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The purpose of this paper is to examine farmers’ access to credit, credit constraint, and productivity in the Northern Savannah ecological zone of Ghana.

Secondary data from the Ghana Feed the Future baseline survey involving a total sample of 2,968 farm households were used. The conditional mixed process (CMP) framework was applied to estimate access to credit, credit constraint, and productivity simultaneously. As a system estimator the CMP corrects for possible heterogeneity and sample selection bias.

The results from the estimations revealed that age, literacy, farm non-mechanized equipment, and group membership were the variables influencing farmers’ access to credit. Credit constraint conditions were determined by household size, locality, group membership, and household durable assets. Finally, the results showed that productivity of farmers was dependent on marital status, household size, locality, farm size, commercialization, farm mechanized equipment, group membership, and household durable assets.

This paper is the first, to the best of the authors’ knowledge, to use the CMP framework to jointly estimate access to credit, credit constraint, and productivity. The results indicate that estimating credit access and constraint models separately would have yielded biased estimates. Thus, this paper informs future research on farmers’ credit access, credit constraint, and productivity for informed policymaking.

This study aims to assess the effects of microfinance institution (MFI) services on the productivity of family farms in Cameroon, in the region of Mbam and Kim. It will be a question, therefore, of determining the level and determiners of the outputs of family farms, in particular those concerned by the cultures of cocoa, beneficiaries of the agricultural services of MFIs.

The authors use the Blinder (1973) and Oaxaca (1973) model of decomposition of the productivity differential between beneficiaries and non-beneficiaries of agricultural credits on a sample of 130 cocoa farming households and four MFIs of the same area between 2008 and 2011.

The yield gap between beneficiaries and non-beneficiaries of agricultural credits is estimated at 0.19 tons per hectare. This gap is explained positively by the financial aid variable, the farm size variable, which is significant in the explanation of the beneficiaries' level of returns and the constant term. On the other hand, all the socio-economic variables of the farmers contribute to reduce this gap of productivity.

This financial assistance from CVECA is essential to increase agricultural yields because it helps to cancel out some structural barriers. However, as this improvement in yields is only possible for large farms, the services of the MFIs would rather favor extensification policies. Nevertheless, the study results are limited by the negative effects of the socio-economic characteristics of the farmers on these yields, the study having been revealed without any selectivity bias.

This study seeks to reverse the trend that in rural areas, MFIs are financing agriculture to increase extensification rather than enhancing intensification in sub-Saharan Africa by challenging the role of MFI services in intensification.

The purpose of this paper is to determine if there are positive microeconomic effects from a state‐funded loan participation program on farm productivity and investment behavior.

The authors take the approach that access to credit solves a liquidity problem. If a credit constraint exists it results in a suboptimal allocation of resources and a reduction in farm output and profitability. A two‐stage regression model approach is used to analyze farmer survey and loan application data. In the first stage, a probit regression model is used to identify the farmers who are likely to be credit rationed. In the second stage, switching regression models are used to observe the effect of credit rationing on farm productivity and on farm investment behavior.

It is found that there are liquidity effects of credit constraints for a significant share of the beginning and low‐resource farmers who participated in the state‐funded farm loan program. After controlling for various farm and farmer characteristics, the estimated productivity and investment demand equations imply that a 1 percent increase in credit received by credit constrained farmers under the state program increased their gross income by about 0.49 percent, and their investments in depreciable assets by about 0.33 percent.

This paper is the first to apply the switching regression model to a state‐funded farm loan program for the purpose of evaluating the financial impacts on farmer participants.

The purpose of this study is to explore the relationship between migration, remittances and agricultural productivity by applying the new economics of labor migration model in the context of north‐west China. The specific objectives are to examine the impacts of rural out‐migration on agricultural productivity in various farming systems, and whether remittances have been reinvested in agriculture.

Cross‐sectional household survey data from three townships were analyzed with the three‐stage least squares (3SLS) regression model.

In multi‐cropping small farming systems, at least in the short run, the loss resulting from losing family labour on lower‐return grain crop production is likely to be offset by the gain from investing in capital‐intensive and profitable cash crop production.

This study provides empirical evidence for the MELM theory. It expands Taylor et al.'s studies by comparing investment behavior and production choices among multiple farm activities, and enriches previous studies by showing that the relation between remittances and agricultural investment depends on the farm activities' profitability.

The purpose of this paper is to examine the impact of informal credit access on agricultural productivity in rural Ghana.

Data sets from the Ghana Feed the Future baseline survey involving a total sample of 2,437 rural farm households were used. In order to address the problem of endogeneity and sample selectivity bias, the endogenous switching regression (ESR) model was employed to examine whether rural farm households’ with access to informal credit and those without access differ in terms of their productivity levels and whether access to informal credit affects agricultural productivity.

Estimates from the ESR show that access to informal credit significantly promotes agricultural productivity. Specifically, farmers with access to informal credit were able to achieve a yield of 48.42 kg/ha more than their counterparts without informal credit access. In terms of the counterfactual, farmers without informal credit access would have increased their yield by 57.61 kg/ha if they were to have access to informal credit.

The study was restricted to the savannah ecological zone of Ghana. This limits the extent of generalisation of results.

