{"type": "FeatureCollection", "features": [{"id": "10.1016/j.geoderma.2015.06.015", "type": "Feature", "geometry": null, "properties": {"updated": "2026-09-20T16:15:42Z", "type": "Journal Article", "created": "2015-07-06", "title": "Impact Of Alley Cropping Agroforestry On Stocks, Forms And Spatial Distribution Of Soil Organic Carbon \u2014 A Case Study In A Mediterranean Context", "description": "Abstract   Agroforestry systems, i.e., agroecosystems combining trees with farming practices, are of particular interest as they combine the potential to increase biomass and soil carbon (C) storage while maintaining an agricultural production. However, most present knowledge on the impact of agroforestry systems on soil organic carbon (SOC) storage comes from tropical systems. This study was conducted in southern France, in an 18-year-old agroforestry plot, where hybrid walnuts ( Juglans regia  \u00d7  nigra  L.) are intercropped with durum wheat ( Triticum turgidum  L. subsp.  durum ), and in an adjacent agricultural control plot, where durum wheat is the sole crop. We quantified SOC stocks to 2.0\u00a0m depth and their spatial variability in relation to the distance to the trees and to the tree rows. The distribution of additional SOC storage in different soil particle-size fractions was also characterized. SOC accumulation rates between the agroforestry and the agricultural plots were 248\u00a0\u00b1\u00a031\u00a0kg\u00a0C\u00a0ha \u2212\u00a01 \u00a0yr \u2212\u00a01  for an equivalent soil mass (ESM) of 4000\u00a0Mg\u00a0ha \u2212\u00a01  (to 26\u201329\u00a0cm depth) and 350\u00a0\u00b1\u00a041\u00a0kg\u00a0C\u00a0ha \u2212\u00a01 \u00a0yr \u2212\u00a01  for an ESM of 15,700\u00a0Mg\u00a0ha \u2212\u00a01  (to 93\u201398\u00a0cm depth). SOC stocks were higher in the tree rows where herbaceous vegetation grew and where the soil was not tilled, but no effect of the distance to the trees (0 to 10\u00a0m) on SOC stocks was observed. Most of the additional SOC storage was found in coarse organic fractions (50\u2013200 and 200\u20132000\u00a0\u03bcm), which may be rather labile fractions. All together our study demonstrated the potential of alley cropping agroforestry systems under Mediterranean conditions to store SOC, and questioned the stability of this storage.", "keywords": ["[SDV.SA]Life Sciences [q-bio]/Agricultural sciences", "http://aims.fao.org/aos/agrovoc/c_28568", "Juglans regia", "F08 - Syst\u00e8mes et modes de culture", "culture associ\u00e9e", "Triticum turgidum", "630", "spectroscopie infrarouge", "zone m\u00e9diterran\u00e9enne", "[SDV.SA.SDS] Life Sciences [q-bio]/Agricultural sciences/Soil study", "http://aims.fao.org/aos/agrovoc/c_35657", "agroforesterie", "2. Zero hunger", "http://aims.fao.org/aos/agrovoc/c_35927", "[SDV.SA] Life Sciences [q-bio]/Agricultural sciences", "soil organic carbon storage", "http://aims.fao.org/aos/agrovoc/c_29563", "soil organic carbon saturation", "04 agricultural and veterinary sciences", "deep soil organic carbon stocks", "http://aims.fao.org/aos/agrovoc/c_207", "s\u00e9questration du carbone", "P31 - Lev\u00e9s et cartographie des sols", "http://aims.fao.org/aos/agrovoc/c_4060", "mati\u00e8re organique du sol", "P33 - Chimie et physique du sol", "Visible and near infrared spectroscopy", "571", "structure du sol", "[SDV.SA.SDS]Life Sciences [q-bio]/Agricultural sciences/Soil study", "Juglans nigra", "particle-size fractionation", "Particle-size fractionation", "12. Responsible consumption", "Soil organic carbon saturation", "visible and near infrared spectroscopy", "http://aims.fao.org/aos/agrovoc/c_33452", "http://aims.fao.org/aos/agrovoc/c_3081", "http://aims.fao.org/aos/agrovoc/c_4059", "Deep soil organic carbon stocks", "15. Life on land", "http://aims.fao.org/aos/agrovoc/c_331583", "cartographie des fonctions de la for\u00eat", "K10 - Production foresti\u00e8re", "soil mapping", "Soil mapping", "culture en couloirs", "http://aims.fao.org/aos/agrovoc/c_7958", "Soil organic carbon storage", "http://aims.fao.org/aos/agrovoc/c_7196", "0401 agriculture", " forestry", " and