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Christensen, J.H., Hewitson, B., Busuioc, A., Chen, A., Gao, X., Held, I., Jones, R., Kolli, R.K., Kwon, W.T., Laprise, R., Magaña Rueda, V., Mearns, L., Menéndez, C.G., Räisänen, J., Rinke, A., Sarr, A. and Whetton, P. (2007) Regional Climate Projections. In: Solomon, S., Qin, D., Manning, M., Chen, Z., Marquis, M., Averyt, K.B., Tignor, M. and Miller, H.L., Eds., Climate Change 2007: The Physical Science Basis, Cambridge University Press, 1-94.
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has been cited by the following article:

• TITLE: Predicting the Drought-Induced Yield Loss of Cotton in the Southern Plains Region of the United States Using a Drought Index

  AUTHORS: Prem Woli, Charles R. Long, Gerald R. Smith, Francis M. Rouquette, Jr.

  KEYWORDS: ARID, Cotton, Drought, Index, Model, Phase, Prediction, Stage, Water Stress, Yield

  JOURNAL NAME: Agricultural Sciences, Vol.16 No.7, July 21, 2025

  ABSTRACT: The semi-arid Southern Plains region of the United States holds a large share of cotton (Gossypium hirsutum L.) production in this country. This region is vulnerable to drought and is projected to experience a drier climate in the future. Droughts that coincide with the critical phenological phases of cotton can be remarkably costly. Although drought cannot be avoided, its impacts can be minimized through appropriate mitigation measures if it is predicted in advance. Predicting the yield loss of cotton due to drought is an important need of cotton producers in this region. One reliable way to meet this need is using an agricultural drought index, such as Agricultural Reference Index for Drought (ARID). As a plant physiology-based drought index, ARID can accurately map drought-yield relationships. By using cotton yield and weather data spanning 26 seasons during 1999 to 2024 collected at four locations in the region – Lubbock and Lamesa in Texas and Chickasha and Fort Cobb in Oklahoma – this study developed an ARID-based yield model for predicting the drought-induced yield loss for cotton in this region by accounting for its phenological phase-specific sensitivity to drought. The modeling results showed that, of all the phenological phases of cotton studied, the pinhead square-first bloom phase was the most sensitive to drought, whereas cotton yields were positively impacted by water stress that occurred during the emergence-pinhead square phase. The rational values of the parameters of the yield model indicated that it reasonably could reflect the phenomenon of water stress decreasing the cotton yields in this region. The values of the various measures used to evaluate the model, including the percentage error (22), the Nash-Sutcliffe Index (0.45), and the Willmott Index (0.83), indicated that the yield model performed fairly well. The yield model can contribute to predicting the drought-induced yield loss for cotton in the study region and scheduling irrigation allocation based on phenological phase-specific sensitivity to drought stress.