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Simulating future climate change impacts on seed cotton yield in the Texas High Plains using the CSM-CROPGRO-Cotton model

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  • Adhikari, Pradip
  • Ale, Srinivasulu
  • Bordovsky, James P.
  • Thorp, Kelly R.
  • Modala, Naga R.
  • Rajan, Nithya
  • Barnes, Edward M.

Abstract

The Texas High Plains (THP) region contributes to about 25% of the US cotton production. Dwindling groundwater resources in the underlying Ogallala aquifer, future climate variability and frequent occurrences of droughts are major concerns for cotton production in this region. Assessing the impacts of climate change on cotton production enables development and evaluation of irrigation strategies for efficient utilization of groundwater resources in this region. In this study, the CROPGRO-Cotton module within the Cropping System Model (CSM) that is distributed with the Decision Support System for Agrotechnology Transfer (DSSAT) was evaluated for the THP region using measured data from cotton water use efficiency experiments at Halfway over a period of four years (2010–2013). Simulated seed cotton yield matched closely with observed yield during model calibration (average percent error of 0.1) and validation (average percent error of 6.5). The evaluated model was able to accurately simulate seed cotton yield under various irrigation strategies over the four growing seasons. The evaluated CROPGRO-Cotton model was later used to simulate the seed cotton yield under historic (1971–2000) and future (2041–2070) climate scenarios projected by three climate models. On an average, when compared to historic seed cotton yield, simulated future seed cotton yield across the THP decreased within a range of 4–17% when carbon dioxide (CO2) concentration was assumed to be constant at the current level (380ppm) under three climatic model scenarios. In contrast, when the CO2 concentration was assumed to increase from 493ppm (in year 2041) to 635ppm (in year 2070) according to the Intergovernmental Panel on Climate Change (IPCC) A2 emission scenario, the simulated future average seed cotton yield in the THP region increased within a range of 14–29% as compared to historic average yield. When the irrigation amount was reduced by 40% (from 100% to 60%), the average (2041–2070) seed cotton yield decreased by 37% and 39% under the constant and increasing CO2 concentration scenarios, respectively. These results imply that cotton is sensitive to atmospheric CO2 concentrations, and cotton production in the THP could potentially withstand the effects of future climate variability under moderate increases in CO2 levels if irrigation water availability remains at current levels.

Suggested Citation

  • Adhikari, Pradip & Ale, Srinivasulu & Bordovsky, James P. & Thorp, Kelly R. & Modala, Naga R. & Rajan, Nithya & Barnes, Edward M., 2016. "Simulating future climate change impacts on seed cotton yield in the Texas High Plains using the CSM-CROPGRO-Cotton model," Agricultural Water Management, Elsevier, vol. 164(P2), pages 317-330.
    • Handle: RePEc:eee:agiwat:v:164:y:2016:i:p2:p:317-330
    DOI: 10.1016/j.agwat.2015.10.011
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    1. Komlan Koudahe & Aleksey Y. Sheshukov & Jonathan Aguilar & Koffi Djaman, 2021. "Irrigation-Water Management and Productivity of Cotton: A Review," Sustainability, MDPI, vol. 13(18), pages 1-21, September.
    2. Himanshu, Sushil Kumar & Ale, Srinivasulu & Bordovsky, James & Darapuneni, Murali, 2019. "Evaluation of crop-growth-stage-based deficit irrigation strategies for cotton production in the Southern High Plains," Agricultural Water Management, Elsevier, vol. 225(C).
    3. Chen, Xiaoping & Qi, Zhiming & Gui, Dongwei & Gu, Zhe & Ma, Liwang & Zeng, Fanjiang & Li, Lanhai, 2019. "Simulating impacts of climate change on cotton yield and water requirement using RZWQM2," Agricultural Water Management, Elsevier, vol. 222(C), pages 231-241.
    4. 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).
    5. Kothari, Kritika & Ale, Srinivasulu & Bordovsky, James P. & Thorp, Kelly R. & Porter, Dana O. & Munster, Clyde L., 2019. "Simulation of efficient irrigation management strategies for grain sorghum production over different climate variability classes," Agricultural Systems, Elsevier, vol. 170(C), pages 49-62.
    6. Amin, Asad & Nasim, Wajid & Mubeen, Muhammad & Ahmad, Ashfaq & Nadeem, Muhammad & Urich, Peter & Fahad, Shah & Ahmad, Shakeel & Wajid, Aftab & Tabassum, Fareeha & Hammad, Hafiz Mohkum & Sultana, Syeda, 2018. "Simulated CSM-CROPGRO-cotton yield under projected future climate by SimCLIM for southern Punjab, Pakistan," Agricultural Systems, Elsevier, vol. 167(C), pages 213-222.
    7. 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).
    8. 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.
    9. Himanshu, Sushil K. & Ale, Srinivasulu & Bell, Jourdan & Fan, Yubing & Samanta, Sayantan & Bordovsky, James P. & Gitz III, Dennis C. & Lascano, Robert J. & Brauer, David K., 2023. "Evaluation of growth-stage-based variable deficit irrigation strategies for cotton production in the Texas High Plains," Agricultural Water Management, Elsevier, vol. 280(C).
    10. Chen, Yong & Marek, Gary W. & Marek, Thomas H. & Moorhead, Jerry E. & Heflin, Kevin R. & Brauer, David K. & Gowda, Prasanna H. & Srinivasan, Raghavan, 2019. "Simulating the impacts of climate change on hydrology and crop production in the Northern High Plains of Texas using an improved SWAT model," Agricultural Water Management, Elsevier, vol. 221(C), pages 13-24.
    11. Jin, Ning & Tao, Bo & Ren, Wei & He, Liang & Zhang, Dongyan & Wang, Dacheng & Yu, Qiang, 2022. "Assimilating remote sensing data into a crop model improves winter wheat yield estimation based on regional irrigation data," Agricultural Water Management, Elsevier, vol. 266(C).
    12. 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).
    13. 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.
    14. Chen, Yong & Ale, Srinivasulu & Rajan, Nithya & Srinivasan, Raghavan, 2017. "Modeling the effects of land use change from cotton (Gossypium hirsutum L.) to perennial bioenergy grasses on watershed hydrology and water quality under changing climate," Agricultural Water Management, Elsevier, vol. 192(C), pages 198-208.
    15. Garibay, Victoria M. & Kothari, Kritika & Ale, Srinivasulu & Gitz, Dennis C. & Morgan, Gaylon D. & Munster, Clyde L., 2019. "Determining water-use-efficient irrigation strategies for cotton using the DSSAT CSM CROPGRO-cotton model evaluated with in-season data," Agricultural Water Management, Elsevier, vol. 223(C), pages 1-1.
    16. Mauget, Steven & Ulloa, Mauricio & Mitchell-McCallister, Donna, 2022. "Simulated irrigation water productivity and related profit effects in U.S. Southern High Plains cotton production," Agricultural Water Management, Elsevier, vol. 266(C).
    17. Kothari, Kritika & Ale, Srinivasulu & Attia, Ahmed & Rajan, Nithya & Xue, Qingwu & Munster, Clyde L., 2019. "Potential climate change adaptation strategies for winter wheat production in the Texas High Plains," Agricultural Water Management, Elsevier, vol. 225(C).

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