Information on the spatiotemporal variability of soil properties and states within the agricultural landscape is vital to identify management zones supporting precision agriculture (PA). Ground-penetrating radar (GPR) and electromagnetic induction (EMI) techniques have been applied to assess soil properties, states, processes, and their spatiotemporal variability. This paper reviews the fundamental operating principles of GPR and EMI, their applications in soil studies, advantages and disadvantages, and knowledge gaps leading to the identification of the difficulties in integrating these two techniques to complement each other in soil data studies. Compared to the traditional methods, GPR and EMI have advantages, such as the ability to take non-destructive repeated measurements, high resolution, being labor-saving, and having more extensive spatial coverage with geo-referenced data within agricultural landscapes. GPR has been widely used to estimate soil water content (SWC) and water dynamics, while EMI has broader applications such as estimating SWC, soil salinity, bulk density, etc. Additionally, GPR can map soil horizons, the groundwater table, and other anomalies. The prospects of GPR and EMI applications in soil studies need to focus on the potential integration of GPR and EMI to overcome the intrinsic limitations of each technique and enhance their applications to support PA. Future advancements in PA can be strengthened by estimating many soil properties, states, and hydrological processes simultaneously to delineate management zones and calculate optimal inputs in the agricultural landscape.

Electromagnetic induction (EMI) technique is an established method to measure the apparent electrical conductivity (EC_a) of soil as a proxy for its physicochemical properties. Multi-frequency (MF) and multi-coil (MC) are the two types of commercially available EMI sensors. Although the working principles are similar, their theoretical and effective depth of investigation and their resolution capacity can vary. Given the recent emphasis on non-invasive mapping of soil properties, the selection of the most appropriate instrument is critical to support robust relationships between EC_a and the targeted properties. In this study, we compared the performance of MC and MF sensors by their ability to define relationships between EC_a (i.e., MF–EC_a and MC–EC_a) and shallow soil properties. Field experiments were conducted under wet and dry conditions on a silage-corn field in western Newfoundland, Canada. Relationships between temporally stable properties, such as texture and bulk density, and temporally variable properties, such as soil water content (SWC), cation exchange capacity (CEC) and pore water electrical conductivity (EC_w) were investigated. Results revealed significant (p < 0.05) positive correlations of EC_a to silt content, SWC and CEC for both sensors under dry conditions, higher correlated for MC–EC_a. Under wet conditions, correlation of MF–EC_a to temporally variable properties decreased, particularly to SWC, while the correlations to sand and silt increased. We concluded that the MF sensor is more sensitive to changes in SWC which influenced its ability to map temporally variable properties. The performance of the MC sensor was less affected by variable weather conditions, providing overall stronger correlations to both, temporally stable or variable soil properties for the tested Podzol and hence the more suitable sensor toward various precision agricultural practices.

Understanding trace element (TE) composition and behavior in soil solution is extremely important for assessing ecological and human health impacts. Using lysimeters to collect soil solution with minimum alteration to the in situ phase distribution and concentration of TEs will facilitate a more accurate assessment. However, different lysimeter materials and sampling conditions may lead to vastly different results, demonstrating the need for the optimal choice of lysimeter depending upon environmental conditions. There is no general agreement or overview discussing the best lysimeter type and sampling system to use under various conditions. This review provides a critical summary of various lysimeters that can be used to collect soil solutions for the analysis of TEs and thereby provides key guidance for developing the best lysimeter sampling system for conditions and research questions of interest. This includes a range of aspects related to lysimeters, such as different types and materials, the basic principles of design and operation, advantages and disadvantages, challenges and limitations, techniques for cleaning and pretreatment, correct installation procedures, the influence of soil physical and chemical properties on sampling, and existing research gaps within this field.

