Research Article

Journal of Agricultural, Life and Environmental Sciences. October 2020. 205-215
https://doi.org/10.22698/jales.20200018


ABSTRACT


MAIN

  • Introduction

  • Theoretical Framework

  • Analytical Framework and Empirical Modelling

  • Result and Discussion

  • Results of the DEA model

  • Conclusion

Introduction

Agriculture remains a vital tool for the world’s economy. It plays a primordial role in the process of development of a nation through the provision of food items, create employment opportunities, industrial inputs, generate foreign exchange earnings, and contributes to the gross domestic product and expanding markets for industrial outputs. In addition, about 70 percent of income and employment of the world’s rural poor societies and 32 percent of GDP of these countries is gotten from the agricultural sector (World Bank, 2007). Globally, 75% of the world poor in rural areas rely on this sector, agriculture must be part of the world economic growth, poverty reduction, and environmental sustainability (UNDP, 2012). The world’s population will rise from about seven billion today to about eight billion by the year 2030, and possibly to over nine billion by 2050. Faced with a steady population growth, the agricultural sector is expected to double its efforts so as to meet the food demands of this rising population (UN Statistics on World’s Population Prospects 2019).

However, food production is rather decreasing in most developing nations especially in Sub-Saharan Africa. Estimates from the Global Agricultural productivity report indicates that the current rates of total factor productivity (TFP) growth in sub-Saharan Africa (SSA) will meet only 8 percent of its food demands through productivity. This is almost 50 percent lower than the 2014 projection of 15 percent, a troublesome trend (GHI, 2017). This gap between food production and population increase, if not addressed, will lead to a plethora of issues like high food prices, starvation and increased environmental burdens through deforestation. It will also lead to economic and social standstill as opined by Nkamleu et al. (2003).

Recently, there have been numerous policy debates on the various measures necessary to turn around tables and obtain increased productivity in agriculture as well as food production. As purported by GHI (2017), improving productivity and the level of efficiency in farm production are the necessary pathways to increased food production. Improving productivity and promoting yield growth in the agricultural sector, especially amongst smallholder farmers continues to be a necessary step in achieving economic development (Poudel et al., 2015). Productivity growth is achieved either from the efficient use of existing resources or the adoption of new technologies/ techniques in agriculture

Agriculture in Cameroon over the past years has been the main employment sector that employs about 70% of its working population. It equally contributes about 42 percent to the Gross domestic Product (GDP) and serves as food for the population as well as feed for livestock (World Bank, 2014). The country equally produces several agricultural produce meant for local and international markets and it remains one of the world’s main producers of certain commodities which includes cocoa, coffee, banana, and palm oil.

Nevertheless, agriculture is still very labor intensive and still operates at the ‘second generation level’ unlike in developed nations like Germany which are capital intensive and operate on the ‘fourth generational level’. The farming systems are also very heterogeneous with the bulk of farmers still operating at the subsistence level. Categorized 13th producer in Africa, Cameroon is also experiencing an uncertain level of progress in maize production, despite the shortage registered in 2011 (MINADER, 2012). Maize is a main staple crop in Cameroon and grown by most farm families. It also a traditional delicacy for most ethnic groups as they either consumes it in the form of food or drink. As a form of food, it can be eaten as porridge with beans, roasted, boiled and even transformed to maize powder to acquire a corn-paste locally referred as ‘fufu’. It is also traditionally fermented and used for drinking purposes particularly in rural areas. Brewery companies also make use of it in the production of beer. Livestock owners also use it as feed for their animals. Because of its several uses, attention is progressively changing from cultivation for food purposes to cultivation for cash.

Maize has experienced important growth in the last five decades. As shown in Fig. 1 below from the period of 1961-2017, there was an annual average of 744 thousand tons of maize produced in the country. However, The production increased to over approximately 2.3 million tons in 2017, compared to 310 thousand tons in 1985. Maize production and supply from 1976 to 1994 shows a “saw tooth” progress. Before 2000, the domestic production was on average 460,133 tons of maize per year. From 2001 to 2008, it was slightly over 1 million tons per year. Since 2008, there has been a solid annual increase in maize production. This performance may be elucidated by the Government's national policy introduced since 2008 with an accent on input subsidies for fertilizers, improved seeds, equipment and regular counselling and advisory support to farmers. The government of Cameroon in an attempt to raise production and commercialized agriculture partnered with both national and international organizations like the German International Corporation (GIZ) and farm demonstrations, extension visits, seminar and training such as ‘farmer field school’ and the ‘farmer business schools’. It also has set up policies to inspire and increase the export level and adding values to farm products through processing (Achancho, 2013).

http://static.apub.kr/journalsite/sites/ales/2020-032-03S/N0250320S05/images/ales_32_S_05_F1.jpg
Fig. 1.

