Plant root-derived carbon(C)inputs(I_(root))are the primary source of C in mineral bulk soil.However,a fraction of I_(root)may lose quickly(I_(loss),e.g.,via rhizosphere microbial respiration,leaching and fauna feedin...Plant root-derived carbon(C)inputs(I_(root))are the primary source of C in mineral bulk soil.However,a fraction of I_(root)may lose quickly(I_(loss),e.g.,via rhizosphere microbial respiration,leaching and fauna feeding)without contributing to long-term bulk soil C storage,yet this loss has never been quantified,particularly on a global scale.In this study we integrated three observational global data sets including soil radiocarbon content,allocation of photo synthetically assimilated C,and root biomass distribution in 2,034 soil profiles to quantify I_(root)and its contribution to the bulk soil C pool.We show that global average I_(root)in the 0-200 cm soil profile is 3.5 Mg ha^(-1)yr^(-1),~80%of which(i.e.,I_(loss))is lost rather than co ntributing to long-term bulk soil C storage.I_(root)decreases exponentially with soil depth,and the top 20 cm soil contains>60%of total I_(root).Actual C input contributing to long-term bulk soil storage(i.e.,I_(root)-I_(loss))shows a similar depth distribution to I_(root).We also map I_(loss)and its depth distribution across the globe.Our results demonstrate the global significance of direct C losses which limit the contribution of I_(root)to bulk soil C storage;and provide spatially explicit data to facilitate reliable soil C predictions via separating direct C losses from total root-derived C inputs.展开更多
In Australia,the proportion of forest area that burns in a typical fire season is less than for other vegetation types.However,the 2019-2020 austral spring-summer was an exception,with over four times the previous max...In Australia,the proportion of forest area that burns in a typical fire season is less than for other vegetation types.However,the 2019-2020 austral spring-summer was an exception,with over four times the previous maximum area burnt in southeast Australian temperate forests.Temperate forest fires have extensive socio-economic,human health,greenhouse gas emissions,and biodiversity impacts due to high fire intensities.A robust model that identifies driving factors of forest fires and relates impact thresholds to fire activity at regional scales would help land managers and fire-fighting agencies prepare for potentially hazardous fire in Australia.Here,we developed a machine-learning diagnostic model to quantify nonlinear relationships between monthly burnt area and biophysical factors in southeast Australian forests for 2001-2020 on a 0.25°grid based on several biophysical parameters,notably fire weather and vegetation productivity.Our model explained over 80%of the variation in the burnt area.We identified that burnt area dynamics in southeast Australian forest were primarily controlled by extreme fire weather,which mainly linked to fluctuations in the Southern Annular Mode(SAM)and Indian Ocean Dipole(IOD),with a relatively smaller contribution from the central Pacific El Niño Southern Oscillation(ENSO).Our fire diagnostic model and the non-linear relationships between burnt area and environmental covariates can provide useful guidance to decision-makers who manage preparations for an upcoming fire season,and model developers working on improved early warning systems for forest fires.展开更多
Increasingly countries are seeking to reduce emission of greenhouse gases from the agricultural industries,and livestock production in particular,as part of their climate change management.While many reviews update pr...Increasingly countries are seeking to reduce emission of greenhouse gases from the agricultural industries,and livestock production in particular,as part of their climate change management.While many reviews update progress in mitigation research,a quantitative assessment of the efficacy and performance-consequences of nutritional strategies to mitigate enteric methane(CH_(4))emissions from ruminants has been lacking.A meta-analysis was conducted based on 108 refereed papers from recent animal studies(2000-2020)to report effects on CH4 production,CH_(4) yield and CH_(4) emission intensity from 8 dietary interventions.The interventions(oils,microalgae,nitrate,ionophores,protozoal control,phytochemicals,essential oils and 3-nitrooxypropanol).Of these,macroalgae and 3-nitrooxypropanol showed greatest efficacy in reducing CH_(4) yield(g CH_(4)/kg of dry matter intake)at the doses trialled.The confidence intervals derived for the mitigation efficacies could be applied to estimate the potential to reduce national livestock emissions through the implementation of these dietary interventions.展开更多
