil nutrient status and the crop responses growing on it.
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Maize is a crop with high demand of soil nutrients. To produce one ton of maize grain, the plant
removes 24 kg N, 3 kg P, 23 kg K, 5 kg Ca and 4 kg Mg from the soil (Fageria et al., 2011). This
highlights the need for regular soil fertility maintenance. However, under intensively cultivated
fields like in Wolaitawhere soil fertility management practices areinadequate (Fanuel,
2015),farmers would fail to supply the required nutrients. Consequently, abnormal colors,
stunted growth, stripes on leaves that are suspected to be nutrient deficiency symptoms have
been commonly observed onmaize plant (personal observation). A hidden hunger on the crop is
also speculated. In this regard, tissue analysis to know the amount of absorbed nutrients and
monitor imbalances, insufficiencies and excess nutrients (Høgh-Jensen et al., 2009; Ramulu and
Raj, 2012) is very crucial.
Different factors are affecting soil-plant relationship. Thus, combined use of soil and plant
analysis is believed to evaluate the complex interaction, get an accurate picture of limiting
nutrients and design corrective actions(Høgh-Jensen et al., 2009; Aref, 2011). Ideally, soil and
tissue nutrient concentrations are expected to be positively correlated for most nutrients (Melgar
et al., 2001). Nevertheless, factors such as soil nutrient level, soil physical conditions, genotype
or climate influence the required nutrient concentrations in plants. As a consequence, sufficient
nutrient uptake from deficient soils (Akhter, 2011) was reported. This could be related to
modification of rhizosphere environment (Fageria and Baligar, 2005; Diatta and Grzebisz, 2006;
Akhter, 2011) and nutrient interaction (Loide, 2004; Høgh-Jensen et al., 2009). This would also
call for the need to know more about soil-plant interaction prior to fertilizer interventions.
Maize is a major grain crop in Wolaita. The grain is used for consumption and for market. In
addition, the stalk is used for fencing, fuel, feed and source of income. The yield under low input
cropping varied from 1.54±0.50 to 2.54±0.9 t/ha (Fanuel, 2015). In order to improve nutrient
application, nutrient use efficiency and crop yield; knowledge on soil-crop interaction has a great
practical importance. However, extensive survey dealing with soil-plant nutrient relations is
scant. Thus, it is hypothesized that most of the intensively cultivated fields under low to no-input
systems in Wolaita area are likely to be deficient in macro and micronutrients, and maize plants
irrespective of varieties grown on these soils can verify the limiting nutrients and existing
interactions. The objective of this study was, therefore, to evaluate soil and plant nutrient status
and investigate soil plant nutrient relationships under low-to-no input soil systems.
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5.2. MATERIALS AND METHODS
5.2.1. Description of the Study Area
The study was conducted in Damot Gale, Damot Sore and Sodo Zuria districts, Wolaita zone,
Southern Nations’, Nationalities’ and Peoples’ Regional State (SNNPRS) of Ethiopia (Figure
1.1) during 2013. The districts are located between 037°35'30" - 037°58'36"E and 06°57'20" 07°04'31"N. The study area covers about 84,000 ha. The area has a bimodal rainfall pattern with
mean annual precipitation of 1355 mm (Figure1.2). The temperature ranges between 17.7 to 21.7
°C with an average of 19.7 °C (NMA, 2013). The elevation in studied districts varied from 1473
to 2873 m.a.s.l (Figure 1.3). The area is predominantly characterized by mid highland agroecology and Eutric Nitisols associated with Humic Nitisols are the most prevalent soils (Tesfaye,
2003). Agriculture in the study area is predominantly small-scale mixed subsistence farming.
The farming system is mainly based on continuous cultivation without any fallow periods. A
brief description about the area is indicated under section 1.1.
5.2.2. Soil and Plant Sampling and Analysis
Irrespective of crop variety and soil types, a total of 50 randomly selected maize growing fields
from the three districts were taken for soil and plant sample collection. From each field, relevant
information regarding topography, cropping history, soil fertility management practices and
estimated yield was recorded using a short structured questionnaire. Sampling fields were
georeferenced using the Geographical Positioning System (GPS). The survey was conducted
during 2013.
5.2.2.1. Soil sampling and analysis
Soil samples were taken from0-20 cm depth. The disturbed and undisturbed soil samples were
taken using augur and core sampler, respectively. About 10 to 15 sub-samples from each field
were taken to form one kg composite sample. After soil processing (drying, grinding and
sieving), soil physicochemical properties like texture, bulk density (BD), pH, soil organic carbon
(OC), macro and micronutrient contents and cation exchange capacity (CEC) were analyzed.
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Particle size distribution (PSD) was analyzed by laser diffraction method using laser scattering
particle size distribution analyzer (Horiba- Partica LA-950V2) (Stefano et al., 2010). Soil BD
was determined using the core method as described by Anderson and Ingram (1993). Soil pH
(1:2 soil: water suspension) was measured with a glass electrode (model CP-501) (Mylavarapu,
2009). Available P, available S, exchangeable basic cations (Ca, K, Mg and sodium (Na)) and
extractable micronutrients (Fe, manganese (Mn), Zn, Cu and B) were determined using MehlichIII multi-nutrient extraction method (Mehlich, 1984).The concentration of elements in the
supernatant was measured using inductively coupled plasma (ICP) spectrometer. Mid-infrared
diffused reflectance spectral analysis was also used to determine the amount of soil OC, total N
and CEC. The available soil Mn content was determined using manganese activity index (MnAI)
as described by Karltun et al. (2013). Particle size distribution, pH, OC, TN, CEC were analyzed
at the National Soil Testing Center (NSTC), Addis Ababa, Ethiopia while Ca, Mg, K, Na, B, Cu,
Fe, Mn and Zn were analyzed in Altic B.V., Dronten, The Netherlands.
5.2.2.2.Leaf sampling and analysis
For the determination of tissue nutrient, ear leaves adjacent to the uppermost developing ear
were considered during crop tasseling. The ear leaves are best indicators of mineral nutrients
(Campbell and Plank, 2000; Ramulu and Raj, 2012) and about 15 leaves per field were collected
from different plants. These samples were homogenized to make one representative sample for a
field.
Leaves were first washed with distilled water, oven dried at 70 °C for 48 hours to a constant
weight. Then after, samples were ground and stored in airtight plastic bags. From the composite
samples, sub-samples were taken for analyzing macronutrients (N, P, K, Ca, Mg) and
micronutrients (Cu, Fe, Mn, and Zn). The tissue analysis was carried out at NSTC, Addis Ababa,
Ethiopia, in accordance with the procedures indicated in Sahelemeden and Taye (2000). Nitrogen
was determined using Kjeldahl distillation procedure. Plant digests using concentrated nitric acid
(HNO3) and 30% hydrogen per oxide (H2O2) were prepared to extract and analyze for P, K, Ca,
Mg, Cu, Fe, Mn and Zn using Atomic Absorption Spectrophotometer. Phosphorous and K
concentration of the digests were measured using spectrophotometer and flame photometer,
respectively.
