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The purpose of this paper is to analyze the impact of farmers’ agricultural production mode transformation, from the perspective of agricultural division of labor and cooperation, on their agricultural production efficiency including technical efficiency, pure technical efficiency and scale efficiency.

This paper analyzes the impact of the agricultural production mode’s transformation on farmers’ agricultural production efficiency, based on the classical theory of division of labor and specialization, transaction costs and cooperation. It uses 2013 survey data from 396 farms in 15 Chinese provinces to explore the contributing factors of agricultural production efficiency using a double selection model (DSM), which can correct the endogenous selection bias in farmers’ decisions.

Farmers that participate in agricultural division of labor and cooperation means transform their agricultural production from a traditional self-sufficient mode to one that is specialized and intensive. Agricultural division of labor measured by farmers’ participation in an agricultural division of labor in the production stages, or in agricultural products, and agricultural cooperation measured by farmers’ participation in farmers’ cooperatives significantly and positively influence their agricultural production efficiency after correcting farmers’ endogenous selection bias.

This paper proposes a unified framework to analyze the impact of farmers’ agricultural production mode transformation on their production efficiency. Further, it builds a DSM for an empirical analysis to avoid the endogenous biases in farmers’ self-selection behavior. This paper also provides ways for policy makers to improve farmers’ agricultural production efficiency from the modern agricultural production perspective.

The purpose of this paper is to analyze the relationship between farm size and agricultural production efficiency from the aspects of output and profit in order to find an optimal farm size that achieves both output and profit efficiency in agricultural production in China.

This study uses the 2012 China Family Panel Studies survey data and employs the stochastic frontier analysis (SFA) models to investigate empirically the relationship between farm size and agricultural production efficiency.

The study finds that there is an inverted-U curve relationship between farm size and output efficiency and a U-shaped curve relationship between farm size and profit efficiency in agricultural production in China. Based on the empirical results, the study estimates that the appropriate farm size is around 10–40 mu and the optimal farm size is around 20–40 mu both in terms of output efficiency and profit efficiency in Chinese agricultural production under the current agricultural technology and land management system.

The findings of this study suggest that appropriate land consolidation will bring more benefits to farmer households and agricultural production efficiency. There are some policy implications. First, governments should give long term and more stable land using rights to farmers through extending the period of land contract and verifying land using rights. Second, governments should encourage transfers of land using rights and promote land consolidation. But the implementation of this policy should consider regional differences and not be used for blindly pursuing increasing land size. Third, land consolidation should be accompanied with the development of specialized agricultural services.

The paper makes two major contributions to the literature. First, the authors use the SFA model to investigate the relationship between land size and agricultural production efficiency. Second, the authors establish two SFA models – the stochastic frontier output analysis model and the stochastic frontier profit analysis model – to estimate the optimal land size to achieve both output and profit efficiency of agricultural production in China.

The development of digital inclusive finance appears to be able to solve the difficulty of traditional finance, which cannot completely cover agriculture and farmers and provides better financial services and products to Chinese farmers. Thus, it improves the farmers' enthusiasm for agricultural production. The purpose of this paper is to clarify whether this goal is indeed being achieved.

This paper theoretically analyzes the mechanism that influences the effect of digital inclusive finance on rural households' agricultural production decisions and conducts an empirical study based on a sample from the Chinese family database (CFD).

First, the development of digital financial inclusion in general can encourage rural households to reduce agricultural production. Second, the negative effect of digital inclusive finance on households' agricultural output is realized by widening the gap between the efficiency of non-agricultural economic activities and the efficiency of agricultural production. The wider the gap is, the lower the enthusiasm of households for agricultural production. Third, the mediating effect of “digital financial inclusion – difference in efficiency – agricultural output” has a significant negative effect on households with low agricultural production efficiency, but not households with high agricultural production efficiency. Digital inclusive finance has no significant effect on the difference in efficiency between the two economic activities of high-efficiency households, but a greater difference in efficiency between the two economic activities corresponds to higher enthusiasm of households for agricultural production.

To the best of our knowledge, this paper is the first to analyze the impact of digital financial inclusion on Chinese farmers' agricultural production. The findings of this study can provide policy-related insights to help local governments promote the development of digital finance in China's agricultural economy.

The purpose of this paper is to analyze agricultural total factor productivity (TFP) and input redundancies in different regions of China, and to bring out the policy implications for improving efficiency in agricultural production as well as environment protection.

Based on the provincial panel data during 1995-2014, the agricultural productivity of China and its regional disparity are analyzed. First, the agricultural TFP and its decomposition are dynamically evaluated by means of data envelopment analysis-Malmquist productivity index. Second, the agricultural radial production efficiency in year 2014 and the input redundancy changes from 1995 to 2014 are measured based on the BCC-slacks-based measure model.

