1.Abtew, W. and Melesse, A. 2013. Crop yield estimation using remote sensing and surface energy flux model, evaporation and evapotranspiration: measurements and estimations. Springer Netherlands, Dordrecht, Pp: 161-175.
2.Adab, H. 2007. Modeling of the colza yield by multiple regression technique in gis a case study: sabzevar township, M.Sc. Thesis, Tarbiat Modarres University, Tehran, Iran. (In Persian)
3.Adab, H., Farajzadeh, M., Filekesh, A. and Ismaili, R. 2014. Preparation of autumn rapeseed yield map using perceptron neural network case study: SabzevarCity. Geogr. Space, 13: 41. 171-180.(In Persian)
4.Alhendawi, R.A., Römheld, V., Kirkby, E.A. and Marschner, H. 1997. Influence of increasing bicarbonate concentrations on plant growth, organic acid accumulation in roots and iron uptake by Barley, Sorghum, and Maize. J. Plant Nutr. 20: 12. 1731-1753.
5.Amirabadizadeh, H. 1997. Plant coverage in sabzevar region, Forest and Rangeland Research Institute of Ministry of Jihad-e Sazandegi, Tehran. Pp: 21-31. (In Persian)
6.Angadi, S., Cutforth, H., McConkey, B. and Gan, Y. 2003. Yield adjustment by canola grown at different plant populations under semiarid conditions. Crop Sci. 43: 4. 1358-1366.
7.Azarakhshi, M., Farzadmehr, J., Eslah, M. and Sahabi, H. 2013. An investigation on trends of annual and seasonal rainfall and temperature in different climatologically regions of iran. J. Range Water. Manage. 66: 1. 1-16.
8.Bairagi, G.D. and Hassan, Z.U. 2002. Wheat crop production estimation using satellite data. J. Ind. Soc. Rem. Sens.
30: 4. 213-219.
9.Bakhsh, A., Kanwar, R.S. and Malone, R.W. 2007. Role of landscape and hydrologic attributes in developing and interpreting yield clusters. Geoderma, 140: 3. 235-246.
10.Balakrishnan, P., Saleem, A. and Mallikarjun, N. 2011. Groundwater quality mapping using geographic information system (GIS): a case study of gulbarga city, karnataka, india. Afric. J. Environ. Sci. Technol. 5: 12. 1069-1084.
11.Baniasadi, M., Babaei, Gh.R., Zeraati, H.A. and Memari, F. 2007. Application of bootstrap open sampling method in logistic regression and its application in analyzing data related to patientswith breast cancer. Quarterly J. Sch. Publi. Health Inst. Publi. Health Res.4: 1. 9-18. (In Persian)
12.Böhner, J. and Antonic, O. 2009. Chapter 8 land-surface parameters specific to topo-climatology. In: H. Tomislav and I.R. Hannes (Editors), Developments in soil science. Elsevier, Pp: 195-226.
13.Böhner, J., McCloy, K.R. and Strobl, J. 2006. SAGA-analysis and modelling applications, University of Goettingen.
14.Breiman, L. 2001. Random Forests. Mach. Learn. 45: 1. 5-32.
15.Choong, J. 2012. Powerful forecasting with ms excel. Kindle Edition.
16.Crane, E. 1972. Bees in the pollination of seed crops. Bee Res. Assoc. 163p.
17.Delaplane, K.S., Mayer, D.R. and Mayer, D.F. 2000. Crop Pollination by Bees. Cabi, 332p.
18.Dehshiri, A. and Modares-Sanavy, S.A.M. 2017. Effects of salinity on yield quantity and quality of three rapeseed (brassicc napus) cultivars under different atmospheric carbon dioxide concentrations. J. Crop Prot. 9: 4. 1-16.
19.Ebrahimi-Pak, N. 2010. Determaination on potential evapotranspiration canola using lysimeter method. Proceedings of 10th National Seminar on Irrigationand Evapotranspiration. Shahid Bahonar University of Kerman, 7p. (In Persian)
20.Edmeades, G.O., Bolanos, J., Lafitte, H.R. and Rajaram, S. 1989. Traditional approaches to breeding for drought resistance in cerea. 1435-0653, ICSU andCAB1, Wallingford, UK.
21.Efron, B. 1981. Nonparametric standard errors and confidence intervals. Can. J. Stat. 9: 2. 139-158.
22.Farajzadehasl, M. and Zarrin, A. 2002. Rainfed wheat yield modeling according to agrometeorological parameters in west azarbayjan provinde. Modares J.6: 71-96. (In Persian)
23.Farshad, M.D. and Sadeh, J. 2014. Short-circuit fault location in high-voltage direct current transmission lines using a generalized regression neural network and random forest algorithm. Computational Intel. Elect. Eng. Smart Sys. Elec. Eng. 4: 2. 1-14.
