INTERUNIVERSITY PROGRAMME
IN
PHYSICAL LAND RESOURCES
Ghent University
Vrije Universteit Brussel
Belgium
The effects of Faidherbia albida leaf litter on N dynamics and the soil
microbial community on two soil types in Zambia
Promoter:
Prof. Dr. Ir. Stefaan De Neve (LA12)
Tutor:
Master dissertation submitted in partial
fulfillment of the requirements for the
degree of Master of Science in Physical
Land Resources
David Buchan (LA12)
by Jones Yengwe (Zambia)
Academic Year 2010 - 2011
This is an unpublished M.Sc. dissertation and is not prepared for further distribution. The
author and the promoter give the permission to use this Master dissertation for consultation
and copy parts of it for personal use. Every other use is subject to the copyright laws; more
specifically the source must be extensively specified when using results from this Master
dissertation.
Gent,
The Promoter,
The Author,
Prof. Dr. ir. Stefaan De Neve (LA12)
Yengwe Jones
i
ACKNOWLEDGEMENT
There are many people who supported and encouraged me before and during my studies in
Belgium. Firstly, I would to sincerely thank my promoter Prof. Dr. ir. Stefaan De Neve and
my tutor David Buchan, for giving me an opportunity to study under their supervision. It has
been a valuable interaction and a learning experience for me. I am also grateful for your
patience, encouragement, continuous guidance and your valuable comments, and effort that
you put in revising and correcting this thesis. Special thanks also go to Luke, Mathieu and
Sophie for their technical assistance. I would also like to acknowledge the support and
encouragement I got from Mr. Victor Shitumbanuma and Dr. Elijah Phiri, and the Soil
Science department staff at the University of Zambia.
Secondly, a lot appreciation goes to my family for their never ending support and
encouragement throughout my academic and whole life. Without you I would not be where I
am today. Thank you!
Many thanks go to VLIR-UOS for awarding me a scholarship and subsequently giving me a
chance to study and stay in Gent. My stay in Gent was pleasant because of the many friends
and people I met from various countries with different social and cultural backgrounds. This
gave life in Gent a different perspective from books and lecture rooms.
My heartfelt gratitude goes to God almighty for the protection and guidance throughout my
life.
Finally, I wish to dedicate this thesis to my late father and mentor Mr. Samuel Kakusa
Yengwe, your words and deeds are not forgotten.
25th August 2011
Jones Yengwe
ii
Abstract
Nitrogen is considered as one of the most limiting plant nutrient in Zambian agriculture.
Despite the availability of artificial fertilizers, the problem still persists due to the expensive
nature of the commodity. To try and overcome nutrient deficits, nitrogen-fixing trees (NFTs)
are used to restore nutrient cycling and soil fertility. One of such trees (NFTs) being
promoted and used by farmers in Zambia to provide part of the required N on their fields is
Faidherbia albida (locally called the Musangu tree). This study was carried out to see
whether the positive influence of F.albida on crop yield is due to litter fall addition to the soil
or also due to more long-term modifications of the soil bio-chemical properties under the
canopy or influence zone of the trees. Soil and litter samples were collected from two sites
with different soils (Chongwe and Monze). At each site, two fields with F.albida trees were
randomly selected and soils were collected from 4 trees within each field. Soils were sampled
randomly inside the canopy and also outside the canopy. An 8 week incubation experiment
was conducted to determine N mineralization, Cmic and microbial community size and
composition. Soils from under the canopy had significantly higher Corg, total N, base cations
and initial mineral N. They also had lower C/N ratios but were not significantly different
from soils outside the canopies. Addition of litter increased amount of N mineralized in all
soils. Litter addition improved N mineralization rates, Cmic and microbial communities in
soils especially in soils outside the canopy. Total PLFAs also showed an increasing trend
when litter was added to the soils. The positive influence of F.albida could be attributed to
both long-term modifications of the soil bio-chemical properties under the canopy and litter
addition. The effect of canopy was also significant as observed by higher microbial activity
and community in soils under the canopy compared to those outside it. Both effects of canopy
and litter were more prominent on soils from Monze compared to Chongwe soils. Therefore,
F.albida leaf litter could improve short-term SOM and microbial activity. However,
establishing permanent F.albida trees within the fields is much more advantageous to the
farmers and would greatly improve SOM content and nutrient availability, thereby reversing
soil fertility depletion in the long-term. Further studies on soils from more sites to have a
general overview of F.albida‟s effect on bio-chemical properties and on various soils types
needs to be conducted.
iii
TABLE OF CONTENTS
ACKNOWLEDGEMENT .......................................................................................................ii
Abstract ................................................................................................................................... iii
TABLE OF CONTENTS ....................................................................................................... iv
LIST OF FIGURES ................................................................................................................ vi
LIST OF TABLES .................................................................................................................vii
List of abbreviations ............................................................................................................ viii
1. INTRODUCTION................................................................................................................ 1
1.1 Background of the study and problem statement ............................................................. 1
1.2 Research significance....................................................................................................... 4
1.3 Objectives ........................................................................................................................ 4
2. LITERATURE REVIEW ................................................................................................... 5
2.1 The nutrient problem........................................................................................................ 5
2.1.1 Nutrient cycling ........................................................................................................ 5
2.2 Trees and soil fertility ...................................................................................................... 7
2.2.1 Nitrogen addition by NFTs ....................................................................................... 9
2.2.1.1 Faidherbia albida .............................................................................................. 11
2.3 Nitrogen management .................................................................................................... 12
2.3.1. Nitrogen cycle ........................................................................................................ 13
2.3.2 Methods in N2 fixation measurements .................................................................... 14
2.4 Factors influencing decomposition and nitrogen release ............................................... 15
2.4.1 Litter quality............................................................................................................ 16
2.4.2 Soil microbial community....................................................................................... 16
2.4.2.1 Microbial biomass ............................................................................................ 16
2.4.2.2 Microbial community composition .................................................................. 19
2.4.3 Environmental conditions ....................................................................................... 20
3. MATERIALS AND METHODS ...................................................................................... 21
3.1 General description of the study area............................................................................. 21
3.2 Field sampling ................................................................................................................ 22
3.3 Soil characterization....................................................................................................... 22
3.4 Leaf litter characterization ............................................................................................. 25
3.5 Incubation Experiment ................................................................................................... 25
3.5.1 Sample preparation ................................................................................................. 25
iv
3.5.2 Bio-chemical analyses ............................................................................................ 27
3.6 Statistical analyses ......................................................................................................... 28
4. RESULTS ........................................................................................................................... 29
4.1 Soil and leaf litter characteristics ................................................................................... 29
4.2 Nitrogen mineralization ................................................................................................. 30
4.3 Soil organic carbon and Microbial biomass carbon ....................................................... 33
4.4 Soil microbial community.............................................................................................. 36
4.5 Relationship between parameters .................................................................................. 41
5. DISCUSSION ..................................................................................................................... 42
5.1 Influence of tree canopy................................................................................................. 42
5.2 Influence of litter addition ............................................................................................. 44
5.3 Comparison of tree canopy and litter addition effect between sites .............................. 47
6. CONCLUSION AND RECOMMENDATIONS ............................................................. 48
REFERENCES ....................................................................................................................... 50
v
LIST OF FIGURES
Figure 1. Maize grown under Faidherbia albida canopy, nitrogen-fixing trees in association
with crops in the same field simultaneously and using the tree biomass as a nutrient source
for crops..................................................................................................................................................7
Figure 2. Nitrate accumulates in the subsoil of this oxisol, near Maseno, Western Kenya. The
full line corresponds to the later stage and the dotted line indicates the initial amount of
Nitrate N. (a) Maize alone is unable to access this pool except in very top layer, while
Sesbania sesban (b) depletes it............................................................................................................9
Figure 3. A simplified nitrogen cycle under agro-forestry showing major stores and
flows......................................................................................................................................................15
Figure 4. Zambia‟s agro-ecological zones.......................................................................................22
Figure 5. Factorial design of the incubation experiment...............................................................27
Figure 6. Mineral N evolution in Chongwe soils...........................................................................32
Figure 7. Mineral N evolution in Monze soils................................................................................32
Figure 8. Net N mineralization during incubation..........................................................................33
Figure 9. Mineral N at week 8 of incubation..................................................................................34
Figure 10. Microbial biomass carbon in Chongwe soils...............................................................36
Figure 11. Microbial biomass carbon in Monze soils....................................................................36
Figure 12. Labile Corg(K2SO4) evolution in Chongwe soils..............................................................37
Figure 13. Labile Corg(K2SO4) evolution in Monze soils..................................................................37
Figure 14. Microbial biomass carbon at week 4 of incubation.....................................................38
Figure 15. Concentration of biomarker indicator Gram+ bacteria in soils from 2 sites...........40
Figure 16. Concentration of biomarker indicator Gram- bacteria in soils from 2 sites............40
Figure 17. Concentration of biomarker indicator Fungi in soils from 2 sites.............................41
Figure 18. Concentration of biomarker indicator Actinomycetes in soils from 2 sites.............41
Figure 19. Scatter-plot showing correlation between Total PLFAs and Cmic.............................42
vi
LIST OF TABLES
Table 1. Estimates of N fixed by symbiotic legumes in Africa ............................................................ 10
Table 2. Plant N content (kg N ha-1) from leguminous tree prunings in alley cropping systems ......... 11
Table 3. Annual yield of maize from alley cropping using prunings from N2-fixing trees in Africa ... 11
Table 4. Yield of cereal, cotton and legumes under and away from F.albida tree (2007/2008 season)
.............................................................................................................................................................. 13
Table 5. Biomarker fatty acids .............................................................................................................. 20
Table 6. CEC and particle size distribution of soils from the two sites ................................................ 30
Table 7. Initial soil characteristics. Differences between treatment means according to Tukey‟s HSD
post hoc test (p<0.05) are indicated by different letters in the same column. Standard deviations are
given in parentheses. ............................................................................................................................. 30
Table 8. Initial readily-available pools of soil nutrients. Differences between treatment means
according to Tukey‟s HSD post hoc test (p<0.05) are indicated by different letters in the same
column. Standard deviations are given in parentheses.......................................................................... 31
Table 9. ANOVA output for initial soil characteristics and readily-available pools of soil nutrients .. 31
Table 10. ANOVA output for total mineral N of soils from the two sites ............................................ 32
Table 11. Zero order N mineralization rates ......................................................................................... 34
Table 12. Amounts of nitrogen applied and cumulative N mineralized from F.albida leaf litter. ....... 35
Table 13. ANOVA output for Microbial biomass carbon for soils from the two sites ......................... 36
Table 14. The percent change in Cmic due to litter addition at 4 weeks of incubation. ......................... 37
Table 15. Concentration of biomarker PLFAs for Gram+, Gram-, Bacteria, Fungi, and AMF (nmol g-1
soil), and ratios of bacteria to fungi, Gram+/Gram- bacteria, cy17:0/16:1ω7 and cy19:0/18:1ω7.
Differences between treatment means according to Tukey‟s HSD post hoc test (p<0.05) are indicated
by different letters in the same column. Standard deviations are given in parentheses. ....................... 39
Table 16. Relative abundance of Gram+, Gram-, Actinomycete, Fungi, AMF and Protozoa (mol %).
Differences between treatment means according to Tukey‟s HSD post hoc test (p<0.05) are indicated
by different letters in the same column. Standard deviations are given in parentheses. ....................... 40
Table 17. Overview of significance Pearson correlation between parameters ..................................... 41
vii
List of abbreviations
CI
Chongwe soils from inside the canopy
CIL
Chongwe soils from inside the canopy with litter
CO
Chongwe soils from outside the canopy
COL
Chongwe soils from outside the canopy with litter
MI
Monze soils from inside the canopy
MIL
Monze soils from inside the canopy with litter
MO
Monze soils from outside the canopy
MOL
Monze soils from outside the canopy with litter
NFTs
Nitrogen fixing trees
BNF
Biological nitrogen fixation
SOM
Soil organic matter
C/N ratio
Carbon to Nitrogen ratio
Cmic
Microbial biomass carbon
Corg
Soil organic carbon
Corg(K2SO4)
Labile organic carbon extracted with K2SO4
TOC
Total organic carbon
PLFA
Phospholipid fatty acid
viii
1. INTRODUCTION
1.1 Background of the study and problem statement
Zambian agriculture is faced with many challenges: political, socio-economical and
environmental problems. With an increase in the country‟s population to about 10 million
people (CSO, 2000); there is a corresponding increase in demand for food which
subsequently requires that food production by farmers goes up. This has put pressure on
farmers who are now extending their fields into marginal and environmentally fragile lands
(Ajayi et al., 2007).