Maize production in Cameroon, 1961–2017.

Source: faostat, 2018.

Maize production in Cameroon has encountered numerous problems like rudimentary production methods, low work productivity and poor organization of actors despite progress registered from the last decade. These problems stemmed from production changing at a far slower pace than the population growth. Moreover, the growth of agro-industries and the cumulative demand of neighboring countries contribute to increasing the deficit between national demand and supply. This production gap is all preoccupying because maize output from traditional farmers remains minor (1.5-2.6 t/ha) and is usually lower by 50% to 80% than optimal yield which is effortlessly reachable with research-driven technology (MINADER, 2014). Therefore, Cameroon moved from being a net maize exporter to a net importer since the 1980s. This has progressively reduced its trade deficit, especially since the latest up surge of international food product prices (Gergely, 2002).

Subsistence farmers are usually inundated with imperfect and asymmetric information. As a result, they usually experience high costs due to high inefficiency level of production process (Sadoulet & de Janvry, 1995). The performance of farms have continuously been assessed through the use of the concept of economic efficiency (Poudel et al., 2015). Economic efficiency refers to the aptitude of a firm or a producing unit to produce a maximum set of outputs with the lowest cost combinations. It includes both technical and allocation efficiency, though it is more fundamental to use technical efficiency as it stresses the efficient use of scarce resources. Technical efficiency refers to the ability of a firm or a producing unit to produce more using the same set of inputs with an available technology or producing the same output with smaller combination of inputs.

In rural areas, most small-scale farmers are amongst the vulnerable groups. The majority been women, children and the aged. It is alleged that a high proportion of these farmers are inefficient due to over or under utilization of some of the factors of production. This in turn leads to food insecurity and poverty (Baloyi et al., 2011). The technical efficiency of small-scale farmers can be easily improved given the existing level of technology which enables them to produce maximum output from a given level of inputs and hence enhance productivity. However, empirical studies on the technical efficiency of small-scale farmers is partial and information on farmer’s production situations remains low mostly in Sub-Saharan countries including Cameroon. Limited studies have been conducted on the technical efficiency of small scale maize farmers in the study area. The major goal of any production system is the attainment of an optimally high level of output with a given amount of effort or input (Rahman, 2013).

The main objective of the study is to analyze technical efficiency(TE) of small-scale maize farmers in Foumbot and Foumban sub divisions of Cameroon. The specific objectives are to determine the levels of technical efficiency of maize production among small-scale farmers in the study area, and to identify farmers’ characteristics affecting technical efficiency in maize production in the study areas. Based on the specific objectives, the following hypotheses were made; H0: there is no significantly high level of technical efficiency of small scale maize farmers in the study area. H1: farmer characteristics are not statistically significant in explaining technical efficiency of small-scale maize farmers.

Theoretical Framework

Numerous works have been done on technical efficiency using Data Envelopment Analysis (DEA) technique and the stochastic frontier approach. Chimai (2011), for example, estimated the technical efficiency of maize production and its determinants in the growing of maize in Zambia using the DEA method and the Ordinary Least Squares (OLS) to realize the set objectives. It was discovered that technical efficiency was low averaging 34% and only about 5% of the sampled farmers were 100% efficient, while 78% of them were at least 50% technically efficient. This shows that there were low efficiencies in production suggesting potential to improve. It was also revealed that great number of dependents, access to agricultural credit, and value of household assets had a positive correlation with technical efficiency. Household size, use of animal draught power for tilling and family size had a negative relation on technical efficiency, while household heads’ formal education levels, ages and gender were not significantly important in explaining technical inefficiency.

Various analysis on the technical efficiency (TE) of numerous commodities such as peanut, cotton, tomato, and maize in Cameroon have been conducted (Binam et al., 2005; Neba et al., 2010; Kane et al., 2012; Akamin et al., 2017), using a two staged stochastic frontier analysis (SFA), parametric and non-parametric DEA methods. Theses studies found a technical efficiency levels of 45% to 65% at the most. Their analysis equally revealed that education and participation in farmer clubs or associations stood very high. In addition, In terms of inputs, farmyard manure and farm equipment were very productive. Equally, female farmers showed better TE attributes than their male counterparts. Labor availability equally showed the expected positive influence on TE.

