Measure Of Soil Quality Research Articles

Oribatid Mites in a Humid Mediterranean Environment under Different Soil Uses and Fertilization Management

Measuring soil quality and the use of indicators for its evaluation is a worldwide challenge. In Garrotxa Volcanic Zone Natural Park (northeastern Spain), different parameters related to oribatid mites as indicators of soil quality were evaluated under different land uses: forest, pasture, and a biennial double-crop rotation of forage crops. In forage crops, previous fertilization management (one based on mineral fertilizers, three on cattle manure, and one using both types) was also evaluated. Three samplings (April, June, and September) were performed over one season. Fifty-four oribatid species belonging to 28 families were identified. Abundance was the lowest in June for all land uses (average of 1184 individuals m−2). In the study period, abundance, diversity (Shannon index, H’), and dominance (Berger–Parker index, d) varied with different land uses, with the highest values of abundance and H’ in forests (9287 individuals m−2 and 2.19, respectively) and the lowest dominance in forests (d = 0.29) without differences between the other uses. Additionally, in the studied parameters, no differences were associated with previous fertilization management in forage crops. Hypochthoniella minutissima, Xenillus (X.) tegeocranus characterized the forest system, Epilohmannia cylindrica minima the forage crops, and Tectocepheus sarekensis the pasture. In pasture, the dominance of the parthenogenetic species Tectocepheus sarekensis raises concerns about potential management constraints.

Impact of soil and water quality on the sustainable management of mangrove-compatible brackishwater aquaculture practices in Indonesia

The widespread degradation of mangroves has been attributed to their conversion into other land uses and purposes, most notably their destruction to construct extensive brackishwater aquaculture ponds. This study investigated the relationship between environmental factors and management alternatives for an integrated mangrove-aquaculture system in Derawan Island District, Berau Regency, East Kalimantan Province, Indonesia. The study collected a total of 56 soil and water samples from around 50 ha to assess environmental limiting factors for integrated mangrove-brackishwater aquaculture management. The measured soil quality variables included field pH (pHF), pH of hydrogen peroxide extract (pHFOX), peroxide oxidizable sulfur (SPOS), pyrite (FeS2), organic matter (OM), nitrogen-total (N-TOT), iron (Fe), and aluminum (Al). Water quality variables included salinity, temperature, pH, dissolved oxygen (DO), different forms of nitrogen (NH3–ammonia, NO3–nitrate, and NO2–nitrite), phosphate (PO4), Fe, and total organic matter (TOM). The study employed a hydrological restoration approach to suggest a mangrove-friendly aquaculture pond design and layout. The study generally showed that Acid Sulfate Soils (ASS) significantly restricted the existing mixed-mangrove aquaculture pond management by negatively impacting water quality. This study highlights that, given the characteristics of ASS-affected aquaculture ponds, reducing pond size for shrimp culture (mangrove-to-pond ratio of 80% and 20%) will simplify managing ASS environmental limiting factors through cost-effective remediation techniques and a Balanced Fertilization Strategy (BFS). The reduced shrimp pond size will also provide direct opportunities for mangrove restoration and allow effective hydrological restoration. The economic benefit of the proposed pond design and management model focuses on diversifying production units which include shrimp aquaculture ponds, milkfish or tilapia culture in the peripheral canals of the mangrove compartment, juvenile shrimp production, and periodic harvest from the primary pond canal. The study findings are particularly applicable to ponds that have already been built in ASS to improve productivity in addition to supporting the mangrove rehabilitation program. Converting mangrove and ASS-affected land is not advisable for constructing new aquaculture ponds.

Soil quality index in some cropping systems in plot 17 of Wanagama forest, Gunungkidul, Yogyakarta, Indonesia

<p><span>The Wanagama 1 Forest, owned by Universitas Gadjah Mada in Playen, is an educational and research forest that is home to different species of trees that are managed and treated differently. Finding the quality index values for different cropping strategies in plot 17 of Wanagama Educational Forest 1 was the aim of this study. The soil quality index was determined using three methods: simple addition, scoring and weighting, and summation. The results show that in plot 17 of the Wanagama forest, the highest soil quality index values were found for ebony species and the lowest soil quality index values were found for mahogany and </span><em><span>Eucalyptus</span></em><span> species. The results showed that all land uses with different types of forest plants had lower soil quality indices. The stepwise analysis results showed that porosity, organic C, cation exchange capacity (CEC), P availability, K availability, and C biomass influenced the soil quality index. Measuring soil quality can help you learn more about soil properties and how to improve it through effective management.</span></p>

