برآورد عملکرد پتانسیل برنج و نیاز کودی در استان گیلان با استفاده از سامانه اطلاعات جغرافیایی و مدل سازی گیاه زراعی

نوع مقاله : مقاله پژوهشی

نویسندگان

1 دانشجوی دکتری، گروه زراعت، دانشکده تولید گیاهی، دانشگاه علوم کشاورزی و منابع طبیعی گرگان، گرگان، ایران

2 استاد، گروه اگروتکنولوژی، دانشکده کشاورزی، دانشگاه فردوسی، مشهد، ایران

3 استاد، گروه مهندسی آب، واحد لاهیجان، دانشگاه آزاد اسلامی، لاهیجان، ایران

4 دانشیار، گروه زراعت، دانشکده تولید گیاهی، دانشگاه علوم کشاورزی و منابع طبیعی گرگان، گرگان، ایران

5 استادیار پژوهش، موسسه تحقیقات برنج کشور، سازمان تحقیقات، آموزش و ترویج کشاورزی، رشت، ایران

چکیده

مقدمه: مدیریت منطقی در استفاده بهینه از منابع کود که اساساً تجدیدناپذیر هستند و در صورت استفاده بدون پایه علمی می­توانند تأثیرات زیست­محیطی زیادی داشته باشند، بسیار مهم است. ابزارهای تصمیم­گیری علمی محدودی جهت کاربرد منابع کود وجود دارد. نرم­افزار FertiliCalc-Fertigate برنامه­ای جهت محاسبه میزان مصرف کودهای NPK در طول فصل رشد به­صورت مقرون به­صرفه و پایدار است. امروزه سامانه اطلاعات جغرافیایی در برنامه‌ریزی مکانی با تعیین پراکنش پدیده­ها و روی هم­گذاری نقشه‌ها و تفسیر داده‌های بوم‌شناختی، در مراحل مختلف برنامه­ریزی کاربرد گسترده­ای دارد. همچنین پتانسیل عملکرد در یک منطقه را می­توان با استفاده از آزمایش­های میدانی و مدل­های شبیه­سازی برآورد کرد. مدل ORYZA2000، یکی از مدل‌های کارآمد در بررسی پتانسیل عملکرد برنج است که رشد و نمو گیاه برنج را در شرایط مطلوب، محدودیت آبی و محدودیت نیتروژن شبیه‌سازی می‌کند. در این مطالعه سعی شد تا با تلفیق مدل ORYZA2000 و سامانه اطلاعات جغرافیایی (GIS)، عملکرد پتانسیل در سطح استان گیلان برآورد شود. همچنین پس از تعیین عملکرد پتانسیل، میزان نیاز کودهای NPK نیز با استفاده از نرم­افزار FertiliCalc-Fertigate برآورد شد.
مواد و روش­ ها: این پژوهش به­منظور بررسی عملکرد پتانسیل برنج در سطح استان گیلان با استفاده از مدل ORYZA2000 صورت گرفت. پس از واسنجی و اعتبارسنجی مدل در سطح آزمایش مزرعه، از این مدل جهت ارزیابی عملکرد پتانسیل برنج در 12 ایستگاه­ همدیدی استان گیلان استفاده شد. از پردازش تصاویر ماهواره لندست 8 جهت تفکیک مزارع برنج استان گیلان استفاده و محدوده مورد مطالعه با استفاده از طبقه­بندی نظارت­شده جدا شد. برآورد عملکرد پتانسیل در سطح استان گیلان با تلفیق محیط GIS و مدل ORYZA2000 صورت گرفت. میزان تابش