Soybean growth is determined by the interaction of genetic, environmental, and management factors. In the context of future climate and climate extremes, understanding genotype by environment interaction (GxE) will be crucial for selecting resilient breeding lines and optimizing management practices to minimize stress. This requires an in depth elucidation of stressful weather conditions and differing temporal responses of genotypes to those conditions. In field studies, however, the environment is often treated as a static factor, and the specific effects of weather variability on crop growth remain poorly understood. Here, we present a longitudinal dataset comprising 17,247 high-resolution RGB images of soybean breeding lines collected throughout eight years in Eschikon, Switzerland. Top-of-canopy images were acquired throughout the entire growing seasons and complemented by hourly weather data, enabling a comprehensive analysis of soybean growth dynamics under varying field conditions. High spatio-temporal image resolution allows detailed analysis of growth dynamics and GxE, supporting identification of stress-tolerant genotypes to improve yield prediction and yield stability.

Societal Impact Statement Soil is a vital resource for life on Earth. Although many soils are degraded, societal awareness of soil‐related issues is limited. To highlight soil as a key resource and fascinating study system, we initiated a citizen science project together with 1000 participants investigating soil health across Switzerland using buried cotton underpants. The degree of underpants decomposition was interpreted as a measure for soil biological activity and soil health. This work demonstrates that underwear decomposition varied widely across land‐use types and that buried underwear is a simple and easily interpretable tool to gain a first impression of soil health. Summary Soil is a vital resource for plant growth. It hosts a major part of global species diversity and is key for ecosystem functioning and it forms the basis of human civilization. Yet soils are rapidly degrading, threatening food production, biodiversity, and Earth system functioning. This threat is largely unrecognized in broader society, hindering effective steps towards soil protection. In a citizen science approach, involving 1000 citizen scientists burying more than 2000 cotton underpants and 12,000 teabags across 1000 locations, we aimed to assess soil biological activity and soil health in Switzerland. Decomposition of underpants and tea bags was considered as proxies for soil biological activity and soil health. Underwear decomposition differed greatly across land‐use types, indicating significant variation in soil biological activity among sites. Land use was the most important predictor of decomposition processes, followed by soil chemical parameters. Private gardens, the land‐use type with the highest levels of soil organic carbon and nutrients, exhibited the greatest biological activity, whereas lawns showed the lowest, with croplands and grasslands falling in between. Overall, this project demonstrates that underpants can serve as a simple and easy‐to‐interpret tool, providing informative insights into the state of soils. Moreover, they are perfectly suited to raise attention in society for soil. We propose the “Underpants Index” as an accessible, engaging method to assess soil decomposition processes and soil fertility and raise public awareness for soil as a vital, yet threatened resource.

Abstract. Agriculture is the largest anthropogenic source of nitrous oxide (N2O), primarily due to nitrogen (N) fertilization. Understanding how the influence of key drivers and the relative contribution of source processes change throughout the cropping season is crucial for developing effective strategies to mitigate N2O emissions. In this study, we combined high-resolution eddy covariance flux measurements and stable isotope analyses over one winter wheat cropping season and the subsequent summer cover crop season. Two phases, crop establishment and early spring, were identified as critical periods for N2O emissions, characterized by a mismatch between N supply and plant demand, resulting in surplus soil mineral N and elevated N2O fluxes under favorable environmental conditions. Gross primary productivity (GPP), used as a proxy for crop N uptake, suppressed N2O emissions, especially under high soil moisture, highlighting the importance of active vegetation in mitigating emissions. Source partitioning, based on stable isotopes, revealed denitrification as the dominant process of N2O production, driven by poor soil drainage and high soil moisture. Over the nine-month winter wheat season, the Tier 1 N2O emission factor was 1.8 %, with cumulative emissions of 5.5 kg N2O-N ha−1, offsetting 70 % of the net CO2 uptake. Our findings emphasize the need to better synchronize N supply with crop demand and to adopt agronomic practices that promote rapid crop establishment to mitigate N2O emissions in cropping systems.

