Journal of Agricultural Economics最新文献

Suppressions in public data severely limit the usefulness of spatial data and hinder research applications. In this context, data imputation is necessary to deal with suppressed values. We present and validate a flexible data imputation method that can aid in the completion of under-determined data systems. The validations use Monte Carlo and optimisation modelling techniques to recover suppressed data tables from the 2017 US Census of Agriculture. We then use econometric models to evaluate the accuracy of imputations from alternative models. Various metrics of forecast accuracy (i.e., MAPE, BIC, etc.) show the flexibility and capacity of this approach to accurately recover suppressed data. To illustrate the value of our method, we compare the livestock water withdrawal estimations with imputed data and suppressed data to show the bias in research applications when suppressions are simply dropped from analysis.

This paper accounts for spatial effects by benchmarking farms against their k-nearest neighbours (KNN) and measuring their inefficiency in a non-parametric dynamic by-production setting. The optimal number of neighbours $��k�$ against which farms are compared corresponds to the value of $��k�$ that maximises the Moran I test for spatial autocorrelation of the good and the bad output of the farms' two sub-technologies. The inefficiency scores for farms' good output, variable inputs, investments and bad outputs are then computed and compared with those calculated based on a global technology, which benchmarks all farms together. The application focuses on an unbalanced panel of specialised Dutch dairy farms over the period 2009–2016 that contains information on their exact geographical locations. The results suggest that the inefficiency scores exhibit statistically significant differences between the KNN and the global model. Specifically, the inefficiencies are generally deflated when a KNN technology is considered, suggesting that ignoring spatial effects can overestimate inefficiency.

Despite a wealth of case-specific insights from agricultural adoption studies, we lack systematic evidence on which technology characteristics matter for adoption across different innovation contexts. We synthesise the results of 304 quantitative farm-level adoption studies for a wide range of agricultural innovations across more than 60 countries using multi-level meta regression. Our results show that land, capital and knowhow are generally more important when an innovation uses the respective factor intensively, but this effect is reduced in contexts where the factor is abundant. Our findings have implications for the design of rural development and agricultural extension programmes. Both should consider the interplay of geographic context and innovation characteristics to develop more effective sustainable intensification strategies.

The European Union (EU) budget allocated to agri-environmental schemes (AES) has increased consistently over the past 20 years. European citizens should expect effective spending of these public funds, so investigation of the effects of these schemes on both environmental benefits and farm efficiency is warranted. We examine the effects of European agri-environmental schemes on farm-level eco-efficiency. Our analysis combines data envelopment analysis and impact assessment methods to evaluate the impact of scheme payments on eco-efficiency measures. Our results suggest that there is considerable scope for eco-efficiency improvements, both for dairy and crop production. Results also show that the average change in eco-efficiency scores does not vary significantly between AES participants and non-participants, which questions the effectiveness of present AES.

We investigate the effects of diet diversity on health outcomes indicated by the body-mass index (BMI) of Kenyan women in their reproductive age (15–49 years). We estimate the demand for diet diversity (which is a proxy for diet quality) and analyse its relationship with BMI by allowing the effect of diet diversity to vary along the conditional BMI distribution. Results show that diet diversity is associated with a beneficial effect on the lower and upper tails of the BMI distribution, that is, dietary diversity improves BMI for underweight individuals while, at the same time, it reduces BMI for overweight/obese individuals. Specifically, doubling the diet diversity is associated with a 14.7% increase in BMI for underweight women and a 7.0% reduction in BMI of obese women. These results support the hypothesis that diet diversity is associated with optimal BMI and, thus, better health, contributing to the policy discourse concerning the double burden of malnutrition in developing countries.

We use a detailed dataset to examine the impact of social networks, conditional on contextual and individual confounders, on farmers' adoption of competing improved soybean varieties in Ghana. Based on the contagion conceptual framework, we employ a spatial autoregressive multinomial probit model to examine how neighbours' varietal and cross-varietal adoption of improved varieties affect a farmer's adoption decision in the social network. Our results show that adoption decisions in a network tend to converge on one variety, such that beyond a threshold of adopting neighbours of that improved variety, the cross-varietal effects tend to lose significance in the network. If the shares of adopting neighbours of the improved varieties are equal, we find evidence that farmers are not more likely to adopt either improved variety compared to farmers with no neighbours who have adopted the improved varieties. The findings demonstrate the significance of neighbourhood effects in the adoption of competing technologies.