برای کل حوزه از تابعPoints Solar Radiation  در GIS محاسبه شد. سپس از رابطه بین مقدار تابش رسیده طی فصل رشد برنج و عملکرد پتانسیل برآورد شده در مدل ORYZA2000، عملکرد پتانسیل محاسبه و بر مبنای کاربری زراعی استان گیلان به کل سطح تعمیم داده شد. نیاز کودی زمین­ها با استفاده از نرم­افزار FertiliCalc-Fertigate 1.0 محاسبه شد. جهت بررسی نیاز کودی در سطح استان، ابتدا تعداد 320 نقطه در سطح محدوده کشت برنج استان به­صورت تصادفی انتخاب و در هر نقطه بر اساس اطلاعات مورد نیاز از جمله میزان عملکرد پتانسیل و اطلاعات خاک، نیاز کودی هر نقطه تعیین شد. سپس درون­یابی نقاط صورت گرفت و نتایج مطالعه به صورت نقشه­های نیاز کودی ارایه شد.
یافته­ های تحقیق: نتایج این مطالعه نشان داد که میزان تابش مزارع برنج طی فصل رشد در منطقه گیلان از 2552 تا 6259 (به‌طور میانگین 4405 مگاژول بر مترمربع) در سال 1395 و از 2423 تا 5337 (به‌طور میانگین 3880 مگاژول بر مترمربع) در سال 1396 بود. کم­ترین میزان تابش دریافتی در مناطق مرکزی حوزه بود که دلیل آن می‌تواند شرایط توپوگرافی منطقه باشد. با استفاده از رابطه رگرسیونی بین تابش رسیده طی فصل رشد و میزان عملکرد پتانسیل، نقشه عملکرد پتانسیل مزارع برنج تهیه شد. بر اساس نتایج، عملکرد پتانسیل در مزارع برنج استان گیلان در سال 1395 بین 4416 تا 7038 کیلوگرم در هکتار (با میانگین 5160 کیلوگرم در هکتار) و در سال 1396 بین 4558 تا 7180 کیلوگرم در هکتار (با میانگین عملکرد 5302 کیلوگرم در هکتار) متغیر بود. نتایج این مطالعه نشان داد که رهیافت تلفیق مدل ORYZA2000 و GIS از توانایی مناسبی برای شبیه­سازی عملکرد پتانسیل در منطقه مورد مطالعه برخوردار است. بررسی سطوح نیاز کودی در زمین­های برنج استان گیلان نیز نشان داد که جهت دستیابی به عملکرد پتانسیل، مصرف 262 تا 274 کیلوگرم در هکتار کود پتاسیم، 116 تا 171 کیلوگرم در هکتار کود نیتروژن و 8 تا 12 کیلوگرم در هکتار کود فسفر نیاز است. همچنین، بر اساس نیازهای کودی محاسبه شده در این مطالعه، پتاسیم مهم­ترین نقش را جهت دستیابی به عملکرد پتانسیل برنج در سطح استان گیلان داشت.
نتیجه ­گیری: به ­نظر می­رسد در بسیاری از مناطق استان گیلان، کاربرد کودهای NPK در مقدار و زمان نامناسب سبب کاهش عملکرد برنج می­شود. نتایج این تحقیق می­تواند الگوی مصرف مناسب کود را مشخص کند تا از طریق کارشناسان برنج به کشاورزان توصیه و ضمن دستیابی به حداکثر عملکرد برنج، از مشکلاتی چون آبشویی فسفر در اثر مصرف بیش از حد آن خودداری شود.