fisheries", "http://aims.fao.org/aos/agrovoc/c_1374847637217", "U30 - M\u00e9thodes de recherche"]}, "links": [{"href": "https://doi.org/10.1016/j.geoderma.2015.06.015"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Geoderma", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "10.1016/j.geoderma.2015.06.015", "name": "item", "description": "10.1016/j.geoderma.2015.06.015", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.1016/j.geoderma.2015.06.015"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2015-12-01T00:00:00Z"}}, {"id": "10.1016/j.soilbio.2021.108466", "type": "Feature", "geometry": null, "properties": {"updated": "2026-09-20T16:16:14Z", "type": "Journal Article", "created": "2021-11-03", "title": "Improved global-scale predictions of soil carbon stocks with Millennial Version 2", "description": "Abstract   Soil carbon (C) models are used to predict C sequestration responses to climate and land use change. Yet, the soil models embedded in Earth system models typically do not represent processes that reflect our current understanding of soil C cycling, such as microbial decomposition, mineral association, and aggregation. Rather, they rely on conceptual pools with turnover times that are fit to bulk C stocks and/or fluxes. As measurements of soil fractions become increasingly available, it is necessary for soil C models to represent these measurable quantities so that model processes can be evaluated more accurately. Here we present Version 2 (V2) of the Millennial model, a soil model developed to simulate C pools that can be measured by extraction or fractionation, including particulate organic C, mineral-associated organic C, aggregate C, microbial biomass, and low molecular weight C. Model processes have been updated to reflect the current understanding of mineral-association, temperature sensitivity and reaction kinetics, and different model structures were tested within an open-source framework. We evaluated the ability of Millennial V2 to simulate total soil organic C (SOC), as well as the mineral-associated and particulate fractions, using three independent data sets of soil fractionation measurements spanning a range of climate and geochemistry in Australia (N\u00a0=\u00a0495), Europe (N\u00a0=\u00a0175), and across the globe (N\u00a0=\u00a0659). When using all the data together (N\u00a0=\u00a01329), the Millennial V2 model predicted SOC (RMSE\u00a0=\u00a03.3\u00a0kg\u00a0C m\u22122, AIC\u00a0=\u00a0675,      R   i  n   2     \u00a0=\u00a00.31,      R   o  u  t   2     \u00a0=\u00a00.26) better than the widely-used first-order decomposition model Century (RMSE\u00a0=\u00a03.4\u00a0kg\u00a0C m\u22122, AIC\u00a0=\u00a0696,      R   i  n   2     \u00a0=\u00a00.21,      R   o  u  t   2     \u00a0=\u00a00.18) across sites, despite the fact that Millennial V2 has an increase in process complexity and number of parameters compared to Century. Millennial V2 also reproduced the observed fraction of C in MAOM and larger particle size fractions for most latitudes and biomes, and allows for a more detailed understanding of the pools and processes that affect model performance. It is important to note that this study evaluates the spatial variation in C stock only, and that the temporal dynamics of Millennial V2 remain to be tested. The Millennial V2 model updates the conceptual Century model pools and processes and represents our current understanding of the roles that microbial activity, mineral association and aggregation play in soil C sequestration.", "keywords": ["2. Zero hunger", "[SDU.OCEAN]Sciences of the Universe [physics]/Ocean", "550", "Mineral association", "Atmosphere", "Soil organic carbon stocks", "[SDU.OCEAN] Sciences of the Universe [physics]/Ocean", " Atmosphere", "15. Life on land", "551", "Microbial decomposition", "01 natural sciences", "[SDU.ENVI] Sciences of the Universe [physics]/Continental interfaces", " environment", "13. Climate action", "Soil carbon modeling", "[SDU.ENVI]Sciences of the Universe [physics]/Continental interfaces", "environment", "0105 earth and related environmental