Assessment and GIS Mapping of Soil Quality Indicators of Agroecological Unit 9 of Kerala, India

Rainfed Inceptisol soils, despite their agricultural potential, pose serious problems, including soil erosion, low fertility, nutrient imbalance, and low soil organic matter, and ultimately lead to poor soil quality. To address these constraints, two long-term experiments were initiated to study conservation agricultural practices, comprising conventional and low tillage as well as conjunctive use of organic and inorganic sources of nutrients in Inceptisol soils of Agra center of the All-India Coordinated Research Project for Dryland Agriculture (AICRPDA). The first experiment included tillage and nutrient-management practices, whereas the second studied only conjunctive nutrient-management practices. Both used pearl millet (Pennisetum americanum (L.) Linn) as test crop. These experiments were adopted for soil quality assessment studies at 4 and 8 years after their completion, respectively, at the Central Research Institute for Dryland Agriculture (CRIDA), Hyderabad, India. Soil quality assessment was done by identifying the key indicators using principal component analysis (PCA), linear scoring technique (LST), soil quality indices (SQI), and relative soil quality indices (RSQI). Results revealed that most of the soil quality parameters were significantly influenced by the management treatments in both the experiments. In experiment 1, soil quality indices varied from 0.86 to 1.08 across the treatments. Tillage as well as the nutrient-management treatments played a significant role in influencing the SQI. Among the tillage practices, low tillage with one interculture + weedicide application resulted in a greater soil quality index (0.98) followed by conventional tillage + one interculture (0.94), which was at par with low tillage + one interculture (0.93). Among the nutrient-management treatments, application of 100% organic sources of nutrients gave the greatest SQI of 1.05, whereas the other two practices of 50% nitrogen (N) (organic) + 50% (inorganic source) (0.92) and 100% N (inorganic source) (0.88) were statistically at par with each other. The various parameters that emerged as key soil quality indicators along with their percentage contributions toward SQI were organic carbon (17%), exchangeable calcium (Ca) (10%), available zinc (Zn) (9%), available copper (Cu) (6%), dehydrogenase assay (6%), microbial biomass carbon (25%) and mean weight diameter of soil aggregates (27%). In experiment 2, SQI varied from 2.33 to 3.47, and 50% urea + 50% farmyard manure (FYM) showed the greatest SQI of 3.47, which was at par with 100% RDF + 25 kg zinc sulfate (ZnSO4) (3.20). Under this set of treatments, the key soil quality indicators and their contributions to SQI were organic carbon (19%), available N (20%), exchangeable Ca (3%), available Zn (4%) and Cu (17%), labile carbon (20%), and mean weight diameter of soil aggregates (17%). The quantitative relationship established in this study between mean pearl millet yields (Y) and RSQI irrespective of the management treatments for both the experiments together could be quite useful to predict the yield quantitatively with respect to a given change in soil quality for these rainfed Inceptisols. The methodology used in this study is not only useful to these Inceptisols but can also be used for varying soil types, climate, and associated conditions elsewhere in the world.

Application of soil quality index to determine the effects of different vegetation types on soil quality in the Yellow River Delta wetland

Soil quality assessment under different long-term rice-based cropping systems in a tropical dry savanna ecology of northern Nigeria

Establishing a suitable and useful soil quality (SQ) assessment tool is imperative for the accurate evaluation of the effect of environmental changes on SQ. This study constructed four soil quality indexes (SQIs) based on different minimum data sets and weighted additive models to evaluate the influence of grassland degradation on SQ in northwest Guizhou, China. A total of 19 soil properties, including six physical properties, six chemical properties, and seven microbial properties, were measured at soil depths 0–20 cm to construct the SQIs. Results showed that 18 soil indicators were selected as the potential SQ indicators in the total data set. Based on the principal component analysis, four indicators, soil organic carbon (SOC), mean weight diameter, α-glucosidase, and β-acetylglucosaminidase, were selected in the minimum data set (MDS). However, six indicators, SOC, pH, β-1,4-xylosidase, β-acetylglucosaminidase, Clay, and Bulk Density, were selected for the selective MDS. Despite the notable inter-correlation among the four established SQIs, the SQI derived from the selective MDS and weighted additive model demonstrated heightened sensitivity and capacity for differentiation with respect to grassland degradation because of the high values of F and CV. Grassland degradation significantly reduced the SQ, and the value of SQ under severely degraded grassland was reduced by 51% compared with that under non-degraded grassland. Under the lightly degraded grassland, the reduction in soil physical quality was the primary reason for the total SQ decline, while the reduction in soil microbial and chemical reduction resulted in a significant decline in total SQ under the severely degraded grassland. In conclusion, greater attention should be paid to the SQ reduction resulting from grassland degradation in the study area, and the SQI established by selective MDS and weighted additive model should be used as a suitable and useful SQ assessment tool to evaluate the influence of environmental changes on SQ in Southwest China and other similar areas.

