80% of arable land in Africa has low soil fertility and suffers from physical soil problems. Additionally, significant amounts of nutrients are lost every year due to unsustainable soil management ...practices. This is partially the result of insufficient use of soil management knowledge. To help bridge the soil information gap in Africa, the Africa Soil Information Service (AfSIS) project was established in 2008. Over the period 2008-2014, the AfSIS project compiled two point data sets: the Africa Soil Profiles (legacy) database and the AfSIS Sentinel Site database. These data sets contain over 28 thousand sampling locations and represent the most comprehensive soil sample data sets of the African continent to date. Utilizing these point data sets in combination with a large number of covariates, we have generated a series of spatial predictions of soil properties relevant to the agricultural management--organic carbon, pH, sand, silt and clay fractions, bulk density, cation-exchange capacity, total nitrogen, exchangeable acidity, Al content and exchangeable bases (Ca, K, Mg, Na). We specifically investigate differences between two predictive approaches: random forests and linear regression. Results of 5-fold cross-validation demonstrate that the random forests algorithm consistently outperforms the linear regression algorithm, with average decreases of 15-75% in Root Mean Squared Error (RMSE) across soil properties and depths. Fitting and running random forests models takes an order of magnitude more time and the modelling success is sensitive to artifacts in the input data, but as long as quality-controlled point data are provided, an increase in soil mapping accuracy can be expected. Results also indicate that globally predicted soil classes (USDA Soil Taxonomy, especially Alfisols and Mollisols) help improve continental scale soil property mapping, and are among the most important predictors. This indicates a promising potential for transferring pedological knowledge from data rich countries to countries with limited soil data.
Soils are widely recognized as a non-renewable natural resource and as biophysical carbon sinks. As such, there is a growing requirement for global soil information. Although several global soil ...information systems already exist, these tend to suffer from inconsistencies and limited spatial detail.
We present SoilGrids1km--a global 3D soil information system at 1 km resolution--containing spatial predictions for a selection of soil properties (at six standard depths): soil organic carbon (g kg-1), soil pH, sand, silt and clay fractions (%), bulk density (kg m-3), cation-exchange capacity (cmol+/kg), coarse fragments (%), soil organic carbon stock (t ha-1), depth to bedrock (cm), World Reference Base soil groups, and USDA Soil Taxonomy suborders. Our predictions are based on global spatial prediction models which we fitted, per soil variable, using a compilation of major international soil profile databases (ca. 110,000 soil profiles), and a selection of ca. 75 global environmental covariates representing soil forming factors. Results of regression modeling indicate that the most useful covariates for modeling soils at the global scale are climatic and biomass indices (based on MODIS images), lithology, and taxonomic mapping units derived from conventional soil survey (Harmonized World Soil Database). Prediction accuracies assessed using 5-fold cross-validation were between 23-51%.
SoilGrids1km provide an initial set of examples of soil spatial data for input into global models at a resolution and consistency not previously available. Some of the main limitations of the current version of SoilGrids1km are: (1) weak relationships between soil properties/classes and explanatory variables due to scale mismatches, (2) difficulty to obtain covariates that capture soil forming factors, (3) low sampling density and spatial clustering of soil profile locations. However, as the SoilGrids system is highly automated and flexible, increasingly accurate predictions can be generated as new input data become available. SoilGrids1km are available for download via http://soilgrids.org under a Creative Commons Non Commercial license.
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 spatio-temporal statistical modeling framework.
