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The aim of this paper is to analyze the effects of access to credit on maize productivity in Mali, by identifying the determinants of credit market participation and maize yield, and then estimating the impact of credit on maize yield.

This study analyzes the impact of credit on maize productivity using data from the World Bank 2014 Living Standards Measurement Survey (LSMS) on Mali, and the endogenous switching regression (ESR) model.

The results suggest a positive effect of credit on maize yield in Mali. Farm size, production shocks and the female gender exert negative effects on credit market participation, unlike education, intercropping with cotton, male family labor and fertilizer use which show positive effects. Farm size has a negative effect on maize yield, but both male family labor, and fertilizer use exert positive effects. Although the use of credit improves agricultural yields, the results show a greater potential effect for rationed producers, than credit users.

These results suggest that implementing a credit strategy that allows those currently excluded from the credit market, to participate, could substantially increase productivity and maize production output in furtherance of the country's food security strategy. Gender-based targeting is needed to bridge the gender gap in access to credit.

As far as the authors are aware, this study is the first to explore the credit-farm productivity links in Mali, while addressing selection bias.

The purpose of this paper is twofold. First, the impact of the North American Free Trade Agreement (NAFTA) on the agribusinesses of corn production in Mexico is analyzed, taking into special consideration the policy of encouragement to small producers, productive restructuring and identification of positive and negative effects. Second, the evolution of the US–Mexican maize belts (1994–2017) is analyzed, establishing the economic and political impacts with respect to NAFTA.

The paper opted for a documentary meta-analysis study using data from the United States Department of Agriculture (USDA) and the System of Agricultural and Fishery Information (SIAP) in Mexico. The data were completed with documentary analysis of research on maize productivity.

Provided is the information about the impacts of maize belts in the United States (US) and Mexico, where it was determined that the leading states maintained productive hegemony to a greater and lesser extent and that Mexico experienced a productive reorientation. The findings show that it is a myth that there are losers in the maize agroindustry of Mexico and the United States as it is suggested that after twenty-four years they have become complementary.

Summarized is the state of knowledge from 1994 to 2017, aligned to the databases of the United States and Mexico.

A need to study the relation between the productive evolution of maize production and NAFTA is identified.

This paper aims to investigate how climatic, market and policy factors interact to determine food production in Togo. Specifically, we estimate acreage and yield response to market prices, weather and policy changes for maize and rice.

We use panel data estimators in a Seemingly Unrelated Regressions Equation (SURE) model with region-level data from the Food and Agriculture Organization statistics department and the National Oceanic and Atmospheric Administration (NOAA) of the US Department of commerce.

We found lower fertilizer price and higher grain price effects on maize acreage and yield. In addition, we found a positive effect of expected rice price on both its acreage and yield. As expected, rainfall during planting months has a significant impact on both maize (April) and paddy (May) acreage allocations. Similarly, total rainfall during the growing season has a positive impact on both maize and paddy yields. Moreover, recent agricultural policy initiative designed to boost domestic food production has significantly increased acreage and yield for maize, and yield for paddy, especially the strategy for agricultural growth.

The dataset includes region-level observations from 1991 to 2012 which limits the observation span. However, we had enough variability in key variables to determine the estimated coefficients.

Although the dataset is limited in time (1991–2012) and uses national-level output prices, this investigation reveals that cropland allocation to maize and rice is sensitive to fertilizer and grain prices, weather expectations and policy interventions. These findings provide evidence for sustainable food production and productivity enhancement in Togo.

Understanding drivers of cropland allocation and cereal yield contribute to better food security and poverty reduction in developing countries, especially Togo.

Prior to this study, little was known on the effect of price, climate and policy on cropland allocation in Togo. This investigation contributes significantly to filling this knowledge gap and provides insights for effective interventions.

This study examines the heterogeneous correlations between rural farmers' land renting behavior and their grain production when they experienced a significant price decline.