sciences"]}, "links": [{"href": "https://doi.org/10.1016/j.soilbio.2021.108466"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Soil%20Biology%20and%20Biochemistry", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "10.1016/j.soilbio.2021.108466", "name": "item", "description": "10.1016/j.soilbio.2021.108466", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.1016/j.soilbio.2021.108466"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2022-01-01T00:00:00Z"}}, {"id": "10.5061/dryad.f4m6k", "type": "Feature", "geometry": null, "properties": {"updated": "2026-09-20T16:19:29Z", "type": "Dataset", "title": "Data from: Spatial variability in soil organic carbon in a tropical montane landscape: associations between soil organic carbon and land use, soil properties, vegetation, and topography vary across plot to landscape scales", "description": "unspecifiedPresently, the lack of data on soil organic carbon (SOC) stocks in  relation to land-use types and biophysical characteristics prevents  reliable estimates of ecosystem carbon stocks in montane landscapes of  mainland SE Asia. Our study, conducted in a 10\u202f000\u202fha landscape in  Xishuangbanna, SW China, aimed at assessing the spatial variability in SOC  concentrations and stocks, as well as the relationships of SOC with  land-use types, soil properties, vegetation characteristics and  topographical attributes at three spatial scales: (1) land-use types  within a landscape (10\u202f000\u202fha), (2) sampling plots (1\u202fha) nested within  land-use types (plot distances ranging between 0.5 and 12\u202fkm), and (3)  subplots (10\u202fm radius) nested within sampling plots. We sampled 27  one-hectare plots \u2013 10 plots in mature forests, 11 plots in regenerating  or highly disturbed forests, and 6 plots in open land including tea  plantations and grasslands. We used a sampling design with a hierarchical  structure. The landscape was first classified according to land-use types.  Within each land-use type, sampling plots were randomly selected, and  within each plot we sampled within nine subplots. SOC concentrations and  stocks did not differ significantly across the four land-use types.  However, within the open-land category, SOC concentrations and stocks in  grasslands were higher than in tea plantations (P\u2009&lt;\u20090.01 for  0\u20130.15\u202fm, P\u2009=\u20090.05 for 0.15\u20130.30\u202fm, P\u2009=\u20090.06 for 0\u20130.9\u202fm depth). The SOC  stocks to a depth of 0.9\u202fm were 177.6\u202f\u00b1\u202f19.6 (SE) Mg\u202fC\u202fha\u22121 in tea  plantations, 199.5\u202f\u00b1\u202f14.8\u202fMg\u202fC\u202fha\u22121 in regenerating or highly disturbed  forests, 228.6\u202f\u00b1\u202f19.7\u202fMg\u202fC\u202fha\u22121 in mature forests, and  236.2\u202f\u00b1\u202f13.7\u202fMg\u202fC\u202fha\u22121 in grasslands. In this montane landscape,  variability within plots accounted for more than 50\u202f% of the overall  variance in SOC stocks to a depth of 0.9\u202fm and the topsoil SOC  concentrations. The relationships of SOC concentrations and stocks with  land-use types, soil properties, vegetation characteristics, and  topographical attributes varied across spatial scales. Variability in SOC  within plots was determined by litter layer carbon stocks (P\u2009&lt;\u20090.01  for 0\u20130.15\u202fm and P\u2009=\u20090.03 for 0.15\u20130.30 and 0\u20130.9\u202fm depth) and slope  (P\u2009\u2264\u20090.01 for 0\u20130.15, 0.15\u20130.30, and 0\u20130.9\u202fm depth) in open land, and by  litter layer carbon stocks (P\u2009&lt;\u20090.001 for 0\u20130.15, 0.15\u20130.30 and  0\u20130.9\u202fm depth) and tree basal area (P\u2009&lt;\u20090.001 for 0\u20130.15\u202fm and  P\u2009=\u20090.01 for 0\u20130.9\u202fm depth) in forests. Variability in SOC among plots in  open land was related to the differences in SOC concentrations and stocks  between grasslands and tea plantations. In forests, the variability in SOC  among plots was associated with elevation (P\u2009&lt;\u20090.01 for 0\u20130.15\u202fm  and P\u2009=\u20090.09 for 0\u20130.9\u202fm depth). The scale-dependent relationships between  SOC