Potential natural vegetation (PNV) is the vegetation cover in equilibrium with climate, that would exist at a given location if not impacted by human activities. PNV is useful for raising public ...awareness about land degradation and for estimating land potential. This paper presents results of assessing machine learning algorithms-neural networks (nnet package), random forest (ranger), gradient boosting (gbm), K-nearest neighborhood (class) and Cubist-for operational mapping of PNV. Three case studies were considered: (1) global distribution of biomes based on the BIOME 6000 data set (8,057 modern pollen-based site reconstructions), (2) distribution of forest tree taxa in Europe based on detailed occurrence records (1,546,435 ground observations), and (3) global monthly fraction of absorbed photosynthetically active radiation (FAPAR) values (30,301 randomly-sampled points). A stack of 160 global maps representing biophysical conditions over land, including atmospheric, climatic, relief, and lithologic variables, were used as explanatory variables. The overall results indicate that random forest gives the overall best performance. The highest accuracy for predicting BIOME 6000 classes (20) was estimated to be between 33% (with spatial cross-validation) and 68% (simple random sub-setting), with the most important predictors being total annual precipitation, monthly temperatures, and bioclimatic layers. Predicting forest tree species (73) resulted in mapping accuracy of 25%, with the most important predictors being monthly cloud fraction, mean annual and monthly temperatures, and elevation. Regression models for FAPAR (monthly images) gave an R-square of 90% with the most important predictors being total annual precipitation, monthly cloud fraction, CHELSA bioclimatic layers, and month of the year, respectively. Further developments of PNV mapping could include using all GBIF records to map the global distribution of plant species at different taxonomic levels. This methodology could also be extended to dynamic modeling of PNV, so that future climate scenarios can be incorporated. Global maps of biomes, FAPAR and tree species at one km spatial resolution are available for download via http://dx.doi.org/10.7910/DVN/QQHCIK.
•The search for sustainability in healthcare is seen internationally as urgent.•Healthcare is being systemically re-conceptualised as co-produced.•Failure to address system boundaries underlies ...opposition towards novel approaches.•Omitting professional identities of stakeholders obstructs change.•Community Operational Research can help facilitate reflection on social identity.
Healthcare worldwide faces severe quality and cost issues, and the search for sustainability in healthcare establishes a grand challenge. Public interest is growing in a systemic re-conceptualising of healthcare, from primarily a consumerist problem of individual need for treatment to a need for communities themselves to become more effective in systemic prevention, coping and caring. In community led approaches, scarce resources are moved away from ever-increasing consumerist services to empower, develop and enable communities to plan their own health and community improvements in mutually interdependent patterns of care often seen as ‘co-production’. This approach is exemplified by the innovative NUKA system of community led healthcare which originated in Alaska and which was trialled in Scotland in 2012, where it did not achieve similar acclaim as in the United States. In the Scottish NUKA trial opposition from professionals meant the trial was ended early. Our research found that omitting to account for the strong professional identity of GPs and other practice staff was instrumental in the failure of the trial. Beyond deficiencies inadequately considering professional identities, the trial also failed to engage the community and its patients as owners and architects of the system. We argue that the root cause of these problems was a more general critical systemic failure to manage participatory boundaries and associated identities. Community Operational Research practitioners have developed relevant theories, methodologies and methods to address issues of participation and identity, so could make a significant contribution to opening up new solutions for community led healthcare.
Methods for rapid estimation of soil properties are needed for quantitative assessments of land management problems. We developed a scheme for development and use of soil spectral libraries for rapid ...nondestructive estimation of soil properties based on analysis of diffuse reflectance spectroscopy. A diverse library of over 1000 archived topsoils from eastern and southern Africa was used to test the approach. Air‐dried soils were scanned using a portable spectrometer (0.35–2.5 μm) with an artificial light source. Soil properties were calibrated to soil reflectance using multivariate adaptive regression splines (MARS), and screening tests were developed for various soil fertility constraints using classification trees. A random sample of one‐third of the soils was withheld for validation purposes. Validation r2 values for regressions were: exchangeable Ca, 0.88; effective cation‐exchange capacity (ECEC), 0.88; exchangeable Mg, 0.81; organic C concentration, 0.80; clay content, 0.80; sand content, 0.76; and soil pH, 0.70. Validation likelihood ratios for diagnostic screening tests were: ECEC <4.0 cmolc kg−1, 10.8; pH <5.5, 5.6; potential N mineralization >4.1 mg kg−1 d−1, 2.9; extractable P <7 mg kg−1, 2.9; exchangeable K <0.2 cmolc kg−1, 2.6. We show the response of prediction accuracy to sample size and demonstrate how the predictive value of spectral libraries can be iteratively increased through detection of spectral outliers among new samples. The spectral library approach opens up new possibilities for modeling, assessment and management of risk in soil evaluations in agricultural, environmental, and engineering applications. Further research should test the use of soil reflectance in pedotransfer functions for prediction of soil functional attributes.
