Our understanding of the relationship between the decomposition of soil organic matter (SOM) and soil temperature affects our predictions of the impact of climate change on soil-stored carbon. One current opinion is that the decomposition of soil labile carbon is sensitive to temperature variation whereas resistant components are insensitive. The resistant carbon or organic matter in mineral soil is then assumed to be unresponsive to global warming. But the global pattern and magnitude of the predicted future soil carbon stock will mainly rely on the temperature sensitivity of these resistant carbon pools. To investigate this sensitivity, we have incubated soils under changing temperature. Here we report that SOM decomposition or soil basal respiration rate was significantly affected by changes in SOM components associated with soil depth, sampling method and incubation time. We find, however, that the temperature sensitivity for SOM decomposition was not affected, suggesting that the temperature sensitivity for resistant organic matter pools does not differ significantly from that of labile pools, and that both types of SOM will therefore respond similarly to global warming.

a b s t r a c tAfrican farming systems are highly heterogeneous: between agroecological and socioeconomic environments, in the wide variability in farmers' resource endowments and in farm management. This means that single solutions (or 'silver bullets') for improving farm productivity do not exist. Yet to date few approaches to understand constraints and explore options for change have tackled the bewildering complexity of African farming systems. In this paper we describe the Nutrient Use in Animal and Cropping systems -Efficiencies and Scales (NUANCES) framework. NUANCES offers a structured approach to unravel and understand the complexity of African farming to identify what we term 'best-fit' technologiestechnologies targeted to specific types of farmers and to specific niches within their farms. The NUANCES framework is not 'just another computer model'! We combine the tools of systems analysis and experimentation, detailed field observations and surveys, incorporate expert knowledge (local knowledge and results of research), generate databases, and apply simulation models to analyse performance of farms, and the impacts of introducing new technologies. We have analysed and described complexity of farming systems, their external drivers and some of the mechanisms that result in (in)efficient use of scarce resources. Studying sites across sub-Saharan Africa has provided insights in the trajectories of change in farming systems in response to population growth, economic conditions and climate variability (cycles of drier and wetter years) and climate change. In regions where human population is dense and land scarce, farm typologies have proven useful to target technologies between farmers of different production objectives and resource endowment (notably in terms of land, labour and capacity for investment). In such regions we could categorise types of fields on the basis of their responsiveness to soil improving technologies along soil fertility gradients, relying on local indicators to differentiate those that may be managed through 'maintenance fertilization' from fields that are highly-responsive to fertilizers and fields that require rehabilitation before yields can improved. Where human population pressure on the land is less intense, farm and field types are harder to discern, without clear patterns. Nutrient cycling through livestock is in principle not efficient for increasing food production due to increased nutrient losses, but is attractive for farmers due to the multiple functions of livestock. We identified trade-offs between income generation, soil conservation and community agreements through optimising concurrent objectives at farm and village levels. These examples show that future analyses must focus at farm and farming system level and not at the level of individual fields to achieve appropriate targeting of technologies -both between locations and between farms at any given location. The approach for integrated assessment described here can be used ex ante to explore the potential of bes...

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To predict the response of C-rich soils to external change, models are needed that accurately reflect the conditions of these soils. Estimation of Carbon in Organic Soils -Sequestration and Emissions (ECOSSE) is a model that allows simulations of soil C and N turnover in both mineral and organic soils using only the limited meteorological, land-use and soil data that is available at the national scale. Because it is able to function at field as well as national scales if appropriate input data are used, field-scale evaluations can be used to determine uncertainty in national simulations. Here we present an evaluation of the uncertainty expected in national-scale simulations of Scotland, using data from the National Soil Inventory of Scotland. This data set provides measurements of C change for the range of soils, climates and land-use types found across Scotland. The simulated values show a high degree of association with the measurements in both total C and change in C content of the soil. Over all sites where land-use change occurred, the average deviation between the simulated and measured values of percentage change in soil C was less than the experimental error (11% simulation error, 53% measurement error). This suggests that the uncertainty in the national-scale simulations will be ~11%. Only a small bias in the simulations was observed compared to the measured values, suggesting that a small underestimate of the change in soil C should be expected at the national scale (-4%).