本研究利用冬小麦起身期和拔节期冠层光谱数据,选用反映冬小麦长势信息的优化土壤调节植被指数OSAVI(Optimization of soil-adjusted vegetation index)与CERES-Wheat模型相结合进行变量施肥管理(变量区),对变量施肥模型进行验证并进行...本研究利用冬小麦起身期和拔节期冠层光谱数据,选用反映冬小麦长势信息的优化土壤调节植被指数OSAVI(Optimization of soil-adjusted vegetation index)与CERES-Wheat模型相结合进行变量施肥管理(变量区),对变量施肥模型进行验证并进行了优化,旨在为变量施肥提供理论依据。展开更多
A field experiment using a split-plot randomized complete block design with three replications was carried out to determine relationships between spectral indices and wheat grain yield （GY）, to compare the performan...A field experiment using a split-plot randomized complete block design with three replications was carried out to determine relationships between spectral indices and wheat grain yield （GY）, to compare the performance of four vegetation indices （VIs） for GY prediction, and to study the feasibility of VI to estimate grain protein content （GPC） in winter wheat. Two typical winter wheat （Triticum aestivum L.） cultivars ＇Xuzhou 26＇ （high protein content） and ＇Huaimai 18＇ （low protein content） were used as the main plot treatments and four N rates, i.e., 0, 120, 210, and 300 kg N ha^-1, as the sub-plot treatments. Increasing soil N supply significantly increased GY and GPC （P ≤ 0.05）. For the two cultivars combined, significant and positive correlations were found between four VIs and GY, with the strongest relationship observed when using the green ratio vegetation index （GRVI） at mid-filling. Cumulative VI estimates improved yield predictions substantially, with the best interval being heading to maturity stage. Similar results were found between VI and grain protein yield. However, when using cumulative VI, GPC showed no significant improvement. The strong relationship between leaf N status and GPC （R2 =0.9144 for ＇Xuzhou 26＇ and R2 = 0.8285 for ＇Huaimai 18＇） indicated that canopy spectra could be used to predict GPC. The strong fit between estimated and observed GPC （R2 = 0.7939） indicated that remote sensing techniques were potentially useful predictors of grain protein content and quality in wheat.展开更多
文摘本研究利用冬小麦起身期和拔节期冠层光谱数据,选用反映冬小麦长势信息的优化土壤调节植被指数OSAVI(Optimization of soil-adjusted vegetation index)与CERES-Wheat模型相结合进行变量施肥管理(变量区),对变量施肥模型进行验证并进行了优化,旨在为变量施肥提供理论依据。
基金Project supported by the National Natural Science Foundation of China (No.30400278)National High Technology Research and Development Program (863 Program) of China (No.2006AA10Z129)
文摘A field experiment using a split-plot randomized complete block design with three replications was carried out to determine relationships between spectral indices and wheat grain yield （GY）, to compare the performance of four vegetation indices （VIs） for GY prediction, and to study the feasibility of VI to estimate grain protein content （GPC） in winter wheat. Two typical winter wheat （Triticum aestivum L.） cultivars ＇Xuzhou 26＇ （high protein content） and ＇Huaimai 18＇ （low protein content） were used as the main plot treatments and four N rates, i.e., 0, 120, 210, and 300 kg N ha^-1, as the sub-plot treatments. Increasing soil N supply significantly increased GY and GPC （P ≤ 0.05）. For the two cultivars combined, significant and positive correlations were found between four VIs and GY, with the strongest relationship observed when using the green ratio vegetation index （GRVI） at mid-filling. Cumulative VI estimates improved yield predictions substantially, with the best interval being heading to maturity stage. Similar results were found between VI and grain protein yield. However, when using cumulative VI, GPC showed no significant improvement. The strong relationship between leaf N status and GPC （R2 =0.9144 for ＇Xuzhou 26＇ and R2 = 0.8285 for ＇Huaimai 18＇） indicated that canopy spectra could be used to predict GPC. The strong fit between estimated and observed GPC （R2 = 0.7939） indicated that remote sensing techniques were potentially useful predictors of grain protein content and quality in wheat.
