Variability Of Soil Properties Research Articles

Determining soil conservation strategies: Ecological risk thresholds of arsenic and the influence of soil properties

The establishment of ecological risk thresholds for arsenic (As) plays a pivotal role in developing soil conservation strategies. However, despite many studies regarding the toxicological profile of As, such thresholds varying by diverse soil properties have rarely been established. This study aims to address this gap by compiling and critically examining an extensive dataset of As toxicity data sourced from existing literature. Furthermore, to augment the existing information, experimental studies on As toxicity focusing on barley-root elongation were carried out across various soil types. The As concentrations varied from 12.01 to 437.25 mg/kg for the effective concentrations that inhibited 10% of barley-root growth (EC10). The present study applied a machine-learning approach to investigate the complex associations between the toxicity thresholds of As and diverse soil properties. The results revealed that Mn-/Fe-ox and clay content emerged as the most influential factors in predicting the EC10 contribution. Additionally, by using a species sensitivity distribution model and toxicity data from 21 different species, the hazardous concentration for x% of species (HCx) was calculated for four representative soil scenarios. The HC5 values for acidic, neutral, alkaline, and alkaline calcareous soils were 80, 47, 40, and 28 mg/kg, respectively. This study establishes an evidence-based methodology for deriving soil-specific guidance concerning As toxicity thresholds.

Residue Kinetics of Insecticide Pymetrozine in Rice Field Soil

Soils are the most diverse and complex ecosystem in the world. In addition to providing humanity with 98.8% of its food, soils provide a broad range of other services, from carbon storage and greenhouse gas regulation, to flood mitigation and providing support for our sprawling cities. Therefore, protection of soil health from various pollutants including pesticides is important for its future sustenance. The pesticide Pymetrozine widely used in rice fields for the control of aphids and whiteflies is also registered in India for crop protection in Paddy. This study aimed to examine how long the pesticide Pymetrozine persists and how it breaks down in different types of soil in West Bengal. This research was conducted through laboratory experiments using LC-MS/MS at Bidhan Chandra Krishi Viswavidyalaya, Nadia. Applied two doses of Pymetrozine and analyzed soil samples over 30 days. Pymetrozine levels decreased by 95-97% after 15 days and fell below detectable levels after 30 days. Dissipation followed first-order kinetics, with a faster rate in new alluvial soil compared to red lateritic soil. This difference could be due to variations in soil properties, like organic carbon content and water retention.

Influence of nitrogen substitution at an equivalent total nitrogen level on bacterial and fungal communities, as well as enzyme activities of the ditch bottom soil in a rice-fish coculture system.

Rice-fish coculture system (RFS) operates through effectively utilizing water and land resources in a complementary form, but it requires more efficient utilization of fertilizer and feed without compromising rice yield. However, the knowledge of how to regulate the proportion of nitrogen (N) supplied from fertilizer and feed at an equivalent total N levelto improve the benefits of RFS remains limited. Therefore, four treatments (S0: RFS with 0% N fromfertilizer and 100% N from feed; S25: RFS with 25% N from fertilizer and 75% N from feed; S50: RFS with 50% N from fertilizer and 50% N from feed; S75: RFS with 75% N from fertilizer and 25% N from feed) were conducted to assess the variation of ditch bottom soil properties, microbial communities and enzyme activities, as well as to obtain the optimal ratio of N supplied from fish feed and fertilizer. The experiments showed that the contents of soil organic matter, total carbon and total N, and the activities of urease, N-acetyl-β-D-glucosaminidase, protease, β-1,4-glucosidase and catalase in the ditch bottom soil significantly reduced in S25 treatment, compared with the other three treatments. Ammonium N content decreased with increasing percentage of the basal fertilizer, whereas nitrate N content and pH value showed an adverse trend. However, the bacterial and fungal communities were unaffected by the ratio shifts between fertilizer-N and feed-N, but their dominant phyla were influenced by the ditch bottom soil N level. Moreover, the bacterial community composition was positively related to nitrate N, whereas fungal diversity was positively correlated with pH, ammonium N and nitrate N, and urease. We also found that the treatment of N input with 25% N from fertilizer and 75% N from feed can reduce N deposition in the ditch bottom soil in the rice-fish coculture system. Our findings indicate that under the equivalent total N input level, the relative higher ratio of N from fish feed increased (S0 treatment) or reduced (S25 treatment) the deposition of N in the ditch bottom soil, and improved fish production, but decreased rice yield; while the higher ratio of N from basal fertilizer increased the transportation of nutrients into the ditch bottom soil and rice yield, but reduced fish production. So when considering multi-balance and multiple benefits, we recommend that a selective substitution ratio within 50% ~ 75% from fish feed to substitute for the basal fertilizer under the equivalent total N input may achieve a good balance of rice and fish production improvement, and reduce nutrients wastage to the ditch bottom, as well as alleviate the potential of non-point source pollution. This study also provides an evidence for regulating and optimizing the ratio of N supplied from fertilizer and fish feed at an equivalent total N level through monitoring the nutrient accumulation in ditch bottom soil in the rice-fish coculture system. © 2024 Society of Chemical Industry.

