Selecting Promising Soil Quality Indicators for Monitoring Soil Management Effects Based on 10 European Long‐Term Field Experiments

Developments in soil biology and methods to characterize soil organic carbon have the potential to deliver novel soil quality indicators that can help to identify soil management practices that sustain soil productivity and environmental resilience. This thesis aimed at investigating the suitability of a range of soil biological and biochemical parameters as novel soil quality indicators for agricultural management. The soil parameters, selected through a literature review, comprised different labile organic carbon fractions (hydrophilic dissolved organic carbon (Hy-DOC), dissolved organic carbon (DOC), permanganate oxidizable carbon (POXC), hot water extractable carbon (HWEC) and particulate organic matter carbon (POMC), ordered here from the smallest to the largest proportion of the total organic carbon), soil disease suppressiveness measured with a Pythium-Cress bioassay, nematode communities characterized with amplicon sequencing and qPCR, and microbial community level physiological profiling (CLPP) measured with MicroResp TM . We tested the sensitivity of the novel indicators to tillage and organic matter addition in 10 European long-term field experiments, and assessed their relationship with already existing soil quality indicators linked to soil functioning. Lastly, the results of these experimental chapters are interpreted relative to each other and to the broader body of literature on soil quality assessments. Moreover, pros and cons of the novel indicators are discussed, and possibilities and needs for future research are outlined. Reduced tillage increased carbon availability, disease suppressiveness, nematode richness and diversity, the stability and maturity of the food web, and microbial activity and functional diversity. Organic matter addition had a weaker role in sustaining soil quality, possibly due to the different compositions of the organic matter inputs in the longterm field experiments that were sampled. Random forest analysis showed that POXC was the indicator that discriminates soil management most, and structural equation modelling showed its central role in nutrient cycling, carbon sequestration, biodiversity conservation, erosion control and disease regulation/suppression. The novel indicators proposed here have great potential to improve existing soil quality assessment schemes, but their usefulness is still to be validated and optimized.

Developments in soil biology and in methods to characterize soil organic carbon can potentially deliver novel soil quality indicators that can help identify management practices able to sustain soil productivity and environmental resilience. This work aimed at synthesizing results regarding the suitability of a range of soil biological and biochemical properties as novel soil quality indicators for agricultural management. The soil properties, selected through a published literature review, comprised different labile organic carbon fractions [hydrophilic dissolved organic carbon, dissolved organic carbon, permanganate oxidizable carbon (POXC), hot water extractable carbon and particulate organic matter carbon], soil disease suppressiveness measured using a <i>Pythium</i>-<i>Lepidium</i> bioassay, nematode communities characterized by amplicon sequencing and qPCR, and microbial community level physiological profiling measured with MicroResp^TM. Prior studies tested the sensitivity of each of the novel indicators to tillage and organic matter addition in ten European long-term field experiments (LTEs) and assessed their relationships with pre-existing soil quality indicators of soil functioning. Here, the results of these previous studies are brought together and interpreted relative to each other and to the broader body of literature on soil quality assessment. Reduced tillage increased carbon availability, disease suppressiveness, nematode richness and diversity, the stability and maturity of the food web, and microbial activity and functional diversity. Organic matter addition played a weaker role in enhancing soil quality, possibly due to the range of composition of the organic matter inputs used in the LTEs. POXC was the indicator that discriminated best between soil management practices, followed by nematode indices based on functional characteristics. Structural equation modeling shows that POXC has a central role in nutrient retention/supply, carbon sequestration, biodiversity conservation, erosion control and disease regulation/suppression. The novel indicators proposed here have great potential to improve existing soil quality assessment schemes. Their feasibility of application is discussed and needs for future research are outlined.

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.

