تعیین مهمترین عوامل موثر بر عملکرد باغات زیتون شمال ایران

نوع مقاله : مقالات پژوهشی

نویسندگان

1 مرکز تحقیقات و آموزش کشاورزی و منابع طبیعی گیلان، سازمان تحقیقات،آموزش و ترویج کشاورزی،رشت

2 واحد علوم و تحقیقات، دانشگاه آزاد اسلامی، تهران

3 .

چکیده

یکی از روش­های نوین در ارزیابی اراضی نسبت به کاربری­های مختلف، مدل­سازی یا شبیه­سازی کاربری مورد نظر است. با توجه به اهمیت عوامل موثر بر عملکرد باغات زیتون کشور، این ارزیابی در شمال ایران در بیش از 80 باغ زیتون شهرستان رودبار استان گیلان که مهمترین باغات زیتون کشور واقع شده است با استفاده از مدل شبکه عصبی مصنوعی انجام شد. عملکرد محصول باغات زیتون تحت تاثیر عوامل مختلف خاکی، آبی، توپوگرافی و اقلیمی می­باشند، که در مجموع بیست و هشت عامل موثر بر عملکرد زیتون انتخاب و به عنوان متغیرهای ورودی مدل انتخاب شدند. این متغیرها عبارت بودند از خصوصیات خاک شامل EC، میزان مواد خنثی شونده (TNV)، درصد کربن آلی، فسفر قابل جذب، پتاسیم قابل جذب، درصد رس و درصد سیلت، خصوصیات آب آبیاری شامل EC و میزان آب آبیاری، خصوصیات توپوگرافی شامل ارتفاع و شیب، غلظت عناصر غذایی در برگ شامل نیتروژن، فسفر، پتاسیم، آهن و روی، عوامل اقلیمی شامل ساعات آفتابی، میزان تبخیر، میزان متوسط دما به عنوان متغیرهای ورودی و عملکرد محصول به عنوان خروجی مدل­­های شبکه عصبی مصنوعی در نظر گرفته شد. نمونه­­های خاک، آب و برگ از باغات به طور همزمان در مرداد ماه جمع آوری شده و عوامل توپوگرافی از طریق میان­یابی در محیط GIS و با استفاده از نرم­افزار  ArcGisبدست آمد و اطلاعات مربوط به عوامل اقلیمی نیز از ایستگاه‌های هواشناسی منطقه و روش میان­یابی بدست آمد. پس از تعیین بهترین ساختار شبکه عصبی با میزان R آزمون 80 درصد، به کمک آنالیز حساسیت به روش Hill، عکس­العمل مدل به هر یک از متغیرهای ورودی بررسی و مهمترین فاکتورهای تأثیرگذار بر عملکرد محصول زیتون به دست آمد. بر اساس نتایج آنالیز حساسیت، مهم­ترین پارامترهای مؤثر در عملکرد محصول زیتون، به ترتیب نیتروژن برگ، فسفر خاک، تبخیر زمستان، تبخیر تابستان، میانگین دمای پاییز، ساعات آفتابی تابستان، میزان پتاسیم برگ، شوری خاک، شوری آب و شیب می­باشند. که به طور خلاصه می­توان به الویت تامین عناصر غذایی ماکرو المنت مانند نیتروژن و فسفر و تامین رطوبت مورد نیاز با توجه به تبخیر منطقه حتی در فصل زمستان برای باغداران تاکید نمود.

کلیدواژه‌ها


عنوان مقاله [English]

Determination of the Most Important Factors Affecting Yield of Olive (Olea europaea L.) Orchards in the North of Iran

نویسنده [English]

  • A. Mohammadi Torkashvand 2
2 .
چکیده [English]

