Chinese Journal of Agrometeorology ›› 2018, Vol. 39 ›› Issue (03): 177-184.doi: 10.3969/j.issn.1000-6362.2018.03.005

Previous Articles     Next Articles

Prediction on Heat Index of Rice in Heilongjiang Province Based on Grey Model

WANG Qiu-jing, MA Guo-zhong, WANG Liang-liang, ZHU Hai-xia, DU Chun-ying, JIANG Li-xia   

  1. 1. Innovation and Opening Laboratory of Regional Eco-Meteorology in Northeast, China Meteorological Administration/Meteorological Academician Workstation of Heilongjiang Province/Heilongjiang Institute of Meteorological Sciences, Harbin 150030, China; 2. Heilongjiang Meteorological Observatory, Harbin 150030
  • Received:2017-06-16 Online:2018-03-20 Published:2018-03-23

Abstract: Based on eleven agro-meteorological observation stations, Heilongjiang province was divided into three regions, namely east region, west region and south region, by using the data of temperature and rice development from 1971 to 2016. The GM (1, 1) forecasting model for the Heat Index was established from May to August for rice in every region with differential equation dynamic modeling. Then the Heat Index was dynamically forecasted, and chilling damage was monitored during growing season of rice. The results showed that the model assessed well the Heat Index during growing season of rice, and the index had corresponded well with chilling damage year of rice. The association degrees were more than 0.88 between simulation results and the original data, and they passed the association degree test and residual tests. The average regression calculating accuracies of these models were 94.6% to 97.6% from 1971 to 2010, and the monthly forecast effects for July and August were generally better than those for May and June in each region. The average forecast accuracy was above 97% from 2011 to 2016. The results indicated that these models had better simulated effect. The GM (1, 1) was feasible to forecast the Heat Index of Rice during growing season, and to achieve dynamic assessment for chilling damage of rice during growing season.

Key words: GM (1, 1) forecasting model, Chilling damage, Rice, The Heat Index