Artificial intelligence and machine learning in environmental impact prediction for soil pollution management – case for EIA process

Scientific predictions are a key component of Environmental Impact Assessments (EIA), which can indicate the level of change within an environmental sphere (e.g., soil). As part of the EIA process, decision-making in mitigating complex environmental problems such as maintaining soil quality can be challenging, especially in data-sparse locations. Artificial Intelligence (AI) can ameliorate but the literature suggests that the deployment of Machine Learning (ML) techniques in soil research is concentrated mostly in developed countries. The potential of ML in managing soil pollution from complex mixture of heavy metals, petroleum hydrocarbons, and physicochemical factors is rarely explored. To address this research gap, we built robust models that increase the accuracy of impact prediction based on new experimental soil data from a data-sparse region of Africa (i.e., Nigeria). The algorithms applied are artificial neural networks (ANN), support vector regression (SVR), regression tree (RT), and random forest (RF). The study also implemented a multivariate linear regression (MLR) model as a baseline. Key findings include (a) the MLR model performed less than the machine learning models largely due to the nonlinearity of data; (b) Log-normalization helped to improve the predictive capability of all models as the effects of statistical variability were removed; (c) the RF model had the best performance in terms of correlation coefficient, mean absolute error, and root mean square error, and (d) the machine learning models showed improved performance with increased correlation and lower error between the actual and predicted soil electrical conductivity values. Our results imply that data sparsity may no longer be an excuse for the non-use of quantitative impact prediction in Environmental Impact Assessment (EIA) processes. This could change how EIAs are conducted and enhance sustainability in natural resource exploitation, globally. Future work will apply algorithms for automated feature selection to obtain optimal subset of soil quality measurements that will further improve the accuracy of the models.

Difficulties in using land use pressure and soil quality indicators to predict water quality

Intensive agriculture can impair river water quality. Soil quality monitoring has been used to measure the effect of land use intensification on water quality at a point and field scales but not at the catchment scale. Other farm scale land use pressures, like stocking rate and the value of land, which relate to land use intensity are now publicly available, nationally. We therefore tested whether point scale soil quality measures, together with newly available farm scale land use pressures (land valuation and stocking rate) and existing catchment and climatic characteristics could help predict the behaviour of water quality data across 192 catchments in New Zealand. We used a generalised additive model to make predictions of the change in nitrogen fractions (r2 = 0.65–0.71), phosphorus fractions (r2 = 0.51–0.70), clarity and turbidity (r2 = 0.42–0.46), and E. coli (r2 = 0.35) over 15 years. The state and trend of water quality was strongly related to a refined farm scale land use classification, and to catchment and climatic characteristics (e.g. slope, elevation, and rainfall). Relationships with point scale soil quality measures and the land use pressures were weak. The weak relationship with land use pressures may be caused by using a single snapshot in time (2022), which cannot account for lag times in water quality response but leaves room for additional temporal data to improve predictive power. The weak relationship to soil quality measures was probably caused by limited data points (n = 667 sites) that were unrepresentative of land use, and areas of catchment subject to processes like runoff or leaching. While national soil quality measures might be useful for evaluating environmental risk at the field or farm scale, without a large increase in sampling, they were not relevant at the catchment scale. Additional analyses should be performed to determine how many samples would be needed to detect a change using an environmentally focused soil test that can guide water quality management.

Effects of returning paddy field to wetland on composition and stability of soil aggregates in the Yellow River Delta.

