THE ANALYSIS OF FERTILIZER USE AND
AGRICULTURAL PRODUCTIVITY: (Case of La’ilay
Maychew Woreda, Tigray, Ethiopia)
BY
ABRHALEY TSEHAYE
To be presented on 14th International Conference on the Ethiopian Economy,
organized by EEA
Addis Abeba, Ethiopia
May 2016
Abstract
The study was conducted in Woreda La’ilay Maychew, central Tigray, Ethiopia on Farm
productivity and yield of the small holder farmers. This study tried to analyze the effect of
inorganic fertilizers on farm productivity and identify the factors that influence probability and
intensity of adoption of inorganic fertilizer with a research question of “Does the marginal effect
of fertilizers is significantly high?
The role of the agricultural sector interms of its contribution to the study area’s economy is
immense. Further, the success and failure of the economy is highly correlated to the
performance of this sector. To accelerate the sector’s growth and increase its contribution to the
overall economic growth the application of modern inputs particularly inorganic fertilizers in the
sector plays a great role.
The starting point for this research work was the farm investment theory of Feder, Just, and
Zilberman who analyses technology adoption and productivity growth. The multi-stage sampling
method was used to select both villages and respondents. A total of 131 plots from four peasant
associations were used for the analysis.
The first model estimated was Cobb-Douglas production function. It was found that fertilizer is
positively and significantly impacted agricultural productivity. In addition, other results from this
multivariate regression analysis shows that age of head, labor, livestock and land have a
positive and statistical significant effect.
Factors influencing the extent and intensity of fertilizer adoption on major cereals were
analyzed. For that matter a double hurdle model of probability and intensity of fertilizer adoption
was estimated. Significant variables like Labor, education, livestock, quality of plot (lemteuf);
and plots, distance from near town affect probability and intensity of adoption of inorganic
fertilizer positively; and negatively respectively. Additionally, it was found that Age, household
size, extension, credit and improved Varity; and chemicals, distance from plots, compost, plot,
quantity of seed sown affect probability of adoption positively; and negatively respectively. In
contrast, plot area, chemicals and quantity of seed sown affects intensity of fertilizer significantly
and positively. Possible policy interventions on fertilizer prices, belief of high fertilizer application
burns crops, complementary inputs to fertilizer i.e. Irrigation, compost and improve varieties is
required.
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1.
Introduction
Agriculture dominates the Ethiopian economy. It is the major supplier of raw materials to food
processing, beverage and textile industries. It accounts for more than 85% of the labour force and
90% of the export earnings (MOFED 2005; RATE 2003; NBE 2007/08, cited in Kefyalew,
2011).
Despite the importance of agriculture in the economy, Agricultural production is characterized
by subsistence orientation, low productivity, low level of technology and inputs, lack of
infrastructures and market institutions, and extremely vulnerable to rainfall variability. The
economy of Ethiopia is based largely on low productive techniques - where farm production
heavily depends on traditional and backward techniques of production on fragmented lands for
its success or failure. According to CSA (2008), the national level application rates of inorganic
fertilizers are very low. For example an application rate of major cereals does not exceed
57kg/ha which is far below the recommended once i.e. 200 kg per hectare.
One of the major biophysical factors for agricultural production, soil fertility, has marginally and
traditionally been maintained through long fallow periods in Ethiopia. Expanding population and
food requirements, however, had led to a reduction or elimination of the fallow period and have
pushed farmers in many areas onto more marginal lands. Distressfully, the fuel wood deficit is
increasingly being made up by substituting dung and crop residues, thus leading to a drastic
decline in the use of animal manures and residues for fertility improvement programs which
drives modest crop yields.
Per capita cereal production is low and yields are low and stagnant despite the large emphasis
given to agriculture. Development of the sector has been modest in recent decades, regardless of
repeated attempts, large portion of the country remain food insecure. To quote Schltz’s (1964,
p.3) observation “The man who farms as his forefathers did, cannot produce much food, no
matter how rich the land or how hard he work”. Sadly speaking this applies to the majority of
Ethiopian agriculture today. Once again he add “The farmer who has access to and knows how
use what science knows about soils, plants, animals and machines can produce an abundance of
food though the land be poor”, cited by Jon Pycroft (2008)
The current government adopted ADLI strategy since 1994/95. The main facets of this strategy
in the agriculture have been the generation, adoption and diffusion of new farm technologies and
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setting up credit schemes (EEA 2002; Asefa and Zegey 2003; Weeks and Geda 2004 as cited by
Kefyalew, 2011). In the mobilization of small farmers with focus on productivity improvement,
the agricultural development practices have been operationalzed through PADETES (EDRI,
2004).
Although inorganic fertilizers have been widely promoted, despite upward trends in fertilizer use
in Ethiopia over the past few years, it is still widely viewed that fertilizer use and application
rates are not high enough to meet national food security and agricultural development goals.
Application rates by most peasants are well below the recommended rates (200 kg per hectare
according to the latest recommendation).
According to FAO (1995) about 80% of agricultural production in Ethiopia was accounted to
cereals, of which 45-50% taken up by the major staple grain, teff, the remaining on wheat,
barley, maize and sorghum. Fertilizer use is concentrated on cereals followed by pulses and oil
seeds respectively. In 2007/08 the national level amount of fertilizer applied in cereals, pulses
and oil seeds were 3,962, 160 and 136 thousands quintals respectively CSA (2008/09). Teff,
wheat and maize cultivation account for the majority of fertilizer use though small holder usage
of fertilizer technology packages vary dramatically between seasons EEA/EEPRI (2006).
Teff is the crop with the largest share in fertilizer use among the cereals (40%), followed by
wheat and maize with respective shares of 29% and 20% during 1994/95-2005/06. In terms of
application rate per hectare of cultivated land, wheat accounted for the largest share (57kg/ha),
followed by teff and maize respectively.
Agricultural production increased by less than 1% between 1980 and 1990. In the mean time,
the rate of population growth averaged 3%, resulting in a widening gap between food supply and
demand. Rate of food self sufficiency and per capita availability of food declined to 58% and
below the recommended intake of 2,100 calories /day respectively (Mulat, Ali, Jayne, 1997).
In recent years, nevertheless, agricultural production has improved owing to a more favorable
policy environment, increased use of fertilizers, and good weather1. However agricultural
1
An estimated 19% increase in area planted to food crops, a 16% increase in productivity and a
40% increase in output was reported (CSA, 1995/96, Bulletin No. 152).
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productivity is still low and stagnant despite the large emphasis given to agriculture and for that
matter per capita cereal production is low (Kefyalew, 2011).
These days, it’s believed that heightening crop productivity is possible through effectively
promoting efficient and sustainable use of inorganic fertilizers. Compared to organic wastes or
manures, commercial fertilizers are relatively more concentrated, making them cheaper to
transport and store.
The focus of this paper is on chemical (commercial) fertilizers. Using such fertilizers represents
a distinct agricultural technology that has great potential to raise agricultural output, yield and
income as has been demonstrated most famously in the so-called Green Revolution of South
Asia. But adoption of modern inorganic fertilizers has been slow and generally less successful in
Ethiopia, and indeed across Africa.
The study focuses on La’ilay Maychew woreda which is located in Central zone in Tigray state.
The area’s temperature ranges from 25o to 30o Celsius during the dry season, and as low as 10 o
Celsius during the wet season. Due to variation in agro-ecology rainfall varies between 300mm
and 700mm every year. Soil types vary between clay, sandy soils and fertile soils (CSA, 2007).
This is a mixed farming area with both crop production and livestock rearing activities.
Agricultural activities are entirely dependent on the kiremt rains from June to September. Main
crops grown are maize, teff, sorghum, wheat and finger millet. Maize and sorghum are stable
food crops and Teff-high value crop, is sparingly consumed and mainly sold to earn income to
purchase the cheaper stables.
Farm production, yield and income of small holder farmers of the study area remain low because
of a variety of distortions and institutional deficiencies. Cereal productivities of Tigray region in
general and central zone in particular does not exceed 24 quintal per hectare. For example yield
of maize, wheat, sorghum, finger millet, barley and teff, in central zone where the study area is
located, was 19.18, 18.69, 17.55, 16.79, 14.35 and 12.32 respectively CSA (2011). Some of the
common reasons for this modest grain yields are natural hazards like erratic rainfall and
hailstorm, depletion of soil fertility in no possibility of fallow, land degradation, insufficient
cultivable land, chronic hazards affecting crops, land shortage for extensive farming and border
insecurity with Eritrea blocked access to trade, pasture and water for livestock etc CSA (2007).