The power of on-farm data for improved agronomy

Advances in technology and analytics to support data-driven agriculture has important implications for global food security and environmental sustainability. However, relatively few studies have investigated the potential to leverage the power of on-farm data for improved agronomy at scale using geospatial machine learning methods. Working in high-yielding rice systems of Uruguay, we developed a geospatial framework to identify yield-limiting factors across 55,000 ha annually of cropland over four seasons (2018–2021 harvest years), while also testing for tradeoffs in the environmental footprint related to nitrogen (N) fertilizer use. Our application of geographically-weighted random forest models showed that crop management decisions influenced rice yield more than variation in soil properties, highlighting the potential for improved agronomy to boost crop production by 1.4–1.8 Mg ha−1 across regions. Seeding date, variety, P rate, and K rate were the most important variables controlling yield, but with significant variation across fields. When these factors were optimized by farmers, the risk of environmental N losses or soil N mining did not increase, highlighting the potential for sustainable intensification by improving N use efficiency. These findings present a pathway for harnessing the benefits of increasingly available on-farm data to identify yield-limiting factors while minimizing negative environmental externalities at the field-level. To enable the development of such geospatial frameworks in other regions, new partnerships are required to engage stakeholders and promote data sharing and collaboration among farmers, researchers, and industry, helping guide regional extension programs and orient future investments in agricultural research.

Time series prediction of reservoir bank landslide failure probability considering the spatial variability of soil properties

Historically, landslides have been the primary type of geological disaster worldwide. Generally, the stability of reservoir banks is primarily affected by rainfall and reservoir water level fluctuations. Moreover, the stability of reservoir banks changes with the long-term dynamics of external disaster-causing factors. Thus, assessing the time-varying reliability of reservoir landslides remains a challenge. In this paper, a machine learning (ML) based approach is proposed to analyze the long-term reliability of reservoir bank landslides in spatially variable soils through time series prediction. This study systematically investigated the prediction performances of three ML algorithms, i.e. multilayer perceptron (MLP), convolutional neural network (CNN), and long short-term memory (LSTM). Additionally, the effects of the data quantity and data ratio on the predictive power of deep learning models are considered. The results show that all three ML models can accurately depict the changes in the time-varying failure probability of reservoir landslides. The CNN model outperforms both the MLP and LSTM models in predicting the failure probability. Furthermore, selecting the right data ratio can improve the prediction accuracy of the failure probability obtained by ML models.

