P119 Berisso an Analysis of the Impact of Climate Variation Ethiopia

7/25/2019 P119 Berisso an Analysis of the Impact of Climate Variation Ethiopia

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An Analysis of the Impact of Climate Variation on Cereal Crops

Productivity in Central Ethiopia

Oumer BERISSO

Department of Economics,

College of Business and Economics,

Addis Ababa University, Ethiopia

E-mail: [email protected]

March 12, 2016

Abstract

This study investigates the impacts of climate variation on cereal crop productivity over a period

of 15 years in Ethiopia. Consistent with previous productivity studies in sub-Saharan Africa, the

study confirmed the importance and strong dependence between most of the climate related

variables and cereal crops productivity. Descriptive statistics show that average annual rainfall,

and maximum temperature decrease over time in all three agro-ecological zones considered in

the study. Panel data estimation results indicated which inputs significantly enhance cereal crops

productivity and which ones including climate variables (temperature and rainfall) influences

cereal crops productivity negatively. Moreover, regression result shows evidence of agro-

ecological differences and crop productivity regress over time. These findings are important and

can be used to initiate different government policy options when planning climate change

adaptation strategies and agricultural policies tailored to support various agro-ecological zones

across the country. The study recommends that policies that would improve extension services,

farmers education, supply of agricultural production inputs and developing climate change

adaptation strategies suitable designed to meet the needs of different agro ecological zones

should be pursued.

Keywords: Climate variation; cereal crops productivity; agro-ecological zone; panel data; rural

Ethiopia;

JEL Classification Codes: D24;O13; O33;O47;Q12; Q54


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1. INTRODUCTION

Research findings on climate change have revealed that climate variability and change have

significant impacts on global and regional food production systems particularly on the

productivity of common staple food crops in the tropical sub-humid climatic zone (UN-

OHRLLS, 2009). This results in reduced food production, leading to higher food prices and

making food less affordable for poor people and consequently to food insecurity by affecting the

four key dimensions of food security; i.e. food availability, access, stability and utilization

dimensions.

Ethiopia is a highly populated agrarian economy in Africa; dominated by subsistent farmers

making it one of the most vulnerable countries to climate variability in the continent. Agriculture

is an important sector in Ethiopia, as it directly provides employment and livelihood to more than

83% of the population, contributes to about 85% to total export earnings and 46% to GDP,

supplies around 73% of the raw material requirement of agro-based domestic industries (AfDB,

2011). It is also the major source of food for the population and hence the prime contributing

sector to food security. However, the countrys crop production is rainfall dependent, being

produced by small holders and subsistent farmers who have less capacity to adaptation of climate

change; who usually cultivate land areas of less than 1 hectare and collectively account for

approximately 95 % of the countrys agricultural production (FAO, 2009).

Ethiopias economy and ecological system are fragile and vulnerable to climate change. The

country is characterized by diverse topographic features that have led to the existence of a range

of agro-climatic zones each with distinctly variant climatic conditions, such as: Lowland,

Midland, and Highland. Among these zones, the lowland that receives the lowest and most

erratic rainfall rates, notably the Central Rift Valley (CRV) region, experiences frequent natural

hazards such as sudden flooding, recurrent droughts and chronic water stress that are aggravated

by climate change and its variability.

Ethiopias agricultural sector, with cereals as major food crops, is especially vulnerable to the

adversities of weather and climate change and is characterized with poor productivity. Cereals in

Ethiopia are particularly important to the countrys food security being a principal dietary staple

for most of the population, comprising about 2/3 of the agricultural GDP and 1/3 of the national


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GDP and source of income for the majority of the people. The country is one of the largest cereal

producers in Africa, yet it is a net importer to meet the populations food demands. Different

cereals are grown in different geographic areas; the primary cereals grown in Ethiopia are teff,

maize, sorghum, barley, wheat and millet. According to, Kassahun (2011) cereals production has

a share of more than 80% of area and 86% of crop production in the country. More than 12

million private smallholders have engaged in the production of crop agriculture.

According to the recent five-year Growth and Transformation Plan (GTP, 2010) of the Ethiopian

government, Ethiopia annually loses 2% to 6% of its total production due to effect of climate

change. To tackle this, the Government of Ethiopia has set the Growth and Transformation Plan

(GTP) and Climate-Resilient Green Economy (CRGE) strategy as parts of its active agricultural

policy. The CRGE focuses on certain critical natural resource endowments and challenges

(climate change, food security, energy, etc.). The government has set a key goal to increase

agricultural output, and to ensure food security in Ethiopia. Such a plan needs to be accompanied

by a continuous research work. Hence in this mixed farming dominated country, it is important

to assess the impact of climate change on crop productivity and efficiency and its implication to

the countrys food security under varying climatic conditions.

This study is expected to contribute to the existing literature regarding climate change issues, and

is designed to bridge the gaps identified. A brief survey of the stochastic production frontier

literature shows that several empirical works have been undertaken to investigate impacts of

climate change on Ethiopian agriculture with different methodologies (see Mintewab et al., 2014;

Zerihun, 2012; Bachewe et al., 2010; and Bamlaku et al., 2009). Some of these studies focus on

national level assessments. Nonetheless, climate change may have area-specific effects, for

example, agro-ecology based analyses may provide a better insight into the impact of climate

change. Some investigate impacts that are on single crop or two crops. Others focus only on crop

production, disregarding the role of livestock production.

In general studies of impact of climate change that include major climatic factors on cereal crop

yield and assessment of its indirect impact on food security are very scanty in Ethiopia.

Moreover, to the best of available knowledge, no study has analyzed determinants of cereal crops

productivity between agro-ecological zones in improving agricultural productivity and links to

climate change. Hence in this mixed farming dominated country, it is necessary to undertake


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more research to assess the impact of climate change and role of production factor variability in

the countrys crop production under varying climatic conditions.

Accordingly, this research is designed to bridge the gaps in the literature identified above, by

considering analysis of climate variations impact in different agro-ecological zones, on major

staple cereal crops, in main cereal crop producing regions in the country. Hence the study will

contribute to the analysis and discussion of economic impact of climate variation on productivity

in Ethiopian cereal crops production. The output of the research will provide a quantified

assessment about the impact of climate variation on productivity which has implication for the

countrys food security. Moreover, as climate change and its impact is an emerging global

concern, the output of this research will serve as supplementary information to the available

literature dealing with the impact of climate change on yield and food security.

The rest of the paper is organized as follows. Section 2 presents an overview of the literature on

impacts of climate variations on crop productivity in developing countries and Ethiopia. In

Section 3, data employed and the econometric methodologies used in the study are presented.

