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The relationship between farm size and land productivity is a hotly debated issue in the study of agricultural economics and development economics. The purpose of this paper is to explore the causes leading to the inverse productivity relationship by examining the relationship between farm size and factor inputs.

With a large panel data set of farm households in China during 2010–2011, this study uses the factor demand models to examine the relationship between farm size and per-mu labor and non-labor inputs while employing a stochastic frontier production function in determining the difference of labor efforts in farming operation across farm sizes. Moreover, the models for value-added margins and profits are used to further determine producer behavior of small-size farms.

Results of this study show that, as compared to larger farms, smaller farms not only utilize more labor and non-labor inputs per mu, but also benefit from a higher labor effort. Moreover, smaller farms concentrate more on grain output and cash costs while focusing less on the family labor input costs in an effort to maximize value-added margins rather than profits. The higher yields on smaller farms are thus a result of the utilization of a relatively higher level of labor and non-labor inputs along with skilled-oriented precision farming technology. The inverse productivity relationship is explained by the behavior of small-size producers with employment constraints, leading to smaller farms generating a higher yield than larger farms.

While Sen (1966), Feder (1985), Eswaran and Kotwal (1986) and others have theoretically derived the causal relationship between the incomplete factor markets, especially incomplete labor markets, and the inverse productivity, empirical studies to test the causal relationships are limited. In particular, a solid foundation based on an empirical analysis is lacking when it comes to explaining the inverse productivity in China. Results of this study are expected to have significant policy implications in terms of the understanding of small-size producer behavior and the associated mechanism underlying the inverse relationship between farm size and land productivity.

The purpose of this paper is to examine the long-term relationship between farm size and productivity in China at the national level.

In contrast to the micro-data examination conducted by earlier literature, in this study, the authors use household aggregate panel data on 29 provinces in China for 1988–2012. Using the panel data PMG model, the authors control the factor of difference in land quality due to the fixed effect in each province, and the authors consider the difference in the long-run coefficients of farm size and land productivity rather than the difference in their short-run relationship. Thus, the authors examine the long-term relationship between farm size and productivity. Furthermore, the authors examine the robustness of this relationship in the long-term using samples of rice, wheat and corn production by region.

In contrast with the findings presented previously, the authors find that the relationship between farm size and agricultural productivity is statistically positive in the long term.

The relationship between farm size and agricultural productivity is a key research issue in agricultural and development economics. In China, many studies have provided evidence of the inverse relationship between farm size and agricultural productivity at the family farm level. However, this inverse relationship seems to reflect specific regions and specific periods in the relationship between farm size and land productivity. At the nationwide level, in the long-term, this is not an inverse but a positive relationship. It is desirable to expand farm size for the long-term development of agriculture.

The farm size-productivity relationship has long been the subject of debate among development economists. Few studies address this issue for China, and those that do only with outdated data sets poorly representing the current situation after the past decade of rapid change, which includes the rapid development of land rental markets, village labor out-migration and use of farm machines. Meanwhile, many studies have researched this relationship for Indian, which is undergoing similar changes except for the development of active land rental markets. The purpose of this paper is to measure the farm size-productivity relationship under the situations of rapid transformation in China and India.

Based on the data of 325 Jiangxi and 400 Allahabad rice farmers in 2011, the survey covered multiple plots of each household in one/multiple growing season(s). The authors use the production function approach and the yield approach, and control for farmland quality, imperfect factor markets, and farm size measurement error, to identify the farm size-productivity relationship.

The regressions show that land yields increase with plot size both by season and over the year in China. This may be one of the reasons that farm sizes are growing in some areas. In India, however, the inverse farm size-productivity relationship is observed by the study, despite recent changes. Moreover, land yields increase with farm machine use in both China and India. This result contributes to the debate over whether mechanization improves yields or just expands the land frontier.

The paper empirically estimates the farm size-productivity relationship under rapid agrarian transformation in both China and India based on a unique data set collected by the authors in a detailed primary survey. The paper considers measurement error in the analysis, which adds values to this type of analysis.