Optimizing nitrogen (N) efficiency in European silage maize cultivation requires better quantification of N savings beyond existing regulations and identifying further opportunities to enhance efficiency. This study focuses on understanding how site and management factors influence net N mineralization. A comprehensive dataset from silage maize field trials across five Central European countries was analyzed using a multistage framework to develop a linear regression equation for estimating net N mineralization during the growing season based on key parameters known before the growing season. The final regression model, with a Radj2 of 0.38, identified ten significant variables affecting net N mineralization. Evaluation of German and French fertilization frameworks, using representative regional use cases, indicated that national guidelines may underestimate or insufficiently capture net N mineralization. Major factors influencing soil net N mineralization included management practices impacting biomass quantity and the carbon-to-N ratio of plant residues, with N fertilization showing a negative effect on net N mineralization. Weather conditions and soil clay contributed to the observed variability to a lesser extent. For silage maize following grain cereals, a predominant cropping sequence within the investigated regions, the highest net N mineralization was achieved by combining straw removal with a frost-killed non-legume cover crop, suggesting potential for reduced N fertilization without yield loss.

This review provides an overview of the accuracy of soil property predictions using the most common proximal soil sensing (PSS) techniques in precision agriculture (PA), both standalone and in combination with one another or with environmental covariates. Based on 114 scientific papers, the accuracy of soil property estimates was evaluated by calculating the normalized root mean square error (NRMSE) using root mean square error (RMSE) values and the range of the predicted soil property. Soil properties, PSS techniques, covariate types, and the type of model employed for predictions were the factors around which accuracy results were sorted. Additionally, we estimated PSS service costs based on both the literature and on a market study with questionnaires for private companies operating in the PA sector. Our literature analysis indicates that diffuse reflectance spectroscopy (DRS) was able to estimate the greatest number of soil properties with a high accuracy compared to the other PSS techniques. The most popular applications of DRS are to determine soil organic matter, nutrients, and soil texture, although most of the applications are primarily lab-based. X-ray fluorescence (XRF) is the second most popular technique for soil property estimation; however, in contrast to DRS, most estimations are in-field applications with portable XRF sensors. The use of XRF is widespread in determining elemental concentrations. On-the-go techniques such as electromagnetic induction (EMI) or gamma-ray spectroscopy (γ-ray) accounted for lower accuracy compared to point-based techniques (e.g., DRS, XRF, time-domain reflectometry). However, they are widely used by companies, as they have vast potential to delineate PA management zones in the field, and are suitable for on-the-go mapping of soil properties such as mineralogy, texture, salinity, water content, cation exchange capacity, and soil depth. The combined use of PSS techniques generally doesn’t outperform the singular application, although the number of samples collected for calibration, and specific combinations of sensors, covariates, and modeling techniques, combined correctly, may enhance the predictions of soil properties using PSS techniques applied singularly. However, these outcomes tend to depend on local site characteristics. Differences were found between the analysis of costs collected from the literature and from the companies’ survey. The estimated cost of surveying a hectare with PSS oscillates between 15.5€/ha and 130€/ha, according to research data, whereas our company survey resulted in an interval between 142 and 362€/ha. Price variability was influenced by personnel costs, fieldwork, data and reporting, sample analysis, and equipment. Besides, increases in the final prices can be attributed to accessibility and difficulties related to field work, as well as traveling to the area of interest. This review aims to serve as a reference for encouraging the adoption of current and available sensing technologies by farmers, policymakers, and companies by providing helpful insights into the suitability of different PSS techniques for mapping various soil properties, their associated costs, and what is available in the market. We foresee that availing PSS services will become cheaper with technological advances. Thus, it will become a standard approach in the future, as it is the most feasible way for producing high-resolution maps and affordable soil property information.

Background: The controlled uptake long-term ammonium nutrition (CULTAN) fertilization technique consists of injecting a concentrated ammonium solution into the soil and aims to positively impact crop physiology and N use efficiency. Aims: This study assesses whether CULTAN can contribute to lower N leaching while maintaining yields in temperate regions with an annual precipitation of around 1000 mm or higher. Methods: We analyzed a 12-year lysimeter experiment with two consecutive 6-crop rotations and a 3-year field experiment with winter wheat and maize in Switzerland. CULTAN was compared to a conventional surface application of ammonium nitrate fertilizer (ConvF). Results: CULTAN achieved at least similar yields compared to ConvF in both studies and had a 38% lower yield-scaled N leaching in the lysimeters. In both studies, CULTAN displayed higher nitrogen recovery efficiency (NRE) compared to ConvF, with an increase ranging from 8% to 17% depending on crop type, although a statistical significance was only found for winter wheat in the field study. NRE and N leaching were only weakly correlated, indicating that other N pathways are affected in the CULTAN fertilization system. Finally, we suggest that the timing and placement of the CULTAN injection need to be better adapted to the plant physiology and pedoclimatic conditions for optimal nutrient use and crop yields. Conclusion: In areas of high nitrate concentration in the groundwater, CULTAN can be an effective fertilization strategy complementing loss reduction measures.