24.Ghafouri-Kesbi, F., Rahimi Mianji, Gh., Honarvar, M. and Nejati, J.A. 2017. The regulation and application of random forest algorithm in genomic evaluation. Res. Anim. Prod. 7: 13. 185-178.(In Persian)
25.Ghorbani, Kh. and Soltani, A. 2014. The effect of climate change on soybean yield in gorgan. J. Plant Prod. Res.
21: 2. 67-85. (In Persian)
26.Godfray, H.C.J., Beddington, J.R., Crute, I.R., Haddad, L., Lawrence, D., Muir, J.F., Pretty, J., Robinson, S., Thomas, S.M. and Toulmin, C. 2010. Food security: the challenge of feeding 9 billion people. Sci. 327: 5967. 812-818.
27.Hanjra, M.A. and Qureshi, M.E. 2010. Global water crisis and future food security in an era of climate change. Food Policy, 35: 5. 365-377.
28.Häntzschel, J., Goldberg, V. and Bernhofer, C. 2005. GIS‐based regionalisation of radiation, temperature and coupling measures in complex terrain for low mountain ranges. Meteorol. Appl. 12: 1. 33-42.
29.Hatamvand, M., Hasanloo, T., Dehghan Nayeri, F., Shiranirad, A.H., Tabatabaei, S.A. and Hosseini, S.M. 2015. Evaluation of some physiological and biochemical indices of canola cultivars in response to drought stress. J. Environ. Stresses Crop Sci. 7: 2. 173-185.
30.Hodgson, A. 1979. Rapeseed adaptation in Northern New South Wales. III.* Yield, yield components and grain quality of Brassica campestris and Brassica napus in relation to planting date. Austr. J. Agric. Res. 30: 1. 19-27.
31.Honar, T., Sabet, S.A., Kamgar, H.A. and Shams, S. 2011. Calibration of crop system model for growth simulation and yield estimation of canola. J. Water Soil. 25: 3. 593-605. (In Persian)
32.Honar, T., Sabet-Sarvestani, A., Sepaskhah, A., Kamgar-Haghighi, A. and Shams, S. 2012. Simulation of soil water content and yield of canola using CRPSM. J. Sci. Technol. Agric. Nat. Resour. 16: 59. 45-57. (In Persian)
33.Hosaini, M.T., Siosemarde, A., Fathi, P. and Siosemarde, M. 2008. Application of artificial nearal network (ann)
and multiple regression for estimating assessing the performance of dry farming wheat yield in Ghorveh Region, Kurdestan Province. Agric. Res.7: 1. 41-54. (In Persian)
34.Iqbal, J., Read, J.J., Thomasson, A.J. and Jenkins, J.N. 2005. Relationships between soil–landscape and dryland cotton lint yield. The National Aeronautical and Space Administration-funded Remote Sensing Technology Center at Mississippi State University (NASA grant number NCC13-99001). Soil Sci. Soc. Amer. J. 69: 3. 872-882.
35.Jan, Z., Abrar, M., Bashir, S. and Mirza, A.M. 2009. Seasonal to inter-annual climate prediction using data mining knn technique. In: D.M.A. Hussain, A.Q.K. Rajput, B.S. Chowdhry andQ. Gee (Editors), Wireless Networks, Information Processing and Systems: International Multi Topic Conference, IMTIC 2008 Jamshoro, Pakistan, April 11-12, 2008 Revised Selected Papers. Springer Berlin Heidelberg, Berlin, Heidelberg, Pp: 40-51.
36.Jay, P. 2000. Weather and yield. Iranian Students Booking Agency. Translated by Kafi, M. Ganjali, M. Nezami, A. and Shariatmadar, F.: 311p. (In Persian)
37.Jey, P. 2014. Climate and yield ofcrops. J. Jihad University, Mashhad, 311p. (In Persian)
38.Kamkar, B., Dorri, M.A. and Teixeira, J.A. 2014. Assessment of land suitability and the possibility and performance of a canola (Brassica Napus L.) – Soybean (Glycine Max L.) Rotation in Four Basins of Golestan Province, Iran.The Egypt. J. Rem. Sens. Space Sci.17: 1. 95-104.
39.Kandiannan, K., Karthikeyan, R., Krishnan, R., Kailasam, C. and Balasubramanian, T.N. 2002. A crop–weather model for prediction of rice (Oryza sativa L.) yield using an empirical-statistical technique. J. Agron. Crop Sci. 188: 1. 59-62.
40.Karimbeigi, H., Nazarian-Firouzabadi, F., Khademi, M. and Mousav, E. 2016. Assessment of genetic diversity among some oilseed rape (Brassica nupus L.) plants, using single sequence repeats (SSR) molecular markers. J. Plant Genet. Res. 3: 1. 45-56. doi:10.29252/ pgr.3.1.45.