Sanchez (2002) highlights degradation of the soil resource through salinity, nutrient mining
(depletion of soil nutrient reserves in sub-sahelien sandy soils due to insufficient
fertilization), erosion, long term monoculture, and loss of biodiversity as a serious threat to
tropical production systems.
Therefore, current smallholder agricultural practices in Zambia are aiming at increasing yield
by using power harnessed in nature. Thus, farmers are being encouraged to use agro-forestry
soil fertility practices, green manures and other organic amendments to improve soil fertility
(Ajayi et al., 2007). An improvement in soil fertility by planting legumes as intercrops to fix
nitrogen reduces the cost of application of artificial fertilizer, as part of the N fertilizer is
supplied by the legumes.
Agro-forestry, which can be considered as a form of intercropping, assumes that trees
maintain soil fertility (Palm, 1995), because higher crop yields are observed near trees or
where trees were previously grown. However, some agro-forestry systems are used for other
purposes like erosion control, timber production, and fodder and fuel production. Therefore
agro-forestry comes in many forms and the type of tree used is dependent on the aim that the
people practicing it want to achieve. Various trees are used in agro-forestry, one of the groups
being nitrogen fixing trees (NFTs). Trees are selected based on the soil and climate
conditions, understory shade intensity, land and labour availability, use/product requirements,
and marketing possibilities (NFTA, 1993). For agricultural purposes, besides the above
properties, a farmer will look for a tree that improves yields when intercropped with a crop.
Nitrogen is arguably the one of most limiting plant nutrients in Zambian agriculture
(Sanchez, 1976). The problem persists despite the availability of artificial fertilizer to remedy
the situation; this is because most farmers cannot afford the commodity (Dakora and Keya,
1996). Farmers will continue to have lower yields if no affordable methods to improve
nitrogen availability are encouraged. To try and overcome nutrient deficits, nitrogen-fixing
trees are used to restore nutrient cycling and soil fertility. These trees are often deep-rooted,
allowing them to gain access to nutrients in the subsoil layers, while the production and
shedding of leaf litter to the ground nourishes soil life, which in turn support more plant life
(Elevitch and Wilkinson, 1998).
Management of biological N2 fixation and prunings from NFTs is very important because
they provide most of the required N for agricultural productivity (Vance and Graham, 1995;
Palm, 1995). These two sources of N are certainly the cheapest and probably the most
effective for maintaining a good yield in agriculture. However, the quality of prunings is
variable. The amount of nutrients which the prunings provide is determined by the production
rate and nutrient concentrations, both of which depend on the climate, tree species, soil type,
plant part, tree density and the pruning regime (Palm, 1995). Apart from the above factors,
nutrient provision by prunings also depends on how readily or rapidly they decompose in the
soil.
Nitrogen Fixing Trees (NTFs) are trees and shrubs that have the ability, through a symbiotic
association with certain soil bacteria, to take nitrogen out of the air and use it for growth.
NFTs‟ ability to take nitrogen from the air and pass it on to other plants through the cycling
of organic matter make them a major source of nitrogen fertility in tropical ecosystems
(Elevitch and Wilkinson, 1998). One of such trees (NFTs) being promoted and used by
farmers in Zambia to provide part of the required N on their fields is Faidherbia albida
(locally called the Musangu tree).
Faidherbia (syn. Acacia) albida is a monotypic genus in the legume subfamily Mimosoideae
(NTFA, 1995). The tree is mostly found in drier areas of sub-saharan Africa and possesses
the unusual characteristic - termed „reverse phenology‟ - of shedding its leaves just before the
onset of the rainy season (Giller, 2001). Trees can reach 25 m in height with canopies of 800
m2. The farmers take advantage of this large canopy area by maintaining the trees within the
cropping area, thus providing organic matter and N, as well as moisture retention under the
2
canopy (Dancette and Poulain, 1969; Bernhard-Reversat, 1982; Kamara and Haque, 1992;
Rhoades, 1995). Shedding leaves during the rainy season and having them during the dry
season favours crop production under the canopy and reduces the need for a fallow period on
poor soils (NTFA, 1995). There is an improvement in the microclimate (improved rainfall
infiltration, reduced evapotranspiration and temperature extremes) and increase in crop yields
due to the mulch created by falling litter and the canopy shade effect (CTFT, 1988).
The benefits of Faidherbia albida have been known for many years in Zambia and this has
led the Conservation Farming Unit (CFU) in the country to promote the tree for farmers to
establish their natural fertility. Increases in yields for some crops planted under the canopy
have been reported in Zambia (Simunji et al., 2009) but it is unclear whether these benefits
result solely from the effect of the tree. The positive effects have also been observed under
canopies of several other non-legume trees, suggesting that the fertility effect may in part be
due to resting animals‟ dung and urine as well as the excrements of perching birds (Rhoades,
1997; Van den beldt, 1992).
There is very little known about nutrient dynamics in agro-forestry systems with much
research concentrating on the above ground transfers. It is still unclear on mechanisms that
contribute most to higher crop yields in agro-forestry systems, with some suggesting physical
changes in the surrounding environment (reduced erosion risk, and protection from wind),
while higher concentrations of nutrients from leaf litter, and overall improvement in soil
properties may also be considered. Szott et al. (1991) recognized the importance of belowground litter production in the decomposition and nutrients but there is inadequate
information about this. Using limited data, various authors have suggested that this is due to
larger root turnover which is also responsible for large vegetation-to-soil nutrient fluxes in
natural systems (Jordan and Escalante, 1980; Cuevas and Medina, 1988, Sanford, 1985).
However, amount of nutrients re-translocated by roots before their death is still unknown.
It is against this background that a study was conducted to investigate whether the positive
influence of the tree is due to leaf litter addition or due to long-term soil modifications. This
was investigated by measuring the mineralization of N from F.albida litter and comparing
soil properties below and beyond the tree canopies. The quality of leaf litter is variable and
depends amongst other things on climate conditions and nutrient content as stated above.
Therefore, if the positive influence on observed yield increase is due to litter addition, this
3
can be used as a „quick fix‟ to improve soil fertility by properly managing leaf litter. If on the
other hand the positive influence is because of long-term soil modification then farmers have
to maintain the trees on their fields for a period of time to improve overall soil fertility of the
land and/or actively plant F.albida trees within the fields as much as possible.
1.2 Research significance
Management of organic residues on-field in the tropics is important because they are a source
of the much-needed nutrients. Establishing the effect of leaf litter addition on nitrogen
dynamics and also how the microbial community is influenced would help explain the
mechanisms underlying the observed yield increase. The study will assist the organisations
promoting the tree to explain to the farmers the benefits of having the tree in their fields. It is
important to understand that no single technology can reverse nutrient depletion in the long
term, but suitable (appropriate) technologies should be selected by farmers to reduce the cost
of inputs by supplying part of the N in smallholders‟ farms.
1.3 Objectives
The general objective of the study is to see whether the positive influence of F.albida on crop
yield is due to litter fall addition to the soil or also due to more long-term modifications of the
soil bio-chemical properties under the canopy or influence zone of the trees. But
improvements of physical properties and their physical protection from e.g. erosion cannot
also be ruled out to having positive influence on crop yield.
Nitrogen mineralization is a measure of one aspect of soil fertility and N has been selected
because it is widely deficient in many African soils, and is thereby one of the „most‟ limiting
plant nutrients on many farmers‟ fields (Ikerra et al., 1999). We also looked at the response
of the soil microbial community to litter addition, and whether soil microbial communities
differed under and outside the tree canopy, specifically if certain microbes such as fungi or
mycorrhiza or actinomycetes are promoted by tree canopy.
4
Specific objectives are:
1. to compare N mineralization rates between unamended soils and soils to which litter
was added;
2. relate whether the amount of N produced in soils to which litter was added is related
to microbial biomass dynamics and determine which type of microbes are dominant
in the decomposition of Faidherbia albida leaf litter in the two soils; and
3. to compare the above two processes/mechanisms and their magnitude between two
different soil types.
5
2. LITERATURE REVIEW
2.1 The nutrient problem
The tropics have been described as the most potentially productive cropping environments in
the world due to sufficient heat, light and moisture. But the yields in these areas are far from
optimal mostly because of lack of nutrients for plant growth in many soils (Giller, 2001).
Nutrient deficiency in most Zambian soils is common, which Smithson and Giller (2002)
attribute to the fact that most tropical soils are very old and the expanding clays (having
negative charge) have broken down over time and hence are unable to adsorb positively
charged cations. The absence of a negative charge and high rainfall leads to the easy leaching
of nutrients beyond the rooting depth of most (annual) crops. Continuous cropping of the
fields with very few external inputs has led to fertility depletion, estimated to be as much as
22kg N, 2.5kg P and 15 kg K of annual losses (Smithson and Giller, 2002; Smaling et al.,
1997). Knowledge on how to best manage the problem soils is needed given the poor
availability of soil nutrients in tropical soils. Frequent additions of large amounts of lime are
important to correct acidity problems, mainly Al-toxicity, and nutrient deficiencies in light
textured soils (Smithson and Giller, 2002).
2.1.1 Nutrient cycling
In a natural environment nutrient cycling is efficient with very small inputs and outputs from
the system. In most agricultural systems the opposite is however true where nutrient cycling
is limited, while inputs and outputs are large, and the soil is not continuously protected by a
continuous ground cover of decomposing litter (Sanchez et al., 1997). The two extremes
above are encompassed by agro-forestry with some research showing added value to soil
processes when competition for growth resources between tree and crop components is
properly managed (Ong and Huxley, 1996).
Nutrient cycling in agro-forestry systems benefits soil properties, crop production and
environmental protection by: (i) increased nutrient inputs to the soil, (ii) enhanced internal
cycling, (iii) decreased nutrient losses from the soil, and (iv) the provision of other
environmental benefits (Sanchez et al., 1997).
6
(i) Increased nutrient inputs
Trees increase nutrient inputs to the soil in an agro-forestry system by capturing nutrients
from atmospheric deposition, biological nitrogen fixation (BNF), and from deep in the
subsoil, and storing them in their biomass. The nutrients become inputs to the soil when the
tree biomass is added to and decomposed in the soil (Figure 1).
Figure 1. Maize grown under Faidherbia albida canopy, nitrogen-fixing trees in association with crops in the
same field simultaneously and using the tree biomass as a nutrient source for crops.
(ii) Enhanced nutrient cycling
The addition of in situ-grown plant material to the soil as litter-fall, root decay, green
manures, crop residue return, and its subsequent decomposition result in the formation of
organic forms of soil N.
(iii) Decreased nutrient losses from the soil
Runoff, erosion and leaching account for about half of N losses from top soil. Agro-forestry
systems have been found to decrease nutrient losses by runoff and erosion to minimal
amounts (Lal, 1989; Smaling, 1993; Young, 1989).
7
(iv) Environmental benefits
The cover from the litter and canopy also dampens temperature and moisture fluctuations.
Radial growth of roots loosens the topsoil improving porosity in subsoil when roots
decompose (Sanchez et al., 1985). Unlike in agricultural systems where soil protection and
root penetration are restricted to periods of crop cover, usually annual, in perennial agroforestry the two processes take place continuously (Sanchez et al., 1997).
2.2 Trees and soil fertility
Trees impact soil properties differently from annual crops; this is because of the long
residence time, larger biomass accumulation, and longer-lasting, more extensive root systems
(Sanchez et al., 1997).
Most available nutrients in the tropics are not in the soil but in living biomass. Based on
nutrient recycling principles, the technologies (agro-forestry with NFTs) take advantage of
the knowledge that, although nitrogen is one of the most limiting macro nutrients in tropical
soils, it is highly abundant in the atmosphere. NTFs replenish soil fertility by transforming
atmospheric nitrogen and making it available in the soil (Ajayi et al., 2007). NFTs are
perfectly suited to jump-start organic matter production on a site because apart from
enhancing overall fertility by accumulation of nitrogen and other nutrients, they establish
readily, grow rapidly, and can be coppiced for timber/mulch/forage (Elevitch and Wilkinson,
1998).
(i) Biological nitrogen fixation
Quantifying the magnitude of BNF in agro-forestry is methodically difficult, but Giller and
Wilson (1991) estimate the overall annual rate to be in the range 25-280 kg N ha-1 yr-1,
although other authors have indicated higher fixation rates depending on the method used.
However, this range is highly variable with a 10-fold difference in N fixation rates.
