شبیه سازی عملکرد و بهره وری مصرف آب گیاه سویا تحت شرایط کم آبیاری و مقادیر مختلف کود نیتروژن با استفاده از مدل DSSAT
محورهای موضوعی : مدیریت آب در مزرعه با هدف بهبود شاخص های مدیریتی آبیاری
امیر نیک اختر
1
,
علی نشاط
2
,
نجمه یزدان پناه
3
,
علی عبدزادگوهری
4
,
ابراهیم امیری
5
1 - محقق، بخش تحقيقات خاک و آب، مركز تحقيقات و آموزش كشاورزي و منابع طبيعي هرمزگان، سازمان تحقيقات و آموزش كشاورزي، بندرعباس، ايران.
2 - دانشيار، گروه مهندسي آب، واحد کرمان، دانشگاه آزاد اسلامي، کرمان، ايران.
3 - دانشيار، گروه مهندسي آب، واحد کرمان، دانشگاه آزاد اسلامي، کرمان، ايران.
4 - محقق، بخش تحقيقات مديريت آب در مزرعه، موسسه تحقيقات خاک و آب، سازمان تحقيقات، آموزش و ترويج کشاورزي، کرج، ايران.
5 - استاد، گروه مهندسي آب، واحد لاهيجان، دانشگاه آزاد اسلامي، لاهيجان، ايران.
کلید واژه: رقم ویلیامز, زیست توده, مدل سازی گياهي, نیازآبی,
چکیده مقاله :
زمینه و هدف: تنش¬آبی و کود نیتروژن بر بسیاری از فرآیندهای فیزیکی و شیمیایی مرتبط با بهره¬وری مصرف آب در سویا اثر منفی دارد. پیشبینی پاسخ عملکرد برای ارزیابی استراتژیهای مدیریت آبیاری و کود از اهمیت خاصی برخوردار است. یکی از مدل¬های پشتیبانی تصمیم در سویا، مدل CSM-CROPGRO-Soybean می¬باشد که در بسته نرم¬افزاری DSSAT قرار دارد. ﺗﺤﻘﻴﻘﺎت در ﻣﺰرﻋﻪ ﺑﺮاي ﺗﻌﻴﻴﻦ راﻫﻜﺎرﻫﺎي ﺑﻬﻴﻨﻪ در کشاورزی اﻧﺠﺎم ﻣﻲ¬ﺷﻮﻧﺪ و این مقوله ﻋﻼوه ﺑﺮ ﻫﺰﻳﻨﻪ ﺑﺮ ﺑﻮدن، زﻣﺎن ﺑﺮ ﻧﻴﺰ ﻣﻲ¬ﺑاشند، لذا هدف از این پژوهش، استفاده از ﻣﺪل ﺷﺒﻴﻪﺳﺎزي DSSAT جهت ارزیابی عملکرد و بهره¬وری مصرف آب گیاه سویا تحت شرایط تنش آبی و کود نیتروژن در استان هرمزگان بود. روش پژوهش: پژوهش حاضر به صورت طرح کرت¬های خرد شده در قالب طرح بلوک کامل تصادفي در سه تکرار در استان هرمزگان و در شهرستان حاجی¬آباد در سال¬های 1400 و 1401 انجام شد. عامل اصلي شامل بدون آبياري و تامين 40، 60، 80، 100 و 120 درصد نياز آبي و عامل فرعي مقادير کود نيتروژن شامل مصرف صفر، 50، 100، 150 و 200 کیلوگرم بر هکتار بود. دادهﻫﺎ و اﻃﻼﻋﺎت ﻣﻮرد ﻧﻴﺎز ﺑﺮاي اﺟﺮاي ﻣﺪل شامل ﻣﻮﻗﻌﻴﺖ ﻣﻜﺎﻧﻲ، اﻃﻼﻋﺎت ﻫﻮاﺷﻨﺎﺳﻲ، اﻃﻼﻋﺎت ﺧﺎک¬شناسی و ﻋﻤﻠﻴﺎت زراﻋﻲ می¬باشد و برآورد در مدل با استفاده از ترکیب روش¬هاي گرافیکی و آماري انجام شد. مقایسه مقادیر و پراکنش داده¬هاي شبیه¬سازي و اندازه¬گیري شده با نمودار و خط 1:1 ارائه شد. یافته¬ها: مقادیر آب مصرفي در تیمارهای 40، 60، 80، 100 و 120 درصد نياز آبي در سال 1400 به ترتیب 265، 354، 444، 533 و 623 میلی¬متر و در سال 1401 به ترتیب 259، 347، 435، 541 و 632 میلی¬متر بود. ریشه میانگین مربعات خطاي نسبی (RMSEn) بر مبنای سال¬های 1400 و 1401 نشان داد که عملکرد دانه، غلاف و زیست توده و بهره¬وری مصرف آب مبتنی بر عملکرد دانه، غلاف و زیست¬توده در سال¬ اول به ترتیب 162/0، 161/0، 099/0، 304/0، 454/0 و 223/0 درصد و در سال دوم به ترتیب 195/0، 172/0، 106/0، 349/0، 485/0 و 247/0 درصد بود. شاخص توافق ویلموت (d) در سال¬ 1400 برای عملکرد دانه، غلاف و زیست توده به ترتیب 902/0، 891/0 و 939/0درصد و برای بهره¬وری مصرف آب مبتنی بر عملکرد دانه، غلاف و زیست¬توده به ترتیب 828/0، 810/0 و 970/0 درصد و در سال 1401، برای عملکرد دانه، غلاف و زیست توده به ترتیب 872/0، 885/0 و 936/0 درصد و برای بهره¬وری مصرف آب مبتنی بر عملکرد دانه، غلاف و زیست¬توده به ترتیب 889/0، 766/0 و 961/0 درصد بود. نزدیک بودن این شاخص به عدد یک، نشان¬دهنده قابل