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

Climate data were incorporated into the EPIC model to estimate annual impacts of alternative climate scenarios on crop yields for barley, corn, cotton, hay, potatoes, rice, sorghum, soybeans, and wheat over the period 2010–2115, in addition to a baseline period of 1980-2009. The EPIC model is a field level biophysical process model that simulates crop yield/biomass production, soil evolution, and their mutual interaction given detailed farm management practices and climate data (Williams <a xmlns:xlink="http://www.w3.org/1999/xlink" class="cite" href="#erl518630bib53" id="fnref-erl518630bib53"="">1995</a>). Crop growth is simulated by calculating the potential daily photosynthetic production of biomass.

Daily potential growth is based on the concept of radiation-use efficiency by which a fraction of daily photosynthetically-active solar radiation is intercepted by the plant canopy and converted into plant biomass. Daily gains in plant biomass are affected by vapor pressure deficits, atmospheric CO<sub="">2</sub> concentrations, environmental controls and stresses such as limitations in water and nutrients. Thus, EPIC can account for the effects of climate-induced changes in temperature, precipitation, and other variables, including extreme temperature and precipitation events affecting agriculture, on potential yields. EPIC also accounts for the effects of change in CO<sub="">2</sub> concentration and vapor pressure deficit on the radiation-use efficiency, leaf resistance and transpiration of crops (Stockle <em="">et al</em> <a xmlns:xlink="http://www.w3.org/1999/xlink" class="cite" href="#erl518630bib47" id="fnref-erl518630bib47"="">1992a</a>). This feature combined with EPIC's comprehensive representation of agroecosystems processes makes it able to simulate the probable effects of CO<sub="">2</sub> and climate change on complex cropping systems that vary in soil, crops, crop rotations and management practices (Stockle <em="">et al</em> <a xmlns:xlink="http://www.w3.org/1999/xlink" class="cite" href="#erl518630bib48" id="fnref-erl518630bib48"="">1992b</a>)<sup xmlns:xlink="http://www.w3.org/1999/xlink" class="fnref"=""><a class="fnref" href="#erl518630fn7"="">14</a></sup> .

Consistent with the expectation that crop production regions may change over time under climate change scenarios, the EPIC model was used to simulate the potential migration of crops into new production areas (following Adams <em="">et al</em> <a xmlns:xlink="http://www.w3.org/1999/xlink" class="cite" href="#erl518630bib2" id="fnref-erl518630bib2"="">2004</a> and Beach <em="">et al</em> <a xmlns:xlink="http://www.w3.org/1999/xlink" class="cite" href="#erl518630bib10" id="fnref-erl518630bib10"="">2010a</a>). In particular, crop production was simulated on potentially suitable areas within 100 km of historical production regions in all directions<sup xmlns:xlink="http://www.w3.org/1999/xlink" class="fnref"=""><a class="fnref" href="#erl518630fn8"="">15</a></sup> <sup="">,</sup> <sup xmlns:xlink="http://www.w3.org/1999/xlink" class="fnref"=""><a class="fnref" href="#erl518630fn9"="">16</a></sup> . The entire range of potential area for each crop was simulated under both dryland and irrigated conditions for each of the IGSM-CAM ensemble members and for the MIROC climate projections as were changes in crop timing and maturity dates. The main focus was on generating results for potential yields and irrigated crop water under existing cropping practices and possible adaptations for incorporation into FASOM-GHG. These scenarios are based on climate and environmental conditions, but unconstrained by potential limits on water availability for irrigated crop use for incorporation into FASOM-GHG (see section <a xmlns:xlink="http://www.w3.org/1999/xlink" class="secref" href="#erl518630s3-3"="">3.3</a> below), which establishes crop mix and production practices based on economic considerations and irrigation constraints.

Finally, EPIC was simulated with the CO<sub="">2</sub> concentration pathways from each scenario (reaching about 830 ppm by 2100 under the Reference and 460 ppm under the Policy scenario) as well as with holding CO<sub="">2</sub> concentrations constant at baseline levels (400 ppm) in both scenarios throughout the simulation period. This sensitivity analysis enables the examination of the influence of other climate impacts separately from the influence of CO<sub="">2</sub> fertilization of crops.

