Climate change impacts on US agriculture and forestry: benefits of global climate stabilization

Abstract

Increasing atmospheric carbon dioxide levels, higher temperatures, altered precipitation patterns, and other climate change impacts have already begun to affect US agriculture and forestry, with impacts expected to become more substantial in the future. There have been numerous studies of climate change impacts on agriculture or forestry, but relatively little research examining the long-term net impacts of a stabilization scenario relative to a case with unabated climate change. We provide an analysis of the potential benefits of global climate change mitigation for US agriculture and forestry through 2100, accounting for landowner decisions regarding land use, crop mix, and management practices. The analytic approach involves a combination of climate models, a crop process model (EPIC), a dynamic vegetation model used for forests (MC1), and an economic model of the US forestry and agricultural sector (FASOM-GHG). We find substantial impacts on productivity, commodity markets, and consumer and producer welfare for the stabilization scenario relative to unabated climate change, though the magnitude and direction of impacts vary across regions and commodities. Although there is variability in welfare impacts across climate simulations, we find positive net benefits from stabilization in all cases, with cumulative impacts ranging from $32.7 billion to $54.5 billion over the period 2015-2100. Our estimates contribute to the literature on potential benefits of GHG mitigation and can help inform policy decisions weighing alternative mitigation and adaptation actions.

Nitrogen deposition and climate change effects on tree species composition and ecosystem services for a forest cohort

“…, Duveneck et al. , Beach ), to our knowledge this is the first study that applies species‐specific estimates of N and climate sensitivity to model future changes in forest composition and to assess the effect of these changes on forest ecosystem services.…”

Adaptation in Agriculture

“…According to IPCC [2], the atmospheric carbon geographically variability [5,[14][15][16]. Estimates in Beach et al [17] show 30% projected increases in dryland corn yields in the southern US by year 2100, but little effects on the dryland corn yields in the western US and the Great Plains. Farmers have been observed to adapt on a regionally specific basis [5].…”

“…Finally, before starting we note that CC is not always as a negative factor but, depending on the region and situation, might also bring positive impacts to agriculture [14][15][16][17][18][19][20]. For example, Reilly et al [15] presented results where moderate CC increases and the associated drivers can lead to increased cotton yield due to the effects of carbon dioxide and the drought tolerant nature of cotton.…”

What are the effects of Agro-Ecological Zones and land use region boundaries on land resource projection using the Global Change Assessment Model?

“…However, the traditional IAM modeling paradigm does not include climate impacts, leaving an unexplored question regarding the representativeness of AEZ-averaged values in land use modeling under changing climate, in which case the AEZs may not retain their expected homogeneity. Note that the use of AEZs is not ubiquitous in land use modeling; other strategies to disaggregate geopolitical regions using primarily non-climatic criteria include grid cell boundaries (e.g., MAgPIE, Dietrich et al 2014;GLOBIOM, Havlik et al, 2013), hydrologic watersheds (e.g., IMPACT, Rosegrant et al 2012), and province/state boundaries (e.g., FASOMGHG, Beach et al 2015). While these non-climatic boundaries avoid some issues associated with AEZs in the context of climate change, they can still suffer from lack of representativeness if they are not carefully defined, and they still generate model uncertainty associated with a particular delineation of the land surface.…”

“…Gridded climate projections are input to various impact models, and several recent and ongoing assessments have aggregated grid-or farm-scale crop model output to larger scales in order to allow climate impact assessments using models of different scales (e.g., von Beach et al 2015). For the AgMIP inter-model comparison, Müller and Robertson (2014) Increasingly, studies are including climate change impacts on land projection to incorporate these feedbacks, and as these feedbacks are implemented the potential for increasing error due to spatial mismatch also increases.…”