Every year, climate change causes $73 million in loss to New Mexico’s agriculture (Niemi, 2009), and despite the numerous incentivizing conservation programs, the state remains one with the lowest adoption rates in the nation (Wade et al., 2015). Thus, understanding specialty crop growers’ perceptions of climate change is a crucial element to develop mitigation strategies that will ensure resiliency of the state’s agriculture. Thus, in this study, we use survey data of 300 farmers from New Mexico to: (a) determine how they perceive climate change; (b) assess their awareness of incentivizing conservation programs; and (c) elicit their willingness and how they would adapt their daily behavior (e.g. recycling, waste management), and agricultural practices (e.g. reduced tillage, cover crops, nitrogen management) to climate change. Three main hypotheses are tested in this study: (H1): Climate change perceptions are equally influenced by demographic characteristics and peer-counseling; (H2): Farmers lack literacy regarding climate change incentivizing programs. And, (H3): Farmers who perceive climate change are more likely to adapt. Descriptive statistics are used to test (H1), while for H2 and H3, a sample selection model is needed to study adaptation willingness among climate change believers. The study uses Heckprobit models to address sample selection bias that may occur if the “don’t believe” respondents were excluded from the estimation. We estimate two models, one for the daily behavior adaptation, and one for agricultural activity adaptation. Two equations per model are then, estimated where both dependent variables are dichotomous: (y_1i), the selection equation that evaluates the probability that grower i believes in climate change, taking the value of one when it’s the case, and takes the value of zero if otherwise. (y_2i), the probit equation that elicits grower i’s willingness to adapt, equals one when the grower is willing to adapt, and zero otherwise. We consider attitudes toward sustainability, land attachment, familiarity with conservation programs, information means (how the grower informs himself on conservation practices), and demographics, as factors for the equations. All estimations will be carried out by the likelihood method using STATA. We expect that growers who perceive climate change will be more likely to change their behavior as well as adopting conservation practices to accommodate climate change. Understanding conservation practices adoption preferences in the state, will help improving the design of adaptation policies to better fit climatic conditions and growers’ needs.
