Performance assessment of the AquaCrop model to estimate rice yields under alternate wetting and drying irrigation in the coast of Peru

Rossana Porras-Jorge, Lia Ramos-Fernández, Waldo Ojeda-Bustamante, Ronald Ontiveros-Capurata
2020 Scientia Agropecuaria  
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 m 3 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
more » ... igation 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. Website: http://revistas.unitru.edu.pe/index.php/scientiaagrop Facultad de Ciencias Agropecuarias Universidad Nacional de Trujillo Scientia Agropecuaria 11(3): 309 -321 (2020) SCIENTIA AGROPECUARIA Cite this article: Porras-Jorge, R.; Ramos-Fernández, L.; Ojeda-Bustamante, W.; Ontiveros-Capurata, R.E. 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. ORCID R. Porras-Jorge https://orcid.org/0000-0002-6900-8671 L. Ramos-Fernández https://orcid.org/0000-0003-3946-7188 W. Ojeda-Bustamante https://orcid.org/0000-0001-7183-9637 R.E. Ontiveros-Capurata https://orcid.org/0000-0002-5094-0469 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. 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.
doi:10.17268/sci.agropecu.2020.03.03 fatcat:qb4ydx2bxfcfngxlptfimusdeu