Introduction: The mismanagement of natural resources has led to low soil quality and high vulnerability to soil erosion in most parts of Iran. To have a sustainable soil quality, the assessment of effective soil quality indicators are required. The soil quality is defined as the capacity of a soil to function within natural and/or managed ecosystem boundaries. Among approaches which are suggested for soil quality assessment like soil card design, test kits, geostatistical methods and soil quality indices (SQIs), SQIs are formed by combination of soil indicators which resulted from integration evaluation of soil physical, chemical and/or biological properties and processes complement by existing/measureable data, sensitive to land use changes, management practices and human activities and could be applied in different ecosystems. As the measurement and monitoring of all soil quality indicators is laborious and costly, many researchers focused on limited soil quality indicators. There are many methods for identification and determination of minimum data set that influence on soil quality such as linear and multiple regression analysis, pedotransfer functions, scoring functions, principle component analysis and discriminant analysis. Among these methods, principle component analysis is commonly used because it is able to group related soil properties into small set of independent factors and to reduce redundant information in original data set. The objective of this research was to investigate the effects of land use change on soil quality indicators and also the determination of minimum effective soil quality indicators for assessment of soil quality in Choghakhor Lake basin, Chaharmahal and Bakhtiari province, Iran. Materials and Methods: To meet the goal, Latin hypercube sampling method was applied by using slope, land use and geological maps and 125 composite soil samples were collected from soil surface (0-20 cm). After pretreatments, 27 physical and chemical soil properties like clay, sand and silt content, bulk density (ρb), porosity, organic carbon (OC), particulate organic carbon in macro aggregate (POCmac), particulate organic carbon in micro aggregates (POCmic), proportion of particulate organic carbon in macro aggregates to micro aggregates (POCmac/mic), mean weight diameter (MWD), macro porosity (Mac pore), air content, available water content (AWC), relative water content (RWC), effective porosity (Feff), Dexter index (S), porosity, acidity (pH), electrical conductivity (EC), Nitrogen (N), Phosphorous (P), Iron (Fe), manganese (Mn), Zinc (Zn), Cadmium (Cd), lead (Pb), Copper (Cu) and Cobalt (Co) were measured using appropriate methods. Results and Discussion: The impact of different land use types on soil quality was evaluated by measuring several soil properties that are sensitive to stress or disturbance and comparison of them. The results showed that measured values of OC, POCmac, POCmic, POCmac/mic, P, Fe, Zn, Mn, Cu, ρb, MWD, AWC, air content and S were in order orchards > crop land > good rangelands > dry lands > weak rangelands. In this region, land use changes have different effects on soil quality. The alternation of good pasture lands to orchard and crop lands caused to enhancement of soil quality parameters. The variation of good pasture to dry land and degradation of good pasture in this area led to decreasing of soil quality. The principle component analysis (PCA) was employed as a data reduction tool to select the most appropriate indicators of site potential for the study area from the list of indicators. Based on PCA, 8 components with eigenvalues ≥ 1 were selected that explained 99.96 percent of variance. The prominent eigenvectors in components were selected using Selection Criterion (SC). The results revealed that the most important component, was the first component with the most dominant measured soil property of Cu. 12 soil quality parameters base on SC were determined in the first component. Stepwise discriminate analysis also was applied for determination significant soil quality indicators from 12 soil parameters. Our result showed that the minimum data set influencing on soil quality were Zn followed by POCmac/mic, clay %, Cu, Mn and P, respectively. Conclusion: The results suggested that the permanent crop management (Orchard and crop land) had generally a positive impact on soil quality, while dry land and degradation of good pasture had a negative impact on soil quality. Our study suggested that the PCA method and stepwise discriminant analysis for determination of minimum data set can be used in Chughakhur lake basin. In this study from27 of physical and chemical soil properties, the fertility factors such as the content of Zn, Cu, Mn and P and the proportion of particle organic carbon in macro aggregate to micro aggregate and also soil texture components can be used to the minimum data set that evaluates soil quality. These parameters mostly depend on soil management system.

The present study was conducted at Kovilpatti Centre of All India Coordinated Research Project for Dryland Agriculture (AICRPDA) to assess the impact of conjunctive nutrient management practices on soil quality parameters, to identify the key indicators of soil quality, to compute the soil quality indices (SQIs) and to identify the best conjunctive nutrient management practices from the view point of soil quality improvement in sorghum + cowpea intercropping (C1), sorghum sole cropping (C2) and cowpea sole cropping (C3) systems. The treatments were comprised of T1: control, T2: 100% N (inorganic), T3: 25 kg N (compost), T4: 15 kg N (compost) + 20 kg N (inorganic) and T5: 15 kg N (green leaf) + 20 kg N (inorganic). The results of the present study clearly indicated that the conjunctive nutrient treatments significantly influenced organic carbon (OC), available phosphorus (P), sulphur (S), zinc (Zn) and copper (Cu) contents of soil in all the three systems; the potassium (K) and manganese (Mn) were significantly influenced in the C2 and C3 cropping systems. The dehydrogenase activity (DHA) was significantly highest under T3 in sole sorghum system with highest value of 1.41 μg TPF ha−1 g−1 of soil, which was on par with T4. The microbial biomass carbon (MBC) was significantly influenced by sole cowpea and sole sorghum systems, with highest values of 125.1 and 114.0 μg g−1 of soil under T3, respectively. However, the labile carbon (LC) content was significantly influenced in all the three cropping systems, with the highest LC content of 338.3 (T3), 285.2 (T3) μg g−1 and 339.64 (T4) of soil under sorghum + cowpea, sole sorghum and sole cowpea systems, respectively. The mean weight diameter of soil aggregates (MWD) was significantly influenced by the treatments in the sole sorghum system with the highest value of 0.50 mm with the T5 treatment. When all the cropping systems were viewed together, the final set of key soil quality indicators for these Vertisol soils emerged were: EC, OC, available P, available S, available Zn, LC, MBC and BD. From the view point of RSQI, the order of superiority of the conjunctive nutrient management treatments was: T3: 25 kg N (compost) (0.95)=T4: 15 kg N (compost) + 20 kg N (inorganic) (0.95) > T5: 15 kg N (green leaf) + 20 kg N (inorganic) (0.84) > T2: 100% N (inorganic) (0.73) > T1: control (0.55).

