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
Sheath blight of rice, caused by Rhizoctonia solani AG 1. 1a, has become the most important disease and caused serious yield losses in some major rice-growing regions in China in recent years. In the present study, fi...Sheath blight of rice, caused by Rhizoctonia solani AG 1. 1a, has become the most important disease and caused serious yield losses in some major rice-growing regions in China in recent years. In the present study, field plot experiment was conducted to examine the relationships between disease intensity and inoculum density （ID）, the seasonal disease epidemic dynamics, and yield reductions due to disease damages. Results from the experiment demonstrated that the areas under progress curves of disease severity and those of percent rice tillers diseased were positively and closely related to the relative initial ID of the pathogen. The inoculum density-disease （IDD） relationships were simulated and the impractical linear models were obtained. Both logistic and Gompertz functions could be used to simulate the disease progress dynamics in time, but the progress curves of the disease severity were modeled better by the Gompertz than by logistic function. However, the Richards function was found to be the best in simulating the disease progress curves when a most appropriate value was chosen for the shape parameter m by using the computer software Epitimulator. Sheath blight infection decreased rice yield very significantly and a yield reduction of 40% was recorded in rice crop with the highest inoculum density. Rice yield was linearly and negatively correlated with the disease severity and the percent tillers affected. The simulated models for all these relationships were computed through executing Epitimulator software and were presented in this paper.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Sheath blight of rice, caused by Rhizoctonia solani AG 1. 1a, has become the most important disease and caused serious yield losses in some major rice-growing regions in China in recent years. In the present study, field plot experiment was conducted to examine the relationships between disease intensity and inoculum density （ID）, the seasonal disease epidemic dynamics, and yield reductions due to disease damages. Results from the experiment demonstrated that the areas under progress curves of disease severity and those of percent rice tillers diseased were positively and closely related to the relative initial ID of the pathogen. The inoculum density-disease （IDD） relationships were simulated and the impractical linear models were obtained. Both logistic and Gompertz functions could be used to simulate the disease progress dynamics in time, but the progress curves of the disease severity were modeled better by the Gompertz than by logistic function. However, the Richards function was found to be the best in simulating the disease progress curves when a most appropriate value was chosen for the shape parameter m by using the computer software Epitimulator. Sheath blight infection decreased rice yield very significantly and a yield reduction of 40% was recorded in rice crop with the highest inoculum density. Rice yield was linearly and negatively correlated with the disease severity and the percent tillers affected. The simulated models for all these relationships were computed through executing Epitimulator software and were presented in this paper.
基金Jiangsu Graduate in Scientific Research and Innovation (No.CX07B_048z)the Special Program for Scientific Research in Public Welfare Meteorological Services (No. GYHY200806008)
