Adoption of Environment-Friendly Agricultural Practices with Background Risk: Experimental Evidence

Abstract

Agriculture is one of the economic sectors most exposed to exogenous risks such as climate hazards and price volatility on agricultural markets. Agricultural policies targeting the adoption of environment-friendly but potentially risk-increasing practices cannot ignore this challenge. Farmers have indeed to decide if they take the foreground risk associated with the adoption of environment-friendly practices, while simultaneously facing exogenous background risk beyond their control. Using a theoretical model and a public good experiment, we analyse the adoption of agri-environmental practices and the effect of agri-environmental subsidies in a context where risks are both foreground and background. While most of the literature on background risk focuses on its impact on individual decisions, we analyse the influence of background risk in a context of strategic uncertainty (contribution to a public good). The results highlight the potential synergies between greening the CAP and supporting risk management. We find that background risk discourages the adoption of green practices, although it affects all farmland independently from the farmer’s choice of practices (environment friendly or conventional). An incentive payment per hectare of land farmed with green practices increases the adoption of risk-increasing practices but is significantly less effective in the presence of background risk.

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Fig. 1

Notes

  1. 1.

    While, in theory, agri-environmental measures aim at compensating the total costs of implementing environment-friendly practices, it is not always the case in practice. First, because it provides a fixed payment to farmers, calculated on the average costs in a given region. As a result, some farmers, facing higher costs, have no incentive to contract such measures. Second, because additional costs, such as transaction or learning costs, are not always accounted for in the design of the payments. This increases the total costs for farmers, who, in turns, have no incentive to engage in such contracts (Ducos et al. 2009; Espinosa-Goded et al. 2013). While this is an important barrier to agri-environmental measures adoption, this is not the main focus of our paper. Here, we only consider payments that cover the full costs of implementation.

  2. 2.

    Laboratory and field experiments have been designed to test whether background risk affects the risk behavior of individuals. Laboratory experiments conducted by Lusk and Coble (2008), Lee (2008) and Beaud and Willinger (2014) concluded that an individual exposed to background risk (whether fair or unfair) would be willing to take fewer foreground risk. With regards to field experiments, Harrison et al. (2007) find that increasing background risk increases risk aversion in the US rare coins market. In contrast, Bchir and Willinger (2013) found that Peruvians living in high-risk areas (due to volcano mudflows) are less risk-averse than those living with lower background risk levels, but their result holds only for low income individuals (no significant results for higher incomes). Herberich and List (2012) carried out an experiment similar to that of Harrison et al. (2007) comparing US farmers and students, but they find no conclusive results regarding the impact of background risk on risk aversion.

  3. 3.

    There are many different risk taxonomies in the agricultural economics literature [see OECD (2009) for a review of the different classifications of agricultural risks]. For example, one can distinguish between output and price uncertainty, sometimes including other sources such as technological or policy uncertainty (Moschini and Hennessy 2001). Another useful categorization in dynamic context is that of «non-embedded risks», i.e. risks that are beyond control of the decision maker because all decisions are made initially, versus «embedded risks», i.e. risks that can be influenced by farmers’ adaptive behaviour due to sequential decisions (Hardaker et al. 1991; Dorward 1999; Ridier et al. 2016). While both taxonomies are certainly relevant, we rely here on another one to distinguish between background risk (which is beyond farmers’ control and affects all crops) and foreground risk (which only affects crops grown according to environment-friendly practices: Farmers may therefore choose to avoid the foreground risk by not engaging in such practices).

  4. 4.

    Indeed, most environment-friendly practices do not provide access to different prices for the products since they cannot be labelled or certified or are not well-known by the consumers (Bazoche et al. 2013). Moreover, there is no consensus on the impact of environment-friendly practices on yields and yields variability, notably because yield level has many determinants interacting with each other (Lechenet et al. 2017). Finally, CAP direct payments are paid whatever the choice of practices made by a farmer (as long as the land is maintained in good agricultural and environmental conditions and the three greening requirements are fulfilled).

  5. 5.

