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Biochar, produced through the pyrolysis of biomass and green waste, offers significant potential as a soil amendment to enhance soil health and sustainability in agriculture. However, the current Measurement, Reporting, and Verification (MRV) systems for biochar predominantly focus on carbon credits/offsets, neglecting crucial aspects related to its usability and suitability as a soil amendment on agricultural fields. Through an examination of recent findings, this perspective explores the integration of geochemical tracers, functional group (hydroxyl, carboxyl, phenolic, lactonic, etc.) analysis, and nutrient dynamics into MRV procedures/systems to create a more comprehensive framework. By examining the applicability of these indicators, this paper identifies key gaps and proposes a more robust MRV approach. Such a system would not only facilitate better assessment of biochar's agronomic benefits but also guide its optimal use in various soil types and agricultural practices. [ABSTRACT FROM AUTHOR]

Soil health is a critical component of nature‐based solutions (NbS), underpinning ecosystem multifunctionality and resilience by supporting biodiversity, improving carbon sequestration and storage, regulating water flow and enhancing plant productivity. For this reason, NbS often aim to protect soil health and restore degraded soil. Robust monitoring of soil health is needed to adaptively manage NbS projects, identify best practices and minimize trade‐offs between goals, but soil assessment is often underrepresented in NbS monitoring programmes. This paper examines challenges and opportunities in selecting suitable soil health metrics. We find that standardization can facilitate widespread monitoring of soil health, with benefits for stakeholders and user groups. However, standardization brings key challenges, including the complexity and local variability of soil systems and the diverse priorities, skills and resources of stakeholders. To address this, we propose a flexible, interdisciplinary approach combining soil science, ecology and socio‐economic insights. We introduce an interactive tool to help users select suitable soil and biodiversity metrics, which are context and scale‐specific, and suggest avenues for future research. We conclude that integrating soil health into NbS through new and improved monitoring approaches, newly available datasets, supportive policies and stakeholder collaboration can enhance the resilience and effectiveness of NbS, contributing significantly to global sustainability goals. [ABSTRACT FROM AUTHOR]

Advancements in technology have recently enabled to assess soil aggregate stability (SAS) using digital devices. To address the need for a faster and more efficient method of measuring SAS, we have developed a simple yet effective approach using a specialized device. The innovative method named SlakeLight involves measuring the changes in light transmittance as aggregates undergo slaking. The device consists of the measuring chamber, which is placed on a LED light source with a surface-homogeneous distribution of luminosity. During the disintegration process of aggregates immersed in water, reduction in the light emitted to the photodiodes is proportional to SAS. The functionality of the device was tested using topsoil samples from two field fertilization trials. The recorded SAStrans values were compared with the wet sieving method (WSA) and SLAKE test. The new method showed a strong correlation with both reference methods (r = 0.89 for WSA, r = -0.86 for SLAKE). The device was able to detect a statistically significant differences in SAS between the grassland and the cropland at both sites. Although differences in SAStrans were not significant between different fertilization treatments unlike WSA, the simplicity and speed of the measurement increase the potential of the method for practical implementation in agriculture, surpassing the limitations of traditional and labor-intensive laboratory techniques.

Advancements in technology have recently enabled to assess soil aggregate stability (SAS) using digital devices. To address the need for a faster and more efficient method of measuring SAS, we have developed a simple yet effective approach using a specialized device. The innovative method named SlakeLight involves measuring the changes in light transmittance as aggregates undergo slaking. The device consists of the measuring chamber, which is placed on a LED light source with a surface-homogeneous distribution of luminosity. During the disintegration process of aggregates immersed in water, reduction in the light emitted to the photodiodes is proportional to SAS. The functionality of the device was tested using topsoil samples from two field fertilization trials. The recorded SAStrans values were compared with the wet sieving method (WSA) and SLAKE test. The new method showed a strong correlation with both reference methods (r = 0.89 for WSA, r = -0.86 for SLAKE). The device was able to detect a statistically significant differences in SAS between the grassland and the cropland at both sites. Although differences in SAStrans were not significant between different fertilization treatments unlike WSA, the simplicity and speed of the measurement increase the potential of the method for practical implementation in agriculture, surpassing the limitations of traditional and labor-intensive laboratory techniques. [ABSTRACT FROM AUTHOR]

Measuring soil structure stability has always been a challenge, and various approaches have been proposed, mainly related to measuring soil aggregate stability upon wetting. This paper presents a rapid and cost-effective tool for evaluating soil structural stability named ASTAVIT, which stands for Aggregate STability Assessment using VIdeo Tests. The ASTAVIT principle involves visually monitoring the spreading of aggregates. This has already been implemented in the SLAKES smartphone application (now renamed Moulder), which simplifies the measurement of soil aggregates with minimal equipment. The aim of this work was to develop a robust, adaptable, and representative enough method that can be widely used in soil science laboratories. The protocol has been modified to use a 3D-printed plate, which source file is provided with this paper, to record the immersion of up to 96 individual aggregates in water. The increase in the projected area of the aggregates during slaking is tracked using image recognition software, ImageJ. The final stability index is determined based on this area increase. Soil structural stability can be assessed within an hour using a procedure that involves placing aggregates on a plate, filming, and analyzing. This method provides an objective evaluation of soil stability in a timely manner. The amount of soil used per test is similar to that used in Le Bissonnais tests (ISO 10930), ensuring representative results. The ASTAVIT index demonstrates expected behaviors of aggregate stability, as evidenced by its correlation with other soil characteristics and its ability to differentiate between soils that have undergone different tillage practices. An indicative classification of the ASTAVIT index into four categories of soil stability, similar to the Le Bissonnais tests, is proposed. ASTAVIT is expected to facilitate a broader implementation of structural stability studies. • This paper describes an alternative method for measuring aggregate stability. • The evaluation is based on changes in the visual area of the slaking aggregates. • The method is low labor and high throughput. • It uses a custom 3D printed support and LED illumination. • The FAST method effectively discriminates between cropland and grassland. [ABSTRACT FROM AUTHOR]

