Winter wheat freeze injury is one of the main agro-meteorological disasters affecting wheat production. In order to evaluate the severity of freeze injury on winter wheat systematically, we proposed a grey-system model (GSM) to monitor the degree and the distribution of the winter wheat freeze injury. The model combines remote sensing (RS) and geographic information system (GIS) technology. It gave examples of wheat freeze injury monitoring applications in Gaocheng and Jinzhou of Hebei Province, China. We carried out a quantitative evaluation method study on the severity of winter wheat freeze injury. First, a grey relational analysis (GRA) was conducted. At the same time, the weights of the stressful factors were determined. Then a wheat freezing injury stress multiple factor spatial matrix was constructed using spatial interpolation technology. Finally, a winter wheat freeze damage evaluation model was established through grey clustering algorithm (GCA), and classifying the study area into three sub-areas, affected by severe, medium or light disasters. The evaluation model were verified by the Kappa model, the overall accuracy reached 78.82% and the Kappa coefficient was 0.6754. Therefore, through integration of GSM with RS images as well as GIS analysis, quantitative evaluation and study of winter wheat freeze disasters can be conducted objectively and accurately, making the evaluation model more scientific.

Winter wheat freeze injury is one of the main agro-meteorological disasters affecting wheat production. In order to evaluate the severity of freeze injury on winter wheat systematically, we proposed a grey-system model (GSM) to monitor the degree and the distribution of the winter wheat freeze injury. The model combines remote sensing (RS) and geographic information system (GIS) technology. It gave examples of wheat freeze injury monitoring applications in Gaocheng and Jinzhou of Hebei Province, China. We carried out a quantitative evaluation method study on the severity of winter wheat freeze injury. First, a grey relational analysis (GRA) was conducted. At the same time, the weights of the stressful factors were determined. Then a wheat freezing injury stress multiple factor spatial matrix was constructed using spatial interpolation technology. Finally, a winter wheat freeze damage evaluation model was established through grey clustering algorithm (GCA), and classifying the study area into three sub-areas, affected by severe, medium or light disasters. The evaluation model were verified by the Kappa model, the overall accuracy reached 78.82% and the Kappa coefficient was 0.6754. Therefore, through integration of GSM with RS images as well as GIS analysis, quantitative evaluation and study of winter wheat freeze disasters can be conducted objectively and accurately, making the evaluation model more scientific.

The influence of climate on winter wheat yields were examined in two important global breadbaskets-the Picardy Region of northern France and the Rostov Oblast of southern Russia. Thirty-year climatologies were established for each region and the magnitude of change between 1973 and 2010 was quantified for a variety of climate variables important to crop development. Using a "first differences" analysis, the aspects of climate that winter wheat yields have been most sensitive to were identified and the impact of changes in these variables on winter wheat yield trends was quantified. A number of aspects of climate have changed at unprecedented rates in the two regions. Between 1973 and 2010, summer precipitation totals decreased by 61% and maximum summer temperatures increased by 4 degrees C in Rostov, while fall precipitation totals decreased by 9% and maximum spring temperatures increased by 2.4 degrees C in Picardy. In addition, winter wheat yields were strongly correlated with a number of climate variables, although the most important drivers of yield variability differed between the two regions. May and June average temperatures explained 49% (p<0.0001) and 16% (p<0.05) of interannual yield variability in Rostov, while in Picardy, November precipitation and minimum summer temperatures explained 26% (p <= 0.001) and 23% (p<0.01). The climate variables that exhibited significant historical trends were often not the climate drivers that winter wheat yields were strongly correlated with in Rostov. Therefore, it appears that recent climate change has not significantly impacted winter wheat yield trends thus far in the region. However, in Picardy, there was partial overlap in the climate variables that winter wheat yields were most responsive to and those that have already exhibited significant changes overtime. Consequently, climate change has likely caused an 11% decrease in winter wheat yield trends in the region. (C) 2013 Elsevier B.V. All rights reserved.

The influence of climate on winter wheat yields were examined in two important global breadbaskets—the Picardy Region of northern France and the Rostov Oblast of southern Russia. Thirty-year climatologies were established for each region and the magnitude of change between 1973 and 2010 was quantified for a variety of climate variables important to crop development. Using a “first differences” analysis, the aspects of climate that winter wheat yields have been most sensitive to were identified and the impact of changes in these variables on winter wheat yield trends was quantified. A number of aspects of climate have changed at unprecedented rates in the two regions. Between 1973 and 2010, summer precipitation totals decreased by 61% and maximum summer temperatures increased by 4°C in Rostov, while fall precipitation totals decreased by 9% and maximum spring temperatures increased by 2.4°C in Picardy. In addition, winter wheat yields were strongly correlated with a number of climate variables, although the most important drivers of yield variability differed between the two regions. May and June average temperatures explained 49% (p<0.0001) and 16% (p<0.05) of interannual yield variability in Rostov, while in Picardy, November precipitation and minimum summer temperatures explained 26% (p≤0.001) and 23% (p<0.01). The climate variables that exhibited significant historical trends were often not the climate drivers that winter wheat yields were strongly correlated with in Rostov. Therefore, it appears that recent climate change has not significantly impacted winter wheat yield trends thus far in the region. However, in Picardy, there was partial overlap in the climate variables that winter wheat yields were most responsive to and those that have already exhibited significant changes over time. Consequently, climate change has likely caused an 11% decrease in winter wheat yield trends in the region.