This study was conducted for the Noun Division, West Region of Cameroon which has eight sub divisions namely: Foumban, Foumbot, Magba, Malantouenn, Bangourai, Massangam, Kouoptamo and Koutaba. The Noun Division covers a total land area of 7,687 km2 and is located in the West-Central part of Cameroon (Yerima and Van, 2005). Two production zones were identified based on the importance of maize production in these areas : Foumban and Foumbot subdivisions. The criteria used to select these areas are the agro-climatic conditions such as soil type, precipitation level and natural vegetation, and socio-demographic conditions as well. In the absence of a recent agricultural census, sampling of the farm surveyed was carried out randomly from the list of farmers available. Sampling allows us to randomly select 148 farmers in the study areas.

Analytical Framework and Empirical Modelling

Basically, many authors have used two approaches to analyze technical efficiency since the pioneer works of Farrell in 1957. The SFA is a parametric analysis that offers econometric approach in the measurement of efficiency (Aigner et al., 1977) while the DEA is a non-parametric technique which uses mathematical programming to derive efficiency (Charnes et al., 1978).

A two-step methodology was employed for the study. Primarily, the DEA model was used to determine the level of technical efficiency in the study areas because of its deterministic nature and its assumption that all deviations from the frontier is as a result of inefficiencies. The DEA has been successfully used in the fields of operation research and production economics in assessing efficiency (Odeck, 2007). DEA can either be Constant Return to Scale (CRS) or Variable Return to Scale (VRS). CRS is appropriate when all decision-making units are assumed operating at an optimal scale, or otherwise VRS is appropriate. Maize farmers in the study areas usually experience variations in agricultural production caused by factors such as financial constraints, imperfect competition, fluctuating input prices and unreliable labor supply. The use of VRS, developed by Banker et al (1984) was therefore appropriate in order to account for these variations as shown below, and the model is given below for N decision making unit (DMU), each producing Y outputs by using K diverse inputs (Coelli et al., 2002).

MaxK=1SVkYkMaxmj=1UjXjpK=1SVkYkis·tj=1mUjXjiK=1SVkYkiVk,Uj0k,j Where k= 1 to s; j= 1 to m; i= 1 to n; Vk = weight given to output k; Ui= weight given to input j Yki= amount of output k produced by DMU i Xji = Amount of input j utilized by DMU i

In the second stage, technical efficiency scores obtained were regressed on farm and farmers’ characteristic variables to detect their impact on technical efficiency. Technical efficiency scores lies between 0 and 1, therefore the two-limit Tobit regression model was applied; Thus, the model is formulated as follows: (Table 1).

UI*=β0+j=01βjZij+μiUi=1ifUI*1U*if0<UI*<10ifUI*

Table 1.

Description of variables used in the Tobit model

Name Definition Description of variables
Malehd Male head Dummy variable: If the household is male headed=1or 0 if female headed
H/age Household age Age (years) of household head
H/size Household size Household size (continuous)
H/edu Household education Number of years spend in formal schooling
Prod advice Production Advice Dummy = 1 if received production advice, 0 if otherwise
Adptill Adopted tillage 1=Hand digging; 2= animal draught power; 3= Not ploughed
HLabor Hired labor Dummy: 1 if tilled with hired labor 0 if otherwise
Offincm Off farm income Income from off farm activities in Fcfa
Asset Household assets Value of all household assets in Fcfa
Farm size Farm size used to produce maize Maize farm size in hectares
Seed rate Maize seed rate Quantity of maize seed used per hectare
Manure Manure Dummy = 1 if used manure, 0 if otherwise
Improve seed Improved seed varieties Dummy = 1 if used improved seed varieties, 0 if local varieties
Clbmbr Club/association membership Dummy =1 if the household head belonged to any club, 0 if otherwise
EXPR Farming experience Dummy = 1 if more than 5 years, 0 if less than 5 years

Where i refers to the DMU; Ui is the efficiency scores of the DMU; Ui*is the latent efficiency; βj are parameters and μi is the error term such that N-(0;δ2); Zij represent farm and farmers characteristics. Thus, the Tobit model used in this study was specified as follows:

Efficiencyscores=β0+β1Malehd+β2H/age+β3H/edu+β4HHsize+β5Prodadvice+β6Adptill+β7Labor+β8Offincm+β9Asset+β10Srgmfarmsize+beta11manure+β12Improvseed+β13Clubmbr+β14Expr+μi

