The Construction and Application of Assessing Index to Crop Growing Condition

It is generally accepted that crop growing assessment can reveal its temporary condition and response to weather and climate when crop growing condition is assessed in a reasonable and effective way. To address this, normal field observation and remote sensing monitoring are the major techniques to quantify the crop growing condition. However, limited by their time-efficiency and uncertainties in algorithm, there are some deficiencies within them. Crop model, as an alternative way, is proposed to detect crop growing condition, with the advantage of its better mechanism and timeliness. Currently, WOFOST and ORYZA2000 are widely used to simulate the growth of winter wheat, spring maize, summer maize, single-season rice and double-season rice. Derived from WOFOST and ORYZA2000, the daily outputs, i.e., the development stage, leaf area index and total aboveground production, are simulated from 2001. Three outputs are selected as impacted variables for crop growing, and quantified through membership function. In the initial stage of crop growth, development stage, leaf area index and total aboveground production generate comprehensive effect, and they are weighted aggregated to a integrated index by the respective weight of 0.3, 0.3 and 0.4 according to expert scoring method. In the later stage, development stage and total aboveground production are the major factors influencing crop condition, and they are aggregated to a integrated index by the weight derived from their relative relationship with dry weight of storage organs. The assessing index is keeping well with experimental observation and remote sensing monitoring, implying its effectivity in evaluation service. The crop growing condition is assessed on any day during the growing season, and the corresponding daily integrated index is built in datasets under Crop Growth Simulating and Monitoring System in China (CGMS-China). According to daily integrated index, crop growing condition is divided into 5 levels, i.e., better, good, normal, bad and worse. Based on CGMS-China, daily crop growing condition is illustrated in the spatial distribution, which can distinguish the regional or local distinction of condition. Moreover, assessing index is spatially aggregated to the index at the province scale, which is a base quantity for comparing the provincial crop growing condition, corresponding to the assessing scale of yield prediction in agrometeorological services. Under the typical weather conditions, assessing index is efficient in specific regions and even local stations. For example, impacts of high temperature and drought from 21 July to 10 August in 2018 are well performed in the assessing index change at spatial and temporal scales. The assessing index for crop growing assessment based on crop model can provide more accurate and quantifier outputs, fitting with the demand of modern agriculture and agrometeorological service.