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 examine the differences in the rural household agricultural income by farmers’ education while exploiting a nationally representative household survey data set, i.e. the India Human Development Survey-2005, in the rural Indian context. The author seeks to answer the question: how much variation in the household net agricultural income per acre of land cultivated can farmers’ education explain?

The author has employed the ordinary least squares regression model with village fixed effects. The author also analysed the data using some exploratory statistics.

The author finds that farmers’ education significantly increases the net household farm income per acre of land cultivated last year. The results are robust to the inclusion of the five educational degree categories (dummies) in lieu of the years of schooling variable. The results are also robust to its decomposition into that for men and for women separately. Women farmers’ education has an amplified impact on farm incomes. The author also confirms the inverse relationship between the household agricultural income and land area cultivated, which is consistent with the huge literature on the negative relationship between land size-class and farm productivity.

In a developing country with a not-so-modernized agriculture sector and low adoption of newer farming technologies, this validated importance of education in explaining the differences in rural farm earnings has guiding policy implications in that a positive return to farmer schooling signals the need for increased investments in the farmers’ education and awareness so as to enhance farm incomes and productivity. The special policy thrust on education of women and women farmers is critical to ensuring higher farm incomes and outcomes.

The literature on the impact of farmers’ education on rural household agricultural income is very sparse. To the best of the author’s knowledge, this issue has not been addressed before in the Indian context. The author explains the contribution of farmer education to farm income in rural Indian households. The author also revisits the negative relationship between farm income and land size holdings in the Indian agriculture.

The emergence of mega-farms in middle-income countries like Brazil and Ukraine and the efforts to consolidate small farms into larger ones in China and elsewhere have suggested that new institutional arrangements and technology progress may increase the significant farm economies of size, and therefore challenging the classical inverse relationship (IR) between productivity and farm size. The purpose of this paper is to conduct a research to examine the existence of IR in China’s cereal production and further explore the possible mechanism.

Based on the panel data concerning farms from 31 provinces in China in 2009–2014, technology efficiency and allocation efficiency in cereal production were measured based on the Translog production function.

In China, an IR exists between wheat, rice and maize production after controlling the related variables. Further, the presence of this IR is due to allocation efficiency rather than technology efficiency. Results of technology efficiency show that there is no significant difference between different size groups; in other words, even the larger size groups enjoy a higher technology efficiency in rice production. Results of allocation efficiency demonstrate that, compared with the larger farms, smaller holders tend to invest more in factors which are beneficial in growing productivity. Hence, yields of small-size farms exceed those of large-size farms, which consequently leads to the IR.

The scale consolidation of cropland is harmless to the efficiency of factor utilization and, conversely, may contribute in improving the technology efficiency of specific crops (like rice). Based on the results of allocation efficiency estimation, the extent of misallocation in large scales is relatively less due to their less input of fertilizer and pesticide; thus, the size-improving policy of farmland may contribute to the ease of the non-point pollution in agriculture.

The existing discussions of the IR in the context of recent China mainly focus on the accurate verification of IRs. This paper steps forward from the perspective of technology efficiency and allocation efficiency and explores the exact resources of IR with up-to-date and representative data and hopes to find some new conclusions.

The purpose of this paper is to examine the connections of agricultural productivity, access to credit and farm size in Africa using Ghana as a case study.

The paper employs mixed methods – quantitative and qualitative strategies for data collection and analyses. The hierarchical competitive model was used for the quantitative analyses supplemented with qualitative analyses using key informant interviews, focus group discussions and household case studies.

The results show that there is significant relationship between credit from formal and informal sources and agricultural productivity. Thus access to formal and informal credit increases farm household agricultural productivity by about 0.10 (p=0.05) and 0.45 (p < 0.01), respectively. The quadratic terms of formal and informal credit as well as farm size were found to significantly influence agricultural productivity. The implication of this is that the relationships between formal credit, informal credit and farm size on one hand and agricultural productivity on the other are non-linear in nature. The interactions of formal credit with informal credit; informal credit with farm size; and formal and informal credit with farm size have significant relationships with agricultural productivity. The amount of remittance received by farm households has negative and insignificant influence on agricultural productivity. Market access is also an insignificant determinant of agricultural productivity in Ghana.