We used well-timed panel data obtained from a two-round survey held in 2013 and 2017 among 621 households in the North China Plain. The empirical analyses were conducted by using the pooled ordinary least squares (OLS) and fixed effects models.

Rural tenants were having heterogeneous responses in land renting behavior and agricultural production when there was a price decline. A group of optimistic tenants (as professional farmers) were more likely to enlarge the farm scale for grain production through land rental markets but decrease variable investment levels (and subsequently decreased productivity) to cope with price decline. In contrast, nonprofessional farmers (the other rural tenants) were rather pessimistic about market performance, and they significantly decreased their grain production area to cope the price decline, but there was no decrease in grain productivity through reducing variable inputs.

This study contributes to the extant literature on the relationship between farmers' land renting-in behavior and agricultural production. By dividing the tenants into professional and nonprofessional farmers, we argue that there is a significant heterogeneous correlation between rural tenants' land renting behavior and grain production when farmers experience a price decline.

This study aims to discuss climate change, by modifying the timing of several agricultural operations, reduce the efficiency and yield of inputs leading to a lower production level. The reduction of the effects of climate change on production yields and on farmers' technical efficiency (TE) requires the adoption of adaptation strategies. This paper analyses the impact of climate change adaptation strategies adopted on maize farmers' TE in Benin.

This paper uses an endogeneity-corrected stochastic production frontier approach based on data randomly collected from 354 farmers located in three different agro-ecological zones of Benin.

Estimation results revealed that the adoption of adaptation strategies improve maize farmers' TE by 1.28%. Therefore, polices to improve farmers' access to climate change adaptation strategies are necessarily for the improvement of farmers' TE and yield.

The results of this study contribute to the policy debate on the enhancement of food security by increasing farmers' TE through easy access to climate change adaptation strategies. The improvement of farmers' TE will in turn improve the livelihoods of the communities and therefore contribute to the achievement of Sustainable Development Goals 1, 2 and 13.

This study contributes to theoretical and empirical debate on the relationship between adaptation to climate change and farmers' TE. It also adapts a new methodology (endogeneity-corrected stochastic production frontier approach) to correct the endogeneity problem due to the farmers' adaptation decision.

This research investigated the economic feasibility of using maize as feedstock to produce bioethanol in South Africa. The purpose of the study was to generate economic data from a maize-fed bioethanol plant and use it to perform a comparative analysis between the profitability that is generated by the maize exports to Southern African Development Community (SADC) countries and the profitability generated by the bioethanol plant in South Africa.

This study used a combination of qualitative and quantitative methods to gather data. The mixed method approach was chosen owing to the nature of the study which required an analysis of qualitative and quantitative data in order to achieve its objectives.

The findings from a qualitative instrument indicated that a majority of respondents were in favour of the decision of excluding maize for bioethanol production made by the South African Government. Findings from quantitative analysis revealed that the profitability of the bioethanol plant was largely influenced by the prices of feedstock and bioethanol.

This research was a deterministic feasibility study which ignores the risk associated with price fluctuation of raw materials and products. A probabilistic feasibility study was recommended (Monte Carlo simulation). Such economic data can also help policymakers and investors to make informed decisions.

The study recommended the need to produce bioethanol from the maize cultivated in available arable soils in South Africa, thus alleviating the cost burden of importing oil and obnoxious environmental effects.

The purpose of this paper is to measure technical efficiency and examine its determinants while disentangling unobserved time-invariant heterogeneity from actual inefficiency using comprehensive household-level panel data.

This paper estimates technical efficiency based on the true random-effects stochastic production frontier estimator with a Mundlak adjustment. By utilising comprehensive panel data with 4,694 observations from 39 districts of four major maize-producing regions in Ethiopia, the author measures technical efficiency and examine its determinants while disentangling unobserved time-invariant heterogeneity from technical inefficiency. By using competing stochastic production frontier estimators, the author provides insights into the influence of farm heterogeneity on measuring farm efficiency and the subsequent impact on the ranking of farmers based on their efficiency scores.