The IGSM-CAM (all five ensemble members) and MIROC climate projections were incorporated into the MC1 dynamic vegetation model to assess differences in forest growth rates. MC1 is a spatially explicit gridded model that contains linked modules simulating biogeography, biogeochemistry, and fire disturbance (Bachelet <em="">et al</em> <a xmlns:xlink="http://www.w3.org/1999/xlink" class="cite" href="#erl518630bib7" id="fnref-erl518630bib7"="">2001</a>, <a xmlns:xlink="http://www.w3.org/1999/xlink" class="cite" href="#erl518630bib8" id="fnref-erl518630bib8"="">2003</a>). The MC1 simulations (Mills <em="">et al</em> <a xmlns:xlink="http://www.w3.org/1999/xlink" class="cite" href="#erl518630bib32" id="fnref-erl518630bib32"="">2015</a>) accounted for the estimated effects of CO<sub="">2</sub> fertilization on plant growth. The model was simulated annually from 2010–2115 for each of the scenarios, along with a baseline period of 1980–2009.

The variable used to capture differences in growth was net primary productivity of aboveground forest, mapped to FASOM-GHG forest types and regions. To smooth out the large level of annual variability from dynamic vegetation models like MC1 (Mills <em="">et al</em> <a xmlns:xlink="http://www.w3.org/1999/xlink" class="cite" href="#erl518630bib32" id="fnref-erl518630bib32"="">2015</a>) and to provide FASOM-GHG with long-term yield trends, a 30 year moving average was used<sup xmlns:xlink="http://www.w3.org/1999/xlink" class="fnref"=""><a class="fnref" href="#erl518630fn10"="">17</a></sup> . The difference in forest yield was assumed to be equal to the percentage difference in net primary productivity between future years and the average for the baseline as in Irland <em="">et al</em> (<a xmlns:xlink="http://www.w3.org/1999/xlink" class="cite" href="#erl518630bib23" id="fnref-erl518630bib23"="">2001</a>). Importantly, the effects of wildfire, pest, and disease on yield are not reflected in the net primary productivity estimates<sup xmlns:xlink="http://www.w3.org/1999/xlink" class="fnref"=""><a class="fnref" href="#erl518630fn11"="">18</a></sup> .

The effects of climate change on production, crop mix, and markets were simulated using the model FASOM-GHG for a six-region aggregation of the United States<sup xmlns:xlink="http://www.w3.org/1999/xlink" class="fnref"=""><a class="fnref" href="#erl518630fn12"="">19</a></sup> . This regional differentiation is important for reflecting the different conditions across the United States and therefore generating an accurate picture of the potential national-level impacts. FASOM-GHG (Adams <em="">et al</em> <a xmlns:xlink="http://www.w3.org/1999/xlink" class="cite" href="#erl518630bib1" id="fnref-erl518630bib1"="">2005</a>, Beach <em="">et al</em> <a xmlns:xlink="http://www.w3.org/1999/xlink" class="cite" href="#erl518630bib11" id="fnref-erl518630bib11"="">2010b</a>) is a forward-looking model of the forest and agriculture sectors that simulates the allocation of land across both sectors over time and the associated impacts on commodity markets. The model solves a constrained dynamic optimization problem that maximizes the net present value of the sum of producers' and consumers' surplus over time. The model is constrained such that total production is equal to total consumption, technical input/output relationships hold, and total US land use remains constant (with some land migrating out to developed uses and becoming unavailable for forestry and agriculture). In addition, the model includes detailed accounting for changes in net GHG emissions.

Land categories included in the model are specified as follows:

• Cropland is land suitable for crop production that is being used to produce either traditional crops (e.g., corn, soybeans) or dedicated energy crops (e.g., switchgrass).
• Cropland pasture is managed land suitable for crop production (e.g., relatively high productivity) that is being used as pasture, but can be used for crops without additional improvement.
• Pasture is grassland pasture that is less productive than cropland pasture and would need to incur costs to be improved before it could be used as cropland.
• Grazed forest is woodland area used for livestock grazing. Grazed forest areas are assumed to produce no forest products and cannot be converted to any other alternative use.
• Rangeland is unimproved land where a significant portion of the natural vegetation is native grasses and shrubs. This land is used for livestock grazing, but is considerably less productive than our other grassland categories and is not permitted to transfer to other land uses.
• Forestland refers to private timberland, which has several subcategories based on productivity, some of which can convert to cropland or pasture. Public forestland is not explicitly tracked and is assumed to remain constant over time, but supplies an exogenous amount of timber into FASOM-GHG forest commodity markets consistent with recent policy.
• Developed land is assumed to increase over time at an exogenous rate for each region based on projected changes in population and economic growth.
• Conservation Reserve Program (CRP) land is specified as land that is voluntarily taken out of crop production and enrolled in the USDA's CRP. This land is generally marginal cropland