In this paper we aim to show a proof-of-principle approach to detect and monitor weed management using glyphosate-based herbicides in agricultural practices. In a case study in Germany, we demonstrate the application of Sentinel-2 multispectral time-series data. Spectral broadband vegetation indices were analysed to observe vegetation traits and weed damage arising from herbicide-based management. The approach has been validated with stakeholder information about herbicide treatment using commercial products. As a result, broadband NDVI calculated from Sentinel-2 data showed explicit feedback after the glyphosate-based herbicide treatment. Vegetation damage could be detected after just two days following of glyphosate-based herbicide treatment. This trend was observed in three different application scenarios, i.e., during growing stage, before harvest and after harvest. The findings of the study demonstrate the feasibility of satellite based broadband NDVI data for the detection of glyphosate-based herbicide treatment and, e.g., the monitoring of latency to harvesting. The presented results can be used to implement monitoring concepts to provide the necessary transparency about weed treatment in agricultural practices and to support environmental monitoring.

Although many Soil Spectral Libraries (SSLs) have been created globally, these libraries still have not been operationalized for end-users. To address this limitation, this study created an online Brazilian Soil Spectral Service (BraSpecS). The system was based on the Brazilian Soil Spectral Library (BSSL) with samples collected in the Visible–Near–Short-wave infrared (vis–NIR–SWIR) and Mid-infrared (MIR) ranges. The interactive platform allows users to find spectra, act as custodians of the data, and estimate several soil properties and classification. The system was tested by 500 Brazilian and 65 international users. Users accessed the platform (besbbr.com.br), uploaded their spectra, and received soil organic carbon (SOC) and clay content prediction results via email. The BraSpecS prediction provided good results for Brazilian data, but performed variably for other countries. Prediction for countries outside of Brazil using local spectra (External Country Soil Spectral Libraries, ExCSSL) mostly showed greater performance than BraSpecS. Clay R2 ranged from 0.5 (BraSpecS) to 0.8 (ExCSSL) in vis–NIR–SWIR, but BraSpecS MIR models were more accurate in most situations. The development of external models based on the fusion of local samples with BSSL formed the Global Soil Spectral Library (GSSL). The GSSL models improved soil properties prediction for different countries. Nevertheless, the proposed system needs to be continually updated with new spectra so they can be applied broadly. Accordingly, the online system is dynamic, users can contribute their data and the models will adapt to local information. Our community-driven web platform allows users to predict soil attributes without learning soil spectral modeling, which will invite end-users to utilize this powerful technique. [ABSTRACT FROM AUTHOR]

Soil health encompasses a range of biological, chemical, and physical soil properties that sustain the commercial and ecological value of agroecosystems. Monitoring soil health requires a comprehensive set of diagnostics that can be cost-prohibitive for routine analyses. The soil microbiome provides a rich source of information about soil properties, which can be assayed in a high-throughput, cost-effective way. We evaluated the accuracy of random forest (RF) and support vector machine (SVM) regression and classification models in predicting 12 measures of soil health, tillage status, and soil texture from 16S rRNA gene amplicon data with an operationally relevant sample set. We validated the efficacy of the best performing models against independent datasets and also tested best practices for processing microbiome data for use in machine learning. Soil health metrics could be predicted from microbiome data with the best models achieving a Kappa value of ∼0.65, for categorical assessments, and a R2 value of ∼0.8, for numerical scores. Biological health ratings were better predicted than chemical or physical ratings. Validation with independent datasets revealed that models had general predictive value for soil properties, including yield. The ecological profiles of several taxa important for model accuracy matched the observed relationships with soil health, including Pyrinomonadaceae, Nitrososphaeraceae , and Candidatus Udeaobacter. Models trained at the highest taxonomic resolution proved most accurate, with losses in accuracy resulting from rarefying, sparsity filtering, and aggregating at higher taxonomic ranks. Our study provides the groundwork for developing scalable technology to use microbiome-based diagnostics for the assessment of soil health. • 16S rRNA gene-based surveys of the soil microbiome can predict soil health metrics. • Predicting biological soil health had the highest potential utility. • Models trained at the highest taxonomic resolution were most accurate. • Taxa with predictive value had clear ecological associations with soil health. • Microbiome machine learning may serve as a low-cost tool for assessing soil health. [ABSTRACT FROM AUTHOR]

In this paper we aim to show a proof-of-principle approach to detect and monitor weed management using glyphosate-based herbicides in agricultural practices. In a case study in Germany, we demonstrate the application of Sentinel-2 multispectral time-series data. Spectral broadband vegetation indices were analysed to observe vegetation traits and weed damage arising from herbicide-based management. The approach has been validated with stakeholder information about herbicide treatment using commercial products. As a result, broadband NDVI calculated from Sentinel-2 data showed explicit feedback after the glyphosate-based herbicide treatment. Vegetation damage could be detected after just two days following of glyphosate-based herbicide treatment. This trend was observed in three different application scenarios, i.e., during growing stage, before harvest and after harvest. The findings of the study demonstrate the feasibility of satellite based broadband NDVI data for the detection of glyphosate-based herbicide treatment and, e.g., the monitoring of latency to harvesting. The presented results can be used to implement monitoring concepts to provide the necessary transparency about weed treatment in agricultural practices and to support environmental monitoring. [ABSTRACT FROM AUTHOR]