Section 4 presents and discusses empirical findings and Section 5 concludes the paper.

2. REVIEW OF THE EMPIRICAL LITERATURE

A considerable number of studies on impact of climate change on agricultural crop productivity

have been conducted in developing and developed countries. In particular, several empirical

works have been undertaken to investigate the impact of climate variation on Ethiopian

agriculture at different levels and with different research methodologies. In what follows, we

review the studies that have focused on developing countries in general followed by a review of

studies in Ethiopia in particular.

2.1 Impact of climate variation on crop productivity in developing countries

Liangzhi et al., (2005) investigated climate impact on Chinese wheat yield growth, using crop-

specific time series and cross-section data from 1979 to 2000 for twenty-two major wheat

producing provinces in China and the corresponding climate data such as temperature, rainfall,

and solar radiation during this period. They found that a 1 percent increase in wheat growing

season temperature reduces wheat yields by about 0.3 percent. They reported also rising


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temperature over the two decades prior to their study accounts for a 2.4 percent decline in wheat

yields in China while the majority of the wheat yield growth, 75 percent, comes from increased

use of physical inputs.

Guiteras (2009) estimated the impact of climate change on Indian agriculture using feasible

generalized least squares (FGLS) estimation method. Their results suggested that climate change

is likely to impose significant costs on the Indian economy unless farmers can quickly recognize

and adapt to increasing temperatures. They reported that such rapid adaptation may be less

plausible in a developing country, where access to information and capital for adjustment is

limited.

Gupta et al. (2012) analyzed impact of climate change on crop yields with implications for food

security and poverty alleviation, using quartile regression for aggregate crop yield. The study

combined historical crop yield data on two of the major crops grown around the world, rice and

maize, with the respective temperature and precipitation data from 66 countries in the world

during1971-2002. The study found that increases in temperature and precipitation exceeding a

certain threshold can be damaging for both rice and maize yields, while increases in the

variability of the climatic variables has a greater negative effect on countries with lower yields

for rice.

Kumar and Sharma (2013) analyzed the impact of climate change on agricultural productivity in

quantity terms, value of production in monetary terms and food security in India based on

secondary data for the duration of 1980 to 2009. Their regression analysis was based on Cobb-

Douglas production type model. Their results showed that for most of the food grain crops, non-

food grain crops in quantity produced per unit of land and in terms of value of production

climate variation cause negative impact. The reported adverse impact of climate change on the

value of agricultural production and food grains indicates food security threat to small and

marginal farming households. The study also reported econometric estimation on state wise foodsecurity index which reveals that food security been adversely affected due to climatic

fluctuations.

Addai and Owusu (2014) analyzed sources of technical efficiency of Maize frmers across

various Agro Ecological Zones of Ghana, based on a panel data analysis using a stochastic


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production frontier model for a sample of 453 maize farmers. They reported that extension;

mono cropping, land ownership and access to credit positively influence technical efficiency.

High input price, inadequate capital and irregularity of rainfall are the most pressing problems

facing maize producers in the forest, transitional and savannah zones respectively.

Mulwa (2015) analyzed the overall farm efficiency, and the influence of climatic factors, and

agro-ecological zone factors on farm level efficiency in Kenya using a two stage semi-parametric

model. They used rural household survey data sets for the periods 2004, 2007, and 2010 that

were collected from 2,297, 1342 and 1313 households for the three years respectively; spread

over 24 districts in Kenya. Results indicate that farming in Kenya is highly inefficient,

recording efficiency levels of 15%, 12%, and 18% for the years 2004, 2007 and 2010. The study

reported temperature, rainfall, Standardized Precipitation-Evapotranspiration Index (SPEI),

altitude and adaptation strategies all influence farming efficiency in the country negatively and

positively and at different magnitudes.

2.2 Impact of climate variation on crop productivity in Ethiopia

Deressa and Hassan (2009) analyzed economic impact of climate change on crop production in

Ethiopia using the Ricardian model and cross-sectional data from farm households in different

agro-ecological zones of the county. They reported that climate, household and soil variables

have a significant impact on the net crop revenue per hectare. Moreover, they concluded that the

reduction in net revenue per hectare by the year 2100 would be more than the reduction by the

year 2050. Additionally, results indicated that the net revenue impact of climate change is not

uniformly distributed across the different agro-ecological zones of Ethiopia.

Bamlaku et al., (2009) investigate efficiency variations and factors causing inefficiency across

agro-ecological zones, Ethiopia using stochastic frontier analysis. They were collected data from

254 randomly selected households conducted in the three districts of East Gojjam zone. Their

stochastic frontier production function estimate revealed a mean technical efficiency of 75.68%.

They reported that a statistically significant difference in technical efficiency among agro-

ecological zones with highland zones scoring the highest leading to a rejection of the hypothesis

of no significant efficiency difference. F-test also showed a statistically significant difference in

technical efficiency among agro-ecological zones with highland zones scoring the highest


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leading to a rejection of the hypothesis of no significant efficiency difference. On the other hand,

maximum likelihood estimates indicated positive and significant elasticities for inputs such as

land, labor, draft power and fertilizer. Besides, education, proximity to markets, and access to

credit were found to reduce inefficiency levels significantly. However, neither extension visits

nor trainings on farmland management brought positive impacts in affecting the efficiency level

of farmers.

Zerihun (2012) during his analysis of the impacts of climate change on crop yield and yield

variability in Ethiopia investigated the impacts of climate change on mean and variance of crop

yields over a period of 28 years. He used a stochastic production function and estimated the

effects of rainfall on crop yields and its variations and found that the effects of the seasonal

rainfalls differ across crops and regions. His simulation results showed that the negative impacts

of future climate change entail serious damage on production of Teff and Wheat, but relatively

maize yield will increase in 2050. In addition, they reported that the future crop yield levels

would largely depend on future technological development, which have improved yield over

time despite changing climate.

Mekonnen (2012) in his analysis of climate variability and its economic impact on agricultural

crops using Ricardian approach analyzed marginal effects of temperature and rainfall on

agricultural crop productivity based on farm data generated from 174 farmers. Regressing of net

revenue, it is reported that climate, socio-economic and soil variables was found to have a

significant impact on the farmers net revenue per hectare. Their results from marginal analysis

have shown that a 1C increase in temperature during the main rainy and dry seasons reduced the

net revenue. On the other hand, a 1C increase in temperature marginally during the short rainy

and autumn seasons was found to increase the net revenue per hectare. Also it is reported that an

increasing precipitation by 1mm during the main rainy and dry seasons reduced the net revenue

per hectare.