Landowners choose how to allocate land based on the net present value of the relative returns to alternative forest types and management regimes compared with the returns to alternative crop production activities, all within constraints on land suitability for alternative activities. For instance, when a forest landowner harvests their timber, they would compare whether they could receive higher returns from reforesting (under any one of multiple potential management regimes) relative to other potential uses of their land and could decide to plant crops or convert to pasture instead of reforesting.

FASOM-GHG was run under the model's typical baseline assumptions, which include fixed climate (future climate is assumed not to change from historical averages so there are no climate change effects on yields). The yield data in FASOM-GHG was then modified relative to the fixed climate baseline using EPIC crop yields and MC1 forest yield projections under the Reference and Policy climate scenarios (all assuming full CO<sub="">2</sub> fertilization). The percentage differences in potential agricultural and forestry yields from EPIC and MC1 were calculated for regions consistent with those in FASOM-GHG and applied to the FASOM-GHG fixed climate baseline regional crop and forest yield data<sup xmlns:xlink="http://www.w3.org/1999/xlink" class="fnref"=""><a class="fnref" href="#erl518630fn13"="">20</a></sup> . Agricultural yield data are based on USDA data for historical values and projected out to 2115 to represent technological improvements using annual percentage or absolute differences in yields calculated from historical data on crop yield improvements (Beach <em="">et al</em> <a xmlns:xlink="http://www.w3.org/1999/xlink" class="cite" href="#erl518630bib11" id="fnref-erl518630bib11"="">2010b</a>). Annual yield growth rates for crops range between 0% (rye) to 1.9% (silage), while those for livestock range between 0.4% (eggs) to 2.3% (milk). Therefore, baseline fixed climate yields in 2100 may be several times higher than in 2000 for some commodities<sup xmlns:xlink="http://www.w3.org/1999/xlink" class="fnref"=""><a class="fnref" href="#erl518630fn14"="">21</a></sup> . Yields have grown rapidly over past decades and most models projecting future production include technological progress, though there is of course uncertainty regarding its magnitude. Forestry yields are assumed constant for a given management intensity classification, though landowners can shift between management intensity classifications following harvest. FASOM-GHG was solved out to 2115 in 5 year intervals, yielding a dynamic simulation of prices, production, management, consumption, and GHG emissions, along with other environmental and economic indicators<sup xmlns:xlink="http://www.w3.org/1999/xlink" class="fnref"=""><a class="fnref" href="#erl518630fn15"="">22</a></sup> .

As noted in section <a xmlns:xlink="http://www.w3.org/1999/xlink" class="secref" href="#erl518630s3-1"="">3.1</a>, EPIC was used to simulate potential yields in areas adjacent to historical production areas and for both dryland and irrigated production for each crop. To account for climate effects on water availability for irrigation, water supply shifts were supplied to FASOM-GHG based on data obtained from a water supply-and-demand model used in the CIRA project (<a xmlns:xlink="http://www.w3.org/1999/xlink" class="cite" href="#erl518630bib12" id="fnref-erl518630bib12"="">Boehlert</a> <em="">et al</em> in review)<sup xmlns:xlink="http://www.w3.org/1999/xlink" class="fnref"=""><a class="fnref" href="#erl518630fn16"="">23</a></sup> . However, the actual production areas, yields, outputs, and other market outcomes simulated for this study are all determined within FASOM-GHG. In addition to accommodate potential crop expansion and/or adoption of irrigation in new areas, it is also possible that production and/or irrigation for certain crops will cease in some existing production areas depending on how differences in yields and production costs affect relative profitability of different land uses.

The biophysical simulation results reveal sizable effects on productivity from the Policy scenario relative to the Reference scenario, mostly positive but some negative. Yield impacts vary considerably across scenarios and between dryland versus irrigated areas. Generally, yields are most positively affected by the emissions stabilization scenario in the Southern US region, while the Northeast benefits the least. Greater yield benefits of stabilization in the Southern US are consistent with expectations and with the results of our Reference scenario, which indicate that the Southern US would experience some of the most negative effects on yields with unabated climate change. However, the patterns of simulated yield differences for a given climate scenario are complex and depend heavily on the individual crop, irrigation status, interactions with changes in precipitation that affect water availability, regional soils, and many other factors.

