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利用稳定性分析、多性状选择指数和不同灌溉制度下的基因型-环境互作对适应普遍变暖的气候智能型水稻(Oryza sativa L.)基因型进行鉴定。

Climate-smart rice (Oryza sativa L.) genotypes identification using stability analysis, multi-trait selection index, and genotype-environment interaction at different irrigation regimes with adaptation to universal warming.

机构信息

Department of Genetics and Plant Breeding, Faculty of Agriculture, Bangladesh Agricultural University, Mymensingh, 2202, Bangladesh.

Rice Breeding Innovation Platform, International Rice Research Institute, 4031, Los Banos, Laguna, Philippines.

出版信息

Sci Rep. 2024 Jun 15;14(1):13836. doi: 10.1038/s41598-024-64808-9.

Abstract

Climate change has brought an alarming situation in the scarcity of fresh water for irrigation due to the present global water crisis, climate variability, drought, increasing demands of water from the industrial sectors, and contamination of water resources. Accurately evaluating the potential of future rice genotypes in large-scale, multi-environment experiments may be challenging. A key component of the accurate assessment is the examination of stability in growth contexts and genotype-environment interaction. Using a split-plot design with three replications, the study was carried out in nine locations with five genotypes under continuous flooding (CF) and alternate wet and dry (AWD) conditions. Utilizing the web-based warehouse inventory search tool (WIST), the water status was determined. To evaluate yield performance for stability and adaptability, AMMI and GGE biplots were used. The genotypes clearly reacted inversely to the various environments, and substantial interactions were identified. Out of all the environments, G3 (BRRI dhan29) had the greatest grain production, whereas G2 (Binadhan-8) had the lowest. The range between the greatest and lowest mean values of rice grain output (4.95 to 4.62 t ha) was consistent across five distinct rice genotypes. The genotype means varied from 5.03 to 4.73 t ha depending on the environment. In AWD, all genotypes out performed in the CF system. With just a little interaction effect, the score was almost zero for several genotypes (E1, E2, E6, and E7 for the AWD technique, and E5, E6, E8, and E9 for the CF method) because they performed better in particular settings. The GGE biplot provided more evidence in support of the AMMI study results. The study's findings made it clear that the AMMI model provides a substantial amount of information when evaluating varietal performance across many environments. Out of the five accessions that were analyzed, one was found to be top-ranking by the multi-trait genotype ideotype distance index, meaning that it may be investigated for validation stability measures. The study's findings provide helpful information on the variety selection for the settings in which BRRI dhan47 and BRRI dhan29, respectively, performed effectively in AWD and CF systems. Plant breeders might use this knowledge to choose newer kinds and to design breeding initiatives. In conclusion, intermittent irrigation could be an effective adaptation technique for simultaneously saving water and mitigating GHG while maintaining high rice grain yields in rice cultivation systems.

摘要

气候变化导致淡水资源短缺,给灌溉带来了严峻的形势,这主要是由于目前的全球水危机、气候多变性、干旱、工业部门对水资源需求的增加以及水资源污染等原因。在大规模、多环境试验中准确评估未来水稻基因型的潜力可能具有挑战性。准确评估的一个关键组成部分是检查生长环境和基因型-环境互作的稳定性。本研究采用裂区设计,三次重复,在 9 个地点进行,五个基因型在连续淹水(CF)和交替湿润和干燥(AWD)条件下进行。利用基于网络的仓库库存搜索工具(WIST)确定水分状况。为了评估稳定性和适应性的产量表现,使用 AMMI 和 GGE 双标图。基因型对不同的环境明显表现出相反的反应,并且确定了大量的相互作用。在所有环境中,G3(BRRI dhan29)的谷物产量最高,而 G2(Binadhan-8)的产量最低。五个不同水稻基因型的稻谷产量最大和最小平均值之间的范围(4.95 至 4.62 t ha)一致。基因型均值根据环境的不同在 5.03 至 4.73 t ha 之间变化。在 AWD 中,所有基因型在 CF 系统中的表现都优于在 CF 系统中的表现。由于某些基因型(AWD 技术的 E1、E2、E6 和 E7,以及 CF 方法的 E5、E6、E8 和 E9)在特定环境中的表现更好,因此得分几乎为零,交互作用效应很小。GGE 双标图为 AMMI 研究结果提供了更多的证据。研究结果表明,当评估多个环境下品种表现时,AMMI 模型提供了大量信息。在分析的五个品系中,有一个品系的多性状基因型理想型距离指数排名最高,这意味着它可能会被调查以验证稳定性措施。本研究结果为 BRRI dhan47 和 BRRI dhan29 分别在 AWD 和 CF 系统中有效应用的环境中的品种选择提供了有价值的信息。植物育种者可以利用这些知识来选择新的品种,并设计育种计划。总之,间歇性灌溉可能是一种有效的适应技术,可在保持水稻高产的同时节约水和减少 GHG。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/44d5/11180187/0855c71aad0d/41598_2024_64808_Fig1_HTML.jpg

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