Prediction of Crop Response to Atmospheric Greenhouse Gas Concentration and Climate Parameters

Predicting crop productivity responses to rising atmospheric greenhouse gas concentrations and resultant climate changes is critical for developing robust adaptation strategies to ensure global food security. This chapter provides a comprehensive review of the diverse modeling techniques and integrated assessment approaches being used to forecast crop yields under different climate change scenarios. The direct physiological impacts of elevated carbon dioxide and temperature levels on crop growth processes such as photosynthesis and phenology are discussed. The review highlights the need to also consider interactions of these effects with other climatic factors like changing rainfall patterns, extreme events, and biotic stresses. The limitations of tightly controlled environment experiments versus field trials in capturing the complexity of real production systems are analyzed. The capabilities and constraints of statistical empirical crop models versus process-based dynamic simulation models are also assessed, including their representation of adaptation responses. Several key challenges are identified that constrain the accuracy and utility of crop climate impact projections, including issues with spatial and temporal scaling, cascading model uncertainties, lack of quality input data, and inadequate collaboration across disciplines. The review emphasizes that continued advances in climate impact modeling strengthened interdisciplinary partnerships, expanded data networks and observation systems, as well as sustained investments in research infrastructure, and human capital will be essential to improve prediction capabilities. This can enable the development of robust adaptation strategies, calibrated to local contexts, to transform agricultural systems and ensure resilient and sustainable food production under progressive climate change.