The results showed that the overall agricultural TFP of China grew 4.3 percent annually during 1995-2014, mainly as a result of technical progress. However, the declines of technical efficiency and scale efficiency slowed down the agricultural TFP growth. The TFP growth in the Western region and Central region far exceeded the Eastern region in last few years. In 2014, most effective decision-making units were in the Western region. The input redundancies in the agricultural production increased substantially after 2006, especially for the pesticide use amount, reservoir capacity and agricultural machinery power.

Combining the dynamic and static analyses, the paper fulfilled the study of China’s agricultural productivity and the input redundancies in recent years, and also presented the regional disparities.

Whether there exists an inverse relationship (IR) between farm size and its efficiency remains a hotly debated question among agricultural economists. In most studies to date, farm efficiency is measured by land productivity. Thus, the IR actually measures the relationship between farm size and land productivity. The purpose of this paper is to examine and understand the IR from a novel angle by using multiple definitions of farm efficiency indicators like labor productivity, profit ratio, total factor productivity (TFP) and technical efficiency (TE).

By using the farm-level panel data from Hubei province in China from 1999 to 2003, this paper employs the two-way fixed effect model of panel data and the stochastic frontier analysis of Battese and Coelli model to investigate the relationship between farm size and its production efficiency derived from the multiple definitions of production efficiency indicators including land productivity, labor productivity, profit ratio, TFP and TE.

The study confirmed the IR between land productivity and farm size, as in many formal studies. However, the relationship between farm size and other agricultural efficiency indicators may be positive, negative or uncorrelated at, depending on how the farm efficiency is defined. Therefore, the paper concluded that the relationship between farm size and its production efficiency is mixed. This paper provides economic explanations for the IR through the comprehensive study using the expansion of agricultural efficiency indicators.

Because different agricultural efficiency indicators have different policy implications for China's future agricultural and land policy, the findings have tremendous policy implications, particularly in terms of the current debate on large or small farm development strategy, the also so-called “go big or small” agricultural strategy. In this sense, the Chinese household responsibility system has played a critical role in its agriculture and will continue to play a critical role in terms of social security and social equality. Any reform to this system should proceed with caution.

While most existing studies only try to explain the IR from the perspective of land productivity, this paper attempts to propose a novel angle to examine the IR by using multiple definitions of agricultural efficiency and hopes to find some new conclusions.

The purpose of this paper is to assess the efficiency of agricultural production in South Africa from 1970 to 2014, using an integrated two-stage fuzzy approach.

Fuzzy technique for order preference by similarity to ideal solution is used to assess the relative efficiency of agriculture in South Africa over the course of the years in the first stage. In the second stage, fuzzy regressions based on different rule-based systems are used to predict the impact of socio-economic and demographic variables on agricultural efficiency. They are compared with the bootstrapped truncated regressions with conditional α levels proposed in Wanke et al. (2016a).

The results show that the fuzzy efficiency estimates ranged from 0.40 to 0.68 implying inefficiency in South African agriculture. The results further reveal that research and development, land quality, health expenditure–population growth ratio have a significant, positive impact on efficiency levels, besides the GINI index. In terms of accuracy, fuzzy regressions outperformed the bootstrapped truncated regressions with conditional α levels proposed in Wanke et al. (2015).

Policies to increase social expenditure especially in terms of health and hence productivity should be prioritized. Also policies aimed at conserving the environment and hence the quality of land is needed.

The paper is original and has not been previously published elsewhere.

The purpose of this paper is to analyze the extent of aging in the agricultural labor force and its effect on farm production in a province of China.

The analysis uses panel data for the years 2004 through 2008 from a representative sample of farm households in Liaoning province. Descriptive statistics reveal the age structure of the agricultural labor force and correlations between labor force age and production characteristics. A translog stochastic frontier production function and technical inefficiency model is employed to analyze the effect of aging of the labor force on the technical efficiency of crop production.

The paper finds an accelerating trend towards aging of the agricultural labor force in the data. Results from the stochastic frontier production function and efficiency analysis reveal that household‐level technical efficiency increases until maximum efficiency is reached when the average age of the household labor force is 45, after which efficiency declines.

Aging of China's rural labor force may affect efficiency and productivity in crop production. Agricultural policies may need to pay more attention to the aging of the agricultural labor force. Some measures should be taken to address the pattern of migration, and policies to improve the social and economic environment in rural areas for younger workers should be developed. Also, extension programs could help older farmers to maintain efficient farming methods.

This is one of very few analyses of the effects of aging on production efficiency for a developing country, as well as for China. The analysis uses a unique panel dataset that covers 24 counties, 1,890 rural households, and more than 6,000 individuals, with each household tracked for five years. Most of the literature estimating technical efficiency carries out the analysis at the individual level; in China and other developing countries, farming is carried out at the household level. We have adapted the methodology to apply to situations where the unit of analysis is the household.

