Risk Assessment of Different Maize (Zea mays L.) Lodging Types in the Northeast and the North China Plain Based on a Joint Probability Distribution Model

Abstract

Mastering the lodging risk of planting environment is of great significance to the optimal layout of maize varieties and the breeding of lodging resistant varieties. However, the existing lodging risk models are still at the stage of single or multi-factors independent analysis, and lack of assessment for different lodging types. To address this issue, based on the mechanism of different lodging types, the Archimedean copula function was used to describe the joint probability distribution of wind speed and precipitation, and the lodging risk assessment model of maize was established. By comparing the goodness of fit, when the rank correlation coefficient of these two is positive and negative, the corresponding optimal joint probability distribution functions are the Gumbel copula and Frank copula. According to the spatial distribution of lodging risk, the area from Liaodong Bay northward to Tongyu, Jilin province in the Northeast and the North China Plain has a high frequency of lodging, in which the probability of stalk lodging is two to four times that of root lodging. Finally, we discussed how to apply the lodging risk distribution results to optimize the maize variety test sites to improve the efficiency and reliability of the existing test system. The method proposed in this paper comprehensively considers the synergistic effect of multiple factors and can provide technical support for other risk assessment.

Generalizability of a Random Forest-based model of maize lodging built with satellite image data and its application to monitoring and evaluating maize lodging risks

“…Although combinations of key climatic factors affect regional lodging risks [6], the relationship between lodging and interacting meteorological factors is too complex to establish an accurate model to predict regional lodging risks. Remote sensing technologies, which allow fast and accurate field monitoring, have been widely used to monitor crop lodging [7][8][9].…”

Perspectives on “Earth Observation and GIScience for Agricultural Applications”

“…The choice of input data and the implemented methodical approach alongside the discussions borne from the study are of interest to the farming sector and drinking water suppliers as well as the climate change impact assessment domain. The paper by Zan et al [2] deals with the assessment of lodging risk for different maize in Northern China by using a joint probability model. They used a joint probability distribution of distinct environmental variables (i.e., wind speed and precipitation) to assess the lodging risk, the typology, and spatial distribution and frequency in the study area.…”