Economic impacts of integrated pest management (IPM) farmer field schools (FFS): evidence from onion farmers in the Philippines

Abstract

This article comprehensively examines the impact of integrated pest management-farmer field school (IPM-FFS) on yield, insecticide expenditures, labor expenditures, herbicide expenditures, fertilizer expenditures, and profit, based on data from onion producers in the Philippines. Propensity score matching (PSM) and regression-based approaches that account for potential bias due to selection problems from observable variables are used to achieve the objective of the study. Sensitivity of our IPM-FFS impact results to potential bias due to "selection on unobservables" was also assessed. We find that farmers who participate in the IPM-FFS training program have statistically lower insecticide expenditures than the non-IPM-FFS farmers. But we do not find any evidence that the IPM-FFS training program significantly affects yield and the other inputs. There is some evidence indicating that IPM-FFS farmers may have statistically higher profit levels than non-IPM-FFS producers, but these results are sensitive to and may still be invalidated by bias due to unobservable variables. Since IPM-FFS seem to only significantly reduce insecticide use, policymakers and extension educators may need to adjust the IPM-FFS curriculum to further emphasize (or include) other agronomic practices that also optimize the use of other inputs like labor, fertilizer, and herbicides. The more efficient use of all inputs would likely reduce total expenditures and eventually translate to higher incomes.JEL classifications: Q10, Q12, Q16

Adoption of High-Yielding Groundnut Varieties: The Sustainability of a Farmer-Led Multiplication-Dissemination Program in Eastern Uganda

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“…The propensity scores are then used to match treated and control observations. Alternative matching methods include nearest neighbor (with or without replacement), kernel, caliper, and radius [86,87]. Next, balance is examined by comparing the means of the matching variables between treated and control groups.…”

“…The robustness of ATET estimates to bias from unobservables is examined under the Rosenbaum Bounds Delta (Г) framework [3,87,90]. The framework relies on the assumption of a confounding variable W that affects the likelihood of being treated.…”

Yield and income effects of rice varieties with tolerance of multiple abiotic stresses: the case of green super rice (GSR) and flooding in the Philippines

“…This can be effectively addressed by matching the GSR and non‐GSR groups based on the probability of treatment called propensity score matching (PSM). In this study, we follow the common steps in implementing the PSM method (see Caliendo and Kopeinig, ; Sanglestsawai et al., ). Initially, it requires the estimation of propensity scores using a parametric binary response model (probit or logit) and “testing the balancing property.” The second step is to do the matching of GSR with non‐GSR farmers using various alternative procedures.…”

Neighbourhood Effects in Pesticide Use: Evidence from the Rural Philippines

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