Soil Laboratory Analysis Research Articles

Enhancing Green Bean Production on Acid Oxisols Using Basalt Fines in the Cameroon Western Highlands

Rock fines have been proposed as alternative to chemical fertilizers, but their application rates and performances on various crops are still poorly understood. This work aims to test the fertilizing potential of basalt fines for Green beans production on acid Oxisols. A field experiment using a completely randomized block design (CRBD) was conducted on a 60 m² plot.". Treatments were: T0(0), T1(5 t ha₋1 basalt fines), T2(10 t ha₋1 basalt fines), T3(15 t ha₋1), T4(20 t ha₋1 basalt fines), T5(5 t ha₋1 poultry manure), T6 (110 Kg. ha₋1 NPK 14₋23₋14). Fieldwork was followed by laboratory analysis of soils before and after treatment of the experimental units. The main results revealed that T0 was very acidic, but basalt and poultry manure (PM) application reduced acidity while NPK further increased soil acidity. The exchangeable bases and base saturation also increased after treatment with basalt and PM. The yield of green beans was such that T4>T1>T6>T3>T5>T0 >T2. Economically, all treatments were negative except T1 (Benefit-to-cost ratio, BCR=1.35<2), suggesting that none of the treatments can be popularized for green beans production. Nevertheless, production could be increased based on appropriate farming and chemical techniques aimed at reducing soil acidity and boosting nutrient capital reserves. This study addresses a critical need for environmentally friendly alternatives to chemical fertilizers, aligning with sustainable agriculture goals. To increase its applicability and impact, more focus should be placed on the long-term environmental advantages and real-world uses.

Multi-Sensor Soil Probe and Machine Learning Modeling for Predicting Soil Properties.

We present a data-driven, in situ proximal multi-sensor digital soil mapping approach to develop digital twins for multiple agricultural fields. A novel Digital Soil CoreTM (DSC) Probe was engineered that contains seven sensors, each of a distinct modality, including sleeve friction, tip force, dielectric permittivity, electrical resistivity, soil imagery, acoustics, and visible and near-infrared spectroscopy. The DSC System integrates the DSC Probe, DSC software (v2023.10), and deployment equipment components to sense soil characteristics at a high vertical spatial resolution (mm scale) along in situ soil profiles up to a depth of 120 cm in about 60 s. The DSC Probe in situ proximal data are harmonized into a data cube providing vertical high-density knowledge associated with physical-chemical-biological soil conditions. In contrast, conventional ex situ soil samples derived from soil cores, soil pits, or surface samples analyzed using laboratory and other methods are bound by a substantially coarser spatial resolution and multiple compounding errors. Our objective was to investigate the effects of the mismatched scale between high-resolution in situ proximal sensor data and coarser-resolution ex situ soil laboratory measurements to develop soil prediction models. Our study was conducted in central California soil in almond orchards. We collected DSC sensor data and spatially co-located soil cores that were sliced into narrow layers for laboratory-based soil measurements. Partial Least Squares Regression (PLSR) cross-validation was used to compare the results of testing four data integration methods. Method A reduced the high-resolution sensor data to discrete values paired with layer-based soil laboratory measurements. Method B used stochastic distributions of sensor data paired with layer-based soil laboratory measurements. Method C allocated the same soil analytical data to each one of the high-resolution multi-sensor data within a soil layer. Method D linked the high-density multi-sensor soil data directly to crop responses (crop performance and behavior metrics), bypassing costly laboratory soil analysis. Overall, the soil models derived from Method C outperformed Methods A and B. Soil predictions derived using Method D were the most cost-effective for directly assessing soil-crop relationships, making this method well suited for industrial-scale precision agriculture applications.

