Lodging in maize is one of the major problems in maize production worldwide,which causes serious yield and economic losses annually.By evaluating cultivar lodging resistance performance in target growing environments ...Lodging in maize is one of the major problems in maize production worldwide,which causes serious yield and economic losses annually.By evaluating cultivar lodging resistance performance in target growing environments before cultivar extension and application,the risks and losses can be significantly reduced.In this study,a GIS-based quantitative method for evaluating maize cultivar lodging resistance performance in target growing environments was established based on full cognition of environment stress,cultivar resistance,and the interaction between them.At first,comprehensive environment lodging stress is measured by three factors:1)extreme wind event in maize vegetative stage which is the direct factor,2)soil potassium content in target growing environment which is an indirect factor affecting corn stem sturdiness,and 3)planting density which is a human influence factor.Quantification methods of extreme probability analysis,spatial interpolation,normalization,and so on were used.Then,maize cultivar lodging resistance was determined using cumulative frequency distribution analysis of tested lodging data.At last,an evaluation matrix was established combining environment lodging stress and cultivar lodging resistance together,which was very simple and easy to understand method and the result is promising providing good direct support in practical cultivar application.The method used in this study,at county-level,cultivar-level and stress-level with GIS,can facilitate the identification of better-adapted growing environments for a specific maize cultivar,and provide direct support for maize cultivar recommendation and extension,so as to reduce the risk and loss of lodging in maize.It is more easy-operational and feasible than traditional surveying approach,especially for large-scale spatial trend analysis.So it is of both academic significance in accelerating precision agriculture development and practical significance in improving maize cultivar application.展开更多
Rapid and nondestructive monitoring of the temporal dynamic changes of agronomic traits of lodging maize is crucial for evaluating the growth recovery status.The purpose of this study is to assess the time-series chan...Rapid and nondestructive monitoring of the temporal dynamic changes of agronomic traits of lodging maize is crucial for evaluating the growth recovery status.The purpose of this study is to assess the time-series changes in maize growth recovery after lodging using unmanned aerial vehicle(UAV)hyperspectral technology.Based on the Entropy method,canopy height(CH)and canopy coverage(CC)were used to represent the canopy structure index(CSI),while leaf chlorophyll content(LCC)and plant water content(PWC)were used to represent the physiological activity index(PAI).Based on the theory of normal(skewed)distribution,the growth recovery grade(GRG)of lodging maize was divided based on the estimated CSI and PAI values.The main results were as follows:(a)With the advance of days after lodging(DAL),CH was decreased after increasing,while other agronomic traits exhibited a downward trend.(b)The R^(2) values for the CH,CC,LCC,and PWC estimation model were 0.75,0.69,0.54,and 0.49,respectively,while the MAPE values were 14.03%,8.84%,16.62%,and 6.22%,respectively,in the testing set.(c)The growth recovery of lodging maize was classified using the threshold based on estimated CSI and PAI,achieving an overall accuracy of 77.68%.Therefore,the method for evaluating maize growth recovery after lodging proved effective in monitoring lodging damage.This study provided a reference for the efficient and nondestructive monitoring of growth recovery in lodging maize using UAV-based hyperspectral images.展开更多
基金We acknowledge the China Meteorology Administration and the Beijing Jinsenonghua Seed Technology Co.,Ltd.for providing essential raw data for this study,and are very thankful that the study is funded by the National Natural Science Foundation of China(41301084)the Hunan Provincial Natural Science Foundation of China(13JJ6075)and the constructing program of the key discipline in Huaihua University.The authors are also very grateful to the anonymous reviewers who gave constructive comments and suggestions on this manuscript.
文摘Lodging in maize is one of the major problems in maize production worldwide,which causes serious yield and economic losses annually.By evaluating cultivar lodging resistance performance in target growing environments before cultivar extension and application,the risks and losses can be significantly reduced.In this study,a GIS-based quantitative method for evaluating maize cultivar lodging resistance performance in target growing environments was established based on full cognition of environment stress,cultivar resistance,and the interaction between them.At first,comprehensive environment lodging stress is measured by three factors:1)extreme wind event in maize vegetative stage which is the direct factor,2)soil potassium content in target growing environment which is an indirect factor affecting corn stem sturdiness,and 3)planting density which is a human influence factor.Quantification methods of extreme probability analysis,spatial interpolation,normalization,and so on were used.Then,maize cultivar lodging resistance was determined using cumulative frequency distribution analysis of tested lodging data.At last,an evaluation matrix was established combining environment lodging stress and cultivar lodging resistance together,which was very simple and easy to understand method and the result is promising providing good direct support in practical cultivar application.The method used in this study,at county-level,cultivar-level and stress-level with GIS,can facilitate the identification of better-adapted growing environments for a specific maize cultivar,and provide direct support for maize cultivar recommendation and extension,so as to reduce the risk and loss of lodging in maize.It is more easy-operational and feasible than traditional surveying approach,especially for large-scale spatial trend analysis.So it is of both academic significance in accelerating precision agriculture development and practical significance in improving maize cultivar application.
基金supported by the National Key Research and Development Program of China(grant number 2023YFD2301500).
文摘Rapid and nondestructive monitoring of the temporal dynamic changes of agronomic traits of lodging maize is crucial for evaluating the growth recovery status.The purpose of this study is to assess the time-series changes in maize growth recovery after lodging using unmanned aerial vehicle(UAV)hyperspectral technology.Based on the Entropy method,canopy height(CH)and canopy coverage(CC)were used to represent the canopy structure index(CSI),while leaf chlorophyll content(LCC)and plant water content(PWC)were used to represent the physiological activity index(PAI).Based on the theory of normal(skewed)distribution,the growth recovery grade(GRG)of lodging maize was divided based on the estimated CSI and PAI values.The main results were as follows:(a)With the advance of days after lodging(DAL),CH was decreased after increasing,while other agronomic traits exhibited a downward trend.(b)The R^(2) values for the CH,CC,LCC,and PWC estimation model were 0.75,0.69,0.54,and 0.49,respectively,while the MAPE values were 14.03%,8.84%,16.62%,and 6.22%,respectively,in the testing set.(c)The growth recovery of lodging maize was classified using the threshold based on estimated CSI and PAI,achieving an overall accuracy of 77.68%.Therefore,the method for evaluating maize growth recovery after lodging proved effective in monitoring lodging damage.This study provided a reference for the efficient and nondestructive monitoring of growth recovery in lodging maize using UAV-based hyperspectral images.
