Climate Action Initiative

Information on heat-related health risks that is based on meteorological data and forecasts is important for outdoor workers, farm managers, and many other stakeholders, but visualization and interpretation of climate data in a user-friendly way remains a challenge. This proof-of-concept research developed Sol, an artificial intelligence (AI) chatbot to conveniently interpret and communicate heat stress risks through smartphones and other devices. Sol is integrated into iHeatApp, a wet bulb globe temperature (WBGT) forecast visualization tool, to turn complex climate data into practical guidance with bilingual output in English and Spanish. The chatbot leverages an open source large language model (LLM) constrained by prompt engineering designs to reduce hallucinations and encourage domain-specific responses. The chatbot input may be either text or screenshot of the app's interface. Guided by our tutorial materials, we gathered farmworker feedback on the chatbot's ability to translate complex weather metrics into risk mitigation actions using focus groups of farmworkers from the Imperial Valley, California. When WBGT forecast values reach the “danger” category, the chatbot recommends wearing light, breathable clothing and increasing water breaks to prevent heat stress. Our app can help both farmworkers and their managers adapt work scheduling decisions to avoid the risks of peak heat and other health hazards and to mitigate labor productivity loss.

Do electric vehicles (EVs), and the expansion of curbside charging infrastructure in particular, undermine efforts to reduce vehicle travel? As EVs gain market share, decisionmakers may be less inclined to support policies such as bicycle lanes and reduced parking if they compete for space with EV chargers. We find no evidence, however, of policy substitution. Surveys in Los Angeles indicate that public support for policies to reduce driving is not swayed by the availability of EV charging. Furthermore, interviews in North American cities reveal that local officials are cautious about locking in scarce curbspace for EVs, and universally prioritized curb uses such as bicycle and bus lanes, street trees, and parklets. In our sample of early adopter cities, EV charging is not currently diminishing the political will for policies to reduce driving. However, this caution has slowed the rollout of curbside charging in North America.

Abstract Heavily managed urban streams, agricultural drainages, and least modified natural streams are rarely analyzed within a single framework, even though modern landscapes include a mix of them. Urbanization, agriculture, deforestation, mining, and other intensive land uses have dramatically altered fluvial systems. Most existing stream classification frameworks were developed for natural streams and rely only on natural geomorphic attributes. The few that address artificial streams focus exclusively on engineered (artificial) characteristics and usually address only a single land-use setting. As a result, these frameworks do not capture the full diversity of stream conditions found in mixed land-use watersheds that are influenced by both natural processes and human interventions. Recognizing this problem, our study proposes a joint geomorphic classification framework that collects and analyzes field data that captures both engineering and geomorphic attributes of streams in mixed land-use watersheds. We implemented this joint framework in a prime example of a mixed land-use watershed—the San Francisco Bay Area, California, USA. We collected a dataset for 164 stream sites. By using hierarchical cluster analysis on 92 variables computed from the field data, we identified 12 regional stream types, including six artificial and six natural ones. Our results demonstrate that this joint framework differentiates unique natural and artificial stream types in urban, agricultural, and wilderness settings. It also captures the subtle internal variability within artificial and natural stream types. Field photograph interpretation corroborates statistical findings that the diverse variables effectively represent real-world conditions of both types. Overall, by blending natural characteristics and the degree of human intervention, our classification provides critical insights that enhance our ability to understand river behavior in heavily managed urbanized, agricultural, and wilderness regions. It holds significant promise for informing targeted restoration and conservation strategies, ultimately contributing to more effective river management and sustainable watershed planning.

BACKGROUND: Farmworkers in the United States are exposed to extreme heat and demanding physical labor, placing them at increased risk for heat-related illnesses (HRI). This study examined individual, occupational, and household factors associated with self-reported HRI among men and women farmworkers in Imperial Valley, California. METHODS: This cross-sectional study used a convenience and snowball sampling approach to recruit agricultural workers through a local community organization. Participants were administered an in-person interview on their experience with HRI and potential risk factors. A multivariate regression analysis was conducted to identify variables related to HRI. RESULTS: From 300 participants, 119 (39.7%) experienced HRI, defined as reporting at least three HRI symptoms. In the multivariate regression analysis, HRI odds were significantly increased in women (aOR: 2.36, 95%CI: 1.35-4.15), in those reporting heat-related sleep problems (aOR: 3.25, 95%CI: 1.50-7.02), face covering use (aOR: 1.94, 95%CI: 1.09-3.45) and those working more than 5 days per week (aOR: 2.19, 95%CI: 1.23-3.91). Older age had a modest association with lower odds of HRI (aOR: 0.98, 95%CI: 0.96-0.99) and more than one break per day (aOR: 0.59, 95%CI: 0.35-1.00, p = 0.053) was associated with marginally lower HRI odds. CONCLUSIONS: As climate change continues to intensify occupational heat exposure among agricultural workers, interventions should extend beyond purely workplace-focused efforts and address cumulative exposure, non-occupational factors and structural constraints. Report of heat-related sleep problems has been associated with HRI relatively recently. Future research should establish the occupational, structural, and biological pathways contributing to the increased HRI odds for women farmworkers.

