Abstract:The main objectives of this study were (1) to evaluate the SPI, and three variations of the Palmer Drought Severity Index (PDSI) (the original PDSI (Orig-PDSI), a self-calibrated version (SC-PDSI) and a modified scheme employing Priestlay-Taylor's approach to compute potential evapotranspiration (PET) instead of Thornthwaite's method) and their respective moisture anomaly indices for assessing rainfed common wheat and durum wheat yield in two pilot crop regions in north and central Greece, and (2) to assess the vulnerability of wheat production to climate change, using the most appropriate drought index, with future scenarios provided by the Hadley Centre regional climate model HadRM3. The yield models that performed best at high-drought risk years (the Orig-PDSI index in the northern region and the SC-PDSI in the southern region, explaining 82.5 to 84.7% and 92% of the measured yield variability, respectively) were also the most effective at predicting the observed wheat yields when soil moisture was not an important yield-limiting factor. However, the strength of the relationship between the drought indices and the crop yields was much weaker. Improving the representation of PET in the PDSI algorithm did not improve the model's performance. The sensitivity of the two types of wheat to HadRM3 projections for the high-drought risk years differed dramatically between the two pilot districts, with extremely decreased yields of 3.14 tn ha −1 expected in the southern district and much smaller changes expected in the northern district (−4.6 vs +6.7% for durum wheat and common wheat, respectively). For the low-drought risk years, the yield models in the northern region predicted lower yields by 30 to 60 kg ha −1 . A positive yield response by 30 kg ha −1 was found for the southern district.

Successful uses of crop models in technology transfer and decision support tools require that coefficients describing new cultivars be available as soon as the cultivars are marketed. The objectives of this study were (i) to develop an approach to estimate cultivar coefficients for the CROPGRO–Soybean model from typical information provided by crop performance tests, (ii) to evaluate the suitability of yield trial data for deriving genetic coefficients and site‐specific soil traits for use in crop models, and (iii) to explore the extent to which our approach allowed the crop model to reproduce observed genotype × environment (GE) interactions, cultivar ranking, and year‐to‐year yield variability. Crop performance tests typically record harvest maturity date, seed yield, seed size, height, and lodging. A stepwise procedure using data on 11 cultivars grown at five sites in Georgia over 4 to 10 yr efficiently decreased the root mean square error (RMSE) between observed and predicted data. For ‘Stonewall’, a maturity group VII cultivar, the RMSE of 769 kg ha−1 between the actual and modeled seed yield, estimated initially by means of the existing general maturity group coefficients, was reduced to 404 kg ha−1 For the same cultivar, the initial RMSE of 5.3 and 9.3 d between the actual and simulated anthesis and harvest maturity dates, respectively, estimated by means of the existing general maturity group coefficients, were reduced to 2.9 and 5.8 d. In addition to deriving useful information on site characteristics and cultivar traits, our approach has enabled CROPGRO to satisfactorily mimic the genotypic yield ranking and much of observed genotype × environment interactions. Across all environments, the difference in genotype ranking based on yield between measured and predicted values was one or less for 61% of the environments.

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Abstract. We compared the responses of the CERES and EPIC crop models, for wheat and corn, to two different climate change scenarios of different spatial scales applied to the central Great Plains. The scenarios were formed from a high-resolution regional climate model (RegCM) and a coarse resolution general circulation model, which provided the initial and boundary conditions for the regional model. Important differences in yield were predicted by the two models for the two different scenarios. For corn, CERES simulated moderate yield decreases for both scenarios, while EPIC simulated a decrease for the coarse scenario but no change for the fine scale scenario. Differences in the simulation of wheat yields were also found. These differences were traced to the contrasting ways in which the models form final yield, even though their strategies for simulating potential total biomass are similar. We identify the crop model type as an important uncertainty in impacts assessment in addition to the spatial resolution of climate change scenarios.