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Vol. 9(1), pp. 19-25, 2021
Copyright ©2021, the copyright of this article is retained by the author(s)
Tutor, Science Department, Saint Catherine Senior High School, Sogakofe.
Finally, the major constraints faced by rice farmers in the study area were low purchasing price of rice, lack of government support, difficulty in accessing capital and erratic rainfall patterns. All other things being equal, if these constraints are removed or minimised rice farmers on the Weta irrigation scheme would be more efficient in production.
The study recommended among others that the Ministry of Food and Agriculture should intensify training of farmers on how to improve upon their production activities through the efficient combination of inputs by establishing demonstration farms within the vicinity of farmers.
Francis K. Kavi
E-mail: kastro8k@ yahoo.com
Rice is considered as the second most important grain food staple in Ghana, next to maize, and its consumption keeps increasing as a result of population growth, urbanization and change in consumer habits [Ministry of Food and Agriculture (MoFA, 2009)].. The total rice consumption in Ghana in 2005 amounted to about 500,000 tonnes and this is equivalent to per capita consumption of 22 kilograms per annum. According to MoFA, per capita consumption of rice per annum is estimated to increase to 63 kilograms by 2018 as a result of rapid population growth and urbanization.
Between 1996 and 2005, paddy rice production in Ghana was in the range of 200,000 and 280,000 tonnes (130,000 to 182,000 tonnes of milled rice) with large annual fluctuations. In 2010, rice was the 10th agricultural commodity in Ghana by value of production while it ranked 8th in terms of production quantity for the period 2005-2010 (MoFA, 2010). Average rice yield in Ghana is estimated to be 2.5 tonnes/hectare while the achievable yield is 6–8 tonnes/hectare. This significant yield potential can be tapped through improvements in agronomic practices and adoption of underutilized beneficial technologies. Also, rice occupies roughly 4 per cent of the total crop harvested area, although it accounts for about 45 per cent of the total area planted to cereals (MoFA, 2009). In addition to being a staple food mainly for high income urban populations, rice is also an important cash crop in the communities in which it is produced.
Rice is also the most imported cereal in the country accounting for 58 per cent of cereal imports (CARD, 2010) accounting for 5 per cent of total agricultural imports in Ghana over the period 2005-2009. Ghana largely depends on imported rice to make up for the deficit in rice supply. On the average, annual rice import in Ghana is about 400,000 tonnes (MoFA, 2009). It is therefore important for stakeholders in the food and agriculture sector to ensure increased and sustained domestic production of good quality rice for food security, import substitution and savings in foreign exchange.
Domestic rice production satisfies around 30 to 40 per cent of demand with a corresponding average rice import bill of US $450 million annually (MoFA, 2010). The massive dependency on rice imports has always been a concern for policy makers, especially after food prices soared in 2008. However, import duties and other taxes as well as interventions to boost productivity and quality of local rice do not seem to produce any substantial impact on Ghana’s import bill.
In May, 2008, Ghana was one of the first countries within the Coalition for Africa Rice and Development to launch its National Rice Development Strategy (NRDS) for the decade 2009 – 2018. The main objective of the NRDS is to double domestic production by 2018, implying a 10 per cent annual growth rate and enhance quality to stimulate demand for domestically produced rice. These increases will most likely come from utilizing potential irrigable lands and valley bottoms with water supply, promoting rice production, and increasing the productivity of existing growers.
The inability of local rice production to meet domestic demand can be attributed to the inability of the rice farmers to obtain maximum output from the resources committed to the enterprise (Kolawole, 2009). According to Rahman, Mia and Bhuiyan (2012), farm level performance can be attained in two alternate ways: either by maximizing output with the given set of inputs or by minimizing production cost to produce a prescribed level of output. The former concept is known as technical efficiency which is a measure of a farm firm’s ability to produce maximum output from a given set of inputs under certain production technology. It is a relative concept in so far as the performance of each production unit is usually compared to a standard. The standard may be used on farm-specific estimates of best practice techniques (Herdt&Mandac, 1981) but more usually by relating farm output to population parameters based on production function analysis (Timmer, 1971).
A technically efficient farm operates on its frontier production function. Given the relationship of inputs in a particular production function, the farm is technically efficient if it produces on its production function to obtain the maximum possible output, which is feasible under the current technology. Put differently, a farm is considered to be technically efficient if it operates at a point on an isoquant rather than interior to the isoquant.
