A Multicriteria Model for Estimating Coffea arabica L. Productive Potential Based on the Observation of Landscape Elements

Jorge Eduardo F. Cunha, George Deroco Martins, Eusímio Felisbino Fraga Júnior, Silvana P. Camboim and João Vitor M. Bravo ()
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Jorge Eduardo F. Cunha: Insitute of Agricultural Sciences, Campus Monte Carmelo, Federal University of Uberlandia, Monte Carmelo 38500-000, Minas Gerais, Brazil
George Deroco Martins: Insitute of Geography, Campus Monte Carmelo, Federal University of Uberlandia, Uberlandia 38500-000, Minas Gerais, Brazil
Eusímio Felisbino Fraga Júnior: Insitute of Geography, Campus Monte Carmelo, Federal University of Uberlandia, Uberlandia 38500-000, Minas Gerais, Brazil
Silvana P. Camboim: Departament of Geomatics, Campus Polytechnic Center, Federal University of Parana, Curitiba 81530-900, Parana, Brazil
João Vitor M. Bravo: Insitute of Geography, Campus Santa Monica, Federal University of Uberlandia, Uberlandia 38408-100, Minas Gerais, Brazil

Agriculture, 2023, vol. 13, issue 11, 1-16

Abstract: Understanding a crop’s productive potential is crucial for optimizing resource use in agriculture, encouraging sustainable practices, and effectively planning planting and preservation efforts. Achieving precise and tailored management strategies is equally important. However, this task is particularly challenging in coffee cultivation due to the absence of accurate productivity maps for this crop. In this article, we created a multicriteria model to estimate the productive potential of coffee trees based on the observation of landscape elements that determine environmental fragility (EF). The model input parameters were slope and terrain shape data, slope flow power, and orbital image data (Landsat 8), allowing us to calculate the NDVI vegetation index. We applied the model developed to coffee trees planted in Bambuí, Minas Gerais, Brazil. We used seven plots to which we had access to yield data in a recent historical series. We compared the productivity levels predicted by the EF model and the historical productivity data of the coffee areas for the years 2016, 2018, and 2020. The model showed a high correlation between the calculated potential and the annual productivity. We noticed a strong correlation (R 2 ) in the regression analyses conducted between the predicted productive potential and the actual productivity in 2018 and 2020 (0.91 and 0.93, respectively), although the correlation was somewhat weaker in 2016 (0.85). We conclude that our model could satisfactorily estimate the yearly production potential under a zero-harvest system in the study area.

Keywords: coffee; crop estimation; productive potential; environmental fragility; sustainability (search for similar items in EconPapers)
JEL-codes: Q1 Q10 Q11 Q12 Q13 Q14 Q15 Q16 Q17 Q18 (search for similar items in EconPapers)
Date: 2023
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