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Fig. 5.4 Impact of climate change under the IPCC SRES A1F1 and B1A scenarios (2080s) on C sequestration potential of UK arable soils using the RothCUK model. FYM = additional 10tha-1yr-1 farmyard manure, STRAW = additional 5tha-1yr-1 cereal straw, A1F and B1A = IPCC SRES HadCM3 A1F1 and B1A climate change scenarios for the 2080s respectively. SUM = calculated by summing values from runs changing only climate or land management changes in NPP calculated by the Lund-Potsdam-Jena model, and land-use change scenarios derived from the IPCC SRES story lines. While climate effects were predicted to decrease carbon stocks, the predicted increases in NPP across Europe (and hence carbon input) and technological improvements were predicted to slow these losses. Considering all these factors, Smith et al. (2005) found European cropland soils to show a small (1-7tCha-1) increase in soil carbon on per unit area basis under future climate. Accounting for the large predicted decrease in cropland area, total European cropland C stocks is expected to decline in all scenarios from 11 Pg in 1990 by 4-6Pg (39-54%) by 2080.

Smith et al. (2005) assumed that changes in carbon inputs to the soil were proportional to increases in NPP and hence, the influence of NPP on C inputs in their study should be regarded as the maximum possible. The range of possible changes in C returns, due to changes in yield associated with improvements in technology, was calculated using equations for the lowest (winter wheat) and highest (winter oilseed rape) soil carbon return per unit yield increase (Smith et al. 1996). Under the B1 scenario (2050), a 62% change in yield resulted in a maximum and minimum change in carbon return of 19 and 6%, respectively. The predicted SOC stocks under each assumption differed by about 9%, equivalent to an uncertainty of 1 Pg for cropland soils. Uncertainty of the impact of NPP on soil C inputs was 9% (1 Pg) of croplands SOC estimates. Further, an un-quantified uncertainty was introduced in the NPP estimates, as NPP estimates by LPJ were not nitrogen limited, meaning that the CO2 response could have been overestimated. However, this potential uncertainty fell within the quantified uncertainty range for NPP given above, where the effect could range from 0 to the impact predicted (assuming C returns to the soil increase in proportion to NPP). This study demonstrates considerable regional uncertainties in the response of C inputs, and soil C changes under climate change (Smith et al. 2005).

Felzer et al. (2005) used the TEM terrestrial ecosystem model to assess future ozone and climate impacts on global carbon sequestration, including the impacts of CO2 fertilization, but excluding land use changes. They predicted overall gains in global cropland carbon storage due to climate change by 2100, although ozone damage to crops could significantly offset these increases, with the largest damages occurring in the Southeast and Midwestern regions of the United States, Eastern Europe, and Eastern China.

5.3.2 Uncertainties in Climate Impacts on Cropland Greenhouse Gas Mitigation Potential

Uncertainties in climate impacts on cropland GHG mitigation potential could arise from uncertainties in (a) climate change, (b) how cropland GHG fluxes respond to climate change, (c) climate impacts on land use and management changes, (d) and other uncertainties/processes. Uncertainties in climate change could also arise from missing or incomplete descriptions of processes in GCMs, uncertainties in future emissions of greenhouse gases, uncertainties in GCM parameters, or differences between models. The latter two can be investigated using multi-member ensemble runs of the same climate model run with different parameter sets (Murphy et al. 2004), and by inter-model comparisons (Friedlingstein et al. 2006). Falloon et al. (2006b) used data from four members of a large multi-member climate model parameter perturbation ensemble with the RothC soil carbon model to assess uncertainties in climate impacts on global soil carbon storage under natural vegetation. Global soil carbon changes under climate change were found to cover both small gains and large losses (mean 91.5 Pg C, range 356.8 Pg C) depending on the climate sensitivity of the ensemble member (Fig. 5.5), with the role of litter inputs (NPP) dominating the response of soil carbon globally and regionally, and temperature and moisture playing a smaller role globally. Smith et al. (2005) found uncertainty of 4.5% in European cropland soil carbon response to climate due to differences in the climate produced by different climate models. Uncertainties in future emissions of greenhouse gases can be investigated using a wide range of emissions scenarios (IPCC 2000). Whilst a range of responses of soil C changes to emissions scenarios of up to 50% were found by Smith et al. (2005) and Falloon (2004), different scenarios are generally considered more likely to affect the magnitude of response rather than the pattern or sign of change (Falloon et al. 2007a).