Phosphorus is an important nutrient in agro-forestry systems because the growth and Nfixation of trees can be limited by its deficiencies: large differences were found in growth and
P-use efficiency among and within NFTs species (Sanginga et al., 1994, 1995). Therefore,
selection of NFTs should also take into consideration tolerance of low available P at an early
growth stage (Sanchez et al., 1997).
8
(ii) Deep nitrate capture
Tree roots extend beyond the rooting depth of crops, taking up nutrients deep in the subsoil
and adding them to the topsoil by shedding tree litter (Sanchez et al., 1997). Mekonnen et al.
(1997) report that subsoil nitrate accumulation is attributed to greater formation of nitrate by
soil organic matter (SOM) mineralization in the topsoil than the crop can absorb. Since nitrate
is very soluble, it easily leaches out of the topsoil if not immediately used by plants or
microorganism. Sesbania sesban fallows deplete this pool, thus capturing a resource that was
unavailable to the maize crop at depths of 0.5-2.0 m (Figure 2). NFTs growing on soils with
high quantities of subsoil nitrate ideally should rapidly take up the nitrate before it is leached
out and meet its N requirements through BNF, thereby acting as a biological safety net in
agro-forestry systems (Sanchez et al., 1997).
0
20
40
Nitrate N (kg ha–1 0.5 m layer–1)
60
80
0
20
40
60
80
0.5
1.0
d(m)
1.5
2.0
(a)
(b)
Figure 2. Nitrate accumulates in the subsoil of this oxisol, near Maseno, Western Kenya. The full line
corresponds to the later stage and the dotted line indicates the initial amount of Nitrate N. (a) Maize alone is
unable to access this pool except in very top layer, while Sesbania sesban (b) depletes it. (Adapted from
Hartemink et al. and Buresh, unpublished data.)
(iii) Biomass transfer
Leafy biomass cut off from trees and leaf litter are incorporated into crop fields as a source of
nutrients in agro-forestry systems (Sanchez et al., 1997). While the quantities of biomass
9
farmers are able to apply are often sufficient to supply N to a maize crop with a moderate
grain yield of 4 t ha-1, they can seldom supply sufficient P to that crop (Palm, 1995).
2.2.1 Nitrogen addition by NFTs
Nutrient cycling on farms is mediated by trees integrated with crops in time and/or space
where nutrient supply and availability is increased, while losses are reduced from the crop
root zone. However, quantitative information is lacking on biological nitrogen fixation and
nutrients recycled in semi-arid agro-forestry due to the control of nutrients losses by trees
through processes such as deep root nutrient capture (Rao et al., 1997). The potential for
exploiting nutrient cycling in simultaneous agro-forestry systems is constrained by a low and
erratic water supply in the semi-arid areas of Africa.
Difficulties in N2 fixation methodologies have seen only few tree legumes being assessed for
N2 fixation. Despite this, the role of these species in agro-forestry has recently gained
increased attention. Restoration of N fertility in some of Africa‟s degraded soils has been
tested by the use of N2-fixing trees such as Acacia, Sesbania, Albizia, Prosopis, Glyricidia
and Leucaena spp for their symbiotic capacity and agro-forestry potential (Kang et al., 1990).
Estimates show a wide range in the amounts of N fixed by these species, which also tends to
vary within species, ranging from 36 to 581 kg N ha-1 (Table 1).
Table 1. Estimates of N fixed by symbiotic legumes in Africa
Country
N fixed (Kg N ha-1y-1)
Reference
Nigeria
448-548
Sanginga et al. (1995)
Nigeria
304
Danso et al. (1992)
Sesbania rostrata
Senegal
505-581
Ndoye and Dreyfus (1988)
Sesbania sesban
Senegal
43-102
Ndoye and Dreyfus (1988)
Gliricidia sepium
Nigeria
108
Danso et al. (1992)
Acacia holosericia
Senegal
36-108
Peoples and Herridge (1990)
Legume species (tree
and shrub legumes)
Leucaena
Peoples and Herridge (1990) highlighted that in some tree legumes, symbiotically-fixed N
accounts for about 50% of the plant‟s N nutrition. As shown in Tables 2 and 3, leaf prunings
usually contain substantial amounts of N in their tissues and can serve as a vital source of N
for cereal crop production (Kang et al., 1990).
10
Cultivation of crops in alleys and continuous pruning of the hedges (leguminous trees) is an
important cropping pattern that is being researched in Africa (Table 2). The species cultivated
in alleys may be cereals or legumes, but the high N content of prunings has tended to
encourage the production of cereals such as maize (Dakora and Keya, 1997).
Table 2. Plant N content (kg N ha-1) from leguminous tree prunings in alley cropping systems
Alley legume
N (kg ha-1)
Country
Reference
Sesbania sesban
448
Kenya
Oni et al. (1990)
Leucaena leucocephala
643
Kenya
Oni et al. (1990)
Gliricidia
41.1
Togo
Schroth and Lehmann (1995)*
Calliandra
26.5
Togo
Schroth and Lehmann (1995)
*Data for two prunings only in 1995.
Table 3. Annual yield of maize from alley cropping using prunings from N2-fixing trees in Africa
Cereallegume mix
Maize grain
yield (t ha-1)
Yield
increase
(%)*
N from
applied fert.
(kg ha-1)
Soil
type
Country
Reference
40-90
N from
legume
pruning
(kg N ha-1)
200
MaizeCalliandra
3.1
54
Alfisol
Nigeria
Gichuru and
Kang (1989)
MaizeSesbania
3.0
77
ND
448
ND
Kenya
Oni et al.
(1990)
MaizeLeucaena
2.8
77
ND
643
ND
Kenya
Oni et al.
(1990)
MaizeLeucaena
3.3
47
60
ND
Alfisol
Nigeria
Siaw et al.
(1991)
MaizeAcioa
3.5
25
60
ND
Alfisol
Nigeria
Siaw et al.
(1991)
MaizeLeucaena
ND
95
60-120
251
ND
Zambia
Matthews et
al. (1992)
(%)* yield increase compared to legume-free cereal yield
ND = not determined
It is interesting to note that there is massive addition of N with relative small maize yields.
This raises questions on the methods used to determined amounts of N added to soils. The
determination of fixed N and N additions in agro-forestry systems is methodologically a
challenge and this may result to over estimation of the amounts in some research (Table 2-3).
Observing the substantial amounts of N fixed or added from prunings, one would expect the
nitrogen problem to be less in tropical agriculture with agro-forestry systems. This is not the
case however; due to poor management, the N deficiency problem still persists because often
most of the N is lost by leaching out of the crop root zone (Smithson and Giller, 2002).
11
2.2.1.1 Faidherbia albida
Faidherbia albida is a leguminous fast-growing tree with an extensive rapidly developing
taproot system enabling it to reach adequate underground moisture, and can reach heights of
25 meters (Saka, 1989). F.albida is commonly intercropped with annual crops because of its
ability to fix nitrogen, draw water and nutrients from deep soil layers, and overall having a
beneficial effect on soil fertility under the canopy (Bernard, 2002). Perhaps one of the
important characteristics of the tree is that it goes dormant and sheds its foliage during the
early rainy season. This is important because at this time the field crops are being established
and therefore, the competition for light, nutrient or water during the growing season is
reduced significantly (De Schutter, 2010).
Soils under the tree canopy are highly fertile, having high contents of organic matter,
nitrogen, phosphorus and exchangeable cations (K, Na, Ca and Mg) (Lofstrand, 2005).
The nitrogen fixation and extraction of nutrients from deeper soil layers by the tree roots
account for the high soil fertility under the tree canopy. The nutrients are later returned to the
soil as leaf litter when the tree sheds its leaves and decompose benefitting crops under its
canopy. Saka (1989) highlights the shedding of its leaves which act as mulch on the ground
before the onset of rain and partial shading from the branches as one of the beneficial
properties of F.albida which reduces heat stress and evapotranspiration.
In a seven year study in Malawi, the yield of maize was shown to have stabilized at about 3 t
ha-1 without the application of fertilizer under the tree canopy whereas that grown away from
the canopy could only yield 500 kg ha-1 (ZNFU and CFU, 1998). Le Houerou (2005)
observed that millet cultivated under F. albida gave about 2.5 times higher yields than millet
cultivated away the canopy. According to De Schutter (2010), yields of unfertilized maize in
Zambia under the canopy averaged 4.1 t ha-1 compared to 1.3 t ha-1 away from the tree
canopy. The increase in yield of the crops under the canopy as compared to those away the
canopy can be as high as 44% (Table 4).
12
Table 4. Yield of cereal, cotton and legumes under and away from F.albida tree (2007/2008 season)
Yield (kg ha-1)
Treatment
Maize
Sorghum
Cotton
Cowpea
Guar
Groundnuts
Soybeans
2208
716
1528
1125
1968
944
1000
Under tree canopy
2819
971
1878
1447
1917
1694
1125
% increase
28
36
23
29
-3
44
13
Away from canopy
(control)
Source: GART year book, 2009
2.3 Nitrogen management
The problem of nitrogen deficiency is more pronounced in Africa‟s tropics because of poor
soil management. Giller et al. (1997) highlights the difficulty with regards to building up
large reserves of soil N in tropical soils and if present, N is mostly in poorly available forms.
The soil N build up is also retarded by the high decomposition rate and low application rates
of organic residues. Therefore, a continuous supply of N (through fertilisation, green
manuring, legume rotations and leguminous tree-shrub fallows) is necessary in tropical
agriculture to alleviate this deficiency.
Green manuring, legume rotations and leguminous tree-shrub intercropping are important in
N management of tropical soils but even under good conditions the amount of N2 fixed to
maintain productivity is difficult to achieve, especially where P is also deficient - which is the
case for most tropical soils (Smithson and Giller, 2002). To offset the estimated N losses in
sub-Saharan Africa an average of 30 kg N ha-1 year-1 must be fixed, but legumes grown in Pdeficient soils may only fix an average of 10 kg N ha-1 (Giller and Cadisch, 1995; Giller et
al., 1997) - unlike in north-west Europe where an average of 20 kg N ha-1 is received from
deposition. Again, these figures of the average amount fixed are way too low compared to
data provided from various researches in the tropics; see (Table 2-3). Legume rotations or
intercrops are common in smallholder agriculture, but since most involve a harvest off-take in
grain or fodder, net N additions are small (Giller and Cadisch, 1995).
Intercropping with leguminous trees would improve soil N content for smallholder farmers,
but even then the amounts of N are small and the technologies should be targeted on the basis
of soil characteristics, and fit in farmers‟ cropping systems. Therefore, leguminous tree
13
intercropping coupled with small doses of inorganic fertilizer where possible, offers a viable
path to increased agricultural productivity in the tropics (Smithson and Giller, 2002).
Mafongoya et al. (1998) suggests that the efficiency of nutrient use depends on the efficiency
of acquisition of nutrients (nutrient capture) and the efficiency with which the nutrients are
utilized by the growing plant (conversion efficiency). The authors further propose two shortterm strategies that can be used to manipulate litter decomposition to improve nutrient use
efficiency: (1) the regulation of the rate of nutrient release to coincide with crop demand, and
(2) the provision a more favourable environment for plant growth. In the long-term, effects of
SOM pools build-up may be more important than short-term nutrient release, but usually
farmers are looking for immediate results from the addition of organic amendments.
2.3.1. Nitrogen cycle
Nitrogen cycling in agro-forestry systems is driven primarily by transfer processes through
which nutrients move into (inputs) and out of (outputs) the system. Nitrogen fluxes of interest
include inputs (precipitation and interception, fertilizer additions, atmospheric fixation),
outputs (leaching, harvesting, fire, denitrification), nutrient release and retention in soil
(mineralization and immobilization), plant uptake, and litter fall (Trautmann and Porter,
2002).
These transformations can be carried out via either biotic or abiotic processes. Nitrogen is
fixed by soil bacteria (Rhizobium) living in association with roots of particular legumes. In
this relationship, the bacteria fix the nitrogen into plant-available NH4+ in exchange for
proteins and carbohydrates from the plant. Organisms that feed on the plants ingest the
nitrogen and release it as organic wastes, while denitrifying bacteria convert nitrate-N into
gaseous forms, thereby returning it to the atmosphere - after nitrifiers have converted
ammonium-N into nitrate-N (Trautmann and Porter, 2002).
