کاربرد تابع تصادفی غیر نرمال پلاتو در تعیین سطح بهینه اقتصادی مصرف نهاده کودهای شیمیایی در تولید غلات آبی (محصولات گندم و جو آبی)

نوع مقاله : مقالات پژوهشی

نویسندگان

1 گروه ترویج و آموزش کشاورزی، دانشگاه بوعلی سینا، همدان

2 گروه اقتصاد کشاورزی، دانشگاه سید جمال الدین اسدابادی

3 دانشگاه بوعلی سینا- گروه اقتصاد کشاورزی

4 دانشگاه بوعلی سینا- اقتصاد کشاورزی

چکیده

در سال‌های گذشته، مصرف بیش از اندازه کودهای شیمیایی، اثرات و پیامدهای زیست‌محیطی نامطلوبی مانند آلودگی آب و خاک و بروز مشکلاتی در مورد وضعیت سلامت انسان‌ها، به همراه داشته است. به دلیل اینکه استفاده نابهینه کودهای شیمیایی می‌تواند خطرات جدی برای محیط و سلامت جامعه ایجاد کند، در این مطالعه سطح بهینه اقتصادی مصرف کودهای شیمیایی ازته، فسفاته و پتاسه در تولید گندم و جو آبی ایران، با استفاده از تابع تصادفی غیر نرمال پلاتو و رویکرد بیزین، طی سال‌های زراعی 86-1385 تا 96-1395 برآورد شد. داده‌های مورد نیاز نیز از آمارنامه‌های کشاورزی و بانک هزینه تولید محصولات زراعی وزارت جهاد کشاورزی جمع‌آوری شد. پس از بررسی و تجزیه و تحلیل داده‌ها با استفاده از نرم‌افزار SAS، نتایج نشان داد که میانگین مصرف بهینه کودهای شیمیایی ازته، فسفاته و پتاسه در تولید گندم آبی به‌ترتیب 05/117، 71/97 و 68/39 کیلوگرم در هکتار و در مورد جو آبی به‌ترتیب 00/29، 17/75 و 81/81 کیلوگرم در هکتار است. براساس نتایج، کشاورزان در تولید گندم آبی، کودهای شیمیایی (ازته، فسفاته و پتاسه) را بیش‌تر از مقدار بهینه استفاده می‌کنند، به طوری‌که میانگین مصرف بهینه کودهای شیمیایی ازته، فسفاته و پتاسه در تولید گندم آبی، به‌ترتیب به میزان56/46 ، 34/25 و 95/10 کیلوگرم در هکتار، کم‌تر از مقدار فعلی مصرف کودهای شیمیایی در کشور است. همچنین نتایج نشان می‌دهند که میزان مصرف فعلی کودهای ازته و فسفاته در تولید جو آبی کشور نیز یشتر از سطح بهینه محاسبه شده می‌باشد. لذا به منظور تخصیص بهینه عوامل تولید و نیز جلوگیری از اثرات زیست‌محیطی نامطلوب مصرف بی‌رویه این نهاده مهم توصیه می‌شود.

کلیدواژه‌ها

موضوعات


عنوان مقاله [English]

Application of Non-normally Distributed Stochastic Plateau Function in Determining the Optimal Economic Level of Chemical Fertilizer Inputs Usage in Irrigated Cereals (Irrigated Wheat and Barley Crops)

نویسندگان [English]

  • H. Balali 1
  • H. Shahbazi 2
  • Z. Seid Mohammadi 3
  • M. Bani Asadi 4
1 Department of Agriculture Extension and Education, Bu-Ali Sina University
2 Faculty of Agricultural Economics, Sayyed Jamaleddin Asadabadi University
3 Bu Ali Sina University
4 Bu Ali Sina University
چکیده [English]