کلیدواژه‌ها


عنوان مقاله [English]

Estimating rice potential yield and fertilizer requirements in Guilan province using GIS and crop modeling

نویسندگان [English]

  • Pooya Aalaee Bazkiaee 1
  • Behnam Kamkar 2
  • Ebrahim Amiri 3
  • Hossein Kazemi 4
  • Mojtaba Rezaei 5
1 Ph.D. Student, Department of Agronomy, Faculty of Plant Production, Gorgan University of Agricultural Sciences and Natural Resources, Gorgan, Iran
2 Professor, Department of Agrotechnology, Faculty of Agriculture, Ferdowsi University of Mashhad, Mashhad, Iran
3 Professor, Department of Water Engineering, Lahijan Branch, Islamic Azad University, Lahijan, Iran
4 Associate Professor, Department of Agronomy, Faculty of Plant Production, Gorgan University of Agricultural Sciences and Natural Resources, Gorgan, Iran
5 Research Assistant Professor, Rice Research Institute of Iran, Agricultural Research, Education and Extension Organization (AREEO), Rasht, Iran
چکیده [English]

Introduction
Rational fertilizer management is crucial in the efficient use of resources that are basically non-renewable and that can have a great environmental impact when used without scientific basis. The availability of scientifically sound decision-making tools for rational fertilization  is scarce. FertiliCalc-Fertigate software is a program to determine the consumption of NPK fertilizers during the growing season in a cost-effective and sustainable way. Today, the geographic information system is widely used in spatial planning by determining the distribution of phenomena and combining maps and interpreting ecological data in different stages of planning. Also, the potential yield in an area can be estimated using field tests and simulation models. The ORYZA2000 model is one of the efficient models in investigating rice potential yield, which simulates the growth and development of rice plants under favorable conditions, water limitation and nitrogen limitation. In this study, an attempt was made to estimate the potential yield in Guilan province by integrating the ORYZA2000 model and geographic information system. Also, after determining the potential yield, the NPK fertilizer requirement estimated using FertiliCalc-Fertigate software.
Materials and methods
This research was conducted to investigate the potential yield of rice in Guilan province using the ORYZA2000 model. After calibrating and validating the model at the field test level, the model was used to evaluate the potential yield of rice in 12 synoptic stations of Guilan province. The processing of Landsat 8 satellite images was used to separate rice fields in Guilan province and the studied area was separated using supervised classification. The estimation of potential yield in Guilan province was done by combining GIS environment and ORYZA2000 model. The amount of radiation for the whole area was calculated from the Points Solar Radiation function in GIS. Then, from the relationship between the amount of radiation received during the rice growing season and the potential yield estimated in the ORYZA2000 model, the potential yield was calculated and generalized to the whole area based on the agricultural land use of Guilan province. Fertilizer requirement of lands was calculated using FertiliCalc-Fertigate 1.0 software. In order to evaluate  the fertilizer requirement at the province level, first, 320 points were randomly selected in the rice cultivation area of the province and the fertilizer requirement of each point was determined based on the required information, including potential yield and soil information. Then the points were interpolated and the study results were presented in the form of fertilizer requirement maps.
Research findings
The results showed that the amount of radiation in rice fields in the Guilan area, during the growing season, was between 2552 to 6259 MJ/m2 (average 4405 MJ/m2) in 2016 and from 2423 to 5337 MJ/m2 (average 3880 MJ/m2) in 2017. The lowest amount of received radiation was in the central areas of the basin, which can be due to the topographic conditions of the area. Using the regression relationship between radiation during the growing season and potential yield, a potential yield map of rice fields was prepared. Based on the results, potential yield in rice fields of Guilan province was between 4416 to 7038 kg/ha (with an average of 5160 kg/ha) in 2016 and between 4558 to 7180 kg/ha (with an average yield of 5302 kg/ha) in 2017. Based on these results, the combined approach of ORYZA2000 model and GIS has a good ability to simulate potential yield in the study area. Estimation the levels of fertilizer requirements in the rice fields of Gilan province showed that in order to achieve the potential yield, 262 to 274 kg/ha of potassium fertilizer, 116 to 171 kg/ha of nitrogen fertilizer, and 8 to 12 kg/ha of phosphorus fertilizer are needed. Based on the calculated fertilizer requirement, potassium has played the most important role in achieving the potential yield of rice in the province.
Conclusion
It seems that in many areas of Gilan province, the application of NPK levels in an inappropriate amount and time causes a decrease in rice yield. The results of this research can recommend the appropriate fertilizer consumption pattern to farmers through rice experts so as to achieve maximum yield and avoid problems such as phosphorus leaching due to excessive consumption.

کلیدواژه‌ها [English]

