In response to agriculture's vulnerability and contribution to climate change, many governments are developing initiatives that promote the adoption of mitigation and adaptation practices among farmers. Since most climate policies affecting agriculture rely on voluntary efforts by individual farmers, success requires a sound understanding of the factors that motivate farmers to change practices. Recent evidence suggests that past experience with the effects of climate change and the psychological distance associated with people's concern for global and local impacts can influence environmental behavior. Here we surveyed farmers in a representative rural county in California's Central Valley to examine how their intention to adopt mitigation and adaptation practices is influenced by previous climate experiences and their global and local concerns about climate change. Perceived changes in water availability had significant effects on farmers' intention to adopt mitigation and adaptation strategies, which were mediated through global and local concerns respectively. This suggests that mitigation is largely motivated by psychologically distant concerns and beliefs about climate change, while adaptation is driven by psychologically proximate concerns for local impacts. This match between attitudes and behaviors according to the psychological distance at which they are cognitively construed indicates that policy and outreach initiatives may benefit by framing climate impacts and behavioral goals concordantly; either in a global context for mitigation or a local context for adaptation.

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Describing spatiotemporal patterns of agricultural biodiversity may be an important step toward better understanding its effect on agroecosystem services.

Aim Modelling complex environmental and ecological processes over large geographic areas is challenging, particularly when basic research and model development for such processes has historically been at the local scale. Moving from local toward global analysis brings up numerous issues related to data processing, aggregation, tradeoffs between model quality and data quality, and prioritization of data collection and/or compilation efforts. We studied these issues in the context of modelling emissions of N2O (a potent greenhouse gas) from agricultural soils. Location Global. Methods We developed metamodels of the DeNitrification–DeComposition (DNDC) model, a mechanistic model that simulates greenhouse gas emissions from agricultural soils, to estimate global N2O emissions from maize and wheat fields. We ran DNDC for a diverse sample of global climate and soil types, and fitted the model output as a function of (sometimes simplified) model input variables, using the random forest machine learning algorithm. We used the metamodels to estimate global N2O emissions from maize and wheat at a very high spatial resolution (c. 1 km2) and examined the effects of different approaches of using soil data as well as the effects of spatial aggregation of soil and climate data. Results The average coefficient of determination (R2) between holdout data (DNDC output not used to construct the metamodel) and metamodel predictions was 0.97 for maize and 0.91 for wheat. The metamodels were sensitive to soil properties, particularly to soil organic carbon content. Global emission estimates with the metamodel were highly sensitive to the spatial aggregation and other forms of generalization of soil data, but much less so to aggregation of climate data. Main conclusions Using a simplified metamodel with data of high spatial resolution could produce results that are more accurate than those obtained with a full mechanistic model and lower‐resolution data.

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