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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.

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.

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.

AbstractLivestock contributes approximately one‐third of global anthropogenic methane (CH4) emissions. Quantifying the spatial and temporal variations of these emissions is crucial for climate change mitigation. Although country‐level information is reported regularly through national inventories and global databases, spatially explicit quantification of century‐long dynamics of CH4 emissions from livestock has been poorly investigated. Using the Tier 2 method adopted from the 2019 Refinement to 2006 IPCC guidelines, we estimated CH4 emissions from global livestock at a spatial resolution of 0.083° (~9 km at the equator) during the period 1890–2019. We find that global CH4 emissions from livestock increased from 31.8 [26.5–37.1] (mean [minimum−maximum of 95% confidence interval) Tg CH4 yr−1 in 1890 to 131.7 [109.6–153.7] Tg CH4 yr−1 in 2019, a fourfold increase in the past 130 years. The growth in global CH4 emissions mostly occurred after 1950 and was mainly attributed to the cattle sector. Our estimate shows faster growth in livestock CH4 emissions as compared to the previous Tier 1 estimates and is ~20% higher than the estimate from FAOSTAT for the year 2019. Regionally, South Asia, Brazil, North Africa, China, the United States, Western Europe, and Equatorial Africa shared the majority of the global emissions in the 2010s. South Asia, tropical Africa, and Brazil have dominated the growth in global CH4 emissions from livestock in the recent three decades. Changes in livestock CH4 emissions were primarily associated with changes in population and national income and were also affected by the policy, diet shifts, livestock productivity improvement, and international trade. The new geospatial information on the magnitude and trends of livestock CH4 emissions identifies emission hotspots and spatial–temporal patterns, which will help to guide meaningful CH4 mitigation practices in the livestock sector at both local and global scales.

AbstractLivestock contributes approximately one‐third of global anthropogenic methane (CH4) emissions. Quantifying the spatial and temporal variations of these emissions is crucial for climate change mitigation. Although country‐level information is reported regularly through national inventories and global databases, spatially explicit quantification of century‐long dynamics of CH4 emissions from livestock has been poorly investigated. Using the Tier 2 method adopted from the 2019 Refinement to 2006 IPCC guidelines, we estimated CH4 emissions from global livestock at a spatial resolution of 0.083° (~9 km at the equator) during the period 1890–2019. We find that global CH4 emissions from livestock increased from 31.8 [26.5–37.1] (mean [minimum−maximum of 95% confidence interval) Tg CH4 yr−1 in 1890 to 131.7 [109.6–153.7] Tg CH4 yr−1 in 2019, a fourfold increase in the past 130 years. The growth in global CH4 emissions mostly occurred after 1950 and was mainly attributed to the cattle sector. Our estimate shows faster growth in livestock CH4 emissions as compared to the previous Tier 1 estimates and is ~20% higher than the estimate from FAOSTAT for the year 2019. Regionally, South Asia, Brazil, North Africa, China, the United States, Western Europe, and Equatorial Africa shared the majority of the global emissions in the 2010s. South Asia, tropical Africa, and Brazil have dominated the growth in global CH4 emissions from livestock in the recent three decades. Changes in livestock CH4 emissions were primarily associated with changes in population and national income and were also affected by the policy, diet shifts, livestock productivity improvement, and international trade. The new geospatial information on the magnitude and trends of livestock CH4 emissions identifies emission hotspots and spatial–temporal patterns, which will help to guide meaningful CH4 mitigation practices in the livestock sector at both local and global scales.