Changes in Soil Carbon Storage After Cultivation

Mann, L.K.

doi:10.3334/cdiac/tcm.007

Cited by 49 publications

(73 citation statements)

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“…The modeled magnitude and time scale for this ''withdrawal'' of N from the system is within published estimates for temperate regions of 20-50 yr and 20-50% of total SOM (Jenny and Raychaudhuri 1960, Mann 1986, Post and Mann 1990, Davidson and Ackerman 1993. But after the initial peak, all terms in the N vector and productivity of the system decline as SOM 2 declines.…”

Section: Ntillsupporting

confidence: 75%

An Analytical Approach to Ecosystem Biogeochemistry Modeling

Baisden

Amundson

2003

Ecological Applications

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Models of terrestrial ecosystem carbon and nutrient biogeochemistry commonly utilize several compartments of organic matter (OM) to simulate the storage and turnover of OM in plant-soil systems. While much work has focused on numerical simulations of ecosystems to understand responses to perturbations, relatively little work explores the analytical properties of the mathematical systems used and their implications for the understanding of terrestrial ecosystems. We explore carbon and nutrient dynamics using a 4 ϫ 4 transfer matrix, which is similar to the mathematical core of commonly used simulation models (e.g., CENTURY). The C and N vectors contain one pool for photosynthesizing biomass and several pools for soil detritis. The model yields steady-state solutions for the C pool size of each compartment dependent solely on the amount of the nutrient (nitrogen) stored within the compartment and the C:nutrient ratio. Furthermore, the C and nutrient storage in the model compartments depends on the inputs and losses of nutrients to and from the system as well as the coefficients for turnover from and transfer between compartments. When examining the transient behavior of the system, the eigenvalues for the system provide a useful tool for predicting and understanding the characteristic time scales with which plant-soil systems respond to perturbations. We use the model eigenvalues to examine ecosystem development from freshly exposed parent material as well as the response to a ''catastrophic'' disturbance event. Ultimately, the model's analytical tools can be extended to characterize soil organic matter (SOM) responses to several major land-use changes occurring in agroecosystems-including the initiation of tillage, the application of industrial fertilizers, and the introduction of reduced-tillage practices. Analysis of the eigenvalues for these cases suggests that agroecosystems tend to lose SOM more rapidly following the initiation of tillage than they can regain SOM after the initiation of conservation-tillage practices. Additionally, the amount of N export (harvesting or loss) places an important control on the storage and future productivity of agricultural systems. Overall, we suggest that the model-with straightforward and accessible steady-state solutions and eigenvalues-offers a complementary perspective to more complex numerical simulations.

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Section: Ntillsupporting

confidence: 75%

An Analytical Approach to Ecosystem Biogeochemistry Modeling

Baisden

Amundson

2003

Ecological Applications

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“…They can reduce soil erosion and the associated loss of nutrients (Dabney 1998) and act directly on soil fertility by improving soil organic matter and promoting N 2 fixation by legumes. Increasing soil organic matter is also a key to preserving soil fertility (Mann 1986). It is encouraged by systems that minimize soil disturbance and maximize the retention of crop residues (mulching, Fig.…”

Section: Multiple Cropping Systems To Reduce Environmental Impactsmentioning

confidence: 99%

Multiple cropping systems as drivers for providing multiple ecosystem services: from concepts to design

Gaba

Lescourret

Boudsocq

et al. 2014

Agron. Sustain. Dev.

287

190

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Provisioning services, such as the production of food, feed, and fiber, have always been the main focus of agriculture. Since the 1950s, intensive cropping systems based on the cultivation of a single crop or a single cultivar, in simplified rotations or monocultures, and relying on extensive use of agrochemical inputs have been preferred to more diverse, self-sustaining cropping systems, regardless of the environmental consequences. However, there is increasing evidence that such intensive agroecosystems have led to a decline in biodiversity as well as threatening the environment and have damaged a number of ecosystem services such as the biogeochemical nutrient cycles and the regulation of climate and water quality. Consequently, the current challenge facing agriculture is to ensure the future of food production while reducing the use of inputs and limiting environmental impacts and the loss of biodiversity. Here, we review examples of multiple cropping systems that aim to use biotic interactions to reduce chemical inputs and provide more ecosystem services than just provisioning. Our main findings are the identification of underlying ecological processes and management strategies related to the provision of pairs of ecosystem services namely food production and a regulation service. We also found gaps between ecological knowledge and the constraints of agricultural practices in taking account of the interactions and possible trade-offs between multiple ecosystem services as well as socioeconomic constraints. We present guidelines for the design of multiple cropping systems combining ecological, agricultural, and genetic concepts and approaches.

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“…For example, the effect of land use changes can be assessed using microbial community metrics such as bacterial community structure and composition [10,11], enzymatic activity [12][13][14], and soil organic carbon [15][16][17][18]. Researchers have tied the status of Soil Organic Carbon (SOC) to the activity of soil enzymes [19,20], including phosphatases [14].…”

Section: Introductionmentioning

confidence: 99%

Assessing the Diversity and Composition of Bacterial Communities across a Wetland, Transition, Upland Gradient in Macon County Alabama

et al. 2013

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Wetlands provide essential functions to the ecosphere that range from water filtration to flood control. Current methods of evaluating the quality of wetlands include assessing vegetation, soil type, and period of inundation. With recent advances in molecular and bioinformatic techniques, measurement of the structure and composition of soil bacterial communities have become an alternative to traditional methods of ecological assessment. The objective of the current study was to determine whether soil bacterial community composition and structure changed along a single transect in Macon County, AL. Proteobacteria were the most abundant phyla throughout the soils in the study (ranging from 42.1% to 49.9% of total sequences). Phyla Acidobacteria (37.4%) and Verrucomicrobia (7.0%) were highest in wetland soils, Actinobacteria (14.6%) was highest in the transition area, and Chloroflexi (1.6%) was highest in upland soils. Principle Components Analysis (relative abundance) and Principle Coordinates Analysis (PCoA) (Unifrac weighted metric) plots were generated, showing distinction amongst the OPEN ACCESSDiversity 2013, 5 462 ecosystem types through clustering by taxonomic abundance and Unifrac scores at 3% dissimilarity, respectively. Selected soil properties (soil organic carbon and phosphatase enzyme activity) also differed significantly in transition soil ecosystem types, while showing predominance in the wetland area. This study suggests that with further study the structure and composition of soil bacterial communities may eventually be an important indicator of ecological impact in wetland ecosystems.

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Changes in Soil Carbon Storage After Cultivation

Cited by 49 publications

References 0 publications

An Analytical Approach to Ecosystem Biogeochemistry Modeling

An Analytical Approach to Ecosystem Biogeochemistry Modeling

Multiple cropping systems as drivers for providing multiple ecosystem services: from concepts to design

Assessing the Diversity and Composition of Bacterial Communities across a Wetland, Transition, Upland Gradient in Macon County Alabama

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