Introduction: Agriculture is one of the basic sectors of any country and is very important in creating employment and production of industrial raw materials. Although the most important role of agriculture in any country is to provide the food security. The world's population is growing, and resources are dwindling. Therefore, feeding the growing population of the world requires more agricultural production. One of the ways to increase agricultural production is to increase yield per hectare. Chemical fertilizers significantly increase production per hectare. But excessive use of chemical fertilizers can also lead to environmentally externalities such as groundwater pollution, reduced quality of agricultural products and endanger human health and the environment. Therefore, the optimal use of production inputs in the agricultural sector is essential. Unfortunately, despite the emphasis of agricultural economists on the optimal use of production inputs, this issue has been taken for granted by farmers and policymakers in the agricultural sector. The purpose of this study is to determine the optimal economic level of use of chemical fertilizers (nitrogen, phosphate and potash) in the production of irrigated wheat and barley.
Materials and Methods: In order to determine the optimal economic level of chemical fertilizer inputs (nitrogen, phosphate and potash) in the production of irrigated wheat and barley in Iran, Bayesian approach and non-normally distributed stochastic plateau function, based on the developed Von Liebig algorithm were used. The estimation of the optimal amount of input usage depends on the functional form and the distribution assumptions based on the production data. The stochastic plateau function is one of the functions has been used to determine the optimal amount of inputs (especially chemical fertilizers). The stochastic plateau function provides insight into why farmers may over-use inputs. The efficiency of the linear stochastic plateau function is better than nonlinear and polynomial functions, and it estimates a more realistic pattern of farmers' expected profits, because the function is stochastic. For simple model estimation, only the input of chemical fertilizers (nitrogen, phosphate and potash) is considered as the limiting resource. If it is assumed that the threshold point is related to the intercept, which represents the yield of crops without input consumption, the equation of the stochastic plateau function is written as the following relation:





(1)

 




Where the yield of the crops in Iran, K is the amount of input in the crop production,  and θ are the coefficients of the yield function that must be estimated, and  is the transmitter intercept that represents all random variables. The used data in this study were collected from agricultural statistics and the production cost database of the Agriculture Ministry. The panel data were collected during 2007-2017 period.
Results and Discussion: Based on the results of the study, the average optimal consumption of nitrogen fertilizer in the production of irrigated wheat and irrigated barley in Iran was estimated 117.05 and 29.00 kg/ha, respectively, while the current average consumption of nitrogen fertilizer in the production of irrigated wheat and barley is 163.626 and 38.75 kg/ha, respectively. In other words, during the years 2007 to 2017, the amount of nitrogen fertilizer used in the production of irrigated wheat was 46.576 kg/ha (equivalent to 28.46%) and in the production of irrigated barley was 9.75 kg/ha (equivalent to 25.16%) more than the optimal level. Also, the potential yield of irrigated wheat and barley with respect to nitrogen fertilizer input was estimated 2754.5 and 2549.80 kg/ha, respectively, in the Bayesian method. The average optimal use of phosphate fertilizer in production of irrigated wheat in Iran was estimated as 97.70 kg/ha, while the current average consumption of phosphate fertilizer in production of irrigated wheat is equal to 123.06.02 kg/ha. In other words, during the years 2007 to 2017, the amount of phosphate fertilizer used in the production of irrigated wheat in Iran was 25.362 kg per hectare (equivalent to 20.609%) more than the optimal level. Also, the potential yield of irrigated wheat due to phosphate fertilizer input, about 2904.54 kg/ha has been obtained in Bayesian method. the average optimal consumption of potash fertilizer in the production of irrigated wheat and irrigated barley in Iran was estimated 39.68 and 81.81 kg/ha, respectively, while the current average consumption of potash fertilizer in the production of irrigated wheat and barley is 50.64 and 134.18 kg/ha, respectively. In other words, during the years 2007 to 2017, the amount of potash fertilizer used in the production of irrigated wheat was 10.96 kg/ha (equivalent to 21.65%) and in the production of irrigated barley was 52.37 kg/ha (equivalent to 39.02%) more than the optimal level.
Conclusion: According to the results of present study, farmers in the production of wheat and barley use chemical fertilizers (nitrogen, phosphate and potash) more than the optimal amount, so that the average optimal use of chemical fertilizers of nitrogen, phosphate and potash in the production of irrigated wheat, respectively 28.52, 20.59 and 78.36, and in the production of irrigated barley, the average optimal use of nitrogen and potash chemical fertilizers, respectively 74.84 and 39.03% per hectare, are less than the current amount of chemical fertilizer use in the country. According to the results of the study, in order to more efficiently use of chemical fertilizers and to reduce environmental pollution caused by their use in agricultural production, the government should reduce the direct payment of chemical fertilizer subsidies. Regarding the elimination of subsidies and pricing of chemical fertilizers (nitrogen, phosphate and potash), the importance of the type of fertilizer in crop production, input production elasticity and input demand elasticity should be considered.