Multispectral imaging satellites such as Sentinel-2 are considered a possible tool to assist in the mapping of soil organic carbon (SOC) using images of bare soil. However, the reported results are variable. The measured reflectance of the soil surface is not only related to SOC but also to several other environmental and edaphic factors. Soil texture is one such factor that strongly affects soil reflectance. Depending on the spatial correlation with SOC, the influence of soil texture may improve or hinder the estimation of SOC from spectral data. This study aimed to investigate these influences using local models at 34 sites in different pedo-climatic zones across 10 European countries. The study sites were individual agricultural fields or a few fields in close proximity. For each site, local models to predict SOC and the clay particle size fraction were developed using the Sentinel-2 temporal mosaics of bare soil images. Overall, predicting SOC and clay was difficult, and prediction performances with a ratio of performance to deviation (RPD) > 1.5 were observed at 8 and 12 of the 34 sites for SOC and clay, respectively. A general relationship between SOC prediction performance and the correlation of SOC and clay in soil was evident but explained only a small part of the large variability we observed in SOC prediction performance across the sites. Adding information on soil texture as additional predictors improved SOC prediction on average, but the additional benefit varied strongly between the sites. The average relative importance of the different Sentinel-2 bands in the SOC and clay models indicated that spectral information in the red and far-red regions of the visible spectrum was more important for SOC prediction than for clay prediction. The opposite was true for the region around 2200 nm, which was more important in the clay models.

BACKGROUND:Understanding genotype-environment interactions of plants is crucial for crop improvement, yet limited by the scarcity of quality phenotyping data. This Data Note presents the Field Phenotyping Platform 1.0 data set, a comprehensive resource for winter wheat research that combines imaging, trait, environmental, and genetic data. FINDINGS:We provide time-series data for more than 4,000 wheat plots, including aligned high-resolution image sequences totaling more than 153,000 aligned images across 6 years. Measurement data for 8 key wheat traits are included-namely, canopy cover values, plant heights, wheat head counts, senescence ratings, heading date, final plant height, grain yield, and protein content. Genetic marker information and environmental data complement the time series. Data quality is demonstrated through heritability analyses and genomic prediction models, achieving accuracies aligned with previous research. CONCLUSIONS:This extensive data set offers opportunities for advancing crop modeling and phenotyping techniques, enabling researchers to develop novel approaches for understanding genotype-environment interactions, analyzing growth dynamics, and predicting crop performance. By making this resource publicly available, we aim to accelerate research in climate-adaptive agriculture and foster collaboration between plant science and machine learning communities.

In variety testing and breeding of wheat (Triticum aestivum L.), it is crucial to know the timing of phenological stages and the senescence behavior of genotypes to select for locally adapted varieties. Sound knowledge of the timing of phenological stages also allows for a more meaningful interpretation of measurements such as yield, quality, or disease ratings. In the presence of stresses, only a combined characterization of phenology and environmental conditions can allow for insights into unraveling stress resistance and stress avoidance. Capturing these traits visually in the field is very time-consuming. Here, a semimobile PhenoCam setup was used to track phenology and senescence from ear emergence to full maturity. PhenoCams mounted on field masts took images of wheat plot trials on a daily basis. In a partial least squares regression analysis, the temporal features of multiple vegetation indices were combined in one model to track phenology and senescence. The method was compared with visual reference methods and repeated drone flights with a multispectral camera. The Pearson's correlation between visual reference methods and PhenoCam predictions was stronger than 0.8, often above 0.9, for most stages. An economic analysis showed that PhenoCams are economically interesting, especially for observing remote experimental sites. Thus, PhenoCams offer a cost-effective replacement for visual ratings of phenology and senescence, particularly in the context of multienvironment trials.

