Maize dominates the South African food system, being both a dietary staple [1
] and crucial commodity in food security and reduction of malnutrition [2
]. However, producing more of this crop by expanding the area cultivated among black farmers has come up against severe challenges [3
], including the on-going land reform programme which is failing to effectively redistribute the land held by white commercial farmers. At the same time, traditional tenure practices continue to concentrate land in the hands of local chieftains and their cronies [5
]. Thus, the unequal distribution of agricultural land that was institutionalized by apartheid rule to the disadvantage of the black population has remained unchanged, as farm sizes remain small within black areas [6
]. According to Moyo [7
], much of the deteriorating welfare conditions in SA derive from the slow pace of land reform and restricted access to farming inputs. In addition, Moyo [8
], Cotula et al. [9
], as well as Jayne et al. [10
], reported that inequitable land distribution is a legacy of apartheid, which prevented SA black farmers from competing in the agricultural market [11
The African National Congress (ANC) government is now considering changes in the relevant provisions in the legislation to allow for land expropriation without compensation to hopefully accelerate the pace of the land reform process [12
]. Redistributing resources is important because land ownership distribution was the driving force behind the struggle of the black population [13
]. The sense of urgency to achieve redress is also manifested in the plethora of government programmes accompanying the land reform programme [14
At the very highest levels, the government’s commitment to creating jobs and reducing inequality and poverty in the nation has been regularly stressed [15
]. To achieve these goals, it is often stated that the government is committed to revitalizing the irrigation schemes in the poor areas of the country. During his presidency (2009–2017), President Jacob Zuma promised that infrastructures would be rehabilitated as a means to rebuild and uplift communities around the irrigation schemes and stimulate profitable agribusiness through a comprehensive programme that would train and build farmers’ capacity to manage their businesses profitably and sustainably [15
In spite of this, there has been huge disgruntlement and disappointment about the implementation of land reforms. The earlier expectation was that by 2014, the black farmers’ ownership of the country’s agricultural land would have increased by as much as 30% [6
]. Despite the actions taken by the government to redress the legacy of apartheid, evidence [6
] shows that the land transfer process under the land reform programme has been far from efficient, with the slow pace resulting in that deadline being missed. Expectedly, the substantial investment for restructuring the agricultural sector has failed to yield the anticipated redistribution of land and improvements in rural welfare, especially for the black population. At present, particularly in the former homeland of SA, farmlands have become smaller and crops are cultivated mostly on food plots and homestead gardens where farmers live [16
]. At the same time, practices remain traditional and basic, inevitably resulting in the persistent low productivity of smallholder agriculture [17
2. Literature Review
Seeking answers to the question as to the role of farm size in farm productivity, several studies have focused on the relationship between farm size and performance of farms. In relation to that, studies have evaluated the socioeconomic and political precursors of concerns over land availability, distribution, and implications for levels of farm investment. In addition, the literature has examined government farmer support arrangements such as public subsidies and their effects on cultivated area and productivity achieved in terms of diverse efficiency measures.
The South African land question is still hotly debated on both academic and policy platforms. A situation where the minority white population owned the bulk of the agricultural land remains puzzling, and largely explains the discontentment among the black population [6
]. Inequality in the distribution of landownership [16
] is seen to account for worsening poverty and delay in the emergence of a modern growth economy [18
]. It is contended that unequal distribution of landownership does this through stalling the development of institutions that create or promote human–capital institutions such as government-owned schools [18
]. Without a doubt, the effect will differ from country to country and may be pervasive in some, as noted by an Advisory Panel set up to guide the government on the expropriation proposal [19
]. For instance, the Panel deplores the continuing deprivations faced by the black population on account of the enduring legacy of land dispossession through forced removals and various coercive and repressive actions by the erstwhile Apartheid regime in South Africa [19
]. It would seem that, regardless of the form that land dispossession takes, be it the communist collectivization process implemented in Slovenia [21
], or the forced removals in South Africa, it almost always results in the arrested development of one group or the other as a consequence of the inequalities it engenders.
According to Bojnec and Ferto [22
], farm size is often employed to explain differences in farm performance. The question is: does farm size affect agricultural productivity? Theoretically, farm size influences a range of farm resource allocation decisions, including labour use patterns [22
]. The reasons adduced for such influence include “…production and pecuniary economies of size, management ability to cope with risk management strategies, and the likelihood of off-farm employment” [23
], as cited by [22
]. The contention surrounding more land may arise from the common-sense notion that “more is better than less” and that agricultural development should be accompanied by more resources, including land, per production unit, which invariably translates to increasing farm size. According to Gollin [24
], the narrative that expansion of farm size is consistent with “agricultural development and economic growth” is a commonly held one. The fact that developed countries generally have larger average farm sizes than poorer, less developed countries [24
] could be a natural driver of the desire for more land in poorer countries. However, in the face of growing land shortage under the pressure of expanding populations, which in turn leads to the need for a larger output from farms, there is both academic and policy interest to assess the role of farm size in agricultural productivity.