This paper provides new insights on whether the scale of production (farm size as proxy) and access to financial services (credit as a proxy) matter in promoting agricultural productivity in Africa using Ghana as a case study. Thus the paper is of relevance to policy-makers and practitioners in Africa and Ghana in particular who are seeking to make informed policy decisions on effectively incorporating credit provision into the agricultural transformation agenda of the continent.

This study investigates the effect of renting in land on farm productivity, and the impacts of rented-in land size and transaction partner type on farm productivity.

Data from the 2015 China Household Finance Survey are analyzed using an extended regression model and the two-stage least squares method.

Farm households that rent in land are likely to achieve higher farm productivity, and ignoring endogeneity underestimates the positive effect of land renting-in. Further evidence indicates that rented-in land size has an insignificant impact on farm productivity, and that there is no difference in farm productivity between lessees renting-in land from acquaintances and those renting-in land from non-acquaintances. These results may be caused by the higher degree of marketization of land rentals between acquaintances in China. With increasing competition in agricultural factor markets, in theory, rented-in land size should not affect farm productivity.

Overall, the analysis suggests that renting in land improves farm productivity, which supports the land transfer policies that have been rolled out in recent decades in China. However, our finding that rented land size does not affect farm productivity, consistent with the results in the literature, implies that the Chinese government should no longer subsidize or prefer large farms with low productivity. More attention should be paid to small lessees and market-oriented land rentals between acquaintances. Promoting the marketization of land transfers inside acquaintance networks could realize the potential of the land market, especially if land transfers decrease.

This study identifies the effects of renting in land, rented-in land size and type of rental transaction partner on farm productivity using nationally representative data. The findings imply that the government should pay more attention to the marketization of land rentals between acquaintances. Although existing studies regard land rental between acquaintances as informal and of low efficiency, the recent evidence shows that China's land markets are changing, and policy makers should adjust their policies accordingly.

This paper aims to present a synergistic approach that combines both construction and environmental expertise to lay the groundwork for a model that can be used to estimate the productivity rate and emissions from construction equipment activities.

The proposed estimating tool is developed by combining the productivity rate model from a reliable construction estimating data sources and the calculation algorithm employed by the EPA's NONROAD model. In order to develop productivity models, simple earthwork activities involving bulldozer, excavator, and dump truck were selected.

The MLR approach proved to be a useful alternative for estimating productivity rate of three pieces of equipment. The MLR models for the productivity rate can explain high percentage of the variability in the data. The models are good to be used as a benchmark for estimating NO_X and PM emissions from some certain types of construction equipment performing earthwork activities. The productivity rate from this model (lcy/hr) is used with emission factors (g/hp‐hr) from EPA's NONROAD model to estimate the total emissions.

The estimating tool proposed in this paper will be an effective means for assessing the environmental impacts of construction activities and will allow equipment owners or fleet managers, policy makers, and project stakeholders to evaluate more sustainable alternatives. The tool will help the contractor to estimate the total expected pollutant emissions for the project, which would be valuable information for a preliminary environmental assessment of the project.

Although there are already methods and models for estimating productivity for construction equipment, there currently is not a means for doing estimates of air pollutant emissions at the same time, particularly for NO_X and PM.

We demonstrate in a laboratory experiment that the effectiveness of performance-contingent incentives is inversely related to risk-aversion levels. For about 16.5% of participants, performance fails to improve under performance-pay, and the probability of such failure increases with risk-aversion. This phenomenon works in part through the reduced effort level of more risk-averse individuals when effort level is positively correlated with risk exposure. It is also associated with higher self-reported levels of stress by more risk-averse people working under performance-contingent pay. We find no evidence of such stress causing decrements in the quality of effort affecting performance after controlling for effort level. However, controlling for effort, more risk-averse participants perform better under a fixed salary, leaving less room for improvement under performance-pay.