Since the Cambodian economy is largely dependent on agricultural production, it is important to understand the effects of climate change on rice production, the primary staple crop of Cambodia. This study assessed the economic impacts of climate change in Cambodia to provide an appropriate set of policy suggestions that could lead to sustainable agricultural productivity and economic growth. The results from the GLAM-Rice crop model and various climate models indicate that Cambodia will be severely affected by climate change, which will lead to lower rice production and economic growth. The changes in rice yield under the RCP 8.5 and RCP 4.5 baseline scenarios reduced the GDP by 8.16% and 10.57%, respectively. By employing an investment model based on a real options framework, the economic effects and feasibility of adaptation strategies such as irrigation and adjustment of planting dates are identified. The analysis indicates that irrigation is a feasible option and the most efficacious strategy to reduce the negative impacts of climate change for the agricultural sector. The index of economic feasibility for irrigation, defined by the ratio of the current realized agriculture value-added to the identified threshold, is 0.6343 and 0.8803 under the RCP 8.5 and RCP 4.5 baseline scenarios, respectively. The results suggest that the priority choice for adaptation measure be in order of irrigation, 20-day later adjustment, and 20-day earlier adjustment.

The United States is one of the top rice exporters in the world, but warming temperatures and other climate trends may affect grain yield and quality. The use of crop models as decision support tools for a climate impact assessment would be beneficial, but suitability of models for representative growing conditions need to be verified. Therefore, the ability of CERES-Rice and ORYZA crop models to predict rice yield and growing season duration in the Mississippi Delta region was assessed for two widely-grown varieties using a 34-year database. CERES-Rice simulated growth duration more accurately than ORYZA as a result of the latter model’s use of lower cardinal temperatures. An increase in base and optimal temperatures improved ORYZA accuracy and reduced systematic error (e.g., correlation coefficient increased by 0.03–0.27 and root mean square error decreased by 0.3–1.9 days). Both models subsequently showed acceptable skill in reproducing the growing season duration and had similar performance for predicting rice yield for most locations and years. CERES-Rice predictions were more sensitive to years with lower solar radiation, but neither model accurately mimicked negative impacts of very warm or cold temperatures. Both models were shown to reproduce 50% percentile yield trends of more than 100 varieties in the region for the 34-year period when calibrated with two representative cultivars. These results suggest that both models are suitable for exploring the general response of multiple rice cultivars in the Mississippi Delta region for decision support studies involving the current climate. The response of rice growth and development to cold injury and high temperature stress, and variation in cultivar sensitivity, should be further developed and tested for improved decision making tools.

Crop models are frequently used to assess the impact of climate change responses. Evaluation of model performance against empirical data is crucial to establish confidence, particularly for rice (Oryza sativa L.), one of the world’s important cereal crops. Data from soil-plant-atmosphere-research (SPAR) chambers and field plots were used to assess three versions of the ORYZA model to a range of climate conditions. The three versions were: V1–the original, V2–V1 plus a revised heat stress component, and V3–V2 plus a coupled leaf-level gas exchange algorithm. Comparison against SPAR datasets, which covered a range of temperatures at two CO₂ levels, indicated successive improvement in yield predictions with the model version. Root Mean Square Error (RMSE) decreased by 520 and 647 kg ha⁻¹ for V2 and V3, respectively, and Wilmott’s index of agreement improved by 10 and 12% compared with V1 when averaged across 20 treatments and three cultivars. Similar improvements were observed from 17 field dataset simulations with two additional varieties. These results indicated the importance of improving heat sterility functions and carbon assimilation methodologies that incorporate direct responses to air temperature and CO₂ concentration in rice models. Accounting for cultivar differences in thermal sensitivity is also an important consideration for climate assessments.