Application of SSM-iCrop2 Model for Yield and Water Balance Simulation under Farmers’ Conditions s

آب و خاک

Article 1, Volume 38, Issue 3 - Serial Number 95, July and August 2024, Pages 319-301   

Document Type: Research Article

DOI: 10.22067/jsw.2024.84807.1343

Authors

S. JafarNodeh¹; A. Soltani^{* 1}; E. Soltani²; A. Dadrasi³; S. Rahban¹

¹Department of Agronomy, Colleg of Plant Production, Gorgan University of Agricultural Sciences and Natural Resources, Gorgan, Iran, respectively.

²Department of Agronomy and Plant Breeding, Aboureyhan Campus, University of Tehran, Pakdasht, Iran

³Researcher in Environmental Center, Charles University, Prague, Czech Republic

Abstract

Introduction
Accurate knowledge of water balance components is necessary to optimize water consumption in agriculture. On the other hand, measuring water balance components is expensive and difficult. Therefore, the use of models that can simulate water balance values is important for water management in agriculture and water used by plants. Crop simulation models have been turned into essential tools for studying plant production systems. In the SSM-iCrop2 models, it is presumed that diseases and weeds are optimally managed and will not affect growth and yield. Additionally, except in cases where the model accounts for specific nutrients such as nitrogen, it is generally assumed that nutrient deficiencies are eliminated through fertilization. Therefore, parameterized and evaluated models are designed to fit these conditions. These factors are present in the field and affect crop growth and yield as well as water use. However, in several cases it is required to estimate yield and water balance components and irrigation water volume under grower conditions. Naturally, models parameterized using experiments are unable to simulate these conditions. Therefore, a model must be prepared so that it can simulate the real conditions of farmers. In this study, the SSM-iCrop2 model has been calibrated for the real conditions of farmers, and the purpose of this study is to use the SSM-iCrop2 model in simulating water performance and water balance for farmers.
 
Materials and Methods
In this study, the SSM-iCrop2 model was calibrated for farmers conditions using variables such as yield and harvest index, which are available for farmers’fields or are cheap to measure. The effect of factors such as pests and diseases, weeds and unsuitable nutrients, density and sowing date entered the model along with the calibration of three parameters of radiation use efficiency, maximum leaf area and maximum harvest index for farmers’ fields. Calibration was done by comparing the performance of farmers against the performance simulated by the model and by changing the parameters of radiation use efficiency (IRUE), maximum leaf area (LAIMX) and maximum harvest index (HIMAX). This calibration was done at Hashem Abad station in Gorgan for irrigated rice (paddy) and wheat. The simulated actual yield was calibrated with the actual yield. Due to the acceptable simulation of actual yields after calibration, it was presumed that other estimates made by the model are also reliable.
 
Results and Discussion
Measurement of water balance and other estimates of the model from growth and yield formation in the grower fields is expensive, but a calibrated model can estimate them at a low cost. In this study, it was shown that with the model calibrated for farmers' conditions, not other easily measured information (such as the irrigation water volume) can be obtained, with the assumption that the model accurately captures this information as well as performance. To evaluate the simulated real performance model, it was compared with the actual performance of farmers (Agricultural Jihad Report) after calibration. In addition to phenology, the SSM model simulates traits related to growth and yield, evapotranspiration values, irrigation water volume, runoff, available soil water during planting and harvesting, cumulative drainage, etc. The output of the model shows the amount of irrigation water is needed for a certain amount of performance in a given place (with specified rainfall and transpiration). The irrigation water volume calculated by the model was compared with the results of field tests from previous studies conducted by researchers at agricultural research centers. It was found that the model's output and the observed values were in good agreement. The root mean square error for rice and wheat was 216.6 and 157.6 kg per hectare, respectively, and the coefficient of variation and correlation coefficient were 4 and 85% for rice and 3 and 94% for wheat, respectively. Then, the irrigation water volume estimated by the model was evaluated and validated with the measured irrigation water volume in different crops (in Golestan province for different years). Based on the results of the evaluation, the coefficient of variation and the correlation coefficient for the simulated irrigation water volume were 8.9 and 98%, respectively, compared with the observed value. This calibration was done for rice (paddy) and irrigated wheat in the fields of Gorgan town, and the simulation and running were done using the meteorological statistics recorded in Hashem Abad weather station, Gorgan. Noting the fact that the actual yield has been simulated with good accuracy after the calibration, it was assumed that the other estimates of the model are also reliable. Thus, the calibrated model estimates them with low cost and appropriate accuracy and can complement field experiments.
 