Assessing the effects of different long-term ecological engineering enclosures on soil quality in an alpine desert grassland area

Irrigation is one way of utilizing the land resources to enhance agricultural production. Irrigation crop production is crucial in the present study area due to its arid and semi-arid climatic characteristics. However, little is known about the influence of different cropping and land management practices on soil quality (SQ). This study aimed to determine the effects of different cropping systems and land management practices on variability of SQ indicators in the Central Rift Valley of Ethiopia (CRVE). To this end, 45 disturbed surface (0‒20 cm) and 24 undisturbed (upper 7 cm) soil samples were collected from four adjacent farms: large-scale perennial farms (LSPF), large-scale annual farms (LSAF), smallholder subsistence annual farms (SHAF), and non-cultivated lands (NCL). Soil analyses were made for selected SQ indicators – particle size analysis, bulk density, soil water content, organic matter, pH, total nitrogen, available potassium and phosphorus, exchangeable bases, and cation exchange capacity. One-way analysis of variance (ANOVA) and Pearson correlation coefficient (r) were computed. Key informants’ interview was conducted to substantiate the data obtained from soil laboratory analyses. As the results confirmed, different cropping and land management practices had significant effects on some SQ indicators. Soil organic matter, total nitrogen, available P, and available K declined significantly (P < 0.05) in the soils of LSAF and SHAF. This is attributed to land management-induced problems such as frequent tillage practice of mono-cropping, high level of mechanization, removal of crop residues/above-ground biomass in LSAF, and use of low external inputs and overcultivation without appropriate land management practices in SHAF. However, LSPF practice resulted in the improvement of key SQ indicators, next to NCL. Therefore, LSPF can be an alternative cropping and land management practice to achieve sustainable agricultural production and land management in semi-arid irrigated lands of CRVE and in places with similar environments.

To precisely comprehend the impacts and determine the most suitable nutrient management and organic farming practices for sustainable agro‐ecosystems, assessment of soil quality at surface and sub‐surface depth is crucial. This study aims to assess the soil quality in response to different farming practices under maize‐wheat sequences. At two soil depths (0–0.15 m; and 0.15–0.30 m), physical, chemical and biological indicators of soil quality were evaluated under eleven nutrient management practices, including 100% NPK (RDF); 100% NPK + FYM at 10 t ha −1 (RDFM); 100% NPK + lime (RDFL); Organic farming practices (OF)s; natural farming system practiced using products of Desi cow (NFS‐ DC ); natural farming system practiced using products of Crossbred cow (NFS‐ CC ); natural farming system practiced using products of Buffalo (NFS‐ BF ); OF+25%NPK; NFS‐ DC + 25%NPK; NFS‐ CC + 25%NPK; and NFS‐ BF + 25%NPK, which were arranged in a completely randomised‐block design. For the soil quality assessment, minimum data set (MDS) of quality indicators was chosen using principal component analysis, and soil quality index (SQI) was then determined using a weighted additive technique. Results revealed that maize and wheat grain equivalent yield, available nutrients (N, P and K) were recorded higher under RDFM, followed by RDFL. However, water holding capacity, bulk density, aggregate stability, saturated hydraulic conductivity, available S, organic carbon, microbial count, microbial biomass C and N, and enzymatic activities greatly improved under OF+ 25%NPK. Among NFS practices, NFS‐ DC outperformed NFS‐ CC and NFS‐ BF in all the studied parameters. Available N, S, K, organic carbon, microbial biomass C, mean weight diameter and bulk density were selected as MDS. At 0–0.15 m soil depth, soil quality index (SQI) was recorded highest under organic farming+25%NPK (0.99), followed by RDFM (0.98), while the lowest value of SQI was observed under RDF (0.88). NFS‐ DC witnessed 1.27% and 1.97% higher values of SQI over NFS‐ CC and NFS‐ BF , respectively. The SQI value decreased in the sub‐surface soil layer (0.15–0.30 m), however treatment‐wise trend remains the same. At surface soil (0–0.15 m soil depth), available N contributed the most (35.7%) to SQI, followed by OC (27.9%), MBC (26.9%), available K (2.34%), S (0.91%) and MWD (0.51%). This study advocates the addition of organic manures with small quantity of chemical fertilisers that is, organic farming practices +25%NPK for maintaining the soil health and quality which could sustain/enhance system' productivity in a long run.