اطمینان بودن مقادیر شبیه¬سازي شده است. نتیجهگیری: نتایج حاصل از ارزیابی مدل DSSAT نشان داد که این مدل قادر به شبیه¬سازی با دقت قابل قبولی در عملکرد سویا و تغییرات رطوبتی در خاک است. به¬طور کلی ﺑﺮ اﺳﺎس ﻧﺘﺎﯾﺞ آﻣﺎري، ﺷﺒﯿﻪ¬ﺳﺎزي ﻋﻤﻠﮑﺮد داﻧﻪ، غلاف و زیست¬توده تحت¬ﺗﺄﺛﯿﺮ نیازهای مختلف آﺑﯿﺎري و سطوح متفاوت کود نیتروژن، ﻗﺎﺑﻞ ﻗﺒﻮل بود و به¬نظر می¬رسد استفاده از این مدل به¬عنوان ابزاری راهگشا جهت پشتیبانی پژوهش¬های علمی و ارتقاء تصمیم¬گیری¬ها در مدیریت مصرف آب در سویا در منطقه مورد مطالعه قابل توصیه می¬باشد.
Background and Aim: Water and fertilizer stress have a negative effect on many physical and chemical processes related to the efficiency of water productivity in soybean, thus leading to a decrease in the yield and quality of the plant. Predicting yield response for evaluating irrigation and fertilizer management strategies is of particular importance for making decisions. One of the decision support models in soybean is the CSM-CROPGRO-Soybean model, which is included in the DSSAT software package. The researches in the farm to determine the optimal solutions are done in agriculture and this item, in addition to the cost, is also time consuming, so the aim of this research is to use the DSSAT simulation model to evaluate the yield and water productivity in soybean plant under the conditions of water stress and nitrogen fertilizer were in Hormozgan province. Method: The current research was idone in the form of split plots in the form of a randomized complete block design in 3 replications, in Hormozgan province and in Haji Abad city in the years 2021 and 2022. The main factor includes no irrigation and supply of 40, 60, 80, 100 and 120% of water requirement and the sub-factor of nitrogen fertilizer amounts included consumption of zero, 50, 100, 150 and 200 kg/hectare. The data and information needed to implement the model include location, meteorological information, soil information and agricultural operations, and the estimation in the model was done using a combination of graphic and statistical methods. Comparison of values and distribution of simulated and measured data was presented with 1:1 graph and line. Results: The amounts of water use in the treatments of 40, 60, 80, 100 and 120 percent of water requirement in 1400 were 265, 354, 444, 533 and 623 mm, respectively and in 1401 were 259, 347, 435, 541 and 632 mm, respectively. The root mean square of the relative error (RMSEn) based on the years 1400 and 1401 showed that the yield of seeds, pods and biomass and the water productivity based on the yield of seeds, pods and biomass in the first year were 0.162, 0.161, 0.099, 0.304, 0.454 and 0.223%, and in the second year it was 0.195, 0.172, 0.106, 0.349, 0.485 and 0.247%, respectively. Wilmot agreement index (d) in the year 1400 for seed yield, pod and biomass respectively 0.902, 0.891 and 0.939% and for water productivity based on seed yield, pod and biomass respectively 0.828, 0.810 and 0.970 percent. In 1401 were for