Assessment of CSM–CERES–Rice as a Decision Support Tool in the Identification of High-Yielding Drought-Tolerant Upland Rice Genotypes

Abstract

Drought is considered as one of the critical abiotic stresses affecting the growth and productivity of upland rice. Advanced and rapid identification of drought-tolerant high-yielding genotypes in comparison to conventional rice breeding trials and assessments can play a decisive role in tackling climate-change-associated drought events. This study has endeavored to explore the potential of the CERES–Rice model as a decision support tool (DST) in the identification of drought-tolerant high-yielding upland rice genotypes. Two experiments mentioned as potential experiment (1) for model calibration under optimum conditions and an experiment for yield assessment (2) with three irrigation treatments, (i) a control (100% field capacity [FC]), (ii) moderate stress (70% FC), and (iii) severe stress (50 % FC), were conducted. The results from the yield assessment experiment indicated that the grain yield of the studied genotypes decreased by 24–62% under moderate stress and by 43–78% under severe stress as compared to the control. The values for the drought susceptibility index (DSI) ranged 0.54–1.38 for moderate stress and 0.68–1.23 for severe stress treatment. Based on the DSI and relative yield, genotypes Khao/Sai, Dawk Kham, Dawk Pa–yawm, Goo Meuang Luang, and Mai Tahk under moderate stress and Dawk Kha, Khao/Sai, Nual Hawm, Dawk Pa–yawm, and Bow Leb Nahag under severe stress were among the top five drought-tolerant genotypes as well as high-yielding genotypes. The model accurately simulated grain yield under different irrigation treatments with normalized root mean square error < 10%. An inverse relationship between simulated drought stress indices and grain yield was observed in the regression analysis. Simulated stress indices and water use efficiency (WUE) under different irrigation treatments revealed that the identified drought-tolerant high-yielding genotypes had lower values for stress indices and an increasing trend in their WUE indicating that the model was able to aid in decision support for identifying drought-tolerant genotypes. Simulating the drought stress indices could assist in predicting the response of a genotype under drought stress and the final yield at harvest. The results support the idea that the model could be used as a DST in the identification of drought-tolerant high-yielding genotypes in stressed as well as non-stressed conditions, thus assisting in the genotypic selection process in rice crop breeding programs.

Application of Smart Techniques, Internet of Things and Data Mining for Resource Use Efficient and Sustainable Crop Production

“…Simulation models have been introduced for yield simulations which are assisting in understanding the behaviors of varying yield in relation to fluctuating environment, nutrient, water, pest, diseases, and other field conditions [38,75,76]. The CROPGRO model [77] was especially designed for simulating different grain legume crops such as soybean, groundnut, and common dry bean crops.…”

Optimizing Nitrogen Fertilization to Enhance Productivity and Profitability of Upland Rice Using CSM–CERES–Rice

“…The model also performed well in predicting the phenology for different sowing windows. Other studies simulating the phenology of rice have indicated that CSM-CERES-Rice was efficient in predicting phenological responses of rice [26,45,46]. Model performance was reasonable at simulating phenology for SD1, particularly in the first season.…”

Assessment and Application of EPIC in Simulating Upland Rice Productivity, Soil Water, and Nitrogen Dynamics under Different Nitrogen Applications and Planting Windows

“…Concentration of NO 3 -N in irrigation water (ppm) 2.50 (0-1000) Parm (20) Microbial decay rate, adjusted soil water-temperature-oxygen equation 0.50 (0.5-1.5) Parm (27) Lower limit nitrate concentration, maintained soil nitrate concentration 0.50 (0-10) Parm (63) Upper limit of N concentration in percolating water (ppm) 100 (100-10,000) Observed data are presented as means with ± standard errors of 3 experimental replications and computations.…”

“…Already developed crop simulation models include those that explain the conversion pro-cess of water, N and carbon balances to predict the growth, water use and nutrient uptake, the crop yields and other crop, soil, and environment components [23]. Numerous crop models have been used for a range of applications, such as the determination of optimal planting dates and N fertilization [24,25], variations in cultivar response to the environment and drought [26,27], soil water movement and N dynamics [28], and assessing the impacts of climate change [29]. The environmental policy-integrated climate (EPIC) model [30] is one of various widely used models that can simulate interactions of soil water, plant nutrient dynamics and carbon cycling in response to agricultural management practices and intercropping [31].…”