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Abstract Resolving regional carbon budgets is critical for informing land-based mitigation policy. For nine regions covering nearly the whole globe, we collected inventory estimates of carbon-stock changes complemented by satellite estimates of biomass changes where inventory data are missing. The net land–atmospheric carbon exchange (NEE) was calculated by taking the sum of the carbon-stock change and lateral carbon fluxes from crop and wood trade, and riverine-carbon export to the ocean. Summing up NEE from all regions, we obtained a global ‘bottom-up’ NEE for net land anthropogenic CO2 uptake of –2.2 ± 0.6 PgC yr−1 consistent with the independent top-down NEE from the global atmospheric carbon budget during 2000–2009. This estimate is so far the most comprehensive global bottom-up carbon budget accounting, which set up an important milestone for global carbon-cycle studies. By decomposing NEE into component fluxes, we found that global soil heterotrophic respiration amounts to a source of CO2 of 39 PgC yr−1 with an interquartile of 33–46 PgC yr−1—a much smaller portion of net primary productivity than previously reported.

AbstractLivestock are a critically important component of the food system, although the sector needs a profound transformation to ensure that it contributes to a rapid transition towards sustainable food systems. This chapter reviews and synthesises the evidence available on changes in demand for livestock products in the last few decades, and the multiple socio-economic roles that livestock have around the world. We also describe the nutrition, health, and environmental impacts for which the sector is responsible. We propose eight critical actions for transitioning towards a more sustainable operating space for livestock. (1) Facilitate shifts in the consumption of animal source foods (ASF), recognising that global reductions will be required, especially in communities with high consumption levels, while promoting increased levels in vulnerable groups, including the undernourished, pregnant women and the elderly. (2) Continue work towards the sustainable intensification of livestock systems, paying particular attention to animal welfare, food-feed competition, blue water use, disease transmission and perverse economic incentives. (3) Embrace the potential of circularity in livestock systems as a way of partially decoupling livestock from land. (4) Adopt practices that lead to the direct or indirect mitigation of greenhouse gases. (5) Adopt some of the vast array of novel technologies at scale and design incentive mechanisms for their rapid deployment. (6) Diversify the protein sources available for human consumption and feed, focusing on the high-quality alternative protein sources that have lower environmental impacts. (7) Tackle antimicrobial resistance effectively through a combination of technology and new regulations, particularly for the fast-growing poultry and pork sectors and for feedlot operations. (8) Implement true cost of food and true-pricing approaches to ASF consumption.

AbstractPast assessments report negative impacts of the climate crisis in boreal areas; but milder and shorter winters and elevated atmospheric CO2may provide opportunities for agricultural productivity potentially playing a significant role in future food security. Arable cropping systems are expanding in boreal areas, but the regional mainstay will likely continue to be livestock production. Agroecological models can when appropriately calibrated and evaluated, facilitate improved productivity while minimising environmental impacts by identifying system interactions, and quantifying greenhouse gas emissions, soil carbon stocks and fertiliser use. While models designed for temperate and tropical zones abound, few are developed specifically for boreal zones, and there is uncertainty around the performance of existing models in boreal areas. We reviewed model performance across boreal environments and management systems. We identified a dearth of modelling studies in boreal regions, with the publication of three or less papers per year since the year 2000, constituting a significant research gap. Models IFSM and BASGRA_N performed best in grassland production, DNDC best in predicting soil N2O and NH3emissions. No model outperformed all others, strengthening the case for ensemble modelling. Existing agroecological models would be worthy of further evaluation, providing model improvements designed for boreal systems.

AbstractSeveral lines of evidence point to European managed grassland ecosystems being a sink of carbon. In this study, we apply ORCHIDEE‐GM a process‐based carbon cycle model that describes specific management practices of pastures and the dynamics of carbon cycling in response to changes in climatic and biogeochemical drivers. The model is used to simulate changes in the carbon balance [i.e., net biome production (NBP)] of European grasslands over 1991–2010 on a 25 km × 25 km grid. The modeled average trend in NBP is 1.8–2.0 g C m−2 yr−2 during the past two decades. Attribution of this trend suggests management intensity as the dominant driver explaining NBP trends in the model (36–43% of the trend due to all drivers). A major change in grassland management intensity has occurred across Europe resulting from reduced livestock numbers. This change has ‘inadvertently’ enhanced soil C sequestration and reduced N2O and CH4 emissions by 1.2–1.5 Gt CO2‐equivalent, offsetting more than 7% of greenhouse gas emissions in the whole European agricultural sector during the period 1991–2010. Land‐cover change, climate change and rising CO2 also make positive and moderate contributions to the NBP trend (between 24% and 31% of the trend due to all drivers). Changes in nitrogen addition (including fertilization and atmospheric deposition) are found to have only marginal net effect on NBP trends. However, this may not reflect reality because our model has only a very simple parameterization of nitrogen effects on photosynthesis. The sum of NBP trends from each driver is larger than the trend obtained when all drivers are varied together, leaving a residual – nonattributed – term (22–26% of the trend due to all drivers) indicating negative interactions between drivers.