Research on Changes in Risk of Chilling Damage to Maize in Heilongjiang Province since 1980

Based on data of daily average temperature observed during maize growing period as well as data of different growing periods of maize at 24 meteorological stations in Heilongjiang Province from 1980 to 2010, changes in risk of chilling damage to maize since 1980 were analyzed. Initially, the risk of the hazard factor was calculated by adopting the criterion of ＂Comprehensive Decision System of Chilling Damage to Maize in Heilongjiang Province＂. Then, choosing the planting area of maize at 75 stations as the exposure degree index, risk zones of exposure degree were concluded. Afterwards, risk zones of maize sensitivity to chilling damage were outlined based on maize yield per unit area. At last, a comprehensive evaluation model of chilling damage to maize in Heilongjiang Province was established, and Heilongjiang Province was divided into 5 grades of risk zones according to the model. The results showed that compared with the period before 1995, the risk of chilling damage to maize was severer in the west area of Songnen Plain, and previous sub-low or medium risk of chilling damage to maize in the west of Sanjiang Plain changed into subhigh or hi qh risk since the middle 1980s.

Prediction of chilling damage risk in maize growth period based on probabilistic neural network approach

Low temperature chilling damage is one of the most serious disasters in maize production,which is a typical non-linear complex issue with numerous influencing factors and strong uncertainty.How to predict it is not only a hot theoretical research topic,but also an urgent practical problem to be solved.However,most of the current researches are focusing on post-disaster static descriptive assessment rather than pre-disaster dynamic predictive analysis,resulting in the problems such as no indicative result and low accuracy.In this study,the satisfaction rate of environmental accumulated temperature for maize production was used to measure the chilling damage risk,and a model for maize chilling damage risk prediction based on probabilistic neural network was constructed.The model was composed of input layer,pattern layer,summation layer and output layer.The obtained results showed that the prediction accuracy for the most serious risk level was as high as 0.91,and the rates of the Type I Error and Type II Error made by the model were 0.1 and 0.09,respectively.This indicated that the model employed was promising with good performance.The results of this research are of both theoretical significance for providing a new reference method of pre-disaster prediction to study maize chilling disaster risk and practical significance for reducing maize production risk and ensuring yield safety.