Assessment of Factors That Affect the Performance of

Agricultural Production, in the Case of Amhara Region,

Ethiopia
Melkamu Ayana Zeru
Department of Statistics, Haramaya University, Harar, Ethiopia.
DOI: 10.4236/as.2018.98073 PDF HTML XML 1,242 Downloads 3,122 Views
Citations

Abstract
The agricultural sector is the basis of livelihood for a large proportion of society in
Ethiopia. In the three political regimes in modern Ethiopia, the Imperial, the military and
the Ethiopian people revolutionary democratic front (EPRDF), agriculture has been
regarded as a critical sector. The Agricultural Development Led Industrialization (ADLI)
is the national policy of the country. Regardless of the government policy attention and
investment, there is a long way to go for smallholders to ensure food self-sufficiency.
Agriculture is the base of our food, transformation to industrialization, climatic change
control system. Agriculture is the soul of our sovereignty for development as well as
poverty reduction for individuals and country level. In Ethiopia, population density is
high and has been increasing and agricultural land has been decreasing because of
fragmenting or converting it into residential plots. To meet the domestic food
requirements, use of improved production technologies developed by research is come
out to be important. Therefore, the goal of this study was to analyze factors that affect
the performance of agricultural products in Amahara region national state and to
determine the highly significant input factors for producing high and qualified
agricultural outputs. Data regarding total agricultural outputs and its input factors in
study area of Amahara region from 2010 to 2018 was obtained from Amhara national
state branch of the Ethiopian institute of agricultural sector. Correlation analyses were
used to examine the strength of the relationship between each of the determinant
factors with total agricultural output, while multiple regression analysis was employed
to examine the simultaneous effects of several independent variables on the dependent
variable, total agricultural outputs. These analyses were employed through the
packages R and Stata to achieve the main objectives of the study. All of the
independent variables were highly correlated with the total agricultural output. The
overall regression model was highly significant (p-value < 0.01) with F = 45.532. The R-
squared value implies that 93.8% percent of the changes in average total agricultural
outputs are successfully explained by the variables used in the model of this study. If
we take model size into account, 91.8% percent of the variation in average total
agricultural output was explained by the values of the independent variables.
Specifically, among the independent variables irrigated land, fertilizer, improved seed
and pesticides are the most significant factors for total products (p-value < 0.05).

Keywords
Agriculture, Assessment, Total Output, Determinant Factors

Share and Cite:

