Proximal Soil Research Articles

Determination of soil organic carbon by conventional and spectral methods, including assessment of the use of biostimulants, N-fertilisers, and economic benefits

Soil organic carbon (SOC) is a very important indicator of soil quality, where even small changes can have large effects on the global carbon cycle, climate change, soil fertility and plant growth conditions. The aims of this study were to investigate changes in soil SOC using two types of biostimulants, to confirm the quality of different SOC determination methods, and to estimate fertiliser requirements and winter crop income and costs. Two methods were used to determine the concentration of SOC: the first was conventional sampling of two soil layers and laboratory analysis. The second method measured SOC using a mounted proximal soil sensor with an integrated visible and near-infrared (VIS-NIR) spectroscopic system. Experimental studies were carried out in three scenarios, of which SC1 used a biostimulant consisting of two components: one for soil (humus) and one for plants (leaf-special), SC2 used a biostimulant consisting of many bacterial species and SC3 was a control without biostimulant. The results of the 3-year experimental studies showed that the use of VIS-NIR spectroscopy in the field allows the prediction of soil SOC concentrations with sufficient accuracy. The coefficient of determination R2 ranged from 0.819 to 0.959 compared to the conventional laboratory SOC test. In the SC1 and SC2 scenarios with biostimulants, R2 was higher than in the SC3 control. The use of biostimulants significantly increased the yield of winter wheat and winter oilseed rape and reduced the need for nitrogen fertiliser per tonne of grain yield for both winter wheat (23.0–37.3 %) and winter oilseed rape (6.3–32.1 %). The economic analysis showed that the application of biostimulants increased the relative profit in all years from EUR 54 ha−1 (for winter oilseed rape) to EUR 143 ha−1 (for winter wheat) compared to the control scenario.

Rapid in-field soil analysis of plant-available nutrients and pH for precision agriculture—a review

AbstractThere are currently many in-field methods for estimating soil properties (e.g., pH, texture, total C, total N) available in precision agriculture, but each have their own level of suitability and only a few can be used for direct determination of plant-available nutrients. As promising approaches for reliable in-field use, this review provides an overview of electromagnetic, conductivity-based, and electrochemical techniques for estimating plant-available soil nutrients and pH. Soil spectroscopy, conductivity, and ion-specific electrodes have received the most attention in proximal soil sensing as basic tools for precision agriculture during the last two decades. Spectral soil sensors provide indication of plant-available nutrients and pH, and electrochemical sensors provide highly accurate nitrate and pH measurements. This is currently the best way to accurately measure plant-available phosphorus and potassium, followed by spectral analysis. For economic and practicability reasons, the combination of multi-sensor in-field methods and soil data fusion has proven highly successful for assessing the status of plant-available nutrients in soil for precision agriculture. Simultaneous operation of sensors can cause problems for example because of mutual influences of different signals (electrical or mechanical). Data management systems provide relatively fast availability of information for evaluation of soil properties and their distribution in the field. For rapid and broad adoption of in-field soil analyses in farming practice, in addition to accuracy of fertilizer recommendations, certification as an official soil analysis method is indispensable. This would strongly increase acceptance of this innovative technology by farmers.

Remote and Proximal Sensors Data Fusion: Digital Twins in Irrigation Management Zoning.