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5.2.3. Statistical Analysis
Descriptive statistics such as mean, standard deviation, minimum, maximum and median was
employed. Correlation analysis was performed to assess relationships among soil and plant
nutrient contents. Variation in soil properties was determined using the coefficient of variation
(CV) and rated as low (< 20%), moderate (20 - 50%) and highly variable (> 50%) according to
Aweto (1982) cited in Amuyou et al. (2013). Data analysis was carried out using Microsoft excel
and Statistical Package for Social Sciences (SPSS) software version 20.
5.3. RESULTS AND DISCUSSION
5.3.1. Maize Field Management and Soil Characteristics
5.3.1.1. Production environment and soil management
The survey result revealed that maize cultivation in the sampled fields has been carried out on
varied topographical categories (Table 5.1) ranging from flat (< 4%) to hilly (17%) slope
positions. The agro-ecology lies between lowland (< 1500 m.a.s.l) and mid highland (1500 2300 m.a.s.l) environments. Maize production is based on continuous cultivation without any
fallow periods. On about 74% of the farmers' fields have practiced double cropping in a year per
field (data not indicated). Complete removal of crop residues including rootuprooting on the
sampledfields has been commonly practiced. In addition, farmerson sampled fields have never
used lime to correct acidity.
For managing soil fertility, farmers have been using di-ammonium phosphate (DAP), urea and
farmyard manure (FYM) as sources of fertilizers. The commercial fertilizers, as reported by
farmers, were introduced since the time of WADU (Wolaita Agricultural Development Unit)
which was more than 35 years. Di-amonium phosphate was applied at higher rates than urea. In
the surveyed fields, DAP application on sampled maize fields varied from 0 to 80 kg ha-1 with a
median of 60.0 kg ha-1and urea application varied from 0 to 80 kg/ha with a median value of 30
kg ha-1; while FYM ranged from nil to 5 tha-1 with median of nil. The wider range of fertilizer
use implies soil fertility management differences among maize growing farmers. Inorganic
fertilizer application rates were lower than the reported blanket fertilizer recommendations for
128
maize (100 kg ha-1 for DAP and urea, each) (Fanuel and Gifole, 2013; Getachew and Aune,
2014).
Overall, the observed soil fertility management practices were not sufficient enough to meet the
crop nutrient demand. This may lead to heavy nutrient depletion and associated yield losses
particularly for maize which is known by its high nutrient requirement (Fageria et al., 2011).
Inadequate soil management interventions might have been among the major reasons for the low
maize yield in the surveyed area (Table 5.1). Farmers estimation of maize yield also showed high
variability, which could also be a reflection of the variation in soil fertility and management.
Table 5.1. Maize production environment, fertilizer rates and farmers estimated yield in surveyed
area of Woliata Zone, southern Ethiopia
Descriptive Altitude Slope
DAP
Urea
FYM
YO
YF
-1
-1
-1
-1
statistics (m.a.s.l) (%)
(kgha ) (kgha ) (tha )
(t ha )
(t ha-1)
Mean
1910.4
5.4
53.5
27.4
0.3
0.67
2.35
StD
104.7
3.6
25.3
27.4
1.0
0.35
1.01
Median
1901.5
5.0
60.0
30.0
0.0
0.60
2.00
Minimum
1471.0
1.0
0.0
0.0
0.0
0.0
0.80
Maximum
2184.0 17.0
80.0
80.0
5.0
1.60
6.00
N
50.0
50.0
50.0
50.0
50.0
50.0
50.0
YO=Yield with no fertilizer, YF= Yield with fertilizer
5.3.1.2. Selected Soil physico-chemical properties
The soils in the present study area varied substantially in their physical and chemical properties.
About 70 and 30% of maize growing fields were found to be silty loam and clay, respectively, in
their textural class. Soil bulk density varied between 0.86 and 1.39 gcm-3 with overall mean
value of 1.13 ± 0.11 gcm-3 (Table 5.2). The bulk density was ideal for proper root development
(Landon, 2014).
Soil pH showed low variabilityrangingfrom 5 to 7 (Table 5.3) and is categorized under strongly
acidic (pH < 5.5) to neutral (6.6 – 7.3) as per the ratings of EthioSIS (2014). About 80% of the
sampled fields were found to be favorable for maize in terms of pH except those fields having
pH < 5.5, which require liming and/or organic material application. The soil OC contents were in
the range of nil to 3.0% (Table 5.3), which are rated as very low (< 1.7%) to low (2-4%) based
129
on the ratings of soil test values interpretation by Landon (2014). Total nitrogen content also
followed the trend of soil OC and varied between nil to 0.3%; in which about 50% of samples
were rated as very low (< 0.1%), 28% low [0.1-0.15%] and 22% optimum (0.15-0.3%]
(EthioSIS, 2014).Therecorded soil OC and TN values were expected in the maize growing fields
of the study area where there is complete removal of biomass form the field, lower application
rate of fertilizers and continuous cultivation that favors rapid rate of mineralization (Girma and
Endalkachew, 2013; Okubay et al., 2015).
Table 5.2. Soil particle size distribution and bulk density of maize growing fields in Wolaita
Zone, southern Ethiopia
Descriptive
Sand
Silt
Clay
BD
Soil
statistics
(%)
(%)
(%)
(gcm-3) texture
Mean
11.2
23.9
65.0
1.21
StD.
3.2
6.3
8.9
0.08
Median
11.0
22.0
68.0
1.21
Clay
Minimum
7.0
17.0
40.0
1.05
Maximum
19.0
41.0
73.0
1.39
N
15.0
15.0
15.0
14.0
Mean
22.8
57.5
19.7
1.10
StD.
8.3
7.5
9.8
0.11
Median
21.0
58.0
19.0
1.09
Silt
Minimum
10.0
35.0
8.0
0.86
loam
Maximum
40.0
68.0
50.0
1.36
N
35.0
35.0
35.0
35.0
Mean
19.3
47.4
33.3
1.13
StD.
