Developing an Algorithm for Selecting the Most Suitable Crops based on Climatic Conditions (Case Study: Soumar Plain in Kermanshah Province)

Document Type : Research Paper

Authors

1 Department of Irrigation and Reclamation Engineering, Faculty of Agriculture and Technology Engineering, University College of Agriculture and Natural Resources, University of Tehran

2 Professor, Department of Irrigation and Reclamation Engineering, Faculty of Agriculture and Technology Engineering, University College of Agriculture and Natural Resources, University of Tehran

3 Department of Industrial Engineering, Faculty of Industrial Engineering, Iran University of Science and Technology, Tehran, Iran

Abstract

Because of the key role of the agricultural sector in achievement of food security, increasing climatic variations due to global change and the dependence of agricultural yields to climatic conditions, it is essential to study the long-term relationship between climatic conditions and agricultural yields in order to coordinate agricultural activities with climate change trend. In this study, a screener algorithm considering the climatic conditions of the region has been developed to rank the most suitable agricultural products with the climatic conditions of Soumar plain in Kermanshah province. For this purpose, the sensitivity of the defined agroecosystem to climatic conditions of the region was calculated using the Shannon-Wiener index. Then, using Multiple Linear Regression method and SPSS software, regression models were developed between climatic data and crop yield data. In the next step, the accuracy of the developed models was confirmed considering the conditions of using linear regression for all models. Afterward, the weight of effective climatic parameters was determined using pairwise comparison methods. According to the results, the minimum monthly temperature parameter with weight of 0.169 and the average monthly wind speed parameter with weight of 0.032 were considered the most and the least effective climatic parameters, respectively. Finally, crops ranking in the study area was completed using TOPSIS method and calculating Ci index which shows the score of each crop. According to the results, bean, barley and canola with the Ci of 0.601, 0.537 and 0.564 and tobacco, tomato and fodder corn with the Ci of 0.376, 0.513 and 0.518 show the most and the least compatibility with the climate conditions of the region, respectively.