This study provides a rigorous econometric analysis of the impacts of access to informal credit on agricultural productivity in rural Ghana. The study contributes to the current debate on the link between access to informal credit and agricultural productivity and provides valuable input for policymakers.

This paper examines the performance of smallholder rice farms established using improved planting technologies – broadcasting, dibbling and transplanting – under different production systems – rain-fed and irrigation – in Ghana.

Using recent cross-sectional data of 200 smallholder rice farmers from the upper east region of Ghana, this study employed multinomial logit model and descriptive and inferential statistics for the analysis.

The results revealed that rice production under irrigation system contributes significantly to increasing farm productivity and profitability. Rice farmers who adopted dibbling and transplanting technologies under both irrigation and rain-fed production system obtained higher productivity and profitability than those who used broadcasting technology. Adoption of improved rice planting technologies by smallholder farmers is significantly influenced by education, farm size, improved rice varieties, sales outlets, hired labour and percentage of paddy sold.

The sample size is relatively small, even though findings are still very important in terms of policy formulation for improved smallholder farm performance in a developing country like Ghana.

This study calls for collaborative efforts by government, donor agencies and NGOs to establish irrigation facilities and/or expand existing ones, increase sensitization and dissemination of improved planting technologies, as well as intensify the input subsidy programme in Ghana.

To the best of the authors knowledge, this is the first study that focuses on farmers' choice of rice planting technologies under irrigation and rain-fed production systems, and how these technologies impact on smallholder farm performance in Ghana.

Agriculture is under pressure to produce more food under increasingly variable climate conditions. Consequently, producers need management innovations that lead to improved physical and financial productivity. Currently, farm accounting technologies lack the sophistication to allow producers to analyse productivity of water. Furthermore water-related agricultural technology (“agtech”) systems do not readily link to accounting innovations. This study aims to establish a conceptual and practical framework for linking temporal, biophysical and management decision-making to accounting by develop a soil moisture and climate monitoring tool.

The paper adopts an exploratory mixed-methods approach to understand supply of and demand for water accounting and water-related agtech; and bundling these innovations with farm accounting to generate a stable tool with the ability to improve agricultural practices over time. Three phases of data collection are the focus here: first, a desk-based review of water accounting and water technology – including benchmarking of key design characteristics of these methods and key actor interviews to verify and identify trends, allowing for conceptual model development; second, a producer survey to test demand for the “bundled” conceptual model; third and finally, a participant-based case study in potato-farming that links the data from direct monitoring and remote sensing to farm accounts.

Design characteristics of water accounting and agtech innovations are bundled into an overall irrigation decision-making conceptual model based on in-depth review of available innovations and verification by key actors. Producer surveys suggest enough demand to pursue practical bundling of these innovations undertaken by developing an integrated accounting, soil moisture and climate monitoring tool on-farm. Productivity trends over two seasons of case study data demonstrate the pivotal role of accounting in leading to better technical irrigation decisions and improving water productivity.

The model can assist practitioners to gauge strengths and weaknesses of contemporary water accounting fads and fashions and potential for innovation bundling for improved water productivity. The practical tool demonstrates how on-farm irrigation decision-making can be supported by linking farm accounting systems and smart technology

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 impact of National Rural Employment Guarantee Scheme (NREGS) on farm sector wage rate. This identification strategy rests on the assumption that all districts across India would have had similar wage trends in the absence of the program. The author argues that this assumption may not be true due to non-random allocation of districts to the program’s three phases across states and different economic growth paths of the states post the implementation of NREGS.

To control for overall macroeconomic trends, the author allows for state-level time fixed effects to capture the differences in growth trajectories across districts due to changing economic landscape in the parent-state over time. The author also estimates the expected farm sector wage growth due to the increased public work employment provision using a theoretical model.

The results, contrary to the existing studies, do not find support for a significantly positive impact of NREGS treatment on private cultivation wage rate. The theoretical model also shows that an increase in public employment work days explains very little of the total growth in cultivation wage post 2004.

This paper looks specifically at farm sector wage growth and the possible impact of NREGS on it, accounting for state specific factors in shaping farm wages. Theoretical estimates are presented to overcome econometric limitations.

This study examines the adoption drivers of scale-appropriate mechanization in Nepal's maize-based farming systems. The authors also assess the contribution of scale-appropriate mechanization to the United Nations Sustainable Development Goals (SDGs) of zero hunger (SDG2) and no poverty (SDG1).

Propensity score matching and doubly robust inverse probability-weighted regression adjusted methods were applied to estimate the effects of mini-tiller adoption. These methods control the biases that arise from observed heterogeneities between mini-tillers users and nonusers.

The study findings show that farm size, labor shortages, draft animal scarcity, market proximity, household assets and household heads' educational level influence the adoption of mechanization in Nepal. Mechanized farms exhibited enhanced maize productivity, profits and household food self-sufficiency. Reduced depth and severity of poverty were also observed. Nevertheless, these effects were not uniform; very small farms (≤0.41 ha) facing acute labor shortages benefited the most.

The study results suggest that policymakers in developing nations like Nepal may wish to expand their emphasis on scale-appropriate mechanization to improve farm productivity and household food security, reduce poverty and contribute to the SDGs.

This first-of-its-kind study establishes the causal effects between scale-appropriate farm mechanization and SDG1 (no poverty) and SDG2 (zero hunger) in a developing nation.