9.0
17.0
23.0
0.11
Median
17.0
52.0
23.0
1.12
Total
Minimum
7.0
17.0
8.0
0.86
Maximum
40.0
68.0
73.0
1.39
N
50.0
50.0
50.0
49.0
F-test
27.2*** 231.8*** 236.8*** 9.9**
CV (%)
46.6
35.9
69.1
9.7
**, *** significant at p < 0.01 and 0.001, respectively.
The soil available P content was found to be highly variable among the sampled fields (Table
5.3). It varied from 0.05 to 68 mg kg-1 with median value of 6mg kg-1. However, the majority
(94%) of maize growing fields were found to be deficient in available P based on the critical
level (30 mg kg-1) suggested by EthioSIS (2014). The high variability in available P content may
reflect differences in management as well as soil differences in terms of soil pH and texture. In
130
the present study, soil available P showed significant (p < 0.01) and positive correlation (r =
0.44) with pH. The clay textured maize fields had 2.8 mg kg-1compared to the silt loam (13.5 mg
kg-1) soils which might be attributed to P fixation. The low application rates of P containing
fertilizers, continuous crop uptake, losses due to erosion and fixation by acidic soils in the maize
growing fields might be linked to the inadequate P levels recorded in the studied soils. Many
studies also reported low available P content in cultivated fields (Tekalign et al., 2002; Wakene
and Heluf, 2003; Abreha et al., 2012 and Fanuel and Gifole, 2013).
The available S content of soil samples on about 96% of maize growing fields was found to be
deficient considering 20 mg kg-1as the adopted critical level (EthioSIS, 2014) (Table 5.3). The
tradition of not using S fertilizers, high crop nutrient removal and low soil OM content might be
the likely causes for the low status of soil available S. In Ethiopia, low soil S status has become a
widespread problem. Different authors associate the lower S content with low OM, as it is the
major source of total S on the surface soils (Tekalign and Haque, 1987; Itanna, 2005; Nand et al.,
2011 and Habtamu, 2015). Furthermore, Abreha (2013) associated the low S content with acidic
soil reaction, as it aggravates the adsorption of the sulfate ion (SO42--S) with aluminum and Fe
compounds.
There existed variability of exchangeable bases (Table 5.3). Exchangeable calcium was
dominant in the exchangeable site followed by Mg2+, K+ and Na+. Overall, the sampled soils had
high content of the basic cations that could be adequate to support plant growth. Exchangeable
Ca (Cmol (+) kg-1) ranged from 2 -16 which is rated as low (2 - 5) to high (10 - 20) level
according to Maria and Yost (2006). Exchangeable Mg (Cmol (+) kg-1)varied from 0.5 to 3
which is rated as low (0.5 - 1.5) to medium (1.51-3.3) according to Landon (2014). The low level
of Ca and Mg could be related to soil acidity, which is caused by leaching. The exchangeable K
(Cmol (+) kg-1) ranged from 0.5 to 4, which is close and above the critical range (0.5 Cmol (+)
kg-1), based on the ratings used for Ethiopian soils (EthioSIS, 2014). The K: Mg ratio ranged
from 0.3 to 1.4. Loide (2004) suggested indicative K: Mg ratio of 0.7:1 and 1:1 for clay and
loamy textured soils. In view of this, about 30 and 84% of maize fields having clay and loam
textured respectively were found to show Mg induced K deficiency. This implies that soil Mg
excessively dominates the exchange complex in relation to K; as a result exchangeable K uptake
131
would be reduced. The CEC showed less variability among surveyed fields (Table 5.3). The
amount ranged between 16 - 29 Cmol(+) kg-1which is rated as moderate (15 - 25Cmol(+) kg-1) to
high (25 - 40Cmol(+) kg-1) level (Landon, 2014).
The soil micronutrient contents revealed variability among maize growing fields. The mean
values were in the order Mn > Fe > Zn > B > Cu. Boron content ranged from 0.3 to 1.4 mg kg1
and Cu ranged from nil to 1.3 mg kg-1(Table 5.3). The nutrient status based on EthioSIS (2014)
ratings showed that about 94% of the sampled maize fields were found to be deficient for B (<
0.8 mg kg-1) and Cu (< 0.9 mg kg-1). The range for Fe in soil samples varied from 76 to 198 mg
kg-1, whereas Zn ranged between 1 to 22 mg kg-1(Table 5.3). About 4 and 2% of the fields were
deficient in Fe (< 80 mg kg-1) and Zn (< 1.5 mg kg-1), respectively. Manganese (i.e. MnAI)
varied from 228 to 826 mg kg-1and it was found to be adequate (> 25 mg kg-1) for crop growth.
In general, soils low in OM are more often deficient in B than soils with high OM content; and
Zn deficiency is mostly not expected on acidic soil (Aref, 2011). In confirmation with this study,
EthioSIS (2014), Eyob (2014), Mohammed (2014) and Tegbaru (2014) reported B deficiency;
and sufficiency of Fe and Mn from different parts of Ethiopia. Furthermore, Teklu (2004) and
Abebe and Endalkachew (2012) indicated the deficiency of Cu in Nitisols of western Ethiopia.