Crop nitrogen (N) management in agricultural fields is crucial in preventing various environmental and socio-economic issues arising from excess N use. However, precise crop N management (PNM) is hindered by its intensive data requirements, high cost, and time requirements. Digital image processing (DIP) offers a promising approach to overcoming these challenges, and numerous studies have explored its application in N management. This review aims to analyze research trends in applying DIP for N management over the past 5 years, summarize the most recent studies, and identify challenges and opportunities. Web of Science, Scopus, IEEE Xplore, and Engineering Village were referred to for literature searches. A total of 95 articles remained after the screening and selection process. Interest in integrating machine learning and deep learning algorithms with DIP has increased, with the frequently used algorithms—Random Forest, Support Vector Machine, Extreme Gradient Boost, and Convolutional Neural Networks—achieving higher prediction accuracy levels. In addition, image data using more variables as model inputs, including agriculture sensors and meteorological data, have increased prediction accuracy. Nonetheless, several challenges associated with DIP, including obtaining high-quality datasets, complex image processing steps, costly infrastructure, and a user-unfriendly technical environment, still need to be addressed.

The efficiency of oil contaminated pulp and paper mill sludge (PPMS) stabilization by Eisenia fetida strongly depends on optimal temperature conditions for nutrient recovery and organic pollutant reduction. In Western Newfoundland, Canada, fluctuations in environmental temperature (< 0 °C to 20 °C), limit the year-round viability of vermicomposting. Therefore, PPMS (TS) and PPMS amended cow manure (TSC) were vermicomposted over a 70-day period at three controlled temperatures (12 °C, 17 °C, and 22 °C) to assess the effects of thermal conditions on PPMS stabilization, and vermiremediation efficiency, and population dynamics of E. fetida. While pH remained stable at 17 °C and 22 °C, it decreased significantly at 12 °C (p < 0.000), whereas electrical conductivity increased significantly across all treatments (p < 0.000). The biodegradability coefficient was significantly higher (p < 0.008) in TS at 17 °C and 22 °C while maximum carbon (C) loss and nitrogen (N) enrichment occurred in TSC at 22 °C, resulting in a reduced C:N (<20) by day 56. Oil degradation was faster in TSC compared to TS at 17 °C and 22 °C. A significant increase in macronutrients (P, Na, K, Ca, Mg) and micronutrients (B, Mn, Fe, Al) were observed at 17 °C and 22 °C with the highest enrichment in TSC, followed by TS (p < 0.001). A slight increase in potentially toxic elements (As, Co) was detected in TSC, whereas Zn and V were more prominent in TS at 12 °C. TS exhibited higher levels of Cd, Cu, Ni, and Cr, while Pb and Mo were elevated in TSC at 22 °C. However, all remained below Canadian Council of Ministers of the Environment (CCME) guideline limits. Growth and reproduction of E. fetida and vermicompost production were significantly enhanced (p < 0.0001) at 17 °C and 22 °C, peaking on day 42 while lower at 12 °C. Overall, temperature between 17 °C and 22 °C optimized PPMS vermicomposting and are feasible from late spring to mid fall in Corner Brook, Newfoundland and Labrador, Canada.

The pulp and paper mill industry in Corner Brook, Newfoundland and Labrador, Canada produces 150 Mg/day of pulp and paper mill sludge (PPMS) with a high moisture content (70–80%). Instead of the current practice of burning, PPMS may be repurposed as vermicompost. However, the optimum population density of Eisenia fetida to maximize the bioconversion process and its influence on population dynamics are largely unknown. The current study aimed at determining how stocking densities affect the vermicomposting time, vermicompost quality, and population dynamics of E. fetida. By using three stocking densities—1.13 g (TL), 2.04 g (TM), and 3.01 g (TH)—the E. fetida per kg of PPMS and the quality of the vermicompost were determined by chemical analysis followed by a germination test and bioassay on Raphanus sativus seedlings. The total vermicompost produced in TL (66.3%) and TM (68.8%) made the bioconversion process quicker in 60 days. The addition of more E. fetida (TH) delayed the process of vermicomposting by up to 90 days (p < 0.003). Overall, 0.87 (TM) E. fetida/L of PPMS was found to be the optimum population density for obtaining the best quantity and quality of vermicompost. The highest earthworm biomass was harvested in TL, followed by TM and TH, as 3.2, 1.3, and 0.9-fold, respectively, compared to the introduced biomass in 60 days (p < 0.008). The mean growth rate (2.6 mg/worm/day), biomass gain (3.49 mg/g), and reproduction rate (3.9 cocoon/worm) were also significantly higher (p < 0.023) in TL compared to TM and TH in 60 days. Therefore, the present study shows the importance of using an optimum stocking density to maximize the bioconversion process in PPMS, especially in cooler regions.