Result and Discussion

The variables used in DEA analysis were subject to descriptive statistics as presented in Table 2 before the technical efficiency scores were generated. These variables were similar in both subdivisions and the same variables were used in the computation of the technical efficiency indices (TEIs) or scores using DEA model. As shown in the Table 2, there were three inputs and one output. The inputs used included the land area in hectares planted with maize, the quantity of maize seeds planted and labor. As indicated, the size of the land used was as small as 7ha in Foumbot and 6ha in Foumban with an average of 3ha and 2ha in Foumbot and Foumban, respectively. The mean quantity of seeds planted in Foumbot and Foumban was 7.5 kg and 5.5 kg, respectively. Labor was measured in terms of days/person where 1 day/person was equivalent to 8hours of work in a day. Output was the maize harvested measured in kilograms. The mean quantities of maize output harvested were 322.3kg and 117.3 kgs in Foumbot and Foumban subdivisions, respectively (Table 2).

Table 2.

Summary statistics of variables used in the technical efficiency analysis

Input\ Output Variables Foumbot Sub Division Foumban Sub Division
Min Max Mean S.D. Min Max Mean S.D.
Output Maize Harvested(Kgs) 200 8,750 1622.2 1721.9 100 1,500 649.3 398.8
Inputs Maize land size(Ha) 1 7 3.3 1.617 1 6 2.5 1.5
Seed Quantity(Kgs) 5 10 7.5 78.8 3 8 5.5 16.9
Labor 4 60 32 17.5 2 79 40.5 31.31

Results of the DEA model

The overall technical efficiency of maize producers in Foumbot and Foumban subdivisions is high averaging 62%. This implies that, on average, the DMUs were able to obtain around 62% of full potential output from a given input with the existing technology.

On the other hand, this equally implies that around 38% of the potential production is lost by the DMUs due to technical inefficiency. There is thus a possibility of increasing technical efficiency in maize production in the subdivisions by 38% through better use of available resources given the existing state of technology. These results appear to concur with those of Wakili (2012) who found an average TE of 72% for maize production in Adamawa State in Nigeria.

Equally, out of the total 148 DMUs surveyed only 35% of them were identified as being DEA efficient, while the technical efficiency level of the inefficient DMUs ranged from 0.20-0.89. There is, therefore, a potential to increase output by 65% (level of technical inefficiency) from the existing levels of inputs use. Policy strategies meant at enhancing technical efficiency in the short run must lay emphasis on the effective and efficient use of the existing technology transfer instruments, which enhances the capacity of the farms to efficiently use the physical inputs. The determinants of technical efficiency are presented in Table 3 below.

Table 3.

Results of the Tobit regression

Variables Overall Foumban Foumbot
Coefficient t-ratio Coefficient t-ratio Coefficient t-ratio
Male-headed HHs
Age of the HHH
Education of the HHH
HH size
Assets
Farming experience
Land tenure
Club membership
Seed rates used
Use of improved seed
Size of land
Land preparation Hired
Manure use
Extension services
HH off-farm income
Fertilizer use
Constant
δ2
Pseudo R2
Log likelihood
Prob. Chi-square
LR chi2
0.009
-0.008
0.028*
-0.022
0.028
0.078*
0.071
0.022*
-0.001
0.096
-0.017
-0.058
0.007*
0.062*
0.070*
-0.033
0.079
1.130*
0.021
0.576
-44.928
0.000
121.87
0.15
-1.64
2.19
-1.60
1.04
2.37
1.49
2.38
-0.42
1.39
-9.60
-1.25
2.12
2.91
2.50
-0.73
1.23
3.72
0.0147
-0.009
0.069*
-0.041
0.040
0.074*
0.139
0.039*
-0.001
0.167
-0.013
0.061
0.019*
0.006*
-
0.190
0.021
0.843
0.027
0.606
-17.300
0.000
53.10
0.15
-1.23
2.69
-1.00
1.09
2.86
2.02
2.25
-0.31
1.60
-5.41
0.60
2.21
2.06
-
1.39
0.20
1.98
0.064
-0.006
0.030*
0.050
-0.060
0.058*
0.073
0.019*
-0.001
0.039
-0.214
-0.128
0.028*
0.305*
0.104*
-0.118
0.141
1.644*
0.023
0.844
-9.304
0.000
100.82
1.10
-1.44
2.17
2.11
-1.50
3.26
1.10
2.30
-0.17
0.48
-9.59
-2.28
2.43
2.40
2.87
-1.85
1.91
4.60

*: statistically significant at the 5% level.