The effects of biochars produced from the residues of locally grown crops on soil quality variables and indexes

Sensitivity of labile carbon fractions to tillage and organic matter management and their potential as comprehensive soil quality indicators across pedoclimatic conditions in Europe

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.

Data-driven analysis of soil quality indicators using limited data

Several methods have been used to model reality and explain soil pedogenesis and evolution. However, there is a lack of information about which soil properties truly condition soil quality indicators and indices particularly at the pedon scale and at different soil depths to be used in land management planning. Thus, the main goals of this research were: i) to assess differences in soil properties (particle size, saturation point, bulk density, soil organic carbon, pH and electrical conductivity) at different soil depths (0-30 and 30-60 cm); ii) to check their statistical correlation with soil quality indicators (CEC, total N, Olsen-P, available K, exchangeable Na, calcium carbonate equivalent, Fe, Mn, Zn, and Cu); and, iii) to elaborate a soil quality index and maps for each soil layer. To achieve this, forty-eight soil samples were analysed in the laboratory and subjected to statistical analyses by ANOVA, Spearman Rank coefficients and Principal Component Analyses. Finally, a soil quality index was developed based on indicators of sensitivity. The study was conducted in a semiarid catchment in northeast Iran with irrigated farming and well-documented land degradation issues. We found that: i) organic carbon and bulk density were not similar in the topsoil and subsoil; ii) calcium carbonate and sand content conditioned organic carbon content and bulk density; iii) organic carbon showed the highest correlations with soil quality indicators; iv) particle size conditioned cation-exchange capacity; and, v) heavy metals such as Mn and Cu were highly correlated with organic carbon due to non-suitable agricultural practices. Based on the communality analysis to map of soil quality, CEC, Mn, Zn, and Cu had the highest weights (≥0.11) at both depths, coinciding with the same level of relevance in the multivariate analysis. Exchangeable Na, CaCO3, and Fe had the lowest weights (≤0.1) and N, P, and K had intermediate weights (0.1- 0.11). In general, the map of the soil quality index shows a lower soil quality in the subsoil increment than in the topsoil.

BackgroundSoil quality, which can be inferred using indicators that interact synergistically, is affected by land use types and agricultural management practices. This study assessed the status of soil quality under three adjacent land uses (cultivated, grazing, and fallow) in Kersa subwatershed (622 ha). Soil samples were collected from the surface soil (0–20 cm depth) of the identified land uses with three replications and the soil quality parameters were analyzed. A minimum data set of soil quality indicators were selected from physical, chemical, and biological parameters using the literature review and expert opinion method. Linear scoring functions were used to give the unitless scores for the selected data sets, which were then integrated into a soil quality index (SQI).ResultsThe results revealed that bulk density, aggregate stability, pH, cation exchange capacity (CEC), available P, and soil organic carbon (SOC) had a significant difference in SQI among the different land uses. The soil quality indices were 0.69 for grazing land, 0.62 for cultivated land, and 0.59 for the fallow land. The SQI of all the land uses falls in the intermediate soil quality (0.55 < SQI < 0.70) class.ConclusionIn almost all the quality indicators assessed, the grazing land was superior to the cultivated and fallow lands. Therefore, implementing management practices that enhance soil quality like organic matter-controlled systems is imperative for sustainable agricultural production in the study area.

