Russian Journal of Agricultural and Socio-Economic Sciences, 2013, №11 (23) Ноябрь

Russian Journal of Agricultural and Socio-Economic Sciences, 11(23)

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ECONOMIC ANALYSIS OF SMALL HOLDER RICE PRODUCTION SYSTEMS IN EBONYI 

STATE SOUTH EAST, NIGERIA

Nwaobiala C.U., Researcher

Michael Okpara University of Agriculture, Umudike, Nigeria

E-mail: cunwaobiala@gmail.com

Adesope O.M., Researcher

University of Port Harcourt, Rivers State, Nigeria

E-mail: omadesope@yahoo.co.uk

ABSTRACT
Economic analysis of Upland and Swamp rice production in Ebonyi State, South east Nigeria
was studied and analyzed in 2011 farming season. Purposive and multistage random 
sampling technique was used to select agricultural blocks, circles and rice farmers. The 
sample size was 240 rice farmers (120 Agricultural Development Programme (ADP) Upland 
rice contact farmers and 120 Agricultural Development Programme (ADP) Swamp contact
rice farmers). Data for the analysis were collected from a structured questionnaire. The result 
indicates that mean ages of upland rice farmers was 37.3 years while swamp rice farmers 
had 39.2 years. The mean farming experience for both farmers were 8.5 years (upland rice 
farmers) and 8.8 years (swamp rice farmers) with farm sizes of 1.2 and 1.1 hectares for 
upland rice farmers and swamp rice farmers respectively. Upland rice farmers had an annual 
farm income of 189,410.00 NGN (1,222USD) as against 201,166.00 NGN (1,297.85USD) for 
Swamp rice farmers. The multiple regression (Cobb Douglas) estimates of the determinants 
of output of upland rice showed that coefficients age, farming experience, farm size, variable 
inputs and farm income were positively signed at given levels of probability while capital 
inputs was negative. The Cobb Douglas regression estimates of the determinants of output 
of Swamp rice showed that the coefficients of education, labour cost, farm size, variable 
inputs and farm income were positively signed and significant at given levels of probability as 
well as capital inputs which was negative. The result indicates that net profit from Upland rice 
cultivation was 92,800.00 NGN (598.71USD) with a Benefit Cost Ratio of N1.55 (1.56USD). 
The net profit from Swamp rice cultivation was 132,090.00 NGN (852.19USD) and a Benefit 
Cost Ratio of 1.75 NGN (1.75USD). Access to credit to rice farmers, subsidy on farm inputs, 
dissemination of improved rice technologies by extension agents and formation of farmer 
groups were advocated for increased rice production.

KEY WORDS
Economic; Analysis; Upland rice; Swamp rice; ADP farmers.

The Nigerian rice sector has a lot of potentials for increased rice productivity as the 

country is blessed with abundant rice growing environment. However, WARDA (2004) noted 
that rice policy in Nigeria is characterized by inconsistency shifting, between open and 
protectionist trade policy and such changes hinder the ability of stakeholders to develop long 
term strategies for the growth of the sector. FAO (2004) identified rice as a very important 
primary food source and this is drawn from the understanding that rice–based systems are 
essential for food security, poverty alleviation and improved livelihoods by enhancing the 
socio- economic profile / status of the farmer. Rice is grown in paddies or on upland fields 
depending on the requirements of the particular variety, there is also limited mangrove 
cultivation. Upland rice production is practiced on different ecologies by majority of farmers 
due to it less tedious operation. Upland contributes substantially less the total rice output in 
relation to its share in total area, but still accounts for an important means of rice production 
(WARDA 2003 et al., and Oyewole et al., 2010). In Nigeria, out of 4.6 million hectares 
available for rice production, only 1.7 million hectares are put to rice cultivation, despite that 
its production is labour intensive and labour represents major production costs (Nwachukwu

Russian Journal of Agricultural and Socio-Economic Sciences, 11(23)

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et al., 2008). Rice is produced in the middle belt, south east and some far northern states of 
Nigeria (Audu, 2008).