The IGSM-CAM (wetter of our two GCMs) simulations project increases in national average yields for all three of these crops under the Policy scenario relative to the Reference case for both irrigated and dryland conditions (see figure <a xmlns:xlink="http://www.w3.org/1999/xlink" href="#erl518630f1"="">1</a>). The relative yield benefits of the Policy scenario increase over time across all regions for irrigated crops and across most regions for dryland crops as well. Exceptions are dryland corn and soybeans in the Plains and Western United States, where the yield gains are very small and/or flat over time and are even negative for soybeans in the Western United States. In the MIROC (drier) model, the results for both irrigated and dryland crops are consistently positive for all crops across all regions.

Zoom In Zoom Out Reset image size

Wheat is typically considered a cooler weather crop than corn or soybeans. However, as shown in figure <a xmlns:xlink="http://www.w3.org/1999/xlink" href="#erl518630f1"="">1</a>, percentage increases in wheat yields are less than or similar to corn and soybeans for IGSM-CAM. For MIROC scenarios, percentage increases in wheat yields are similar to or higher than corn and soybeans. In general, the level of temperature increase simulated in MIROC scenarios results in less temperature stress and more favorable conditions then the IGSM-CAM scenarios for wheat. Overall, our simulations of future differences in wheat yields are of similar magnitude to Rosenzweig <em="">et al</em> (<a xmlns:xlink="http://www.w3.org/1999/xlink" class="cite" href="#erl518630bib39" id="fnref-erl518630bib39"="">2013</a>).

Although both Reference and Policy scenarios have similar directional effects on yields in many, but not all (e.g., dryland hay and cotton), cases, our focus here is on the difference between the scenarios. Our study differs from Nelson <em="">et al</em> (<a xmlns:xlink="http://www.w3.org/1999/xlink" class="cite" href="#erl518630bib34" id="fnref-erl518630bib34"="">2013</a>), for instance, in that we are focusing on the benefits of stabilization. Thus, while both Policy and Reference cases may lead to negative impacts on crop yields, consistent with their findings on potential yield effects under climate change, our focus on the benefits of <em="">reducing</em> the impacts of climate change results in an improvement in yields relative to unabated climate change. Table <a xmlns:xlink="http://www.w3.org/1999/xlink" class="tabref" href="#erl518630t1"="">1</a> summarizes the relative difference in yields for major crops under the Policy case relative to the Reference case after the biophysical yield effects in EPIC (discussed above) are incorporated into FASOM-GHG, which determines production practices (e.g. irrigation) and the regional distribution of production based on economic optimization. Therefore, these yield effects reflect both the biophysical differences in yields and the net adjustments in production practices (e.g., irrigation) and regional distribution of production simulated in FASOM-GHG. Overall, the differences in yields tend to be fairly consistent in the direction of impacts across the IGSM-CAM and MIROC climate models, but yield impacts for dryland crops tend to be less positive or more negative under IGSM-CAM. This is consistent with the wetter model finding less negative climate change impacts and conversely, smaller benefits of avoiding climate change. Although the Policy case generally exhibits increased yields under both GCMs, IGSM-CAM and MIROC are consistent in simulating negative impacts for dryland wheat and sorghum and irrigated cotton by the end of the century. The crop that experiences the largest difference in yield impacts between the GCMs is dryland hay, which has rising yields over time with the Policy under MIROC but falling yields over time with the Policy under IGSM-CAM.

Although our primary focus in this study is on the results with CO<sub="">2</sub> fertilization, because the literature finds there will be benefits of higher CO<sub="">2</sub> concentrations for plant growth (Ainsworth and Long <a xmlns:xlink="http://www.w3.org/1999/xlink" class="cite" href="#erl518630bib4" id="fnref-erl518630bib4"="">2005</a>, Sharkey <em="">et al</em> <a xmlns:xlink="http://www.w3.org/1999/xlink" class="cite" href="#erl518630bib43" id="fnref-erl518630bib43"="">2007</a>), we also conducted a sensitivity analysis to explore the impacts of climate change in isolation from CO<sub="">2</sub> benefits. However, we found relatively small differences in the benefits of the stabilization scenario for yields with both increases and decreases in the potential yield benefits across individual crops. Refer to the supplemental online material for selected results from the EPIC scenarios simulated without CO<sub="">2</sub> fertilization.