Evaluating soil quality under various management practices in agroecosystems is crucial for understanding soil functioning. In this study, a soil quality index (SQI) was developed to assess the impact of wheat straw incorporation on soil quality. Several soil attributes, either sensitive or insensitive to straw incorporation, were quantified in a pot experiment, and factor analysis was applied as a dimension reduction technique. Samples were taken from 0–20 cm depth in paddy soil, with treatments including no‐fertilizer control and wheat straw incorporation at rates of 0%, 50%, 100%, and 150% under combined NPK fertilization. The wheat straw used in this study was obtained from a rice–wheat rotation system. Soil properties, including physical (mean weight diameter), chemical (organic carbon, total nitrogen, ratio of organic carbon and total nitrogen, and particulate organic carbon), and biological (microbial biomass carbon and nitrogen, and three enzymes) parameters, were measured to create a minimum data set for the SQI evaluation. Results suggested a significant improvement in SQI under 100%S treatment. While the 100%S treatment enhanced most soil microbial attributes, not all of them contributed to SQI due to their interrelationships. Only geometric mean of microbial biomass (GMB) and geometric mean of enzyme activity (GME) were critical for the SQI, collectively contributing 56.97%. Thus, GMB and GME were essential indicators for distinguishing between straw management systems and could be employed to monitor improvements in soil quality under 100%S treatment. Taken together, the 100%S treatment showed the highest SQI, indicating improved soil capacity and functions compared to the control. Smallholder farmers should consider the 100%S treatment for enhanced soil quality in paddy ecosystems.

Soils provide essential ecosystem services for the existence of ecosystems and biodiversity. It is crucial to understand their quality through the evaluation of ecological processes. However, only some studies estimate the effectiveness of ecosystem restoration based on evaluating soil quality (SQ) indicators. This research evaluated the five most common land-use scenarios in Andean ecosystems within the Natural Reserve of the Civil Society (RNSC) “La Montaña Mágica” under natural forest, coffee plantation, badlands, and active and passive restoration. The main objective was to analyze the physical, chemical, and biological characteristics concerning land use, establish the baseline for SQ indicators in different land use activities, and determine the status of restoration systems for other land uses in the study site. ANOVA and Dunnett's test evaluated SQ parameters. In passive and active restoration, soil bulk density, porosity, and the number of individuals and families of macroinvertebrates were improved. The latter presented low pH and aluminum values but increased potassium compared to other soil uses. The restoration strategies favored changes in SQ indicators due to the contribution of organic carbon, a developed root system, and the recirculation of nutrients in the edaphic system. This study provided information on changes in SQ with soil usage as a practical tool to evaluate ecological restoration methods in natural areas of the eastern Colombian Andes.

Context: The agroecological unit 9 (AEU 9) in Alappuzha district of Kerala represents the south central laterites. The soils are acidic, gravelly, having low activity clay, often underlain by plinthite with low water and nutrient retention capacity. Assessment of soil quality indicators and mapping of resources and soil fertility status is essential for planning and development activities.&#x0D; Aims: Soil quality assessment was made by collecting observations on physical, chemical and biological indicators, soil quality index was computed and generated maps using GIS.&#x0D; Study Design: Survey, collection of soil samples and principal component analysis.&#x0D; Place and Duration of Study: A study was conducted in agroecological unit 9 (south central laterites) in Alappuzha district of Kerala during 2020.&#x0D; Methods: Geo-referenced composite soil samples were collected from 75 locations of AEU9 in Alappuzha district, and were characterized for physical, chemical and biological attributes for evaluating soil quality. Principal component analysis (PCA) was carried out for soil parameters and a minimum data set (MDS) was arrived from seven principal components (PC 1 to PC 7) with eigen values greater than 1. The selected soil quality indicators were categorized into classes, assigned with scores and soil quality index was computed. Based on soil quality index, soils were rated as poor, medium or good. GIS based thematic maps of soil quality indicators and index were prepared.&#x0D; Results: The soils of AEU 9 are sandy loam in texture, strongly acidic (pH 4.5-5.5), medium in organic carbon, low in available N, medium in available P and K, deficient in available Ca, Mg and B. Majority (80%) of the soils are rated poor and 20% rated medium in soil quality. From the study it is concluded that pH, clay%, bulk density, available nutrients N, P, K, Ca and S are the key indicators of soil quality in AEU 9.

Soil quality under conservation practices on farm operations of the southern semiarid pampas region of Argentina