The composition and stability of soil aggregates are important indicators for measuring soil quality, which would be affected by land use changes. Taking wetlands with different returning years (2 and 15 years) in the Yellow River Delta as the research object, paddy fields and natural wetlands as control, we analyzed the changes in soil physicochemical properties and soil aggregate composition. The results showed that soil water content, total organic carbon, dissolved organic carbon and total phosphorus of the returning soil (0-40 cm) showed an overall increasing trend with returning period, while soil pH and bulk density was in adverse. There was no significant change in clay content, electrical conductivity, and total nitrogen content. The contents of macro-aggregates and micro-aggregates showed overall increasing and decreasing trend with returning period, respectively. The stability of aggregates in the topsoil (0-10 cm) increased with returning years. Geometric mean diameter and mean weight diameter increased by 8.9% and 40.4% in the 15th year of returning, respectively, while the mass proportion of >2.5 mm fraction decreased by 10.5%. There was no effect of returning on aggregates in subsoil (10-40 cm). Our results indicated that returning paddy field to wetland in the Yellow River Delta would play a positive role in improving soil structure and aggregate stability.

Enhanced IoT Solution System for Smart Agriculture in Indonesia

This innovative solution encompasses an IoT-based smart agricultural system. The system includes a solar panel power supply, a weather station (monitoring temperature, humidity, air pressure, wind speed and direction, raindrop), an air quality monitoring module (measuring NH4, CO2, and PM2.5 levels), a soil quality measurement module, a microcontroller, a GSM cellular module for internet connectivity, and an automated relay actuator for a water pump. The water pump's operation is contingent upon the soil moisture levels, ensuring efficient irrigation. The utilization of an IoT-driven smart agricultural system enables real-time monitoring of weather conditions, air quality, and agricultural soil conditions. Additionally, it facilitates the remote control of automated water pumps via smartphones—an aspect that remains unattainable within the confines of traditional Indonesian agriculture. Leveraging an Android application on smartphones, this system delivers detailed insights. To present the collected sensor data in accordance with prevailing environmental and soil states, a dedicated Android application has been developed. Moreover, this application facilitates the control of the water pump to irrigate arid soil as required. The data is transmitted via the internet to a cloud server, serving as the intermediary that receives data from the IoT system's sensors positioned at the farm.

Key soil properties and their relationships with crop yields as affected by soil–landscape rehabilitation

AbstractTillage and water erosion induce spatially dependent changes in soil properties that influence productivity. Soil–landscape rehabilitation (returning translocated topsoil to landscape positions of soil loss by erosion) is one method to improve the productivity of severely eroded land. The objective of this study was to investigate relationships among key soil chemical, biological, and physical factors and crop growth and grain yield in eroded and rehabilitated landform positions. Soil–landscape rehabilitation was performed by moving 15–20 cm of topsoil from the lower slope to the upper slope positions of replicate plots; adjacent plots were left in their eroded condition. Crop response was monitored for 6 years. Rehabilitation resulted in large changes in the upper slope, especially in the most eroded landscape positions, where rehabilitated plots had lower inorganic carbon (IC) and higher organic carbon, available macronutrients, water infiltration rates, fungal and bacterial populations, and other measures of soil quality compared with control plots. Differences in surface soil IC that result from extensive erosion exposing calcareous subsoils, explained 70% of the yield variability in the upper slope. In the lower slope, the soil removal treatment was the main predictor of crop yield. Areas of soil removal had relatively high early‐season water content and low fungal and bacterial populations. Improving soil properties in areas of high soil loss by erosion increased both grain yield and yield stability across climatic conditions.

Soil calcium prompts organic carbon accumulation after decadal saline-water irrigation in the Taklamakan desert

Soil organic carbon (SOC), as a crucial measure of soil quality, is typically low in arid regions due to salinization, which is a global issue. How soil organic carbon changes with salinization is not a simple concept, as high salinity simultaneously affects plant inputs and microbial decomposition, which exert opposite effects on SOC accumulation. Meanwhile, salinization could affect SOC by altering soil Ca2+ (a salt component), which stabilizes organic matter via cation bridging, but this process is often overlooked. Here, we aimed to explore i) how soil organic carbon changes with salinization induced by saline-water irrigation and ii) which process drives soil organic carbon content with salinization, plant inputs, microbial decomposition, or soil Ca2+ level. To this end, we assessed SOC content, plant inputs represented by aboveground biomass, microbial decomposition revealed by extracellular enzyme activity, and soil Ca2+ along a salinity gradient (0.60–31.09 g kg−1) in the Taklamakan Desert. We found that, in contrast to our prediction, SOC in the topsoil (0–20 cm) increased with soil salinity, but it did not change with the aboveground biomass of the dominant species (Haloxylon ammodendron) or the activity of three carbon-cycling relevant enzymes (β-glucosidase, cellulosidase, and N-acetyl-beta-glucosaminidase) along the salinity gradient. Instead, SOC changed positively with soil exchangeable Ca2+, which increased linearly with salinity. These results suggest that soil organic carbon accumulation could be driven by increases in soil exchangeable Ca2+ under salinization in salt-adapted ecosystems. Our study provides empirical evidence for the beneficial impact of soil Ca2+ on organic carbon accumulation in the field under salinization, which is apparent and should not be disregarded. In addition, the management of soil carbon sequestration in salt-affected areas should be taken into account by adjusting the soil exchangeable Ca2+ level.

