Alexander J. Koiter

Soil color has long been used as an indicator for soil properties such as soil organic carbon and soil moisture. Recent developments in citizen science have seen the increased use of smartphone cameras for soil color measurements. However, there are high errors associated with this technique. Two major sources of errors are smartphone cameras and lighting conditions. These errors limit the applicability of this technique in citizen science. Existing correction methods for reducing these errors are either ineffective or too complicated or difficult to apply. There is also a lack of systematic analysis on how these correction methods can reduce errors. In this study, we tested the effectiveness of using a color plate as a reference to reduce the errors on color measurements due to the use of different smartphones and taking photos under different lighting conditions. Three types of objects were tested, including the squares on the color plate itself, the color chips in a Munsell soil color book, and soil samples. The results show that the raw values of color parameters showed different patterns of biases with different smartphones and lighting conditions. The calibration reduced the errors consistently for all smartphones under all lighting conditions for the color plate squares. For the Munsell book chips or the soil samples, the calibration did not always reduce the bias but it did reduce the variations in all color parameters among smartphones and lighting conditions and, therefore, improved the precision of color measurements.

Soil color has long been used as an indicator for soil properties such as soil organic carbon and soil moisture. Recent developments in citizen science have seen increased use of smartphone cameras for soil color measurements. However, there are high errors associ-ated with this technique. Two major sources of errors are smartphone camera and lighting condition. In this study we tested the effectiveness of using a color plate as a reference to reduce these errors on color measurements for the squares on the color plate itself, color chips in a Munsell soil color book and soil samples. The results show that the raw values of color parameters showed different patterns of biases with different smartphones and lighting conditions. The calibration reduced the errors consistently for all smartphones under all lighting conditions for the color plate squares. For the Munsell book chips or the soil samples, the calibration did not always reduce the bias but it did reduce the variations of all color parameters among smartphone and lighting condition, therefore, improved the precision of color measurements.

🔗Cómo empezó Mi viaje para crear mi primer paquete de R comenzó cuando recibí un correo electrónico de un colega que trabajaba con muestras de suelo archivadas que se recogieron antes de la llegada de la tecnología GPS.Estas muestras se georreferenciaron utilizando las Descripciones Legales de Levantamientos Originales de Manitoba, que proceden del sistema Dominion Land Survey introducido a finales del siglo XIX para organizar el asentamiento y

🔗How it started My journey in creating my first R package all started when I received an email from a colleague who was working with archived soil samples that were collected prior to the advent of GPS technology.These samples were georeferenced using the Manitoba Original Survey Legal Descriptions, which originate from the Dominion Land Survey system, introduced in the late 19th century to organize the European settlement and colonization of

🔗How it started My journey in creating my first R package all started when I received an email from a colleague who was working with archived soil samples that were collected prior to the advent of GPS technology.These samples were georeferenced using the Manitoba Original Survey Legal Descriptions, which originate from the Dominion Land Survey system, introduced in the late 19th century to organize the European settlement and colonization of

🔗Cómo empezó Mi viaje para crear mi primer paquete de R comenzó cuando recibí un correo electrónico de un colega que trabajaba con muestras de suelo archivadas que se recogieron antes de la llegada de la tecnología GPS.Estas muestras se georreferenciaron utilizando las Descripciones Legales de Levantamientos Originales de Manitoba, que proceden del sistema Dominion Land Survey introducido a finales del siglo XIX para organizar el asentamiento y

Riparian areas play an important role in maintaining water quality in agricultural watersheds by buffering sediment, nutrients, and other pollutants. Recent studies have shown that in some cases riparian areas are a net source of phosphorus (P) in cold climates. This study assessed the impact of cattle grazing or harvesting of riparian areas on the spatial and vertical distribution of water‐extractable phosphorus (WEP). This study measured the WEP in four distinctive sources: biomass, litter, organic layer, and Ah horizon in three riparian locations extending from the edge of the waterbody to the field edge. In addition to a control, three treatments were examined: (1) grazing; (2) high‐density grazing; and (3) mowing. Prior to implementing the treatments, the Ah (0–10 cm) soil was the largest pool of WEP (42.5 mg m⁻², ∼44%); however, the biomass (i.e., standing vegetation) was a considerable proportion of the total (26.3 mg m⁻², ∼25%) WEP pool. The litter and organic layer had median WEP areal densities of 11.1 and 17.7 mg m⁻², respectively. Findings revealed significant reductions in biomass WEP with median reductions of 10.4 and 18.7 mg m⁻² for high‐density grazing and mowing treatments, respectively. This reduction was more pronounced in the lower riparian locations where there was more biomass available to be grazed or mowed. There were no detectable changes in the other sources of WEP across all the treatments. Assessment of the control plots (pre‐ and post‐treatment) clearly indicates that there is considerable small‐scale spatial variability in P measurements in riparian areas. Overall, the results of this study suggest that management practices that target vegetation, including harvesting and short‐term autumn grazing, may be mechanisms to reduce the potential P loss during the snowmelt period. To fully assess the risk of P loss, studies investigating other important riparian processes that also have a demonstrated impact on P mobility, including freeze‐thaw cycles and flooding, are needed.