Does MFIs agricultural credit influence the determinants of the efficiency of SFF which are socio-economic factors of the farmers but also agricultural endowments of family farms? This paper aims to study the contribution of MFI services on improving the technical efficiency of SFFs in Cameroon.

The stochastic frontier analysis (SFA) model permits the estimation of the technical efficiency indicators for beneficiaries and nonbeneficiaries of agricultural credits on a sample of 130 cocoa farming households and four MFIs of the same area between 2008 and 2011. The censored tobit model is used to assess the determinants of technical efficiency.

The results show that the SFF beneficiaries of agricultural credit have an average technical efficiency of 0.68 inferior to that of nonbeneficiaries (0.72) as expected. They are, respectively, at 0.32 and 0.28 of their full productive capacities. The results of the censored Tobit model show that socioeconomic characteristics of the producer such as age and gender explain negatively, while experience explains positively the technical efficiency of SFFs.

Although without any selectivity bias, this study indicates the essential character of the socioeconomic factors in the amplification of the role of the MFIs credit on the efficiency of SFFs.

Strategies to improve the efficiency of SFFs require an increase in MFI credits, primarily targeting young, experienced and female farmers.

This study examines the efficiency of SFFs by highlighting the interaction between the socio-economic factors of farmers and the credit of MFIs. It also points to the problem of monitoring the implementation of agricultural financing.

Agricultural carbon productivity considers the dual goals of “agricultural economic growth” and “carbon emission reduction”. Improving agricultural carbon productivity is a requirement for promoting green and low-carbon development of agriculture. Agricultural production agglomeration is widespread worldwide, but the relationship between agricultural production agglomeration and agricultural carbon productivity is inconclusive. This paper aims to study the impact of agricultural production agglomeration on agricultural carbon productivity, which is conducive to a better understanding of the relationships among agglomeration, agricultural economic development and carbon emission, better planning of agricultural layout to build a modern agricultural industrial system and achieve the goal of carbon peaking and carbon neutrality.

Based on China's provincial data from 1991 to 2019, this paper uses non-radial directional distance function (NDDF) and Metafrontier Malmquist–Luenberger (MML) productivity index to measure total factor agricultural carbon productivity. Subsequently, using a panel two-way fixed effect model to study the effect and mechanism of agricultural production agglomeration on agricultural carbon productivity, and the two-stage least squares method (IV-2SLS) is used to solve endogeneity. Finally, this paper formulates a moderating effect model from the perspective of the efficiency of agricultural material capital inputs.

The empirical results identify that Chinese provincial agricultural carbon productivity has an overall growth trend and agricultural technological progress is the major source of growth. There is an inverted U-shaped relationship between agricultural production agglomeration and agricultural carbon productivity. The input efficiency of agricultural film, machine and water resources have moderating effects on the inverted U-shaped relationship. Agricultural production agglomeration also promotes agricultural carbon productivity by inhibiting agricultural carbon emissions in addition to affecting agricultural input factors and its internal mechanisms are agricultural green technology progress and rural human capital improvement.

This paper innovatively adopts the NDDF–MML method to measure the total factor agricultural carbon productivity more scientifically and accurately and solves the problems of ignoring group heterogeneity and the shortcomings of traditional productivity measurement in previous studies. This paper also explains the inverted U-shaped relationship between agricultural production agglomeration and agricultural carbon productivity theoretically and empirically. Furthermore, from the perspective of agricultural material capital input efficiency, this paper discusses the moderating effect of input efficiency of fertilizers, pesticides, agricultural film, agricultural machines and water resources on agricultural production agglomeration affecting agricultural carbon productivity and answers the mechanism of carbon emission reduction of agricultural production agglomeration.

The purpose of this paper is to examine the impact of agricultural credit on technical efficiency of Ghanaian maize farmers using a unique dataset drawn from the database of Sub-Saharan Africa’s intensification of food crops agriculture (Afrint II) in 2008 period.

In this study, a two-stage estimation procedure is employed to determine impact of agricultural credit on technical efficiency of Ghanaian maize farmers. The first stage utilized probit model while the second stage utilized stochastic frontier approach to estimate impact of credit on technical efficiency of Ghanaian maize farmers.

The study found that farmers are producing below the frontier with average technical efficiency of 47 percent. Policy variables such as credit access; education, extension access and farm size played a stronger role in technical efficiency. Agricultural credit in particular increased technical efficiency by 3.8 percent.

The results should not be extended to the impact of agricultural credit on economic efficiency since the allocative efficiency component is not considered in this study. Also, caution should be taken in the interpretation of these results because the data could not permit the incorporation of all variables that might affect technical efficiency.

The originality of the paper and its contribution to existing literature largely lies from the use of a unique dataset to find evidence of the impact of credit on efficiency in Ghana.