The study results indicate that ignoring unobservable farmer heterogeneity leads to a downwards bias of technical efficiency estimates with a consequent effect on the ranking of farmers based on their efficiency scores. The mean technical efficiency score implied that about a 34% increase in maize productivity can be achieved with the current input use and technology in Ethiopia. The key determinants of the technical inefficiency of maize farmers are the age, gender and formal education level of the household head, household size, income, livestock ownership, and participation in off-farm activities.

While the findings of this study are critical for informing policy on improving agricultural production and productivity, a few important things are worth considering in terms of the generalisability of the findings. First, the study relied on secondary data, so only a snapshot of environmental factors was accounted for in the empirical estimations. Second, there could be other sources of unmeasured potential sources of heterogeneity caused by persistent technical inefficiency and endogeneity of inputs. Third, the study is limited to one country. Therefore, future research should extend the analysis to ensure the generalisability of the empirical findings regarding the extent to which unmeasured potential sources of heterogeneity caused by persistent technical inefficiency, endogeneity of inputs and other unobservable country-specific features – such as geographical differences.

This paper contributes to the literature on agricultural productivity and efficiency by providing new evidence on the influence of unobservable heterogeneity in a farm efficiency analysis. While agricultural production is characterised by heterogeneous production conditions, the influence of unobservable farm heterogeneity has generally been ignored in technical efficiency estimations, particularly in the context of smallholder farming. The value of this paper comes from disentailing producer-specific random heterogeneity from the actual inefficiency.

The purpose of this paper is to analyze the effects of data aggregation and farm‐level crop acreage on the level of natural hedge, i.e. the level of price‐yield correlations, which is an important issue in risk modeling and management.

Swiss FADN data for five crops covering the period 2002‐2009 are used to estimate price‐yield correlations at the farm‐ as well as on an aggregated level. Tobit regressions are used to estimate empirical relationships between the level of natural hedge and the underlying crop acreage.

Price‐yield correlations differ significantly between farm‐ and aggregated‐level. More specifically, the natural hedge observed at the farm‐level is much smaller, i.e. correlations are closer to zero. Taking correlations from aggregated levels thus leads to an underestimation of farm‐level revenue variability. Furthermore, it is found that larger farms have a stronger natural hedge. For instance, a 1 percent increase in area under maize and intensive barley leads to a change in the correlation by −0.02 and −0.08, respectively.

The natural hedge is often approximated with correlations observed at more aggregated levels, e.g. the county level. The results show that this implies errors in risk assessment and modeling as well as insurance applications. Thus, farm‐level estimates should be used. The here presented relationship between price‐yield correlations and farm‐level crop acreage can be used to derive better information on levels of the natural hedge.

Even though the effects of data aggregation on price‐yield correlations have been discussed in earlier research, this paper is the first to also account for on‐farm effects of underlying crop acreage on levels of natural hedge. It is found that this simple relationship can be useful in risk management and modeling applications.

Northern Vietnam is a mountainous area where most farmers' livelihoods depend on maize production. Beside training in farming techniques, since 2013 extension courses on market access have been gradually delivered to maize farmers to help them save production costs and increase earnings.

By applying robust regression and Tobit models to estimate technical efficiency (TE), the difference-in-difference (DID) technique is used in this study to determine the change in the TE of 1,035 farmers in the period 2013–2015 in Northern Vietnam.

The DID estimator indicated the initial success of extension courses by showing a faster increase in the TE of the receivers compared to that of non-receivers. However, co-operation in production and marketing by maize farmers and access to micro-credit resulted in an insignificant impact on the TE of maize farmers in 2015. These low impact results highlighted the inefficiency of the extension and micro-credit programmes and that raised the concern of policymakers. The distance to local market signifies the determination of farmers on their crops affected by how close their location to their input/output marketplaces.

The Land Policy of the government should take into careful consideration measures to exploit all potential production and to mitigate poverty as well as foster the development of the region.

The expansion of extension courses on access to market has been recognised as an effective method to increase farmer's efficiency.