One of the main impacts of water erosion within a watershed is the downstream deposition of sediments in watercourses and decrease in water quality, esigning and implementing effective soil and water conservation practices to address these impacts requires a soil conservation practices. Increasingly, researchers are using sediment source fingerprinting methods which use physical, biological, and geochemical attributes of the soil and sediments as tracers (Tiecher et al., 2015). Identifying sediment sources enables targeted corrective measures, but tracer selection and fingerprinting feasibility are ongoing debates among experts (Lizaga et al., 2020; Owens et al., 2022).This study focuses on identifying sediment sources to develop erosion mitigation plans in a 33.3 km² rural river basin, in southern Brazil, crucial for supplying the municipality of Pelotas. Three primary sediment sources were identified: annual crops, perennial forage (pastures), and gutters (river channels). Samples were collected from the surface horizon (0-20 cm) of agricultural land and perennial pastures. Gutter samples were collected from the underground horizon, where active erosion processes were taking place. In total, 116 source samples were obtained. Nine sediment samples were collected from six sites across the study area every two months during 2021-2022, forming three collections for each sub-area of the river basin (A1, A2, and A3). Traditional fingerprinting methods, utilizing geochemical tracers, total organic carbon, and color coefficient tracers in the visible spectrum, were employed to analyze the soil of the contributing area and the sediments. The FingerPro (v1.1; Lizaga, 2018) mixture model was applied to evaluate the contributions of sediment sources to the collected sediment.This communication presents preliminary results of 37 tracers: 22 geochemical elements, 14 color coefficients, and total organic carbon. Data processing, using FingerPro, was conducted separately by sub-area and sediment collection. Tracer selection involved a-two sequential statistical tests: 1) Kruskal-Wallis (KW) selects tracers with significant differences between at least two sources and 2) Discriminant Function Analysis (DFA) selects optimal tracers that effectively discriminate between the three sediment sources.The results obtained demonstrated that the selected tracers for each sub-area varied considerably. For example, the tracer selection procedure for sub-area A1 resulted only in total organic carbon as a viable tracer while the number tracers selected for the other two sub-areas were seven and five, for A2 and A3, respectively. Notably, the varying sets of tracers being selected for each sub-area indicate that the heterogeneity in soil properties is an important consideration in sediment source fingerprinting studies. Combining samples from the whole river basin may distort sediment dynamics. Tailored approaches are crucial for accurate understanding and management.AcknowledgementsThis study was made possible by the generous support of Brazil-CAPES through a doctoral scholarship (Finance Code 001).ReferencesLizaga et al. 2020. Consensus ranking as a method to identify non-conservative and dissenting tracers in fingerprinting studiesLizaga. 2018. fingerPro 1.1.Owens, P. N. 2022. Sediment source fingerprinting: are we going in the right direction?.Tiecher et al. 2015. Combining visible-based-color parameters and geochemical tracers to improve sediment source discrimination and apportionment

Purpose: The characterization of soil properties is an important part of many different types of agri-environmental research including inventory, comparison, and manipulation studies. Sediment source fingerprinting (i.e., tracing) is a method that is increasingly being used to link sediment sources to downstream sediment. There is currently not a standard approach to characterizing sources and the different approaches to sampling have not been well assessed.Methods: Grid, transect, and likely to erode sampling designs were used to characterize the geochemical, colour, grain size distribution, and soil organic matter content at two sites under contrasting land uses (agricultural and forested). The impact of the three sampling designs on fingerprint selection, source discrimination, and mixing apportionment results was evaluated using a virtual mixture.Results: The sampling design had a significant impact on the characterization of the two sites investigated. While the number and composition of the fingerprints selected varied between sampling designs there was strong discrimination between sources regardless of the sampling approach. There were deviations in the expected apportionment results, but the overall patterns were similar across the three sampling designs.Conclusions: Despite having an impact on the characterization of sources, the sampling design used ultimately had little impact on the conclusions drawn from the final apportionment results. Continued work at the watershed scale is needed to fully evaluate the importance of source sampling on the sediment source fingerprinting approach.