Soil organic carbon (SOC) comprises particulate (POC) and mineral-associated organic carbon (MAOC), which differ in formation, stabilization, and loss mechanisms. While the current global distribution of POC and MAOC is characterized, their vulnerability under future climate scenarios remains unclear. Using 3284 topsoil (0-30 cm) observations from six continents, we identify high-latitude soils as global hotspots of SOC vulnerability under shared socioeconomic pathway scenarios (SSP126, SSP245, and SSP585). Under a high-emission scenario (SSP585), high-latitude soils are projected to lose substantial POC by 2100, accounting for about 81 ± 10% of total SOC losses. These declines are driven by the high proportion of SOC stored as POC (fPOC) and its high temperature sensitivity. We show that fPOC is a robust indicator of SOC vulnerability to climate change. Globally, the projected POC decline corresponds to a cumulative carbon dioxide (CO2) release of 81.34 Pg CO2-equivalent by 2100, highlighting the importance of preserving POC to mitigate climate feedbacks.

Enhanced weathering (EW) is proposed as a key strategy for climate change mitigation and carbon dioxide removal technology. Dissolution of silicate minerals enhances the alkalinity of the pore water, resulting at a shift of the carbonate system towards carbonate and bicarbonate, leading to higher dissolved inorganic carbon when the water is equilibrated with the atmosphere. Here, we evaluated the effects of EW on a crop ecosystem under future climate change conditions within a macro-scale ecotron – an enclosed facility enabling complete quantification of carbon fluxes among the atmosphere, vegetation, soil, and leachates. We monitored all greenhouse gases in deep mesocosms representative of marginal soil conditions and, after liming and fertilization, applied 10 t ha−1 of basalt at the start of the experiment. EW treatment resulted in an almost three-fold enhancement of measured carbon flux into the soil, achieving rates up to 1.5 t ha−1 during the growing season. Moreover, the observed carbon sequestration surpassed the levels expected from weathering processes alone. This is notable because the near-neutral soil pH environment was not favourable to EW kinetics. Therefore, we conclude that EW facilitated significant carbon accrual in our simulated ecosystems via not only carbonate precipitation but also enhanced biogeochemical activities promoting additional carbon storage. Based on these findings, we speculate on the underlying pathways responsible for such outcomes.

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.

Water and N availability are key factors limiting crop yield, particularly in marginal soils. This study evaluated the effects of water and N stress on barley grown in marginal soils using field trials and the AgroC model. Experiments from 2020 to 2022 in Lithuania with spring barley cv. KWS Fantex under two N fertilization treatments on sandy soil provided data for model parameterization. The AgroC model simulated barley growth to assess yield potential and yield gaps due to water and N stress. Potential grain yields (assuming no water or N stress) ranged from 4.8 to 6.02 t DW ha−1, with yield losses up to 54.4% assuming only N stress and 59.2% assuming only water stress, even with the N100 treatment (100 kg N ha−1 yr−1). A synthetic case study varying N fertilization from 0 to 200 kg N ha−1 yr−1 showed that increasing N still enhanced yield, but the optimal rate of 100–120 kg N ha−1 yr−1 depended on climatic conditions, leading to uncertainty in fertilization recommendations. This study underscores the importance of integrating advanced modeling techniques with sustainable agricultural practices to boost yield potential and resilience in marginal soils. Incorporating remote sensing data to capture soil and crop variability is recommended for improving simulation accuracy, contributing to sustainable agriculture strategies in the Baltic–Nordic region.

Sustainable soil management is recognised as a pivotal solution for addressing current and future global challenges, but existing global and national soil property maps often lack the fine-scale resolution required for local or intra-field assessments. Here, we aimed to develop an open access framework to downscale soil property maps using remote and proximal sensor data and test it for predicting soil organic carbon (SOC) and clay across different regions of Europe. To facilitate the dissemination of this framework, we developed the R package "soilscaler", which contains integrated functions for producing downscaled soil maps. This approach uses coarse resolution maps as a baseline, incorporating sensor data and soil observations to train a model explaining local variation of soil properties. We tested the framework in Denmark, Northern Ireland, Lithuania, The Netherlands, and Turkey. For comparison, we also created high-resolution maps using a conventional digital soil mapping (DSM) approach for each field independently. We found that the downscaling performance depends on the quality of the coarse-resolution soil maps, the spatial variability of soil properties within a given field, and the range of inter-field variations in each country. Although the downscaling process showed lower performance than the conventional DSM approach, the results indicate that the downscaled maps better represent local variability than existing national and global soil maps. Additionally, we found that remote sensing sensors generally better represent the spatial distribution of SOC, while proximal soil sensors better capture clay contents. Future studies should focus on gathering more sensor data and correlating it with soil properties to improve predictions based solely on sensor data.

