Determination of Resource Use Efficiency of Rice Farmers in Kaambe District of Guma Local Government Area of Benue State, Nigeria

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2. Materials and Method

2.1. Study Area

Kaambe district is located between latitudes 7°38¹N and 7°52¹N and longitude 8°50¹E and 9°05¹N. The area shares boundary with Saghev ward to the east, on the north by Uvir ward, on the west by Abinsi ward all of Guma local government area of Benue State, and in the south by Tarka local government area of Benue state. The location of the area in Guma local government area is shown on Figure 1 and Figure 2.

Figure 2. Map of Kaambe District Showing Rice Farming Communities (Source: Derived from Ministry of Lands and Survey, Makurdi)

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Agriculture forms the backbone of Kaambe ward economy, engaging more than 70% of the working population in the area. Though the agricultural system was largely subsistent and depended highly on hand tillage with few people practicing mechanize farming. Although, mechanization has gradually crept in, use of farm inputs such as improved seeds, fertilizers, insecticides and herbicides is on the increase in the area ^[13].

Kaambe district is divided into four rice farming communities; Agasha, Tyulen, Mbagbaav and Gberkyon. A total of 100 rice farmers were randomly selected, twenty five each from the four rice farming communities of the area. Although, some the issues where not attended to by the respondents. Data collected for this study include: farm yield, amount of farm inputs such as farm size, fertilizer, planting seeds, herbicides/pesticides, labour and capital. This data were generated from both primary and secondary sources using the following instrument of data collection: questionnaire, interviews and field measurement. The data was analyzed using a regression model to determine the resource use efficiency of rice farmers in the area.

3. Results and Discussion

3.1. Characteristics of Rice Farmers in the Area

Age Distribution of Rice Farmers

Age determines to some extent how productive an individual is in carrying out agricultural activities. It plays an important role most especially in the traditional system of agriculture practiced in the third world countries.

Table 1. Age of respondent

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Table 1 shows that, 68.77% of the respondent were within the age category of 30 to 59 years, thus they are said to be in the productive age to be able to cope with the rigours of rice production. The mean age of respondent is 43.82 years which indicates that they were still very active. The active age of rice farmers implies that, their farm labour is productive.

3.2. Period of Rice Cultivation among Respondents

Rice a major commodity is an increasing important crop in the state and the country at large. Cultivation of this crop decades ago attracted few farmers. Today, it has become a major food and cash crop due to the decline in maize production world over.

Table 2. Period of rice farming in the area

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Table 2 shows that majority of the rice farmers in the study area have stayed in the business of rice cultivation for between 11 to 20 years represented by 43.33% of the respondent. The mean period of rice cultivation of respondent was 15.69 years which indicates that rice cultivation in the study area is a recent phenomenon. However, with the increasing demand of rice, farmers are ready to adopt new rice farming practices in order to better their experience in rice production.

3.3. Efficiency of Resource Allocation by Rice Farmers

The concept of efficiency is at the core of economic theory. It indicates the maximum benefits derivable from optimal allocation and use of resources, in this case farm resources. The theory of production economies is concerned with optimization which implies efficiency. The important role of efficiency in increasing agricultural output has been widely recognized by researchers and policy makers alike. It is no surprise therefore, that considerable effort has been devoted to analysis of farm level efficiency in the developing countries. An underlying premise behind much of this work is that, if farmers are not making efficient use of the existing technology, efforts designed to improve productivity of farmers cannot be achieved. The efficiency of resource allocation by rice farmers in the area were determined by productivity level achieved by farmers as a result of farm inputs.

3.3.1. Determinants of Rice Farmers’ Productivity in the Study Area

The productivity of rice farming in the study area is determined using a regression analytical technique. The formula is stated thus:

(1)

Where: Y- Rice yield (kg)

a - constant

b₁, b₂, b₃, b₄, b₅, b₆ – plane of the variables

X₁– Farm size (ha)

X₂– Fertilizer (kg)

X₃– Herbicide/pesticide (litre)

X₄– Seeds (kg)

X₅ – Labour (man/day)

X₆ – Capital (Naira)

e – Error of estimate

Table 3. Model Summary

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Table 4 shows the factors that determine the productivity level achieved by rice farmers in the study area. It shows that, of the forms in which the variables used to explain the productivity level of the rice farmers were entered. A regression model that accommodates rice yield, their farm size, the quantity of herbicide/pesticide, the quantity of fertilizer, amount of labour used, planting seeds and capital. It showed that the six (6) independent variables regressed against rice yield (the dependant variable) in the study area gave a coefficient of determination (R²) of 0.812 (Table 3). Thus the variation in productivity of rice farmers can be adequately explained (81.6%) by the identified variables (farm size, fertilizer, herbicide/pesticide, seeds, labour and capital). Thus, the six independent variables can explain 81.6% of the productivity of rice farmers. They can determine 81.6% of the variation that can be observed in the productivity level achieved by rice farmers in the study area.

Table 4. Determinants of Productivity level of rice Farmers

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The table further shows that the seeds planted by the respondent was the highest predictor of the productivity level achieved by respondents, indicating that farmers with larger quantity of seeds were more productive. Followed by farmers with large farm size were also more productive than those that cultivated small holdings. The huge investment of farmers that cultivated large farms and quantity of planting seeds propelled them to be more productive in order to be able to recover production cost. The contribution of quantity of planting seeds and farm size is followed by those of herbicide/pesticide, fertilizer, and labour with capital having an insignificant coefficient of determination. These variables can therefore be further exploited to improve productivity level of rice farmers to boast their economic status in the area.

There is thus need to encourage rice farmers to adopt the use of improved varieties and also open up new areas of land for rice cultivation to increase their output. Since, farm size and quantity of planting seeds have the tendency of propelling rice production in the study area. The predictor model of the study area is stated thus:

(2)

Where: Y* is predicted rice yield of the area and Y is estimated rice yield of the area.

3.3.2. Determination of Rice Yield Using Regression Model

Multiple regression analysis is a powerful technique used for predicting the unknown value of a variable from the known value of two or more variables also called the predictors. For instance the yield of rice per hectare depends upon quality of seed, farm size, fertilizer used, pesticide/herbicide and amount of labour used as shown in Table 5. Using equation 3, the following predictions were made for the communities of the study area.

Table 5. Determination of rice yield using the regression model

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The degree of uncertainty inherent in an estimate of rice yield regression plane expressed in units of standard deviation represents the standard error of estimates which is the error of multiple regression model in predicting actual values of rice yield in the area. The formula of standard error of estimate is stated thus:

(3)

Table 6. Standard error estimate

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This implies that predicted values of rice yield of farmers in the study area with the regression model will deviate from actual value by 4.69 standard deviations.

Table 7. Analysis of variance for testing the significance of the regression model

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