Conclusion
This study discovered that the SSM_iCrop2 model, when calibrated for the conditions of farmers' fields, can accurately simulate both growth and yield traits as well as water balance characteristics. Notably, the model provides reliable estimates of irrigation water volume in farming scenarios, a crucial factor for agricultural planning and drought adaptation.

Keywords

Evapotranspiration; Index leaf area; Modeling; Water productivity; Water footprint

References

1. Akbari, Sh., & Soltani, A. (2017). Parameterization and evaluation of simple model for simulation of growth and yield of soybean in Tehran climatic condition. Journal of Crops Improvment, 19(3), 751-765. (In Persian with English abstract). https://doi.org/10.22059/jci.2017.60477
2. Akbari, M. (2011). Soil water balance and crop yield of winter wheat using AquaCrop simulation model. Food Engineering Research, 12(4), 19-34. https://doi.org/10.22092/jaer.2012.100329
3. Akbari, M., Naseri, A., Noorjo, A., Parchami Iraqi, F., Nakhjavani Moghadam, M., Sepehri Sadeghian, S., Alimohammadi Nafchi, R., Eslami, A., Ghasemi, M., Gomrakchi, A., Kamali Pashai, M.A., Kia, A., Guderzi, M., & Behramlu, R.(2021).Determining the usable water of peaches and nectarines in the country.Final report of the research project. Agricultural Engineering and Technical Research Institute, Agricultural Research Organization of the country, registration number
4. Abbasi, N., Taheri, M., Kiani, A., Yusuf Gomrakchi, A., Dehghanian, S.A., & Shahin Rakhsar, P. (2020).Determining the consumption of olive water in the country.Final report of the research project.Technical Research Institute and . Agricultural Engineering, Agricultural Research Organization of the country, registration number https://doi.org/10.4236/jacen.2017.64015
5. Abbasi, F., Jalini, M., Khormian, M., Dehghanian, S.A., Moqbli Doman, A., Nowrozi, M., Yusuf Gomrakchi, A., Taheri, M., Zare Mehrani, A., Kayani, A., Salah, N., Mousavi Fazl, H., Qadoumi Firouzabadi, A., Bayat, P., & Naseri, A. (2021).The role of modern irrigation systems in the management of tomato juice consumption in country.Engineering Research of Irrigation and Drainage Structures, 22(82), 43-64.
6. Arabameri, R., Soltani, A., Zeinali, E., & Torabi, B. (2020). The amount and How to distribute of chickpea and lentil yield gap in Iran. Journal of Crops Improvment, 23(2), 221-234. https://doi.org/10.22059/jci.2021. 294686.2323
7. Baghani, J., Kayani, A.,Firouzabadi, A., Shahrukhnia, M.,Jalini, M., Khosravi, H., Alimohammadi Nafchi, R., Meiri, M., Behramlu, R., Akhwan, K., Salemi, H., Moqbli, A., Nikanfar, R., & Abbasi, M. (2019).Determination of water Consumption of potatoes in the country.Final report of the research project.Technical and Engineering Research Institute. Agriculture, Agricultural Research Organization of the country, registration number 57967.
8. Bregaglio, S., Willocquet, L., Kersebaum, K.Gh., Ferrise, R., Stella, T., Ferreira, T.B., Pavan, W., Asseng, S., & Savary, S. (2021). Comparing process-based wheat growth models in their simulation of yield losses caused by plant diseases. Fild Crops Research, 1-14. https://doi.org/1016/j.fcr.2021.108108
9. Bagheri, V., & Torabi, B. (2015). A simple model for simulation of growth, development and yield of faba bean in Golestan province. Journal of Agricultural Crops Production, 8(2), 133-152. (In Persian with English abstract)
10. Dadrasi, A., Torabi, B., Rahimi, A., Soltani, A., & Zeinali, E. (2020). Parameterization and evaluation of a simple simulation model (SSM-iCrop2) for potato (Solanum tuberosum ) growth and yield in Iran. Potato Research, 63, 545-563. https://doi.org/10.1007/s11540-020-09456-y