Exploring soil erodibility: integrating field surveys, laboratory analysis, and geospatial techniques in sloping agricultural terrains

The escalating trend of land degradation poses a significant challenge, especially in sloping agricultural terrains, driven by the increasing global demand for food and the limited availability of flat arable land. In response to these challenges, farmers are compelled to shift their focus towards cultivating sloping terrains. This research aimed to employ a comprehensive methodology that integrates on-site field surveys, meticulous laboratory soil analyses, and geospatial data for mapping soil erodibility. The parameters under scrutiny encompass various crucial aspects, including soil texture (ranging from coarse sand to very fine sand, silt, and clay), soil structure, organic matter content, and permeability. The meticulous examination of these factors serves as the foundation for calculating soil erodibility, utilizing the well-established Wischmeir and Smith formula developed in 1978. The research findings present a nuanced understanding of soil erodibility in the study location, revealing a spectrum spanning low to very high erodibility. Specific units, such as Unit 1, Unit 2, Unit 3, Unit 7, Unit 9, Unit 10, Unit 13, and Unit 16, exhibit very low to low erodibility. In contrast, Unit 4, Unit 6, Unit 14, and Unit 15 showcase moderate erodibility, while units like Unit 5, Unit 8, Unit 11, Unit 12, Unit 17, and Unit 18 are characterized by moderately high to very high erodibility. These insightful results shed light on the diverse erodibility levels within the studied locations and provide valuable guidance for formulating sustainable land management practices.

Investigation of the Relationship between Topographic and Forest Stand Characteristics Using Aerial Laser Scanning and Field Survey Data

The article thoroughly investigates the relationships between terrain features and tree measurements derived from aerial laser scanning (ALS) data and field surveys in a 1067-hectare forested area. A digital elevation model (DEM) was generated from ALS data, which was then used to derive additional layers such as slope, aspect, topographic position index (TPI), and landforms. The authors developed a mathematical procedure to determine the radii for the topographic position index. The canopy height model was created, and individual trees were segmented using a novel voxel aggregation method, allowing for the calculation of tree height and crown size. Accuracy assessments were conducted between ALS-derived data and field-collected data. Terrain variability within each forest unit was evaluated using characteristics such as standard deviation, entropy, and frequency. The relationships between tree height and the derived topographic features within forest subcompartments, as well as the correlation between the height yield map for the entire area and the TPI layer, were analysed. The authors found strong correlation between the topographic position index and tree heights in both cases. The presented remote-sensing-based methodology and the results can be effectively used in digital forest site mapping, complemented by field sampling and laboratory soil analyses, and, as final goal, in carbon stock assessment.

Rapid soil attribute evaluation for soil security assessments in data-poor environments in the Pacific region

Many global environments face increasing pressures on soil resources, and effective, scalable methods for assessment of soil condition and capital are essential to respond to tangible soil threats. This situation is common across Pacific Island Countries and Territories (PICTs), where high throughput soil analysis laboratories are limited, and issues such as soil organic carbon decline, acidification and fertility declines are present. Soil spectral inference presents an opportunity in such regions to provide rapid insights into soil capital and condition, though the need for robust calibration libraries remains a limiting factor. This work investigates the utility of a regionally appropriate spectral library, the New Zealand Soil Spectral Library (NZSSL) to support the development of soil spectral inference in data-poor environments, such as PICTs, through a case study on the island of Tongatapu in The Kingdom of Tonga. We contrast the performance of existing partial least squares regression (PLSR) models developed for New Zealand soils on soils from Tongatapu and explore the opportunities for enhancement of predictions formed through memory-based learning (MBL) supplemented with local data. Our work shows the potential for cost-effective and timely soil monitoring through soil spectral inference in PICTs. The work further underscores the importance of regional cooperation and data-sharing for addressing soil security.

Assessment of hydropedological characteristics at medium-sized landslide sites in Manafwa catchment, Mount Elgon, Uganda

ABSTRACT Though strongly linked to landslides, hydropedological characteristics are inadequately investigated in Manafwa catchment. This study investigated hydropedological characteristics at medium landslide sites in Manafwa catchment. The design included 12 infiltration experiments that were conducted at each site. To determine the selected soil physical properties, composite soil samples were extracted from the infiltration sites for soil laboratory analysis. Soil cores were extracted to determine saturated hydraulic conductivity (Ksat) and bulk density. The observed high infiltration rates and rapid Ksat underscore the susceptibility of the studied sites to landslide hazards. Additionally, soil organic matter, clay content, and bulk density further contribute to the understanding of the hydropedological factors influencing landslide occurrences. These findings reveal the need for comprehensive investigations into hydropedological characteristics of other landslide categories to assist in landslide risk assessment and management strategies.