41.Kazemi, H. 2014. Agroecological zoning of gorgan agricultural lands for hulless barley cropping based on boolean logic. J. Crop Prod. 6: 4. 165-185.(In Persian)
42.Khorshid Dost, E.M., Hoseini, A.V. and Pour, K. 2012. Suitable Areas for canola cultivation in Kurdistan province using GIS. Water Soil Knowl. 21: 3. 48-37. (In Persian)
43.Lall, U. and Sharma, A. 1996. A nearest neighbor bootstrap for resampling hydrologic time series. Water Resour. Res. 32: 3. 679-693.
44.Lashkari, H., Keikhosravi, B.C. and Ghiyasi, D. 2011. Modeling of topography effects on estimation of potential evapotranspiration using Geographic Information System (Case study- Sabzevar, Iran. Earth Sci. Res.1: 2. 87-102. (In Persian)
45.Liaw, A. and Wiener, M. 2002. Classification and regression by randomforest. R news, 2: 3. 18-22.
46.Lobell, D.B. 2013. The use of satellite data for crop yield gap analysis. Field Crops Res. 143: 56-64.
47.Loeppert, R.H. 1986. Reactions of iron and carbonates in calcareous soils. J. Plant Nutr. 9: 3-7. 195-214.
48.Maas, E. 1986. Salt tolerance of plants. Applied Agric. Res. (USA).
49.Maas, E.V. and Hoffman, G. 1977.Crop salt tolerance-current assessment. J. Irrig. Drain. Div. 103: 2. 115-134.
50.Mathers, H. 2003. What you should know about water, Organ State University, USA.
51.Matlabefard, R., Mjmalakouti, M.J. and Kafee, M. 2009. The effect of pH of irrigation on quantity and quality characteristics of dianthus caryophyllus l. Soil Water Sci. 22: 1. 103-111.(In Persian)
52.Mazhari, M. and Parsapoor, K. 2012. Factors influencing the adoption of rapeseed cultivation (Case study of Khorasan Razavi Province). J. Econ. Agric. Dev. 25: 4. 410-419.
53.Mo, X., Liu, S., Lin, Z., Xu, Y., Xiang Y. and McVicar, T.R. 2005. Prediction of crop yield, water consumption and water use efficiency with a svat-crop growth model using remotely sensed data on the north china plain, Ecol. Model. 183: 2-3. 301-322.
54.Morrison, M. 1993. Heat stress during reproduction in summer rape. Can. J. Bot. 71: 2. 303-308.
55.Morrison, M.J. and Stewart, D.W. 2002. Heat stress during flowering in summer brassica. Crop Sci. 42: 3. 797-803.
56.Najafi, A.B. 2010. Evaluation ofthe effect of climatic parameters on canola in Pars-Abad, Iran to Provide agricultural calendar in the year of 2008/2009, M.Sc. Thesis. University of Ardabil. (In Persian)
57.Nellemann, C., MacDevette, M., Manders, T., Eickhout, B., Svihus, B., Prins, A.G. and Kaltenborn, B.P. 2009. The environmental food crisis: the environment's role in averting future food crises: A UNEP Rapid Response Assessment. UNEP/Earthprint, Norway, 101p.
58.Noori, K. and Jahan Nama, F. 2008. Study of comparative advantage of spring soybean in iran. Research Construct. Agron. Hort. 21: 2. 26-35.(In Persian)
59.Olsson, G. 1955. Wind pollinationof cruciferous oil plants. Sverig. Utsadesforen. Tidskr, 65: 6. 418-422.
60.Omidvar, K. and Dastmoradi, S. 2014. Study of the relationship between climatic elements and canola performance in Kermanshah Province, First National Conference on Climatology. University of Advanced Industrial Technology and Advanced Technology, Kerman, Pp: 32-53. (In Persian)
61.Oshiro, T.M., Perez, P.S. and Baranauskas, J.A. 2012. How many trees in a random forest? .International Workshop on Machine Learning and Data Mining in Pattern Recognition. Springer, Pp: 154-168.
62.ÖZER, H., Oral, E. and Dogru, U. 1999. Relationships between yield and yield components on currently improved spring rapeseed cultivars. Turk. J. Agric. For. 23: 6. 603-608.
63.Ozkan, B. and Akcaoz, H. 2002.Impacts of climate factors on yields for selected crops in the southern turkey. Mitigation Adapt. Strateg. Glob. Chang. 7: 4. 367-380.
64.Parida, A.K. and Das, A.B. 2005.Salt tolerance and salinity effects on plants: a review. Ecotoxicol. Environ. Saf. 60: 3. 324-349.
65.Pasban-Eslam, B. 2013. Evaluation of seed and oil yields and their components and relationships in oilseed rape genotypes under east azarbaijan conditions in iran. Crop Breed. J. 3: 1. 53-59.(In Persian)
66.Reganold, J.P., Papendick, R.I. and Parr, J.F. 1990. Sustainable agriculture. Sci. Amer. 262: 6. 112-120.
