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• W4318318404 abstract "Abstract Context Coffee farms benefits from ecosystem services dependent on pollinators and pest predators. The flow of these biodiversity-based ecosystem services is regulated by landscape-scale processes. Therefore, the landscape structure in which coffee farms are inserted influence coffee production. Objectives The aim of this paper is to investigate how landscape structure can influence coffee production at different scales. We also evaluated the predictive utility of landscape metrics in a spatial (farm level) and aspatial approach (municipality level). Methods We tested the effect of six landscape metrics on coffee production for 25 farms and 30 municipalities in southern Brazil. For the farm level we calculate the metrics at 1 to 5 km radii from the farm's centroid for access its scale of effect. A model selection of Generalized Linear Model (GLM) was run for access the effect of landscape metrics on coffee production for both approaches (spatial and aspatial). Results We obtained that coffee production was negatively influenced by the landscape intensity index, while landscape diversity and the native forest patch density had a positive effect on production. All significant metrics had effects emerging within a 2 km radius at the farm level, but we had no effects at the municipality scale. Our findings suggest that the landscape composition in the immediate surroundings of coffee farms is useful to predict production in a spatially explicit approach. However, these metrics are not able to detect the effect of the landscape when analyzed in an aspatial approach. These findings highlight the importance of the landscape spatial structure, mainly the natural one, in the stability of coffee production. Conclusions This study enhanced the knowledge on the role of the landscape in coffee production dependence on landscape-level processes. This advance can help to improve the sustainability of land use and better planning of agriculture, ensuring food and economic safety. Besides, we offer a framework that can be applied to study any cropping system with available census data (spatialized or not)." @default.
• W4318318404 created "2023-01-28" @default.
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• W4318318404 date "2023-01-27" @default.
• W4318318404 modified "2023-09-26" @default.
• W4318318404 title "Like an “espresso” but not like a “cappuccino”: landscape metrics are useful for predicting coffee production at the farm level, but not at the municipality level" @default.
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• W4318318404 doi "https://doi.org/10.21203/rs.3.rs-2499734/v1" @default.
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