Simulation of Growth and Development of Tomato (Lycopersicon esculentum Mill.) under Drought Stress: 1- Simulation of Soil Water Content, Evapotranspiration, and Green Canopy Cover

Document Type : Research Article

Authors

• E. Farrokhi ¹
• M. Nassiri Mahallati ²
• A. Koocheki ²
• alireza beheshti

¹ Ph.D. Candidate of Agrotechnology Department, Faculty of Agriculture, Ferdowsi University of Mashhad, Iran

² Professor of Agrotechnology Department, Faculty of Agriculture, Ferdowsi University of Mashhad, Iran

https://doi.org/10.22067/jsw.2021.15011.0

Abstract

Introduction: The modeling approach for the simulation of the growth and development of tomatoes in Iran has been overlooked. Calibrated crop simulation models, therefore, are increasingly being used as an alternative means for the 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 settings. This study was conducted as a two-year field experiment with the aim of the simulation of soil water content, evapotranspiration, and green canopy cover of tomato using AquaCrop model under different irrigation regimes at two growth stages in Mashhad climate conditions.
Materials and Methods: A field experiment was conducted over two consecutive seasons (2016-2017) in the experimental field of Ferdowsi University of Mashhad, located in Khorasan Razavi province, North East of Iran. The experiment was laid out in a split-plot design with different irrigation regimes at the vegetative and at the reproductive stage as the main and subplot factors, replicated thrice. In total, 27 plots of 4.5×3 m (13.5 m²) were used at a planting density of 2.7 plants per m². Seedlings were planted in a zigzag pattern into twin rows, with a distance of 1.5 m between them, so there were four twin rows of three meters in each plot. The distance between tomato plants within each twin-row was 0.5 meters. A buffer zone spacing of 3 and 1.5 m was provided between the main plots and subplots, respectively. The following experimental factors were studied: three irrigation regimes (100= 100% of water requirement, 75= 75% of water requirement, 50= 50% of water requirement) and two crop growth stages (V= vegetative stage and R= Reproductive stage). 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. Model accuracy was evaluated using statistical measures, e.g., R², normalized root means square error (NRMSE), model efficiency (E.F.), and d-Willmott. The 2016 and 2017 measured soil and canopy data sets were used for calibration and validation of the AquaCrop model, respectively.
Results and Discussion: For a water-driven model, such as AquaCrop, it is important to evaluate its effectiveness in simulating soil water content. During calibration (2016), the model simulated the soil water content with good accuracy. The simulated soil water content values were close to the observed values during calibration (2016) for all treatments with R² ranging from 0.90 to 0.97, NRMSE in range of 8.47 to 17.96%, d varying from 0.76 to 0.99, and M.E. ranging from 0.87 to 0.96. Validation results indicated the good performance of the model in simulating soil water content for most of the treatments (0.79<R²<0.99, 10.04%<NRMSE<18.65%, 0.77<ME<0.92).
Appropriate parameterization of canopy cover curve is critical for the model to provide accurate estimates of soil evaporation, crop transpiration, biomass, and yield. In general, the calibration results showed good agreement between simulated and observed data for canopy cover development in all treatments with high R² values (>0.87), good E.F. (>0.61), low estimation errors (RMSE, ranging from only 4.5 to 9.2) and high d values (>0.92).
Conclusion: The AquaCrop model (version 6.1) was calibrated and validated for modeling soil water content, evapotranspiration, and green canopy cover for tomatoes under drought stress conditions. In general, soil water content, evapotranspiration, and green canopy cover of tomato were simulated by AquaCrop model with acceptable accuracy in both calibration and validation stages. However, the model performance was more accurate in no and/or moderate stress conditions than in severe water stress environments. In conclusion, the AquaCrop model could be calibrated to simulate the growth and soil water content of tomatoes under temperate conditions reasonably well and become a very useful tool to support the decision on when and how much irrigate.
For R², values > 0.90 were considered very well, while values between 0.70 and 0.90 were considered good. Values between 0.50 and 0.70 were considered moderately well, while values less than 0.50 were considered poor. Root mean square error ranges from 0 to positive infinity and expresses in the units of the studied variable. An RMSE approaching 0 indicates good model performance.