Forest productivity generally increases with climate change in our simulations, rising under both Reference and Policy cases compared with fixed climate. The yield gains using the IGSM-CAM are larger under the Reference than the Policy, especially for hardwoods, indicating that abatement would reduce yields compared with the Reference (see figure <a xmlns:xlink="http://www.w3.org/1999/xlink" href="#erl518630f2"="">2</a>). One potential driver of this higher forest productivity under the IGSM-CAM Reference case in the future is likely the enhanced positive effects of CO<sub="">2</sub> fertilization along with the response to increases in precipitation that take place across much of the forested area in the US. The MIROC climate projections, on the other hand, have higher hardwood yields under the Reference case through 2050, with abatement resulting in higher yields in 2060 and beyond. For softwoods, MIROC results show little differentiation until 2045, when abatement begins to result in higher yield growth and maintains that advantage through 2100. These gains from the Policy case in later years may stem from the increasingly negative effects of the hotter and drier future under the MIROC GCM. It is important to note that these yield estimates do not include the effects of wildfire, which would likely decrease simulated productivity, especially under unmitigated climate change (Rogers <em="">et al</em> <a xmlns:xlink="http://www.w3.org/1999/xlink" class="cite" href="#erl518630bib38" id="fnref-erl518630bib38"="">2011</a>, Mills <em="">et al</em> <a xmlns:xlink="http://www.w3.org/1999/xlink" class="cite" href="#erl518630bib32" id="fnref-erl518630bib32"="">2015</a>). Our results on potential changes in forest productivity in the US are much more positive than some European studies. For instance, Hanewinkel <em="">et al</em> (<a xmlns:xlink="http://www.w3.org/1999/xlink" class="cite" href="#erl518630bib19" id="fnref-erl518630bib19"="">2013</a>) finds that between 21%–60% of European forestlands will be suitable only for a low-valued oak forest by the end of the century. One reason for such differences is that we placed more restrictions on the ability of forests to switch types based on standing inventory and economic considerations rather than permitting changes in forest types based primarily on ecological considerations.

Zoom In Zoom Out Reset image size

In contrast, our study of the US has much smaller negative or even net positive impacts on forests due to CO<sub="">2</sub> fertilization that largely outweighs negative climate impacts and reallocation of forests amongst other marketable species. This result is consistent with Kirilenko and Sedjo (<a xmlns:xlink="http://www.w3.org/1999/xlink" class="cite" href="#erl518630bib24" id="fnref-erl518630bib24"="">2007</a>), who examined the potential impacts of climate change on global forestry. They found that there may be sizable impacts on global forestry production due to climate change as forests shifted towards the poles, but that the United States may face relatively small net impacts of climate change on the forestry sector due to the large stock of existing forests across multiple climate zones, rapid technological change in timber production, and the ability to adapt quickly. Susaeta <em="">et al</em> (<a xmlns:xlink="http://www.w3.org/1999/xlink" class="cite" href="#erl518630bib49" id="fnref-erl518630bib49"="">2014</a>) examined the impact of climate change and new disturbance regimes for timber production in the US South and found that the net impacts depended on tradeoffs between increased forest productivity and higher probabilities of loss due to disturbances. When landowners were able to adapt through switching tree species and other adjustments, Susaeta <em="">et al</em> (<a xmlns:xlink="http://www.w3.org/1999/xlink" class="cite" href="#erl518630bib49" id="fnref-erl518630bib49"="">2014</a>) found that it was possible for them to substantially reduce potential losses or even experience net benefits. The fact that our forest yield impacts are relatively small in magnitude and can be higher under the Reference case than Policy case for some region/climate scenario combinations is consistent with our model reflecting the higher productivity associated with climate change, but not capturing the change in disturbances other than through the impact of fire on average yields.

Consistent with the observed differences in yields over time and across crops, global GHG mitigation under the Policy scenario generally results in lower crop prices compared to the Reference case, although there is almost no difference through 2020 and prices do not really start diverging substantially until 2050. The differences in prices becoming more sizable over time is reflective of the increasing climate change impacts on yields and hence the greater impact of avoiding them through stabilization.

