Search

In order to clarify the prediction accuracy of eight models for predicting the peak occurrence of the first generation larvae of Dendrolimus punctatus and provide basis for the pest control, a catastrophe prediction model was established based on the peak occurrence of the first generation larvae of Dendrolimus punctatus in Qianshan City, Anhui Province from 1983 to 2016, and compared with other seven prediction models. Comparing the forecasting results in 2015 and 2016 with actual value and taking 1 head/plant as the error standard, the errors of multiple regression models with six factors as independent variables, namely, peak occurrence of pupae in overwintering generation, peak occurrence of eggs in the first generation, cumulative population in overwintering generation, peak occurrence of adults in overwintering generation, rainfall in early April and parasitic rate of Trichogrammatid in the first generation eggs of Dendrolimus punctatus, were 0.21 heads/plant and 0.23 heads/plant with accuracy rate of 100%. The errors of stepwise regression model with the same six factors were 0.23 head/plant and 0.29 head/plant. The prediction accuracy of artificial BP neural network model, Markov chain model, contingency table model, stationary time series model, and fuzzy comprehensive evaluation model was 100%, but variance period extrapolation model had an accuracy rate of 88%. The accuracy of catastrophe prediction model was related to the selection of catastrophe threshold. Comprehensive comparison of the above eight models, multiple regression, stepwise regression, artificial BP neural network, Markov chain model, stationary time series model, and catastrophe prediction model were more accurate. [ABSTRACT FROM AUTHOR]

Soil moisture content (SMC) is an important factor affecting crop growth. Based on the field SMC data measured at the Wudaogou experimental station from 1989 to 2019, two typical crop types, wheat and maize, were selected. By combining the groundwater depth, crop growth period, and meteorological factors in the same period, and using classical statistics and redundant analysis (RDA) methods, the characteristics and influencing factors of SMC changes in vertical profiles of different crops were compared and analyzed. The results showed that the SMC and average daily water storage of wheat were greater than that of maize. The crop growth mainly consumed 0–60 cm SMC. The SMC in this area was moderately variable; the SMC of 0–30 cm belongs to the active layer, and the SMC of 30–100 cm belongs to the sub-active layer. The RDA method identified ground temperature, groundwater depth, relative humidity, and the wheat growing period as the main factors affecting soil moisture variation in wheat fields; groundwater depth, relative humidity, and water vapor pressure differences were the main factors affecting soil moisture variation in maize fields. The results can provide a basis for accurate prediction of soil water dynamics and thus provide a reference for irrigation decision-makers. [ABSTRACT FROM AUTHOR]