DO INDUSTRY AND ACADEMIC EXPERTS DIFFER IN WEIGHTING CRITERIA FOR AGRICULTURAL PLANNING AND IN THEIR LOSS AVERSION?

##plugins.themes.bootstrap3.article.main##

##plugins.themes.bootstrap3.article.sidebar##

Published Apr 20, 2016
Gregory Yom Din

Abstract

Agricultural planning models account for changing weather conditions and for high food price volatility. Applying the criteria of maximum farmer’s profit in various years enables the use of multi-criteria techniques. This paper presents the application of the Analytical Hierarchy Process (AHP) to estimate the importance of these criteria. The study contributes to the literature by a) addressing expert opinions on the importance of the criteria of profit in normal, dry years, and in years when agricultural prices rise; b) examining differences in weighting criteria between industry and academic experts; c) taking into account the loss averse behavior for these two groups of experts. Industry and academic experts were interviewed in the agricultural region of North-Eastern Israel. Changes in profit are approximated by a linear utility function with a positive slope (for both losses and gains) which is steeper for losses (profit in dry years) than for gains (profit in years when prices rise). The AHP method allows for the identification of the importance of the criteria for all respondents as a whole and for academic experts as compared to industrial experts. The share of loss averse respondents and coefficients of loss aversion are identified for both groups of experts. These coefficients are used for explaining differences between industry and academic expert opinions in estimating the criteria importance.

How to Cite

Yom Din, G. (2016). DO INDUSTRY AND ACADEMIC EXPERTS DIFFER IN WEIGHTING CRITERIA FOR AGRICULTURAL PLANNING AND IN THEIR LOSS AVERSION?. International Journal of the Analytic Hierarchy Process, 8(1). https://doi.org/10.13033/ijahp.v8i1.355

Downloads

Download data is not yet available.
Abstract 1767 | PDF Downloads 203

##plugins.themes.bootstrap3.article.details##

Keywords

AHP, farmers profit, dry year, prices rise, consistency, loss averse

References
Abdellaoui, M., Bleichrodt, H., & Paraschiv C. (2007). Loss aversion under prospect theory: A parameter-free measurement. Management Science, 53(10), 1659-1674. doi: http://dx.doi.org/10.1287/mnsc.1070.0711

Arriaza, M. & Gómez-Limón, J.A. (2003). Comparative performance of selected mathematical programming models. Agricultural Systems, 77(2), 155-171. doi:10.1016/S0308-521X(02)00107-5

Ashta, A. and Otto, P.E. (2011). Project valuation in the presence of loss aversion during economic crises. Strategic Change, 20(5‐6), 171-186. Doi: 10.1002/jsc.894

Bjørndal, T., Herrero, I., Newman, A., Romero, C., & Weintraub, A. (2012). Operations research in the natural resource industry. International Transactions in Operational Research, 19(1-2), 39-62.

Bocquého, G., Jacquet, F., Reynaud, A. (2014). Expected utility or prospect theory maximisers? Assessing farmers' risk behaviour from field-experiment data. European Review of Agricultural Economics, 41(1), 135-172. doi: 10.1093/erae/jbt006

Brunelli, M. (2015). Introduction to the Analytic Hierarchy Process. Switzerland: Springer International Publishing. doi: 10.1007/978-3-319-12502-2.

Calzadilla, A., Rehdanz, K., Betts, R., Falloon, P., Wiltshire, A., & Tol, R. S. (2013). Climate change impacts on global agriculture. Climatic change, 120(1-2), 357-374. doi:10.1007/s10584-013-0822-4

Central Bureau of Statistics, Israel. (2015). Database of price indices by subject. http://www.cbs.gov.il/reader/?MIval=%2Fprices_db%2FPricesDB_SecondSelect_E.html&Radio1=1_3

Chavez, M.D., Berentsen, P.B.M., & Oude Lansink, A.G.J.M. (2012). Assessment of criteria and farming activities for tobacco diversification using the Analytical Hierarchical Process (AHP) technique. Agricultural Systems, 111, 53-62. doi:10.1016/j.agsy.2012.05.006

Coelho, L.A. G., Pires, C.M.P., Dionísio, A.T., & Serrão, A.J.D. C. (2012). The impact of CAP policy in farmer's behavior – a modeling approach using the Cumulative Prospect Theory. Journal of Policy Modeling, 34(1), 81-98. doi:10.1016/j.jpolmod.2011.03.009

Dury, J., Schaller, N., Garcia, F., Reynaud, A., & Bergez J. E. (2012). Models to support cropping plan and crop rotation decisions. A review. Agronomy for sustainable development, 32(2), 567-580. doi: 10.1007/s13593-011-0037-x

FAO. (2010). AQUASTAT database. Available at www.fao.org/nr/water/aquastat/main/index.stm

FAO. (2011). The state of the world’s land and water resources for food and agriculture (SOLAW) – Managing systems at risk. Food and Agriculture Organization of the United Nations, Rome and Earthscan, London.