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A substantial proportion of the topography of Tigray is mainly rugged and mountainous land. Its
economy is highly dependent on traditional and rain fed agriculture. The natural resources have
been degraded as a result of wars and intensive cultivation. Besides, rapid population growth
hastens the shortage of land leading to a repetitive tillage of land. Thus soil fertility has
decreased and hence its productivity. The region’s agriculture mainly relies on cultivation of
crops and livestock production. In addition, the mountainous land feature of the region specially
and the country generally is another hindrance for using mechanized farming except in few
areas. Oxen are the only means of traction and are considered as the real indicator of household
wealth.
Modernization of agriculture through use of modern farming inputs such as fertilizers,
machineries and high yield seeds have paramount effect on increasing productivity and reducing
poverty. Rising land productivity with the prevailing diminishing sizes of land may have great
importance at providing the food supplies at least for subsistent living for the ever increasing
population. However, due to the natural hindrances (including topography) and the country’s
backwardness, the adoption of modern inputs is not usually seen exercised in the desired manner.
Given this day light facts though farm yield is modest, the disparity in farm productivity is still
large in the study area. This may be, due to large variations in the use of farm technologies
across farm villages with slight difference in agro-ecological characteristics. Such variations
provide an opportunity to evaluate the effects of fertilizer adoption on farm yield using cross
sectional data collected considering possible agro-ecological zones.
Adoption of farm inputs in the study area, thus, raises some concerns, as many other input
adoption schemes did in other districts of the region. First and for most the input distribution
packages to the study area is not environmental attribute base and the input diffusion tends to
deliver inputs of a given quantity to all farm villages regardless of soil type, nature of climate
and crop response. Thus farm inputs may not be intensively adopted and used as expected
because of the adoptive behavior of the farmers and topographical contexts.
While there have been some attempts to evaluate the impacts of adoption of modern agricultural
inputs particularly inorganic fertilizers on farm productivity, most of them deal with impact
analysis of services provided like development agents’ extension services, access to credit,
education, irrigation, short term farmer training on input usage and management etc, Gine and
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Klonner (2006), Tomoya and Takashi (2010). In the literature little work was done at cross
section level in relation to agro-ecological conditions like soil type, climate, crop type and input
package(s). One can observe the dearth of literatures most of them were single crop focused case
studies. Thus the researcher is initiated to work in this area by which the mentioned gaps will be
bridged.
A few problems, among others, are also mentioned to show that the topic in hand is quite
important. Can inorganic fertilizers help enhance the productivity of land in the area given
conducive agro-ecological cofactors? And does the marginal effects of fertilizers is significantly
high?
The primary objective of the paper is to analyze the effect of modern farm inputs particularly
inorganic fertilizers on farm productivity in the study area.
The specific objectives of the paper are:
 To analyze effect of fertilizer on farm productivity
 To identify factors influencing probability of adoption of inorganic fertilizer
 To identify factors influencing the intensity of fertilizer use
2.
Methodology and description of study area
2.1 Description of the Study Area
Tigray is one of the regional states in Ethiopia. It has an area of 84,721.77 km2 and a population
of 4,664,071. Of the total, 80.5 percent of the population resides in purely rural areas with an
average population density of 55.1 per square kilometer (CSA, 2010). Tigray borders with the
state of Eritrea and Sudan and two regional states in Ethiopia, Amhara and Afar.
It has five
zones and one special zone, Mekelle city. According to CSA (2010) report, excluding Mekelle
the region has 45 rural and urban Woredas.
Tigray region has three main agro-ecological zones; namely “Dega” (i.e., highland areas),
“woinadega” (i.e., midland areas), and “Kolla” (i.e., lowlands) (Fredu et al., 2008).
This study focuses on La’ilay Maychew woreda in Central zone in Tigray Region. La’ilay
Maychew woreda is bordered on the south by Naeder Adet, on the west by Tahtay Maychew, on
7
the north by Mereb Lehe and on the east by Adwa. The administrative center of this woreda is
Axum. It is located between latitudes 14o10’N and longitudes 38o45’E.2
Based on the 2007 National Census conducted by CSA, this woreda has a total rural population
of 72,625. In the woreda population has increased by 16.67% compared to 1994 census. Among
the rural population 36,203 are men and 36,422 women. With an area of 1,873.35 square
kilometers, La’ilay Maychew woreda has a population density of 38.77/km2 which is less than
the zone average of 56.29 persons per square kilometers CSA (2008).
The study area encompasses almost all kinds of agro-ecological zones. For that matter it is more
favorable to the cultivation of wheat, maize, sorghum and teff. It is selected for this study
because it is one of the most cultivated areas in the region and has a high response crops to
fertilizer (Gunjal et al., 1980).
2.2 Types and Sources of Data
The data that were gathered have a host of household related variables as well as plot level data
on the plots’ biophysical features, production history and input use. Quantitative and qualitative
data were collected from primary and secondary sources. Primary data were collected from 131
sample plots (80 households)3 that were selected from sample rural kebeles. The primary data of
sample households include information on: household demographic characteristics (education,
age, family size, and sex), livestock holdings, data on land and plot characteristics4 (land and plot
size, slope, quality), institutional factors (credit and extension services), land management (soil
conservation practices) and inputs (fertilizer, compost, seed types, quantity of seed and Varity
sown per plot) and labor used in fertilizer use.
Secondary data about population, age structure, land use pattern, farming systems, infrastructure
situation, crop production trend, meteorological data (annual rainfall and minimum and
maximum temperature), prices of inputs and outputs (fertilizers, improved Varieties, farmer’s
outputs in market) etc. were collected from published and unpublished sources.
2
Extracted from Wikipedia, the free encyclopedia.mht, this page was last modified on 22 January 2011 at 05:30.
Most farm households in the study area own more than one plot
4
In this study rented in and rented out plots were dropped, thus we do have 131 owner-operated plots.
3
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2.3 Sampling Technique and Sample Size
A multi-stage sampling technique was used to draw sample farmers for the study. Out of the total
of 12 known rural “Tabias” and one major city, four “Tabias” purposely were selected for study
purpose based on the livelihood classification5 of the area, where rural kebeles were identified in
agro-climatic zones. Access to irrigation, major cereals 6 producing areas and accessibility were
considered during selection of “Tabias”. These “Tabias” represent one of the major cereal
growing areas in the woreda where improved technologies were partially adopted by farmers.
Each “Tabia” has four “Kushets” or “Gots”7. From the selected “Tabias”, random sampling was
used to select two “Kushets” from each, in which respondents are selected through the same
procedure. A total of 80 farm households (131 sample plots) were surveyed with 20 farm
households from each “Tabia”. Therefore, both a multi stage sampling and random sampling is
employed.
2.4 Methods of Data Collection
A questionnaire survey was conducted to assess farmers’ opinions pertaining to inputs, outputs,
prices and other socio-economic variables. Formal and informal discussions were held with
farmers so as to generate additional information for the study.
To carry out data collection four enumerators were recruited from the locality based on
educational background, the knowledge and experience in agriculture. The enumerators were
trained on the content of the questionnaire, methods of data collection, and interview techniques
for one day for each before the actual data collection.
2.5 Methods of Data Analysis
2.5.1 Descriptive Statistics
By applying descriptive statistics, one can compare and contrast different categories of the
sample units with respect to the desired characteristics. The descriptive statistics used in this
study include mean, standard deviation, percentages and frequency of occurrence. Chi-square
5
The Woreda has four livelihood zones based on the Livelihood Profile of Tigray Region, Ethiopia (2007) :
1) Central Mixed Crop-has 3 “Tabias” 2) Mereb Basin-Has 1 “Tabia” 3) West Central Teff-has 7 “Tabias” 4) Werie
Catchment-has 1 “Tabia” and one unknown zone with one “Tabia”
Here “Tabias” in local saying is to mean Kebeles or peasant associations
6
Include teff, wheat, maize, barley and sorghum-depend on exploratory survey
7
Locally “Kushet” or “Got” is to mean-Village, neighbor.
9
and t- tests were used to test for the significance of the discrete and continuous variables,
respectively.
2.5.2 Specification of Econometric Models
The purpose of this study is to investigate the effect of inorganic fertilizer on agricultural
productivity and analyze determinants of the probability and intensity of inorganic fertilizer.
Two theoretical models were required to achieve the predetermined objectives. These are model
of agricultural productivity and model for agricultural technology adoption particularly inorganic
fertilizer.
2.5.2.1 Model of Agricultural Productivity
Empirical work shows that the factors that can affect the productivity in agriculture are
numerous. These factors can be classified into four broad categories: environmental, input use
and technology, governmental policies and demographic variables. These factors affect
economic agents which in turn constrain the agricultural sector (weeks and Geda 2004; Ruttan
2002; as cited by Kefyalew, 2011).
The dependent variable in the household level is value of output of major cereals produced by
the household. Modern capital investments are not very common for the rural households in the
study area. The best proxies for capital are land and TLU. Regarding labour use, the agricultural
system in the area is predominantly dependent on family labor. The common proxy is the
number of labors in man equivalence that are working in farming activities. The other set of
independent variables include: fertilizer (sum of Dap and Urea), credit and extension access, land
size, gender and education status of the head. As robustness check for effect of fertilizer on
productivity the estimated parameter of output model was compared to the corresponding Yield
model.
