Afforestation can mitigate the export of water, sediment, and dissolved or adsorbed contaminants to river systems, but identifying effective intervention sites requires accounting for multiple flow-related criteria and their spatial interactions. This paper presents a multi-criteria heuristic approach that extends CAMF (Cellular Automata-based Heuristic for Minimizing Flow), originally designed to select cells from a rasterized landscape for interventions that minimize sediment yield at target sites. We integrated the Distance-to-Ideal-Point (DIST2IP) algorithm in CAMF, enabling the selection of cells where intervention can minimize two or more flows simultaneously. The multi-criteria CAMF was applied ex post to the radioactively contaminated Niida river catchment, Fukushima prefecture, Japan, to identify 1,000 cells within decontaminated zones where immediate afforestation would have maximally reduced both sediment and residual 137Cs export. The 1,000 best cells selected by DIST2IP, representing 4% of the decontaminated cells, would have reduced sediment export by 22% and 137Cs export by 6%. Selected cells are within the union of cells identified by the two single-criteria optimizations and are predominantly close to water bodies, confirming that blocking flow paths before they connect to the river system is most effective.

Antimicrobial resistance (AMR) in agricultural systems poses a critical "One Health" challenge, impacting animal, environmental, and human health. However, the field-scale dynamics of antibiotics within the soil-plant continuum, and their combined effects with organic fertilizers in driving antibiotic resistance genes (ARGs), remain poorly resolved. Here, we applied a stable isotope tracing approach using fully 13C-labeled sulfamethoxazole (13C10-SMX) in a lettuce field to track antibiotic dynamics across the soil-plant continuum. The half-lives of newly introduced 13C10-SMX in the labeled antibiotic (LA) and combined organic fertilizer and labeled antibiotic (OF&LA) treatments were 34.66 and 24.76 days, respectively. Rhizosphere soils showed early accumulation, with root-to-leaf translocation factors ranging from 0.16 to 0.34. OF&LA treatment amplified the relative abundance of ARGs by up to 5.35-fold in soils and 2.38-fold in roots, sustaining broad ARG enrichment. Mobile genetic elements (MGEs) emerged as the strongest direct driver of ARGs (β = 0.88), while 13C10-SMX exerted stronger indirect effects on ARGs than fertilizer inputs. Notably, nine high-risk ARGs were detected in lettuce leaves despite negligible antibiotic residue levels. These findings provide direct field-scale evidence that antibiotics and organic fertilizers jointly contribute to AMR propagation, highlighting the urgent need for integrated management strategies to mitigate agricultural AMR risks at the human-animal-environment interface.

Afforestation can mitigate the export of water, sediment, and dissolved or adsorbed contaminants to river systems, but identifying effective intervention sites requires accounting for multiple flow-related criteria and their spatial interactions. This paper presents a multi-criteria heuristic approach that extends CAMF (Cellular Automata-based Heuristic for Minimizing Flow), originally designed to select cells from a rasterized landscape for interventions that minimize sediment yield at target sites. We integrated the Distance-to-Ideal-Point (DIST2IP) algorithm in CAMF, enabling the selection of cells where intervention can minimize two or more flows simultaneously. The multi-criteria CAMF was applied ex post to the radioactively contaminated Niida river catchment, Fukushima Prefecture, Japan, to identify 1000 cells within decontaminated zones where the then immediate afforestation would have maximally reduced both sediment and residual 137Cs export. The 1000 best cells selected by DIST2IP, representing 4% of the decontaminated cells, would have reduced sediment export by 22% and 137Cs export by 6%. Selected cells are within the union of cells identified by the two single-criteria optimizations and are predominantly close to water bodies, confirming that blocking flow paths before they connect to the river system is most effective.

