The role of plants and land management in sequestering soil carbon in temperate arable and grassland ecosystems

Rees, Robert M.; Bingham, I. J.; Baddeley, John A.; Watson, C. A.

doi:10.1016/j.geoderma.2004.12.020

Cited by 193 publications

(114 citation statements)

References 183 publications

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“…Other soil C-CO 2 sources include plant root metabolism and the respiration of rhizosphereassociated microorganisms (Mielnick, 1996). These soil C-CO 2 effluxes are regulated by climatic conditions, especially temperature and soil moisture (Buyanovsky et al, 1986;Rees et al, 2005).…”

Section: Introductionmentioning

confidence: 99%

“…Residue decomposition is mainly regulated by climatic conditions, soil nutrient availability and the biochemical composition of the residues, such as lignin and alkaloid contents and C/N ratio (Rees et al, 2005;Padovan et al, 2006). In field studies of crop residue decomposition under soil tillage systems, mesh-bags are frequently applied (Wieder & Land, 1982;Amado et al, 2003;Padovan et al, 2006;De Bona et al, 2006).…”

Section: Introductionmentioning

confidence: 99%

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Long-term C-CO2 emissions and carbon crop residue mineralization in an oxisol under different tillage and crop rotation systems

Campos

Amado

Tornquist

et al. 2011

Rev. Bras. Ciênc. Solo

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Soil C-CO 2 emissions are sensitive indicators of management system impacts on soil organic matter (SOM). The main soil C-CO 2 sources at the soil-plant interface are the decomposition of crop residues, SOM turnover, and respiration of roots and soil biota. The objectives of this study were to evaluate the impacts of tillage and cropping systems on long-term soil C-CO 2 emissions and their relationship with carbon (C) mineralization of crop residues. A long-term experiment was conducted in a Red Oxisol in Cruz Alta, RS, Brazil, with subtropical climate Cfa (Köppen classification), mean annual precipitation of 1,774 mm and mean annual temperature of 19.2 o C. Treatments consisted of two tillage systems: (a) conventional tillage (CT) and (b) no tillage (NT) in combination with three cropping systems: (a) R0-monoculture system (soybean/wheat), (b) R1-winter crop rotation (soybean/wheat/soybean/black oat), and (c) R2-intensive crop rotation (soybean/ black oat/soybean/black oat + common vetch/maize/oilseed radish/wheat). The soil C-CO 2 efflux was measured every 14 days for two years (48 measurements), by trapping the CO 2 in an alkaline solution. The soil gravimetric moisture in the 0-0.05 m layer was determined concomitantly with the C-CO 2 efflux measurements. The crop residue C mineralization was evaluated with the mesh-bag method, with sampling 14, 28, 56, 84, 112, and 140 days after the beginning of the evaluation period for C measurements. Four C conservation indexes were used to assess the relation between C-CO 2 efflux and soil C stock and its compartments. The crop Brazil. residue C mineralization fit an exponential model in time. For black oat, wheat and maize residues, C mineralization was higher in CT than NT, while for soybean it was similar. Soil moisture was higher in NT than CT, mainly in the second year of evaluation. There was no difference in tillage systems for annual average C-CO 2 emissions, but in some individual evaluations, differences between tillage systems were noticed for C-CO 2 evolution. Soil C-CO 2 effluxes followed a bi-modal pattern, with peaks in October/November and February/March. The highest emission was recorded in the summer and the lowest in the winter. The C-CO 2 effluxes were weakly correlated to air temperature and not correlated to soil moisture. Based on the soil C conservation indexes investigated, NT associated to intensive crop rotation was more C conserving than CT with monoculture.Index terms: management systems, no tillage, conventional tillage, greenhouse gases. RESUMO: EMISSÕES DE C-CO 2 DE LONGO PRAZO E A MINERALIZAÇÃO DO CARBONO DOS RESÍDUOS CULTURAIS EM UM LATOSSOLO SOB DIFERENTES SISTEMAS DE PREPARO E ROTAÇÃO DE CULTURAS A emissão de C-CO 2 do solo é considerada um indicador sensível dos impactos de sistemas de manejo sobre a matéria orgânica do solo (MOS). Nos sistemas agrícolas, as principais fontes de C-CO 2 são as decomposições dos resíduos culturais e da MOS, além da respiração das raízes das culturas e da biota do solo. O principal objetivo de...

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Long-term C-CO2 emissions and carbon crop residue mineralization in an oxisol under different tillage and crop rotation systems

Campos

Amado

Tornquist

et al. 2011

Rev. Bras. Ciênc. Solo

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“…Soil carbon gains/losses have been linked to various environmental factors such as climate (Hook and Burke, 2000), land use/land cover (John et al, 2005;Rees et al, 2005), soil moisture/hydrology (Vasques, Grunwald and Myers, 2012b), and topography (Yimer, Ledin and Abdelkadir, 2006). Mallavan, Minasny and McBratney (2010) argued that soil attributes correlate consistently with environmental factors assuming homology of soil-forming factors.…”

