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CROPGRO-Cotton model for determining climate change impacts on yield, water- and N- use efficiencies of cotton in the Dry Savanna of West Africa

Author

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  • Amouzou, Kokou Adambounou
  • Naab, Jesse B.
  • Lamers, John P.A.
  • Borgemeister, Christian
  • Becker, Mathias
  • Vlek, Paul L.G.

Abstract

Cotton is an important cash crop in many West African countries. Hence, its sustainable production will support the national economies as well as the livelihoods of the farming population alike and in turn help easing wide-spread poverty. However, future climate change may affect the productivity of cotton in West Africa. Therefore, the objectives of this study were to (i) parameterize the Cropping System Model- CROPGRO-Cotton to simulate growth, seed cotton yield, and in-season soil water dynamics and nitrogen (N) uptake, and (ii) apply the model to estimate potential climate change impacts on cotton growth, yields, and water- and N- productivity under different soil-fertility management practices. The CROPGRO-Cotton model was first parameterized and evaluated using datasets collected in three field experiments conducted in 2014 and 2015 in the Dry Savanna of northern Benin, West Africa. The model was next applied to determine long-term responses of cotton to historical (1986–2015) and projected climate (2080–2099) for three Representative Concentration Pathways (RCPs 2.6, 4.5, and 8.5). CROPGRO-Cotton accurately simulated in-season soil water dynamics (nRMSE of 12–27%, d-values of 0.79–0.88), N uptake (nRMSE of 31–44%, d-values of 0.89–0.96), and biomass accrual (nRMSE of 31–46% and d-values of 0.91–0.97), as well as seed cotton yield at harvest (nRMSE of 24–39% and d-value of about 0.81). The model predicted higher seed cotton yield with planting dates in June compared to July. Under the climate change scenario of RCP2.6, CROPGRO-Cotton predicted a decrease in water use efficiency (WUE) by 20% without any soil amendment, and by 4% with an integrated soil-crop management compared to the historical run, but an increase of 2% with a high use of mineral fertilizer. With the RCP4.5 and 8.5 scenarios, the predicted changes in WUE varied between −1% and 17% across the soil fertility management options. CROPGRO-Cotton predicted increases in N-partial factor productivity by 7 to 31%. The N uptake varied between −7% and 46% whilst seed cotton yield varied between −7 and 41%. The findings underlined that the predicted future increases in water and N productivity in cotton will be driven by CO2 fertilization, increases in temperatures as well as rainfall variability, but at the expense of soil fertility leading to soil mining. Yet, even if cotton, unlike some other crops in the region, will likely respond positively to climate change, adequate soil fertility management practices are essential to ensure efficient and sustainable water- and N- use in the expected future.

Suggested Citation

  • Amouzou, Kokou Adambounou & Naab, Jesse B. & Lamers, John P.A. & Borgemeister, Christian & Becker, Mathias & Vlek, Paul L.G., 2018. "CROPGRO-Cotton model for determining climate change impacts on yield, water- and N- use efficiencies of cotton in the Dry Savanna of West Africa," Agricultural Systems, Elsevier, vol. 165(C), pages 85-96.
  • Handle: RePEc:eee:agisys:v:165:y:2018:i:c:p:85-96
    DOI: 10.1016/j.agsy.2018.06.005
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    1. Li, Na & Yao, Ning & Li, Yi & Chen, Junqing & Liu, Deli & Biswas, Asim & Li, Linchao & Wang, Tianxue & Chen, Xinguo, 2021. "A meta-analysis of the possible impact of climate change on global cotton yield based on crop simulation approaches," Agricultural Systems, Elsevier, vol. 193(C).
    2. Marcos Jiménez Martínez & Christine Fürst, 2021. "Simulating the Capacity of Rainfed Food Crop Species to Meet Social Demands in Sudanian Savanna Agro-Ecologies," Land, MDPI, vol. 10(8), pages 1-28, August.
    3. 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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