Performance assessment of the AquaCrop model to estimate rice yields under alternate wetting and drying irrigation in the coast of Peru
DOI:
https://doi.org/10.17268/sci.agropecu.2020.03.03Palabras clave:
biomass, canopy cover, performance, water-use efficiency.Resumen
Peru is the second-largest rice producer in Latin America, with 406166 ha grown annually, predominately on the Peruvian north coast. However, rice is primarily irrigated by flooding (93%), which demands high water use (15000-18000 m3 ha−1) owing to low water-use efficiency. Additionally, the intensification of climate change is of great concern as it causes high variability as well as a decreasing trend in water resource availability. Alternate wetting and drying (AWD) irrigation technique reportedly reduce the irrigation volumes while maintaining conventional yield rates. The AquaCrop model was calibrated and assessed to simulate rice yield response to the AWD technique under water shortage conditions on the Peruvian central coast. The AquaCrop model exhibited a “very good” to “good” performance in predicting canopy cover development, soil water content, aerial biomass, and grain yield using performance indicators, such as the Nash-Sutcliffe efficiency coefficient, the RMSE observations standard deviation ratio (RSR), Willmott index, and determination coefficient. The calibrated model showed a good performance of rice under AWD irrigation, indicating that this technique can be used to assess rice production under Peruvian arid conditions.
Citas
Abdul-Ganiyu, S.; Kyei-Baffour, N.; Agyare, W.A.; et al. 2018. Evaluating the effect of irrigation on paddy rice yield by applying the AquaCrop model in Northern Ghana. In Strategies for Building Resilience against Climate and Ecosystem Changes in Sub-Saharan Africa. Springer, Singapore. 93-116 pp.
Allen, R.G.; Pereira, L.S.; Raes, D.; et al. 1998. Crop evapotranspiration – Guidelines for computing crop water requirement. Rome (Italy). FAO. 24-86.
Amiri, E. 2016. Calibration and Testing of the Aquacrop Model for Rice under Water and Nitrogen Management. Communications in Soil Science and Plant Analysis 47(3): 387-403.
Amiri, E; Rezaei, M; Eyshi Rezaei, E; et al. 2014. Evaluation of Ceres-Rice, Aquacrop and Oryza2000 Models in Simulation of Rice Yield Response to Different Irrigation and Nitrogen Management Strategies. Journal of Plant Nutrition 37(11): 1749-1769.
Asibi, A.; Chai, Q.; Coulter, J. 2019. Rice blast: A disease with implications for global food security. Agronomy 9: 451.
Bouman, B.A.M.; Tuong, T.P. 2001. Field water management to save water and increase its productivity in irrigated lowland rice. Agricultural Water Management 49: 11-30.
Carrijo, D.R.; Lundy, M.E.; Linquist, B.A. 2017. Rice yields and water use under alternate wetting and drying irrigation: A meta-analysis. Field Crops Research 203:173-180.
Cheng, W.; Zhang, G.; Zhao, G.; et al. 2003. Variation in rice quality of different cultivars and grain positions as affected by water management. Field Crops Research 80: 245-252.
Cobos, D.R. and Chambers, C. 2010. Calibrating ECH2O Soil Moisture Sensors; Application note; Decagon Devices: Pullman, WA, EE. UU.
Counce, P.A.; Keisling, T.C.; Mitchell, A.J. 2000. A uniform, objective, and adaptive system for expressing rice development. Crop Science 40: 436.
Djaman, K.; Mel, V.C.; Diop, L.; et al. 2018. Effects of alternate wetting and drying irrigation regime and nitrogen fertilizer on yield and nitrogen use efficiency of irrigated rice in the Sahel. Water 10: 711.
FAOSTAT. 2018. United Nations Food and Agriculture Organization of the United Nations Statistics Division Available in: http://www.fao.org/faostat/zh/#data.
Geerts, S.; Raes, D., Garcia, M.; et al. 2008. Could deficit irrigation be a sustainable practice for quinoa (Chenopodium quinoa Willd.) in the Southern Bolivian Altiplano? Agricultural Water Management 95: 909-917.
Geerts, S.; Raes, D., Garcia, M.; et al. 2009. Simulating yield response of quinoa to water availability with AquaCrop. Agronomy Journal 101: 499-508.