Gebreegziabher et al., (2013) investigated crop-livestock inter-linkages and climate change

implications for the Ethiopias agriculture in broader sense using Ricardian approach in the Nile

Basin during the 2004/05 production year. They analyzed the impact of climate change and

weather variation on agriculture, on crops and livestock, both separately and taken together. The

findings suggested that warmer temperature is beneficial to livestock agriculture, while it is


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harmful to the Ethiopian economy from the crop agriculture point of view. Moreover, they

concluded that an increasing/decreasing rainfall associated with climate change is damaging to

both of the agricultural activities.

Mintewab et al., (2014) assessed impacts of weather and climate change measures on agricultural

productivity of households, measured in terms of crop revenue, in the Amhara region of

Ethiopia. They used four waves of survey data, combined with interpolated daily temperature

and monthly rainfall data from the meteorological stations. The findings showed that temperature

effects are distinctly non-linear, but only when the weather measures are combined with the

extreme ends of the distribution of the climate measures. In addition, they reported that rainfall

generally has a less important role to play than temperature, contrary to expectations for rain fed

agriculture.

Accordingly, this research has been designed to bridge the gaps in the literature identified above,

by considering analysis of climate variability and its impacts on agricultural productivity in

different agro-ecological zones, on major staple cereal crops, in main cereal crop producing areas

in Ethiopia. Hence the study has provided valuable information needed to develop agro

ecologically-adaptive strategies in response to the rising climate variation impacts in the country.

Hence, the results can be used to infer the economic implications of climate change on targeted

food crops in the country. Moreover, findings of this research would serve as supplementary

information to the available literature dealing with the impact of climate change on yield and can

play a significant role to enhance and facilitate exchange of climate knowledge and information

among local communities, field experts, policy makers and researchers.

3. DATA AND THE METHOD

3.1 Data and Study Area

This study employed four round panel data of six peasant associations (PAs) in rural Ethiopia.The data was from a panel dataset commonly called the Ethiopian Rural Household Survey

(ERHS) - a longitudinal dataset collected from randomly selected farm households in rural

Ethiopia collected in 1994, 1999, 2004, 2009, and 2014. Originally, the first four waves were

conducted by the Department of Economics at Addis Ababa University, Centre for the Study of

African Economies (CSAE)-University of Oxford, UK and International Food Policy Research


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Institute (IFPRI) in collaboration. Data collection started in 1989 on seven study sites. The 1989

survey was expanded in 1994 by incorporating other survey sites in different regions of the

country. From 1994 onwards data collection has been conducted in a panel framework. The

number of study areas was increased to fifteen with the resulting sample size totalling 1,477

households. The newly included study villages were selected in order to represent the countrys

diverse farming systems. Before a household was chosen, a numbered list of all households was

developed with the help of local PA authorities. Once the list had been constructed, stratified

random sampling was used to select sample households in each village, whereby in each study

site the sample size is proportionate to the population, resulting in a self-weighing sample. The

surveys are conducted on a sample that is stratified over the countrys three major agricultural

systems found in five agro-ecological zones.

The last round survey was extended from original sample by forming a sub-sample of the

original sample covering the said six PAs following a similar sampling strategy and comprising

495 households in 2015 by the researcher. This was implemented in collaboration with the

Department of at Economics Addis Ababa University and the Environment for Development

(EfD), at University of Gothenburg, Swedenthrough Ethiopian Development Research Institute

(EDRI). The survey sites include households in 6 PAs in two regional states (Oromia and

Amhara); regions that represent the largest proportion of the predominantly settled farmers in the

country. The 6 PAs were selected carefully in order to represent the major cereal crops producing

areas that may represent different agro-ecological zones in the regional states of the country. The

PAs are characterized by a mixed farming system, with a household having several field plots for

crop cultivation, and livestock grazing. The contents of the questionnaire that was extracted from

that of ERHS focusing only to those parts required for the intended study.

The data set is comprehensive and addresses households demographic and socio-economic

characteristics such as age, education, and households size; agricultural production inputs use

and outputs, livestock ownership; access to institutions; and ways of climate change adaptationand coping mechanism of the farmers. Moreover, important secondary data needed for the study;

location and the metrological data on climate variables mainly altitude, latitudinal, longitudinal

position of the PAs, temperature and rainfall was obtained from the Ethiopian Meteorology

Authority. It includes monthly observations from the years 1999 to 2014, collected in stations

close to the study villages. The metrological data set includes monthly and annual rainfall, and


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annual maximum and minimum temperature data that were collected by the metrological agents

from stations near to the study villages (PAs). Consequently, this analysis included a total of the

balanced 310 households consisting of 1,240 household level observations over four rounds that

were surveyed in all rounds since 1999 for six PAs.

Variables used in the analysis

The choice of study variables was guided both by review of economic theory application and

other previous empirical work on agricultural production and productivity in general in Ethiopia

and in Developing Countries. Agricultural production and productivity studies in developing

countries including those on Ethiopia showed that traditional agricultural input quantity,

technology variables like fertilizer, pesticides, machinery use and some demographic and

socioeconomic characteristics of the household affected farm production and productivity level.

However, the effect varied in time and space depending on specific situations in the study

countries/areas, making it imperative to test their effects also in Ethiopias cereal crop producers.

For this study the dependent variable; output per cultivated hectors, is measured as the total

monetary value from the different cereal crops grown on a given farm was used. Accordingly, a

continuous variable-yield in logarithm term was selected as the dependent variable. As input

variables we have used traditional agricultural input quantities, technological variables such as

quantities of fertilizer, agro-chemicals and machinery use of the household. The climate

variables are defined as average annual precipitation and average annual temperature; and a set

of regional dummy variables such as agro-ecological zones as explanatory variables. Farm

outputs are captured in kilograms per households per cereal crops in quantities. Due to

aggregation challenges, seed for different crops, and different types of damage control-

agrochemicals variables (pesticides, herbicides, fungicide and insecticides); these variables were

converted in to monetary equivalents. An equivalent conversion was done for each chemicalinput which was then summed up agro-chemicals as damage control inputs. Farm labor was

converted it into man-day equivalent (MDE) units.


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Classification of Agro-ecological Zones in Ethiopia

As indicated in Table 1 below, the agro-ecological zones in Ethiopia vary greatly in terms of

altitude, rainfall, length of crops growing period and average annual temperature. As a general

rule, the higher we move, the colder it becomes and the longer is the growing period.