This study investigates the paleoenvironmental and ecological evolution of Parón Lake, the largest proglacial lake in the Cordillera Blanca, Peru, from the end of the Little Ice Age to the present. By integrating 210Pb-dated sediment core analysis spanning from 1907 to 2016 CE with geochemistry, mineralogy, and remote sensing of glacier retreat, the study examines how shifting sedimentation processes modulated diatom community structure. The sedimentary record was divided into three distinct stratigraphic units reflecting the lake's transition from a high-energy glaciogenic system to a stable, productive ecosystem. Unit C represents a high-energy glaciogenic stage characterized by low total organic carbon, high lithogenic silica, and frequent Glacial Lake Outburst Floods (GLOFs). During this phase, the diatom community was extremely restricted, exhibiting the lowest Shannon Diversity Index (H' ≈ 1.82) and high valve fragmentation due to mechanical stress from catastrophic sediment influx. The transitional phase, Unit B, corresponds to the initial retreat of the Artesonraju glacier and is marked by nutrient pulses of calcium and phosphorus leaching from newly exposed proglacial areas. This unit also shows the emergence of "glacial seeding," where diatoms from cryoconite reservoirs began influencing the lake's biology as the environment stabilized. Finally, Unit A reflects the current state of maximum lacustrine productivity and ecological complexity, coinciding with the most pronounced glacial retreat and basin contraction. Diatom diversity reached its peak (H' ≈ 2.49) in this unit, supported by a stabilized water column and increased biogenic silica availability. The findings demonstrate that the diatom community was actively shaped by the retreat of the Parón basin glaciers, transitioning from a transport-dominated system to a productivity-dominated one. This underscores how Andean warming acts as the primary source of modern lacustrine biodiversity, fundamentally altering hydrodynamics, nutrient availability, and biological connectivity across high-altitude landscapes.

Antimicrobial resistance (AMR) represents a critical One Health challenge, linking human, animal, and environmental health. Agriculture, particularly the use of livestock manure as fertilizer, contributes significantly to the dissemination of antibiotic resistance genes (ARGs) and mobile genetic elements (MGEs) through soils and crops, posing risks to food security and public health. This study integrates multiple experiments to elucidate the fate of antibiotics and the dynamics of ARGs across the manure–soil–plant continuum. Pot experiments with pig manure-amended soil revealed enriched ARGs in the carrot rhizosphere and phyllosphere. Manure application increased ARG bioaccumulation in carrot tubers (up to 124-fold for specific genes) and facilitated transfer from skin to tuber. Estimated daily human ARG intake from manured carrots reached ~3 × 107 copies, but peeling reduced this by 28–91%. Field-scale isotope tracing (13C-labeled sulfamethoxazole) in lettuce demonstrated rapid antibiotic dissipation in non-planted soils (>98% in 180 days), yet rhizosphere accumulation and root-to-leaf translocation. Co-application with swine manure amplified soil ARG abundance (up to 5.35-fold) and root enrichment (2.38-fold), driven primarily by MGEs. High-risk ARGs persisted in leaves despite low residues. In paddy fields, swine compost elevated ARG abundance in soil and rice roots over growth stages, with increased detection frequencies indicating transfer from compost and irrigation water. No significant ARG differences appeared in grains across treatments. Metagenomic analysis via 13C-DNA stable isotope probing distinguished antibiotic-degrading bacteria (ADB) from non-degrading ones in contrasting soils. ADB harbored diverse chromosomal ARGs co-localized with MGEs and degradation genes, suggesting high horizontal gene transfer potential and soil-specific resistome networks. Hydroponic lettuce studies showed that manure sterilization reduced endophytic ARG/MGE subtypes by 50–86%, diminished pathogenic bacteria, and lowered high-risk ARG intake, highlighting its efficacy for risk mitigation. These findings provide comprehensive evidence that manure application propagates AMR through synergistic antibiotic–fertilizer effects, MGE-mediated transfer, and plant uptake. Integrated management, including manure sterilization, peeling of root vegetables, and soil-specific strategies, is essential to mitigate risks at the human–animal–environment interface.