Monitoring soil organic carbon (SOC) is essential for assessing the sustainability of soil usage. This study explores the spatial variability and mapping of SOC in Lithuania’s Nevėžis Plain using various interpolation methods, with an emphasis on understanding the impacts of soil typological units, moisture regimes, and erosion on SOC distribution. A total of 275 soil samples were collected from agricultural croplands at depths of 0–10 cm, supplemented by 38 samples from previous studies. The SOC map was created based on the contours of the Lithuanian soil geodatabase. Statistical analysis revealed that the distribution of SOC in the studied area was significantly influenced by soil moisture and the degree of erosion. Based on these findings, SOC mapping was conducted according to the contours of Lithuanian soils. Comparing the interpolation methods that were analyzed, it was found that the kriging, RBF, and EBK methods fail to adequately capture the minimum and maximum values of SOC, while the IDW fails to adequately capture only the minimum values. In summary, the integrated geographical approach is complex but applicable to SOC mapping. This method facilitates the creation of adaptable SOC maps that are both geographically and pedologically informed. Key principles to apply this approach for future research and practical application should include establishing a statistically reliable data foundation, categorizing samples based on contrasting soil moisture regime, degrees of erosion, and land use patterns, and developing contouring principles along with a criteria algorithm that enables accurate spatial interpolation of average SOC values.

Growing crops on marginal lands is a promising solution to alleviate the increasing pressure on agricultural land in Europe. Such crops will however be at the same time exposed to increased drought and pathogen prevalence, on already challenging soil conditions. Some sustainable practices, such as Silicon (Si) foliar fertilization, have been proposed to alleviate these two stress factors, but have not been tested under controlled, future climate conditions. We hypothesized that Si foliar fertilization would be beneficial for crops under future climate, and would have cascading beneficial effects on ecosystem processes, as many of them are directly dependent on plant health. We tested this hypothesis by exposing spring barley growing on marginal soil macrocosms (three with, three without Si treatment) to 2070 climate projections in an ecotron facility. Using the high-capacity monitoring of the ecotron, we estimated C, water, and N budgets of every macrocosm. Additionally, we measured crop yield, the biomass of each plant organ, and characterized bacterial communities using metabarcoding. Despite being exposed to water stress conditions, plants did not produce more biomass with the foliar Si fertilization, whatever the organ considered. Evapotranspiration (ET) was unaffected, as well as water quality and bacterial communities. However, in the 10-day period following two of the three Si applications, we measured a significant increase in C sequestration, when climate conditions where significantly drier, while ET remained the same. We interpreted these results as a less significant effect of Si treatment than expected as compared with literature, which could be explained by the high CO2 levels under future climate, that reduces need for stomata opening, and therefore sensitivity to drought. We conclude that making marginal soils climate proof using foliar Si treatments may not be a sufficient strategy, at least in this type of nutrient-poor, dry, sandy soil.

