Posted by Kathryn Schwartz on November 06, 2013

METHODS1. The Study Areas

The study was conducted in Osun and Oyo states of Nigeria. These two states are among the six states constituting the South western zone of Nigeria. The other four states are Ekiti, Lagos, Ogun and Ondo States. Oyo state spans an area of 28,454 square kilometers (2,845,400 Ha) while Osun state spans an area of 9,251 square kilometers (925,100 Ha) (FOS, 1997). The two states have two distinct ecological zones: The moist forest to the south and the intermediate savannah to the north. The climates in the areas are of tropical type with two distinct rainfall patterns suitable for upland rice production. The rainy season, which marks the agricultural production season is normally between the months of April and October.

The heaviest rainfalls are recorded between the months of June and August while driest months are November to March. The average total annual rainfall ranges between 1000mm and 1500mm with high daily temperature ranging between 280C and 300C (FAOSTAT, 2004). Agriculture is the main occupation of the people and small-scale traditional farming system predominates in the area. The major food crops grown in the areas are: maize, rice, yam, cassava and cocoyam.

2. Sources of Data and Sampling Techniques

Primary data which were supplemented with secondary data were used for this study. The primary data were obtained through sample survey using structured questionnaire, administered by trained enumerators under the supervision of the researcher. The secondary data were obtained from publications of the Federal Office of Statistics (FOS), Food and Agricultural Organization (FAO), State Agricultural Development Programmes (ADPs), journals and other relevant publications.

The study used a combination of purposive and multi-stage random sampling techniques in order to obtain the relevant data. The first stage involved purposive selection of the two Local Government Areas (LGAs) noted for rice cultivation in each of Osun and Oyo states. These are Atakunmosa East and Oriade LGAs from the Ife/Ijesa agricultural zone in Osun state and Ogo-Oluwa and Surulere LGAs from Ogbomoso agricultural zone in Oyo state. The second stage involved random selection of six towns/villages from the list of rice-growing towns/villages obtained from the Information Unit of each LGA-making a total of twenty-four villages comprising of twelve villages from each state.

The last stage involved a simple random sampling of thirteen rice farmers from each of the 24 villages in both states. Thus, a total of 312 farmers out of all the population of rain-fed upland rice farmers were interviewed, using a structured questionnaire with interview schedule. However, 300 well-completed copies of the questionnaire, representing 150 respondents from each of Osun and Oyo states were used for the analysis.

3. Methods of Data Analysis

Descriptive statistics, budgeting technique and stochastic frontier production function were employed to analyse the data for the study.

a. Descriptive Statistics

The descriptive statistics used were: mean, standard deviation, minimum and maximum values. They were used to discuss the demographic and production characteristics of rice farmers.

b. Budgeting Technique

The budgeting technique involved the use of gross margin (GM) to determine the profitability of the rice cultivation. The GM is as specified by equation (1) below.

GMi = TR – TVCi  (1)

Where: GM = Gross margin (N/farmer);

TR = Total revenue from the sale of paddy rice (N);

TVC = Total variable cost (N);

The TR from the sale of paddy rice was the value of total sales and imputed value of paddy used for home consumption. Other measures of profitability and economic performance of rain-fed upland rice production adopted for this study were:

(a) Benefit Cost Ratio= TR/TC (2)

1. the log, hence it is easy to fit data with the Cobb-Douglas form;

2. the estimated co-efficients are the direct elasticities of production and,

3. the sum of estimated coefficients are used to deduce returns to scale (RTS) directly.

The model of the stochastic frontier production for the estimation of the TE is specified as:

Yi=f (Xi,P)+Vi-Ui (6)

This is log-linearised as:

ln Yi = Po + P1 ln X 1i + b2 ln X 2i + P3 ln X3i + b4 ln X 4i + b5 ln X5 i + P6 ln X 6i + b7 ln X 7i + Vi – Ui (7)

household sizes. It is expected that the family members of a farm operator will contribute labour to farm work, thus, the farmer’s family in the study areas assisted in planting, weeding, bird scaring and harvesting of rice. The mean amount of credit available to rice farmers in Osun state was about N6, 101 while the mean amount of credit available to their counterpart in Oyo state was about N10, 967. It is expected that the larger the amount of credit available to rice farmers, the greater the encouragement of these farmers in the adoption of improved technologies to enhance rice productivity. The mean output of rice paddy production was about 2183kg from an average of 1.3ha of farmland in Osun state and 2200kg from an average of 1.9ha of farmland in Oyo state, while the average revenue was about N65, 486 in Osun state and N74, 349 in Oyo state. The crop output of any farmer depends on the size of farm he operates. It is revealed from the results in table 1 that farm sizes cultivated are generally small scale in nature (Olayide, 1980). Average profit made by rice farmers in Osun State was about N41, 132.74 and about N44, 476.8 in Oyo state.

Demo-graphic Variable Osun State Oyo State
Min Max Mean SD Min Max Mean SD
Age (Years) 21 70 44.3 11.3 20 62 37.5 6.7
Education (Years) 1 15 3.2 3.4 1 17 4.2 3.9
Experience (Years) 2 52 14.1 9.9 1 34 10.5 7.0
Household Size (No) 1 15 3.2 3.4 1 20 6.7 3.2
Product-ion Variable
Farm Size (Ha) 0.3 6.0 1.3 0.9 0.1 13.0 1.9 1.7
Amount of Credit (N) 1000 60000 6100.7 8838.3 500 45000 10966.9 10232.3
Output (kg) 500 9500 2183.3 1387.97 150 10000 2200.4 1516.7
Revenue (N) 18000 285000 65486.4 41650.14 600 455000 74349.3 51078.6
Profit (N) -2675 208050 41132.74 34393.6 -19600 237700 44476.8 32602.5

Min = minimum values; Max = maximum values; SD = standard deviation.

(US1 dollar =132 Nigeria naira)

Source: Results obtained from Data Analysis.

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