Table 1: Traditional classification of Agro-ecological Zones in Ethiopia

Agro-ecological Zone

Average Annual

Rainfall(mm)

Altitude

(meters)

Average Annual

Temperature (oC)

Length of Growing

Period (days)

Upper highland (cold and

moist) 1200-2200 >3200


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It can be observed that the three agro-ecological zones are fairly represented in the study sites.

This may allow inter-regional comparisons of the results. Moreover, according to the information

obtained from the agricultural bureaus of each district, there are differences in the types of major

crops grown in each agro-ecological zone in the study area. For instance, the dominant crop

grown in lowland area is sorghum, whereas teff and barley are dominant in midland area and

highland areas respectively.

3.2. Econometric Methodology

Conceptual model

For this study we used Cobb-Douglas type production function (CDPF) called a production

model using total production value per unit land for each farm called Yield as a dependent

variable utilizing appropriate panel data for time period, 1999 to 2014. This production model

assume that climatic factors are important input factor for growth of crop (Nastis et al., 2012)

while it assumes agricultural production being a function of many variables and other physical

inputs. These include crop cultivated area, labor, fertilizer, , irrigated area, agrochemicals,

number of oxen owned, agricultural machinery use; and climate variables such as temperature

and precipitation.

Hence, we hypothesis total cereal crops production as a function of categories of explanatory

variables such as physical agricultural inputs, technology management, and climate factors; and

formulate the following functional form for this study as:

FMISCFCAALfTP Cli,,,,,)1(

Where; TP represents total cereal crops production value which is the net production. AL

represents the total number of economically active member of households in cereal crops

production, CA is total cereals cultivated land area used for cereal crops production, CF

represents the total consumption of chemical fertilizer, IS represents the total quantity of

improved seeds used, M represents the total number of machines in use in cereal crops

production, and CliF represents climate variables which are temperature and precipitation.


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Now, dividing total cereal crops production value (TP) by total cereal cultivated land area,

equation (1) will become-

FMISCFALfCATP Cli,,,,/)2(

where; TP/CA is total cereals production of per unit land or yield.

Hence, based on the above hypothesis incorporating the non-climatic factors such as household

demographic and socioeconomic characteristics (HHC), other factors like agro-ecological

variables and time as dummy variable to capture technical change; we formulated the following

general functional form of crop yield model using a panel data context based on single-equation

production model, for this study as:

iteYearAgEVHHCCliVXY tkitititit

*)exp(**)3( 0

Equation (3) can be transformed into the logarithmic form, to be written as:

it

t

tt

n

itn

k

kk

h

ithit

j

jit yearHHCAgEVCliVXY lnln)4( 0

where: ln is the natural logarithm; ),...,3,2,1(,),...,3,2,1( TttNii ; j and h indexes farm, time

period and inputs respectively; Y is total cereals production of per unit land or yield of the i-th

farm/household at time t; X is the jth

traditional agricultural input quantity and other technology

variables include the quantities of fertilizer, alsagrochemic and machinery use of the ith

household at time t.HHCs is household demographic and socioeconomic characteristics, CliVs

are climate variables including annual average rainfall, annual average maximum and minimum

of temperature during crops growing season respectively; AgEVs are a set of regional dummy

variables such as agro- ecological zones that are included to represent time-persistent factors, and

finally year is years in the panel period, as a time-variant time trend variable that will be used torepresent the factor due to technological change during these period. s, s, s,s and s are

the regression coefficient for respective variables to be estimated and itiit uc is the

composite error term in the model, which is decomposed into unobserved heterogeneity part (c i)

and the so called idiosyncratic error (u it) components.


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Equation (4) represents the log linear form of the production function model. Similar model was

used by Nastis et al., (2012) to analysis the climatic impact on agricultural productivity in Greek;

by Gupta et al., (2012) to investigate the climatic impact on rice, sorghum and millet productivity

utilizing panel in India; by Kassahun, A., (2011) to analysis the Impact of Climate Variability on

Crop Production in Ethiopia. Similar model was also used by Kumar and Sharma (2013);

Shakeel et al., (2012); and Rukhsana (2011) for similar analysis in India.

3.3 Empirical Model Specification

For empirical applications after including the major variables (climate variability factors and

production input factors); incorporating possible household demographic and socioeconomic

characteristics and agro-ecological and time dummy variables in equation (4) above, we specify

household specific cereal crops yield empirical model using panel data set as:

it

t

ttk

q

k

kn

N

n

nit

it

ititit

yearAgEVHHCAMINTAAMAXT

AAMINRF

livestockloberyield

11

it109

it87it6it5

it4it3210

)Alnln

AAMAXRFlnlnmachinerylnalsagrochemicln

seedln)fertilizerlnlnln)(ln)5(

where: AARF, AAMAXT and AAMINT are annual average rainfall, annual average maximum

and minimum of temperature in entire crop duration respectively; HHC is household

demographic and socioeconomic characteristics, and agro-ecological and time dummy variables

as explained before. s, s, s and s are the regression coefficient for respective variables to

be estimated and itis the composite error term in the model.

4. EMPIRICAL RESULT AND DISCUSSION

4.1. Descriptive Results

Descriptive statistics of the data

The descriptive statistics of the variables used in the analysis are presented in Table 3 below. It

presents the summery statistics and evolution of agricultural production output and input

variables and major characteristics of households over the period 1999-2014. Farm outputs are

captured in kilograms per farm, with a mean of 1,696.9 kilograms, minimum of 25 and

maximum 27,800 kilograms for the surveys. According to Table 3, the mean of output in


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kilogram in the sample was about 1,097.1 kilograms in 1999 which rose steadily to 2,407.3

kilograms in 2014. This shows that output increased over time. Farm average yield is also

captured in kilograms per acreage quantities, with a mean of 1,305.5 kilograms ranging from

28.6 kilograms to 10,667.7 kilograms per farm for the four waves of data. This was obtained by

using 223.06 man-days of labor per farm, although there was a wide variation, ranging from 3 to

2,546 man-days; and 188 kilogram of seed use ranging from 6 kilogram to 1,500 kilogram per

farm of seed sown on average of 1.57 hectares cultivated farm land. Fertilizer application was

minimal with an average of 103.53 kilogram of fertilizers was used per household against a mean

cultivated land area of 1.57 hectares; while they expenses on average 51.3 birr for agro-

chemicals was use per households.