Rice production in Nigeria between 2001 and 2003 was estimated at 2.03 million mega 

grams while consumption was 3.96 million grams. The balance of 1.90 million mega grams 
was obtained by importation (FAO, 2004). Nigeria is the largest producer of rice in West 
Africa producing over 40% of the regions total production (Singh et al., 1997 and FAOSTAT, 
2007). In the past 30 years, production has increased six folds with Nigeria producing 3.3 
and 3.6 million tons of paddy rice in 2000 and 2005 respectively (FAOSTAT, 2004 and 
2007). Africa accounts for only about 2% of the worlds output of rice. Current production 
stands at 2.8 million tons with a deficit of 1.6million tons excluding the quantity smuggled 
through the porous borders (USAID, 2008).The successive programmes launched to 
increase rice production have not been able to reduce the resulting rice deficit. The 
imposition of a ban on rice import from 1985 to 1995 and ensuring increase in the relative 
price against other major staples boosted rice production mainly through area increase. Past 
policies did not help local rice producers to secure significant market share and imports have 
increased since the lifting of the ban and successive increase in the import tariff from 50% to 
100%. Imported rice represents more than 20% of agricultural imports and half of total rice 
consumption (WARDA, 2003). In spite of the relative increase in the price of rice per capita, 
consumption has maintained the upward trend showing that rice has become a structural 
component of Nigeria diet with a low price elasticity of demand (WARDA, 2003). Massive 
importation of food especially rice in recent years is an indicator of poor state of nations 
agricultural and technologies development, occasioned by poor productive propensity of the 
farmers. Increase in agricultural import is a disincentive to local farmers to produce and may 
cause reduction in farming population which can subsequently lead to a reduction in 
agricultural output. The production process involving costs and the magnitude of costs 
influence the magnitude of profit (Anuebunwa, 2004 and 2007). Many studies on rice 
production were geared towards maximizing profit, ignoring socio- economic factors of the 
farmers which influence and contribute to rice production. Problems of rice production are 
identified as relatively high production costs, relatively poor producer prices and marketing 
systems, this results to low returns and subsequently decline in rice production (FAO, 2008; 
Ugwugwu, 2008). In view of the above, this study tends to analyze the economics of 
smallholder rice production systems in Ebonyi State, Nigeria. The specific objectives of this 
paper are to:

a.
describe selected socio-economic characteristics of ADP Upland and Swamp rice 

farmers in the study area.

b.
determine the influence of socio-economic variables on the output of ADP Upland 

and Swamp rice farmers in the study area.

c.
estimate the cost and returns of Upland and Swamp rice farming in the study area.

Hypotheses:
Ho1: Socio-economic characteristics of rice farmers do not have significant relationship 

on the output of ADP Upland and Swamp rice Contact farmers.

Ho2: Upland and Swamp rice farming are not profitable.

METHODOLOGY

The study was conducted in Ebonyi State South east Nigeria, which is one of the major 

rice producing areas in Nigeria. The State has 13 Local Government Areas and 3 agricultural 
zones namely Ebonyi North (Izzi, Abakaliki, Ohaukwu and Ebonyi Blocks): Ebonyi Central 
(Ishielu, Ikwo, Ezza South and Ezza North Blocks) and Ebonyi South (Afikpo, Afikpo South, 
Onicha and Ohazara Blocks). The State has a land mass of approximately 5,932km2 and lies 
within latitudes 41N and 141N of the Equator and longitudes 3o E and 150 E of Greenwich 
meridian. The State has a population of about 2.3 million people (NPC, 2006). The State has 
an average rainfall of 1200mm – 2000mm with temperature ranging from 33oC in the dry 
season and about 16oC to 18oC in the rainy season (EBADEP, Annual Record, 2005). Rice 
farming is predominantly practised by farmers in the State.