], Berry and Cline [26
], Cornia [27
], Bhatt and Bhat [28
], Ladvenicova and Miklovicova [29
], and Akudugu have reported a negative relationship between farm size and productivity [30
] across the world. Studies carried out in transitional economies such as Slovenia have yielded diverse and sometimes contradictory results [21
] The assumed relationship which informed the whole question of redistributing land seems to lack credibility within the SA context, where it is difficult to relate output to land holding. In their seminal work, Sen [30
] and Shultz [31
], drew the same conclusion. This negative relationship presented smallholder farmers as efficient and productive farming units. However, interventions to improve the livelihooods of small farmers have not borne out these theoretical or empirical findings (Berry and Cline [26
]; Deolalikar [32
]; Cornia [27
]; Binswanger et al. [33
]; Vollrath [34
]; Hazell [35
]) in different contexts that contend that farm size and the performance of farms are inversely related. The same relationship was observed in data obtained from households in Kenya, Malawi, Tanzania and Uganda [36
]. Given these conflicting claims, it is difficult to be definitive about the farm-size–efficiency nexus in the case of South Africa. Several notable commentators, including Van Zyl et al. [37
], have expressed misgivings about studies that attempt to measure economies of scale in South African commercial agriculture in the pre-reform era, describing them as “not reliable”. More significant is that the studies in which Van Zyl et al. [37
] and Ngwenyi et al. [38
] disputed quite animatedly in the early- to mid-1990s referred exclusively to commercial agriculture, where land holdings ranged between 32 and 4700 ha (Van Zyl [39
]), which do not apply to small farms in the former homeland areas [16
] in the current era of agricultural restructuring and intensified efforts to integrate black farmers into the nation’s agricultural economy. Concern over the pace of the land reform programme is definitely not helped by advice that are not based on “sound analysis”, as Van Zyl et al. [36
] enjoined. The fact that those earlier studies focused on wheat production is also significant; maize is not only the dietary staple, but is also the most popular crop in the smallholder farming system. According to Thapa and Gaiha [40
], the farm-size–efficiency relationship varies with the food commodity group analyzed.
Information clarifying the farm-size–efficiency relationship and its determinants are important policy indicators for policy makers to chart the interventions capable of increasing the food production capacity of farmers. Therefore, an informed decision is relevant to gaining a better understanding of the economic farm-size–efficiency relationship and to provide better guidelines on how to pursue the land reform programme.
The present study revisits the foregoing questions with respect to smallholder maize production for the first time in the country. In line with this, the study estimates the output elasticity with respect to each of the inputs used. Similarly, it estimates the total cost elasticity with respect to each input’s average cost and identified causal factors. In a situation where about 25 years of agrarian reforms have produced little or no change in small farmer circumstances, interrogating these fundamental questions is urgent. The remaining sections of this paper present and discuss the results regarding the aforementioned objectives and conclude with appropriate recommendations.
4. Results and Discussion
4.1. Descriptive statistics of Farmer and Farm Characteristics
The mean values of the variables modelled for each of the study sites as well as the combined dataset are presented in Table 2
along with their statistical significance for the sampled farmers.
4.2. Farm size and Production Efficiency Assessment of Maize
The interaction between size of farmland and how efficient farmers organize farm inputs have important policy implications on the aggregate crop output. To understand this relationship, the maximum likelihood estimate of the stochastic trans-log production frontier was applied to the data. According to Table 3
, the value of sigma squared is 20.01 and is highly statistically significant (p
This result indicates that the model was significant with good fit. Further, the half-normal distributional assumption was adequate. The results on Table 4
lean in same direction.
The variance ratio parameter (γ) in Table 3
revealed a value greater than zero and large (0.99). This estimate implies that farm performance is influenced strongly by farmers’ agronomic practices rather than random variability. The technical efficiencies generated post-estimates indicate that mean technical efficiency averaged at 70%, and ranged from 22% to 99.8%. From the results, maize farmers could optimize maize production by paying more attention to the different agronomic practices adopted and input organization in cultivating the crop. This estimate further confirms the suitability of the stochastic frontier model for this study, since it accounted for the inefficiency effect going by the statistical value of the likelihood ratio (148.5) test.