Introduction In order to land classification for agricultural and natural resources, the most important criterion and factor is the production or yield of the lands. The best way to evaluate a method is to interact between the yield and potential of the product with the land specification involved in the production. One of the new methods in the land evaluation for different uses is the modeling or simulation of the intended use. Artificial neural networks are one of the new tools that are used today to simulate yield and determine the effective factors in the production of agricultural crops. New land evaluation methods include different modeling techniques, which these new methods, including simulations, are used in land valuation methods to test the ability of models for determining the relationship mentioned. An artificial neural network is one of the techniques that can do nonlinear analysis. It is important to recognize the most important input parameters to the predictive models of olive yield, which are also considered to be effective variables in production. Through the process of sensitivity analysis, valuable information about the sensitivity of the model to its input variables can be provided to the designer and model architect. In north of Iran, in the southern areas of Gilan Province, the most important olive gardens of the country are located in the southern part of the country. Different soil and water, topographical and climatic factors affect the yield of olive orchards.
Materials and Methods Climatic data, soil and water characteristics, topographic characteristics and leaf nutrition elements as input variables and olive yield were used as output models. Twenty-eight factors were selected as the most important factors or variables affecting the yield of olive orchards. These input variables included soil properties: EC, (TNV), organic carbon percentage, available phosphorus, available potassium, clay percentage and silt percentage; irrigation water characteristics including: EC and content of irrigation water; topographic characteristics including: altitude and the slope; the concentration of nutrients in leaves included: nitrogen, phosphorus, potassium, iron and zinc; climatic factors including: sunny hours, evaporation rate, average temperature and olive yield were considered as output of artificial neural network model.
Results and Discussion Using the MATLAB software by artificial neural network, the best structure of this network was obtained for the component of the yield of olive. The trained structure had 28 input nodes in 5 groups and one output node. The number of hidden nodes, 38 nodes and the most appropriate number of repetitive learning based on the test and error, 20 were determined for olive product yield. After determining the best structure of the neural network with a R-test of 80%, using Hill's sensitivity analysis, the model's response to each of the input variables was studied and the most important factors influencing the yield of olive oil were obtained. Based on the results of sensitivity analysis, the most important parameters affecting the yield of olive are content of leaf nitrogen, soil phosphorus, winter evaporation, summer evaporation, average autumn temperature, summer sunshine, leaf potassium, salinity, salinity, and slope, respectively. In general, this study showed that the amount of nitrogen and phosphorous is the most important factor for the production of olives, and then variables that are related to the amount of water in the soil, (summer evaporation, average autumn temperature, summer sunshine), were identified as important. The concentration of potassium, iron and zinc in the leaf as a nutrient element and water and soil salinity from stressors for plants and slope are important topographical factors that affected the amount of soil depth and soil water content as the most important factors in olive crop production.
Conclusion In brief, the role of micro and macro nutrients and the factors affecting the maintenance of water in the soil and providing moisture for the plant. In addition to nutrients, gardeners should consider soil and water salinity, which is a stressor for plants, as well as slope, which is important topography factor that effective on soil depth and water available and nutrients for plants as the most important factors in olive production.

کلیدواژه‌ها [English]

