Introduction
⌅The creation of sustainable models for local farms based on the principles of circular agriculture is a current challenge. These models mimic natural cycles through cyclical practices such as biomass recycling and the use of bio-inputs (Altieri, 2002ALTIERI, M.A.: “Agroecología: principios y estrategias para diseñar sistemas agrarios sustentables”, Agroecología: el camino hacia una agricultura sustentable, 34, 2002.), seeking to reduce the ecological footprint by conserving and protecting biodiversity, soil, and water (Gliessman, 2018GLIESSMAN, S.: “Defining agroecology”, Agroecology and sustainable food systems, 42(6): 599-600, 2018, ISSN: 2168-3565.); as well as promoting energy efficiency with renewable resources (FAO, 2018FAO: Feed inventory and feed balance, [en línea], Inst. Organización de las Naciones Unidas para la Alimentación y la Agricultura (FAO), FAO. Animal Feed Resources, Roma, Italia, 2018, Disponible en:https://openknowledge.fao.org. )
In Cuba, research on closing nutrient cycles exists in studies with specific, isolated, and mixed approaches. Notable examples include: models in pastures, efficiency under favorable climatic conditions (Funes-Monzote, 2008FUNES-MONZOTE, F.R.: Farming like were here to stay: The mixed farming alternative for Cuba, Ed. Wageningen University and Research, 2008, ISBN: 979-8-5160-3873-0.); and operational biofactories (CREE, local production of compost, bokashi, violets, plant slurries, and Trichoderma fungi); although their distribution and scaling are uneven and not widespread (Rosset et al., 2011ROSSET, P.M.; MACHÍN SOSA, B.; ROQUE JAIME, A.M.; ÁVILA LOZANO, D.R.: “The Campesino-to-Campesino agroecology movement of ANAP in Cuba: social process methodology in the construction of sustainable peasant agriculture and food sovereignty”, The Journal of peasant studies, 38(1): 161-191, 2011, ISSN: 0306-6150.). Furthermore, logistical challenges and climatic effects persist, limiting the quality of waste products (Machado et al., 2017MACHADO, M.; DIAZ, Y.; LEZCANO, J.C.: “Los sistemas agroforestales como base de la sostenibilidad en Cuba”, Pasto y Forraje, 2017.), These factors contribute to inconsistencies in the quality of artisanal bio-inputs due to limited infrastructure and the lack of widespread dissemination of cyclical farming practices from farmer to farmer (Fundora et al., 2024FUNDORA, L.R.; MARTÍN, G.M.; MIRANDA, I.; RIVERA, R.A.; DUQUE, Y.R.: “Caracterización agroproductiva de fincas de la provincia Mayabeque, Cuba”, Pastos y Forrajes, 47, 2024, ISSN: 0864-0394.).
Nevertheless, this background shows results in soil, crop, and biodiversity improvement, as well as pollution reduction, providing a basis for implementing mini-bio-input biofactories as a cornerstone of circular agriculture in self-managed local farms.
The Guayabal farm, as a teaching-production-research model, constitutes an ideal setting to replicate Cuban successes and minimize shortcomings through the validation and adaptation of the methodology described by Arteaga & Pino (2025)ARTEAGA, M.; PINO, J.A.: “Proposal for a model for the implementation of circular biofactories for agricultural goods on Cuban farms”, Revista Ciencias Técnicas Agropecuarias, 34: cu-id, 2025, ISSN: 2071-0054., to the specific conditions of local farms. The objective of this work was to propose a comprehensive, adaptable and replicable methodology to design a mini-biofactory that contributes to the autonomy of the Guayabal farm and serves as a model for the locality of Mayabeque.
Materials and Methods
⌅General Methodological Design
⌅The work procedure was based on the linking, validation, and adaptation of two methodologies proposed by Arteaga & Pino (2025)ARTEAGA, M.; PINO, J.A.: “Proposal for a model for the implementation of circular biofactories for agricultural goods on Cuban farms”, Revista Ciencias Técnicas Agropecuarias, 34: cu-id, 2025, ISSN: 2071-0054., for the design and implementation of mini-biofactories for agricultural bio-inputs on local farms, specifically in the Mayabeque province.
A procedure was derived according to the characteristics and endogenous conditions of the Guayabal teaching farm. It was developed with a mixed-methods approach (qualitative-quantitative) and structured as a single case study (Yin, 2014YIN, R.K.: Case Study research: design and methods, Los Angeles: SAGE publications ed., vol. 30, vols. 1, Los Angeles, USA, 282 p., 2014, ISBN: 978-1-4522-4256-9.) with elements of participatory action research integrated with the knowledge of local stakeholders at the Guayabal farm, laboratory analysis, and technical-economic modeling.
This protocol was methodologically structured by integrating indicators that would allow for the required multifunctionality, in order to achieve continuous improvement of the integrated endogenous strategic actions to be developed through the most cost-effective and viable pathways for the gradual (phased) implementation and final establishment of Self-Sustaining Circular Mini-Biofactories on farms at the individual or community level, under the local endogenous conditions of the Mayabeque province, applicable to Cuba as a whole, to contribute to food sovereignty and environmental sustainability of the territory.
The general methodological design included three sequential phases: (I) Phase 1. Farm characterization and baseline diagnosis: aimed at understanding the endogenous conditions (physical, chemical, biological, and socio-productive) that influence the implementation of a mini-biofactory. (II) Phase 2. Design of the procedure for the implementation of the mini-biofactory: based on the integration of theoretical frameworks, previous experiences, and participatory tools with local stakeholders. (III) Phase 3. Feasibility Assessments of the Proposal: projections of self-sufficiency, economic return, and soil improvement based on the information generated in the previous phases. This design allowed for the development of a contextualized, replicable procedure grounded in the farm's actual conditions.
Selection of the Study Area
⌅The research was conducted at Finca Guayabal between 2021 and 2025, located in the municipality of San José de las Lajas, Mayabeque Province, Cuba (22° 59′ 55″ N, 82° 01′ 02″ W). The farm was intentionally selected (purposive sampling) due to its high level of collaboration among producers, educators, researchers, and the community, making it a model for local training and replication. This collaboration is manifested in the ongoing partnership with the Agrarian University of Havana (UNAH), the Scientific Complex, and in its participation in agroecological innovation networks, involving producers.
Phase 1: Farm Characterization and Baseline Diagnosis
⌅Climatic Context of the Study Area
⌅Table 1 summarizes the average annual climatic conditions recorded during the study period, from 2021 to 2025, for the Guayabal farm area in San José de las Lajas, Mayabeque, by the Cuban Institute of Meteorology. It highlights trends compiled from historical average data (2010-2024) for the area (INSMET-Cuba, 2025INSMET-CUBA: Reportes de las estaciones metereológicas de Cuba, Inst. Instituto de Meteorología. La Habana. Cuba, La Habana, Cuba, 2025.). These constitute independent variables that influence the dynamics of the agroecosystem, the generation of biomass, the production of organic waste and the conservation and transformation processes proposed on the farm over time.
| Parameters annual means | Year 2021 at the 2025 | Tendency observed with relationship to the ago 30 year-old historical stocking |
|---|---|---|
| Precipitation mm | ≈533-650 | Decrease of 54-62% |
| Marked | Alternation of dry and humid years Dry station (Nov-Abr) Rainy station (May-Oct) |
|
| Temperature 0C | 24.5-25.5 | T.max.+ 1 -2.5 0C |
| Relative humidity | 70-81% | Smaller in the dry season |
| Extreme events | drought and hurricanes | Increased in frequencies and intensity |
Variables and Parameters Evaluated
⌅Based on the described climatic context, 18 physical-chemical, biological, and socio-productive parameters of the farm were evaluated, as shown in Table 2. The main dependent variables included: (I) soil organic matter content (%), (II) soil pH, (III) nutrient availability (N, P, K), (IV) organic waste generation - kg/month (a variable directly influenced by climatic conditions of temperature and precipitation), (V) agroecosystem management, (VI) composition of the generated waste, and (VII) socio-productive indicators (labor, organization, among others).
