Integrated modeling of environmental factors and crop yield: a review

Purpose – Agricultural production significantly emits greenhouse gases and pollutants, degrading environmental systems that, in turn, impact crop yields. Achieving climate neutrality and Sustainable Development Goals necessitates balancing climate action, pollution control and sustainable agriculture. This requires a systematic understanding of the links between environmental factors and crop yields, particularly the impact mechanisms of agriculture–environment interactions and advances in integrated climate–biophysical–economic modeling. However, existing research remains fragmented across various disciplines and domains. This paper aims to provide a review and conclude in this field. Design/methodology/approach – This paper first identifies the key research boundaries on environmental factors and modeling tools through bibliometric analysis. It then summarizes the interaction mechanism between environmental systems and crop yields, and further systematically reviews the advantages, disadvantages and applicability of different modeling approaches in this field. Findings – This review synthesizes and examines the key biophysical mechanisms linking environmental factors to crop yields, focusing on four major drivers: climate change, ozone pollution, nitrogen fertilizer use and soil organic carbon. It identifies critical modeling challenges, including scale mismatches between biophysical and economic models, spatial resolution limitations in ozone effects, underrepresentation of nitrogen’s economic costs and long-term dynamics of soil organic carbon. Research limitations/implications – Addressing these gaps requires integrating process-based and statistical models, multisource data calibration, constructing marginal cost curves and leveraging remote sensing and geospatial technologies. These advances are essential for developing policies that align agricultural productivity with environmental sustainability. Originality/value – This paper synthesizes key biophysical mechanisms and explores the development trajectories and characteristics of modeling tools, identifying the associated strengths, practical applicability, key limitations and methodological challenges. It also proposes directions for future research to enhance analytical frameworks.