Validation of AquaCrop Model for Simulation of Winter Wheat Yield and Water Use Efficiency under Simultaneous Salinity and Water Stress

آب و خاک

Article 5, Volume 29, Issue 1, March 2015, Pages 67-84   

Document Type: Research Article

DOI: 10.22067/jsw.v0i0.31304

Authors

M. Mohammadi^{* 1}; B. Ghahraman¹; K. Davary¹; H. Ansari¹; A. Shahidi²

¹Ferdowsi University of Mashhad

²University of Birjand

Abstract

Introduction: FAO AquaCrop model (Raes et al., 2009a; Steduto et al., 2009) is a user-friendly and practitioner oriented type of model, because it maintains an optimal balance between accuracy, robustness, and simplicity; and it requires a relatively small number of model input parameters. The FAO AquaCrop model predicts crop productivity, water requirement, and water use efficiency under water-limiting and saline water conditions. This model has been tested and validated for different crops such as maize, sunflower and wheat (T. aestivum L.) under diverse environments. In most of arid and semi-arid regions water shortage is associated with reduction in water quality (i.e. increasing salinity). Plants in these regions in terms of water quality and quantity may be affected by simultaneous salinity and water stress. Therefore, in this study, the AquaCrop model was evaluated under simultaneous salinity and water stress. In this study, AquaCrop Model (v4.0) was used. This version was developed in 2012 to quantify the effects of salinity. Therefore, the objectives of this study were: i) evaluation of AquaCrop model (v4.0) to simulate wheat yield and water use efficiency under simultaneous salinity and water stress conditions in an arid region of Birjand, Iran and ii) Using different treatments for nested calibration and validation of AquaCrop model.
Materials and Methods: This study was carried out as split plot design (factorial form) in Birjand, east of Iran, in order to evaluate the AquaCrop model.Treatments consisted of three levels of irrigation water salinity (S1, S2, S3 corresponding to 1.4, 4.5, 9.6 dS m-1) as main plot, two wheat varieties (Ghods and Roshan), and four levels of irrigation water amount (I1, I2, I3, I4 corresponding to 125, 100, 75, 50% water requirement) as sub plot. First, AquaCrop model was run with the corresponding data of S1 treatments (for all I1, I2, I3, and I4) and the results (wheat grain yield, average of soil water content, and ECe) were considered as the “basic outputs”. After that and in the next runs of the model, in each step, one of the inputs was changed while the other inputs were kept constant. The interval of variation of the inputs was chosen from -25 to +25% of its median value. After changing the values of input parameters, the model outputs were compared with the “basic outputs” using the sensitivity coefficient (Sc) of McCuen, (1973). Since there are four irrigation treatments for each salinity treatment, the model was calibrated using two irrigation treatments for each salinity treatment and validated using the other two irrigation treatments. In fact, six different cases of calibration and validation for each salinity treatment were [(I3 and I4), (I2 and I4), (I1 and I4), (I2 and I3), (I1 and I3), and (I1 and I2) for calibration and (I1 and I2), (I1 and I3), (I2 and I3), (I1 and I4), (I2 and I4), and (I3 and I4) for validation, respectively]. The model was calibrated by changing the coefficients of water stress (i.e. stomata conductance threshold (p-upper) stomata stress coefficient curve shape, senescence stress coefficient (p-upper), and senescence stress coefficient curve shape) for six different cases. Therefore, the average relative error of the measured and simulated grain yield was minimized for each case of calibration. After calibrating the model for each salinity treatment, the model was simultaneously calibrated using six different cases for three salinity treatments as a whole.
Results and Discussion: Results showed that the sensitivity of the model to crop coefficient for transpiration (KcTr), normalized water productivity (WP*), reference harvest index (HIo), θFC, θsat, and maximum temperature was moderate. The average value of NRMSE, CRM, d, and R2 for soil water content were 11.76, 0.055, 0.79, and 0.61, respectively and for soil salinity were 24.4, 0.195, 0.72, and 0.57, respectively. The model accuracy for simulation of soil water content was more than for simulation of soil salinity. In general, the model accuracy for simulation yield and WP was better than simulation of biomass. The d (index of agreement) values were very close to one for both varieties, which means that simulated reduction in grain yield and biomass was similar to those of measured ones. In most cases the R2 values were about one, confirming a good correlation between simulated and measured values. The NRMSE values in most cases were lower than 10% which seems to be good. The CRM values were close to zero (under- and over-estimation were negligible). Based on higher WP under deficit irrigation treatments (e.g. I3) compared to full irrigation treatments (e.g. I1 and I2), it seems logical to adopt I3 treatment, especially in Birjand as a water-short region, assigning the remaining 25% to another piece of land. By such strategy, WP would be optimized at the regional scale.
Conclusion: The AquaCrop was separately and simultaneously nested calibrated and validated for all salinity treatments. The model accuracy under simultaneous case was slightly lower than that for separate case. According to the results, if the model is well calibrated for minimum and maximum irrigation treatments (full irrigation and maximum deficit irrigation), it could simulate grain yield for any other irrigation treatment in between these two limits. Adopting this approach may reduce the cost of field studies for calibrating the model, since only two irrigation treatments should be conducted in the field. AquaCrop model can be a valuable tool for modelling winter wheat grain yield, WP and biomass. The simplicity of AquaCrop, as it is less data dependent, made it to be user-friendly. Nevertheless, the performance of the model has to be evaluated, validated and fine-tuned under a wider range of conditions and crops.