Experimental model updating of slope considering spatially varying soil properties and dynamic loading

AbstractThe widespread threat posed by slope structure failures to human lives and property safety is widely acknowledged. Additionally, natural soil often displays spatial variability due to geological deposition and other factors. Therefore, predicting the seismic response of slopes subjected to ground motions and inversely analyzing the spatial distribution of soils remains an unresolved issue. In the present work, a shaking table experimental test is first designed and carried out, in which a soft‐soil slope dynamic system is established. To capture the seismic response of the soft‐soil slope, specifically the experimental characteristic of acceleration and soil pressure response in both spatial domain and time domain, a series of sensors were pre‐embedded in the slope. Subsequently, a model updating approach is proposed for slope seismic analysis that incorporates spatial variability of soil. In addition, to enhance computational efficiency, the dimensionality reduction of Karhunen–Loève expansion method is introduced to reduce inverse analysis parameters. On the basis of 34 samples collected from experimental data, it is shown that near‐fault pulse‐like ground motions deliver greater concentrated energy, causing more severe damage to slope structures, especially the topsoil layer. Furthermore, using data obtained from a shaking table test subjected to ground motion Recorded Sequence Number 158H1 from the Pacific Earthquake Engineering Research Center NGA‐West2 database as an example, it is also shown that the proposed approach demonstrates high accuracy in predicting the spatial distribution of the maximum shear modulus in soil slope dynamic systems. The present work not only addresses the challenges posed by mainshock–aftershock effects but also highlights the potential of model updating approaches to enhance the understanding of slope behavior under seismic loading conditions.

Properties and Fungal Communities of Different Soils for Growth of the Medicinal Asian Water Plantain, Alisma orientale, in Fujian, China.

The Asian water plantain, Alisma orientale (Sam.) Juzep, is a traditional Chinese medicinal plant. The dried tubers of the Alisma orientale, commonly referred to as Alismatis rhizome (AR), have long been used in traditional Chinese medicine to treat a variety of diseases. Soil properties and the soil microbial composition are known to affect the quality and bioactivity of plants. Here, we sought to identify variations in soil fungal communities and soil properties to determine which would be optimal for cultivation of A. orietale. Soil properties, heavy metal content, and pesticide residues were determined from soils derived from four different agricultural regions around Shaowu City, Fujian, China, that had previously been cultivated with various crops, namely, Shui Dao Tu (SDT, rice), Guo Shu Tu (GST, pecan), Cha Shu Tu (CST, tea trees), and Sang Shen Tu (SST, mulberry). As fungi can either positively or negatively impact plant growth, the fungal communities in the different soils were characterized using long-read PacBio sequencing. Finally, we examined the quality of A. orientale grown in the different soils. Our results show that fungal community diversity of the GST soil was the highest with saprotrophs the main functional modes in these and SDT soils. Our data show that GST and SDT soils were most suitable for A. orientale growth, with the quality of the AR tubers harvested from GST soil being the highest. These data provide a systematic approach at soil properties of agricultural lands in need of replacement and/or rotating crops. Based on our findings, GST was identified as the optimal soil for planting A. orientale, providing a new resource for local farmers.

Interpolation of non-stationary geo-data using Kriging with sparse representation of covariance function

Interpolation of spatially varying measurements, e.g., spatial variation of a geotechnical property along depths, is often needed in many disciplines, including geotechnical engineering. Among various interpolation methods, geostatistical methods, such as Kriging, are popular since they are able to not only provide the best estimates but also quantify the interpolated uncertainty. However, Kriging relies on an assumption of data stationarity, and a transformation from non-stationary data to stationary data by, e.g., detrending, is required when using Kriging to interpolate non-stationary data. Detrending, however, is a tricky and long-lasting challenge in geo-data analyses. To tackle this challenge, this study develops a novel Kriging method for direct interpolation of spatially non-stationary data without detrending. A sparse representation of covariance function is proposed for Kriging interpolation of spatially non-stationary data, bypassing the tricky detrending process. The proposed Kriging method is particularly appealing for non-stationary geotechnical site investigation data often encountered in geotechnical practice (e.g., spatial variation of soil properties over different soil layers along depth). Equations are derived for the proposed Kriging method, and it is illustrated and validated using numerical examples. Results show that the proposed Kriging method with sparse representation of covariance function is directly applicable to spatially non-stationary data and has the advantages of improved accuracy and reduced interpolation uncertainty when interpolating spatially varying, non-stationary data.

