GIS-based regional classification for overwintering of southern root-knot nematode in Shaanxi Province

Abstract

Southern root鄄knot nematode, Meloidogyne incognita, is an important pathogen of vegetables, and was first observed in South China. With the development of indoor agricultural facilities, it was found for the first time in Shaanxi in 2000, and now is widely distributed through different ecological vegetable growing zones in Northern and Southern Shaanxi, and the Guanzhong area. M. incognita has become a devastating soilborne disease, causing great economic losses in vegetable production. It survives at soil depths of 5 to 15 cm, and overwinters in the soil as eggs or second instar larvae. Soil temperature is an important factor affecting its overwintering, and this is dependent on climate and plant conditions. Air and soil temperature data for open fields can be obtained from meteorological stations, but soil temperature data for indoor agricultural facilities is not available, and thus mathematical models need to be established to simulate soil temperatures under different cultivation conditions. Each winter ( November to the following March) from 2009 to 2012, air and soil temperatures in four agricultural systems in four ecological regions ( Yanan, Shangluo, Yangling, and Dali) in Shaanxi

Low-temperature derived temporal change in the vertical distribution of Sesamia inferens larvae in winter, with links to its latitudinal distribution

“…Although artificial because the linear model based on the point data might not represent the actual regional patterns, the linear relationship between air and soil temperature has indeed been studied in different areas [ 47 , 48 ]. Hong [ 24 ] has taken advantage of this linear relationship from 96 weather stations in Shanxi, China, and classified the overwintering sites of the southern root-knot nematode. These results also show that the underground low temperature threshold defined by the ULLT 50 could be extrapolated to other underground overwinter species.…”

“…The calculation of survival rates in different locations from 2007 to 2014, resulted in identification of the total proportion of insect survival over the 8 years. Based on the proportion of each location, a potential distribution based on annual minimum temperature threshold could be produced with ArcMap (version 10.2, ESRI, Redlands, CA, USA) [24].…”

Occurrence Regionalization of Kiwifruit Brown Spot in Sichuan

“…Kistner-Thomas et al [ 10 ] assessed the relationship between grasshopper density survey data and 72 biologically relevant GIS-based environmental variables and developed a regression model to predict the mean density of an adult grasshopper from 2012 to 2016. At present, in the research on the suitable regionalization of diseases and pests, correlation analysis, weight coefficient distribution, and assignment of each impact factor are first carried out with mathematical methods; then the comprehensive factors are graded by the index classification method, the data are rasterized by GIS, the value of the vacant area is inserted, and the regionalization results are finally determined [ 11 ]. The accuracy of the model primarily relies on the key factors.…”