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The role of pest pressure in technical and environmental inefficiency analysis of Dutch arable farms: an event-specific data envelopment approach

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Abstract

This article provides estimates of farm technical and environmental inefficiency that recognize the effect of pest pressure on farmers’ production environment. This effect is modeled through the use of an event-specific production technology, which is empirically implemented using Data Envelopment Analysis (DEA). A regional biodiversity variable and two variables reflecting impacts of pesticides on farmland biodiversity are used to partition the data into high and low pest infestation events. The DEA representation is applied to data from Dutch arable farms. Results show that the degree of inefficiency overstatement from a model that ignores the event-specific nature of the production technology increases with pest infestation. Mean environmental inefficiency of the sample farms is low, implying that these farms are, on average, minimizing their impacts on farmland biodiversity. Environmental inefficiency provides an indicator of farm-level environmental sustainability that could help towards a more effective distribution of farm-support payments and make agriculture more environmentally sound.

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Notes

  1. This model measures production uncertainty in the use of pest control methods by adding an error term in the damage control function. This error term accounts for randomness in pest populations and pesticide applications.

  2. Our motivation to define a CRS technology is based on the fact that land in the Dutch arable sector is a scarce input that constrains the cash crop sector (Guan et al. 2005; Skevas et al. 2013) thus making it difficult for farmers to increase their farm size (a review of the broader Netherlands agricultural FADN database shows negligible changes in land over time). Both Guan et al. (2005) and Skevas et al. (2013) report that Dutch farms operate approximately at constant returns to scale. Moreover, Kuosmanen (2005) notes that WD of undesirables is satisfied only under CRS and the specification of WD with variable returns to scale underestimates the production possibilities and can have direct implications for the results of the analysis. Yet, when relaxing the CRS assumption we found an insignificant underestimation of our results.

  3. While Simar and Wilson (2007) define Algorithm 1 for radial distance functions, we apply it to the case of the directional distance functions. However, since we use the actual quantities of inputs and outputs as directional vector, our inefficiency measures are radial in nature (Färe and Grosskopf 2003).

  4. Running a truncated regression for every event produces similar results.

  5. Other pesticides include herbicides, insecticides, growth regulators, rodenticides, additives (i.e., mineral oil), ground disinfectants, detergents, sulfur, and, unclassified products.

  6. One AWU is equivalent to one person working full-time on the holding (EC 2001).

  7. Water organisms include mainly aquatic insects, while biological controllers include, among others, ladybugs, predatory mites, and hymenopteran parasitoids (CLM 2010).

  8. As of March 2013, CLM has increased the acceptable score for aquatic organisms to 100 impact points, and all pesticide-specific impact points for these organisms have been raised with a factor 10.

  9. The data partition resulted in 240 and 246 observations for the high and low pest infestation event, respectively. The 5 years in the high pest infestation state have, respectively, 44, 44, 48, 57 and 47 observations. The five years in the low pest infestation state have, respectively, 52, 50, 51, 50 and 43 observations.

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Authors and Affiliations

  1. Department of Agricultural and Applied Economics, University of Missouri, 200 Mumford Hall, Columbia, MO, 65211, USA

    Theodoros Skevas

  2. Department of Agricultural and Consumer Economics, University of Illinois, 335 Mumford Hall, 1301 W Gregory Drive, Urbana, IL, 61801, USA

    Teresa Serra

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  1. Theodoros Skevas
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  2. Teresa Serra
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Correspondence to Theodoros Skevas.

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Skevas, T., Serra, T. The role of pest pressure in technical and environmental inefficiency analysis of Dutch arable farms: an event-specific data envelopment approach. J Prod Anal 46, 139–153 (2016). https://doi.org/10.1007/s11123-016-0476-0

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