Russian Journal of Agricultural and Socio-Economic Sciences, 11(23)

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Purposive and multistage sampling techniques were used in the selection of 

agricultural blocks, circles and rice farmers. Purposively, Ebonyi State was chosen because 
it is among the major rice growing areas in Nigeria. The three agricultural zones namely 
Ebonyi North, Ebonyi Central and Ebonyi South were used for the study. First, 2 blocks were 
randomly selected from the three agricultural zones to give a total of 6 blocks (Ebonyi North 
– Izzi and Abakaliki Blocks : Ebonyi Central – Ikwo and Ezza South Blocks and Ebonyi South 
– Onicha and Ohazara Blocks). Also, 2 circles each were randomly selected from the 
selected blocks which gave a total of 12 circles. Furthermore, 10 ADP Upland rice Contact
farmers were randomly selected from the selected circles to give a total of 120 ADP Upland 
Contact farmers. Finally, 120 ADP Swamp rice Contact farmers were selected from the areas 
where the Upland rice farmers were chosen to give a grand sample size of 240 ADP rice 
Contact farmers. Descriptive statistics such as frequency distribution, mean counts and 
percentages were used to realize objective i while multiple regression analysis achieved 
objectives ii and iii.

Theoretical Framework. The multiple regression studies involve the nature of 

relationship between a dependent variable and two or more explanatory variables. The 
techniques produce estimates of the standard error of multiple regression and coefficients of 
multiple determinants. In implicit form, the statement that a particular variable of interest (Y) 
is associated with a set of the other variables (XI) is given as:

yi = F (X1, X2,..................XS) (1),

where yi is the dependent variable, and XI...........X2 is a set of a k variables.

The coefficients of multiple determination measures the relative amount of variation in 

the dependent variable (Yi) explained by the regression relationship between Y and the 
explanatory variables (X1). The F- Statistics tests the significance of the coefficients of the 
explanatory variables as a group. It tests the null hypothesis of no evidence of significant 
statistical regression relationship between Yi and the xi s as against the alternative 
hypothesis of evidence of significant statistical relationship. The critical F- value has an n and 
n-k-1 degrees of freedom, where n is the number of respondents and k is the number of 
explanatory variables. The standard error of regression coefficients is the measure of error 
about the regression coefficients. The nature of the relationship between an outcome 
variable (Yi) and a set of explanatory variables (X1) can be modelled using different functional 
forms. The four commonly used algebraic (functional) forms are: linear, semi- log, 
exponential and Cobb Douglas.

The four functional multiple regression models were employed to select the one that 

has provided the best fit. The choice of best functional form was based on the magnitude of 
the R2 value, number of significant variables, size and sign of regression coefficients as they 
conform to a priori expectation. The four functional forms were specified implicitly as follows;

(i)
Linear Function 
Y = b0+ b1X1+ b2X2+ b3X3+ b4X4+ b5X5+ b6X6 + b7X7+ b8X8+ b9X9+ei

(ii)
Semi – log function 
Y= Lnb0+b1LnX1+ b2LnX2+ b3LnX3+ b4LnX4+ b5LnX5+ b6LoX6 +b7LoX7 +b8LoX8 +b9LoX9 +ei

(iii)
Exponential function 
LnY = b0+ b1X1+ b2X2+ b3X3+ b4X4+ b5X5+ b6X6 + b7X7 + b8X8 + b9X9 + ei

(iv)
Cobb Douglas Function 
LnY = Lnb0+b1LnX1+b2LnX2+b3LnX3+b4LnX4+b5LnX5+ b6LnX6 + b7LnX7 + b8LnX8 + b9LnX9 + 
ei

Y= Output of Rice in Kg/ tons; X1 = Age in years;
X2 = Household Size; X3 = Education
X4 = Farming Experience in Years; X5 = Farm Size in Hectares
X6 = Labour Cost in Naira; X7 = Variable Inputs in Kg
X8 = Capital Inputs in NGN/USD; X9 = Farm Income in NGN/USD
ei = error term

Russian Journal of Agricultural and Socio-Economic Sciences, 11(23)

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The budgetary technique was used to determine the profitability of rice production

systems in the study area. Gross margin analysis is one of the budgetary techniques and it 
basically measures the difference between total returns and total variable costs. Gross 
margin of rice farming is the total value of production (Total revenue) and the variable costs 
of production. The total revenue refers to the gross income accruing from rice farms as a 
result of the sales of processed rice. This is obtained by multiplying the unit price of average 
processed rice by the quantity sold. The variable costs are those costs that vary with the total 
level of output. The addition of total variable costs and total fixed costs gives the overall costs 
incurred in production. However, for the purpose of arriving at fixed costs of the rice farms for 
a given year, the straight line depreciation method was used taken into consideration the 
expected life span of the different fixed cost items. Using the straight line method, the annual 
depreciation expenses are calculated on the fixed costs, which are then used to get the net 
farm profit. The benefit cost ratio which expresses the return in investment a farmer gets in 
producing a commodity is derived by dividing total revenue by total costs used in production.