کلیدواژه‌ها [English]

  • Bayesian method
  • Fertilizer
  • Iran
  • Optimum consumption
  • Stochastic Plateau Function
  • Wheat
  1. Abdi Rokni K., Hussain Yekani S.A., Kashiri F., and Abedi S. 2019. Effect of optimizing chemical fertilizer use on cropping pattern in positive mathematical planning (Case study: Sari Gherbaran). Journal of Agricultural Economics Research 11(2): 263-276. (In Persian with English abstract)
  2. Astolfi A. 2006. Optimization an introduction. Imperial College London. Available at: http://www3.imperial. ac.uk/pls/portallive/docs/1/7288263. PDF. (Visited: December 2015)
  3. Behnood H.R., Zamani S., and Naderan A. 2020. Impact assessment of the construction of extremist bus lines on the frequency of the accident on urban highways (Case study: Resalat Tehran highway). Journal of Transportation Engineering 10(3): 603-615. (In Persian with English abstract)
  4. Chandini K.R., Kumar R., and Prakash O. 2019. The Book Research Trends in Environmental Sciences. The Impact of Chemical Fertilizers on Our Environment and Ecosystem. Edition: 2nd. AkiNik Publications. pp: 69-86.
  5. Djordjevic S.S. 2019. Some unconstrained optimization methods. DOI: http://dx.doi.org/10.5772/intechopen.83679.
  6. Ehsani M.A., Keshavarz H., and Keshavarz M. 2018. The impact of monetary and financial policies on employment fluctuations with an emphasis on private sector employment. Journal of Economic Growth and Development Research 7(26): 125-144. (In Persian with English abstract)
  7. Falahi E., Khalilian S., and Ahmadian M. 2014. Determining the optimal economic value of water consumption and environmental pollutants in water wheat production (Case study: Seyedan Farouk plain, Marvdasht county. Journal of Agricultural Economics and Development 22(87): 139-162. (In Persian with English abstract)
  8. Gelman A. 2006. Prior distributions for variance parameters in hierarchical models. Bayesian Analysis 1(3): 515-533.
  9. Glickman M.E., and van Dyk D.A. 2003. Basic bayesian methods. Methods in Molecular Biology 404: 319-338.
  10. Goni M., Mohammed S., and Baba B.A. 2007. Analysis of resource-use efficiency in rice production in the lake Chand area of borno state, Nigeria. Journal of Sustainable Development in Agriculture and Environment 3(7): 31-37.
  11. Grzenda W. 2015. The advantages of bayesian methods over classical methods in the context of credible intervals. Information Systems in Management 4(1): 53-63.
  12. Hezhaber Kiani K. 2000. Assess and determine the optimal economic use of inputs in the cultivation of wheat. Agricultural Economics and Development 28: 77-108. (In Persian with English abstract)
  13. Jahan M., and Amiri M.B. 2018. Optimizing application rate of nitrogen, phosphorus and cattle manure in wheat production: an approach to determine optimum scenario using response-surface methodology. Journal of Soil Science and Plant Nutrition 18(1): 13-26.
  14. Makian S.N., Rostami M., and Ramazani H. 2019. Analyzing the relationship between robbery and income inequality with the Bayesian approach (Case study: Iran). Journal of Economic Research (Sustainable Development and Growth) 23(76): 137-158. (In Persian with English abstract)
  15. Makian S.N., Rostami M., Farhadi D., and Zabul M.A. 2019. Impact of unemployment on crime in Iran (hierarchical approach of the bayesian panel - poisson). Iranian Journal of Economic Research 23(76): 137-158. (In Persian with English abstract)
  16. Mirzaie S.h., Zakerinia M., Sharifan H., and Shahabifar M. 2015. Determination of optimum cropping Pattern by the Minimum Maximum Method (MMAS) of Ant System (Case Study: Golestan Dam Irrigation and drainage network). Iranian Journal of Irrigation and Drainage 9(1): 66-74. (In Persian with English abstract)
  17. Moazen V., Mojtahedi M., and Dashti G. 2015. Economics of input consumption in onion production in Tabriz plain. International conference on “Management Economics and Agricultural Sciences”. Bandar Anzali Free Zone, International Center for Academic Communication, Academic Entrepreneurship Development, 22 July 2015. (In Persian with English abstract)