Understanding the role of soil texture in soil-water management amid climate change is crucial for sustainable agriculture as it influences water availability, nutrient dynamics, erosion control, carbon sequestration, and overall soil health. Therefore, having soil texture mapping is an important decision tool for establishing sustainable resource management. In this study, we present findings from soil sampling conducted in 2023 and a decade earlier from Hydrological Open-Air Laboratory (HOAL) in Petzenkirchen, Lower Austria. The PARIO system was used to analyse soil particle distribution utilising the Integral Suspension Pressure (ISP) method. This method utilizes the stokes’ law to calculate the particle size distribution based on changes in suspension pressure and temperature. The change of suspension pressure as well the temperature is measured at 10- seconds intervals following the chemical and physical dispersion, along with the pretreatment of soil samples involving the removal of organic matter, soluble salts and determination of sample dry weight. The analysis of soil texture from the 2023 soil sampling, conducted using the PARIO system, revealed a predominant silty clay loam structure, with a particle distribution of 9% sand, 56% silt, and 35% clay, aligning closely with results from a decade prior. Concurrently, we utilized Gamma-Ray Sensor (GRS) technology to measure the spatial activity concentrations (Bq.kg-1) of 40K (potassium), 238U (uranium), and 232Th (thorium) over more 20 points across the fields. The aim was to correlate these radionuclide concentrations with soil texture data using a Python-based correlation model. Preliminary results showed the best correlation between 40K radionuclide concentrations versus clay (R2 = 0.8) and silt (R2 = 0.7) and 238U versus silt (R2= 0.7). Thus, spatial monitoring of 40K and 238U with mobile GRS can be used for spatial determination of clay and silt. Nevertheless, further analysis is essential to compare and validate these results with a more extensive dataset encompassing additional soil texture data. These preliminary results demonstrate the potential of monitoring 40K and 238U concentrations by a portable gamma sensor for soil texture mapping in agricultural land. Further analysis and validation are required to verify the robustness of this model.

Hopanoids are produced by bacteria and are commonly found in terrestrial and marine environments. In modern environments, hopanoids mostly occur in the biological 17β,21β(H) configuration. Over geological time (106 to 108 years), thermal degradation changes their stereochemistry to the thermally mature 17α,21β(H) configuration. However, in modern acidic peat-forming environments, the ‘thermally mature’ C31 17α,21β(H)-homohopane dominates over the biological ββ stereoisomer, with an increase in the relative abundance of the αβ stereoisomer at lower pH. Based on this pH dependency, hopane isomerisation ratios have been used to reconstruct pH in ancient peat-forming environments. However, the environmental controls on hopane isomerisation remain poorly constrained and it is unclear whether this proxy is also applicable in mineral soils. Here, we analysed hopane distributions in mineral soils characterised by a wide range of mean annual temperature and pH. We show that mineral soils are dominated by diploptene, an unsaturated C30 hopanoid synthesised by a wide range of bacteria. In our soil dataset, there are relatively few thermally mature αβ hopanes – even within acidic mineral soils – and there is no relationship between hopane isomerisation ratios and pH. We propose that mineral protection in these soil environments selectively protects hopanoids from rapid degradation and subsequent isomerisation in modern samples. This provides a plausible explanation for the lack of 17α,21β hopanes in modern acidic mineral soil and suggests that the C31 hopane ββ/(αβ + ββ) should only be employed as a quantitative pH proxy in peats. Moving forward, we propose that hopane isomerisation ratios can help fingerprint the delivery of (acidic) peat into the marine realm and build upon other biomarker-based proxies developed to trace the input of terrestrial OC into the marine realm.

Climate change poses a significant threat to soil quality and global food security, with projections indicating potential crop yield declines of 17% by 2050. Simultaneously, agriculture contributes to an estimated 24% of all greenhouse gas (GHG) emissions. The dynamics of carbon (C) and nitrogen (N) play a pivotal role in GHG emissions and soil C sequestration, yet further research is needed on how management practices influence these dynamics. To address these challenges and provide data can facilitate efficient resource utilization in agricultural production, an incubation experiment was conducted to provide data on the impact of management options on C sequestration and GHG emissions from agricultural soils. The experiment took place in 850 mL glass jars under controlled conditions at 60% water-filled pore space and a temperature of 25°C. Composite soil samples, derived from a moderately fertile soil (2-3% SOC) from Grabenegg, Austria, at a depth of 0-15 cm, were subjected to five treatments: 1) control, 2) labeled urea, 3) inhibitor and labeled urea, 4) biochar + 15N labeled urea, and 5) inhibitor and biochar and labeled urea. The 15N-labeled urea (5% atom excess) was applied at a rate of 150 kg N ha-1, while biochar was applied at 2% of the soil by dry mass basis. A neon inhibitor which includes NBPT to limit nitrogen loss into the atmosphere as ammonia and DCD to reduce leaching, were applied at a rate of 4 mL per 100g urea as instructed by the manufacturer. All treatments were replicated four times. Soil and gas samples were collected on days 1, 3, 8, 15, 24, 31, 38, 45, 52, and 59 after treatment application. Gas samples were collected over a two hour period each day. Soil samples were analyzed for pH, soluble organic C, and mineral-N (NH4+, NO3-), while gas samples were analyzed using a gas chromatograph (GC) for NO2, CO2, and CH4. Preliminary results indicate that the addition of biochar increased soil C content, aligning with expectations from prior studies and that the addition of the inhibitor had a discernible impact on the pathways of nitrogen in the study samples. The use of isotopic methods and GHG measurements can furnish critical data supporting the most efficient use of resources for both climate mitigation and adaptation.

