Olivyn Angeles

From ORYZA2000 to ORYZA (v3): An improved simulation model for rice in drought and nitrogen-deficient environments

Highlights • Improvements in ORYZA (v3) were identified and compared to ORYZA2000.• Soil carbon, nitrogen, and temperature dynamic modules were developed.• Algorithms for the effects of environmental stresses on rice growth were improved.• Case studies confirmed successful improvement of the model.

Combining Limited Multiple Environment Trials Data with Crop Modeling to Identify Widely Adaptable Rice Varieties

Multi-Environment Trials (MET) are conventionally used to evaluate varietal performance prior to national yield trials, but the accuracy of MET is constrained by the number of test environments. A modeling approach was innovated to evaluate varietal performance in a large number of environments using the rice model ORYZA (v3). Modeled yields representing genotype by environment interactions were used to classify the target population of environments (TPE) and analyze varietal yield and yield stability. Eight Green Super Rice (GSR) and three check varieties were evaluated across 3796 environments and 14 seasons in Southern Asia. Based on drought stress imposed on rainfed rice, environments were classified into nine TPEs. Relative to the check varieties, all GSR varieties performed well except GSR-IR1-5-S14-S2-Y2, with GSR-IR1-1-Y4-Y1, and GSR-IR1-8-S6-S3-Y2 consistently performing better in all TPEs. Varietal evaluation using ORYZA (v3) significantly corresponded to the evaluation based on actual MET data within specific sites, but not with considerably larger environments. ORYZA-based evaluation demonstrated the advantage of GSR varieties in diverse environments. This study substantiated that the modeling approach could be an effective, reliable, and advanced approach to complement MET in the assessment of varietal performance on spatial and temporal scales whenever quality soil and weather information are accessible. With available local weather and soil information, this approach can also be adopted to other rice producing domains or other crops using appropriate crop models.

Describing the physiological responses of different rice genotypes to salt stress using sigmoid and piecewise linear functions

Highlights • Rice crop responses to soil salinity can be fitted with a logistic function with three parameters.• Tolerant genotype, BRRI Dhan 47 presented 50% of reduction in leaf net photosynthesis and transpiration at soil salinity higher than 14 dS m−1.• Growth of tolerant genotype as BRRI Dhan 47 was significantly reduced at soil salinity higher than 5 dS m−1.

Varietal improvement options for higher rice productivity in salt affected areas using crop modelling

Highlights • Rice yield gains with improvement in salinity tolerance were quantified using field experiments and crop modelling.• The rice variety BRRI dhan47 presented an optimum tolerance for current salinity conditions in Satkhira, Bangladesh.• Improving salinity tolerance of IR64 by 1% resulted in a yield gain of 0.30 – 0.40% in Satkhira, Bangladesh.