  • Food security
  • ORYZA2000 model
  • Potential yield
Aggarval, P. K., Karla, N., Bandyopadhyay, S. K. and Selvarjan, S. 1995. A systems approach to analyze production options for wheat in India. In Eco-regional approaches for sustainable land use and food production (pp. 167-186). Springer, Dordrecht.
Aggarval, P. K., Talukdar, K. K. and Mall, R. K. 2000. Potential yields of rice-wheat system in the Indo-Gangetic plains of India (p. 16). Facilitation Unit, Rice-Wheat Consortium for the Indo-Gangetic Plains.
Agricultural Statistics. 2018. Volume I: Crop products. 2018-19. Office of Statistics and Information Technology, Deputy Director of Planning and Economic Affairs. Ministry of agriculture jihad. 90 pages. (In Persian)
Ahmadi Alipour, H. 2017. Modeling of production and yield gap of wheat in Golestan province. Master Thesis. Gorgan University of Agricultural Sciences and Natural Resources. 91 pages. (In Persian with English Abstract)
Ambreen, R., Qiu, X. and Ahmad, I. 2011. Distributed modeling of extraterrestrial solar radiation over the rugged terrains of Pakistan. Journal of Mountain Science. 8: 427–436. 
Amiri, E. and Rezaei, M. 2010. Evaluation of water–nitrogen schemes for rice in Iran, using ORYZA2000 model. Communications in Soil Science and Plant Analysis. 41: 2459–2477.
Ata-Ul-Karim, S. T., Liu, X., Lu, Z., Zheng, H., Cao, W. and Zhu, Y. 2017. Estimation of nitrogen fertilizer requirement for rice crop using critical nitrogen dilution curve. Field Crops Research. 201: 32-40.
Badsar, M. 2014. Yield gap estimation in wheat fields using GIS, RS and SSM model (A case study: Qaresso basin, Gorgan distinct). Master's thesis. Gorgan university of agricultural sciences and natural resources. 95 pp. (In Persian)
Bhatia, V., Singh, P., Wani, S., Chauhan, G., Rao, A. K., Mishra, A. and Srinivas, K. 2008. Analysis of potential yields and yield gaps of rainfed soybean in India using CROPGRO-Soybean model. Agricultural and Forest Meteorology. 148: 1252-1265.
Boonwichai, S., Shrestha, S., Babel, M. S., Weesakul, S. and Datta, A. 2019. Evaluation of climate change impacts and adaptation strategies on rainfed rice production in Songkhram River Basin, Thailand. Science of the Total Environment. 652: 189-201.
Bouman B. A. M., Kropff, M. J., Tuong, T. P., Wopereis, M. C. S., Ten Berge H. F. M. and Van Laar H. H. 2001. ORYZA2000: modeling lowland rice. International Rice Research Institute, Los Banos.
Cao, B., Hua, S., Ma, Y., Li, B. and Sun, C. 2017. Evaluation of ORYZA2000 for Simulating Rice Growth of Different Genotypes at Two Latitudes. Agronomy journal. 109: 2613-2629.
Chen, C., Baethgen, W. E. and Robertson, A. 2013. Contributions of individual variation in temperature, solar radiation and precipitation to crop yield in the North China Plain. 1961–2003. Climate Change. 116: 767-788.
Cochran W. G. 1963. Sampling Techniques. 2nd ed. New York: John Wiley and Sons, Inc.
Dehkordi, P. A., Nehbandani, A., Hassanpour-bourkheili, S. and Kamkar, B. 2020. Yield Gap Analysis Using Remote Sensing and Modelling Approaches: Wheat in the Northwest of Iran. International Journal of Plant Production. 14: 443-452.
Delgado, A. and Scalenghe, R. 2008. Aspects of phosphorus transfer in Europe. Journal of Plant Nutrition and Soil Science. 171: 552-575.
Deng, N., Grassini, P., Yang, H., Huang, J., Cassman, K. G. and Peng, S. 2019. Closing yield gaps for rice self-sufficiency in China. Nature Communications. 10: 1-9.
Drenth, H., Ten Berge, F. F. M. and Riethoven, J. J. M. 1994. ORYZA simulation modules for potential and nitrogen limited rice production SARP Research Proceedings. Wageningen, the Netherlands.
Espe, M. B., Yang, H., Cassman, K. G., Guilpart, N., Sharifi, H. and Linquist, B. A. 2016. Estimating yield potential in temperate high-yielding, direct-seeded US rice production systems. Field Crops Research. 193: 123-132. 
