YANG Y C, CHENG Y H, LIANG L Q, LIAO L P, WANG T Y, ZHANG H Y, YANG X X, HU J Q. Simulation of response of sugarcane growth to meteorological drought scenarios based on Aqua Crop model in Nanning[J]. Chinese Journal of Eco-Agriculture, 2022, 30(12): 1900−1912. DOI: 10.12357/cjea.20220315
Citation: YANG Y C, CHENG Y H, LIANG L Q, LIAO L P, WANG T Y, ZHANG H Y, YANG X X, HU J Q. Simulation of response of sugarcane growth to meteorological drought scenarios based on Aqua Crop model in Nanning[J]. Chinese Journal of Eco-Agriculture, 2022, 30(12): 1900−1912. DOI: 10.12357/cjea.20220315

Simulation of response of sugarcane growth to meteorological drought scenarios based on Aqua Crop model in Nanning

  • Rain-fed sugarcane is mainly cultivated in Nanning, an area with extensive hills and karst. The soil water deficit caused by meteorological drought is a major factor affecting sugarcane growth and yield in this region. The cane yield has experienced huge losses due to drought over the years. Therefore, the daily standardized weighted average precipitation index (SWAP) was calculated using 0.1° grid daily meteorological data from 1979−2018, and the meteorological drought characteristics and possible drought event scenarios during the sugarcane growth period were identified. Finally, the Aqua Crop model was employed to simulate and reveal the mechanisms of sugarcane growth, biomass, and yield accumulation in response to meteorological droughts of multiple intensities and durations. The results showed that the duration, intensity, and frequency of meteorological drought presented significant spatial heterogeneity in Nanning, and meteorological drought events mainly occurred during the sugarcane growth stages of sprouting, stem elongation, and maturity. In addition, seasonal droughts with durations longer than 30 days and sudden droughts with durations less than 30 days occured alternately in the study area. The Aqua Crop model showed good simulation accuracy with the yield determination coefficient (R2) reaching 0.92, and the root mean square error as 3.84%, which were achieved after the sensitivity analysis by the Extended Fourier Amplitude Test (EFAST) and the crop parameter localization for the model. That is, the Aqua Crop model had good simulation accuracy and practical value in this study. The simulation results of a typical meteorological drought year demonstrated that the cane yield (Y) and biomass accumulation (B) were sensitive to meteorological drought of all intensities. However, transpiration (Tr) was sensitive to meteorological drought only during the tilling and stem elongation stages, and canopy coverage (CC) appears to have a significant lag effect in response to meteorological drought. The variation in the above four factors showed an obvious response when the meteorological drought lasted for 15 days or more during the sprouting stage. Nevertheless, the variation in the above four factors appeared to be a significant response when the meteorological drought lasted for only 5 days or more during the stem elongation stage. There was no significant response of the above four factors to meteorological drought for all intensities and durations in the maturity stage. In terms of different meteorological drought intensity scenarios, the reduction rate variations of cane yield (Yw), biomass (Bw) and transpiration (Trw) were, respectively, 0−24.0%, 0−18.5%, and 0−15.9% when the duration of light drought increased from 5 to 35 days; 25.0%−37.0%, 20.0%−29.3%, and 8.0%−24.4% when the duration of moderate drought increased from 15 to 45 days; and 33.5%−40.0%, 26.2%−31.7%, and 18.9%−25.7% when the duration of severe drought increased from 35 to 50 days. These results reveal the quantitative mapping relationship between sugarcane growth process, cane yield accumulation, and meteorological drought for all intensities and durations in the study area, which plays an important scientific supporting role in the chain transmission mechanism analysis of sugarcane drought among meteorological drought, soil moisture, and sugarcane growth, in multi-stage drought early warning systems, and in the intelligent management of drought dynamic risk.
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