    Coyle (1999), Isik (2002) Serra et al. (2006) and Bontems and Nauges (2019) have also analyzed farmers’ production decisions in a mean–variance framework in the context of multiple risks [including background risk for Bontems and Nauges (2019)]. Our contribution extends the analysis of production decisions in a risky environment to a public good game context.

  6. 6.

    The assumption of quasi-concavity for \(V\left( {\mu ,v} \right)\) implies that the second order condition \(\partial^{2} U/\partial g_{i}^{2} < 0\) holds.

  7. 7.

    This assumption is realistic since, in the current CAP, the payments associated with agri-environmental contracts are set-up to cover up the opportunity cost of adopting green practices. Concerning the green payment, while its level was not chosen to compensate the opportunity cost of adoption greening requirements, it has been shown that it is higher than the compliance costs for a large majority of farmers (Louhichi et al. 2018).

  8. 8.

    While in reality the ecosystem services can benefit a larger perimeter where several farmers are operating, we have used the smallest possible group (2) to simplify the experiment. There is much literature on the effect of group size on contributions in public good games. For instance, Isaac and Walker (1988) found no difference between groups of 4 and groups of 10 people. To our knowledge, there is no experimental evidence on the differences in the behaviours of individuals interacting in pairs (in a prisoners’ dilemma) and in groups of 4 persons.

  9. 9.

    In treatments ForeOnly and BackOnly, the table with the individual payoff had 2 lines corresponding to the two possible outcomes of the draw. While in treatment Fore&Back, the table had 4 lines, corresponding to the four different outcomes combining the two draws.

  10. 10.

    The main advantage of using random effects over fixed effects estimations is that it allows for covariates that are constant over time, such as the individual characteristics of the participants (Demidenko 2005).

  11. 11.

    Moreover, according to proposition 3 (see Appendix A3), the contribution to environment-friendly practices decreases when the covariance between both risks increases. We could therefore assume that the reduction of the contribution to environment-friendly practices in the treatment Fore&Back would be even more pronounced if the experiment had been designed with positively correlated risks.

References

  1. Acemoglu D, Jensen MK (2013) Aggregate comparative statics. Games Econ Behav 81:27–49

    Google Scholar 

  2. Acs S, Berentsen P, Huirne R, Asseldonk M (2009) Effect of yield and price risk on conversion from conventional to organic farming*. Aust J Agric Resour Econ 53:393–411

    Google Scholar 

  3. Agreste (2017) Mémento de la statistique agricole Pays de Loire. http://agreste.agriculture.gouv.fr/IMG/pdf/R5217C02.pdf

  4. Ambiaud E (2011) Diversité du monde agricole. http://agreste.agriculture.gouv.fr/IMG/pdf_analyse321106.pdf

  5. Babcock BA, Fraser RW, Lekakis JN (2003) Risk management and the environment in agriculture: a key policy theme. In: Babcock BA, Fraser RW, Lekakis JN (eds) Risk management and the environment: agriculture in perspective. Springer, Dordrecht, pp 1–8. https://doi.org/10.1007/978-94-017-2915-4_1

    Google Scholar 

  6. Balliet D, Parks C, Joireman J (2009) Social value orientation and cooperation in social dilemmas: a meta-analysis. Group Process Intergroup Relat 12(4):533–547

    Google Scholar 

  7. Bardaji I, Garrido A (2016) Research for AGRI committee—state of play of risk management tools implemented by member states during the period 2014–2020: National and European Framework. http://www.europarl.europa.eu/RegData/etudes/STUD/2016/573415/IPOL_STU(2016)573415_EN.pdf

  8. Bazoche P, Bunte F, Combris P, Giraud-Héraud E, Seabra-Pinto A, Tsakiridou E (2013) Willingness to pay for pesticides’reduction in EU: nothing but organic? Eur Rev Agric Econ 41(1):87–109

    Google Scholar 

  9. Bchir MA, Willinger M (2013) Does the exposure to natural hazards affect risk and time preferences? Some insights from a field experiment in Perú. LAMETA, Universitiy of Montpellier. https://econpapers.repec.org/paper/lamwpaper/13-04.htm

  10. Beaud M, Willinger M (2014) Are people risk vulnerable? Manag Sci 61(3):624–636

    Google Scholar 

  11. Beedell J, Rehman T (2000) Using social-psychology models to understand farmers’ conservation behaviour. J Rural Stud 16(1):117–127

