Soil Health Assessment Research Articles

Combining Soil Immobilization and Dressing Techniques for Sustaining the Health of Metal-Contaminated Arable Soils

The combination of lime immobilization of metals and soil dressing has been a prevalent practice in Korea for remediating metal-contaminated arable soils. However, there have been limited reports on whether this method effectively sustains soil health after remediation, particularly in arable soils. This study undertook a comparative assessment of the soil health index (SHI) across metal-contaminated arable lands, arable soils remediated with lime immobilization and soil dressing, and uncontaminated soils. A total 389 soil samples were collected from these sites and analyzed for nineteen indicators encompassing physical, chemical, and biological properties. To assess soil health, these indicators were screened using principal component analysis, yielding five minimum data set (MDS) indicators: total nitrogen, clay content, dehydrogenase activity, bacterial colony-forming units, and available phosphorus. Among these MDS indicators, total nitrogen exhibited the highest value as the principal component contributing to soil health assessment. Scores of the MDS indicators exhibited significant correlation with those of total data set indicators, affirming the appropriateness of the soil health assessment adopted in this study. The SHI of the remediated arable soils (0.48) surpassed those of the contaminated soils (0.47) and were statistically comparable to those of the uncontaminated forest (0.51) and upland (0.51) soils. The health of the contaminated soils demonstrated a high dependence on soil properties rather than metal concentrations. These findings underscore the robustness of the combined immobilization and soil dressing method for sustaining the health of contaminated arable soils post-remediation.

SHAPEv1.0 Scoring curves and peer group benchmarks for dynamic soil health indicators

AbstractThe soil health concept has evolved over the past several decades, recognizing that the response of dynamic soil properties to management is dependent on site‐specific factors. The Soil Health Assessment Protocol and Evaluation (SHAPE) tool provides scores and benchmark values by forming soil peer groups based on site‐specific, climate‐edaphic characteristics. Data for model development were compiled from the Cornell Soil Health Laboratory and the Kellogg Soil Survey Laboratory databases. The SHAPEv1.0 interpretation curves produce scores between 0% and 100% for measured laboratory values that reflect the quantile within a population conditional cumulative distribution function along with measures of uncertainty. The original SHAPE tool was developed for soil organic carbon and has been expanded to include two wet aggregate stability methods, permanganate oxidizable organic carbon, autoclaved citrate extractable protein, and 4‐day microbial respiration. In addition, SHAPE provides site‐specific benchmark values at user‐defined percentiles within a given soil peer group. The difference between the actual measured value and the selected benchmark value represents the soil health opportunity gap. Case studies in Missouri and Texas show that the SHAPE scoring curves are sensitive to land‐use and management practices across multiple soil types and provide a regionally relevant interpretation of key soil health indicators.

Application of Artificial Intelligence and Sensor Fusion for Soil Organic Matter Prediction.

Soil organic matter (SOM) is one of the best indicators to assess soil health and understand soil productivity and fertility. Therefore, measuring SOM content is a fundamental practice in soil science and agricultural research. The traditional approach (oven-dry) of measuring SOM is a costly, arduous, and time-consuming process. However, the integration of cutting-edge technology can significantly aid in the prediction of SOM, presenting a promising alternative to traditional methods. In this study, we tested the hypothesis that an accurate estimate of SOM might be obtained by combining the ground-based sensor-captured soil parameters and soil analysis data along with drone images of the farm. The data are gathered using three different methods: ground-based sensors detect soil parameters such as temperature, pH, humidity, nitrogen, phosphorous, and potassium of the soil; aerial photos taken by UAVs display the vegetative index (NDVI); and the Haney test of soil analysis reports measured in a lab from collected samples. Our datasets combined the soil parameters collected using ground-based sensors, soil analysis reports, and NDVI content of farms to perform the data analysis to predict SOM using different machine learning algorithms. We incorporated regression and ANOVA for analyzing the dataset and explored seven different machine learning algorithms, such as linear regression, Ridge regression, Lasso regression, random forest regression, Elastic Net regression, support vector machine, and Stochastic Gradient Descent regression to predict the soil organic matter content using other parameters as predictors.

