RESUMO
Crop rotation is an important strategy for pest reduction. For mono-, or oligophagous pests that overwinter at a previously infested site, crop rotation means that the pests must find new host crop sites in the following year, and it is more efficient if a pest-specific distance is applied. Here, we report the development of a GIS-based tool for efficient cultivation planning using the example of the pest complex pea moth (Cydia nigricana) and grain and green peas (Pisum sativum). Monitoring data for four consecutive years (2016-2019) from 513 sites were used. Infestation of pea seeds and the distance to the previous year's pea sites were recorded. An adjustable Python script was developed by means of infestation-distance-correlation as a pest and crop-specific minimum migration distance (MD). The output of the tool is a risk map as decision support for cultivation planning. It shows different risk buffers with distances from 1261 m to 1825 m, depending on the cultivation type. The web tool is easily adjustable to other pests and crops anywhere in the world. The tool helps to prevent damages caused by agricultural, mono-, or oligophagous insect pests and consequently reduces pesticide applications for the benefit of the environment and biodiversity.
RESUMO
The prediction of the post-diapause emergence is the first step towards a comprehensive decision support system that can contribute to a considerable reduction of pesticide use by forecasting a precise spraying date. The cumulative field emergence can be described as a function of the cumulative development rate. We investigated the impact of seven constant temperatures and five light regimes on post-diapause development in laboratory experiments. Development rate depended significantly on temperature but not on photoperiod. We therefore fit non-linear thermal performance curves, a better and more modern approach over past linear models, to describe the development rate as a function of temperature. The four parameter Brière function was the most suitable and was subsequently applied to temperature data from 36 previous pea fields, where pea moth emergence was measured with pheromone traps in Northern Hesse (Germany). In order to describe the variation in development times between individuals, we fit five nonlinear distribution models to the cumulative development rate as a function of cumulative field emergence. The three parameter Gompertz model was selected as the best fitted model. We validated the model performance with an independent field data set. The model correctly predicted the first moth in the trap and the peak emergence in 81.82% of cases, with an average deviation of only 2.00 and 2.09 days respectively.