Seed and labour used in maize production exhibited positive and significant positive effects (Table 3
). This suggests that an increase in this input in the right proportion will increase maize production, ceteris paribus
. However, fertilizer was significant but had a negative sign along with farm size. This suggests that fertilizer was under/over-utilized in cultivating maize. The linear term of farm size appeared to be neutral in predicting changes in the production of maize. The implication is that if other important factors are carefully combined, a farmer will make more profit using the present farm size. In Table 4
, only labour and fertilizer exhibited statistical significance in the frontier model estimates in the top panel.
shows that the squared term of farm size was unimportant in predicting maize production. The squared terms of fertilizer, seed and labour were significant, but the squared term of labour has a negative sign, which suggests that as labour increases, maize production decreases. This result could mean that using more labour on a fixed size of land might lead to labour redundancy and a labour surplus whose withdrawal would leave output virtually unchanged.
For the interaction terms, the estimates indicate positive and significant effects for farm size and fertilizer as well as for fertilizer and labour, whereas negative but significant effects were indicated for fertilizer and seed and for seed and labour. These interactions provide insights into the importance of appropriate resource combination to optimize maize output.
4.3. Estimating the Cost Function
The revenue accruable to a farmer is a function of farmers’ capability to effectively allocate financial resources to production inputs. In assessing the cost implication of maize produced in the study area, the maximum likelihood estimate of the Cobb-Douglas cost function was adopted (Table 5
The statistically significant Sigma value (p
= 0.03) (Table 5
) suggests a good fit. Further, the gamma value of 0.96 (p
= 0.02), suggests that 96 percent of deviations from the maximum feasible level was due to cost inefficiency.
The result in Table 5
shows that most of the cost expended on inputs had a direct bearing on the aggregate cost of producing maize by the enumerated farmers. However, this was not the case with respect to farm size probably because of market imperfections in the absence of a functional rural land market. This suggests that, being a fixed input resource in farming, farmers would acquire land regardless of the cost implication. It could be inferred that farmers hold farmland more as a way of life than an economic resource, cultivating it for the pride of owning a farm rather than optimizing land use.
The quantity of maize produced and the cost of maize production are also seemingly linked. Specifically, the cost of maize production will increase by less than the proportion by which the quantity of maize increases. The coefficients in the model represent the elasticity of the cost of production with respect to the various costs of inputs.
The estimated elasticity of production and cost in maize production are presented in Table 6
, which suggests an increasing return to scale of maize production in the study area, meaning that both maize area and output will increase by the same proportions.
In the maize farms, it was observed that the production of maize was inelastic and associated with farm size, fertilizer, seed and labour, meaning that these variables were efficiently utilized. Put differently, their use was in the second stage of the production function, which is regarded as the economically efficient stage of production, although the highest profit point within that region still needs to be located. The cost of maize production, on the other hand, revealed an inelastic relation with the cost of fertilizer, cost of seed and cost of labour.
4.4. Determinants of Production Efficiency in Maize Farming
The overall output in a given production system depends on a combination of factors. Based on the 2-step approach, the determinants of efficiency in maize production are as shown in Table 7
. The results show that the coefficients of the technical, cost and economic efficiency differ substantially.
shows that only association membership and farm size are insignificant and not related to technical inefficiency out of the ten variables fitted in the model. Of the variables that significantly influenced technical efficiency, it was shown that gender, marital status, education, credit, experience and farm size were directly related to technical inefficiency. An increase in these variables will therefore result in an increase in technical efficiency. On the other hand, association, extension and main occupation were directly related to technical inefficiency, which means that an increase in these variables will result in a decrease in the level of technical inefficiency.
A gender effect on technical efficiency was assessed and the results suggest a strong significant and positive effect. This result could suggest that male farmers were more technically efficient in the production of maize. This result concurs with Kibara’s [66
] finding that male farmers are more efficient. But the result contradicts the findings of Onyenweaku and Effiong [67
]. Married respondents, as a variable, had an indirect relationship with technical inefficiency. This suggests that married farmers were more technically efficient.
Education was negatively related to technical inefficiency of the enumerated farmers, suggesting that education improves technical efficiency. Owen et al. [68
] and Addai and Owusu [69
] report similar results.
Household size had a positive coefficient in the inefficiency estimate, which means that larger household sizes may not improve technical efficiency. While this negated the popular notion that households with many members often use family labour on their farms, the findings presented by Essilfie [70
] suggest that families with a large household size have more obligations, which diverts resources away from farming operations to household maintenance needs.