    Google Scholar 

  12. Binswanger HP (1980) Attitudes toward risk: experimental measurement in rural India. Am J Agric Econ 62(3):395–407

    Google Scholar 

  13. Bocquého G, Jacquet F, Reynaud A (2014) Expected utility or prospect theory maximisers? Assessing farmers’ risk behaviour from field-experiment data. Eur Rev Agric Econ 41(1):135–172

    Google Scholar 

  14. Bontems P, Nauges C (2019) Production choices with water markets and risk aversion: the role of initial allocations and forward trading. Eur Rev Agric Econ 46(4):579–608

    Google Scholar 

  15. Bougherara D, Gassmann X, Piet L, Reynaud A (2017) Structural estimation of farmers’ risk and ambiguity preferences: a field experiment. Eur Rev Agric Econ 44(5):782–808

    Google Scholar 

  16. Brunette M, Choumert J, Couture S, Montagne-Huck C (2015) A meta-analysis of the risk aversion coefficients of natural resource managers evaluated by stated preference methods. Working papers—Cahiers du LEF no. 2015–13, Laboratoire d’Economie Forestiere, AgroParisTech-INRA. https://ideas.repec.org/p/lef/wpaper/2015-13.html

  17. Carpenter J, Seki E (2011) Do social preferences increase productivity? Field experimental evidence from fishermen in Toyama Bay. Econ Inq 49(2):612–630

    Google Scholar 

  18. Charness G, Gneezy U (2010) Portfolio choice and risk attitudes: an experiment. Econ Inq 48(1):133–146

    Google Scholar 

  19. Charness G, Gneezy U (2012) Strong evidence for gender differences in risk taking. J Econ Behav Organ 83(1):50–58

    Google Scholar 

  20. Charness G, Gneezy U, Imas A (2013) Experimental methods: eliciting risk preferences. J Econ Behav Organ 87:43–51

    Google Scholar 

  21. Chavas J-P, Holt MT (1996) Economic behavior under uncertainty: a joint analysis of risk preferences and technology. Rev Econ Stat 78(2):329–335

    Google Scholar 

  22. Chèze B, David M, Martinet V (2020) Understanding farmers’ reluctance to reduce pesticide use: a choice experiment. Ecol Econ 167:106349

    Google Scholar 

  23. Coble KH, Hanson T, Miller JC, Shaik S (2003) Agricultural insurance as an environmental policy tool. J Agric Appl Econ 35(2):391–405

    Google Scholar 

  24. Coyle BT (1999) Risk aversion and yield uncertainty in duality models of production: a mean–variance approach. Am J Agric Econ 81(3):553–567

    Google Scholar 

  25. Crosetto P, Filippin A (2016) A theoretical and experimental appraisal of four risk elicitation methods. Exp Econ 19:613–641

    Google Scholar 

  26. Dave C, Eckel C, Johnson C, Rojas C (2010) Eliciting risk preferences: when is simple better? J Risk Uncertain 41:219–243

    Google Scholar 

  27. Deck C, Lee J, Reyes JA, Rosen CC (2013) A failed attempt to explain within subject variation in risk taking behavior using domain specific risk attitudes. J Econ Behav Organ 87:1–24

    Google Scholar 

  28. Deck C, Lee J, Reyes J (2014) Investing versus gambling: experimental evidence of multi-domain risk attitudes. Appl Econ Lett 21(1):19–23

    Google Scholar 

  29. Demidenko E (2005) Introduction: why mixed models? In Mixed models. Wiley, Berlin, pp 1–44. https://onlinelibrary.wiley.com/doi/abs/10.1002/0471728438.ch1

  30. Diamond DW (1984) Financial intermediation and delegated monitoring. Rev Econ Stud 51(3):393–414

    Google Scholar 

  31. Dickinson DL (1998) The voluntary contributions mechanism with uncertain group payoffs. J Econ Behav Organ 35(4):517–533

    Google Scholar 

  32. Dorward A (1999) Modelling embedded risk in peasant agriculture: methodological insights from northern Malawi. Agric Econ 21(2):191–203