The scientific field of precision agriculture employs increasingly innovative techniques to optimize inputs, maximize profitability, and reduce environmental impact. However, obtaining a high number of soil samples is challenging in order to make precision agriculture viable. There is a trade-off between the amount of data needed and the time and resources spent to obtain these data compared to the accuracy of the maps produced with more or fewer points. In the present study, the research was based on an exhaustive dataset of apparent electrical conductivity (aEC) containing 3906 points distributed along 26 transects with spacing between each of up to 40 m, measured by the proximal soil sensor EM38-MK2, for a grain-producing area of 72 ha in São Paulo, Brazil. A second sparse dataset was simulated, showing only four transects with a 400 m distance and, in the end, only 162 aEC points. The aEC map via ordinary kriging (OK) from the grid with 26 transects was considered the reference, and two other mapping approaches were used to map aEC via sparse grid: kriging with external drift (KED) and geographically weighted regression (GWR). These last two methods allow the increment of auxiliary variables, such as those obtained by remote sensors that present spatial resolution compatible with the pivot scale, such as data from the Landsat-8, Aster, and Sentinel-2 satellites, as well as ten terrain covariates derived from the Alos Palsar digital elevation model. The KED method, when used with the sparse dataset, showed a relatively good fit to the aEC data (R2 = 0.78), with moderate prediction accuracy (MAE = 1.26, RMSE = 1.62) and reasonable predictability (RPD = 1.76), outperforming the GWR method, which had the weakest performance (R2 = 0.57, MAE = 1.78, RMSE = 2.30, RPD = 0.81). The reference aEC map using the exhaustive dataset and OK showed the highest accuracy with an R2 of 0.97, no systematic bias (ME = 0), and excellent precision (RMSE = 0.56, RPD = 5.86). Management zones (MZs) derived from these maps were validated using soil texture data from clay samples measured at 0-10 cm depth in a grid of 72 points. The KED method demonstrated the highest potential for accurately defining MZs for irrigation, producing a map that closely resembled the reference MZ map, thereby providing reliable guidance for irrigation management.

Calibrated SoilOptix ® estimates of soil pH and exchangeable cations in three agricultural fields in western Canada – implications for managing spatially variable soil acidity

Spatial variability in soil pH is a major contributor to within-field variations in soil fertility and crop productivity. An improved understanding of the spatial variability of soil pH within agricultural fields is required to determine liming requirements for precision farming. This study with the use of proximal sensors, firstly assessed the spatial pattern of soil pH and how it can be used to determine site-specific, spatially variable lime requirements. Secondly, the effects of soil pH on soil concentrations of nitrate nitrogen (N03-N), phosphorus (P), potassium (K), sulfur (SO4-S), calcium (Ca), magnesium (Mg), soil organic matter (SOM), aluminum (Al), and manganese (Mn) were assessed in three study fields in central Alberta, Canada. Soil pH varied between 4.5 and 7.5 across all field sites. The field-scale coefficient of variation (CV %) for soil pH, Al and Mn ranged between 4.39 and 7.50 %, 7.33–13.72 %, and 7.33–13.72 % across the three sites. The other soil properties showed low, moderate, and high variability, with field-scale CVs ranging between 6.39 and 17.70 % for SOM and 24.33–91.39 % for SO4-S. Soil pH exhibited positive correlations with both Ca and Mg, across all fields. Negative correlations were observed between soil pH and Al across all fields. A principal component analysis (PCA) was performed for all soil parameters and two principal components accounted for 50 %, 54.9 %, and 76.8 % of the total variance in field 1, field 2, and field 3, respectively. Geostatistical semivariance indicated a strong spatial dependence of all chemical parameters across fields. Large regions within a field were strongly acidic (pH < 5.5) and required lime applications ranging from 0 to 6 t ha−1. We conclude that proximal soil sensors can be calibrated to soil properties, enabling variable rate lime recommendations on spatially variable fields for the management of soil acidity.

Dynamic soil hydraulic resistance regulates stomata.

The onset of stomatal closure reduces transpiration during drought. In seed plants, drought causes declines in plant water status which increases leaf endogenous abscisic acid (ABA) levels required for stomatal closure. There are multiple possible points of increased belowground resistance in the soil-plant atmospheric continuum that could decrease leaf water potential enough to trigger ABA production and the subsequent decreases in transpiration. We investigate the dynamic patterns of leaf ABA levels, plant hydraulic conductance and the point of failure in the soil-plant conductance in the highly embolism-resistant species Callitris tuberculata using continuous dendrometer measurements of leaf water potential during drought. We show that decreases in transpiration and ABA biosynthesis begin before any permanent decreases in predawn water potential, collapse in soil-plant hydraulic pathway and xylem embolism spread. We find that a dynamic but recoverable increases in hydraulic resistance in the soil in close proximity to the roots is the most likely driver of declines in midday leaf water potential needed for ABA biosynthesis and the onset of decreases in transpiration.

