Research on World Agricultural Economy

Strategic Site Selection for Agricultural Innovation: Utilizing AHP to Enhance Rural Development in Colombia's Cabuya Industry

Santiago A. Roa‑Ortiz

Faculty of Natural Sciences and Engineering, University of Bogotá Jorge Tadeo Lozano, Bogotá D.C. 111321, Colombia

Favio Cala‑Vitery

Research & Development Department, Colombian Agricultural Research Corporation AGROSAVIA, Mosquera 250047, Colombia

DOI: https://doi.org/10.36956/rwae.v7i1.2304

Received: 12 June 2025 | Revised: 9 August 2025 | Accepted: 25 August 2025 | Published Online: 15 December 2025

---

Abstract

The strategic deployment of agricultural technologies is essential for promoting rural development in contexts marked by inequality, such as Colombia. This study examines the site selection of a prototype machine for extracting fiber, juice, and bagasse from giant cabuya (Furcraea foetida), a crop with underutilized potential for generating economic and environmental benefits. To address this challenge, the Analytical Hierarchy Process (AHP) was applied as a multicriteria decision-making framework that integrates infrastructure, social capital, and economic outlook. The methodology combined hierarchical modeling with expert assessments gathered through workshops and interviews with producers, associations, and local stakeholders in four major cabuya-producing regions. A total of 36 paired comparison matrices were constructed to evaluate alternatives, ensuring validity through consistency ratios below established thresholds. Results indicate that La Guajira is the most suitable location for deploying the prototype, followed by Antioquia, Nariño, and Santander. These findings highlight the advantages of using multidimensional criteria to inform decisions, moving beyond narrow productivity-based or politically influenced approaches. This study contributes to agricultural innovation and policy design by showing how AHP supports transparent, participatory, and evidence-based allocation of resources. The model not only improves governance and reduces biases but also provides a replicable tool for aligning technological investments with local socio-economic capacities, thereby fostering sustainable and inclusive rural development.

Keywords: Multicriteria Decision‑Making; Agricultural Technology; Furcraea foetida; R&D Technology Transfer

---

References

[1] Jones-Garcia, E., Krishna, V.V., 2021. Farmer adoption of sustainable intensification technologies in the maize systems of the Global South. A review. Agron. Sustain. Dev. 41, 8. DOI: https://doi.org/10.1007/s13593-020-00658-9

[2] Golfam, P., Ashofteh, P.-S., Rajaee, T., et al., 2019. Prioritization of Water Allocation for Adaptation to Climate Change Using Multi-Criteria Decision Making (MCDM). Water Resources Management. 33(10), 3401–3416. DOI: https://doi.org/10.1007/s11269-019-02307-7

[3] Roa-Ortiz, S.A., Cala-Vitery, F., 2025. The Current Agricultural Technology Transfer Policy in Colombia Modeled through Dynamic Performance Governance. Research on World Agricultural Economy. 6(1), 319–337. DOI: https://doi.org/10.36956/rwae.v6i1.1263

[4] UN, 2015. Transforming Our World: The 2030 Agenda for Sustainable Development. Resolution Adopted by the General Assembly on 25 September 2015. 42809, 1–13. Available from: https://docs.un.org/en/A/RES/70/1 (cited 15 June 2025).

[5] FAO, 2017. The State of Food and Agriculture Leveraging Food Systems for Inclusive Rural Transformation. In Population and Development Review. 19 (1). Available from: http://uni-sz.bg/truni11/wp-content/uploads/biblioteka/file/TUNI10042440(1).pdf (cited 15 June 2025).

[6] Britz, W., Van Ittersum, M., Oude Lansink, A., et al., 2012. Tools for Integrated Assessment in Agriculture. State of the Art and Challenges. Bio-based and Applied Economics. 1(2), 125–150. DOI: https://doi.org/10.13128/BAE-11232

[7] De Luca, A.I., Iofrida, N., Leskinen, P., et al., 2017. Life cycle tools combined with multi-criteria and participatory methods for agricultural sustainability: Insights from a systematic and critical review. Science of The Total Environment. 595, 352–370. DOI: https://doi.org/10.1016/j.scitotenv.2017.03.284

[8] Guitouni, A., Martel, J.-M., 1998. Tentative guidelines to help choosing an appropriate MCDA method. European Journal of Operational Research. 109(2), 501–521. DOI: https://doi.org/10.1016/S0377-2217(98)00073-3

[9] Ruvalcaba García, A., González Morán, T., 2020. Analysis and selection of optimal sites for wind farms: case study, region north of Mexico. Atmósfera. 34(1), 121–131. DOI: https://doi.org/10.20937/ATM.52664