IoT Based Smart Agriculture Monitoring System

The rapid advancement of Internet of Things (IoT) technology has revolutionized the field of agriculture by enabling efficient and intelligent monitoring systems. This paper proposes an IoT-based smart agriculture monitoring system that incorporates sensors to measure soil quality, temperature, air moisture, and soil moisture. The system utilizes an ARM Cortex microcontroller to gather data from these sensors and performs predictive analysis. The system is designed to address the challenges faced by farmers in monitoring crucial parameters for optimal crop growth. In conclusion, the proposed IoT- based smart agriculture monitoring system offers an efficient and comprehensive solution for monitoring essential parameters in agricultural environments. Keywords – DHT11 Temperature and Humidity sensor, Arm Cortex Rp2040, LCD, pH sensor, Moisture sensor, Predictive analysis, Blynk IOT.

Soil microbial communities are sensitive to differences in fertilization intensity in organic and conventional farming systems.

Intensive agriculture has increased global food production, but also impaired ecosystem services and soil biodiversity. Organic fertilization, essential to organic and integrated farming, can provide numerous benefits for soil quality but also compromise the environment by polluting soils and producing greenhouse gases through animal husbandry. The need for reduced stocking density is inevitably accompanied by lower FYM inputs, but little research is available on the impact of these effects on the soil microbiome. We collected soil samples from winter wheat plots of a 42-year-old long-term trial comparing different farming systems receiving farmyard manure at two intensities and measured soil quality parameters and microbial community diversity through DNA metabarcoding. High-input fertilization, corresponding to 1.4 livestock units (LU) improved the soil's nutritional status and increased soil microbial biomass and respiration when compared to low-input at 0.7 LU. Bacterial and fungal α-diversity was largely unaffected by fertilization intensity, whereas their community structure changed consistently, accompanied by an increase in the bacterial copiotroph-to-oligotroph ratio in high-input systems and by more copiotrophic indicator OTUs associated with high than low-input. This study shows that reduced nutrient availability under low-input selects oligotrophic microbes efficiently obtaining nutrients from various carbon sources; a potentially beneficial trait considering future agroecosystems.

Soil quality variation and its driving factors within tropical forests on Hainan Island, China

AbstractSoil quality is a critically important component of soil within ecosystems, and our understanding of soil quality variation and its driving factors in tropical forests is limited. This study collected soil samples at 0–20 cm depths from two typical tropical forests (tropical rainforest and rubber plantation) on Hainan Island, China's largest tropical island, during dry and rainy seasons. A soil quality index (SQI) using principal component analysis was applied to measure soil quality based on 21 soil properties. The soil properties' spatial and seasonal changes and their driving factors were simultaneously collected for a holistic analysis of the ecosystems. The results were summarized as follows: (i) The SQI of the rubber plantation decreased by 26.48% compared to the tropical rainforest on the island, while four soil properties investigated (soil pH, total phosphorus, cellulose decomposition, and actinomycetes) increased. (ii) The SQI of different soil types in the two tropical forests displayed an apparent discrepancy. Within the same soil type, the SQI of tropical rainforests showed no obvious differences, while there was a notable spatial discrepancy among rubber plantations in dispersed geographical positions belonging to varied climatic zones. (iii) The SQI of the tropical rainforest was significantly higher in the wet season than in the dry season. In contrast, there were no significant differences between the two seasons for rubber plantations, indicating that soil quality in the tropical rainforest was more sensitive to seasonal changes than soil quality in rubber plantations. Overall, we concluded that except for land use and soil type, spatial variability and seasonal pattern also played essential roles in soil quality on the tropical island. The results from the SQI are expected to provide novel information regarding the sustainable use of tropical forests on Hainan Island.