Developing Vis–NIR libraries to predict cation exchange capacity (CEC) and pH in Australian sugarcane soil

In sugarcane growing areas of Queensland Australia, management of soil condition requires information about soil chemical properties (i.e., cation exchange capacity [CEC], and pH), because they are used to make fertiliser recommendations. While laboratory analysis is cost prohibitive the development of visible near–infrared (Vis–NIR) spectroscopy libraries might be useful. The aims of this study was to compare: i) linear (i.e., Partial least squares regression [PLSR]) and machine learning (i.e., Cubist, Random Forest [RF] and Support vector machine [SVM]) algorithms in terms of their calibration strength (i.e., coefficient of determination (R2); ii) depth-specific (i.e., topsoil [0 – 0.3 m], subsurface [0.3 – 0.6 m], subsoil [0.6 – 0.9 m] and deep subsoil [0.9 – 1.2 m]) and multi-depth libraries; iii) prediction R2, agreement (Lin’s concordance correlation coefficient [LCCC]) and accuracy (ratio of performance to interquartile [RPIQ]); and iv) minimum number of calibration sample locations (i.e., n = 400, 350, …, 50). For depth-specific calibration for CEC, strong calibration R2 was achieved for the subsurface (i.e., RF [0.95], Cubist [0.93], PLSR [0.88] and SVM [0.81]), and for pH, the largest R2 was for deep subsoil (i.e., RF [0.91], followed by PLSR [0.80], Cubist [0.79] and SVM [0.56]), with subsurface, and deep subsoil similar, with topsoil R2 smaller. In terms of model prediction of depth-specific, Cubist was superior to PLSR, RF and SVM for CEC, and PLSR was the best for pH. For example, agreement for topsoil CEC was substantial for Cubist (0.80) and PLSR (0.80), but moderate for RF (0.69) and poor for SVM (0.59). For subsurface (0.9), subsoil (0.91) and deep subsoil (0.92), the agreements were perfect for CEC using Cubist. In terms of multi-depth calibration for CEC it was strong for RF (R2 = 0.94), Cubist (0.92), PLSR (0.81) and SVM (0.72), but for prediction agreement, Cubist (LCCC = 0.92) was perfect, with PLSR (0.85), RF (0.84) and SVM (0.83) substantial. This was also the case for pH (0.84) substantial. However, there are efficiencies in developing a multi-depth calibration. Moreover, the minimum number of calibration sample sites required was 300 (i.e., 1,200 samples) (1.78 sites/ha). The results have implications for using a Vis–NIR library to replace traditional soil laboratory analysis and for fertiliser recommendations for sugarcane soil.

Spatial analysis of leachate penetration at Lemna dumpsite, Calabar: Implications for sustainable waste management in Cross River State

This study rigorously investigated the spatial analysis of leachate penetration at Lemna dumpsite, located in Calabar, Cross River State, Nigeria. Purposeful soil sampling, performed at specific intervals (5 m, 25 m, and 50 m) along the Electrical Resistivity profile line within the dumpsite, was augmented by water sample collection from five boreholes near Lemna dumpsite. Utilizing Electrical Resistivity Tomography (ERT) and Vertical Electric Sounding (VES) survey techniques, resistivity data were systematically gathered to comprehensively analyze the Leachate Penetration in the Lemna dumpsite. Laboratory analysis of soil and borehole water quality focused on Benzene, Toluene, Ethylbenzene, and Xylene (BTEX), with paired sample t-tests applied for statistical scrutiny. Analyzing the ERT and VES data employed sophisticated techniques embedded in Resistivity Two Dimension Invasion software and Advanced Geosciences Incorporation Earth Imager software. Substantial disparities (p < 0.05) emerged in the paired sample t-tests for BTEX in soil compared to National Environmental Standard Regulation and Enforcement Agency (NESREA) limits. Similarly, BTEX in borehole water displayed significant differences (p < 0.05) when compared to World Health Organization (WHO) standards, raising alarming concerns about the safety and portability of groundwater in the area. The examination of dumpsite leachate penetration revealed a resistivity anomaly of 8.01 Ωm and an inverse depth of 12.4 m, underscoring profound environmental implications and necessitating immediate remediation efforts. Additionally, Vulnerability and Aquifer Protective Capacity Index (VES) results, with a rating of <0.1, indicated severely compromised aquifer protective capacity, emphasizing the vulnerability of groundwater resources to further contamination. Our study advocates for strategic management, remediation, and monitoring measures to prevent contamination and safeguard water quality in the region.