11. Dehghan, H., Alizadeh, A., & Haghayeghi, S.A. (2011). Water balance components estimating in farm scale using simulation model SWAP (Case study: Neyshabur region). Water and Soil, 24(6), 1265-1275. (In Persian with English abstract). https://doi.org/10.22067/JSW.VOIO.7513
12. Diepen, C.V., Wolf, J., Keulen, H.V., & Rappoldt, C. (1989). WOFOST: A simulation model of crop production. Soil Use and Management, 5(1), 16-24. https://doi.org/10.1111/j.1475-2743.1989.tb00755.x
13. Ghanem, M.E., Marrou, H., Biradar, C., & Sinclair, T.R. (2015). Production potential of lentil (Lens culinaris) in East Africa. Agricultural Systems, 137, 24-38. https://doi.org/10.1016/j.agsy.2015.03005
14. Guiguitant, J., Marrou, H., Vadez, V., Gupta, P., Kumar, S., Soltani, A., Sinclair, T.R., & Ghanem, M.E. (2017). Relevance of limited-transpiration trait for lentil (Lens culinaris) in South Asia. Field Crops Research, 209, 96-107. https://doi.org/10.1016/j.fcr.2017.04.013
15. Hearn, A.B. (1994). OZCOT: A simulation model for cotton crop management. Agricultural Systems, 44, 257-299. https://doi.org/10.1016/0308.521x(94)90223-3
16. International Benchmark Sites Network for Agrotechnology Transfer. (1993). The IBSNAT Decade. Department of Agronomy and Soil Science, College of Tropical Agriculture and Human Resources, University of Hawaii, Honoluly, Hawaii.
17. Jones, J.W., Hoogenboom, G., Porter, C.H., Boote, K.J., Batchelor, W.D., Hunt, L.A., Wilkens, P.W., Singh, U., Gijsman, A.J., & Ritchie, J.T. (2003). The DSSAT cropping system model. European Journal of Agronomy, 18, 235-265. https://doi.org/1016/s1161-0301(02)00107-7
18. Kiani, A., Kamali, M.A., & Abbasi, F.(2022).Investigating the efficiency of soybean irrigation water in fields of Golestan province. Irrigation and drainage of Iran, 16(1), 69-82. (In Persian)
19. Karamat, S., Torabi, B., Soltani, A., & Zeinli, A. (2020). Evaluation of production potential and rice yield gap in Iran using SSM-iCrop2 model.Cereal Research, 11(3), 191-175. (In Persian). https://doi.org/22124/CR.2022. 20959.1696
20. Kamari, H., Zeinli, A., Soltani, A., & Qadrifar, F. (2019). Parameterization and evaluation of SSM-iCrop2 model for predicting cotton growth and yield in Iran. Simulation of cotton growth and yield using SSM-iCrop2 model.Iranian Cotton Researches, 8(2), 72-49. (In Persian). https://doi.org/22092/IJCR.2021.127700.1137
21. Messina, C.D., Sinclair, T.R., Hammer, G.L., Curan, D., Thompson, J., Oler, Z., Gho, C., & Cooper, M. (2015). Limited-transpiration trait may increase maize drought tolerance in the US Corn Belt. Agronomy Journal, 107(6), 1978-1986. https://doi.org/102134/agronj15.0016
22. McCown, R.L., Hammer, G.L., Hargreaves, J.N.G., Holzworth, D.P., & Freebairn, D.M. (1996). APSIM: a novel software system for model development, model testing, and simulation in agricultural system research. Agricultural Systems, 50, 255-271. https://doi.org/10.1016/0308-521X(94)00055-V
23. Manschadi, A.M., Kaul, H.-P., Eitzinger, J., Friedel, J., Pötsch, E., Bodner, G., & Neubauer, T. (2019). Farm/IT – innovative digital technologies for strengthening the resilience of Austrian farming systems to climate risks. Presented at the 20. Österreichischer Klimatag, Climate Change Centre Austria (CCCA), 20. KLIMATAG - Facetten der österreichischen Klimaforschung, Vienna, Austria, p. 139.
24. Manschadi, A.M., Palka, M., Fuchs, W., Neubauer, T., Eitzinger, J., & Oberforster, M. (2022). Performance of the SSM-iCrop model for predicting growth and nitrogen dynamics in winter wheat. European Journal of Agronomy, 135, 126-487. https://doi.org/10.1016/j.eja.2022.126487
