Social learning to reduce pesticides: Evidence from a French agricultural extension programme
Open accessAbstract
Social learning is likely to play a crucial role in disseminating new agricultural technologies and driving the agroecological transition in European countries. We evaluated a French pesticide reduction programme designed to train farmers and promote practices through demonstration days on participating farms. Using pseudo-panel data from surveys conducted before and after the programme’s launch, we found evidence of decreased pesticide use among cohorts linked to farms attending demonstration days. Our analysis, supported by a placebo test and various robustness checks, suggests that peer-sharing in training programmes can scale up effectively at no additional cost.
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- Albizua, A., E. M. Bennett, G. Larocque, R. W. Krause, and U. Pascual (2021). Social networks influence farming practices and agrarian sustainability. PloS one 16(1), e0244619.
- Bareille, F., R. Chakir, and D. Keles (2024, June). Weather shocks and pesticide purchases. European Review of Agricultural Economics 51(2), 309-353.
- Bartolini, F. and D. Vergamini (2019). Understanding the spatial agglomeration of participation in agri- environmental schemes: The case of the tuscany region. Sustainability 11(10), 2753.
- Beaman, L., A. BenYishay, J. Magruder, and A. M. Mobarak (2021). Can network theory-based targeting increase technology adoption? American Economic Review 111(6), 1918-1943.
- Beaman, L. and A. Dillon (2018). Diffusion of agricultural information within social networks: Evidence on gender inequalities from mali. Journal of Development Economics 133, 147-161.
- Beketov, M. A., B. J. Kefford, R. B. Schäfer, and M. Liess (2013). Pesticides reduce regional biodiversity of stream invertebrates. Proceedings of the National Academy of Sciences 110(27), 11039-11043.
- Bellemare, M. F. and C. J. Wichman (2020). Elasticities and the inverse hyperbolic sine transformation. Oxford Bulletin of Economics and Statistics 82(1), 50-61.
- Benyishay, A. and A. M. Mobarak (2019). Social learning and incentives for experimentation and communica- tion. The Review of Economic Studies 86(3), 976-1009.
- Brunelle, T., R. Chakir, A. Carpentier, B. Dorin, D. Goll, N. Guilpart, F. Maggi, D. Makowski, T. Nesme, J. Roosen, et al. (2024). Reducing chemical inputs in agriculture requires a system change. Communications Earth & Environment 5(1), 369.
- Chabé-Ferret, S., P. Le Coent, V. David-Legleye, and V. Delannoy (2023). Non-monetary incentives to increase enrollment in payments for environmental services. European Review of Agricultural Economics 50(4), 1401- 1427.
- Chantre, E. and A. Cardona (2014). Trajectories of french field crop farmers moving toward sustainable farm- ing practices: change, learning, and links with the advisory services. Agroecology and Sustainable Food Systems 38(5), 573-602.
- Chen, J. and J. Roth (2024). Logs with zeros? some problems and solutions. The Quarterly Journal of Economics 139(2), 891-936.
- Conley, T. G. and C. R. Udry (2010). Learning about a new technology: Pineapple in Ghana. American Economic Review 100(1), 35-69.
- Crane-Droesch, A. (2018). Technology diffusion, outcome variability, and social learning: evidence from a field experiment in Kenya. American Journal of Agricultural Economics 100(3), 955-974.
- Damalas, C. A. (2021). Farmers' intention to reduce pesticide use: The role of perceived risk of loss in the model of the planned behavior theory. Environmental Science and Pollution Research 28(26), 35278-35285.
- Deaton, A. (1985). Panel data from time series of cross-sections. Journal of Econometrics 30, 109-126.
- Di Falco, S., A. Doku, and A. Mahajan (2020, 1). Peer effects and the choice of adaptation strategies. Agricultural Economics (United Kingdom) 51(1), 17-30.
- Dias, M., R. Rocha, and R. R. Soares (2023). Down the river: Glyphosate use in agriculture and birth outcomes of surrounding populations. Review of Economic Studies 90(6), 2943-2981.
- Fletcher, J. and H. Noghanibehambari (2024). The siren song of cicadas: early-life pesticide exposure and later-life male mortality. Journal of Environmental Economics and Management 123, 102903.
- Foster, A. D. and M. R. Rosenzweig (1995). Learning by doing and learning from others: Human capital and technical change in agriculture. Journal of political Economy 103(6), 1176-1209.
- Foster, A. D. and M. R. Rosenzweig (2010). Microeconomics of technology adoption. Annu. Rev. Econ. 2(1), 395-424.
- Gardes, F., G. J. Duncan, P. Gaubert, M. Gurgand, and C. Starzec (2005). Panel and pseudo-panel estimation of cross-sectional and time series elasticities of food consumption: The case of US and Polish data. Journal of Business & Economic Statistics 23(2), 242-253.
- Genius, M., P. Koundouri, C. Nauges, and V. Tzouvelekas (2014). Information transmission in irrigation technology adoption and diffusion: Social learning, extension services, and spatial effects. American Journal of Agricultural Economics 96(1), 328-344.