132
Table 5.3. Selected soil chemical properties of maize growing fields in Wolaita Zone, southern Ethiopia
pH
OC
TN
P
S
Ca
Mg
Mean
5.6
%
2.2
%
0.2
mg kg-1
2.8
mg kg-1
13.1
5.1
Cmol(+)kg-1
1.5
1.1
0.8
0.5
StD
0.6
0.9
0.1
2.2
4.7
3.0
0.7
0.8
0.7
0.2
0.2
34.1
135.0
2.2
2.4
Median
6.0
2.0
0.2
2.0
13.0
4.0
1.0
1.0
1.0
0.4
0.4
109.0
529.0
4.0
19.0
Minimum
5.0
0.0
0.0
0.05
5.0
2.0
0.5
0.5
0.0
0.3
0.0
76.0
301.0
1.0
16.0
Maximum
7.0
3.0
0.3
7.0
21.0
12.0
3.0
3.0
2.0
0.8
0.8
198.0
787.0
10.0
25.0
N
15.0
15.0
15.0
15.0
15.0
15.0
15.0
15.0
15.0
15.0
15.0
15.0
15.0
15.0
15.0
Mean
6.5
1.7
0.1
13.5
9.8
9.3
1.8
1.4
1.1
0.5
0.4
124.5
484.9
9.9
20.7
StD
0.5
0.6
0.1
13.6
3.0
2.5
0.5
0.7
0.3
0.2
0.3
22.7
135.1
4.6
3.2
Median
6.0
2.0
0.1
11.0
9.0
8.0
2.0
1.0
1.0
0.5
0.4
127.0
489.0
8.0
20.0
Minimum
6.0
1.0
0.0
1.0
5.0
5.0
1.0
1.0
1.0
0.3
0.1
79.0
228.0
4.0
16.0
Maximum
7.0
3.0
0.2
68.0
16.0
16.0
3.0
4.0
2.0
1.4
1.3
175.0
826.0
22.0
29.0
N
35.0
35.0
35.0
35.0
35.0
35.0
35.0
35.0
35.0
35.0
35.0
35.0
35.0
35.0
35.0
Mean
6.2
1.8
0.1
10.3
10.8
8.0
1.7
1.3
1.0
0.5
0.4
123.0
498.5
8.4
20.4
StD
0.7
0.7
0.1
12.4
3.9
3.3
0.6
0.7
0.5
0.2
0.2
26.4
135.3
4.6
3.0
Median
6.0
2.0
0.1
6.0
10.5
8.0
2.0
1.0
1.0
0.4
0.4
121.0
497.0
7.0
20.0
Minimum
5.0
0.0
0.0
0.05
5.0
2.0
0.5
0.5
0.0
0.3
0.0
76.0
228.0
1.0
16.0
Maximum
7.0
3.0
0.3
68.0
21.0
16.0
3.0
4.0
2.0
1.4
1.3
198.0
826.0
22.0
29.0
N
50.0
50.0
50.0
50.0
50.0
50.0
50.0
50.0
50.0
50.0
50.0
50.0
50.0
50.0
50.0
25.3***
3.2NS
2.4NS
5.0*
0.9 NS
0.01
0.4 NS
1.2 NS
15.8***
1.3 NS
21.4
27.1
55.5
14.8
Texture
Clay
Silt
Loam
Total
F-test
25.9***
5.6*
15.2***
9.2**
9.1**
K
Na
B
Cu
Fe
MnAI
mg kg-1 mg kg-1 mg kg-1mg kg-1
0.4
119.6
530.2
Zn
CEC
4.9
Cmol(+) kg-1
19.7
NS
CV (%)
10.8
40.1
66.0
120.1
36.2
40.7
36.1
54.4
46.4
39.3
59.4
*,**, *** significant at p< 0.05, 0.01 and 0.001, respectively. NS=Non significant difference
133
5.3.2. Leaf Nutrient Content and Relationship with Soil Nutrients
5.3.2.1. Leaf macronutrient concentration
There was moderate variability of tissue N content among maize ear leaves. The N content
varied from 0.67 to 2.50% with an overall mean of 1.51 ± 0.46% (Table 5.4). This is however
below the critical values of 2.80% reported by Schwab et al. (2007) and Fageriaet al. (2011). The
result suggests that N was inadequate in the plant. The deficiency of N in the tissues was
reflectedmore than the soil status that showed 78%low TN (Figure 5.1.A). Deficiency of foliar N
was predictable from the observed lower N application rates (27.4 ± 27.4 kg ha-1 urea, 0.3 ± 1.0 t
ha-1 FYM) by maize growing farmers of the study area. In addition, low soil OC and TN content,
loss of N due to its high mobility in the soil, and continuous uptake without fallowing, low return
of crop residues and overall very low soil fertility replenishment activities might explain the low
tissue N content. Corroborating this result, Høgh-Jensen et al. (2009) and Akhter (2011)
observed crop responses to the applied N. For the study area, soil management practices such as
the use of N bearing fertilizers, intercropping, and rotation with legumes are needed to raise the
TN content.
Phosphorus content of maize ear leaves varied between 0.14 and 0.39% with an overall mean of
0.21 ± 0.05% (Table 5.4). The deficiency of available P in the soil was reasonably reflected in
the tissue analysis results (Figure 5.1.B). About 84% of maize leaf samples had P at and below
the critical level of 0.25% set by Fageriaet al. (2011) and Schwab et al.(2007). This is in
agreement with Getachew and Girma (2004) who reported a positive correlation between P in
soil and P in maize plant.
Data on K tissue concentration indicated the presence of variabilityamong leaf samples. The K
content ranged from 1.0 to 3.0% with an overall mean of 1.76 ±.0.66% (Table 5.4). Regardless
of the adequate levels of soil K, about 44 to 54% of leaf samples were found to be deficient in K
when compared to the critical level of 1.7% (Fageriaet al., 2011) and 1.8% (Schwab et al.,
2007), respectively (Figure 5.1.C). The lower tissue content of K was not expected form K
sufficient soils of the study area. The K deficiency could explain that other factors like K fixation
and/or nutrient interactions in the soil are important in affecting the K availability. The soil
134
analysis result suggested the presence of imbalances between soil K and Mg contents (Loide,
2004). There is thus an evidence to speculate that Mg induced K deficiency limited available K
uptake and led to K deficiency in the leaves. Furthermore, Kajiru (nd) also reported non-direct
relationship between K values and availability to plants due to loss of some K through fixation
by clay minerals. This finding suggests that K fertilization appears to be necessary in the area. In
the present study, moderate variability of Ca and Mg concentrations of maize leaf samples were
observed. The Ca and Mg contents ranged between 0.55 – 2.0% and 0.15 – 0.20%, with mean
concentrations of 1.19 ± 0.37% and 0.17 ±.0.02%, respectively (Table 3). Calcium and Mg
contents in the leaves were found to be sufficient (Figures 5.1.D and E) compared to the critical
levels of 0.25 and 0.15%, respectively as suggested by Schwab et al. (2007).