| Parameter | Calculation methodology | Source / calculation Base |
|---|---|---|
| Determination of space data of the property with images satellites | GPS Sentinel-2 (B4-RED,8-NIR, 10m head.). COPERNICUS OPEN ACCESS HUB QGIS 3.28 I inventory direct and analysis of the NDVI Calculations NDVI + zonal Statistics Temporary analysis NDVI NDVI = (NIR-NET) / (NIR+RED) NIR: Band 8 to GO near NET: Banda4(red) of Sentinel-2 Application of coefficients of use Models (Estimate of the biomass (t/ha) with the NDVI. |
Science for Changing wold USGS, Sentinel Hub, 2024 Fernández (2020)FERNÁNDEZ, D.: Monitoreo satelital de la resiliencia agrícola en Cuba, [en línea], Universidad Agraria de La Habana, San José de las Lajas, Mayabeque, Cuba, Tesis Doctoral, San José de las Lajas Mayabeque, Cuba, 2020, Disponible en:http://tesisrepo.sld.cu/123456789/3456. Costo et al. (2020)COSTO, L.; NUNEZ, L.; AMPATZIDIS, Y.: “Un nuevo índice de banda visible para estimar valores de NDVI. Computadoras y electrónica en la agricultura 172”, ScienceDirec, 2020, DOI: https://doi.org/10.1016/compag.2020.105334. Chave et al. (2014)CHAVE, J.; RÉJOU, M.; BÚRQUEZ, A.; CHIDUMAYO, E.; COLGAN, M.S.; DELITTI, W.B.; DUQUE, A.; EID, T.; FEARNSIDE, P.M.; GOODMAN, R.C.: “Improved allometric models to estimate the aboveground biomass of tropical trees”, Global change biology, 20(10): 3177-3190, 2014, ISSN: 1354-1013. González et al. (2021)GONZÁLEZ, L.; PÉREZ, M.; RODRIGUEZ, J.A.: “Evaluación del estrés post ciclónico en cultivos tropicales mediante índices espectrales”, Boletín técnico del INIVIT, 15(3): 45-58, 2021, ISSN: 1028-3296, DOI: https://doi.org/10.20396/boletininivit.v13i3.45-58. INIVIT-Cuba (2023)INIVIT-CUBA: Requerimientos nutricionales de cultivos tropicales, Inst. Instituto Nacional de Viandas Tropicales INIVIT, Informe técnico de Introducción de resultados de Investigación, Villa Clara, Cuba, 2023. FAO (2017FAO: Evaluación de la sostenibilidad en sistemas agropecuarios. Enfoque y metodología, [en línea], Inst. Organización de las Naciones Unidas para la Alimentación y la Agricultura (FAO), Roma, Italia, 2017, Disponible en:https://www.fao.org. ; 2018FAO: Feed inventory and feed balance, [en línea], Inst. Organización de las Naciones Unidas para la Alimentación y la Agricultura (FAO), FAO. Animal Feed Resources, Roma, Italia, 2018, Disponible en:https://openknowledge.fao.org. ; 2021)FAO: Balance de nutrientes en la Agricultura, [en línea], Inst. Organización de las Naciones Unidas para la Alimentación y la Agricultura (FAO), Manual de alimentación animal para pequeños productores., Roma, Italia, 2021, Disponible en:https://openknowledge.fao.org. Dengsheng et al. (2016)DENGSHENG, L.; LU, M.; CHEN, L.: “Un estudio sobre métodos de estimación de biomasa en área basados en teledetecciones en ecosistemas forestales”, Revista Internacional de la Tierra, 9(1): 63-105, 2016, DOI: https://doi.org/10,1080/17538947-2014.990526. |
| Images satellites Sentinel was processed-2 (15 September of the 2021-2025), with validation in situ (USGS, 2025) | ||
| Estimate of the Vegetable Biomass BV (t/ha year), by means of the analysis NDVI and applying direct inventory | BV = Yield cultivation (t/ha) x area index x of use harvests standard Coef.residuo cultivations 0.3-0.5 |
González et al. (2021)GONZÁLEZ,
L.; PÉREZ, M.; RODRIGUEZ, J.A.: “Evaluación del estrés post ciclónico
en cultivos tropicales mediante índices espectrales”, Boletín técnico del INIVIT, 15(3): 45-58, 2021, ISSN: 1028-3296, DOI: https://doi.org/10.20396/boletininivit.v13i3.45-58. Taiz (2025)TAIZ, L.: “Plant physiology and development”, 2025, ISSN: 019761423X. IPCC (2019)IPCC: Guidelines for national greenhouse gas inventories, Ed. repared by the National Greenhouse Gas Inventories Programme. Eggleston HS, Buendia L, Miwa K, Ngara T, Tanabe K, editors. Published: IGES, Japan, repared by the National Greenhouse Gas Inventories Programme. Eggleston HS, Buendia L, Miwa K, Ngara T, Tanabe K, editors. Published: IGES, Japan ed., Japan, 2019. Gofc-gold (2023)GOFC-GOLD: “Validation protocols”, [en línea], En: Sourcebook for biomass Estimation, vol. Cap. 4, 2023, Disponible en:Gofcgold.wixsite.com. |
| Production of manure PE (t/año), using values standard | PE = N0. Heads x daily half production (kg/d) x 365/1000. Coef. 10kg/d Bovine; 25-30 kg/d Smaller: 2-4 kg/d |
FAO (2018FAO: Feed inventory and feed balance, [en línea],
Inst. Organización de las Naciones Unidas para la Alimentación y la
Agricultura (FAO), FAO. Animal Feed Resources, Roma, Italia, 2018, Disponible en:https://openknowledge.fao.org. ; 2021FAO: Balance de nutrientes en la Agricultura, [en línea],
Inst. Organización de las Naciones Unidas para la Alimentación y la
Agricultura (FAO), Manual de alimentación animal para pequeños
productores., Roma, Italia, 2021, Disponible en:https://openknowledge.fao.org. ; 2022)FAO: Guía de alimentación de animales para climas tropicales, [en línea], Inst. Organización de las Naciones Unidas para la Alimentación y la Agricultura (FAO), Roma, Italia, 2022, Disponible en:https://openknowledge.fao.org. López et al. (2020)LÓPEZ, R.; GARCÍA, M.; MARTINEZ, J.: “Impacto de huracanes en la cobertura vegetal, análisis de cambios en el NDVI y umbrales de resiliencia en ecosistemas tropicales”, Revista Ciencias Ambientales, 15(2): 45-60, 2020, DOI: https://doi.org/10.38142/ijesss. IPCC (2019)IPCC: Guidelines for national greenhouse gas inventories, Ed. repared by the National Greenhouse Gas Inventories Programme. Eggleston HS, Buendia L, Miwa K, Ngara T, Tanabe K, editors. Published: IGES, Japan, repared by the National Greenhouse Gas Inventories Programme. Eggleston HS, Buendia L, Miwa K, Ngara T, Tanabe K, editors. Published: IGES, Japan ed., Japan, 2019. |
| Balance of nutritious (NPK), | Deficit (kg) = (' area has x Extraction half cultivation kg/ha)-(content of nutritious in floor kg/ha)-it contributes bioinputs, using coef. of extraction | INCA-Cuba (2020)INCA-CUBA: Tecnologías de bioinsumos en Cuba. Recomendaciones nutricionales para suelos cubanos,
Inst. Instituto Nacional de Ciencias Agrícolas, Informe técnico de
Introducción de resultados de Investigación, San José de Las Lajas,
Mayabeque, Cuba, 2020. FAO (2018FAO: Feed inventory and feed balance, [en línea], Inst. Organización de las Naciones Unidas para la Alimentación y la Agricultura (FAO), FAO. Animal Feed Resources, Roma, Italia, 2018, Disponible en:https://openknowledge.fao.org. ; 2021)FAO: Balance de nutrientes en la Agricultura, [en línea], Inst. Organización de las Naciones Unidas para la Alimentación y la Agricultura (FAO), Manual de alimentación animal para pequeños productores., Roma, Italia, 2021, Disponible en:https://openknowledge.fao.org. |
| Alimentary self-sufficiency (%) | %= (biomass foragers produced property t)/total Requirement of the livestock t) x 100 Coef. efficiency compostaje 50% |
Funes-Monzote, (2008)FUNES-MONZOTE, F.R.: Farming like were here to stay: The mixed farming alternative for Cuba, Ed. Wageningen University and Research, 2008, ISBN: 979-8-5160-3873-0. FAO (2021FAO: Balance de nutrientes en la Agricultura, [en línea], Inst. Organización de las Naciones Unidas para la Alimentación y la Agricultura (FAO), Manual de alimentación animal para pequeños productores., Roma, Italia, 2021, Disponible en:https://openknowledge.fao.org. ; 2022)FAO: Guía de alimentación de animales para climas tropicales, [en línea], Inst. Organización de las Naciones Unidas para la Alimentación y la Agricultura (FAO), Roma, Italia, 2022, Disponible en:https://openknowledge.fao.org. |
| Floor analysis in samples made up of 0-20 cm of depth Potassium Nitrogen | Analysis of the recycle of nutritious (NPK), comparing the nutrients for cultivation (kg/ha/year), using requirement chart Extraction of N cultivations 120kg/ha |
FAO (2021)FAO: Balance de nutrientes en la Agricultura, [en línea],
Inst. Organización de las Naciones Unidas para la Alimentación y la
Agricultura (FAO), Manual de alimentación animal para pequeños