Spatial-temporal variability in nitrogen use efficiency: Insights from a long-term experiment and crop simulation modeling to support site specific nitrogen management

Within-field soil heterogeneity can lead to large variation in nitrogen use efficiency (NUE). Crop simulation models provide a multi-faceted approach to management considering both soil and plant interactions. However, research using crop models for investigating within field variation in NUE is limited, in part because of challenges quantifying spatially variable soil model parameters. Here soil apparent electrical conductivity (ECa) and measured soil properties were used to map spatial variations in soil characteristics across a Long-Term Experiment in Norfolk, England. The relationship between plot ECa across the 3 ha experiment and agronomic data across three different nitrogen rates (0, 110, and 220 kg N ha-1) over five wheat years (2010–2020) was quantified. The Sirius crop model was parameterized for two soils representing the extremes of ECa. Sirius was validated using recorded plot data. Site-specific optimal nitrogen and associated leaching risks were simulated across 29 years of weather data. Variation in soil properties had significant impact on measured NUE. At 220 kg N ha-1 mean observed yields across 5 years ranged from 9.0 to 10.7 t ha-1 and grain protein from 11.6% to 11% on the low EC and high EC plots, respectively. On average fertiliser grain N recovery was 19.7 kg N ha-1 lower on the low ECa plots. Sirius simulated the variation in yield, grain protein and grain N recovery to a good level of accuracy with RRMSE of 19.5%, 15.4% and 19.5%, respectively. Simulated optimal nitrogen on the low EC soils was on average 12 kg N ha-1 lower, with >1 in 4 years with optimal nitrogen <200 kg N ha-1. Our work demonstrated that using a combination of proximal soil EC scans and targeted soil sampling we can optimize the data requirements for model parameterisation to support site-specific N management.

Effect of training sample size, sampling design and prediction model on soil mapping with proximal sensing data for precision liming

Site-specific estimation of lime requirement requires high-resolution maps of soil organic carbon (SOC), clay and pH. These maps can be generated with digital soil mapping models fitted on covariates observed by proximal soil sensors. However, the quality of the derived maps depends on the applied methodology. We assessed the effects of (i) training sample size (5–100); (ii) sampling design (simple random sampling (SRS), conditioned Latin hypercube sampling (cLHS) and k-means sampling (KM)); and (iii) prediction model (multiple linear regression (MLR) and random forest (RF)) on the prediction performance for the above mentioned three soil properties. The case study is based on conditional geostatistical simulations using 250 soil samples from a 51 ha field in Eastern Germany. Lin’s concordance correlation coefficient (CCC) and root-mean-square error (RMSE) were used to evaluate model performances. Results show that with increasing training sample sizes, relative improvements of RMSE and CCC decreased exponentially. We found the lowest median RMSE values with 100 training observations i.e., 1.73%, 0.21% and 0.3 for clay, SOC and pH, respectively. However, already with a sample size of 10, models of moderate quality (CCC > 0.65) were obtained for all three soil properties. cLHS and KM performed significantly better than SRS. MLR showed lower median RMSE values than RF for SOC and pH for smaller sample sizes, but RF outperformed MLR if at least 25–30 or 75–100 soil samples were used for SOC or pH, respectively. For clay, the median RMSE was lower with RF, regardless of sample size.