25. Nehbandani, A.R., Soltani, A., Zeinali, E., Raeisi, S., & Rajabi, R. (2015). Parameterization and evaluation of SSM soybean model for prediction of growth and yield of soybean in Gorgan. Journal of Plant Production Research, 22(2), 1–26. (In Persian with English abstract). https://doi.org/20.1001.1.23222050.1394.22.3.1.2
26. Pourshirazi, S., Soltani, A., Zainli, A., & Torabi, B. (2023). Parameterization and evaluation of a simple simulation model (SSM-iCrop2) for alfalfa growth and yield in Iran. Agroecology Journal, 15, 169-189. (In Persian with English abstract). https://doi.org/10.22067/agry.2021.70433.1044
27. Rassam, G., & Soltani, A. (2007). Optimizing chickpea production management under rainfed conditions using the Computer simulation. In: Proceeding of 2nd National Symposium of Organic Farming, Oct 17–18. University of agriculture Sciences and Natural Resourcess, Gorgan. Iran. (In Persian). https://doi.org/10.22059/jci.2021. 294686.2323
28. Razzaghi, M., Kiani, A., & Abiyar, N. (2019). Cultivation and drying of rice, a technical and economic solution for rice production in the conditions of Golestan province. Agricultural Water Management, 7(1), 33-44. (In Persian)
29. Raes, D., Steduto, P., Hsiao, T.C., & Fereres, E. (2009). AquaCrop Reference Manual. FAO. Land and Water Division, Rome, Italy.
30. Rahban, S., Torabi, B., Soltani, A., & Zeinali, E. (2021). Using SSM-iCrop model to predict phenology, yield, and water productivity of canola (Brassica napus) in Iran condition. Agroecology Journal, 13(1), 157-177. https://doi.org/10.22067/JAG.V13I2.84057
31. Schoppach, R., Soltani, A., Sinclair, T.R., & Sadok, W. (2017). Yield comparison of simulated rainfed wheat and barley across Middle-East. Agricultural Systems, 153, 101-108. https://doi.org/10.1016/j.agsy.2016.12017
32. Sinclair, T.R., Messina, C.D., Beatty, A., & Samples, M. (2010). Assessment across the United States of the benefits of altered soybean drought traits. Agronomy Journal, 102(2), 475-482. https://doi.org/10.2134/agronj2009.0195
33. Sinclair, T.R., Soltani, A., Marrou, H., Ghanem, M., & Vadez, V. (2020). Geospatial assessment for crop genetic and management improvements. Crop Science. https://doi.org/10.1002/csc2.20106
34. Soltani, A., & Sinclair, T.R. (2011). A simple model for chickpea development, growth and yield. Field Crop Research, 124(2), 252-260. https://doi.org/10.1016/j.fcr.2011.06.021
35. Soltani, A., & Sinclair, T.R. (2012). Modeling physiology of crop development, growth and yield, CABI, Wallingford, UK.
36. Soltani, A., Maddah, V., & Sinclair, T.R. (2013). SSM-Wheat: a simulation model for wheat development, growth and yield. International Journal of Plant Production, 7(4), 711-740. https://doi.org/10.22069/IJPP.2013.1266
37. Soltani, A., & Sinclair, T.R. (2015). A comparison of four wheat models with respect to robustness and transparency: simulation in a temperate, sub-humid environment. Field Crop Research, 175, 37-46.
38. Soltani, A., Nehbandani, A., Zeinali, A., Torabi, B., Zand, A., Ghasemi, S., Alasti, A., Dadrasi, A., Hossain, R., Alimaghm, S.M., Zahed, M., Mohamad Zadeh, Z., Kamari, H., Arabameri, R., Fayazi, H., Rahban, S., Mohamadi, S., & Keramat, S. (2018). Providing a gap Atlas for the performance and potential of important crops in the country in present and futureclimates. Gorgan. Sirang Press.
39. Soltani, A., Nehbandani, A., Aalimaqham, S.A., Dadarsi, A., Torabi, B., Zeinli, A., Zand, A., Ghasemi, S., Barani, H., Alasti, A., Hosseini, R., Zahed, M., Fayazi, H., Kamri, H., Arab Ameri, R., Mohammadzadeh, Z., Rahban, S., Pourshirazi, S., Mohammadi, S.,Keramat, S., Sosraei, N., Ashnavar, M., & Ahmadi, M. (2019). Modeling the growth and production of vegetation on a large scale with SSM-iCrop2: crops, vegetables, orchards and pastures.Gorgan University of Agricultural Sciences and Natural Resources, research project report
