Abstract
Coffee farms receive ecosystem services that rely on pollinators and pest predators. Landscape-scale processes regulate the flow of these biodiversity-based services. Consequently, the coffee farms’ surrounding landscape impacts coffee production. This paper investigates 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). We tested the effect of landscape structure on coffee production for 25 farms and 30 municipalities in southern Brazil. We used seven landscape metrics at landscape and class levels to measure the effect of landscape structure. At the farm level, we calculated metrics in five buffers from 1 to 5 km from the farm centroid to measure their scale of effect. We conducted a model selection using the generalized linear model (GLM) with a Gamma error distribution and inverse link function to evaluate the impact of landscape metrics on coffee production in both spatial and aspatial approaches. The landscape intensity index had a negative effect on coffee production (AICc = 375.59, p < 0.001). The native forest patch density (AICc = 390.14, p = 0.011) and landscape diversity (AICc = 391.18, p = 0.023) had a positive effect on production. All significant factors had effects at the farm level in the 2 km buffer but no effects at the municipality level. Our findings suggest that the landscape composition in the immediate surroundings of coffee farms helps predict production in a spatially explicit approach. However, these metrics cannot detect the impact 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. This study enhanced the knowledge of 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. Furthermore, our framework provides a method that can be useful to scrutinize any cropping system with census data that is either spatialized or not.
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The analysis R code and data used in this study are available in the GitHub repository: https://github.com/FJeronimo42/LikeEspressonotlikeCappuccino.
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Acknowledgements
We thank all the farmers who contributed to this study by providing interviews and producing all the coffee consumed during the preparation of this manuscript; Renato S. Marcantônio (Viveiro Saragoça), Leandro Thomacheski (UFPR), Cezar F. Araujo Junior (IAPAR), Vauller Furtado (Integrada Cooperativa Agroindustrial), Robson L. B. Ferreira, and Mauricio T. Roll (Cocamar Cooperativa Agroindustrial), for mediating contact with coffee farmers; Candice M. R. Santos and Lucas B. Fernandes (CONAB), for providing the first-hand Paraná state coffee production mapping; Augusto Colombo, Luan Passos, and Jaqueline Paes with field data collection; the members of the Interactions and Reproductive Biology Laboratory (UFPR) for their discussions and contributions on this paper; and finally, the reviewers for their constructive comments that helped to improve this manuscript.
Funding
The Coordination for the Improvement of Higher Education Personnel – CAPES Brazil (Finance Code 001—scholarship to F.F.J.), National Council for Scientific and Technologic Development – CNPq Brazil (fellowship 312580/2020–7 to I.G.V.) funded the work, CNPq/MCTIC/IBAMA/ABELHA (grant 400590/2018–2).
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Fernando Fortunato Jeronimo and Isabela Galarda Varassin conceived the study, hypothesis, and statistical analyses, and developed, co-wrote, reviewed, and approved the manuscript. Fernando Fortunato Jeronimo sampled the data and wrote and ran the code for analyzes, figures, and tables.
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Highlights
• Landscape configuration influences coffee production.
• Coffee production is higher in landscapes with a higher proportion of natural vegetation. Landscape metrics are better predictors of coffee production in a spatial approach.
• Landscape aspatial approach can hide the effects of landscape on coffee production.
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Jeronimo, F., Varassin, I.G. 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. Environ Monit Assess 195, 1515 (2023). https://doi.org/10.1007/s10661-023-12139-z
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DOI: https://doi.org/10.1007/s10661-023-12139-z