A comparison of ground motions predicted through one-dimensional site response analyses and three-dimensional wave propagation simulations at regional scales

One-dimensional (1D) site response analysis (SRA), which considers vertically propagating seismic waves from the bedrock to the surface, has been a common technique among geotechnical engineers to examine site-specific ground shaking. However, observations from past earthquakes and analytical studies indicate that idealizations ingrained in 1D SRA may be too severe to capture the ground truth, such as the omissions of spatial variability of soil properties, surface topography, and basin and directivity effects. Physics-based three-dimensional ground motion simulations (GMSs) can incorporate these factors and yield more reliable predictions. In this study, we utilize ground motions from 57 physics-based broadband (from 0 to 8–12 Hz) GMS for a region of Istanbul. A total of 2912 sites with experimentally measured soil profiles that are distributed over the 30 km-by-12.5 km area are also modeled as soil columns and analyzed through 1D SRA. The ground responses from 1D SRA and three-dimensional (3D) GMS are then compared for all 57 earthquake scenarios. These systematic comparisons are then used for examining model features that are correlated with variations in the ratios of various ground motion intensity measures (IMs) and for developing regression-based formulas that can be used for determining simple factors for the considered region to correctly scale (up or down) the site-specific ground motion intensities obtained from 1D SRA, including peak ground acceleration (PGA), peak ground velocity (PGV), and spectral acceleration ( Sa) values.

Variation of Soil Properties of Unsaturated Zone with Depth and Time at an Agricultural Site in Kuwait

Variation of Soil Properties of Unsaturated Zone with Depth and Time at an Agricultural Site in Kuwait

Hydrosedimentological monitoring in runoff plots of a degraded tropical area under restoration of the Brazilian Atlantic Forest

Rainfall erosion is a concerning process for landowners, government agencies and civil society as it involves soil loss, an indispensable natural resource for the production of food, fibers, and biofuels, as well as a means to support enterprises or even preservation of large conservation areas for sustainability and potential carbon sink. Specific Projects using runoff and soil loss plots have been adopted in different parts of the world, contributing to the understanding of erosion dynamics. This research is the result of extensive fieldwork carried out on a rural property undergoing forest restoration through a Payment for Environmental Service initiative. Four runoff plots with dimensions of 22m x 4m x 0.5m (length x width x depth) connected to sediment catchment tanks were implemented on a convex slope to analyze erosion in four different treatments: planting native seedlings, seedlings with green manure, natural regeneration with selective pruning, and exposed soil. The results of monitoring over approximately one year showed that 80.15% of all accumulated surface runoff occurred in the plot with exposed soil, as well as 99.15% of all soil loss. The treatment that presented the best conservation rates was natural regeneration, since there was no need to dig holes to introduce seedlings, causing less soil disturbance. Thus, it was concluded that land use and changes in land use are one of the main factors controlling erosion and surface runoff in the study area, however, in the long term, variations in soil properties due to vegetation may reflect more strongly this dynamic.

Influence of Cross-Correlation on the Modelled Uncertainty in Stress–Strain Behavior of Soft Clays

AbstractReliability-based design is increasingly being applied to geotechnical problems because it allows the robust consideration of various sources of uncertainty, such as the inherent variability of soil properties. Some soil properties, however, are mutually dependent, and ignoring this cross-correlation may lead to biased estimates of the probability of unsatisfactory performance. Hence, in this study, the Gaussian copula was used to evaluate how the applied cross-correlation or independence between the compressibility properties affects the uncertainty in the stress–strain response of two marine soft clays. Two settlement calculation methods were considered: the compression index and Janbu tangential stiffness methods. The correlation coefficients were defined from the site-specific oedometer data at two extensively studied clay sites, and from a database. The simulated oedometer curves were compared to the observed variability in the site-specific data. The settlements in the clay sublayers were then computed, and different cases were compared by means of boxplots. It is concluded that the Janbu method leads to a significant overestimation of uncertainty in settlement if the cross-correlation between the compressibility parameters is ignored. On contrary, the compression index method seems less sensitive to the assumed correlation structure, and as such, the parameters can be treated as independent in most cases.