135
Table 5.4. Leaf tissue analysis of samples collected from maize growing fields in Wolaita Zone, southern Ethiopia
Texture
N
P
K
Ca
Mg
Cu
Fe
%
Clay
Silt Loam
Total
Mn
Zn
-1
mg kg
Mean
1.53
0.20
1.80
1.09
0.17
8.27
388.80
146.47
49.87
StD
0.40
0.04
0.86
0.35
0.02
1.44
170.43
60.95
35.57
Median
1.40
0.19
2.00
0.93
0.16
8.00
346.00
162.00
38.00
Minimum
0.91
0.14
1.00
0.71
0.15
5.00
215.00
68.00
12.00
Maximum
2.36
0.27
3.00
1.81
0.20
11.00
892.00
293.00
138.00
N
15.00
15.00
15.00
15.00
15.00
15.00
15.00
15.00
15.00
Mean
1.50
0.22
1.74
1.23
0.17
6.91
332.00
78.94
49.91
StD
0.49
0.06
0.56
0.38
0.02
3.17
126.42
23.65
28.88
Median
1.37
0.21
2.00
1.25
0.17
7.00
287.00
72.00
43.00
Minimum
0.67
0.14
1.00
0.55
0.15
1.00
183.00
40.00
18.00
Maximum
2.50
0.39
3.00
2.00
0.20
14.00
626.00
137.00
172.00
N
35.00
35.00
35.00
35.00
35.00
35.00
35.00
35.00
35.00
Mean
1.51
0.21
1.76
1.19
0.17
7.32
349.04
99.20
49.90
StD
0.46
0.05
0.66
0.37
0.02
2.82
141.70
49.26
30.67
Median
1.38
0.20
2.00
1.19
0.17
8.00
315.50
80.00
43.00
Minimum
0.67
0.14
1.00
0.55
0.15
1.00
183.00
40.00
12.00
Maximum
2.50
0.39
3.00
2.00
0.20
14.00
892.00
293.00
172.00
N
50.00
50.00
50.00
50.00
50.00
50.00
50.00
50.00
50.00
F-test
0.05NS
1.94 NS
0.08 NS
1.44 NS
0.44 NS
2.49 NS
1.71
32.36***
0.00 NS
CV(%)
30.4
25.0
37.3
31.4
9.47
38.5
40.6
49.7
61.5
*** significant at p< 0.001. NS=Non significant difference
136
3.5
A
Leaf P (%)
Leaf TN (%)
2.8
2.1
1.4
0.7
0.0
0.00
0.15
0.45
0.40
0.35
0.30
0.25
0.20
0.15
0.10
0.05
0.00
0.30
B
0
15
Soil TN (%)
Soil P (mg
C
3.60
Leaf Ca (%)
2.70
Leaf K (%)
30
1.80
0.90
0.00
Soil K (Cmolc kg-1)
0.25
60
D
2
4 6 8 10 12 14 16 18
Soil Ca (Cmolc kg-1)
E
Leaf Mg (%)
0.2
0.15
0.1
0.05
0
0
0.5
1
1.5
2
2.5
3
75
kg-1)
2.25
2.00
1.75
1.50
1.25
1.00
0.75
0.50
0.25
0.00
0
0.0 0.5 1.0 1.5 2.0 2.5 3.0 3.5 4.0
45
3.5
Soil Mg (Cmolc kg-1)
Figure 5.1.Relationship between soil and maize leaf macronutrients (N, P, K, Ca and Mg). The
broken line indicates the critical level.
137
5.3.2.2. Leaf micronutrient concentration
There existed variability among micro nutrient concentrations of maize leaves. The range and
mean concentration (mg kg-1) values indicate: Cu (1 - 14, 7.32 ± 2.82), Fe (183 to 892, 394.04 ±
141.7), Mn (40 - 293, 99.2 ± 49.3) and Zn (12-172, 49.9 ± 30.7), respectively (Table 5.4). The
Cu level on about 28% of the leaf samples were found to be below the critical level of 6 mg kg1
suggested by Fageriaet al. (2011). In contrast to soil analysis results that have shown 100% soil
Cu deficiency, tissue analysis did not reflect entire deficiency (Figure 5.2.A). Similarly, Asgelilet
al. (2007) and Wondwosen and Sheleme (2011) in pot experiments conducted on maize grown
under Cu deficient soils of Ethiopia indicated the absence of responses to Cu (plant height, dry
matter yield and nutrient concentrations). The existing relationship on soil and plant tissue Cu
contents could be linked with the ability of the plant to absorb Cu, greater soil exploration due to
greater root mass and/or root hairs, increased Cu solubility due to root exudates, influence of soil
pH and more efficient Cu transport from roots to shoots (Havlin et al., 2009). Furthermore,
acidification of the rhizosphere from the applied ammonium sourced N fertilizers and other
acidic reactions was reported to enhance the availability and uptake of Cu, B, Fe, Mn and Zn by
roots (Fageria and Baligar, 2005; Diatta and Grzebisz, 2006; Akhter, 2011).
The tissue concentrations of Fe and Mn revealed that all samples were found to be above the
critical level of 21 and 20 mg kg-1, respectively based on the norm developed by Fageriaet al.
(2011). Similarly, 92% of leaf samples had leaf Zn content above the critical level of 20 mg kg1
indicated by Schwab et al. (2007) and Fageriaet al. (2011). Sufficient micro nutrient (Fe, Mn
and Zn) concentrations in the leaves were in agreement with their soil concentrations (Figures
5.2.B, C and D). This could suggest the existence of adequate amounts of these elements for
maize production and their deficiency may not be expected in the study area. Correspondingly,
Wondwosen and Sheleme (2011) from pot experiment conducted using soil collected from
Wolaita area have reported adequate amount of Fe, Mn and Zn in maize plant grown under high
levels of the indicated micronutrients.
138
A
12
Leaf Fe (ppm)
Leaf Cu (ppm)
15
9
6
3
0
0.0
0.9
Soil Cu (mg kg-1)
350
1000
900
800
700
600
500
400
300
200
100
0
1.8
0
C
300
Leaf Zn (ppm)
Leaf Mn (ppm)
250
200
150
100
50
0
0
300
600
Soil Mn (mg kg-1)
900
B
80
160
Soil Fe (mg kg-1)
240
200
180
160
140
120
100
80
60
40
20
0
D
0 2 4 6 8 10 12 14 16 18 20 22 24
Soil Zn (mg kg-1)
Figure 5.2. Relationship between soil and maize leaf micronutrients (Cu, Fe, Mn and Zn). The
broken line indicates the critical level.
139
5.3.2.3. Soil and Plant nutrient relationships
In order to evaluate whether a particular nutrient in the maize ear leaf is generally in a similar
trend with soil nutrient status, simple correlation analysis was performed (Table 5.5). The result
of soil-plant interaction for all investigated nutrients, except Mn, showed a positive correlation
with their respective nutrients (Table 5.5). Nonetheless, the interaction revealed significantly (p
< 0.01) positive correlation (r = 0.70, 0.40, 0.50) for P, Ca and Cu, respectively. On the other
hand, tissue N, K, Mg, Fe, Mn and Zn did not show significant correlation with their respective
soil nutrient contents. The presence of non-significant correlation indicates that not only the soil
nutrient content, but also some other factors might have caused the poor interaction. In general,
low to no nutrient application rate, mobility of nutrients in the soil, the ionic imbalance, fixation
and soil pH as well as genetic variability might be responsible for the non-significant correlation
between tissue and soil nutrient concentration.
The influence of soil pH and other soil nutrient on the tissue nutrient concentration were also
evaluated (Table 5.5). Soil pH was found to have a significant (p < 0.01) and positive correlation
(r = 0.4) with leaf tissue P and Mg concentration; and negative (r = - 0.4, -0.7) influences on
tissue Fe and Mn contents, respectively. The correlation matrix further revealed the existance of
significantly (p < 0.05) positive interaction of tissue P with soil P, K, Ca, Mg, Cu and Zn
contents. In addition, Ca uptake showed significantly (p < 0.05) positive interaction with soil P,
K, Ca, and Cu contents (Table 5.5). Copper uptake had significantly (p < 0.05) positive
correlation with soil Cu (r = 0.5) and Mn (r = 0.3) contents. Data regarding Fe uptake indicated
significantly (p < 0.05) positive interaction with total N (r = 0.3) and negative (r = -0.3)
interaction with pH. Furthermore, significantly (p < 0.05) negative correlation of Mn uptake with
soil pH, P, K, Ca, Mg and Zn and positive interaction with soil N were recorded (Table 5.5). In
agreement with the present findings, Høgh-Jensen et al. (2009) indicatedhigher P supply reduced
Mn uptakes and they also reported positive interaction between P and K; and increase of leaf P
and Zn due to increase in leaf N. In addition, Getachew and Girma (2004) reported highly
significant positive correlation between P application and P, K and Mg concentrations in the
plant. Aref (2012) also reported antagonism between Zn in the soil and leaf Mn content.