Multiple Soil Health Assessment Methods for Evaluating Effects of Organic Fertilization in Farmland Soil of Agro-Pastoral Ecotone

The incorporation of organic fertilizer is an important practice to improve the sustainability and productivity of crop production and decrease environmental pollution from crop-livestock systems in global agriculture. However, establishing an evaluation dataset is still the main challenge for quickly and effectively assessing the effect of management measures on farmland soil health. Hereby, we developed a minimum dataset (MDS) using three methods (network analysis (NA), random forest analysis (RF), and principal component analysis (PCA)). Based on MDS and two scoring functions (nonlinear (NL) and linear scoring curve (L)), the SHI (soil health index framework) was constructed to assess soil health conditions under four fertilization treatments (no fertilization, CK; only chemical fertilizer, NPK; only cow manure, MF; 50% chemical fertilizer + 50% cow manure, NPKM) in the northern ecotone of China. The results showed that the MDS-based on SHIs were positively correlated with each other and SHI-TDS (total dataset), verifying the consistency of soil health assessment models. Higher R2 was observed in the fitting of SHIs based on NA and TDS, which suggested that nMDS (minimum dataset based on network analysis) could represent most of the information in the TDS. The SHI-NL-nMDS (based on network analysis and nonlinear scoring curve) has the highest ability of sensitivity and accuracy, which indicates that compared with PCA and RF, the SHI based on NA can better embody farmland sol ecosystem functions. In addition, crop yield was significantly positive relative to SHI (soil health index). The incorporation of cow manure and chemical fertilizer improved soil health and increased crop yield. These results indicate that network analysis was a reliable technology for determining the minimum dataset in the evaluation of farmland soil health, and incorporating livestock manure could improve soil health and crop yield in this study area.

A soil health assessment tool for vegetable cropping systems in tropical soils based on beta-glucosidase, arylsulfatase, and soil organic carbon

Vegetable production is characterized by a strong dependence on external inputs and intense tillage, which can harm soil health (SH) by reducing organic matter levels, soil biota diversity, and overall ecosystem functions. Tools to assess SH affected by organic and conventional vegetable production systems are crucial and still scarce. This study aimed to develop a practical and reliable SH assessment tool for vegetable production systems in tropical soils by integrating Cornell's Comprehensive Assessment of Soil Health (CASH) with the four-quadrant model (4QM). Furthermore, the study evaluated the relationship between management practices and SH status. Arylsulfatase (ARYL) and β-glucosidase (GLU) activities, and soil organic carbon (SOC) were analyzed in 90 soil samples (0 to 10 cm depth) collected from 53 organic and conventional vegetable farms in the Federal District, Brazil. The adoption of practices aimed at improving or maintaining SH was significantly greater on organic farms than on conventionally managed farms, resulting in a SOC content and ARYL activity 27 % and 86 % higher in organic farms than in conventional systems, respectively (P < 0.05). ARYL proved to be more accurate in separating organic and conventional fields than GLU, highlighting its potential for SH evaluation in vegetable production systems. The integration of CASH and the 4QM, along with ARYL, GLU, and SOC measurements, effectively addressed the complexity of both organic and conventional vegetable production systems. This combined approach facilitated differentiation between systems based on varying levels of adoption of best management practices (BMPs). Further research is needed to assess the performance of this approach across different soil types and regions where vegetables are cultivated.

Machine learning-based soil quality assessment for enhancing environmental monitoring in iron ore mining-impacted ecosystems

In November 2015, a catastrophic rupture of the Fundão dam in Mariana (Brazil), resulted in extensive socio-economic and environmental repercussions that persist to this day. In response, several reforestation programs were initiated to remediate the impacted regions. However, accurately assessing soil health in these areas is a complex endeavor. This study employs machine learning techniques to predict soil quality indicators that effectively differentiate between the stages of recovery in these areas. For this, a comprehensive set of soil parameters, encompassing 3 biological, 16 chemical, and 3 physical parameters, were evaluated for samples exposed to mining tailings and those unaffected, totaling 81 and 6 samples, respectively, which were evaluated over 2 years. The most robust model was the decision tree with a restriction of fewer levels to simplify the tree structure. In this model, Cation Exchange Capacity (CEC), Microbial Biomass Carbon (MBC), Base Saturation (BS), and Effective Cation Exchange Capacity (eCEC) emerged as the most pivotal factors influencing model fitting. This model achieved an accuracy score of 92% during training and 93% during testing for determining stages of recovery. The model developed in this study has the potential to revolutionize the monitoring efforts conducted by regulatory agencies in these regions. By reducing the number of parameters that necessitate evaluation, this enhanced efficiency promises to expedite recovery monitoring, simultaneously enhancing cost-effectiveness while upholding the analytical rigor of assessments.