Gilbert, C.L., & Mugera, H. K. (2014). Food commodity prices volatility: The role of biofuels. Natural Resources 2014. doi: http://dx.doi.org/10.4236/nr.2014.55019

Isin, S. & Miran, B. (2005). Farmers' attitudes toward crop planning in Turkey. Journal of Applied Sciences, 5, 1489-1495. doi: 10.32923/jas.2005.1489.1495

Kahneman, D. & Tversky A. (1979). Prospect theory: An analysis of decision under risk. Econometrica: Journal of the Econometric Society, 47(2), 263-291. doi: http://www.jstor.org/stable/1914185

von Lampe, M., Willenbockel, D., Ahammad, H., Blanc, E., Cai, Y., Calvin, K., Fujimori, S., Hasegawa, T., Havlik, P., Heyhoe, E., Kyle, P., Lotze-Campen, H., Mason d'Croz, D., Nelson, G.C., Sands, R.D., Schmitz, C., Tabeau, A., Valin, H., van der Mensbrugghe, D., & van Meijl H. (2014). Why do global long-term scenarios for agriculture differ? An overview of the AgMIP Global Economic Model Intercomparison. Agricultural Economics, 45, 3–20. doi: 10.1111/agec.12086

Lehmann, N., Briner, S., & Finger R. (2013). The impact of climate and price risks on agricultural land use and crop management decisions. Land Use Policy, 35, 119-130. doi:10.1016/j.landusepol.2013.05.008

Liu, E.M. (2013). Time to change what to sow: Risk preferences and technology adoption decisions of cotton farmers in China. Review of Economics and Statistics, 95(4), 1386-1403. doi:10.1162/REST_a_00295

Loewenstein, G.F. (1988). Frames of mind in intertemporal choice. Management Science, 34(2), 200-214. doi: http://dx.doi.org/10.1287/mnsc.34.2.200

Mustajoki, J., & Hamalainen, R.P. (2000). Web-HIPRE: Global decision support by value tree and AHP analysis. INFOR J, 38(3), 208-220.

Pelizaro, C., Benke, K., & Sposito, V. (2011). A modelling framework for optimisation of commodity production by minimising the impact of climate change. Applied Spatial Analysis and Policy, 4, 201-222. doi: 10.1007/s12061-010-9051-7

Rădulescu, M., Rădulescu, C.Z., & Zbăganu, G. (2014). A portfolio theory approach to crop planning under environmental constraints. Annals of Operations Research: 1-22. doi:10.1007/s10479-011-0902-7

Rai, V. (2013). Expert elicitation methods for studying technological change under uncertainty. Environmental Research Letters, 8(4), 041003. doi:10.1088/1748-9326/8/4/041003

Rivers, L. & Arvai, J. (2007). Win some, lose some: The effect of chronic losses on decision making under risk. Journal of Risk Research, 10(8), 1085-1099. doi:10.1080/13669870701615172


Rutten, M., Shutes, L.,¬¬ & Meijerink, G. (2013). Sit down at the ball game: How trade barriers make the world less food secure. Food Policy, 38, 1-10. doi:10.1016/j.foodpol.2012.09.002

Saaty, T.L. (1977). A scaling method for priorities in hierarchical structures. Journal of Mathematical Psychology, 15(3), 234-281. doi:10.1016/0022-2496(77)90033-5

Saaty, T. (1980). The Analytic Hierarchy Process. New York : McGraw-Hill. doi:10.1080/00137918308956077
Saaty, T.L. (1990). How to make a decision: the analytic hierarchy process. European Journal of Operational Research, 48(1), 9-26. doi:10.1016/0377-2217(90)90057-I

Saaty, T.L. (2008). Decision making with the analytic hierarchy process. International Journal of Services Sciences, 1(1), 83-98.
doi: http://dx.doi.org/10.1504/IJSSCI.2008.017590

Saaty, T.L., & Vargas, L. G. (2007). Dispersion of group judgments. Mathematical and Computer Modelling, 46(7), 918-925. doi:10.1016/j.mcm.2007.03.004

Salo, A.A., & Hämäläinen, R.P. 1997. On the measurement of preferences in the analytic hierarchy process. Journal of Multi‐Criteria Decision Analysis, 6(6), 309-319. doi: 10.1002/(SICI)1099-1360(199711)6:6<309::AID-MCDA163>3.0.CO;2-2

Sengar A., Sharma V., Agrawal R., & Bharti, K. (2014). Prioritisation of barriers to rural markets: integrating fuzzy logic and AHP. International Journal of Business and Emerging Markets, 6(4), 371-394. doi: http://dx.doi.org/10.1504/IJBEM.2014.065584

Shishank, S., Dekkers, & R. (2013). Outsourcing: decision-making methods and criteria during design and engineering. Production Planning & Control, 24(4-5), 318-336. doi:10.1080/09537287.2011.648544
Stokes, J.R., & Tozer, P.R. (2002). Sire selection with multiple objectives. Agricultural Systems, 73(2), 147-164. doi:10.1016/S0308-521X(01)00079-8

Tadesse, G., Algieri, B., Kalkuhl, M., & von Braun, J. (2014). Drivers and triggers of international food price spikes and volatility. Food Policy, 47, 117-128. doi:10.1016/j.foodpol.2013.08.014

Tey, Y.S., & Brindal, M. (2015). Factors Influencing Farm Profitability. In Sustainable Agriculture Reviews, pp. 235-255, Switzerland: Springer International Publishing. doi: 10.1007/978-3-319-09132-7_5

Thornton, P.K., Ericksen, P.J. Herrero, M., & Challinor, A.J. (2014). Climate variability and vulnerability to climate change: a review. Global Change Biology, 20(11), 3313-3328. doi: 10.1111/gcb.12581

Toledo, R., Engler, A., & Ahumada, V. (2011). Evaluation of risk factors in agriculture: an application of the Analytical Hierarchical Process (AHP) methodology. Chilean Journal of Agricultural Research, 71(1), 114-121. http://dx.doi.org/10.4067/S0718-58392011000100014

Tversky, A., & Kahneman, D. (1992). Advances in prospect theory: Cumulative representation of uncertainty. Journal of Risk and Uncertainty, 5(4), 297-323. doi: 10.1007/BF00122574
Section
Articles