140
Table 5.5. Pearson correlation matrix for relationship between soil and maize leaf nutrients in Wolaita Zone, Southern Ethiopia.
Leaf
Soil
P
K
Ca
Mg
Cu
Fe
Mn
Zn
pH
N
P
K
Ca
Mg
Cu
Fe
Mn
Zn
Leaf N
0.31*
0.03
-0.24
0.17
0.03
0.21
0.20
0.47**
0.05
0.12
0.10
0.01
-0.06
0.01
0.08
0.13
0.02
0.04
Leaf P
1.00
0.02
0.18
0.22
0.53**
-0.02
-0.25
0.10
0.39**
0.04
0.63**
0.50**
0.31*
0.29*
0.35*
-0.02
0.23
0.52**
1.00
0.08
-0.07
-0.15
0.00
-0.25
0.13
-0.07
0.07
0.01
0.20
-0.04
0.08
-0.26
-0.13
-0.08
-0.31
*
-0.12
0.06
0.18
*
Leaf K
Leaf Ca
Leaf Mg
Leaf Cu
Leaf Fe
Leaf Mn
Leaf Zn
1.00
0.04
1.00
0.08
0.08
-0.15
-0.31
1.00
0.10
0.12
1.00
0.37
**
1.00
-0.02
0.26
-0.08
0.36
-0.06
0.03
-0.08
*
0.37
**
0.33
*
0.17
-0.24
0.37
**
0.13
0.34
*
*
0.26
-0.05
0.06
0.11
0.20
0.07
0.46**
0.10
0.33*
0.01
-0.08
0.16
0.08
-0.01
-0.07
0.08
-0.08
-0.37**
0.00
0.22
0.13
0.34
0.19
0.20
0.04
0.01
-0.33
*
0.15
-0.74
**
1.00
-0.04
0.25
-0.04
0.14
-0.04
-0.04
0.15
0.04
0.15
0.11
1.00
-0.51**
0.44**
0.52**
0.73**
0.45**
0.17
-0.32*
0.19
0.40**
1.00
0.01
0.02
-0.19
0.02
0.27
0.21
0.21
0.14
0.00
0.12
0.62**
0.28
Soil pH
Soil N
Soil P
Soil K
Soil Ca
Soil Mg
Soil Cu
Soil Fe
Soil Mn
Soil Zn
*, ** Correlation is significant at p < 0.05 and 0.01, respectively
0.33
*
0.47
**
-0.16
-0.30
1.00
0.04
*
-0.26
-0.46
0.53
1.00
**
**
-0.71
0.56
-0.14
**
**
-0.58
**
*
0.24
0.30
0.45**
0.49**
0.16
-0.28*
0.32*
0.46**
1.00
0.53**
0.36**
-0.13
0.30*
0.59**
1.00
**
0.33*
0.21
-0.08
0.41
1.00
-0.17
0.40**
0.31*
1.00
-0.09
0.07
1.00
0.23
1.00
141
5.4. CONCLUSION
In conclusion, the traditional soil fertility management practices were not adequate to replenish
the high amount of nutrients mined by the demanding maize crop. This led to low levels of
most macro and some micronutrients in the soil. This was partly reflected in the leaf or tissue
test results. In addition, the influence of soil nutrient imbalances on the uptake of nutrientsspecifically on K was observed. The overal result obtained (irrespective of crop variety and
soil type) is an indicative of the existing situations. Furthermore, it also showed that soil test
should be complemented with tissue analysis. Therefore, soil management practices and
fertilizer application that would address observed nutrient limitations N, P, K, S, B and Cu are
recommended for realizing better yield. In addition, further studies considering variety and
soil type is suggested.
5.5. ACKNOWLEDGEMENTS
We would like to thank Ministry of Education (MOE) for the scholarship, the Ethiopian Soil
Information System (EthioSIS) at the Agricultural Transformation Agency (ATA) for financial
support. We are very grateful for all assistances, knowledge and experiences we have got from
the farmers in Damot Gale, Damot Sore and Sodo Zuria districts.
142
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6. GENERAL SUMMARY AND CONCLUSIONS
Agriculture in Ethiopia has long been a priority and focus of the national policy. The sector
employs about 85% of the population, generates over 40% of GDP and 80% of export
earnings. It plays a significant role in improving food security. However, declining soil
fertility has beena serious challenge to agricultural activities. Inadequate soil management
practices, continuous land use pressure and natural factors have been posing a challenge to soil
and crop productivity. In the countryincluding in Wolaita zone, uniform soil fertility
management using blanket fertilizer recommendation approach has been implemented. Owing
to the differnces in soil managment, agro-ecology and inherent soil properties, soil fertility
spatial variation would be obvious. Hence, the blanket managment approach used throughout
the country could not address yield limiting nutrients in the soils and enhance the level of crop
productivity up to the expectations. Thus, adequate site-specific knowledge about the soil
health and fertility conditions is required for adopting efficient and corrective soil management
interventions. Therefore, this study was conducted.
To evaluate (1) landscape characteristics of agricultural lands and farmers' soil fertility
management practices (2) soil fertility status (3) analyze spatial variability, produce map and
recommend fertilizer types and (4) soil-plant nutrient status in Damot Gale, Damot Sore and
Sodo Zuria districts, Wolaita zone, southern Ethiopia. The study is part of the national soil
fertility mapping initiative (EthioSIS) and is aimed to contribute information towards
enriching the national soil information database. To achieve the intended objectives, about 789
field plots were surveyed during 2013 and analyzed for various soil fertility and chemical
parameters. Landscape characteristics of agricultural lands, indigenous soil knowledge and
farmers' soil management practices were investigated. In addition, geospatial analysis,
mapping and fertilizer type recommendations were performed on soil laboratory
results.Furthermore, as maize is among the major grain crops in the area, 50 maize growing
fields were also used to evaluate soil-plant interactions.