Table 3: Descriptive statistics of input-output variables and some households characteristics over

1999-2014 years (N=310 farms observed each 4 years, Overall observations = 1,240)

1999 2004 2009 2014 ALL

Variable Mean Mean Mean Mean Mean

Yield 883.6 1031.4 1274.9 2032 1306

Output 1097.1 1273.7 2009.3 2407 1697

Farm size 1.49 1.48 1.74 1.58 1.57

Fertilizer 97.77 87.95 86.54 141.8 103.5

Agrochemicals 27.74 23.65 56.76 97.09 51.31

Farm labor 205.9 266.17 178.12 242 223.1

Machinery 0 41 444 157 161

Livestock 6.18 4.5 7.79 8.57 6.76

No of oxen 1.7 1.37 1.76 1.74 1.64

Number of plot 3.68 2.97 3.63 3.89 3.54

Credit amount 508 209 568 646 483

Head age 52.41 52.84 50.65 50.54 51.61

Household head educ. 3.7 4.47 6.43 4.43 4.76

Household marital status 1.92 2.04 1.91 2.02 1.97

Household family size 6.32 4.63 5.77 5.7 5.6

Source: Authors calculations.


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The average farm land holding per farm household in the sample was below two hectares. The

number of plots owned by smallholder under cereal crops cultivation, that measure land

fragmentation averaged to 3.54 with a maximum of 16 plots. The average livestock ownership

was 6.76 units (tropical livestock units) and average oxen ownership was around 1.64 that is

almost two oxen per farm household, ranging from 0 to 9 oxen per farm household.

Table 4 shows that for combined panels, the majority of farmers are males, in which male-

headed households constituted 900 (72.58%) of the total sample. The age of the household head

is an important factor as it determines whether the household benefits from the experience of

older farmers or the risk taking attitude of younger farmers. For this penal mean household age

about 51.61years of age with minimum and maximum of 17 and 103 years respectively, while

household size ranged from 1 to16 members, with a mean of approximately 6 members. The

household size between the three years appeared to be different; in which 1999 year recorded

highest family size; while 2014 averaged with less family size, reflecting a natural process by

which children exit from the household as they become older. Household size has an important

implication for agricultural labor supply and household food security issues. Large family size

could imply availability of adequate labor and more demand for household consumption.

Table 4: Descriptive statistics of dummy variables over time; 1999-2014 years

1999 2004 2009 2014 ALL

Freq. Percent Freq. Percent Freq. Percent Freq. Percent Freq. Percent

Sex 241 77.74 241 77.74 199 64.2 219 70.7 900 72.58

Agri. Ext. service 134 43.23 73 23.55 123 39.7 117 37.7 447 36.05

Credit use 150 48.39 196 63.23 175 56.5 155 50 676 54.52

Manure use 196 63.23 223 71.94 184 59.4 197 63.6 800 64.52

Crop damage 182 58.71 294 94.84 186 60 166 53.6 828 66.77


A total of 447 (36.1%) of the farmers have reported contact with extension agents but have very

few contacts with extension agents in a month, only (1.6 times on average, or 1-4 times per

month). Almost half of the sampled farmers have access to credit of which 676 (54.5%) have

access to credit that can be from formal credit Institutions informal credit sources either from

relatives, friends or local lenders. They also used low levels of credit; credit amount averaged

approximately 483 birr per farm with maximum of 4,000 birr.


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Combining the four panels, the educational level of the household head also varied over the years

with mean schooling being 5 years (Table 5). In which majority of the sample respondents, that

is 701 (56.56%) did not attained any formal education; hence are illiterate in which 512(41.3%)

of them did not attained any schooling, 38 (3.06%) some religious (Church/Mosque) schooling

and 151 (12.2%) of them attained adult literacy program participation. About 539 (43.44%) of

them have attended formal education ranging from elementary schooling to territory level

education; out of which 454 (36.6%) of them have completed primary education, 1-8; 48

(3.87%) completed junior secondary education, 9-10; 26 (2.1%) completed senior secondary

education, 11-12; 11 (0.89%) have territory schooling in which few have completed university

education.

Table 5: Descriptive statistics of households educational characteristics by schooling level

1999 2004 2009 2014 ALL

Schooling Freq. Percent Freq. Percent Freq. Percent Freq. Percent Freq. Percent

Illiterate 110 35 142 45.81 133 42.9 127 41 512 41.3

Religious School 22 7.1 6 1.94 9 2.9 1 0.32 38 3.06

Adult Literacy 1 0.3 45 14.52 76 24.5 29 9.35 151 12.2

Primary educ. 149 48 102 32.9 80 25.8 123 39.7 454 36.6

Joiner 2ry educ. 17 5.5 8 2.58 4 1.29 19 6.13 48 3.87

Senior 2ry educ. 7 2.3 6 1.94 5 1.61 8 2.58 26 2.1

Territory educ. 4 1.3 1 0.32 3 0.97 3 0.97 11 0.89

Observation 310 100 310 100 310 100 310 100 1,240 100


Comparison between the Agro-Ecological Zones

The PAs vary in a range of agro-climatic conditions (i.e., rainfall, temperature, and elevation). In

addition, we also included altitude as an indicator of elevation of the PAs. This is directly related

to the agro-ecological zones, which are mainly classified based altitude, rainfall, and

temperature. In terms of agro-ecology related variables including altitude, temperature and

rainfall during crops growing season; the study covers the area that can be classified in to three

agro-ecological zones (lowland, midland and highland) in the country.

When we look the situation of crops production output and yield across agro-ecological zones,

we found that as one move from highland zones to that of lowland zones, crop yields is found to


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decrease. Using descriptive summery Table 6 below shows that the mean of output and

productivity is higher in highland areas followed by midland agro-ecological area; while the least

output and yield is observed in lowland areas.

When we look the situation of climate variables across agro-ecological zones, as the descriptive

summery below shows for the four panels, the study area range in altitude from 1,351-2,750

meters with mean of 1953 meters above sea level. The midland agro-ecological zone occupies

the largest percentage followed by lowland and highland agro-ecological zones respectively. In

general, for the four panels, the mean of average annual rainfall is 73.9 mm that varies from

49.4-108.3 mm; while mean of average annual maximum temperature is 25.9oC that varies from

19.7-33.1oC; and mean of average annual minimum temperature is 10.4

oC, fluctuating from 4.8-

14.5oCin the study area.