Advances in Precision Agriculture Technologies for Sustainable Crop Production

Precision agriculture (PA) encompasses a set of technologies that allow farmers to collect data on variations within fields and manage crops at a more granular level. PA technologies include sensors, satellite imagery, information management tools and variable rate application systems. Together, these enable data-driven, site-specific decision making to optimize productivity while minimizing environmental impacts. This review examines recent advances in PA technologies and their role in supporting sustainable crop production. Key developments reviewed include remote sensing platforms and techniques, proximal soil sensors, variable rate systems, robotics and automation, and decision support tools powered by artificial intelligence and machine learning. Challenges and future directions are also discussed. Widespread adoption of PA technologies has the potential to increase yields and profitability for farmers while reducing use of inputs such as water, nutrients, and pesticides. However, barriers to adoption exist, including costs, technical complexity, and integration challenges. Continued innovation and knowledge transfer will be critical to unlocking the full promise of PA for sustainable agriculture.

Prevalence of trace gas-oxidizing soil bacteria increases with radial distance from Polloquere hot spring within a high-elevation Andean cold desert.

High-elevation arid regions harbor microbial communities reliant on metabolic niches and flexibility to survive under biologically stressful conditions, including nutrient limitation that necessitates the utilization of atmospheric trace gases as electron donors. Geothermal springs present "oases" of microbial activity, diversity, and abundance by delivering water and substrates, including reduced gases. However, it is unknown whether these springs exhibit a gradient of effects, increasing their impact on trace gas-oxidizers in the surrounding soils. We assessed whether proximity to Polloquere, a high-altitude geothermal spring in an Andean salt flat, alters the diversity and metabolic structure of nearby soil bacterial populations compared to the surrounding cold desert. Recovered DNA and metagenomic analyses indicate that the spring represents an oasis for microbes in this challenging environment, supporting greater biomass with more diverse metabolic functions in proximal soils that declines sharply with radial distance from the spring. Despite the sharp decrease in biomass, potential rates of atmospheric hydrogen (H2) and carbon monoxide (CO) uptake increase away from the spring. Kinetic estimates suggest this activity is due to high-affinity trace gas consumption, likely as a survival strategy for energy/carbon acquisition. These results demonstrate that Polloquere regulates a gradient of diverse microbial communities and metabolisms, culminating in increased activity of trace gas-oxidizers as the influence of the spring yields to that of the regional salt flat environment. This suggests the spring holds local importance within the context of the broader salt flat and potentially represents a model ecosystem for other geothermal systems in high-altitude desert environments.

Relationships between Soil Moisture and Visible–NIR Soil Reflectance: A Review Presenting New Analyses and Data to Fill the Gaps

The ability to precisely monitor soil moisture is highly valuable in industries including agriculture and civil engineering. As soil moisture is a spatially erratic and temporally dynamic variable, rapid, cost-effective, widely applicable, and practical techniques are required for monitoring soil moisture at all scales. If a consistent numerical relationship between soil moisture content and soil reflectance can be identified, then soil spectroscopic models may be used to efficiently predict soil moisture content from proximal soil reflectance and/or remotely sensed data. Previous studies have identified a general decrease in visible–NIR soil reflectance as soil moisture content increases, however, the strength, best wavelengths for modelling, and domain of the relationship remain unclear from the current literature. After reviewing the relevant literature and the molecular interactions between water and light in the visible–NIR (400–2500 nm) range, this review presents new analyses and interprets new 1 nm resolution soil reflectance data, collected at &gt;20 moisture levels for ten soil samples. These data are compared to the results of other published studies, extending these as required for further interpretation. Analyses of this new high-resolution dataset demonstrate that linear models are sufficient to characterise the relationship between soil moisture and reflectance in many cases, but relationships are typically exponential. Equations generalising the relationship between soil MC and reflectance are presented for a number of wavelength ranges and combinations. Guidance for the adjustment of these equations to suit other soil types is also provided, to allow others to apply the solutions presented here and to predict soil moisture content in a much wider range of soils.

