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Quantifying crop water stress factors from soil water measurements in a limited irrigation experiment

Author

Listed:
  • Saseendran, S.A.
  • Trout, T.J.
  • Ahuja, L.R.
  • Ma, L.
  • McMaster, G.S.
  • Nielsen, D.C.
  • Andales, A.A.
  • Chávez, J.L.
  • Ham, J.

Abstract

A correct simulation of crop responses to water stress is essential for a system model. In this study, we investigated three methods of quantifying water deficit stresses based on soil water measurements and their effects on simulating grain yield, biomass and canopy cover of corn (Zea Mays L.). Experimental data were collected for six irrigation treatments designed to replace 40 to 100% of potential crop evapotranspiration (ETc) losses during the growing season, from 2008 to 2011 near Greeley, Colorado in a sandy loam soil (Limited Irrigation Research Farm, LIRF). Water available for plant uptake (PAW, plant available water) and the maximum PAW (MAW) in the soil were calculated for a constant 1 m soil profile from 45 days after planting till maturity. Water deficit stress factors were calculated as ratios of (1) PAW to alfalfa reference crop evapotranspiration (ETr) (WSF1), (2) PAW to MAW (WSF2), and (3) WSF2 to ETr (WSF3). Average WSF1, WSF2 and WSF3 over the growing season were related to end of the season grain yield, biomass, and fraction canopy cover measurements. These stress factors were implemented in the RZWQM2 cropping system model and the calibrated results compared with those obtained from using current stress factors in CERES-maize module in RZWQM2. The best simulation of the measured grain yields, biomass and LAI was obtained using WSF3. The modified model was also tested for simulating dryland and limited irrigation studies at Akron, CO, and irrigated corn in a sandy loam soil at Zaragoza, Spain and in a sandy soil at Gainesville, Florida, USA. In general, WSF3 gave slightly better simulations of grain yields, biomass and LAI than WSF2, WSF1 and the original stress factor.

Suggested Citation

  • Saseendran, S.A. & Trout, T.J. & Ahuja, L.R. & Ma, L. & McMaster, G.S. & Nielsen, D.C. & Andales, A.A. & Chávez, J.L. & Ham, J., 2015. "Quantifying crop water stress factors from soil water measurements in a limited irrigation experiment," Agricultural Systems, Elsevier, vol. 137(C), pages 191-205.
  • Handle: RePEc:eee:agisys:v:137:y:2015:i:c:p:191-205
    DOI: 10.1016/j.agsy.2014.11.005
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    5. Chen, Xiaoping & Qi, Zhiming & Gui, Dongwei & Sima, Matthew W. & Zeng, Fanjiang & Li, Lanhai & Li, Xiangyi & Gu, Zhe, 2020. "Evaluation of a new irrigation decision support system in improving cotton yield and water productivity in an arid climate," Agricultural Water Management, Elsevier, vol. 234(C).
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    7. Saseendran S. Anapalli & Srinivasa R. Pinnamaneni & Daniel K. Fisher & Krishna N. Reddy, 2021. "Vulnerabilities of irrigated and rainfed corn to climate change in a humid climate in the Lower Mississippi Delta," Climatic Change, Springer, vol. 164(1), pages 1-18, January.
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    10. Haomiao Cheng & Shu Ji & Hengjun Ge & Mohmed A. M. Abdalhi & Tengyi Zhu & Xiaoping Chen & Wei Ding & Shaoyuan Feng, 2022. "Optimizing Deficit Irrigation Management to Improve Water Productivity of Greenhouse Tomato under Plastic Film Mulching Using the RZ-SHAW Model," Agriculture, MDPI, vol. 12(8), pages 1-13, August.
    11. Zhang, Chao & Xie, Ziang & Wang, Qiaojuan & Tang, Min & Feng, Shaoyuan & Cai, Huanjie, 2022. "AquaCrop modeling to explore optimal irrigation of winter wheat for improving grain yield and water productivity," Agricultural Water Management, Elsevier, vol. 266(C).
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