Soil health reduction following the conversion of primary vegetation covers in a semi-arid environment

A degraded forest is the outcome of a degradation process that has adverse effects on ecosystem functions and services. This phenomenon results in alterations of soil physicochemical and biological properties, which serve as valuable indicators for assessing soil health that has been recognized as a crucial component of soil quality. For several decades, the conversion of forested areas into rangeland has been documented in specific regions of the world. There is a widespread lack of global understanding regarding the lasting consequences of land degradation on soil health indicators. The present study aims to investigate the impact of forest degradation on soil health indicators in a mountainous semi-arid region located in northern Iran. The study area was predominantly forested, but due to human activities over the past 30 years, it has been transformed into three distinct land uses: forest, forest-rangeland ecotones and rangeland. In each of these land covers, a total of 20 litter (O-horizon) and 20 soil (from two depths of 0–15 and 15–30 cm) samples were collected in the summer (August 2022) season. According to our results, the highest litter thickness, P and Mg were in forest ecosystem, the lowest in rangeland ecosystem. The findings indicated that following the conversion of forest to rangeland, there was a decrease in soil aggregate stability, porosity, soil organic matter, POC, PON, NH4+, NO3− and nutrient levels, while soil bulk density increased. The forest ecosystem showed notably higher C and N stocks (45 and 5.21 Mg ha−1) in comparison to the rangeland (38 and 3.32 Mg ha−1) ecosystem. In addition, P, K, Ca, and Mg exhibited elevated levels within the total root of the forest ecosystem (2.12, 1.23, 0.71, and 0.38 %, respectively), whereas the lower values (1.29, 1.01, 0.43, and 0.23 %, respectively) were found in the rangeland ecosystem. Following the shift of land cover from forest to rangeland, soil fauna, microflora populations, soil enzymes and microbial activities decreased (about 1–2 times higher in the forestland). This research emphasizes the urgent need to advance sustainable management practices to prevent further degradation and promote the implementation of restoration or rehabilitation techniques in degraded forests. Despite being conducted in a semi-arid region situated in northern Iran, the findings of this study have considerable value for the sustainable management of soil and land conservation in various other semi-arid regions around the world.

Texture and organic matter associations with soil functional properties in crop and conservation land uses in North Carolina

AbstractSoil texture and organic matter are considered overarching factors controlling a diversity of soil processes and properties. However, a more complete understanding of how these factors quantitatively affect other soil properties has been lacking due to either limited scope of land use and management conditions, confounding climate and environmental conditions with management, or narrow range of soil and management conditions. This study evaluated the strength of associations of sand and total soil N concentrations with soil physical, chemical, and biological properties and processes from 310 surface‐soil (0–10‐cm depth) samples (87% Ultisols, 4% Inceptisols, and ≤3% each of Alfisols, Spodosols, Entisols, and Histosols) collected under conventional‐till and no‐till cropland, grassland, and woodland in four physiographic regions of North Carolina. Sand concentration was most strongly associated with dry‐stable mean‐weight diameter (MWD), MWD product, and sieved soil density (|r| &gt; 0.63, p &lt; 0.001). Total soil N concentration was most strongly associated with particulate organic C and N and soil water content at water‐holding capacity (|r| &gt; 0.74, p &lt; 0.001). Soils under unfertilized woodlands deviated the most from general associations, suggesting that organic matter quality could be unique, and requires further investigation. Overall, sand concentration associated more closely with soil physical properties and total soil N concentration associated closely with both soil physical and biogeochemical properties. Soil chemical properties were only weakly associated with either property. This study suggests that soil texture and organic matter would be complementary, additional properties in holistic soil health assessments that already included routine soil chemical analyses.