[10] Korhonen, P.J., Wallenius, J., Genc, T., et al., 2021. On rational behavior in multi-attribute riskless choice. European Journal of Operational Research. 288(1), 331–342. DOI: https://doi.org/10.1016/j.ejor.2020.05.056

[11] Robinson, P., Johnson, P.A., 2021. Pandemic-Driven Technology Adoption: Public Decision Makers Need to Tread Cautiously. International Journal of E-Planning Research. 10(2), 59–65. DOI: https://doi.org/10.4018/IJEPR.20210401.oa5

[12] Ahsan, K., Rahman, S., 2017. Green public procurement implementation challenges in Australian public healthcare sector. Journal of Cleaner Production. 152, 181–197. DOI: https://doi.org/10.1016/j.jclepro.2017.03.055

[13] Bersani, C., Guerisoli, C., Mazzino, N., et al., 2015. A multi-criteria methodology to evaluate the optimal location of a multifunctional railway portal on the railway network. Journal of Rail Transport Planning & Management. 5(2), 78–91. DOI: https://doi.org/10.1016/j.jrtpm.2015.06.003

[14] Kauf, S., Tłuczak, A., 2018. Solving the problem of logistics center location based on the AHP method. MATEC Web of Conferences. 184, 04024. DOI: https://doi.org/10.1051/matecconf/201818404024

[15] Colak, H.E., Memisoglu, T., Gercek, Y., 2020. Optimal site selection for solar photovoltaic (PV) power plants using GIS and AHP: A case study of Malatya Province, Turkey. Renewable Energy. 149, 565–576. DOI: https://doi.org/10.1016/j.renene.2019.12.078

[16] Herrera-Seara, M.A., Aznar Dols, F., Zamorano, M., et al., 2010. Optimal location of a biomass power plant in the province of Granada analyzed by multi-criteria evaluation using appropriate geographic information system according to the analytic hierarchy process. Renewable Energy and Power Quality Journal, 8(1). DOI: https://doi.org/10.24084/repqj08.484

[17] Saaty, R.W., 1987. The analytic hierarchy process—what it is and how it is used. Mathematical Modelling. 9(3–5), 161–176. DOI: https://doi.org/10.1016/0270-0255(87)90473-8

[18] Cervantes-Zapana, M., Yagüe, J.L., De Nicolás, V.L., et al., 2020. Benefits of public procurement from family farming in Latin-AMERICAN countries: Identification and prioritization. Journal of Cleaner Production. 277, 123466. DOI: https://doi.org/10.1016/j.jclepro.2020.123466

[19] Saaty, T.L., 1986. Axiomatic Foundation of the Analytic Hierarchy Process. Management Science, 32(7), 841–855. Available from: http://www.jstor.org/stable/2631765 (cited 15 June 2025).

[20] De Marinis, P., Sali, G., 2020. Participatory analytic hierarchy process for resource allocation in agricultural development projects. Evaluation and Program Planning. 80, 101793. DOI: https://doi.org/10.1016/j.evalprogplan.2020.101793

[21] Saaty, T.L., 2008. Decision making with the analytic hierarchy process. International Journal of Services Sciences. 1(1), 83–98. DOI: https://doi.org/10.1504/IJSSCI.2008.017590

[22] Padma, K., Vaisakh, K., 2017. Application of AHP method for optimal location of SSSC device under different operating conditions. In Proceedings of the 2017 2nd IEEE International Conference on Electrical, Computer and Communication Technologies, ICECCT, Coimbatore, India, 2017. pp. 1–6. DOI: https://doi.org/10.1109/ICECCT.2017.8117972

[23] Ikram, M., Sroufe, R., Zhang, Q., 2020. Prioritizing and overcoming barriers to integrated management system (IMS) implementation using AHP and G-TOPSIS. Journal of Cleaner Production. 254, 120121. DOI: https://doi.org/10.1016/j.jclepro.2020.120121

[24] Saaty, T.L., 1988. What is the Analytic Hierarchy Process?. In: Mitra, G., Greenberg, H.J., Lootsma, F.A., et al. (Eds.), Mathematical Models for Decision Support. NATO ASI Series, vol 48. Springer Berlin Heidelberg: Berlin, German. pp. 109–121. DOI: https://doi.org/10.1007/978-3-642-83555-1_5

[25] Aguarón, J., Moreno-Jiménez, J.M., 2003. The geometric consistency index: Approximated thresholds. European Journal of Operational Research. 147(1), 137–145. DOI: https://doi.org/10.1016/S0377-2217(02)00255-2