Intrinsic water use efficiency (iWUE) is a critical characteristic for optimizing cassava (Manihot esculenta Crantz) performance under climate change. Stable isotope composition provides a valuable tool for estimating iWUE, yet the key drivers of isotope variation across the cassava canopy remain unclear. In this study, conducted at 17 farms across three agroecological zones in the Eastern Democratic Republic of Congo, we examined how agronomic practices (fertilizer application and weeding) influence carbon (δ¹³C) and oxygen (Δ¹⁸O) isotope composition at different canopy positions and in carbohydrate pools during the bulk root initiation stage. Physiological and morphological variables were measured at noon across the upper, middle, and lower canopy of cassava plants grown on-farm during the rainy season. These variables were related to δ¹³C and Δ¹⁸O in bulk leaf material, extracted cellulose, and soluble sugars.Fertilizer application increased δ¹³C of soluble sugars (+0.6 ‰, p < 0.1) and bulk (+0.3 ‰, p < 0.1) in the drier zone, suggesting enhanced iWUE under fertilized conditions. Path analysis showed that leaf nitrogen concentration became increasingly correlated with δ¹³C from the upper to the lower canopy, while the influence of stomatal conductance declined. In upper-canopy leaves, higher stomatal conductance was associated with elevated vapour pressure deficit (VPD), possibly due to co-varying increased light intensities. Assumptions of the dual isotope approach related to Δ¹⁸O were not met, and therefore require further investigation. These findings provide new insights into the drivers of iWUE in cassava, highlighting the roles of canopy position and agronomic practices. This knowledge can inform strategies to improve cassava resilience and productivity under climate change.

The intricate interplay among plant water dynamics, nutritional content, and soil health is pivotal for unravelling the complexities inherent in plant materials, forging a direct link to the intricate web of the water-energy-food nexus. This investigation aims to find more accessible ways of evaluating the interplay between soil characteristics, water use in agriculture, and plant health, contributing crucial insights to sustainable agricultural practices that align with the SDGs 2030 Agenda for zero hunger, better environment, and enhanced human well-being.Cassava, as a staple crop in many developing countries is the focal point for this study, aiming for proof of a more affordable and accessible way of accessing the impact of water scarcity and nutrient deficiency. This understanding becomes particularly crucial in the development of effective digital technologies tailored to enhance the sustainability of agricultural practices, fostering a balance within the intersection of water, energy, and food systems.The core objective of this research is to assess the efficacy of Mid-Infrared Spectroscopy (MIRS) in predicting Carbon-13 (δ13C) signatures in cassava, establishing correlations between MIR spectral features and reference C-13 data obtained through Isotope Ratio Mass Spectrometry (IRMS). While Near-Infrared Spectroscopy (NIRS) and IRMS have demonstrated acceptable accuracy in modelling C-13 content in plant material, the underexplored potential of Mid-Infrared Spectroscopy (MIRS) holds promise, given its proven prediction potential with soil parameters as well as the small, required sample size which make it even more affordable, accessible, and sustainable. By grounding this investigation in the larger objective of managing the resource use efficiently, the calibration and validation process aims to contribute to the development of a broadly applicable methodology, across geographic boundaries and mediums and enhancing the collective understanding of the interdependencies within the water-energy-food nexus. Carbon-13 (δ13C) signatures in cassava offer invaluable insights into water use and transpiration efficiency and with a data-driven decision-making approach, not only informs farmers about optimal irrigation levels but also contributes to the broader discourse on sustainable resource management. Leveraging a dataset comprised of more than 700 cassava plant samples, this study employs Mid-Infrared Spectroscopy (MIRS) to predict δ13C content primarily in leaf material, utilizing Partial Least-Squares Regression (PLSR) to develop a robust model. Preliminary findings indicate that the indirect estimation is possible. The model's prediction performance, assessed through accepted statistical metrics such as R2 and RMSE, sheds light on the potential of MIRS for plant parameter prediction as an indicator of best soil and water management practices.