40. Soltani, A., Alimagham, S.M., Nehbandani, A., Torabi, B., Zeinali, E., Dadrasi, A., Zand, E., Ghassemi, S., Pourshirazi, S., Alasti, O., Hosseini, R.S., Zahed, M., Arabameri, Mohammadzadeh, Z., Rahban, S., Kamari, H., Fayazi, H., Mohammadia, S., Keramat, V., Vadez, van Ittersum, M.K., & Sinclair, T.R. (2020a). SSMiCrop2: A simple model for diverse crop species over large areas. Agricultural Systems, 1-7. https://doi.org/10.1016/ j.agsy.2020.102855
41. Soltani, A., Alimagham, S.M., Nehbandani, A., Torabi, B., Zeinali, E., Dadrasi, A., Zand, E., Ghassemi, S., Pourshirazi, S., Alasti, O., Hosseini, R.S., Zahed, M., Arabameri, R., Mohammadzadeh, Z., Rahban, S., Kamari, H., Fayazi, H., Mohammadi, S., Keramat, S.,Vadez, V., van Ittersum, M.K., & Sinclair, T.R. (2020b). SSM-iCrop2: A simple model for diverse crop species over large areas. Agricultural Systems, 182, 102855.
42. Soltani, A., Alimagham, S.M., Nehbandani, A., Torabi, B., Zeinali, E., Zand, E., Ghassemi, S., Vadez, V., Sinclair, T.R., & van Ittersum, M.K. (2020c). Modeling plant production at country level as affected by availability and productivity of land and water. Agricultural Systems, 183, https://doi.org/10.1016/j.agasy.2020.102859
43. Stockle, C.O., Donatelli, M., & Nelson, R. (2003). CropSyst, a cropping system simulation model. European Journal of Agronomy, 18, 289-307. https://doi.org/10.1016/S1161-0301(02)00109-0
44. Shiukhy Soqanloo, S., Mousavi Baygi, M., Torabi, B., & Raeini Sarjaz, M. (2023). Evaluating the SSM model efficiency in simulating the wheat growth under water stress conditions. Journal of Water and Soil, 37(3), 353-366. (In Persian with English abstract). https://doi.org/10.22067/jsw.2023.80355.1237
45. Saeidi, R., Ramezani Etedali, H., Sotoodehnia, A., Nazari, B., & Kaviani, A. (2021). Evaluation of AquaCrop model for estimating of changes process of soil moisture, evapotranspiration and yield of maize under salinity and fertility stresses. Environment Stresses In Crop Sciences, 14(1), 195-210. https://doi.org/10.22077/escs.2020.2473.1652
46. Torabi, B., Ebrahimi, N., Soltani, A., & Zeinali, E. (2020). Parameterization and evaluation of SSM_iCrop model for prediction of growth and development of Faba bean in climatic conditions of Gorgan. Journal of Crops Improvement, 22, 531-542. (In Persian with English abstract). https://doi.org/10.22059/jci.2020.273742.2148
47. Vadez, V., Halilou, O., Hissene, H.M., Sibiry-Traore, P., Sinclair, T.R., & Soltani, A. (2017). Mapping water stress incidence and intensity, optimal plant populations, and cultivar duration for African groundnut productivity enhancement. Front. Plant Science, 8, 432.
48. Van Evert, F.K., & Campbell, G.S. (1994). CropSyst: A collection of object-oriented simulation models of agricultural system. Agronomy Journal, 86, 325-331. https://doi.org/10.2134/agronj1994.00021962008600020022x
49. Williams, J.R., Jones, C.A., Kiniry, J.R., & Spanel, D.A. (1989). The EPIC crop growth model. Trans. ASAE, 32(2), 497-0511.
50. Wolfgang Fuchs, D. (2021). Parameterisation and evaluation of the crop growth model SSM-iCrop for winter wheat grown in Eastern Austria. Doctor rerum naturalium technicarum Universität für Bodenkultur Wien. Department of Crop Sciences. Institute of Agronomy. 127p.
51. Zahed, Mm, Soltani, A., Zeinali, E., Totabi, B., Zand, S., & Alimagham, S.M. (2019). Modeling of irrigated wheat yield potential and gap in Iran. Journal of Crop Production, 12(3), 35-52. (In Persian with English abstract). https://doi.org/22069/EJCP.2019.15436.2150