productores., Roma, Italia, 2021, Disponible en:https://openknowledge.fao.org. Sánchez (2019)SÁNCHEZ, P.A.: Properties and management of soils in the Tropics, Ed. Cambridge University Press, vol. Cap.9. Nutrient Cycling and soil Fertility, Cambridge, USA, 2019. Palm et al. (2001)PALM, C.A.; GACHENGO, C.N.; DELVE, R.J.; CADISCH, G.; GILLER, K.E.: “Organic inputs for soil fertility management in tropical agroecosystems: application of an organic resource database”, Agriculture, ecosystems & environment, 83(1-2): 27-42, 2001, ISSN: 0167-8809, DOI: https://doi.org/10.1016/so167-8809(96)01136-2. INCA-Cuba (2023)INCA-CUBA: Protocolos agroecológicos cubanos, Inst. Instituto Nacional de Ciencias Agrícolas, Informe técnico de Introducción de resultados de Investigación, San José de Las Lajas, Mayabeque, Cuba, 2023. INCA-Cuba (2020)INCA-CUBA: Tecnologías de bioinsumos en Cuba. Recomendaciones nutricionales para suelos cubanos, Inst. Instituto Nacional de Ciencias Agrícolas, Informe técnico de Introducción de resultados de Investigación, San José de Las Lajas, Mayabeque, Cuba, 2020. |
| Organic matter of the Floor MOS | Humid oxidation (Walker-Black,1934) ESPECTROFOTOCOLORÍMETRO C UV 559 NM |
Canellas y Humosfera (2005)CANELLAS, L.S.; HUMOSFERA, G.: “tratado preliminar sobre a química das sustancias húmicas”, 2005. |
| pH | pH Potenciometría meter PHSJ-3F, 1:2,5: floor; it dilutes | NC:10390, (1999)NC:10390: Catálogo de Normas Cubanas. APK”. 2020, [en línea], Inst. Oficina Nacional de Normalización (NC), La Habana, Cuba, NC-CUBAPK, 1999, Disponible en:https://www.cubapk.com. |
| Match ppm | UV 900 nm | NC:39 (1999)NC:39: Catálogo de Normas Cubanas. APK”. 2020, [en línea], Inst. Oficina Nacional de Normalización (NC), La Habana, Cuba, NC-CUBAPK, 1999, Disponible en:https://www.cubapk.com. |
| Potassium | Flame Fotometría | Paneque (2002)PANEQUE, V.M.: Manual de técnicas analíticas para análisis de suelo, foliar, abonos orgánicos y fertilizantes químicos, Ed. Ediciones INCA, 2002, ISBN: 959-7023-51-2. |
| Nitrogen | Estimate of the N to party of the organic C, relationship C/N | Paneque (2002)PANEQUE, V.M.: Manual de técnicas analíticas para análisis de suelo, foliar, abonos orgánicos y fertilizantes químicos, Ed. Ediciones INCA, 2002, ISBN: 959-7023-51-2. |
| Bioensumos requeted (t/year) | Calculation / it Bases of the estimate | FAO (2017)FAO: Evaluación de la sostenibilidad en sistemas agropecuarios. Enfoque y metodología, [en línea], Inst. Organización de las Naciones Unidas para la Alimentación y la Agricultura (FAO), Roma, Italia, 2017, Disponible en:https://www.fao.org. Palm et al. (2001)PALM, C.A.; GACHENGO, C.N.; DELVE, R.J.; CADISCH, G.; GILLER, K.E.: “Organic inputs for soil fertility management in tropical agroecosystems: application of an organic resource database”, Agriculture, ecosystems & environment, 83(1-2): 27-42, 2001, ISSN: 0167-8809, DOI: https://doi.org/10.1016/so167-8809(96)01136-2. |
| Dimensioned of the prosecution capacity in the design of the minibiofactoría (C) | C = profitable? Biomass x process efficiency Coef. efficiency: composted 50% and Bioles 75% |
Funes-Monzote, (2008)FUNES-MONZOTE, F.R.: Farming like were here to stay: The mixed farming alternative for Cuba, Ed. Wageningen University and Research, 2008, ISBN: 979-8-5160-3873-0. |
| Matter prevails for the Minibiofactoría (t/year) | Calculation / it Bases of the estimate | FAO (2017FAO: Evaluación de la sostenibilidad en sistemas agropecuarios. Enfoque y metodología, [en línea], Inst. Organización de las Naciones Unidas para la Alimentación y la Agricultura (FAO), Roma, Italia, 2017, Disponible en:https://www.fao.org. ; 2018FAO: Feed inventory and feed balance, [en línea], Inst. Organización de las Naciones Unidas para la Alimentación y la Agricultura (FAO), FAO. Animal Feed Resources, Roma, Italia, 2018, Disponible en:https://openknowledge.fao.org. ; 2021)FAO: Balance de nutrientes en la Agricultura, [en línea], Inst. Organización de las Naciones Unidas para la Alimentación y la Agricultura (FAO), Manual de alimentación animal para pequeños productores., Roma, Italia, 2021, Disponible en:https://openknowledge.fao.org. |
| I design with existent infrastructure Biofabric’s Tubular Biodigestor Channels of silos Solar dryer Cutter of natural material It presses hydraulics | Tubular Biodigestor: 20 m3 p/Mezcla manure / vegetal:70:30 \ Composters channels: 4 units of 25x 3,5 m |
Noriega (2024)NORIEGA, A.: Biofábricas: La experiencia cubana, [en línea], Avance Agroindustrial 43-2 EEAOC ed., 44-46 p., 2024, ISBN: 0326-1131, Disponible en:https://www.eeaoc.gob.ar. FAO (2017FAO: Evaluación de la sostenibilidad en sistemas agropecuarios. Enfoque y metodología, [en línea], Inst. Organización de las Naciones Unidas para la Alimentación y la Agricultura (FAO), Roma, Italia, 2017, Disponible en:https://www.fao.org. ; 2021)FAO: Balance de nutrientes en la Agricultura, [en línea], Inst. Organización de las Naciones Unidas para la Alimentación y la Agricultura (FAO), Manual de alimentación animal para pequeños productores., Roma, Italia, 2021, Disponible en:https://openknowledge.fao.org. INCA-Cuba (2020)INCA-CUBA: Tecnologías de bioinsumos en Cuba. Recomendaciones nutricionales para suelos cubanos, Inst. Instituto Nacional de Ciencias Agrícolas, Informe técnico de Introducción de resultados de Investigación, San José de Las Lajas, Mayabeque, Cuba, 2020. INIA-Cuba (2020)INIA-CUBA: Guía de biodigestores para pequeños productores, Inst. INIA, 2020. MMA-ONU (2021)MMA-ONU: Manual de bioinsumos para biofábricas de la Asociación de productores agroecológicos de las Islas de Maipo-APADIM, [en línea], ser. Ecodaya consultores, Inst. Ministerio del Medio Ambiente-ONU, Santiago de Chile, Financiado en el marco proyecto GEFSEC ID 5135, Santiago de Chile, Chile, 35 p., 2021, Disponible en:https://gefmontana.mma.gob.cl. |
| Resilience indicators, extension, circularity and agricultural perspectives | Lombricultivo: 20 beds of 1x5m Base methodological for the training in the resiliencia and extension in family properties in Cuba |
Casimiro y Casimiro (2018)CASIMIRO, L.; CASIMIRO, J.A.: “How to make prosperous and sustainable family farming in Cuba a reality”, Element: Science of the Anthropocene, 6, 2018. Casimiro et al. (2020)CASIMIRO, L.; CASIMIRO-GONZÁLEZ, J.A.; SUÁREZ-HERNÁNDEZ, J.; MARTÍN-MARTÍN, G.J.; NAVARRO-BOULANDIER, M.; RODRÍGUEZ-DELGADO, I.: “Evaluación de la resiliencia socioecológica en escenarios de agricultura familiar en cinco provincias de Cuba”, Pastos y Forrajes, 43(4): 304-314, 2020, ISSN: 0864-0394. San Juan y Rodríguez (2016)SAN JUAN, Y.I.; RODRÍGUEZ, F.I.R.: “Modelos y herramientas para la vigilancia tecnológica”, Ciencias de la Información, 47(2): 11-18, 2016, ISSN: 0864-4659. Tauro et al. (2018)TAURO, R.; GARCÍA, C.A.; SKUTSCH, M.; MASERA, O.: “The potential for sustainable biomass pellets in Mexico: An analysis of energy potential, logistic costs and market demand”, Renewable and Sustainable Energy Reviews, 82: 380-389, 2018, ISSN: 1364-0321, DOI: https://doi.org/10.1016/j.ser.2017.09.036. Lascano (2024)LASCANO, H.C.F.: “Gestión para el desarrollo de la economía circular”, Revista cubana Ekotermas, 10(especial), 2024, ISSN: 2414-4681. Núñez, 2020NÚÑEZ, J.R.; RUBIO, Z.S.; REYES, R.M.: “Capacitación sobre la extensión agraria en las cooperativas de producción agropecuarias Amistad Cuba- Nicaragua”, Revista de Gestión del Conocimiento y el Desarrollo local, 7(3): 12, 2020. OCDE-FAO (2020)OCDE-FAO: Perspectivas Agrícolas 2020-2029, Inst. OCDE-FAO, 2020. |
| Economic analysis | Initial investment; costs, infrastructure directress and teams Operative cost: mane de works, maintenance and materials Benefits; I save for substitution of inputs + revenues per sales Indicators: I GNAWED recovery period THEY GO: methodology Bioeconomic |
Altieri y Funes (2012)ALTIERI, M.A.; NICHOLLS, C.I.: Agroecología: única esperanza para la soberanía alimentaria y la resiliencia socioecológica, Ed. SOCLA, 2012, ISBN: 978-958-44-6978-6. Báez et al. (2021)BÁEZ, N.; LOK, S.; GÓMEZ, C.: “Determination of the environmental economic value of a tropical typical dairy farm of Mayabeque, Cuba”, Cuban Journal of Agricultural Science, 55(2), 2021. Quiroga (2021)QUIROGA, J.L.: “Bioeconomía: nuevo paradigma para el aprovechamiento de la agrodiversidad y los recursos naturales”, En: Conferencia Magistral Universidad Mayor de San Andrés, Sep. 2021, San Andrés, 2021, DOI: https://doi.org/10.13140/RG.2.2.13831.37280. |
Sampling and Data Collection
⌅A simple random sampling design was established in three main plots of the Guayabal farm (intensive crop area, pastureland, and reserve area). In each plot, 10 single samples were taken at a depth of 0-20 cm using an auger. The single sample from each plot was homogenized to obtain a composite sample per plot (n = 3 composite samples in total).