The main uncertainties in cropland GHG flux responses to climate change relate to how GHG fluxes respond to changes in temperature, moisture and carbon and

Member 3, 2xC02 - 1XC02

Member 4, 2xC02 - 1XC02

Member 3, 2xC02 - 1XC02

Member 4, 2xC02 - 1XC02

Relationships Global Carbon Pool
Fig. 5.5 Changes in global soil carbon stocks (kg C m-2) from four different HadCM3 single parameter perturbation ensemble members - difference between 2xCO2 and 1xCO2

nitrogen inputs to soils. There is presently no consensus on the temperature sensitivity of soil carbon stocks (Davidson and Janssens 2006) and therefore, considerable and unquantifiable uncertainty. However, Knorr et al. (2005) suggest that these conflicting opinions are compatible with long-term temperature sensitivity of SOC turnover and may be explained by rapid depletion of labile SOC combined with the negligible response of non-labile SOC on experimental time scales. Since non-labile SOC may be more sensitive to temperature than labile SOC, the long-term positive feedback of soil decomposition could be even stronger than predicted by global models (Knorr et al. 2005). While the impact of soil temperature on soil carbon storage has been the subject of considerable debate (Giardina and Ryan 2000; Fang et al. 2005; Knorr et al. 2005; Davidson and Janssens 2006), the influence of soil moisture on large-scale soil carbon stocks has received little attention used twelve soil moisture-respiration functions with the RothC soil carbon model and data from a coupled-climate carbon cycle GCM to investigate the impact of heterotrophic respiration dependent on soil moisture on the global climate-carbon cycle feedback under natural vegetation. Considerable uncertainty in the soil carbon changes was found due to response of soil respiration to soil moisture - the range of global soil carbon changes from 1860 to 2100 was 71.8 PgC (minimum —54.1 PgC, maximum 17.7PgC), considering temperature, moisture and litter changes, and 60.9PgC (minimum —17.8 Pg C, maximum 43.1 Pg C) considering only changes in moisture (Fig. 5.6).

In contrast, the response of soil carbon to changes in C and N inputs is well understood. Although it is difficult to directly measure total C and N inputs to soil, since there are both above and below ground components (Falloon 2001) and there is a strong linear relationship between soil C storage and C inputs (Paustian et al. 1997; Buyanovsky and Wagner 1998), up to a certain 'saturation limit'. Errors in predicted SOC values, because of uncertainties in the size and quality of C inputs, have been assessed by Falloon et al. (1998) and Falloon (2001). Using data from a global network of long-term experiments and the RothC soil carbon model, Falloon et al. (1998) found that C inputs accounted for 60% of the variance in soil C stocks. SOC values were found to be more sensitive to C input quantity than quality, and C input quality was most important for forest systems with relatively small impacts on cropland SOC estimates (Falloon 2001). Halving or doubling the quantity of C inputs, when simulating a 120-year experiment, resulted in differences in SOC of 41 and +80%, respectively, reducing the fit to measured data by 28-71%, and an error of 10% in C inputs could result in an error of 14.35% in SOC stocks. Altering the quality of C inputs resulted in errors in modelled SOC of 0.2-11.6%, similar to the errors in C input of 5-8% calculated by Jenkinson et al. (1999). Long-term data sets of changes in SOC are often used to evaluate SOC models and estimate C inputs. However, most long-term data sets have only mean SOC values and no estimate of the error about the mean. Falloon and Smith (2003b) showed that when using data sets that do not include estimates of the error about the mean, it is not possible to reduce the error between modelled and measured SOC below 6.5-8.5% even with site specific calibration; equivalent errors for model runs using regional default C input values were 12-34%.

