通过作物模拟建模探索气候变化的影响,并确定泰国湄南河下游流域水稻可持续生产的潜在适应战略

IF 2.9 4区 环境科学与生态学 Q3 ENVIRONMENTAL SCIENCES
Sakron Vilavan, Debesh Das, Hayat Ullah, Shubham Anil Gade, Sheikh Faruk Ahmed, Suriyan Cha-um, Patchara Praseartkul, Avishek Datta, Sushil Kumar Himanshu
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引用次数: 0

摘要

泰国湄南河下游流域(CPRB)是一个主要的水稻产区,随着洪水和干旱的日益频繁,该流域的缺水问题也日益严重,因此需要采取有效的适应战略来维持农业生产率。本研究利用 DSSAT-CERES-Rice 模型评估了气候变化对水稻产量和灌溉用水的影响。根据这些研究结果,推荐了基于基因型和管理的潜在适应策略。利用 2017-2018 年和 2021-2022 年期间在泰国亚洲理工学院进行的田间试验数据对该模型进行了校准和评估。比较了基准期(2010-2022 年)和未来气候期(世纪早期:2023-2040 年;世纪中期:2041-2070 年;世纪晚期:2071-2100 年)的谷物产量和灌溉用水量。未来气候预测基于 NEX-GDDP-CMIP6项目的五个全球气候模型(GCMs),在三种情景(SSP126、SSP245 和 SSP585)下进行。模型校准和评估显示了非常好的性能统计,校准和评估期间的 d 指数均为 0.85。模型模拟结果表明,在 SSP245 和 SSP585 条件下,预计本世纪末 CPRB 下部的最高气温和最低气温将分别升高约 2 ℃ 和约 4 ℃。因此,预计到本世纪末,在 SSP585 条件下,水稻产量将下降 33%,灌溉用水量将增加 53%。根据研究结果,建议采取以下基于基因型和管理的主要适应战略:(1) 培育耐热水稻品种,以减轻未来气候情景下的产量损失;(2) 培育谷粒灌浆期延长的水稻品种,以提高灌溉用水量和产量;(3) 将播种日期提前 1-2 周(基准播种日期为 7 月 20 日),并将施肥日期提前 1-2 周、(4) 优化灌溉阈值(可灌溉的土壤剩余水量),在不影响产量的情况下减少灌溉用水量。总之,研究结果突出表明了基因型改良和适应性管理措施在减轻气候变化对 CPRB 下游地区水稻生产不利影响方面的重要性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Exploring the impacts of climate change and identifying potential adaptation strategies for sustainable rice production in Thailand’s Lower Chao Phraya Basin through crop simulation modeling

The lower Chao Phraya River Basin (CPRB) in Thailand, a major rice-producing area, is grappling with increased water scarcity alongside more frequent floods and droughts, necessitating effective adaptation strategies to sustain agricultural productivity. This study assesses the impacts of climate change on rice yield and irrigation water use, using the DSSAT-CERES-Rice model. Based on these findings, potential genotype- and management-based adaptation strategies were recommended. The model was calibrated and evaluated using the data from field experiments conducted at the Asian Institute of Technology, Thailand during 2017–2018 and 2021–2022. The grain yield and irrigation water use between baseline (2010–2022) and future climate periods (early-century: 2023–2040, mid-century: 2041–2070, and late-century: 2071–2100) were compared. Future climate projections were based on five Global Climate Models (GCMs) from the NEX-GDDP-CMIP6 project under three scenarios (SSP126, SSP245, and SSP585). The model calibration and evaluation demonstrated very good performance statistics, with a d-index of 0.85 during both calibration and evaluation. The model simulations indicated that the maximum and minimum temperatures in the lower CPRB are projected to increase by ~ 2 °C and ~ 4 °C in the late century under SSP245 and SSP585, respectively. Consequently, rice yields are projected to decline by up to 33%, and irrigation water usage to increase by 53% under SSP585 by the late century. Based on the findings, the following major genotype- and management-based adaptation strategies are recommended: (1) Developing heat-tolerant rice cultivars to mitigate yield losses under future climate scenarios, (2) Developing rice cultivars with extended grain-filling duration to enhance both irrigation water use and yield, (3) Shifting the planting date 1–2 weeks earlier (from baseline planting date of 20 July), and shifting fertilizer application date 1–2 weeks earlier (from baseline fertilizer application date of 20 September) for the panicle initiation stage to improve yield, and (4) Optimizing irrigation thresholds (remaining soil water at which to irrigate) to reduce irrigation water use without compromising yield. Overall, the findings highlight the importance of genotype improvement and adaptive management practices in mitigating the adverse effects of climate change on rice production in the lower CPRB.

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来源期刊
Environmental Monitoring and Assessment
Environmental Monitoring and Assessment 环境科学-环境科学
CiteScore
4.70
自引率
6.70%
发文量
1000
审稿时长
7.3 months
期刊介绍: Environmental Monitoring and Assessment emphasizes technical developments and data arising from environmental monitoring and assessment, the use of scientific principles in the design of monitoring systems at the local, regional and global scales, and the use of monitoring data in assessing the consequences of natural resource management actions and pollution risks to man and the environment.
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