Technical efficiency in agriculture production is an important element in the pursuit of output growth. A high level of technical efficiency implies that output is being maximized given the available technology. In this situation, output growth will be achieved through the introduction of new technology that will shift the production frontier outward. A low level of technical efficiency, on the other hand, indicates that output growth can be achieved given current inputs and available technology. Therefore, it is important to determine the degree of technical efficiency among farmers, and if low technical efficiency is found, to investigate the factors that will increase efficiency.
Measurement of productive efficiency of a farm relative to other farms or to the best practice in an industry has long been of interest to agricultural economists. From an applied perspective, measuring efficiency is important because it is the first step in a process that might lead to substantial resource savings. These resource savings have important implications for both policy formulation and farm management (Bravo-Ureta & Rieger, 1991). For individual farms, gains in efficiency are particularly important in periods of financial stress since the efficient farms are likely to generate higher incomes and thus stand a better chance of surviving and prospering.
In an economy where resources are scarce and opportunities for new technologies are lacking, further increase in output can best be brought about through improvement in the productivity of the crop. In this context, technical efficiency in the production of a crop is of paramount importance.
Measurement of technical efficiency (TE) provides useful information on competitiveness of farms and potential to improve productivity, with the existing resources and level of technology (Abdulai &Tietje, 2007). Moreover, investigating factors that influence technical efficiency offers important insights on key variables that might be worthy of consideration in policy-making, in order to ensure optimal resource utilisation.
Several studies have been conducted on rice production in Ghana. However, most of these studies on rice focused on other areas rather than the technical efficiency of production. Examples are: “Impact of improved varieties on the yield of rice producing households in Ghana” (Wiredu, et al., 2010); “Cooking characteristics and variations in nutrient content of some new scented rice varieties in Ghana”(Diako et al., 2011), “Rice price trends in Ghana” (Amanor-Boadi, 2012) and “Patterns of adoption of improved rice technologies in Ghana’’(Ragasa et al., (2013).
Even though some studies have been conducted on technical efficiency in rice production in Ghana, most of these studies are concentrated in Northern Ghana; especially on the Tono Irrigation Scheme, for example Technical efficiency in rice production at the Tono irrigation scheme in Northern Ghana (Donkoh, Ayambila & Abdulai, 2012).
Besides, in the Ketu North District rice is a major food crop and its production serves as a source of employment for many years. Yet, not much study has been conducted to determine the technical efficiency of rice farmers.
The aforementioned reasons informed a study to be conducted to determine the technical efficiency in rice production on the Weta irrigation scheme in the Ketu North District of the Volta Region of Ghana. This would fill the knowledge gap and inform policy decisions.
Objectives of the study
Generally, the study seeks to measure the technical efficiency in rice production in the Ketu North District of the Volta Region. The specific objectives include:
1. To estimate the level of technical efficiency of rice farmers in the district.
2. To identify and rank the constraints in rice production with respect to technical efficiency.
The following research questions have been developed to guide the study.
1. What are the levels of technical efficiency in rice production in the district?
2. What are the constraints in rice production on the Weta Irrigation Scheme?
The study seeks to test the following hypothesis:
H1: Rice farmers are fully technically efficient.
The study employed cross-sectional survey research design to measure the technical efficiency of rice farmers. In this design, a sample of the population was selected from which data were collected to answer research questions of interest. It is called cross-sectional because the information that were gathered about the phenomenon represent what existed at only one point in time. A cross-sectional study is one that produces a ‘snapshot’ of a population at a particular point in time. The single ‘snapshot’ of the cross-sectional study provides researchers with data for either a retrospective or a prospective enquiry (Louis, Lawrence & Keith, 2007). This research design is appropriate as it makes inference about the effect of one or more explanatory variables on the dependent variable by recording observations and measurements on a number of variables at the same point in time (Gay, 1992).
Amedahe (2002) defines population as the target group about which the researcher is interested in gaining information and drawing conclusions. The target population was all rice farmers in the Ketu North District of the Volta Region. The accessible population for the study included all the 1,024 rice farmers on the Weta irrigation scheme in the Ketu North District.