The other nitrogen available to plants, besides the N obtained directly through symbiosis, is
derived from the mineralisation of easily decomposable soil organic matter. However this
fraction is relatively small in size compared to the total soil organic N pool and has a high
turnover rate (Young, 1989). This store is renewed from three sources: litter (above-ground
plant residues and root residues), SOM and organic fertilizers (Figure 3). The largest nitrogen
store is that bound up in the organic polymers of soil humus (Figure 3); this is mineralized
14
slowly, at the same rate as the decomposition constant for soil carbon, or about 3-4% per year
(Young, 1989).
Figure 3. A simplified nitrogen cycle under agro-forestry showing major stores and flows. Source: Young 1989
2.3.2 Methods in N2 fixation measurements
Many factors such as microbial, soil, and environmental variables influenced by agronomic
practices determine legumes‟ ability to fix atmospheric N2 (Peoples et al., 1995; Giller,
2001). This then sets aside the assumption that legumes will always fix significant amounts
of N (Peoples et al., 2009a). It is difficult to quantify the amount of N2 fixed, despite nitrogen
fixation associated with trees and shrubs playing a major role in the functioning of many
ecosystems, from natural woodlands to plantations and agro-forestry systems (Boddey et al.,
2000). Different approaches have been used to quantify the amount of N 2 fixed by legumes
but there is no single “correct” way of measuring N2 fixation that takes into account all
situations (Peoples et al., 2009b).
The basic principle of all methodologies for assessing N2 fixation is to quantify legume N2
fixation by simply measuring the total amount of N accumulated in plant tissue during a
given period of time. The various common methods used for determining N2 fixation are: (i)
15
nitrogen balance, (ii) nitrogen difference, (iii) acetylene reduction, (iv) hydrogen reduction,
(v) ureides, (vi) 15N2, and (vii) 15N-dilution.
Among these the most frequently used methods are:

Nitrogen difference, which compares the legume total N to that of a neighbouring non
N2-fixing plant where the difference in N is assumed to be the fixed N.

Ureides method makes use of the N-solute composition in the xylem sap of many
tropical and subtropical legumes which changes from one dominated by ureides in N2fixing plants to one dominated by nitrate and amino acids in plants utilizing soil N.
Therefore an indirect measure of legume‟s dependence on N2 fixation can be linked to
the abundance of ureides relative to other N solutes (Peoples and Herridge, 1990;
Herridge and Danso, 1995).

The Nitrogen 15 isotope techniques (Nitrogen-15 natural abundance and Nitrogen-15
enrichment) use the principle that there is a difference between the concentrations of
15
N in atmospheric N2 and those of plant-available soil N.
Most of the methods outlined above are very difficult to apply for woody perennials in the
determination of N2 fixation. Some of the problems involved in measuring N2 fixation by
woody perennials include:
(i) the long-term, perennial nature of growth and the seasonal or year-to-year changes in
patterns of N assimilation ( Ladha et al., 1993; Peoples et al., 1996);
(ii) large plant-to-plant variations in growth and nodulation potential, which typically occur
even within a single genotype or provenance used for agro-forestry.
2.4 Factors influencing decomposition and nitrogen release
Both biotic (litter quality and microbial composition) and abiotic (climate conditions and soil
physical and chemical properties) factors regulate plant litter decomposition (Swift et al.,
1979; Zhou et al., 2008).
Supply of N and maintenance of soil organic matter (SOM) in conventional agro-ecosystems
are important processes which are influenced by the nature of plant residues. The increase in
nutrient availability in the soil from addition and decomposition of plant material such as leaf
litter fall, root decay, green manures and crop residues results in the formation of organic
16
forms of soil N (Sanchez et al., 1997). In agro-forestry systems like alley cropping, food
crops take up N from the prunings of N2-fixing leguminous trees after their decomposition
(Kang et al., 1985). Mineralization of soil organic N grown in-situ converts it to nitrate or
ammonium ions in soil solution which are readily available to plants. Under ideal conditions
in alley cropping, the nutrients contained in the prunings should be released at rates which are
synchronized with the nutrient demand rate of the crop in the field (Seneviratne et al., 1999).
It has been observed generally that large amounts of prunings give higher SOM levels and
increased crop yields, but Sanchez and Miller (1986) caution that total SOM does not
generally relate to increased crop yield. Nutrients releases from SOM are more dependent on
the biologically active fraction than on total SOM levels.
2.4.1 Litter quality
Tree species in agro-forestry systems vary in litter quality, usually determined by the C/N
ratio of their leaves (Palm and Sanchez, 1991; Constantinides and Fownes, 1994). But other
authors have observed a slow release of N even from leguminous litter with low C/N ratios,
emphasizing that C/N ratios should not be considered as the only determinant of litter quality
and its potential for N release (Vallis and Jones, 1973). High quality materials readily
mineralize, while low quality ones decompose slowly and eventually form part of SOM
pools. Therefore for decomposition, various parameters have been suggested to predict the N
mineralization apart from total N content and C/N ratio of the plant material. In the case for
observed slow release of N from low C/N ratio plant material, the presence of polyphenols
which bind proteins reduced the N release (Vallis and Jones, 1973). The importance of the
lignin/N ratio for leaf litter decomposition and the polyphenol/N ratio for predicting leaf N
release have been suggested as determinants of litter quality (Melillo et al., 1982; Palm and
Sanchez, 1991). With no consensus regarding which chemical parameter is the best
determinant of decomposition and N release, Seneviratne et al. (1999) suggests the initial
concentrations of C, N, lignin, polyphenols and their ratios with N to be considered as the
major determinants.
17
2.4.2 Soil microbial community
2.4.2.1 Microbial biomass
Microbes are responsible for many nutrient transformations involving both macro and micro
nutrients which in turn influence nutrient availability and soil health and quality (Alexander,
1977). The main driving force in the decomposition of organic materials is the soil microbial
biomass and it is frequently used as an important index of biological status in soil and as an
indicator of changes in chemical and physical properties resulting from soil management and
environmental stresses in agricultural ecosystems (Brookes, 1995; Trasar-Capeda et al.,
1998). Microbial biomass is a labile nutrient source and an agent of transformation and
cycling of organic matter and plant nutrients in the soil (Sicardi et al., 2004). Microbial
biomass carbon (Cmic) represents the living component of SOM and responds rapidly to
changes in soil management (Saswati and Vadakepuram, 2010). The size and activity of
microbial biomass determine the nutrient availability and productivity of agro-ecosystems. Its
turnover is a dynamic process responding quickly to changes in environmental conditions
such as climate (temperature, moisture), input of nutrients and disturbance (management
systems) (Friedel et al., 1996).
Identification of biological indicators of soil quality is critical. Microbial and biochemical
parameters are used as soil fertility indicators because of their sensitivity to any changes in
the environment and some biological indicators such as microbial biomass play a central role
in the cycling of C and N (Visser and Parkison, 1992; Doran and Parkin, 1994; Brookes,
1995; Elliott et al., 1996).
Organic materials added to the soil are a source of nutrients; therefore their effects on the soil
microbial biomass should be taken into consideration (Perucci et al., 2000). Understanding
fully the role macro and microorganisms play in soil productivity, that is, transformations and
availability of nutrients, still remains a challenge (Zhenli et al., 2003). Despite the challenge,
the estimation of soil microorganisms and the many below ground activities they undertake is
useful in soil biology research.
18
2.4.2.2 Microbial community composition
The plate count technique was for a long time considered to be a reliable way to describe the
community composition of soil microbes (Frostegard et al., 2010). Usually the conventional
methods (culture technique) used to study soil microbial community structure accounts for a
very small proportion (thought to represent only about 1%) of soil microbial community as
compared to PLFA profile analysis (Tunlid and White, 1992). The technique has its
limitations and according to Amann et al. (1995), about 80–99% of species of
microorganisms have not yet been cultured. Thus only a small percentage of soil
microorganisms could ever be cultured and counted by plate dilution techniques. Kirk et al.
(2004) reported that the development of fatty acid analysis and numerous DNA- and RNAbased methods would overcome problems associated with non-culturable microorganisms.
Therefore, phospholipid fatty acids (PLFAs) found in all living cells were seen as potential
biomarkers for community characterization (Zelles et al., 1992; Guckert and White, 1986;
Petersen et al., 1997). Zelles (1999) stresses that PLFA profiles give information on
community structure fingerprints rather than exact species compositions and thus provide a
broad diversity of measurements at phenotypic level. To study the molecular microbial
diversity, genomics approaches which isolate DNA, RNA and other genetic materials have
been developed. These include DNA reassociation, DNA–DNA and mRNA: DNA
hybridization and DNA cloning and sequencing (Kirk et al., 2004).
Phospholipids rapidly degrade after cell death, therefore, a quantitative insight into soils‟
viable or active microbial biomass can be provided by the total PLFA content of a soil
(Frostegard et al., 2010). Balkwill et al. (1998) observed a significant correlation between
total phospholipid content and other methods used to measure microbial biomass.
PLFAs considerably differ among specific groups of microorganisms and this makes them
useful biomarkers for specifically identifying important microbial groups (Tunlid and White,
1992; Zelles et al., 1992). The microbial groups which can be distinguished on the basis of
PLFA profiles are bacteria (of which Gram-positive and Gram-negative bacteria can be
discerned), fungi (including the sub-group/phylum of the mycorrhizal fungi), actinomycetes,
and protozoa (Tunlid and White, 1992; Vestal and White, 1989; Frostegard et al., 1993;
O‟leary and Wilkinson, 1988) (Table 5).
19
Table 5. Biomarker fatty acids
Group
Bacteria
Biomarker
Gram-positive
Branched chain fatty acids (br 17: 0,
br 18:0, i17:0, a17:0, i16:0, i16:1,
10Me16:0, 10Me17:0), iso and anteiso
isomers
of 15:0
Gram-negative
Cyclopropane fatty acids (cy17:0, cy19:0)
Fungi
PUFA 18:2ω6 (linoleic acid)
Actinomycetes
10Me16:0, 10Me17:0 and 10Me18:0
Arbuscular mycorrhizal fungi
16:1ω5
Source: (Zelles, 1999)
The use of PLFAs as indicators of environmental stress is reinforced by several of their
features such as them being key components of the microbial membrane, therefore
responding to both intracellular and extracellular environmental conditions. Changes in
PLFA composition of microbial membranes can be caused by environmental disturbances
resulting in a shift of the soil microbial community structure (Guckert et al., 1986; Heipieper
et al., 1995; Heipieper et al., 1996). Depending on the environmental changes or stress, the
cyclopropyl to mono-unsaturated fatty acid ratios can be altered as in the case where tillage
has been reported to increase relative cy17:0 and cy19:0 productions by 70 and 86%
respectively (Chaudhary et al., 2005). When the cells enter a stationary phase, cyclopropyl
fatty acids (cy17:0 and cy19:0) are formed by transmethylation of their equivalent/related cis
monounsaturated fatty acids (16:1ω7, 18:1ω7). The now modified cyclopropyl fatty acids
are more stable and not easily metabolized by bacteria and hence help in maintaining a
functional living membrane by reducing the membrane lipid losses during stress conditions
(Guckert et al., 1986). These features of PLFAs can be used as indicators to monitor and
assess environmental stress or changes.
2.4.3 Environmental conditions
Soil temperature and moisture are important factors in decomposition because they affect the
activities of microorganisms (Brinson, 1977; Singh, 1969). In India, Kaushal and Verma
20
(2003) reported a rapid decomposition in rainy season due to the favorable effect of soil
moisture and temperature on the decomposers.
Soil moisture affects litter decomposition in various ways where in very dry conditions,
decomposition is inhibited and is slow in very wet conditions because of the development of
anaerobic conditions. A high decomposition has been observed at about 60 to 80 percent of
the water-holding capacity of the soil (Kononova, 1975).
The rate of litter decomposition is primarily determined by temperature (Anderson, 1991;
Hobbie, 1996). Kononova (1975) found the optimum temperature to be between 30 and 40oC.
The other factors important for litter decomposition are aeration and soil pH/soil reaction.
For proper activity of microorganisms involved in decomposition of organic matter, good
aeration is important. Reddy and Patrick (1974) reported that decomposition is faster under
aerobic conditions; this is because most of the microorganisms involved in the decomposition
(fungi and actinomycetes) need aerobic conditions. The type, density and activity of
microorganisms (fungi, bacteria and actinomycetes) involved in decomposition are directly
affected by soil pH. The pH therefore affects the decomposition rate of organic matter with
the rate being reported to be higher in neutral soils than in acidic soils (McCauley et al.,
2009).
