Wheat (Triticum aestivum L.) production is a major economic activity in most regional and rural areas in the Southern Plains, a semi-arid region of the United States. This region is vulnerable to drought and is projec...Wheat (Triticum aestivum L.) production is a major economic activity in most regional and rural areas in the Southern Plains, a semi-arid region of the United States. This region is vulnerable to drought and is projected to experience a drier climate in the future. Since the interannual variability in climate in this region is linked to an ocean-atmospheric phenomenon, called El Niño-Southern Oscillation (ENSO), droughts in this region may be associated with ENSO. Droughts that occur during the critical growth phases of wheat can be extremely costly. However, the losses due to an impending drought can be minimized through mitigation measures if it is predicted in advance. Predicting the yield loss from an imminent drought is crucial for stakeholders. One of the reliable ways for such prediction is using a plant physiology-based agricultural drought index, such as Agricultural Reference Index for Drought (ARID). This study developed ENSO phase-specific, ARID-based models for predicting the drought-induced yield loss for winter wheat in this region by accounting for its phenological phase-specific sensitivity to drought. The reasonable values of the drought sensitivity coefficients of the yield model for each ENSO phase (El Niño, La Niña, or Neutral) indicated that the yield models reflected reasonably well the phenomena of water stress decreasing the winter wheat yields in this region during different ENSO phases. The values of various goodness-of-fit measures used, including the Nash-Sutcliffe Index (0.54 to 0.67), the Willmott Index (0.82 to 0.89), and the percentage error (20 to 26), indicated that the yield models performed fairly well at predicting the ENSO phase-specific loss of wheat yields from drought. This yield model may be useful for predicting yield loss from drought and scheduling irrigation allocation based on the phenological phase-specific sensitivity to drought as impacted by ENSO.展开更多
To develop a suitable method for monitoring wheat yield loss caused by drought for dry farming areas in northwestern China, daily ET0 and ETC were calculated using KC and FAO- PM from 1961 to 2000, and wheat evapotr...To develop a suitable method for monitoring wheat yield loss caused by drought for dry farming areas in northwestern China, daily ET0 and ETC were calculated using KC and FAO- PM from 1961 to 2000, and wheat evapotranspiration with an interval of 10 days was estimated with soil water balance equation for the mountainous areas in southern Ningxia, China. Actual water consumption and water requirements of wheat during growing season was calculated using soil water balance equation by correcting leakage of soil water and run-off of precipitation every year. A model for estimation of yield loss by drought was established based on crop growth-water consumption function and yield potential. The results show that it is an effective method for monitoring drought and estimating yield loss. This method is suitable for monitoring drought and estimating yield loss of wheat in dry farming areas in northwestern China.展开更多
文摘Wheat (Triticum aestivum L.) production is a major economic activity in most regional and rural areas in the Southern Plains, a semi-arid region of the United States. This region is vulnerable to drought and is projected to experience a drier climate in the future. Since the interannual variability in climate in this region is linked to an ocean-atmospheric phenomenon, called El Niño-Southern Oscillation (ENSO), droughts in this region may be associated with ENSO. Droughts that occur during the critical growth phases of wheat can be extremely costly. However, the losses due to an impending drought can be minimized through mitigation measures if it is predicted in advance. Predicting the yield loss from an imminent drought is crucial for stakeholders. One of the reliable ways for such prediction is using a plant physiology-based agricultural drought index, such as Agricultural Reference Index for Drought (ARID). This study developed ENSO phase-specific, ARID-based models for predicting the drought-induced yield loss for winter wheat in this region by accounting for its phenological phase-specific sensitivity to drought. The reasonable values of the drought sensitivity coefficients of the yield model for each ENSO phase (El Niño, La Niña, or Neutral) indicated that the yield models reflected reasonably well the phenomena of water stress decreasing the winter wheat yields in this region during different ENSO phases. The values of various goodness-of-fit measures used, including the Nash-Sutcliffe Index (0.54 to 0.67), the Willmott Index (0.82 to 0.89), and the percentage error (20 to 26), indicated that the yield models performed fairly well at predicting the ENSO phase-specific loss of wheat yields from drought. This yield model may be useful for predicting yield loss from drought and scheduling irrigation allocation based on the phenological phase-specific sensitivity to drought as impacted by ENSO.
文摘To develop a suitable method for monitoring wheat yield loss caused by drought for dry farming areas in northwestern China, daily ET0 and ETC were calculated using KC and FAO- PM from 1961 to 2000, and wheat evapotranspiration with an interval of 10 days was estimated with soil water balance equation for the mountainous areas in southern Ningxia, China. Actual water consumption and water requirements of wheat during growing season was calculated using soil water balance equation by correcting leakage of soil water and run-off of precipitation every year. A model for estimation of yield loss by drought was established based on crop growth-water consumption function and yield potential. The results show that it is an effective method for monitoring drought and estimating yield loss. This method is suitable for monitoring drought and estimating yield loss of wheat in dry farming areas in northwestern China.