Access to credit clearly improves technical efficiency, a finding supported by Addai and Owusu [69
] who observed that farmers with access to credit generally performed better.
Main occupation had a negative relationship with technical inefficiency. This implication is that farmers whose main occupation is farming could be technically less efficient farmers than farmers reporting farming as main occupation. This result is in accord with Abdulai and Huffman [71
]. It could be as a result of the greater access to additional finance enjoyed by persons employed off-farm, which enables them to access better technology. They are also likely to be more knowledgeable about applying improved technology to their farming.
The relationship between experience and technical efficiency was indirect. This outcome presents farmers with more experience as more technically efficient than those with less experience. This outcome agrees with the findings of Addai and Owusu [69
] where they reported that experienced farmers were likely to be more technically efficient.
Being a member of an association, access to credit and the main occupation are important determinants of cost efficiency. Being a member of an association and main occupation were directly related to cost efficiency while credit had an indirect bearing on cost efficiency. Specifically, the positive relationship between being a member of an association and cost efficiency implies that farmers who are members of farmer associations were more cost efficient than their counterparts. The negative relationship between credit and cost efficiency implies that farmers with access to credit were less cost-efficient than their counterparts.
The positive relationship between main occupation and cost efficiency implies that respondents whose primary occupation is farming are more cost-efficient than their counterparts.
Married farmers, educational status, experience and farm size were found to be the important determinants of economic efficiency. Married farmers had an indirect relationship with economic efficiency. This implies that economic efficiency declines with marriage. On the other hand, education, experience and farm size were directly related to economic efficiency in maize farming, meaning that when these variables increase, economic efficiency could also increase.
Specifically, the negative coefficient on married farmers’ variable implies that married farmers were less economically efficient than their counterparts. The positive relationship between education and economic efficiency implies that educated farmers were more economically efficient in maize farming than less educated farmers.
The positive sign on farm size variable implies that farmers with larger farm sizes were more economically efficient than those with smaller farm sizes. This result is supported by the results of Magreta et al. [72
], to the effect the larger the farm sizes, the more economically efficient the farmers.
Researchers, including Wadud [73
] and Ogundari and Ojo [74
], have regularly pondered the question of the relative strengths of the techniques employed to measure efficiency, more specifically making the distinction between parametric and non-parametric approaches. However, investigating which of the measures is a better predictor of firm health has not been done in the literature. However, such an insight would be helpful. For instance, when confronted with the urgent need to make a decision, a policymaker would benefit from knowledge as to which measure conveys a better picture of the firm’s capacity to deliver on performance. From Table 7
, technical efficiency seemed more sensitive to changes in input and output levels than either allocative or economic efficiency. Of the 10 explanatory variables examined, two appeared to have a neutral impact on technical efficiency, while seven were neutral for allocative efficiency, and six were neutral for economic efficiency. It would seem that TE more precisely reflects the conditions in and around the farm, and can therefore be a more reliable basis for decision.
5. Practical Implications and Conclusions
This paper has provided estimates of efficiency in relation to economic farm size and the determinants of production efficiency in maize farming in South Africa’s Eastern Cape Province. It was revealed that deviations from the optimal output arose from the inefficient practices of smallholder maize farmers in the area. The observation that the linear and squared terms in the model were not crucial in predicting maize production could suggest that the efficient use of available land, rather than the expansion of cultivated areas, can, on its own, lead to an increase in maize production. In light of the slow pace of the land reform, this is probably the most reasonable option open to smallholders and policy makers.
An increasing return to the scale of production was observed for maize, an indication that maize farming could make the farmers earn more. However, it was also observed that appropriate resource input combination and utilization could make farmers more efficient. The study has shown that a number of factors determine the technical, cost and economic efficiency of maize production and that the effects of these factors are not uniform across the spectrum of the technical, cost and economic efficiencies of farmers producing maize. It is clear from the results that farmer’s experience, education, extension contact, membership of farmers’ association and access to credit are important determinants of farm performance, albeit to different degrees.
These findings point to the importance of farmer support for improving production efficiency. The proper identification and prioritization of key factors is a crucial need. A clear policy is needed to ensure that the necessary coordination of the key factors and processes is provided as part of the package of agrarian restructuring and land reform. Land alone cannot make farmers successful entrepreneurs, as has been revealed by the experience of the last quarter of a century, during which the country has implemented a land reform programme. A whole range of complimentary inputs are needed to make the programme successful. Moreover, extension services would immensely promote farmers’ participation in activities that disseminate innovative practices and the adoption of technology that aims to make farmers more efficient.