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The Chinese pilot target-price-based subsidy program (TSP) on the cotton market in Xinjiang region started in 2014 and is regarded as an effective policy, motivating cotton farmers and reducing cotton imports. This paper develops and applies a partial equilibrium model of the cotton market with regional details and linkages to the rest of the world to quantify the market and welfare impacts of a nationwide TSP. The results show a significant increase in domestic output and decrease in imports without significantly reducing current national welfare as long as the target price does not go below 120 percent of market price. In addition, measures that restrict the release of cotton stock to the domestic market would help the government in reaching its objective of supporting cotton farmers and reducing import.

This paper investigates the Maximum Acquisition Values (MAVs) of weeding robots and their determinants in both organic and conventional sugar beet farming in Germany. The MAV is defined in this paper as the price of the weeding robot that renders the same net profit as the current weeding methods. For our analysis, a Monte Carlo simulation approach is used, combined with empirical data and data collected from weeding robot companies. The results show that the MAVs of mechanical weeding robots for organic farming are substantially higher than that of spot spraying robots for conventional farming. Technology attributes are more influential than labour cost in determining the MAVs of weeding robots: in organic farming, technology attributes such as area capacity and weeding efficiency impact the MAVs of mechanical weeding robots the most; in conventional farming, supervision intensity and the robot’s ability to save herbicides are the most influential factors. The wage rate of unskilled labour, relevant for manual weeding, plays a more important role in determining the MAVs than that of skilled labour, relevant for supervision of the robot. This implies that a shortage of seasonal workers and hence increases in the wage of low-skilled labour could be important drivers of the adoption of mechanical weeding robots. Plot characteristics such as plot size and mechanisation level only have limited impacts on the MAVs.

The bioeconomy strategy of the European Union aims to balance three distinct goals: food security, the sustainable use of renewable resources for industrial purposes, and environmental protection. This study uses an integrated computable general equilibrium model to simulate the impacts of selected elements of the EU bioeconomy strategy on German agriculture at national and regional level up to 2050. An improved productivity of the crop sector substantially increases production and export/import ratio of crop outputs and reduces crop prices, while only moderately expanding cropland. The improved crop productivity would help to reduce the competition for resources between non-food and food biomass use as well as between crop and livestock production. An increasing conversion efficiency of agricultural biomass for use in biorefineries alone is unlikely to have a significant impact on the German bioeconomy. Overall, the results suggest the need for further efforts to improve crop productivity and effective complementary measures supporting the development of transformative technologies and changes in consumer preferences to ensure a minimum level of biomass use in the chemical sector.

Technological change co‐determines agri‐environmental performance and farm structural transformation. Meaningful impact assessment of related policies can be derived from farm‐level models that are rich in technology details and environmental indicators, integrated with agent‐based models capturing dynamic farm interaction. However, such integration faces considerable challenges affecting model development, debugging and computational demands in application. Surrogate modelling using deep learning techniques can facilitate such integration for simulations with broad regional coverage. We develop surrogates of the farm model FarmDyn using different architectures of neural networks. Our specifically designed evaluation metrics allow practitioners to assess trade‐offs among model fit, inference time and data requirements. All tested neural networks achieve a high fit but differ substantially in inference time. The Multilayer Perceptron shows almost top performance in all criteria but saves strongly on inference time compared to a Bi‐directional Long Short Term Memory.