Developing a soil quality index to compare soil fitness for agricultural use under different managements in the Mediterranean environment

Armenise, Elena; Redmile-Gordon, Marc; Stellacci, Anna Maria; Ciccarese, A.; Rubino, Pietro

doi:10.1016/j.still.2013.02.013

Cited by 247 publications

(162 citation statements)

References 35 publications

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“…W i in Eq. (4) was derived by the percentage of the total variance explained by the factor in which the soil property had the highest load divided by the total variance explained by all the factors with eigenvalues ≥ 1 (Masto et al, 2008;Armenise et al, 2013). In Step (7) CSDI values were categorized into number of desired (3) classes of degradation using their z score value as obtained by…”

Section: Statistical Analyses and Index Developmentmentioning

confidence: 99%

Development of a composite soil degradation assessment index for cocoa agroecosystems in southwestern Nigeria

2017

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Abstract. Cocoa agroecosystems are a major land-use type in the tropical rainforest belt of West Africa, reportedly associated with several ecological changes, including soil degradation. This study aims to develop a composite soil degradation assessment index (CSDI) for determining the degradation level of cocoa soils under smallholder agroecosystems of southwestern Nigeria. Plots where natural forests have been converted to cocoa agroecosystems of ages 1-10, 11-40, and 41-80 years, respectively representing young cocoa plantations (YCPs), mature cocoa plantations (MCPs), and senescent cocoa plantations (SCPs), were identified to represent the biological cycle of the cocoa tree. Soil samples were collected at a depth of 0 to 20 cm in each plot and analysed in terms of their physical, chemical, and biological properties. Factor analysis of soil data revealed four major interacting soil degradation processes: decline in soil nutrients, loss of soil organic matter, increase in soil acidity, and the breakdown of soil textural characteristics over time. These processes were represented by eight soil properties (extractable zinc, silt, soil organic matter (SOM), cation exchange capacity (CEC), available phosphorus, total porosity, pH, and clay content). These soil properties were subjected to forward stepwise discriminant analysis (STEPDA), and the result showed that four soil properties (extractable zinc, cation exchange capacity, SOM, and clay content) are the most useful in separating the studied soils into YCP, MCP, and SCP. In this way, we have sufficiently eliminated redundancy in the final selection of soil degradation indicators. Based on these four soil parameters, a CSDI was developed and used to classify selected cocoa soils into three different classes of degradation. The results revealed that 65 % of the selected cocoa farms are moderately degraded, while 18 % have a high degradation status. The numerical value of the CSDI as an objective index of soil degradation under cocoa agroecosystems was statistically validated. The results of this study reveal that soil management should promote activities that help to increase organic matter and reduce Zn deficiency over the cocoa growth cycle. Finally, the newly developed CSDI can provide an early warning of soil degradation processes and help farmers and extension officers to implement rehabilitation practices on degraded cocoa soils.

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Section: Statistical Analyses and Index Developmentmentioning

confidence: 99%

Development of a composite soil degradation assessment index for cocoa agroecosystems in southwestern Nigeria

2017

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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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“…Initially, efforts were made to define soil quality and later transform such concept into something measurable (Armenise et al, 2013).…”

Section: Introductionmentioning

confidence: 99%

Soil Physical Quality Indicators and Refinement of the Evaluation Method through the Srelative

Alencar¹,

Chaves²,

Freire³

et al. 2018

JAS

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This study aimed to verify the efficiency of indicators of measure of physical attributes' alterations and to refine the S relative determination method in order to increase its sensitivity to soil physical alterations. Soils under Ficus carica L. cultivation (with 0, 20, 40 and 60% of liquid bovine biofertilizer in the irrigation depth) and under forest were used. Parameters evaluated included soil granulometry, soil bulk and particle density, soil water retention curve (SWRC), porosity and the indices S and S relative . The experimental design was completely randomized with four replicates. For S relative refinement, with the SWRC containing only textural porosity, the soil was dispersed in water and with the addition of 1 N sodium hydroxide (with and without removal of sodium through washing). An ANOVA was performed for 0, 20, 40 and 60% of biofertilizer in 0-10, 10-20 and 20-30 layers; Dunnett test was used to compare the mean values of S-index and S relative -index. With respect to four methods to obtain the S relative -index the means were compared by Tukey test. Tests of line parallelism and intercept were performed for the regressions between each of the soil physical variables and S relative -index obtained. It was found that S and S relative indices were sensitive to soil physical alterations caused by the application of the biofertilizer; Srelative-index was sensitive to variation in soil bulk density and total porosity and the S relative -index obtained from the method of soil dispersion in water is more sensitive to soil physical alterations in comparison to S relative -index obtained through ADFE.

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Developing a soil quality index to compare soil fitness for agricultural use under different managements in the Mediterranean environment

Cited by 247 publications

References 35 publications

Development of a composite soil degradation assessment index for cocoa agroecosystems in southwestern Nigeria

Development of a composite soil degradation assessment index for cocoa agroecosystems in southwestern Nigeria

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

Soil Physical Quality Indicators and Refinement of the Evaluation Method through the Srelative

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