Agricultural & Applied Economics Association

ABSTRACT The Orinoquía region is one of Colombia's principal agricultural frontiers and has been shaped by long‐standing armed‐conflict dynamics that continue to influence land governance, producer incentives, and investment decisions. Efforts to promote sustainable cattle production emphasize two key practices: sustainable pasture management (including rotational grazing systems) and ecologically appropriate stocking rates. However, their adoption varies widely because of institutional, economic, and biophysical constraints. This study applies a dual binary choice framework to examine the adoption decisions associated with these two practices and to assess whether they operate sequentially or independently. Results show that tenure security, measured through formal landownership, is strongly associated with the adoption of sustainable pasture management. In contrast, access to technical assistance is negatively associated with adoption, suggesting issues of program quality, targeting, or reverse causality. Older and more experienced producers are less likely to adopt, indicating path dependence. For stocking‐rate decisions, rotational and alternating grazing systems exhibit lower probabilities of meeting sustainable thresholds, likely reflecting implementation challenges in extensive systems. Larger finishing areas also tend to be managed less sustainably, and descriptive evidence suggests that unsustainable stocking in our sample is more frequently associated with understocking than overstocking, with 239 producers (70%) below 1 animal per hectare. These findings illustrate how structural and institutional conditions, including those linked to conflict legacies, shape sustainability transitions in the Orinoquía and can inform more tailored policy interventions for frontier livestock regions.

The Computer Age has enabled an explosion in Bayesian inference. Historically, agricultural and resource economists have contributed to the development and application of Bayesian econometrics. This article describes Bayesian econometrics in agricultural and resource economics and highlights its use. We detail the basics of Bayesian methodology and computation, and provide an accompanying empirical example. We then examine the strengths and weaknesses of the Bayesian approach, particularly for applications in agricultural and resource economics. Lastly, we consider frontier Bayesian methods and how they might be used to obtain improved inference, make more accurate predictions, or solve computational challenges.

Pollution permit trading is of interest due to its potential to meet pollution control targets at minimum cost without market designers knowing polluters’ abatement costs. This potential has been substantially realized in applications to air emissions but, with a very few exceptions, not to water quality protection. Yet substantial interest in water quality trading remains. We explore the economic literature on water quality trading, focusing on class of trades generating the most interest: trades between point sources and agricultural nonpoint sources, or PS-NPS trading. We describe how NPS pollution processes and institutional environments create unique market design challenges, prompting the development of a specialized literature. The principal challenges stem from NPS emissions being unobservable and stochastic, preventing actual NPS emissions from serving the traditional role of a quantifiable, tradeable property right. Additionally, PS-NPS trading programs generally operate in complex institutional environments that limit the ability to regulate agricultural sources and also include pre-existing environmental policies. We describe that, while the integration of PS and NPS control efforts under PS-NPS markets has the potential to improve the overall efficiency of pollution control, good market design is key to realizing these gains. Well-designed markets will incentivize trades so that cost-reducing abatement reallocations do occur, with these trades either reducing or not substantially increasing environmental risks brought about by NPS uncertainties.

This study explores the relationship between China's food imports and the ecological environment (mainly agricultural carbon emissions) of its trading partners. Using panel data from 15 major food trading partners from 2001 to 2021, we apply the Feasible Generalized Least Squares (FGLS) method to examine the relationship between China's food imports and agricultural carbon emissions of trading partners. The results indicate that the relationship supports the Environmental Kuznets Curve (EKC) hypothesis, exhibiting an inverted U-shape. This means that as food trade openness increases, trading partners' ecological environment first deteriorates and then improves after crossing a turning point. We also find that the EKC shape exhibits national and product heterogeneity. The EKC is inverted U-shaped for high-income countries and U-shaped for low-income countries. It is inverted U-shaped for soybean imports and U-shaped for corn imports. Based on 2021 data, expanding China's soybean imports from the United States, Brazil, and increasing corn imports from Brazil, Uruguay, Russia, the United States, and Myanmar are beneficial for improving the ecological environment of these countries. Furthermore, we discover that food trade impacts trading partners' ecological environment through production scale, industrial structure, and technological progress, with production scale being the main path for the inverted U-shaped curve. Increasing trade openness has a positive U-shaped impact on the environment through technological and structural effects, but not enough to offset the negative scale effects. Our study contributes by confirming the EKC relationship and turning point between food trade and ecological environment, clarifying its national and product heterogeneity, and identifying the main impact channels. The findings have policy implications for optimizing China's food import structure and promoting sustainable trade and development.