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Ecophysiological crop models encode intra-species behaviors using parameters that are presumed to summarize genotypic properties of individual lines or cultivars. These genotype-specific parameters (GSP’s) can be interpreted as quantitative traits that can be mapped or otherwise analyzed, as are more conventional traits. The goal of this study was to investigate the estimation of parameters controlling maize anthesis date with the CERES-Maize model, based on 5,266 maize lines from 11 plantings at locations across the eastern United States. High performance computing was used to develop a database of 356 million simulated anthesis dates in response to four CERES-Maize model parameters. Although the resulting estimates showed high predictive value (R2 = 0.94), three issues presented serious challenges for use of GSP’s as traits. First (expressivity), the model was unable to express the observed data for 168 to 3,339 lines (depending on the combination of site-years), many of which ended up sharing the same parameter value irrespective of genetics. Second, for 2,254 lines, the model reproduced the data, but multiple parameter sets were equally effective (equifinality). Third, parameter values were highly dependent (p<10−6919) on the sets of environments used to estimate them (instability), calling in to question the assumption that they represent fundamental genetic traits. The issues of expressivity, equifinality and instability must be addressed before the genetic mapping of GSP’s becomes a robust means to help solve the genotype-to-phenotype problem in crops.

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Ecophysiological crop models encode intra-species behaviors using constant parameters that are presumed to summarize genotypic properties. Accurate estimation of these parameters is crucial because much recent work has sought to link them to genotypes. The original goal of this study was to fit the anthesis date component of the CERES-Maize model to 5266 genetic lines grown at 11 site-years and genetically map the resulting parameter estimates. Although the resulting estimates had high predictive quality, numerous artifacts emerged during estimation. The first arose in situations where the model was unable to express the observed data for many lines, which ended up sharing the same parameter value. In the second (2254 lines), the model reproduced the data but there were often many parameter sets that did so equally well (equifinality). These artifacts made genetic mapping impossible, thus, revealing cautionary insights regarding a major current paradigm for linking process based models to genetics.

The experiment was conducted with four levels of nitrogen (40, 80,120 and 160 kg/ha) and 3 different cultivars (Prithivi hybrid), Masuli (HYV) and Sunaulo Sugandha (Aromatic).RMSE value (747.35 kg/ha, 1.106 days, 2.58 days and 0.004 kg/ha) and D-stat value (0.793, 0.99, 0.99 and 0.633) for grain yield, anthesis days, maturity days, and individual grain weight respectively. The objective of this study was to identify whether CSM-CERES-Rice model can be used in Nepalese condition and to evaluate the sensitivity of model with impact of climate change on rice production. Eight different climate scenarios were built by perturbing maximum and minimum temperature (± 4°C), CO2 (± 20ppm), solar radiation (±1MJ/m2/day) using interactive sensitivity analysis mode in DSSAT. Among the scenario evaluated, temperature (± 40°C), CO2 concentration (+20 ppm) with change in solar radiation (±1MJ m-2 day-1) resulted maximum increase in yield (by 62, 41 and 42%) under decreasing climatic scenarios and sharp decline in yield (by 80, 46 and 40%) was observed under increasing climate change scenarios, in Prithivi, Masuli and Sunaulo Sugandha cultivars respectively.Not surprisingly, increasing yield by (48, 25 and 27 %) and decrease in yield by(77, 41 and 34) by perturbing only maximum and minimum temperature by (± 4) shows that the temperature is most sensitive for yield potentiality of cultivars than other. CERES-Riceversion 4.0 was well calibrated in Chitwan Nepal condition. The model applications show that model could be a tool for precision decision-making. There was variation in yield in response to the change in climatic scenario in the study. RMSE value (747.4 kg/ha, 1.11days, and 2.58 days), and d-stat (0.79, 0.99 and 0.99) for grain yield, anthesis, and maturity days confirm the possibility of CERESRiceuse in Nepalese agriculture. The finding showed that there was sharp decrease in rice yield due to change in temperature, CO2 and solar radiation. Climatic scenario developed by CERES-Rice model in sensitivity analysis resulted yield reduction up to 80%. Among the cultivar, hybrid rice shows more vulnerability with climate change. Decrease in yield were mainly associated with lowering growth duration along with increasing temperature, where as there is very less counter effect of increasing carbon dioxide concentration and solar radiation. Agronomy Journal of Nepal (Agron JN) Vol. 3. 2013, Page 11-22 DOI: http://dx.doi.org/10.3126/ajn.v3i0.8982