Assessment of yield and water productivity in soybean (Glycine max) with AquaCrop
DOI:
https://doi.org/10.15517/gpmbn475Keywords:
biomass, canopy cover, hydraulic conductivity, simulation, sensitivity, consumptive useAbstract
Introduction. Simulation models are a tool to study crop behavior under different climatic and water conditions and agronomic management practices. Objective. To evaluate the AquaCrop model for estimating yield and water productivity in soybean var. CIGRAS-06. Materials and methods. The study was carried out at the Estación Experimental Agrícola Fabio Baudrit Moreno of the University of Costa Rica, in Alajuela, Costa Rica, from June 6 to October 23, 2018. The AquaCrop v. 7.1 model was used to simulate soybean crop development and yield. Simulated data on canopy cover, biomass production, and yield were compared with experimental data from a plot planted with soybean variety CIGRAS-06. Soil parameters measured in the field and generated with pedotransfer equations were used. Results. Predictions of yield, total biomass, and canopy cover were good (similarity values: d ≥ 0.97), but predictions of leaf coverage during the early crop cycle were susceptible to improvement. Differences between the two types of soil parameters used did not significantly affect the final simulation. Conclusions. AquaCrop successfully simulated soybean yield, biomass, and leaf coverage. Water productivity simulation was higher than values reported in the literature.
Downloads
References
Boligon Minuzzi, R., Do Amaral Frederico, C., & Freire da Silva, T. G. (2017). Estimation of soybean agronomic performance in climatic scenarios for the Southern Brazil. Revista Ceres, 64(6), 567–573. https://doi.org/10.1590/0034-737X201764060002
Chinchilla Hidalgo, K. (2023). Validación del modelo AquaCrop para estimar el rendimiento de dos variedades comerciales de frijol común (Phaseolus vulgaris L.) bajo riego deficitario controlado [Tesis de Licenciatura, Universidad de Costa Rica]. Repositorio SIBDI de la Universidad de Costa Rica. https://repositorio.sibdi.ucr.ac.cr/handle/123456789/22237
Cordero, J. (2023, febrero 20). Agricultores retoman el cultivo de soya en Guanacaste con semilla UCR. Universidad de Costa Rica. https://www.ucr.ac.cr/noticias/2023/2/20/agricultores-retoman-el-cultivo-de-soya-en-guanacaste-con-semilla-ucr.html
Day, P. R. (1965). Particle fractionation and particle-size analysis. In C. A. Black, D. D. Evans, D. D. Ensminger, J. L. White, & F. E. Clark (Eds). Methods of soil analysis (pp. 545–567). American Society of Agronomy. https://doi.org/10.2134/agronmonogr9.1.c43
Dos Santos Farias, D. B., Neiva Rodrigues, L., & Alves Souza, S. (2024). AquaCrop model assessment for simulating soybean response under water stress. Ciência Rural, 54(5), Artículo e20230168. https://doi.org/10.1590/0103-8478cr20230168
Fajardo, H., García, M., Raes, D., & Van Gaelen, H. (2016). Validación del modelo AquaCrop para diferentes niveles de fertilidad en el cultivo de Quinua en el Altiplano Boliviano. Revista CINTEX, 21(2), 31–52. https://revistas.pascualbravo.edu.co/index.php/cintex/article/view/16
Food and Agriculture Organization of the United Nations. (s.f.). FAOSTAT: Database on crop yields. Retrieved April 30, 2025, from https://www.fao.org/faostat/en/
Forsythe, W. (1985). Manual de laboratorio: física de suelos. (2.ª ed.). Instituto Interamericano de Cooperación para la Agricultura.
González-Robaina, F., López-Vargas, D., Cisneros-Zayas, E., Herrera-Puebla, J., & Cid-Lazo, G. (2019). Calibración y análisis de sensibilidad del modelo AquaCrop para frijol en suelo Ferralítico Rojo Compactado. Ingeniería Agrícola, 9(4), 3–12. https://revistas.unah.edu.cu/index.php/IAgric/article/view/1166
Gutiérrez, M. V., Soto, D., & Alpízar, M. (1997). Cuarenta años de observaciones meteorológicas en la Estación Experimental Fabio Baudrit Moreno. BOLTEC, 30(2), 1-14.