21
3. MATERIALS AND METHODS
3.1 General description of the study area
Zambia is located in the south-central part of Africa between latitudes 8°15‟ and 18°7‟ south
of the equator, and the longitudes 22° to 34° east of the Greenwich Meridian. The country is
landlocked and covers an area of 752,610 km2. Zambia can be roughly divided into 3 agroecological regions based on the average annual rainfall (Figure.4). Region I has the warmest
and driest climatic conditions, it receives less than 800 mm rainfall per year and covers the
eastern and southern rift valley areas and the southern part of the Western and Southern
Provinces. Region II is further divided into three zones which receive between 800 and 1000
mm rainfall per year. Zone IIa1 receives an annual mean rainfall of 818 mm, while Zones
IIa2 and IIb receive higher rainfall of 941 mm and 930 mm respectively. Thurlow et al.
(2008) separated this region into three zones since most of Zambia‟s economic activities are
centered around Lusaka. Region III covers the northern part of the country and receives
annual rainfall above 1000 mm (Figure 4).
Figure 4. Zambia‟s agro- ecological zones (source: Thurlow et al., 2008)
22
The soils were collected from the Chongwe and Monze areas. Chongwe is located about 50
km east of Lusaka, capital city of Zambia (Lusaka province) and Monze (Southern province)
is about 190 km south of Lusaka. These two places were selected because they are in the
same agro-ecological zones, have abundance of Faidherbia in the areas, and the tree is being
promoted in both areas. At both sites, the trees were of similar age (more than 25 years old);
though the farmers did not know the exact age of the trees. The crop that is mostly grown is
maize and farmers had been cultivating on the fields for more than 10 years. Little or no crop
rotation is practiced, but if done, groundnuts, maize, cotton and sunflower are rotated mostly
on farmers‟ demonstration plots.
3.2 Field sampling
A site represents an area from which the soil samples were collected. In this case two sites,
Chongwe and Monze, were chosen for sample collection. A single representative field was
identified in each site, in which four trees were randomly selected for soil sampling within
the same field. Each tree (station) was considered as a single replicate, so in each field, four
replicates were effectively sampled. For each replicate, quadrants were made on the ground
centered around the tree (station) from which soil was sampled at several random points from
0-20 cm depth layer. Soils from outside the canopy were also randomly sampled at various
points away around the tree canopy. Soil from each replicate was put in a bucket and mixed
to make a composite sample. This was repeated separately for both the soil under the canopy
and outside the canopy (about 5 m from the canopy). A total of 16 samples were collected,
that is, sites Chongwe and Monze had eight different samples each (four from under the
canopy and the other four from outside the canopy), from the four stations in each site. From
each station tree leaves were also collected, combined and dried for later use as an
amendment in the incubation experiment. Core ring samples (undisturbed samples) were also
collected for each station within and outside the canopy for the determination of bulk density.
The soils were transported to Belgium for the incubation experiment and other analyses.
3.3 Soil characterization
The soils were air dried, crushed in a ceramic mortar and passed through a 2 mm sieve in
preparation for chemical analyses.
23
Soil texture was determined by the sieving and pipette method according to Gee and Bauder
(1986). Since the amounts of Iron and Aluminum were low there was no modification to the
method.
For the extraction of mineral N, 20.0 g of soil was weighed in 250 ml plastic erlenmeyer
flasks and 60 ml of 1N KCl solution added. The flasks were put on the shaker for 1 hour and
later filtered. The filtrate was collected in plastic bottles and placed in the freezer (-18 oC)
until analysis. Nitrate-N and ammonium-N were determined colorimetrically by continousflow anlaysis (Chem-lab 4, Skalar Anaytical, Breda, the Netherlands). The N mineralized
from litter (Norg) at each sampling time (t) was defined as:
Norg=([Ni]t - [Ni]t=0)amended - ([Ni]t - [Ni]t=0)unamended,
Where Ni is the sum of NO3- and NH4+.
The amount of net N mineralized in kg ha-1 was obtained by multiplying the released
amounts by the respective soil bulk densities and average depth of 20 cm expressed over a
hectare.
To determine the C/N ratio of the soils, C and N concentrations of the soil were measured
with Variomax CNS elemental analyser (Elementar GmbH, Hanau, Germany) from finely
ground soils.
Soil organic carbon (Corg) was determined using the Walkley and Black method (1934).
About 1g of soil was weighed into 500 ml erlenmeyer flasks, and 10ml 1N potassium
dichromate (K2CrO3) was added. Then 20 ml of concentrated sulphuric acid (H2SO4) was
added and left to stand for 30 minutes after which about 150 ml of distilled water was added.
Concentrated orthophosphoric acid (H3PO4), 10 ml, was added and drops of ferroine
indicator. The samples were then titrated with iron sulphate (Fe [II] SO4). The Walkey and
Black method was used for Corg determination, whereas the total C from the elemental
analyzer was used for calculating the C/N ratio. Both measurements of organic C were
comparable however only very minor differences were observed in C concentrations.
24
For bulk density, undisturbed soil samples were weighed and put in the oven for 24 hours at
105oC. The samples were allowed to cool in a dessicator and weighed to get the oven dry
mass used to calculate the bulk density of the soil.
The soil pH was determined both in H2O and 1N KCl with a soil: solution ratio of 1:2.5 using
a pH meter (Thermo Orion, model 420A+). Using 50 ml beakers, 10 g of soil sample was
weighed, 25 ml of 1N KCl and 25 ml of H2O added to determine pH-KCl and pH-H2O
respectively.
Due to greater uncertainty of the applicability of different P determination methods for
tropical soils, two methods were used to see if there was a difference in the concentration of P
extracted. Each extractant was specifically developed to assess plant available P in different
soils with different conditions, for example, as in acid soils, Al-P and Fe-P are usually found,
whereas in calcareous soils, Ca-P is found. Ammonium oxalate is a more rigorous extractant
than ammonium lactate and is suitable for soils having Al and Fe.
Readily-available pools of the nutrients Na, K, Ca, Mg, and P were determined by weighing 5
g of soil into a 250 ml plastic bottle and extracting with 100 ml of ammonium lactate. The
extraction was read for the nutrients using the Inductively Coupled Plasma (ICP)
spectrometer (iCAP 6000 series, Thermo Fisher Scientific Inc, Newington, USA).
A different extracting solution was used for the determination of P, Al, and Fe. Ammonium
oxalate is effective at extracting Fe and Al from poorly available forms. About 5 g of soil was
weighed into a 250 ml plastic bottle and extracted with 100 ml ammonium oxalate. The
filtered solution was then read with the Inductively Coupled Plasma (ICP) spectrometer
(iCAP 6000 series, Thermo Fisher Scientific Inc, Newington, USA).
Soil phosphorus was also determined using a modified Scheel method (1936). Measurements
from this method were compared with the ICP readings since the both used ammonium
lactate as the extracting solution. First standards (0, 1, 2, 3, 4, 5, 6 ppm) were prepared from
25 ppm P stock solution by pipetting 0, 1, 2, 3, 4, 5, 6 ml into 50 ml volumetric flasks. About
5 ml of sample solution extracted with ammonium lactate was pipetted into 50 ml volumetric
flasks. To the samples, 5 ml of Scheel I solution was added and immediately followed by
another 5 ml of Scheel II solution. The solutions were left to react for 15 minutes and then 10
25
ml of Scheel III was added. The volumetric flasks were made to volume using ammonium
lactate, capped and shaken. They were left for 15 minutes to react and read for P using a
spectrophotometer (UV visible, 50Conc, Varian).
The CEC was determined using the leaching method of soil with 1N NH4OAc pH 7 followed
by washing with 95% ethanol. This was further washed with 1N KCl and collected in 500 ml
volumetric flasks (Chapman, 1965). The amount of NH4+-N was read with the continuous
flow device to determine the concentration (Chem-lab 4, Skalar Analytical, Breda, the
Netherlands).
3.4 Leaf litter characterization
The moisture content of the leaf litter was determined by weighing 10g of leaf litter in a
moisture can and putting it in the oven at 105oC for 24 hours.
The air-dried leaf litter was finely ground for C and N concentrations measurement with
Variomax CNS elemental analyser (Elementar GmbH, Hanau, Germany).
3.5 Incubation Experiment
3.5.1 Sample preparation
For the incubation experiment PVC tubes with a diameter of 4.7 cm were used in which 100g
of air dry soil was weighed. The soils were moistened to 50% total soil porosity
corresponding to a gravimetric water content of 0.19 and 0.17 g g-1 soil for the Chongwe and
Monze soils respectively. Half of the cores from each site were amended with the F. albida
litter collected during sampling, the rest were left unamended (Figure 5). The amount of leaf
litter was calculated at 2 t ha-1 dry matter (corresponding to adding ca. 0.39g per 100g soil).
Litter was mixed well with moist soil and compacted to the required height in the tubes
depending on the soil bulk density. The amount of litter added was taken as the quantity of
litter that falls from the tree based on Dunham (1989) - though the author estimated lower
amounts of litter fall of about 1 t ha-1, while others have used as much as 4 t ha-1 for
incubation experiments (Gnankambary, 2007). This was recalculated to the surface area of
the tubes used. The tubes were covered with parafilm; perforations were made to allow air
exchange and incubated at 25oC in the incubation chamber. Moisture content was kept
constant during the incubation period by regularly adding water equivalent to the amount lost.
26
Four replicate tubes from each treatment were destructively sampled at 2-week intervals to
give 4 sampling dates. At each sampling date, mineral N and Cmic were determined. For
PLFAs only two dates (week 2 and 6) were determined. For each sampling, moisture content
was also determined.
Figure 5. Factorial design of the incubation experiment. Soils from Chongwe were; under canopy without litter
(CI), under canopy with litter (CIL), outside canopy without litter (CO) and outside canopy with litter (COL).
Monze soils were; MIL and MI, under canopy with and without litter and MOL and MO for outside canopy with
and without litter addition, respectively. This gives a total of 8 treatments.
3.5.2 Bio-chemical analyses
The Cmic was determined using the fumigation-extraction technique (Vance et al., 1987).
Using 250 ml plastic erlenmeyer flasks and a 50 ml beakers, 20.0 g of soil sample was
weighed and 60 ml 0.5N K2SO4 was added to the flasks. The mixture was put on the shaker
for 1 hour and filtered with Whatman 5 filter paper. The extracts were immediately put in the
freezer at -20 oC until reading. Organic carbon contents of the extracts were determined with
a TOC analyser (TOC-VCPN, Shimadzu Corporation, Kyoto, Japan) and these were used to
for Cmic calculated data. The microbial biomass was determined by subtracting non-fumigated
from fumigated samples. To convert TOC to Cmic in soils, a kEC value of 0.35 for tropical
soils was assumed (Sparling et al., 1990). Corg(K2SO4) is the labile carbon which is extracted
from the non-fumigated samples during Cmic determination.
The fatty acid composition of the phospholipids in the soil was used to describe the structure
of the microbial community. A modified Bligh and Dyer technique (1959) was used to
27
extract PLFAs. About two gram of freeze-dried soil was weighed into glass tubes and
extracted twice with 3.6 ml phosphate buffer (P-Buffer) pH 7.0, 4 ml chloroform and 8 ml
methanol in that exact order. The tubes were placed on the shaker for 1 hour and centrifuged
for 10 minutes at 2500 rpm (21˚C).To clean and dry separatory funnels, 8 ml chloroform and
8 ml P-Buffer were added to each funnel (to aid the phase separation). After centrifuging the
solution was decanted into the funnels. The funnels were then gently shaken for about 30
seconds and placed in the fume hood, covered with black bags and left overnight for phase
separation. The next day, the lower organic phase was drained into clean glass tubes and was
dried under N2. The vacuum tank with SPE cartridges (Chromabond, Macherey-Nagel
GmbH, Düren, Germany) inserted in the top of the lid was assembled. The dried lipids were
re-dissolved in chloroform and transferred to the SPE cartridge and this was repeated twice,
rinsing down the sides of the glass tube. The phospholipids were selectively collected in
methanol, and the neutral and glycolipids, collected in chloroform and acetone respectively,
were discarded. The methanol fraction was dried using N2 as above. After drying, the tubes
were stored at -20˚C until the next day. The samples were re-dissolved with 1 ml methanol:
toluene (1:1v/v) and 1 ml methanolic KOH. Tubes were closed with caps, vortexed and
incubated at 35˚C for at least 30 minutes to allow for transesterification to occur. After
incubation, 2 ml hexane: chloroform (4:1 v/v) was added and again vortexed and 1 ml 1 M
acetic acid was added followed by 2 ml distilled water to neutralize samples; tubes were
closed with caps and vortexed again. The tubes were centrifuged for 5 minutes at 2000 rpm
(21˚C). After centrifuging, the upper (hexane) phase was transferred into hexane-rinsed small
pointed tubes. This was repeated 3 times. The hexane layer was dried with N2. After drying
out with N2, the fatty acids were redissolved in 300 μl n-hexane containing nonadecanoate
fatty acid (19:0) as an internal standard, vortexed and sonicated for 10 minutes using an
ultrasonic bath. The entire solution was transferred from the glass tubes to GC vials, which
were immediately closed with caps and stored at -20 ˚C. PLFAs were determined by GC-MS
on a Thermo Focus GC coupled to a Thermo DSQ MS (Thermo Fisher Scientific Inc.,
Waltham, USA) in electron ionization mode. The sums of marker PLFA concentrations for
selected microbial groups were calculated as follows. Markers for Gram-positive bacteria
were calculated as the sum of i14:0, i15:0, a15:0, i16:0, a16:0, i17:0 and a17:0. Markers for
Gram-negative bacteria were cy17:0 and cy19:0. The total bacteria community was taken as
the sum of Gram positive and negative markers in addition to 15:0 and 17:0. The sum of
10Me16:0 and 10Me18:0 were used as markers for actionomycetes. Markers for fungi were
28
18:1ω9, 18:2ω6 and 18:3ω3. The signature fatty acid 16:1ω5 was used for arbuscular
mycorrhizal fungi (AMF) and 20:4 and 20:5 for protozoa (Kozdroj and Van Elsas, 2001).
