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Enhancing waterlogging tolerance in cotton through agronomic practices

Journal of Cotton Research 2023年第4期6卷 306-307页

作者： ZHANG Yanjun DONG Hezhong Institute of Industrial Crops Shandong Academy of Agricultural SciencesJinan 250100China

Recent publications have highlighted significant progress in utilizing agronomic interventions to alleviate waterlogging stress in cotton *** on these advancements,we provide a concise comment on the effects and underlying mechanisms of various strategies such as utilizing stress-tolerant cotton varieties,applying nitric oxide(NO),and implementing ridge ***,we recommend a combination of measures to enhance cotton’s ability to withstand waterlogging and reduce yield losses.

关键词： Cotton Waterlogging agronomic practice

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Improved Yield Prediction of Ratoon Rice Using Unmanned Aerial Vehicle-Based Multi-Temporal Feature Method

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Rice science 2023年第3期30卷 247-256,I0039-I0042页

作者： ZHOU Longfei MENG Ran YU Xing LIAO Yigui HUANG Zehua LÜZhengang XU Binyuan YANG Guodong PENG Shaobing XU Le College of Resources and Environment Huazhong Agricultural UniversityWuhan 430070China National Key Laboratory of Crop Genetic Improvement/Hubei Hongshan Laboratory/Key Laboratory of Crop Ecophysiology and Farming System in the Middle Reaches of the Yangtze River Ministry of Agriculture and Rural Affairs/College of Plant Science and TechnologyHuazhong Agricultural UniversityWuhan 430070China College of Agriculture Northeast Agricultural UniversityHarbin 150006China Harbin Institute of Technology Institute for Artificial Intelligence Co. LtdHarbin 150000China

Pre-harvest yield prediction of ratoon rice is critical for guiding crop interventions in precision ***,the unique agronomic practice(i.e.,varied stubble height treatment)in rice ratooning could lead to inconsistent rice phenology,which had a significant impact on yield prediction of ratoon ***-temporal unmanned aerial vehicle(UAV)-based remote sensing can likely monitor ratoon rice productivity and reflect maximum yield potential across growing seasons for improving the yield prediction compared with previous ***,in this study,we explored the performance of combination of agronomic practice information(API)and single-phase,multi-spectral features[vegetation indices(VIs)and texture(Tex)features]in predicting ratoon rice yield,and developed a new UAV-based method to retrieve yield formation process by using multi-temporal features which were effective in improving yield forecasting accuracy of ratoon *** results showed that the integrated use of VIs,Tex and API(VIs&Tex+API)improved the accuracy of yield prediction than single-phase UAV imagery-based feature,with the panicle initiation stage being the best period for yield prediction(R^(2) as 0.732,RMSE as 0.406,RRMSE as 0.101).More importantly,compared with previous multi-temporal UAV-based methods,our proposed multi-temporal method(multi-temporal model VIs&Tex:R^(2) as 0.795,RMSE as 0.298,RRMSE as 0.072)can increase R^(2) by 0.020-0.111 and decrease RMSE by 0.020-0.080 in crop yield *** study provides an effective method for accurate pre-harvest yield prediction of ratoon rice in precision agriculture,which is of great significance to take timely means for ensuring ratoon rice production and food security.

关键词： ratoon rice yield prediction unmanned aerial vehicle multi-temporal feature agronomic practice stubble height

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