@article{fdi:010054212,
  title = {{C}oupled information diffusion-pest dynamics models predict delayed benefits of farmer cooperation in pest management programs},
  author = {{R}ebaudo, {F}. and {D}angles, {O}livier},
  editor = {},
  language = {{ENG}},
  abstract = {{W}orldwide, the theory and practice of agricultural extension system have been dominated for almost half a century by {R}ogers' "diffusion of innovation theory". {I}n particular, the success of integrated pest management ({IPM}) extension programs depends on the effectiveness of {IPM} information diffusion from trained farmers to other farmers, an important assumption which underpins funding from development organizations. {H}ere we developed an innovative approach through an agent-based model ({ABM}) combining social (diffusion theory) and biological (pest population dynamics) models to study the role of cooperation among small-scale farmers to share {IPM} information for controlling an invasive pest. {T}he model was implemented with field data, including learning processes and control efficiency, from large scale surveys in the {E}cuadorian {A}ndes. {O}ur results predict that although cooperation had short-term costs for individual farmers, it paid in the long run as it decreased pest infestation at the community scale. {H}owever, the slow learning process placed restrictions on the knowledge that could be generated within farmer communities over time, giving rise to natural lags in {IPM} diffusion and applications. {W}e further showed that if individuals learn from others about the benefits of early prevention of new pests, then educational effort may have a sustainable long-run impact. {C}onsistent with models of information diffusion theory, our results demonstrate how an integrated approach combining ecological and social systems would help better predict the success of {IPM} programs. {T}his approach has potential beyond pest management as it could be applied to any resource management program seeking to spread innovations across populations.},
  keywords = {},
  booktitle = {},
  journal = {{P}los {C}omputational {B}iology},
  volume = {7},
  numero = {10},
  pages = {e1002222},
  ISSN = {1553-734{X}},
  year = {2011},
  DOI = {10.1371/journal.pcbi.1002222},
  URL = {https://www.documentation.ird.fr/hor/fdi:010054212},
}