Reduced rainfall has been identified as a highly probable consequence of climate change in certain regions of Zambia. This is particularly concerning for small-holder farmers, who heavily rely on rainfall and are the primary producers of the country’s staple food, such as maize. The resulting decrease in production significantly impacts national food security. Recognizing the potential of irrigated agriculture to improve food security and sustain production levels, the Zambian Agricultural Research Institute (ZARI) has been actively engaged in research since 2021. Their focus is on enhancing irrigation and soil fertility management under conditions of reduced water availability.To address these challenges, a research trial was initiated at the ZARI research station in 2021. This trial aims to identify the optimal and sustainable water and nitrogen application for achieving maximum maize production in irrigated crop systems. Access tubes were installed in each subplot to monitor soil moisture to a depth of 1 m before and after irrigation on a weekly basis.This paper assesses the stored water in the root zone (up to 1 m) with interplay between amount of nitrogen fertilizer applied and water application level.In the 2021 season, the results indicate that significantly more water was retained averagely throughout the growing season in treatments with higher nitrogen levels, especially under reduced irrigation water applications (50% and 75% ETc). A similar trend was observed in the 2022 season, albeit only for 50% ETc. The increased stover yield may have contributed to reduced evaporation, minimizing losses. As nitrogen application levels rise, the ability to store soil water in the profile appears to increase. However, further analyses of soil moisture depth and root systems are needed to determine whether excess water in deficit-irrigated treatments is obtained from lower depths or if (and how much) water is lost in optimally irrigated treatments.

Increasing demand for land and resources in Himalayan catchments is altering hydrological processes and threatening freshwater ecosystems. Sediment mobilization and nutrient fluxes, especially during monsoon rainfall events, are intensifying the degradation of water bodies. This study investigates land cover change and its effects on nutrient dynamics in the Phewa Lake catchment, Nepal. Landsat imagery from 1990 to 2021, processed through Google Earth Engine, was used to map land changes. Nutrient loading for the two time periods was estimated with the InVEST model. Surface soils were sampled across the catchment to analyze nitrogen and phosphorus distribution, while their particle-bound transport to the lake was assessed through riverbed sediments and the suspended sediments collected during monsoon rainfalls. Pre-monsoon water quality was examined to evaluate eutrophication levels across different lake zones. Results reveal forest recovery in the upper catchment, but agricultural land in the lower catchment is being rapidly converted to urban areas. While forest recovery has enhanced sediment retention, nutrient inputs to the lake, particularly nitrogen and phosphorus, have increased. Fertilizer leaching and untreated sewage emerge as key sources in rural and urban areas, respectively. Seasonal constraints of the dataset may underestimate the overall extent of water quality deterioration, as indicated by high nutrient loads in monsoon suspended sediments. Overall, this study highlights the dual effect of land cover change: forest regrowth coincides with rising nutrient discharge. Without timely interventions, growing urban populations in the region may face worsening water quality challenges.