and its controlling factors demonstrate that studies that aim to  investigate the land-use effects on SOC need an appropriate sampling  design reflecting the controlling factors of SOC so that land-use effects  will not be masked by the variability between and within sampling plots.", "keywords": ["Soil organic carbon stocks", "Land-use type", "Soil characteristics", "15. Life on land"], "contacts": [{"organization": "de Bl\u00e9court, Marleen, Corre, Marife D., Paudel, Ekananda, Harrison, Rhett D., Brumme, Rainer, Veldkamp, Edzo,", "roles": ["creator"]}]}, "links": [{"href": "https://doi.org/10.5061/dryad.f4m6k"}, {"rel": "self", "type": "application/geo+json", "title": "10.5061/dryad.f4m6k", "name": "item", "description": "10.5061/dryad.f4m6k", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.5061/dryad.f4m6k"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2018-06-27T00:00:00Z"}}, {"id": "3210439835", "type": "Feature", "geometry": null, "properties": {"updated": "2026-09-20T16:22:53Z", "type": "Journal Article", "created": "2021-11-03", "title": "Improved global-scale predictions of soil carbon stocks with Millennial Version 2", "description": "Abstract   Soil carbon (C) models are used to predict C sequestration responses to climate and land use change. Yet, the soil models embedded in Earth system models typically do not represent processes that reflect our current understanding of soil C cycling, such as microbial decomposition, mineral association, and aggregation. Rather, they rely on conceptual pools with turnover times that are fit to bulk C stocks and/or fluxes. As measurements of soil fractions become increasingly available, it is necessary for soil C models to represent these measurable quantities so that model processes can be evaluated more accurately. Here we present Version 2 (V2) of the Millennial model, a soil model developed to simulate C pools that can be measured by extraction or fractionation, including particulate organic C, mineral-associated organic C, aggregate C, microbial biomass, and low molecular weight C. Model processes have been updated to reflect the current understanding of mineral-association, temperature sensitivity and reaction kinetics, and different model structures were tested within an open-source framework. We evaluated the ability of Millennial V2 to simulate total soil organic C (SOC), as well as the mineral-associated and particulate fractions, using three independent data sets of soil fractionation measurements spanning a range of climate and geochemistry in Australia (N\u00a0=\u00a0495), Europe (N\u00a0=\u00a0175), and across the globe (N\u00a0=\u00a0659). When using all the data together (N\u00a0=\u00a01329), the Millennial V2 model predicted SOC (RMSE\u00a0=\u00a03.3\u00a0kg\u00a0C m\u22122, AIC\u00a0=\u00a0675,      R   i  n   2     \u00a0=\u00a00.31,      R   o  u  t   2     \u00a0=\u00a00.26) better than the widely-used first-order decomposition model Century (RMSE\u00a0=\u00a03.4\u00a0kg\u00a0C m\u22122, AIC\u00a0=\u00a0696,      R   i  n   2     \u00a0=\u00a00.21,      R   o  u  t   2     \u00a0=\u00a00.18) across sites, despite the fact that Millennial V2 has an increase in process complexity and number of parameters compared to Century. Millennial V2 also reproduced the observed fraction of C in MAOM and larger particle size fractions for most latitudes and biomes, and allows for a more detailed understanding of the pools and processes that affect model performance. It is important to note that this study evaluates the spatial variation in C stock only, and that the temporal dynamics of Millennial V2 remain to be tested. The Millennial V2 model updates the conceptual Century model pools and processes and represents our current understanding of the roles that microbial activity, mineral association and aggregation play in soil C sequestration.", "keywords": ["2. Zero hunger", "[SDU.OCEAN]Sciences of the Universe [physics]/Ocean", "550", "Mineral association", "Atmosphere", "Soil organic carbon stocks", "[SDU.OCEAN] Sciences of the Universe [physics]/Ocean", " Atmosphere", "15. Life