Sample size and sampling technique
Hummelbrunner, Rak and Gray (1996) explain sampling as selecting a portion of the population that is most representative of the population. The study employed a two-stage sampling technique to select the participants for the study. The rice farmers on the Weta Irrigation Scheme were grouped into 11 sections by the Irrigation Development Authority. Considering each section as a cluster, six sections were selected at random at the first stage. At the second stage, a total of 290 rice farmers were chosen from the six sections using proportionate random sampling technique to form the sample for the study. A sampling frame was obtained from the Irrigation Development Authority. The computer software programme known as excel was then used to generate a list of randomly selected numbers within a specified range. Rice farmers with those randomly selected numbers were then identified and interviewed. This sample size was determined using the sample size determination table produced by Krejcie and Morgan (1970). However, out of the 290, only 285 rice farmers were reached, giving a response rate of 98.3 per cent.
The structured interview schedule was developed by the researcher and used to collect data relating to technical efficiency in rice production from the respondents (farmers). It contained both open-ended and close ended questions. A structured interview is an interview in which the specific questions to be asked and the order of the questions are predetermined and set by the researcher. It is based on a strict procedure and a highly structured interview guide which is no different from a questionnaire. The structured interview is, in reality, a questionnaire read by the interviewer as prescribed by the researcher. The rigid structure determines the operations of this research instrument and allows no freedom to make adjustments to any of its elements such as content, wording or order of questions (Amedahe, 2002).
Data were collected on socio-economic characteristic of farmers, input and output quantities and the constraints faced by rice farmers. The structured interview schedule comprised four sections; namely, A, B,C and D. Section A covered the farm and farmer- specific characteristics such as the age of the farmer, sex of farmer, household size, educational level, marital status, off-farm work, farming experience and years of formal education. Section B of the interview schedule dealt with the production activities of the farmer such as methods of weed control, access to technical training, access to credit, access to agricultural extension services and number of times of producing rice in a cropping year. Section C of the interview schedule provided information on the inputs used and the output obtained by the farmer. These included information on land, labour, materials used for planting, fertilizer, equipment, chemical use and output obtained. The last section, D covered the constraints that farmers face in the production of rice in the district. This included input, production, and marketing constraints.
Pre-testing of instrument
Sarantakos (1997) defines a pre-test as small tests of single elements of the research instrument which are predominantly used to check eventual ‘mechanical’ problems of the instrument. The instrument was pre-tested before it was used for data collection. The pre-test was undertaken in November, 2014 using 30 respondents who cultivated rice in the South Tongu District. This helped to check the adequacy of response categories, ambiguity and respondents’ interpretation of certain questions, thereby making it possible for adjustments to be made where necessary. Inaccuracies identified during the pre-testing were corrected before the actual data collection took place.
The reliability of the instrument was established using the Cronbach’s alpha reliability coefficient. The reliability coefficient was estimated at 0.75. According to Cohen, Manion and Morrison (2007), the widely acceptable minimum standard of internal consistency is 0.70. Therefore, the reliability coefficient of 0.75 is interpreted as high; implying that the individual items or sets of items on the instrument would produce results consistent with the overall instrument.
Data collection procedure
Data were collected by the researcher and two field assistants during the 2014/2015 cropping season. The selection of the field assistants took into consideration their level of education and their ability to speak the local language of the farmers. A visit was paid to the study area by the researcher with an introductory letter from the Department of Agricultural Economics and Extension, University of Cape Coast to inform the District Director of Agriculture, the Irrigation Scheme Manager, the Sectional Heads and the rice farmers about the study a month ahead of the data collection date. A two-day training programme was organised to equip the field assistants with interviewing skills and to explain to them the various items on the instrument. A second visit was paid to the study area to agree on the date and duration for data collection with the rice farmers and their Sectional Heads a week before data collection began. Data collection was done for a period of two months.
Description of farmer- specific variables for the study
Educational level: It is measured by the number of years of schooling by the farmer. Education promotes the adoption of better management practices and resource use which contributes to the efficiency levels of farmers. Findings from a study by Ahzar (1991) show that education enables one to make better choices regarding input combination and use of existing resources. Hence, it is anticipated that education would influence technical efficiency positively.
Age: It was measured in years and it is used as a proxy for farming experience. Chukwuji, Inoni and Ike (2007) have indicated that older farmers are less efficient than the younger ones. This has been attributed to the fact that older farmers are less willing to adopt new ideas in their production activities.
