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The study of interactions and feedbacks between plants and soils is a rapidly expanding research area, and a primary tool used in this field is to perform glasshouse experiments where soil biota are manipulated. Recently, there has been vigorous debate regarding the correctness of methods for carrying out these types of experiment, and specifically whether it is legitimate to mix soils from different sites or plots (mixed soil sampling, MSS) or not (independent soil sampling, ISS) to create either soil inoculum treatments or subjects. We performed the first empirical comparison of MSS vs ISS approaches by comparing growth of two boreal tree species (Picea abies and Pinus sylvestris) in soils originating from 10 sites near the boreal forest limit in northern Sweden, and 10 sites in the subarctic region where boreal forests may potentially expand as a result of climate change. We found no consistent differences in the conclusions that we reached whether we used MSS or ISS approaches. We propose that researchers should not choose a soil handling method based on arguments that one method is inherently more correct than the other, but rather that method choice should be based on correct alignment with specific research questions and goals.

The assessment of soil organic carbon (SOC) stocks relies on several key factors, including the total SOC content of the soil, the bulk density (BD) of the soil, and the depth of the sampled layers. However, traditional methods, particularly those using volumetric cylinders for undisturbed soil sampling, present significant logistical challenges for large-scale projects due to their costly and time-consuming nature, often requiring the excavation of trenches. In response, automated probes, which are commonly used in geological studies, offer a promising alternative, but their application in the context of BD soil studies remains under discussion. The objective of this study was to evaluate the efficacy of a tractor-mounted probe in comparison to the conventional core method for the collection of undisturbed soil samples across a range of soil textures and depths. The results indicated that the coefficient of variation (CV) for the probe’s bulk density (BD) measurements ranged from 3 to 15% in sandy soils but remained consistently below 5% in clay-sandy-loam and clay textures. Despite small differences in BD values between the methods used, SOC stock assessments showed only minor variations across all layers, regardless of soil texture. Therefore, this study demonstrated that the tractor-mounted probe represents a viable and scalable solution for conducting large-scale SOC stock assessments, despite its susceptibility to minor variations in sandy soil conditions. This research contributed to the field of automated soil sampling tool validation and offered alternatives for field operationalization in large-scale carbon projects. It also raised new questions for further investigation into other field conditions

A traditional grid model for soil sampling may suffer from poor efficiency and low accuracy. With a nonferrous metal processing plant as the study area, a three-dimensional kriging interpolation model was built based on this plant’s preliminary investigation data for arsenic (As), and a detailed survey sampling programme was proposed. The sampling density at the pollution interval of the surface soil was estimated by the coefficient of variation method, and the sampling depth was determined by the pollution interval of the vertical prediction results. The results showed that the encrypted soil sampling distribution optimisation method obtains greater pointing accuracy with fewer points. The sampling accuracy was 87.62% after optimising the depth of pointing. Moreover, this approach could save 66.13% of the sampling costs and 56.93% of the testing costs compared to a full deployment programme. This study provides a new and cost-effective method for predicting the extent of contamination exceedance at a site and provides valuable information to guide post-remediation strategies for contaminated sites.

The use of soil residual herbicides, along with other practices that diversify weed management strategies, have been recommended to improve weed management and deter the progression of herbicide resistance. Although soil characteristics influence recommended application rates for these herbicides, the common practice is to apply a uniform dose of soil residual herbicides across fields with variable soil characteristics. Mapping fields for soil characteristics that dictate the optimal dose of soil residual herbicides could improve the efficiency and effectiveness of these herbicides, as well as improve environmental stewardship. The objectives of this research were to develop and quantify the accuracy of management zone classifications for variable-rate residual herbicide applications using multiple soil data sources and soil sampling intensities. The maps were created from soil data that included (i) Soil Survey Geographic database (SSURGO), (ii) soil samples (SS), (iii) soil samples regressed onto soil electrical conductivity (EC) measurements (SSEC), (iv) soil samples with organic matter (OM) data from SmartFirmer® (SF) sensors (SSSF), and (v) soil samples regressed onto EC measurements plus OM data from SmartFirmer® sensor (SSECSF). A modified Monte Carlo cross validation method was used on ten commercial Indiana fields to generate 36,000 maps across all sources of spatial soil data, sampling density, and three representative herbicides (pyroxasulfone, s-metolachlor, and metribuzin). Maps developed from SSEC data were most frequently ranked with the highest management zone classification accuracy compared to maps developed from SS data. However, SS and SSEC maps concurrently had the highest management zone classification accuracy of 34% among maps developed across all fields, herbicides, and sampling intensities. One soil sample per hectare was the most reliable sampling intensity to generate herbicide application management zones compared to one soil sample for every 2 or 4 hectares. In conclusion, soil sampling with ECa data should be used for defining the management zones for variable-rate (VR) residual herbicide applications.