Gram+/Gram- and bacteria/fungi ratios were calculated by dividing the respective sums of
marker fatty acids. The cyclopropyl to mono-unsaturated fatty acid ratios were determined by
taking ratios of fatty acids cy17/16:1ω7 and cy19/18:1ω7.
3.6 Statistical analyses
The statistical analyses were performed using the SPSS software package (SPSS version 16.0
for Windows). One-way ANOVA followed by Tukey‟s HSD post-hoc test were performed to
compare the means of the initial parameters. For the parameters mineral N and Cmic during
the incubation experiment, split-plot 3 factorial ANOVA was used to compare differences
due to treatment (litter amendment; with or without litter, 2 levels ), placement (whether
under or outside canopy, 2 levels) and date of sampling (4 levels). The data was split
according to site before univariate 3 factorial ANOVA was performed. Correlations between
various parameters were determined using Pearson‟s correlation as computed in SPSS 16.
29
4. RESULTS
4.1 Soil and leaf litter characteristics
The soil texture for the sites is summarized in Table 6 and was classified using the USDA
textural classification. The soil pH-KCl range was from 5.66 to 5.77 and the initial Corg
ranged from 9.8 to 21.3 mg C g-1 soil. The soils under the tree canopy had a high Corg and
total N as compared to those outside the canopy (Table 7). The Corg under the canopies
averaged 20.8 mg C g-1 soil compared to only 11.2 mg C g-1 soil outside canopies (p<0.05)
(Table 9). Total N under the canopy soil was also high averaging 1.78 mg N g-1soil compared
to 0.77 mg N g-1 soil for the soils outside the canopies. However, there was no significant
difference in mean Corg and total N for soils under and also soils outside the canopy between
sites. There was no significance difference in the bulk densities among the soils, but the soils
under canopies had lower values compared to those outside the canopies (Table 7). The soils
CO, CI and MO were not significantly different with respect to Mg and K concentrations but
MI differed significantly from CO and CI. The soil from CO had significantly less Ca as
compared to soils from MI but did not significantly differ from CO and MO (Table 8). There
were no differences in P concentration among the soils but soils under the canopies had
relatively high concentrations, averaging 0.12 mg P g-1 compared to 0.08 mg P g-1 for soils
outside the canopy. The initial mineral N was again significantly higher in soils under
canopies than in those outside (Table 8).
Table 6. CEC and particle size distribution of soils from the two sites
Site
CEC
(cmol kg-1)
Textural class
(USDA)
Sand
(%)
Silt
(%)
Clay
(%)
Chongwe
6.12 (0.58)
Loamy sand
81
9
10
Monze
11.09 (0.06)
Sandy loam
71
21
8
Table 7. Initial soil characteristics. Differences between treatment means according to Tukey‟s HSD
post hoc test (p<0.05) are indicated by different letters in the same column. Standard deviations are
given in parentheses.
Soil
CI
CO
MI
MO
Bulk density
(g cm-3)
1.25 (0.05)a
1.32 (0.05)a
1.29 (0.07)a
1.38 (0.11)a
pH-KCl
1:2.5
5.7 (0.2)a
5.7 (0.2)a
5.8 (0.2)a
5.8 (0.5)a
C:N
ratio
12.63 (0.6)a
16.35 (1.8)b
12.57 (0.2)a
13.61 (1.6)a
30
Corg
(mg g-1)
21.3 (0.3)a
9.8 (0.3)b
20.3 (0.6)a
12.5 (0.3)b
Total N
(mg g-1)
1.75 (0.01)a
0.63 (0.02)b
1.80 (0.05)a
0.90 (0.03)b
Mineral N
(mg kg-1)
39.2 (17.6)a
3.7 (1.5)b
37.8 (10.3)a
7.8 (3)b
Table 8. Initial readily-available pools of soil nutrients. Differences between treatment means
according to Tukey‟s HSD post hoc test (p<0.05) are indicated by different letters in the same
column. Standard deviations are given in parentheses.
Soil
Ca
(mg g-1)
Mg
(mg g-1)
K
(mg g-1)
P
(mg g-1)
CI
15.9 (2.6)ab
0.15 (0.01)a
0.09 (0.04)a
0.11 (0.05)a
CO
7.9 (2.9)a
0.09 (0.03)a
0.06 (0.02)a
0.07 (0.02)a
MI
19.4 (7.7)b
0.28 (0.10)b
0.21 (0.05)b
0.13 (0.07)a
MO
14.4 (6.7)ab
0.19 (0.09)ab
0.12 (0.05)ab
0.09 (0.06)a
Table 9. ANOVA output for initial soil characteristics and readily-available pools of soil nutrients
Parameter
Source
Sum of Squares
df
Mean
F
Sig.
13.498
.000
.128
.941
9.069
.002
10.564
.001
3.121
.066
4.921
.019
.825
.505
8.153
.003
2.363
.122
13.329
.000
Square
-1
Mineral N (mg kg )
pH-KCl
-1
OC(mg g )
-1
K (mg g )
-1
Ca (mg g )
-1
Mg (mg g )
-1
P (mg g )
CN ratio
Bulk density (g cm-3)
Total N (mg g-1)
Between Groups
4317.398
3
1439.133
Within Groups
1279.438
12
106.620
Between Groups
.032
3
.011
Within Groups
1.004
12
.084
Between Groups
4.004
3
1.335
Within Groups
1.766
12
.147
Between Groups
.056
3
.019
Within Groups
.021
12
.002
Between Groups
279.789
3
93.263
Within Groups
358.574
12
29.881
Between Groups
.071
3
.024
Within Groups
.057
12
.005
Between Groups
.008
3
.003
Within Groups
.036
12
.003
Between Groups
37.793
3
12.598
Within Groups
18.543
12
1.545
Between Groups
.039
3
.13
Within Groups
.067
12
.006
Within Groups
.043
3
.014
Between Groups
.013
12
.001
The air-dried leaf litter had a C:N ratio of 15.18, with a total N content of 3.04 % N and a
residual moisture content of 10.8 %.
31
4.2 Nitrogen mineralization
The Figures 6 and 7 show mineral N (mg kg-1 soil) extracted throughout the 8 week
incubation period. Generally higher N amounts were observed for soils collected under the
tree canopy (inside) than those outside it.
Figure 6. Mineral N evolution in Chongwe soils
Figure 7. Mineral N evolution in Monze soils
Table 10. ANOVA output for total mineral N of soils from the two sites
Site
Source
Type III Sum
of Squares
df
Mean Square
F
Sig.
Chongwe
date
18178.606
3
6059.535
21.474
.000
placement
62200.145
1
62200.145
220.423
.000
treatment
3811.217
1
3811.217
13.506
.001
date * placement
259.317
3
86.439
.306
.821
date * treatment
579.296
3
193.099
.684
.567
placement * treatment
691.719
1
691.719
2.451
.126
date * placement *
3715.736
3
1238.579
4.389
.010
date
24346.648
3
8115.549
21.862
.000
placement
26041.630
1
26041.630
70.151
.000
treatment
3674.718
1
3674.718
9.899
.003
date * placement
705.632
3
235.211
.634
.598
date * treatment
1768.410
3
589.470
1.588
.208
placement * treatment
80.139
1
80.139
.216
.645
date * placement *
969.996
3
323.332
.871
.464
treatment
Monze
treatment
32
The factors placement, date and treatment had significant effects on the release of mineral N
(all p<0.05 in ANOVA) at both sites. Interaction effects between date and placement, date
and treatment, and placement and treatment were not significant (p>0.05) for both sites. The
interaction effect of date, placement, and treatment was significant for the Chongwe site but
not for Monze site, meaning the effect of date on mean amount of N mineralised differed
according to combinations of placement and treatment (Table 10).
Figure 8. Net N mineralization during incubation
The net N mineralized was high for soils sampled from inside canopy after 2 weeks. For CI
soils, immobilization was unexpectedly observed after 6 weeks of incubation, whereas for MI
soils, immobilization occurred in week 4 and thereafter renewed mineralization took place for
the rest of the incubation. The calculated immobilization in MI at week 4 may be due to only
small differences between mineral N levels between amended and unamended soils. The soils
outside the canopy showed an increase in net N mineralized during the entire incubation
period (Figure 8).
33
Figure 9. Mineral N at week 8 of incubation
The mineral N released at week 8 was high for amended soils outside the canopy and this was
the same case for soils from under the canopy except for Chongwe soil (Figure 9).
The N mineralization data was fitted with a zero order kinetics model.
Nmin = k * T + C
Where;
Nmin is the cumulative net N mineralized;
k is the mineralization rate (day–1);
T is time (day) and;
C is intercept parameter.
Table 11. Zero order N mineralization rates
Site
Chongwe
Monze
Mineralization rate (mg N kg-1 soil day-1)
Under
Outside
With litter
Without litter
With litter
Without litter
1.57
2.13
1.52
0.86
1.83
1.43
1.61
0.97
The N mineralization rate range was between 0.86 and 2.13 mg N kg-1 soil day-1 (Table 11).
The rates were on average higher in soils with added litter (MIL, MOL, COL and CIL)
compared to unamended soils.
34
Table 12. Amounts of nitrogen applied and cumulative N mineralized from F.albida leaf litter.
Site
Chongwe
Under
N
concentration
(%)
3.04
Outside
Monze
Under
3.04
Outside
N applied
(mg kg-1 soil)*
Mineralized N
(mg kg-1 soil)
-22
Mineralized
N
(%)
-20
Net N
released
(kg ha-1)
-56
107
106
35
33
35
109
28
25
28
107
31
29
31
*Amount of N applied differ due to differences in the residual moisture of each soil
The amount of cumulative N mineralized tended to be higher for soils outside the canopy
than those collected within the canopy (Table 12).
4.3 Soil organic carbon and Microbial biomass carbon
The Cmic was determined for the two soils at different time intervals during the incubations.
The fumigated samples for week 2 were unintentionally disposed of and therefore the data for
this period is missing.
The Figures 10 and 11 show the evolution of Cmic during the incubation with the highest Cmic
at 4 weeks of incubation and a decrease thereafter. The soils from inside the canopy had an
overall higher Cmic throughout the incubation period. From ANOVA output Table 13, the
factors treatment, placement and date had significant effects on the increase in Cmic at both
sites (all p<0.05). The interaction effect between date and placement was significant for
Chongwe while this was not the case for Monze. All other interaction effects were not
significant for soils from both sites.
35
Table 13. ANOVA output for Microbial biomass carbon for soils from the two sites
site
Source
Type III Sum
df
Mean Square
F
Sig.
of Squares
Chongwe
date
936435.924
2
468217.962
45.125
.000
placement
860206.709
1
860206.709
82.902
.000
treatment
214860.717
1
214860.717
20.707
.000
date * placement
80520.648
2
40260.324
3.880
.030
date * treatment
21236.321
2
10618.160
1.023
.370
placement * treatment
8061.431
1
8061.431
.777
.384
date * placement *
10486.806
2
5243.403
.505
.608
date
1569279.769
2
784639.884
23.044
.000
placement
1034167.650
1
1034167.650
30.373
.000
treatment
469593.497
1
469593.497
13.792
.001
date * placement
39096.967
2
19548.483
.574
.568
date * treatment
115326.003
2
57663.002
1.694
.198
placement * treatment
19594.615
1
19594.615
.575
.453
date * placement *
22321.430
2
11160.715
.328
.723
treatment
Monze
treatment
Figure 10. Microbial biomass carbon in Chongwe soils
Figure 11. Microbial biomass carbon in Monze soils
36
Figure 12. Labile Corg(K2SO4) evolution in Chongwe soils.
