Exploring variability in rangeland soil organic carbon stocks across California (USA) using a voluntary monitoring network

Carey, Chelsea J.; Weverka, Jacob; DiGaudio, Ryan; Gardali, Thomas; Porzig, Elizabeth L.

doi:10.1016/j.geodrs.2020.e00304

Cited by 11 publications

(8 citation statements)

References 60 publications

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“…The soil organic carbon plays a valuable role in ecosystem services to improve rangelands' productivity and sustainability (Carey et al, 2020). Rangelands, especially in the arid region, are an ecologically and economically important land use type, and preserving soil organic carbon (SOC) can promote pasture production (Sarkar et al, 2020;Zhao et al, 2019).…”

Section: Introductionmentioning

confidence: 99%

“…Spatial prediction of soil organic matter (SOM) is important to understand the SOM storage and the emphasis of the SOM in the global carbon cycle and environmental issues in arid and semiarid region (Ebrahimzadeh et al, 2021). So, it is a necessary step in the management of SOC to understand spatial variability and its relationship with state factors such as terrain attributes, climatic parameters, and soil properties (Carey et al, 2020). Identifying the most important factors affecting SOCS variation in the landscape related to choose the optimal environmental covariates, therefore, is necessary to provide set of environmental variables that are achieved in low cost or easily accessible (Brungard et al, 2015;Miller et al, 2015).…”

Section: Introductionmentioning

confidence: 99%

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Spatial prediction of soil organic carbon stocks in an arid rangeland using machine learning algorithms

Rostaminia

Rahmani

Mousavi

et al. 2021

Environ Monit Assess

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method. Robust and popular random Forest (RF), cubist (CB) along with random forest-ordinary kriging (RF-OK), and cubist-ordinary kriging (CB-OK) hybrid ML models were applied to the prediction of SOCS. Ten-fold CV was implemented for modeling performance and uncertainty map. According to data analysis, the maximum, minimum, and average values of SOCS are 44.50, 10.50, and 20.50 (ton. ha −1 ) at the surface depth (0-30 cm), respectively. In general, normalized and standardized height covariates had a higher effect related to other predictors. On the other hand, two remote sensing (RS) indices, including salinity ratio (salinity) and GNDVI index, had a better impact on SOCS variability. The external validation of model performance indicated that RF-OK with (R 2 = 0.75, RMSE = 6.33 ton. ha −1 ) with the high and low uncertainty range (3.33-9.50 ton. ha −1 ) was the outperformed ML model in compare with other models as RF (R 2 = 0.65, RMSE = 7.38 ton. ha −1 ), CB-OK (R 2 = 0.56, RMSE = 9.22 ton. ha −1 ), and CB (R 2 = 0.33, RMSE = 10.42 ton. ha −1 ). In general, the hybrid models improved the accuracy of RF and CB with increased 0.11 until 0.23 of R 2 , and 1.05 to 1.2 (ton. ha −1 ) decreased RMSE of model's prediction. Hence, we conclude that the topographic attributes (especially normalized and standardized height) were the most critical factors in controlling surface SOCS in arid rangelands when combining with robust RF ML model, and optimized soil sampling methods like RF-cLHS can prepare acceptable soil properties maps.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Spatial prediction of soil organic carbon stocks in an arid rangeland using machine learning algorithms

Rostaminia

Rahmani

Mousavi

et al. 2021

Environ Monit Assess

View full text Add to dashboard Cite

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“…A central approach by the State to meet this goal is through doubling investments in land conservation and management, with the aim of using these investments to sequester more than 80 million metric tons of CO 2 by 2045 (California Air Resources Board 2019). However, California's diverse climate and topography yield highly variable patterns of vegetation productivity (Larsen et al 2015, Becchetti et al 2016, Carey et al 2020, posing a challenge for the State in understanding how to prioritize land management and conservation investments. A greater understating of the spatiotemporal variation in terrestrial ecosystem productivity and the environmental drivers of this variation are critical if we are to enhance conservation decision making and optimize the potential for terrestrial vegetation to store carbon.…”

Section: Introductionmentioning

confidence: 99%

Climate seasonality and extremes influence net primary productivity across California’s grasslands, shrublands, and woodlands

Alexander,

McCafferty,

Fricker

et al. 2023

Environ. Res. Lett.