The Gross margin analysis adopted in this paper is in accordance with Nwaobiala, 

(2010):

GM = Σpi (Qi - ΣpjXi) … (i),

where GM = Gross Margin; Pi = Unit price of output; Qi = Quantity of each output; Pj = Unit 
of each input; Xi = Quantity of each input.

NR = GM – TFC … (ii)

BCR = TR / TC,

where NR = Net Revenue; TFC = Total fixed costs derived by depreciation of fixed costs; 
BCR = Benefit Cost Ratio; TR = Total Revenue; TC = Total Costs.

RESULTS AND DISCUSSION

Socio-economic Characteristics of ADP Contact Rice Farmers in Ebonyi State, Nigeria.

The result indicates that the mean age of ADP Upland rice contact farmers was 37.70 years 
while the Swamp rice contact farmers was 39.20 years. Also 5.1 and 5.2 were household 
sizes of ADP Upland and Swamp rice contact farmers respectively. The ADP Upland rice 
and Swamp rice contact farmers had 8.5 and 8.8 years of farming experiences respectively 
and mean farm sizes of 1.2 hectares (ADP Upland rice contact farmers) and 1.1 hectares 
(ADP Swamp rice contact farmers). The annual farm income of ADP Upland rice contact 
farmers was 189,410.00 NGN (1,222USD) while ADP Swamp rice Contact farmers had 
201,166.00 NGN (1,297.85USD).

Socio economic Factors Influencing the Output of Upland Rice in Ebonyi State, Nigeria.

Our study shows the Ordinary Least Square (OLS) multiple regression estimates of the 
determinants of Upland rice Output in Ebonyi State, Nigeria. The Cobb Douglas functional 
form was chosen as lead equation based on the high R2 value, sizes and numbers of 
significant variables and agreement with a priori expectation. The R2 value of 0.7412 
indicates 74.12% variability in output of Upland rice was explained by the independent 
variables. The F- value (9.874) was highly significant indicating a regression of good fit.

The coefficient of age (0.00471) was positively signed and significant at 5% level of 

probability. This implies that as age increases the probability of the farmer to produce more 
quantities of upland rice increases. This is against a priori expectation. Probably because 
accumulated knowledge and experience of farming systems a farmer had acquired pays off 
over a long period of time, while costs occur in earlier stages (Bonabana – Wabbi, 2002).

The coefficient of farming experience (0.3714) was positively signed and significant at 

10% level of probability. This is in agreement with a priori expectation. The positive sign 
implies that an increase in farming experience will lead to a corresponding increase in the 
output of upland rice. This may be that farmers had acquired encouraging return from upland 
rice cultivation and thus will continue with it anticipating continued benefits. This result is in 

Russian Journal of Agricultural and Socio-Economic Sciences, 11(23)

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consonance with the findings of Nwaobiala, (2010) where there was a relationship between 
farming experience and output of rice farmers in Ebonyi State.

The coefficient of farm size (0.00129) which was positive was highly significant at 1% 

level of probability. This implies that an increase in farm size will result to an increase in the 
output of Upland rice in the study area. This is in agreement with a priori expectation. This 
result is in conformity with the findings of Ezeh, (2006) as he found a positive influence of 
farm size on the output of Upland rice in Abia State, Nigeria.

The coefficient of variable inputs (11970.41) was positively signed and significant at 5% 

level of probability, implying that an increase in quantities of variable inputs used in the 
production of Upland rice will increase output of Upland rice. This is in agreement with a 
priori expectation. This result is in consonance with the findings of Onyenweaku et al., (2010) 
as they found a positive relationship between rice seeds and quantities of fertilizer used in
rice production in Abia State, Nigeria.

The coefficient of capital inputs (- 0.00524) which was depreciated was negatively 

related to output of Upland rice. This implies that any increase in the cost of capital inputs will 
bring about increase in the output of Upland rice. This is in agreement with a priori 
expectation. Mbanasor and Obioha (2003) asserted that capital inputs used in any farming 
operation have a resultant effect on the output of the enterprise.