  • Artificial neural network
  • Gilan
  • Olive (Olea europaea L.)
  • Modeling
  • sensitivity analysis
1- Aguilera F., and Valenzuela. L.R, 2014. Forecasting olive crop yields based on long-term aerobiological data series and bioclimatic conditions for the southern Iberian Peninsula. Spanish Journal of Agricultural Research 2014 12(1): 215-224, Instituto Nacional de Investigacion Tecnologia Agraria Alimentaria (INIA) .Available http ://dx.doi.org /10.5424 /sjar/2014121-4532
2- Agyare W.A., S.J. Park., and P.L.G. V lek. 2007. Artificial neural network estimation of saturated hydraulic conductivity. Vadose Zone J. 6:423-431.
3- Albrizio R., Todorovic M., Matic T., and Stellacci A. M. 2010. Comparing the interactive effects of water and nitrogen on durum wheat and barley grown in a Mediterranean environment. Field Crop Research 115: 179-190.
4- Alcoz M.M., F.M. Hons and V.A. Haby. 1993. Nitrogen fertilization timing effect on wheat production, nitrogen uptake efficiency, and residual soil nitrogen. Agron. J. 85:1198-1203.
5- Anonymous. 2017. Statistical Booklet of Gilan Jihad Agriculture Organization, Ministry of Agriculture Jihad.
6- Ayoubi S., and Jalalian A. 2006. Land Evaluation (Agriculture and Natural Resources Applications). Isfahan University of Technology Publ. (in Persian)
7- Ayoubi S., Zamani S. M. and Khormali F. 2009. Wheat Yield Prediction through Soil Properties Using Principle Component Analysis, Iranian Journal of Soil and Water Research, 40 (1), 51-57. (In Persian)
8- Bagheri S., Gheysari M., Ayoubi Sh. and Lavaee N. 2012. Silage maize yield prediction using artificial neural networks. Journal of Plant Production, 19(4): 77-95. (in Persian with English abstract).
9- Bagherzadeh A., Ghadiri E., Souhani Darban.A. R.,and Gholizadeh.A. 2016. Land suitability modeling by parametric-based neural networks and fuzzy methods for soybean production in a semi-arid region. Model. Earth Syst. Environ. (2016) 2:104 .DOI 10.1007/s40808-016-0152-4
10- Black C.A., Evans D.D., White J.L., Ensminger L.E., and Clark F.E. 1965. Methods of Soil Analysis, Agronmy Monograph No. 9, Part II: Chemical and microbiological properties, Am.Soc. Argon ., Madison, WI, USA.
11- Bremmer JM., Mulvaney CS. 1982. Total nitrogen. Methods of Soil Analysis, Agronmy Monograph No. 9, Part II: Chemical and microbiological properties, Am.Soc. Argon., Madison, WI, USA.
12- Chartzoulakis K. 2005. Salinity and olive: growth, salt tolerance, photosynthesis and yield. Agriculture Water Management. 78: 108–121
13- Chen Z. X., Ren J. Q., Zhou Q. B., and H. J, Tang. 2008. Regional yield estimation for winter wheat with MODIS-NDVI data in Shandong, China. International Journal of Applied Earth Observation and Geoinformation, 10:403−413.
14- Correia P. J., I. Anast´acio M. Da F´e Candeias and M. A.Martins-Louc¸˜ao “Nutritional diagnosis in carob-tree: relationships between yield and leaf mineral concentration,” Crop Science, vol. 42, no. 5, pp. 1577–1583, 2002.
15- Doraiswamy P. C., Moulin S., Cook P. W., and V., Stern. 2003. Crop yield assessment from remote sensing. Photogrammetric Engineering and Remote Sensing, 69: 665−674
16- Duarte F., N. Jones and L. Fleskens.2008. “Traditional olive orchards on sloping land: sustainability or abandonment?” Journal of Environmental Management, vol. 89, no. 2, pp. 86–98, 2008.
17- Erel R. 2008. Flowering and Fruit Set of Olive Trees in Response to Nitrogen, Phosphorus, and Potassium. J. AMER. SOC. HORT. SCI. 133(5):639–647
18- Galan C., Garcia-Mozo H., Vazquez L., Ruiz Valenzuela L.,Diaz de la Guardia C., and Dominguez E. 2008. Modeling olive crop yield in Andalusia, Spain. Agron J 100: 98-104
19- Gee G.W., and Bauder J.W. 1986. Particle size analysis. In: Klute A. (Ed), Methods of soil analysis. Part 1. 2nd ed. Agron. Monogr. 9. ASA. Madison. WI, USA. pp: 383-411.
20- Graaff J. De., and L. A. A. J. Eppink.1999. “Olive oil production and soil conservation in southern Spain, in relation to EU subsidy policies,” Land Use Policy, vol. 16, no. 4, pp. 259–267, 1999.
21- Heydari M. 2006. Identification of dominant soils and the effect of their properties on leaf concentration, quantity and quality of pistachio in Anar region, Rafsanjan. MSc. Thesis, Soil Science Department, Agricultural College, University of Shahrekord, Shahrekord, Iran. (In Persian with English abstract).
22- Hill M. C. 1998. Methods and guidelines for effective model calibration. U.S.Geological
23- Hosseini-Mazinani M., Torkzaban B.2013. Iranian olive catalogue "Morphological and molecular characterization of Iranian olive germplasm". National Institute of Genetic Engineering and Biotechnology.2013. 978-964-8516-23-4.