FacebookTwitterLinkedInSina WeiboShare
Zeru, M. (2018) Assessment of Factors That Affect the Performance of Agricultural
Production, in the Case of Amhara Region, Ethiopia. Agricultural Sciences, 9, 1058-1069.
doi: 10.4236/as.2018.98073.
1. Introduction
Nodaway, Agriculture is the base of our food, transformation to industrialization,
climatic change control system. Agriculture is also the base the society for development
as well as poverty reduction for individuals and in country level. The current
government of Ethiopia is highly involved in the agricultural sector and, through its
developmental state theory, has put the highest level of investment into the sector [1].
The agricultural production in our country come from both in private peasant holdings in
large as well as in medium commercial farms, where the commercial farms consist of
the state and private investment commercial frames [2]. Ethiopia is one of the largest
African countries with a population of 73.9 million people. The country shares bound-
arise with Eritrea to the north, Kenya to the south, Somalia to the east and Sudan to the
west. According to the Central Statistical Agency [3] of Ethiopia, the majority (83.8%) of
Ethiopians reside in the rural areas. Hence, subsistence and rain-fed agriculture is the
economic base and means of livelihood of the majority of these people. The contribution
of agriculture to growth domestic product in Ethiopia is above the average contribution
of Sub-Saharan Africa. The share of the agricultural sector in Sub-Saharan Africa is
around 40 percent [4]. On the other hand, the contribution of the agricultural sector to
GDP in Ethiopia is 41 percent [5]. In Ethiopia, population density is high and has been
increasing and agricultural land has been decreasing because of fragmenting or
converting it into residential plots. To meet the domestic food requirements, use of
improved production technologies developed by research is come out to be important.
Agriculture in our country is the foundation of the economy. The structure of this
economy is characterized by an overall whelming depends up on agriculture. The
contribution of agriculture to gross domestic product (GDP) is quite high about 40% of
GDP in our country and the primary role of agriculture was to provide sufficient and low
priced food as well as man power for the expanding of industrial economy which was
through out to be the dynamic leading sector in any overall strategy of economy
development [6].
Investment is one of the means of achieving agricultural development for the given
appropriate strategy and favorable policy environment and the most volatile micro
economic variable in which trying to achieve economic growth and development. We do
not have strong observed growing faster than economic with strong state intervention
[7]. In the last decade per capital agriculture production in Amahara is 5.6% while
population growing at 3% per annual and hence food production has been below
demand and it result in chronic food deficit and this poor performance of agricultural
sector has an impact on the other sector of the economy [8].
Rapid population growth aggravates the problem of agricultural performance in the
society where agricultural inputs like labor force, fertilizer, improved seed and
pesticides are limited. Here is a considerable body of literature that favors the idea that
agricultural growth serves as an engine of growth and that irrigation-led technological
changes are the eye drivers behind the growth of productivity in the agriculture sector
in Asia [9].
But, in Ethiopia, the ultimate goal of Agricultural Development Led Industrialization
(ADLI) strategy is for the industry to take the lead.
Due to these limitation land productivity is stagnating, technological development is at
rudimentary stage, and resource distributions are quite uneven.
Economists traditionally have analyzed the agricultural development in terms of its
relationship to the growth of overall economy. While the physiatrists, Viewed agriculture
as the engine of growth angling surpluses large enough to stimulate growth order sector
of economy. On the other hand the classical economists believed that the diminishing of
marginal returns to agricultural land would eventually lead to overall economic stag-
nation or the stable condition [10]. As the majority of the population is engaged in the
agricultural sector, it is the critical sector for the future. To show the importance of
agriculture [11] stated that it is in the agricultural sector that the battle for long-term
economic development will be won or lost. In the act of output in agricultural production
uses a Variety of goods and services called factors (determinants) out puts. Past studies
have already demonstrated that there are a number of factors that might
simultaneously have contributed to agriculture growth in the world, the same as in
Ethiopia. This factor includes labor force, irrigated land, improved seeds, fertilizer,
pesticides and other related factors. Workers react on incentives, created by the
institutional and economic environment, by changing their labor effort and thus the
intensity of production factors to count for the different factors that affect production
out-put, different production function shifters are include in the model [12] and [13].
Finally, Macours analyzed that the output is highly depends on labor force, temperature,
irrigated land and improved seed but fertilizer is the insignificant variable. Among these
variables the labor force and temperature has negative relationship with the output
production while improved seed and irrigated land has positive or direct relationship
with that of the output production. In some countries the evidence of an inverse
relationship between farm irrigated land and its productivity is more mixed than
elsewhere in our country. This is partly a result of imperfect policy distortions and also
that the discrimination against smallholder farmers. Large farms have often been more
pro table due to frequently occupy land of high quality, have better access to credit an
extension services and use more non labor inputs, such as fertilizers, pesticides, high-
yield seed varieties, and irrigation land.
The goal of this study was to analyze factors that affect the performance of agricultural
products in Ethiopia particularly in Amhara regional state. The state of Amhara is
located in the north western and north central part of Ethiopia. The State shares
common borders with the state of Tigray in the north, Afar in the east, Oromiya in the
south, Benishangul/Gumuz in the south west, and the Republic of Sudan in the west.
Amhara is topographically divided into two main parts, namely the highlands and
lowlands. The highlands are above 1500 meters above sea level and comprise the
largest part of the northern and eastern parts of the region while the lowland part
covers mainly the western and eastern parts with an altitude between 500 - 1500
meters above sea level. The annual mean temperature for most parts of the region lies
between 15˚C - 21˚C. The State receives the highest percentage (80%) of the total
rainfall in the country. The highest rainfall occurs during the summer season, which
starts in mid-June and ends in early September.
This study was basically conducted in order to determine the highly significant input
factors for producing agricultural outputs and also to determine the type of relationship
exist between the dependent and the predictor variables in the case of Amahara
National state. In the process data regarding total agricultural outputs and its input
factors with study, from 2010 to 2018 was obtained from Amahara state of the
Ethiopian Institute of Agricultural Sector. Correlation analyses were used to examine the
strength of the relationship between each of the determinant factors with total
agricultural output, while multiple regression analysis was employed to examine the
simultaneous effects of several independent variables on the dependent variable total
agricultural outputs. These analyses were employed through the packages R and Stata
to achieve the main objectives of the study.