Keywords

• AquaCrop model
• CropWat
• Deficit irrigation
• Modelling
• Soil water balance

20.1001.1.20084757.1400.35.3.1.0

Main Subjects

• Irrigation

References

1- Abrha B., Delbecque N., Raes D., Tsegay A., Todorovic M., Heng L.E.E., Vanutrecht E., Geerts S.A.M., Garcia-Vila M., and Deckers S. 2012. Sowing strategies for barley (Hordeum vulgare L.) Based on modelled yield response to water with aquacrop. Experimental Agriculture 48: 252-271.

2- Agricultural Statistics of Iran. 2018. The yearbook of agriculture statistics of Iran. Bureau of statistics and information technology, The ministry of Agriculture, Tehran, Iran. 232 pages. (In persian)

3- 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.

4- Andarzian B., Bannayan M., Steduto P., Mazraeh 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.

5- Araya A., Habtu S., Hadgu K.M., Kebede A., and Dejene T. 2010a. Test of AquaCrop model in simulating biomass and yield of water deficient and irrigated barley (Hordeum vulgare). Agricultural Water Management 97: 1838-1846.

6- 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.

7- Bahmani O., and Eghbalian S. 2018. Simulating the Response of Sugarcane Production to Water Deficit Irrigation Using the AquaCrop Model. Agricultural Research 7: 158-166.

8- Carlson T.N., and Ripley D.A. 1997. On the relation between NDVI, fractional vegetation cover, and leaf area index. Remote Sensing of Environment 62: 241-252.

9- Cusicanqui J., Dillen K., Garcia M., Geerts S., Raes D., and Mathijs E. 2013. Economic assessment at farm level of the implementation of deficit irrigation for quinoa production in the Southern Bolivian Altiplano. Spanish Journal of Agricultural Research11(4): DO - 10.5424/sjar/2013114-4097.

10- Doorenbos J., and Kassam A. 1979. Yield response to water. FAO Irrigation and Drainage, Paper No. 33: 257.

11- FAO STAT. 2018. The State of the World’s Land and Water Resources for Food and Agriculture (SOLAW)-Managing Systems at Risk. Food and Agriculture Organization of the United Nations, Rome and Earth scan, London.

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

13- Farahani H.J., Izzi G., and Oweis T.Y. 2009. Parameterization and Evaluation of the AquaCrop Model for Full and Deficit Irrigated Cotton. Agronomy Journal 101: 469-476.

14- Farrokhi E., Nassiri Mahalati M., Koocheki A., Beheshti S.A. 2022. Simulation of growth and development of tomato (Lycopersicon esculentum Mill.) under drought stress: 2- simulation of water productivity, above ground biomass and yield. Water and Soil. (Accepted). (In Persian with English abstract)

15- Farrokhi E., Nassiri Mahalati M., Koocheki A., Beheshti S.A. 2021. Light extinction coefficient and radiation use efficiency in different growth stages of tomato exposed to different irrigation regimes. Environmental Stresses in Crop Sciences. (Accepted). (In Persian with English abstract)

16- Foster T., Brozović N., and Butler A.P. 2014. Modeling irrigation behavior in groundwater systems. Water Resources Research 50: 6370-6389.

17- 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.

18- García-Vila M., and Fereres E. 2012. Combining the simulation crop model AquaCrop with an economic model for the optimization of irrigation management at farm level. European Journal of Agronomy 36: 21-31.

19- Geerts S., Raes D., and Garcia M. 2010. Using AquaCrop to derive deficit irrigation schedules. Agricultural Water Management 98: 213-216.

20- 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.

21- Grassini P., Yang H., Irmak S., Thorburn J., Burr C., and Cassman K.G. 2011. High-yield irrigated maize in the Western U.S. Corn Belt: II. Irrigation management and crop water productivity. Field Crops Research 120: 133-141.

22- 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.

23- 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.

24- 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.

25- Jiang Z., Huete A.R., Chen J., Chen Y., Li J., Yan G., and Zhang X. 2006. Analysis of NDVI and scaled difference vegetation index retrievals of vegetation fraction. Remote Sensing of Environment 101: 366-378.