Remote Sensing and Kriging with External Drift to Improve Sparse Proximal Soil Sensing Data and Define Management Zones in Precision Agriculture

The precision agriculture scientific field employs increasingly innovative techniques to optimize inputs, maximize profitability, and reduce environmental impacts. Therefore, obtaining a high number of soil samples to make precision agriculture feasible is challenging. This data bottleneck has been overcome by identifying sub-regions based on data obtained through proximal soil sensing equipment. These data can be combined with freely available remote sensing data to create more accurate maps of soil properties. Furthermore, these maps can be optimally aggregated and interpreted for soil heterogeneity through management zones. Thus, this work aimed to create and combine soil management zones from proximal soil sensing and remote sensing data. To this end, data on electrical conductivity and magnetic susceptibility, both apparent, were measured using the EM38-MK2 proximal soil sensor and the contents of the thorium and uranium elements, both equivalent, via the Medusa MS1200 proximal soil sensor for a 72-ha grain-producing area in São Paulo, Brazil. The proximal soil sensing attributes were mapped using ordinary kriging (OK). Maps were also made using kriging with external drift (KED), and the proximal soil sensor attributes data, combined with remote sensing data, such as Landsat-8, Aster, and Sentinel-2 images, in addition to 10 terrain covariables derived from the digital elevation model Alos Palsar. As a result, three management zone maps were produced via the k-means clustering algorithm: using data from proximal sensors (OK), proximal sensors combined with remote sensors (KED), and remote sensors. Seventy-two samples (0–10 cm in depth) were collected and analyzed in a laboratory (1 sample per hectare) for concentrations of clay, calcium, organic carbon, and magnesium to assess the capacity of the management zone maps created using analysis of variance. All zones created using the three data groups could distinguish the different treatment areas. The three data sources used to map management zones produced similar map zones, but the zone map using a combination of proximal and remote data did not show an improvement in defining the management zones, and using only remote sensing data lowered the significance levels of differentiating each zone compared to the OK and KED maps. In summary, this study not only underscores the global applicability of proximal and remote sensing techniques in precision agriculture but also sheds light on the nuances of their integration. The study’s findings affirm the efficacy of these advanced technologies in addressing the challenges posed by soil heterogeneity, paving the way for more nuanced and site-specific agricultural practices worldwide.

Multinational prediction of soil organic carbon and texture via proximal sensors

AbstractNovel technologies help to monitor the environmental impact of human activities, but tests involving datasets from several countries, encompassing a large variability of soil properties, are still scarce. This study utilized proximal sensors to predict soil organic carbon (OC) and soil texture of samples from Brazil, France, India, Mozambique, and United States. A total of 1749 samples were analyzed by portable X‐ray fluorescence (pXRF) spectrometry and visible near‐infrared diffuse reflectance spectroscopy. Sand (R2 = 0.89), silt (0.87), and clay (0.84) predictions were very accurate, despite contrasting climates, soil parent materials, and weathering degrees. Soil OC predictions were similarly successful (0.74) using samples from five countries. pXRF was the optimal sensor for soil texture predictions. The addition of international data may improve local models. Proximal soil sensing can be successfully used with a multinational soil database offering a clean, rapid, and accurate alternative to estimate soil texture and OC with international datasets.