2. Methodology
The study design that were employed was specific time data based on the number of
out relines from (2010-2018). The area where the study would cover was Amahara state
and data used for this study were obtained from Amahara National state Agricultural
 office, Northern part of Ethiopia. In this study, the dependent variable was the variable
that measures the performance of agricultural products in the case of Amahara National
State and Agricultural output which is expressed in terms of quintal per hectare was
used as the response variable.
Also, five potential explanatory variables were considered in this study. The descriptions
of these covariant are presented in Table 1 below.
S/N Variable Description
1 GDP Agricultural output expressed in terms of quantal per hectare
2 Fertilizer The amount of fertilizer used per hectare
3 Labor force The number of individual person used for work
4 Improved seed The type of seed used
5 Irrigated land The size of land which was irrigated
6 pesticides The amount of pesticides in litter per hectare to protect disease
Table 1. List of study variables.
Method of Data Analysis
Pearson correlation coefficient measures the linear relationship between the dependent
variable and the independent variables. The correlation between any two variables
always lies between −1, 1 and 0. If r is −1, this figure indicates that the dependent and
the in-dependent variables have absolutely negative relationship while r is 1 showing
the existence of absolute positive relationship. But correlation coefficient of 0 implies
that there is no any linear relationship between dependent and independent variables.
Regression analysis consists of techniques for modeling the relationship between a
response variable and one or more explanatory variables or predictors and it answers
questions about the dependence of a response variable on one or more predictors,
including prediction of future values of a response, discovering which predictors are
important, and estimating the impact of changing a predictor or a treatment on the
value of the response. Consequently, this leads to use the multiple linear regression
model in order to achieve the main and specific objectives of the study. Many methods
have been developed to determine various parametric relationships between response
variable and independent variables. These methods typically depend on the form of
parametric regression function and the distribution of the error term in a regression
model. Generally, most regression models are written as a function of predictor
variables and random error.
Yi=f(x1,x2,⋯,xk)+εiYi=f(x1,x2,⋯,xk)+εi(1)
where k is the number of repressor’s that are involved in the regression model, Y i is the
additive random error term and Xi=(x1,x2,⋯,xn)Xi=(x1,x2,⋯,xn) is a function that
describes the relationship between y and x1,x2,⋯,xkx1,x2,⋯,xk.
The parameters need to be estimated so that the model gives the best t to the data.
These parameters are estimated based on least squares method principle. In this study
the least squares principle was utilized to derive estimates of the regression
parameters. Suppose that n ≥ k observations are available, and let Y i denote the
ith observed response and Xi denote the ith observation or levels of repressors Xi. Then
the simple linear regression model of our study becomes:
yi=β0+∑i=1n∑j=1kβjxij, k=1,2,3,4,5yi=β0+∑i=1n∑j=1kβjxij, k=1,2,3,4,5(2)