To identify factors which contribute to the productivity of the farm households, the CobbDouglas production function model was used. Despite its limitation in terms of its restrictive
properties imposed on the production structure like fixed returns to scale and elasticity of
substitution always equal to unity, it is chosen for its simplicity, convenient to specify and
interpret. Limiting aspects of Cobb-Douglas production function have been weighed against
potential shortcomings of more flexible functional forms, such as degrees of freedom problems
10
and multi-collinearity between independent variables (Anders, 1998; Ashenafi, 2006; Kefyalew,
2011).
By choosing a Cobb-Douglas production function the relationship between agricultural
production and the input factors is linear in logarithms with constant returns to scale. In its
simplest form Cobb-Douglas production function can be expressed as:
(1)
Where Xs are representing the explanatory variables and
is the error term. For applying the
ordinary linear regression as it is easy for mathematical manipulation and interpretation, the
double log Cobb Douglas production function was estimated:
Log(Y)= f[log(fertilizer)(+)8,log(Household size)(+), Age of HHH (+/-), Sex of HHH(+),
Education status of HHH (+), land size(+), log(TLU)(+),irrigation(+), off-farm income(?),
Credit(+),
Extension
(+),
Labor(+),
plot
slope
type(?),fertilizer*Dummies(+)+Error term]
Where Y- Value of output / Value of yield,
(-),
plot
quality
(+),
crop
(2)
HHH-household head, and Fertilizer- Dap+Urea
2.5.2.2 Model Used in Agricultural Technology Adoption (Inorganic Fertilizer)
Different researchers used different models for analyzing the determinant of technology
adoption. In principle, the decisions on whether to adopt and how much to adopt can be made
jointly or separately (Berhanu and Swinton, 2003; as cited by Hassen Beshir, 2012). The Tobit
model was used to analyze under the assumption that the two decisions are affected by the same
set of factors (Greene, 2003).However, the decision to adopt may well precede the decision
about the intensity of use and hence the explaining variables in the two stages may differ. Tobit
is an extension of the probit model and it is one approach to deal with the problem of censored
data (Johnston and Dinardo, 1997). In the double-hurdle model, on the other hand, both hurdles
have equations associated with them, incorporating the effects of farmer's characteristics and
circumstances. Such explanatory variables may appear in both equations or in either of them
8
The signs in the brackets show the expected signs of the coefficients
11
(Teklewold et al., 2006). Empirical studies have also indicated that a variable appearing in both
equations may have opposite effects in the two equations. The double-hurdle model, developed
by Cragg (1971), has been extensively applied in several empirical studies such as (Burton et al.,
1996; Newman et al., 2001; Berhanu and Swinton, 2003; Teklewold et al., 2006; as cited by
Hassen et al, 2012).
As already noted, in this study a double hurdle model was used to identify factors affecting the
probability and intensity of inorganic fertilizers. The double-hurdle model is a parametric
generalization of the Tobit model, in which two separate stochastic processes determine the
decision to adopt and the level of adoption of technology. The two stage questions in a typical
DH model are: i) Have you adopted inorganic fertilizers for your major cereals-Adoption
decision (yes/no)? And ii) If yes, how much inorganic fertilizer in kg you applied given different
constraints-Intensity Decision (kg)? Therefore, the double-hurdle model has an adoption (D)
decision with an equation:
(3)
,
Being
i
N (0, 1)
a latent variable that takes the value 1 if a farmer adopts inorganic fertilizer
technology and zero otherwise, Zi is a vector of household characteristics and α is a vector of
parameters. This function is probit model estimation for adoption decision of households.
The second hurdle, the intensity of adoption, is then modeled considering non-zero use of
fertilizer. The Tobit model, however, assumes a zero amount of adoption of inorganic fertilizers
as a lack of positive demand for new technology. The level of adoption (Y) decision has an
equation:
(4)
,
Where
Vi N (0, 1)
is the observed proportion of agricultural technologies and
is a vector of household
socioeconomic characteristics and β is a vector of parameter. The fourth equation is estimated
12
using truncated regression.
From (3) and (4) Ui and Vi are stochastic error terms, which
represents omitted, yet relevant but difficult to capture variables and measurement errors. It is
assumed both to be normally, identically and independently distributed.
The above equations tell us that two thresholds should be passed in order to observe a positive
level of inorganic fertilizer use. These are the adoption threshold, that is if the farmer has
adopted inorganic fertilizers, and the level threshold, that is the farmer has applied a non-zero
inorganic fertilizer.
The log-likelihood function for the double-hurdle model that nests a univariate probit model and
a truncated regression model is given following Cragg, (1971) by:

 



LogL =  ln 1  ( x1i 1) x 2i 2 
  

0



(5)
where, “0” indicates summation over the zero observations in the sample, while “+” indicates
summation over positive observations, and Ф and  refer to the standard normal probability and
density functions respectively,
and
model and the Truncated model respectively,
represents independent variables for the Probit
and
are parameters to be estimated for each
model.
Under the assumption of independency between the error terms, the log-likelihood function of
the double-hurdle model is equivalent to the sum of the log likelihoods of a truncated regression
model and a univariate probit model. Consequently, the log-likelihood function of the doublehurdle model can be maximized, without loss of information, by maximizing the two
components separately: the probit model (overall observations) followed by a truncated
regression on the non-zero observations. A hypothesis test for the double hurdle model against
the Tobit model was examined9.
9
2
The likelihood ratio test statistics Γ can be computed (Greene 2000) as: Γ =-2[lnLT-(lnLP+lnLTR)] ~ k, where LT is
the likelihood for the Tobit model; LP is the likelihood for the probit model; LTR is the likelihood for the truncated
regressions model; and k is the number of independent variables in the equations. If the test hypothesis is written
as:
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2.6 Measurements and Definitions of Variables Used in Both Models
The definitions of the dependent and independent variables follow the structure of the analytical
models that are employed as provided below.
The Dependent variables of Output, Probit and truncated regression models
The dependent variable of output model is value of output in kg and the dependent variable of
probit model has a dichotomous value depending on the farmers’ decision either to adopt or not
to adopt the inorganic fertilizers. However, the truncated regression model has a continuous
value which should be the intensity, the use and application of the technology. In this case, it
indicates the amount of inorganic fertilizer applied in kilogram. The inorganic fertilizers in
question are Dap and Urea.
The Independent variables and their definitions used in both models
Adoption literatures provide a long list of factors that may influence the adoption of agricultural
technologies. Generally, farmers’ decision to use improved agricultural technologies and the
intensity of use in a given period of time are hypothesized to be influenced by a combined effect
of various factors such as household characteristics, socio-economic and physical environments
in which farmers operate.
The explanatory variables (demographic, physical, socio-institutional and plot characteristics )
included in the empirical models were selected following the literature on farm level investment
theory (Feder et al., 1985; Feder et al., 1992; Clay et al., 1998; Berhanu and Swinton, 2003) and
are explained on Table 2.1 on the appendix.
3. RESULTS AND DISCUSSIONS
3.1
Demographic Characteristics
Table 3.110 gives the demographic characteristics of the sample farm household heads. The table
presents the mean (proportion) and standard deviations for each variable for adopters, non-
H0: = and H1:
, then H0 was rejected on a pre-specified significance level, provided Γ >
2
k , confirming the
superiority of the double-hurdle specification over the Tobit model. In such a case, the decision to state a positive
value for inorganic fertilizer adoption and the decision about how much to state appear to be governed by different
processes.
10
I recommend to visit tables, and figures on the appendix part
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adopters and the total sample. T-value and χ2-value are also presented for the statistical
comparison of the mean (proportion) values between adopters and non-adopters.
The result indicates that 77.1% of the heads of the household are male (Table 3.1). Out of the
total sample households, 22.9% of the household heads are women, who are single, widowed or
Divorced. The survey result of chi-square test (χ2=0.1057) revealed that there is insignificant
difference between the decision to adopt and sex of household heads.
The age of the sample household heads ranges from 30 to 75 years. The mean age of the
household heads is 51.4 years with a standard deviation of 11.2. The mean age of respondents’
shows slight variation between adopting and non- adopting and the relationship is statistically
non-significant (Table 3.1).
The number of family members of the sample households varies from a minimum of 2 to a
maximum of 12 and the mean family size was 5.5 persons per household. There is variation
between the two groups with average of 5.6 persons for adopters and 5.4 for non-adopters (Table
3.1).
Regarding the educational status of the household heads, two educational levels were identified
i.e. illiterate and write and read. From the survey results, about 60.31% of the household heads
were illiterate, 39.69% have the skill to write and read. In addition there was a variation in
educational level of household heads between adopting and non-adopting. From adopting heads
39.39% can write and read and 60.61% were illiterate.
