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Breeding high yielding water-deficit tolerant rice is considered a primary goal for achieving the objectives of the sustainable development goals, 2030. However, evaluating the performance of the pre-breeding-promising parental-lines for water deficit tolerance prior to their incorporation in the breeding program is crucial for the success of the breeding programs. The aim of the current investigation is to assess the performance of a set of pre-breeding lines compared with their parents. To achieve this goal a set of 7 pre-breeding rice lines along with their parents (5 genotypes) were field evaluated under well-irrigated and water-stress conditions. Water stress was applied by flush irrigation every 12 days without keeping standing water after irrigation. Based on the field evaluation results, a pre-breeding line was selected to conduct physiological and expression analysis of drought related genes at the green house. Furthermore, a greenhouse trial was conducted in pots, where the genotypes were grown under well and stress irrigation conditions at seedling stage for physiological analysis and expression profiling of the genotypes. Results indicated that the pre-breeding lines which were high yielding under water shortage stress showed low drought susceptibility index. Those lines exhibited high proline, SOD, TSS content along with low levels of MDA content in their leaves. Moreover, the genotypes grain yield positively correlated with proline, SOD, TSS content in their leaves. The SSR markers RM22, RM525, RM324 and RM3805 were able to discriminate the tolerant parents from the sensitive one. Expression levels of the tested drought responsive genes revealed the upregulation of OsLEA3 , OsAPX2 , OsNAC1 , OSDREB2A , OsDREB1C , OsZIP23 , OsP5CS , OsAHL1 and OsCATA genes in response to water deficit stress as compared to their expression under normal irrigated condition. Taken together among the tested pre-breeding lines the RBL112 pre-breeding line is high yielding under water-deficit and could be used as donor for high yielding genes in the breeding for water deficit resistance. This investigation withdraws attention to evaluate the promising pre-breeding lines before their incorporation in the water deficit stress breeding program.
Breeding high yielding water-deficit tolerant rice is considered a primary goal for achieving the objectives of the sustainable development goals, 2030. However, evaluating the performance of the pre-breeding-promising parental-lines for water deficit tolerance prior to their incorporation in the breeding program is crucial for the success of the breeding programs. The aim of the current investigation is to assess the performance of a set of pre-breeding lines compared with their parents. To achieve this goal a set of 7 pre-breeding rice lines along with their parents (5 genotypes) were field evaluated under well-irrigated and water-stress conditions. Water stress was applied by flush irrigation every 12 days without keeping standing water after irrigation. Based on the field evaluation results, a pre-breeding line was selected to conduct physiological and expression analysis of drought related genes at the green house. Furthermore, a greenhouse trial was conducted in pots, where the genotypes were grown under well and stress irrigation conditions at seedling stage for physiological analysis and expression profiling of the genotypes. Results indicated that the pre-breeding lines which were high yielding under water shortage stress showed low drought susceptibility index. Those lines exhibited high proline, SOD, TSS content along with low levels of MDA content in their leaves. Moreover, the genotypes grain yield positively correlated with proline, SOD, TSS content in their leaves. The SSR markers RM22, RM525, RM324 and RM3805 were able to discriminate the tolerant parents from the sensitive one. Expression levels of the tested drought responsive genes revealed the upregulation of OsLEA3 , OsAPX2 , OsNAC1 , OSDREB2A , OsDREB1C , OsZIP23 , OsP5CS , OsAHL1 and OsCATA genes in response to water deficit stress as compared to their expression under normal irrigated condition. Taken together among the tested pre-breeding lines the RBL112 pre-breeding line is high yielding under water-deficit and could be used as donor for high yielding genes in the breeding for water deficit resistance. This investigation withdraws attention to evaluate the promising pre-breeding lines before their incorporation in the water deficit stress breeding program.
In agriculture, especially in crop breeding, innovative approaches are required to address the urgent issues posed by climate change and global food security. Artificial intelligence (AI) is a revolutionary technology in wheat breeding that provides new approaches to improve the ability of crops to withstand and produce higher yields in response to changing climate circumstances. This review paper examines the incorporation of artificial intelligence (AI) into conventional wheat breeding methods, with a focus on the contribution of AI in tackling the intricacies of contemporary agriculture. This review aims to assess the influence of AI technologies on enhancing the efficiency, precision, and sustainability of wheat breeding projects. We conduct a thorough analysis of recent research to evaluate several applications of artificial intelligence, such as machine learning (ML), deep learning (DL), and genomic selection (GS). These technologies expedite the swift analysis and interpretation of extensive datasets, augmenting the process of selecting and breeding wheat varieties that are well-suited to a wide range of environmental circumstances. The findings from the examined research demonstrate notable progress in wheat breeding as a result of artificial intelligence. ML algorithms have enhanced the precision of predicting phenotypic traits, whereas genomic selection has reduced the duration of breeding cycles. Utilizing artificial intelligence, high-throughput phenotyping allows for meticulous examination of plant characteristics under different stress environments, facilitating the identification of robust varieties. Furthermore, AI-driven models have exhibited superior predicted accuracies for crop productivity and disease resistance in comparison to conventional methods. AI technologies play a crucial role in the modernization of wheat breeding, providing significant enhancements in crop performance and adaptability. This integration not only facilitates the growth of wheat cultivars that provide large yields and can withstand stressful conditions but also strengthens global food security in the context of climate change. Ongoing study and collaboration across several fields are crucial to improving and optimizing these AI applications, ultimately enhancing their influence on sustainable agriculture.
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