Altitude and Agroecological Conditions in Production, Profitability, and Risks of Alfalfa (Medicago Sativa) Cultivation in the Highlands of Lima, Peru
DOI:
https://doi.org/10.59072/rper.vi74.837Keywords:
Alfalfa, profitability of permanent crop, risks in agriculture, stochastic simulation, agroecological conditionsAbstract
Alfalfa (Medicago sativa) is a crucial legume as fodder for livestock, especially in high Andean regions. Despite its growing national importance, production remains limited by agroecological and climatic factors, mainly altitude. In this context, this study estimated the profitability and risks of alfalfa production in the district of San Andrés de Tupicocha, located in the highlands of Lima, Peru. To this end, surveys were conducted with producers during the year 2024, collecting information on yields, costs and risk perceptions. Profitability was analyzed using a stochastic Monte Carlo simulation of the Net Present Value (NPV), complemented by statistical tests comparing two zones with different altitudes and agroecological conditions. On average, the NPV/ha was S/ 159,555; with S/ 134,832 in the high zone (up to 4000 m.a.s.l) and S/ 188,286 in the low zone (up to 2500 m.a.s.l). The high zone showed a higher probability of economic losses, mainly due to frost and heavy rains. These findings suggest that, in order to improve productive efficiency and mitigate risks, it is necessary to implement strategies differentiated according to altitude and local agroecological characteristics. This differentiation will allow optimizing land use and available resources, favoring the sustainability of alfalfa production in the district.
References
Acharya, J. P., Lopez, Y., Gouveia, B. T., de Bem Oliveira, I., Resende Jr, M. F., Muñoz, P. R., & Rios, E. F. (2020). Breeding alfalfa (Medicago sativa L.) adapted to subtropical agroecosystems. Agronomy, 10(5), 742. https://doi.org/10.3390/agronomy10050742
Amorim, F. R. D., Guimarães, C. C., Afonso, P., & Tobias, M. S. G. (2024). Forecasting Cost Risks of Corn and Soybean Crops through Monte Carlo Simulation. Applied Sciences, 14(17), 8030. https://doi.org/10.3390/app14178030
Bashi-Pizarro, M. S. (2021). Caracterización tecnico-económica de sistemas agroforestales de café (Coffea arabica L.) en fundos cafetaleros de dos microcuencas de Pichanaki, Junín-Perú. Tesis pregrado
Bazán, R. V.; Yamada, A. G.; Coronado, S. L.; & Fuentes, N. N. (2017). Comportamiento Productivo de la Alfalfa (Medicago sativa) de la Variedad Caravelí Sometida al Pastoreo en el Valle de Huaral. Revista de investigaciones veterinarias del Peru, 28(3), 743. https://doi.org/10.15381/rivep.v28i3.13359
Ben-Horin, M., & Kroll, Y. (2017). A simple intuitive NPV IRR consistent ranking. The Quarterly Review of Economics and Finance, 66, 108–114. https://doi.org/10.1016/j.qref.2017.01.004
Blain, G., Assuero, S. G., & Berone, G. D. Altura de corte en alfalfa: producción y calidad nutritiva del forraje= Cutting height in alfalfa: production and nutritional quality of forage.
Bover-Felices, K., & Suárez-Hernández, J. (2023). Contribución del enfoque de la agroecología en el funcionamiento y estructura de los agroecosistemas integrados. Estación Experimental de Pastos y Forrajes Indio Hatuey, Universidad de Matanzas, Ministerio de Educación Superior.