The average available labor was estimated to be 3.28 man-days for sample households, 3.29
man-days for adopting and 3.28 man-days for non- adopting. The statistical analysis showed
insignificant difference in amount of labor (man days) for adopting and non- adopting (Table
3.1).
Both groups of the sample reported that they face labor shortage during main crop seasons as the
use of hired labor was very low may be due to the imperfect labor market.
3.2 Institutional Services and Resource Endowments
Table 3.2 presents access to institutional services and resource endowments of household heads.
The nature and discussion of the table is like table 3.1.
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Off-farm activity is source of capital for small holder farmers as it helps to finance cash deficit
by rural farm households. Farm households who were involved in off-farm activities tend to
intensify less their own crop production. There are cases when off-farm activity looks relatively
attractive compared to on farm activities, which attracts the attention of households. The return
obtained from off-farm activities also influences the farmer decision to adopt inorganic
fertilizers.
The survey showed that 44.27% of the farmers are involved in various forms of off-farm
activities. The result also indicates that there is difference in adoption among the heads that have
access to off-farm activities.
Livestock which is an indicator of wealth status and a means of holding an asset in rural areas
plays an important role in the farming system of the study area. Cattle, sheep and goat, and
chicken are kept by farmers for purpose of income source, draft power and food.
The result showed the average livestock holding for sample households was 4.43 TLU, while the
average livestock holding for adopter and non-adopter groups were 4.23 and 5.06 TLU,
respectively (Table 3.2). This result showed that TLU was the basic factor for adopting inorganic
fertilizer. The t- test revealed that the mean difference between adopter and non–adopter
household heads was statistically significant (at p <0.05).
Land holding per sample households ranged between 0.125 ha and 1.875 ha. On average the land
holding of the sample households is 1.04 ha, while the average of adopting is 1.04 ha and of nonadopting is 1.02 ha (Table 3.2). The mean difference is found to be statistically non-significant.
Credit in the form of cash or kind was provided to sample households. There are different
sources of credit. Among all, institutions like micro finance and office of agriculture take the
leading role in providing credit in cash or in kind to farmers. About 67.94% of sample
households got credit either in the form of cash or in kind from government, informal local
institutions, and private money lenders and from friends and relatives. About 65.66 and 75
percent of users of credit were adopting and non- adopting, respectively. The chi-square test
shows insignificant difference between adopting status of households and credit (Table 3.2).
The agricultural extension services provided to farmers is believed to be the main source of
information about improved agricultural technologies and it is widely accepted that substantial
16
productivity increases could be achieved when farmers get appropriate extension services. About
96.97 percent of adopting, 34.38 percent of non- adopting and 81.68 percent of the whole sample
respondents had access to extension services. This indicates that adopting had relatively better
extension service than non– adopting, and that access to extension service is positively related to
the adoption decision of farmers. The chi-square test indicated that there is significance
difference between proportions of the two categories of sample household groups using
extension services at less than 1% level of significance (table 3.2).
3.3 Plot Characteristics and Other Inputs used
The farmers’ decision to adopt a new technology is influenced not only by the total landholding
but also by the size of each plot. The average plot size of sample households in the study area
ranges from a minimum of 0.0625 ha to a maximum of 1.25 ha with average plot size of 0.4 ha.
Furthermore, there was a difference between the average plot size of the two groups with 0.42 ha
and 0.37 ha for adopting and non– adopting, respectively (Table 3.3).
Most farm households managed better nearer plots than distant plots on average due to close
observation of the changes on nearer plots and the additional time and labor required reaching
distant plots. The distance of plots from homesteads varies from a minimum of 0 minutes up to a
maximum of 90 minutes. The average distance is 17, 5 and 14 minutes among adopting, nonadopting and the total sample households, respectively (Table 3.3). The mean difference is
statistically significant at p <0.01.
The quality and physical feature of the plots determine the decision to adopt in farm plots
productivity improvement. Based on the observation of the sample households as well as the
expert evaluation of DAs each farm plot was classified into flat, gentle slope, and steep (Table
3.3). Out of the whole plots of sample farm households about 67.94%, 23.66% and 8.40% are
flat, gentle and steep slopes, respectively.
Furthermore, there was a certain variation in slope of the plots between adopting and nonadopting households. Thus, about 71.72%, 22.22% and 6.06% of the adopting and 56.25%,
28.13% and 15.63% of the non- adopting households’ plots were flat, gentle and steep sloped
respectively. The chi square test indicated that there is significant difference between slope
categories of the plots among the two groups, the adopting and non- adopting households (table
3.3).
17
Enhancing agricultural productivity of the study area using inorganic fertilizers was
complemented by some inputs like use of compost, improved Varity, chemicals and access to
irrigation. Among the sample plots 50.38%, 50.38% and 19.85% had used compost, improved
Varity and irrigation. Mean while 36.36%, 49.49% and 26.26% of adopting and 93.75%, 53.13%
and 0% of non-adopting households’ plots were used compost, improved Varity and irrigable
plots respectively. A significant statistical difference between adopters and non-adopters was
observed in proportion of compost used and irrigable plots at less than 1% probability but
insignificance statistical difference in proportion of seed type.
On average 0.5, 0.54 and 0.51 liter of chemicals and 21.9, 18.5 and 21.07 kg of seed per plot for
adopting, non-adopting and total sample were used. But there is no statistical significance in
mean difference of chemicals and quantity of seed among the two groups of the sample.
3.4 Extent of Fertilizer Use in the Major Cereals
Table 3.4 shows the number of plots, average land cultivated, average plot area cultivated,
percentage of fertilizer applied, and amount of fertilizer used for the five major cereals averaged
over the plots. The average number of plots which were cultivated with a particular crop and the
plot land cultivated is largest for teff. On average about 42 plots of the sample were cultivated
under teff which is roughly one third of the total number of plots covered by the survey. The
average plot land size used for cultivating teff was about 0.4 ha. The total area cultivated over all
plots combined was highest for teff (16.8 ha, averaged over all plots cultivated teff). Maize is
the second crop in terms of total plot area cultivated i.e. 13.86 ha. Farmers cultivate on average
0.42 ha maize.
The percentage of any of the two fertilizers (Dap or Urea or both) applied per plot per crop
ranges from 50.3% to 97.6%. Teff ranked first followed by wheat while barley is the lowest.
The fertilizer use per hectare of plot land was also described. It should be noted that the data are
at the plot level (by averaging we can also generate household level data).
The average amount of fertilizer per hectare of plot area is therefore the amount of fertilizer
applied on plots of respective major crops. This might understate the amount of application rate
of farmers in situation where the farmers cultivate a minor crop on more than one plots and when
fertilizer is applied in some of these plots. The average amount of fertilizer (Dap + Urea) per unit
of plot area was highest for teff followed by sorghum and barley is the lowest. The data show
18
that the plots using both dap and urea seem to apply less than the recommended level of 200kg
per ha.
The existence of no gap between the mean of dap and urea used per plots on some crops is
consistent with the extension recommendations that require proper combination of dap and urea.
Some works stated 100 kg of each of dap and urea per ha of cultivated land as a recommended
level (Fufa and Hassen, 2005). The exception here is for crops sorghum and teff which has small
gap between mean of dap and urea. The next section explores the intensity of fertilizer use
among the major crops.
Table 3.5 shows the use of fertilizer in kg as well as their average per hectare of plot of
cultivated area for the crops under consideration, by fertilizer appliers only, based on the data set.
It is essential to explore the level of fertilizer applied per ha of plot area as it has important
policy implications. The individual averages of fertilizer use per ha of plot area cultivated were
not small on average terms. It should be noted that this does not necessarily imply that the
application rates were as per recommended level of 100 kg of dap and 100 kg of urea per ha of
plot area cultivated.
The average intensity of fertilizer use for all major cereals was 34.12, 37.35 and 71.46 for Dap,
Urea and Dap + Urea respectively. Average intensity of use of fertilizer for the major cereals
varies from 35.63 kg (Barley) to 87.33 kg (sorghum).
The simple average does not reveal variation. Variation in intensity of fertilizer is thus discussed
next. The quintile plots (Figure 3.1 and 3.2) shows that there is a high degree of variation among
the plots in the application rates of fertilizer in both urea and daps. Quarter of the plots have a
low level of application rates while the rest plots have a high level. (Note: quantile plots denote
the ordered values of Urea and Dap against the quantiles of a uniform distribution (the line).
3.6 The Effect of Fertilizer Use on Agricultural Productivity
The summary statistics of the basic demographic characteristics of the farmers, plot
characteristics and resource endowment and institutional services were discussed in the last few
pages. Here summary statistics of value of output, value of yield and the target variable fertilizer
is presented in table 3.6.
