Climate change impacts and adaptations for fine, coarse, and hybrid rice using CERES-Rice

Nasir, Irfan Rasool; Rasul, Fahd; Ahmad, Ashfaq; Asghar, Hafiz Naeem; Hoogenboom, Gerrit

doi:10.1007/s11356-019-07080-z

Cited by 15 publications

(9 citation statements)

References 36 publications

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“…The model was effectively able to predict the yield of all of the genotypes under the applied treatments. The results are supported by the findings of Cheyglinted et al [68], Hussain et al [32], and Nasir et al [35] in that the model was able to able to simulate the yield. Vilayvong et al [69] also found that CSM-CERES-Rice adequately accounted for predicting yields and it could be used to define suitable management practices for improved rice production.…”

Section: Discussionsupporting

confidence: 84%

“…CSM-CERES-Rice is a physiologically based model which simulates daily photosynthesis, growth, respiration, biomass partitioning, and plant development as a function of input information such as soil characteristics, management scenarios, cultivar parameters, and prevailing daily weather conditions [33]. Model performance for various crop management strategies in number of agro-environments and in climate impact assessments has also been evaluated [24][25][26][28][29][30]34,35]. The drought stress index which is one of valuable outputs of CERES-Rice that could be utilized in predicting a genotypic yield response under a diverse range of management strategies and could lead to decision support in various management options including yield forecast and genotype recommendation.…”

Section: Introductionmentioning

confidence: 99%

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

et al. 2023

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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.

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Section: Discussionsupporting

confidence: 84%

Section: Introductionmentioning

confidence: 99%

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

et al. 2023

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“…Similarly, it is an immediate need to control such practices in agriculture which lead to GHGs emissions, i.e., N 2 O emissions from the application of chemical fertilizers, and CH 4 emissions from livestock and rice production systems (Herrero et al, 2016 ; Allen et al, 2020 ). Similarly, alternate wetting and drying and rice intensification are important to reduce the GHGs emission from rice crops (Nasir et al, 2020 ). Carbon can be restored in soil by minimizing the tillage, reducing soil erosions, managing the acidity of the soil, and implementing crop rotation.…”

Section: Discussionmentioning

confidence: 99%

“…On the other hand, climate variability could reduce crop water productivity by 32% under RCP 4.5, or 29% under RCP 8.5 by 2080's in rice crops (Boonwichai et al, 2019 ). In China and Pakistan, high temperature adversely affects the booting and anthesis growth stages of rice ultimately resulting in yield reduction (Zafar et al, 2018 ; Nasir et al, 2020 ). Crop models like DSSAT and APSIM have projected a yield reduction of both rice and wheat crops up to 19 and 12% respectively by 2069 due to a rise of 2.8°C in maximum and 2.2°C in minimum temperature in Pakistan (Ahmad et al, 2019 ).…”

Section: Introductionmentioning

confidence: 99%

Impact of climate change on agricultural production; Issues, challenges, and opportunities in Asia

et al. 2022

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Agricultural production is under threat due to climate change in food insecure regions, especially in Asian countries. Various climate-driven extremes, i.e., drought, heat waves, erratic and intense rainfall patterns, storms, floods, and emerging insect pests have adversely affected the livelihood of the farmers. Future climatic predictions showed a significant increase in temperature, and erratic rainfall with higher intensity while variability exists in climatic patterns for climate extremes prediction. For mid-century (2040–2069), it is projected that there will be a rise of 2.8°C in maximum temperature and a 2.2°C in minimum temperature in Pakistan. To respond to the adverse effects of climate change scenarios, there is a need to optimize the climate-smart and resilient agricultural practices and technology for sustainable productivity. Therefore, a case study was carried out to quantify climate change effects on rice and wheat crops and to develop adaptation strategies for the rice-wheat cropping system during the mid-century (2040–2069) as these two crops have significant contributions to food production. For the quantification of adverse impacts of climate change in farmer fields, a multidisciplinary approach consisted of five climate models (GCMs), two crop models (DSSAT and APSIM) and an economic model [Trade-off Analysis, Minimum Data Model Approach (TOAMD)] was used in this case study. DSSAT predicted that there would be a yield reduction of 15.2% in rice and 14.1% in wheat and APSIM showed that there would be a yield reduction of 17.2% in rice and 12% in wheat. Adaptation technology, by modification in crop management like sowing time and density, nitrogen, and irrigation application have the potential to enhance the overall productivity and profitability of the rice-wheat cropping system under climate change scenarios. Moreover, this paper reviews current literature regarding adverse climate change impacts on agricultural productivity, associated main issues, challenges, and opportunities for sustainable productivity of agriculture to ensure food security in Asia. Flowing opportunities such as altering sowing time and planting density of crops, crop rotation with legumes, agroforestry, mixed livestock systems, climate resilient plants, livestock and fish breeds, farming of monogastric livestock, early warning systems and decision support systems, carbon sequestration, climate, water, energy, and soil smart technologies, and promotion of biodiversity have the potential to reduce the negative effects of climate change.

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“…The newest version of DSSAT (version 4.7.5) can simulate 42 crops (Hoogenboom et al, 2019). Rice researchers use the CSM-CERES-Rice option in DSSAT to study the responses of rice to climate change (Gupta and Mishra, 2019;Nasir et al, 2020), to forecast KDML 105 fragrant rice yield (Kaeomuangmoon et al, 2020), to manage nitrogen (Zhang et al, 2018) and for yield gap analysis (Phakamas, 2015). Therefore, the use of crop simulation models is very important in agriculture where it provides valid information for decision making.…”

Section: Introductionmentioning

confidence: 99%

Small scale method for estimation of genetic coefficients of photoperiod-insensitive rice using generalized likelihood uncertainty estimation

2023

ANRES

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Importance of the work: Genetic coefficients are important parameters for simulations of rice yield performance in crop growth models. Most genetic coefficients (GCs) are obtained from large experiments. Objectives: To estimate the GCs of seven photoperiod-insensitive rice cultivars for four planting dates in a pot experiment. Materials & Methods: Input data (soil, weather, management, plant parameters) were collected and used to calibrate the GCs of seven rice cultivars using the GLUE estimator in the DSSAT version 4.7 package. The data were collected from four planting dates: 1) 23 Nov 2019; 2) 23 Dec 2019; 3) 23 Jan 2020; and 4) 23 Feb 2020. The data from planting dates 1, 3 and 4 were used for calibration of the GCs, whereas the data from planting date 2 were used for evaluation of the GCs. Results: Good prediction qualities of the model for most cultivars were indicated for days to anthesis and days to physiological maturity; however, there were poor prediction qualities for almost all cultivars for their biomass and grain weight. Main finding: This information should be useful for further investigations of GCs in rice. Although the results were contrary to the initial hypothesis, the method showed promise for further use in rice modeling research if the method can be improved by experimental management, the use of suitable reference plants for each cultivar and running the model for an appropriate cycle. It was possible to obtain some reliable GCs from small-scale experiments, so the experiment should be improved to obtain better results. Further investigations should focus on the optimum scale and weather data specific to experimental sites.

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Climate change impacts and adaptations for fine, coarse, and hybrid rice using CERES-Rice

Cited by 15 publications

References 36 publications

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

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

Impact of climate change on agricultural production; Issues, challenges, and opportunities in Asia

Small scale method for estimation of genetic coefficients of photoperiod-insensitive rice using generalized likelihood uncertainty estimation

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