Results revealed that population pressure in the studied area havebeen compelling farmers to
exert a land-use pressure for agriculture to the extent of ecologically fragile steep slope
topographic positions. Consequently, higher bulk density and lower contents of available P,
148
exchangeable Kand extractable (B, Cu, Fe and Zn) on slopping landscapes thanflat positions
were recorded. This in turn brought negative consequences on crop productivity. Farmers in
the studied area have knowledge to classify, manage and use soils as perceived soil categories.
Evaluation of locally classified soil types using laboratory analysis did not indicate consistent
performances of each soil type. However, comparison made among major soil type/groups
(e.g. fertile and infertile) showed an observable relation when evaluated using soil chemical
analysis and yield of crops. Though clear demarcation of each soil type is needed, the existing
indigenous knowledge has an attractive outlook for easy communication with farmers during
soil fertility interventions. Farmers in the area tend to allocate fertilizing resources based on
the soil types. They use organic fertilizers for perennial and vegetable crops which are
common near to homestead, while the inorganic fertilizers were mostly allocated for annual
crop growing in the out fields. In general, from the present study, it is concluded that the
farmers' soil fertility management practices such as return of crop residue, fallowing, fertilizer
application by farmers were not adequate to bring about soil fertility improvement and good
yield returns.
The soil samples in the studied area have shown significant spatial variability. This reflects the
apparent differences in soil fertility management and inherent soil variability. In spite of this
fact, the soil fertility parameters such as soil OC, total N, available P, S and micronutrient (B
and Cu) were found at low level. The problem has been observed in almost all of the
agricultural lands including land use types which the farmers thought were fertile soils. This
indicates that improved soil management is required in all fields such as home garden, middle
and distant fields. Particularly, the striking soil fertility which was observed on perennials and
other vegetable crops growing fields was not expectable as it has been given special attention
by the farmers. This justifies that how soil fertility is gettingpoor in the area; and also demands
further investigation to come up with additional concluding remarks. In addition, the present
study identified strongly acidic areas (pH < 5.5) that need reclamation through liming. But the
study has limitation to indicate the rate; and hence this issue should be clearly addressed in the
future studies. In general, the present study can be considered as a useful source of information
in overcoming the philosophy of promoting only N and P containing fertilizers in the area.
149
Soil properties in the present finding have shown a considerable spatial variability. This
variability for most of the soil parameters was captured and quantified with sampling scheme
used in the present study. The generated soil fertility map in the studied area also revealed the
low levels of soil OC, TN, available P, K, S, B and Cu. The map also indicated the need for
site specific nutrient management interventions. The soil fertility constraints seek immediate
attention for sustaining crop production in the area. In this regard, the need for eight blended
fertilizer types has been identified. However, some of them were found on very few areas/
very few farms/. Hence, in order to address the majority of farmers in the studied area, three
blended fertilizers (NPKSBCu, NPSBCu and NPKSB) need to be used. Further refinement of
fertilizer rates targeting the major crops of the study area should be thought in the future
studies.
Farmers' soil fertility management practices were not enough to replenish the nutrients taken
up by the crops like maize. It led to the deficiency of essential nutrients in the soil. This was
partly reflected in the leaf or tissue test results. In addition, the influence of soil nutrient
imbalances
on
the
availability and
uptake
of
nutrients-specifically on
K
was
observed.However, the soil-plant nutrient investigation in the present study did not examine
the S and B status. Their deficiency under existing soil fertility management practices would
be suspected to prevail in the crop (e.g. maize) growing fields. Nonetheless, the soil S and B
with crop interaction under low to no input conditions should be addressed in the future
studies. In conclusion, the soil-plant nutrient investigation revealed that soil test results should
be complemented with tissue analysis for better soil fertility management and yield.
Based on the findings of this study, the following recommendations are forwarded:
 Interventions such as soil conservation, locally accessible organic matter application,
use of bio-fertilizers and balanced inorganic fertilizer application to restore the soil
fertility and productivity are recommended.
 In order to solve the major soil fertility constraints and realizing better yield in the
studied districts gradual buildup of the soil OM and use of the three blended fertilizer
types (NPKSBCu, NPSBCu and NPKSB) are recommended.
150
 For effectiveness of site specific fertilizer intervention, calibration works on newly
introduced fertilizer types such as rate, time, method of application and their economic
return for the major crops grown in the study areas are recommended.
 For efficient utilization, the developed soil fertility atlas has to be cross validated.
 For fine tuning of observed soil fertility study outputs, locally tailored critical values
for all nutrients need to be developed.
 In order to facilitate easy communication and share the current findings widely with the
local farmers, systematic integration of scientific approach with traditional soil
classification should be implemented for effective soil fertility management
interventions.
151
7. APPENDIX
152
Appendix Table 1. Topography and slope category of sampled agricultural fields in the
studied districts.
Slope
(%)
Topography
Flat
Almost Flat
Gentle Slope
Strongly Slopping Plain
Hilly Sloping
Maximum recorded slope (% )
Damot
Gale
Proportion (%)
Damot
Sodo
Sore
Zuria
(N=243)
(N=216)
<2
2-4
4-8
8 -16
> 17
17
4
43
21
15
58
Total
(N=789)
(N=330)
14
3
52
25
6
31
14
6
50
18
12
58
15
5
49
20
11
58
Numbers in parenthesis indicate the sample size (Source: EthioSIS sampling guide line).
Appendix Table 2. Farmers' soil management practices (%) on sampled agricultural fields in
the studied districts.
Practices
Damot Gale (%)
Damot Sore (%)
Sodo Zuria (%)
Total (%)
Fallowing
N=243
2.5
N=216
0.5
N=330
1.2
N=789
1.4
97.5
99.5
98.8
98.6
N=210
N=188
N=276
N=674
One
35.7
47.9
32.2
37.7
Two
61.9
51.1
61.2
58.6
Three
2.4
N= 210
1.1
N= 188
6.5
N=276
3.7
N=674
34
66
43
57
42
58
39
61
N=243
98
N=216
94
N=330
96
N=789
96
2
6
4
4
N= 210
N= 188
N=276
N=674
59
41
57
43
58
42
58
42
N= 210
N= 188
N=276
N=674
23
77
34
66
20
80
25
75
Yes
No
Crop intensity Yr-1
Rotation
No
Yes
Residue Management
Clear
Retain
Inorganic fertilizer use
Yes
No
Organic fertilizer use
Yes
No
"N" indicates number of sampled lands used to compute the proportions (Source: Survey result,2013)
153
Appendix Table 3. Correlation between fertilizer use, slope and crop yield in the studied
districts.