Imbach, P., Chou, S. C., Lyra, A., Rodrigues, D., Rodriguez, D., Latinovic, D., Siqueira, G., Silva, A., Garofolo, L., & Georgiou, S. (2018). Future climate change scenarios in Central America at high spatial resolution. PLoS One, 13(4), Artículo e0193570. https://doi.org/10.1371/journal.pone.0193570
Intergovernmental Panel on Climate Change. (2019). Special report on climate change and land. Chapter 2: Land–climate interactions. https://www.ipcc.ch/srccl/chapter/chapter-2/
Kreutz Rosa, S. L., Moretti de Souza, J. L., De Oliveira, C. T., & Tsukahara, R. Y. (2023). Calibration and validation of the AquaCrop model to estimate soybean production in the Campos Gerais, Parana State, Brazil. Agricultural Engineering International: CIGR Journal, 25(4), 1–13. https://cigrjournal.org/index.php/Ejounral/article/view/8245
Lu, Y., Chibarabada, T. P., McCabe, M. F., De Lannoy, G. J. M., & Sheffield, J. (2021). Global sensitivity analysis of crop yield and transpiration from the FAO-AquaCrop model for dryland environments. Field Crops Research, 269, Artículo 108182. https://doi.org/10.1016/j.fcr.2021.108182
Mata, R., Vázquez, A., Rosales, A., & Salazar, D. (2016). Mapa digital de suelos de Costa Rica (Escala, 1:200000). Asociación Costarricense de la Ciencia del Suelo.
Ministerio de Agricultura y Ganadería. (2015). Estrategia 2015-2034 y plan de acción para la ganadería baja en carbono en Costa Rica: síntesis informativa. https://www.mag.go.cr/bibliotecavirtual/AV-1174.pdf
Morales-Santos, A., García-Vila, M., & Nolz, R. (2023). Assessment of the impact of irrigation management on soybean yield and water productivity in a subhumid environment. Agricultural Water Management, 284, Artículo 108356. https://doi.org/10.1016/j.agwat.2023.108356
Organización para la Cooperación y el Desarrollo Económico, & Organización de las Naciones Unidas para la Alimentación y la Agricultura. (2023). OCDE-FAO Perspectivas Agrícolas 2023–2032. https://doi.org/10.1787/2ad6c3ab-es
Porras-Jorge, R., Ramos-Fernández, L., Ojeda-Bustamante, W., & Ontiveros-Capurata, R. (2020). Performance assessment of the AquaCrop model to estimate rice yields under alternate wetting and drying irrigation in the coast of Peru. Scientia Agropecuaria, 11(3), 309–321. https://doi.org/10.17268/sci.agropecu.2020.03.03
Quesada-Chacón, D., Barfus, K., & Bernhofer, C. (2021). Climate change projections and extremes for Costa Rica using tailored predictors from CORDEX model output through statistical downscaling with artificial neural networks. International Journal of Climatology, 41(1), 211–232. https://doi.org/10.1002/joc.6616
Raes, D., Steduto, P., Hsiao, T. C., & Fereres, E. (2023). AquaCrop: Version 7.1 reference manual [Software Manual]. Food and Agriculture Organization of the United Nations.