Figure 13. Labile Corg(K2SO4) evolution in Monze soils
The Figures 12 and 13 show the evolution of an indicator of labile Corg that is K2SO4
extractable. At both sites Corg(K2SO4) was higher for soils under canopies compared to soils
outside canopies. However, there was a remarkable difference in Corg(K2SO4) for Chongwe soils
under the canopy and outside it (Figure 12). This was not the case for soils from Monze
which showed no significant difference of Corg(K2SO4) for soils MI and MO (Figure 13).
Table 14. The percent change in Cmic due to litter addition at 4 weeks of incubation.
Site
Chongwe
Monze
Cmic unamended
(mg C kg-1 soil)
Under
736
Cmic from
amendment
(mg C kg-1 soil)
197
Cmic change relative to
Cmic unamended
(%)
27
Outside
381
189
50
Under
864
336
39
Outside
494
333
67
The Cmic change due to litter addition at 4 weeks of incubation ranged from 27% to 67%. As
is the case with mineral N above, largest relative increases of Cmic due to litter addition was
observed in „less fertile‟ outside canopy soils and not soils under the canopy (Table 14).
37
Figure 14. Microbial biomass carbon at week 4 of incubation
4.4 Soil microbial community
The PLFA data of the two dates was averaged because there was no apparent change in the
concentration between these two dates. The concentration of total PLFAs was higher in
amended soils from each site but not significantly different from unamended soils.
The concentration of biomarker for fungi was highest in MIL and lowest in CO. Fungi
concentration in MIL was significantly higher than in MO, COL, CO and CI but was not
different to soils CIL, MI and MOL (Table 15). The marker PLFAs for AMF in CIL, CI and
MI were significantly higher compared to CO and COL but they did not significantly differ
from those in MO and MOL. The marker PLFAs for Gram+ and Gram- bacteria were higher
in MI and MIL than other soils but were not different from CI and MOL soil. The highest
cy17:0/16:1ω7 ratio was in CO and COL but there were no significant differences between
treatments. However, the cy19:0/18:1ω7 ratio was significantly higher in CI and CIL than in
all other soils except for CO. There was also a reduction in bacteria/fungi ratio in the
amended soil as compared to their controls (Table 15). The relative abundance for the
biomarkers for Gram- bacteria was significantly higher in soils from inside the canopies
compared to outside. Relative abundance of biomarker Gram+ bacteria and biomarker for
fungi generally increased with the addition of litter in all soils, MIL and CO having the
highest and lowest abundance, repectively (Table 16).
38
Table 15. Concentration of biomarker PLFAs for Gram+, Gram-, Bacteria, Fungi, and AMF (nmol g-1 soil), and ratios of bacteria to fungi, Gram+/Grambacteria, cy17:0/16:1ω7 and cy19:0/18:1ω7. Differences between treatment means according to Tukey‟s HSD post hoc test (p<0.05) are indicated by
different letters in the same column. Standard deviations are given in parentheses.
Site/
Bacteria/
Gram+/
treatment
Total PLFAs
Gram+
Gram-
Fungi
AMF
Fungi
Gram-
cy17:0/16:1ω7
cy19:0/18:1ω7
CI
20.26 (3.63)abc
4.15 (0.96)abc
1.45 (0.30)ab
1.62 (0.36)abc
0.64 (0.22)b
3.78 (0.24)bc
2.86 (0.26)a
1.01 (0.19)a
0.79 (0.06)d
CIL
23.89 (5.39)bc
5.05 (1.37)bc
1.77 (0.48)b
2.04 (0.55)bcd
0.81 (0.27)b
3.63 (0.37)bc
2.87 (0.29)a
0.96 (0.21)a
0.78 (0.07)d
CO
15.77 (1.74)a
3.09 (0.58)a
0.94 (0.13)a
1.14 (0.18)a
0.45 (0.10)a
3.99 (0.39)c
3.30 (0.43)ab
1.06 (0.12)a
0.69 (0.04)cd
COL
16.45 (2.60)a
3.29 (0.57)a
1.01 (0.19)a
1.41 (0.31)ab
0.50 (0.13)a
3.44 (0.30)bc
3.25 (0.13)ab
1.06 (0.13)a
0.68 (0.06)bc
MI
24.46 (5.07)bc
5.48 (1.36)c
1.70 (0.46)b
2.14 (0.55)bcd
0.85 (0.20)b
3.61 (0.37)bc
3.25 (0.36)ab
0.88 (0.11)a
0.61 (0.04)abc
MIL
25.44 (6.05)c
5.75 (1.58)c
1.75 (0.51)b
2.59 (0.92)d
0.87 (0.22)b
3.18 (0.39)ab
3.29 (0.26)ab
0.86 (0.08)a
0.59 (0.04)ab
MO
18.26 (3.53)ab
3.73 (0.92)ab
1.10 (0.25)a
1.52 (0.44)abc
0.58 (0.17)ab
3.60 (0.60)bc
3.40 (0.35)b
0.95 (0.18)a
0.59 (0.09)ab
MOL
20.73 (4)abc
4.18 (1.01)abc
1.28 (0.29)ab
2.26 (0.63)cd
0.65 (0.19)ab
2.71 (0.46)a
3.27 (3.27)ab
0.92 (0.19)a
0.58 (0.08)a
Table 16. Relative abundance of Gram+, Gram-, Actinomycete, Fungi, AMF and Protozoa (mol %).
Differences between treatment means according to Tukey‟s HSD post hoc test (p<0.05) are indicated
by different letters in the same column. Standard deviations are given in parentheses.
Site/
treatment
CI
Gram+
Gram-
Actinomycete
Fungi
AMF
Protozoa
19.35 (1.15)abc
6.80 (0.46)b
7.27 (0.31)ab
7.57 (0.53)ab
2.92 (0.50)ab
1.45 (0.22)abc
CIL
20.07 (1.37)abc
7.04 (0.60)b
7.08 (0.24)ab
8.11 (0.79)abc
3.15 (0.47)ab
1.30 (0.25)ab
CO
18.27 (1.77)a
5.57 (0.34)a
7.04 (0.36)ab
6.76 (0.50)a
2.65 (0.30)a
1.75 (0.15)b
COL
18.65 (0.61)a
5.75 (0.31)a
6.79 (0.30)a
7.98 (0.72)ab
2.78 (0.40)ab
1.73 (0.19)b
MI
21.42 (1.69)bc
6.61 (0.52)b
7.46 (0.32)b
8.37 (0.67)bc
3.35 (0.15)b
1.24 (0.25)ab
MIL
21.49 (1.44)c
6.55 (0.53)b
7.29 (0.43)ab
9.56 (1.32)cd
3.26 (0.24)b
1.19 (0.26)a
MO
19.18 (1.67)ab
5.67 (0.54)a
7.22 (0.66)ab
7.78 (1.10)ab
2.94 (0.42)ab
1.58 (0.25)bc
MOL
18.97 (1.49)a
5.81 (0.34)a
6.70 (0.34)a
10.20 (1.46)d
2.94 (0.45)ab
1.44 (0.24)abc
Figure 15. Concentration of biomarker indicator
Gram+ bacteria in soils from 2 sites
Figure 16. Concentration of biomarker indicator
Gram- bacteria in soils from 2 sites
40
Figure 17. Concentration of biomarker indicator
Fungi in soils from 2 sites
Figure 18. Concentration of biomarker indicator
Actinomycetes in soils from 2 sites
4.5 Relationship between parameters
There were positive significant correlations between bio-chemical parameters (Table 17).
Figure 19 shows a scatter plot for the best correlation between the parameters, Cmic and total
PLFAs.
Table 17. Overview of significance Pearson correlation between parameters
Corg(K2SO4)
Mineral N
Cmic
Corg(K2SO4)
-
Mineral N
0.527**
-
** Correlation is significant at the 0.01 level (2-tailed)
* Correlation is significant at the 0.05level (2-tailed)
41
Cmic
0.579**
0.217*
-
Total PLFA
0.490**
0.425**
0.874**
Figure 19. Scatter-plot showing correlation between Total PLFAs and C mic
42
5. DISCUSSION
5.1 Influence of tree canopy
The soils under the tree canopy had an initial Corg ranging between 20.3 to 21.3 mg g-1 soil
whereas those from outside the canopy had a lower content of 9.8 to 12.5 mg g-1 soil. Most of
the crop residue is left on the fields in these areas, even with this; there is still a huge
difference in the Corg between soils inside and outside canopies. This difference could be as a
result of soils under the canopies benefitting from both leaf litter and crop residues whereas
that outside source of Corg is mostly crop residue. These findings are consistent with those of
Nyberg and Högberg (1995) and Breman and Kessler (1997), who found higher Corg in soils
under compared to outside canopy and attributed it to the continuous addition of organic
matter in form of leaf litter under the tree canopy. Apart from SOM being an energy source
for soil microorganisms and a substrate for soil micro and macro fauna, it also influences
litter decomposition, nutrient release and cation exchange capacity (Gnankambary et al.,
2007). Other soil nutrients; P, Mg and Ca were also higher for soils under the canopy tree
than outside it. This is consistent with studies by Kho et al. (2001); Lofstrand (2005) and
Bayala et al. (2002), who found that the soils under tree canopy of other Acacia species had
increased total soil nutrient contents compared with the open field. The high concentration of
nutrients under the canopy could be due to to relocation of nutrients mined from deep in the
soil by tree roots, while others have also suggested that resting animals‟ dung and urine and
birds‟ excrements would increase soil N and P under the canopy (Sanchez et al., 1997;
Rhoades, 1997; Van den Beldt, 1992). The soils from under the canopy had more than double
higher total N content compared to soils from outside the canopy, averaging 1.78 mg g-1 soil
and 0.77 mg g-1 soil, respectively. The high total N was also reflected from relatively low
C/N ratios for soils under the canopy with an average of 12.60 compared to a ratio of 14.98
outside it.
Soil N availability is often investigated by estimates of nitrogen mineralization (Verchot et al.
2001; Anderson 2002; Picone et al., 2002). The soils from under the canopy had high initial
mineral N content and throughout the incubation period (Table 7 and Figures 6-7). Breman
and Kessler (1997) also found high amounts of soil N under tree canopies and attributed it to
prolonged litter and plant residue addition over the years. The high amounts of N during
incubation indicate that there were already high amounts of mineralizable N in the soils under
43
the canopy. The soils from under canopies also had greater N mineralization rates compared
to the ones from outside the canopy (Table 11). More residues from leaf litter and crops over
time, clearly resulted in high amounts of mineral N and N mineralization rates for soils under
as also observed by Nyberg and Högberg (1995). The amount of mineral N released is
however lower than that reported by Rhoades (1995) who found 60 mg N kg-1 soil released in
the first month of a field incubation experiment under the F.albida canopy. On the other
hand, Sall et al. (2006) reported an average net immobilization of 27 mg N kg-1 soil with the
addition of F.albida litter in their 10-day incubation experiment. They related the
immobilization to an increase in microbial activity which subsequently increased the demand
for N in the amended soils.
The Cmic of a soil has generally been assumed to be a sensitive indicator of long-term trends
in SOM (Sparling et al., 1998). It represents the living component of SOM and changes
rapidly with changes in carbon supply. Cmic is both the source of labile nutrients and agent of
transformation and cycling organic matter and plant nutrients in the soil and this makes it an
important index of biological status in soils (Sicardi et al., 2004). The overall Cmic from soil
inside the canopy was higher than those outside the canopy during the incubation. This
difference can be attributed to the high initial Corg in the soils from under the canopy relative
to the soils outside. The soils under the canopy had higher carbon content and microbial
diversity as compared to the soils outside, as also observed by others e.g. (Dilly and
Nannipieri, 2001). These observations of higher microbial activity under various species of
Acacia were also made by Traoré et al. (2007) and Gnankambary et al. (2008) who went
further to indicate the influence of optimum soil temperature and higher soil water as some of
the factors that could also support higher microbial activity under the tree canopy. The results
are confirmed by those in literature of Sarmiento and Bottner (2002) who showed that the
potential mineralization of a soil is positively correlated with its carbon content and in
particular its richness in carbohydrates. The activity of microorganisms depends directly on
the various pools of the organic matter in the soil (Degens et al., 2000) and soils under
canopies had more Corg leading to high microbial activity.
