Journal Paper Digests

Journal Paper Digests 2024 #4

Preserving soil data privacy with SoilPrint: A unique soil identification system for soil data sharing
Spatial evaluation of the soils capacity and condition to store carbon across Australia
Suitability of microbial and organic matter indicators for on-farm soil health monitoring
Developing scoring functions based on soil texture to assess agricultural soil health in Quebec, Canada
Flexible marked spatio-temporal point processes with applications to event sequences from association football
Modelling calibration uncertainty in networks of environmental sensorsGet accessArrow

Modelling calibration uncertainty in networks of environmental sensorsGet accessArrow

Networks of low-cost environmental sensors are becoming ubiquitous, but often suffer from poor accuracies and drift. Regular colocation with reference sensors allows recalibration but is complicated and expensive. Alternatively, the calibration can be transferred using low-cost, mobile sensors. However, inferring the calibration (with uncertainty) becomes difficult. We propose a variational approach to model the calibration across the network. We demonstrate the approach on synthetic and real air pollution data and find it can perform better than the state-of-the-art (multi-hop calibration). In Summary: The method achieves uncertainty-quantified calibration, which has been one of the barriers to low-cost sensor deployment.

Flexible marked spatio-temporal point processes with applications to event sequences from association football

We develop a new family of marked point processes by focusing the characteristic properties of marked Hawkes processes exclusively on the space of marks, allowing a separate model specification for the occurrence times. We develop a Bayesian framework for their inference and prediction that can naturally accommodate covariate information to drive cross-excitations, offering broad flexibility for real-world applications. The framework is applied to in-game event sequences from association football, resulting in inferences about previously unquantified characteristics of game dynamics, extraction of event-specific team abilities and predictions for event occurrences, such as goals or fouls in a specified interval of time.

Developing scoring functions based on soil texture to assess agricultural soil health in Quebec, Canada

Adoption of soil health indicators to assess physical, biological, and chemical properties involves adapting their interpretation for a specific region using scoring functions. Accordingly, we used data provided from 1166 soil samples distributed between fine-, medium-, and coarse-textured soils, collected in agricultural areas across the province of Quebec, Canada, and analyzed for 15 soil health indicators. Scoring functions were calculated according to the means and standard deviations obtained for each soil health indicator by textural group. Three scoring types were used: “more-is-better”, “less-is-better”, and “optimum-is-best”. The results showed that 12 indicators were significantly influenced by soil texture and need separate scoring functions, except for wet aggregate stability, penetration resistance of the surface hardness (0–15 cm), and pH. This led to the development of one to three scoring functions for each soil health indicator. Correlation analysis between soil health indicators was also investigated to better understand relationships between soil physical, biological, and chemical properties. We observed that soil biological indicators were moderately to strongly correlated with each other (r = 0.59–0.74) and with soil physical indicators (r = 0.60–0.76). Overall, the results of this study led to the development of new scoring functions based on soil texture to interpret soil health indicators objectively and accurately for the benefit of Quebec farmers and agricultural stakeholders. The findings of this study demonstrated the need to adapt scoring functions to better account for the impact of regional factors on agricultural soils for the interpretation of soil health indicators.

Suitability of microbial and organic matter indicators for on-farm soil health monitoring

In addition to standard laboratory testing of soil samples, on-farm soil health monitoring methods are needed to help farmers assess progress in adopting new management practices. However, there is currently a lack of studies evaluating the suitability of semi-quantitative on-farm indicators to accurately rank target soil properties according to laboratory results. Therefore, this study assessed methods with potential for field use compared to common laboratory approaches for the determination of (i) soil organic carbon (SOC), (ii) carbon (C) fractions and (iii) microbial activity. The comparison allowed the evaluation of the validity, practicality and cost-effectiveness of the approaches. For this purpose, three sites in north-eastern Austria with contrasting soil textures (light, medium, heavy) and two different management systems (namely ‘pioneer’ and ‘standard’) were selected. Pioneer soils are managed long-term according to principles of soil health using conservation agricultural practices while neighbouring fields under standard management represent conventional practices. Beyond texture and site differences, both laboratory and field-adapted approaches revealed differences between the pioneer and standard systems. Overall, management-specific differences were most pronounced in the light and heavy textured soil. Although the laboratory methods provided more accurate results with less variability, the field-based approaches still identified trends in soil health parameters in the pioneer system. Our study can thus serve as a guide for the selection of suitable parameters and methods for assessing soil health in different areas of research and practical application.

Spatial evaluation of the soils capacity and condition to store carbon across Australia

The soil security concept has been put forward to maintain and improve soil resources inter alia to provide food, clean water, climate change mitigation and adaptation, and to protect ecosystems. A provisional framework suggested indicators for the soil security dimensions, and a methodology to achieve a quantification. In this study, we illustrate the framework for the function soil carbon storage and the two dimensions of soil capacity and soil condition. The methodology consists of (i) the selection and quantification of a small set of soil indicators for capacity and condition, (ii) the transformation of indicator values to unitless utility values via expert-generated utility graphs, and (iii) a two-level aggregation of the utility values by soil profile and by dimension. For capacity, we used a set of three indicators: total organic and inorganic carbon content and mineral associated organic carbon in the fine fraction (MAOC) estimated via their reference value using existing maps of pedogenons and current landuse to identify areas of remnant genosoils (total organic and inorganic carbon) and the 90th percentile for MAOC. For condition we used the same set of indicators, but this time using the estimated current value and comparing with their reference-state values (calculated for capacity). The methodology was applied to the whole of Australia at a spatial resolution of 90 m 90 m. The results show that the unitless indicator values supporting the function varied greatly in Australia. Aggregation of the indicators into the two dimensions of capacity and condition revealed that most of Australia has a relatively low capacity to support the function, but that most soils are in a generally good condition relative to that capacity, with some exceptions in agricultural areas, although more sampling of the remnant genosoils is required for corroboration and improvement. The maps of capacity and condition may serve as a basis to estimate a spatially-explicit local index of Australia’s soil resilience to the threat of decarbonization.

Soil is an indispensable resource with critical implications in various fields such as agriculture, environmental science, climate change, hydrology, ecology, and geoscience. Accuracy and accessibility of soil data are crucial for informed decision making. However, the sharing and harmonization of soil data present significant challenges, particularly owing to the lack of a comprehensive identification system that ensures privacy and stewardship in a federated data sharing framework. Moreover, the inherent heterogeneity of soil properties across space and time complicates the establishment of connections between soil profiles and their corresponding properties. To address these challenges, a novel and persistent soil-data identifier, called SoilPrint, akin to a fingerprint, was proposed. SoilPrint utilizes a mathematical algorithm to effectively integrate the properties of soil profile layers (SPLP) with Geohashes, providing an efficient solution. The incorporation of SoilPrint streamlines the data federation process within a secure and distributed ledger, eliminating the need for complex data mapping or alignment. This approach ensures data privacy throughout the sharing process and addresses concerns associated with data management. To demonstrate the practical applications of SoilPrint, a case study using soil data from Ontario, Canada was presented. The results underscored the unique identification capabilities of SoilPrint for soil profiles and their associated properties, establishing it a promising tool for soil data management. SoilPrint facilitates data tracking, reuse, and analysis, thereby enhancing the efficiency and effectiveness of soil-related research and decision-making processes.