seed yield, pod and biomass 0.872, 0.885 and 0.936 percent respectively and for water productivity based on seed yield, pod and biomass respectively 0.889, 0.766 0 and 0.961 percent. The closeness of this index to the number one, it indicates the reliability of the simulated values. Conclusion: In general, based on the statistical results, the simulation of seed, pod and biomass yields under the effect of different irrigation requirements and different levels of nitrogen fertilizer was acceptable and it seems that the use of the model as a useful tool to support scientific research and improving decisions in water use management in soybeans in the study area are recommended.
Abdzad Gohari, A., & Babazadeh, H. (2023). Simulation of yield and water productivity of Cowpea cultivars under deficit irrigation conditions using the DSSAT model. Iranian Irrigation and Drainage, 3(1), 215-232 (in Persian).
Adhikari, P., Ale, S., Bordovsky, J.P., Thorp, K.R., Modala, N.R., Rajan, N., & Barnes, E.M. (2016). Simulating future climate change impacts on seed cotton yield in the Texas High Plains using the CSM-CROPGRO-Cotton model. Agricultural Water Management, 164, 317-330.
Allen, R.G., Pereira, L.S., Raes, D., & Smith, M. (1998). Crop Evapotranspiration-Guidelines for Computing Crop Water Requirements-FAO Irrigation and Drainage Paper 56, 300. FAO, Rome, pp. 6541.
Antolin, L.A., Heinemann, A.B., & Marin, F.R. (2021). Impact assessment of common bean availability in Brazil under climate change scenarios. Agricultural Systems, 191, 1-9.
Babazadeh, H., & Sarai Tabrizi, M. (2013). Calibration of SWAP Model for Simulating Crop Yield, Biological Yield and Soybean Water Use Efficiency. Journal of Irrigation Sciences and Engineering, 35(4), 83-96. [in Persian]
Bao, Y., Hoogenboom, G., McClendon, R.W., & Paz, J.O. (2015). Potential adaptation strategies for rainfed soybean production in the south-eastern USA under climate change based on the CSM-CROPGRO-Soybean model. The Journal of Agricultural Science, 798-824.
Bhatia, V.S., Singh, P., Wani, S.P., Chauhan, G.S., Rao, A.K., Mishra, A.K., & Srinivas, K. (2008). Analysis of potential yields and yield gaps of rainfed soybean in India using CROPGRO-Soybean model. Agricultural and Forest Meteorology, 1252-1265.
Biel, W., Gawęda, D., Łysoń, E., & Hury, G. (2017). The effect of variety and agrotechnical factors on nutritive value of soybean seeds. Acta Agrophysica. 24, 395-404.
Boote KJ, Porter C, Jones JW, Thorburn PJ, Kersebaum KC, Hoogenboom G, White JW, Hatfield JL. 2016. Sentinel site data for crop model improvement definition and characterization. Advances in Agricultural Systems Modeling, 7, 125-158.
Boote, K. J., Jones, J.W., Mishoe, J.W., & Wilkerson, G.G. (1986). Modelling growth and yield of groundnut. Agri meteorology of Groundnut: Proceeding of an International Symposium, ICRISAT Sahelian Center, Niamey, Niger. 21-26 Aug, 1985, ICRISAT, Patancheru, A. P. 502 324, India, pp. 243-254.