FAO. 2018. Food and Agricultural Organization of the United Nations (sited in: http://www,fao.org/index_en.htm/, 1/1/2020.
Fatemi, B. and Rezaei, Y. 2006. Basic of Remote Sensing. Azade publication. 257 pages. (In Persian).
Fu, W., Tunney, H. and Zhang, C. 2010. Spatial variation of soil nutrients in a dairy farm and its implications for site-specific fertilizer application. Soil and Tillage Research. 106: 185-193.
Gharineh, M. H., Bakhshandeh, A. M., Andarzian, B. and Fayezizadeh, N. 2012. Agro-climatic zonation of Khouzestan province based on potential yield of irrigated wheat using WOFOST model. Agroecology. 4: 255-264. (In Persian with English Abstract).
Godfray, H. C. J., Beddington, J. R., Crute, I. R., Haddad, L., Lawrence, L., Muir, J. F., Pretty, J., Robinson, S., Thomas, S. M., Toulmin, C. 2010. Food Security: the challenge of feeding 9 billion people. Science. 327: 812-818.
Guo, Y., Wu, W. and Bryant, C. R. 2019. Quantifying spatio-temporal patterns of rice yield gaps in double-cropping systems: A case study in pearl river delta, China. Sustainability. 11: 1-22.
Hajjarpour, A., Soltani, A. and Torabi, b. 2016. Using boundary line analysis in yield gap studies: Case study of wheat in Gorgan. Crop Production. 8: 183-201. (In Persian with English Abstract)
Hussain, S., Huang, J., Huang, J., Ahmad, S., Nanda, S., Anwar, S.and Zhang, J. (2020). Rice production under climate change: adaptations and mitigating strategies. In Environment, climate, plant and vegetation growth (pp. 659-686). Springer, Cham.
Islam, A. and Muttaleb, A. 2016. Effect of potassium fertilization on yield and potassium nutrition of Boro rice in a wetland ecosystem of Bangladesh. Archives of Agronomy and Soil Science. 62: 1530-1540.
Kahabka, J. E., Van Es, H. M., McClenahan, E. J. and Cox, W. J. 2004. Spatial analysis of maize response to nitrogen fertilizer in central New York. Precision Agriculture. 5: 463-476.
Kassie, B. T., Van Ittersum, M. K., Hengsdijk, H., Asseng, S., Wolf, J. and Rotter, R. P. 2014. Climate-induced yield variability and yield gaps of maize (Zea mays L.) in the Central Rift Valley of Ethiopia. Field Crops Research. 160: 41-53.
Kazemi, H. 2012. Ecological crop zoning of Golestan province in order to develop a suitable cultivation pattern. PhD thesis in agriculture. Trabiat Modares university. 280 pages. (In Persian with English Abstract).
Kazemi, H., Tahmasebi Sarvestani, Z., Kamkar, B., Shataei, Sh. and Sadeghi, S. 2012. Evaluation of geostatistical methods for estimating and zoning of macronutrients in agricultural lands of Golestan province. Journal of Water and Soil Science. 22: 201-218. (In Persian with English Abstract)
Khaliq, T., Gaydon, D. S., Cheema, M. J. M. and Gull, U. 2019. Analyzing crop yield gaps and their causes using cropping systems modelling–a case study of the Punjab rice-wheat system, Pakistan. Field Crops Research. 232: 119-130.
Lobell, D. B., Cassman, K. G. and Field, C. B. 2009. Crop Yield Gaps: Their Importance, Magnitudes, and Causes. Annual Review of Environment and Resources. 34: 179-204.
Meghdadi, N., Soltani, A., Kamkar, B. and Hajjarpour, A. 2014. Agroecological zoning of Zanjan province for estimating yield potential and yield gap in dryland-base chickpea production systems. Plant Production Science. 21: 27-49. (In Persian with English Abstract)
Nasiri Mahallati, M. 2000. Modeling of crop growth processes. Mashhad University Jihad Publications. 274 pages. (In Persian)
Nassiri Mahallati, M. and Koocheki, A. 2009. Agroecological zoning of wheat in Khorasan provinces: stimating yield potential and yield gap. Iranian Journal of Field Crops Research. 7: 695-709. (In Persian)
