Abstract
AbstractNutrient emissions from agricultural land are now widely recognized as one of the key contributors to poor water quality in local lakes, rivers, and streams. Nutrient trading for nonpoint sources, including farms, has been suggested as a regulatory tool to improve and protect water quality. However, farmers' attitudes suggest that they are resistant to adopting the unfamiliar technologies and farm management practices that may be required under such a scheme. This study develops a model of farmers' resistance to change and demonstrates how this affects their adoption of new mitigation technologies under nutrient trading regulation. We present the model and derive some of its properties in the two‐farmer case.
Highlights
Nutrient emissions from non-point sources, such as agricultural land, are increasing recognized as one of the key contributors to poor water quality
The NManager model (Anastasiadis et al 2011) make several simplifying assumptions in order to model the performance of six different designs of nitrogen regulation in the Lake Rotorua catchment (New Zealand)
The paper is set out as follows: In the remainder of this section we review some of the literature relevant to agricultural adoption, and briefly describe the workings of a nutrient trading scheme
Summary
Nutrient emissions from non-point sources, such as agricultural land, are increasing recognized as one of the key contributors to poor water quality. The NManager model (Anastasiadis et al 2011) make several simplifying assumptions in order to model the performance of six different designs of nitrogen regulation (including a nitrogen trading scheme) in the Lake Rotorua catchment (New Zealand). These assumptions include: farmers are willing to change, farmers respond optimally to a nitrogen price, and farmers’ decisions are independent of their past decisions and the decisions of other farmers.∗. Farmers’ inertia depends on their past behavior, the past behavior of other farmers, and the passing of time This is a novel and somewhat challenging approach as it involves quantifying something that is difficult to identify and measure.
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