Greaves, G.; Wang, Y. 2016. Assessment of FAO AquaCrop Model for Simulating Maize Growth and Productivity under Deficit Irrigation in a Tropical Environment. Water 8: 557.
Guo, D.X.; Chen, C.F.; Guo, P.Y.; et al. 2018. Evaluation of AquaCrop model for Foxtail Millet (Setaria italica) growth and water use with plastic film mulching and no mulching under different weather conditions. Water 10: 836.
Heros, E.; Gómez, L.; Sosa, G. 2014. Utilización de los índices de selección en la identificación de genotipos de arroz (Oryza sativa L.) tolerantes a sequía. Producción Agropecuaria y Desarrollo Sostenible 2: 11-31
Hsiao, T.C.; Heng, L.; Steduto, P.; et al. 2009. AquaCrop-El modelo de cultivo de la FAO para simular la respuesta de rendimiento al agua: III. Parametrización y pruebas para maíz. Agronomy Journal 101: 448-459.
Hussein, F.; Janat, M.; Yakoub, A. 2011. Simulating cotton yield response to deficit irrigation with the FAO AquaCrop model. Spanish Journal of Agricultural Research 9: 1319-1330.
Khan, M.U.; Li, P.; Amjad, H.; et al. 2019. Exploring the potential of overexpressed OsCIPK2 rice as a nitrogen utilization efficient crop and analysis of its associated rhizo-compartmental microbial communities. International Journal of Molecular Science 20: 3636.
Leib, B.G.; Jabro, J.D. 2003. Matthews, GR Field evaluation and performance comparison of soil moisture sensors. Soil Science168: 396-408.
Lin, L.; Zhang, B.; Xiong, L.H. 2012. Evaluating yield response of paddy rice to irrigation and soil management with application of the AquaCrop model. American Society of Agricultural and Biological Engineers 55: 839-848.
Liu, J.; Pattey, E. 2010. Retrieval of leaf area index from top-of-canopy digital photography over agricultural crops. Agricultural and Forest Meteorology 150: 1485-1490.
Maniruzzaman, M.; Talukder, M.; Khan, M.; et al. 2015. Validation of the AquaCrop model for irrigated rice production under varied water regimes in Bangladesh. Agricultural Water Management 159: 331-340.
McMaster, G.; Wilhelm, W. 1997. Growing degree-days: one equation, two interpretations. Agricultural and Forest Meteorology 87: 291-300.
Mkhabela, S.M.; Bullock, P.R. 2012. Performance of the FAO AquaCrop model for wheat grain yield and soil moisture simulation in Western Canada. Agricultural Water Management 110: 16-24.
Mondal, M.S.; Saleh, A.F.; Razzaque Akanda M.A.; et al. 2015. Simulando la respuesta de rendimiento del arroz al estrés de salinidad con el modelo AquaCrop. Environmental Science Process Impacts 17: 1118-1126
Moriasi, D.N.; Arnold, J.G.; Liew, M.W.V.; et al. 2007. Model evaluation guidelines for systematic quantification of accuracy in watershed simulations. American Society of Agricultural and Biological Engineers 50: 885-900.
Nuruzzaman, M.; Yamamoto, Y.; Nitta, Y.; et al. 2000. Varietal differences in tillering ability of fourteen japonica and indica rice varieties. Soil Science and Plant Nutrition 46: 381-391.
Orasen, G.; De Nisi, P.; Lucchini, G.; et al. 2019. Continuous flooding or alternate wetting and drying differently affect the accumulation of health-promoting phytochemicals and minerals in rice brown grain. Agronomy 9: 628.
Otukei, J.R.; Blaschke, T. 2010. Land cover change assessment using decision trees, support vector machines and maximum likelihood classification algorithms. International Journal of Applied Earth Observation and Geoinformation 12: 27-31.
Pereira, L.S.; Paredes, P.; Sholpankulov, E.D.; et al. 2009. Irrigation scheduling strategies for cotton to cope with water scarcity in the Fergana Valley, Central Asia. Agricultural Water Management 96: 723-735.
Pirmoradian, N.; Davatgar, N. 2019. Simulating the effects of climatic fluctuations on rice irrigation water requirement using AquaCrop, Agricultural Water Management 213: 97-106.