19
As can be seen the average value of output (3160) and yield (8030) of the major crops deviates
across the plots by about 3127 and 4635 respectively. On average, farmers used about 26 and 28
kg of dap and urea in production of the major crops. All plots in the sample are observed using
both types of fertilizer inputs-the minimum being 10 kg and the maximum of 250 kg for both
inputs for adopters. There is no problem of fertilizer mix usage in the study area, almost all plots
have used proportional combination of dap and urea, though not as per the recommendation.
3.6.1 Partial Correlations
Correlation is an indicator of whether a relationship exists or not between variables. It also shows
the strength and direction of the relationship. Table 3.7 shows that the quantities of (Dap + Urea)
applied are positively correlated with the total production of teff, maize and sorghum. The
correlations are highly significant in most of the cases for both Dap and Urea. In relative terms
both Dap and Urea applications are highly correlated with teff and maize productions. The
correlation of fertilizer use and production is, however, insignificant in the case of barley and
wheat. Both barley and wheat in the study area have no improved variety. Therefore there is
seemingly a lower return to fertilizer use.
3.6.2 Econometrics Approach
This section shows how fertilizer affects agricultural production with a multifactor production
function. The model specification and method of estimation was discussed in chapter three and
discussion of estimation results is given in the following subsection.
The necessary data explorations are conducted before proceeding to the estimation. One of the
problems encountered when linear production functions are used is multicollinearity.
Examination of pair-wise and partial correlations of the variables revealed this problem.
Correlation among explanatory variables yields a change in sign and significance of estimated
parameters. This problem is also observed in most non-linear and interactive variables during the
preliminary estimations. Dropping theses variables reduces the extent of the problem. Hence the
model was finally reduced to a Cobb-Douglas function. The result obtained show that the model
in general is significant at less than 1% probability level. Further, the adjusted R2=66.80 tells all
the explanatory variables together explains 67 percent of variations in the farmers productivity.
The regression results obtained are presented in table 3.8.
20
The Ramsey RESET test using powers of the fitted values of the outcome variable with F (3, 67)
= 1.32 shows a sound acceptance of the null hypothesis (model has no omitted variables) and
thus the Cob-Douglas function best fit the data. Breusch-pagan/Cook-Weisberg test for
Heteroscedasticity, which is defined only for non-robust standard error, showed that running the
ordinary regression would have yielded in heteroskedastic error term. Thus robust option was
add while estimating the model through which the ill was cured. The VIF test of multicollinearity
problem was also tolerable with the rule of thumb. Most of the explanatory variables are also
significant and with the expected sign (Table 2.1). The discussions therefore focus on the OLS
estimates.
Fertilizer, the target variable of this work, is significant at less than 5% probability level. The
elasticity of value of production with fertilizer is 0.32 indicating that, with other factors
remaining constant, a 10% increase in fertilizer use increase the value of crop production by
32%. Similar results for robustness are also obtained by estimating the natural logarithm of
value of yield as a dependent variable, where fertilizer had significant and positive effect on
value of yield The significance of fertilizer in both models reveals that its effect is
unambiguously significant.
Although the significance of fertilizer is confirmed, the magnitude with which the value of
production responds to a change in fertilizer use is low. Its coefficient is, however, the second
largest next to land. This evidence of the estimation underscores the relative importance of
fertilizer in improving total production and productivity. The magnitude of the estimated
coefficient is also consistent with some other findings like Bumb (1995), Morris et al (2007),
Chencho and Bart (2010) and Kefyalew (2011). Kefyalew (2011) found that fertilizer use is
significant with elasticity values of 2.15% for five major crops.
3.6.3 Other Correlates of Agricultural Productivity
The area of land cultivated for harvest is significant and positive at less than 1% level of
probability similar to Umar et al (2011) findings. This is, however, not a sustainable solution for
increased productivity. The land holding size in the study area is continuously declining due to
the continuous population growth and recently land administration policy change. Moreover, a
real expansion is mostly toward marginal lands that have lower soil fertility.
21
Plot characteristics like slope and soil fertility of the plot, agro-ecology variable (Dummy kolla)
and dummies of crop types were taken as covariates in the model as Hussain and Perera (2004)
consider in their analysis. The lemteuf (at p<0.05) and dagetama and miaze both at p<0.1 in
value of yield model were variables that affect significantly and negatively value of production
and value of yield respectively. This finding is consistent with Marenya and Barrett (2009b),
who found farmers with poor soils are in the trap of low productivity in western Kenya. But
Interaction of plot characteristics (soil fertility and slope) and crop type (barley, maize, sorghum
and wheat) with fertilizer resulted in high multicollinearity problem and hence were excluded.
The dummy variable kolla was insignificant, when it interact with fertilizer it affects value of
production significantly and negatively at less than 1% probability level. This is may be due to
the water intensive nature of fertilizer.
Rural farm activities are dependent on the number of livestock owned. All types of livestock
owned have a direct effect on value of production though the degree of importance varies.
Livestock is also an important indicator of wealth of the households. Therefore, it is highly
associated with a higher application rate of inputs, particularly fertilizer. Livestock ownership in
tropical livestock units (TLU) was used to show the effect of livestock on value of production
and was significant and positive at less than 10% probability level. This finding was consistent
with Kefyalew (2011).
The agricultural system in the study area is predominantly dependent on family labor. Labor was
approximated by the number of man day equivalence that is involved in farming activities. It was
found significant at less than 0.05 probability indicating that having more labor man day in a
family does improve the value of production. This is due to the reason that more labor is required
in agricultural production processes at different stages. This finding is consistent with Fakayode
et al (2008) and Umar et al (2011).
Similarly, education level of the head is significant and positive factor for value of production.
Farmers who have more education better add for their value of production and yield than those
who lack knowledge.
A dummy for gender of the head is insignificance. It indicates that there is no significance
difference in the value of production among male and female headed households. Other studies,
however, found that male headed is advantageous compared to female headed. EEA (2002)
22
found, on average, male headed households are better-off as they have more land to cultivate,
more labor and more livestock. These benefits seem to arise because females are denied basic
rights in terms of access to education and other resources compared to males. The insignificant
coefficient of gender of the household dummy might be because women’s rights are now more
respected than they were in previous times. This can serve as evidence for the role of
empowering women in the growth of rural poor.
Credit and extension variables are highly correlated with the probability and level of fertilizer
use. Dummy for access to extension and credit both were insignificant in the production model.
The importance of credit and extension is revealed by their significance effect on the adoption
and level of application of fertilizers which is examined in the next section. This is due to the
fact that the important channels though which credit and extension affects agricultural
productivity is by improving the adoption rate of fertilizer.
Simple descriptive evidence from the survey shows that only 82% of the total sample plots were
involved in soil conservation measures (Table 3.9). Out of the total plots under no soil
conservation measure, majority of them are with no need or problem of soil degradation. It is
essential to improve awareness of those that are not involved in conservation activities through
technical support and advice. Another soil fertility enhancing technique is the use of organic
manures and compost. About 50% of the plots are using organic manure (Table 3.9). This looks
less promising achievements because a sustained production of manures helps to substitute the
inorganic fertilizers which farmers are less likely to be able to afford given the continual increase
in the price of fertilizer11. Besides, the organic manures contain soil moisture for a long time and
this enhances adequate water availability for crop growth (FAO, 1998). However, the intensity of
manure use by farmers is usually very low. Manure is used mostly on small plots that are located
around the household’s residence. This is associated with labor constraints and probably even
with manure quantity constraints as Kefyalew (2011) explained.
11
The price of fertilizer is increasing monthly e.g. the price of Urea and Dap after three months was most probably
(current price + 30 for each).
23
3.7 Factors Affecting Adoption and Intensity of Inorganic fertilizers
3.7.1 Qualitative Evidence
The levels of fertilizer application per ha of plot area is not consistent with the DAs and
agricultural experts recommendations for the area. High fertilizer price is the major problem
stated by most respondents, followed by small farmers understanding of “use of high fertilizer as
per recommendation burn crop and reduces soil fertility”, with respective shares of 48.09% and
26.72% (Table 3.10). The price problem is as expected because the price of fertilizer has been
increasing at higher rate than cereal prices (EEA 2001/02. For instance, in the previous
production period in the targeted area price of major crops varies from 500 to 1500 ET Birr per
quintal for barley and teff respectively. But price of Dap and Urea for the same period was 1473
and 1160 ET Birr respectively. Additionally, price of improved varieties 12 of some of the major
crops was incomparable with price of farm gate output in post harvest period (La’ilay Maychew
rural development office, 2013). This finding may indicate price barrier for complementary
inputs like improved Varieties may paralyze effectiveness of fertilizers for productivity
improvement.
Shortage of supply, late arrival, and lack of credit were not reported as major problems of the
study area. This evidence realizes the effectiveness of fertilizer dissemination in the study area.
Despite the stated problems 11.45% cultivators of the plots reported no problem.