基金supported by the National Key Research and Development Program(Grant No.2021YFE0114500)the Strategic Priority Research Program of the Chinese Academy of Sciences(Grant No.XDA26010103)the Major Program for Basic Research Project of Yunnan Province(Grant No.202101BC070002)。
文摘Plant root-derived carbon(C)inputs(I_(root))are the primary source of C in mineral bulk soil.However,a fraction of I_(root)may lose quickly(I_(loss),e.g.,via rhizosphere microbial respiration,leaching and fauna feeding)without contributing to long-term bulk soil C storage,yet this loss has never been quantified,particularly on a global scale.In this study we integrated three observational global data sets including soil radiocarbon content,allocation of photo synthetically assimilated C,and root biomass distribution in 2,034 soil profiles to quantify I_(root)and its contribution to the bulk soil C pool.We show that global average I_(root)in the 0-200 cm soil profile is 3.5 Mg ha^(-1)yr^(-1),~80%of which(i.e.,I_(loss))is lost rather than co ntributing to long-term bulk soil C storage.I_(root)decreases exponentially with soil depth,and the top 20 cm soil contains>60%of total I_(root).Actual C input contributing to long-term bulk soil storage(i.e.,I_(root)-I_(loss))shows a similar depth distribution to I_(root).We also map I_(loss)and its depth distribution across the globe.Our results demonstrate the global significance of direct C losses which limit the contribution of I_(root)to bulk soil C storage;and provide spatially explicit data to facilitate reliable soil C predictions via separating direct C losses from total root-derived C inputs.
基金supported by the National Natural Science Foundation of China(42088101 and 42030605)support from the research project:Towards an Operational Fire Early Warning System for Indonesia(TOFEWSI)+1 种基金The TOFEWSI project was funded from October 2017-October 2021 through the UK’s National Environment Research Council/Newton Fund on behalf of the UK Research&Innovation(NE/P014801/1)(UK Principal InvestigatorAllan Spessa)(https//tofewsi.github.io/)financial support from the Natural Science Foundation of Qinghai(2021-HZ-811)。
文摘In Australia,the proportion of forest area that burns in a typical fire season is less than for other vegetation types.However,the 2019-2020 austral spring-summer was an exception,with over four times the previous maximum area burnt in southeast Australian temperate forests.Temperate forest fires have extensive socio-economic,human health,greenhouse gas emissions,and biodiversity impacts due to high fire intensities.A robust model that identifies driving factors of forest fires and relates impact thresholds to fire activity at regional scales would help land managers and fire-fighting agencies prepare for potentially hazardous fire in Australia.Here,we developed a machine-learning diagnostic model to quantify nonlinear relationships between monthly burnt area and biophysical factors in southeast Australian forests for 2001-2020 on a 0.25°grid based on several biophysical parameters,notably fire weather and vegetation productivity.Our model explained over 80%of the variation in the burnt area.We identified that burnt area dynamics in southeast Australian forest were primarily controlled by extreme fire weather,which mainly linked to fluctuations in the Southern Annular Mode(SAM)and Indian Ocean Dipole(IOD),with a relatively smaller contribution from the central Pacific El Niño Southern Oscillation(ENSO).Our fire diagnostic model and the non-linear relationships between burnt area and environmental covariates can provide useful guidance to decision-makers who manage preparations for an upcoming fire season,and model developers working on improved early warning systems for forest fires.
基金funded by the NSW Climate Change Fund through the NSW Primary Industries Climate Change Research Strategy。
文摘Increasingly countries are seeking to reduce emission of greenhouse gases from the agricultural industries,and livestock production in particular,as part of their climate change management.While many reviews update progress in mitigation research,a quantitative assessment of the efficacy and performance-consequences of nutritional strategies to mitigate enteric methane(CH_(4))emissions from ruminants has been lacking.A meta-analysis was conducted based on 108 refereed papers from recent animal studies(2000-2020)to report effects on CH4 production,CH_(4) yield and CH_(4) emission intensity from 8 dietary interventions.The interventions(oils,microalgae,nitrate,ionophores,protozoal control,phytochemicals,essential oils and 3-nitrooxypropanol).Of these,macroalgae and 3-nitrooxypropanol showed greatest efficacy in reducing CH_(4) yield(g CH_(4)/kg of dry matter intake)at the doses trialled.The confidence intervals derived for the mitigation efficacies could be applied to estimate the potential to reduce national livestock emissions through the implementation of these dietary interventions.
