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Modelling responses of cotton growth and yield to pre-planting soil moisture with the CROPGRO-Cotton model for a mulched drip irrigation system in the Tarim Basin

Author

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  • Wang, Xingpeng
  • Wang, Hongbo
  • Si, Zhuanyun
  • Gao, Yang
  • Duan, Aiwang

Abstract

Pre-planting soil moisture plays an important role in regulating plant emergence rate and yield, yet there is a paucity of studies on the response of cotton growth and yield to pre-planting soil moisture in arid regions. In this study, the CROPGRO-Cotton model was used to simulate cotton growth and yield in a mulched drip irrigation system, with the interaction of different pre-planting soil water contents and irrigation levels during the cotton growing season. This was undertaken to determine the optimal pre-planting soil water contents and irrigation levels that would produce the highest cotton yield and biomass values in the model simulations. Experimental data of cotton phenology, and the biomass and yield at the maturing stage in 2017 and 2018 was used to calibrate and verify the DSSAT-CROPGRO-Cotton model outputs. Based on the calibrated CROPGRO-Cotton model, scenario simulation was performed using three irrigation levels (24 mm, 30 mm, 36 mm) and eight pre-planting soil water contents [1.2 θFC, 1.1 θFC, θFC (field water holding capacity), 0.9 θFC, 0.8 θFC, 0.7 θFC, 0.6 θFC, and 0.5 θFC]. The results showed that the simulated cotton phenology and seed cotton yield produced by the calibrated CROPGRO-Cotton model showed good fits with the observed values, thus, satisfying the accuracy requirement for large-scale, mulched, drip irrigated cotton field simulations. However, large deviations were observed between simulated and observed biomass values. According to the simulations, the maximum seed cotton yield and biomass can be obtained from a pre-planting soil water content of 0.8 θFC - θFC, and the maintenance of irrigation levels at 30–36 mm during the cotton growing season. These simulation results may serve as reference data for cotton crop production and irrigation management in the Tarim Basin.

Suggested Citation

  • Wang, Xingpeng & Wang, Hongbo & Si, Zhuanyun & Gao, Yang & Duan, Aiwang, 2020. "Modelling responses of cotton growth and yield to pre-planting soil moisture with the CROPGRO-Cotton model for a mulched drip irrigation system in the Tarim Basin," Agricultural Water Management, Elsevier, vol. 241(C).
    • Handle: RePEc:eee:agiwat:v:241:y:2020:i:c:s0378377419323832
    DOI: 10.1016/j.agwat.2020.106378
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    Citations

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    Cited by:

    1. Quan, Hao & Ding, Dianyuan & Wu, Lihong & Qiao, Ruonan & Dong, Qin'ge & Zhang, Tibin & Feng, Hao & Wu, Lianhai & Siddique, Kadambot H.M., 2022. "Future climate change impacts on mulched maize production in an arid irrigation area," Agricultural Water Management, Elsevier, vol. 266(C).
    2. Hongbo Wang & Hui Cao & Fuchang Jiang & Xingpeng Wang & Yang Gao, 2022. "Analysis of Soil Moisture, Temperature, and Salinity in Cotton Field under Non-Mulched Drip Irrigation in South Xinjiang," Agriculture, MDPI, vol. 12(10), pages 1-15, October.
    3. Leo, Stephen & De Antoni Migliorati, Massimiliano & Nguyen, Trung H. & Grace, Peter R., 2023. "Combining remote sensing-derived management zones and an auto-calibrated crop simulation model to determine optimal nitrogen fertilizer rates," Agricultural Systems, Elsevier, vol. 205(C).
    4. Himanshu, Sushil Kumar & Fan, Yubing & Ale, Srinivasulu & Bordovsky, James, 2021. "Simulated efficient growth-stage-based deficit irrigation strategies for maximizing cotton yield, crop water productivity and net returns," Agricultural Water Management, Elsevier, vol. 250(C).
    5. Yuhui Yang & Dongwei Li & Weixiong Huang & Xinguo Zhou & Zhaoyang Li & Xiaomei Dong & Xingpeng Wang, 2022. "Effects of Subsurface Drainage on Soil Salinity and Groundwater Table in Drip Irrigated Cotton Fields in Oasis Regions of Tarim Basin," Agriculture, MDPI, vol. 12(12), pages 1-14, December.
    6. Tsakmakis, I.D. & Gikas, G.D. & Sylaios, G.K., 2021. "Integration of Sentinel-derived NDVI to reduce uncertainties in the operational field monitoring of maize," Agricultural Water Management, Elsevier, vol. 255(C).
    7. Desheng Wang & Chengkun Wang & Lichao Xu & Tiecheng Bai & Guozheng Yang, 2022. "Simulating Growth and Evaluating the Regional Adaptability of Cotton Fields with Non-Film Mulching in Xinjiang," Agriculture, MDPI, vol. 12(7), pages 1-20, June.

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