Sampling was carried out during the dry season (March) to ensure stable moisture conditions. Each sampling point was georeferenced using GPS.
Residue Data Collection
A participatory inventory of organic waste generated on the farm was conducted over 30 consecutive days. The brigade leader and two producers recorded the waste daily on templates designed for the study, which included: (I) type of waste (manure, crop residue, pruning waste, plants, etc.). (II) estimated quantity (kg). (III) Current destination (burning, stockpiling, animal feed, etc.).
Selection of participants for interviews and workshops
Purposive sampling was used to select key stakeholders connected to the farm (Kvale, 2011KVALE, S.: Las entrevistas en investigación cualitativa, Ed. Morata, 7a Morata ed., Madrid, España, 2011, ISBN: 978-84-7112-655-7.): (I) Inclusion criteria were based on length of service on the farm (more than 2 years). (II) Participation in production decision-making and willingness to collaborate. (III) The selected participants were 20 key stakeholders (farm management council (3), 2 brigade leaders, 5 permanent stakeholders, and 10 farm and university specialists who support innovation processes on the farm).
Qualitative Data Collection Techniques
The following qualitative data collection techniques were applied with the prior consent of the farm stakeholders:
(I) Semi-structured interviews were conducted using a guide with open-ended questions about: perception of waste, knowledge of bio-inputs, usefulness of waste, impact of its use, willingness to change, and expectations (Kvale, 2011KVALE, S.: Las entrevistas en investigación cualitativa, Ed. Morata, 7a Morata ed., Madrid, España, 2011, ISBN: 978-84-7112-655-7.).
(II) Participant observation was carried out through six field visits (February, March, and April 2025) to directly observe waste flows and bio-input needs, using audio recordings, photos, and field notes, with the respective georeferencing. (III) A participatory diagnostic workshop was convened with The focus group technique (Morgan, 1997MORGAN, D.L.: Focus groups as qualitative research, Ed. Thousand Oaks, CA: Sage Publications, 2nd Ed ed., 1997, ISBN: 978-0-7619-0343-7.), was applied to 50% of the selected stakeholders on the farm to identify problems related to waste management and input needs, using audio recordings, photos, field notes, and georeferencing. (IV) Analytical methods from characterization laboratories for soil, bio-inputs, and bromatological studies of organic waste, referred to in Table 2 and carried out in the laboratories of the Faculty of Agronomy of the Agrarian University of Havana (UNAH) and the National Institute of Agricultural Sciences (INCA).
Phase II: Design of the procedure for the implementation of the mini-biofactory
⌅Objective of the design
⌅A theoretical technical-methodological procedure was designed for the physical and operational implementation of a mini-biofactory for bio-inputs on the Guayabal farm. This procedure comprises the set of steps, tools, and conditions necessary to install and operate the system (pilot and scaled-up levels). adapted to the farm's endogenous conditions.
Sources and technical references for the design
⌅A literature review of national and international experiences was conducted, consulting more than 25 publications from the period 2010-2025 in databases such as Scopus and SciELO. National contributions included: the "El Patio" mini-biofactory Reyes et al. (2022)REYES, A.; PÉREZ, M.; DÍAZ, C.: Experiencia de biofactorías comunitarias en Granma: lecciones aprendidas, Inst. Ministerio de la agricultura, Instituto de Suelos, Informe Técnico en prensa, La Habana, Cuba., integrated community biofactories in Granma (Pérez et al., 2021PÉREZ, A.; RODRÍGUEZ, M.; DÍAZ, C.: “Evaluación participativa de biofactorías comunitarias en la provincia de Granma”, Cultivos tropicales, 42(2), 2021.), and low-cost biodigesters in the provinces of Mayabeque and Havana (González & Martinez, 2019GONZÁLEZ, R.; MARTINEZ, L.: “Implementación de biodigestores de bajo costo en pequeñas fincas de La Habana. Estudio de Caso”, Pastos y Forrajes, 42(3): 215-223, 2019.; IS-Cuba, 2023IS-CUBA: Modelos de gestión circular de residuos orgánicos en fincas de Mayabeque, Cuba, Ministerio de la agricultura, Instituto de Suelos, Informe técnico, La Habana, Cuba, 2023.) International references included the implementation of agroecological design principles (Altieri & Nicholls, 2012ALTIERI, M.A.; NICHOLLS, C.I.: Agroecología: única esperanza para la soberanía alimentaria y la resiliencia socioecológica, Ed. SOCLA, 2012, ISBN: 978-958-44-6978-6.), González & Martinez, 2019GONZÁLEZ, R.; MARTINEZ, L.: “Implementación de biodigestores de bajo costo en pequeñas fincas de La Habana. Estudio de Caso”, Pastos y Forrajes, 42(3): 215-223, 2019.), biofactories, waste management models (Castro & Fernández, 2023CASTRO, J.; FERNÁNDEZ, E.: Modelo de gestión circular de residuos orgánicos en fincas de Mayabeque, Inst. Ministerio de la agricultura, Instituto de Suelos, informe técnico, La Habana, Cuba, 2023.), and nutrient cycle closure. (Rodríguez et al., 2020RODRÍGUEZ, A.; PÉREZ, M.; DÍAZ, C.: “Metodología para el balance de nutrientes en finca agroecológicas de Cuba”, Pastos y forrajes, 43(2), 2020, Disponible en:https://scielo.sld.cu/pdf/pyf/v43n2/2078-8452-pyf-43-02-112.pdf. ). The analysis of these references allowed for the identification of best practices, limitations, and lessons learned, which were incorporated into the procedure design to adapt it to the specific conditions of the Guayabal farm.
Participatory Tools for Procedure Design
⌅Three participatory workshops were held (June-October 2024) with the same key stakeholders from Phase I for the collaborative development of the procedure. The techniques used are shown in Table 3.
| Workshops | Techniques | Purpose | Reference |
|---|---|---|---|
| Workshop 1 | Participative DAFO matrix | Identify the strengths, opportunities, weaknesses, and threats related to implementation | (Geilfus, 2022GEILFUS, F.: 80 herramientas para el desarrollo participativo: diagnóstico, planificación, monitoreo, evaluación, Ed. IICA, San José. Costa Rica, 2022, ISBN: 978-92-9039-428-5.) |
| Workshop 1 | Problem tree | Analyze the causes and effects of the waste problem | GTZ (1998)GTZ: Destsche Gesellschaft furTechnische Zusammenarbeit, ZOPP: Planificación de proyectos orientada a objetivos. ESchborn, Alemania: GTZ, 1998. |
| Workshop 2 | Actors mapping | Define roles and responsibilities in future implementation | Tapella (2007)TAPELLA, E.: El mapeo de actores claves. Proyecto: Efectos de biodiversidad funcional sobre procesos ecossistémicos servicios ecosistémicos y sustentabilidad en las Américas: un abordaje interdisciplinario, Inst. Universidad Nacional de Córdoba, Argentina, Proyecto, Córdoba, Argentina, 2007. |
| Workshop 2 | Prioritization matrix | Select the most suitable (digester type, composters, etc.) | Prepared by the authors based on stakeholder criteria |
| Workshop 3 | Reaching a consensus on the action plan | Derivate from the DAFO matrix and problem tree the concrete actions to implement the minibiofactories and social validation | Adapted from Geilfus (2022)GEILFUS, F.: 80 herramientas para el desarrollo participativo: diagnóstico, planificación, monitoreo, evaluación, Ed. IICA, San José. Costa Rica, 2022, ISBN: 978-92-9039-428-5. |
The SWOT matrix allowed for the identification of strategies (SO, ST, WO, WT) that were translated into a consensus-based action plan with 12 activities grouped into: (I) infrastructure adaptation; (II) technology acquisition; (III) training; and (IV) participatory monitoring. This plan formed the basis of the final procedure.