The coefficient of farm income (14.35774) was positively signed and highly significant 

at 1% level of probability. This implies that any increase in farm income will lead to an 
increase in output of Upland rice farmers. The result is in agreement with the findings of 
Nwaobiala et al., (2009) where they found a positive relationship between farm income and 
output of Agip- Green River Project crop farmers in Rivers State, Nigeria.

Socio economic Factors Influencing the Output of Swamp Rice in Ebonyi State, 

Nigeria. Our study shows the Ordinary Least Square (OLS) multiple regression estimates of
the determinants of Swamp rice Output in Ebonyi State, Nigeria. The Cobb Douglas 
functional form was chosen as lead equation based on the high R2 value, sizes and numbers 
of significant variables and agreement with a priori expectation. The R2 value of 0.8214 
indicates 82.14% variability in output of Swamp rice were explained by the independent 
variables. The F- value (10.241) was highly significant indicating a regression of good fit.

The coefficient of education (0.00941) was positively signed and significant at 10% 

level of probability. This implies that increased level of education will lead to increase in the 
output of Swamp rice in the study area. This is in agreement with a priori expectation.
Generally education is thought to create a favourable mental attitude for acceptance of new 
practices which in return leads to increased output (Ezeh and Nwachukwu, 2010).

The coefficient of labour cost (0.06157) was positively signed and significant at 5.0% 

level of probability. This implies that an increase in the cost of labour will lead to a 
corresponding increase in the output of Swamp rice in the study area. This is in 
disagreement with a priori expectation. Since Swamp rice cultivation requires tedious and 
labour intensive operations, skilled labour needed for farming operation may be too costly to 
hire. This result is in conformity with the findings of Nwaobiala, (2013) who found a positive 
relationship between cost of labour used by IFAD farmers and their farm output in Abia and 
Cross River States, Nigeria. Umebali, (2007) who reported that labour is expensive and has 
driven farmers to use both family and hired labour. The high cost of labour in small holder 
agriculture is due to ageing of farmers and migration of youths from rural to urban areas.

The coefficient of farm size (0.60387) which was positive was highly significant at 1% 

level of probability. This implies that an increase in farm size will result to an increase in the 
output of Swamp rice in the study area. This is in agreement with a priori expectation. Farm 
size is therefore a strong determinant of Swamp rice output in Ebonyi State.

The coefficient of variable inputs (0.12473) was positively signed and highly significant 

at 1.0% level of probability, implying that an increase in quantities of variable inputs used in 
the production of Swamp rice will increase output of Swamp rice. This is in agreement with a 
priori expectation. This indicates that variable inputs are strong determinant of the output of 
Swamp rice in the study area.

Russian Journal of Agricultural and Socio-Economic Sciences, 11(23)

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The coefficient of capital inputs (- 0.005789) which was depreciated was negatively 

related to output of Swamp rice. This implies that any increase in the cost of capital inputs 
will bring about increase in the output of Upland rice. This is in agreement with a priori 
expectation. Therefore depreciation of capital inputs is a strong determinant of Swamp rice 
output in the study area.

The coefficient of farm income (0.05945) was positively signed and highly significant at 

1.0% level of probability. This implies that any increase in farm income will lead to an 
increase in output of Swamp rice farmers. This is in agreement with a priori expectation. 
Therefore farm income is a strong determinant of Swamp rice output in the study area.

Profitability Analysis of Upland Rice Farming in Ebonyi State, Nigeria. Our study shows 

the gross margin analysis of upland rice farmers in the study area. This posted a total 
revenue of two hundred and fifty nine thousand naira (N259.000.00) (16.71 USD). The 
average annual or seasonal cost incurred in rice production in Ebonyi state was on hundred 
and thirty thousand six hundred naira (N130,600) (842.58 USD). The Gross Margin was one 
hundred and twenty eight thousand four hundred naira (N128,400.00) (828.38 USD) .The 
total fixed cost per hectare is Thirty five thousand six hundred naira (N35,600.00) (229.67 
USD). Thus, each farmer is left with average net profit of Ninety two thousand eight hundred
naira only (N92,800.00) (598.71 USD). The Benefit Cost Ratio of Upland rice farming was 
1:55, which means that every NI ($1) a farmer used in the cultivation of Upland rice he 
realizes N1.55k.