Evaluation of farmland soil health and optimization of evaluation system under different green manure applications in a semi‐arid irrigation area

AbstractHealthy soil is essential for maintaining food security and enhancing ecosystem functions. Green manure application improves soil fertility and is an alternative to chemical fertilizers; however, research on optimizing soil health assessment systems and providing comprehensive and accurate information for local farmland green manure management models remains limited. In this study, we used maize and potato intercropped with green manure farmland in the main crop production area of a semi‐arid irrigation area in Northwest China as the research object, integrated the regional soil environment characteristics, and used principal component analysis and other methods to establish a minimum index data set including physicochemical, biological, and yield indices. A soil health assessment system applicable to the study region was constructed and optimized by drawing the scoring curve. The results showed that green manure treatment exerted a positive effect on the soil bulk density, nutrient content, extracellular enzyme activity, and the yield of maize and potato fields. This evaluation method showed that both green manure treatments could improve the soil health index of two major crop fields, with scores of 6.9 (3.7–8.6) and 6.2 (4.5–7.0) for maize and potato farmland, respectively, with a greater positive effect caused on maize. This evaluation system can not only reflect the soil conditions of farmland accurately given the application of green manure in the semi‐arid irrigation areas of northwest China but also provide reference for the promotion of organic fertilizers such as green manure and the resolution of regional soil health differences.

Microbial transformation of soil organic matter under varying agricultural management systems in Ukraine.

This paper presents comparative studies on the content and structure of organic matter (OM) and the activity of microbiological cellulose destruction in three types of Ukrainian soils intensively used in agricultural production. The highest content of humus in the arable layer (4.9%), OM (410 t ha-1), and total carbon (30.9 mg C g-1 soil) was determined in chernic phaeozems, which is 2.2-2.5 times higher than in albic retisols. The soil of natural ecosystems is characterised by a high content of microbial carbon (Cmic) in the carbon fraction of organic soil compounds. In arable soils, the content and reserves of humus and soil organic matter (SOM) have decreased by an average of 1.5-2 times. The most considerable loss of humus reserves in the soil profile was identified in albic retisols (1.96-1.44 times) and the smallest in chernic phaeozems (1.27-1.81 times). During the long-term systematic application of mineral fertilisers, the Corg content decreased by 8-21% in chernic phaeozems, 12-33% in greyzemic phaeozems, and 6-38% in albic retisols. A significant difference of 2.1-8.0 times was determined regarding the number of aerobic cellulolytic microorganisms and 1.3-3.3 times in the potential cellulolytic activity of the studied soils. The high number of cellulose-destroying microorganisms is characteristic of chernic phaeozems with a high content of OM in the soil; the advantage over other types of studied soils was 1.4 times and 7.8 times for greyzemic phaeozems and albic retisols, respectively. Among the studied soil types, high values of CO2 emissions were identified in chernic phaeozems. Intensive agricultural practices in Ukrainian soils have significantly altered the content and composition of organic matter, leading to reduced humus and soil organic matter reserves. The study also underscores the importance of considering the abundance of cellulose-destroying microorganisms and their potential activity in assessing soil health and sustainability.

A field assessment to validate the assumptions of the Tea Bag Index (TBI) as a measure of soil health

This study evaluated key assumptions underlying the Tea Bag Index (TBI) as a soil health assessment tool through a time series conducted under field conditions. The TBI assesses soil health by measuring the decomposition rates of standardized green and red tea over a 90-day incubation period. Our research was conducted on a degraded 33.6-ha agricultural site in North Saanich, British Columbia, where we compared two plots: a reference site and a grazed site, each representing distinct land use conditions.The first assumption evaluated was that the TBI metrics of S (stabilization rate) and k (decomposition rate) follow exponential decay models. While S adhered to this assumption, k did not, suggesting additional as yet undetermined influences on decomposition rates. We introduced new biological metrics, Bg and Br, which followed polynomial models of decay, and may reflect the decomposition of different materials over time by different soil organisms.The second assumption tested was that a 90-day incubation period adequately captures the timeframe required for labile litter consumption. Our findings indicated that decomposition variance plateaued after 105 days, and that an increase in incubation time by 35 days increased the precision of mathematical decomposition models by a mean value of 18.1 %.The final assumption tested was that litter decomposition rate varies with land use. Our findings affirm that land use does affect litter decomposition rate, although the specific sources of that variance remain unclear.TBI accuracy can be improved by extending the litter incubation time to at least 105 days. Incorporating biological metrics (Bg and Br) increases the TBI's precision. Our results reaffirm the TBI's sensitivity to land use differences, highlighting its potential as a cost-effective tool for assessing soil health under varying conditions.