Fig. 5.6 Changes in global total soil carbon from 1860 values using the RothC model and different soil moisture-respiration functions driven by HadCM3LC outputs changing (a) all forcings (soil temperature, moisture and plant carbon inputs) and (b) soil moisture only

There is considerable uncertainty in future changes in C and N inputs to soils, which depend on both how crops respond to climate change and on changes in land use, crop technology and management. Betts et al. (2006) examined the uncertainties in natural vegetation responses to climate change, diagnosing Net Primary Productivity from the QUMP ensemble. Although a global mean increase in NPP under doubled-CO2 climate change was predicted, the climate-related uncertainty

(5-95 percentile range > 0.6 kg Cm-2 yr-1) was larger than the mean change in many regions. NPP in most regions was considered likely to increase, although decreases were simulated in a small number of cases. This was due to the impact of elevated CO2 on NPP - the impact of climate on NPP is uncertain (Friedlingstein et al. 2006). In Amazonia, NPP decreases in parts of the basin in all simulations with HadCM3 were found, with the geographical extent of the NPP varying very widely between simulations. Challinor et al. (2005) investigated uncertainty in crop responses over India to doubled CO2 using the same four ensemble members used by Falloon et al. (2006b), described earlier in this chapter. In all four ensemble members, the impact of as doubled CO2 climate on the mean and standard deviation of yield was marked. Differences in simulated yields across ensemble members varied geographically, while differences in the inter-annual mean, and especially the standard deviation, varied in magnitude and sign across grid cells. Variations were greater in the doubled-CO2 climate than in the present-day climate. Uncertainty in crop model parameters also led to marked differences in crop yield. However, the impact of crop parameter perturbation was more spatially systematic than that of climate parameter perturbation. Challinor et al. (2005) concluded that the major causes of uncertainty in the simulation of crop yield under doubled CO2 included uncertainty in temperature (particularly how crop duration responds to increases in temperature), and in the CO2 fertilization effect. As discussed above, the relationship between changes in NPP and C inputs under climate change is complex, since it involves both biological and technological aspects, and hence, a further source of uncertainty exits.

Uncertainties in climate impacts on future land use and management could have significant impacts on cropland GHG fluxes. Since future changes in land use, management and crop technology are dependent on patterns of future development, it is extremely difficult to quantify the likelihood of any particular scenario. Despite this, in the European scale studies of Smith et al. (2005), uncertainty associated with the land-use and technology scenarios was not quantified, but worst-case quantified uncertainties were 22.5% for croplands, equivalent to a potential errors in C sequestration of 2.5 Pg SOC, or 42-63% of the predicted SOC stock change. In UK-scale recent studies (1990-2000), land use impacts on soil carbon, uncertainty in input soil, climate and land use databases were considered likely to be <25%, although a tentative overall uncertainty range of 50-100% was considered reasonable (Falloon et al. 2006a). Climate change could also limit the feasibility of different land management options (Falloon et al. 2007a). For instance, if the predicted dieback of the Amazon forest (Cox et al. 2000, 2004; Betts et al. 2004; Cowling et al. 2004; Scholze et al. 2006) is correct, then clearly land management options favouring forestry will be severely limited in the Amazon region. Considerable reductions in forest cover are also expected due to the expansion of logging schemes and clearance of land for agricultural expansion (Laurance et al. 2004).

