Soybeans are a globally important crop,both commercially and nutritionally.Accurate mapping of soybean cultivation is essential for optimizing production and informing market strategies.However,traditional sample-driv...Soybeans are a globally important crop,both commercially and nutritionally.Accurate mapping of soybean cultivation is essential for optimizing production and informing market strategies.However,traditional sample-driven soybean mapping algorithms often rely on extensive,representative datasets,which can limit their applicability across different regions and periods.In contrast,existing sample-free soybean mapping methods have yet to fully exploit key physiological traits,such as chlorophyll content,canopy greenness,and water content,that are essential for distinguishing soybeans from other crops,particularly during peak growth stages when many crops share similar spectral characteristics.To address these limitations,this study introduces an innovative approach:the spectral Gaussian mixture model(SGMM)for globalscale soybean mapping.Specifically,the SGMM develops a novel Bhattacharyya coefficient weighting method to optimize spectral probabilistic separability between soybeans and other crops.Moreover,it identifies an accurate soybean mapping timeframe,named the optimal time window,to refine spectral feature extraction across varying environmental conditions and crop calendars.Unlike previous methods that rely on fixed thresholds or a limited set of spectral indices,our SGMM offers a probabilistic mapping framework that dynamically adapts to regional variations in soybean growth.The SGMM was validated across multiple soybean-producing regions,showing high accuracy with average overall accuracies of 0.875 in China,0.907 in the United States,0.895 in Argentina,and 0.884 in Brazil.Furthermore,the provincial-level estimates of soybean areas correlated strongly with official statistics,highlighting the model’s reliability and scalability for global soybean mapping.By leveraging key physiological insights and optimizing spectral feature extraction,the SGMM provides an efficient,scalable solution for global agricultural monitoring and can serve as a reference for mapping other crops.展开更多
Plant height is one of the most important traits in soybean. The semi-dwarf soybean cultivars could improve the ability of lodging resistance to obtain higher yield. To broaden the dwarfism germplasm resources in soyb...Plant height is one of the most important traits in soybean. The semi-dwarf soybean cultivars could improve the ability of lodging resistance to obtain higher yield. To broaden the dwarfism germplasm resources in soybean, 44 dwarf mutants were identified from a gamma rays mutagenized M-2 population. Two of these mutants, Gmdwf1（Glycine max dwarf 1） and Gmdwf2（Glycine max dwarf 2）, were investigated in this study. Genetic analysis showed that both mutants were inherited in a recessive manner and their mutated regions were delimited to a 2.610-Mb region on chromosome 1 by preliminary mapping. Further fine mapping study proved that the two mutants had a common deletion region of 1.552 Mb in the target region, which was located in a novel locus site without being reported previously. The dwarfism of Gmdwf1 could not be rescued by gibberellin（GA） and brassinolide（BR） treatments, which indicated that the biosynthesis of these hormones was not deficient in Gmdwf1.展开更多
基金supported by the National Natural Science Foundation of China(Project No.42371363)the National Key Research and Development Program of China(Project No.2023YFB3907603)funded by the Fundamental Research Funds for the Central Universities and the Independent Innovation Research Funds for Graduate Students of China Agricultural University.
文摘Soybeans are a globally important crop,both commercially and nutritionally.Accurate mapping of soybean cultivation is essential for optimizing production and informing market strategies.However,traditional sample-driven soybean mapping algorithms often rely on extensive,representative datasets,which can limit their applicability across different regions and periods.In contrast,existing sample-free soybean mapping methods have yet to fully exploit key physiological traits,such as chlorophyll content,canopy greenness,and water content,that are essential for distinguishing soybeans from other crops,particularly during peak growth stages when many crops share similar spectral characteristics.To address these limitations,this study introduces an innovative approach:the spectral Gaussian mixture model(SGMM)for globalscale soybean mapping.Specifically,the SGMM develops a novel Bhattacharyya coefficient weighting method to optimize spectral probabilistic separability between soybeans and other crops.Moreover,it identifies an accurate soybean mapping timeframe,named the optimal time window,to refine spectral feature extraction across varying environmental conditions and crop calendars.Unlike previous methods that rely on fixed thresholds or a limited set of spectral indices,our SGMM offers a probabilistic mapping framework that dynamically adapts to regional variations in soybean growth.The SGMM was validated across multiple soybean-producing regions,showing high accuracy with average overall accuracies of 0.875 in China,0.907 in the United States,0.895 in Argentina,and 0.884 in Brazil.Furthermore,the provincial-level estimates of soybean areas correlated strongly with official statistics,highlighting the model’s reliability and scalability for global soybean mapping.By leveraging key physiological insights and optimizing spectral feature extraction,the SGMM provides an efficient,scalable solution for global agricultural monitoring and can serve as a reference for mapping other crops.
基金supported by the National Natural Science Foundation of China (31171571 and 31571692)the One Hundred Person Project of the Chinese Academy of Sciences
文摘Plant height is one of the most important traits in soybean. The semi-dwarf soybean cultivars could improve the ability of lodging resistance to obtain higher yield. To broaden the dwarfism germplasm resources in soybean, 44 dwarf mutants were identified from a gamma rays mutagenized M-2 population. Two of these mutants, Gmdwf1（Glycine max dwarf 1） and Gmdwf2（Glycine max dwarf 2）, were investigated in this study. Genetic analysis showed that both mutants were inherited in a recessive manner and their mutated regions were delimited to a 2.610-Mb region on chromosome 1 by preliminary mapping. Further fine mapping study proved that the two mutants had a common deletion region of 1.552 Mb in the target region, which was located in a novel locus site without being reported previously. The dwarfism of Gmdwf1 could not be rescued by gibberellin（GA） and brassinolide（BR） treatments, which indicated that the biosynthesis of these hormones was not deficient in Gmdwf1.