Assessment of agricultural land suitability for cereal crops based on the analysis of soil physico-chemical characteristics

This research aimed at evaluation of a given land resource suitable for selected rain fed crops production (malt barley, wheat and teff) in Ambesh watershed. It also quantified suitable land and classified into the land mapping units (LMUs), and presents a land suitability map. Land suitability assessment (LSA) made using climatic condition, topography, soil physical and chemical properties as a major factor integrated with a multi criteria decision making (MCDM) analysis in Ambesh watershed. Fourteen composite soil samples were taken by categorizing the watershed into different land mapping units (LMUs) and analyzed in a soil laboratory. Climatic data, rainfall was obtained from two meteorological stations nearby to the study area. Temperature data derived from Landsat 8 satellite thermal bands data. Data obtained from the soil laboratory and others were finally analyzed using ArcGIS environment and priority estimation tool (PriEsT) software’s. Weighted Sum Overlay was implemented to investigate the final LSA map of the watershed. Results revealed that LMUs, VRe–LPq and LPK.Pq–FLc LMUs has higher overall suitability for all the selected rain fed crops. However, LMUs (VRe–NTu and NTu–VRe) has lower overall suitability values particularly for S1 suitability class (0.05% and 10.6%, respectively). The least suitable LMU is VRe–NTu with 0.05% S1 suitability class and above 99% of the land laid under the suitability classes of moderately suitable, marginally suitable and not suitable for the selected land utilization types. Moreover, about 219.06 ha (17.76%), 217.6 ha (17.64%), 168.9 ha (13.7%), of land are highly suitable for malt barley, teff and wheat crop production, respectively. In conclusion, during MCDM, classifying the land into closer homogeneities (LMU) an important application of LSA integrated with remote sensing and GIS for a better decision making. Meanwhile, majority (above two third’s) of the land in the watershed is under moderate and marginally suitable, it needs intensive land management activities to increase the land qualities and obtain high yields. LSA recommended before land utilization decision has to be made. It is also important to classifying the land into LMUs to make it more homogeneous for sample taking and reducing the prestigious soil laboratory analysis costs.

Belowground bud banks and land use change: roles of vegetation and soil properties in mediating the composition of bud banks in different ecosystems.

Belowground bud banks play integral roles in vegetation regeneration and ecological succession of plant communities; however, human-caused changes in land use severely threaten their resilience and regrowth. Although vegetation attributes and soil properties mediate such anthropogenic effects, their influence on bud bank size and composition and its regulatory mechanisms under land use change have not been explored. We conducted a field investigation to examine impacts of land use change on bud bank size and composition, vegetation attributes, and soil properties in wetlands (WL), farmlands (FL), and alpine meadow (AM) ecosystems in Zhejiang Province, China. Overall, 63 soil samples in close proximity to the vegetation quadrats were excavated using a shovel, and samples of the excavated soil were placed in plastic bags for onward laboratory soil analysis. The total bud density (1514.727 ± 296.666) and tiller bud density (1229.090 ± 279.002) in wetland ecosystems were significantly higher than in farmland and alpine meadow ecosystems [i.e., total (149.333 ± 21.490 and 573.647 ± 91.518) and tiller bud density (24.666 ± 8.504 and 204.235 ± 50.550), respectively]. While vegetation attributes critically affected bud banks in WL ecosystems, soil properties strongly influenced bud banks in farmland and alpine meadow ecosystems. In wetland ecosystems, total and tiller buds were predominantly dependent on soil properties, but vegetation density played a significant role in farmlands and alpine meadow ecosystems. Root sprouting and rhizome buds significantly correlated with total C in the top 0 - 10 cm layer of farmland and alpine meadow ecosystems, respectively, and depended mainly on soil properties. Our results demonstrate that land use change alters bud bank size and composition; however, such responses differed among bud types in wetland, farmland, and alpine meadow ecosystems.

