The impact of extreme heat on crop yields is an increasingly pressing issue given anthropogenic climate warming. However, some of the physical mechanisms involved in these impacts remain unclear, impeding adaptation-relevant insight and reliable projections of future climate impacts on crops. Here, using a multiple regression model based on observational data, we show that while extreme dry heat steeply reduced U.S. corn and soy yields, humid heat extremes had insignificant impacts and even boosted yields in some areas, despite having comparably high dry-bulb temperatures as their dry heat counterparts. This result suggests that conflating dry and humid heat extremes may lead to underestimated crop yield sensitivities to extreme dry heat. Rainfall tends to precede humid but not dry heat extremes, suggesting that multivariate weather sequences play a role in these crop responses. Our results provide evidence that extreme heat in recent years primarily affected yields by inducing moisture stress, and that the conflation of humid and dry heat extremes may lead to inaccuracy in projecting crop yield responses to warming and changing humidity.
Smallholder Knowledge of Local Climate Conditions Predicts Positive On-Farm Outcomes
Abstract People’s observations of climate change and its impacts, mediated by cultures and capacities, shape adaptive responses. Adaptation is critical in regions of rainfed smallholder agriculture where changing rainfall patterns have disproportionate impacts on livelihoods, yet scientific climate data to inform responses are often sparse. Despite calls for better integration of local knowledge into adaptation frameworks, there is a lack of empirical evidence linking both smallholder climate observations and scientific data to on-farm outcomes. We combine smallholder observations of past seasonal rainfall timing with satellite-based rainfall estimates in Uganda to explore whether farmers’ ability to track climate patterns is associated with higher crop yields. We show that high-fidelity tracking, or alignment of farmer recall with recent rainfall patterns, predicts higher yields in the present year, suggesting that farmers may translate their cumulative record of environmental knowledge into productive on-farm decisions, such as crop selection and timing of planting. However, tracking of less-recent rainfall (i.e., 1–2 decades in the past) does not predict higher yields in the present, while climate data indicate significant trends over this period toward warmer and wetter seasons. Our findings demonstrate the value of smallholder knowledge systems in filling information gaps in climate science while suggesting ways to improve adaptive capacity to climate change.
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- Award ID(s):
- 1740201
- NSF-PAR ID:
- 10411714
- Date Published:
- Journal Name:
- Weather, Climate, and Society
- Volume:
- 14
- Issue:
- 3
- ISSN:
- 1948-8327
- Page Range / eLocation ID:
- 671 to 680
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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null (Ed.)Amid climate change, biodiversity loss and food insecurity, there is the growing need to draw synergies between micro-scale environmental processes and practices, and macro-level ecosystem dynamics to facilitate conservation decision-making. Adopting this synergistic approach can improve crop yields and profitability more sustainably, enhance livelihoods and mitigate climate change. Using spatially explicit data generated through a public participatory geographic information system methodology (n = 37), complemented by spatial analysis, interviews (n = 68) and focus group discussions (n = 4), we explored the synergies between participatory farmer-to-farmer agroecology knowledge sharing, farm-level decisions and their links with macro-level prioritization of conservation strategies. We mapped farm conditions and ecosystem services (ES) of two village areas with varying knowledge systems about farming. Results of the farm-level analysis revealed variations in spatial perception among farmers, differences in understanding the dynamics of crop growth and varying priorities for extension services based on agroecological knowledge. The ES use pattern analysis revealed hotspots in the mapped ES indicators with similarities in both village areas. Despite the similarities in ES use, priorities for biodiversity conservation align with farmers’ understanding of farm processes and practices. Farmers with training in agroecology prioritized strategies that are ecologically friendly while farmers with no agroecology training prioritized the use of strict regulations. Importantly, the results show that agroecology can potentially contribute to biodiversity conservation and food security, with climate change mitigation co-benefits. The findings generally contribute to debates on land sparing and land sharing conservation strategies and advance social learning theory as it pertains to acquiring agroecological knowledge for improved yield and a sustainable environment.more » « less
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Global sea level rise (SLR) may present the most urgent climate change adaptation challenge facing coastal communities today. The direction is clear, impacts are manifesting now, and the pace of rise is likely to accelerate. As a result, many coastal communities have begun planning their adaptation response and some are quite far along in the process. At the same time, evolving science provides new observations, models, and understanding of land-ocean dynamics that can increase clarity while also in many ways increase uncertainty about the scope, timing, and regional nature of SLR. The planning, design, and construction of water infrastructure has a relatively long timeline (up to 30 years), and thus the evolution of scientific knowledge presents challenges for communities already planning for SLR based on previous information. When does science become actionable for decision-makers? Are there characteristics or thresholds that could cause communities decide to move from one set of scenarios to another, or change approaches altogether? This talk focuses on two important studies different in kind but dominating the conversation about SLR adaptation planning today. First, DeConto and Pollard (2016) have suggested significantly higher upper end projections for Antarctic ice sheet melt, which increase both global and regional SLR above most previously assumed upper limits. Second, probabilistic projections using model output and expert elicitation as presented in Kopp et al (2014) are increasingly appearing in federal reports and planning-related documents. These two papers are pushing the boundaries of the science-to-planning interface, while the application of this work as actionable science is far from settled. This talk will present the outcome of recent conversations among our diverse author team. The authors are engaged in SLR planning related contexts from many angles and perspectives and include the aforementioned Kopp and DeConto as well as representatives of the City of San Francisco, Army Corps of Engineers, Environmental Protection Agency, and engineering consultant community. Attendees of this session will hear a presentation demonstrating co-production in process, including topics about which the authors have and have not agreed upon to date, with some attention to next steps in the process.more » « less
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Abstract Objectives Human responses to climate variation have a rich anthropological history. However, much less is known about how people living in small‐scale societies perceive climate change, and what climate data are useful in predicting food production at a scale that affects daily lives.
Methods We use longitudinal ethnographic interviews and economic data to first ask what aspects of climate variation affect the agricultural cycle and food production for Yucatec Maya farmers. Sixty years of high‐resolution meteorological data and harvest assessments are then used to detect the scale at which climate data predict good and bad crop yields, and to analyze long‐term changes in climate variables critical to food production.
Results We find that (a) only local, daily precipitation closely fits the climate pattern described by farmers. Other temporal (annual and monthly) scales miss key information about what farmers find important to successful harvests; (b) at both community‐ and municipal‐levels, heavy late‐season rains associated with tropical storms have the greatest negative impact on crop yields; and (c) in contrast to long‐term patterns from regional and state data, local measures show an increase in rainfall during the late growing season, indicating that fine‐grained data are needed to make accurate inferences about climate trends.
Conclusion Our findings highlight the importance to define climate variables at scales appropriate to human behavior. Course‐grained annual, monthly, national, and state‐level data tell us little about climate attributes pertinent to farmers and food production. However, high‐resolution daily, local precipitation data do capture how climate variation shapes food production.
- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.1175/WCAS-D-21-0131.1