The use of the soil microbial community to indicate ecosystem status has been extensive
because they are the major contributor to nutrient cycling and food webs within the soil
(Harris, 2003). In both soils, the biomarker PLFAs were significantly greater in soils from
under the canopies compared to the soils from outside the canopies (Figures 15-18).
44
Biomarker PLFA for Gram+, Gram-, Fungi and AMF were high for soils under the canopy
compared to soils away from the canopies. The relatively high Corg in soils under the canopy
acted as a substrate source leading to higher microbial activity in soils from under canopies.
AMF were generally high in soils from under the canopy because of more root activity during
the growing season under these soils as compared to the soils away from the canopy (Table
15).
5.2 Influence of litter addition
Addition of litter generally increased the amounts of mineral N released in all soils except for
CIL towards the end of the incubation (Figures 6 and 7). The increase was relatively more
pronounced on soils outside canopy whereas the benefits were marginal under the canopy.
This may be due to reduced mineralization rate and subsequent low N release caused by high
initial soil N availability under the canopies. Lui et al. (2010) found that high N contents in
soils slowed the decomposition rates. Various authors have given possible explanations for
the decrease in decomposition rate resulting directly from an increase in soil N availability.
Ostertag and Hobbie (1999) and Hobbie and Vitousek (2000) attributed the limitation of
other nutrients such as P to be limiting for decomposer microbes. Higher soil N availability
may inhibit lignin decomposition in litter due to inhibiting the synthesis of lignolytic
enzymes or form more decay-resistant complexes from brokendown lignin products (Liu et
al., 2010) and decreased microbial biomass and microbial activity are some of the reasons
forwarded for reduced decomposition rates.
Generally, net N mineralized from the litter was high for soils outside the canopy. It was
however surprising that immobilization took place at week 4 in Monze and weeks 6 to 8 for
Chongwe soils but this may have been because of variation in the soil because each tree was
taken as a replicate (Figure 8). The unexplainable variability could also be due to pretreatment of the soil by air-drying and then re-wetting it before the experiment. The soils
were therefore no longer in their original state during the incubation. The soil Chongwe
outside canopy had the highest percent of N mineralized (Table 12). An amount of about 35
kg N ha-1 and 31 kg N ha-1 was released from the litter for the Chongwe and Monze outside
canopy soils, respectively.
45
It is clear from the Figures 10 and 11 that the addition of litter increased the Cmic in soils from
both sites. The addition of litter stimulated microbial activity and the increase could be
attributed to microorganisms which respond quickly to the addition of energy-rich
compounds (Sall et al., 2003). Sall et al. (2006) reported that the addition of F.albida litter
increased the microbial activity as measured by the quantity of CO2-C produced and that this
activity was higher than in unamended soils. This is in agreement with several studies that
have shown that adding organic matter to soils increases microorganism activities (Crecchio
et al., 200; Dinesh et al., 2000).
As was the case for higher net mineral N outside the canopy, the percentage increase of Cmic
due to litter addition was higher for soils outside the canopy as compared to those under it
(Table 14). From Table 17, the correlation between Cmic data and Corg(K2SO4) was significant at
(p<0.01). The correlation between Cmic and mineral N was low but significant (p<0.05). Rice
et al. (1996) reported the use of measurement of microbial biomass and activity to investigate
nitrogen availability. A reduction in Cmic with time can be explained by the reduction of
substrate availability, microbial community structure and accumulation of recalcitrant
compounds in soil (Saswati and Vadakepuram, 2010).
By and large, there was an increase in total PLFAs due to the addition of F.albida litter in all
soils. The use of total amount of PLFAs as indicators of microbial biomass in soils has been
undertaken by many researchers (Frostegard et al., 1991; Petersen et al., 1991; Baath et al.,
1992; Zelles et al., 1992, 1994, 1995, 1997). A significant positive correlation between total
PLFAs and Cmic was observed in the study (Table 17 and Figure 19). This is confirmed by
other authors who also found good correlations between the total PLFAs and the microbial
biomass determined by different methods and thereby using the measured total amount
PLFAs to assess microbial biomass in the soil e.g. (Zelles et al., 1992, 1994, 1995). The shift
in PLFA fraction indicated a change in microbial community structure. Zelles (1999) also
found an increase and shift in PLFA profiles when plant residues were added to the soils. The
total PLFAs also had a significant positive correlation with Corg(K2SO4) and mineral N
indicating that microorganisms increase with an increase in Corg(K2SO4) in the soil (Table 17).
The amended soils had an increase of biomarker Gram-negative (Gram-) compared to
unamended soils at both sites with CIL and CO having the highest and lowest, respectively
(Table 15). This was due to an increase in organic matter and substrate availability in
46
amended soils. The observation is supported by Bohme et al. (2005) and Zelles et al. (1992),
who also found an increase in biomarker Gram- bacteria with the addition of organic matter
to the soil. This indicates that there are Gram- bacteria that respond quickly and positively to
litter addition, more so than Gram+ bacteria.
Absolute amounts of bacteria were in general high in amended soils because they utilized
readily decomposable compounds such as carbohydrates. This is consistent with other studies
of PLFAs where authors have documented an increase in bacteria with the addition of readily
decomposable organic residue e.g. (Bardgett and McAlister, 1999). Fungal contribution to
soil quality is significant; they promote soil aggregation and release nutrients by
decomposing a large proportion of plant residues. Relative abundance of biomarker of fungi
also increased in amended soils, because with the addition of litter, the recalcitrant and
insoluble material decomposed by fungi, also accumulated over time. Therefore, the increase
in relative abundance of biomarkers of bacteria and fungi suggests that they both make use of
and decompose the litter added.
In this study, the factors related to nutrient availability are the determinants of change in
microbial community structure. The change in bacterial to fungal ratio is thought to be related
to the relative proportions of easily assimilated and more recalcitrant substrate that prevails in
many residues. The bacteria/fungal ratios were high in controls compared to the amended
soils with the CO and MOL being the highest and lowest ratios respectively. The ratios are
low in amended soils due to the increase in the relative abundance of fungal biomarkers
following litter addition. It can therefore be speculated that as the readily decomposable
organic residues decrease, the activity of fungi increases while that of bacteria reduces due to
high content of recalcitrant and insoluble material decreasing the bacteria/fungi ratio.
However to confirm this, more changes in bacteria/fungal ratio over time should have been
observed.
The changes in PLFA patterns such as an increased ratio of cyclopropyl fatty acids to their
monoenoic precursor fatty acids can present evidence of stress. There were no significant
differences in the cy17:0/16:1w7 ratios for all but the amended soils had lower ratios
compared to the controls. The mean ratio was highest in CO indicating high stress and MIL
had the lowest mean ratio (Table 15). The ratio cy19:0/18:1w7 also followed the same trend
as cy17:0/16:1w7 in that the ratios were lower for amended soil compared to the controls.
47
Bossio and Scow (1998) also found lower cyclo/precursor ratios in soils where there were
organic residue inputs, reflecting the higher amount of substrates available to
microorganisms. The increase in mean ratios of cyclopropyl fatty acids to their monoenoic
precursor fatty acids have been associated with starvation (nutrient stress) (Guckert et al.,
1986), therefore low availability of substrate in the controls could be the cause of high ratios
in these soils. Therefore, the relative abundance of fatty acids cy17:0 and cy19:0 can
potentially be used as bioindicators of stress in agricultural fields suggesting low organic
residue input.
5.3 Comparison of tree canopy and litter addition effect between sites
The average mineralization rate for soils from Monze was higher than that of soils from
Chongwe. The relatively high N mineralization rate can be attributed to Monze soils being
more „fertile‟, that is, having higher CEC, Corg, total N and soil nutrients as compared to the
Chongwe soils. Since microbial activity is controlled by soil physical and chemical
conditions (compaction, temperature and oxygen, substrate availability and biological
conditions) (Grant et al., 1993); Monze soil were overall „better‟ than Chongwe in terms of
these conditions. However, Chongwe site had the highest net N mineralized from soils
outside the canopy.
As already highlighted above, the change in Cmic due to litter addition was more in soils
outside the canopies. The Monze site had an overall higher Cmic turnover during the
incubation compared to soils from Chongwe. Again the initial properties, such as high Corg
and microbial diversity of the soil meant that the microbes stabilized early and had enough
energy sources in the initial stages of the incubation (Dilly and Nannipieri, 2001; Breman and
Kessler, 1997). PLFAs also followed the same trend as that of mineral N and Cmic results, to
be exact, higher in Monze soils compared to Chongwe soils. The effect of canopy and litter
addition on biomarker indicator PLFAs was more significant in Monze soils (Figures 15-18).
The relative abundance all biomarker PLFAs with the exception of Protozoa increased with
an addition of litter and concentrations were higher in soils under the canopy, indicating
relatively high abundance of microbial communities.
48
6. CONCLUSION AND RECOMMENDATIONS
Surprisingly Chongwe under canopy with litter soil had a low N mineralization rate, while
other amended soils generally had higher rates compared to their controls. The low rate in
this soil could have been due to high amounts of mineralizable N already present in soils
under the canopy.
In most publications authors have suggested that in order to have high crop yields,
application of litter should supplement rather than substitute N fertilizer (Kang et al., 1985;
Sanchez and Palm, 1996), because some of the N released is lost by various process e.g.
leaching. Resource-poor farmers would therefore only be required to purchase the extra N
fertilizer to supplement that provided by the tree litter. The residual effect of organic inputs
on soil fertility is much greater than that of inorganic fertilizers, because N is usually slowly
released from SOM as opposed to an immediate release from inorganic fertilizer. From
observed improved N release and mineralization rates, it can be concluded that the addition
of F.albida litter to soil could improve N mineralization rates and amount of N released.
Soil microorganisms are important for nutrient release and transfer from organic materials.
The magnitude of microbial biomass has been used as an indicator of soil quality and their
contribution to nutrient dynamics in soils (Insam, 2001). In our study, addition of litter
improved microbial biomass and activity in all soils. Despite the soils from under the canopy
having high initial Cmic, it was again interesting to observe that soils outside the tree canopy
had the highest change in Cmic with the addition of litter after 4 weeks. This just showed that
the addition of litter boosted microbial biomass more and their increase was more obvious in
soils outside the canopy. However, overall the soils from under the canopy still had more Cmic
throughout the incubation. The addition of organic inputs has a quicker influence on Cmic than
the change in total organic matter (Powlson et al., 1987; Brookes and Ocio, 1990). Therefore,
to increase long-term sustainability of soil fertility, that is, by improving levels of SOM,
available nutrients and soil microbial activity Chander et al. (1998) suggested the adoption of
tree-crop combinations for smallholder farmers.
Phospholipid fatty acids (PLFAs) represent qualitatively and quantitatively the current living
microbial community in soils. All PLFA biomarkers increased in concentration with the
addition of litter which is a carbon source. The cyclopropyl fatty acids/precursors fatty acids
49
used as an indicator of nutritional stress shifted in accordance to C availability. Higher ratios
were observed in unamended soils while amended soils had lower ratios comparatively.
Although these shifts in ratios were difficult to understand and were not clear-cut. Largely,
total PLFAs were high in soil under the tree canopy, Monze under canopy with litter and
Chongwe outside soils having the highest and lowest microbial community, respectively.
This corresponds with high Cmic for in these soils.
The view that F.albida trees in agro-forestry maintain soil fertility among other functions is
based mainly on observation of good amounts of nutrients - particularly N - in their leaf
litters. The amount and rate of N released due to litter addition were significantly different
from the controls, indicating a more direct short-term supply of nutrients from the
decomposition of leaf litter. There was a significant difference in mineral N released due to
placement, implying that SOM formed from cumulative input of organic residues under the
canopy could also be a source of nutrients from its mineralization.
In general, the sites differ appreciably in some, but not other, properties in response to the
litter addition treatments and effect of canopy. This difference emphasises the importance of
site characteristics (meteorology, soil, fertility etc.) when drawing conclusions regarding
microbiological soil properties.
Thus, F.albida leaf litter could improve short-term SOM and microbial activity. However,
establishing permanent F.albida trees within the fields is much more advantageous to the
farmers and would greatly improve SOM content and nutrient availability, thereby reversing
soil fertility depletion in the long-term. Further studies on soils from more sites to have a
general overview of F.albida‟s effect on bio-chemical properties and on various soils types
needs to be conducted.
50
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