Simulation of Growth and Development of Tomato (Lycopersicon esculentum Mill.) under Drought Stress: 2- Simulation of Water Productivity, Above Ground Biomass and Yield

آب و خاک

Article 2, Volume 35, Issue 5 - Serial Number 79, November and December 2021, Pages 627-643   

Document Type: Research Article

DOI: 10.22067/jsw.2021.15035.0

Authors

E. Farrokhi; M. Nassiri Mahallati^* ; A. Koocheki; alireza beheshti

Agrotechnology Department, Faculty of Agriculture, Ferdowsi University of Mashhad, Iran

Abstract

Introduction: Predicting yield is increasingly important to optimize irrigation under limited available water to enhance sustainable production. Calibrated crop simulation models therefore increasingly are being used an alternative means for rapid assessment of water-limited crop yield over a wide range of environmental and management conditions. AquaCrop is a multi-crop model that simulates the water-limited yield of herbaceous crop types under different biophysical and management conditions. It requires a relatively small number of explicit and mostly-intuitive parameters to be defined compared to other crop models, and has been validated and applied successfully for multiple crop types across a wide range of environmental and agronomic setting. This study was conducted as a two-year field experiment with the aim of the simulation of water productivity, above ground biomass and fresh and dry yield of tomato using AquaCrop model under different irrigation regimes applied at two growth stages in Mashhad climate conditions.
Materials and Methods: A two-year field experiment was conducted during 2016-2017 growing seasons in the experimental field of Ferdowsi University of Mashhad located in Khorasan Razavi province, North East of Iran. The water-driven AquaCrop model developed by FAO was calibrated and validated to simulate water productivity, above-ground biomass and yield of tomato crop under varying irrigation regimes. AquaCrop was calibrated and validated for tomato under full (100% of water requirements) and deficit (75 and 50% of water requirements) irrigation regimes at vegetative (100V, 75V, and 50V) and reproductive stages (100R, 75R, and 50R). Model performance was evaluated in terms of the normalized root mean squared error (NRSME), the Nash–Sutcliffe model efficiency coefficient (EF), Willmott’s index of agreement (d) and coefficient of determination (R²). The drip irrigation method was used for irrigation. The tomato water requirement was calculated using CROPWAT 8.0 software. The irrigation water was supplied based on total gross irrigation and obtained irrigation schedule of CROPWAT. The 2016 and 2017 measured data sets were used for calibration and validation of AquaCrop model, respectively.
Results and Discussion: Calibration results showed good agreement between simulated and observed data for water productivity in all treatments with high R² value (0.93), good ME (0.23), low estimation errors (RMSE=0.09 kgm³) and high d value (0.85). The goodness of fit results showed that measured WP values were closer to simulated WP values for the validation season (2017) than for the calibration season (2016). During calibration, (2016), the model simulated the biomass with good accuracy. The simulated above ground biomass values were close to the observed values during calibration (2016) for all treatments with R² ranging from 0.92 to 0.99, NRMSE in range of 7.4 to 23%, d varying from 0.94 to 1, and ME ranging from 0.71 to 0.98. Validation results indicated good performance of model in simulating above ground biomass for most of the treatments (0.92 < R² < 0.98, 6.5% < NRMSE < 21.3%, 0.76 < ME < 0.99). During validation (2017 growing season), overall, the trend of biomass growth (or accumulation) was captured well by model. However, the range of biomass of simulation errors was high, especially in treatments with higher stress. Accurate simulation of the response of yield to water is important for agricultural production, especially in an arid region where agriculture depends closely heavily on irrigation. During validation, the model predicted dry and fresh yield satisfactorily (NRMSE = 15.64% and 11.80% for dry and fresh yield, respectively).
Conclusion: In general, the AquaCrop model was able to simulate the observed water productivity, above ground biomass and yield of tomato satisfactorily in both calibration and validation stage. However, the model performance was more accurate in non- and/or moderate stress conditions than in sever water-stress environments. In conclusion, the AquaCrop model could be calibrated to simulate growth and yield of tomato under temperate condition, reasonably well, and become a very useful tool to support decision on when and how much irrigate. This study provides the first estimate of the soil and plant parameter values of AquaCrop for simulation of tomato growth in Iran. Model parameterization is site specific, and thus the applicability of key calibrated parameters must be tested under different climate, soil, variety, irrigation methods, and field management.

Keywords

AquaCrop model; Deficit irrigation; Modelling; Vegetative and reproductive stages; Water productivity

References

1- Ahmadi S. H., Mosallaeepour E., Kamgar-Haghighi A.A., and Sepaskhah A.R. 2015. Modeling Maize Yield and Soil Water Content with AquaCrop Under Full and Deficit Irrigation Managements. Water Resources Management 29: 2837-2853.

2- Araya A., Keesstra S.D., and Stroosnijder L. 2010b. Simulating yield response to water of Teff (Eragrostis tef) with FAO's AquaCrop model. Field Crops Research 116: 196-204.

