Abstract: The empirical evidence of decline in ecosystem services (ES) over the last century has reinforced the call for ES quantification, monitoring and valuation. Usually, only provisioning ES are marketable and accounted for, whereas regulating, supporting and cultural ES are typically non-marketable and overlooked in connection with land-use or management decisions. The objective of this study was to quantify and value total ES (marketable and non-marketable) of diverse production systems and management intensities in Denmark to provide a basis for decisions based on economic values. The production systems were conventional wheat (Cwheat), a combined food and energy (CFE) production system and beech forest. Marketable (provisioning ES) and non-marketable ES (supporting, regulating and cultural) ES were quantified by dedicated on-site field measurements supplemented by literature data. The value of total ES was highest in CFE (US$ 3142 ha(-1) yr(-1)) followed by Cwheat (US$ 2767 ha (1) yr(-1)) and beech forest (US$ 2328 ha(-1) yr(-1)). As the production system shifted from Cwheat – CFE-beech, the marketable ES share decreased from 88% to 75% in CFE and 55% in beech whereas the non-marketable ES share increased to 12%, 25% and 45% of total ES in Cwheat, CFE and beech respectively, demonstrating production system and management effects on ES values. Total ES valuation, disintegrated into marketable and non-marketable share is a potential way forward to value ES and `tune’ our production systems for enhanced ES provision. Such monetary valuation can be used by policy makers and land managers as a tool to assess ES value and monitor the sustained flow of ES. The application of ES-based valuation for land management can enhance ES provision for maintaining the productive capacity of the land without depending on the external fossil-based fertilizer and chemical input. (C) 2013 Elsevier Ltd. All rights reserved.

Abstract: Projections of climate change impacts on crop yields are inherently uncertain(1). Uncertainty is often quantified when projecting future greenhouse gas emissions and their influence on climate(2). However, multi-model uncertainty analysis of crop responses to climate change is rare because systematic and objective comparisons among process-based crop simulation models(1,3) are difficult(4). Here we present the largest standardized model intercomparison for climate change impacts so far. We found that individual crop models are able to simulate measured wheat grain yields accurately under a range of environments, particularly if the input information is sufficient. However, simulated climate change impacts vary across models owing to differences in model structures and parameter values. A greater proportion of the uncertainty in climate change impact projections was due to variations among crop models than to variations among downscaled general circulation models. Uncertainties in simulated impacts increased with CO2 concentrations and associated warming. These impact uncertainties can be reduced by improving temperature and CO2 relationships in models and better quantified through use of multi-model ensembles. Less uncertainty in describing how climate change may affect agricultural productivity will aid adaptation strategy development and policymaking.

Abstract: In order to feed the world we need innovative thinking on how to increase agricultural production whilst also mitigating climate change. Agriculture and land-use change are responsible for approximately one-third of total anthropogenic greenhouse gas (GHG) emissions but hold potential for climate change mitigation but are only tangentially included in UNFCCC mitigation policies. To get a full estimate of GHG emissions from agricultural crop production both energy-based emissions and land-based emissions need to be accounted for. Furthermore, the major mitigation potential is likely to be indirect reduction of emissions i.e. reducing emissions per unit of agricultural product rather than the absolute emissions per se. Hence the system productivity must be included in the same analysis. This paper presents the Kaya-Porter identity, derived from the Maya identity, as a new way to calculate GHG emissions from agricultural crop production by deconstructing emissions into five elements; the GHG intensity of the energy used for production (kg CO2-eq./MJ), energy intensity of the production (MJ/kg dry matter), areal productivity (kg dry matter/ha), areal land-based GHG emissions (CO2-eq./ha) and area (ha). These separate elements in the identity can be targeted in emissions reduction and mitigation policies and are useful to analyse past and current trends in emissions and to explore future scenarios. Using the Kaya-Porter identity we have performed a case study on Danish crop production and find emissions to have been reduced by 12% from 1992 to 2008, whilst yields per unit area have remained constant. Both land-based emissions and energy-based emissions have decreased, mainly due to a 41% reduction in nitrogen fertilizer use. The initial identity based analysis for crop production presented here needs to be extended to include livestock to reflect the entire agricultural production and food demand sectors, thereby permitting analysis of the trade-offs between animal and plant food production, human dietary preferences and population and resulting GHG emissions. (C) 2012 Elsevier B.V. All rights reserved.