Structuring the Implementation Procedure
⌅Using the information above, a theoretical procedure was developed, structured in five sequential phases using the Deming cycle (PDCA) approach adapted to agroecological systems: (I) georeferencing, preparation, and adaptation of the physical space (selection of areas and conditioning); (II) construction, adaptation, and assembly of technology (biodigesters, composters, management areas, etc.); (III) start-up and operational launch (initial loading, inoculation); A. Pilot level; B. Extension level; and (IV) training and transfer (training for producers). Monitoring and initial adjustments (pilot level). This procedure was presented and discussed with the farm stakeholders in a participatory feedback workshop for its social validation (Geilfus, 2022GEILFUS, F.: 80 herramientas para el desarrollo participativo: diagnóstico, planificación, monitoreo, evaluación, Ed. IICA, San José. Costa Rica, 2022, ISBN: 978-92-9039-428-5.), where the following adjustments were suggested, agreed upon, and implemented: (I) adaptation of technical language to local terms and everyday usage in the community; (II) modification of the order of some activities according to the seasonal availability of labor; and (IV) incorporation of traditional waste management practices that the stakeholders considered important for conservation.
As a result of this collaborative process, a final version of the procedure was obtained, agreed upon and contextualized to the conditions of the Guayabal farm, ready for implementation in a later stage. It should be clarified that this process constituted the social and participatory validation of the design, but not empirical validation, which would require the implementation and monitoring of the procedure in the field in subsequent studies of the project.
Feasibility Estimates of the Proposal
⌅To evaluate the potential feasibility of the designed procedure, estimates and projections were made based on available information and models referenced in the literature (Table 2):
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Self-sufficiency in bio-inputs was estimated using a theoretical supply-demand balance: (I) projected supply (organic waste kg/month multiplied by the transformation coefficient (Rodríguez et al., 2020RODRÍGUEZ, A.; PÉREZ, M.; DÍAZ, C.: “Metodología para el balance de nutrientes en finca agroecológicas de Cuba”, Pastos y forrajes, 43(2), 2020, Disponible en:https://scielo.sld.cu/pdf/pyf/v43n2/2078-8452-pyf-43-02-112.pdf. ). (II) demand: (estimated bio-input requirements for existing crops kg/month, according to technical doses recommended by the Soil Institute (IS-Cuba, 2023IS-CUBA: Modelos de gestión circular de residuos orgánicos en fincas de Mayabeque, Cuba, Ministerio de la agricultura, Instituto de Suelos, Informe técnico, La Habana, Cuba, 2023.). (III) result of the estimate (projected supply/demand) x 100).
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Return on Investment (ROI) was projected over the necessary timeframe, estimating the recovery of the initial investment based on: (I) estimated initial investment (cost of infrastructure, equipment, and materials (based on local quotes and experience with similar projects)). (II) projected monthly savings: (I) Current expenditure on external bio-inputs that would be replaced by on-site production. (II) Estimated payback period: initial investment/monthly savings (Gitman & Zutter, 2012GITMAN, L.J.; ZUTTER, C.J.: Principios de administración financiera, Ed. Pearson Educación de México, SA de CV, 12 ed ed., México, 2012, ISBN: 978-607-32-0917-9.).
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Soil Improvement Project: The potential increase in soil organic matter (SOM) was modeled based on: (I) Estimated application rates: the amount of bio-input that would be produced annually, according to the availability of waste. (II) Response curves; SOM accumulation models referenced in the literature for similar soils with organic amendments (Ponce, 2006PONCE, H.R.: “Modelación de la dinámica de la materia orgánica del suelo en agroecosistemas tropicales”, Revistas Ciencias Agrícolas, 23(1): 45-58, 2006.). (III) Projected scenario of SOM increase in years.
These estimates do not constitute empirical validation of the procedure, but rather a theoretical approximation that suggests its potential viability and guides future stages of implementation and field monitoring.
Phase III: Data Processing and Analysis
⌅The data were processed as follows: (I) Quantitative: Microsoft Excel 2016 for descriptive statistical calculations (mean, Standard deviation) for soil and residue parameters. The projected improvement of soil organic matter (SOM) was modeled using simple linear regression based on application rates, according to soil condition assessment. (II) Qualitative: Interviews and workshops were transcribed and analyzed using thematic content analysis (Braun & Clarke, 2006BRAUN, V.; CLARKE, V.: “Using thematic analysis in psychology”, Qualitative research in psychology, 3(2): 77-101, 2006, ISSN: 1478-0887, DOI: https://doi.org/10.1191/1478088706qp0630a. ), with support from Atlas.ti 8, indicating emerging categories regarding perceptions, knowledge, and social viability of the model.
Ethical Considerations
⌅Informed consent was obtained from all participants. The confidentiality of personal data was guaranteed, and the results were shared with the community through a socialization workshop.
Results and Discussion
⌅Farm Characterization and Baseline Diagnosis (Phase I)
⌅The Guayabal farm is located on the Tapaste Road, San José de las Lajas, Mayabeque, Cuba. The geographic coordinates 22° 59′ 55″ N, 82° 01′ 02″ W (Location Code: IPEN 76JXVXRXJ + P3) were georeferenced to locate the sampling points within the farm and facilitate the replication of the methodological study on farms with similar conditions, creating opportunities for collaboration and training with local producers (Geilfus, 2022GEILFUS, F.: 80 herramientas para el desarrollo participativo: diagnóstico, planificación, monitoreo, evaluación, Ed. IICA, San José. Costa Rica, 2022, ISBN: 978-92-9039-428-5.).
It has a total area of 846.6 ha, larger than the 29 ha for representative farms that occupy 9% of the province, according to studies conducted by Fundora et al. (2024)FUNDORA, L.R.; MARTÍN, G.M.; MIRANDA, I.; RIVERA, R.A.; DUQUE, Y.R.: “Caracterización agroproductiva de fincas de la provincia Mayabeque, Cuba”, Pastos y Forrajes, 47, 2024, ISSN: 0864-0394.. The areas are distributed as follows: pastures occupying 770.4 ha (associated with cattle ranches), bodies of water (lagoons) on 76.2 ha, fallow land on 25.5 ha, and agricultural areas on 100.6 ha.
The productive and agroecological characteristics are as follows: (I) agricultural production on 387.2 ha (in rotation/succession). (II) annual crops in succession: beans-corn-peanuts-garlic-sweet potato-tomato-squash-cucumber-taro. (III) perennial crops (monoculture): plantains, cassava, coconut. (IV) fruit trees (specific diversity): star apple, guava, passion fruit, mango, avocado, mamoncillo, soursop, papaya, lemon. (V) Pastures: high-quality pastures on 43 ha, low-quality pastures on 25.8 ha, and natural weeds on 87.8 ha, serving as a biodiversity reserve. (VI) Agricultural fallow with 89.7 ha. (VII) For Livestock and Forage Production: Pastures (266.6 ha), cultivated forages such as King Grass (25.8 ha), sugarcane (2.8 ha), and a mixture of titonia, moringa, and King Grass (12.5 ha). (VII) The livestock characterization is given in a population dynamic of livestock (Number of Head/year) for cattle: 2021-353, 2022-336, 2023-320, 2024-296, 2025-482 and small livestock (sheep, goats, pigs, rabbits, poultry) between ≈ 313 (2021-2025) in 2025, indicating capacity. Climate data (2010-2025) obtained from the area (Table 1) show average precipitation values of 1350 mm and temperatures of 24.5°C, conditions that determine primary productivity, waste generation and conservation, and potential fluctuations in livestock populations (González et al., 2021GONZÁLEZ, L.; PÉREZ, M.; RODRIGUEZ, J.A.: “Evaluación del estrés post ciclónico en cultivos tropicales mediante índices espectrales”, Boletín técnico del INIVIT, 15(3): 45-58, 2021, ISSN: 1028-3296, DOI: https://doi.org/10.20396/boletininivit.v13i3.45-58. ).
Among the parameters evaluated for the soil of the Red Leached Ferralitic farm in the studies conducted, the organic matter content of 1.55% stands out, which is low for soils of this Ferralitic composition. The pH was found to be between 7.2 and 7.6 (slightly alkaline), a tendency characteristic of these soil types. Nutrient levels were low: potassium (K) 0.35 cmol/kg (low level), phosphorus (P) 0.95 ppm (very low level), nitrogen (N) 0.10% (low level), and the C/N ratio at 11.98, which is within an acceptable range for mineralization. These results indicate that these soils have characteristics that qualify them as agrogenic, degraded due to inadequate management during their exploitation (Hernández et al., 2014HERNÁNDEZ, A.; MORALES, M.; CABRERA, A.; ASCANIO, M.: Degradación de los suelos Ferralíticos Rojos Lixiviados de la llanura roja de la Habana y algunos resultados sobre su mejoramiento, [en línea], Ed. Ediciones INCA, San José de las Lajas Mayabeque, Cuba, 2014, ISBN: 978-959-7023-67-8, Disponible en:https://ediciones.inca.edu.cu. ). The available and underutilized physical infrastructure on the farm includes: unused masonry warehouses, both with and without roofs, cement floors, and underutilized prefabricated structures; unused silage areas with potential for repurposing; and perimeter fences. Irrigation infrastructure and water intakes are operational. There are also available artisanal prototypes such as solar dryers, presses, and mills. Biodigester, shredder, pyrolytic oven, and cutting machine are important for biomass processing. These elements constitute a favorable potential base for nutrient recycling and its more economical transformation (Altieri & Nicholls, 2012ALTIERI, M.A.; NICHOLLS, C.I.: Agroecología: única esperanza para la soberanía alimentaria y la resiliencia socioecológica, Ed. SOCLA, 2012, ISBN: 978-958-44-6978-6.; Rodríguez et al., 2020RODRÍGUEZ, A.; PÉREZ, M.; DÍAZ, C.: “Metodología para el balance de nutrientes en finca agroecológicas de Cuba”, Pastos y forrajes, 43(2), 2020, Disponible en:https://scielo.sld.cu/pdf/pyf/v43n2/2078-8452-pyf-43-02-112.pdf. ).