Cantaro-Segura, J. L., Delgado-Palma, D., & Cayetano-Robles, J. L. (2021). Caracterización de la crianza de cuyes en una zona de la sierra de Huarochirí-Perú. Revista de Investigación e Innovación Agropecuaria y de Recursos Naturales, 8(2), 72-78. https://doi.org/10.53287/hffs7980xc24q
Celestin, M., Vasuki, M., Boakye, M. M., & Kumar, A. D. (2025). Monte Carlo simulations for assessing the impact of market uncertainty on investment portfolios. International Journal of Scientific Research and Modern Education (IJSRME), 11(2), 19-29. https://doi.org/10.5281/zenodo.14904146
Cesca, I. G., Postali, F. A. S., & Parente, V. (2024). Economic flexibilities and opportunities for sugar cane plants: A real options valuation case. Cleaner and Circular Bioeconomy, 7, 100074. https://doi.org/10.1016/j.clcb.2024.100074
Chizmar, S., Castillo, M., Pizarro, D., Vasquez, H., Bernal, W., Rivera, R., ... & Cubbage, F. (2020). A discounted cash flow and capital budgeting analysis of silvopastoral systems in the Amazonas region of Peru. Land, 9(10), 353. https://doi.org/10.3390/land9100353
Crookston, B., Boren, D., Yost, M., Sullivan, T., Creech, E., Barker, B. & Reid, C. (2025). Irrigation technology, irrigation dose, and crop genetic impacts on alfalfa yield and quality. Agricultural Water Management. 311(1), 109366. https://doi.org/10.1016/j.agwat.2025.109366
Das, T. K., Behera, B., Nath, C. P., Ghosh, S., Sen, S., Raj, R., ... & Paramanik, B. (2024). Herbicides use in crop production: An analysis of cost-benefit, non-target toxicities and environmental risks. Crop protection, 181, 106691. https://doi.org/10.1016/j.cropro.2024.106691
Dogbatse, J. A., Arthur, A., Padi, F. K., Konlan, S., Quaye, A. K., Owusu-Ansah, F., et al. (2020). Influence of acidic soils on growth and nutrient uptake of cocoa (Theobroma cacao L.) varieties. Commun. Soil Sci. Plant Anal. 51, 2280–2296. https://doi.org/10.1080/00103624.2020.1822384
dos Santos, I.G., Rocha, J.d., Vigna, B.B. (2020). Exploring the diversity of alfalfa within Brazil for tropical production. Euphytica, 216, 72 https://doi.org/10.1007/s10681-020-02606-w
Durand, P. (2011). Sembrando y cosechando agua. Proceso de adopción tecnológica y gestión del agua en la comunidad campesina de Cullpe: Una experiencia de autogestión campesina y cambio social. Revista Andina, 51(1): 9-41.
Feng, Y., Shi, Y., Zhao, M., Shen, H., Xu, L., Luo, Y., et al. (2022). Yield and quality properties of alfalfa (Medicago sativa L.) and their influencing factors in China. Eur. J. Agron. 141, 126637 https://doi.org/10.1016/j.eja.2022.126637
Frej, E. A., Ekel, P., & de Almeida, A. T. (2021). A benefit-to-cost ratio based approach for portfolio selection under multiple criteria with incomplete preference information. Information Sciences, 545, 487-498. https://doi.org/10.1016/j.ins.2020.08.119
Gómez de la Torre Barrúa, J.; & Ibañez Blancas, A. N. (2023). Encuentros y desencuentros percibidos entre comuneros y docentes en torno a la innovación en las amunas de San Andrés de Tupicocha, Huarochirí, Perú. Collectivus. Revista de Ciencias Sociales, 10(1), 81- 110. https://doi.org/10.15648/Collectivus.vol10num1.2023.3566
Gómez, R., Diez, R., Vasquez, C. & Vargas, J. (2022) Rentabilidad del cultivo de tara Caesalpinia spinosa (Molina) Kuntze en Apurímac, Perú. Anales Científicos. 83(2), 175-184. https://doi.org/10.21704/ac.v83i2.1960
Hidalgo-Gonzales, C. D. R. (2021). Análisis comparativo de la rentabilidad económica de sistemas agroforestales y cultivos tradicionales de dos comunidades nativas, en el distrito de Irazola, Ucayali. Tesis pregrado