Sex: It was measured as a dummy variable; one, if the farmer was a male and zero, if the farmer was a female. Male farmers were expected to be more technically efficient than female farmers.
Extension services: It shows whether the farmer had access to extension services during the cropping season. It was measured as a dummy variable; one, if farmer had access to extension service and zero, if otherwise. Extension services provided to farmers enable them to learn better farm management practices and efficient use of resources.
Off-farm activities: It indicates whether the farmer engaged in other economic activities aside from rice farming during the 2014/2015 cropping season. Those who engage in different economic activities at the same time are not fully committed to any of the activities thereby leading to technical inefficiency. However, the additional income earned from off-farm activities can be used to purchase farm inputs. It was measured as a dummy variable and had a value of one, if the farmer engaged in other off farm work whilst a value of zero indicates that rice production was a full time occupation.
Access to credit: It was measured as a dummy variable; one if the farmer had access to credit, and zero if otherwise. Access to credit, defined as the availability of loans and other financial aids to the farmer helps to ease the financial constraints faced by farmers. Farmers who have access to credit tend to have higher technical efficiency than those who do not have access to credit (Binam et al., 2004).
Household size: It includes the number of people who were living with the farmer during the 2014/2015 cropping season. It was expected that large family size would have a positive relationship with technical efficiency as they provide labour for farming activities.
Experience: The number of years engaged in rice farming. Bozoglu and Ceyhan (2007) concluded that farmers with more years of farming experience reduce their technical inefficiency level by ensuring the optimal usage of time and inputs. Therefore, it was expected that farming experience would have a significant relationship with rice output.
Membership of farmer based organization. This indicates whether the rice farmer belongs to a farmer based organization or not. It was measured as a dummy variable and had a value of one if the farmer belonged to an organization and zero if otherwise. This variable was expected to increase the yield of rice.
Descriptive statistics, including the mean, frequencies, charts and standard deviation were used to describe the socio-economic characteristics of farmers. The stochastic production frontier analysis, a parametric approach in measuring technical efficiency was employed in this study. The transcendental logarithmic (translog) form of production function was then fitted to the production function to estimate technical efficiency level of rice farmers and the determinants of technical efficiency simultaneously (Research questions one and two). Data were analysed using the SPSS version 21 and the R programming software.
Specification of the models
The explicit translog stochastic frontier production function used in this study is given in equation (vii):
The inefficiency model of the stochastic frontier function is given by:
The translog function was adopted in order to estimate the level of technical efficiency in a way consistent with the theory of production function after preliminary testing for the most suitable functional forms of the model under the data set available using the generalised likelihood ratio test(Griffiths, Hill & Judge, 1993). The generalised likelihood- ratio test statistic is of the form:
Asymptotically, the test statistic has a Chi-square distribution with the degree of freedom equal to the difference in the number of parameters between the models. Here, the null tested was that the Translog functional form does not represent the data more adequately than the Cobb-Douglas. The results of the likelihood ratio test presented in Table 1 show a p-value of 0.05676 which is statistically significant at the 10 per cent significance level indicating the rejection of the Cobb-Douglas functional form in favour of the more flexible translog. Thus the null hypothesis was rejected.
RESULTS AND DISCUSSION
Description of socioeconomic characteristics of rice farmers and production parameters
Table 2 presents the summary statistics of farmer-specific characteristics as well as production parameters. It can be observed from Table 6 that on average, rice farmers in the study area had 19 years of farming experience, with a minimum of 2 years and a maximum of 36 years. Table 6 also shows that the mean number of years of formal education was 5 years with a minimum of zero and a maximum of 13 years. Also, the mean extension contacts was twice a year. This is relatively low considering the importance of extension in agriculture. The low extension contacts imply that not much information got to the farmers as far as innovations and technologies are concerned. Table 6 also indicates that on average, rice farmers in the study area produced an output of 6059.9 kilograms of rice per hectare using an average of 1.66 hectares of land, 21.15 litres of weedicide per hectare, 492.33 kilograms of fertilizer per hectare, 16.98 litres of pesticide per hectare, 625 person days of labour per hectare, 275 kilograms of seeds per hectare, GH¢608.50 worth of irrigation facilities per hectare and GH¢40.75 worth of equipment per hectare. The minimum output of rice was 3250 kilograms/hectare and the maximum was 22000 kilograms/hectare. The large variation in output of rice in the study area can be attributed to variations in their levels of technical efficiency.