Soil Health Assessment in Brazilian Subtropical Oxisol Under Land Use and Long-term Tillage Systems

Soil Health Assessment in Brazilian Subtropical Oxisol Under Land Use and Long-term Tillage Systems

A 1 billion euro mission: A Soil Deal for Europe

AbstractSoils have achieved prominence in the political agenda of the European Commission with the proposal for a Soil Monitoring Law and the ambitious Soil Mission research framework. The EU Soil Observatory (EUSO) used the latest state‐of‐the‐art pan‐European datasets to propose a preliminary assessment of soil health in the EU based on 18 soil degradation proxy indicators. The body of knowledge will soon be enriched thanks to the investment of 1 billion euros towards the Mission ‘A Soil Deal for Europe’, which has the ambition to promote the development of new harmonized bottom‐up and top‐down soil health indicators. New data and knowledge are also anticipated through the national soil monitoring schemes to support the implementation of the Soil Monitoring Law. We present the Soil Mission roadmap towards assessing and achieving soil health in the EU by 2030 to meet Green Deal objectives. We introduce the EUSO Soil Health Dashboard, a soil degradation indicator tool using soil health indicators developed by the European Soil Data Centre (ESDAC) (2012–2023) that will contribute to Soil Monitoring Law assessments.

Reduction of Nitrate Leaching and Threats to Surface Water Under Conservation Tillage

Highlights Conservation agriculture practice (NT) improved soil organic matter, a principal indicator of soil health, soil health score (SHS) and CO2 respiration, and other soil physical and chemical properties vs. conventional till (CT) practice. Conservation agriculture practice (NT) promoted soil health functions under sufficient precipitation distribution and ample temperature conditions and aided the soil's retention ability to resist nitrate leaching and loads in subsurface drainage discharge systems. Uneven weather conditions, drought, and other variable agronomic practices rendered soil conditions unfavorable to retain nitrate and induced adverse effects by increasing the nitrate leaching and loads in NT vs. CT. The limited experiment period presented significant challenges for the evaluation and synthesizing of soil health motivations and barriers in the NT vs. CT. Abstract. An edge-of-field subsurface drainage discharge system removes excess subsurface water from agricultural fields to improve the conditions of the soil system making them favorable for plant growth. Conversely, edge-of-fields can increase the nitrate (NO3 -) offsite movement to surface water bodies, which ultimately impacts water quality. In St. Johns, MI. Two on-farm large areas were initiated in 2013 for conservation (NT) and conventional till (CT) practices, of which two microscale fields, an area of 4 acres for each, were delineated in 2019 for NT and CT practices under a corn/soybean crop rotation. NT and CT fields were monitored and evaluated through full water years (WYs) 2019-2022. The objective was to use an innovative approach in coupling edge-of-field with soil health assessments to learn how improved soil health functions are influencing nitrate leaching and loads. NT practice enhanced SOM, other physical properties, and soil health parameters, promoting soil health functions compared to CT practice under conditions of adequate precipitation and favorable temperatures. Additionally, it improved the soil's ability to retain and resist nitrate loads in the 2020 water year, with reductions of 9.6 and 18.3 kg/ha, respectively. In 2021, WY nitrate loads reported nearly an equal value in NT vs. CT (4.8 and 4.4 kg/ha, respectively). Factors of uneven weather conditions and drought, variable agronomic practices, and residual nitrogen after corn, rendered soil conditions unfavorable to retain nitrate and induced adverse effects by increasing the nitrate leaching and loads in NT vs CT in 2022 WY (8.1 and 5.8 kg/h, respectively). Therefore, the limited evaluation period (2019-2022 WYs) presented significant challenges and revealed no affirmative evidence that the improved soil health functions under NT reduced the nitrate leaching loads in all WYs of this research. Further research is needed to prove the impact of NT on improving soil health functions to sustainably reduce nitrate leaching and loads in edge-of-fields. Keywords: Conservation no-till (NT), Conventional till (CT), Nitrate load, Soil health functions, Subsurface drainage discharge, Water quality.

