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Within and among farm variability of coffee quality of smallholders in southwest Ethiopia
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Number of Authors: 92023 (English)In: Agroforestry Systems, ISSN 0167-4366, E-ISSN 1572-9680, Vol. 97, no 5, p. 883-905Article in journal (Refereed) Published
Abstract [en]

The biophysical drivers that affect coffee quality vary within and among farms. Quantifying their relative importance is crucial for making informed decisions concerning farm management, marketability and profit for coffee farmers. The present study was designed to quantify the relative importance of biophysical variables affecting coffee bean quality within and among coffee farms and to evaluate a near infrared spectroscopy-based model to predict coffee quality. Twelve coffee plants growing under low, intermediate and dense shade were studied in twelve coffee farms across an elevational gradient (1470–2325 m asl) in Ethiopia. We found large within farm variability, demonstrating that conditions varying at the coffee plant-level are of large importance for physical attributes and cupping scores of green coffee beans. Overall, elevation appeared to be the key biophysical variable influencing all the measured coffee bean quality attributes at the farm level while canopy cover appeared to be the most important biophysical variable driving the above-mentioned coffee bean quality attributes at the coffee plant level. The biophysical variables driving coffee quality (total preliminary and specialty quality) were the same as those driving variations in the near-infrared spectroscopy data, which supports future use of this technology to assess green bean coffee quality. Most importantly, our findings show that random forest is computationally fast and robust to noise, besides having comparable prediction accuracy. Hence, it is a useful machine learning tool for regression studies and has potential for modeling linear and nonlinear multivariate calibrations. The study also confirmed that near-infrared spectroscopic-based predictions can be applied as a supplementary approach for coffee cup quality evaluations.

Place, publisher, year, edition, pages
2023. Vol. 97, no 5, p. 883-905
Keywords [en]
Coffee quality, Near-infrared spectroscopy (NIRS), MANRandom forest, PEROVA
National Category
Agricultural Science
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URN: urn:nbn:se:su:diva-216001DOI: 10.1007/s10457-023-00833-3ISI: 000942711600001Scopus ID: 2-s2.0-85149148962OAI: oai:DiVA.org:su-216001DiVA, id: diva2:1748247
Available from: 2023-04-03 Created: 2023-04-03 Last updated: 2023-05-12Bibliographically approved

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Tack, Ayco J. M.Hylander, KristofferZewdie, Beyene

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Tack, Ayco J. M.Hylander, KristofferZewdie, Beyene
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Department of Ecology, Environment and Plant Sciences
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