The validity domain of sensor fusion in sensing soil quality indicators

Soil health has gained increasing attention under the rapid development of industrialization and the requirement for green agriculture. Therefore, up-to-date soil information related to soil health is urgently needed to ensure food security and biodiversity protection. Previous studies have shown the potential of proximal soil sensing in measuring soil information, while it remains challenging to get cost-efficient and robust estimates of multiple soil health indicators simultaneously via sensor fusion. In this study, we investigated the potential of visible near-infrared (vis-NIR), and mid-infrared (MIR) spectroscopy as well as three model averaging methods in predicting three soil health properties, including soil organic matter (SOM), pH, cation exchange capacity (CEC). The model averaging methods are not only used for model fusion but also for high-level sensor fusion, which include Granger-Ramanathan (GR), Bayesian Model Averaging and Spectral-Guided Ensemble Modelling (S-GEM). Here, S-GEM is a recently proposed algorithm that can improve soil spectroscopic prediction by including spectral information in ensemble modelling. Four widely used prediction models were evaluated, including partial least square regression, Cubist, memory based learning and convolutional neural network. For SOM, sensor fusion based on model averaging algorithms was comparable to that of Sensorsingle + Modelmultiple (MIR singly based on S-GEM) with R2 of 0.86. However, MIR only with S-GEM performed the best among all methods (LCCC of 0.92, RMSE of 3.66 g kg−1 and RPIQ of 3.68). The 10-fold cross-validation results indicated that Sensorsingle + Modelmultiple (MIR singly based on S-GEM) performed best among all methods for pH, with R2 of 0.84, LCCC of 0.90, RMSE of 0.45 and RPIQ of 3.65. For CEC, Sensormultiple + Modelmultiple based on GR performed best with R2 of 0.66, LCCC of 0.80, RMSE of 3.48 cmol + kg−1 and RPIQ was 2.22. Our results also showed that sensor fusion failed to improve spectral prediction of soil information when the performance among sensors differed a lot (△R2 > 0.2), and the use of a single best sensor is therefore suggested in this case. When the sensors have a close model performance (△R2 < 0.2), Sensormultiple + Modelmultiple based on GR was recommended. The outcome of this study can provide a reference for determining the validity domain of sensor fusion methods in improving the accuracy of soil health prediction.

Design and Analysis of Proximal Soil Sensing System Based on Electrochemical ISE Sensor for Total Nitrogen and Potassium Fertility Assessment

Traditional agriculture is the most practiced form of agriculture around the world. Conventional agriculture depends on applying harmful chemicals to the soil and plants, which are mostly synthetically formulated in a laboratory. A modern approach is known as Precision Agriculture (PA) has been introduced in the last two decades produce healthier crops and higher yields compared to conventional agriculture. By using precision agriculture, the farmers can improve their farming techniques and solve decades-old problems. From the study, plants suffering from a lack of N and K show decreasing photosynthetic activity and plants’ abilities to resist disease, leading to reduced crop yield quality and overall quantity. To solved this, the assessment and data analysis for N and K is required. This study aims to get the accurate value of N and K fertilizer used in the field to help restore the ecosystem and optimize yield while remaining environmentally sustainable. The fertilizer A and B will fill and mix into the aqueous solution, then proximal soil sensing ISE sensor will measure the concentration of Nitrate and Potassium ions in aqueous samples. The feedback signal from the ISE sensor will enter the Raspberry Pi controller and analysed through the Internet of Things (IOT) platform using the Node Red and Phyton simulation. Lastly, the data will be processed and monitored through the LCD. The evaluation performance and the result of the ISE Sensor will be validated using a correlation method. The correlation of the soil sensor and the lab analysis has shown that the right amount of NK fertilizer can help the farmer to manage the plant in the field.

Spatial pattern consistency and repeatability of proximal soil sensor data for digital soil mapping

AbstractData from proximal soil sensors can facilitate digital soil mapping at high spatial resolutions. However, their use for predicting static soil properties, such as texture, is affected by spatio‐temporal changes in environmental and measurement conditions. In this research study, seasonal changes in spatial patterns and repeatability of data provided by a platform that simultaneously measures the red (Red) and near infrared (NIR) reflectance, apparent soil electrical conductivity (ECa), temperature, and volumetric moisture content of topsoil (at 3–6 cm depth) were assessed. Test fields are located in Southern Finland with textures dominated by clay and fine sandy till. During single scans, mean relative differences between the data from duplicated measurement points ranged from ~4% to 6% and were the highest for temperature and Red values. The consistency of spatial patterns across seasons (spring and autumn 2021 and 2022) was the highest for ECa values, and the lowest for NIR. ECa and moisture were significant for predicting the clay contents at a cereal grain crop site, whereas temperature was significant at grass ley sites. Errors were generally lower when using spring data compared with autumn data (RMSE ranging from 4.8% to 11.1% for the data from different fields and measurement dates). For the fields, where static soil properties change at small spatial scales, spatially detailed moisture and temperature data support the understanding of seasonal changes in the spatial patterns derived from multi‐sensor data, and the corresponding changes in the performance of calibration models.