Profitability Analysis of Swamp Rice Farming in Ebonyi State, Nigeria. Our study shows 

the gross margin analysis of Swamp rice farmers in the study area. This posted a total 
revenue of three hundred and six thousand six hundred and thirty naira (N306,630.00)
(1978.26 USD). The average annual or seasonal cost incurred in rice production in Ebonyi 
State was one hundred and thirty nine thousand naira (N139,000.00) (896.77 USD). The 
Gross Margin was one hundred and sixty seven thousand six hundred and thirty naira 
(N167,630.00) (1081.48 USD). The total fixed cost per hectare is thirty six thousand five 
hundred and forty naira (N36,540.00) (235.74 USD). Thus, each farmer is left with an
average net profit of one hundred and thirty two thousand and ninety naira only 
(N132,090.00) (852.19 USD). The Benefit Cost Ratio of Upland rice farming was 1:75, which 
implies that, for every NI ($1) a farmer uses in the cultivation of Upland rice he realizes 
N1.75k.

CONCLUSION AND RECOMMENDATIONS

Rice production is a major source of livelihood for the resource poor farmers in Ebonyi 

State, Nigeria. The study has examined and determined the socio- economic factors that 
influence both Upland and Swamp rice output in the study area to include age, education, 
farming experience, farm size, variable inputs, capital inputs and farm income. The net profit 
of the farmers were also estimated indicating that Swamp rice farming was profitable than 
Upland rice farming in the study area.

Based on the findings the following recommendations were drawn:
– Reviewing the Land Use Act of 1990 is critical so that most of the fertile land held by 

government will be released to rice farmers. This will help boost rice production;

– Educational centres should be established by government in the rural areas, this will 

stimulate learning and adoption of improved rice technology packages by farmers;

– Subsidy and availability of farm inputs as improved rice seeds, fertilizers, herbicides 

among others by relevant agencies will increase rice production;

– Formation of farmer groups (cooperatives) is advocated so that farmers can pull their 

resources together to access credit and improved farm inputs in order to boost production;

– Rural facilities such as good roads, markets and agro based industries should be 

established by government. This will help curb rural- urban migration by the youths thereby 
help in providing skilled labour required in rice production and easy evacuation of farm 
produce.

Russian Journal of Agricultural and Socio-Economic Sciences, 11(23)

9

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Russian Journal of Agricultural and Socio-Economic Sciences, 11(23)

11

AN ECONOMETRIC ANALYSIS OF THE RELATIONSHIP BETWEEN AGRICULTURAL 

PRODUCTION AND ECONOMIC GROWTH IN ZIMBABWE

Alexander Mapfumo, Researcher

Great Zimbabwe University, Masvingo, Zimbabwe

E-mail: allymaps@gmail.com

ABSTRACT
In Less Developed Countries (LDCs) like Zimbabwe, agricultural production has been 
regarded by several studies as a paramount prerequisite for industrialization and economic 
growth. The idea behind this view is that, as agricultural production increases, countries are 
able to produce more food with less labour input which allows them to feed their growing 
population while releasing labour for the manufacturing sector and other sectors of the 
economy hence the process will lead to economic growth. The main objective of this study 
was to investigate how agricultural production affected economic growth in Zimbabwe from 
1980-2010. The Log linear growth regression model was employed where gross domestic 
product was the dependant variable and the explanatory variables are the major crop 
products and factors which affect it. Four major crops which were included in the model are 
tobacco, maize, coffee and cotton. Moreover, a dummy variable for the prevailing weather 
conditions was also included in the model. The regression analyses were performed using 
Econometric-views 3 (E-views 3) statistical package. Regression was carried out on time 
series data for the period 1980 to 2010. The data was tested for stationarity and for 
autocorrelation. Problems of non stationarity of data were corrected by differencing the 
trending series. Results from the empirical analysis provide strong evidence indicating that 
agricultural production is important in improving the wellbeing of countries especially in 
LDCs. The results from this study suggest that the value of agricultural production of 
tobacco, maize and cotton positively affects economic growth in Zimbabwe from 1980 to 
2010.

