Relative influence of climate and agroenvironmental factors on wireworm damage risk in maize crops

Abstract

A large-scale survey was carried out in 336 French fields to investigate the influence of soil characteristics, climate conditions, the presence of wireworms and the identity of predominant species, agricultural practices, field history and local landscape features on the damage caused by wireworms in maize. Boosted regression trees, a statistical model originating from the field of machine learning, were fitted to survey data and then used to hierarchize and weigh the relative influence of a large set of variables on the observed damage. Our study confirmed the relevance of an early assessment of wireworm populations to forecast crop damage. Results have shown that climatic factors were also major determinants of wireworm damage, especially the soil temperature around the sowing date, with a strong decrease in damage when it exceeds 12°C. Soil characteristics were ranked third in importance with a primary influence of pH, but also of organic matter content, and to a lesser extent of soil texture.Field history ranked next, in particular our findings confirmed that a long lasting meadow appeared favourable to wireworm damage. Finally, agriculture practices and landscape context (especially the presence of a meadow in the field vicinity) were also shown to influence wireworm damage but more marginally. Overall, the predicted damage appeared highly correlated to the observed one allowing us to produce the framework of a decision-support system to forecast wireworm risk in maize crop.

Drivers of tropical soil invertebrate community composition and richness across tropical secondary forests using DNA metasystematics

“…The larvae of the Elateridae are (known as ‘wireworms’) live in soil and/or under bark and feed and process decaying soil organic matter 56 . The wireworm larval stage can span from 2 to 6 years, and in some cases even 10 years 56 – 58 . During this larval period, wireworms specifically feed underground making them persistent residents of soil for some time.…”

Coffee tree architecture and its interactions with microclimates drive the dynamics of coffee berry disease in coffee trees

“…We applied a statistical method from the field of machine learning, namely, BRT 34,35 , to build a stochastic, nonlinear regression model from our dataset. This method is attracting increasing interest in ecology and epidemiology 36–40 . BRT combines the advantages of regression trees (capable of handling qualitative and quantitative predictors and accommodating missing data, and prior information or data transformation is not needed) and boosting (improves prediction accuracy by combining many simple models) 36 .…”

Alternative Strategies for Controlling Wireworms in Field Crops: A Review

“…They also found that soil pH was a strong predictor for the abundance of A. obscurus and A. ustulatus. Based on a large-scale survey carried out in 336 maize fields over three years in France, Poggi et al [25] concluded that soil characteristics had a prominent influence on wireworm damage risk, ranking them third after the presence of wireworms and climatic variables, with both pH and organic-matter content also being major factors. The effects of soil texture, drainage, and other factors can be found in the literature (see for example Furlan et al [23]).…”