- Guichard, L., F. Dedieu, M.-H. Jeuffroy, J. M. Meynard, R. Reau, and I. Savini (2017). Le plan eco- phyto de réduction d'usage des pesticides en france: décryptage d'un échec et raisons d'espérer. Cahiers Agricultures 26(1), 1-12.
- Guillerm, M. (2017). Pseudo-panel methods and an example of application to household wealth data. Economie et Statistique 491(1), 109-130.
- Jacquet, F., J.-P. Butault, and L. Guichard (2011). An economic analysis of the possibility of reducing pesticides in french field crops. Ecological economics 70(9), 1638-1648.
- Krishnan, P. and M. Patnam (2014). Neighbors and extension agents in Ethiopia: Who matters more for technology adoption? American Journal of Agricultural Economics 96(1), 308-327.
- Läpple, D. and H. Kelley (2015). Spatial dependence in the adoption of organic drystock farming in Ireland. European Review of Agricultural Economics 42(2), 315-337.
- Lewis, D. J., B. L. Barham, and B. Robinson (2011). Are there spatial spillovers in the adoption of clean technology? The case of organic dairy farming. Land Economics 87(2), 250-267.
- Manski, C. F. (1993). Identification of endogenous social effects: the reflection problem. Review of Economic Studies.
- Mobius, M. and T. Rosenblat (2014). Social learning in economics. Annual Review of Economics 6(Volume 6, 2014), 827-847.
- Moffitt, R. (1993). Identification and estimation of dynamic models with a time series of repeated cross-sections. Journal of Econometrics 59(1-2), 99-123.
- Morgan, S. L. (2011). Social learning among organic farmers and the application of the communities of practice framework. Journal of Agricultural Education and Extension 17(1), 99-112.
- Paroissien, E., L. Latruffe, and L. Piet (2021, November). Early exit from business, performance and neighbours' influence: a study of farmers in France. European Review of Agricultural Economics 48(5), 1132-1161.
- Sampson, G. S. and E. D. Perry (2019). Peer effects in the diffusion of water-saving agricultural technologies. Agricultural Economics 50(6), 693-706.
- Schmidtner, E., C. Lippert, B. Engler, A. M. Häring, J. Aurbacher, and S. Dabbert (2012). Spatial distri- bution of organic farming in Germany: does neighbourhood matter? European Review of Agricultural Economics 39(4), 661-683.
- Schmidtner, E., C. Lippert, B. Engler, A. M. Häring, J. Aurbacher, and S. Dabbert (2012, September). Spatial distribution of organic farming in Germany: does neighbourhood matter? European Review of Agricultural Economics 39(4), 661-683.
- Verbeek, M. (1996). Pseudo panel data. In The econometrics of panel data, pp. 280-292. Springer.
- Yoder, L., A. S. Ward, K. Dalrymple, S. Spak, and R. Lave (2019). An analysis of conservation practice adoption studies in agricultural human-natural systems. Journal of environmental management 236, 490-498.
- Zhang, Y., L. Bedoussac, C. Zhang, W.-F. Cong, M. Guinet, R. Nandillon, and N. Munier-Jolain (2024). Pesticide use is affected more by crop species than by crop diversity at the cropping system level. European Journal of Agronomy 159, 127263. Data reference: Enquête PKGC 2021 (http://doi.org/10.34724/CASD.56.5275.V1) Enquête PKGC 2017 (https://doi.org/10.34724/CASD.56.3033.V1) Enquête PhytoGC 2014 (https://doi.org/10.34724/CASD.162.1639.V1) Enquête PKGC 2011 (https://doi.org/10.34724/CASD.56.310.V1) Enquête PKGC 2006 (https://doi.org/10.34724/CASD.56.309.V1) · DOI
- Authors
- Deperrois, R., Fadhuile, A., & Subervie, J.
- Affiliations
- Institut polytechnique de Grenoble (FR)
- Centre National de la Recherche Scientifique (FR)
- Corresponding author
- Deperrois, Rose
- Venue
- European Review of Agricultural Economics
- Publisher
- Oxford University Press (OUP)
- Published
- 2025-10-29
- Volume
- 52
- Issue
- 5
- Pages
- 1089-1141
- ISSN
- 0165-1587 (ISSN-L)
- 1464-3618
- OpenAlex
- W4407393808
- Cited by
- 2
- References
- 0
- Citations by year
- 2026: 2
- FWCI
- 3.53
- Type
- journal article
- Language
- en
- Open access
- green
- hal.inrae.fr
- Licence
- Locations
- European Review of Agricultural Economics (publishedVersion)
- HAL (submittedVersion, other-oa)
- HAL (submittedVersion)
- Funders
- ANR
- French National Research Agency
- FAST (20-PCPA-0005)
- Investissements d’avenir’ programme (ANR-15-IDEX-0002)
- Topics
- Life Sciences › Agricultural and Biological Sciences › General Agricultural and Biological Sciences › Agricultural Innovations and Practices
- Life Sciences › Agricultural and Biological Sciences › General Agricultural and Biological Sciences › Agricultural Development and Management
- Social Sciences › Safety Research › Agricultural economics and policies
- Keywords
- social learning, pesticide reduction, agricultural extension, farmer training, agroecological transition, peer learning, technology diffusion, placebo tests, French agricultural policy
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