Agnieszka Wnuk

Soil-based ecosystem services (SESs) are various benefits provided by soils to society or the environment, and their assessment supports sustainable agricultural soil management aimed at preventing further soil degradation. Individual SES evaluation procedures need a set of adequate indicators to support countries in monitoring the status of soil health. Among them, soil organic carbon (SOC) is indicated as one of the main, widely accepted and practicable attributes reflecting the proper functioning of soils. This paper aimed to present the recent state-of-the-art on SOC’s role in modelling and mapping individual SESs. Therefore, the PRISMA method was applied to select 138 research articles. The results showed that SOC data has been applied in evaluations of provisioning, regulating and supporting SESs. Based on our findings, we recommend paying special attention to SOC monitoring systems at different scales and database preparations following the primary modelling rule, garbage in—garbage out, enhancing the reliability of various models and their applicability across different scales. The proper selection of input data and assessment methods is crucial for accurately evaluating ecosystem services while minimising the risk of misinterpretation or ineffective policy and management decisions. Despite the existence of many types of models for the evaluation of SESs, we want to highlight that for the preservation of consistency and harmonisation, the proper modelling framework should be kept. In our studies, we highlighted that a comprehensive modelling workflow that integrates DSM-derived SOC data, scale-aware validation and uncertainty propagation into SESs is crucial to achieve reproducible, high-quality modelling approaches. Future research should focus on a systematic review of the usability of SES indicators, refining methodologies and expanding their use in national-scale assessments to support sustainable decision making.

The structure of a scientific article is crucial for clearly conveying research findings. Modern scientific publications combine text with various elements—such as tables, graphs, images, diagrams, and maps—that support the narrative and aid data interpretation. Understanding how these components influence a publication’s reception and scientific impact is essential. This study analyzes differences among 15 soil science journals (indexed in the Web of Science) in terms of visual elements, tables, number of authors, and article length. The journals had a 5-year Impact Factor (2023) ranging from 0.9 (Soil and Environment) to 10.4 (Soil Biology and Biochemistry). The Kruskal–Wallis test and Bonferroni-adjusted Dunn’s post hoc tests revealed statistically significant differences across all variables (p < 0.05). The relationships were further assessed using Pearson’s correlation, based on the median number of authors and article length, as well as the percentage of articles that include at least one element of a given type (e.g., table, graph, image, diagram, or map). Key findings show that journals with a higher impact factor tend to publish articles with more authors (r = 0.62, p = 0.014), use diagrams more frequently (r = 0.69, p = 0.004), and include fewer tables (r = –0.85, p < 0.001). These results suggest that journals with a higher 5-year IF tend to include articles with a greater number of authors and a higher frequency of diagram use, while relying less on tables.

Methane (CH₄) is a potent greenhouse gas with a significant impact on short- and medium-term climate forcing, and its atmospheric concentration has been increasing rapidly in recent decades. This study aims to analyze spatio-temporal patterns of atmospheric methane concentrations between 2019 and 2025, focusing on comparisons between regions characterized by high and low emission intensities. Level-3 XCH₄ data from the TROPOspheric Monitoring Instrument (TROPOMI) onboard the Sentinel-5 Precursor satellite were used, which were aggregated into seasonal and annual composites. High-emission regions, such as the Mekong Delta, Nile Delta, Eastern Uttar Pradesh and Bihar, Central Thailand, Lake Victoria Basin, and Eastern Arkansas, were contrasted with low-emission areas including Patagonia, the Mongolian Steppe, Northern Scandinavia, the Australian Outback, the Sahara Desert, and the Canadian Shield. The results show that high-emission regions exhibit substantially higher seasonal amplitude in XCH₄ concentrations, with an average seasonal variation of approximately 30.00 ppb, compared to 17.39 ppb in low-emission regions. Methane concentrations generally peaked at the end of the year (Q4) and reached their lowest levels during the first half of the year (Q1 or Q2), particularly in agriculturally dominated regions. Principal component and cluster analyses further confirmed a strong spatial differentiation between high- and low-emission regions based on both temporal trends and seasonal behavior. These findings demonstrate the potential of satellite remote sensing to monitor regional methane dynamics and highlight the need for targeted mitigation strategies in major agricultural and wetland zones.

This study compares the methane emission estimates from the Intergovernmental Panel on Climate Change with satellite-based measurements from Sentinel-5P to assess trends in global methane emissions and concentrations. Focusing on the countries listed in Annex I of the United Nations Framework Convention on Climate Change and the key sectors of Agriculture, Energy, industrial processes and product use, land use, land use change, forestry, and Waste, this analysis uses data from 1990 to 2021, evaluated through the Mann–Kendall trend test. The findings reveal a decline in methane emissions reported by the IPCC, particularly in the Energy and Waste sectors, driven by stricter environmental regulations and technological advancements in these regions. However, the satellite data from the TROPOspheric Monitoring Instrument for 2019–2024 indicate an increasing trend in atmospheric methane concentrations, suggesting that the reductions reported in the inventories may be insufficient to offset ongoing or previously accumulated emissions. The discrepancies between the IPCC inventories and the satellite observations highlight the challenges in methane source attribution and the limitations of relying solely on inventory-based methods. This study demonstrates the potential of integrating high-resolution satellite data with the traditional methodologies to improve the accuracy of methane emission estimates. Such an approach provides a more comprehensive understanding of methane dynamics, particularly in regions where natural and anthropogenic sources overlap. The findings of this study contribute to a better understanding of global methane emission trends and their implications for climate change. Integrating satellite observations into national inventories has practical applications for enhancing methane monitoring, improving emission reporting, and supporting global climate goals through the more effective and sustainable management of methane emissions.