Silver origins and spatial distribution in soils of southwestern France: A first assessment with cokriging

Ag is a rare, non-essential metal with many applications in different forms, including nanoparticles (Ag-NPs). Its release into the soil environment can represent a risk, due to the toxicity of silver. This study is based on the analysis of samples from the two sampling campaigns of the Soil Quality Measurement Network, RMQS1 and RMQS2, to establish the spatial distribution and origin of Ag in the soils of southwestern France (area of 90,293 km2). Given the progress of the second campaign (completion in 2027), we chose to do a cokriging to estimate the spatial distribution, with Pb, Cd or Cu as potential secondary variables. The results of the analyzes of the 35 samples of the RMQS2 indicate a range of Ag concentration in the soils going from 0.015 to 0.222 mg kg−1 and a median of 0.081 mg kg−1. Correlations were found between the concentrations of Ag Cd, Cu and Pb, and also between Ag concentrations and distance to Pb mines and deposits (r = 0.62). This suggests a common origin for Ag and Pb linked to historical exploitations of argentiferous galena. No link could be demonstrated between the concentration of Ag and the high density of anti-hail ground generators in the region. The analysis of the (auto-)variograms showed that Pb is the most relevant variable compared to Cd and Cu to achieve a cokriging. Thus, based on Ag concentrations from RMQS2 and Pb concentrations from RMQS1 (356 samples), a linear co-regionalization model was established and the co-kriging of Ag concentrations in soils calculated. Compared to ordinary kriging, cokriging improved the quality of the interpolation (decrease in the root mean square error).

Assessment of Industrial Waste on Soil Pollution in Asaba, Delta State, Nigeria

The study evaluated the effect of industrial waste on soil pollution in Asaba, Delta State. The procedure adopted the ex post facto research model comprising a comprehensive range of simultaneous measures of industrial waste collection and soil quality in the selected industrial areas in Asaba. Industries in Asaba were selected based on the nature and type of industrial waste generated at random. Six existing industrial locations in Asaba were selected. The collected soil samples were used to investigate the soil pH, Zinc, Lead, Chromium, Silver and Copper concentration levels of the soils. Multiple regression was used to ascertain the effect of industrial waste on the soil in Asaba. 188071 Kg of industrial waste containing flammable, corrosive and toxic substances were generated at industrial landfills in Asaba. The industrial waste generated exceeds WHO standards. This is an implication that the industrial waste generated in Asaba is hazardous. The study revealed that most soils in industrial dumpsites are acidic and this is a reflection of the industrial waste's corrosive ability. Soils in the industrial dump sites are very high in Zinc, lead and chromium contents. Asaba Expressway 2 industrial dump sites have the highest amount of zinc and lead content, while Summit Junction industrial dump site has the highest content of silver. At a P<0.05 significance level, the calculated F value is 3.11 while the critical table value is 41.95. Thus, the toxicity in the soils in Asaba is significantly dependent on the chemical (Zinc, lead and Chromium) properties of industrial waste generation. Furthermore, as Zinc, lead and Chromium properties in industrial waste increase, the toxicity of the soil increases as well. Thus polluting the soils in Asaba industrial areas. The results obtained from this study portrayed that the Zero waste approach would be the recommended choice for sustainable waste management.

Soil quality measurement for sustainable soybean yield agroforestry system under different crop rotation models

Abstract. Alam T, Suryanto P, Handayani S, Supriyanta, Wulandari RA, Anshari A, Aisya AW, Purba AE, Widowati R, Taryono. 2022. Soil quality measurement for sustainable soybean yield agroforestry system under different crop rotation models. Biodiversitas 23: 6155-6163. Soil quality is essential for sustaining the soybean yield in an agroforestry system. This study determined the soil quality variables that affect soybean yield under different crop rotation models in the agroforestry system with kayu putih (Melaleuca cajuputi). A 2-year experiment was conducted during the dry season (March-June 2021) and the wet season (November 2021-February 2022) at Menggoran Forest Resort, Playen District, Gunungkidul Regency, Special Province of Yogyakarta, Indonesia. All of the trials during each season were laid out in a randomized complete block design with three blocks as replication. The treatment crop rotation model consisted of soybean planting after fallow (FS), soybean planting after maize (MS), soybean planting after rice (RS), and continuous soybean planting (SS). The observations were soybean yield and 21 soil quality variables. The data were analyzed using analysis of variance, multiple factor analysis, heatmap cluster, generalized pair plot, and standardized stepwise regression. The increase in soybean yield during the dry season was affected by TB, Fe, and N in the soil, while it was affected by TF, Fe, and silt during the wet season. Soil quality and soybean yield can be improved by applying organic matter and soil amendment.