Keywords: Biomass, Plant modeling, Sensitivity analysis

Keywords

Biomass; Plant modeling; sensitivity analysis

References

1- Afyooni D. 2005. The effect of seeding rate on wheat cultivars performance under salinity stress. Journal of Agriculture, 7(2): 7-16. (in Persian with English abstract)

2- Alizadeh H.A., Nazari B., Parsinejad M., Ramezani-Eetedali H., Janbaz H.R. 2010. Evaluation of AquaCrop model on wheat deficit irrigation in Karaj area.Iranian Journal of Irrigation and drainage, 2(4):273-283. (in Persian with English abstract)

3- Andarziana B., Bannayanb M., Stedutoc P., Mazraeha H., Barati M.E., Barati M.A., and Rahnama A. 2011. Validation and testing of the AquaCrop model under full and deficit irrigated wheat production in Iran. Agricultural Water Management, 100:1-8.

4- Babazadeh H., and Sarai-Tabrizi M. 2012. Assessment of AquaCrop model under soybean deficit irrigation management conditions. Journal of Water and Soil, 26(2): 329-339, (in Persian with English abstract)

5- Bastiaanssen W.G.M., Allen R.G., Droogers P., D’Urso G., and Steduto P. 2007. Twenty five years modeling irrigated and drained soils: State of the art. Agricultural Water Management, 92(3), 111–125.

6- Doorenbos J., and Kassam A.H. 1979. Yield Response to Water. Irrigation and Drainage Paper No. 33. FAO, Rome.

7- Egli D.B., and Bruening W. 1992. Planting date and soybean yield: Evaluation of environmental effects with a crop simulation model: SOYGRO, Agricultural and Forest Meteorology Journal, 62:19-29.

8- Fayyaz F., Kheradnam M., Assad M.T. 2006. Evaluation of the morphophysiological traits heritability drought stress conditions in dread wheat genotypes (Triticum aestivum L.). Agricultural Sciences and Technology Journal, 20(5):35-46. (in Persian with English abstract)

9- Fereres E., and Soriano M.A. 2007. Deficit irrigation for reducing agricultural water use. Journal of Experimental Botany, 58, 147–159.

10- Geerts S., Raes D., Garcia M., Miranda R., Cusicanqui J.A., Taboada C., Mendoza J., Huanca R., Mamani A., Condori O., Mamani J., Morales B., Osco V., and Steduto P. 2009. Simulating yield response of quinoa to water availability with AquaCrop. Agronomy Journal, 101: 499–508.

11- Hajiabadi M.R., Sadeghzadeh A., and Soltani H.R. 2003. Determination of irrigation suitable date of wheat using evaporation pan data in Birhand region. Agriculture and Natural Resources Research Center of Khorasan. n. 109-12-56854, pp. 85. (in Persian)

12- 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.

13- Hsiao T.C., Heng L.K., Steduto P., Rojas­Lara 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.

