10 pages, Ongoing climate change and associated food security concerns are pressing issues globally, and are of particular concern in the far north where warming is accelerated and markets are remote. The objective of this research was to model current and projected climate conditions pertinent to gardeners and farmers in Alaska. Research commenced with information-sharing between local agriculturalists and climate modelers to determine primary questions, available data, and effective strategies. Four variables were selected: summer season length, growing degree days, temperature of the coldest winter day, and plant hardiness zone. In addition, peonies were selected as a case study. Each variable was modeled using regional projected climate data downscaled using the delta method, followed by extraction of key variables (e.g., mean coldest winter day for a given decade). An online interface was developed to allow diverse users to access, manipulate, view, download, and understand the data. Interpretive text and a summary of the case study explained all of the methods and outcomes. The results showed marked projected increases in summer season length and growing degree days coupled with seasonal shifts and warmer winter temperatures, suggesting that agriculture in Alaska is undergoing and will continue to undergo profound change. This presents opportunities and challenges for farmers and gardeners.
25pgs, We combine farm accounting data with high-resolution meteorological data, and climate scenarios to estimate climate change impacts and adaptation potentials at the farm level. To do so, we adapt the seminal model of Moore and Lobell (2014) who applied panel data econometrics to data aggregated from the farm to the regional (subnational) level. We discuss and empirically investigate the advantages and challenges of applying such models to farm-level data, including issues of endogeneity of explanatory variables, heterogeneity of farm responses to weather shocks, measurement errors in meteorological variables, and aggregation bias. Empirical investigations into these issues reveal that endogeneity due to measurement errors in temperature and precipitation variables, as well as heterogeneous responses of farms toward climate change may be problematic. Moreover, depending on how data are aggregated, results differ substantially compared to farm-level analysis. Based on data from Austria and two climate scenarios (Effective Measures and High Emission) for 2040, we estimate that the profits of farms will decline, on average, by 4.4% (Effective Measures) and 10% (High Emission). Adaptation options help to considerably ameliorate the adverse situation under both scenarios. Our results reinforce the need for mitigation and adaptation to climate change.