Farmworkers are particularly vulnerable to heat stress, which can escalate into heat-related illnesses such as heat exhaustion or heat stroke, and can also impact productivity loss. Heat exposure varies considerably depending on season, work-shift timing, acclimatization, and workload. We examine the frequency of heat stress exceedance in the Imperial and Coachella Valleys of southern California using wet bulb globe temperature (WBGT) calculated using the outputs from Weather Research and Forecasting (WRF) model for a reference year of 2020. The critical WBGT threshold of 80℉ is exceeded for more than 500 hr in August, with considerable exceedance in all key harvesting months of April, May, and June. The threshold is exceeded most frequently in August, with daytime (nighttime) exceedance of 96.3% (38.0%) in the Imperial and 72.5% (11.9%) in the Coachella Valleys. Occupational WBGT limits are also exceeded in most crop environments for unacclimatized workers, and even for acclimatized workers during date and sugarcane harvests. Adjusting the work schedule to an early morning or late evening shift can reduce workers' productivity loss by up to 40%. Based on our findings, we propose several strategies to mitigate heat risks among farmworkers: (a) adjusting work hours to include cooler morning or evening hours (b) using WBGT instead of air temperature to monitor heat exposure (c) adopting variable rest-break schedules (d) reducing nighttime heat exposure during summer to allow recovery from daytime heat (e) taking extra precaution in high-risk crop zones (f) avoiding nighttime irrigation during summer, and (g) revising thresholds and definitions used to identify heatwaves.

The United States agriculture sector needs to adapt to and mitigate its contributions to climate change, especially with adverse impacts such as intense and frequent drought, flooding, wildfire, and extreme heat that affect crop yields, livestock health, farm infrastructure, and farmer incomes. Recently, federal and state governments have introduced new ‘climate-smart agriculture’ (CSA) policies and programs to help farmers mitigate greenhouse gas (GHG) emissions from farming operations. However, farmers face persistent barriers to accessing government resources, which necessitates an understanding of power dynamics among CSA program actors and existing institutions and structures in the agricultural sector. I used the California Department of Food and Agriculture (CDFA) CSA incentive programs to investigate these dynamics. I examined how farmer access to CSA programs and the broader program goal of GHG mitigation are enabled or constrained by program actors involved in program implementation and decision-making, and external institutional and structural ‘conditions.’ I explored if and what foci should be incorporated into the California CSA approach to further achieve GHG mitigation and broader climate change adaptation. To assess these objectives in the context of the CSA programs’ implementation and agenda-setting policy process stages, I employed qualitative methods. Drawing from conservation social sciences, political sciences, and human geography, I used a research approach based in concepts of power – the mechanisms of power and the enabling and constraining outcomes – and theories of institutions and structures. The findings demonstrate persisting challenges within the implementation process that ultimately constrain farmers’ access to the CSA programs. External policies, market dynamics, farmland and labor access, structural discrimination, and asymmetrical power dynamics in program agenda-setting further constrain farmer access to programs and the programs’ ability to mitigate GHG emissions. My findings highlight potential foci to address structural constraints for GHG mitigation and adaptation at the farm level for the CSA approach utilized in California. I recommend considering a more holistic, multi-policy approach to address climate change mitigation and adaptation in agriculture and I argue for future participatory action research with farmers, farm workers, and rural communities to determine mitigation and adaptation needs.

The following report chronicles the professional project completed in partial fulfillment for the degree of Master of Science in Environmental Sciences and Management at California Polytechnic State University, San Luis Obispo. This professional project spanned fifteen months and was conducted with the support of the Sustainable Land Initiative under the supervision of Professor Nicholas Babin. Two educational workshops resulted from the endeavor, thus contributing to the Central Coast’s agriculture community through increased knowledge access and professional networks. Additionally, a two-year road map was developed for future Sustainable Land Initiative events. This plan was intended to be used as a reference point as the organization continues to develop and uplift regenerative farming practices within the region. This report provides the context of the development of the workshops through a literature review, whereas the future outreach plan was informed by survey results from the organized workshops.

This paper investigates the persistence of agricultural practices funded by the California Department of Food and Agriculture's Office of Environmental Farming and Innovation (CDFA OEFI). The central inquiry revolves around determining the most effective behavior model for analyzing persistence, comparing the Reasoned Action Approach (RAA), Comprehensive Action Determination Model (CADM), and CADM augmented with structural variables. The study's methodology integrates literature review, analysis of OEFI-funded practices, and statistical modeling to assess persistence levels. Contrary to existing literature, our findings reveal significantly higher levels of persistence than anticipated. Moreover, through model comparison, CADM augmented with political economic variables emerges as the superior model for analyzing and predicting persistence in agricultural practices funded by CDFA OEFI. These results contribute to a deeper understanding of behavioral determinants in sustainable agricultural practices and offer insights into optimizing policy interventions for long-term practice adoption and environmental impact.

Between 1995 and 2014, California wine production increased by 79%. While the industry has made significant efforts to improve environmental performance, existing data on water consumption and $CO_2$ emissions often rely on studies from foreign regions, which may not accurately reflect California's specific production schemes. Because environmental profiles vary widely based on life-cycle factors, this study utilizes Life Cycle Analysis (LCA) to assess the impact of red wine produced on the Central Coast of California under various production scenarios.The cradle-to-gate analysis examined variables including bottle weight reduction, transportation distances, and varying ratios of machine versus hand-harvesting. Results revealed that the bottling process contributes the most significant environmental impacts, accounting for 82.6% of total global warming potential (GWP) and 65.4% of eutrophication potential. Furthermore, transportation and bottle weight are critical factors, contributing 10.6% of ecotoxicity potential, 11.6% of GWP, and 7.84% of eutrophication potential. Notably, reducing bottle weight by 26.1% resulted in a 20.0% reduction in GWP and a 13.2% reduction in eutrophication potential.These findings provide a scientific basis for producers to adopt best practices for environmentally conscious wine production and inform consumer decision-making. Additionally, these results facilitate the assessment of produce against established sustainability standards. Given the regional variability of certification programs across California and the United States, this data offers a quantitative basis for streamlining audit procedures and supporting the overall certification process.