Exploring the untapped potential of hand-feel soil texture data for enhancing digital soil mapping: Revealing hidden spatial patterns from field observations

Digital soil mapping (DSM) is commonly conducted using input soil attributes derived from laboratory analyses of geographically referenced samples. Field observations are often abundant and can offer a dense source of soil data that has the potential to enhance DSM predictions. However, they are not widely used due to to concerns about subjectivity and data quality. This study investigates the usefulness of hand-feel soil texture (HFST) data for DSM. We processed HFST data obtained from forest soils in France from two inventory campaigns: (i) HFST determination from systematic 1 km2 grid observations in France utilizing a specialized soil texture triangle, and (ii) HFST observations from soil survey samples, using a different texture triangle. Both sets of HFST data were used as input soil variables, with the same covariates, for predicting topsoil texture In a sizable, forested area through a DSM method. By employing independent sampling and laboratory soil analyses in selected areas, we uncovered measurement bias in one of the datasets. However, intriguingly, these biased observations identified subtle yet highly specific and unexpected patterns of sands in terraces due to alluvial deposits along small rivers. Thus, field soil observations, even if they are biased, should not be dismissed solely based on their overall predictive performance. It is essential to carefully examine predicted maps and covariates to determine whether patterns may have pedological and/or lithological origins and if they are pertinent for enhancing DSM predictions, enhancing soil process understanding, and meeting the requirements of end users. Numerous HFST are available worldwide, these datasets are usually disregarded for DSM. Here we contend that efforts should be put in recovering these data, and their potential for enhancing DSM and deepening our understanding of soil processes.

Spatio-Temporal variability of soil primary macro and micro nutrients – An empirical analysis on Anaimalai block, Coimbatore District, India

This study reveals Spatio-temporal prognostication of macro and micronutrients present in entisols, inceptisols and alfisols soil groups at Anaimalai block of Coimbatore district, India through empirical analysis. The soil primary macronutrients and micronutrients for the period 2021–2032 were procured from Autoregressive Integrated Moving Average (ARIMA), Linear Regression (LR) and Logistic Regression (Log R) models by setting out 2005–2020 soil nutrient datasets as input. 1627 soil samples were taken out through composite sampling method in the month of February 2021. The laboratory soil analysis was carried out for macro and micronutrients computation. Computed values were validated with the 2021 soil nutrient empirical models. R2 values 0.88, 0.83, 0.85, 0.89, 0.88, 0.92, 0.90, 0.92 and 0.85 for soil pH, EC, Available nitrogen, Available phosphorus, Available potassium, Iron, Manganese, Copper and Zinc respectively divulges the reliability of ARIMA model in forecasting the soil nutrients and are spatially exposed. Spatio-temporal visualization of soil nutrients for a decade with large nutrient database as input along with field validation brings out promising forecasting model that reverberates on futuristic policy making for attaining sustainable agro-productivity.