Table 6: Descriptive statistics of some important farm characteristics by agro-ecological zones

for all years

Lowland Midland Highland ALL

Variable Mean Min Max Mean Min Max Mean Min Max Mean Min Max

Yield 630.9 28.6 4000 1517.4 200 10666.7 1714.5 114.3 8000 1305.5 28.6 10666.7

Output 1413.2 25.0 11000 1746.9 50.0 27800 1929.5 50.0 10000 1696.9 25.0 27800

Aarf 72.1 49.4 106.1 72.1 58.9 108.3 78.7 75.5 85.3 73.9 49.4 108.3

Aamaxt 30.6 27.2 33.1 26.5 22.9 30.0 20.0 19.7 20.2 25.9 19.7 33.1

Aamint 13.0 10.7 14.5 11.4 9.2 13.5 6.1 4.8 7.1 10.4 4.8 14.5

Observation 372 528 340 1240

Percent 30 42.58 27.42 100


Looking climate variables across agro-ecological zones; mean of average annual rainfall in

lowland agro-ecological zone is 72.1 mm that varies from 49.4-106.1mm; while mean of average

annual maximum temperature is 30.6oC that varies from 27.2-33.1

oCand the mean of average

annual minimum temperature is 13.0oC, fluctuating from 10.7-14.5

oC.Similarly mean of average

annual rainfall in midland agro-ecological zone is 72.1 mm varying from 58.9-108.3mm; mean

of average annual maximum temperature is 26.5oC that varies from 22.9-30.0

oC and the mean of

average annual minimum temperature is 11.4oC, fluctuating from 9.2-13.5

oC. For highland agro-

ecological zone mean of average annual rainfall is 78.7 mm that varies from 75.5-85.3mm; while


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mean of average annual maximum temperature is 20.0oC that varies from 19.7-20.2oC and the

mean of average annual minimum temperature is 6.1oC, fluctuating from 4.8-7.1

oC.

4.2. Econometric Regression Results

Various testing results

For the econometric result several multiple regressions models were run for selection of

appropriate penal model to estimate best fit of models. Certain variables in different models were

dropped from the regression due to high insignificant level of respective variable. Finally three

panel regression models Pooled, Random-effect and Fixed-effect regression models were used.

Random effect regression model was used to identify the agro-climatic impact on dependent

variable and the fixed effect model to identify the time effect in the data (Gupta et al., 2012).

Beside several estimation diagnoses for the econometric models were also performed.

Accordingly, the Variance Inflation Factor (VIF) was used to detect multicollinearity. The VIF

values for all the independent variables confirm that there is virtually no multicollinearity as their

specific values were less than 10 (VIF < 10). Another potential problem may be omitted variable

bias where some temperature-related variables that affect cereal crops yield but have been left

out. For this we performed the Ramsey (1969) regression specification error test (RESET) for

omitted variables. The test reveals that (Prob > F = 0.6322 > 0.05) indicates that there are no

omitted variables for this particular model; therefore, there is no need to improve the

specification of the model.

To check for the presence of unobserved household heterogeneity Breusch-Pagan Lagrange

Multiplier test was used. The result of the test reveals that there is no unobserved household

heterogeneity, as the p-value 0.3367 > 0.05. To check the quandary of the fixed and random

effect regression model estimates, Hausman specification test (Wooldridge, 2002) was used. It

test a null hypothesis that random effects estimation gives consistent and efficient coefficientsversus alternative hypothesis that random effects coefficients would be inconsistent. The result

of the test showed that fixed effects as a more efficient model against random effects; as its p-

value is less than 1 percent critical level suggesting that the random effect model is strongly

rejected. Hence fixed effect estimation will be applied in this regression analysis. Furthermore,

for heteroscedasticity test, we used a heteroscedasticity robust method. Hence as presented


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above, since the model has passed all the regression hurdles, we therefore conclude that the

model adequately fits the data.

Analysis of the estimation results

Table 8 below presents the regression results on panel data-set. In an overall view, as can be seen

from the table, the results obtained from the three models conformed well to expectation; as most

of the explanatory variables in the regression result are statistically significant and of expected

signs. Thus, the models adequately fit the data set relatively well. Moreover, the use of robust

standard errors helps the model to diminish heteroscedasticity. In particular, the Pooled and

random-effects estimates for parameters of most of the explanatory variables are significant at

the 5 percent level or below with the expected signs. The fixed effects estimates differ slightly

from that of Pooled and random-effects with some improvements and all parameters are still

significant at the 5 percent level or below for both models. Hence, after assessing the three

modes estimates we choose only to refer the random-effects results in the rest of this paper.

As shown in Table 8 below the coefficient for fertilizer use, agricultural labor use,

agrochemicals, livestock ownership measured in TLUs, number of owned oxen (animal draft

power), participation in agricultural extension services and households head education

significantly enhances cereal crops productivity level. A positive sign indicates that lack of these

physical assets would hamper the agricultural activities and hence cereal crops productivity. On

the hand coefficient for cereal sown/cultivated farm land size, agricultural farm machinery

implements and households head age negatively impacted cereal crops productivity level.

The estimated coefficients of fertilizer use are statistically significant, depicted positively

significant enhancement in productivity level values at 1 percent significance level. Therefore, an

increment in the use of modified fertilizer varieties by 1 percent will increase output hence

productivity level by 3 percent. Crop production is labor-intensive activity in Ethiopia.

Accordingly, labor availability showed positively and significantly affected cereal crops

productivity at 1 percent significance level. This indicates that an increment in labor supply by 1

percent will increase crops productivity by 6 percent. In this paper the agricultural labor

aggregation was compiled from three sources such as traditional labor sharing groups, family

labor and hired labor. Hence in this regard, in this study the effect of farm labor on cereal crops


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productivity cannot be over emphasized, as about 70 percent of our sampled households

characterized by having family size more than 4 members in the household in which more of

members were adults. In fact, the literature argues that an increase in the number of adults in the

family could increase crops productivity if the increased in this resource is devoted to crop

production.

As regards livestock ownership measured in TLUs, its coefficients were positively and

significantly associated with crops productivity at 1 percent significance level. The coefficient

indicates that an increment in livestock number by 1 percent will increase output by more than 9

percent. The positive sign for livestock ownership indicates that the availability of this asset is

essential in several respects. For instance, farmers who have livestock can sale and buy farm

inputs such as seeds, fertilizers and other chemicals, apart from smoothing their incomes and

better nourish their families with animal products such as milk and meat. They also use dung

cakes to fertilize homesteads. Besides, pack animals are used for timely transportation of the

crops to a threshing point. Since threshing is conducted using animal power, the availability of

livestock especially during peak periods is vital. It helps reduce post-harvest loses. The results in

this study are in line with the findings of several other empirical works (Abdulahi and Eberlin,

2001; Ahmed et al., 2002).

Regression result also shows that number of oxen owned has a positive and statistically

significant impact on cereal crops productivity at 5 percent significance level. This evides draft

oxen ownership is important factor for cereal crops productivity; and indicating the importance

of the lack of oxen ownership as a major constraint to productivity. Its regression coefficient

shows that 1 percent rise in number of oxen owned has increased cereal crops productivity by

about 0.12 percent.

