A Taxonomically Based Ordinal Estimate of Soil Productivity for Landscape-Scale Analyses

Schaetzl, Randall J.; Krist, Frank J.; Miller, Bradley A.

doi:10.1097/ss.0b013e3182446c88

Cited by 69 publications

(41 citation statements)

References 1 publication

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“…Outbreaks (solid perimeters) are grouped into zones (dashed perimeters) and black regions indicate where outbreaks occurred in more than 50% of potential outbreak periods from 1916 to 1984. Recent DFTM outbreaks in California south of previously known outbreaks are also indicated on the map (: 1996Sequoia National Forest and j: 2009-2012. The site of collection of DFTM from the Sierra San Pedro Martir National Park, Baja California Norte, Mexico is also indicated (*).…”

Section: Genetic Analysesmentioning

confidence: 97%

“…Criteria included A. concolor basal area (m 2 ha À1 ); percentage of A. concolor in a stand; tree density (ha À1 ); quadratic mean diameter of A. concolor (cm DBH); terrain position index; and soil drainage index for predicting risk (i.e., basal area loss) from DFTM outbreaks (Krist et al, 2010;Schaetzl et al, 2012). Each criterion was weighted for importance as a predictor in the model (modified from Eastman, 1999) (Table 1).…”

Section: Risk Modelingmentioning

confidence: 99%

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Impact of the first recorded outbreak of the Douglas-fir tussock moth, Orgyia pseudotsugata, in southern California and the extent of its distribution in the Pacific Southwest region

Coleman¹,

Jones²,

Courtial³

et al. 2014

Forest Ecology and Management

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Section: Genetic Analysesmentioning

confidence: 97%

Section: Risk Modelingmentioning

confidence: 99%

Impact of the first recorded outbreak of the Douglas-fir tussock moth, Orgyia pseudotsugata, in southern California and the extent of its distribution in the Pacific Southwest region

Coleman¹,

Jones²,

Courtial³

et al. 2014

Forest Ecology and Management

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“…Although we have only estimated long-term nutrient contents using relatively scarce data, the maps produced could be used to derive various higher-level data products, such as nutrient mass balance maps, when combined with soil bulk density data, Soil Fertility Index maps (Schaetzl et al 2012) and/or nutrient gap (deficiency) maps. Such maps can be beneficial for non-specialist audiences who are nevertheless interested in spatial distributions of soil nutrients.…”

Section: Usability Of Produced Mapsmentioning

confidence: 99%

Soil nutrient maps of Sub-Saharan Africa: assessment of soil nutrient content at 250 m spatial resolution using machine learning

Hengl

Leenaars

Shepherd

et al. 2017

Nutr Cycl Agroecosyst

252

153

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Spatial predictions of soil macro and micro-nutrient content across Sub-Saharan Africa at 250 m spatial resolution and for 0-30 cm depth interval are presented. Predictions were produced for 15 target nutrients: organic carbon (C) and total (organic) nitrogen (N), total phosphorus (P), and extractable-phosphorus (P), potassium (K), calcium (Ca), magnesium (Mg), sulfur (S), sodium (Na), iron (Fe), manganese (Mn), zinc (Zn), copper (Cu), aluminum (Al) and boron (B). Model training was performed using soil samples from ca. 59,000 locations (a compilation of soil samples from the AfSIS, EthioSIS, One Acre Fund, VitalSigns and legacy soil data) and an extensive stack of remote sensing covariates in addition to landform, lithologic and land cover maps. An ensemble model was then created for each nutrient from two machine learning algorithms- random forest and gradient boosting, as implemented in R packages ranger and xgboost-and then used to generate predictions in a fully-optimized computing system. Cross-validation revealed that apart from S, P and B, significant models can be produced for most targeted nutrients (R-square between 40-85%). Further comparison with OFRA field trial database shows that soil nutrients are indeed critical for agricultural development, with Mn, Zn, Al, B and Na, appearing as the most important nutrients for predicting crop yield. A limiting factor for mapping nutrients using the existing point data in Africa appears to be (1) the high spatial clustering of sampling locations, and (2) missing more detailed parent material/geological maps. Logical steps towards improving prediction accuracies include: further collection of input (training) point samples, further harmonization of measurement methods, addition of more detailed covariates specific to Africa, and implementation of a full spatiotemporal statistical modeling framework.

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“…토양의 생산력 (soil productivity)은 특정 경영 조건하에 서 작물 혹은 어떤 식물을 생산할 수 있는 토양의 능력으로 알려져 있다 (Schaetzl et al, 2012). 이러한 토양의 생산능 력을 몇 가지 지표로써 예측하고자 하는 노력은 토양 및 농 업분야에서 오랫동안 연구되어 왔다 (Hussain et al, 1999).…”

Section: Introductionunclassified

Assessment of Sustainable Production on Paddy Field Treated with Green Manure Crops Using Sustainability Index

Kim¹,

Kim²,

Park³

et al. 2014

Korean Journal of Soil Science and Fertilizer

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Assessment of sustainable production on a cropland can help to determine the most proper management practices. In this study, we evaluated the sustainable production on paddy field treated with green manure crops using sustainability index which based on nutrient index, microbiological index, and crop index related to nutrient-supplying capacity. Especially choosing appropriate indicators from a minimum data set (MDS) were used the principal components analysis (SI-2) as well as expert opinion (SI-1) usually used in sustainability index. Six treatments including the two tillage treatments and two green manure crops were investigated as follows; (i)

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A Taxonomically Based Ordinal Estimate of Soil Productivity for Landscape-Scale Analyses

Cited by 69 publications

References 1 publication

Impact of the first recorded outbreak of the Douglas-fir tussock moth, Orgyia pseudotsugata, in southern California and the extent of its distribution in the Pacific Southwest region

Impact of the first recorded outbreak of the Douglas-fir tussock moth, Orgyia pseudotsugata, in southern California and the extent of its distribution in the Pacific Southwest region

Soil nutrient maps of Sub-Saharan Africa: assessment of soil nutrient content at 250 m spatial resolution using machine learning

Assessment of Sustainable Production on Paddy Field Treated with Green Manure Crops Using Sustainability Index

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