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Anaya‐Romero, M., Abd‐Elmabod, S. K., Muñoz‐Rojas, M., Castellano, G., Ceacero, C. J., Alvarez, S., & De la Rosa, D. (2015). Evaluating soil threats under climate change scenarios in the Andalusia Region, Southern Spain. Land Degradation & Development, 26(5), 441-449.
Angulo-Martínez, M., López-Vicente, M., Vicente Serrano, S. M., & Beguería, S. (2009). Mapping rainfall erosivity at a regional scale: a comparison of interpolation methods in the Ebro Basin (NE Spain).
Balan, B., Mohaghegh, S., & Ameri, S. (1995). State-of-the-art in permeability determination from well log data: Part 1-A comparative study, model development. In SPE Eastern Regional Meeting. Society of Petroleum Engineers.
Barnes, B. V., Zak, D. R., Denton, S. R., & Spurr, S. H. (1998). Forest Ecology. John Wiley & Sons. INC, Newyork.
Borrelli, P., Diodato, N., & Panagos, P. (2016). Rainfall erosivity in Italy: a national scale spatio-temporal assessment. International Journal of Digital Earth9(9), 835-850.
Bouroncle, C., Imbach, P., Rodríguez-Sánchez, B., Medellín, C., Martinez-Valle, A., & Läderach, P. (2017). Mapping climate change adaptive capacity and vulnerability of smallholder agricultural livelihoods in Central America: ranking and descriptive approaches to support adaptation strategies. Climatic Change141(1), 123-137.
Brookfield, H., & Stocking, M. (1999). Agrodiversity: definition, description and design. Global environmental change9(2), 77-80.
Bryan, E., Ringler, C., Okoba, B., Roncoli, C., Silvestri, S., & Herrero, M. (2013). Adapting agriculture to climate change in Kenya: Household strategies and determinants. Journal of environmental management114, 26-35.
Chen, S., Chen, X., & Xu, J. (2013). Impacts of climate change on corn and soybean yields in China. Agricultural and Applied Economics Association 2013 AAEA and CAES joint Annual Meeting, Washington, DC. 120-145.
Fallsolyman, M., Hajipour, M., & Sadeghi, H. (2014). Performance comparison of multi index decision making (TOPSIS-AHP) for suitable site selection cultivation planting of pistachio in Mokhtaran plain of Birjand in GIS environment. Journal of Geographical sciences, 13(31), 133-155. (In Farsi)
Finger, R. & Schmid, S. (2008) Modeling agricultural production risk and the adaptation to climate change. Journsl of Agricultural Finance Review, 12: 2541.
Ghalegolab Behbahani, A., Khoshbakht, K., Tabrizi L., Davari, A., & Vaisi, H. (2013). A comparative assessment of agrobiodiversity indices in farms, gardens and home gardens (case study of jajrood basin). Journal of Agroecology, 5(2), 161-168. (In Farsi)
Ghorbani, R. (2016). General Ecology (3ed Ed.). Mashhad: Jahad daneshgahi. (In Farsi)
Goldani, M., Bannayan, M. & Naderi M.R. (2017). Stratification of Isfahan province regarding crop plants biodiversity during 2003-2012. Journal of Plan Research, 30(1), 155-172. (In Farsi)
Habibi, A. (2016). Applied education of SPSS software (4th Ed.). Pars Modir. (In Farsi)
Isalou, A., Ebrahimzadeh, H., & Shahmoradi, B. (2014). Feasibility study of old inefficient urban area interference using analytical network process model: the case study of district 6 of Qom. Journal of Geography and Development, 12(34), 57-68. (In Farsi)
Javadzadeh, M., & Saljooghianpour, M.  (2018). Biodiversity of agronomical crops in Sistan and Balouchestan Province, Iran. Agroecology Journal, 14(2), 31-50. (In Farsi)
Kamali, G.H., Sadaghianpour, A., Sedaghat Kerdar, A., & Asgari, A. (2008). The climatic zoning of dryland wheat in Eastern Azarbaijan. Journal of Water and Soil, 22(2), 467-483. (In Farsi)
Keshavarz, M. (2018). Addressing compatibility of the farm management strategies with climate changes: The casestudy of Fars prvince. Iranian Agricultural Extension and Education Journal, 14(2), 107-123. (In Farsi)
Kokic, P. Heaney, A. Pechey, L. Crimp, S. and Fisher, B. (2005). Predicting the impacts on agriculture: a case study Australian commodities. Journal of climate change, 12: 123-140.
Koocheki, A., Nassiri Mahallati, M., Hassanzadeh Aval, F., Mansoori, H., Amiri, S.R., Zarghani, H., & Karimian, M. (2013). Agrobiodiversity of vegetable crops in agroecosystems in Iran. Iranian Journal of Applied Ecology, 4(2), 1-12. (In Farsi)
Koocheki, A., Nassiri Mahallati, M., & Jafari, L. (2015). Evaluation of climate change effect on agricultural production of Iran: predicting the future agroclimatic conditions. Iranian Journal of Field Crops Research, 13 (4): 651-664. (In Farsi)
Koocheki, A., Nasiri Mahalati, M., Sharifi, H.R., Zand, E., & kamali, Gh.A. (2001). A simulation study for growth, phenology and yield of wheat cultivers under the doubled CO2 concentration in Mashhad conditions. Journal of Desert, 6(2), 117-127. (In Farsi)
Labus, M. P., Nielsen, G. A., Lawrence, R. L., Engel, R., & Long, D. S. (2002). Wheat yield estimates using multi-temporal NDVI satellite imagery. International Journal of Remote Sensing, 23(20), 4169-4180.
Magurran, A. E. (1988). Ecological diversity and its measurement. Princeton university press.
Mahmoodi, A., & Rasoolzadeh, N. (2016). Determining investment priorities in agriculture sector in Qazvin province using hierarchical analysis method. Journal of Agricultural Economics Researches, 8(2), 1-16. (In Farsi)
Massey Jr, F. J. (1951). The Kolmogorov-Smirnov test for goodness of fit. Journal of the American statistical Association46(253), 68-78.
Meffe, G., & Carroll, R. (1997). Principles of Conservation Biology 2nd EditionSunderland. MA Sinauer Associates.
Mertz, O., Mbow, C., Reenberg, A., & Diouf, A. (2009). Farmers’ perceptions of climate change and agricultural adaptation strategies in rural Sahel. Environmental management43(5), 804-816.
Montgomery, D. C., Peck, E. A., & Vining, G. G. (2012). Introduction to linear regression analysis (Vol. 821). John Wiley & Sons.
Muñoz-Rojas, M., Pereira, P., Brevik, E. C., Cerdà, A., & Jordán, A. (2017). Soil Mapping and Processes Models for Sustainable Land Management Applied to Modern Challenges. In Soil Mapping and Process Modeling for Sustainable Land Use Management (pp. 151-190). Elsevier.
Nassabian, Sh., & Sadr Alashrafi, M. (2004). Study of the effects of rainfall and temperature on strategic agronomy crops. Journal of Agricultural Sciences, 10(1), 35-50.‌ (In Farsi)
Rasooli, S.J., Nasiri Mahalati, M., Naseri Yazdi, M.T., & Ghorbani, R. (2016). Determining prediction model of canola yields based on agrometeorological and climatic parameters in Mashhad region of Iran. Journal of Soil and Water and Soil, 30(4), 1322-1333. (In Farsi)
Reidsma, P., Lansink, A. O., & Ewert, F. (2009). Economic impacts of climatic variability and subsidies on European agriculture and observed adaptation strategies. Mitigation and Adaptation Strategies for Global Change, 14(1), 35.
Rencher, A. C., & Schaalje, G. B. (2008). Linear models in statistics. John Wiley & Sons.
Safari Shali, R., & Getabi, K. (2015). Comprehensive guide to using SPSS in survey research (quantitative data analysis) (6th Ed.). Tehran: Loyeh. (In Farsi)
Seo, S. N. (2013). An essay on the impact of climate change on US agriculture: weather fluctuations, climatic shifts, and adaptation strategies. Climatic Change121(2), 115-124.
Seshadri, S., Hariharan, P., Chhatre, A., & Devalkar, S. (2016). Crop Diversification to Reduce Exposure to Climatic Changes: Associated Risks and Mitigation Strategies. ISB Insight.
Smale, M., Meng, E., Brennan, J. P., & Hu, R. (2003). Determinants of spatial diversity in modern wheat: examples from Australia and China. Agricultural Economics28(1), 13-26.
Stocking, M. (2001). Agrodiversity: a positive means of addressing land degradation and sustainable rural livelihoods. In Land degradation (pp. 1-16). Springer, Dordrecht.
Tatari, M., Koochekian, A., & Nasiri Mahalati, M. (2009). Dryland wheat yield prediction using precipitation and edaphic data by applying of regression models. Iranian Journal of Field Crops Research, 7(2), 357-365. (In Farsi)
Tavakoli, A., Liaghat, A., & Alizadeh, A. (2014). Determination of effective parameters on climate production functions for rainfed barley and sensitive analysis at cold and semicold regions of Lorestan province. Journal of Soil and Water Resources Conservation, 3(2), 57-72. (In Farsi)
Thrupp, L. A. (1998). Cultivating diversity: agrobiodiversity and food security. World Resources Institute.