    Google Scholar 

  33. Ducos G, Dupraz P, Bonnieux F (2009) Agri-environment contract adoption under fixed and variable compliance costs. J Environ Plan Manag 52(5):669–687

    Google Scholar 

  34. Eeckhoudt L, Gollier C, Schlesinger H (1996) Changes in background risk and risk taking behavior. Econometrica 64(3):683–689

    Google Scholar 

  35. Eichner T (2008) Mean variance vulnerability. Manag Sci 54(3):586–593

    Google Scholar 

  36. Eichner T, Wagener A (2011) Increases in skewness and three-moment preferences. Math Soc Sci 61(2):109–113

    Google Scholar 

  37. Espinosa-Goded M, Barreiro-Hurlé J, Dupraz P (2013) Identifying additional barriers in the adoption of agri-environmental schemes: the role of fixed costs. Land Use Policy 31:526–535

    Google Scholar 

  38. European Commission (2017a) The future of food and farming. https://ec.europa.eu/agriculture/sites/agriculture/files/future-of-cap/future_of_food_and_farming_communication_en.pdf

  39. European Commission (2017b) The future of food and farming. https://ec.europa.eu/agriculture/sites/agriculture/files/future-of-cap/future_of_food_and_farming_communication_en.pdf

  40. Fischbacher U, Gachter S (2010) Social preferences, beliefs, and the dynamics of free riding in public goods experiments. Am Econ Rev 100(1):541–556

    Google Scholar 

  41. Frechette GR (2011) Laboratory experiments: professionals versus students. No. ID 1939219, Social Science Research Network. https://papers.ssrn.com/abstract=1939219

  42. Gangadharan L, Nemes V (2009) Experimental analysis of risk and uncertainty in provisioning private and public goods. Econ Inq 47(1):146–164

    Google Scholar 

  43. Gneezy U, Imas A (2017) Chapter 10—Lab in the field: measuring preferences in the wild. In: Banerjee AV, Duflo E (eds) Handbook of economic field experiments. Handbook of field experiments. North-Holland, London, pp 439–464. http://www.sciencedirect.com/science/article/pii/S2214658X16300058

  44. Gneezy U, Potters J (1997) An experiment on risk taking and evaluation periods. Q J Econ 112(2):631–645

    Google Scholar 

  45. Goeree JK, Holt CA, Laury SK (2002) Private costs and public benefits: unraveling the effects of altruism and noisy behavior. J Public Econ 83(2):255–276

    Google Scholar 

  46. Gollier C (2001) The economics of risk and time. MIT Press, Cambridge

    Google Scholar 

  47. Gollier C, Pratt JW (1996) Risk vulnerability and the tempering effect of background risk. Econometrica 64(5):1109–1123

    Google Scholar 

  48. Guillou M, Guyomard H, Huyghe C, Peyraud JL (2013) Le projet agro-écologique: Vers des agricultures doublement performantes pour concilier compétitivité et respect de l’environnement. http://www.ladocumentationfrancaise.fr/var/storage/rapports-publics/134000352.pdf

  49. Hardaker JB, Pandey S, Patten LH (eds) (1991) Farm planning under uncertainty: a review of alternative programming models. Rev Mark Agric Econ 59:9–22

  50. Harrison GW, List JA (2004) Field experiments. J Econ Lit 42(4):1009–1055

    Google Scholar 

  51. Harrison GW, List JA, Towe C (2007) Naturally occurring preferences and exogenous laboratory experiments: a case study of risk aversion. Econometrica 75(2):433–458

    Google Scholar 

  52. Hellerstein D, Higgins N, Horowitz J (2013) The predictive power of risk preference measures for farming decisions. Eur Rev Agric Econ 40(5):807–833

    Google Scholar 

  53. Herberich DH, List JA (2012) Digging into background risk: experiments with farmers and students. Am J Agric Econ 94(2):457–463

    Google Scholar 

  54. Huang W-Y (2002) Using insurance to enhance nitrogen fertilizer application timing to reduce nitrogen losses. J Agric Appl Econ 34(1):131–148