Instituto Nacional de Estadística e Informática [INEI] & Ministerio de Desarrollo Agrario y Riego [MIDAGRI]. (2023). Encuesta Nacional Agropecuaria 2022. Instituto Nacional de Estadística e Informática. https://www.inei.gob.pe
INIA. (2024). INIA lanza primera variedad de alfalfa de secano que destaca por su alta persistencia y potencial productivo | INIA. https://www.inia.cl/2024/07/08/inia-lanza-primera-variedad-de-alfalfa-de-secano-que-destaca-por-su-alta-persistencia-y-potencial-productivo/
Inostroza, F. (2022). Alfalfa, una alternativa de alta rentabilidad para los productores de la precordillera de Ñuble. Praderas. RedAgrícola. https://hdl.handle.net/20.500.14001/68783
Javadi, A., Ghahremanzadeh, M., Sassi, M., Javanbakht, O., & Hayati, B. (2024). Impact of climate variables change on the yield of wheat and rice crops in Iran (application of stochastic model based on Monte Carlo simulation). Computational Economics, 63(3), 983-1000. https://doi.org/10.1007/s10614-023-10389-0
Jin, Y., Lee, S., Kang, T., Park, J., & Kim, Y. (2023). Capacity Optimization of Rainwater Harvesting Systems Based on a Cost–Benefit Analysis: A Financial Support Program Review and Parametric Sensitivity Analysis. Water, 15(1), 186. https://doi.org/10.3390/w15010186
Kabir, M. J., Gaydon, D. S., & Cramb, R. (2025). Evaluation of crop and pond deepening adaptations to climate change in saline coastal Bangladesh: Benefit cost and risk analysis. Agricultural Water Management, 308, Article 109274. https://doi.org/10.1016/j.agwat.2024.109274
Kunwar, S. R., Fonsah, E. G., & Escalante, C. L. (2025). An Assessment of Profitability Using Monte Carlo Simulation Approach: A Case of Georgia Blueberry Industry. Journal of Food Distribution Research, 56(1).
Liu, J., Li, Y. P., Huang, G. H., Zhuang, X. W., & Fu, H. Y. (2017). Assessment of uncertainty effects on crop planning and irrigation water supply using a Monte Carlo simulation based dual-interval stochastic programming method. Journal of Cleaner Production, 149, 945-967. https://doi.org/10.1016/j.jclepro.2017.02.100
Maaß, O., & Kehlenbeck, H. (2024). Cost–Benefit Analysis of Monitoring Insect Pests and Aerial Spraying of Insecticides: The Case of Protecting Pine Forests against Dendrolimus pini in Brandenburg (Germany). Forests, 15(1), 104. https://doi.org/10.3390/f15010104
Maggauer, K., & Fina, B. (2025). Monte Carlo simulation-based economic risk assessment in energy communities. Energy Reports, 13, 987-1003. https://doi.org/10.1016/j.egyr.2024.12.046
Mahmudiono, T., Yasin, G., Jasim, S. A., Alghazali, T. A. H., Kadhim, M. M., Iswanto, A. H., ... & Panduro-Tenazoa, N. M. (2022). Analyzing food production risk with Monte Carlo simulation. Food Science and Technology, 42, e03522. https://doi.org/10.1590/fst.03522
Majerova, I., Michna, P., Lebiedzik, M., Nevima, J., & Turečkova, K. (2022). Implementation of a navigation system: Economic verification in a local hospital. PLoS One, 17(10), e0276996. https://doi.org/10.1371/journal.pone.0276996
McNulty, M. J., Kelada, K., Paul, D., Nandi, S., & McDonald, K. A. (2021). Techno-economic process modelling and Monte Carlo simulation data of uncertainty quantification in field-grown plant-based manufacturing. Data in Brief, 38, 107317. https://doi.org/10.1016/j.dib.2021.107317
Ministerio de Desarrollo Agrario y Riego [MIDAGRI] (2024). Boletín Estadístico Mensual El Agro en Cifras – Mayo 2024. Dirección General de Estadística, Seguimiento y Evaluación de Políticas.
