Projecting Soil Organic Carbon Distribution in Central Chile under Future Climate Scenarios

Rojas, Luis Alejandro; Adhikari, Kabindra; Ventura, Stephen J.

doi:10.2134/jeq2017.08.0329

Cited by 33 publications

(13 citation statements)

References 44 publications

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“…This study focuses on Latin America, where site-or region-specific modeling efforts report high explained variance when mapping SOC (Reyes-Rojas et al, 2018). Accurate SOC maps are required to identify areas with the potential for soil carbon sequestration, and distinguish them from areas with high SOC.…”

Section: Introductionmentioning

confidence: 99%

No silver bullet for digital soil mapping: country-specific soil organic carbon estimates across Latin America

Guevara

Olmedo

Stell

et al. 2018

SOIL

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Abstract. Country-specific soil organic carbon (SOC) estimates are the baseline for the Global SOC Map of the Global Soil Partnership (GSOCmap-GSP). This endeavor is key to explaining the uncertainty of global SOC estimates but requires harmonizing heterogeneous datasets and building country-specific capacities for digital soil mapping (DSM). We identified country-specific predictors for SOC and tested the performance of five predictive algorithms for mapping SOC across Latin America. The algorithms included support vector machines (SVMs), random forest (RF), kernel-weighted nearest neighbors (KK), partial least squares regression (PL), and regression kriging based on stepwise multiple linear models (RK). Country-specific training data and SOC predictors (5 × 5 km pixel resolution) were obtained from ISRIC -World Soil Information. Temperature, soil type, vegetation indices, and topographic constraints were the best predictors for SOC, but country-specific predictors and their respective weights varied across Latin America. We compared a large diversity of country-specific datasets and models, and were able to explain SOC variability in a range between ∼ 1 and ∼ 60 %, with no universal predictive algorithm among countries. A regional (n = 11 268 SOC estimates) ensemble of these five algorithms was able to explain ∼ 39 % of SOC variability from repeated 5-fold cross-validation. We report a combined SOC stock of 77.8 ± 43.6 Pg (uncertainty represented by the full conditional response of independent model residuals) across Latin America. SOC stocks were higher in tropical forests (30 ± 16.5 Pg) and croplands (13 ± 8.1 Pg). Country-specific and regional ensembles revealed spatial discrepancies across geopolitical borders, higher elevations, and coastal plains, but provided similar regional stocks (77.8 ± 42.2 and 76.8 ± 45.1 Pg, respectively). These results are conservative compared to global estimates (e.g., SoilGrids250m 185.8 Pg, the Harmonized World Soil Database 138.4 Pg, or the GSOCmap-GSP 99.7 Pg). Countries with large area (i.e., Brazil, Bolivia, Mexico, Peru) and large spatial SOC heterogeneity had lower SOC stocks per unit area and larger uncertainty in their predictions. We highlight that expert opinion is needed to set boundary prediction limits to avoid unrealistically high modeling estimates. For maximizing explained variance while minimizing prediction bias, the selection of predictive algorithms for SOC mapping should consider density of available data and variability of country-specific environmental gradients. This study highlights the large degree of spatial uncertainty in SOC estimates across Latin America. We provide a framework for improving country-specific mapping efforts and reducing current discrepancy of global, regional, and country-specific SOC estimates.

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Section: Introductionmentioning

confidence: 99%

No silver bullet for digital soil mapping: country-specific soil organic carbon estimates across Latin America

Guevara

Olmedo

Stell

et al. 2018

SOIL

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“…Considerable efforts have been made to assess the response of SOC stocks in topsoil to warming (Davidson and Janssens, 2006; Conant et al, 2011; Crowther et al, 2016). Most SOC measurements were limited to the top 10‐ to 30‐cm depths, and few papers accounted for subsoil SOC stocks (Cavigelli et al, 2018; Dell et al, 2018; Gollany and Polumsky, 2018; Reyes Rojas et al, 2018). Findings from these studies identified critical gaps in knowledge.…”

Section: Discussionmentioning

confidence: 99%

“…Four papers in the section (summarized below) applied statistical models to develop maps of current SOC stocks across different geographical regions and spatial scales, including a 15‐km 2 area in southeastern Brazil (Costa et al, 2018), two different 100‐km 2 regions in South Africa (Vågen et al, 2018), a 100,000‐km 2 region of the northwestern United States (Flathers and Gessler, 2018), and a 150,000‐km 2 region in central Chile (Reyes Rojas et al, 2018). Two of these efforts (Flathers and Gessler, 2018; Reyes Rojas et al, 2018) used random forest statistics with the scorpan modeling approach of McBratney et al (2003), which uses seven categories of input data to make SOC predictions: known soil attributes, climatic values, organisms present, relief, parent material, age, and spatial location. In addition to assessing current SOC stocks, Reyes Rojas et al (2018) extended their effort to assessing future climate effects.…”