The paper presents the control architecture of a crawler mobile robot designed and developed to sample potentially contaminated lands. The robot, developed in the framework of an Italian national project named ROBILAUT, carries a driller with a customized sampling mechanism to implement on-site the required quartering, and it is controlled to move the drilling device on specific points acquired in real time before the mission starts. The paper describes the software architecture for the navigation and control, focusing on the control framework of the robotic platform. Specifically, the robot exhibits a differential drive kinematics with actuators’ constraints, and two different control strategies have been experimentally tested for comparison both in a structured environment and in the real site in May 2023.

The rapid monitoring and accurate estimation of dynamic changes in soil organic carbon (SOC) can make great efforts in understanding the global carbon cycle. Traditional field survey is the main approach to obtain soil data and measure SOC content. However, the limited number of soil samples and the sampling cost hinder the quality of digital soil mapping. This research aims to explore the sensitive of sampling density in digital soil mapping, and then design a suitable soil sampling plan based on a series of sampling indices. Headwall hyperspectral images (400–1700 nm) were used to estimate the SOC map by partial least squares regression (PLSR) and PLSR kriging (PLSRK). Three traditional soil sampling methods (random, grid, and Latin hypercube sampling) with 10 classes of sampling densities (6.26, 2.79, 1.57, 1.01, 0.69, 0.53, 0.39, 0.30, 0.26, and 0.20 ha−1) were designed. The R2, root mean square error (RMSE) and ratio of standard deviation to RMSE (RPD) were used to evaluate the prediction accuracy in digital soil mapping by ordinary kriging. Three new indices, namely, the ratio of sampling efficiency to performance (RSEP), the density of soil samples index and the comprehensive evaluation index of prediction accuracy, were used to select a suitable soil sampling plan. Results showed that (1) the prediction accuracy of PLSRK (RPD = 2.00) was higher by approximately 11.73% than that of PLSR (RPD = 1.79), and the hyperspectral images provided an actual referential SOC map for the study of soil sampling; (2) the grid sampling plan performed better than the random and Latin hypercube sampling methods, and the quality of SOC map improves with the increase of the sampling density, and (3) the computer simulation and field verification indicated that RSEP is one feasible index in designing a suitable soil sampling plan.

Though more costly than petroleum-based fuels and a minor component of overall military fuel sources, biofuels are nonetheless strategically valuable to the military because of intentional reliance on multiple, reliable, secure fuel sources. Significant reduction in oilseed biofuel cost occurs when grown on marginally productive saline-sodic soils plentiful in California’s San Joaquin Valley (SJV). The objective is to evaluate the feasibility of oilseed production on marginal soils in the SJV to support a 115 ML yr−1 biofuel conversion facility. The feasibility evaluation involves: (1) development of an Ida Gold mustard oilseed yield model for marginal soils; (2) identification of marginally productive soils; (3) development of a spatial database of edaphic factors influencing oilseed yield and (4) performance of Monte Carlo simulations showing potential biofuel production on marginally productive SJV soils. The model indicates oilseed yield is related to boron, salinity, leaching fraction, and water content at field capacity. Monte Carlo simulations for the entire SJV fit a shifted gamma probability density function: Q = 68.986 + gamma (6.134,5.285), where Q is biofuel production in ML yr−1. The shifted gamma cumulative density function indicates a 0.15–0.17 probability of meeting the target biofuel-production level of 115 ML yr−1, making adequate biofuel production unlikely.