  18. Moein-Al-dini A. 2016. Investigation of economic factors of wheat production in Fars province. 3rd International conference on “Modern Research in Management, Economics and Humanities”. Georgia Batum, Karin Institute of Excellence Conference, 5 June. 2016. (In Persian with English abstract)
  19. Mojtahedi M., and Dashti G. 2015. Economics of input consumption in water wheat production in Grammy County. 4th national congress on “Organic and Conventional Agriculture”. Ardabil, Mohaghegh Ardebili University, 19-20 August 2015. (In Persian)
  20. Naal Bandi Aghdam L., Dashti Gh., and Ajly J. 2013. Comparative evaluation of consumption factors of blue wheat production factors in the small and large farms of Ahar city. Journal of Agricultural Science and Sustainable Production 23(2): 85-97. (In Persian with English abstract)
  21. NazamFar H. 2017. Forecasts of urban development using the geographic information system integration method and the bayesian probabilistic model (Case study: Ardebil city). Journal of Human Geography Research 49(2): 357-370. (In Persian with English abstract)
  22. Ouedraogo F.B., and Brorsen B.W. 2014. Bayesian estimation of pptimal nitrogen rates with a non-normally distributed stochastic plateau function. Southern Agricultural Economics Association (SAEA), Annual Meeting, Dallas, Texas, 1-4 February 2014.
  23. RaheliNamin B., Mortazavi S., and Salmanmahiny A. 2016. Optimizing cultivation of agricultural products using socio-economic and environmental scenarios. Environmental Monitoring and Assessment 188(11): 627-644.
  24. Rahman K.M., and Zhang D. 2018. Effects of fertilizer broadcasting on the excessive use of inorganic fertilizers and environmental sustainability. Sustainability 10(3): 2-15.
  25. Salahshor H., Shaabani E., and Kazemian J. 2018. Earthquake risk assessment and calculation “parameters using the Bayesian approach at several selected stations in Iran. International conference on “Contemporary Iranian Civil, Architecture and Urban Development”. Tehran, Shahid Beheshti University, August 25. (In Persian with English abstract)
  26. Schoot R., Van D., Kaplan D., Denissen J., Asendorpf J.B., Neyer F.J., and van Aken M.A.G. 2014. A gentle introduction to bayesian analysis: applications to developmental research. The Journal Child Development 85(3): 842-860.
  27. Semerci A. 2013. Determining the resource use effciency in wheat (Triticum aestivum L.) production: A case study of Edirne Province-Turkey. Bulgarian Journal of Agricultural Science 19: 314-324.
  28. Seyedan S.M. 2003. Productivity analysis and determination of optimum use of potato production factors in Hamadan city. Iranian Journal of Agricultural Science 34(4): 1003-1010. (In Persian with English abstract)
  29. Shams A., and Moghaddam Fard Z.H. 2017. Factors affecting wheat farmers' attitudes toward organic farming. Polish Journal of Environmental Studies 26(5): 2207-2214.
  30. Stonecka I., Lukasik K., and Fornalski K.W. 2019. Simplified bayesian method: application in cytogenetic biological dosimetry of mixed n + γ radiation fields. Radiation and Environmental Biophysics 58(1): 49-57.
  31. Sun W., and Yuan Y.X. 2006. Optimization Theory and Methods, Nonlinear Programming. Springer, Switzerland.
  32. Arora R.K. 2015. Optimization Algorithms and Applications. Taylor and Francis Group, New York, pp: 466.
  33. Tembo G., Brorsen B.W., Epplin F.M., and Tostao E. 2008. Crop input response functions with stochastic plateaus. American Journal of Agricultural Economics 90(2): 424-434.
  34. Tiffin R., and Balcombe K. 2010. The determinants of technology adoption by UK farmers using bayesian model averaging, the cases of organic production and computer usage. Australian Journal of Agricultural and Resource Economics 55(4): 579-598.
  35. Tumusiime E., Brorsen B.W., Mosali J., Johnson J., Locke J., and Biermacher J.T. 2011. Determining optimal level of nitrogen fertilizer using random parameter models. Journal of Agricultural and Applied Economics 43(4): 541-552.
  36. Upadhyaya A. 2017. Application of optimization technique for crop planning to improve farm productivity of ICAR-RCER, Patna, India. Journal of Agrisearch 4(1): 68-70.
CAPTCHA Image