67.Riffkin, P., Potter, T. and Kearney,G. 2012. Yield performance of late-maturing winter canola (Brassica Napus L.) Types in the high rainfall zone of southern australia. Crop Pasture Sci.63: 1. 17-32.
68.Sadat Seyed Mohammadi, N., Allahdadi, A., Seyed Mohammadi, E., Sarafraz, E. 2013. The variety of some physiological and morphological characteristics of spring rapeseed varieties under irrigation intervals and regimes. Physiol. Plant.
4: 16. 5-17. (In Persian)
69.Sadeh, J., and Farshad, M. 2014. Fault locating in hvdc transmission lines using generalized regression neural network and random forest algorithm. Comput. Intel. Electr. Eng. 4: 2. 1-14. (In Persian)
70.Sairam, R.K. and Srivastava, G.C. 2002. Changes in antioxidant activity insub-cellular fractions of tolerant and susceptible wheat genotypes in response to long term salt stress. Plant Sci.162: 6. 897-904.
71.Savin, I.Y., Stathakis, D., Negre, T. and Isaev, V.A. 2007. Prediction of crop yields with the use of neural networks. Russ. Agric. Sci. 33: 6. 361-363.
72.Serele, C.Z., Gwyn, Q.H.J., Boisvert, J.B., Pattey, E., McLaughlin, N. and Daoust. G. 2000. Corn yield prediction with artificial neural network trained using airborne remote sensing and topographic data. Geoscience and Remote Sensing Symposium, 2000. Proceedings. IGARSS 2000. IEEE. International. IGARSS Honolulu (USA), Pp: 384-386.
73.Singh, H., Singh, K.P., Jarwal, S.D., Singh, T., Tonk, D.S. and Faroda, A.S. 1989. Water production function for indian rape. J. Oilseeds Res. 6: 2. 316-321.
74.Sudduth, K.A., Drummond, S.T., Birrell, S.J. and Kitchen, N.R. 1996. Analysis of spatial factors influencing crop yield, precision agriculture. American Society of Agronomy, Crop Science Societyof America, Soil Science Society of America, Madison, WI, Pp: 129-139.
75.Sun, R. and Zhang, B. 2016. Topographic effects on spatial pattern of surface air temperature in complex mountain environment. Environ. Earth Sci. 75: 7. 621.
76.Tachikawa, T., Hato, M., Kaku, M. and Iwasaki, A. 2011. Characteristics of aster gdem version 2. Geoscience and Remote Sensing Symposium (IGARSS), 2011 IEEE International, Pp: 3657-3660.
77.Tikle, S., Saboori, M.J. and Sankpal, R. 2012. Spatial distribution of ground water quality in some selected parts of Pune City, Maharashtra, India using GIS. Curr. World Environ. 7: 2. 281-286.
78.van den Berg, R.A., Hoefsloot, H.C.J., Westerhuis, J.A., Smilde, A.K. andvan der Werf, M.J. 2006. Centering, scaling, and transformations: improving the biological information content of metabolomics data. BMC Genomics,7: 142-142.
79.Wadsworth, R.M. 1959. An optimum wind speed for plant growth. Annals of Botany, 23: 1. 195-199.
80.Williams, I.H., Martin, A. and White, R. 1987. The effect of insect pollination on plant development and seed production in winter oil-seed rape (brassica napus L.). J. Agric. Sci. 109: 01. 135-139.
81.Wu, X., He, J., Yip, T. and Zhang, P. 2015. A two-stage random forest method for short-term load forecasting. PowerTech, 2015 IEEE Eindhoven. IEEE, Pp: 1-6.
82.Yanegh, A. and Khajeh-hosseini, M. 2014. Effects of field conditions on emergence of oilseed rape seed lots grown in Khorasan Province. Iranian J. Field Crops Res. 12: 1. 9-16. (In Persian)
83.Yazdani, V., Davari, K., Ghahreman, B. and Kafi, M. 2015. Assesment of the water-salinity production function models-canola application in the Mashhad Area. J. Irrig. Water Eng.5: 18. 32-53. (In Persian)
84.Young, L.W., Wilen, R.W. and Bonham‐Smith, P.C. 2004. High temperature stress of brassica napus during flowering reduces micro‐and megagametophyte fertility, induces fruit abortion, and disrupts seed production. J. Exp. Bot. 55: 396. 485-495.
85.Zevenbergen, L.W. and Thorne, C.R. 1987. Quantitative analysis of land surface topography. Earth Surf. Process. Landf. 12: 1. 47-56.
86.Zhao, Y. and Zhang, Y. 2008. Comparison of decision tree methods for finding active objects. Adv. Space Res. 41: 12. 1955-1959.
)