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Assessment of the FAO AquaCrop model in the simulation of rainfed and supplementally irrigated maize, sugar beet and sunflower

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  • Stricevic, Ruzica
  • Cosic, Marija
  • Djurovic, Nevenka
  • Pejic, Borivoj
  • Maksimovic, Livija

Abstract

Farming in Serbia is traditionally rainfed. Analyses show that drought events of varying severity are frequent in this region, although there is no specific pattern. There is a distinct need for an objective assessment of the impact of drought on strategic field crops, to solve the dilemma whether irrigation is required or not. For this reason, and based on available field data, the FAO AquaCrop water driven model was selected to simulate yield and irrigation water use efficiency (IWUE) for three major field crops (maize, sunflower, and sugar beet), under two scenarios: (1) natural water supply and adequate supply of nutrients, and (2) supplementary irrigation and adequate supply of nutrients. The experiments presented here were conducted between 2000 and 2007 in northern Serbia, where chernozem soil is prevalent. Data of 2003 cropping seasons were used for local calibration, whereas the remaining years for validation. Results were such that local calibration resulted in very minor changes of AquaCrop coefficients (e.g., maize basal crop coefficient, sunflower harvest index, etc.). Simulated maize yield levels exhibited the greatest departure from measured data under irrigation conditions (-3.6 and 3.3% during an extremely dry and an extremely wet year, respectively). Simulated sunflower yield levels varied by less than 10% in 8 out of 10 comparisons. The most extreme variation was noted during the extremely wet year. The difference between simulated and measured values in the case of sugar beet was from -10.2 to 12.2%. Large differences were noted only in two or three cases, under extreme climatic conditions. Statistical indicators - root mean square error (RMSE) and index of agreement (d) - for all three crops suggested that the model can be used to highly reliably assess yield and IWUE. This conclusion was derived based on low values of RMSE and high values of d (in the case of maize and sugar beet 0.999 for both yield and IWUE, and in the case of sunflower 0.999 for yield and 0.884 for IWUE). It is noteworthy that under wet conditions, the model suggested that sunflower and sugar beet do not require irrigation, as confirmed by experimental research. These data are significant because they show that the AquaCrop model can be used in impartial decision-making and in the selection of crops to be given irrigation priority in areas where water resources are limited.

