Agricultural Planning with Quantitative Models

Authors

DOI:

https://doi.org/10.47633/86vt6976

Keywords:

agricultural planning, linear programming, time series, Prophet, FAOSTAT, maize, wheat

Abstract

This study presents an agricultural planning approach based on the use of quantitative forecasting and optimization models to improve the allocation of arable land between maize and wheat. Using official data from the Food and Agriculture Organization of the United Nations (FAO, 2023), time series methods were implemented through the Prophet model to estimate the evolution of future production (Taylor & Letham, 2018). In addition, classical linear programming was applied to determine the optimal distribution of hectares to be cultivated, maximizing expected yield under conditions of resource constraints (Dantzig, 1963). The analysis suggests that integrating forecasting techniques with optimization models enables more efficient agricultural decision-making grounded in real data. Furthermore, the potential of these tools to generate predictive scenarios that guide both strategic and operational planning at the national or regional level is highlighted. This study provides evidence of how technology and data analysis can positively transform the design of sustainable agricultural policies.

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Author Biographies

  • Cristina Urbina-Céspedes, Instituto Tecnológico de Costa Rica. Cartago, Costa Rica

    Researcher

  • Mauricio Alonso Campos-Cerdas, Instituto Tecnológico de Costa Rica. Cartago, Costa Rica

    Researcher

  • Juan Bautista Núñez-Parrales, Instituto Tecnológico de Costa Rica. Cartago, Costa Rica

    Researcher

References

Rodríguez, A. (2023). Estrategias de desarrollo económico para el siglo XXI. Editorial Académica.

Dantzig, G. B. (1963). Linear programming and extensions. Princeton: Princeton University Press.

Taylor, S. J., & Letham, B. (2018). Forecasting at scale. The American Statistician, 72(1), 37–45. doi:https://doi.org/10.1080/00031305.2017.1380080

Food and Agriculture Organization of the United Nations (FAO). (2023). Retrieved from FAOSTAT statistical database: https://www.fao.org/faostat/en/#data/QCL

Castillo, E., Delgado, O., de León, H., Escartin, L., Saéz, Y., & Collado, E. (2021). Mejoramiento del uso de suelo en la agricultura mediante herramientas basadas en optimización. I+D Tecnológico, 17(2). https://doi.org/10.33412/idt.v17.2.3144

Custodio, J. M., Billones, R. K., Concepcion, R., & Vicerra, R. R. (2024). Optimization of Crop Harvesting Schedules and Land Allocation Through Linear Programming. Process Integration and Optimization for Sustainability, 8(1). https://doi.org/10.1007/s41660-023-00357-4

Esteso, A., Alemany, M. M. E., Ortiz, Á., & Iannacone, R. (2022). Crop planting and harvesting planning: Conceptual framework and sustainable multi‐objective optimization for plants with variable molecule concentrations and minimum time between harvests. Applied Mathematical Modelling, 112. https://doi.org/10.1016/j.apm.2022.07.023

Ji, H., He, X., Wang, W., & Zhang, H. (2023). Prediction of Winter Wheat Harvest Based on Back Propagation Neural Network Algorithm and Multiple Remote Sensing Indices. Processes, 11(1). https://doi.org/10.3390/pr11010293

Piedra Rivas, M. C., Banegas Campoverde, C. M., & Castillo Ortega, Y. (2021). Modelo de optimización de la cadena de distribución de la agricultura familiar campesina en las parroquias Quingeo y Santa Ana del Cantón Cuenca. ConcienciaDigital, 4(2). https://doi.org/10.33262/concienciadigital.v4i2.1630

Rambauth Ibarra, G. E. (2022). Agricultura de Precisión: La integración de las TIC en la producción Agrícola. Computer and Electronic Sciences: Theory and Applications, 3(1). https://doi.org/10.17981/cesta.03.01.2022.04

Desai, M., & Shingala, A. (2023). Time Series Prediction of Wheat Crop based on FB Prophet Forecast Framework. ITM Web of Conferences, 53. https://doi.org/10.1051/itmconf/20235302014

Hunt, J., Rees, H. van, Hochman, Z., Carberry, P., Holzworth, D., Dalgliesh, N., Poulton, P., Rees, S. van, Huth, N., & Peake, A. (2006). Yield Prophet ®: An online crop simulation service. Proceedings of the 13th ASA Conference,"Ground Breaking Stuff", January.

La portada presenta el título de la edición especial, Investigación desde las aulas, los logos de la Revista Académica Arjé, la Universidad Técnica Nacional y del Instituto Tecnológico de Costa Rica, y la ilustración digital de dos estudiantes rodeados de elementos representativos de las temáticas abarcadas en este número.

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Published

2026-02-13

Issue

Vol. 9 No. 1 (especial) (2026): Research from the Classroom

Section

Peer-reviewed publications (articles, essays, literature reviews)

License

Copyright (c) 2026 Cristina Urbina-Céspedes, Mauricio Alonso Campos-Cerdas, Juan Bautista Núñez-Parrales

Creative Commons License

This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.

All articles in the Revista Académica Arjé are published under the Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License (CC BY-NC-SA 4.0).

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More information at: https://creativecommons.org/licenses/by-nc-sa/4.0/deed.en

How to Cite

Urbina-Céspedes, C., Campos-Cerdas, M. A., & Núñez-Parrales, J. B. (2026). Agricultural Planning with Quantitative Models. Academic Journal Arjé , 9(1 (especial), 1-17. https://doi.org/10.47633/86vt6976

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