26- Johnson L.F., and Trout T.J. 2012. Satellite NDVI Assisted Monitoring of Vegetable Crop Evapotranspiration in California’s San Joaquin Valley. Remote Sensing 4.

27- 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.

28- Kim D., and Kaluarachchi J. 2015. Validating FAO AquaCrop using Landsat images and regional crop information. Agricultural Water Management 149: 143-155.

29- 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: 419-427.

30- Mhizha T. 2010. Increase of yield stability by staggering the sowing dates of different varieties of rainfed maize in Zimbabwe.

31- 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.

32- Nyakudya I.W., and Stroosnijder L. 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 146: 280-296.

33- Paredes P., de Melo-Abreu J.P., Alves I., and Pereira L.S. 2014. Assessing the performance of the FAO AquaCrop model to estimate maize yields and water use under full and deficit irrigation with focus on model parameterization. Agricultural Water Management 144: 81-97.

34- Pirmoradian N., and Davatgar N. 2019. Simulating the effects of climatic fluctuations on rice irrigation water requirement using AquaCrop. Agricultural Water Management 213: 97-106.

35- 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.

36- Richey A.S., Thomas B.F., Lo M.H., Reager J.T., Famiglietti J.S., Voss K., Swenson S., and Rodell M. 2015. Quantifying renewable groundwater stress with GRACE. Water Resources Research 51: 5217-5238.

37- 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.

38- Schewe J., Heinke J., Gerten D., Haddeland I., Arnell N.W., Clark D.B., Dankers R., Eisner S., Fekete B.M., Colón-González F.J., Gosling S.N., Kim H., Liu X., Masaki Y., Portmann F.T., Satoh Y., Stacke T., Tang Q., Wada Y., Wisser D., Albrecht T., Frieler K., Piontek F., Warszawski L., and Kabat P. 2014. Multimodel assessment of water scarcity under climate change. Proceedings of the National Academy of Sciences 111: 3245-3250.

39- Shrestha N., Raes D., Vanuytrecht E., and Sah S.K. 2013. Cereal yield stabilization in Terai (Nepal) by water and soil fertility management modeling. Agricultural Water Management 122: 53-62.

40- Steduto P., Hsiao T.C., Fereres E., and Raes D. 2012. "Crop yield response to water," fao Rome.

41- 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.

42- Tavakoli A.R., Mahdavi Moghadam M., and Sepaskhah A.R. 2015. Evaluation of the AquaCrop model for barley production under deficit irrigation and rainfed condition in Iran. Agricultural Water Management 161: 136-146.

43- Taylor R. 2014. When wells run dry. Nature 516: 179-180.

44- Todorovic, M., Albrizio, R., Zivotic, L., Saab, M.-T. A., 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.

45- 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.

46- Tsegay, A. 2012. Improving Crop Production by Field Management Strategies Using Crop Water Productivity Modeling: Case Study of Tef (Eragrostistef (Zucc.) Trotter) Production in Tigray, Ethiopia. PhD Manuscript.

47- Tsegay A., Raes D., Geerts S., Vanuytrecht E., Abraha B., Deckers J., Bauer H., and Gebrehiwot K. 2012. Unravelling crop water productivity of tef (Eragrostis Tef (Zucc.) Trotter) through AquaCrop in northern Ethiopia. Experimental Agriculture 48: 222-237.

48- Van Ittersum M.K., Cassman K.G., Grassini P., Wolf J., Tittonell P., and Hochman Z. 2013. Yield gap analysis with local to global relevance—A review. Field Crops Research 143: 4-17.

49- 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.

50- Zeleke K.T., Luckett D., and Cowley R. 2011. Calibration and Testing of the FAO AquaCrop Model for Canola. Agronomy Journal 103: 1610-1618.

51- Zinyengere N., Mhizha T., Mashonjowa E., Chipindu B., Geerts S., and Raes D. 2011. Using seasonal climate forecasts to improve maize production decision support in Zimbabwe. Agricultural and Forest Meteorology 151: 1792-1799.