Table 2: Summary statistics of production parameters and farmer characteristics
Source: Field survey data, 2015.
Frequency distribution of technical efficiency of rice farmers
Table 3 shows the frequency distribution of technical efficiency of rice farmers. The mean level of technical efficiency of rice farmers was 70.7 per cent with a minimum of 29.6 per cent and a maximum of 96.3 per cent. This shows that there was a wide disparity among rice farmers in their level of technical efficiency. This, in turn, indicates that there was an opportunity to improve the existing level of production of rice in the study area through enhancing the level of technical efficiency of rice farmers.
The mean level of technical efficiency further implies that the level of output of rice in the study area could be increased on an average by about 29.3 per cent if appropriate measures are taken to improve the level of efficiency of rice farmers. In other words, there was a possibility of increasing the yield of rice by about 29.3 per cent using the available resources in an efficient manner without introducing a new technology.
The results in the table 3 also show that 45.26 per cent of the respondents operated below the mean level of technical efficiency. Thus the null hypothesis that rice farmers in the Ketu North District are not fully technically efficient is not rejected.
Constraints to rice production
The views of respondents were sought on the seriousness of the constraints faced in rice production in the study area. The constraints were tabulated and ranked on a 5-point Likert scale as follows: 1-Very high constraint; 2-High constraint; 3-Low constraint; 4-Very low constraint; 5- No constraint. The result is presented in Table 4. The Kendall’s coefficient of concordance (Kendall’s W) of 0.598 shows that an agreement exists among the rice farmers on the ranking of constraints to rice production at 1 percent significant level. This implies that rice farmers in the study area agreed on the rankings of the constraints that limit them in the production of rice. Therefore the null hypothesis that there is no concordance among the rice farmers on the ranking of the constraints they faced in rice production is rejected in favour of the alternative.
***——- Statistically significant at 1%
a———–Kendall’s coefficient of concordance.
Source: Field survey data, 2015
It can be observed from Table 5 that the first three constraints faced by rice farmers were low purchasing price of rice, lack of government support and difficulty in accessing capital. These constraints were interrelated. For instance, lack of government support in the form of provision of capital for production compels farmers to access loans from unscrupulous moneylenders at exorbitant interest rates. Most of these money lenders also buy and sell rice for profit therefore they dictate the price. Moreover, the dire need for money to settle debts owed by the farmer to friends and relatives compels them to sell their produce at the prevailing market price which is usually low. The farmers had, therefore, become price takers. Further, the low price offered could not cover production cost, making it difficult for farmers to purchase the required inputs for the next season. This finding confirms the finding of Matanmi et al. (2011) who cited financial constraints and access to inputs as serious challenges facing irrigated rice productions at the Kwara State, Nigeria. The finding is also consistent with those of Alarima, et al. (2011) and Bempomaa(2014) who found that lack of financial agencies to support production; poor capital base and low purchasing price of rice were the major constraints facing rice and maize production in Nigeria and Ghana respectively.
The next constraint faced by rice farmers was the erratic nature of rainfall in the study area. Although the rice farmers were on an irrigation scheme, inadequate rainfall makes irrigation water unavailable since the water level in the dams becomes very low during the minor raining season. On the other hand, excessive rainfall leads to flooding. This finding lends support to that of Musime et al. (2005) that change in rainfall pattern was a major constraint limiting rice production in the Bugiri District, Kampala, Uganda.
At the bottom of the table is the constraint of lack of storage facilities. In fact, almost all the respondents did not see this as a problem because all the sections on the irrigation scheme had a place for storage. Also, there was ready market for rice and the financial pressure on farmers to sell their produce to enable them pay debts could not permit them to store their produce for some time to attract higher prices.
Table 5: Ranking of constraints faced by rice farmers
CONCLUSIONS AND RECOMMENDATIONS
Based on the findings, it can be concluded that rice farmers in the study area were not fully technically efficient. With the mean technical efficiency estimated at 70.7 per cent, there was an opportunity for the rice farmers to increase their output by 29.3 per cent through efficient reallocation of the available resources without introducing a new technology.
From the conclusions drawn, the study recommends that the Ministry of Food and Agriculture should intensify training of farmers on how to improve upon their production activities through the efficient combination of inputs by establishing demonstration farms within the vicinity of farmers since the farmers were not fully efficient in production.
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