Boote, K.J., Jones, J.W., Hoogenboom, G., & Pickering, N.B. (1998). The CROPGRO model for grain legumes. In: Tsuji, G.Y., Hoogenboom, G., Thornton, P.K. (Eds.), Understanding Options for Agricultural Production. Springer Science and Business Media, Dordrecht, pp. 99-128.
Boulch, Elmerich, C., Djemel, A., & Lange, B. (2021). Evaluation of soybean (Glycine max L.) adaptation to northern European regions under different agro-climatic scenarios. In Silico Plants, 3(1), pp. 1-13.
Clark, M., & Tilman, D. (2017). Comparative analysis of environmental impacts of agricultural production systems, agricultural input efficiency, and food choice. Environmental Research Letters, 1-11.
D’Amour, C.B., Wenz, L., Kalkuhl, M., Steckel, J.C., & Creutzig, F. (2016). Teleconnected food supply shocks. Environmental Research Letters, 035007.
Das, H.P. (2003). Water use efficiency of soybean and its yield response to evapotranspiration and rainfall. Journal of Agricultural Physics, 3(1), 35-39.
Dias, G.V.S., Silva, E.H.F.M., Vieira Junior, N.A., & Marin, F.R. (2020). Simulation of the water footprint of soybeans in Mato Grosso based on climate change projections. Agrometeoros, 27, 155-163.
Dokoohaki, H., Gheisari, M., Mousavi, S. F., & Mirlatifi, S.M. (2012a). Estimation soil water content under deficit irrigation by using DSSAT. Journal of Water and Irrigation Management . 2 (1): 1-14. [in Persian].
Dokoohaki, H., Gheysari, M. and Karimi Jafari, M. (2012b). Applying the DSSAT model to determine the yield response factor under different growth stage. Third National Conference on Comprehensive Water Resources Management. Sari Agricultural Sciences and Natural Resources University. (In Persian).
Edreira, J.I.R., Guilpart, N., Sadras, V., Cassman, K.G., van Ittersum, M.K., Schils, R.L., & Grassini, P. (2018). Water productivity of rainfed maize and wheat: a local to global perspective. Agricultural and Forest Meteorology, 259, 364-373.
Er-Raki, S., Bouras, E., Rodriguez, J.C., Watts, C.J., Lizarraga-Celaya, C., & Chehbouni, A. (2020). Parameterization of the AquaCrop model for simulating table grapes growth and water productivity in an arid region of Mexico. Agricultural Water Management, 106585, 106585.
FAOSTAT, (2022). Food and Agriculture Organization of the United Nations. Stadistic Division [WWW Document]. URL http://faostat3. fao.org/faostat-gateway/go/to/down. (Accessed 6.14.16).
Farhani pad, P., Paknezhad, F., Ilkaei, M., Habibi, D., & Davoodi Fard, M. (2011). Simulation yield on yield components of soybean (Williams cv.) on the effect of planting date with CROPGRO soybean. Journal of Agriculture and Plant Breeding, 8(4): 31-41. [in Persian].
Garcia, A., Persson, T., Guerra, L.C., & Hoogenboom, G. (2010). Response of soybean genotypes to different irrigation regimes in a humid region of the southeastern USA. Agricultural Water Management, 97, 981-987.
Gheysari, M., Loescher, H.W., Sadeghi, S.H., Mirlatifi, S.M., Zareian, M.J., & Hoogenboom, G. 2015. Water-yield relations and water use efficiency of maize under nitrogen fertigation for semiarid environments: experiment and synthesis. In: Sparks, D.L. (Ed.), Advances in Agronomy, pp. 175–229.
Godwin, D.C., & Allan, C.J. (1991). Nitrogen dynamics in soil-plant systems. Modeling Plant and Soil Systems 287-321.
Godwin, D.C., & Singh, U. (1998). Nitrogen balance and crop response to nitrogen in upland and lowland cropping systems. In: Tsuji, G.Y., Hoogenboom, G., Thornton, P.K. (Eds.), Understanding Options for Agricultural Production. Springer Science and Business Media, Dordrecht, pp. 55-77.
Haghjoo, M., & A. Bahrani. (2015). Simulation of Grain Yield and Biomass of Corn at Different Irrigation Regimes and Nitrogen Application. Journal of Crop Ecophysiology. 9(1): 167-176. [in Persian].
