Hiroe Yoshida

Uncertainties in predicting rice yield by current crop models under a wide range of climatic conditions

Predicting rice (Oryza sativa) productivity under future climates is important for global food security. Ecophysiological crop models in combination with climate model outputs are commonly used in yield prediction, but uncertainties associated with crop models remain largely unquantified. We evaluated 13 rice models against multi-year experimental yield data at four sites with diverse climatic conditions in Asia and examined whether different modeling approaches on major physiological processes attribute to the uncertainties of prediction to field measured yields and to the uncertainties of sensitivity to changes in temperature and CO2 concentration [CO2 ]. We also examined whether a use of an ensemble of crop models can reduce the uncertainties. Individual models did not consistently reproduce both experimental and regional yields well, and uncertainty was larger at the warmest and coolest sites. The variation in yield projections was larger among crop models than variation resulting from 16 global climate model-based scenarios. However, the mean of predictions of all crop models reproduced experimental data, with an uncertainty of less than 10% of measured yields. Using an ensemble of eight models calibrated only for phenology or five models calibrated in detail resulted in the uncertainty equivalent to that of the measured yield in well-controlled agronomic field experiments. Sensitivity analysis indicates the necessity to improve the accuracy in predicting both biomass and harvest index in response to increasing [CO2 ] and temperature.

Improved Climate Risk Simulations for Rice in Arid Environments

We integrated recent research on cardinal temperatures for phenology and early leaf growth, spikelet formation, early morning flowering, transpirational cooling, and heat- and cold-induced sterility into an existing to crop growth model ORYZA2000. We compared for an arid environment observed potential yields with yields simulated with default ORYZA2000, with modified subversions of ORYZA2000 and with ORYZA_S, a model developed for the region of interest in the 1990s. Rice variety ‘IR64’ was sown monthly 15-times in a row in two locations in Senegal. The Senegal River Valley is located in the Sahel, near the Sahara desert with extreme temperatures during day and night. The existing subroutines underestimated cold stress and overestimated heat stress. Forcing the model to use observed spikelet number and phenology and replacing the existing heat and cold subroutines improved accuracy of yield simulation from EF = −0.32 to EF =0.70 (EF is modelling efficiency). The main causes of improved accuracy were that the new model subversions take into account transpirational cooling (which is high in arid environments) and early morning flowering for heat sterility, and minimum rather than average temperature for cold sterility. Simulations were less accurate when also spikelet number and phenology were simulated. Model efficiency was 0.14 with new heat and cold routines and improved to 0.48 when using new cardinal temperatures for phenology and early leaf growth. The new adapted subversion of ORYZA2000 offers a powerful analytic tool for climate change impact assessment and cropping calendar optimisation in arid regions.

A taxonomy-based approach to shed light on the babel of mathematical models for rice simulation

For most biophysical domains, differences in model structures are seldom quantified. Here, we used a taxonomy-based approach to characterise thirteen rice models. Classification keys and binary attributes for each key were identified, and models were categorised into five clusters using a binary similarity measure and the unweighted pair-group method with arithmetic mean. Principal component analysis was performed on model outputs at four sites. Results indicated that (i) differences in structure often resulted in similar predictions and (ii) similar structures can lead to large differences in model outputs. User subjectivity during calibration may have hidden expected relationships between model structure and behaviour. This explanation, if confirmed, highlights the need for shared protocols to reduce the degrees of freedom during calibration, and to limit, in turn, the risk that user subjectivity influences model performance. A taxonomy-based approach was used to classify AgMIP rice simulation models.Different model structures often resulted in similar outputs.Similar structures often led to large differences in outputs.User subjectivity likely hides relationships between model structure and behaviour.Shared protocols are still needed to limit the risks during calibration.

Modeling the effects of N application on growth, yield and plant properties associated with the occurrence of chalky grains of rice

Abstract The objective of this study was to propose a model for explaining rice responses to a wide range of N application rates in various growth attributes associated with the occurrence of chalky grains. We improved the sub-model for N uptake process of a previous rice model which was originally developed for explaining genotypic and environmental variations in the whole growth processes, considering the difference in the rate of N loss from the plant-soil system between indigenously supplied soil mineral N and fertilizer N. A total of 80 growth datasets of cultivar ‘Koshihikari’ grown at Shiga prefecture, Japan, in 2010 was utilized for the calibration and validation of the model. The rice growth model well explained the above-ground biomass growth (RMSD = 78.7 g m−2) and rough dry grain yield (RMSD = 83.2 g m−2) for the validation data-set, simultaneously. The simulated carbohydrate content available per single spikelet was negatively correlated with the observed percentage of the milky-white grain which includes white-cored grain (r = −.77, p < .001) for all the data-sets of calibration and validation. On the other hand, the observed percentage of the sum of white-back and white-base grains was closely correlated with the simulated plant N content available per single spikelet (r = −.59, p < .001). It was suggested that the present rice growth model would rationally explain the effects of N application on the occurrence of the chalky grains through the dynamic change of the carbohydrate content and plant N content available per single spikelet.

Causes of variation among rice models in yield response to CO2 examined with Free-Air CO2 Enrichment and growth chamber experiments

The CO2 fertilization effect is a major source of uncertainty in crop models for future yield forecasts, but coordinated efforts to determine the mechanisms of this uncertainty have been lacking. Here, we studied causes of uncertainty among 16 crop models in predicting rice yield in response to elevated [CO2] (E-[CO2]) by comparison to free-air CO2 enrichment (FACE) and chamber experiments. The model ensemble reproduced the experimental results well. However, yield prediction in response to E-[CO2] varied significantly among the rice models. The variation was not random: models that overestimated at one experiment simulated greater yield enhancements at the others. The variation was not associated with model structure or magnitude of photosynthetic response to E-[CO2] but was significantly associated with the predictions of leaf area. This suggests that modelled secondary effects of E-[CO2] on morphological development, primarily leaf area, are the sources of model uncertainty. Rice morphological development is conservative to carbon acquisition. Uncertainty will be reduced by incorporating this conservative nature of the morphological response to E-[CO2] into the models. Nitrogen levels, particularly under limited situations, make the prediction more uncertain. Improving models to account for [CO2] × N interactions is necessary to better evaluate management practices under climate change.