KEYWORDS
Economic growth; Agricultural production; Zimbabwe.

In developing countries like Zimbabwe, an increase in agricultural production will mean 

more adequate food supplies and use of scarce foreign currency for other imports (Sherman,
1960). Moreover, when a country attains food surplus, more foreign currency will be available 
for development to other sectors of the economy. Eventually, non agricultural sectors of the 
economy would achieve sufficient momentum to provide employment for many of the 
underemployed workers in agricultural and non agricultural sectors leading to economic 
growth. Consequently, improvements in performance of the economy can be initiated by 
developments in agriculture.

There is a need to know the relationship of agriculture production to economic growth 

since development policies in Zimbabwe has been primarily based on the assumption that 
agriculture production is of paramount importance to the performance of the Zimbabwean 
economy.

Various agricultural performance indicators provide evidence of the relative 

deterioration of agricultural production since independence. For instance, the total 
agricultural production per capita and the food production per capita index have been falling, 
particularly since 2000. This partly explains the rampant food shortages that Zimbabwe has 
witnessed, with consequent increases in domestic food prices and the dramatic increases in 
agricultural imports that have been observed since 2000. Yield levels usually averaging 
below 1t ha-1 have resulted in persistent cereal deficits despite the large area put under 
production each year. Declining soil fertility, erratic precipitation patterns, high input costs 
and unstable market conditions have all affected agricultural production in Zimbabwe.

Agricultural production in Zimbabwe. According to FAO (2012), Zimbabwe has a 

diversified agriculture sector which accounts 11 to 20 percent of the country’s annual gross 

Russian Journal of Agricultural and Socio-Economic Sciences, 11(23)

12

domestic product being generated by agriculture as well as 45 percent of exports. Moreover, 
it also provides over 70 percent of the population directly and indirectly employment and 
among those who are directly linked to farming about 75 percent rely on rainfed farming 
systems. The agricultural sector is composed of large scale commercial farming and small 
scale farmers, with the latter occupying more land area but located in regions where land is 
relatively infertile with more unreliable rainfall. Zimbabwe is a tropical country which generally 
experiences a dry savannah climate. Farmland in Zimbabwe is divided into five natural 
regions on the basis of soil type, rainfall amounts and temperature and climatic factors (refer 
to Figure 1) (Mapfumo et al, 2012).

Figure1 – Agro-ecological zones in Zimbabwe (Source: FAO, 2012)

Model specification. This study used a modified log linear growth model used by Fan, 

Hazel and Thorat (2000). The dependant variable is Gross Domestic Product (GDP) and the 
independent variables include the production of Tobacco, Maize, Coffee and Cotton. 
According to FAO (2009), the crops outlined are the major crops grown in Zimbabwe. The 
outlined crops were considered as part of the analysis, basing on their contribution to 
agricultural production in Zimbabwe.

The log linear regression model is as follows:

Log GDP = β0 + β1Log TOBC + β2 Log COFFE+ β3 Log COTN+ β4 Log MAIZE+ β5 Dummy,

where Log GDP is the logarithm for Gross Domestic Product (GDP), β0 is a constant and β1,
β2, β3, β4 and β5 are parameters to be estimated. Log TOBC, COFFE, COTN and Log MAIZE
are the logarithms for the value of agricultural production for Tobacco, Coffee, Cotton and 
Maize respectively. The dummy variable represents the prevailing weather conditions.

Variables used in the model. Gross Domestic Product (GDP). In this study GDP is 

taken as a proxy for economic performance which measures the total amount of goods and 
services produced in an economy (Lipsey & Crystal, 1999). It measures how big the 
economy is and has been chosen as a favourable indicator in this case because it captures 
all the variables that concerns economic growth. GDP has been included because it 
measures the output produced in any one period at prices of the same base year.

Tobacco (TOBC). Zimbabwe is the world’s third largest producer of flue cured tobacco 

after the USA and Brazil (FAO, 2012). Tobacco has always dominated the agricultural export 
composition , with the largest shares of & 8% and 67% in 1992 and 1993 respectively, were