Soil quality estimation using environmental covariates and predictive models: an example from tropical soils of Nigeria

BackgroundInformation addressing soil quality in developing countries often depends on results from small experimental plots, which are later extrapolated to vast areas of agricultural land. This approach often results in misinformation to end-users of land for sustainable soil nutrient management. The objective of this study was to estimate the spatial variability of soil quality index (SQI) at regional scale with predictive models using soil–environmental covariates.MethodsA total of 110 composite soil samples (0–30 cm depth) were collected by stratified random sampling schemes at 2–5 km intervals across the Cross River State, Nigeria, and selected soil physical and chemical properties were determined. We employed environmental covariates derived from a digital elevation model (DEM) and Sentinel-2 imageries for our modelling regime. We measured soil quality using two approaches [total data set (TDS) and minimum data set (MDS)]. Two scoring functions were also applied, linear (L) and non-linear (NL), yielding four indices (MDS_L, MDS_NL, TDS_L, and TDS_NL). Eleven soil quality indicators were used as TDS and were further screened for MDS using principal component analysis (PCA). Random forest (RF), support vector regression (SVR), regression kriging (RK), Cubist regression, and geographically weighted regression (GWR) were applied to predict SQI in unsampled locations.ResultsThe computed SQI via MDS_L was classified into five classes: le 0.38, 0.38–0.48, 0.48–0.58, 0.58–0.68, and ge 0.68, representing very low (class V), low (class IV), moderate (class III), high (class II) and very high (class I) soil quality, respectively. GWR model was robust in predicting soil quality (R2 = 0.21, CCC = 0.39, RMSE = 0.15), while RF was a model with inferior performance (R2 = 0.02, CCC = 0.32, RMSE = 0.15). Soil quality was high in the southern region and low in the northern region. High soil quality class (> 49%) and moderate soil quality class (> 14%) dominate the study area in all predicted models used.ConclusionsStructural stability index, sand content, soil oganic carbon content, and mean weight diameter of aggregates were the parameters used in establishing regional soil quality indices, while land surface water index, Sentinel-2 near-infrared band, plane curvature, and clay index were the most important variables affecting soil quality variability. The MDS_L and GWR are effective and useful models to identify the key soil properties for assessing soil quality, which can provide guidance for site-specific management of soils developed on diverse parent materials.

A Critical Review for Real-Time Continuous Soil Monitoring: Advantages, Challenges, and Perspectives.

Most soil quality measurements have been limited to laboratory-based methods that suffer from time delay, high cost, intensive labor requirement, discrete data collection, and tedious sample pretreatment. Real-time continuous soil monitoring (RTCSM) possesses a great potential to revolutionize field measurements by providing first-hand information for continuously tracking variations of heterogeneous soil parameters and diverse pollutants in a timely manner and thus enable constant updates essential for system control and decision-making. Through a systematic literature search and comprehensive analysis of state-of-the-art RTCSM technologies, extensive discussion of their vital hurdles, and sharing of our future perspectives, this critical review bridges the knowledge gap of spatiotemporal uninterrupted soil monitoring and soil management execution. First, the barriers for reliable RTCSM data acquisition are elucidated by examining typical soil monitoring techniques (e.g., electrochemical and spectroscopic sensors). Next, the prevailing challenges of the RTCSM sensor network, data transmission, data processing, and personalized data management are comprehensively discussed. Furthermore, this review explores RTCSM data application for updating diverse strategies including high-fidelity soil process models, control methodologies, digital soil mapping, soil degradation, food security, and climate change mitigation. Finally, the significance of RTCSM implementation in agricultural and environmental fields is underscored through illuminating future directions and perspectives in this systematic review.

