Mechanisms to mitigate global climate change by sequestering carbon (C) in different 'sinks' have been proposed as at least temporary measures. Of the major global C pools, terrestrial ecosystems hold the potential to capture and store substantially increased volumes of C in soil organic matter (SOM) through changes in management that are also of benefit to the multitude of ecosystem services that soils provide. This potential can only be realized by determining the amount of SOM stored in soils now, with subsequent quantification of how this is affected by management strategies intended to increase SOM concentrations, and used in soil C models for the prediction of the roles of soils in future climate change. An apparently obvious method to increase C stocks in soils is to augment the soil C pools with the longest mean residence times (MRT). Computer simulation models of soil C dynamics, e.g. RothC and Century, partition these refractory constituents into slow and passive pools with MRTs of centuries to millennia. This partitioning is assumed to reflect: (i) the average biomolecular properties of SOM in the pools with reference to their source in plant litter, (ii) the accessibility of the SOM to decomposer organisms or catalytic enzymes, or (iii) constraints imposed on decomposition by environmental conditions, including soil moisture and temperature. However, contemporary analytical approaches suggest that the chemical composition of these pools is not necessarily predictable because, despite considerable progress with understanding decomposition processes and the role of decomposer organisms, along with refinements in simulation models, little progress has been made in reconciling biochemical properties with the kinetically defined pools. In this review, we will explore how advances in quantitative analytical techniques have redefined the new understanding of SOM dynamics and how this is affecting the development and application of new modelling approaches to soil C.

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The term 'carbon sequestration' is commonly used to describe any increase in soil organic carbon (SOC) content caused by a change in land management, with the implication that increased soil carbon (C) storage mitigates climate change. However, this is only true if the management practice causes an additional net transfer of C from the atmosphere to land. Limitations of C sequestration for climate change mitigation include the following constraints: (i) the quantity of C stored in soil is finite, (ii) the process is reversible and (iii) even if SOC is increased there may be changes in the fluxes of other greenhouse gases, especially nitrous oxide (N 2 O) and methane. Removing land from annual cropping and converting to forest, grassland or perennial crops will remove C from atmospheric CO 2 and genuinely contribute to climate change mitigation. However, indirect effects such as conversion of land elsewhere under native vegetation to agriculture could negate the benefit through increased CO 2 emission. Re-vegetating degraded land, of limited value for food production, avoids this problem. Adding organic materials such as crop residues or animal manure to soil, whilst increasing SOC, generally does not constitute an additional transfer of C from the atmosphere to land, depending on the alternative fate of the residue. Increases in SOC from reduced tillage now appear to be much smaller than previously claimed, at least in temperate regions, and in some situations increased N 2 O emission may negate any increase in stored C. The climate change benefit of increased SOC from enhanced crop growth (for example from the use of fertilizers) must be balanced against greenhouse gas emissions associated with manufacture and use of fertilizer. An over-emphasis on the benefits of soil C sequestration may detract from other measures that are at least as effective in combating climate change, including slowing deforestation and increasing efficiency of N use in order to decrease N 2 O emissions.

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On file RONO: 00Increasing the inputs of nutrients has played a major role in increasing the supply of food to a continually growing world population. However, focusing attention on the most important nutrients, such as nitrogen (N), has in some cases led to nutrient imbalances, some excess applications especially of N, inefficient use and large losses to the environment with impacts on air and water quality, biodiversity and human health. In contrast, food exports from the developing to the developed world are depleting soils of nutrients in some countries. Better management of all essential nutrients is required that delivers sustainable agriculture and maintains the necessary increases in food production while minimizing waste, economic loss and environmental impacts. More extensive production systems typified by 'organic farming' may prove to be sustainable. However, for most of the developed world, and in the developing world where an ever-growing population demands more food, it will be essential to increase the efficiency of nutrient use in conventional systems. Nutrient management on farms is under the control of the land manger, the most effective of whom will already use various decision supports for calculating rates of application to achieve various production targets. Increasingly, land managers will need to conform to good practice to achieve production targets and to conform to environmental targets as well.Peer reviewe

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How we manage farming and food systems to meet rising demand is pivotal to the future of biodiversity. Extensive field data suggest impacts on wild populations would be greatly reduced through boosting yields on existing farmland so as to spare remaining natural habitats. High-yield farming raises other concerns because expressed per unit area it can generate high levels of externalities such as greenhouse gas (GHG) emissions and nutrient losses. However, such metrics underestimate the overall impacts of lower-yield systems, so here we develop a framework that instead compares externality and land costs per unit production. Applying this to diverse datasets describing the externalities of four major farm sectors reveals that, rather than involving tradeoffs, the externality and land costs of alternative production systems can co-vary positively: per

SUMMARYA computer model is presented that describes the flow of nitrogen between crop and soil on the field scale. The model has a compartmental structure and runs on a weekly time-step. Nitrogen enters via atmospheric deposition and by application of fertilizer or organic manures, and is lost through denitrification, leaching, volatilization and removal in the crop at harvest. Organic nitrogen is contained within three of the model compartments – crop residues (including plant material dying off through the growing season), soil microbial biomass and humus. Inorganic nitrogen is held in two pools as NH4+ or NO3-. Nitrogen flows in and out of these inorganic pools as a result of mineralization, immobilization, nitrification, leaching, denitrification and plant uptake. The model requires a description of the soil and the meteorological records for the site – mean weekly air temperature, weekly rainfall and weekly evapotranspiration. The model is designed to be used in a ‘carry forward’ mode – one year's run providing the input for the next, and so on. The model also allows the addition of 15N as labelled fertilizer, and follows its progress through crop and soil. Data from a Rothamsted field experiment in which the fate of a single pulse of labelled N was followed over several years were used to set the model parameters. The model, thus tuned, was then tested against other data from this and two contrasting sites in south-east England. Over a period of 4 years, the root mean square (R.M.S.) difference between modelled and measured quantities of labelled N remaining in the soil of all three sites was c. 7·5 kg N/ha, on average. The root mean square error in the measurements was c. 2·5 kg/ha. Similarly, the R.M.S. difference between modelled and measured recovery of labelled N by the crop was 0·6, compared with 0·3 kg/ha in the measurements themselves.