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Terrestrial vegetation is a substantial carbon sink and plays a foundational role in regional and global climate change mitigation strategies. The state of California, USA, commits to achieving carbon neutrality by 2045 in part by managing terrestrial ecosystems to sequester more than 80 MMT of CO2. We used a 35-year remotely sensed net primary productivity (NPP) product with gridded climate, soil, topography, and vegetation data to evaluate spatiotemporal drivers of NPP variation and identify drivers of NPP response to extremes in water availability within and among California’s major grassland, shrubland, and woodland ecosystems. We used generalized boosted models and linear mixed effects models to identify influential predictors of NPP and characterize their relationships with NPP across seven major vegetation cover types. Climate seasonality, specifically greater precipitation and warmer minimum temperatures in early spring and winter, was associated with greater NPP across space, particularly in chaparral, blue oak, and grassland systems. Temporal variation in maximum annual temperature and climatic water deficit showed a negative relationship with temporal variation in NPP in most vegetation cover types, particularly chapparal and coastal scrub. We found a significant decrease in NPP over time in most vegetation types, appearing to coincide with California’s recent mega-drought. However, response to water availability extremes differed by vegetation type. Grasslands, for example, showed a pronounced ability to upregulate NPP in extreme wet years while maintaining relatively high baseline NPP in extreme dry years. Our analysis characterizes several climate risks and conservation opportunities in using California’s natural lands to store carbon. Namely, shifts in climate seasonality and water availability extremes present risks to the ability of most of these systems to fix carbon, yet hotspots of NPP resilience may exist and could be enhanced through conservation and restoration. Additional mechanistic work can help illuminate these opportunities and prioritize conservation decision making.

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“…The magnitude of soil SOC stocks depends on the complex interaction between temperature, moisture, soil properties (e.g., texture, pH values), tree species, forest management, and litter quality (Burke et al., 1989; Carey et al., 2020; Lal, 2005). With climate change, however, not only temperatures and moisture will change in the future, but also the amount and quality of litterfall due to intentional or unintentional changes in tree species.…”

Section: Introductionmentioning

confidence: 99%

Spatial 3D mapping of forest soil carbon stocks in Hesse, Germany

Heitkamp

Ahrends

Evers

et al. 2021

J. Plant Nutr. Soil Sci.

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Background Forest soils are an important reservoir of organic carbon (OC) and a potential source or sink for atmospheric CO2. Prediction of OC stock changes under ongoing climatic and management changes requires a spatial explicit base. Knowledge of the vertical distribution of OC stocks is very important, since varying soil layers may be affected differently by environmental change. Aim Three‐dimensional regionalization of OC stocks of the forest floor (FFC) and 5 cm depth increments of the mineral soil (SOC) of Hesse, Germany. Methods Datasets of the second National Forest Soil Inventory (NFSI II) were used for parametrization of hierarchical generalized additive models (hGAM). Validation was performed by a 10 times repeated 10‐fold cross‐validation, and spatial model uncertainty was assessed. Results Depth‐dependent validation indicated that model performance was best between 15 and 60 cm (amount of variance explained ≈ 0.5). All covariates showed plausible partial effects. FFC stocks were predicted to be highest under coniferous forest with a high influence of N deposition. Climate and potential cation exchange capacity affected SOC stocks markedly, whereas soil class and parent material were most important for the depth distribution. Overall, average predicted OC stocks were between 78.0 and 92.5 t ha−1, amounting to 67.6 to 80.1 Mt for all forest soils of Hesse. Between 16% and 24% were stored in the forest floor. Sixty‐eight percent to 69% of predicted SOC stocks were stored in the upper 30 cm. Model uncertainty was highest at locations with high elevation or ground water influence. Conclusions This work provides the first spatial explicit database for OC stocks of forest soils in Hesse at an intermediate scale. Stocks can be assessed flexibly for varying depth from the forest floor down to 100 cm. Uncertainty analysis informs about locations, where the model results have to be handled with care.

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Exploring variability in rangeland soil organic carbon stocks across California (USA) using a voluntary monitoring network

Cited by 11 publications

References 60 publications

Spatial prediction of soil organic carbon stocks in an arid rangeland using machine learning algorithms

Spatial prediction of soil organic carbon stocks in an arid rangeland using machine learning algorithms

Climate seasonality and extremes influence net primary productivity across California’s grasslands, shrublands, and woodlands

Spatial 3D mapping of forest soil carbon stocks in Hesse, Germany

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