Prediction of soil nutrients through PLSR and SVMR models by VIs-NIR reflectance spectroscopy

Though soil nutrients play important roles in maintaining soil fertility and crop growth, their estimation requires direct soil sampling followed by laboratory analysis incurring huge cost and time. In this research work, soil nutrients were predicted using VIs-NIR reflectance spectroscopy (range 350–2500 nm) with Partial Least Squares Regression (PLSR) and Support Vector Machine Regression Model (SVMR) model through principal component analysis. Two hundred soil samples were collected from Tarekswar, Hooghly, West Bengal, India to predict eight selected soil nutrients, such as soil organic carbon (OC), pH, available nitrogen (N), available phosphorus (P), available potassium(K), electric conductivity (EC), zinc (Zn) and soil texture (sand, silt, and clay) levels. The OC content was predicted with sound accuracy (R2: 0.82, RPD: 2.28, RMSE: 0.13, RPIQ: 4.15 FD-SG), followed by P (R2: 0.71, RPD: 1.83, RMSE: 4575, RPIQ: 3.44 1st derivative). The soil parameters sensitive to the particular band of visible spectrum were also identified viz. wavelengths of 409, 444, 591 and 592 nm for OC, 430 and 505 nm for P, 464 nm for K; 580 nm for Zn, 492,511,596 and 698 nm for N; 493, 569 and 665 nm for EC; 492,567 and 652 nm for pH; 457 nm for sand and 515 nm for clay.The soil nutrient levels were predicted by PLSR and SVMR models through PCA and Sentinel 2 imagery and soil suitability map were also generated for seven soil parameters such as OC, pH, EC, N, P, K and clay content. Through map query tool in ArcGIS software environment the PLSR and SVMR model successfully identify the suitability class with level of accuracy of 87.2% and 88.9%, respectively, against the direct soil analysis based suitability mapping.The machine learning technique based soil nutrient and soil suitability prediction can be easily adopted in different regions. This will reduce the cost of laboratory soil analysis and optimize the total time requirement.

Prediction of some selected soil properties using the Hungarian Mid-infrared spectral library

Routine soil chemical and physical laboratory analysis provides a better understanding of the soil by evaluating its quality and functions. Demands for the development of national Mid-infrared (MIR) spectral libraries for predicting soil attributes with high accuracy have risen substantially in the recent past. Such MIR spectral library is usually regarded as a fast, cheap and non-destructive technique for estimating soil properties compared to laboratory soil analysis. The main objective of this research was to assess the performance of the Hungarian MIR spectral library in estimating four soil properties namely: Cation Exchange Capacity (CEC), Exchangeable Mg and Ca and pH water at different scenarios. Archived soil samples were scanned and spectra data were saved in the Fourier transform infrared spectrometer OPUS software. Preprocessed filtering, outlier detection and calibration sample selection methods were applied to the spectral library. MIR calibration models were built for soil attributes using partial least square regression method and the models were validated with sample predictions. R2, RMSE and RPD were used to assess the goodness of calibration and validation models. MIR spectral library had the ability to estimate soil properties such as CEC and exchangeable Ca and Mg through various scale models (national, county and soil type). The findings showed that the Hungarian MIR spectral library for estimation of soil properties has the ability to provide good information on national, county and soil type scales at different levels of accuracy.

Soil Analysis GIS-Based Fertility Assessment and Soil Mapping of Cardamom Growing Area, Pandam, Ilam, Nepal

Abstract: The objective of this research was to map the status of soil nutrients in the farmlands of cardamom development centre, Pandam, Fikkal, Ilam. The research was carried out in January 2021, and soil laboratory analysis was performed in February 2021, after soil sample collection. n the soil lab of the Agriculture Knowledge Center, Ilam, soil samples were examined for texture, PH, N, P2O5, K2O, and organic matter using a conventional analytics procedure. The ARC-GIS version 10 was used to prepare the soil-fertility maps. The data revealed two predominant textural classes; sandy loam (70%) and loam (30%).The soil PH (5.19±0.08) was slightly acidic in majority of the study site i.e. 83.3% of the research location was found to be acidic and 16.6% of the location was found to be neutral soil. While the organic matter(5.3%±0.0096) and total nitrogen content (0.263%±0.0038) of the study area was found to be high in about two-third of sampling location, phosphorous (49.61kg/ha±0.89) was found to be medium in majority of the site i.e. 90% medium, 6.67% high and 3.33% was low. Available potassium (82.78kg/ha±7.71) was found to be low i.e. 60% of the research location was found to be low in Total potassium, 39.9% was medium and 0.01% was high in potassium. The research was carried out in January 2021, and soil laboratory analysis was performed in February 2021, after soil sample collection