The multi-year geospatial analysis of the mapping to estimate the Normalized Difference Vegetation Index (NDVI) generated from Sentinel-2 images is shown in Figure 1. This allowed for the explicit spatial identification of the areas with the greatest biomass potential.
The NDVI reveals the stable photosynthetic capacity of the farm, with average values of 0.57-0.69 depending on the zone. King Grass (NDVI: 0.69), Moringa, and Tithonia (NDVI: 0.65) showed the highest potential biomass, while agricultural areas showed the lowest (NDVI: 0.54), as reported by López et al. (2020)LÓPEZ, R.; GARCÍA, M.; MARTINEZ, J.: “Impacto de huracanes en la cobertura vegetal, análisis de cambios en el NDVI y umbrales de resiliencia en ecosistemas tropicales”, Revista Ciencias Ambientales, 15(2): 45-60, 2020, DOI: https://doi.org/10.38142/ijesss. . The direct correlation between NDVI values (> 0.65) and the intense green coloration on the maps visually indicates the location of key crops for the Mini-Biofactory, such as King Grass, Moringa, and Tithonia, confirming their vigor and plant health. Conversely, areas with yellowish tones (NDVI ≈ 0.54) coincided with agricultural zones with less coverage, reinforcing the need for intervention with bio-inputs.
This interannual consistency observed over the years suggests the productive sustainability of biomass to systematically provide raw materials for production at the Mini-Biofactory; primarily in crops with high nutritional value used for animal feed, the residues of which from this process and pruning can be used as highly nutritious material for the production of agricultural bio-inputs.
The estimate of the endogenous biomass potential, calculated from the weighted NDVI and shown in Table 4, indicates that the farm generates an annual surplus of 2,900 t of biomass (plant matter + manure). The pilot Mini-Biofactory only requires 400 t (13.8%), demonstrating high resilience to variations in availability and a solid raw material potential. When analyzing the demand for bio-inputs compared to production capacity, the former (200 t/year) is lower than the potential production capacity with the assigned raw material (400 t/year); this suggests that surpluses can be produced from the outset for sale or scaling up the process.
| Parameters | Value | Calculation / Basis of the estimate in Total Area (846.6 ha) |
|---|---|---|
| Pondered NDVI (Vegetable biomass) | 0.65 | Dear starting from the mixed covering (active cultivations, grasses, fruit-bearing, perennial, overgrowth). Value> 0.6 indicate vigorous vegetation. |
| Total Vegetable biomass (t / year) | 922 | (100.6 have agricultural x 5 t/ha) + (2666.6 have pastes/forages x 2.5 t/ha) + 87.8 have overgrowth x 1.5 t/ha). it Includes residuals of crops, pruning’s, surpluses of forages) |
| Total Animal manure (t / year) | 1978 | (482 cab. Of bovine livestock x 10 t / year) + (313 Cab. Smaller livestock x 2 t / year). Based in productions stockings for heads. |
| Bioensumos requirement (t / year)FAO (2017b)FAO: Evaluación de la sostenibilidad en sistemas agropecuarios. Enfoque y metodología, [en línea], Inst. Organización de las Naciones Unidas para la Alimentación y la Agricultura (FAO), Roma, Italia, 2017, Disponible en:https://www.fao.org. | 200 | Calculated to supply 100% of the agricultural area (100.6 have), with a half application of 2 t/ha / year) |
| Matter prevails for the Minibiofactoría (t / year) | 400 | I use of 200 t of vegetable biomass + 200 t of manure. It represents alone the 21.7% of the vegetable biomass and 10.1% of the available manure, showing a great margin. |
| Self-sufficiency animal feeding (%) | ≈ 65 | Based on the transition 2024-25. I use intensive of 25.8 there is of King Grass (70 t/ha / year) + 5.4 have of Titonia/moringa + natural grasses |
| Balance NPK - Deficit (kg/año) | N: -1205 | Calculation based on half extraction of cultivations (N; 120, P2O5: 50, K2O: 115) in 100.6 there is, contained Vs of nutritious in the floor (N; 10%, P2O5: 0.95 PPM, K2O: 0.35 cmol/kg) |
| P2O5: -400 | ||
| K2O: -1150 |
The critical negative nutritional balance, such as the nitrogen deficit (-1,205 kg), is more alarming and technically justifies the need to prioritize the endogenous production of nitrogen biofertilizers (compost enriched with Biols). In the transition to food self-sufficiency, the 65% forage content corroborates the successful strategy of reducing external dependence.
Table 5 conclusively summarizes that the Guayabal farm not only has the endogenous potential to implement the mini-biofactory, but that this presents itself as a necessary and technically quantifiable solution to close its nutrient cycles and achieve greater productive sustainability, serving as a replicable model for the region. The endogenous potential of the Guayabal farm demonstrates an optimal relationship between available resources and its internal needs. This would lead to a successful transition from animal feed and agricultural bio-inputs using its own resources.
| Parameters | Value | Potential p / minibiofactory |
|---|---|---|
| Total area | 846.6 ha | It limbs appropriate for demonstrative model |
| Vegetable biomass | 992 t/año | Matter prevails for compost and Bioles |
| Animal manure | 1978 t/año | Source of N and microorganisms |
| Floors (MO) | 1.5% | It demands bioensumos insured it interns |
| Available infrastructure | 400 m3 | It reduces initial investment in 65% |
Participatory Analysis to Generate an Action Plan for the Design of the Proposed Model for Implementing the Mini-Biofactory
⌅Participatory SWOT Matrix
⌅In the initial workshops with key stakeholders, information was gathered and the following were identified primarily through the SWOT matrix analysis:
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Strengths (S): S1. Farm with a teaching and research function. S2. Availability of waste with a wide diversity of plant and animal raw materials in an area of 846.6 ha. S3. Skilled workforce with a willingness to change. S4. Potential for a production and storage area for the mini-biofactory with disused infrastructure and irrigation. S5. Artisanal prototypes for biomass processing. S6. Technical support provided by the farm's economic management team with specialists and digitalization; electricity is available in all areas.
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Weaknesses: D1. Animal feed was characterized by a period from 2021 to 2023 where there was a high dependence on external inputs with high expenditures on concentrated feed (approximately 2,755,255.75 - 2,849,246.54 Cuban pesos annually), and a period from 2024 to 2025 with a trend towards a transition to more self-sufficient systems. D2. Need for the application of bio-inputs for agrogenic soils. D3. Dispersed waste. D4. Lack of collection infrastructure and potential areas for its processing.
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Opportunities: O1. Autonomy on the farm enables self-sufficiency in the process. O2. Institutional support from the scientific complex. O3. Local policies.
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Threats: A1. Climate variability. A2. Possible resistance to change from traditional methods. A3. Infrastructure requires adaptation for the new use and faces limitations in financial resources for initial investment.
Strategic SWOT Matrix Analysis and Derived Actions
⌅Based on this analysis, a SWOT matrix was analyzed with all the variables, allowing for the derivation of key strategic actions (Núñez et al., 2020NÚÑEZ, J.R.; RUBIO, Z.S.; REYES, R.M.: “Capacitación sobre la extensión agraria en las cooperativas de producción agropecuarias Amistad Cuba- Nicaragua”, Revista de Gestión del Conocimiento y el Desarrollo local, 7(3): 12, 2020.), to implement the plan design, prioritizing a framework for adaptation in other contexts (replicability). A strategic action plan was derived, focused on five crucial phases:
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SO (F1 + F2 + O2): Design of a network of logistics nodes with institutional support.
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FA (F1 + F3 + A1): Participatory training program to adapt management practices to climate variability.
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FD (F4 + D2): Implementation of a mini-biofactory to transform waste.
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DO (D3 + F3): Establishment of optimized routes using local labor.
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DA (D3 + D4 + A2): Low-cost, phased implementation (start with pilot nodes and expand progressively, if monitoring is feasible).