The value of Pseudo R2 of our censored regression model was 0.5756 which implies that 57% of the variations in maize output was due to the factor inputs. Our variance parameter sigma squared shows the goodness of fits of the specified distributional assumptions of the composite error term. A positive value simply means that the distributional assumption of our error term was of good fit. The overall LR Chi2 indicates the significant of the entire model. Since the Prob. Chi-square is less than 5%, this means our entire model was significant.

Out of the 17 variables, six were found to influence technical efficiency positively and statistically significant at 5% level. These variables include education levels of household heads in terms of years spent in formal schooling, years of experience in maize farming, HH membership in farmer associations, hired labor, use of manure and production advice on maize production through extension services. This implies that an increase in these variables respectively improved technical efficiency of maize production.

Farming experience was positive and significant at 5% level implying that as farming experience increases, this tends to increase farmers’ capacity to do better hence, they become more technically efficient. These results are consistent with those of Gul et al (2009) and Padilla-Fernandez and Nuthall (2009), but contrary to those of Ajewole and Folayan (2008). Farmers with more years of farming experience are normally better placed to acquire the skills needed for selecting suitable new technology over time.

Educated farmers tend to adopt and respond speedily to the use of better-quality technologies such as harvesting and soil conservation technologies and the agronomic practices such as appropriate spacing and thinning, which could positively influence the technical efficiency of maize. According to Wakili (2012) and Njeru (2010), farmers with low levels of education are often less receptive to improved farming techniques. These farmers provide poor supervision and are often very slow in responding to emergencies such as outbreak of crop diseases or pests.

The existence of positive and significant relationship between membership to farmer associations and technical efficiency was an important finding. This suggests that the households who belonged to farmer associations or clubs or related organizations were more likely to benefit from better access to inputs such as improved maize varieties and information on improved farming practices. Similar results were also realized by Wakili (2012) and Chiona (2011).

Hired labor had a positive and significant coefficient at 5% significance level. This implies that those households who used hired labor were more efficient than those who only used family labor in the production processes of maize. This could be attributed to the fact that hired labor acts as an incentive for the households to be more efficient as the cost of hired labor is expensive, hence, the productivity per unit of hired labor is high. Comparable results were reported by Chimai (2011) and Elibariki and Shuji (2008).

Production advice on maize through extension services given to the households had a direct and significant relationship with technical efficiency. This implies that those households that received production advice on maize were more technically efficient than their counterparts who never received any advice. This corroborates with the work of Wakili (2012), Chiona (2011), Javed et al (2010). Through production advice from the extension agents, farmers were able to get first-hand information on new agricultural innovations and techniques that would ensure increased maize production in the study areas.

Conclusion

For the study, a non-parametric method and Data Envelopment Analysis (DEA) were employed, which allows for numerous inputs and outputs and imposes no assumptions on the functional form. The sample size for this study involved 148 respondents consisting of farmers aged 20+ collected through a multistage random sampling technique in the study area.

In the first stage of our analysis, the DEA model was used to determine the level of technical efficiency of small scale farmers in the study area. In the second stage, we employed the censored dependent Tobit model which permitted us to capture the impacts of farmers’ characteristics on technical efficiency. The mean technical efficiency of 0.61 and 0.64 were obtained at variable returns to scale from Foumbot and Foumban subdivisions respectively. These values simply shows that there still exist potentials of increasing the level of technical efficiency in the study areas. Technical efficiency indices were equally regressed on farmers’ characteristics and significant results were obtained.

Based on the results, we could conclude that formal education levels of the HHHs, years of maize farming experience, membership to farmer associations, hired labor, use of manure and farmers receiving production advice through extension services had a positive and significant effects on technical efficiency implying that an increase in these variables with everything equal will increase the level of technical efficiency of small-scale farmers in the study areas. It was therefore recommended that farmers should be encouraged to use organic manure, which is locally and cheaply available. They should be trained on how to make farmyard manure or compost manure using local materials at their disposal. The training should be conducted through demonstrations during the field days or agricultural shows.Equally, it was recommended that the development/improvement of training and extension services should be encouraged. Farmers need to access training and extension services in order for them to correctly appraise their investments. Technical skills training such as agronomy, post-harvest handling and processing is an important component in rationalizing production and marketing of the crop.

Finally, appropriate policy formulation and/or review should be undertaken to provide an enabling environment to encourage related basic education for both the old and the young farmers. The implementation of policies that encourage farmers to join cooperatives and farmers’ associations should be established in the short-run as well.

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