Soil health assessment of an acidic red soil agricultural area and its restoration with biochar soil conditioners

AbstractIn light of growing concerns regarding food security and soil health, there is increasing emphasis on the assessment of soil health and efforts to improve or maintain the soil health of cultivated land. This case study Niutian Town in Jiangxi Province in southern China was selected as a typical red soil and a major grain‐producing area. This research involves an integrated evaluation model based on Entropy weight TOPSIS and emergy analysis to assess the soil health of cultivated land. An evaluation system was developed, comprising 31 indicators selected on the basis of resources and environments, with an emphasis on sensitive and promising microorganisms category indicators. Additionally, experiments were conducted to assess the ecological risk of soil pollution and develop novel soil conditioners. Our results indicate that Pb, Hg and Cd present a moderate ecological risk on 92.1% of the cultivated land. Furthermore, 56.8% of the cultivated land was ‘sub‐healthy’ or ‘unhealthy’. Biochar was found to be a soil conditioner with a good adsorption effect, with absorption rates reaching up to 99.9% and 88.3% for Pb and Cd, respectively. Additionally, g‐C3N4 was added to address pesticide contamination, which showed an adsorption rate of up to 75.2% for atrazine. This work develops a targeted remediation approach based on assessment results to address regional cropland soil health issues.

First surveillance of pesticides in soils of the perimeter of Tadla, a Moroccan sugar beet intensive area.

With the long-term application of pesticides on sugar beet farms in the irrigated perimeter of Tadla in Morocco for over 50years, pesticide monitoring is necessary to assess soil health. The objective of our study was to monitor multiple pesticide residues in topsoil samples collected from post-harvest sugar beet fields and verify their migration to deep soil layers. Topsoil and deep soil samples were collected from arbitrarily selected sugar beet fields in the IPT. In this study, a target-screening method was applied. All target pesticides were detected in soil samples, with tefluthrin being the most frequently detected pesticide. The residue with the highest concentration in soil samples was DDE. All the soil samples contained a mixture of pesticide residues, with a maximum of 13 residues per sample. The total pesticide content decreased toward more profound layers of soil, except in one field where it reached a concentration of 348µg/kg at the deeper soil layer. For pesticides detected at the three soil depths, only tefluthrin concentration increased in the deep soil layer. The results provide comprehensive and precise information on the pesticide residue status in sugar beet soils warning against the multiple risks that this contamination can cause. This study indicates the need of regular monitoring of pesticides over a large area of the perimeter to enable decision-makers to pronounce the impacts of the extension and intensification of sugar beet cultivation at the irrigated perimeter of Tadla.

A full-length SSU rRNA-based workflow for high-resolution monitoring of nematode communities reveals direct and indirect responses to plant-based manipulations

Agricultural intensification has resulted in a decline in soil biodiversity, and concerns about the deterioration of the biological condition of soils prompted the development of measures to restore soil life. Due to the overwhelming biodiversity of soils, evaluation of such measures is not straightforward, and proxies are used to assess soil health. Because of their trophic diversity, high abundance, and relatively well-characterized ecologies, nematodes are often used as soil health indicators. However, the scarcity of informative morphological characters hampers the upscaling of this proxy. Here we present a community analysis approach that uses nanopore sequencing to generate full-length sequences of small subunit ribosomal DNAs (SSU rDNA). Cover cropping is a common agricultural practice that stimulates soil life, and we mapped the effects of ten cover crop treatments on nematode communities in a field experiment. These analyses included the monitoring of a high-impact plant-parasite, Meloidogyne chitwoodi. In total, 132 nematode samples were analyzed, and 65 nematode taxa were detected, mostly at the species level, including representatives of all trophic groups. As a validation, all samples were analyzed microscopically for M. chitwoodi, and a comparison of count and DNA read data revealed highly similar results. Treatments did not only affect plant-parasitic nematodes but also free-living nematodes in a cover crop-specific manner. Free-living nematodes from the same trophic group, and even congeneric species, responded differentially to plant-mediated manipulations of the soil microbiome. Hence, nanopore-based SSU rDNA sequencing could facilitate a substantial refinement of the use of nematodes as indicators for soil health.