A decline in prices does not necessarily have to be the outcome of higher yields, but often results from the greater production efficiency experienced leading to higher production volumes. That is what we are seeing in our scenarios, an increase in production of most commodities due to higher yields even though they are using less land in many cases. As a consequence of higher production and lower prices, US exports become more competitive and exports rise for the majority of traded agricultural commodities. The increased diversion of domestic production to export markets will tend to increase domestic prices relative to having no increase in exports, but we are still seeing domestic prices fall over time.

Under the IGSM-CAM projections, the Policy scenario results in lower crop prices for most of the century for seven out of the nine crops directly simulated using EPIC, with hay and barley being the only crops with consistently higher prices under the Policy case. For the MIROC projections, the Policy scenario results in lower crop prices compared to the Reference case for eight out of the nine crops, with hay being the only crop with higher prices under the Policy case. Figure <a xmlns:xlink="http://www.w3.org/1999/xlink" href="#erl518630f3"="">3</a> presents the crop price index generated using FASOM-GHG, which is a weighted average of all crop price changes in the model. As would be expected, generally rising yields under the Policy case relative to the Reference case result in less pressure on land resources and declining commodity prices. Because MIROC has more positive impacts on yields overall, it is not too surprising that the crop price index declines more under MIROC than under IGSM-CAM.

Zoom In Zoom Out Reset image size

The differences in expected yields and prices as well as in yield and price variability for both crops and forests between the Policy and Reference scenarios result in differences in acreage allocation. Figure <a xmlns:xlink="http://www.w3.org/1999/xlink" href="#erl518630f4"="">4</a> presents the average simulated differences in land cover at the regional level under both IGSM-CAM and MIROC climate conditions over the latter half of this century. Overall, the higher crop yields experienced under the Policy case compared with the Reference result in less cropland being brought into production relative to the Reference case under MIROC climate conditions, as less land is required to meet demand. Cropland tends to convert to cropland pasture and forests, with most of the cropland reductions and forest increases concentrated in the Midwest region. Under IGSM-CAM climate conditions, there is a small net increase in cropland, occurring primarily in the Plains region, while the Midwest experiences a decline in cropland. Forest productivity is relatively higher under the Policy scenario under MIROC climate conditions but is just the opposite for IGSM-CAM, which influences decisions about land conversion based on the relative profitability of alternative land uses.

Zoom In Zoom Out Reset image size

The changes experienced within individual regions are up to a few million acres, but are generally relatively small in percentage terms given the large US land area. Most changes in land cover are less than 1%, though there are some exceptions such as cropland pasture in the Plains region, which declines by almost 13% in the Policy relative to the Reference as that land shifts into cropland.

Reallocation of land between cropland and other uses has implications for the provision of ecosystem services, including promoting habitat and biodiversity (Lawler <em="">et al</em> <a xmlns:xlink="http://www.w3.org/1999/xlink" class="cite" href="#erl518630bib25" id="fnref-erl518630bib25"="">2014</a>). Given the large quantities of carbon sequestered in forests, even relatively small land movements can have sizable impacts on net GHG emissions. In addition, given that we are finding increases in crop yields under the Policy case relative to the Reference case that result in reduced demand for cropland, global stabilization may result in an increased provision of ecosystem services and wildlife habitat relative to unabated climate change. This is particularly true in the Midwest, which experiences the largest reduction in cropland. For instance, the prairie pothole region, which is important habitat for migratory birds, would potentially face less pressure to convert natural lands to cropland in the Policy case compared with the Reference case.