24- Kaul M., Hill R.L. and Walthall, C. 2005. Artificial neural networks for corn and soybean yield prediction. Agricultural Systems, 85: 1-18.
25- Keshavarzi A., Sarmadian F., Sadeghnejad M., and Pezeshki P.2010.Developing pedotransfer functions for estimating some soil properties using artificial neural network and multivariate regression approaches.ProEnvironment, 3: 322-330.
26- Khairunniza-Bejo S., Mustaffha S., Ishak W., Ismail W. 2014.Application of artificial neural network in predicting crop yield. A review. Journal of Food Science and Engineering, 4,1–9.
27- Khakural, B.R., Robert, P.C., and Huggins, D.R. 1999. Variability of corn/soybean yield and soil/landscape properties across a southwestern Minnesota landscape. In Proceedings of the fourth international conference on precision agriculture, pp: 573-579.
28- Maselli F., and F. Rembold. 2001. Analysis of GAC NDVI data for cropland identification and yield forecasting in Mediterranean African countries. Photogrammetric Engineering and Remote Sensing, 67: 593−602.
29- McLean EO. 1982. Soil pH and lime requirement. Methods of Soil Analysis, Agronmy Monograph No. 9, Part II: Chemical and microbiological properties, Am. Soc. Argon., Madison, WI, USA. pp. 199-233.
30- Mehnatkesh A. 2012. Modeling of Soil and landscape and prediction of dryland wheat production using different models in central Zagros, Ph.D. thesis in Isfahan University of Technology(In Persian with English Summary)
31- Mehnatkesh A., and Ayoubi S., and Dehghani A.2017. Determination of the Most Important Factors on Rainfed Wheat Yield by Using Sensitivity Analysis in Central Zagros. Iranian Journal of Field Crops Research Vol. 15, No. 2, summer. 2017, p. 257-266 (In Persian with English Summary)
32- Menhaj M., 2002. Neural Network Foundations. Iran Amir Kabir University of Technology Publications.
33- Miao Y., Mulla D.J. and Rober P.C. 2006. Identifying important factors influencing corn yield and grain quality variability using artificial neural networks. Precision Agriculture, 7: 117–135.
34- Michelakis N. 2002. Olive Orchard Management: Advances and problems. Proc. 4th International Symposium on Olive Growing. Eds. C. Vitagliano and G.P. Martelli. Acta Hort. 586: 239-245.
35- Mohammadi H., and Zinanlou A., and Roshan A. 2008. Olive Temperature Compatibility Modeling (Olea europaea L.) in Iran, Geographical Research, No. 64, pp. 37-51. (in Persian)
36- Montazer A.A., Azedegan B., and Shahraki M. 2009. Performance evaluation of artificial neural network models in estimation of yield and water productivity of wheat on the basis of climate factor andconsumption water-nitrogen fertilizer. Iranian Journal of Water Research 3 (5): 17-29. (In Persian withEnglish Summary).
37- Niazian M., Sadat-Nooria S.A., Abdipourc M.2018. Modeling the seed yield of Ajowan (Trachyspermum ammi L.) using artificial neural network and multiple linear regression models.Industrial Crops & Products 117 (2018) doi.org/10.1016/j. indcrop.2018.03.013–234.
38- Norouzi M. 2009. Prediction of rainfed wheat yield using artificial neural network in Ardal district of Chaharmahal and Bakhtiari province. M.Sc. Thesis, Collage of Agriculture, Isfahan University of Technology, Isfahan, Iran. 112 p. (In Persian with English Summary)
39- Olsen S. R. and L. E. Sommers. 1982. Phosphorus. In: A. L. Page (Eds.), Methods of Soil Analysis, Agron. No. 9, Part 2: Chemical and microbiological properties, 2th ed. PP. 403-430
40- Page MC., Sparks DL., Noll M., Hendricks GJ. 1987. Kinetics and mechanisms of potassium release from sandy middle Atlantic Coastal Plain Soils. Soil Science Society of AmericaJournal 51: 1460-1465.
41- Quanqi L. Baodi D., Yunzhou Q., Mengyu L., and Jiwang Z. 2010. Root growth, available soil water, and water-use efficiency of winter wheat under different irrigation regimes applied at different growth stages in North China. Agr. Water Manage. 97, 1676–1682
42- Rapoport HF., Hammami SBM., Martins P., Perez-Priego O., and Orgaz F. 2012. Influence of water deficits at different times during olive tree inflorescence and flower development.Environ Exp Bot 77: 227-233.
43- Ribeiro H., Cunha M., Abreu I. 2008. Quantitative forecasting of olive yield in northern Portugal using a bioclimatic model. Aerobiologia 24: 141-150.
44- Richards LA. 1954. Diagnosis and improvement of saline and alkaline soils. USDA Handbook No. 60. U.S. Government Printing Office, Washington, DC. 160 pp.
45- Rossiter D. G. 2003. Biophysical models in land evaluation. Encyclopedia of life support system (EOLSS), EOLSS pub. UK.16pp.