14- Jones C.A., Kiniry J.R., and Dyke P.T. 1986. CERES-Maize: A simulation model of maize growth and development, User's guide of CERES-Maize. Texas University Press College Station (USA).

15- Kroes J.G., and Van Dam J.C. 2008. Reference manual SWAP version 3.2., Alterra Green World Research, Wagenningen, Report 1649 (Available at: www.alterra.nl/models/swap).

16- Kuo S.F., Lin B.J., and Shieh H.J. 2006. Estimation irrigation water requirements with derived crop coefficients for upland and paddy crops in ChiaNan Irrigation Association, Taiwan. Agricultural Water Management, 82:433-451.

17- Lopez-Urrea R., Montoro A., Gonza lez-Piqueras J., Lopez-Fuster P., and Fereres E. 2009. Water use of spring wheat to raise water productivity. Agricultural Water Management, 96:1305-1310.

18- Marinov D., Querner E., and Roelsma J. 2005. Simulation of water flow and nitrogen transport for a Bulgarian experimental plot using SWAP and ANIMO models. Journal of Contaminant Hydrology, 77: 145-164.

19- Mebane, V.J., Day R.L., Hamlett J.M., Watson J.E., and Roth G.W. 2013. Validating the FAO AquaCrop model for rainfed maize in Pennsylvania. Agronomy Journal, 105(2):419-427.

20- Meyer G.E., Curry R.B., Streeter J.G., and Baker C.H. 1981. Simulation of reproductive processes and senescence in indeterminate soybeans. Transactions of the ASABE. 24 (2):421- 429.

21- Mkhabela M.S., and 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.

22- Owies T., Zhang H., and Pala M. 2000. Water use efficiency of rainfed and irrigated bread wheat in Mediterranean Environment, Agronomy Journal, 92:231-238.

23- Raes D. 2002. Reference manual of Budget model. K. U. Leuven, Faculty of Agricultural and Applied Biological Sciences, Institute for Land and Water Management, Leuven, Belgium.

24- Raes D., Steduto P., Hsiao T.C., and Fereres E. 2009. AquaCrop-the FAO crop model for predicting yield response to water: II. Main algorithms and software description. Agronomy Journal, 101:438–447.

25- Raes D., Steduto P., Hsiao T.C., and Fereres E. 2012. Reference manual AquaCrop, FAO, Land and Water Division, Rome, Italy.

26- Salemi H., Mohd Soom M.A., Lee T.S., and Mousavi S.F., Ganji A., and KamilYusoff M. 2011. Application of AquaCrop model in deficit irrigation management of Winter wheat in arid region. African Journal of Agricultural Research,. 610: 2204-2215.

27- Shamsnia S.A., and Pirmoradian N. 2013. Simulation of rainfed wheat yield response to climatic fluctuations using AquaCrop model (case study: Shiraz region in southern of Iran). International Journal of Engineering Science Invention, 2(4):51-56.

28- Singh R. 2004. Simulation on direct and cyclic use of saline waters for sustaining Cotton-Wheat in a semi-arid area of north-west India. Agricultural Water Management, 66: 153-162.

29- Steduto P., Hsiao T.C., Raes D., and Fereres E. 2007. On the conservative behavior of biomass water productivity. Irrigation Science. 25:189–207.

30- Steduto P., Hsiao T.C., Raes D., and Fereres E. 2009. AquaCrop-the FAO crop model to simulate yield response to water: I. concepts and underlying principles. Agronomy Journal, 101:426-437.

31- Todorovic M., Albrizio R., Zivotic L., Abi Saab M., Stöckle C., and Steduto P. 2009. Assessment of AquaCrop, CropSyst, and WOFOST models in the simulation of sunflower growth under different water regimes. Agronomy Journal, 101: 509–521.

32- Van Dam J.C., Groenendijk P., Hendriks R.F.A., and Kroes J.G. 2008. Advances of modeling water flow in variably saturated soils with SWAP. Vadose Zone Journal, 7:640-653.

33- Zamani Gh.R. 2004. Ecophysiological aspects of wild oat competition with wheat under salinity stress. Ph.D. Thesis. Ferdowsi Univesity of Mashhad.