3- Battilani A., Letterio T., and Chiari G. 2015. Aquacrop model calibration and validation for processing tomato crop in a sub-humid climate. pp. 167-174. International Society for Horticultural Science (ISHS), Leuven, Belgium.

4- Doorenbos J., and Kassam A. 1979. Yield response to water. Irrigation and drainage paper 33, 257.

5- Evett S.R., and Tolk J.A. 2009. Introduction: Can Water Use Efficiency Be Modeled Well Enough to Impact Crop Management? Agronomy Journal 101: 423-425.

6- FAO. 2019. Downloads for AquaCrop (Version 6.1) Standard Window Program and Plug-in Program. http://www.fao.org/nr/water/aquacrop.html.

7- Farrokhi E., Nassiri Mahallati M., Koocheki A., and Beheshti S.A. 2021. Simulation of growth and development of tomato (Lycopersicon esculentum Mill.) Under drought stress: 1- simulation of soil water content, evapotranspiration and green canopy cover. Journal of Water and Soil 35(3): 299-318. (In Persian with English abstract)

8- Foster T., Brozović N., Butler A.P., Neale C.M.U., Raes D., Steduto P., Fereres E., and Hsiao T.C. 2017. AquaCrop-OS: An open source version of FAO's crop water productivity model. Agricultural Water Management 181: 18-22.

9- Greaves E.G., and Wang Y.-M. 2016. Assessment of FAO AquaCrop Model for Simulating Maize Growth and Productivity under Deficit Irrigation in a Tropical Environment. Water 8.

10- Heng L.K., Hsiao T., 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.

11- Heuvelink E. 2018. Tomatoes, 2nd edition. Boston, MA: CABI.

12- 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.

13- Ismail S., El-Abedin T.Z., El-Ansary D., and Abdel-Al A. 2015. Modification of FAO Crop Model to Simulate Yield Response to Water for Peach Trees. Misr Journal Agriculture Engineering 32: 145-172.

14- Katerji N., Campi P., and Mastrorilli M. 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 130: 14-26.

15- Linker R., Ioslovich I., Sylaios G., Plauborg F., and Battilani A. 2016. Optimal model-based deficit irrigation scheduling using AquaCrop: A simulation study with cotton, potato and tomato. Agricultural Water Management 163: 236-243.

16- Ngouajio M., Wang G., and Goldy R. 2007. Withholding of drip irrigation between transplanting and flowering increases the yield of field-grown tomato under plastic mulch. Agricultural Water Management 87: 285-291.

17- Patanè C., and Cosentino S.L. 2010. Effects of soil water deficit on yield and quality of processing tomato under a Mediterranean climate. Agricultural Water Management 97: 131-138.

18- Patanè C., Tringali S., and Sortino O. 2011. Effects of deficit irrigation on biomass, yield, water productivity and fruit quality of processing tomato under semi-arid Mediterranean climate conditions. Scientia Horticulturae 129: 590-596.

19- Pawar G.S., Kale M.U., and Lokhande J.N. 2017. Response of aquacrop model to different irrigation schedules for irrigated cabbage. Agricultural Research 6: 73-81.

20- 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. Agronomy Journal 101: 438-447.

21- Sandhu R., and Irmak S. 2019. Performance of AquaCrop model in simulating maize growth, yield, and evapotranspiration under rainfed, limited and full irrigation. Agricultural Water Management 223: 105687.

22- 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.

23- Stricevic R., Cosic M., Djurovic N., Pejic B., and Maksimovic L. 2011. Assessment of the FAO AquaCrop model in the simulation of rainfed and supplementally irrigated maize, sugar beet and sunflower. Agricultural Water Management 98: 1615-1621.

24- Tsakmakis I.D., Kokkos N.P., Gikas G.D., Pisinaras V., Hatzigiannakis E., Arampatzis G., and Sylaios G.K. 2019. Evaluation of AquaCrop model simulations of cotton growth under deficit irrigation with an emphasis on root growth and water extraction patterns. Agricultural Water Management 213: 419-432.

25- Vanuytrecht E., Raes D., Steduto P., Hsiao T.C., Fereres E., Heng L.K., Garcia Vila M., and Mejias Moreno P. 2014. AquaCrop: FAO's crop water productivity and yield response model. Environmental Modelling & Software 62: 351-360.

26- Wang F., Kang S., Du T., Li F., and Qiu R. 2011. Determination of comprehensive quality index for tomato and its response to different irrigation treatments. Agricultural Water Management 98: 1228-1238.

27- WRG (2030 Water Resources Group). 2009. Charting Our Water Future. The Barilla Group, The Coca-Cola Company, The International Finance Corporation, McKinsey & Company, Nestle S.A. Available from: http://www.2030 wrg.org/wp-content/uploads/2014/07/Charting-Our-Water- Future-Final.pdf

28- WWAP (World Water Assessment Programme), 2012. The United Nations World Water Development Report 4: Managing Water under Uncertainty and Risk. Paris, France. UNESCO. Available from: https://www.zaragoza.es/ contenidos/medio ambiente/onu// newsletter12/ 789-eng-sum-ed4.pdf