Jamieson, P.D., Porter, J.R., & Wilson, D.R. (1991). A test of the computer simulation model ARCWHEAT on wheat crops grown in New Zealand. Field Crops Research, 27, 337-350.
Jones, C.A., & Kiniry., J.R. (1986). CERES-Maize: A simulation model of maize growth and development. Texas A&M Univ. Press, College Station.
Jons, J. W., B. A. Keat & Porer. C.H. (2001). Aproachest mdula mdeldevelopment. Agric. Systems 70: 421-443.
Kahraman, A. 2017. Nutritional value and foliar fertilization in soybean. J. Elem. 22, 55–66.
Karam, F., Karaa, K., & Tarabey, N. (2005). Effects of deficit irrigation on yield and water use efficiency of some crops under semi-arid conditions of the Bekaa valley of Lebanon. , Amman, Jordan, 2 (1), 139-155.
Lich, M.A., Wright, D., Lenssen, & A.W., (2013). Soybean Response to Drought, Agriculture. Iowa State University Extension and Outreach, Ames, IA (USA).
Maiorano, A., P. Martre, S. Asseng, F. Ewert, C. Müller, R.P. Rötter, A.C. Ruane, M.A. Semenov, D. Wallach, E. Wang, P.D. Alderman, B.T.Kassie, C. Biernath, B. Basso, D. Cammarano, A.J. Challinor, J. Doltra, B. Dumont, E. Eyshi Rezaei, S. Gayler, K.C. Kersebaum, B.A. Kimball, A.K. Koehler, B. Liu, G.J. O’Leary, J.E. Olesen, M.J. Ottman, E. Priesack, M. Reynolds, P. Stratonovitch, T. Streck, P. J. Thorburn, K. Waha, G.W. Wall, J.W. White, Z. Zhao, & Zhu., Y. (2017). Crop model improvement reduces the uncertainty of the response to temperature of multi-model ensembles. Field Crops Research, 202, 5-20.
Majidian, M., & Ghadiri, H. (2002). The effect of moisture stress and different amounts of nitrogen fertilizer in different stages of growth on the yield of yield components, water use efficiency and some physiological characteristics of corn plants. Iranian Journal of Agricultural Sciences, 33(3), 492-499. [in Persian]
Marchand, P., Carr, J.A., Dell’Angelo, J., Fader, M., Gephart, J.A., Kummu, M., & Ratajczak, Z. (2016). Reserves and trade jointly determine exposure to food supply shocks. Environmental Research Letters, 095009.
Montoya, F. García, C. Pintos, F. & Otero. A. (2017). Effects of irrigation regime on the growth and yield of irrigated soybean in temperate humid climatic conditions. Agricultural Water Management 193: 30-45.
Mourtzinis, S. Gurpreet Kaurb, John M. Orlowskib, Charles A. Shapiroc, Chad D. Leed, Charles Wortmannc, David Holshousere, Emerson D. Nafzigerf, Hans Kandelg, Jason Niekampf, William J. Rossh, Josh Loftoni, Joshua Vonkf, Kraig L. Roozeboomj, Kurt D. Thelenk, Laura E. Lindseyl, Michael Statonm, Seth L. Naeven, Shaun N. Casteelo, William J. Wieboldp, Shawn & Conleya., P. (2018). Soybean response to nitrogen application across the United States: A synthesis-analysis. Field Crops Research, 215, 74-82.
Panda, R.K., S.K. Behera, and P.S. Kashyap. 2004. Effective management of irrigation water for maize under stressed condition. Agric. Water Manage. 66: 181-203.
Quansah, J.E., Welikhe, P., El Afandi, G., Fall, S., Mortley, D., & Ankumah, R. (2020). CROPGRO-soybean model calibration and assessment of soybean yield responses to climate change. American Journal of Climate Change, 297-316.
Ritchie, J. T., & Otter, S. (1985). Description and performance of CERES-Wheat: a useroriented wheat yield model. In: ARS Wheat Yield Project. ARS-38. Natl Tech Info Serv, Springfield, Missouri, pp. 159-175.
