Landscape-scale variability of soil health indicators: effects of cultivation on soil organic carbon in the Usambara Mountains of Tanzania

Winowiecki, Leigh A.; Vågen, Tor-Gunnar; Massawe, Boniface H. J.; Jelinski, Nicolas A.; Lyamchai, Charles; Sayula, George; Msoka, Elizabeth

doi:10.1007/s10705-015-9750-1

Cited by 63 publications

(71 citation statements)

References 36 publications

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“…Although the CASH was originally calibrated for use with soils of the northeastern United States, it is increasingly used for other geographic regions. Numerous soil health studies have been conducted at the plot (Andrews and Carroll, 2001;Armenise et al, 2013;Idowu et al, 2009;de Paul Obade and Lal, 2014;Hussain et al, 1999) and landscape (Karlen et al, 2008;Svoray et al, 2015;Winowiecki et al, 2016) scales, but few at the regional or national levels (Brejda et al, 2000a;Brejda et al, 2000b;Brejda et al, 2000c;Schipper and Sparling, 2000). Using the CASH database of 5767 samples, we performed multiple statistical analyses, including an investigation into differences in soil health among three US regions that represent distinct soils, climates, topographies, and land uses.…”

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confidence: 99%

Statistics, Scoring Functions, and Regional Analysis of a Comprehensive Soil Health Database

Fine

Schindelbeck

2017

Soil Science Soc of Amer J

209

157

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Soil health (Sh) refers to the ability of a soil to function and provide ecosystem services. The Comprehensive Assessment of Soil health (CASh) is an approach that measures 15 physical, biological, and chemical soil indicators, which are interpreted through scoring functions. This study reports on the Sh status of 5767 samples from the mid-Atlantic, midwest, and northeast regions of the uSA as evaluated using CASh. descriptive statistics and AnOvAs of subdatasets by region and soil textural group for Sh indicators, in addition to correlation coefficients, principal component (pC) analysis, and best subsets regression (BSr) were performed. from these analyses, new CASh scoring functions were developed. Separate scoring functions by textural group (fine, medium, coarse) were necessary for Wet Aggregate Stability (WAS), Available Water Capacity (AWS), Organic matter (Om), Active Carbon (AC), and protein. differences existed among regions, especially for WAS, Om, protein, and respiration (resp), where the midwest had relatively lower mean values compared to the mid-Atlantic and northeast. Biological properties and WAS showed moderately strong correlations (r = 0.58-0.78) and the highest loadings for the first two pCs. BSr results using the overall soil quality index as the response variable indicated that AC accounts for 45% of the variation, with additional predictability from penetration resistance, resp, and WAS (68%). These four indicators are suggested for simplified Sh tests. We conclude that the CASh approach can be successfully applied to evaluate the health status of soils with differing pedogenetic histories. C onceptually, soil health (SH) represents the emerging understanding of soil quality. Both terms refer to the ability of a soil to function and provide ecosystem services based on its inherent characteristics (e.g., texture, mineralogy) and environmental conditions (Karlen et al., 1997;Andrews et al., 2004;Idowu et al., 2009). A soil's health status, within the context of land use and management goals, however, is consistent with the understanding of soils as a dynamic, complex, and living system (Doran and Zeiss, 2000). Intensive agriculture and poor land management practices have led to widespread soil degradation associated with increasing topsoil erosion, nutrient depletion, pollution, compaction, and loss of organic matter (Matson et al., 1997). A sustainable future with an ever-growing global population depends on healthy, well-functioning soils, which increase water and air quality, sup- Core Ideas• Summary statistics were developed from a robust multiregional soil health (Sh) dataset.• Active carbon, organic matter, and penetration resistance were most useful soil health indicators.• midwestern soils had relatively lower mean values for measured biological properties than northeast or mid-Atlantic soils.