Remediation Efficiency and Soil Properties of TCE-Contaminated Soil Treated by Thermal Conduction Heating Coupled with Persulfate Oxidation

Less attention was paid to the remediation of volatile organic compounds (VOCs) contaminated soil treated by thermal conduction heating (TCH) coupled with chemical oxidization. In this study, the lab-scale remediation experiments of trichloroethylene (TCE)-contaminated soil by TCH and TCH coupled with persulfate (TCH + PS) were performed to explore the influences of PS usage, temperature, reaction time, and the variation of soil properties. TCE was removed from contaminated soils using TCH with a temperature lower than boiling point, and the removal ratio of TCE reached 78.21% with a reaction time of 6h at 60 °C. In the TCH + PS treatments, the removal ratio increased to 87.60~99.50% when the PS dosage was increased from 7.0 mmol/kg to 17.5 mmol/kg at 60 °C. However, the usage efficiency of PS had no positive relationship with oxidant usage and temperature. The treatment with 14 mmol/kg PS after 3h at 50 °C had the highest PS usage ratio of 3.05. In addition, soil pH and soil organic matter (SOM) did not decrease significantly in the TCH-only treatment, while the content of SOM declined by almost 50% after the TCH + PS treatment. Overall, it was concluded that TCH + PS achieved higher removal efficiency, whereas TCH had less disturbance on soil pH and SOM. As such, the applicability of TCH-only or TCH + PS treatments is site-specific.

Impact of humic acid on various properties of soil and crop productivity- A review

To raise the amount and quality of agricultural produce, numerous studies have been conducted in recent years. The development of agriculture is not just influenced by mechanization and new hybrid seeds; soil quality also plays a significant role. Lack of organic matter in soil frequently renders it unsuitable for plant growth. To address this problem and increase agricultural yields, humic compounds have started to be applied to soil in many parts of the world. Humic acid (HA) can now be applied to crops or soil using a variety of methods, including soil application, foliar application, seed treatment, root dipping, and drip irrigation, as HA is available in various forms viz., granule, powder, flakes, liquid and also in conjunction with inorganic fertilizers. However, soil application is the most effective in numerous studies. Numerous experiments on crops were carried out to determine the ideal dosage of humic acid, and it was found that character, yield, and soil characteristics all had a favourable impact on crop production. This review clarifies the significance of humic acid for various crops and the health of the soil, as well as how it increases agricultural output.

Investigation on wave-induced responses of heterogeneous seabed around a buried pipeline based on nonstationary random field model

The instability of the seabed poses a threat to the structural integrity and functionality of submarine pipelines. This numerical study investigates wave-induced responses in a heterogeneous seabed around a buried pipeline, considering spatial variability in soil properties through nonstationary random field models. Three scenarios of spatial variability for soil parameters changing with depth are simulated using both stationary and nonstationary random field models via the Karhunen-Loève expansion method. The discrete random fields are validated in terms of correlation structures and statistical characteristics. The stochastic finite element method, incorporating random fields and a poroelastic seabed model through a LiveLink code, is employed to examine probabilistic outcomes of dynamic soil responses and liquefaction zone distributions within a probabilistic framework. Contrastive analyses are conducted for seabed models with different random field scenarios, in addition to deterministic analyses. The results of the stochastic analysis show that the increasing trend of soil strength with depth has significant effects on the seabed responses and transient liquefaction characteristics around a pipeline.