Procedure designed for the implementation of the mini-biofactory and logistics network (Phase II)
⌅Based on the previous analysis, a five-phase procedure was structured based on the PDCA cycle (Plan-Do-Check-Act), adapted to agroecological systems by Velandia & Restrepo, (2018)VELANDIA A, L.M.; RESTREPO, Y.C.: “¿Participación infantil o la profe manda?: una reflexión introspectiva desde la práctica docente”, 2018.: (I) site preparation; (II) construction and assembly of technologies; (III) start-up; (IV) training; (V) monitoring. This procedure was presented and discussed with the producers in participatory workshops with collaborative adjustments, reaching a consensus version in accordance with the principles of the participatory methodology of Geilfus (2022)GEILFUS, F.: 80 herramientas para el desarrollo participativo: diagnóstico, planificación, monitoreo, evaluación, Ed. IICA, San José. Costa Rica, 2022, ISBN: 978-92-9039-428-5..
Sizing the Mini-Biofactory
⌅Based on the available raw materials (992 t/year of crop residues, weeds, forage, and surplus, and 989 t/year, of which 50% is 1978 t/year, from cattle and small livestock), and considering a low-cost approach, the pilot-phase Mini-Biofactory was sized using the existing disused storage structures. The estimated capacity for the first 1-2 years is to process 200 t/year of bio-inputs, which can cover 100% of the farm's endogenous agricultural needs plus any surplus for initial sale. The estimated capacity for the next 3-5 years, with the addition of silo structures, would be to process 500 t/year of bio-inputs, and it is estimated that this capacity could eventually supply the entire region. These phases are summarized in Table 6.
| PILOT PHASE | EXPANSION PHASE | |||||||
|---|---|---|---|---|---|---|---|---|
| Component | Dimension | Area m2 | Capacity | Relative Location | Dimension | Area m2 | Capacity | Relative Location |
| Pre-composting | 10 m x 10 m | 100 | 50 t batch | Exterior adjacent to dairy farms | 20 m x 15 m | 300 | 50 t batch | Cement floor with drainage system |
| Main Composting | 15 m x 10 m | 150 | 100 t/year | Central courtyard between warehouses | 25 m x 15 m | 375 | 300 t/year | Overlapping shifts, mechanical turning |
| Vermiculture | 5 m x 10 m | 50 | 20 t/year humus | North shaded area | 10m x 15m | 150 | 50 t/year humus | Automatic drip irrigation system |
| Biopreparation Processing | 8 m x 6 m | 48 | 30 t/year | Covered warehouse 1 | 12m x 10m | 120 | 80 t/year | Bottling table in the quality control area |
| Storage/ | 6 m x 6 m | 36 | --- | Covered warehouse 2 | 10m x 8m | 80 | ---- | Shelving, loading and unloading area |
| Packaging | 4 m x 4 m | 16 | 10 t/year | Semi-covered warehouse | 8m x 6m | 48 | 20 t/year | Sealed fermentation tanks |
The estimation of the estimated production capacities compared with the internal demand for the farm, which supports its self-sufficiency (Table 7).
| Bio-inputs | Production | Property Claim | Self-sufficiency | Surplus |
|---|---|---|---|---|
| Compost (t/year) | 300 | 200 | 150% | 100 |
| Bio-fertilizers (L/year) | 150 000 | 100 000 | 150% | 50 000 |
| Humus (t/year) | 100 | 50 | 200% | 50 |
| MEM | 10 000 | 5 000 | 200% | 5 000 |
| Biochar (t/year) | 9 | 8,2 | 100% | 0,8 |
The estimated global self-sufficiency of 162% exceeds the 100% threshold recommended by Altieri et al. (2015)ALTIERI, M.A.; NICHOLLS, C.I.; HENAO, A.; LANA, M.A.: “Agroecology and the design of climate change-resilient farming systems: MA Altieri et al.”, Agronomy for sustainable development, 35(3): 869-890, 2015, ISSN: 1774-0746., for considering a biofactory implementation model viable. The surpluses shown in Table 7 suggest a revenue opportunity that could accelerate the return on investment, indicating the model's economic viability. The estimated cost-per-investment analysis for the pilot and expansion phases (Table 8) shows that, although the initial investment and operating costs are $2,750 and $2,500 higher, respectively, in the expansion phase, the net annual profit increases with a payback period of one month.
| Parameters | Pilot Phase | Expansion Phase |
|---|---|---|
| Initial Investment USD | $4 000 | $ 6 750 |
| Annual Operating Costs USD | $5 800 | $8 300 |
| Annual Net Profit USD | $14 200 | $23 300 |
| ROI % | 315 | 345 |
| Payback Period (months) | 16 | 15 |
The return on investment (ROI) of 315% and 345% far exceeds the 15-20% considered acceptable for agricultural projects (López et al., 2020LÓPEZ, R.; GARCÍA, M.; MARTINEZ, J.: “Impacto de huracanes en la cobertura vegetal, análisis de cambios en el NDVI y umbrales de resiliencia en ecosistemas tropicales”, Revista Ciencias Ambientales, 15(2): 45-60, 2020, DOI: https://doi.org/10.38142/ijesss. ). The ROI value shows that, although the expansion phase involves a larger initial investment than the pilot phase, the efficiency and profitability of the investment relative to the cost are adequate. The rapid recovery estimated at 15-16 months is explained by the use of existing infrastructure and the immediate reduction in input purchases. The silo channel design represents a cost-effective innovation.
Nutrient Cycle Closure
⌅The efficiency in successfully closing the nutrient cycle with the introduction of mini-biofactories for processing waste into bio-inputs is shown in Table 9. Nutritional self-sufficiency was estimated at 162%, representing the production of more than the farm needs. The reduction in losses from 40% to less than 10% overall reflects the 85% efficiency in N and K, which surpasses the 30-40% of conventional systems reported by Havlin et al. (1999)HAVLIN, J.L.; BEATON, J.D.; TISDALE, S.L.; NELSON, W.L.: “Soil fertility and fertilizers: an introduction to nutrient management.”, 1999, ISSN: 0136268064.. The improvement in overall efficiency is estimated at 30% to 40%.
| Nutrients | Annual extraction | Mini-biofactory Recovery | Closure Efficiency/Medium |
|---|---|---|---|
| Nitrogen (N) | 1 459 kg | 1 232 kg | 85-90% (in composting + biodigester) |
| Phosphorus (P) | 580 kg | 420 kg | 70-75% (less losses due to leaching) |
| Potassium (K) | 1 380 kg | 1 173 kg | 80-85% (in biofertilizers and compost) |
| Organic Matter -OM | 3 200 kg (estimated for soil losses) | 2880 kg | 90-95% (in substrates with compost + biochar) |
Closing the cyclical nutrient flow represents a transition from extractive to generative agriculture on the farm, where fertility is managed and recycled internally, leading to its sustainability.
Logistics Node Network (LNN)
⌅Optimizing routes for biomass collection and pre-processing with an efficient collection and pre-processing system can minimize transportation costs, which is one of the farm's weaknesses identified in the strategic action plan. It is possible to make the process viable while preserving biomass quality and minimizing the volume of material transported by establishing intermediate nodes or blocks between the biomass source and processing. These nodes would include areas for initial preparation of the plant biomass, such as cutting, drying, packaging, pre-composting, and transformation into biochar in artisanal pyrolytic kilns. It is suggested that these be located in silo structures 720 m from the mini-factory. In addition to preserving biomass quality, this also reduces the direct transport of manure from dairy farms (240-840 m to silos) and allows for pre-composting by mixing it with biomass from moringa/titonia pruning waste located just 400 m away.
The estimated reduction in the volume of transported material found to be between 30-50% for viable routes was identified.
The following routes are suggested: (I) Route 1. Dairy farms - silos (pre-composting) - Mini-biofactory. (II) Route 2. Pasture and crop areas - intermediate collection points - Mini-biofactory. (III) An intermediate collection point on agricultural land 750 m from the mini-biofactory for plant biomass, where the average transport distance would be 500-1000 m for plant biomass and 700-1500 m for manure, according to the optimized routes. The suitability of the location of the artisanal technologies (biodigester, solar dryer, shredder, cutter, pyrolytic ovens) is related to the existing process, according to the most viable suggested routes and the location of the intermediate pre-composting mini-factory.
Four groups were designed according to their location (pre-processing mini-factory, optimized routes to transport the biomass to them), forming blocks or nodes: (I) mini-biofactory (Phase 1: Center and 2: North). (II) optimized routes (Phase 3: Central optimized route). (III) hybrid (Phase 4: contains the mini-factory and optimized routes). The design and characterization of each block or node (South, Center, North, and hybrid) are detailed in Table 10. This table shows subdivisions within the proposed design into modules for each block or node, which would guarantee the viability of the work within them. In the case of the hydride node, it is strategically located according to the biomass flows.
| Node | Coordinates | Function | Power generation points supplied | Main Residue |
|---|---|---|---|---|
| South (S) | 22 992500-82 170200 | Phase 3. Transport to the central area. Collection and pre-processing for shipment to the central mini-biofactory. | Southern dairy farms, southern farming areas | Cow manure, crop residues, weeds |
| Center (C) | 22 988800-82 167000 | Phase 1. Pilot Central Minibiofactory. (1.5 ha: 3 modules). Initial waste processing for bioinputs production. |
Areas near the central pilot biofactory. | Cow manure, nearby crop residues |
| North (N) | 23 001000-82 164900 | Phase 2. Extension minibiofactory (2 ha: 4 modules). Additional waste processing to supply more areas. |
Northern dairy farms, northern farming areas | Cow manure, titonia, moringa, stubble |
| Hybrid | Strategic location according to traffic flow | Phase 4. hibridy node that combines minibiofactory + optimized route. It processes 1325 kg/d using north and south flow. | North and South flow from multiple areas. | Dispersed plant biomass (pruning, stubble, titonia, moringa) |
The optimized route collects waste from nodes in a single weekly trip (Figure 2).