    Google Scholar 

  55. Isaac RM, Walker JM (1988) Group size effects in public goods provision: the voluntary contributions mechanism. Q J Econ 103(1):179–199

    Google Scholar 

  56. Isik M (2002) Resource management under production and output price uncertainty: implications for environmental policy. Am J Agric Econ 84(3):557–571

    Google Scholar 

  57. Kim K, Chavas J-P, Barham B, Foltz J (2014) Rice, irrigation and downside risk: a quantile analysis of risk exposure and mitigation on Korean farms. Eur Rev Agric Econ 41(5):775–815

    Google Scholar 

  58. Knapp S, van der Heijden MGA (2018) A global meta-analysis of yield stability in organic and conservation agriculture. Nat Commun 9(1):3632

    Google Scholar 

  59. Lechenet M, Dessaint F, Py G, Makowski D, Munier-Jolain N (2017) Reducing pesticide use while preserving crop productivity and profitability on arable farms. Nat Plants 3(3):nplants20178

    Google Scholar 

  60. Ledyard JO (1995) Public goods: a survey of experimental research. In: Kagel J, Roth A (eds) Handbook of experimental economics. Princeton University Press, Princeton, pp 111–194

    Google Scholar 

  61. Lee J (2008) The effect of the background risk in a simple chance improving decision model. J Risk Uncertain 36(1):19–41

    Google Scholar 

  62. Lefebvre M, Papaïx J, Mollot G, Deschodt P, Lavigne C, Ricard J-M, Mandrin J-F, Franck P (2017) Bayesian inferences of arthropod movements between hedgerows and orchards. Basic Appl Ecol 21(Supplement C):76–84

    Google Scholar 

  63. Levati MV, Morone A (2013) Voluntary contributions with risky and uncertain marginal returns: the importance of the parameter values. J Public Econ Theory 15(5):736–744

    Google Scholar 

  64. Levati MV, Morone A, Fiore A (2009) Voluntary contributions with imperfect information: an experimental study. Public Choice 138(1–2):199–216

    Google Scholar 

  65. Louhichi K, Ciaian P, Espinosa M, Perni A, Gomez y Paloma S (2018) Economic impacts of CAP greening: application of an EU-wide individual farm model for CAP analysis (IFM-CAP). Eur Rev Agric Econ 45(2):205–238

    Google Scholar 

  66. Lusk JL, Coble KH (2008) Risk aversion in the presence of background risk: evidence from an economic experiment. In: Risk aversion in experiments. Research in experimental economics. Emerald Group Publishing Limited, New York, pp 315–340. https://www.emeraldinsight.com/doi/abs/10.1016/S0193-2306%2808%2900006-9. Accessed Aug 31, 2018

  67. Menapace L, Colson G, Raffaelli R (2013) Risk aversion, subjective beliefs, and farmer risk management strategies. Am J Agric Econ 95(2):384–389

    Google Scholar 

  68. Meyer J (1987) Two-moment decision models and expected utility maximization. Am Econ Rev 77(3):421–430

    Google Scholar 

  69. Midler E, Pascual U, Drucker AG, Narloch U, Soto JL (2015) Unraveling the effects of payments for ecosystem services on motivations for collective action. Ecol Econ 120:394–405

    Google Scholar 

  70. Moschini G, Hennessy DA (2001) Chapter 2 Uncertainty, risk aversion, and risk management for agricultural producers. In: Handbook of agricultural economics. Agricultural production. Elsevier, Berlin, pp 87–153. http://www.sciencedirect.com/science/article/pii/S1574007201100058

  71. Murphy RO, Ackermann KA, Handgraaf M (2011) Measuring social value orientation. SSRN Electronic Journal. http://www.ssrn.com/abstract=1804189. Accessed July 20, 2017

  72. Narloch U, Pascual U, Drucker AG (2012) Collective action dynamics under external rewards: experimental insights from Andean farming communities. World Dev 40(10):2096–2107

    Google Scholar 

  73. OECD (2009) Managing risk in agriculture: a holistic approach. http://www.oecd.org/tad/agricultural-policies/managingriskinagricultureaholisticapproach.htm