COVID-19 has caused major disruptions to agricultural supply chains around the world. Researchers and policy-makers are interested in identifying means to reduce the disruptive effects caused by the pandemic. We investigate the impacts of COVID-19 on the operation of e-commerce stores (in short, e-stores) specialising in agricultural inputs. The difference-in-differences method (DID) is employed to estimate the causal relationship between COVID-19 and online sales of agricultural inputs using data from 54,244 agricultural input e-stores registered in 118 prefecture-level cities across 15 provinces and hosted on two major Chinese e-commerce platforms. The results show that COVID-19 led to a substantial growth in monthly sales of agricultural input e-stores, and this growth of online sales varied across store scales and by types of agricultural inputs. In particular, e-stores selling seeds and seedlings experienced a larger growth in sales than stores selling agricultural machinery and implements, and the mid- and larger-scaled e-stores experienced more growth of sales than micro- and small-scaled e-stores. Further analysis reveals that the growth of online sales of agricultural inputs was driven mainly by an increase in the quantity of customer orders (QCO). The findings of this paper underscore the importance of e-commerce in ensuring the resilience of the agricultural supply chain during the pandemic period.

Taxes on fats and sugar-sweetened beverages are deployed in the developed world to encourage healthier diets. How effective might such fiscal instruments be in emerging economies? We evaluate the impacts of a subsidy for palm oil, introduced as part of the public distribution system in three Indian states. Using variants of the difference-in-differences approach, we find that palm oil consumption increased, particularly in rural areas, as a result of the subsidy, and traditional oils were displaced by cheaper palm oil. However, the intervention did not significantly alter overall edible oil consumption. These results are robust to different specifications, alternative estimation samples, and the exclusion of households who may have been potential beneficiaries of other interventions. Impacts were higher in Tamil Nadu than in other states, and were higher for vegetarian households in rural areas. There was only weak evidence of spillover income effects on other food groups. Given India's dual burden of malnutrition, our analysis suggests that fiscal policy interventions have the potential to effectively nudge consumer choices towards healthier edible oil consumption.

The agricultural sector is currently confronted with the challenge to reduce greenhouse gas (GHG) emissions, whilst maintaining or increasing production. Energy-saving technologies are often proposed as a partial solution, but the evidence on their ability to reduce GHG emissions remains mixed. Production economics provides methodological tools to analyse the nexus of agricultural production, energy use and GHG emissions. Convexity is predominantly maintained in agricultural production economics, despite various theoretical and empirical reasons to question it. Employing non-convex and convex frontier frameworks, this contribution evaluates energy productivity change (the ratio of aggregate output change to energy use change) and GHG emission intensity change (the ratio of GHG emission change to polluting input change) using Hicks-Moorsteen productivity formulations. We consider GHG emissions as by-products of the production process by using a multi-equation model. Given our empirical specification, non-convex and convex Hicks-Moorsteen indices can coincide under certain circumstances, which leads to a series of theoretical equivalence results. The empirical application focuses on 1,510 observations of Dutch dairy farms for the period of 2010–2019. The results show a positive association between energy productivity change and GHG emission intensity change, which calls into question the potential of on-farm, energy-efficiency-increasing measures to reduce GHG emission intensity.

New plant engineering techniques (NPETs) may significantly improve both production and quality of foods. Some consumers and regulators around the world might be reluctant to accept such products and the global market penetration of these products may remain low. We develop a parsimonious economic model for R&D investment in food innovations to identify conditions under which NPET technology emerges in the context of international trade. The framework integrates consumers' willingness to pay (WTP) for the new food, the uncertainty of R&D processes, the associated regulatory cost of approval, and the competition between domestic and foreign products. With generic applicability, the model enables the quantitative analysis of new foods that could be introduced in markets and then traded across borders. We apply the framework to a hypothetical case of apples improved with NPETs. Simulation results suggest that import bans and high values of sunk cost can reduce R&D investment in NPETs to suboptimal levels.