Potential interactions between food production and climate mitigation are explored for two situations in sub-Saharan Africa, where deforestation and land degradation overlap with hunger and poverty. ...Three agriculture intensification scenarios for supplying nitrogen to increase crop production (mineral fertilizer, herbaceous legume cover crops—green manures—and agroforestry—legume improved tree fallows) are compared to baseline food production, land requirements to meet basic caloric requirements, and greenhouse gas emissions. At low population densities and high land availability, food security and climate mitigation goals are met with all intensification scenarios, resulting in surplus crop area for reforestation. In contrast, for high population density and small farm sizes, attaining food security and reducing greenhouse gas emissions require mineral fertilizers to make land available for reforestation; green manure or improved tree fallows do not provide sufficient increases in yields to permit reforestation. Tree fallows sequester significant carbon on cropland, but green manures result in net carbon dioxide equivalent emissions because of nitrogen additions. Although these results are encouraging, agricultural intensification in sub-Saharan Africa with mineral fertilizers, green manures, or improved tree fallows will remain low without policies that address access, costs, and lack of incentives. Carbon financing for small-holder agriculture could increase the likelihood of success of Reducing Emissions from Deforestation and Forest Degradation in Developing Countries programs and climate change mitigation but also promote food security in the region.
Tanzania is one of the countries that has embarked on a national programme under the United Nations collaborative initiative on Reducing Emissions from Deforestation and forest Degradation (REDD). ...Tanzania is currently developing the capacity to enter into a carbon monitoring REDD+ regime. In this context spatially representative soil carbon datasets and accurate predictive maps are important for determining the soil organic carbon pool. The main objective of this study was to model and map the SOC stock for the 0–30-cm soil layer to provide baseline information for REDD+ purposes. Topsoil data of over 1400 locations spread throughout Tanzania from the National Forest Monitoring and Assessment (NAFORMA), were used, supplemented by two legacy datasets, to calibrate simple kriging with varying local means models. Maps of SOC concentrations (g kg−1) were generated for the 0–10-cm, 10–20-cm, 20–30-cm, 0–30-cm layers, and maps of bulk density and SOC stock (kg m−2) for the 0–30-cm layer. Two approaches for modelling SOC stocks were considered here: the calculate-then-model (CTM) approach and the model-then-calculate approach (MTC). The spatial predictions were validated by means of 10-fold cross-validation. Uncertainty associated to the estimated SOC stocks was quantified through conditional Gaussian simulation. Estimates of SOC stocks for the main land cover classes are provided. Environmental covariates related to soil and terrain proved to be the strongest predictors for all properties modelled. The mean predicted SOC stock for the 0–30-cm layer was 4.1 kg m−2 (CTM approach) translating to a total national stock of 3.6 Pg. The MTC approach gave similar results. The largest stocks are found in forest and grassland ecosystems, while woodlands and bushlands contain two thirds of the total SOC stock. The root mean squared error for the 0–30-cm layer was 1.8 kg m−2, and the R2-value was 0.51. The R2-value of SOC concentration for the 0–30-cm layer was 0.60 and that of bulk density 0.56. The R2-values of the predicted SOC concentrations for the 10-cm layers vary between 0.46 and 0.54. The 95% confidence interval of the predicted average SOC stock is 4.01–4.15 kg m−2, and that of the national total SOC stock 3.54–3.65 Pg. Uncertainty associated with SOC concentration had the largest contribution to SOC stock uncertainty. These findings have relevance for the ongoing REDD+ readiness process in Tanzania by supplementing the previous knowledge of significant carbon pools. The soil organic carbon pool makes up a relatively large proportion of carbon in Tanzania and is therefore an important carbon pool to consider alongside the ones related to the woody biomass. Going forward, the soil organic carbon data can potentially be used in the determination of reference emission levels and the future monitoring, reporting and verification of organic carbon pools.