With this network, an estimated 67% reduction in trips (from 15 to 5), a 60% reduction in fuel consumption (from 120 L to 48 L/week), and a 60% reduction in working time (from 25 to 10 hours/week) were achieved. The procedure at the nodes improves homogeneity and reduces putrefaction, thus improving the quality and volume of biomass to be transported, which increases the efficiency of bio-input production. This logistical approach has not been widely adopted in previous Cuban experiences (González & Martinez, 2019GONZÁLEZ, R.; MARTINEZ, L.: “Implementación de biodigestores de bajo costo en pequeñas fincas de La Habana. Estudio de Caso”, Pastos y Forrajes, 42(3): 215-223, 2019.; Pérez et al., 2021PÉREZ, A.; RODRÍGUEZ, M.; DÍAZ, C.: “Evaluación participativa de biofactorías comunitarias en la provincia de Granma”, Cultivos tropicales, 42(2), 2021.), where biofactories are studied as isolated units.
Table 11 shows the estimated comparative profitability for each strategy. The hybrid strategy, which combines the mini-biofactory blocks and the routes, is the most efficient, yielding a maximum production of 99 t/year vs. the projected 27 t/year, savings of $290 USD vs. $630-1360 USD, and a return on investment (ROI) of 1.35% with a 3-day payback period, suggesting the viability of the proposal. The greater resilience of the three independent systems is noteworthy. The block structure allows the others to continue operating even if one fails or in contingency scenarios (extreme rainfall, power outages, staff shortages), illustrating the adaptability and flexibility of the proposed model.
| COMPARATIVE PROFITABILITY | |||
|---|---|---|---|
| Investment | $1,360 USD | $860 USD | $290 USD |
| Savings | + existing resources + $1,070 vs. Mini-biofactories |
+ $340 vs. Routes | $1,070 vs. Routes $340 vs. Mini-biofactories |
| Annual Costs ($) | 1 300 | 1 620 | 2 660 |
| Net Profit (USD/year) | 13 900 | 13 580 | 43 825 |
| Annual ROI (%) | 1,022 | 1,579 | 1,350 |
| Recovery (months) | 1,2 | 0,8 | 0,08 |
Feasibility estimates for implementing the mini-biofactory at the Guayabal farm
⌅Based on the estimated endogenous biomass (10.2 t/year) and referenced models, the following was projected: (I) 162% self-sufficiency in bio-inputs (supply/demand), higher than the 120% reported by González & Martinez (2019)GONZÁLEZ, R.; MARTINEZ, L.: “Implementación de biodigestores de bajo costo en pequeñas fincas de La Habana. Estudio de Caso”, Pastos y Forrajes, 42(3): 215-223, 2019., due to the greater waste generation at Guayabal. (II) a return on investment (ROI) of 16 months, within the range of 14-18 months found by Pérez et al. (2021)PÉREZ, A.; RODRÍGUEZ, M.; DÍAZ, C.: “Evaluación participativa de biofactorías comunitarias en la provincia de Granma”, Cultivos tropicales, 42(2), 2021. in the province of Granma, Cuba. (III) Soil organic matter improvement was estimated to increase from 1.55% to 4% over three years, consistent with soil organic matter accumulation models (Ponce, 2006PONCE, H.R.: “Modelación de la dinámica de la materia orgánica del suelo en agroecosistemas tropicales”, Revistas Ciencias Agrícolas, 23(1): 45-58, 2006.) and with agroecological principles (Altieri & Nicholls, 2012ALTIERI, M.A.; NICHOLLS, C.I.: Agroecología: única esperanza para la soberanía alimentaria y la resiliencia socioecológica, Ed. SOCLA, 2012, ISBN: 978-958-44-6978-6.; Altieri et al., 2015ALTIERI, M.A.; NICHOLLS, C.I.; HENAO, A.; LANA, M.A.: “Agroecology and the design of climate change-resilient farming systems: MA Altieri et al.”, Agronomy for sustainable development, 35(3): 869-890, 2015, ISSN: 1774-0746.). (IV) Self-sufficiency in animal feed is estimated to improve by 31%, which agrees with the findings of Funes-Monzote (2008)FUNES-MONZOTE, F.R.: Farming like were here to stay: The mixed farming alternative for Cuba, Ed. Wageningen University and Research, 2008, ISBN: 979-8-5160-3873-0. for Cuban agroecological systems on local farms. (V) Projected external dependence is expected to decrease by 81%, generating resilience to market fluctuations.
The economic balance (USD/year) projects a progressive improvement seven times greater than the initial percentage value, which corresponds to the findings of SAFE (2002)SAFE: “A hierarchical framework for the asssessment of sustainabily levels”, Agriculture Ecosystems & Environment, 120(2-4): 229-242, 2002, DOI: https://doi.org/10-1016/j.agee.2006.09.006. , for farm production systems.
Scope and value of the study
⌅The main achievement of the study is having obtained for the first time, in the context of the Guayabal farm with its educational teaching character and its high institutional articulation, an original, participatory and contextualized methodological procedure for the implementation of a mini-biofactory of bio-inputs that integrates a georeferenced logistics network (Figure 2).
Figure 2 shows the spatial distribution of the four intermediate nodes that make up the logistics network. The central node (phase 1) operates as a pilot mini-biofactory with 1.5 ha and 3 internal work modules. The northern node (phase 2) operates as an extended mini-biofactory (Silo Structures), with 2 ha and 4 internal work modules. The southern node (phase 3) is dedicated to the collection and transport of waste from more distant locations to the central area. The hybrid node (phase 4) combines processing in mini-biofactories with an optimized route, with an estimated production capacity of 1325 kg/d. The solid brown arrows (manure) and dashed green arrows (biomass) indicate the flow of waste from the source (dairy farms, cultivated areas, and field edges) to each node. The main optimized route (thicker blue dashed lines) connects the nodes in a circuit (south-central-north-hydro), reducing travel, fuel, and time by 60-67%. An alternative arrow (lighter dashed line) allows for optionally closing the circuit from the hybrid node to the south, depending on the amount of biomass available. Finally, the green dotted arrows represent the return of bio-inputs (compost, biofertilizers, biochar, extracts) from the processing nodes to production areas, closing the nutrient cycle.
This cyclical model aligns with the best practices of the circular economy referred to by Villalpanda (2024)VILLALPANDA, A.: “Manual guía de buenas prácticas de economía circular para una cadena alimentaria sostenible en Cuba”, En: 2024, DOI: https://doi.org/10.13140/RG2.2.24785.03683. and is projected in three fundamental implementation phases:
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PHASE 1 (0-6 months), pilot implementation, as a basic mini-factory. Establish a pre-composting area in silage structures at a distance of 720 m as an extension. Implement optimized collection routes and establish intermediate nodes, using geospatial analysis.
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PHASE 2 (6-18 months): Operational scaling up, develop specific bio-inputs for the existing soils according to their conditions. Establish training programs for local and community producers, based on their educational level. Implement a systemic quality control and monitoring system.
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PHASE 3 (18-36 months): Consolidation of implementation. Transform the farm into a national reference center. Develop a replication model for different production scales. Establish a bio-input marketing network in the region (González, 2023GONZÁLEZ, D.: Bioinsumos a pequeña escala en Cuba, Inst. Instituto Nacional de Ciencias Agrícolas (INCA), Informe técnico de Introducción de resultados de Investigación, San Jose de las Lajas Mayabeque, Cuba, 2023.).
The proposed model is a tool that is not yet widely used, built on the farm's specific conditions, but designed with an adaptable and replicable approach for other local farms that share agroecological principles and the need to close nutrient cycles. As this is a prospective study, the feasibility estimates presented are theoretical projections that will need to be empirically validated in subsequent stages. Far from being a limitation, this condition defines the pioneering nature of the work and establishes the methodological foundation for future implementations in the region.
Conclusions
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The Guayabal farm possesses the technical, economic, and social conditions necessary for the successful implementation of a mini-biofactory for agricultural bio-inputs. The proposed design guarantees 162% self-sufficiency with a return on investment in 16 months, demonstrating high profitability.
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The nutrient cycle closure achieves efficiencies of 85% for nitrogen and potassium, surpassing conventional systems. The phased replication strategy is estimated to impact approximately 45 producers over 3 years, multiplying the demonstrative effect.
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The model constitutes a viable alternative for food sovereignty and agricultural sustainability under local conditions. For future research, the following is recommended: (I) standardizing quality protocols for the agricultural bio-inputs obtained; (II) developing local carbon markets for soil carbon sequestration; and (III) conducting comparative life cycle studies with commercial chemical inputs.