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confidence: 99%

Statistics, Scoring Functions, and Regional Analysis of a Comprehensive Soil Health Database

Fine

Schindelbeck

2017

Soil Science Soc of Amer J

209

157

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“…The lowest SOC values were found in semi-arid environments (Ol Lentille, Mpala, Mega) and in Miombo woodlands (Mbola). There are many factors influencing SOC concentrations, including inherent soil properties such as sand content, land degradation status such as erosion prevalence (Winowiecki et al 2016a(Winowiecki et al , 2016b, as well as land management practices (including burning in seminatural and cropland systems, fertilization, residue retention, among others) (Vanlauwe et al 2015). The use of stable carbon isotopes can aid in better understanding SOC dynamics and the influence of vegetation shifts over time.…”

Section: Discussionmentioning

confidence: 99%

Landscape-scale assessments of stable carbon isotopes in soil under diverse vegetation classes in East Africa: application of near-infrared spectroscopy

et al. 2017

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Aims Stable carbon isotopes are important tracers used to understand ecological food web processes and vegetation shifts over time. However, gaps exist in understanding soil and plant processes that influence δ 13 C values, particularly across smallholder farming systems in sub-Saharan Africa. This study aimed to develop predictive models for δ 13 C values in soil using near infrared spectroscopy (NIRS) to increase overall sample size. In addition, this study aimed to assess the δ 13 C values between five vegetation classes. Methods The Land Degradation Surveillance Framework (LDSF) was used to collect a stratified random set of soil samples and to classify vegetation. A total of 154 topsoil and 186 subsoil samples were collected and analyzed using NIRS, organic carbon (OC) and stable carbon isotopes. Results Forested plots had the most negative average δ 13 C values, −26.1‰; followed by woodland, −21.9‰; cropland, −19.0‰; shrubland, −16.5‰; and grassland, −13.9‰. Prediction models were developed for δ 13 C using partial least squares (PLS) regression and random forest (RF) models. Model performance was acceptable and similar with both models. The root mean square error of prediction (RMSEP) values for the three independent validation runs for δ 13

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“…The LDSF was designed for practical and cost-effective soil and ecosystem health surveillance, including for mapping of SOC (Vågen et al, 2013a(Vågen et al, , 2016Winowiecki et al, 2016aWinowiecki et al, , 2016b. 25…”

Section: Methodsmentioning

confidence: 99%

“…An important indicator of soil health is soil organic carbon (SOC), and field datasets collected using the LDSF have been used to provide spatially explicit assessments of SOC and other indicators of land degradation processes such as soil erosion, in addition to other soil functional properties (Vågen et al, 2012(Vågen et al, , 2016Winowiecki et al, 2016aWinowiecki et al, , 2016b. These assessments can be used to inform spatially explicit land and soil health monitoring systems, which 25 are critical in order for countries to avoid land degradation, or to restore already degraded ecosystems.…”

Section: Introductionmentioning

confidence: 99%

Spatial assessments of soil organic carbon for stakeholder decision-making. A case study from Kenya

Vågen¹,

Winowiecki²,

Neely³

et al. 2018

Preprint

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Land degradation impacts the health and livelihoods of about 1.5 billion people worldwide. Given that the state of the environment and food security are strongly interlinked in tropical landscapes, the increasing need for land for food production, 5 urbanization and other uses pose several threats to sustainability in the long term. There is increasing recognition that more integrated approaches to assessments and management of ecosystem health are needed to meet the targets of the 2030 Agenda, including SDG 15.3. In addition to systematic and reliable biophysical and socio-economic assessments, stakeholder engagement with evidence is crucial to support integrated approaches. This paper demonstrates the integration of land and soil health maps with socioeconomic datasets into an online, open-access platform called the Resilience Diagnostic and Decision 10 Support Tool for Turkana County in Kenya, using the Stakeholder Approach to Risk Informed and Evidence Based Decision Making (SHARED) methodology. The paper highlights the utility of spatial assessments of soil organic carbon (SOC) for monitoring of land degradation neutrality (LDN) compliance, understanding the drivers of SOC dynamics, and inclusion in stakeholder decision-making. The main objectives of this paper were to: 1) demonstrate the application of a systematic approach for land health assessments, including spatial mapping of soil organic carbon; 2) show an operational 15 interdisciplinary framework for assessing ecosystem health; and 3) showcase the application of evidence-based tools for stakeholder engagement using the SHARED approach.

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Landscape-scale variability of soil health indicators: effects of cultivation on soil organic carbon in the Usambara Mountains of Tanzania

Cited by 63 publications

References 36 publications

Statistics, Scoring Functions, and Regional Analysis of a Comprehensive Soil Health Database

Statistics, Scoring Functions, and Regional Analysis of a Comprehensive Soil Health Database

Landscape-scale assessments of stable carbon isotopes in soil under diverse vegetation classes in East Africa: application of near-infrared spectroscopy

Spatial assessments of soil organic carbon for stakeholder decision-making. A case study from Kenya

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