Grain water content(GWC)is a key determinant for mechanical harvesting of maize(Zea mays).In our previous research,we identified a quantitative trait locus,qGWC1,associated with GWC in maize.Here,we examined near-isog...Grain water content(GWC)is a key determinant for mechanical harvesting of maize(Zea mays).In our previous research,we identified a quantitative trait locus,qGWC1,associated with GWC in maize.Here,we examined near-isogenic lines(NILs)NILL and NILH that differed at the qGWC1 locus.Lower GWC in NILL was primarily attributed to reduced grain water weight(GWW)and smaller fresh grain size,rather than the accumulation of dry matter.The difference in GWC between the NILs became more pronounced approximately 35 d after pollination(DAP),arising from a faster dehydration rate in NILL.Through an integrated analysis of the transcriptome,proteome,and metabolome,coupled with an examination of hormones and their derivatives,we detected a marked decrease in JA,along with an increase in cytokinin,storage forms of IAA(IAA-Glu,IAA-ASP),and IAA precursor IPA in immature NILL kernels.During kernel development,genes associated with sucrose synthases,starch biosynthesis,and zein production in NILL,exhibited an initial up-regulation followed by a gradual down-regulation,compared to those in NILH.This discovery highlights the crucial role of phytohormone homeostasis and genes related to kernel development in balancing GWC and dry matter accumulation in maize kernels.展开更多
利用高光谱遥感技术监测作物水分状况和籽粒产量,对于调控作物生长、优化水分管理和改善产量形成具有重要意义。本研究玉米品种选用正红505,于2018—2019年在四川雅安和仁寿的试验田设置4个水分处理(正常水分、轻度、中度和重度干旱),...利用高光谱遥感技术监测作物水分状况和籽粒产量,对于调控作物生长、优化水分管理和改善产量形成具有重要意义。本研究玉米品种选用正红505,于2018—2019年在四川雅安和仁寿的试验田设置4个水分处理(正常水分、轻度、中度和重度干旱),分析玉米在拔节期(V6)、抽雄期(VT)和灌浆期(R^(2))的冠层含水量(canopy water content,CWC)与籽粒产量的定量关系,利用植被指数和连续小波变换对光谱反射率数据进行处理,采用线性回归方法构建CWC定量反演模型,进一步探索以CWC为桥梁建立的玉米籽粒产量的预测模型效果。结果表明,(1)利用小波特征构建的CWC估测模型的预测效果高于植被指数,V6、VT和R^(2)期分别以小波特征gaus3770,64、rbio3.31635,2和rbio3.3838,2构建的线性回归模型检验精度较高,R^(2)分别为0.770、0.291和0.233。(2)CWC与玉米籽粒产量间建立的线性回归模型均达极显著水平(P<0.01),V6、VT和R^(2)期的R^(2)分别为0.596、0.366和0.439。(3)基于光谱反射率构建的产量预测模型以V6期小波特征gaus3770,64的验证效果最好(R^(2)=0.577,RMSE=1.625 t hm^(–2)),可作为预测玉米籽粒产量的最佳时期。因此,本研究提出的“光谱反射率—冠层含水量—产量”建模方法能够实现对玉米籽粒产量的精确估测,为未来大面积监测玉米生产力提供了理论依据。展开更多
基金supported by the Jiangsu province Seed Industry Revitalization project[JBGS(2021)002]Beijing Germplasm Creation and Variety Selection and Breeding Joint Project[NY2023-180].
文摘Grain water content(GWC)is a key determinant for mechanical harvesting of maize(Zea mays).In our previous research,we identified a quantitative trait locus,qGWC1,associated with GWC in maize.Here,we examined near-isogenic lines(NILs)NILL and NILH that differed at the qGWC1 locus.Lower GWC in NILL was primarily attributed to reduced grain water weight(GWW)and smaller fresh grain size,rather than the accumulation of dry matter.The difference in GWC between the NILs became more pronounced approximately 35 d after pollination(DAP),arising from a faster dehydration rate in NILL.Through an integrated analysis of the transcriptome,proteome,and metabolome,coupled with an examination of hormones and their derivatives,we detected a marked decrease in JA,along with an increase in cytokinin,storage forms of IAA(IAA-Glu,IAA-ASP),and IAA precursor IPA in immature NILL kernels.During kernel development,genes associated with sucrose synthases,starch biosynthesis,and zein production in NILL,exhibited an initial up-regulation followed by a gradual down-regulation,compared to those in NILH.This discovery highlights the crucial role of phytohormone homeostasis and genes related to kernel development in balancing GWC and dry matter accumulation in maize kernels.
文摘利用高光谱遥感技术监测作物水分状况和籽粒产量,对于调控作物生长、优化水分管理和改善产量形成具有重要意义。本研究玉米品种选用正红505,于2018—2019年在四川雅安和仁寿的试验田设置4个水分处理(正常水分、轻度、中度和重度干旱),分析玉米在拔节期(V6)、抽雄期(VT)和灌浆期(R^(2))的冠层含水量(canopy water content,CWC)与籽粒产量的定量关系,利用植被指数和连续小波变换对光谱反射率数据进行处理,采用线性回归方法构建CWC定量反演模型,进一步探索以CWC为桥梁建立的玉米籽粒产量的预测模型效果。结果表明,(1)利用小波特征构建的CWC估测模型的预测效果高于植被指数,V6、VT和R^(2)期分别以小波特征gaus3770,64、rbio3.31635,2和rbio3.3838,2构建的线性回归模型检验精度较高,R^(2)分别为0.770、0.291和0.233。(2)CWC与玉米籽粒产量间建立的线性回归模型均达极显著水平(P<0.01),V6、VT和R^(2)期的R^(2)分别为0.596、0.366和0.439。(3)基于光谱反射率构建的产量预测模型以V6期小波特征gaus3770,64的验证效果最好(R^(2)=0.577,RMSE=1.625 t hm^(–2)),可作为预测玉米籽粒产量的最佳时期。因此,本研究提出的“光谱反射率—冠层含水量—产量”建模方法能够实现对玉米籽粒产量的精确估测,为未来大面积监测玉米生产力提供了理论依据。