Nazari Far, M., Momeni, R. and Jafari, V. 2006. Evaluation of the effect of radiation on the maximum yield of agricultural products in Karun Basin and zoning of water use efficiency using GIS. The first regional conference on water resources exploitation in Karun and Zayandehrood basins (opportunities and challenges), Shahrekord University. (In Persian with English Abstract).
Neumann, K., Verburg, P. H., Stehfest, E., and Müller, C. 2010. The yield gap of global grain production: A spatial analysis. Agricultural Systems. 103: 316-326.
Potter, P., Ramankutty, N., Bennett, E. M., and Donner, S. D. 2010. Characterizing the spatial patterns of global fertilizer application and manure production. Earth Interactions. 14: 1-22.
Pourhadian, H., Kamkar, B., Soltani, A., and Mokhtarpour, H. 2019. Evaluation of forage maize yield gap using an integrated crop simulation model-satellite imagery method (Case study: four watershed basins in Golestan Province). Archives of Agronomy and Soil Science. 65: 253-268.
Qaswar, M., Jing, H., Ahmed, W., Dongchu, L., Shujun, L., Lu, Z., and Huimin, Z. 2020. Yield sustainability, soil organic carbon sequestration and nutrients balance under long-term combined application of manure and inorganic fertilizers in acidic paddy soil. Soil and Tillage Research. 198: 104569
Raziei, T. 2017. Köppen-Geiger climate classification of Iran and investigation of its changes during 20th century. Earth and Space Physics. 43: 419-439. (In Persian)
Sarkar, M. I. U., Islam, M. N., Jahan, A., Islam, A. and Biswas, J. C. 2017. Rice straw as a source of potassium for wetland rice cultivation. Geology, Ecology, and Landscapes. 1: 184-189.
Schulthess, U., Timsina, J., Herrera, M. J. and McDonald, A. 2013. Mapping fieldscale yield gaps for maize: An example from Bangladesh. Field Crops Research. 143: 151-156.
Soundharajan, B. and Sudheer, K. P. 2013. Sensitivity analysis and auto-calibration of ORYZA2000 using simulation-optimization framework. Paddy and Water Environment. 11: 59-71.
Tari, D. B., Amiri, E. and Daneshian, J. 2017. Simulating the Impact of Nitrogen Management on Rice Yield and Nitrogen Uptake in Irrigated Lowland by ORYZA2000 Model. Communications in Soil Science and Plant Analysis. 48: 201-213.
Torabi, B., Soltani, A., Galeshi, S. and Zinali, E. 2012. Analyzing wheat yield constraints in Gorgan. Crop Production. 4: 1-17. (In Persian with English Abstract).
Villalobos, F. J., Delgado, A., Lopez-Bernal, A. and Quemada, M. 2020. FertiliCalc: A Decision Support System for Fertilizer Management. International Journal of Plant Production. 14: 299-308.
Wang, W., Ding, Y., Shao, Q., Xu, J., Jiao, X., Luo, Y., and Yu, Z. 2017. Bayesian multi-model projection of irrigation requirement and water use efficiency in three typical rice plantation region of China based on CMIP5. Agricultural and Forest Meteorology. 232: 89-105.
Wopereis, M. C. S. 1993. Quantifying the impact of soil and climate variability on rainfed rice production. PhD thesis. Wageningen (Netherlands): Wageningen Agricultural University. 188 pages.
Wopereis, M. C. S., Bouman, B. A. M., Tuong, T. P., Ten Berge, H. F. M. and Kropff, M. J. 1996. ORYZA_W: rice growth model for irrigated and rainfed environments. SARP Research Proceedings. Wageningen (Netherlands): IRRI/AB -DLO. 159 p.
Wu, D., Yu, Q., Lua, C. and Hengsdijk, H. 2006. Quantifying production potentials of winter wheat in the North China Plain. Agronomy Journal. 24: 226-235.
Yaghoobi, M., Aghayari, F. and Mostafavi, K. 2017. Factors affecting wheat yield gap in Savojbolagh, Iran. Advances in Bioresearch. 8: 84-92.
Yousefian, M., Soltani, A., Dastan, S. and Ajamnoroozi, H. 2019. Documenting production process and the ranking factors causing yield gap in rice fields in Sari, Iran. Iran Agricultural Research. 38: 101-109. (In Persian with English Abstract)
Zhang, H., Tao, F. and Zhou, G. 2019. Potential yields, yield gaps, and optimal agronomic management practices for rice production systems in different regions of China. Agricultural Systems. 171: 100-112.