Indian Journal of Agricultural Research

  • Chief EditorT. Mohapatra

  • Print ISSN 0367-8245

  • Online ISSN 0976-058X

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Indian Journal of Agricultural Research, volume 55 issue 4 (august 2021) : 428-433

Modeling and Evaluation of AquaCrop for Maize (zea mays L.) under Full and Deficit Irrigation in Semi-Arid Tropics 

M. Roja, K.S. Kumar, V. Ramulu, Ch. Deepthi
1Department of Agronomy, Centurion University of Technology and Management, Paralakhemundi-761 211, Odisha, India.
Cite article:- Roja M., Kumar K.S., Ramulu V., Deepthi Ch. (2021). Modeling and Evaluation of AquaCrop for Maize (zea mays L.) under Full and Deficit Irrigation in Semi-Arid Tropics. Indian Journal of Agricultural Research. 55(4): 428-433. doi: 10.18805/IJARe.A-5520.
FAO AquaCrop is a simulation model that predicts the effects of soil, climate, water and crop growth on water productivity, yield and its attributes of various crops. In the present study, performance evaluation of AquaCrop model for maize was assessed for rabi maize during 2015 at Water Technology Centre, College of Agriculture, Rajendranagar, Hyderabad. The experiment was laid in a randomized block design with eight treatments in three replications. The treatments comprised of surface and drip irrigation schedules based on Epan viz., surface irrigation at 0.6 IW/CPE ratio (T1), 0.8 IW/CPE ratio (T2), 1.0 IW/CPE ratio (T3), 1.2 IW/CPE ratio (T4), drip irrigation at 0.6 Epan (T5), 0.8 Epan (T6), 1.0 Epan (T7) and 1.2 Epan (T8). The model was evaluated using crop data resulted from the experiment under varying water application methods and levels. Simulation performance was assessed with statistical parameters viz., statistical co-efficient of determination (R2), prediction error (Pe), model efficiency (E), root mean square error (RMSE) and mean absolute error (MAE). The model results are in quite agreement with practical values for grain yield, biomass and water productivity with model efficiency of 0.99, 0.92 and 0.71, coefficient of determination (R2) of 0.90, 0.91 and 0.93 with an RMSE of 0.24, 0.10 and 0.05, respectively. The model prediction errors in simulation of grain yield, biomass and water productivity under all treatments ranged from 1.4% to 11.9%, 1.4% to 16.1% and 4.85% to 25.9%, respectively. The highest and lowest prediction accuracy for grain yield, biomass and water productivity were in drip irrigation at 1.2 Epan and surface irrigation at 0.6 IW/CPE ratios. It is inferred that FAO AquaCrop model is suitable for predicting grain yield, biomass, water productivity and green canopy cover with acceptable range of under and over predictions for maize in semi-arid tropical climate.
  1. Abedinpour, M., Sarangi, A., Rajput, T.B.S. and Singh, M. (2014). Prediction of maize yield under future water availability scenarios using the AquaCrop model. Journal of Agricultural Science. 152:558-574.
  2. Abedinpour, M., Sarangi, A., Singh, M., Pathak, H. and Ahmed, T. (2012). Performance evaluation of AquaCrop model for maize crop in semi arid environment. Agricultural Water Management. 110: 55-65.
  3. Ahmadi, S.H., Mosallaeepour, E., Haghighi, A.A.K. and Sepaskhah, A.R. (2015). Modeling maize yield and soil water content with AquaCrop under full and deficit irrigation managements. Water Resource Management. 29:2837-2853.
  4. Debaeke, P. and Aboudrare, A. (2004). Adaptation of crop management to water-limited environments. European Journal of Agronomy. 21:433-446.
  5. FAO, (2008). HotIssues: Water Scarcity, FAOweblink: http://www.
  6. Fereres, E.M. and Soriano, A. (2007). Deficit irrigation for reducing agricultural water use: Integrated approaches to sustain and improve plant production under drought stress special issue. Journal of Botany. 58:147-159.
  7. Gebreselassie,Y., Mekonen, A. and Kassa, T. (2015). Field experimentation based simulation of yield response of maize crop to deficit irrigation using AquaCrop model, Arba Minch, Ethiopia. African Journal Of of Agricultural Research. 10 (4): 269-280.
  8. Heng, L.K., Hsiao, T.C., Evett, S., Howell. T. and Steduto, P. (2009). Validating the FAO AquaCrop model for irrigated and water deficient field maize. Agronomy Journal. 101: 488-498.
  9. Homayounfar, M., Lai, S.H., Zomorodian, M., Sepaskhah, A.R. and Ganji, A. (2014). Optimal crop water allocation in case of drought occurrence, imposing deficit irrigation with proportional cutback constraint. Water Resource Management. 28:3207-3225.
  10. Hsiao, T.C., Heng, L., Steduto, P., Rojas Lara, B., Raes, D. and Fereres, E. (2009). AquaCrop-The FAO crop model to simulate yield response to water: III. Parameterization and testing for maize. Agronomy Journal. 101:448-459.
  11. Kloss, S., Pushpalatha, R., Kamoyo, K.J. and Schutze, N. (2012). Evaluation of crop models for simulating and optimizing deficit irrigation systems in arid and semi-arid countries under climate variability. Water Resource Management. 26: 997-1014.
  12. Kumar, V. and Ahlawat, I.P.S. (2004). Carry-over of biofertilizers and nitrogen applied to wheat (Triticum aestivum L.) and direct applied N in maize (Zea mays L.) in wheat-maize cropping system. Indian Journal of Agronomy. 49 (4): 233-    236.
  13. Mhizha, T., Geerts, S., Vanuytrecht, E., Makarau, A and Raes, D. (2014). Use of the FAO AquaCrop model in developing sowing guidelines for rainfed maize in Zimbabwe. Water SA. 40:233-244.
  14. Pereira, L.S. (2006). Irrigated agriculture: Facing environmental and water scarcity challenges. In: International Symposium on Water and land management for Sustainable Irrigated Agriculture, Cukurova University, April 4-8, Turkey.
  15. Raes, D., Steduto, P., Hsiao, T.C. and Fereres, E. (2009). AquaCrop-    The FAO crop model to simulate yield response to water: II. Main algorithms and software description. Agron. Journal. 101: 438-447.
  16. Singh, A. (2014). Irrigation planning and management through optimization modelling. Water Resource Management. 28:1-14.
  17. Steduto, P., Hsiao, T., Raes, C.D and Fereres, E. (2009). AquaCrop The FAO crop model to simulate yield response to water: I. Concepts and underlying principles. Agron. Journal. 101: 426-437.

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