The use of remote sensing data methods is affordable for the mapping of soil properties of the plowed layer over croplands. Carried out in the framework of the ongoing STEROPES project of the European Joint H2020 Program SOIL, this work is focused on the feasibility of Sentinel-2 based approaches for the high resolution mapping of topsoil clay and organic carbon (SOC) contents at the within-farm or within-field scales, for cropland sites of contrasted climates and soil types across the Northern hemisphere. Four pixelwise temporal mosaicking methods, using a two years-Sentinel-2 time series and several spectral indices (NDVI, NBR2, BSI, S2WI), were developed and compared for i) pure bare soil condition (maxBSI), ii) driest soil condition (minS2WI), iii) average bare soil condition (Median) and iv) dry soil conditions excluding extreme reflectance values (R90). Three spectral modeling approaches, using the Sentinel-2 bands of the output temporal mosaics as covariates, were tested and compared: (i) Quantile Regression Forest (QRF) algorithm; (ii) QRF adding longitude and latitude as covariates (QRFxy); (iii) a hybrid approach, Linear Mixed Effect Model (LMEM), that includes spatial autocorrelation of the soil properties. We tested pairs of mosaic and spectral approaches on ten sites in Turkiye, Italy, Lithuania, and USA where soil samples were collected and SOC and clay content were measured in the lab. The average RPIQ of the best performances among the test sites was 2.50 both for SOC (RMSE = 0.15%) and clay (RMSE = 3.3%). Both accuracy level and uncertainty were mainly influenced by site characteristics of cloud frequency, soil types and management. Generally, the models including a spatial component (QRFxy and LMEM) were the best performing, while the best spatial mosaicking approaches mostly were Median and R90. The most frequent optimal combination of mosaicking and model type was Median or R90 and QRFxy for SOC, and R90 and LMEM for clay estimation.

The accurate, timely, and non-destructive estimation of maize total-above ground biomass (TAB) and theoretical biochemical methane potential (TBMP) under different phenological stages is a substantial part of agricultural remote sensing. The assimilation of UAV and machine learning (ML) data may be successfully applied in predicting maize TAB and TBMP; however, in the Nordic-Baltic region, these technologies are not fully exploited. Therefore, in this study, during the maize growing period, we tracked unmanned aerial vehicle (UAV) based multispectral bands (blue, red, green, red edge, and infrared) at the main phenological stages. In the next step, we calculated UAV-based vegetation indices, which were combined with field measurements and different ML models, including generalized linear, random forest, as well as support vector machines. The results showed that the best ML predictions were obtained during the maize blister (R2)–Dough (R4) growth period when the prediction models managed to explain 88–95% of TAB and 88–97% TBMP variation. However, for the practical usage of farmers, the earliest suitable timing for adequate TAB and TBMP prediction in the Nordic-Baltic area is stage V7–V10. We conclude that UAV techniques in combination with ML models were successfully applied for maize TAB and TBMP estimation, but similar research should be continued for further improvements.

Hemp (Cannabis sativa L.) is a productive multi-purpose crop that can be cultivated in different climatic conditions including northern latitudes. However, no comprehensive study on the hemp yield potential has been conducted, particularly in high latitude regions. This study investigates hemp development and the suitability of the AgroC model to simulate hemp growth in cool climate. Field experiments were conducted at the Lithuanian Research Centre for Agriculture and Forestry on Endogleyic Endostagnic Endocalcaric Luvisol. The AgroC model was calibrated using data sets from 2019, whereby validation was performed with 2020-2021 data sets. The model provided adequate results when simulating hemp and its individual parts biomass, leaf area index, soil water content, and development stages. The validated model was further used to provide hemp yields and water as well as temperature limited yield gaps for the historical 1990-2021 period. Simulated average hemp biomass potential reached up to 20.1 t ha- 1. Average biomass yield losses due to water stress are 8.7 t ha- 1 (gap 43.3 %), while reduction due to low temperatures may reach 6.4 %, but mean reduction was only 0.4 t ha- 1 (gap 2.0 %). Simulated results showed water stress as the main factor defining hemp yield losses, while low temperatures are of secondary importance.