Spatial and temporal variation of soil properties and soil organic carbon in semi-arid areas of Sub-Sahara Africa

Land cover land use (LCLU) patterns play a crucial role in shaping the biosphere and global climate. This influence is especially pronounced in arid and semi-arid regions, where environmental vulnerabilities are heightened. Studies have shown that LCLU Change is a key concern in the scientific community, particularly regarding its influence on soil health. This study focuses on the Gaborone area in Botswana, which has experienced rapid population growth and LCLU changes from 2002 to 2022.Landsat 7 (2002) and Sentinel-2A (2022) from USGS datasets were used to detect LCLU changes and assess their impacts on soil properties, namely: soil organic carbon (SOC) content, bulk density, soil pH, and texture. Image classification was done using a supervised classification method based on a Maximum Likelihood classifier and accuracy assessment done via a contingency matrix. LCLU change detection was achieved using the post classification comparison approach. Moreover, soil properties analysis was done using the Walkley and Black method, core method, Bouyoucos hydrometric method, and a pH meter, respectively. The effects of LCLU on soil properties and SOC were studied using a Random Forest regression analysis.Water bodies, tree cover, cropland, shrubland, bare land, and build-up were identified as major LCLU classes in the study area, with shrubland being the dominant class during the study period. A significant variation in soil properties was found across the different LCLU types. SOC content, a key determinant of soil fertility and moisture retention, was found to be higher in the tree cover and shrubland classes compared to cropland and built-up areas. A Random Forest regression analysis showed a substantial influence of LCLU on soil properties, particularly SOC dynamics. The study concludes by highlighting the interconnectedness of LCLU changes and soil properties, advocating for integrated land management strategies to balance development and environmental preservation in this climate-vulnerable landscape.

Delineation of Soil Management Zones and Validation through the Vigour of a Fodder Crop

In Mediterranean farming systems, the semi-arid conditions and agricultural ecosystems have made site-specific management an important approach. This method aims to understand and handle the variability of soil properties and crop management, particularly through the utilization of geospatial information and accessible technology. Over three years in a 30 ha experimental field located in the Alentejo region (Portugal), crop establishment was monitored using data from soil apparent electrical conductivity (ECa), remote sensing (Sentinel-2), and in situ soil sampling. The procedure began with Step 1, involving the acquisition of soil spatial information and spatial interpolation. Subsequently, in Step 2, management zones (MZs) for soil characteristics were delineated using a combination of ECa measurements and soil analysis, and Step 3 spanned over three years of gathering meteorological data and crop remote sensing measurements. In Step 4, site-specific crop MZs were delineated by vegetation indexes (VIs). This article aims to increase the importance of in situ and remote assessments to more accurately identify areas with different productive potential. Results showed three MZs based on the percentage of sand, ECa, altimetry, exchangeable calcium, and exchangeable calcium properties, validated by crop VIs (Normalized Difference Vegetation Index (NDVI), Normalized Difference Red-Edge Index (NDRE), and Normalized Difference Moisture Index (NDMI)) over time. Although there are many sensorial techniques available for site-specific management, this paper emphasizes a methodology for the farmer to identify different MZs combining remote and in situ evaluations, supporting new opportunities for a more rational use of natural resources. Based on soil parameters, three site-specific management areas could be selected. NDMI was the index that best explained the MZs created according to soil properties.

Understanding the impact of within-field Olsen P variation on common wheat production in Olsen P deficient soils

This study investigates the influence of Olsen phosphorus (Ps) within-field variability on common wheat production in Ps-deficient soil. It covers a comprehensive investigation conducted over two growing seasons in Tuscany, Italy, aiming to evaluate the impact of within-field Ps variation on common wheat production and crop responses to different agronomic treatments. Field experiments were undertaken, employing 24 treatments, including 4 wheat varieties, 2 seeding densities, and 3 nitrogen fertilization levels. Soil samples were collected to determine within field variability of main soil properties. At harvesting, aboveground biomass was collected to determine grain yield, straw, and total protein content. Firstly, an ANOVA was performed to account for the effect of the agronomical treatments on crop parameters, namely the Agronomical Input Model (AIM). Then, soil total nitrogen (Ns) and Ps, were added as covariates in a new model, namely the Agronomical and Soil Input Model (ASIM). Results showed a significant spatial variability of Ps within the fields, ranging between 13.7 and 17.4 mg kg-1. Despite this minimal variation in Ps, we observed a linear increase in GY of about 1430 kg ha-1 for each increase of 1 mg kg-1 in Ps. Further studies should be conducted to account for non-linear responses at both lower and higher Ps levels. The ASIM model outperformed AIM, indicating a notable increase in predictive accuracy for both grain yield and protein concentration due to its incorporation of soil parameters. The variance explained by ASIM in predicting the grain yield and protein concentration in grain increased by about 11.0 % and 17.3 %, respectively, with respect to AIM. The study emphasizes the necessity of managing within-field Ps variability through targeted fertilization strategies to enhance wheat production in Ps-deficient soils. By addressing soil nutrient variability, this research aims to contribute to more precise and efficient common wheat production systems.