A remaining source of uncertainty is the biophysical impact of land use changes on the climate itself. Plans and strategies for adaptation to climate change require specific and local details of climate change. Although this can be provided by regional climate models, these are typically only used for down-scaling of radiatively-forced global climate change. Significant works (Lean and Warrilow 1989; Betts 2001; Feddema et al. 2001; Betts et al. 2007b) demonstrate that the biophysical effects of land use change are also of large importance. Land use change, particularly deforestation or reforestation, can exert significant impacts on local climates by influencing surface albedo and evaporation. Deforestation in temperate regions leads to a cooling through increased surface albedo (Betts 2001), while continued tropical deforestation is expected to lead to a warming and drying of local climate (Lean and Warrilow 1989). Biogeophysical effects of the Amazon forest dieback are important locally, acting to further reduce rainfall (Betts et al. 2004). Scenarios of greenhouse gas emissions implicitly assume changes in land use, but the direct effects of these changes are often not considered in climate change projections. Feddema et al. (2001) showed that the projected climate change, in some regions, can be significantly affected by the assumed land cover change associated with the emissions scenario. For example, the SRES B1 scenario implies reforestation in mid-latitudes and relatively little tropical deforestation, whereas the A2 scenario implies less mid-latitude reforestation, but extensive tropical deforestation. These differences in projected land cover lead to significant variations in the predicted climate change at regional scales.

Regional climate change studies used for impacts assessments should, therefore, consider uncertainties associated with land cover changes and their biophysical effects. This is particularly important for assessments of agricultural impacts, since consistency between land use and the overlying climate will be crucial (Betts 2005). For instance, Falloon and Betts (2006) showed that land use changes alone could have significant impacts on regional river flows, which would impact the feasibility of GHG mitigation options involving irrigation. Raddatz (2007) also found that agriculture has an impact upon near surface weather elements and regional hydrological cycles, through the physiological and physical properties of the land cover. By changing the availability of energy and water vapour mass for moist deep convection at local and regional scales, and creating latent heat flux discontinuities, agriculture may induce mesoscale circulations that initiate moist deep convection. By altering the level of stored soil moisture, agriculture can also may influence the level of seasonal convective activity within a region (Raddatz 2007).

Irrigation itself can also increase the surface moisture flux and hence reduce the Bowen ratio, exerting a cooling influence on local near-surface temperatures (Betts 2007). Boucher et al. (2004) introduced present-day patterns of irrigation into the Laboratoire de Meteorologie Dynamique (LMD) GCM and found a simulated surface cooling of up to 0.8 K in some regions. Schaeffer et al. (2006) suggested that the use of biofuel may exert a double effect on reducing temperature rise by mitigating CO2 emissions, while maintaining a relatively high surface albedo. Biofuel crops tend to be short in stature and hold less foliage than forests and so the surface albedo of an area of biofuel plantations is higher than a forested landscape. In contrast, since tropical deforestation exerts a warming effect through reduced evapotranspiration (Betts 2007), reforestation (or avoided deforestation) in tropical regions could exert a double cooling effect through carbon sequestration and increased evaporation and cloud cover. Jackson et al. (2005) showed that water resources are directly impacted by forestry activities designed for carbon sequestration. Climate change adaptation plans may, therefore, be inappropriate if based on projections which ignore land use change.

5.4 Conclusions

There is considerable potential for climate mitigation via cropland soil carbon sequestration and GHG reductions. Care should be taken for the choice of alternative land management strategies since there may be additional negative or positive impacts including both socio-economic and environmental aspects. The mitigation potential of cropland management options, which is actually achievable in practice, given economic, political, social, land suitability and other constraints may only be around 10-20% of the biological potential. Alternative cropland management strategies could meet only 2-5% of emissions gaps. However, many such options are most effective during the first 20 years following implementation, so they have a key role to play in any portfolio of emissions reduction measures over the next 20-50 years, while new energy technologies are developed and implemented.

Globally over next 100 years, climate models project temperature increases of 1.4-4.0°C, and significant changes in regional precipitation patterns, whilst CO2 concentrations could reach 2-5 times of present day values. Changes in global land use patterns could occur, with increases and decreases in the present cropland, both possible. While some increases in crop productivity are expected in the short to mid term in Northern mid-high latitudes, globally decreases are likely in the longer term in the absence of adaptation. If present trends towards more harvestable product continue, globally crops are likely to return less C and N to soils due to changes in the harvest index.