We find substantial reallocation of where crops are grown as an adaptation in response to climate change impacts on relative agricultural productivity, consistent with adjustments found by Adams <em="">et al</em> (<a xmlns:xlink="http://www.w3.org/1999/xlink" class="cite" href="#erl518630bib3" id="fnref-erl518630bib3"="">1990</a>, <a xmlns:xlink="http://www.w3.org/1999/xlink" class="cite" href="#erl518630bib2" id="fnref-erl518630bib2"="">2004</a>, <a xmlns:xlink="http://www.w3.org/1999/xlink" class="cite" href="#erl518630bib1" id="fnref-erl518630bib1"="">2005</a>) and Reilly <em="">et al</em> (<a xmlns:xlink="http://www.w3.org/1999/xlink" class="cite" href="#erl518630bib37" id="fnref-erl518630bib37"="">2003</a>) (see figure <a xmlns:xlink="http://www.w3.org/1999/xlink" href="#erl518630f5"="">5</a>). This effect is overshadowed by a general reduction in crop area, however, due to increasing yields under the Policy case relative to the Reference that results in a lower overall demand for land. Switching between crops varies across regions as production of major crops other than wheat tends to decrease substantially in the major production center of the Midwest and shift to the Northeast, Plains, and Southern United States. Under IGSM-CAM, there is movement of cotton from the Midwest to the Plains region and 'other crops' moving from the Midwest and Plains regions to the Northeast and Southern regions. Hay and wheat tend to be expanding in multiple regions as they have relatively low or even negative yield impacts under the Policy case, resulting in a demand for more land to meet demand for those commodities. Under MIROC, we see sorghum moving from the Western US, Midwest, and Northeast into the Plains region, while 'other crop' shift from the Midwest and Northeast to the other three regions. As with IGSM-CAM, wheat is again increasing in most regions given its relative lack of yield benefit from the Policy case. Hay, on the other hand, experiences a net decrease in area under MIROC, with a large decrease in the Midwest and only small increases in the Plains and Northeast as the net increase in hay yields results in decreased demand for hay land area relative to the Reference.

Zoom In Zoom Out Reset image size

As is readily apparent from figure <a xmlns:xlink="http://www.w3.org/1999/xlink" href="#erl518630f6"="">6</a>, the IGSM-CAM results show large increases in net GHG emissions from agriculture and forestry under the IGSM-CAM Policy case, almost all of which are due to differences in forest management (i.e., net carbon losses or reduced gains in carbon storage on existing forestland)<sup xmlns:xlink="http://www.w3.org/1999/xlink" class="fnref"=""><a class="fnref" href="#erl518630fn17"="">24</a></sup> . Under the IGSM-CAM projections, the generally lower forest productivity under the Policy scenario results in substantially less forest carbon sequestration over time because of the direct effects of slower forest growth (less sequestration). Under MIROC climate conditions, however, forests become a larger sink under the Policy than occurs in the Reference case as forest productivity is enhanced. Emissions from livestock rise under the Policy case for both GCMs as higher crop yields result in lower feed prices and expanded livestock production, but GHG emissions related to crop production are generally declining as less area is devoted to crops, given the higher yields. As is often the case in assessing agricultural and forestry sector emissions, changes in forest carbon sequestration greatly outweigh all other sources. In this case, differences in forest productivity effects between the GCMs analyzed yield opposite signs for the change in net GHG emissions with GHG stabilization Policy.

Zoom In Zoom Out Reset image size

One clear implication is that the climate model used has a profound influence on the estimated change in net GHG emissions. Our findings are consistent with a major difference in precipitation patterns observed across these models. While the MIROC model tends to have more precipitation under the Policy case than the Reference case for many key forest-growing regions, the IGSM-CAM model is just the opposite. The effect on forest growth rates and associated carbon storage is reflected very clearly in figure <a xmlns:xlink="http://www.w3.org/1999/xlink" href="#erl518630f6"="">6</a>. While climate conditions such as those under IGSM-CAM would reduce the US LULUCF sink, conditions such as those under MIROC would increase it, other things being equal.

Figure <a xmlns:xlink="http://www.w3.org/1999/xlink" href="#erl518630f6"="">6</a> is showing only the change in cumulative emissions, however, not the absolute emissions. Total US GHG emissions from agriculture and forestry, land use, and land use change are estimated at about −343 MT CO<sub="">2</sub>eq in 2013 (USEPA <a xmlns:xlink="http://www.w3.org/1999/xlink" class="cite" href="#erl518630bib54" id="fnref-erl518630bib54"="">2015</a>) (forestry sink more than outweighs emissions so net emissions from those sectors are currently negative). FASOM-GHG does not fully capture all emissions components that are in the US inventory, but based on the average cumulative changes from 2015–2100 (cumulative net source of 5394 MT CO<sub="">2</sub>eq for Policy relative to Reference over 90 years, or average of 59.9 MT CO<sub="">2</sub>eq increase in net GHG emissions per year over that timeframe), the average change under the Policy with IGSM-CAM is equal to a reduction in the 2013 net sink by about 15–20%. Under MIROC, on the other hand, the Policy scenario provides a cumulative net sink of about 7685 MT CO<sub="">2</sub>eq, or an average sink of about 85.4 MT CO<sub="">2</sub>eq, which is equal to about 40% of the US sink from these sectors in 2013. Other things being equal, the effect of the Policy on net US GHG emissions will become larger over time as seen in figure <a xmlns:xlink="http://www.w3.org/1999/xlink" href="#erl518630f6"="">6</a>, though the percentage impact will depend on the evolution of the US inventory over time.

