Journal of Agricultural Economics最新文献

This paper estimates the impact of trypanosomiasis on crop-livestock production, economic loss and poverty in Ethiopia. We use unique panel data to estimate how the disease affects livestock deaths, production costs and crop production. We then use these estimates to evaluate the disease's total economic loss and its implications on poverty in the study districts and the country. Estimates of the fixed effect regression models indicate that trypanosomiasis increases the value of livestock deaths by 33% and production costs by 63%. Crop production decreases by 14% when trypanosomiasis and oxen death coexist. We estimate that the direct economic loss is about US$58,300 per annum in the study districts. The estimated countrywide economic loss is about US$94 million per annum. Had the government prevented this economic loss and invested it in social protection programmes, it could have lifted about 9000 people in the study districts and about 200,000 people in the country above the national poverty line per annum. These findings indicate that trypanosomiasis remains a major production constraint. Controlling the disease appears to be pro-poor, calling for more policy attention.

The implications of dietary changes for the environment and for human health are well documented, but the impacts on the agricultural sector are less well researched. We fill this gap by specifying scenarios in which European consumers' diets approximate the EAT-Lancet dietary recommendations to varying degrees and estimate the effects on agricultural production, incomes and emissions using an agro-economic modelling framework. The combination of different models allows for a detailed assessment of consequences for the agricultural sector from the global through European NUTS2 level to the farm level at different time scales. Shifting European consumption towards the EAT-Lancet recommendations leads to decreasing production of animal-based products, while production of fruits and vegetables increases sharply. The results indicate that the agricultural sector could benefit from a dietary shift, though the results are mixed at country, regional and farm levels. In particular, countries and regions that are highly specialised in animal farming are likely to lose income—at least in the short run—while regions with higher shares of vegetable and fruit farms can expect income gains. In Germany, pig and poultry farms may experience losses of up to 34% of their income, whereas farms with a high share of vegetables could gain more than 30% in income. Our results have implications for the policies to assist these extensive structural adjustments in response to widespread dietary changes.

Home gardens have been an integral part of the recent food-based interventions aimed at stimulating changes in dietary patterns and improving nutrition. However, evidence of their effects on food security, dietary quality, child anthropometry and incomes is limited, particularly among vulnerable populations groups. Using panel data from a sample of approximately 1900 households from vulnerable population groups in Odisha, India, difference-in-differences and other econometric techniques, we analyse the effects of home gardens on food security, dietary quality, child anthropometry and income. On average, home gardens contribute to better household food security, higher dietary quality of men and women but do not contribute to higher children's dietary quality and anthropometry. Also, home gardens increase monthly per adult equivalent incomes by 37% and reduce the prevalence of poverty by 11.7 percentage points. Quantile regression results suggest that home gardens enhance food security and incomes in all quantiles, but richer farmers benefit more than poorer farmers. Overall, home gardens can enhance household food security, dietary quality of men and women, and income gains among vulnerable farming population groups, but they may not suffice to improve child dietary quality and anthropometry.

Food quality and food safety issues arouse increasing interest and concern among consumers and policy-makers. Consequently, the importance of country-of-origin labelling (COOL) is increasing in business, policy and research. Numerous studies have reported a wide range of estimates for consumers' willingness-to-pay (WTP) for COOL using stated preference methods and, in particular, discrete choice experiments. We apply meta-regression analyses to synthesise the heterogeneous results of 204 WTP for COOL estimates extracted from 59 studies which used discrete choice experiments and were published between 2009 and 2020. Meta-regression analysis allows an adjusted summary proxy to be derived for the WTP for COOL and the determinants of heterogeneity in reported WTP estimates are also investigated. Our results suggest that there is a significant positive WTP for COOL, and also reveal that the reported WTP estimates are unaffected by publication bias. In addition, they show systematic variation in WTP estimates across the context and methodological characteristics of the studies. More precisely, we find that the region and the product (animal- vs. plant-based) analysed, as well as certain characteristics of the choice design (e.g., the number of attributes used, or the inclusion of an opt-out option) can have a significant impact on the estimated WTP for COOL. Finally, our results reveal significant differences in price premiums between various types of COOL (e.g., domestic vs. foreign). This highlights that results from individual primary studies should not be generalised without further consideration of the underlying study design.

Although past studies have separately explored the direct impact of agriculture official development assistance (ODA) and foreign development investment (FDI) on agricultural production, the nexus between these two elements is often neglected. This article aims to understand the linkage between agricultural ODA and FDI, using data from 63 developing countries from 1991 to 2019. Poisson pseudo maximum likelihood estimations reveal that agricultural ODA considerably promotes FDI in the agriculture, fishery and forestry sectors (FDI_aff) by approximately 0.5%, while its impact on FDI in the food, beverages and tobacco industries (FDI_fbt) is overall insignificant. Geographical and ecological conditions play a decisive role in accounting for FDI in agriculture. While coastal and land-rich countries receive a significantly higher amount of FDI_aff and FDI_fbt, tropical countries are evidently more attractive destinations for FDI_fbt. The empirical analysis also shows that a peaceful social environment encourages FDI_aff. Well-established legal systems and reductions in corruption facilitate FDI_aff, whereas the impact of overall institutional quality on agricultural FDI is insignificant. Results suggest that donors prioritise agricultural initiatives with higher positive spillover effects, such as programmes supporting food crop production and agricultural research.

Existing indicators of agricultural protection and support were developed primarily to gauge the market and welfare effects of government policies. They have shed light on a wider range of impacts through their use in economic models and empirical analysis. The rising scale of support to agriculture globally, and continued reliance on market distorting policy instruments, make this work as important as ever. Deeper investments are nonetheless needed to address heightened concerns about the spill-over impacts of agricultural policies on the performance of food systems, in particular with respect to food security and nutrition, rural livelihoods, resource use and the environment, and global emissions. A first area for development is improved measures of policies to correct agriculture's externalities. A priority here is to develop a carbon tax equivalent of sectoral mitigation efforts. A second area is to provide a clearer delineation of government spending on private versus public goods. This would make a valuable contribution to a ‘repurposing’ agenda that seeks to identify how agricultural budgets can be spent more effectively, as well as the scope for transferring resources to wider social priorities, such as public health and climate action.

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.