•SOC stock was mapped and uncertainty assessed using a recent, nationwide dataset.•Two mapping methods were compared: calculate-then-model and model-then-calculate.•Average SOC stock was is 4.1 kg m−2, translating to a total stock of 3.6 Pg.•Uncertainty about SOC concentration contributed most to SOC stock uncertainty.•The SOC maps provide baseline information to prepare Tanzania for REDD+.
There has been growing interest in the use of diffuse infrared reflectance as a quick, inexpensive tool for soil characterization. In studies reported to date, calibration and validation samples have ...been collected at either a local or regional scale. For this study, we selected 3768 samples from all 50 U.S. states and two tropical territories and an additional 416 samples from 36 different countries in Africa (125), Asia (104), the Americas (75) and Europe (112). The samples were selected from the National Soil Survey Center archives in Lincoln, NE, USA, with only one sample per pedon and a weighted random sampling to maximize compositional diversity. Applying visible and near-infrared (VNIR) diffuse reflectance spectroscopy (DRS) to air-dry soil (<
2 mm) with auxiliary predictors including sand content or pH, we obtained validation root mean squared deviation (RMSD) estimates of 54 g kg
−
1
for clay, 7.9 g kg
−
1
for soil organic C (SOC), 5.6 g kg
−
1
for inorganic C (IC), 8.9 g kg
−
1
for dithionate–citrate extractable Fe (FEd), and 5.5 cmol
c kg
−
1
for cation exchange capacity (CEC) with NH
4 at pH
=
7. For all of these properties, boosted regression trees (BRT) outperformed PLS regression, suggesting that this might be a preferred method for VNIR-DRS soil characterization. Using BRT, we were also able to predict ordinal clay mineralogy levels for montmorillonite and kaolinite, with 88% and 96%, respectively, falling within one ordinal unit of reference X-ray diffraction (XRD) values (0–5 on ordinal scale). Given the amount of information obtained in this study with ∼4
×
10
3 samples, we anticipate that calibrations sufficient for many applications might be obtained with large but obtainable soil-spectral libraries (perhaps 10
4–10
5 samples). The use of auxiliary predictors (potentially from complementary sensors), supplemental local calibration samples and theoretical spectroscopy all have the potential to improve predictions. Our findings suggest that VNIR soil characterization has the potential to replace or augment standard soil characterization techniques where rapid and inexpensive analysis is required.
•There is insufficient specific evidence on land degradation to focus action.•Land health problems share many features with public health problems.•Scientific principles of public health surveillance ...are applied to land health.•National land health surveillance systems could generate large development benefit.•Preventive strategies that reduce distal risks at national levels are needed.
Degradation of land health – the capacity of land, relative to its potential, to sustain delivery of ecosystem services – is recognized as a major global problem in general terms, but remains poorly quantified, resulting in a lack of specific evidence to focus action. Land health surveillance and response is designed to overcome limitations of current assessment approaches. It is modelled on science principles and approaches used in surveillance in the public health sector, which has a long history of evidence-informed policy and practice.
Key elements of the science framework are: (i) repeated measurement of land health and associated risk factors using probability based sampling of well defined populations of sample units; (ii) standardized protocols for data collection to enable statistical analysis of patterns, trends, and associations; (iii) case definitions based on specific diagnostic criteria; (iv) rapid low cost screening tests to permit detection of cases and non-cases in large numbers of samples; (v) cost-effectiveness evaluation of interventions based on projected reduction in risks and problem incidence; (vi) design of statistically analysable studies to evaluate interventions in the real-world; (vii) meta-analysis of these data to guide design of public policy and intervention programmes; and (viii) integrating surveillance and the communication and use of results into operational systems as part of regular policy and practice.
The scientific rigour of land health surveillance has potential to provide a sound basis for directing and assessing action to combat land degradation. Specialized national surveillance units should be established to harness and realign existing resources to provide integrated national land health systems. An international unit is needed to provide science and technology support to governments and develop standards, whereas an international agency should coordinate land health surveillance globally. Application of the surveillance framework could result in a shift away from a focus on rehabilitation of severely degraded land towards a preventive approach that focuses more on reducing distal risks at national and regional levels.
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