Ministerio de Desarrollo Agrario y Riego [MIDAGRI] (2025). Boletín estadístico mensual "El agro en cifras" - 2024 [Boletín]. https://www.gob.pe/institucion/midagri/informes-publicaciones/5380407-boletin-estadistico-mensual-el-agro-en-cifras-2024
Ministerio del Ambiente [MINAM]. (2019). Línea de base de la alfalfa con fines de bioseguridad en el Perú. MINAM. https://bioseguridad.minam.gob.pe/wp-content/uploads/2020/02/estudio_lb_alfalfa.pdf
Oktoviany, P., Knobloch, R., & Korn, R. (2021). A machine learning-based price state prediction model for agricultural commodities using external factors. Decisions in Economics and Finance, 44(2), 1063-1085. https://doi.org/10.1007/s10203-021-00354-7
Pawlak, M. (2024). Valuation of real options using Monte Carlo simulation. Procedia Computer Science, 246, 3410-3419. https://doi.org/10.1016/j.procs.2024.09.216
Quispe-Guevara, R. Q. (2022). Producción De Medicago Sativa (Alfalfa), Aplicando Abonos Orgánicos En Época De Invierno. Revista de Investigaciones, 11(1), 55-67. https://doi.org/10.26788/riepg.v11i1.3564
Rahaman, S. U., & Abdul, M. J. (2025). Quantifying uncertainty in economics policy predictions: A Bayesian & Monte Carlo based data-driven approach. International Review of Financial Analysis, 102, 104157. https://doi.org/10.1016/j.irfa.2025.104157
Ríos-Flores, J. L., Ruiz-Torres, J., Cisneros-Vázquez, J. M., Cantú-Brito, J. E., Torres-Moreno, M., & Quiñones, A. M. (2008). Producción, productividad y rentabilidad de la alfalfa (Medicago sativa L) irrigada por bombeo en la Comarca Lagunera de 1999 a 2005. Revista Chapingo-Serie Zonas Áridas VII (2). Universidad Autónoma Chapingo. Texcoco Edo. De México.
Roberts, M., Hawes, C., & Young, M. (2023). Environmental management on agricultural land: Cost benefit analysis of an integrated cropping system for provision of environmental public goods. Journal of Environmental Management, 331, Article 117306. https://doi.org/10.1016/j.jenvman.2023.117306
Sarmiento, N. (2023). Uso de abonos orgánicos e implicancia en el cultivo de cacao (Theobroma cacao L.) en la provincia de San Martín 2022. Tesis pregrado
Sistema Integrado de Estadísticas Agrarias [SIEA]. 2025. Perfil Productivo Regional.
Sokolov, M. V. (2024). NPV, IRR, PI, PP, and DPP: A unified view. Journal of Mathematical Economics, 114, 102992. https://doi.org/10.1016/j.jmateco.2024.102992
Traverso, L., Mazzoli, E., Miller, C., Pulighe, G., Perelli, C., Morese, M. M., & Branca, G. (2021). Cost Benefit and Risk Analysis of Low iLUC Bioenergy Production in Europe Using Monte Carlo Simulation. Energies, 14(6), 1-18. https://doi.org/10.3390/en14061650
Trejo-Pech, C. O., Rodríguez-Magaña, A., Briseño-Ramírez, H., & Ahumada, R. (2024). A Monte Carlo simulation case study on blueberries from Mexico. International Food and Agribusiness Management Review, 27(2), 359-377. https://doi.org/10.22434/IFAMR2023.0052
Vilani, L., Zanin, A., Lizot, M., Trentin, M., Afonso, P., & Lima, J. (2024). A Framework for Investment and Risk Assessment of Agricultural Projects. Journal of Risk and Financial Management, 17(9), 378. https://doi.org/10.3390/jrfm17090378
Yao, X., Qian, L., Changhui, L., & Yi, S. (2022). Effects of altitude and varieties on overwintering adaptability and cold resistance mechanism of alfalfa roots in the Qinghai–Tibet Plateau. Journal of the Science of Food and Agriculture, 103(3), 1001–1012. https://doi.org/10.1002/jsfa.12407
Downloads
Published
How to Cite
Issue
Section
License

This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
RPER is the official journal of the