Section: Environmental Domain Space Of Attributesmentioning

confidence: 99%

Transferability and scaling of soil total carbon prediction models in Florida

Grunwald

Xiong

2014

Preprint

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The applicability, transfer, and scalability of visible/near-infrared (VNIR)-derived soil models are still poorly understood. The objectives of this study in Florida, U.S. were to: (i) compare three methods to predict soil total carbon (TC) using five fields (local scale) and a pooled (regional scale) VNIR spectral dataset, (ii) assess the model's transferability among fields, and (iii) evaluate the up-and down-scaling behavior of TC prediction models. A total of 560 TC-spectral sets were modeled by Partial Least Square Regression (PLSR), Support Vector Machine (SVM), and Random Forest. The transferability and up-and down-scaling of models were limited by the following factors: (i) the spectral data domain, (ii) soil attribute domain, (iii) methods that describe the internal model structure of VNIR-TC relationships, and (iv) environmental domain space of attributes that control soil carbon dynamics. All soil logTC models showed excellent performance based on all three methods with R 2 > 0.86, bias < 0.01%, root mean square prediction error (RMSE) = 0.09%, residual predication deviation (RPD) > 2.70% , and ratio of prediction error to inter-quartile range (RPIQ) > 4.54. PLSR performed substantially better than SVM to scale and transfer models. Upscaled soil TC models performed somewhat better in terms of model fit (R 2 ), RPD, and RPIQ, whereas downscaled models showed less bias and smaller RMSE based on PLSR. Given the many factors that can impinge on empirically derived soil spectral prediction models, as demonstrated by this study, more focus on the applicability and scaling of them is needed.PeerJ PrePrints | http://dx.doi.org/10.7287/peerj.preprints.494v1 | CC-BY 4.0

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“…Carbon stored in soils represents the third largest global C pool (Lal, 2008) and grassland management methods that GHG mitigation and adaptation with farm models 375 increase forage production have the potential to increase soil C stocks (Freibauer et al, 2004;Rees et al, 2005). Soil organic matter (SOM) dynamics and associated GHG emissions are also influenced by modifying the quality and composition of manure that is returned and more importantly, by changes in land use if animal diet changes (e.g.…”

Section: Soil Carbonmentioning

confidence: 99%

Whole-farm models to quantify greenhouse gas emissions and their potential use for linking climate change mitigation and adaptation in temperate grassland ruminant-based farming systems

Prado

Crosson

Olesen

et al. 2013

Animal

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The farm level is the most appropriate scale for evaluating options for mitigating greenhouse gas (GHG) emissions, because the farm represents the unit at which management decisions in livestock production are made. To date, a number of whole farm modelling approaches have been developed to quantify GHG emissions and explore climate change mitigation strategies for livestock systems. This paper analyses the limitations and strengths of the different existing approaches for modelling GHG mitigation by considering basic model structures, approaches for simulating GHG emissions from various farm components and the sensitivity of GHG outputs and mitigation measures to different approaches. Potential challenges for linking existing models with the simulation of impacts and adaptation measures under climate change are explored along with a brief discussion of the effects on other ecosystem services.Keywords: adaptation, farm-models, greenhouse gases, mitigation, ruminants ImplicationsWhole-farm models are valuable tools to study the feedback and feedforward interactions between mitigation of greenhouse gas (GHG) emissions and adaptation to climate change for ruminant-based production systems. All of the processes affecting GHG emissions and farm productivity involve the cycling of C and N within the farm, most of which will be affected by climate change. However, not all farm models include such responses to climatic drivers, and there is a need for further development and testing of the models under diverse climatic conditions. Modelling of the complex interactions between farm components and the environment, including the socio-economic aspects, is instrumental for providing strategic direction to the development of climate and food-related policies. Despite the contribution of nontemperate livestock systems to total global GHG emissions, there is a dearth of specific farm models to assess GHG emissions from these regions. Modelling of soil carbon (C) fluxes is still largely absent or very simplified in many studies even though soil C has the potential to be the largest source or sink of C for grassland-based livestock systems. IntroductionAccording to The Food and Agriculture Organization (FAO) (Steinfeld et al., 2006) livestock production systems contribute about 18% of global anthropogenic greenhouse gas (GHG) emissions. Livestock is also currently one of the fastest growing agricultural subsectors in developing countries (Thornton, 2010), with projections indicating that by 2050 worldwide animal production is expected to increase by 80% compared with 2005 (Alexandratos andBruinsma, 2012). Although demand for non-ruminant meat has been increasing more rapidly than that for ruminant meats and, consequently, the importance of grasslands in livestock production and trade has been declining, increased milk and beef demand, potentially produced via grassland-based systems, is expected to increase (Fiala, 2008;Thornton, 2010). It is thus projected that the global annual growth rate of beef until 2050 will be 1.2%, clos...

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The role of plants and land management in sequestering soil carbon in temperate arable and grassland ecosystems

Cited by 193 publications

References 183 publications

Long-term C-CO2 emissions and carbon crop residue mineralization in an oxisol under different tillage and crop rotation systems

Long-term C-CO2 emissions and carbon crop residue mineralization in an oxisol under different tillage and crop rotation systems

Transferability and scaling of soil total carbon prediction models in Florida

Whole-farm models to quantify greenhouse gas emissions and their potential use for linking climate change mitigation and adaptation in temperate grassland ruminant-based farming systems

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