Plants experience physiological and metabolic changes in response to water deficit during critical stages, such as fruiting. In coffee, the allocation of fresh assimilates and interplay between leaf orientation, leaf age, and carbon changes are unknown. Understanding these strategies would reveal how coffee plants enhance their survival and productivity under water scarcity. Four-year-old Venecia Arabica coffee clones under water stress were pulse labelled with 13C-CO2 in a greenhouse. Three hours after labelling, leaf punches from young and old leaf pairs were collected at 10, 11, 12, and 13 days of water deficit (50% pot capacity/PC). These were analysed to assess 13C enrichment in relation to carbon assimilation and leaf carbon changes over time. Water deficit significantly decreased carbon assimilation by 20-52% compared to well-watered plants, especially in young leaves (p< 0.05). In addition, old leaves on the sun-exposed side performed better in terms of carbon assimilation than those on the shaded side; however, the orientation effect was not evident under stress. At harvest, approximately 15 days of water deficit, carbon allocation exhibited a marked decline, particularly in young leaves. The plants prioritised the allocation of newly assimilated carbon to roots and shoots, and to a lesser extent, to the fruits to support survival, storage, and production. Notably, carbon redistribution resulted in elevated levels of starch and sugar in fruits (by 33% and 51%, respectively), shoots, and roots, accompanied by a reduction in foliar sugar and cellulose contents in young leaves. These findings highlight the complex survival strategies employed by coffee plants, demonstrating their capacity to optimise resource allocation to storage organs and the potential of old leaves in response to drought. The results offer valuable guidance for coffee breeding programs aimed at enhancing the resilience of Coffea arabica to climate-induced water scarcity.

Soil texture plays a fundamental role in influencing water retention, nutrient dynamics, erosion susceptibility, and carbon sequestration, making it essential for sustainable agricultural practices. Accurate monitoring and mapping of soil texture components, such as clay, silt, and sand, are crucial for effective soil and water management. This study explores the potential of combining radionuclide monitoring data and Gamma-Ray Spectrometry (GRS) with quantitative modelling techniques for soil texture estimation, focusing on transferring a predictive model developed in one location to another.The research builds on work conducted in 2023 at the Hydrological Open-Air Laboratory (HOAL) in Petzenkirchen, Lower Austria, to assess the transferability of a predictive model for soil texture to the experimental farm of the University of Natural Resources and Life Sciences (BOKU) in Raasdorf, near Vienna, Austria. Soil sampling campaigns at Petzenkirchen (2023) and Raasdorf (2024) provided input data for the model. Soil texture was analyzed using the PARIO system, which applies the Integral Suspension Pressure (ISP) method, based on Stokes' law, to determine particle size distributions. Portable gamma-ray spectrometry (GRS) was used to measure activity concentrations of radionuclides (40K, 232Th, and 238U) at multiple locations in each field, serving as predictors for soil texture components through a Python-based statistical model initially developed in Petzenkirchen.The integration of GRS data with quantitative modelling revealed critical relationships between radionuclide concentrations and soil texture components. Moderate positive correlations of 232Th (0.59) and 238U (0.72) with silt, and moderate negative correlations with clay (-0.62 and -0.74), indicate that radionuclides preferentially associate with silt-sized particles due to their larger surface area and mineralogical properties. Additionally, a strong inverse relationship between clay and silt (-0.92) reflects their complementary distribution within the soil matrix. Strong correlations were observed between 238U and both silt (R² = 0.8, p = 4.1 × 10⁻⁵) and clay (R² = 0.78, p = 5.6 × 10⁻⁵) demonstrating its predictive potential. These strong associations formed the basis for selecting 238U as a key predictor for soil texture estimation in Raasdorf.The predictive models from Petzenkirchen were applied to estimate silt and clay content in Raasdorf using 238U as a predictor. The model performed well for silt, achieving a mean error of 10% and an RMSE of 0.07 g, indicating strong agreement between observed and predicted values. However, predictions for clay exhibited greater variability, with a mean error of 25% and an RMSE of 0.28 g. This discrepancy highlights the need for localized calibration to address site-specific differences in soil mineralogy and radionuclide binding affinities between the two fields.This research demonstrates how integrating radionuclide monitoring with quantitative soil texture modeling provides a scalable and cost-effective approach for digital soil mapping in agricultural landscapes. Future work will refine the model by leveraging advanced GRS data analysis, such as radionuclide ratios (e.g., 238U/232Th) and spatial variability, to improve clay predictions and assess uncertainties in soil property estimations. These efforts aim to enhance the applicability of digital soil mapping for precision agriculture and sustainable land management.