This study was aimed at assessing farmers’ perception and knowledge of soil quality (SQ) change, in light of scientifically measured SQ indicators in the Wanka watershed, northwestern highlands of Ethiopia. Household survey, participatory SQ status assessment, key informant interview and laboratory analysis of selected SQ indicators were used as data collection tools. Independent samples t test (two-tailed) was used to compare the mean difference of SQ indicators between perceived good and poor SQ status. Farmers recognized that there has been SQ decline in their farm lands over the years. Based on perceived SQ status, farmers categorized their farm plots into good, average and poor classes locally called wofram meret, boda and sis/chincha meret, respectively. The identified principal SQ status indicators used by farmers were yield, plow depth, appearance of undesired weedy plant species, fertilizer requirement of soil, topsoil color and soil workability. These farmers’ assessment of SQ has well-coincided with major scientific quantitative indicators. Accordingly, plots identified by farmers as good SQ status exhibited better soil nutrients than the perceived poor SQ. Available phosphorus (p < 0.01) and exchangeable potassium (K+) (p < 0.01) were significantly higher in the reported good SQ plots. Conversely, sand content (p < 0.01) and bulk density (p < 0.05) were significantly high in poor SQ category. The synergy between perceived SQ status and scientifically measured SQ indicators signifies the relevance of farmers’ soil knowledge in characterizing SQ status of farm plots and manage them accordingly. Thus, strategies that incorporate farmers’ soil knowledge in land evaluation and sustainable land management practices should be developed and promoted.

Soil quality (SQ) indicators such as plant available water (PAW), soil organic carbon (SOC), and microbial biomass carbon (MBC) can reveal agroecological functions; however, their spatial variabilities across contrasting land uses need to be better understood. This study examined the spatial variation of these key SQ indicators as a function of two land-use systems and using topography covariates. We sampled a total of 116 point locations in a native grassland (NG) site and an irrigated cultivated (IC) site located near Brooks, Alberta. Compared with NG, cultivation altered soil pore-size distribution by sharply reducing macroporosity by 25%. However, conditions in the IC soil supported greater accrual of microbial growth (MBC of 601 vs. 812 nmol phospholipid fatty acids g−1 soil) probably due to more availability of water and nutrients. Focusing on the effects of topography on SQ indicators, terrain elevation (by light detection and ranging) and estimated depth-to-water were found to be key controllers of SQ at the two land-use systems. Also, there were gradual increases in both SOC and MBC where estimated water table was deeper, and higher SOC also associated with lower elevation. A comparison of ordinary kriging and cokriging (coK) geostatistical mapping indicated that the coK method performed better as demonstrated by improvements in the accuracies of spatial estimations of PAW, SOC concentration, and MBC. Thus, implementing coK using the aforementioned topography covariates enhances the capability for predictive mapping of SQ, which is particularly useful when spatial data for key SQ indicators are sparse and challenging to measure.

Soil quality assessment under different land uses in an alpine grassland

Establishment of critical limits of indicators and indices of soil quality in rice-rice cropping systems under different soil orders

Soil salinity and acidity are some of the major causes of land degradation and have a negative impact on agricultural productivity. Assessing soil quality (SQ) of soils affected by soil salinity and acidity is required for their sustainable utilization for agricultural production. The aim of the present study was to evaluate the SQ of the salt-affected acid soils of the Indian West Coastal region using the additive and weighted soil quality indices (SQIs). The SQIs were developed using a total dataset (TDS) and a minimum dataset (MDS). The TDS comprised of 15 different soil properties as electrical conductivity (EC), pH, bulk density, soil available nitrogen (N), phosphorus (P), potassium (K), sulfur (S), boron (B), iron (Fe), manganese (Mn), copper (Cu), zinc (Zn) and exchangeable calcium (Ca), magnesium (Mg), and sodium (Na) measured on 300 soil samples (depth 0-0.15m). Based on principal component analysis and correlation analysis, an MDS with soil properties like soil pH, EC, Na, Cu, Mn, and BD was formed. Using two approaches (additive and weighted), two datasets (TDS and MDS), and two scoring methods (linear and non-linear), eight SQIs were developed. The MDS-based linear weighted and non-linear weighted SQI found suitable to evaluate SQ of salt-affected acid soils and SQI had a significant and negative correlation of - 0.83 and - 0.70 (p < 0.01) with EC, respectively. Thus, it is clear that the SQ considerably reduces with an increase in soil salinity. The performance of the MDS-based SQIs was better than the TDS to discriminate different soil salinity classes. The agreement between the linear and non-linear scoring method of SQI had a linear relationship with a coefficient of determination (R2) of 0.91-0.96. Thus, assessing the SQ of salt-affected acid soils using MDS, linear scoring, and weighted approach of the soil quality indexing could save the time and cost involved.