Y=⎡⎣⎢⎢⎢⎢⎢y1y2⋮yn⎤⎦⎥⎥⎥⎥⎥Y=[y1y2⋮yn], X=⎡⎣⎢⎢⎢⎢11⋮1x11x21⋮x51x12x22⋮x52⋯
where,

⋯⋯x15x25⋮x55⎤⎦⎥⎥⎥⎥X=[1x11x12⋯x151x21x22⋯x25⋮⋮⋮⋮1x51x52⋯x55],
β=⎡⎣⎢⎢⎢⎢β0β1⋮βk⎤⎦⎥⎥⎥⎥β=[β0β1⋮βk], ε=⎡⎣⎢⎢⎢⎢ε1ε2⋮εn⎤⎦⎥⎥⎥⎥ε=[ε1ε2⋮εn]
The least squares principle for the multiple linear regression model is to find the
estimates of the regression parameters such that the least squares function is small as
possible. This least square regression function is determined by:
S(β)=∑ε2iS(β)=∑εi2
S(β)=ε′εS(β)=ε′ε
S(β)=(y−xβ)t(y−xβ)S(β)=(y−xβ)t(y−xβ)(3)
Using the concepts of calculus (matrix differentiation), the value of that minimizes the
function S(β) is (xtx)−1xty(xtx)−1xty. Thus, the least square estimator of β is given by:
βˆ=(xtx)−1xtyβ^=(xtx)−1xty(4)
with the variance-covariance matrix of var(β)=δ2(xtx)−1var(β)=δ2(xtx)−1.
All statistical procedures and results extracted from such analysis are valid and have
meaning only if the standard regression assumptions (linearity, non-stochastic
repressors, no multi-linearity, zero mean error, no autocorrelation and normality) are
satisfied. When these assumptions are violated, the standard results quoted do not hold
and an application of them also lead to serious error. In addition, gross violations of the
assumptions lead to an unstable model in the sense that a different sample could lead
to a totally different models with opposite conclusions. The regression assumptions
should be checked before drawing statistical conclusions from the analysis because the
validity of these statistical procedures hinges on the validity of the assumptions. These
assumptions are diagnosing using plot of standardized residuals against each of the
predictor variables should be a random scatter points in the test of linearity, Plots of the
standardized residuals against the predictors or against the fitted values are not only
helpful to study whether a linear regression function is appropriate but also to examine
whether the variance of the error terms is constant and using the a formal test of the
Breusch-Pagan test for detecting the assumption of homoscedasticity. The most
common measure of collinearity is the variance inflation factor for the j th regression
coefficient VIFj. The link between VIFj and collinearity (of the standardized and centered
variables) is through the relationship
VIFj=11−R2jVIFj=11−Rj2(5)
If R2jRj2 equals zero (i.e., no correlation between x j and the remaining independent
variables), then VIFj equals 1. This is the minimum value of variance inflation factor. A
VIF value greater than 10 is an indication of potential colinearity problems [14] and also
the normality of residuals can be measured. A very simple method of checking the
normality assumption is to construct a normal probability plot of the residuals.
Substantial departures from a straight line indicate that the distribution is not normal.
Furthermore, normality of residuals can be ensured by the Shapiro-Wilk normality test.
Whenever data are obtained in a time sequence or some other type of sequence, such
as for adjustment geographic areas, it is a good idea to prepare a sequence plot of the
residuals. When the error terms are independent, we expect the residuals in a sequence
plot to fluctuate in a more or less random pattern around the base line 0. On the other
hand, if we see a clustering of neighboring residuals on one or the other sides of the line
= 0, then such clustering is a sign that the errors are auto correlated. Lack of
randomness can take the form of too much or too little alternation of points around the
zero line. Plot the partial autocorrelation function (PACF) of ordinary least square (OLS)
residuals is also one way of detecting the presence or absence of autocorrelation. All
the bars should be within the confidence band if each residual is not predictable from
the one preceding it. If the function at lag one is outside the 95% upper or lower
confidence limits, then this is an indication that the errors follow the AR(1) process.
Higher order error processes can be detected similarly. So the independence
assumption may or may not be satisfied and to ensure this we can use Durbin-Watson
(DW) test when the process is AR(1) and Breusch-Godfrey (BG) Test for higher order
process.

3. Result and Discussion

The total agricultural products consist of 9 subsequent year’s data from 2010 up to
2018. From this the statistical analysis results are presented in the graph below.
From Figure 1, we observe that the total product increases sharply from

Figure 1. Production outputs in subsequent years.

