Integrating land suitability assessment and socioeconomic indicators for robusta coffee development
DOI:
https://doi.org/10.61511/tafoa.v3i1.2026.3125Keywords:
integrated assessment, land suitability class, robusta coffee, socio-economic indicators, sustainabilityAbstract
Background: Robusta coffee is a strategic commodity supporting rural livelihoods and the national economy. However, average productivity (≈0.7 t/ha/year) remains far below its potential (2.5–3.0 t/ha/year). This gap reflects not only biophysical land constraints but also socio-economic limitations. An integrated assessment combining land suitability and socio-economic conditions is therefore necessary for sustainable development planning. Methods: This study was conducted from September 2020 to March 2021 using a descriptive-exploratory and survey approach. Soil samples were analyzed in university laboratories, and biophysical conditions were evaluated using Land Suitability Classification (LSC) through a matching method based on crop requirements. Socio-economic conditions were measured using a Socio-Economic Index (SEI) calculated through min–max normalization (0–1 scale) with equal indicator weighting. LSC and SEI were integrated to assess development potential and readiness. Findings: All study sites were classified as S3 (marginally suitable), limited by low organic carbon, poor drainage, and shallow soil depth. SEI values ranged from 0.15 to 0.63, indicating varying socio-economic readiness across villages. The integrated analysis shows that development feasibility depends not only on land characteristics but also on farmers’ socio-economic capacity, influencing the sustainability and productivity of robusta cultivation. Conclusion: Integrating LSC and SEI provides a comprehensive framework for evaluating regional development potential. Sustainable robusta expansion requires addressing both land limitations and socio-economic empowerment to reduce the productivity gap. Novelty/Originality of this article: This study proposes a multidimensional LSC–SEI framework that bridges biophysical and socio-economic dimensions, offering a strategic decision-support model for sustainable agricultural planning.
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