Salman, M., García-Vila, M., Fereres, E., Raes, D. & Steduto, P. (2021). The AquaCrop model – Enhancing crop water productivity. Ten years of development, dissemination and implementation 2009–2019 (FAO Water Report No. 47). Food and Agriculture Organization of the United Nations. https://doi.org/10.4060/cb7392en
Saxton, K. E., & Rawls, W. J. (2006). Soil water characteristic estimates by texture and organic matter for hydrologic solutions. Soil Science Society of America Journal, 70(5), 1569–1578. https://doi.org/10.2136/sssaj2005.0117
Secretaría Ejecutiva de Planificación Sectorial Agropecuaria. (2016). Plan Nacional para la Seguridad Alimentaria, Nutrición y Erradicación del Hambre 2025. Plan SAN-CELAC Costa Rica. I Quinquenio. Organización de las Naciones Unidas para la Alimentación y la Agricultura. https://faolex.fao.org/docs/pdf/cos177242.pdf
Soil Survey Staff. (2014). Claves para la taxonomía de suelos (12.ª ed.). Departamento de Agricultura de los Estados Unidos, & Servicio de Conservación de Recursos Naturales. https://www.nrcs.usda.gov/sites/default/files/2022-10/Spanish-Keys-to-Soil-Taxonomy.pdf
Steduto, P., Hsiao, T. C., Fereres, E., & Raes, D. (2012). Crop yield response to water (FAO Irrigation and Drainage Paper No. 66). Food and Agriculture Organization of the United Nations. https://www.fao.org/4/i2800e/i2800e00.htm
Steduto, P., Hsiao, T. C., Raes, D., & Fereres, E. (2009). AquaCrop—The FAO crop model to simulate yield response to water: I. Concepts and underlying principles. Agronomy Journal, 101(3), 426–437. https://doi.org/10.2134/agronj2008.0139s
Terán-Chaves, C. A., García-Prats, A., & Polo-Murcia, S. M. (2022). Calibration and validation of the FAO AquaCrop water productivity model for perennial ryegrass (Lolium perenne L.). Water, 14(23), Artículo 3933. https://doi.org/10.3390/w14233933
Tobía, C., & Villalobos, E. (2004). Producción y valor nutricional del forraje de soya en condiciones tropicales adversas. Agronomía Costarricense, 28(1), 17–25. https://doi.org/10.15517/rac.v28i1.61222
Tornés Olivera, N., Brown Manrique, O., Gómez Masjuan, Y., & Guerrero Alega, A. M. (2016). Evaluación del modelo AquaCrop en la simulación del crecimiento del cultivo del frijol. Revista Ciencias Técnicas Agropecuarias, 25(3), 23–30. https://revistas.unah.edu.cu/index.php/rcta/article/view/450
Ureña, P., Alfaro, E. J., & Soley, F. J. (2016). Propuestas metodológicas para el rellenado de datos ausentes en series de tiempo geofísicas. Guía práctica de uso. Universidad de Costa Rica. https://www.kerwa.ucr.ac.cr/server/api/core/bitstreams/b05ba1d5-3f25-4c5a-85f3-4d93f802752e/content
Van Looy, K., Bouma, J., Herbst, M., Koestel, J., Minasny, B., Mishra, U., Montzka, C., Nemes, A., Pachepsky, Y. A., Padarian, J., Schaap, M. G., Tóth, B., Verhoef, A., Vanderborght, J., Van der Ploeg, M. J., Weihermüller, L., Zacharias, S., Zhang, Y., & Vereecken, H. (2017). Pedotransfer functions in Earth system science: Challenges and perspectives. Reviews of Geophysics, 55(4), 1199–1256. https://doi.org/10.1002/2017RG000581
Villalobos, E., & Camacho, F. (1999). Avances en el mejoramiento genético de la soya en Costa Rica: II. CIGRAS-06 y CIGRAS-10, dos nuevas variedades tropicales. Agronomía Costarricense, 23(1), 61–67.
Yadav, M., Vashisht, B. B., Jalota, S. K., Jyolsna, T., Singh, S. P., Kumar, A., Kumar, A., & Singh, G. (2024). Improving water efficiencies in rural agriculture for sustainability of water resources: a review. Water Resources Management, 38(10), 3505–3526. https://doi.org/10.1007/s11269-024-03836-6
Additional Files
Published
Issue
Section
Categories
License
Copyright (c) 2025 Dania Zúñiga-Herrera, Néstor Felipe Chaves-Barrantes, Marco Vinicio Gutiérrez-Soto, Mayela Monge-Muñoz, Cristina Chinchilla-Soto (Autor/a)
This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.
1. Proposed policy for open access journals
Authors who publish in this journal accept the following conditions:
a. Authors retain the copyright and assign to the journal the right to the first publication, with the work registered under the attribution, non-commercial and no-derivative license from Creative Commons, which allows third parties to use what has been published as long as they mention the authorship of the work and upon first publication in this journal, the work may not be used for commercial purposes and the publications may not be used to remix, transform or create another work.
b. Authors may enter into additional independent contractual arrangements for the non-exclusive distribution of the version of the article published in this journal (e.g., including it in an institutional repository or publishing it in a book) provided that they clearly indicate that the work was first published in this journal.
c. Authors are permitted and encouraged to publish their work on the Internet (e.g. on institutional or personal pages) before and during the review and publication process, as it may lead to productive exchanges and faster and wider dissemination of published work (see The Effect of Open Access).
How to Cite