The differences in prices and the level of production and consumption will lead to impacts on the economic welfare of consumers and commodity producers. One common method of measuring these effects on well-being is in terms of differences in economic surplus, which is a monetary measure of welfare. Consumers' surplus is the difference between the maximum amount consumers would have been willing to pay for a commodity and the actual price paid, whereas producers' surplus is the difference between the minimum amount producers would have accepted to supply a commodity and the actual price received.

Overall, the results of this study show that stabilization results in transfers from producers to consumers as commodity prices decline for goods with relatively inelastic demands. Although the magnitude and temporal distribution of benefits under the Policy case vary across the IGSM-CAM model initializations and MIROC projection, each of our scenarios results in cumulative net gains to consumers that more than outweigh any losses to producers, as has been found in prior studies (e.g., Irland <em="">et al</em> <a xmlns:xlink="http://www.w3.org/1999/xlink" class="cite" href="#erl518630bib23" id="fnref-erl518630bib23"="">2001</a>). Based on the MC1 and EPIC simulated impacts on forest and agricultural productivity, the Policy case results in sizable increases in overall yields and reduced commodity prices compared to unabated climate change in the Reference case. As shown in tables <a xmlns:xlink="http://www.w3.org/1999/xlink" class="tabref" href="#erl518630t2"="">2</a> and <a xmlns:xlink="http://www.w3.org/1999/xlink" class="tabref" href="#erl518630t3"="">3</a>, reducing global GHG emissions under the Policy case is found to increase total surplus in the forest and agriculture sector by a cumulative $32.7 billion to $54.5 billion for the 2015–2100 period across the two GCMs and the different model initializations of IGSM-CAM. The magnitude of welfare impacts in the agricultural sector are much larger than in the forestry sector.

There have been several previous studies examining the potential economic impacts of climate change on US agriculture, but many focus only on major crops, they do not necessarily report national welfare values, and they do not explicitly examine the benefits of mitigation. For instance, Reilly <em="">et al</em> (<a xmlns:xlink="http://www.w3.org/1999/xlink" class="cite" href="#erl518630bib37" id="fnref-erl518630bib37"="">2003</a>), using predictions from GCMs from the Canadian Center Climate Model and Hadley Centre Model found that climate change from 2030–2090 would have positive impacts on welfare ranging between $0.8 billion (2000 $) in 2030 and $12.2 billion in 2090. The main driver of these productivity gains were projected increases in precipitation levels under climate change. Greenstone and Deschenes (<a xmlns:xlink="http://www.w3.org/1999/xlink" class="cite" href="#erl518630bib18" id="fnref-erl518630bib18"="">2007</a>) also found positive climate change impacts using the Hadley model, estimating an increase of $1.3 billion. Schlenker <em="">et al</em> (<a xmlns:xlink="http://www.w3.org/1999/xlink" class="cite" href="#erl518630bib40" id="fnref-erl518630bib40"="">2005</a>) find much more negative impacts, estimating that economic effects of climate change on agriculture need to be assessed differently in dryland and irrigated areas and that pooling the dryland and irrigated counties could potentially yield biased estimates. Based on available data, they estimate the model for dryland counties and the estimated annual losses are about $5 to $5.3 billion for dryland non-urban counties alone. Temperature increases affect crop responses in a non-linear fashion. Using a 55 year panel data on crop yields, Schlenker and Roberts (<a xmlns:xlink="http://www.w3.org/1999/xlink" class="cite" href="#erl518630bib42" id="fnref-erl518630bib42"="">2006</a>) found increases in crop yields (for corn, soybeans, and cotton) with higher temperatures until reaching threshold values. Their results show very large decreases in crop yields toward the end of the century as temperatures exceed these threshold levels. The study estimates that yields of these three crops are expected to decline by 25–44% under a slow warming scenario and 60–79%, respectively, under a quick warming scenario at the end of the century. Thus, the negative effects on agriculture could become very large in the long-term future if temperatures begin to reach threshold levels.

In general, the findings from previous studies have depended heavily on the net changes in precipitation experienced under climate change, but the welfare results from this study fall within the general range of estimates that have been generated.