Assessing the risk of 137Cs root uptake by crops is crucial for nuclear emergency preparedness. However, this risk remains underexplored in low-latitude regions and the Southern Hemisphere compared to mid-latitude areas in the Northern Hemisphere due to lower 137Cs contamination rates, making 137Cs measurements impractically time-consuming. To evaluate 137Cs uptake risk using stable 133Cs, we conducted a batch experiment to determine the optimal amount of trace 133Cs addition one that minimally altered its solid-liquid distribution. Additionally, we performed a pot experiment with paddy rice (Oryza sativa, cv. Koshihikari) using eight soils with diverse clay mineralogy, collected from Fukushima and surrounding areas (2015-2020). The experiment included treatments with and without 133Cs addition and with and without an aging treatment (30 dry-wet cycles). The transfer factor of added 133Cs exhibited a strong linear correlation with that of 137Cs from the Fukushima accident (R2 > 0.95), demonstrating that combining batch experiments with pot cultivation and trace 133Cs addition effectively assessed 137Cs uptake risk in crops. The 137Cs transfer factor was five times lower than that of added 133Cs without wetting/drying cycles, aligning with the expected 137Cs aging post-2011. Indices from the batch experiment, including the solid-liquid distribution coefficient and exchangeable ratio of sorbed 133Cs, identified high-risk soils, suggesting batch experiments alone may suffice for screening. Furthermore, the 137Cs/133Cs ratio in the soil exchangeable fraction and rice shoots indicated similar solid-liquid distribution coefficients, allowing estimation of 137Cs concentrations in soil solutions-otherwise impractically time-consuming-using 133Cs concentrations and the soil exchangeable 137/133Cs ratio.

Plastic materials and their associated additives have emerged as critical environmental concerns, particularly within agricultural systems. These materials not only affect soil properties but also pose potential risks of absorption by plants, thereby facilitating the trophic transfer of contaminants. The measurement of nanoplastic particles (NPs) presents challenges due to their small size and low concentrations. While techniques such as micro-Fourier transform infrared spectroscopy (µFTIR) and micro-RAMAN are commonly used for identifying microparticles, they lack the capability to quantify NPs (

Numerous studies have shown that nitrification inhibitors (NIs) are an effective tool to reduce direct N2O emissions. However, some studies have showed the positive effect of NIs on ammonia volatilization and increase the indirect N2O emission from AV. This study aimed to investigate the effect of nitrapyrin (NP) as a NI and gibberellic acid (GA3) as a plant growth regulator (PGR) on direct and indirect N2O emissions. A randomized complete block design including three treatments and five replicates was used in this study. The treatments were: T1 (control treatment-without N fertilizer), T2 (Urea only), and T3 (Urea+NI+GA3). Urea was applied in three split applications. GA3 was foliar sprayed only at stem elongation stage. NP and GA3 were applied at a rate of 0.51% and 0.03% of the applied N (weight/weight), respectively. Ammonia volatilization was measured with semi-static chambers and direct N2O emission was measured with static chambers. Cumulative N2O was 1.45 ± 0.13 and 1.11 ± 0.10 (kg N2O-N ha-1) in urea alone and urea in combination of NP+GA3. The estimated values of indirect N2O-N produced from AV in urea and urea+NP + GA3 were 0.38 and 0.45 kg N ha− 1, respectively. The results showed that the indirect N2O emission from the ammonia path in this type of soil which has high pH cannot be ignored and should be included in the net emission. Also, the results showed that the increase in the indirect emission of N2O from ammonia path induced by NP is negligible.