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  • Stricevic, Ruzica & Cosic, Marija & Djurovic, Nevenka & Pejic, Borivoj & Maksimovic, Livija, 2011. "Assessment of the FAO AquaCrop model in the simulation of rainfed and supplementally irrigated maize, sugar beet and sunflower," Agricultural Water Management, Elsevier, vol. 98(10), pages 1615-1621, August.
  • Handle: RePEc:eee:agiwat:v:98:y:2011:i:10:p:1615-1621
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    1. Geerts, S. & Raes, D. & Garcia, M., 2010. "Using AquaCrop to derive deficit irrigation schedules," Agricultural Water Management, Elsevier, vol. 98(1), pages 213-216, December.
    2. Araya, A. & Habtu, Solomon & Hadgu, Kiros Meles & Kebede, Afewerk & Dejene, Taddese, 2010. "Test of AquaCrop model in simulating biomass and yield of water deficient and irrigated barley (Hordeum vulgare)," Agricultural Water Management, Elsevier, vol. 97(11), pages 1838-1846, November.
    3. Park, S.J. & Hwang, C.S. & Vlek, P.L.G., 2005. "Comparison of adaptive techniques to predict crop yield response under varying soil and land management conditions," Agricultural Systems, Elsevier, vol. 85(1), pages 59-81, July.
    4. Zand-Parsa, Sh. & Sepaskhah, A.R. & Ronaghi, A., 2006. "Development and evaluation of integrated water and nitrogen model for maize," Agricultural Water Management, Elsevier, vol. 81(3), pages 227-256, March.
    5. Ma, L. & Hoogenboom, G. & Ahuja, L.R. & Ascough II, J.C. & Saseendran, S.A., 2006. "Evaluation of the RZWQM-CERES-Maize hybrid model for maize production," Agricultural Systems, Elsevier, vol. 87(3), pages 274-295, March.
    6. Yu, Q. & Saseendran, S.A. & Ma, L. & Flerchinger, G.N. & Green, T.R. & Ahuja, L.R., 2006. "Modeling a wheat-maize double cropping system in China using two plant growth modules in RZWQM," Agricultural Systems, Elsevier, vol. 89(2-3), pages 457-477, September.
    7. Hong Wu & Donald Wilhite, 2004. "An Operational Agricultural Drought Risk Assessment Model for Nebraska, USA," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 33(1), pages 1-21, September.
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    2. Moursi, Hossam & Kim, Daeha & Kaluarachchi, Jagath J., 2017. "A probabilistic assessment of agricultural water scarcity in a semi-arid and snowmelt-dominated river basin under climate change," Agricultural Water Management, Elsevier, vol. 193(C), pages 142-152.
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    5. Hassanli, Mohammad & Ebrahimian, Hamed & Mohammadi, Ehsan & Rahimi, Amirreza & Shokouhi, Amirhossein, 2016. "Simulating maize yields when irrigating with saline water, using the AquaCrop, SALTMED, and SWAP models," Agricultural Water Management, Elsevier, vol. 176(C), pages 91-99.
    6. Maniruzzaman, M. & Talukder, M.S.U. & Khan, M.H. & Biswas, J.C. & Nemes, A., 2015. "Validation of the AquaCrop model for irrigated rice production under varied water regimes in Bangladesh," Agricultural Water Management, Elsevier, vol. 159(C), pages 331-340.
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    8. Voloudakis, Dimitrios & Karamanos, Andreas & Economou, Garifalia & Kalivas, Dionissios & Vahamidis, Petros & Kotoulas, Vasilios & Kapsomenakis, John & Zerefos, Christos, 2015. "Prediction of climate change impacts on cotton yields in Greece under eight climatic models using the AquaCrop crop simulation model and discriminant function analysis," Agricultural Water Management, Elsevier, vol. 147(C), pages 116-128.
    9. Ran, Hui & Kang, Shaozhong & Li, Fusheng & Du, Taisheng & Tong, Ling & Li, Sien & Ding, Risheng & Zhang, Xiaotao, 2018. "Parameterization of the AquaCrop model for full and deficit irrigated maize for seed production in arid Northwest China," Agricultural Water Management, Elsevier, vol. 203(C), pages 438-450.
    10. Shi, Jianchu & Wu, Xun & Zhang, Mo & Wang, Xiaoyu & Zuo, Qiang & Wu, Xiaoguang & Zhang, Hongfei & Ben-Gal, Alon, 2021. "Numerically scheduling plant water deficit index-based smart irrigation to optimize crop yield and water use efficiency," Agricultural Water Management, Elsevier, vol. 248(C).
    11. Sandhu, Rupinder & Irmak, Suat, 2019. "Assessment of AquaCrop model in simulating maize canopy cover, soil-water, evapotranspiration, yield, and water productivity for different planting dates and densities under irrigated and rainfed cond," Agricultural Water Management, Elsevier, vol. 224(C), pages 1-1.
    12. Hamidreza Kamali & Shahrokh Zand-Parsa, 2017. "Estimation of Sugar Beet Yield and its Dry Matter Partitioning Under Different Irrigation and Nitrogen Levels," Modern Applied Science, Canadian Center of Science and Education, vol. 11(1), pages 143-143, September.
    13. Katerji, Nader & Campi, Pasquale & Mastrorilli, Marcello, 2013. "Productivity, evapotranspiration, and water use efficiency of corn and tomato crops simulated by AquaCrop under contrasting water stress conditions in the Mediterranean region," Agricultural Water Management, Elsevier, vol. 130(C), pages 14-26.
    14. Wu, Hui & Yue, Qiong & Guo, Ping & Xu, Xiaoyu & Huang, Xi, 2022. "Improving the AquaCrop model to achieve direct simulation of evapotranspiration under nitrogen stress and joint simulation-optimization of irrigation and fertilizer schedules," Agricultural Water Management, Elsevier, vol. 266(C).
    15. El Chami, D. & Knox, J.W. & Daccache, A. & Weatherhead, E.K., 2015. "The economics of irrigating wheat in a humid climate – A study in the East of England," Agricultural Systems, Elsevier, vol. 133(C), pages 97-108.
    16. Toumi, J. & Er-Raki, S. & Ezzahar, J. & Khabba, S. & Jarlan, L. & Chehbouni, A., 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, Elsevier, vol. 163(C), pages 219-235.
    17. Seyed Ahmadi & Elnaz Mosallaeepour & Ali Kamgar-Haghighi & Ali Sepaskhah, 2015. "Modeling Maize Yield and Soil Water Content with AquaCrop Under Full and Deficit Irrigation Managements," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 29(8), pages 2837-2853, June.
    18. Sandhu, Rupinder & Irmak, Suat, 2019. "Performance of AquaCrop model in simulating maize growth, yield, and evapotranspiration under rainfed, limited and full irrigation," Agricultural Water Management, Elsevier, vol. 223(C), pages 1-1.
    19. Wellens, Joost & Raes, Dirk & Traore, Farid & Denis, Antoine & Djaby, Bakary & Tychon, Bernard, 2013. "Performance assessment of the FAO AquaCrop model for irrigated cabbage on farmer plots in a semi-arid environment," Agricultural Water Management, Elsevier, vol. 127(C), pages 40-47.
    20. Feng, Dingrui & Li, Guangyong & Wang, Dan & Wulazibieke, Mierguli & Cai, Mingkun & Kang, Jing & Yuan, Zicheng & Xu, Houcheng, 2022. "Evaluation of AquaCrop model performance under mulched drip irrigation for maize in Northeast China," Agricultural Water Management, Elsevier, vol. 261(C).
    21. Shirazi, Sana Zeeshan & Mei, Xurong & Liu, Buchun & Liu, Yuan, 2021. "Assessment of the AquaCrop Model under different irrigation scenarios in the North China Plain," Agricultural Water Management, Elsevier, vol. 257(C).
    22. Mustafa, S.M.T. & Vanuytrecht, E. & Huysmans, M., 2017. "Combined deficit irrigation and soil fertility management on different soil textures to improve wheat yield in drought-prone Bangladesh," Agricultural Water Management, Elsevier, vol. 191(C), pages 124-137.
    23. Nyakudya, Innocent Wadzanayi & Stroosnijder, Leo, 2014. "Effect of rooting depth, plant density and planting date on maize (Zea mays L.) yield and water use efficiency in semi-arid Zimbabwe: Modelling with AquaCrop," Agricultural Water Management, Elsevier, vol. 146(C), pages 280-296.

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