Applying Remote Sensing Methods to Estimate Alterations in Land Cover Change and Degradation in the Desert Regions of the Southeast Iberian Peninsula

Numerous drylands worldwide have experienced degradation of both soil and vegetation in proximity to watering areas. Degradation can be observed in satellite imagery as fading radial brightness belts extending away from the water sources. The main objective of this study was to examine the spatio-temporal patterns of land degradation and rehabilitation in the drylands of the southeast Iberian Peninsula. The brightness index of tasseled cap was discovered to be the best form of spectral transformation for enhancing the contrast between the bright-degraded areas near the points and the darker surrounding areas far from and in between these areas. To comprehend the spatial structure present in spaceborne imagery of two desert sites and three key time periods, semi-variograms were created (mid-late 2000s, around 2015 and 2020). To assess spatio-temporal land-cover patterns, a kriging was used to smooth the brightness index values extracted from 30 m spatial resolution images. To assess the direction and intensity of changes between study periods, a change detection analysis based on kriging prediction maps was performed. These findings were linked to the socioeconomic situation prior to and following the EU economic crisis. The study discovered that degradation occurred in some areas as a result of the region’s agricultural activities being exploited.

Using pXRF and Vis-NIR for characterizing diagnostic horizons of fine-textured podzolic soils in subtropical forests

Proximal soil sensing is a promising tool to quantify soil properties through spectral data and machine learning algorithms for application in pedological investigations. However, the relationship between pedological characteristics and sensor signals is difficult to be elucidated with machine learning models. This study aimed to establish direct linkages between pedological features and data from proximal sensors, namely portable X-ray fluorescence spectrometry (pXRF) and visible and near-infrared spectroscopy (Vis-NIR). Ten podzolic soil profiles, including seven Spodosols, two Ultisols, and one Inceptisol, were described and sampled from Central Taiwan. Principal component analysis (PCA) and k-means clustering method revealed that the major variances of the pXRF were affected by the amounts of spodic materials and organic carbon derived from plant debris. For Vis-NIR, a remarkable absorption of Fe-oxides was observed in the B horizons, and the major variations of Vis-NIR spectra extracted by PCA represented the influence of metal-complexed organic carbon and clay. The principal components (PC) of pXRF and Vis-NIR can be used to separate soil horizons, especially the E horizons were clearly separated as indicated by its high user’s and producer’s accuracies. However, the B and C horizons were poorly separated by the PCs and k-means clustering method. Moreover, the PCs can be used directly to predict the nanocrystalline Fe and Al and the optical density of the oxalate extract. Ten soil profiles were classified based on the predicted values, and nine out of ten soil profiles were in agreement with their classification using chemical extractions. The proposed pXRF and Vis-NIR approaches predicted the chemical criteria of diagnostic horizons and facilitated fast determination of Spodosols.

Small-scale variation in available water capacity of the soil influences height growth of single trees in Southern Germany

Aim of study: Detecting possible small-scale soil effects on height growth of single trees in monospecific stands of three important tree species (Abies alba, Fagus sylvatica, and Picea abies).&#x0D; Area of study: 37 mature stands along an ecological gradient in Southern Germany from the cold and wet “optimal niche zone” to warmer and drier niche zones, including gravelly soils with poor water supply.&#x0D; Material and methods: Measurement of achieved height and age of 15 to 20 sample trees per stand. Estimation of the available water capacity of the soil (AWC) in close proximity to sample trees based on soil texture following the German soil survey guidelines. Examining height growth depending on niche zone and AWC. &#x0D; Main results: On sites (stand level) with the lowest water regime, height growth increased significantly with AWC of microsites. The estimated effect on height growth over the whole range of AWC values was almost 8 m at those sites. In contrast, the effect was negative on optimal sites. For intermediate and marginal sites, the effect was positive, albeit not significant for marginal sites.&#x0D; Research highlights: To our knowledge this is the first study about small-scale effects of AWC on height growth of single trees in temperate European forests. Small-scale soil variability should be considered in future scientific studies and practical evaluation, involving single tree performance at stands with low water regime. This seems particularly important in genetic environmental associations studies and in the process of selecting trees for breeding purposes in such stands.