For the agri-environmental monitoring of Switzerland, nitrogen balances on farm level for all Swiss farms were calculated and aggregated in order to obtain regionalized nitrogen balances. This monitoring attempts to incorporate as much existing data as possible to minimize multiple data collections from farmers. Data from the agricultural policy information system of Switzerland was used as basis for the calculation. This database contains information on livestock numbers, the crops grown, and the direct payments received for each farm. This information was supported with different data sources from federal offices, cantons, agricultural associations, and research institutions. Balances were calculated as a soil-surface balance according to the OECD method, which includes N input via organic and mineral fertilizers, biological N-fixation, atmospheric N-deposition, and seedlings as well as N outputs via plant yields.The regional balances showed a high variability, resulting in an average N surplus of around 105 kg N per hectare of utilized agricultural area in cantons with highly intensive livestock farming and around 16 kg N in cantons with more extensive farming practices, i.e. in mountain regions. On national scale, highest N input occurred via organic fertilizers, whereas mineral fertilizers and biological N-fixation account for around 15% of the total input each.Our approach of calculating N balances on farm level for the whole Swiss farming system has some limitations, which are mainly due to missing or incomplete data sources. As an example, the use of mineral fertilizers had to be estimated by application data of a rather small sample of farms (~300 farms). Nevertheless, the obtained results show that this methodology is a promising tool to gain a regional overview of the environmental status of Swiss farms. Over the years, this approach will be refined and new data (e.g. additional administrative data, satellite data) can be incorporated in order to better estimate the N balances of Swiss farms.

Monitoring crop N status by means of proximal and remote sensing data can help enhancing N use efficiency at various farm scales. This study compares five optical sensor platforms, commonly used in practice and research, based on their usability and accuracy in measuring crop N status at field level. The data were gathered in 2019 in two sites in northeast Switzerland that were cropped with winter wheat (Triticum aestivum). The optical sensor platforms employed included a Sentinel-2 satellite, two different unmanned aircraft systems (UAS fixed-wing and quadcopter), a tractor-mounted system, and a handheld field spectrometer. We used a power regression to compare the measured crop N uptake with spectral vegetation indices computed from the different sensors. The reported normalized difference red-edge (NDRE) index values were distributed in a broad range from 0.17 to 0.74, with the Sentinel-2 satellite records in the higher part of the range (0.59-0.74) and those of the handheld spectrometer in the low range (0.17-0.29). The study's key finding was the information collected was significantly different across the five sensing platforms, in terms absolute values from the sensors. However, the correlations between NDRE values from all sensors and the measured N uptake were comparably robust, with r > 0.8 a root mean square error ranging from 29 to 37 kg N/ha. Furthermore, the N application maps produced for the satellite and UAS platforms showed that the best compromise between detailed spatial resolution and matching of the working width of the machinery used was achieved by resampling the UAS-based maps at 10 m resolution with the calculation used in this study. We concluded that sensor-based N status assessment across different sensing levels can support the improvement of N use efficiency by allowing a more precise management of in-field variability, with the precondition of having a good calibration for climatic location and variety. However, factors such as the degree of detail needed to capture in-field variability while matching the working width should be evaluated for each specific case.

Soil mass balances are used to assess the risk of trace metals that are inadvertently applied with fertilizers into agroecosystems. The accuracy of such balances is limited by leaching rates, as they are difficult to measure. Here, we used monolith lysimeters to precisely determine Cd, Cu, and Zn leaching rates in 2021 and 2022. The large lysimeters ( n = 12, 1 m diameter, 1.35 m depth) included one soil type (cambisol, weakly acidic) and distinct cropping systems with three experimental replicates. Stable isotope tracers were applied to determine the direct transfer of these trace metals from the soil surface into the seepage water. The annual leaching rates ranged from 0.04 to 0.30 for Cd, 2.65 to 11.7 for Cu, and 7.27 to 39.0 g (ha a) (-1) for Zn. These leaching rates were up to four times higher in the year with several heavy rain periods compared to the dry year. Monthly resolved data revealed that distinct climatic conditions in combination with crop development have a strong impact on trace metal leaching rates. In contrast, fertilization strategy (e.g., conventional vs. organic) had a minor effect on leaching rates. Trace metal leaching rates were up to 10 times smaller than fertilizer inputs and had therefore a minor impact on soil mass balances. This was further confirmed with isotope source tracing that showed that only small fractions of Cd, Cu, and Zn were directly transferred from the soil surface to the leached seepage water within two years ( < 0.07 %). A comparison with models that predict Cd leaching rates in the EU suggests that the models overestimate the Cd soil output with seepage water. Hence, monolith lysimeters can help to refine leaching models and thereby also soil mass balances that are used to assess the risk of trace metals inputs with fertilizers.

