Pest Manag Sci
Terrestrial Ecology Research Group, Department for Ecology and Ecosystem Management, Technische Universität München, Freising, Germany.
Published: October 2017
Background: Intercropping can help reduce insect pest populations. However, the results of intercropping can be pest- and crop-species specific, with varying effects on crop yield, and pest suppression success. In Cameroon, okra vegetable is often grown in intercropped fields and sown with large distances between planting rows (∼ 2 m). Dominant okra pests include cotton aphids, leaf beetles and whiteflies. In a field experiment, we intercropped okra with maize and bean in different combinations (okra monoculture, okra-bean, okra-maize and okra-bean-maize) and altered plant densities (high and low) to test for the effects of diversity, crop identity and planting distances on okra pests, their predators and yield.
Results: We found crop identity and plant density, but not crop diversity to influence okra pests, their predators and okra yield. Only leaf beetles decreased okra yield and their abundance reduced at high plant density. Overall, okra grown with bean at high density was the most economically profitable combination.
Conclusions: We suggest that when okra is grown at higher densities, legumes (e.g. beans) should be included as an additional crop. Intercropping with a leguminous crop can enhance nitrogen in the soil, benefiting other crops, while also being harvested and sold at market for additional profit. Manipulating planting distances and selecting plants based on their beneficial traits may thus help to eliminate yield gaps in sustainable agriculture. © 2017 Society of Chemical Industry.
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http://dx.doi.org/10.1002/ps.4636 | DOI Listing |
Background: The cotton jassid, Amrasca biguttula, a dangerous and polyphagous pest, has recently invaded the Middle East, Africa and South America, raising concerns about the future of cotton and other food crops including okra, eggplant and potato. However, its potential distribution remains largely unknown, posing a challenge in developing effective phytosanitary strategies. We used an ensemble model of six machine-learning algorithms including random forest, maxent, support vector machines, classification and regression tree, generalized linear model and boosted regression trees to forecast the potential distribution of A.
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