Regression result showed use of agrochemicals as damage control has a positive and statistically

significant impact on cereals productivity at 1 percent significance level. Its coefficient impliesthat, increase in agrochemicals use by 1 percent has increased the cereals productivity level by

0.03 percent. This implies that, farmers who use agrochemicals on their crops during cultivation

are more productive compared to farmers who do not spray their farms.


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Table 8: Regression result: Impact of climatic and non-climatic variables on cereal crops

productivity. (N: Panel = 310, Overall observations = 1240)

Explanatory-

Variables

Dependent Variable: Ln Aggregate yield of Cereal Crops

Pooled Model Random effect Model Fixed effect Model

Coef. SE(Robust) Coef. SE(Robust) Coef. SE(Robust)

Ln fertilizer 0.028*** 0.01 0.028*** 0.01 0.016 0.011

Ln agrochemicals 0.028*** 0.008 0.028*** 0.008 0.022** 0.009

Ln farm size -0.292*** 0.034 -0.292*** 0.033 -0.373*** 0.037

Ln farm labor 0.061*** 0.016 0.061*** 0.016 0.045*** 0.017

Ln machinery -0.028*** 0.008 -0.028*** 0.008 -0.035 0.010***

Ln livestock 0.096*** 0.022 0.096*** 0.02 0.090*** 0.025

Ln No of oxen 0.117** 0.039 0.117** 0.039 0.104** 0.049

Agri. Ext. service 0.066** 0.033 0.066** 0.033 0.049 0.041

Households head age -0.013** 0.005 -0.013** 0.005 -0.018** 0.007

Households head age sq. 0.010** 0.005 0.010** 0.005 0.013** 0.006

Family size 0.005 0.006 0.005 0.006 0.005 0.006

Households head educ. 0.007*** 0.003 0.007*** 0.002 0.005** 0.003

Households head sex 0.049 0.036 0.049 0.035 -0.032 0.052

Ln annual ave. rainfall 0.422*** 0.084 0.422*** 0.08 0.355*** 0.111

Ln annual ave. temperature -1.736*** 0.196 -1.737*** 0.198 -2.106*** 0.361

Midland 0.518*** 0.054 0.518*** 0.051 -

Highland -0.257** 0.105 -0.257** 0.106 -yr04 0.353*** 0.05 0.353*** 0.048 0.348*** 0.048

yr09 0.557*** 0.045 0.557*** 0.043 0.551*** 0.043

yr14 0.941*** 0.052 0.941*** 0.051 0.977*** 0.053

Constant 9.150*** 0.722 9.151*** 0.713 11.037*** 1.38

F-statistic F( 20,1219) = 80.53*** Wald chi (20) = 1894.66*** F(18,309) = 49.34***

R-squared 0.5827 within = 0.4594 within = 0.4721

between = 0.7486 between = 0.3819

overall = 0.5827 overall = 0.4018

Note: *: Significant at 10 percent; **: Significant at 5 percent; ***: Significant at 1 percent.

Another important factor considered in this analysis was access to extension services represented

by the participation of the household in governmental agricultural extension services and the

number of extension visits received by the farmer. The results of the analysis reveal that

participating in agricultural extension service had a significant and positive impact on cereal


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crops productivity at 1 percent significance level. This shows participation and more number of

contacts with extension agents was associated with greater crops productivity. Thus ceteris

paribus, the corresponding regression coefficient shows that an additional increase in

participation and number of contacts with extension agents could lead to a rise in cereal crops

productivity by 0.07 percent.

The estimates on educational level of the household head show that education affects cereal

crops productivity positively and significantly at 1percent significance level. The positive sign

for education indicates that increase in human capital enhances the productivity of farmers since

they will be better able to allocate family-supplied and purchased inputs, select the appropriate

quantities of purchased inputs and choose among available techniques. The coefficient of this

regression indicates that an increment of households educational level by 1 percent has

increased cereal crops productivity by 1 percent. The results is in line with Battese and Coelli

(1995), who hypothesized education to increase the households ability to utilize existing

technologies and efficient management of production systems and hence attain higher

productivity levels.

Regression result further indicates that cereal cultivated farm land size has a negative and

significant impact on cereal crops productivity, conforming to the inverse farm size-productivity

relationships found in other studies. The estimated coefficient of the extent of farm land area

under cereal cultivation is significant at 1 percent significance level. Therefore, an increment of

cereal cultivated land under cultivation by 1percent will decrease productivity by more than 0.29

percent. The result is similar with what others have found in Tigray (Tesfay, et al., 2005).

Similar directions were obtained by Basnayake and Gunaratne (2002) in Tanzania.

The result further indicates that the use of farm machinery implements have negative sign

significantly at 1 percent significance level. The corresponding coefficient shows that an

additional increase in number of mechanized agriculture implements could lead to a decline incereal crops productivity by 0.03 percent. The result could be explained by the fact that, small

and fragmented land holdings make it difficult to attain economies of scale for smallholders

using machinery implements, indicating a mismatch between machinery implements and realities

at the farm level. This implies given the current landholdings and smallholders resource base,

investment in highly mechanized agriculture might not necessarily translate to high productivity.


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Moreover, most farmers in the sample used archaic and very backward implements such as oxen,

hoe and plow; and only very insignificant portion of our sample used modern inputs such farm

machinery like tractor and combiner to harvest their crop output.

Age of households head is negative and significant across all the regressions, while its square is

positive and significant. This indicates that head age has a strong impact on crop productivity.

The negative sign for the coefficient of the age variable indicates that older household heads are

less productive than younger ones, implying that old farmers are a more decline on their level of

cereal crops output than younger ones. This result may be supported by the result from the

descriptive summery of the study as the age of the farmers studied ranged 17 to 103 years with

an average age of 52 years, implying that farmers in the area are relatively old, a condition that

might have affected productivity negatively, since crop production is labor intensive. Moreover,

the result can be explained in terms of crop production practice. Hussain (1989) argued that older

farmers are less likely to have contact with extension workers and are equally less inclined to use

new techniques and modern inputs, whereas younger farmers, by virtue of their greater

opportunities for formal education, may be more skillful in the search for information and the

application of new techniques. This, could be in return, could owe the younger farmers relatively

better capacity to manage their farm land hence would enable them to improve the level of their

cereal crops productivity.