Improving Soil Health of Commercial Vegetable Home Gardens through Conservation Agriculture in Cambodia

Tillage systems are components of broad agricultural practices that affect soil properties and soil health. These changes include soil respiration, density, moisture, and pH. Conservation agriculture practices have the potential to improve soil health by reducing tillage. In agricultural production, there can be numerous approaches to achieving consistently high yields annually; however, this study specifically looked at conventional tillage and conservation agriculture systems. This study aimed to determine soil fauna biodiversity and soil health under conservation agriculture (CA) and conventional tillage (CT) management practices of vegetable production in Cambodia. Five CA and five CT plots were selected and included in this study. Fifty soil samples were collected from CA and CT plots for soil fauna measurement, and in-situ tests were made using Biofunctool© for soil health assessment. The results showed that the abundance of soil fauna and aggregation stability were greater in CA than in CT. Soil fauna biodiversity enhancement may provide better soil health for soil improvement by adapting farming management practices.

Effects of fertilization practices and understory on soil health and oil palm performances in smallholdings: An Indonesian case study

CONTEXTSmallholders play a considerable role in oil palm production in Indonesia. Their specific constraints lead to contrasted plot management, especially for fertilization and understory vegetation. Studies disentangling the agro-environmental impacts of oil palm smallholders' management practices at the plot scale remain scarce. The extent to which smallholders' practices affect both agronomic and environmental performances is still to be investigated with robust in-field studies to provide improvement tracks. OBJECTIVEThis study aimed at investigating the effects of both fertilization and understory vegetation management on soil health and oil palm performances. It explored a gradient of management practices, within homogeneous soil and climate conditions and historical development. The objective was to isolate and decipher the specific effects of some key practices on the overall plantation performances. METHODSWe surveyed 16 ex-plasma oil palm smallholders' 25-year old plots, gathering detailed information on management and implementing field measurements (48 points). For soil health assessment, we applied Biofunctool® that consists of nine in-site indicators informing on the soil ability to transform carbon, recycle nutrient and maintain its structure. For the oil palm performances, we relied on conventional appraisal parameters, such as yields, leaf area and nutrient leaf analyses. RESULTS AND CONCLUSIONSOur results highlighted that fertilization, especially potassium, played a significant role in sustaining oil palm performances. Understory vegetation had a direct and positive effect on soil structure maintenance indicators. Soil structure improvement was correlated with enhanced oil palm performances. Those results highlighted that, besides nutritional aspects, increasing plot biodiversity through understory vegetation maintenance was associated with enhanced soil health hence potential greater ecosystem functions. SIGNIFICANCEThe findings have implications in terms of better understanding the effects of combined farming practices on the soil-oil palm continuum. It especially highlighted the key role of understory vegetation to improve soil structure maintenance without competing with oil palms in the studied context.

Identifying key factors controlling potential soil respiration in agricultural fields

AbstractSoil respiration is one of the main soil health indicators and is influenced by several factors in agricultural fields. Identifying key factors that control soil respiration is desirable for informed soil management decisions and for promoting and scaling up soil health. This study aimed to (i) quantify the relationships between potential soil respiration and selected soil properties, crops, and slope positions, and (ii) identify key factors controlling these relationships using a neural network model. Ninety soil samples from 0‐ to 5‐ and 5‐ to 20‐cm soil depth were collected from footslope, backslope, and summit in three fields planted with soybean (Glycine max L. Merr.), alfalfa (Medicago sativa L.), and corn (Zea mays L.). The model provided great accuracy (coefficient of determination: 0.96; root‐mean square error: 7.8; and mean absolute deviation: 3.8) and explained nearly 96% of variations in soil respiration across soil depth, crop, and slope positions. Soil depth, ammoniacal nitrogen (NH4‐N), crop types, slope position, and silt content were identified as the top five factors influencing potential soil respiration at the field level. Potential soil respiration was more sensitive to potassium, phosphorus, pH, cation exchange capacity, and mean weight diameter and less sensitive to NH4‐N, nitrate nitrogen, soil organic matter, and clay content. It increased with pH, electrical conductivity, mean weight diameter, potential nitrogen mineralization, and potassium, and it decreased with increasing silt content. Soil from 0 to 5 cm under soybean or at the summit slope position exhibited a higher respiration. Using a small dataset, this pilot study accurately predicted potential soil respiration in agricultural fields and identified key drivers controlling it. The results from this study highlight the complexity of using potential soil respiration as a standalone test for evaluating soil health. This does not diminish the usefulness of potential soil respiration as a soil health indicator to support agricultural management decisions and as a reference in future soil health studies. However, it emphasizes the importance of considering multiple factors when interpreting the significance of soil biological indicators for soil health assessments.