No
Parameters
Correlation
P
coefficient value
1
Yield (with and without fertilizer)
Maize
0.727
0.000
Tef
0.411
0.000
Wheat
0.800
0.000
Haricot bean
0.617
0.000
Field pea
0.576
0.105
Potato
0.706
0.000
Sweet potato
0.519
0.000
Taro
0.740
0.000
2
3
Slope and yield
Slope and maize (without fertilizer)
Slope and Wheat(With fertilizer)
Slope & Haricot bean (without fertilizer)
Slope & Haricot bean (With fertilizer)
-0.166
-0.291
-0.131
-0.150
0.021
0.022
0.040
0.020
Slope and Fertilizer application
Slope & DAP application
Slope & Urea application
Slope & FYM application
-0.047
-0.058
-0.056
0.224
0.131
0.149
Appendix Table 4. Summary of descriptive statistics of soil properties in the maps in the
studied districts.
Soil properties
Mean SD
Min Max
CV(%)
pH
6.13
0.39 5.02 7.28
6
OC (%)
1.94
0.47 0.72 5.0
24
TN (%)
0.14
0.05 0.02 0.47
36
-1
P (mg kg )
4.79
3.56 0.78 26.22
74
S (mg kg-1)
10.56 1.57 6.45 17.50
15
-1
Ca (cmol(+) kg
7.45
1.71 3.65 14.9
23
Mg (cmol(+) kg-1
1.88
0.43 0.7
6
23
K (cmol(+) kg-1
1.09
0.32 0.46 2.55
29
-1
B (mg kg )
0.52
0.11 0.05 1.83
21
Cu (mg kg-1)
0.52
0.19 0.05 2.64
37
-1
Fe (mg kg )
126.86 25.95 42.85 296.23
20
Mn (mg kg-1)
146.17 27.61 67.11 240.13
19
-1
Zn (mg kg )
8.35
3.65 1.02 35.47
44
PBS (%)
55.87 6.78 37.67 72.95
12
-1
CEC (cmol(+) kg
20.97 2.44 11.18 45.13
12
K:Mg
0.65
0.16 0.14 1.48
25
154
Appendix Table 5. EthioSIS ratings used for Ethiopian soils.
Soil parameter
Status
Soil pH (water)
Strongly acidic
Moderately acidic
Neutral
Moderately alkaline
Strongly alkaline
Very low
Low
Optimum
High
Very high
Very low
Low
Optimum
High
Very high
Very low
EthioSIS
Critical
< 5.5
5.6 – 6.5
6.6 – 7.3
7.4 – 8.4
> 8.5
0 – 15
15 – 30
30 – 80
80 - 150
> 150
< 90
90 – 190
190 – 600
600 – 900
> 900
< 30
Low
Optimum
High
Very high
Very low
30 – 50
50 – 70
70 – 80
> 80
<8
Low
8 – 10
Optimum
High
Very high
10 – 18
18 – 25
> 25
Phosphorus (mg/kg)
Potassium (mg/kg)
Calcium saturation
Percentage
Magnesium
saturation percentage
Soil
parameter
Exch sodium
percentage
(ESP)
Sulfur (mg/kg)
Zinc (mg/kg)
Iron (mg/kg)
Manganese
Activity Index
(MnAI)
Status
Very low
Low
Optimum
High
Very high
Very low
Low
Optimum
High
Very high
Very low
Low
Optimum
High
Very high
Very low
EthioSIS
Critical level
< 0.5
0.5 – 1.0
1.0 – 3.5
3.5 – 5
>5
< 10
10 – 20
20 – 80
80 – 100
> 100
<1
1 - 1.5
1.5 – 10
10 – 20
> 20
< 60
Low
Optimum
High
Very high
Low
60 – 80
80 – 300
300 – 400
> 400
< 25
Soil
parameter
Copper
(mg/kg)
Boron (mg/kg)
Total nitrogen
(%)
Organic matter
(%)
EC (mScm-1)
Status
Very low
Low
Optimum
High
Very high
Very low
Low
Optimum
High
Very high
Very low
Low
Optimum
High
Very high
Very low
EthioSIS
Critical level
< 0.5
0.5 – 0.9
1 – 20
20 – 30
> 30
< 0.5
0.5 – 0.8
0.8 – 2.0
2.0 – 4.0
> 4.0
< 0.1
0.1 – 0.15
0.15 – 0.3
0.3 – 0.5
> 0.5
<2
Low
Optimum
High
Very high
Salt free
2.0 – 3.0
3.0 – 7.0
7.0 – 8.0
> 8.0
<2
Very slightly
saline
Slightly saline
Moderately saline
Strongly saline
2–4
4–8
8 – 16
> 16
155
Appendix Table 6. Pearson correlation matrix of selected soil properties in the studied districts.
Sand
Silt
Clay
pH
TN
OC
P
Sand
Silt
Clay
pH
TN
OC
P
S
B
Cu
Fe
Mn
Zn
Ca
K
Mg
CEC
1.0
0.66***
-0.80***
0.28***
-0.35***
-0.24***
0.19***
-0.14***
-0.05
-0.08
0.19***
-0.13***
0.11***
0.20***
0.04
-0.10***
-0.05
1.00
-0.98***
0.31***
-0.20***
-0.07*
0.29***
-0.15***
0.01
0.37***
-0.21***
0.26***
0.28***
0.06*
-0.07*
0.02
1.00
-0.32***
0.25***
0.12***
-0.28***
0.16***
-0.02
0.01
-0.35***
0.20***
-0.24***
-0.28***
-0.06*
0.08***
0.00
1.00
-0.16***
-0.14***
0.44***
-0.35***
0.33***
0.27***
-0.20***
0.37***
0.38***
0.66***
0.65***
0.52***
0.53***
1.00
0.95***
0.13***
0.28***
0.24***
0.30***
-0.04
-0.03
0.20***
-0.03
0.18***
0.06
0.19***
1.00
0.14***
0.25***
0.24***
0.34***
0.01
-0.11***
0.27***
0.00
0.18***
0.01
0.18***
1.00
0.04
0.42***
0.43***
-0.03
0.23***
0.48***
0.55***
0.58***
0.36***
0.52***
1.00
0.05
-0.07*
-0.05
-0.09***
-0.08***
-0.27***
-0.05
-0.22***
-0.08***
1.00
0.35***
-0.14***
0.34***
0.32***
0.39***
0.52***
0.35***
0.43***
-0.04
0.19***
0.40***
0.47***
0.39***
0.43***
0.50***
1.00
-0.42***
-0.02***
-0.09***
-0.30***
-0.22***
-0.27***
1.00
0.25***
0.28***
0.47***
0.28***
0.32***
1.00
0.39***
0.48***
0.21***
0.35***
1.00
0.63***
0.77***
0.89***
1.00
0.62***
0.69***
1.00
0.86***
S
B
Cu
0.04
1.00
Fe
Mn
Zn
Ca
K
Mg
CEC
*,**, *** significant at P< 0.05, 0.01, 0.001, respectively.
1.00