Revisiting pest sampling plans in light of economic uncertainty and risk aversion.

Abstract

Decision-making for pest management in agriculture is often assisted by sampling plans that guide users in determining the need for an intervention. Even though Tuta absoluta is easily recognizable by most tomato growers and that several sampling plans have been developed, adoption of decision-making systems for this pest is still incipient. Two potential obstacles for adoption are market uncertainty and farmer's risk aversion. Both obstacles could be tackled by adopting sampling plans that allow farmers to plan interventions according to rough estimations of economic thresholds and the intuition and experience gained by farmers. In this study, we evaluated four sampling plans using computer simulations and field trials. We compared the efficiency and the ability of each plan to both estimate the actual mean number of larvae per plant and to classify pest populations according to a predefined economic threshold. We also analyzed the time spent, and plants examined by human subjects applying each plan on a tomato crop with a T. absoluta infestation slightly over a predefined economic threshold. We show that sampling plans that deliver the most precise classifications, are poorest in delivering pest density estimations and vice versa. Our findings are consistent for both human subjects and computer simulations. However, the average number of samples required by sampling plans does not reflect the time spent by humans sampling real plants. Our results show that sampling plans based on counts, as opposed to those based on binary data, can efficiently provide reliable information on a current level of T. absoluta infestation relative to an estimated decision threshold. We suggest that sampling plans that promote the creation of farmer's memory, such as those based on counts, may be more suitable to both reduce risk aversion and increase adaptability to market uncertainty.

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Preprints for Agriculture and Allied Sciences
Advisory Board
  • Leisa Armstrong, Edith Cowan University, Australia
  • Arianna Becerril García, Autonomous University of the State of Mexico, Redalyc/AmeliCA, Mexico
  • Susmita Das, Bangladesh Agricultural Research Council
  • Abeer Elhalwagi, National Gene Bank, Egypt
  • Gopinath KA, Central Research Institute for Dryland Agriculture
  • Niklaus Grünwald, USDA Agricultural Research Service
  • Sridhar Gutam, ICAR IIHR/Open Access India
  • Vinodh Ilangovan, Max Planck Institute for Biophysical Chemistry
  • Jayalakshmi M, ANGRAU, India
  • Khelif Karima, Institut National de la Recherche Agronomique d'Algérie
  • Dinesh Kumar, Indian Agricultural Statistics Research Institute
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  • Satendra Kumar Singh, Indian Council of Agricultural Research
  • Devika P. Madalli, DRTC/Indian Statistical Institute, India
  • Prateek Mahalwar, Cellulosic Technologies UG, Germany
  • Bernard Pochet, University of Liège - Gembloux Agro-Bio Tech
  • Vassilis Protonotarios, NEUROPUBLIC
  • Andy Robinson, CABI
  • Paraj Shukla, King Saud University
  • Chandni Singh, Indian Institute for Human Settlements
  • Kuldeep Singh Jadon, ICAR-Central Arid Zone Research Institute, India
  • Rajeev K Varshney, CGIAR/ICRISAT, India
  • Sumant Vyas, ICAR- National Research Centre on Camel, India
  • Oya Yildirim Rieger, Ithaka S+R/ITHAKA, USA
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