基于模式识别的半干旱区雨养春小麦干旱发生状况判别

Discrimination of drought occurrence for rainfed spring wheat in semi-arid area based on pattern recognition

为准确判断作物生长发育过程中农业干旱的发生状况,并预估作物产量,该研究以半干旱区1986-2011年生育期气象和产量资料为基础,分析雨养春小麦产量形成所受因素,以产量变动状况作为春小麦干旱和正常年景的判断标准。采用模式识别法,迭代求解建立可预测春小麦年景的线性分类方程,对半干旱雨养区农业干旱的发生状况进行判定。研究结果表明:半干旱雨养区春小麦产量形成受诸多因素影响。若不剔除其他因素的影响,仅以气象要素为基础无法建立判别方程,从而难以定量判断春小麦生育期农业干旱的发生状况。但在剔除播前50 cm层次土壤相对含水率大于55%的年份后,以主要生育期平均温度和降水量能够建立判别方程预测春小麦年景,从而可以对春小麦生长发育过程中的农业干旱发生状况进行定量分析。同时,5月份降水量对春小麦生长发育具有非常重要的作用,在播前50 cm层次土壤相对含水率小于55%时,只用5月份降水量一个气象要素即可较为准确地模拟估测春小麦产出。该研究可为干旱致害机理的进一步深入探讨提供参考依据。

The mechanism of the damage process for agricultural drought is very complex, and many factors can affect it. Agricultural drought is the main limiting factor for crop yield in rainfed area. For defining drought occurrence during the crop growth, and predicting crop yield, we used pattern recognition based on meteorological data during growing season and yield data of spring wheat in semi-arid rainfed area in Dingxi, China from 1986 to 2011. Owing to the application of deviation for crop yield from its long-term mean to define agricultural drought, we divided the year pattern into two categories： drought series, and normal series on the basis of 30 percent deviation from the mean wheat yield. The iteration method was then applied in order to find a case wherein the drought could be linearly discriminated from normal category. According to our research, we find the spring wheat yield was affected by various factors. They can be categorized as 1） weather conditions, such as temperature, precipitation; 2） farm management factors and crop variety, such as soil tillage, soil depth, planting density, sowing date, crop protection against pests and diseases, and soil fertility level; 3） soil conditions, such as soil physical properties and soil water content. Measuring or estimating some of these factors was often not feasible, and the influence of some other factors may be considered insignificant or constant in an agrometeorological experimental station. It was therefore weather condition alone that can affect crop yield most significantly. However, it was found that no linear relation existed in any cases based on average temperature and precipitation during the main growing period without taking other factors into a consideration. After rejecting years in which the soil relative water content was more than 55%, we can predict if agricultural drought through establishing a linear equation with two parameters, the average temperature and precipitation during the main growth period for spring wheat. From the research, we also found the best parameter to predict the agricultural drought occurrence and factor that determined spring wheat yield was the precipitation in May. A Predictive Equation for spring wheat yield was also established by the least square method based on the precipitation in May. The predictive equation was simple but useful, and it can forecast spring wheat yield one and half month earlier before wheat harvest. Meanwhile, it should be noted that the predictive equation was established after rejecting the years in which the soil relative water content was more than 55%. We suggested that the agricultural drought differ from meteorological drought. As such, we should use the method much more carefully for quantitative prediction of agricultural drought occurrence and crop yield in future research.