on land", "551", "Microbial decomposition", "01 natural sciences", "[SDU.ENVI] Sciences of the Universe [physics]/Continental interfaces", " environment", "13. Climate action", "Soil carbon modeling", "[SDU.ENVI]Sciences of the Universe [physics]/Continental interfaces", "environment", "0105 earth and related environmental sciences"]}, "links": [{"href": "https://doi.org/3210439835"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Soil%20Biology%20and%20Biochemistry", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "3210439835", "name": "item", "description": "3210439835", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/3210439835"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2022-01-01T00:00:00Z"}}, {"id": "10.25338/B8061X", "type": "Feature", "geometry": null, "properties": {"updated": "2026-09-20T16:18:51Z", "type": "Dataset", "title": "Multiple Benefits from Agricultural and Natural Land Covers in the Central Valley, CA", "description": "unspecifiedMethods for Rapid Evidence  Assessment and Benefit/Tradeoff analysis We performed a rapid review of  the literature from the last 10 years focusing on benefits from  agricultural and natural land covers in the Central Valley. We focused our  search on 10 priority agricultural land covers, selected according to  harvested acreage as reported by the California County Agricultural  Commissioners\u2019 2018 Crop Report [30], and 3 priority natural (i.e., not  for production purposes) land covers based on land area in the Central  Valley [98]. See Appendix II for a detailed overview of the search  strategy employed, the inclusion criteria, and the data collected from  each study in the review. The resulting library of research included  reports from peer-review studies as well as publicly available federal or  state surveys/censuses and expert source surveys. In  total, we reviewed 107 studies that included approximately 10 agricultural  land covers and 3 natural land covers, recording over 77 different metrics  for benefits and tradeoffs provisioned by those land covers.\u00a0From the 107  studies we obtained 512 unique observations across land covers and benefit  metrics.\u00a0 To  complement the metrics reported in the peer-reviewed literature, we  included metrics with quality data available in public repositories such  as federal and state censuses, technical reports, and databases. These  metrics were chosen because they provided information to supplement a  benefit category with few examples in recent published literature or  because they described metrics that are more suitable for survey formats  than for the experimental interventions in the studies reviewed above.  These additional datasets included: Crop  production value ($USD  ha<sup>-1</sup>)<sup>\u00a0</sup>   Pesticide use by land cover type (kg applied  ha<sup>-1</sup>)\u00a0 Consumptive water use  (m<sup>3</sup> ha<sup>-1</sup>)\u00a0  Employment (workers ha<sup>-1</sup>) and average  weekly wages earned ($USD worker<sup>-1</sup>  ha<sup>-1</sup>) in the agricultural sector\u00a0 Avian conservation  score The Avian Conservation Score was  developed through a survey of domain experts. In an iterative process, the  expert sources reached a consensus on scores for each landcover type  according to their relative value for nesting, foraging, or roosting  different avian taxa during the breeding and non-breeding seasons. Avian  taxa considered were those for which the Central Valley Joint Venture has  established conservation objectives, including grassland, oak savannah,  and riparian landbirds, waterfowl, shorebirds, and other waterbirds  (Central Valley Joint Venture 2020). Each land cover type was given a  final score on a 0-1 scale representing its relative total value across  taxa and seasons.