2010 to 2018. In half of 2011 the total products starts to decline and reaches minimum
point in 2013, this is basically due to different reasons like change in policy in
government, instability of country in poetics, draught and hence the agricultural sector
cannot increase the output productions.
But after 2013 Starting from 2014 the total product starts to increase continuously
throughout the year with a small decrement within the given years. From this we can
understand that the total product is varying in different year. This variation is due to the
use of different amount of basic input factors in different years (like improved seed,
fertilizer, and irrigated land size). Examination of the results do not reveals any series
violations of the assumptions. Thus, the usual interpretations of regression analyses are
valid since all assumptions concerning the data were met.
Based on the shapiro-wilk normality test (W = 0.9479) with a large p-value (p =
0.3106), shows the residuals satisfy the normality assumption. Furthermore, we can see
that the graph of normal Q-Q plot (the theoretical quintile’s against the sample
quintiles’) from Figure 2, the residual observations lie nearly on the straight line of the
graph or no any observation far from the line which implies that the normality
assumption is fully satisfied. Thus this data are used for model building and for further
statistical analysis as well as for future prediction purpose, because the violation of
normality assumption is the basic question and problem of data analysis. The formal
test of the studentized Breusch-pagan (BP) is 3.8432 with p-value = 0.5722 which is
greater than (α = 0.05) and plot of fitted values against the standardized residuals
(there is no any systematic patterns), indicates the existence of homoscedasticity
(constant variance), in other word the residuals has no any systematic patterns.
Since VIF is a measure of how much the variance of the estimated regression coefficient
β is inflated by the existence of correlation among the predictor variables in the model.
A VIF of value less than 10 means that there is no correlation among the k th predictor
and the remaining predictor variables, and hence

Figure 2. Normal Q-Q plot.

the variance of β is not inflated at all. From the result of Table 2, the VIF values for each
variable are less than 10 indicating the non-existence of multi-collinearity. As the result
of this we conclude that each of the independent variables are not correlated or no
multi-collinearity among the explanatory variables.
In regression, this partial correlation could be found by correlating the residuals from
two different regressions: one is we predict y from x 1, x2, x3, x4 and x5, and secondly, we
can also predict x3 from x1 and x2. Basically, we correlate the parts of y and x 3 that are
not predicted by x1 and x2. According to Figure 3, all of the lags are found within the
confidence limit (between −0.5 and 0.5). This leads to the conclusion that the error
terms are not serially correlated or the assumption of no auto correlation between the
error terms is satisfied. In this study the lag value is statistically significant, which
suggests errors follow the AR(1) model for these data with no correlation.
After the overall test of model assumptions, the multiple linear regression model of the
total output production over the significant explanatory variables was modeled based
on the estimated values of the parameter, based on the individual t-test for the
significance of the individual predictor variables, based on the statistical results of Table
3.
The fitting linear regression model that relating output product with the explanatory
variables is given as:
Y=−219625.734+46.882x1+94.03x2+21.63x3−151.119x4+0.427x5Y=−
219625.734+46.882x1+94.03x2+21.63x3−151.119x4+0.427x5
where x1, x2, x3, x4, and x5 are the effect of pesticides, improved seed, labor force,
fertilizer and irrigated land respectively. The model parameters were interpreted
 Figure 3. Partial autocorrelation function.
Variables Pesticides Irrigated land Improved seed Labor force Fertilizer
VIF 4.747158 6.395043 2.100014 1.170496 1.393434

Table 2. Variance Inflation Factor of the explanatory variables.

Effect Estimate Std. error t-value p-value
Intercept −219,625.734 1,996,540 −0.11 0.289
Pesticides 46.882 21.221 2.209 0.043
Improved seeds 94.030 34.762 2.705 0.016
Labor force 21.630 47.777 0.452 0.657
Fertilizer −151.119 43.682 −3.460 0.00
Irrigated land 0.427 0.100 4.27 0.001
Table 3. Individual t-test of each of the predictor variables.
R-square: 0.938.
as follows. The figure β1 = 46.882 indicate that when the use of pesticides increased by
one litter per hectare the total product is also increased by 46.882 quintal where other
effects are held constant. The figure β2 = 94.03 also shows keeping other input factors
constant, the total output production is increased by 94.03 quintals for every one
quintal addition of improved seed. Similarly, for a unit increase in one hectare of
irrigated land, the total agricultural product will increased by 0.427 quintal in similar
way, β4 = 151.119 reflects when the use of fertilizer increased by one quintal the total
product decreased by 151.119 quintal. In fact this is not surprising because the used
fertilizer might not be comfortable with the soil and it may reduce the total production
output. Furthermore, the amount of used fertilizer might be excess (dose) and hence it
may reduce the total production due to the acidic behavior of the fertilizer. The
coefficient of determination is 0.938 from Table 3, shows that about 93.8% of the
variation in the total output is explained by the explanatory variables, labor force,
irrigated land, fertilizer, improved seed and pesticides while the remaining 6.2% of the
variation is explained by other related factors like type of soil, temperature etc. If we
take model size into account, 91.8% of the variation in average total agricultural output
was explained by the values of the independent variables.