Searchinger, T., Waite, R., Hanson, C., Ranganathan, J., Dumas, P., Matthews, E., & Klirs, C. 2019. Creating a Sustainable Food Future: a Menu of Solutions to Feed Nearly 10 Billion People by 2050 (accessed 16 April 2020).
Silva, E.H.F.M., Boote, K.J., Hoogenboom, G., Gonçalves, A.O., Junior, A.S.A., & Marin, F.R. (2021). Performance of the CSM-CROPGRO-soybean in simulating soybean growth and development and the soil water balance for a tropical environment. Agricultural Water Management, 252, 106929
Singh, A. K., Tripathy, R., & Chopra., U.K. (2008). Evaluation of CERES Wheat and Crop System models for water-nitrogen interactions in wheat crop. Agricultural Water Management, 95, 776-786.
Singh, U., J. T., Ritchie & Tsuji., G.Y. (1991). Simulation models for crop growth: IBSNAT approach. In: International Symposium on Sweet Potato Technology for the 21th Century, Tuskegee University, Tuskegee, Alabama.
Soler, C.M.T., Sentelhas, P.C. & Hoogenboom, G. (2007). Application of the CSM-CERES-Maize model for planting date evaluation and yield forecasting for maize grown off-season in a subtropical environment. European Journal of Agronomy. 27(2), 165-177.
Souza, T.T., Antolin, L.A.S., Bianchini, V.J.M., Pereira, R.A.A., Silva, E.H.F.M., & Marin, F. R. (2019). Longer crop cycle lengths could offset the negative effects of climate change on Brazilian maize. Bragantia.
Thomson, A.M., Calvin, K.V., Smith, S.J., Kyle, G.P., Volke, A., & Patel, P. (2011). RCP4. 5: a pathway for stabilization of radiative forcing by 2100. Climate Change, 77-94.
Timsina, J., Godwin, D., Humphreys, E., Kukal, S.S., & Smith, D. (2008). Evaluation of options for increasing yield and water productivity of wheat in Punjab, India using the DSSAT-CERES-Wheat model. Agric. Water Manag. 1099-1110.
Tyagi, S.D., Khan, M.H., Teixeira D.A., & Silva, J.A. (2011). Yield stability of some soybean genotypes across diverse environments. Int. J. Plant Breed. 5, 37-41.
White, J., & Hoogenboom, G. (2010). Crop response to climate: ecophysiological models. In: Lobell D, Burke M, editors. Climate change and food security, advances in global change research, 37, 59-83.
Wilkerson, G. G. , J. W. Jones, K. J. Boote, K. T. Ingram, J. W. Mishoe, (1983). Modeling soybean growth for crop management. American Society of Agricultural and Biological Engineers. 26: 63-73.
Willmott, C.J. (1982). Some comments on the evaluation of model performance. Bulletin of American Meteorology Society, 63, 1309-1313.
Wright, G.C. (1996). Selection for water-use efficiency in grain legume species. 554-557, In: Michalk, D.L. and J.E. Dratley (eds.), Proceedings of the 8th Australian Agronomy Conference, Toowoomba, Australia.
Yang, J.M., Yang, J.Y., Liu, S., & Hoogenboom, G. (2014). An evaluation of the statistical methods for testing the performance of crop models with observed data. Agricultural Systems, 127, 81-89.
Yang, S.H., G. Wilkerson, R. Hejazi, L. Heiniger, & D. Bowman. (2008). Estimating CSM-CERES-Maize genetic coefficients and soil parameters and evaluating model response to varying nitrogen management strategies under North Carolina Conditions. Ph.D Thesis. USA.
Yousefi, S., Pakenjad, F., & Ilkai, M. (2011). The effect of irrigation management and nitrogen fertilization on soybean plant yield and yield. The first national conference on new topics in agriculture, 25-32. [in Persian].
Salmeron, M., Urrego, Y.F., Isla, R. and Cavero, J. 2012. Effect of non-uniform sprinkler irrigation and plant density on simulated maize yield. Agricultural Water Management, 113: 1-9.
Ramezani Vasokolaei., M. Naftchali., A. Saber Ali., F & Kazemi., S.H. 2022. Evaluation and Simulation of Water Table Management Influence on Rice Yield and its Components Involving DSSAT Model. 12(4): 157-175. [in Persian].