\u00a0 Although our search strategy  reflected <i>a priori </i>selection of focal benefit  categories and metrics, benefit categories were subsequently adjusted to  reflect the actual availability of information on each benefit category  and associated metrics. Of the metrics described in the gap analysis  above, we chose a subset of metrics with the best representation across  land cover types and recategorized them into a suite of benefit  categories: 1) Environmental health or quality, which included air  pollution and pesticide use metrics; 2) Economy, which included  agricultural (crop and forage) production value and livelihood value  metrics; 3) Climate, which included greenhouse gas emission and carbon  storage/sequestration metrics; 4) Water, which included water  quality/pollution and water use metrics, and 5) Wildlife, which included  the Avian Conservation Score. These categories were subsequently used to  calculate a Multiple Benefits Index across land covers (within metrics)  and within specific land covers (across metrics). The Multiple Benefits Index was  calculated by normalizing all of the above metrics to a similar scale to  enable comparison of multiple benefits and tradeoffs across land cover  types. To compare benefit metrics within each landcover, reported values  were converted to the same unit of measure and then transformed to a 0-1  scale by setting the highest reported value across all land covers to 1  and then calculating the remaining values according to the following  formula: where MBI represents the Multiple Benefits Index, or normalized value of X, and X<sub>i</sub> represents a single value in the vector of values for X. Metrics were then categorized <i>post hoc </i>as either \u201cbenefits\u201d or \u201ctradeoffs\u201d depending on their perceived value to the above sectors or interests. Benefits were those metrics that related to provisioning of a desirable service such as pollutant removal, while tradeoffs were metrics that related to provisioning of an undesirable service such as greenhouse gas emissions. Metrics considered tradeoffs were assigned a negative value by multiplying the Multiple Benefits Index by -1. The results of within-land cover benefit/tradeoff analyses were presented in the individual land cover profiles in Section III, while the results of cross-land cover benefit/tradeoff analysis are presented below. To compare land covers across all metrics, we calculated the mean Multiple Benefits Index score for all metrics within a land cover type and then ranked landcovers from highest to lowest mean score. See Appendix III for the rationale behind the selected metrics, along with unit conversions and assumptions made for each metric included in the benefit-tradeoff analysis. Finally, the benefit/tradeoff analysis was placed into the context of a changing environment through the development of a Climate Change Vulnerability Index, similarly to the climate change vulnerability index developed for birds in the Central Valley.\u00a0As with the avian conservation score, we developed a survey for a panel of expert sources. The expert panel scored landcovers according to their estimated vulnerability to climate change based on a combination of sensitivity (intrinsic, physiological factors that contribute to climate change vulnerability) and exposure (extrinsic, environmental factors that contribute to climate change vulnerability) factors. Sensitivity scores and exposure scores were summed separately within each land cover and then multiplied together to derive the overall vulnerability index (sum of sensitivity*sum of exposure).\u00a0 Because it does not represent a specific benefit or tradeoff, but rather a property of individual land covers, the CCVI was not included in the benefit/tradeoff analysis. Instead, it was used as a standalone metric to contextualize benefits and tradeoffs expected from land covers under climate change and the resulting uncertainty surrounding management scenarios. Methods for spatial hotspot/coldspot analysis of ecosystem benefits/tradeoffs <b>Ecosystem Service Metrics and Source Data</b> Land cover data were obtained from the USDA NASS Cropscape Data Layer (CDL2019), and recategorized according to the specifications of this project (Table 1). Riparian zones were determined as a 25 meter buffer around National Hydrological Dataset (NHD) flowlines for natural rivers and