Raes, D.; Steduto P.; Hsiao, T.C.; et al. 2009. AquaCrop—The FAO Crop Model to simulate yield response to water: II. Main algorithms and software description. Agronomy Journal 101: 438-447.
Raes, D.; Steduto, P.; Hsiao, et al. 2018. AquaCrop Version 6.0 - 6.1 Reference Manual June. FAO, Rome, Italy.
Raoufi, R.; Soufizadeh, S.; Larijani, B.A.; et al. 2018. Performance of AquaCrop for simulation of genotypic differences in rice under various seedling ages. Natural resources modeling. 31 pp.
Rau, P.; Bourrel, L.; Labat, D.; et al. 2017. Regionalization of rainfall over the Peruvian Pacific slope and coast. International Journal of Climatology 37: 143-158.
Salemi, H.; Soom, M.A.M.; Lee, T.S.; et al. 2011. Application of AquaCrop model in deficit irrigation management of Winter wheat in arid region. African Journal of Agricultural Research 610: 2204-2215.
Shafiei, M.; Ghahraman, B.; Saghafian, B.; et al. 2014. Uncertainty assessment of the agro‐hydrological SWAP model application at field scale: a case study in a dry region. Agricultural Water Management 146: 324-334.
Singh, A.; Saha, S.; Mondal, S. 2013. Modelling irrigated wheat production using the FAO AquaCrop model in West Bengal, India, for sustainable agriculture. Irrigation and Drainage 62: 50-56.
Steduto, P.; Hsiao T.; Fereres, et al. 2012. Respuesta del rendimiento del cultivo al agua, papel de riego y drenaje de la FAO. FAO Irrigation and Drainage 66, Roma, Italia. 66 pp.
Steduto, P.; Hsiao, T.C.; Raes, D.; et al. 2009. AquaCrop - The FAO Crop model to simulate yield response to water: I. Concepts and Underlying Principles. Agronomy Journal 101: 426-437.
Steduto, P.; Albrizio, R. 2005. Resource-use efficiency of field grown sunflower sorghum, wheat and chickpea. II. Water use efficiency and comparison with radiation use efficiency. Agricultural and Forest Meteorology130: 269-281.
Steduto, P.; Hsiao, T.; Fereres, E. 2007. On the conservative behaviour of biomass water productivity. Irrigation Science 25: 189-207.
Toumi, J.; Er-Raki, S.; Ezzahar, J. et al. 2016. Performance assessment of AquaCrop model for estimating evapotranspiration, soil water content and grain yield of winter wheat in Tensift Al Haouz (Morocco): application to irrigation management. Agricultural Water Management 163: 219-235.
Xu, J.; Bai, W.; Li, Y.; et al. 2019a. Modeling rice development and field water balance using AquaCrop model under drying-wetting cycle condition in eastern China. Agricultural Water Management 213: 289-297.
Xu, Y.; Gu, D.; Li, K.; et al. 2019b. Response of grain quality to alternate wetting and moderate soil drying irrigation in rice. Crop Science 59: 1261-1272.
Zeleke, K.T. 2019. Calibration and validation for faba bean (Vicia faba L.) under different agronomic managements. Agronomy 9: 320.
Zhai, B.; Fu, Q.; Li, T.; et al. 2019. Rice irrigation schedule optimization based on the AquaCrop model: study of the Longtouqiao irrigation district. Water 11: 1799.
Descargas
Publicado
Cómo citar
Número
Sección
Licencia
Los autores que publican en esta revista aceptan los siguientes términos:
a. Los autores conservan los derechos de autor y conceden a la revista el derecho publicación, simultáneamente licenciada bajo una licencia de Creative Commons que permite a otros compartir el trabajo, pero citando la publicación inicial en esta revista.
b. Los autores pueden celebrar acuerdos contractuales adicionales separados para la distribución no exclusiva de la versión publicada de la obra de la revista (por ejemplo, publicarla en un repositorio institucional o publicarla en un libro), pero citando la publicación inicial en esta revista.
c. Se permite y anima a los autores a publicar su trabajo en línea (por ejemplo, en repositorios institucionales o en su sitio web) antes y durante el proceso de presentación, ya que puede conducir a intercambios productivos, así como una mayor citación del trabajo publicado (ver efecto del acceso abierto).