This study examined changes in soil enzymatic activity caused by constant mineral fertilization with NPK and diversified fertilization with Fe and Mo micronutrients. A field experiment was conducted in a completely randomized design with four replications in Siedlce (central-eastern Poland) between 2012 and 2014. Alfalfa (Medicago sativa L.) was used as the test plant. The first factor consisted of fertilization treatments: control; NPK; NPKFe1; NPKMo1; NPKFe1Mo1; NPKFe2; NPKMo2, and NPKFe2Mo2. The second factor was composed of the time of soil sampling (15 August 2012, 20 September 2012, 17 June 2013, and 20 July 2014). Mineral fertilization was applied: N-20; P-22; K-124.5; Fe1-0.5; Mo1-0.5; Fe2-1.0; Mo2-1.0 kg ha−1. Application of molybdenum (Mo2-1.0 kg ha−1) in alfalfa fertilized with NPK was optimal for obtaining the beneficial nitrogenase activity. The applied NPKFe1Mo1 fertilization in alfalfa cultivation was optimized to achieve high dehydrogenases activity, alkaline phosphatase activity, and acid phosphatase activity. The highest of soil urease activity was determined in soil fertilized with NPKFe2Mo2. The biochemical index (BCHI) of soil fertility reached its highest mean value (254.9) after applying the NPKFe1Mo1. A high BCHI soil fertility index indicates the possibility of generating high alfalfa yields and maintaining good soil culture.

The timing of technology adoption is influenced by profitability and farmer ability to bear risk. Innovators are typically more risk tolerant than laggards. Understanding the factors influencing early adoption of precision agriculture (PA) technologies by cotton farmers is important for anticipating technology diffusion over time. The factors influencing the timing of grid soil sampling (GSS), yield monitoring (YMR) and remote sensing (RMS) adoption by cotton producers was evaluated using multivariate censored regression. Data for cotton farmers in 12 states were obtained from a survey conducted in 2009. The factors hypothesized to influence the timing of adoption included farm characteristics, operator characteristics, PA information sources, adoption of other PA technologies, and farm location. The results suggest that different factors influenced when cotton farmers adopted GSS, YMR and RMS after these technologies became commercially available. For example, land ownership was associated with the timing of GSS adoption, but not YMR or RMS adoption; farmer age was correlated with the timing of GSS and YMR adoption, but not RMS adoption; and obtaining PA information from consultants affected the timing of GSS and RMS adoption, but not YMR adoption. The only factors correlated with the early adoption of all three technologies were beliefs that PA would improve environmental quality and the adoption of at least one other PA technology. Thus, the potential for improved environmental quality appears to be a strong adoption motivator across PA technologies, as is the earlier adoption of other PA technologies. This research may be useful for farmers, researchers, Extension personnel, machinery manufacturers, PA information providers and agricultural retailers to anticipate the future adoption of new and emerging PA technologies.

In order to address the low soil breaking rate, poor soil covering performance, and low working efficiency of the existing 3MT-1.8 and PMT-75 grapevine burying machines, two types of improved burying machines, namely the 3MTLJ-1.8 and 3MTXP-1.8, were developed in consideration of the local Ningxia soil conditions. Field experimental results indicated that the soil breaking rate of the 3MTLJ-1.8 machine was 71.44%, and its soil sampling volume increased by approximately 30% compared to that of the 3MT-1.8 machine. It was verified that the self-developed 3MTLJ-1.8 machine can be used in the southern regions of Ningxia. Furthermore, the soil sampling volume of the 3MTXP-1.8 burying machine was 0.24 m3/m, and its soil breaking rate increased by more than 41.42% compared to the standard required volume. The 3MTXP-1.8 machine can be used in the northern areas of Ningxia, where the soil hardness is higher. The results can provide a reference for the development and popularization of grapevine burying machines in Ningxia.