  74. PANEurope. Inspiration note for the development of EU’s common agricultural policy: what changes are needed to make risk management tools a suitable rural development measure? http://www.pan-europe.info/sites/pan-europe.info/files/public/resources/briefings/pan-e-risk-management-tool.pdf

  75. Pedroni A, Frey R, Bruhin A, Dutilh G, Hertwig R, Rieskamp J (2017) The risk elicitation puzzle. Nat Hum Behav 1(11):803

    Google Scholar 

  76. Quiggin J (2003) Background risk in generalized expected utility theory. Econ Theor 22(3):607–611

    Google Scholar 

  77. Reynaud A, Couture S (2012) Stability of risk preference measures: results from a field experiment on French farmers. Theor Decis 73(2):203–221

    Google Scholar 

  78. Ridier A, Chaib K, Roussy C (2016) A dynamic stochastic programming model of crop rotation choice to test the adoption of long rotation under price and production risks. Eur J Oper Res 252(1):270–279

    Google Scholar 

  79. Serra T, Zilberman D, Goodwin BK, Featherstone A (2006) Effects of decoupling on the mean and variability of output. Eur Rev Agric Econ 33(3):269–288

    Google Scholar 

  80. Soane E, Chmiel N (2005) Are risk preferences consistent? The influence of decision domain and personality. Pers Individ Differ 38(8):1781–1791

    Google Scholar 

  81. Thomas F, Midler E, Lefebvre M, Engel S (2019) Greening the common agricultural policy: a behavioural perspective and lab-in-the-field experiment in Germany. Eur Rev Agric Econ 46(3):367–392

    Google Scholar 

  82. Vanslembrouck I, Huylenbroeck GV, Verbeke W (2002) Determinants of the willingness of Belgian farmers to participate in agri-environmental measures. J Agric Econ 53(3):489–511

    Google Scholar 

  83. Vollmer E, Hermann D, Mußhoff O (2017) Is the risk attitude measured with the Holt and Laury task reflected in farmers’ production risk? Eur Rev Agric Econ 44(3):399–424

    Google Scholar 

  84. Weber EU, Blais A-R, Betz NE (2002) A domain-specific risk-attitude scale: measuring risk perceptions and risk behaviors. J Behav Decis Mak 15(4):263–290

    Google Scholar 

  85. Willock J, Deary IJ, Edwards-Jones G, Gibson GJ, McGregor MJ, Sutherland A, Dent JB, Morgan O, Grieve R (1999) The role of attitudes and objectives in farmer decision making: business and environmentally-oriented behaviour in Scotland. J Agric Econ 50(2):286–303

    Google Scholar 

  86. Zuo A, Nauges C, Wheeler SA (2015) Farmers’ exposure to risk and their temporary water trading. Eur Rev Agric Econ 42(1):1–24

    Google Scholar 

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Acknowledgements

The survey on which this paper is based was funded by Angers University in the context of the Project “Verdissement de la politique agricole européenne: étude expérimentale des réactions des agriculteurs”. However, the views expressed are purely those of the authors and may not in any circumstances be regarded as stating an official position of the University. The authors would like to thank S. Blondel for his contribution to the experimental design, B. Goujon for programming the on-line survey, M. Ghali for recruiting the subject at Ecole Supérieure d’Agricultures d’Angers, C. Nauges, D. Bougherara, S. Thoyer and R. Préget for commenting a previous version of the article, and Olivier Midler for english proof reading. Estelle Midler acknowledge fundings from the Alexander Von Humboldt foundation in the framework of the Alexander Von Humboldt professorship endowed by the German federal ministry of research and education. Philippe Bontems acknowledges funding from ANR under Grant ANR-17-EURE-0010 (Investissements d’Avenir program).

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Lefebvre, M., Midler, E. & Bontems, P. Adoption of Environment-Friendly Agricultural Practices with Background Risk: Experimental Evidence. Environ Resource Econ 76, 405–428 (2020). https://doi.org/10.1007/s10640-020-00431-2

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Keywords

  • Common agricultural policy
  • Agri-environmental measures
  • Background risk
  • Lab experiment
  • Risk aversion
  • Public good game

JEL Classification

  • C93
  • D81
  • Q18
  • Q12