bodies of water, limited to non-developed and non-agricultural land cover categories. Air and Water Quality metric obtained from the California Healthy Places Index (HPI) geospatial dataset, Pollution and H<sub>2</sub>O Contamination indices respectively. Habitat quality metric obtained from Department of Fish and Wildlife (CDFW) Areas of Conservation Emphasis (ACE) dataset. Soil organic carbon content and percent clay particles were aggregated from the NRCS SSURGO soil data viewer. Parameter values were aggregated from individual soil horizon by volume up to soil map unit component, and aggregated from map unit component by percent total extent to map units. Theoretical maximum carbon storage was calculated based on percent clay as per Hoyle et al (2011) by the following equation:<br> <b><i>SOC%=0.5482\u00d7 </i></b><b>ln</b><b><i>(clay%)</i></b><b><i>+1.3073</i></b> Soil potential carbon accumulation was calculated by subtracting existing soil carbon stock (SSURGO) from the theoretical maximum calculated as above, and applying a weighting factor based on land cover expected biomass productivity and soil disturbance frequency (Table 1). Rangeland and forest biomass productivity metrics were obtained from SSURGO soil data viewer by map unit component, and aggregated to map unit by percent total extent. Perennial crop biomass productivity data, previously used in orchard life cycle assessment modeling (Marvinney et al 2015, Kendall et al 2015) was obtained from a cooperating agri-services firm operating out of the San Joaquin Valley region, for 14 different tree crops. These data were joined to the CDL2019 perennial crops with average value assigned to any tree crop for which no biomass data was available. Groundwater recharge potential data was obtained from the UC Davis SAGBI dataset. Groundwater depth data was obtained from the Department of Water Resources (DWR) open test well data as the average of measurements from 2015-201 Crop productivity data (5-year mean yield in tons per acre) was obtained from the County Crop Commission (CCC) reports via USDA NASS, and joined to CDL2019 land cover units as well as recategorized land cover units as the mean yield value of any constituent crop types. The CDL 2019 original unit-based productivity analysis is thus the more accurate representation, as less aggregation of yield values was required.<br> \u00a0 <b>Transformation and Aggregation of Ecosystem Service Metrics</b> Linear transformation was used to convert the range of values in each metric dataset to a scale of 0-1, with 0 being \u2018worst\u2019 and 1 \u2018best\u2019 in terms of ecosystem services provided. Combined metrics were generated by averaging the transformed values in the relevant metrics, and applying a linear transformation to re-scale the values to 0-1. Metrics were aggregated to a 5km hex grid covering the Central Valley by area-weighted averaging. Ecosystem service \u2018hot\u2019 and \u2018cold\u2019 spots were generated by extracting hexes with values below 0.2 and above 0.8 for the combination of all examined metrics.<br> <br> \u00a0 Hoyle F.C., Baldock J.A., Murphy D.V. (2011) Soil Organic Carbon \u2013 Role in Rainfed Farming Systems. In: Tow P., Cooper I., Partridge I., Birch C. (eds) Rainfed Farming Systems. Springer, Dordrecht<br> <br> Marvinney EM, Kendall AM, Brodt SB (2015) Life Cycle\u2013based Assessment of Energy Use and Greenhouse Gas Emissions in Almond Production, Part II: Scenario and Sensitivity Analysis. J Ind Ecol 19(6)<br> <br> Kendall AM, Marvinney EM, Zhu W, Brodt SB (2015) Life Cycle\u2013based Assessment of Energy Use and Greenhouse Gas Emissions in Almond Production, Part I: Analytical Framework and Baseline Results. J Ind Ecol (19) 6<br>", "keywords": ["2. Zero hunger", "Soil organic carbon stocks", "groundwater depletion", "environmental quality", "1. No poverty", "annual grasslands", "15. Life on land", "7. Clean energy", "6. Clean water", "12. Responsible consumption", "soil organic carbon", "13. Climate action", "11. Sustainability", "14. 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