No abstract

Agricultural service providers often work closely with producers, and are well positioned to include weather and climate change information in the services they provide. By doing so, they can help producers reduce risks due to climate variability and change. A national survey of United States Department of Agriculture Farm Service Agency (FSA) field staff (n = 4621) was conducted in 2016. The survey was designed to assess FSA employees’ use of climate and weather-related data and explore their perspectives on climate change, attitudes toward adaptation and concerns regarding climate- and weather-driven risks. Two structural equation models were developed to explore relationships between these factors, and to predict respondents’ willingness to integrate climate and weather data into their professional services in the future. The two models were compared with assess the relative influence of respondents’ current use of weather and climate information. Findings suggest that respondents’ perceptions of weather-related risk in combination with their personal observations of weather variability help predict whether an individual intends to use weather and climate information in the future. Importantly, climate change belief is not a significant predictor of this intention; however, the belief that producers will have to adapt to climate change in order to remain viable is. Surprisingly, whether or not an individual currently uses weather and climate information is not a good predictor of whether they intend to in the future. This suggests that there are opportunities to increase employee exposure and proficiency with weather and climate information to meet the needs of American farmers by helping them to reduce risk.

As climate change is expected to significantly affect agricultural systems globally, agricultural farm advisors have been increasingly recognized as an important resource in helping farmers address these challenges. While there have been many studies exploring the climate change belief and risk perceptions as well as behaviors of both farmers and agricultural farm advisors, there are very few studies that have explored how these perceptions relate to actual climate impacts in agriculture. Here we couple survey data from United States Department of Agriculture farm service employees (n=6, 514) with historical crop loss data across the United States to explore the relationship of actual climate-related crop losses on farm to farm advisor perceptions of climate change and future farmer needs. Using structural equation modelling we find that among farm advisors that work directly with farms on disaster and crop loss issues, there is a significant positive relationship between crop loss and perceived weather variability changes, while across all farm advisors crop loss is associated with reduced likelihood to believe in anthropogenic climate change. Further, we find that weather variability perceptions are the most consistently and highly correlated with farm advisors' perceptions about the need for farm adaptation and future farmer needs. These results suggest that seeing crop loss may not lead to climate change belief, but may drive weather variability perceptions, which in turn affect farm adaptation perceptions. This lends further evidence to the debate over terminology in climate change communication and outreach, suggesting that weather variability may be the most salient among agricultural advisors.

No abstract

No abstract

In November/December of 2016, a survey collaboratively designed by the USDA Climate Hubs, FSA, and the University of Vermont was administered to capture FSA field staffs' beliefs and attitudes related to climate change and potential impacts, as well as their perceptions about the risk that weather variability poses for U.S. farmers.

Three storms in the 2017 hurricane season caused $265 billion in damages in the U.S. Southeast and Caribbean, including billions in losses in the agriculture and forestry sector. Climate change projections indicate that such disastrous hurricane seasons are becoming more normal. Working land management sectors need to prepare for this future. However, few studies evaluate hurricane resilience strategies, or challenges faced by land managers surrounding hurricane events. Boundary organizations are critical to hurricane preparedness and recovery, advising land managers before hurricanes, and often supporting recovery efforts. Here, we rely on public advisors’ experiences to understand how land managers pursue hurricane resilience. Using focus groups and an online survey of three agencies in the Southeast U.S. and U.S. Caribbean (n = 607), we identify challenges faced by land managers before and after hurricanes, and the strategies they implement to minimize damage. We learn that land managers are faced with many diverse and unique challenges related to hurricanes, but that long-term planning for hurricane events is uncommon compared to shorter-term preparedness and recovery activities. Efforts towards hurricane resilience should incorporate local needs, align with other land management goals, and increase overall resilience to climate change and related stressors. The results of this research can guide state/territorial and national-level prioritizations regarding hurricane resilience, as well as identify research needs on hurricane resilience strategies.

Forest Inventory and Analysis (FIA) data provides robust information for the United States Forest Service’s (USFS) mid-to-broad-scale planning and assessments, but ecological challenges (i.e., climate change, wildfire) necessitate increasingly strategic information without significantly increasing field sampling. Small area estimation (SAE) techniques could provide more precision supported by a rapidly growing suite of landscape-scale datasets. We present three Regional case studies demonstrating current FIA uses, how SAE techniques could enhance existing uses, and steps FIA could take to enable SAE applications that are user-friendly, comprehensive, and statistically appropriate. The Northern Region uses FIA data for planning and assessments, but SAE techniques could provide more specificity to guide vegetation management activities. State and transition simulation models (STSM) are run with FIA data in the Southwestern Region to predict effects of treatments and disturbances, but SAE could support model validation and more precision to identify treatable areas. The Southern Region used FIA to identify existing longleaf pine stands and evaluate condition, but SAE techniques within FIA tools would streamline analyses. Each case study demonstrates a desire to have FIA data on non-forested conditions and non-tree variables. Additional tools to measure statistical confidence would help maximize utility. FIA’s SAE techniques could add value to a widely used data set, if FIA can support key supplements to basic data and functionality.