Theodoros Skevas

Pesticide use, environmental spillovers and efficiency: A DEA risk-adjusted efficiency approach applied to Dutch arable farming

Pesticides are widely used by crop producers in developed countries to combat risk associated with pests and diseases. However, their indiscriminate use can lead to various environmental spillovers that may alter the agricultural production environment thus contributing to production risk. This study utilises a data envelopment analysis (DEA) approach to measure performance of arable farms, incorporating pesticides’ environmental spillovers and output variance as undesirable outputs in the efficiency analysis and taking explicitly into account the effect of pesticides and other inputs on production risk. The application focuses on panel data from Dutch arable farms over the period 2003–2007. A moment approach is used to compute output variance, providing empirical representations of the risk-increasing or -decreasing nature of the used inputs. Finally, shadow values of risk-adjusted inputs are computed. We find that pesticides are overused in Dutch arable farming and there is a considerable evidence of the need for decreasing pesticides’ environmental spillovers.

How willing are landowners to supply land for bioenergy crops in the Northern Great Lakes Region

Land to produce biomass is essential if the United States is to expand bioenergy supply. Use of agriculturally marginal land avoids the food vs. fuel problems of food price rises and carbon debt that are associated with crop and forestland. Recent remote sensing studies have identified large areas of US marginal land deemed suitable for bioenergy crops. Yet the sustainability benefits of growing bioenergy crops on marginal land only pertain if land is economically available. Scant attention has been paid to the willingness of landowners to supply land for bioenergy crops. Focusing on the northern tier of the Great Lakes, where grassland transitions to forest and land prices are low, this contingent valuation study reports on the willingness of a representative sample of 1124 private, noncorporate landowners to rent land for three bioenergy crops: corn, switchgrass, and poplar. Of the 11% of land that was agriculturally marginal, they were willing to make available no more than 21% for any bioenergy crop (switchgrass preferred on marginal land) at double the prevailing land rental rate in the region. At the same generous rental rate, of the 28% that is cropland, they would rent up to 23% for bioenergy crops (corn preferred), while of the 55% that is forestland, they would rent up to 15% for bioenergy crops (poplar preferred). Regression results identified deterrents to land rental for bioenergy purposes included appreciation of environmental amenities and concern about rental disamenities. In sum, like landowners in the southern Great Lakes region, landowners in the Northern Tier are reluctant to supply marginal land for bioenergy crops. If rental markets existed, they would rent more crop and forestland for bioenergy crops than they would marginal land, which would generate carbon debt and opportunity costs in wood product and food markets.

Investment risk in bioenergy crops

Perennial, cellulosic bioenergy crops represent a risky investment. The potential for adoption of these crops depends not only on mean net returns, but also on the associated probability distributions and on the risk preferences of farmers. Using 6‐year observed crop yield data from highly productive and marginally productive sites in the southern Great Lakes region and assuming risk neutrality, we calculate expected breakeven biomass yields and prices compared to corn (Zea mays L.) as a benchmark. Next we develop Monte Carlo budget simulations based on stochastic crop prices and yields. The crop yield simulations decompose yield risk into three components: crop establishment survival, time to maturity, and mature yield variability. Results reveal that corn with harvest of grain and 38% of stover (as cellulosic bioenergy feedstock) is both the most profitable and the least risky investment option. It dominates all perennial systems considered across a wide range of farmer risk preferences. Although not currently attractive for profit‐oriented farmers who are risk neutral or risk averse, perennial bioenergy crops have a higher potential to successfully compete with corn under marginal crop production conditions.

Does Spatial Dependence Affect the Intention to Make Land Available for Bioenergy Crops

We find spatial dependence in landowners’ stated intentions to make land available for bioenergy crops. Our data are generated from a contingent valuation survey of 599 owners of marginal land in southern Michigan. Employing a Bayesian framework and using these spatially explicit data, we estimate and compare non†spatial probit and spatial Durbin probit models to examine the presence of spatial dependence in land rental intentions. Results show that intentions to rent land for bioenergy crop production are spatially dependent. This spatial dependence arises both from the land supply intentions of nearby landowners and from spatial spillover effects of landowner characteristics and attitudes towards environmental amenities and the disamenities of land rental. We show that ignoring spatial dependence in the intentions of neighbouring landowners to participate in land rental markets for bioenergy feedstocks can lead to distortions that underestimate total effects. Our finding implies that studies of land use and crop supply should test for spatial interactions in order to make accurate inferences.

Farm Capital Investment and Deviations from the Optimal Path

We identify farms’ optimal investment path in capital assets and compare it with their actual investment to assess the direction and extent of deviation from the optimal investment. A probit model is further used to investigate the determinants of the probability that a farmer over†or under†invests in capital assets. We use a panel dataset of Dutch dairy farms over the period 2003–2013, and find that most farms under†invest in capital assets during the study period. Although the number of farms that had over†invested in capital assets is relatively small, these farms account for the biggest share of total investment in capital assets. The probit results show that liquidity, agricultural support payments, age, land tenure and standard output size are important variables explaining the likelihood of over†and under†investment.