Site-specific crop management demands maps which present the content of the main macronutrients. Such maps are prepared based on optimized soil sampling within management zones, which should be quite homogenous according to nutrient content, especially the content of potassium and phosphorus. Delineation of management zones is very often conducted using soil apparent electrical conductivity (EC) or other variables related to soil condition, including satellite-derived vegetation indices. In this study conducted in North-Western Poland, relationships between soil electrical conductivity and the satellite-derived normalized difference vegetation index (NDVI) of various crops (wheat, barley, and rapeseed) with soil pH and content of P, K, and Mg were evaluated. Strong relationships were observed between NDVI of cereals with potassium content in soil. Correlation coefficients for wheat ranged from 0.37 to 0.60 for average potassium content for three years and from 0.05 to 0.63 for barley. Stronger relationships were observed for the years 2018 and 2019 when NDVI was based on Sentinel-2 data, while weaker for year 2017 when Landsat 8 NDVI was used. Relationships between EC and macronutrients content were similar to those observed with NDVI. Satellite-derived NDVI of cereals can be used as a variable for the delineation of within-field management zones. The same relationships were much weaker and not consistent for winter rapeseed.

Among European countries, Poland has the largest gap in the grain yield of winter wheat, and thus the greatest potential to reduce this yield gap. This paper aims to recognize the main reasons for winter wheat yield variability and shed the light on possible reasons for this gap. We used long-term datasets (2008–2018) from individual commercial farms obtained by the Laboratory of Economics of Seed and Plant Breeding of Plant Breeding and Acclimatization Institute (IHAR)-National Research Institute (Poland) and the experimental fields with high, close to potential yield, in the Polish Post-Registration Variety Testing System in multi-environmental trials. We took into account environment, management and genetic variables. Environment was considered through soil class representing soil fertility. For the crop management, the rates of mineral fertilization, the use of pesticides and the type of pre-crop were considered. Genotype was represented by the independent variable year of cultivar registration or year of starting its cultivation in Poland. The analysis was performed using the CART (Classification and Regression Trees). The winter wheat yield variability was mostly dependent on the amount of nitrogen fertilization applied, soil quality, and type of pre-crop. Genetic variable was also important, which means that plant breeding has successfully increased genetic yield potential especially during the last several years. In general, changes to management practices are needed to lower the variability of winter wheat yield and possibly to close the yield gap in Poland.

The paper proposes a graphical method called the CI thermometer that facilitates the analysis of bivariate relations among many variables. The CI thermometer will help scientists present correlation matrices accompanied by additional information on confidence intervals.

Even though data visualization is a common analytical tool in numerous disciplines, it has rarely been used in agricultural sciences, particularly in agronomy. In this paper, we discuss a study on employing data visualization to analyze a multiplicative model. This model is often used by agronomists, for example in the so-called yield component analysis. The multiplicative model in agronomy is normally analyzed by statistical or related methods. In practice, unfortunately, usefulness of these methods is limited since they help to answer only a few questions, not allowing for a complex view of the phenomena studied. We believe that data visualization could be used for such complex analysis and presentation of the multiplicative model. To that end, we conducted an expert survey. It showed that visualization methods could indeed be useful for analysis and presentation of the multiplicative model.

BACKGROUND: Copper (Cu) is a key trace mineral involved in a variety of physiological processes, and is commonly used in poultry production. However, regardless of the inclusion level the majority of Cu is excreted with poultry faeces. We hypothesise that in ovo administration will allow for better utilisation of Cu during embryo development than when supplied post‐natally with feed to growing chickens. Thus, the objective of this study was to evaluate effects of in ovo administration of NanoCu and copper sulfate (CuSO₄) on broiler chicken performance.RESULTS: The study showed the positive influences of Cu nanoparticles and CuSO₄ on broiler chickens performance. Body weight, at the end of the rearing period (day 42) was significantly higher in NanoCu (2206 g) and CuSO₄ (2402 g) groups compared to the control group (2000 g). Both treatment groups had significantly lower feed conversion rate and mortality, and higher percentage of breast and leg muscles in the carcass versus control.CONCLUSION: The in ovo application of Cu colloids may ensure an efficient penetration of Cu into the embryonic tissue with long lasting effects on postnatal growth. The method may provide a successful alternative to using Cu as a feed additive. © 2015 Society of Chemical Industry

A pie chart is a common way of presenting simple statistics—for example, the amount of time devoted to teaching, researching, and administration by the faculty can be illustrated by dividing a circle (‘pie’) into three appropriately sized segments. There has been much discussion about the strengths and weaknesses of such pie charts for a long time, and it is not likely to end soon. 3D pie charts show the same statistics, but in three dimensions. Multiple pie charts can be found where the data for groups to be compared are presented in adjacent pies. Here we argue that, even if a simple pie chart may have some advantages, the same cannot be said for multiple pie charts. Multiple pie charts are difficult to analyze and interpret, especially when comparing adjacent pies.