Land health surveillance and response: A framework for evidence-informed land management

Shepherd, Keith D.; Shepherd, Gemma; Walsh, Markus

doi:10.1016/j.agsy.2014.09.002

Cited by 56 publications

(44 citation statements)

References 25 publications

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“…Although a great deal of soil data exists for Africa, there is little standardization in the sampling design or analytical tests conducted. The Africa Soils Information System is an example of how this situation may be remedied in the future by standardized protocols that examine change at large spatial scales through time [36].…”

Section: Spatial Scalesmentioning

confidence: 99%

“…Still, these evidence-based practices hold great potential for supporting sustainable soil management, and broad improvement will require a coherent policy framework to support their wider adoption and long-term investment by farmers. Fortunately, a growing global demand for good quality, low-cost soils data has been moving forward [36,85]. Such integrated research efforts are necessary to inform national and international efforts that invest in agricultural intensification across SSA [171][172][173].…”

Section: Positive Trajectories and Conclusionmentioning

confidence: 99%

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The State of Soil Degradation in Sub-Saharan Africa: Baselines, Trajectories, and Solutions

et al. 2015

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Abstract:The primary cause of soil degradation in sub-Saharan Africa (SSA) is expansion and intensification of agriculture in efforts to feed its growing population. Effective solutions will support resilient systems, and must cut across agricultural, environmental, and socioeconomic objectives. While many studies compare and contrast the effects of different management practices on soil properties, soil degradation can only be evaluated within a specific temporal and spatial context using multiple indicators. The extent and rate of soil degradation in SSA is still under debate as there are no reliable data, just gross estimates. Nevertheless, certain soils are losing their ability to provide food and essential ecosystem services, and we know that soil fertility depletion is the primary cause. We synthesize data from studies that examined degradation in SSA at broad spatial and temporal scales and quantified multiple soil degradation indicators, and we found clear indications of degradation across multiple indicators. However, different indicators have different trajectories-pH and cation exchange capacity tend to decline linearly, and soil organic carbon and yields non-linearly. Future research should focus on how soil degradation in SSA leads to changes in ecosystem services, and how to manage these soils now and in the future. OPEN ACCESS

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Section: Spatial Scalesmentioning

confidence: 99%

Section: Positive Trajectories and Conclusionmentioning

confidence: 99%

The State of Soil Degradation in Sub-Saharan Africa: Baselines, Trajectories, and Solutions

et al. 2015

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“…Detailed maps of soil nutrients, including micronutrients, are now possible due to the increasing inflow of soil samples collected at field point locations by various government and/or NGO funded projects: e.g. by projects supported by the National Governments of Ethiopia, Tanzania, Kenya, Uganda, Nigeria, Ghana, Rwanda, Burundi and others; and by organizations such as the Bill and Melinda Gates Foundation (Leenaars 2012;Shepherd et al 2015;Towett et al 2015;Vågen et al 2016) and similar, as well as by the private sector.…”

Section: Introductionmentioning

confidence: 99%

“…These maps were derived from a compilation of soil profile data collected from current and previous soil surveys. There is now a growing interest in applying similar spatial prediction methods to produce detailed maps of soil nutrients (including micro-nutrients) for SSA, in order to support agricultural development, intensification and monitoring of the soil resource (Kamau and Shepherd 2012;Shepherd et al 2015;Wild 2016). Detailed maps of soil nutrients, including micronutrients, are now possible due to the increasing inflow of soil samples collected at field point locations by various government and/or NGO funded projects: e.g.…”

Section: Introductionmentioning

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

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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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“…The ambition was to integrate research across multiple scalestrees, farms, landscapes and governance domainsand provide international public goods, including 'change of theory' if existing 'theories of change' were found inadequate. The 'sentinel' terminology was derived from public health, where research to recognize generic patterns can lead to more effective prevention rather than only curing individuals (Shepherd et al 2015). We will here focus on two questions:…”

Section: Introductionmentioning

confidence: 99%

Tropical forest-transition landscapes: a portfolio for studying people, tree crops and agro-ecological change in context

Dewi

Noordwijk

Zulkarnain

et al. 2017

International Journal of Biodiversity Science, Ecosystem Servic

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Nudging the development trajectory of tropical landscapes towards sustainability requires a global commitment and policies that take diverse contexts and forest transitions into account. Out-scaling and upscaling landscape-level actions to achieve sustainable development goals globally need to be based on understanding of extrapolation domains and interconnectivity of products and services. We evaluated three portfolios of tropical landscape observatories and quantified extrapolation domains across ecological zones, stages of forest transition, human development index (HDI), population density and potential prominence of four dominant tropical tree crops (arabica coffee, cacao, rubber and oil palm). The ASB Partnership for Tropical Forest Margins portfolio was focussed on active humid forest margins and the Poverty and Environment Network on early stages of forest transition. The portfolio of sentinel landscapes of the Forests, Trees and Agroforestry (FTA) research programme provides a 5% sample of pantropical area, 8% of people, 9% of tree cover and 10-12% of potential tree crop presence, with quantified biases across zones, transition stages and HDI. In the 'water tower' configuration, relatively high population density coincides with biodiversity, coffee expansion and contested ecosystem services. The extrapolation domain of the FTA portfolio includes trade-off (tree loss) and synergy (restoration) phases of tropical forest transition. ARTICLE HISTORY

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Land health surveillance and response: A framework for evidence-informed land management

Cited by 56 publications

References 25 publications

The State of Soil Degradation in Sub-Saharan Africa: Baselines, Trajectories, and Solutions

The State of Soil Degradation in Sub-Saharan Africa: Baselines, Trajectories, and Solutions

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

Tropical forest-transition landscapes: a portfolio for studying people, tree crops and agro-ecological change in context

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