Integrating additional spectroscopically inferred soil data improves the accuracy of digital soil mapping

Digital soil mapping has been increasingly advocated as an efficient approach to deliver fine-resolution and up-to-date soil information in evaluating soil ecosystem services. Considering the great spatial heterogeneity of soils, it is widely recognized that more representative soil observations are needed for better capturing the soil spatial variation and thus to increase the accuracy of digital soil maps. In reality, the budget for the field work and soil laboratory analysis is commonly limited due to its high cost and low efficiency. In the last two decades, being an alternative to wet chemistry, soil spectroscopy, such as visible-near infrared (Vis-NIR), mid-infrared (MIR) spectroscopy has been developed in measuring soil information in a rapid and cost-effective manner and thus enable to collect more soil information for digital soil mapping (DSM). However, spectroscopically inferred (SI) data are subject to higher uncertainties than reference laboratory analysis. Many DSM practices integrated SI data with soil observations into spatial modelling while few studies addressed the key question that whether these non-errorless soil data improve map accuracy in DSM. In this study, French Soil Monitoring Network (RMQS) and Land Use and Coverage Area frame Survey Soil (LUCAS Soil) datasets were used to evaluate the potential of SI data from Vis-NIR and MIR in digital mapping of soil properties (i.e. soil organic carbon, clay, and pH) at a national scale. Cubist and quantile regression forests were used for spectral predictive modelling and DSM modelling, respectively. For both RMQS and LUCAS Soil dataset, different scenarios regarding varying proportions of SI data and laboratory observations were tested for spectral predictive models and DSM models. Repeated (50 times) external validation suggested that adding additional SI data can improve the performance of DSM models regardless of soil properties (gain of R2 proportion at 3–19%) when the laboratory observations are limited (≤50%). Lower proportion of SI data used in DSM model and higher accuracy of spectral predictive models led to greater improvement of DSM. Our results also showed that a greater proportion of SI data lowered the prediction intervals which may result in an underestimation of prediction uncertainty. The determination of accuracy threshold on SI data for the use in DSM needs to be explored in future studies.

Effect of irrigation regimes on the fertility of soybean and sunflower cultivars planted in repeated periods

The increase in the world’s population means that the demand for food, including vegetable oil and protein products, is increasing day by day. Biometric measurements of the plant, laboratory analysis of soil and plant samples were done using phenological observations “Methods of conducting field experiments”, the amount of protein in soy grains was analyzed based on “Methods for determining the amount of protein in grain and its content”. The results showed that the amount of oil was 58.6% during irrigation according to the pre-irrigation soil moisture content of 65–65–60% relative to the limited field moisture capacity of the 0-50 cm soil layer during irrigation. Furthermore, it was found that when the soil moisture before irrigation was 75-75-65% according to LFMC in the calculation layer of the soil in the 0-50 cm layer in the 1-2-1 system, 1682 m3/ha water was used in soybeans, and it was 2331 m3/ha of sunflower plants.

Smart Soil Property Analysis Using IoT: A Case Study Implementation in Backyard Gardening