3.7.2 Econometric Approach
There are many demographic, socio-economic and agro-climatic factors that contribute in the
fertilizer adoption decision and level of application of fertilizer. In order to identify the
significant factors, both Double Hurdle and Tobit models are applied on the data set. Denoting
that fertilizer adoption decision and intensity of
fertilizer application decisions may not
necessarily be made jointly, and that the factors affecting each decision may be different, thus
this study estimated both Tobit and double hurdle models (Probit and truncated regression
models) separately, and then conducted a likelihood ratio test.
The results of the likelihood ratio test favor the use of the double hurdle model. It shows that the
likelihood ratio test statistic Γ is 86.88, which exceeds the critical value [ 2(19) 48.28] at the
12
Price of improved Varieties for maize, sorghum and teff was 1175, 820 and 1400 ET Birr respectively for the last
production period in the study area.
24
1%-level of significance. Hence a likelihood ratio test rejected the Tobit model in favor of the
double hurdle model. The test confirmed that the adoption decision and intensity of application
of fertilizers are in fact separate for this data set. Thus, plot level decision to state a positive
value for adoption of inorganic fertilizer and the decision about how much to state seem to be
governed by different process. This also confirmed by the result of the Akaike’s information
criterion, which was included as an alternative model selection criterion. The less formal “test”
of comparing the Probit and Tobit estimated coefficients also confirmed the above test results:
this is implied from the existence of several variables that significantly affect adoption decision
without being significant factors for the intensity decision, and vice versa. Even among those that
affect both adoption and intensity, the direction of effect for some is different. The decision to
adopt inorganic fertilizer and the decision about how much to apply appear to be explained by
different processes.
The double hurdle model has two dependent variables: the dependent variables used in the study
were classified as decision to use and intensity of fertilizer use. Intensity of fertilizer use was
measured by kg used per hectare of plot area.
Considering the dependent variables, “frt” is a discrete dummy variable that indicates if the
household’s plot was used any of dap, urea or both fertilizers and “sfrt” describes the level of
fertilizer use that were made on the farm plots. The descriptive statistics show that 75.57% of the
plots use positive fertilizers and 24.43% of the household’s plots indicate that they had not use
so. Plots which make use of fertilizers have average of fertilizer use of 71.46 kg per hectare of
plot area (with standard deviation of 45.52).
3.7.2.1 Determinants of Adoption Decision of Households
The probit regression result from the first stage of the double hurdle model shows that the
probability of adopting inorganic fertilizers is influenced by a wide range of factors. The results
of the probit estimations for adoption decisions are presented in Table 3.11. The table shows the
estimated coefficients, their robust standard errors, and marginal effects. The pseudo-R-squared
and the chi-squared test results are presented at the bottom of the table.
From a total of 19 explanatory variables that were considered in the probit model, only three
variables were found insignificant to influence the probability of fertilizer adoption.
25
The result showed that the significant variables affecting the probability of adoption in the study
area include age, education level (edu), family size (hhsize), labor force in man equivalence (lf),
total livestock holding (TLU), number of plots
(plots), extension (ext), credit, chemicals
(chem), home-plot distance (hmplot), home-near town distance (hmnt), compost, seed type
(seed), quantity of seed (qseed) and Varity of seed sown per plot (Varity) and quality of soil of
plot (lemteuf).
The sign of the coefficient estimates of the variables except chem, compos and qseed confirm the
prior expectation. But sex of head, Plotarea and donk were insignificant to affect probability of
adoption.
Age had a significant positive effect on the probability of adoption at less than 10% level of
significance.
This might be related to the reason that older famers might have gained
knowledge. The result is consistent with the findings of Hassen (2012). However, this effect may
diminish, as the household head gets older.
The probit regression result reveals that the slope coefficient of household size is positive
determinant of fertilizer adoption. It denotes that increase in farmers’ family size is an incentive
for adoption of new fertilizer as more agricultural output is required to meet the family food
consumption needs (Yonannes et al, 1989) or as more family labour is required for adoption of
labour intensive technologies like fertilizer (Hassan et al, 1998a) and Fufa and Hassen (2006).
Access to extension service had the expected positive and significant effect at less than 1%
significant level on probability of adoption due to access to information for fertilizer
technologies. Agricultural extension services are the major sources of information for improved
agricultural technologies to improve the production and productivity of smallholder farms.
Farmers get access to information about improved technologies by contacting the extension
agent. The result is consistent with the finding of Fufa and Hassen (2006) and Hassen (2012)
who make use of probit model.
Having access to credit had the expected positive and significant effect at less than 10%
significant level on probability of adopting inorganic fertilizer due to access to finance for these
technologies. Agricultural credit services are the major sources for improved agricultural
technologies to solve financial constraints. If farmers can get access to credit, they can purchase
improved technologies. The result is consistent with the finding of Hassen (2012).
26
In conformity to the hypothesis distance form residence to plot had negative and significant
effect on probability of adoption at less than 1% level of significance. This is may be due the
requirement of more labor, resource and time. So that plots in nearby residence most probability
would get better adoption than the far once. This finding is consistent with Yonas A., et al
(2008), Team (2011) and Kefyalew (2011).
As Solomon et al (2011) explained knowledge of existing varieties and attributes of improved
varieties are major determinants of adoption of improved technology. In the first hurdle of this
study compost, improved seed, quantity of seed and Varity sown per plot affects adoption
decision of farmers. This outcome was consistent with Yonas A., et al (2008) and Solomon et al
(2011). In the next few paragraphs remain variables were discussed in comparison to truncated
regression results (second stage of Double hurdle model).
3.7.2.2 Determinants of Intensity of Fertilizer Use of Households
Literacy was hypothesized to affect technology adoption and intensity of fertilizer positively
since it increases the capacity of farm households to acquire information and knowledge of
improved technologies and promote the decision to use it on his/her farm. In this study, in
conformity with the hypothesis, education positively and significantly affected the probability
and intensity of use of inorganic fertilizer at less than 10% level of significance. The result is
consistent with the findings of Doss and Morris (2001), Fufa and Hassen (2006) and Team
(2011).
As expected, labour force available had influenced the probability and level of use of inorganic
fertilizer positively at less than 1% level of significance. The probable reason for this finding was
that improved practices are labour intensive and hence the household with relatively high labour
force uses the technologies on their farm plots better than others. This finding is consistent with
the results of Yonas A., et al (2008) and Hassen (2012).
Number of plots had influenced the probability and intensity of use of inorganic fertilizer
negatively at less than 1% level of significance. The reason might be related to the poor
transportation access in the study areas and the land fragmentation problems, as the number of
plot increases the time required to reach the plots and labour required increases. The cost of
intensifying inorganic fertilizer on fragmented plots is likely to be high. This result is consistent
with the findings of Kefyalew (2011) and Team (2011).
27
Ownership of livestock had the expected positive and significant effect on probability and
intensity of inorganic fertilizer. Livestock ownership is considered as an asset that could be used
either in the production process or it could be exchanged for cash (particularly small ruminants)
for the purchase of inputs whenever the need arose. Moreover, livestock is considered as a sign
of wealth and increases availability of cash for adopting technologies. The result is consistent
with the findings of Dereje B., et al (2001), Solomon et al (2011) and Hassen (2012), who apply
double hurdle model in their analysis.
The coefficient of distance to near town market had the expected negative sign and significant
effect on the probability and intensity of adoption of inorganic fertilizer at less than 1%. The
negative sign indicated the importance of proximity to a regular markets leading to better access,
lower transport cost, and timely delivery of inputs and disposal of output and better output price
for farmers. The market is used to buy required input and sell surplus output. Thus the closer
distances of a farmer’s home to the market enables and facilitates marketing of inputs and
outputs. The result is consistent with the finding of Fufa and Hassen (2006), Yonas A., (2011)
and Hassen (2012).
The result indicates that complementary inputs like chemicals, quantity of seed sown and quality
of the plot (lemteuf) affects probability and intensity of inorganic fertilizer differently at different
level of significance. While chemicals and quantity of seed sown affects probability and intensity
of inorganic fertilizers negatively and positively respectively, quality of the plot (lemteuf) do
affect both hurdles positively but at different level of significance. Findings of Solomon et al
(2011) confirms this result.
While farm land was insignificant in the adoption model it affects intensity of fertilizer positively
and significantly at less than 1%. Farm size, in conformity with the hypothesis, had influenced
the intensity of use of inorganic fertilizer positively at less than 1% level of significance. Farm
size is an indicator of wealth and perhaps a proxy for social status and influence within a
community. The result is consistent with the finding of Dereje B., et al (2001), Fufa and Hassen
(2006), Solomon et al (2011) and Team (2011).