The goal of net zero emissions by 2050 requires a reduction in greenhouse gas emissions (GHG emissions) in all sectors, including Swiss agriculture. One approach to this is to optimize nitrogen (N) fertilization using the corrected norms method (korrNorm). By taking soil, climate and cultivation conditions into account using correction factors, the N fertilization norm is tailored to plant requirements, thereby avoiding N surpluses and associated GHG emissions. As part of the AgroCO(2)ncept resource project, the korrNorm was applied to eleven farms in the Zurich Flaachtal over a period of four years (2018-2021) in an ex post analysis and compared with the N fertilization norm and the company's fertilization practice. Depending on the current fertilization practice, applying the korrNorm would result in both realizable N savings of between (-)7.6 kg N ha(-1) and (-)75.1 kg N ha(-1) in twelve years of operation, as well as an increased fertilizer requirement of between (+)1.4 kg N ha(-1) and (+)56.7 kg N ha(-1) in 14 years of operation. A change in fertilization practice to the korrNorm would therefore not lead to GHG emission reductions in all cases. Nevertheless, the korrNorm provides valuable insights for fertilization planning at the farm and plot level and underlines the importance of farm-specific measures for climate protection.

Soil pH and texture are valuable information for agriculture, supporting the achievement of high productivity and low environmental impact, which is the basis for sustainable agricultural production. In this study, we present novel soil mapping techniques that integrate high-spatial-resolution satellite and ground data, surpassing traditional methods in precision and reliability. By synergizing remote sensing data, including polarimetric synthetic aperture and multispectral imagery, with climate and terrain information, alongside coarse-resolution soil data, we achieved high accuracy, with an average error of less than 6%, in predicting soil pH and texture parameters. Notably, the approach allows for detailed mapping at the pixel level, revealing nuanced variability within 10×10 m field pixels. Considering the accuracy, the method establishes itself as a benchmark for field management guidelines integrating a precision sampling approach, offering actual and high spatial resolution information crucial for sustainable agricultural practices. This holistic approach allows new opportunities to revolutionize soil management practices, facilitating variable rate applications, soil moisture, and fertilization mapping and ultimately enhancing agri-environmental sustainability.

In-field soil spectroscopy represents a promising opportunity for fast soil analysis, allowing the prediction of several soil properties from one spectral reading representing one soil sample. This facilitates data acquisition from large amounts of samples through its rapidity and the absence of required chemical processing. This is of particular interest in agriculture, where the chance to retrieve information from soils directly in the field is very appealing. This review is focused on in-field visible to near infrared (Vis-NIR) spectroscopy (350-2500 nm), aimed at analysing soils directly in the field through proximal sensing. The main scope was to explore the available knowledge to identify existing gaps limiting the reliability and robustness of in-field measurement, to foster future research and help transition towards the practical application of this technology. For this purpose, a literature review was performed, and surveyed information encompassed sensor range, carrier platforms in use, sensor type, distance to the soil sample, measurement methodology, measured soil properties and soil management, among many others. From this, we derived a list of tools in use with their spectral measurement properties, including the potential cross-calibration with soil spectral libraries from laboratory spectroscopy of soil samples and potential measured target soil properties. Different instruments and sensors used to measure at varying wavelength ranges and with different spectral qualities are available for a large range of prices. The most frequently analysed soil properties included soil carbon contents (soil organic carbon, soil organic matter, total carbon), texture (clay, silt, sand), total nitrogen, pH and cation exchange capacity. Future perspectives comprise the implementation of larger databases, including different instruments and cropping systems as well as methodologies combining existing knowledge regarding laboratory spectroscopy with in-field methods. The authors highlight the need for a broadly accepted measurement protocol for in-field soil spectroscopy, fostering harmonization and standardization and consequently a more robust application in practice.

Soil organic carbon (SOC) plays a major role in the global carbon cycle and is an important factor for soil health and fertility. Accurate mapping of SOC and other influencing parameters are crucial to guide the optimization of agricultural land management to maintain and restore soil health, to increase soil fertility, and thus to quantify its potential for sequestering CO2. Remote sensing and machine learning techniques offer promising approaches for predicting SOC distribution. In this study, we used remote sensing data and machine learning algorithms to map SOC at regional to large scale, which we then combined with temporospatial and spectral signature-based soil sampling to integrate local ground measurements. A rigorous validation approach was performed where several independent unseen datasets with a high number of samples were used, which additionally involved densely sampled fields. We found that our approach could predict SOC with an average percentage error of less than 10 % with an R2 of 0.91 using support sampling on croplands located on mineral soils, demonstrating the potential of remote sensing, machine learning, and specific ground measurements for mapping SOC. Our results suggest that this approach could make small carbon differences measurable and inform carbon sequestration efforts and improve our understanding of the impacts of land use and field management practices on soil carbon cycling.