Šiame tyrime, derinant aerodistancinius lauko duomenis ir pasitelkiant mašininio mokymosi algoritmus, siekiama prognozuoti kukurūzų teorinį biometano potencialą (TBMP). Šiam tikslui pasiekti kukurūzų lauko eksperimente periodiškai atlikti skrydžiai su bepilotėmis skraidyklėmis, o pagal gautus duomenis apskaičiuoti augalų vegetatyviniai indeksai. Vėliau šie indeksai naudoti kuriant mašininio mokymosi algoritmus ir, tuo pačiu, siekiant prognozuoti TBMP reikšmes. Tyrimų rezultatai parodė, kad kukurūzų biomasės ir TBMP kiekiai svyravo esmingai, t. y. nuo 9,70 t ha-1 iki 15,00 t ha-1, ir, atitinkamai, nuo 4168,42 m3 ha-1 iki 6052,87 m3 ha-1. Nepaisant esmingų biomasės ir TBMP svyravimų, GLM prognozavimo modelis viso kukurūzų auginimo sezono metu tiksliai prognozavo TBMP reikšmes, o R3 kukurūzų tarpsnyje gauti tiksliausi prognozavimo rezultatai (R2 – 0,97, RMSE – 104,25 m3/ha-1, BIAS – 5,26).

The study aims to investigate industrial hemp growing potential in the Nordic-Baltic region, to assess the impact of various agrotechnological measures on macro: calcium, magnesium, potassium, phosphorus, and micronutrient: boron, iron, manganese, copper, zinc accumulation in hemp seeds, and to determine biologically active compounds and beneficial elements: elements cobalt, aluminum, sodium accumulation trends. Obtained results indicate that hemp seeds are rich in K (0.69–0.90%) followed by P (0.41–0.51%), Mg (0.42–0.46%), and Ca (0.10–0.19%). It was determined that Fe (181–334 mg/kg) was the main abundant microelement followed by Mn (58–81 mg/kg), Zn (55–68 mg/kg), Cu (13–20 mg/kg), and B (7–16 mg/kg) independently from applied agrotechnological measures. The amount of biologically active compounds ranged from 6.55 to 12.39 mg/gRUE and from 2.52 to 4.74 mg/gRUE, respectively, for TPC and TFC. A significant amount of beneficial elements Al (6.0–12.6 mg/kg) and Na (0.90–16.1 mg/kg) except Co were obtained. Results presented in this study indicate that seeds of industrial hemp grown in the Nordic-Baltic region could be considered as an important source of essential minerals and biologically active compounds.

Little information is available regarding the performance of the CERES-Maize model under nemoral climate conditions. Therefore, this study aims to estimate and compare major soil-plant N cycle parameters in grain maize (Zea mays L.) crop after application of synthetic and different organic fertilizers solely or in combination in nemoral zone maize production, using the Decision Support System for Agrotechnology Transfer (DSSAT) model. Field experiments carried out during 2015, 2016, and 2017 in Akademija (Lithuania) were considered for model calibration and validation. The model was successfully validated for total aboveground biomass (TAB, R-2 = .89), grain yield (GY, R-2 = .85), and acceptably for leaf area index (LAI, R-2 = .57), total plant N uptake (R-2 = .61), and residual soil mineral N (R-2 = .64). The lower plant N uptake and soil mineral nitrogen (SMN) observed for the pelletized cattle manure (PCM) and green waste compost (GWC) treatments compared to the fertilization with synthetic ammonium nitrate (AN) were successfully captured by the model. Finally, the model provided reasonable predictions of the temporal dynamics of measured soil water content (SWC) and soil temperature. The validated model was further used to provide N loss estimations during the maize growing seasons via leaching and gaseous emissions. The results showed that the CERES-Maize model can successfully be used to simulate maize growth under the extreme climatic conditions of the nemoral zone in combination with different N managements. Nevertheless, additional efforts are needed to verify and fine-tune the model to comprehensively simulate the N cycle, especially losses by drainage water and gaseous( )emissions.