Section: Statistical Models To Assess Soil Organic Carbon Stocksmentioning

confidence: 99%

“…Two of these efforts (Flathers and Gessler, 2018;Reyes Rojas et al, 2018) used random forest statistics with the scorpan modeling approach of McBratney et al (2003), which uses seven categories of input data to make SOC predictions: known soil attributes, climatic values, organisms present, relief, parent material, age, and spatial location. In addition to assessing current SOC stocks, Reyes Rojas et al (2018) extended their effort to assessing future climate effects. Costa et al (2018) compared and evaluated performance of classical multiple linear regression (MLR) and geographically weighted regression (GWR) models to predict SOC and chemical fractions of SOM in the Brazilian southeastern mountainous region.…”

Section: Statistical Models To Assess Soil Organic Carbon Stocksmentioning

confidence: 99%

Section: Statistical Models To Assess Soil Organic Carbon Stocksmentioning

confidence: 99%

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Measurements and Models to Identify Agroecosystem Practices That Enhance Soil Organic Carbon under Changing Climate

Gollany¹,

Venterea

2018

J of Env Quality

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Adapting to the anticipated impacts of climate change is a pressing issue facing agriculture, as precipitation and temperature changes are expected to have major effects on agricultural production in many regions of the world. These changes will also affect soil organic matter decomposition and associated stocks of soil organic C (SOC), which have the potential to feed back to climate change and affect agroecosystem resiliency. This special section brings together multiple efforts to assess effects of climate change on SOC stocks around the globe in grassland, pasture, and crop agroecosystems under varying management practices. The overall goal of these efforts is to identify optimum practices to enhance SOC accumulation. In this article, we summarize the highlights of these papers and assess their broader implications for future research to enhance agroecosystem SOC accumulation and resiliency to climate change. Fourteen of the twenty contributions apply dynamic process-based models to assess climate and/or long-term management impacts on SOC stocks, and four papers use statistical SOC models across landscapes or regions. Also included are one meta-analysis and one longterm study. The models applied in this collection performed well when reliable input data were available, underlining the usefulness of modeling efforts to inform management decisions that enhance SOC stocks. Overall, the findings confirm that most agroecosystems have the potential to store SOC through improved management. However, this will be challenging, particularly for dryland agriculture, unless crop yield and crop biomass increase under projected climate change.

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Relating topography and soil phosphorus distribution in litter‐amended pastures in Arkansas

Adhikari

Braden

Owens

et al. 2021

Agrosystems Geosci & Env

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Poultry producers in northwestern Arkansas fertilize pastures with litter, leading to excessive P buildup on surface soils with risk of contaminating nearby surface waters. Information on the influence of pasture topography on P runoff is limited. Objectives were to assess soil P and P index status in pastures, quantify topographic influence on P distribution, and generate high-resolution P maps for site-specific nutrient management. Soil samples were collected from a commercial farm in a grid design and analyzed for Mehlich-3 P (STP), and dissolved reactive P (DRP). Gburek (GPI) and Sims P indices (SPI) were calculated by considering soil erosion and runoff potentials, STP, and P fertilizer application rate and source. A machine-learning algorithm, based on a random forest model, quantified spatial relationships of STP, DRP, and P indices with topography. The study area was highly variable in topography and soil P levels. High-slope areas bordering a stream and flat areas with lower elevation had greater GPI and SPI. Topography explained up to 50% of variation in STP and DRP distribution and >70% variation in GPI and SPI, which was linked to litter application sites and cattle feeding sites. Key terrain attributes for STP, DRP, GPI, and SPI distribution were elevation, slope position, slope height, valley depth, and valley bottom flatness. Predicted P maps illustrated areas along a stream had lower STP and DRP levels, but greater GPI and SPI. This analysis linked topographic relationships with P distribution, as topography controls the flow and distribution of water and influences farm management practices; therefore, future P management strategies should explicitly incorporate topographic risks.

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Projecting Soil Organic Carbon Distribution in Central Chile under Future Climate Scenarios

Cited by 33 publications

References 44 publications

No silver bullet for digital soil mapping: country-specific soil organic carbon estimates across Latin America

No silver bullet for digital soil mapping: country-specific soil organic carbon estimates across Latin America

Measurements and Models to Identify Agroecosystem Practices That Enhance Soil Organic Carbon under Changing Climate

Relating topography and soil phosphorus distribution in litter‐amended pastures in Arkansas

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