Crop growth and the agricultural ecosystem are both affected by inappropriate soil fertility ranges. Soil fertility management practices help to efficient use of fertilizer and organic compost and selecting appropriate crops based on soil conditions. But, farmers randomly applied fertilizers without analysing their soil nutrients status. These unbalanced agrochemical inputs contaminate agricultural resources. Laboratory soil analysis procedures help to track soil fertility status. However, the laboratory soil analysis practices are insufficient for Indian farmers. Recent research and development show an On-field soil property monitoring system. Furthermore, internet-based soil fertility monitoring aids farmers in increasing productivity and optimizing resources. This paper presents the prototype based on a smart soil fertility prediction system based on the Internet of Things (IoT). The prototype model was tested and analysed on home gardening application and monitoring the spinach crop growth. This soil fertility prediction system encourages cost-effective direct field measurement in agriculture. To test the accuracy of the prototype, the sensor result was compared with the laboratory analysis result. For soil pH measurements the sensor result is 6 and the laboratory test result is 6.7. similarly, for soil EC measurement the sensor value is 0.48 and the laboratory result is 0.64. The soil pH and EC comparison results show that only a slight variation is present between the sensor and laboratory value i.e for pH 0.7 % and EC 0.16% difference are present. The analysed result shows that the proposed prototype model is efficient for on-field monitoring in gardening. Further, the measured data are stored in the cloudMQTT server, and they can be utilized for ago decision-making applications.

Impact of Topography on Soil Properties in Delboatwaro Subwatershed, Southern Ethiopia

Soil characterization and classification are prerequisites for better agricultural productivity and sustainable soil fertility management. This study was previously conducted to characterize and classify DelboAtwaro, a watershed in southern Ethiopia. Three pedons classes were inspected and three representative pits (pedons) opened, i.e. one in each pedon. Pedons were described in accordance with FAO [1] and WRB (2014) in the study area, and then soil samples were collected from recognized horizons of each pedon and analyzed for selected physicochemical properties. The pedons confirmed the variability of the physical, chemical and morphological properties of the soils in the study area. Based on the result of field and laboratory soil analysis, the soil structure was early clay in both aboveground and subterranean strata. The soil chemical reaction used to be somewhat acidic to neutral in reaction (pH 6.1-7.0). The organic carbon (OC) content varied between 1.23 and 1.78% between the respective topographical positions. The cationexchange capacity (CEC) of the soils ranged from 39.8 to 79.9 cmol (+) kg-1 (intermediate to optimal), while the percent base saturation (PBS) ranged from 23.7 to 40.7%. The dominance of the exchangeable bases was once, in descending order, Ca&gt;Mg&gt;K&gt;Na. Soils ranged from low to optimal in TN and very low to low in available P, while the concentrations of micronutrients in the soils were best (Fe), very high (Mn), adequate (Zn), and optimum (Cu). The soil had a molly epipedon with humic diagnostic houses in the subsurface. Hence the soil was categorized as Rhodic Nitisols (Haplic) (US, MS, and LS) according to the WRB for soil resources. In general, slope and land use influenced soil properties in the different topographical locations of the Delbo Atwaro Underwater Catchment, suggesting the need for integrated soil fertility management to sustainably conserve soil organic matter and nutrients.

Machine learning models for predicting soil particle size fractions from routine soil analyses in Quebec

AbstractSoil texture and particle size distribution are important soil properties for understanding and interpreting the multiple processes and interactions in soil agrosystems. However, soil particle size analysis is rarely included in routine soil laboratory analyses due to cost but can be derived from other soil physico‐chemical properties and from more powerful regressions using machine learning techniques. The performance of multiple linear regression and four machine learning algorithms (MLAs)—K‐nearest neighbor (KNN), Random Forest, extra‐gradient boosting (XGBoost), and multilayer neural network (NeuralNetwork)—was compared to predict particle size fractions of clay, sand, and silt using routine analyses of soil pH, cation exchange capacity, Mehlich‐3 extracted elements, and density of dried sieved soil from 8,364 soil samples distributed across Quebec. Particle size fractions were predicted as compositional data using isometric log ratio transformation. The XGBoost model performed best, with RMSE values of 7, 10, and 12% and R2 values of .77, .57, and .73 for the prediction of clay, silt, and sand fractions, respectively. Feature importance classification varied from one model to another, but the best predictors remained the same regardless of the model used. Sieved soil density measured in the laboratory and Mehlich‐3 Mg, K, Fe, and Zn ranked as better predictors of particle size fractions. Predicting soil particle sizes using routine soil physico‐chemical properties and MLAs appears an option for incomplete legacy datasets and a promising economic alternative to current methods carried out in commercial laboratories.