Geospatial Variability of Soil Properties of the Different Villages in Arajiline Block of Varanasi District of Uttar Pradesh

The current study was conducted at the different villages of the Arajiline block of Varanasi district, Uttar Pradesh, India during November, 2022–June, 2023 to evaluate the variability of soil properties and nutrient indexof soils. The random sampling technique was used to collect 40 soil samples at different locations with the help of GPS at 0–15 cm depth. The collected soil samples were air-dried in shade at room temperature, passed through a 2 mm filter paper, and analyzed for different physico-chemical properties. The result revealed that the TS (range 8.2–81.3%), VFS (range 4.2–35.5%), Silt (range 10.1–54.2%), Clay (range 7.7–38.8%), BD (range 1.08–1.45 g cm-3), PD (range 2.04–2.33), Porosity (range 30.24–51.56%) pH (range 6.4-7.8), EC (range 0.1-0.3 dS m-1), OC (0.2-0.6%), and available N, P, K, S, Ca, and Mg varied from 130.12–260.30 kg ha-1, 14.2-23.2 kg ha-1, 145.0–240.0 kg ha-1, 7.5–17.6 kg ha-1, 3.8–8.2 cmol(P+)/kg, and 2.1-4.1 cmol(P+)/kg, respectively. The findings indicated that the studied soils were slightly acidic to moderately acidic and free from salinity hazards; 70% of the soil samples were low in organic carbon, 30% were medium in organic carbon, and 100% of the soil samples were low in available N and medium in available P, K, and S. 100% of soil samples were found to be sufficient in exchangeable Ca and Mg. The findings of this research could help in crop nutrient management, fertilizer recommendation, and decision-making for increasing agricultural production and farmer profitability.

Evaluating Intra-Field Spatial Variability for Nutrient Management Zone Delineation through Geospatial Techniques and Multivariate Analysis

This research study underscores the importance of effectively managing soil nutrients in a site-specific manner to enhance crop productivity while considering the spatial variability of the soil. The objective is to identify subfields with similar soil characteristics, referred to as management zones (MZs), to promote sustainable land utilization. This study was conducted in two central pivot fields located in Southern Idaho, USA, where barley and sugar beets were grown. Soil samples were collected from each field in a grid pattern and analyzed for various chemical properties. These properties included soil pH, organic matter, cation exchange capacity, excess lime, electrical conductivity, total inorganic nitrogen, phosphorus, potassium, calcium, magnesium, zinc, iron, manganese, copper, and boron. Descriptive statistics and normality assessments were performed, and the coefficient of variation was calculated to assess the heterogeneity of soil properties, revealing significant variability. To determine the spatial variability of soil properties, ordinary kriging was used revealing diverse spatial patterns for each location and soil variable examined with moderate to strong spatial dependence. To develop the MZs, a combination of principal component analysis and fuzzy k-means clustering was utilized, and specific parameters that represented the overall variability of soil properties in each field were identified. Based on the identified parameters, two clusters were created in each field. The first management zone (MZ1) exhibited lower values of soil pH, excess lime content, and electrical conductivity compared to the MZ2. Consequently, higher crop productivity was observed in MZ1 in both fields. The biomass yields of barley and sugar beets in MZ1 surpassed those in MZ2. This study highlights the effectiveness of the methodology employed to delineate MZs, which can be instrumental in precise soil nutrient management and maximizing crop productivity.