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Simulation of efficient irrigation management strategies for grain sorghum production over different climate variability classes

Author

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  • Kothari, Kritika
  • Ale, Srinivasulu
  • Bordovsky, James P.
  • Thorp, Kelly R.
  • Porter, Dana O.
  • Munster, Clyde L.

Abstract

The Texas High Plains (THP) is a productive agricultural region, and it relies heavily on the exhaustible Ogallala Aquifer for irrigation water for crop production. Efficient use of irrigation water is critical for the sustainability of agriculture in the THP. Grain sorghum is one of the major crops grown in the region, and it is known for its drought tolerance and lower water requirement compared to other cereal crops such as corn. In this study, the CERES-Sorghum and CROPGRO-Cotton modules of the Decision Support System for Agrotechnology Transfer (DSSAT) were evaluated using data from cotton-sorghum rotation experiments at Halfway, Texas over a period of nine years (2006–2014). The evaluated CERES-Sorghum model was then used to identify the optimum (i) initial soil moisture at planting (ISM); (ii) threshold to start irrigation (ITH); (iii) threshold to terminate irrigation; and iv) deficit/excess (DFI) irrigation strategy for grain sorghum production based on simulated sorghum yield, irrigation water use efficiency (IWUE), and grain water use efficiency (WUE). In addition, the effect of weather conditions on simulated strategies was elucidated by dividing the long-term (1977–2016) weather data into cold, warm, wet, dry, and normal climate variability classes based on the 33rd and 66th percentiles of growing season temperature and precipitation. The DSSAT model adequately simulated the grain sorghum and seed cotton yields during calibration (average Percent Error (PE) of 1.3% (sorghum) and 3.4% (cotton)) and evaluation (average PE of −2.2% (sorghum) and −10.5% (cotton)). The results from long-term simulations indicated that weather conditions played a key role in selecting appropriate irrigation management strategies. Under normal/cold/wet weather, ISM of 75% available water holding capacity (AWC), ITH of 50%, and DFI 85% were found to be adequate for irrigated grain sorghum production. However, in warm/dry weather, ISM of 75%, ITH 60%, and DFI at 100% reduced sorghum yield loss.

Suggested Citation

  • 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.
    • Handle: RePEc:eee:agisys:v:170:y:2019:i:c:p:49-62
    DOI: 10.1016/j.agsy.2018.12.011
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    References listed on IDEAS

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    1. Araya, A. & Kisekka, Isaya & Gowda, Prasanna H. & Prasad, P.V. Vara, 2017. "Evaluation of water-limited cropping systems in a semi-arid climate using DSSAT-CSM," Agricultural Systems, Elsevier, vol. 150(C), pages 86-98.
    2. Tolk, Judy A. & Howell, Terry A., 2003. "Water use efficiencies of grain sorghum grown in three USA southern Great Plains soils," Agricultural Water Management, Elsevier, vol. 59(2), pages 97-111, March.
    3. Timsina, J. & Humphreys, E., 2006. "Performance of CERES-Rice and CERES-Wheat models in rice-wheat systems: A review," Agricultural Systems, Elsevier, vol. 90(1-3), pages 5-31, October.
    4. Attia, Ahmed & Rajan, Nithya & Xue, Qingwu & Nair, Shyam & Ibrahim, Amir & Hays, Dirk, 2016. "Application of DSSAT-CERES-Wheat model to simulate winter wheat response to irrigation management in the Texas High Plains," Agricultural Water Management, Elsevier, vol. 165(C), pages 50-60.
    5. 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.
    6. Timsina, J. & Godwin, D. & Humphreys, E. & Yadvinder-Singh & Bijay-Singh & Kukal, S.S. & Smith, D., 2008. "Evaluation of options for increasing yield and water productivity of wheat in Punjab, India using the DSSAT-CSM-CERES-Wheat model," Agricultural Water Management, Elsevier, vol. 95(9), pages 1099-1110, September.
    7. DeJonge, K.C. & Ascough, J.C. & Andales, A.A. & Hansen, N.C. & Garcia, L.A. & Arabi, M., 2012. "Improving evapotranspiration simulations in the CERES-Maize model under limited irrigation," Agricultural Water Management, Elsevier, vol. 115(C), pages 92-103.
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    1. 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).
    2. 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).
    3. 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).
    4. Xiaoping Chen & Shaoyuan Feng & Zhiming Qi & Matthew W. Sima & Fanjiang Zeng & Lanhai Li & Haomiao Cheng & Hao Wu, 2022. "Optimizing Irrigation Strategies to Improve Water Use Efficiency of Cotton in Northwest China Using RZWQM2," Agriculture, MDPI, vol. 12(3), pages 1-15, March.
    5. 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.

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