Wheat yield modeling using climatic indices and hierarchical clustering

Document Type : Research Article

Authors

University

Abstract

The study of climatic indices with relation to the meteorological data is one of the effective factors in decision making of climate, agriculture, water resource engineering planning and determination of management strategies.The aim of this research is improvement of climatic indices- crop yield modeling with emphases on the model inputs based on the clustering analysis. Data derived from clusters of climatic indices was conducted with mean calculation of each cluster. The investigated indices were 11 climatic indices (Lang, De Martonne, Koppen 1, Koppen 2, Koppen 3, Angstrom, Ivanov, Selyaninov, PEI, VCI and aridity). Simple regression and artificial neural networks were used as modeling of climatic indices- wheat yield in Gilan, Esfahan, Kermanshah and West Azerbaijan provinces. The modified indices with correct structure led to increase of accuracy in crop yield estimation, for example the agreement index of Kermanshah province was increased 12.82% from De Martonne to Koppen 2 index. RMSE related to crop yield eastimatiom using climatic indices compared to the direct use of meteorological data in all provinces decreased 36.66%. The clustering analysis regard to the models input determination increased the models accuracy (Average RMSE of all provinces with clustering=0.7 and without clustering= 1.15, Average RRMSE with clustering=0.29 without clustering= 0.5). Therefore, the synthesize of climatic indices as the model input with proper analysis led to improvement of crop yield modeling.

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References

 Biswas B, Dhaliwal L.K, Singh S.P, Sandhu S.K. Forecasting wheat production using ARIMA model in Punjab. International Journal of Agricultural Sciences.2014; 10(1): 158-161.
[2].Mathieu JA, Aires F. Assessment of the agro-climatic indices to improve crop yield forecasting. Agricultural and Forest Meteorology.2018; 253-254:15-30.
[3].Mousavi-Baygi M, Bannnayan M, Ashraf B, Asadi Oskuei E. Assessment of climatic indices limiting rainfed wheat yield. Ecological Indicators.2016; 62: 298-305. [Persian]
[4].Paltasingh KR, Goyaria P, Mishra R.K. Measuring Weather Impact on Crop Yield Using Aridity Index: Evidence from Odisha.Agricultural Economics Research Review.2012; 25(2): 205-216.
[5].Shaw LH. The effect of weather on agricultural output: A look at methodology. Journal of Farm Economics.1964; 46(1): 218-230.
[6].Oury B. Allowing for weather in crop production model building. Journal of Farm Economics.1965; 47(2): 270-283.
[7].Rolla AL, Nuñez MN, Guevara ER, Meira SG, Rodriguez GR, Zárate MI. Climate impacts on crop yields in Central Argentina. Adaptation strategies. Agricultural Systems.2018; 60: 44-59.
[8].Wang R, Bowling L.C, Cherkauer K.A. Estimation of the effects of climate variability on crop yield in the Midwest USA. Agricultural and Forest Meteorology.2016; 216: 141-156.
[9].Sabziparvar AA, Torkaman M, Maryanaji Z. Investigating the effect of agroclimatic indices and variables on optimum wheat performance (Case study: Hamedan Province). Journal of Water and Soil.2013; 26(6): 1554-1567. [Persian]
[10].Mosaedi A, Mohammadi Moghaddam S, Ghabaei Sough M. Modeling rain-fed wheat and barley based on meteorological features and drought indices. Journal of Water and Soil.2015; 29(3): 730-749. [Persian]
[11].Alizadeh A, Kamali G, Mousavi F, Mousavi Bayeghi M.Weather and climate. Ferdowsi University of Mashhad;2012. [Persian]
[12].Lang R. Verwitterrung und Bodenbildung alsEifuhrung die Bodenkunde, Stuttgart;1920.
[13]. De Martonne E. Une nouvelle function climatologique: I’Indice d’Aridite, La Meteorologie, October, Paris (Cited by Oury, 1965);1926.
[14].Angstrom A A coefficient of humidity of general applicability. Geografiska Annaler,1936; 18: 245-254.
[15].Köppen W. Das geographische System der Klimate. Handbuch der Klimatologie, v.1, Part C (W. Köppen & R. Geiger, eds.). Gerbrüder Bornträger, Berlin,1936: p. C1-C44, (Cited by Oury, 1965).
[16].Ivanov NN. Landscape-climatic zones of the earth surface. Proceedings , All Soviet Geographical Congress, Vol. 1. Publ i cat ion of the Academy of Science of USSR, Leningrad.1948.
[17]. Selianinov GT. On agricultural climate valuation. Proceedings of Agricultural Meteorology.1928; 20: 165–177(in Russian, cited by Meshcherskaya and Blazhevich,1997).
[18].Thornthwaite CW. An approach toward a rational classification of climate. Geogr Rev.1948; 38:55−94.
[19]. Gorbanzadeh Kharazi H, Cheleh Mal Dezfulnejad M. Innovation a new climate classification method based on hydrometeorology. 2014; 2(2):97-108.
[20].Food and Agriculture Organization (FAO). Arid Zone Forestry: A Guide for Field Technicians; FAO: Rome, Italy, 1989.
[21]. Arman N, Salajegheh A, Feiznia S, Ahmadi H, Ghodousi J, and Kiani Rad A. Determination of homogeneous watersheds for estimating erosion and sediment using different cluster analysis methods (Case study: Northern Alborz Range). Journal of Range and Watershed Management.2016; 69(2): 261-273. [Persian]
[22].Khosravi M, Armesh M. Climatic Regionalization of Markazi Province: application offactor and cluster analysis. Geography and Environmental Planning. 2012; 46(2): 87-104. [Persian]
[23].Wang HR, Wang C, Lin X, Kang J. An improved ARIMA model for precipitation simulations. Nonlinear Processes in Geophysics.2014; 21: 1159-1168.
[24].Caubel J, de Cortázar-Atauri I, Launay M, de Noblet-Ducoudré N, Huard F, Bertuzzi P, Graux A. Broadening the scope for ecoclimatic indicators to assess crop climate suitability according to ecophysiological, technical and quality criteria. Agricultural and Forest Meteorology.2015; 207: 94-106.
[25].Lepage M, Bourgeois G, Bélanger G. Indices agrométéorologiques pour l’aide á la eferenc dans un eferen de climat variable et en eference. Centre de eference enagriculture et agroalimentaire du Québec (CRAAQ). Québec, QC, Canada.2012.
[26].Murtagh F, Legendre P. Ward’s hierarchical agglomerative clustering method: which algorithms implement ward’s criterion? Journal of Classification.2014; 31: 274-295.
[27].Gu X, Zhang Q, Singh VP, Chen YD, Shi P. Temporal clustering of floods and impacts of climate indices in the Tarim River basin, China. Global and Planetary Change.2016; 147: 12-24.
Iranian journal of Ecohydrology
Volume 6, Issue 2
July 2019
Pages 479-491

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History

  • Receive Date: 24 November 2018
  • Revise Date: 18 March 2019
  • Accept Date: 18 March 2019
  • First Publish Date: 22 June 2019
  • Publish Date: 22 June 2019

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