28
4. Conclusion and Policy Implications
The general objective of the study was to examine factors that affect the probability and intensity
of adoption of inorganic fertilizer and their productivity effects. In general as part of the
agricultural development-led industrialization program, the Ethiopian government launched the
new extension program. The program was expected to result in abrupt changes in the production
and productivity of Ethiopian agriculture. In spite of intensive efforts to expand the use of
improved agricultural technologies, such as improved varieties and fertilizers, the yield of major
crops and livestock, remained low. There has been a growing concern by researchers, extension
personnel and policy makers about the effectiveness of adoption of improved agricultural
technologies particularly on the area allocated and amount of use of these technologies and
farmers learning process from the program to alleviate the food shortage problem in the country
particularly Tigray Regional state.
The need for applying modern agricultural inputs particularly inorganic fertilizers in the
woreda’s agriculture is not debatable. The agricultural sector of the woreda is well known for its
being traditional and use of backward technologies. Hence the application of modern inputs and
practices, as evidenced from the Green Revolution Applied in Asia and Latin America, can
contribute a lot for productivity enhancement of the sector. The fate of the sector interms of
increasing its contribution to the overall growth of the economy and securing food self
sufficiency depends on the development and application of appropriate technologies.
This study was initiated to identify factors that affect the probability and intensity of farmers’
decision to use improved fertilizer technologies and their productivity effect. There are several
studies on farmers’ adoption of improved agricultural technologies using static and dynamic
models in developing countries including Ethiopia. However, there is no study on this research
problem conducted in the study area.
Cross-section data were used to analyze the effect of fertilizer on agricultural productivity. The
study used data obtained from a survey of farmers and their plots in La’ilay Maychew district
collected for the period March 1 to April 24, 2013. Four different peasant associations were
selected to represent lowland and highland agro-ecological environment in the woreda. Then 131
farmers’ plots were selected using multi-stage random sampling of farm households.
29
Cobb-Douglas production function was estimated to analyze the effect of fertilizer on
productivity. It was found that fertilizer positively and significantly affects productivity. In
addition to that other variables like age and education level of head, household size, labor,
livestock, and land affects agricultural productivity positively and significantly. Mean while,
gentle slope and poor soil quality of plot, maize among others and an agro-ecology variable all
affect value of production and yield negatively and significantly.
Factors that affect the probability and intensity of inorganic fertilizer was analyzed using both
descriptive and econometrics techniques. The adopters of inorganic fertilizer were characterized
by older and slightly with better resource endowment (labour, household size, land, plots and
plot area), better client of agricultural extension service, sown more seed per plot and in far
distance from near town and plots to their residences than non-adopters.
Double hurdle model was employed to study farmers’ decision to adopt and intensity of use of
improved fertilizer technologies. The results of the study provided empirical evidence of the
positive and significant effect of education level of head, labor equivalence, tropical livestock
unit and quality of the plot in enhancing the probability and intensity of adoption of inorganic
fertilizer technologies to increase production. On the other hand, variables like number of plots
and distance from residence to near town affects probability and intensity of adoption of
inorganic fertilizer technologies negatively and significantly.
Mean while, the study found access and availability of extension and credit services, compost,
Varity sown per plot, seed type and home-plot distance had significant effect on probability of
adoption. But the access and availability of extension service was found to be more powerful
than other factors in explaining probability of adoption of inorganic fertilizer technology
adoption. Plot area was insignificant in adoption model but it was significant and positive factor
affecting intensity of inorganic fertilizer use in the second hurdle. Variables like quantity of seed
sown per plot and chemicals were significant factors in both hurdles but differ in direction of
their effect and level of significance.
The results obtained from the study can be used to show some intervention areas, even if a more
detailed study in terms of area coverage and depth is required to arrive at conclusive policy
recommendations.
30
To improve the possible benefits from fertilizer use and to encourage peasants’ adoption of
fertilizer, the study identified the following priority actions that will have the most impact on
fertilizer use both in the short and long run:
Almost half of the farmers surveyed cited high fertilizer prices as the main constraint to their use.
This affects the net benefit of fertilizer use from the farmer’s perspective. This needs
interventions to reduce the price of fertilizer or to manage its rate of increment over time.
Interventions might be in the form of crop specific fertilizer subsidies or cash transfers. Each of
these approaches has their own advantages and disadvantages. A choice between the different
means of reducing fertilizer price has to be based on the costs and benefits of the alternative
methods.
The second problem observed was farmer’s perception of “Application of as per the
recommendation burns crops”. Here deep research is required to know if the farmers view has
ground or not rather than black command of apply as per the recommendation.
Encouraging peasants to apply the proper mix of Dap and Urea and extension follow-up are
advantageous to enhance productivity.
It is shown that the impact of extension contact and credit on the farmer’s adoption decision is
significant, but the impact on productivity is not. This implies the need for enhancing the
knowledge of the extension workers and credit providers through appropriate training so that
they can help the farmers not only to tell to use the modern inputs, but also how to use to benefit
from the possible productivity gains.
Since most of the farmers in the woreda are very poor who can’t afford easily to purchase
modern inputs, there is a need to develop and use local productivity enhancing technologies
available at the farmer’s disposal in addition to the purchased inputs. This includes the use of
organic fertilizers compost and manures.
31
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University of Gothenburg Department of Economics
34
Appendices
Table 2.1 Summary of definitions, measurements and expected signs of variables
Definition of variables
Measurement of variables
Expected sign
Dependent variables
Value of production (Voutput)
Continuous (kilogram)
Value of Yield (Vyield)
Continuous (kilogram)
Adoption of inorganic fertilizers (frt)
Dummy (yes/no)
Amount of inorganic fertilizers (sfrt)
Continuous (kilogram)
Independent variables
Age of the household head (age)
Years
?
*
Education of the household head (edu)
1 if head can write and read
Household size (hhsize)
Number
+
Sex of the household head (sex)
1 if male
+
Labour available in the family (lf)
Man equivalence
+
Farm size (land)
Cultivated area in hectare
+
Plot area (plotarea)
Cultivated area of plot in hectare
+
Livestock owned (tlu)
Tropical Livestock Unit
+
Fertilizer (Dap + Urea) (sfrt)
Kilogram
+
Plots (plot)
Number
-
Access to off farm income (offfarm)
1 if had access
?
Access to extension service (ext)
1 if had access
+
Access to credit (credit)
1 if had access
+
Access to irrigation (irrigation)
1 if had access
+
Residence-plot distance (hmplot)
Walking minutes
-
Residence- nearest market distance (hmnt)
Walking minutes
-
35
+
Donkey (donk)
1 if owned
+
Chemicals (chem)
Liter
+
Compost (compos)
1 if used
+
Seed type (seed)
1 if improved
+
Quantity of seed (qseed)
Kilogram
+
Varity per plot (varity)
1 if Varity per plot was sown
+
Major Crop types(Barley, maize, sorghum, DummiesK
?
wheat)
Plot quality (poor, very poor)(quality)
DummiesK
+
Plot slope (mountainous and geddel)(slop)
DummiesK
-
Variable for agro-ecology
1 if lowlandK
-
Interaction variables (fertilizer*dummyK)
Continuous
+
Characteristics
Where
*
Adopting
Non- adopting
Total sample
(N=99)
(N=32)
(N=131)
-1if head can write and read, and 0 other wise (this apply for all variables that have
same nature)
K
-is dummy category (K=dummy crop type, plot quality and slope, lowland)
?-expectation of variables is indeterminate
Table 3.1: Demographic characteristics of the sample households
36
Adopting
%
Variables
Sex of the household
(N=99)
Non- adopting
%
(N=32)
%
Male
Off-farm activities
Female
Had access
Education
Had not access
Read and write
Credit service
Illiterate
Had access
Had not access
Age
Extension service
Family size
Had access
Labor equivalence
Had not access
75.00
22.22
43.43
25.00
46.88
Total sample
%
(N=131)
0.1057
χ2-value
%
77.78
χ2-value
%
77.10
0.1160
22.90
44.27
0.0153
56.57
39.39
53.13
40.63
55.73
60.61
65.66
Mean
SD
34.34
51.75
11.45
59.38
75.00
Mean
SD
25.00
50.28
10.28
5.58
2.19
96.97
5.38
3.29
1.41
3.03
3.28
39.69
0.9692
34.38
65.63
60.31
67.94
SD
32.06
11.16
T-value
Mean
51.39
1.64
-0.6449
63.3194***
-0.4774
5.53
2.06
81.68
1.18
-0.0472
3.28
1.36
18.32
Source: Own computation from survey, 2013
Table 3.2: Institutional services and Resource endowments of the sample households
37
Mean
Land size
Variables
TLU
SD
1.04
0.37
Adopting
(N=99)
4.23
1.70
Mean
SD
T-value
1.02Non- 0.35
adopting
5.06
(N=32) 1.46
-0.2613
2.4928**
Mean
1.04
Total
sample
(N=131)
4.43
SD
0.37
1.68
Sou
rce:
Ow
n
com
put
atio
n
fro
m
sur
vey,
2013
*** And ** significance at 1% and 5% probability level respectively
Table 3.3 Plot characteristics and inputs used
38
%
χ2-value
3.8422*
%
Slope categories
Flat
71.72
Gentle
22.22
Steep
6.06
Soil quality
Fertile
12.12
Poor
44.44
Very poor
43.43
Varity used?