Field-Scale Evaluation of the Soil Quality Index as Influenced by Dairy Manure and Inorganic Fertilizers

Long-term addition of manure increases soil organic carbon (SOC), provides nutrient supply, enhances soil quality and crop yield (CY), but may also increase global warming potential (GWP). In this study, a long-term experiment was conducted to investigate impacts of organic dairy manure and inorganic fertilizer on the spatial distribution of soil quality indicators in field scale. The experiment was initiated in 2008 (seven years), and includes three manure and two inorganic fertilizer treatments along with a control (no manure or no inorganic fertilizer addition). The study was set into a randomized complete block design with six treatments and four replications in a total of 24 plots with an equal size each of 6 × 18 m (108 m2). Soil physical, chemical and biological properties (total 26 properties) were considered as the total data set and principal component analysis (PCA) was used to determine long-term organic and inorganic fertilizer-induced changes in soil quality. Ordinary kriging interpolation methods were used to predict the spatial distributions of soil quality index (SQI) and mean soil quality values were compared with fertilization treatments by using Duncan’s test. Results showed that most measured soil quality index parameters showed significant differences (p < 0.05). The long-term dairy manure applications had positive impacts on soil quality index parameters where overall SQI scores were higher under high manure (HM) compared to medium manure (MM), low manure (LM), medium fertilizer (MF), high fertilizer (HF), control (CK) by 25%, 27%, 47%, 55% and 92%. A similar trend was observed for CY and GWP. This indicates that long-term dairy manure can be an option to increase SQI values and provide higher CY, however, this may lead to greater GWP.

Cover Crops Enhance Soil Properties in Arid Agroecosystem despite Limited Irrigation

Cover crops (CCs) can enhance the sustainability and resiliency of agroecosystems by providing multiple ecosystem benefits, including soil quality improvement. However, in areas with limited precipitation such as the southwestern USA, cover cropping is challenging. With limited water, it may be difficult to raise cover crops for realizing ecosystem benefits. Research was conducted at two sites in New Mexico over two years to determine if CC under limited irrigation could produce enough biomass to improve soil quality. Treatments included a fallow (control) and monocultures of barley (Hordeum vulgare), Austrian winter peas (Pisum sativum subsp. arvense), mustard (Brassica rapa, var. Caliente 199), and a three-way mixture of these species, grown under three different irrigation regimes. The results indicate that the improvement in soil quality measurements by CCs grown under one supplemental irrigation were comparable to those grown under multiple irrigations. All CC treatments improved the soil dry aggregate size distribution from 2018 to 2020. At the end of the study, the MWD of dry aggregates was higher (3.26 mm) in all CC treatments than in the fallows (2.43 mm) at one site, but at the second site, mustard and mix were comparable to the fallows. Wet aggregate stability increased by 19% in the mix between 2018 and 2020 at one site. Pea plots needed about 23 kg ha−1 less N fertilizer for sweet corn production compared to the fallow treatment at one site. This suggests that CCs can be successfully grown under limited water availability in irrigated arid systems of New Mexico while still improving the soil quality.

Soil Mesofauna Community on Ex-Landfills Land Gunung Tugel Banyumas Regency

This study aims to identify and reveal the soil fauna community which includes individual counts, diversity and dominance as well as the quality of the soil environment in the former landfills Gunung Tugel land, Kedung Randu Village, Patikraja District, Banyumas Regency. The method used is purposive random sampling. Sampling was carried out for six months, during January-June 2021, six times with an interval of one month, at day (06.00 - 08.00) and night (18.00-20.00). Soil sampling and soil quality measurements were carried out at two research locations, location I in the northern and eastern areas of the ex-landfills Gunung Tugel area and location II in the western and southern areas of the ex- landfills Gunung Tugel area. The results of the sampling were identified using a microscope and referred to the book An Introduction to the Study of Insects and the book Ecology of Soil Animals. The results showed that 394 individuals of soil mesofauna were counted which belonged to 12 orders, 15 families and 17 species. The diversity index is classified as moderate, in the range of 0.970 - 1.097 with an average of 1.045. The dominance index is relatively low, ranging from 0.335 to 0.423 with an average of 0.367 and shows that no species dominates. The quality of the soil environment of the former Gunung Tugel landfills is classified as not yet feasible to support the life of the soil mesofauna.