46- Royo C., Aparicio N., Blanco R. and Villegas D. 2004. Leaf and green area development of durum wheat genotypes grown under Mediterranean conditions. Eur. J. Agron. 20: 419–430.
47- Russo C., Cappelletti G.M, Nicoletti G.M., Di Noia A.E., and Michalopoulos G.2016, Comparison of European Olive Production Systems Sustainability 2016, 8, 825; doi:10.3390/su8080825. www.mdpi.com/journal/sustainability
48- Sadras V.O., and Calviño P.A. 2001. Quantification of grain yield response to soil depth in soybean, maize, sunflower, and wheat. Agronomy Journal 93: 577–583
49- Salehi M.H., and HosseinifardS J. 2012. Soil and ground water relationships with pistachio yield in the Rafsanjan area, Iran. Communications in Soil Science and Plant Analysis, 43: 660-671.
50- Sepaskhah A. R., Azizian A., and Tavakoli A. R. 2006. Optimal applied water and nitrogen for winter wheat under variable seasonal rainfall and planning scenarios for consequent crops in a semi-arid region. Agr. Water Manage. 48: 113-122. (In Persian)
51- Seyyed Jalali A. 2015. Determination of Land Production Potential for Wheat in Gotvand and Shooshtar Regions of Khuzestan Province. Land Management Journal, Volume 3, Issue 1.2015. (In Persian)
52- Sharma S.D., Singh R. P., and Sharma C.L.2005.“Periodical changes in foliar macronutrient status of olive,” Acta Horticulturae, vol. 696, pp. 249–254, 2005.
53- Shirdeli A., and Tavasoli A. 2015. Forecast of Saffron Water Performance and Efficiency Using Neural Network Models Based on Climate and Water Factors. Journal of Agriculture and Technology of Saffron Volume 3, Issue 2, Summer 2015, p. 121- 131 University of Torbat Heydarieh Ministry of Science Research Technology, (in Persian)
54- Si C., and Farrell R.E. 2004. Scale-dependent relationship between wheat yield and topographic indices: A wavelet approach. Soil Sci. Soc. Am. J. 68: 577-587
55- Soares J.D.R., Pasqual M., Lacerda W.S., Silva S.O. and Donato S.L.R. 2013. Utilization of artificial neural networks in the prediction of the bunches’ weight in banana plants. Scientia Horticulturae, 155: 24–29.
56- Sudduth K.A., Drummond S.T., Birrell S.J., and Kitchen N.R. 1997. Spatial modeling of crop yield using soil and topographic data. P 439-447. In: Proceedings of the First European Conference on precision agriculture, edited by J.V. Stafford (BIOS Scientific Publishers, Oxford, UK).
57- Sys C., Van Ranst E., and Debaveye J.1993. Land evaluation, Part III. Crop requirements. General Administration for development cooperation, Brussels.
58- Sys C., Van Ranst E., and Debaveye J. 1991. Land evaluation, Part I and II. General Admhnstration for development cooperation, Brussels.
59- Taheri M., and Basirat M., and khoshzaman T., and Mostashari M. and Shakeri M. 2017. Soil Fertilizer and Plant Nutrition Management in Olive Trees, Soil and Water Research Institute of Iran, 2017, (in Persian)
60- Taheri M., Malakouti M. 2000. The necessity of optimum use of fertilizer for increasing the yield and quality improvement of olive in the country. Technical publication No: 66, Agricultural Education Publishing House. Karaj, Iran
61- The International Olive Oil Council. Available online: http://www.internationalolive -oil.org / (accessed on 18 April 2016).
62- Torkashvand A.M., Ahmadi A., Nikravesh N.L. 2017. Prediction of kiwifruit firmness using fruit mineral nutrient concentration by artificial neural network (ANN) and multiple linear regressions (MLR). Journal of Integrative Agriculture, 16, 1634–1644.
63- Touzani A. 1999. Olive farming and the environment. Proc. Of IOOC, Int. Seminar on Genetic Resources, Florence March 1999: 1-12.
64- Tubeileh A., Turkelboom F., Al-Ibrahem A., Thomas R., and Tubeileh. K.S. 2014. Modelling the Effects of Soil Conditions on Olive Productivity in Mediterranean Hilly Areas.International Journal of Agronomy.Volume 2014, Article ID 672123, 12 pages. Hindawi Publishing Corporation .doi:org/10.1155/2014/672123.
65- Velička R., Marcinkevičienė A., Pupalienė R., Butkevičienė L.M, Kosteckas R., Čekanauskas S.,and Kriaučiūnienė Z. 2016. Winter oilseed rape and weed competition in organic farming using non-chemical weed control. Zemdirbyste-Agriculture,103, 11
66- Walkey A., and Black I.A. 1982. An examination of Degtjareff method for determining soil organic matter and a proposed modification of the chromic acid in soil analysis. 1: Experimental. Soil Sci. 79: 459-465.
67- Wall L., Larocue D., and leger P.M.2007.The early explanatory power of NDVI in crop Yield modeling.International Journal of Remote Sensing, 29:2211-2225.
68- Wang L.G., Qiang Qi.Fu., and Liu Y. 2006. Soybean yield forecast application based on Hopfield ANN model. Am. Sci. 2: 85-89.