Background: Understanding genotype-environment interactions of plants is crucial for crop improvement, yet limited by the scarcity of quality phenotyping data. This data note presents the Field Phenotyping Platform 1.0 data set, a comprehensive resource for winter wheat research that combines imaging, trait, environmental, and genetic data. Findings: We provide time series data for more than 4,000 wheat plots, including aligned high-resolution image sequences totaling more than 148,000 aligned images across six years. Measurement data for eight key wheat traits is included, namely canopy cover values, plant heights, wheat head counts, senescence ratings, heading date, final plant height, grain yield, and protein content. Genetic marker information and environmental data complement the time series. Data quality is demonstrated through heritability analyses and genomic prediction models, achieving accuracies aligned with previous research. Conclusions: This extensive data set offers opportunities for advancing crop modeling and phenotyping techniques, enabling researchers to develop novel approaches for understanding genotype-environment interactions, analyzing growth dynamics, and predicting crop performance. By making this resource publicly available, we aim to accelerate research in climate-adaptive agriculture and foster collaboration between plant science and computer vision communities. ### Competing Interest Statement The authors have declared no competing interest.

One challenge in predicting soil parameters using in situ visible and near infrared spectroscopy is the distortion of the spectra due to soil moisture. External parameter orthogonalization (EPO) is a mathematical method to remove unwanted variability from spectra. We created two different EPO correction matrices based on the difference between spectra collected in situ and, respectively, spectra collected from the same soil samples after drying and sieving and after drying, sieving and finely grinding. Spectra from 134 soil samples recorded with two different spectrometers were split into calibration and validation sets and the two EPO corrections were applied. Clay, organic carbon and total nitrogen content were predicted by partial least squares regression for uncorrected and EPO-corrected spectra using models based on the same type of spectra (“within domain”) as well as using laboratory-based models to predict in situ collected spectra (“cross-domain”). Our results show that the within-domain prediction of clay is improved with EPO corrections only for the research grade spectrometer, with no improvement for the other parameters. For the cross-domain predictions, there was a positive effect from both EPO corrections on all parameters. Overall, we also found that in situ collected spectra provided an equally successful prediction as laboratory-based spectra.

The application of visible and near-infrared (vis-NIR) spectroscopy to characterize soil samples has gained growing interest as a fast and cost-effective methodology for soil fertility assessment. In order to profit from the full potential of vis-NIR spectroscopy, the acquisition of soil spectra directly in-situ would increase the possibility to obtain data rapidly and at a high spatial and temporal resolution. In the present study, we test and propose the best practice to characterize a set of fertility-related parameters (i.e. texture, organic carbon, pH, cation exchange capacity and major nutrients) of agricultural soils by measuring vis-NIR spectra in the field. To reach this goal, we compare the spectra obtained from different scanning positions with two portable spectrometers, that is, a micro-electro-mechanical systems (MEMS)-based spectrometer and a research-grade vis-NIR spectrometer. On the basis of 134 soil sampling points, vis-NIR spectra were recorded from: (1) the cutaway side of a soil sample collected with an Edelman auger to a depth of 20 cm, (2) the raw soil surface, as well as (3) the cleaned and smoothed soil surface. Partial least squares regression (PLSR) calibration models were built for the selected soil parameters, scanning positions and different spectral pretreatments for both spectrometers. The model performance was evaluated based on the ratio of performance to interquartile range (RPIQ), the R-2, the root mean squared error (RMSE) and Lin's concordance correlation coefficient (CCC). Overall, the following soil parameters were successfully predicted: clay, sand, pH, organic carbon, cation exchange capacity, total nitrogen and exchangeable magnesium. In contrast, total and exchangeable Ca, K and P, as well as total Mg could not be predicted at a satisfactory level for both the spectrometers. The best scanning position for the successfully calibrated models was along the cutaway sides of the Edelman auger. Although the research-grade spectrometer gave better performance indicators for most of the parameters, the calibrations with the MEMS-based spectrometer still resulted in satisfactory predictions. Based on these findings, the proposed best practice for obtaining in-situ soil vis-NIR scans is to scan along the cutaway sides of a soil core using at least five replicate scans.