GHG fluxes from croplands will be altered by these changes in controlling factors. Temperature increases will generally increase GHG fluxes; the influence of moisture changes will depend on regional patterns of change - for instance, drying alone could increase soil C sequestration. Changes in CO2 concentrations alone are expected to have only small impacts on cropland GHG fluxes. Globally, long-term reduced crop productivity and changing harvest index are likely to reduce C and N inputs to soil, thus reducing soil carbon storage and GHG fluxes in the absence of adaptation measures. The combined impact of these factors is not simply additive, and most holistic systems studies have been conducted for natural ecosystems, not croplands. These studies generally show reductions in global soil carbon storage.

Climate change is likely to reduce cropland soil carbon sequestration and GHG mitigation potential in many regions in the long-term in the absence of mitigation, perhaps with the exception of Northern Hemisphere mid-high latitudes in the short-mid term. Increased ozone concentrations are likely to further reduce GHG mitigation potential, particularly in some of the major global cropping regions. Changes in future cropland GHG mitigation potential are, however, strongly dependent on changes in agricultural technology and crop breeding. Uncertainties in climate change impacts on cropland GHG mitigation potential are large, and derived from several sources including climate predictions, the extent of climate impacts, and changes in future cropland distribution and management. The biophysical impacts of changes in land use and management on climate can be significant, and hence need to be considered with other factors while determining suitable GHG mitigation options for croplands. Current GHG mitigation practices should be, therefore, reassessed to account for both biogeochemical and the biogeophysical forcing, acknowledging the significant opportunities and risks that occur in the complex interactions between agriculture and the environment (Desjardins et al. 2007), as in the preliminary approach of Seguin et al. (2007) using the LPJ model. Research needs include improved representation of croplands in GCMs (Osborne et al. 2007; Bondeau et al. 2007). With fully-coupled crop-climate models such as these, and with adequate account of uncertainty, a robust understanding of soil-atmosphere-crop interactions will emerge. There is a lack of research into the impacts of changes in extreme events on GHG fluxes from croplands, and a need for more detailed information on regional aspects of crop suitability and productivity, technology changes, crop breeding impacts and land use changes is greatly realized. More detailed regional information is particularly critical, if some of the projected regional differences are large in climate, and socio-economic factors. For instance, during the next three decades, Asia will remain the largest global food consumer (consumption could rise from 40 to 55% of the global total between 2000 and 2015) and the largest source of GHG from agriculture (approximately 50% of total emissions). Unless improved management systems are adopted, a substantial increase in GHG emissions from the agri-food sector in Africa and South America will arise due to growing demand for food (Verge et al. 2007).

Regardless of their GHG mitigation potential, maintaining the organic matter content of cropland soils is fundamental in supporting soil quality and sustainable production systems (Bradley et al. 2005), as recognised by the UK Soil Action Plan (Defra 2003) and the EU Thematic Soil Protection Policy (CEC 2004). There are also requirements for nations to maintain and regularly update their Greenhouse Gas Emissions Inventories (GHGEI), category on land use change (LUC) and forestry under the United Nations Framework Convention on Climate Change (UNFCCC) and the EU Monitoring Mechanism for greenhouse gas emissions (for EU members). Thus, by combining information on the impact of both land use change and/or climate change on climate and cropland GHG fluxes, we will be able to make more holistic estimates of the impact of different scenarios of land use in altering regional and global climate. In this way, the relative contribution of land use change and anthropogenic emissions to climate change can be assessed, and land management options may be planned to minimise impacts on the climate. Finally it is important to identify potential synergies between land-based adaptation and mitigation strategies, linking issues of carbon sequestration, emissions of greenhouse gases, land-use change and long-term sustainability of production systems within coherent climate policy frameworks (Smith et al. 2005; Rosenzweig and Tubiello 2007; Easterling et al. 2007). Further, progress in understanding how climate change might impact GHG mitigation potential in croplands, therefore, requires more global, and particularly regional studies taking a holistic systems approach, by including crops and cropland management in earth systems models.

Acknowledgments This work was supported by the Joint Defra and MoD Programme, (Defra) GA01101 (MoD) CBC/2B/0417_Annex C5.

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