4. Conclusion
The explanatory study assessed the determinant factors for the performance of
agricultural output production in Amahara regional state of the country, to make
relevant recommendations about how other nonagricultural sectors transformed to
agricultural sector. The discussion is centered on the most important findings in the
importance of labor, the size of the irrigated land, fertilizer, improved seed and
pesticides on the total output production. The econometric result analysis reveals that
the relation-ship between fertilizer and output, labor force and output, irrigated land
and output, pesticides and output as well as improves seed and output. Among this
relations, irrigated land, pesticides, fertilizer and improved seed had a significant
relation to output products with positive effect while has fertilizer had negative effect
because of the soil in Amahara region has organic fertilizer, this is why fertilizer has
negative effect on output production. Finally this study reveals the most important
factors for agricultural output are: Irrigated land, fertilizer, improved seeds and
pesticide were significant factors for agricultural out ensuring food security is a
challenging endeavor, because of crop production of the region was affected by shallow
soil which was very low in inorganic matter and extremely de deficient in nitrogen and
phosphors. The average crop productivity (quintals/hectare) of the region is lower than
the national average. In addition, the annual food grain consumption of the region is
below the minimum daily requirement of 2400 calories per person. There are also many
households in the list of the Productive Safety Net Program (PSNP) who are unable to fill
the annual food requirements of their families.

Acknowledgements
The author is grateful to the Editor of Agricultural Sciences (AS) in Scientific Research
Academic Publisher and anonymous referees for their helpful comments and
suggestions on the earlier version of this article.
Conflicts of Interest
The authors declare no conflicts of interest.
References
[1] Lefort, R. (2012) Free Market Economy, Developmental State and Part State Hegemony
in Ethiopia: The Case of the Model Farmer. Journal of Modern African Studies, 50, 681-
706.
[2] Centeral Statistical Autority (2007) Crop Production Sample Survey Statistics Bulletin.
[3] Centeral Statistical Autority (2008) Summary and Statistical Report of the 2007
Population and Housing Census: Population Size by Age and Sex. UNFPA, Addis Ababa,
Ethiopia.
[4] Barrios, S., Strobl, E. and Ouattara, B. (2008) The Impact of Climatic Change on
Agricultural Production: Is It Different for Africa. Food Policy, 33, 287-298.
https://doi.org/10.1016/j.foodpol.2008.01.003
[5] MoFED (2012) Sustainable Development and Poverty Reduction Program. Addis Ababa,
Ethipia.
[6] Tadaro, M. (1995) Economic Development. 5th Edition, New York University,
Singapore.
[7] World Bank (1996) From Plan to Market—Executive Summary (Russian). World
 Development Report, World Bank Group, Washington DC.
[8] Minsrty of Planining and Economic Development (1998) Trends on Development and
Annual Report on Macroeconomic Development in Ethiopia.
[9] Hussain, I. and Hanjra, M.A. (2004) Irrigation and Poverty Alleviation: Review of the
Empirical Evidence. Irrigation and Drainage, 53, 1-15.
https://doi.org/10.1002/ird.114
[10] Echer and Staatz (1998) International Agricultural Development. 3rd Edition, The Johns
Hopkins University Press, Baltimore.
[11] Todaro, M. (2000) Economic Development. 7th Edition, Addison Wesley Inc., England.
[12] Liebenstcin, H. (1996) Allocative Efficiency vs. X-Efficiency. American EcOM.
[13] Carter, M. (1984) Issues in the Hidden Economy—A Survey. Economic Record, 60, 209-
221.
https://doi.org/10.1111/j.1475-4932.1984.tb00856.x
[14] Hair, J. and Anderson, R. (1995) Multivariate Data Analysis. 3rd Edition, Macmillan,
New York.