In the EU and world-wide, agriculture is in transition. Whilst we just converted conventional farming imprinted by the post-war food demand and heavy agrochemical usage into integrated and sustainable farming with optimized production, we now have to focus on even smarter agricultural management. Enhanced nutrient efficiency and resistance to pests/pathogens combined with a greener footprint will be crucial for future sustainable farming and its wider environment. Future land use must embrace efficient production and utilization of biomass for improved economic, environmental, and social outcomes, as subsumed under the EU Green Deal, including also sites that have so far been considered as marginal and excluded from production. Another frontier is to supply high-quality food and feed to increase the nutrient density of staple crops. In diets of over two-thirds of the world's population, more than one micronutrient (Fe, Zn, I or Se) is lacking. To improve nutritious values of crops, it will be necessary to combine integrated, systems-based approaches of land management with sustainable redevelopment of agriculture, including central ecosystem services, on so far neglected sites: neglected grassland, set aside land, and marginal lands, paying attention to their connectivity with natural areas. Here we need new integrative approaches which allow the application of different instruments to provide us not only with biomass of sufficient quality and quantity in a site specific manner, but also to improve soil ecological services, e.g. soil C sequestration, water quality, habitat and soil resistance to erosion, while keeping fertilization as low as possible. Such instruments may include the application of different forms of high carbon amendments, the application of macro- and microelements to improve crop performance and quality as well as a targeted manipulation of the soil microbiome. Under certain caveats, the potential of such sites can be unlocked by innovative production systems, ready for the sustainable production of crops enriched in micronutrients and providing services within a circular economy.

Climate change will bring warmer and wetter conditions and more frequent extreme events in the Nemoral climate zone. These changes are expected to affect maize growth and yields. In this study, we applied the AgroC model to assess climate change impact on changes in growing environmental conditions, growing season length, yield and potential yield losses due to multiple abiotic stresses. The model was calibrated and validated using data from dedicated field experiments conducted in Lithuania during four meteorologically contrasting years (2015, 2016, 2017 and 2019). We simulated the climate impacts on rainfed maize for long-term future climate conditions from 2020 to 2100 under the RCP2.6 (low), RCP4.5 (medium) and RCP8.5 (high) emission scenarios. As a result, we found that air temperature, sum of growing degree days and amount of precipitation during the growing season of maize will increase, especially under medium and higher emission scenarios (RCP4.5 and RCP8.5), with significantly positive effect on yields. The simulation results showed that average maize grain yield will increase under RCP2.6 by 69 kg ha−1 per decade, under RCP4.5 by 197 kg ha−1 per decade and under RCP8.5 by 304 kg ha−1 per decade. The future potential maize yield reveals a progressive increase with a surplus of +10.2% under RCP4.5 and +14.4% under RCP8.5, while under RCP2.6 the increase of potential yield during the same period will be statistically not significant. The yield gap under RCP2.6 and RCP4.5 will fluctuate within a rather narrow range and under RCP8.5, it will decrease.

The application of composts could be accompanied by potential hazards to soil and humans, caused by heavy metals and organic persistent pollutants. A total of 115 compost samples from four different origins (green waste composts, sewage sludge composts, mixed municipal waste composts after mechanical-biological treatment and mixed municipal waste compost) were collected to analyse the chemical composition, nutrients levels, seven heavy metals, 15 polycyclic aromatic hydrocarbons (PAHs) and seven polychlorobiphenyls (PCBs). Simulation models were used to estimate the heavy metal accumulation risk in soil and to evaluate the potential ecological risk to environment. After analysing chemical parameters of compost quality, it was found that sewage sludge composts contained the highest amounts of nitrogen (2.98%), phosphorus (4.44%) and organic matter (47.6%), and the highest potassium content (1.20%) was found in mixed municipal composts after mechanical-biological treatment. After having tested all the composts, green waste composts had the lowest content of the following nutrients: nitrogen, phosphorus, potassium and sulphur. High molecular weight PAHs dominated in green waste, sewage sludge and mixed municipal waste composts, and the opposite tendency was observed on mixed municipal waste composts after mechanical-biological treatment; low molecular weight PAHs were abundant. It was determined that, according to the total amount of 15 PAHs (16.54 mg kg−1 d.w.) and 7 PCBs (233.53 μg kg−1 d.w.), the most contaminated composts were produced from mixed municipal waste. As it was expected, the lowest level of PCBs (13.85 μg kg−1 d.w.) was found in green waste composts. Monte Carlo simulations showed that the shortest period in which zinc concentration in soil could increase twice is 2 years when applying continuously mixed municipal waste compost after mechanical-biological treatment. Based on Monte Carlo simulation results from repeated application of green waste composts, mixed municipal waste compost and mixed municipal waste compost after mechanical-biological treatment could double the soil background level of copper in 6 and 3 years respectively. Reducing the content of heavy metals in composts would be of great significance for minimising the damage caused by them.