Characteristics
Yes
46.46
Total number
of
plots
cultivated
No
53.54
Plot Irrigated?
Yes
26.26
No
73.74
Seed type
Improved
49.49
Local
50.51
Compost used?
Yes
36.36
No
63.64
56.25
28.13
15.63
%
67.94
23.66
8.40
Sou
rce:
own
3.5791*
9.38
28.13
62.50
Barley
71.88
15
28.13
11.45
40.46
48.09
6.2638**
Maize
33
Sorghum
20
Teff
52.67
42
47.33
10.4850***
100
19.85
80.15
0.1275
53.13
46.88
50.38
49.62
31.8579***
93.75
6.25
50.38
49.62
Wheat
21
com
put
atio
n
fro
m
sur
vey
data, 2013
***, ** and * implies significant at 1%, 5% and 10% probability level, respectively
Adopting
Variables
Non- adopting
Total sample
(N=32)
(N=131)
(N=99)
Mean
SD
Mean
SD
T-value
Mean
SD
Plot area
0.42
0.24
0.37
0.20
-1.0892
0.40
0.23
Plot distance
17.36
19.19
5
10.92
-3.4624***
14.34
18.29
Chemicals
0.50
0.54
0.54
0.51
0.3303
0.51
0.53
Quantity of seed
21.9
17.48
18.5
13.88
-1.0022
21.07
16.69
Table 3.4: Average number of plots, land cultivated, percentage of fertilizer used and
amount of fertilizer used per plots
39
Average number of plots per farmer
Average plot area cultivated (ha)
Total cultivated plot area (ha)
Average land cultivated per farmer (ha)
% of Dap used
Average amount of Dap (kg)
% of Urea used
Average amount of Urea (kg)
% of (Dap+Urea) used
Average amount of Dap and Urea (kg)
Source: own survey 2013
2.93
0.37
5.55
1.13
53.33
9.5
53.33
9.5
53.33
19
1.73
0.42
13.86
.94
57.58
24.24
57.58
24.24
63.33
48.48
2
0.57
11.4
1.08
75
31.5
75
34
88.24
65.5
3
0.4
16.8
1.11
95.24
34.64
97.62
41.07
97.62
75.71
2.90
0.26
5.46
.97
76.19
16.67
76.19
16.67
76.19
33.33
Table 3.5 Averages fertilizer use (kg) in major cereals (only from fertilizer appliers)
Variables
All major cereals
Barley
Maize
Sorghum
Teff
Wheat
N
99
8
19
15
41
16
Dap
34.12
17.81
42.11
42
35.49
21.88
Urea
37.35
17.81
42.11
45.33
42.07
21.88
Dap+Urea
71.46
35.63
84.21
87.33
77.56
43.75
Source: own survey, 2013
Table 3.6 Descriptive statistics production function
Variable
Value of farm output (kg)
Value of farm yield (kg)
Observation Mean
131
3159.893
131
8030.101
Fertilizer (Dap + Urea) in kg
Dap (kg)
Urea (kg)
131
131
131
54.00763
25.78244
28.22519
Std. Dev. Min Max
3127.039 300 27000
4635.4
1000 28000.
01
50.1207
0
300
22.12744 0
100
30.4667
0
250
Source: own computation from survey data, 2013
Table 3.7 Pair wise correlation coefficients of Dap and Urea use (in kg) with the
production volume of major cereals (in kg)
Crop type
Dap
Urea
Urea + Dap
p-value
Barley
-0.153
-0.153
-0.153
0.5854
Maize
0.707***
0.72***
0.714***
0.0000
Sorghum
0.49**
0.493**
0.5**
0.02273
Teff
0.543***
0.873***
0.843***
0.00007
Wheat
0.167
0.167
0.167
0.468
All cereals
0.504***
0.779***
0.696***
0.0000
Source: own survey 2013
*** And ** implies significant at 1%, 5% probability level, respectively
40
Table 3.8 OLS regression results of a crop value of production function
Explanatory Variables
Coefficients
Robust Std.err
Fertilizer in kilogram (log)
0.32**
0.14
Age of house hold head (year)
0.004*
0.005
Sex of house hold head(1 if male)
0.1
0.13
Household size in number (log)
0.393*
0.231
Labor force in man equivalence (log)
0.054**
0.224
Tropical Livestock units in TLU (log)
0.261*
0.132
Land in hectare (log)
0.647***
0.146
Education level of head (1 if head can write and read)
0.033*
0.02
Irrigation (1 if plot has access)
-0.101
0.176
Extension (1 if head has access)
0.041
0.189
Credit (1 if head has access)
0.005
0.125
Off-farm income (1 if head had access)
0.002
0.097
Lemteuf (1 if the plot’s fertility is poor)
-0.365**
0.18
Dagetama (1 if the plot is mountainous)
-0.282
0.256
Maize (1 if the plot is cultivated maize)
-0.333
0.263
Fertilizer*kolla (interaction variable)
-0.007**
0.003
-cons
7.582***
0.788
No of observations
99
F(16, 82)
16.58
Prob>F
0.0000
2
R
76.28
66.80
Dependent variable: ln(value of output)
***, ** and * implies significant at 1%, 5% and 10% probability level, respectively
Table 3.9 Agricultural productivity enhancing mechanisms and extent of utilization
Type of activity
Soil conservation measure
Irrigation
Compos and manure
Extension visits
Access to Credit
Access to Off-farm
Source: own survey 2013
Total size of plots
131
131
131
131
131
131
Total plots involved under the activity (%)
81.68
19.85
50.38
81.68
67.94
44.27
Table 3.10 Main problems of fertilizer application as per the recommendation
Type of problem
High price
Farmers understanding:
“use of high fertilizer burn crop and reduce soil
fertility”
Shortage of supply
Late arrival
41
Number
complain
63
35
3
5
of Percentage
48.09
26.72
2.29
3.82
2
15
8
131
Lack of credit
No problem
Others
Total
Source: own survey 2013
1.53
11.45
6.11
100
Table 3.11 Results of Probit Model: Factors Affecting Probability of adoption
Explanatory Variables
Estimated Coef.
Robust std.err
0.035*
0.019
0.003
Age
-1.304
0.554
-0.065
Sex
0.541***
0.169
0.043
Hhsize
0.569***
0.216
0.045
Lf
0.108*
0.06
0.009
Edu
-0.699
0.613
-0.056
Plotarea
0.211*
0.124
0.017
Tlu
-0.48***
0.187
-0.038
Plots
4.643***
0.574
0.973
Ext
-0.848
0.562
-0.040
Donk
-1.435***
0.398
-0.114
Chem
-0.05***
0.018
-0.004
Hmplot
-0.029***
0.011
-0.002
Hmnt
0.737*
0.441
0.077
Credit
-1.811***
0.427
-0.182
Compos
0.64*
0.385
0.053
Seed
-0.029**
0.154
-0.002
Qseed
1.021**
0.444
0.093
Varity
0.618**
0.301
0.049
Lemteuf
-0.266
1.351
Constant
131
No of observation
-16.8829
Log likelihood
111.90
Wald Chi2(19)
0.0000
Prob>chi2
2
0.7682
Pseudo R
2
No of iteration
***, ** and * implies significant at 1%, 5% and 10% probability level, respectively
Table 3.12 Results of Truncated model: Factors Affecting Intensity of use of fertilizer
Explanatory Variables
Estimated Coef.
Robust std.err
Age
Sex
Hhsize
Lf
Edu
Plotarea
-0.314
1.057
4.831
11.601***
2.755*
57.537***
0.392
11.58
3.276
4.534
1.442
12.471
42
-0.314
1.057
4.831
11.601
2.755
57.537
Tlu
Plots
Ext
Donk
Chem
Hmplot
Hmnt
Credit
Compos
Seed
Qseed
Varity
Lemteuf
Constant
No of observation
Log likelihood
Wald Chi2(19)
Prob>chi2
Pseudo R2
No of iteration
10.426***
-14.67***
-1.204
0.626
22.535***
-0.264
-0.454***
-11.446
14.944
-9.412
1.006***
-10.754
17.864***
-8.411
3.466
4.332
24.744
15.184
8.122
0.27
0.171
10.243
9.964
7.571
0.294
7.843
5.393
36.175
99
-463.3930
132.54
0.0000
4
10.426
-14.67
-1.204
0.626
22.535
-0.264
-0.454
-11.446
14.944
-9.412
1.006
-10.754
17.864
***, ** and * implies significant at 1%, 5% and 10% probability level, respectively
43