Agricultural lands occupy 37% of the earth's land surface. Agriculture accounts for 52 and 84% of global anthropogenic methane and nitrous oxide emissions. Agricultural soils may also act as a sink or source for CO 2 , but the net flux is small. Many agricultural practices can potentially mitigate greenhouse gas (GHG) emissions, the most prominent of which are improved cropland and grazing land management and restoration of degraded lands and cultivated organic soils. Lower, but still significant mitigation potential is provided by water and rice management, set-aside, land use change and agroforestry, livestock management and manure management. The global technical mitigation potential from agriculture (excluding fossil fuel offsets from biomass) by 2030, considering all gases, is estimated to be approximately 5500-6000 Mt CO 2 -eq. yr K1 , with economic potentials of approximately 1500-1600, 2500-2700 and 4000-4300 Mt CO 2 -eq. yr K1 at carbon prices of up to 20, up to 50 and up to 100 US$ t CO 2 -eq. K1 , respectively. In addition, GHG emissions could be reduced by substitution of fossil fuels for energy production by agricultural feedstocks (e.g. crop residues, dung and dedicated energy crops). The economic mitigation potential of biomass energy from agriculture is estimated to be 640, 2240 and 16 000 Mt CO 2 -eq. yr K1 at 0-20, 0-50 and 0-100 US$ t CO 2 -eq. K1 , respectively.

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Abstract. The stability of soil organic matter (SOM) is a major source of uncertainty in predicting atmospheric CO 2 concentration during the 21st century. Isolating the stable soil carbon (C) from other, more labile, C fractions in soil is of prime importance for calibrating soil C simulation models, and gaining insights into the mechanisms that lead to soil C stability. Long-term experiments with continuous bare fallow (vegetation-free) treatments in which the decay of soil C is monitored for decades after all inputs of C have stopped, provide a unique opportunity to assess the quantity of stable soil C. We analyzed data from six bare fallow experiments of long-duration (>30 yrs), covering a range of soil types and climate conditions, and sited at Askov (Denmark), Grignon and Versailles (France), Kursk (Russia), Rothamsted (UK), and Ultuna (Sweden). A conceptual three pool model dividing soil C into a labile pool (turnover time of a several years), an intermediate pool (turnover time of a several decades) and a stable pool (turnover time of a several centuries or more) fits well with the long term C decline observed in the bare fallow soils. The estimate of stable C ranged from 2.7 g C kg −1 at Rothamsted to 6.8 g C kg −1 at Grignon. The uncertainty Correspondence to: P. Barré (barre@geologie.ens.fr) associated with estimates of the stable pool was large due to the short duration of the fallow treatments relative to the turnover time of stable soil C. At Versailles, where there is least uncertainty associated with the determination of a stable pool, the soil contains predominantly stable C after 80 years of continuous bare fallow. Such a site represents a unique research platform for characterization of the nature of stable SOM and its vulnerability to global change.

Aim Recent changes in crop yields have implications for future global food security, which are likely to be affected by climate change. We developed a spatially explicit global dataset of historical yields for maize, soybean, rice and wheat to explore the historical changes in mean, year-to-year variation and annual rate of change in yields for the period 1982-2006. Location This study was conducted at the global scale. MethodsWe modelled historical and spatial patterns of yields at a grid size of 1.125°by combining global agricultural datasets related to the crop calendar and harvested area in 2000, country yield statistics and satellite-derived net primary production. Modelled yields were compared with other global datasets of yields in 2000 (M3-Crops and MapSPAM) and subnational yield statistics for 23 major crop-producing countries. Historical changes in modelled yields were then examined.Results Modelled yields explained 45-81% of the spatial variation of yields in 2000 from M3-Crops and MapSPAM, with root-mean-square errors of 0.5-1.8 t ha −1 . Most correlation coefficients between modelled yield time series and subnational yield statistics for the period 1982-2006 in major crop-producing regions were greater than 0.8. Our analysis corroborated the incidence of reported yield stagnations and collapses and showed that low and mid latitudes in the Southern Hemisphere (0-40°S) experienced significantly increased year-to-year variation in maize, rice and wheat yields in 1994-2006 compared with that in 1982-93. Main conclusions Our analyses revealed increased instability of yields across a broad region of the Southern Hemisphere, where many developing countries are located. Such changes are likely to be related to recent yield stagnation and collapses. Although our understanding of the impacts of recent climate change, particularly the incidence of climate extremes, on crop yields remains limited, our dataset offers opportunities to close parts of this knowledge gap.

An approach to derive relationships for defining land degradation and desertification risk and developing appropriate tools for assessing the effectiveness of the various land management practices using indicators is presented in the present paper. In order to investigate which indicators are most effective in assessing the level of desertification risk, a total of 70 candidate indicators was selected providing information for the biophysical environment, socio-economic conditions, and land management characteristics. The indicators were defined in 1,672 field sites located in 17 study areas in the Mediterranean region, Eastern Europe, Latin America, Africa, and Asia. Based on an existing geo-referenced database, classes were designated for each indicator and a sensitivity score to desertification was assigned to each class based on existing research. The obtained data were analyzed for the various processes of land degradation at farm level. The derived methodology was assessed using independent indicators, such as the measured soil erosion rate, and the organic matter content of the soil. Based on regression analyses, the collected indicator set can be reduced to a number of effective indicators ranging from 8 to 17 in the various processes of land degradation. Among the most important indicators identified as affecting land degradation and desertification risk were rain seasonality, slope gradient, plant cover, rate of land abandonment, land-use intensity, and the level of policy implementation.