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Leaf area index simulation in cotton grown under surface waterlogging.

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Titel: Leaf area index simulation in cotton grown under surface waterlogging.
Autoren: Wang, Panpan1 (AUTHOR), Wu, Hao1 (AUTHOR) wu_hao@yzu.edu.cn, Han, Xudong2 (AUTHOR), Peng, Zhuoyue1 (AUTHOR), Wang, Yuanning1 (AUTHOR), Ma, Haoyu1 (AUTHOR), Wang, Jingxian3 (AUTHOR)
Quelle: Crop Science. Sep/Oct2025, Vol. 65 Issue 5, p1-16. 16p.
Schlagwörter: *LEAF area index, *COTTON, *AGRICULTURAL industries, *WATERLOGGING (Soils), *TRANSPIRATION (Physics), *PHOTOSYNTHESIS, *DRAINAGE, *CROP growth
Abstract: Leaf area index is one of the important parameters to reflect the crop group structure and has a significant influence on the photosynthesis and transpiration of crops. The cotton surface waterlogging experience was conducted to characterize the changes in leaf area index under different durations of surface waterlogging (0, 3, 5, and 7 days). Based on the surface waterlogging depth and comprehensively considering the aftereffects of surface waterlogging, a surface waterlogging indicator was developed. The leaf area index under surface waterlogging was calculated by logistic equation using meteorological data and the constructed surface waterlogging indicator. The results indicate that the constructed model can accurately simulate the cotton leaf area index under different surface waterlogging degrees. During the calibration and validation periods, the coefficient of determination was >0.74, the normalized root mean square error ranged from 0.08 to 0.18, and the Nash–Sutcliffe efficiency coefficient was >0.70. The results of the sensitivity analysis indicate that the leaf area at emergence (Lg0) and the initial leaf area at senescence (Ls0) had relatively little impact on the simulation results, while the potential maximum values of leaf area expansion and senescence (ag and as), the maximum rate of change in expanding leaf area (ΔLgxmax), and the maximum rate of change in senescing leaf area (ΔLsxmax) had a greater influence on the simulation results. The constructed surface waterlogging indicator can more accurately reflect the impact of surface waterlogging on cotton growth, thereby improving the simulation accuracy of cotton growth and yield under surface waterlogging conditions. The results provide a scientific basis for developing crop growth models and implementing precision management in farmland drainage. Core Ideas: The impact of different surface waterlogging levels on the leaf area index (LAI) of cotton is different.The logistic model can simulate the changes of LAI in cotton under different surface waterlogging conditions.The impact of surface waterlogging stress on the LAI of cotton exhibits a persistent aftereffect. Plain Language Summary: Leaf area index has a significant influence on the photosynthesis and transpiration of crops. In this study, the changes of cotton leaf area index under different surface waterlogging conditions were discussed. Considering the aftereffects of surface waterlogging, a surface waterlogging indicator and a cotton leaf area index calculation model were constructed. The results indicate that the constructed model can accurately simulate the cotton leaf area index under different surface waterlogging degrees. The potential maximum of leaf area expansion has the greatest impact on leaf area index, followed by the maximum rate of change in expanding and senescing leaf area, the potential maximum of leaf area senescence and the leaf area at emergence, and the initial leaf area at senescence. The results provide a scientific basis for developing crop growth models and implementing precision management in farmland drainage. [ABSTRACT FROM AUTHOR]
Datenbank: Academic Search Index
Beschreibung
Abstract:Leaf area index is one of the important parameters to reflect the crop group structure and has a significant influence on the photosynthesis and transpiration of crops. The cotton surface waterlogging experience was conducted to characterize the changes in leaf area index under different durations of surface waterlogging (0, 3, 5, and 7 days). Based on the surface waterlogging depth and comprehensively considering the aftereffects of surface waterlogging, a surface waterlogging indicator was developed. The leaf area index under surface waterlogging was calculated by logistic equation using meteorological data and the constructed surface waterlogging indicator. The results indicate that the constructed model can accurately simulate the cotton leaf area index under different surface waterlogging degrees. During the calibration and validation periods, the coefficient of determination was >0.74, the normalized root mean square error ranged from 0.08 to 0.18, and the Nash–Sutcliffe efficiency coefficient was >0.70. The results of the sensitivity analysis indicate that the leaf area at emergence (Lg0) and the initial leaf area at senescence (Ls0) had relatively little impact on the simulation results, while the potential maximum values of leaf area expansion and senescence (ag and as), the maximum rate of change in expanding leaf area (ΔLgxmax), and the maximum rate of change in senescing leaf area (ΔLsxmax) had a greater influence on the simulation results. The constructed surface waterlogging indicator can more accurately reflect the impact of surface waterlogging on cotton growth, thereby improving the simulation accuracy of cotton growth and yield under surface waterlogging conditions. The results provide a scientific basis for developing crop growth models and implementing precision management in farmland drainage. Core Ideas: The impact of different surface waterlogging levels on the leaf area index (LAI) of cotton is different.The logistic model can simulate the changes of LAI in cotton under different surface waterlogging conditions.The impact of surface waterlogging stress on the LAI of cotton exhibits a persistent aftereffect. Plain Language Summary: Leaf area index has a significant influence on the photosynthesis and transpiration of crops. In this study, the changes of cotton leaf area index under different surface waterlogging conditions were discussed. Considering the aftereffects of surface waterlogging, a surface waterlogging indicator and a cotton leaf area index calculation model were constructed. The results indicate that the constructed model can accurately simulate the cotton leaf area index under different surface waterlogging degrees. The potential maximum of leaf area expansion has the greatest impact on leaf area index, followed by the maximum rate of change in expanding and senescing leaf area, the potential maximum of leaf area senescence and the leaf area at emergence, and the initial leaf area at senescence. The results provide a scientific basis for developing crop growth models and implementing precision management in farmland drainage. [ABSTRACT FROM AUTHOR]
ISSN:0011183X
DOI:10.1002/csc2.70181