UNMANNED AIRCRAFT SYSTEMS AND SATELLITE TECHNOLOGIES FOR TOPSOIL MAPPING IN PRECISION AGRICULTURE

Abstract. The use of satellite/UAS technologies in Precision Agriculture (PA) is increasing significantly and the continuous need of such kind of technologies is generating a great economic impact. This study carried out results by using Unmanned Aircraft Systems (UAS) and Very Hight Resolution (VHR) satellite imagery for the estimation of topsoil variability. The purpose of this contribution concerns the data analysis over two Production Units by evaluating radiometric response along the surface (e.g., spectral index). Our results concern the integration of UAS and satellite, improving the estimation accuracy of soil homogeneous areas variability, over which to manage agricultural practices. The remote sensing data covered by bare soil were isolated from the vegetative matrix along the study area. Thus, by using a statistical approach through the correlation between remote imagery and specific indices, it could be possible to determine variability in each pedological context, determining the zoning over land plots. According to this, in absence of high-resolution proximal soil sensing instrumentation, remote sensing data analysis can provide preliminary information suitable to establish a weighted topsoil/subsoil sampling campaign.

Mapping cation exchange capacity and exchangeable potassium using proximal soil sensing data at the multiple-field scale

Cation exchange capacity (CEC – cmol (+) kg−1) is the capacity of a soil to hold exchangeable cations, one of which is exchangeable potassium (K). The data from both CEC and exchangeable cations are frequently useful for fertiliser recommendations; however, they are expensive to measure in the laboratory. To add value to limited CEC and K data, digital data can be beneficial where large amounts can be acquired expeditiously and are often strongly correlated. In this research, we seek to develop a linear regression (LR) between apparent soil electrical conductivity (ECa – mS m–1) from Veris-3100 shallow (0 – 0.3 m) and deep (0 – 0.9 m) array configurations and measured topsoil (0 – 0.2 m) and subsoil (0.5 – 0.7 m) CEC. Moreover, we compare these LRs, with a LR between estimates of σ derived from the inversion of ECa using a quasi-three-dimensional algorithm (invVeris V1.1) and topsoil and subsoil CEC. We also want to determine the minimum number of calibration sample sites (i.e., 45, 40, 35, …, 5) required to produce a strong LR (i.e., coefficient of determination: R2 > 0.7) and substantial (Lin’s concordance correlation coefficient; LCCC > 0.8) or consistent prediction agreement. This requires dividing the n = 60 sample sites into calibration (n = 45) and validation (n = 15) sets. A similar approach was used to develop a multiple LR (MLR) to predict topsoil K considering digital data (i.e., ECa, elevation and trend surface parameters). While the LRs between topsoil CEC with shallow ECa (R2 = 0.38), and subsoil CEC with deep ECa (0.36) were weak (0.5 > R2 > 0.3), the LR (0.75) between σ and CEC was strong (using the S2 algorithm and a damping factor [λ] = 1). A poor LR (0.47) was also found between K and ECa; however, a MLR model (ECa, elevation and trend surface parameters) was strong (0.73). In terms of minimum calibration sample size for CEC, it was found that n = 10 sites (i.e., at 2 depths) or more were required. With respect to K, the minimum calibration sample size was n = 10 (i.e., single depth). To improve calibration equations and areal prediction agreement of CEC and K, and reduce confidence intervals (CI) across the four fields, we recommend the use of tighter transect spacings (< 6 m), and the inclusion of soil (i.e., small CEC and K) and digital (e.g., small ECa) data from adjacent fields and on nearby farms. The final DSM of CEC and K could be used to prescribe potash (K2O) fertilisers.