On the contrary the age-squared is positively and statistically affected cereal crops productivity

at 5 percent significance level. The findings reveal that older household heads are more

productive than younger ones. This result can be explained as income of the previous research

works such as Beniam et al. (2004), assume that the older a farmer gets, the more experienced

and argued that he/she will appear to be more productive than younger farmers due to their good

managerial skills, which they have learnt over time. Besides, given the importance and

significance of land, labor, capital and other resources in agricultural crop production, it could be

argued that young households are deficient in resources and might not be able to apply inputs or

implement certain agronomic practices sufficiently quickly. In sum, a possible explanation to

these two contrasting effects regarding the age of household head might have neutralized each

other, in such a way that; the older hence more experienced farmers have more knowledge on


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their farm land and traditional practices on agricultural crop production, but are less responsive

to take new ideas.

Another important variable that affected cereal crops productivity significantly in this analysis is

climate related variables. Results from both random and fixed effects analyses showed that

climate variables temperature and rainfall during crops growing season are found to significant

determinants of agricultural productivity. The result shows significant relationships between crop

yield and average annual temperature and annual precipitation at 1 percent significance level in

all cases.

Climate variation effects on yield

The result showed average of annual rainfall variability affected cereal crops productivity

positively and significantly. This is may be due to the fact that; rainfall enhances crop

productivity as it improves the soils capacity and enables it to use the fertilizer and other inputs

effectively (Tchale and Suaer, 2007). The regression coefficients suggest that any increment in

average of annual rainfall (precipitation) by 1 mm will increase cereal crops productivity level by

more than 0.42 percent. Interpreting the result in other way round a decrease in average of annual

precipitation by 1 percent annually would lead to a decrease in cereal crops yield by 0.42

percent.

Contrary to this, average annual temperature was negatively associated to cereal crops

productivity provide evidence that temperature has a negative effect on crop yield though their

effect is not uniform in the corresponding regression coefficients from random effects and fixed

effects models. The coefficients of regression for averaged annual temperature are found to have

large values implying to have large impact. The coefficient indicates that a 1oC rise in average

annual temperature during growing season could reduce cereal crops productivity level by 173

percent. This may be due to increase (downward move) in average annual minimum temperature

or (upward move) in average annual maximum temperature during crops growing season which

in turn leads to a declined in cereal crops productivity.

As expected geographical differences included in regression analysis as a set of regional dummy

variables (lowland, midland and highland) that are included to represent time-persistent, the

agro-climatic differences or regional differences considerably affects cereal productivity


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significantly at 1 percent significance level. It appears that being farming in midland or highland

areas than other areas; contributed cereal crop productivity to increases and being farming in

lowland areas contributed a decrease in cereal crop productivity at 1 percent significance level in

both cases. Hence, in line with descriptive result, it is found that cereal crop yields to rise in

midland area by 0.52 percent; declined in highland area by 0.25 percent compared with the

lowland agro-ecological area.

Lastly, the regression result shows that the estimated coefficients for time dummy variables

categorized for this study are highly significant and positively impacted crops productivity. The

estimated coefficients show that cereal crops productivity is rising through the panel time

largely. The positive sign shows also that there is technological regress or upward shift in the

production function over time between these three time periods. The result evidences that there

has not only been an increase, but also increase in the percentage-value over the past 15years, as

the probability of average cereal crops productivity that raised in 2004 was 0.35 percent, was

0.56 percent in 2009, while it reached about 0.94 percent in 2014; all being significant at 1

percent level compared with the base year ad divided by number of years to get the annual

growth rate.

5. CONCLUSION

A large body of literature demonstrates negative impacts of climate change and variations on the

agricultural sector and its productivity. In particular, as climate change is likely to intensify high

temperature and low precipitation, its most dramatic impacts will be felt by smallholder and

subsistence farmers suffering the brunt of the effects. Considering the Ethiopian agricultural crop

production; it is observed that while the majority of cereal crops yield productivity increase is

due to increased use of physical inputs and the institutional change, the gradual increase in

growing season climatic factors in the last few decades has had a measurable effect on Ethiopian

cereal crops yield productivity.

In this paper, we evaluated the impacts of climatic and non-climatic factors on cereal crops yield;

provided descriptive and econometrics analysis of determinants of Ethiopian cereal crops yield

productivity using four-round panel-data. Consistent with previous findings of productivity

studies in Sub-Saharan Africa, which primarily consider agricultural production inputs and


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climate factors, the results from regression analysis confirm the importance and statistically

strong dependence between of most of the explanatory variables and cereal crops yield in

Ethiopia. Descriptive results show that average annual rainfall, maximum and maximum

temperature decrease over time in all three agro-ecological zones considered in the study.

Econometrics results, random effect and fixed effect estimates indicated inputs such as fertilizer,

agricultural labor, agrochemicals, livestock ownership, number of owned oxen (animal draft

power), agricultural extension service and education level of the household head significantly

enhances cereal crops productivity. On the hand, farm size, agricultural machinery use and

household head age influenced cereal crops productivity negatively significantly.

Furthermore, considering that cereal crops productivity could be influenced by two main

meteorological factors. We find that rainfall variable to have a positive impact on cereal crops

productivity; while maximum temperature variable to have a positive impact hence reduces the

level cereal crops productivity. The result showed average of annual rainfall variability affected

cereal crops productivity positively and significantly. The regression coefficients suggest that

any increment in average of annual rainfall by 1mm will increase cereal crops productivity level

by more than 0.42 percent. Interpreting the result in other way round a decrease in average of

annual precipitation by 1 percent annually would lead to a decrease in cereal crops yield by 0.42

percent. On the other hand a 1oC rise of average annual temperature during crops growing season

could reduce cereal crops yield by 173 percent. This may be due to increase (downward move) in

average annual minimum temperature or (upward move) in average annual maximum

temperature during crops growing season which in turn leads to a declined in cereal crops

productivity. This negative impact would probably become worse with accelerating change of

future climate.

Moreover, regression result shows that dummy variables showing agro-ecological differences are

significant; and also time dummy variables are significant and positively impacted cropsproductivity showing there is technological regress or upward shift in production over time

periods. These outcomes are important and can be used to inform the government on different

policy decision, such as where to emphasize when planning on climate change adaptation

strategies to be promoted, ways to envisage better extension services provisions that are tailored

to the peculiarities of the agro-ecological zones across the country. Thus the study result


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confirmed that the climate change effects contribute to increase inefficiency in agricultural

crop yields in Ethiopia and in the study areas crop production showing impact of climate

change effects due to the low capability of adaptation. The study therefore recommends policies

that would improve extension service, education, supply of agricultural production inputs and

developing climate change adaptation strategies suitable to the different agro-ecological zones

should be pursued.

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