Intensive agricultural practices lead to a deterioration in soil quality, causing a decline in farm productivity and quality, and disturbing the ecosystem balance in command areas. To achieve sustainable production and implement effective soil management strategies, understanding the state and spatial variability of soil quality is essential. The study aims to enhance the understanding of soil quality variability and provide actionable insights for sustainable soil management. In this regard, principal component analysis (PCA) and digital soil mapping were used to assess and map the spatial variability of the soil quality index (SQI) in the Cauvery command area, Mandya district, Karnataka, India. A total of 145 georeferenced soil samples were drawn at 0–15 cm depth and analyzed for physico-chemical properties. PCA was used to reduce the dataset into a minimum dataset as eight important soil indicators and to determine relative weightage factors, which were used for assessing SQI with linear and non-linear scoring methods. For spatial assessment of SQI, the random forest algorithm with environmental covariates was used to map eight soil indicators selected in the minimum dataset. The soil property maps were subjected to linear and non-linear scoring, followed by multiplying with corresponding weightage factors and summation to produce SQI maps. Results reveal that values of SQI calculated using linear scoring, range from 0.10 to 0.64, with a mean of 0.39, while non-linear scoring exhibits a wider range of 0.12 to 0.78 and a mean of 0.48. With a slight higher sensitivity index of 6.5, non-linear scoring proved to be the better scoring method compared to linear scoring. Spatial assessment shows that the R2 and LCC between the calculated and predicted SQI were higher for non-linear scoring (0.66 and 0.66) compared to linear scoring (0.60 and 0.65). The SQI maps reveal high spatial variability with more than 40 percent of soils classified as moderate-to-low index. The soils with low SQI were distributed in eastern parts, whereas western parts exhibited high-to-very-high soil quality. To achieve production goals and improve soil quality in the eastern region, sustainable soil and crop management strategies must be developed, and their effects on soil quality should be assessed.

Soil quality indices of paddy soils in Guilan province of northern Iran: Spatial variability and their influential parameters

&amp;lt;p&amp;gt;LANDSLIDES are one of the destructive geological processes that occur throughout the world. At global scale, the landslides are one of the major natural disaster which deteriorate the soil quality at a very large scale. In the Indian Himalayan Region (IHR), the Garhwal Himalayas of Uttarakhand landslides occurred very frequently in rainy season due to the presence of fragile rocks, active tectonic activity and unplanned anthropogenic activities. Landslides causes the loss of soil nutrients and vegetation which in turn deteriorate the soil quality. They can have an enormous effect on biodiversity and significantly alter the soil quality. The rate of soil development in essential for determining the recovering capacity of soil after the losses occurred due to landslides and erosion.&amp;lt;/p&amp;gt;&amp;lt;p&amp;gt;Therefore, the present study analyzed the natural recovery of soil quality in terms of soil characteristics with the passage of time (chronosequence) in 4 disturbed sites of different ages i.e., 6-year-old (L1 site), 16-year-old (L2 site), 21-year-old (L3 site) and 26-year-old (L4 site) including control (undisturbed) site in the Garhwal Himalayas of Uttarakhand. 76 soil samples were collected from all the selected sites at two depths i.e., 0-15cm and 15-30cm. The collected soil samples were analyzed for various physical (bulk density (BD), particle density (PD), total porosity (TP), moisture content (MC) and sand, silt and clay content) and chemical characteristics (pH, electrical conductivity (EC), soil organic carbon (SOC), soil organic matter (SOM), mineralisable nitrogen (MN), available phosphorus (AP) and available potassium (AK). Principal Component Analysis (PCA) was done with all the 14 variables which are significantly different in order to establish minimum data set (MDS). The MDS includes SOC, AP and clay content on the basis of the PCA results. The soil quality index (SQI) was calculated using Integrated Quality Index (IQI) equation. Landslide affected sites L1, L2, L3 and L4 and control site had mean SQI scores of 0.136, 0.279, 0.447, 0.604 and 0.882, respectively.&amp;lt;/p&amp;gt;&amp;lt;p&amp;gt;The results have demonstrated that the control site had much better soil quality in comparison to the landslide affected sites because of its better nutrients content and better physical characteristics. The results have also shown that the soil quality tends to increase with the age of landslide, but the soil quality has not reached to the pre-disturbance level in a period of 26 years. The SQI shows the variations in landslide affected sites which could be used to detect variations in soils of disturbed areas. The results will also provide crucial information for evaluating the consequences, designing, and implementing restoration strategies.&amp;lt;/p&amp;gt;