The Soil Fertility (SoilFer) project, led by the Land and Water Division at FAO, seeks to enhance agricultural practices and resilience globally, starting with five countries (Guatemala, Honduras, Zambia, Kenya, and Ghana). The project collaborates with governments and relevant national partners to establish comprehensive national monitoring and mapping systems for soil management, catering to the diverse needs of agriculture stakeholders. The Soil and Water Management Laboratory at the Joint FAO/IAEA Center serves as a crucial hub for advancing research and technical expertise in soil and water management using nuclear and related techniques. Through its multifaceted approach in collaboration with the Land and Water Division, the laboratory contributes significantly to the SoilFer project, through the development and implementation of technical training programs for and expert advising on the application of Mid-Infrared Spectroscopy (MIRS), Cosmic Ray Neutron Sensor (CRNS), and Gamma Ray Spectroscopy (GRS) to soil monitoring and mapping.The integration of MIRS, CRNS, and GRS technologies within the SoilFer project forms a robust framework for soil monitoring and mapping, as MIRS has been shown to provide detailed insights into soil composition and carbon content, CRNS offers real-time data on soil moisture dynamics, and GRS contributes to the analysis of radioactive isotopes and elemental composition. Given the integrated nature of landscape processes, the adoption of technological approaches must mirror this complexity. Interconnected ecological, hydrological, and geological processes within landscapes necessitate a holistic and integrated technological framework. This approach ensures that diverse data streams, derived from technologies such as remote sensing, geographic information systems (GIS), and advanced sensor networks, can be harmoniously synthesized. Only through such integration can a comprehensive understanding of landscape dynamics be achieved, facilitating informed decision-making and sustainable management practices across multifaceted environmental systems. The project emphasizes the seamless integration of these advanced technologies with soil monitoring and mapping systems, ensuring a comprehensive and effective approach to soil management practices, while improving national capacity and stakeholder engagement in data-based decision making. The key objectives of the SoilFer project encompass the development of robust national soil information systems, the implementation of decision support systems targeting soil health, and the promotion of sustainable soil management practices. By fostering collaboration and knowledge exchange, the project aspires to build technical, increase agricultural resilience and ensure food security in the participating countries.

Coffee-banana intercropping, widely practiced by smallholder farmers in South America and East Africa, is recognized for its potential to combine sustainability with resilience to climate change. This practice promotes crop diversification, but may also enhance water-use efficiency. However, its effectiveness may vary depending on the local conditions and agricultural practices. The lack of quantitative data on drought stress and the complexity of interactions within coffee-banana intercropping systems pose significant challenges in modelling and optimizing water use efficiency. This study aims to develop and refine innovative methods to assess drought stress in coffee-banana intercropping systems, with a focus on stable carbon isotope values (δ¹³C), leaf temperature, and mid-infrared spectroscopy (MIRS). While stable carbon isotope analysis is a promising tool, its application may face challenges due to factors such as crop size, canopy heterogeneity, banana-coffee canopy overlapping, leaf age, orientation, or position (leaf morphological aspects), leading to variable competition for water and light. These factors affect the way sampling for stable carbon isotope and leaf temperature analysis should be conducted, in addition to physiological differences between coffee genotypes, agronomic practices, and complexities in data interpretation. Sampling and analytical protocols must be adapted to address these factors and their effects, while accounting for leaf morphology and microenvironmental parameters. Initially, we evaluated the influence of these factors on δ¹³C variability in coffee leaf samples, in addition to their correlation with leaf temperature. Samples were collected from a 0.15 hectares experimental farm managed by the Agricultural Research Company of Minas Gerais (EPAMIG) in Brazil, an intercrop of Arabica coffee and Cavendish banana plants at 3.6 a distance apart. Coffee leaves were sampled using a metal puncher and leaf temperature was measured using an infrared thermometer, considering varying levels of sunlight exposure. Ten plants of the Catuaí Vermelho IAC 44 coffee cultivar were randomly selected: five under conventional management (chemical fertilizers) and five under organic management (cattle manure). For each plant, samples were taken at three different heights (Top, Middle and Bottom), three orientations (South, East and West), and two branch sides, including young and mature leaves, resulting in 36 leaves per plant. The poster presents key findings on the variability of δ¹³C isotopes in coffee leaves within a banana-coffee intercropping system and their relationship with leaf temperature under different management practices (organic and conventional). This presentation highlights the observed effects of leaf sampling parameters, such as age, position, and sunlight exposure, on δ¹³C values, as well as the implications for improving drought stress screening methodologies.

Nitrous oxide (N2O) stands out among greenhouse gases due to its global warming potential, surpassing carbon dioxide by 310 times and methane by 16 times over a 100-year period. Its primary source lies in the application of fertilizers to agricultural soil. Despite its significance, traditional methods for understanding the intricate relationships within gross nitrogen (N) transformation processes are limited in their analytical depth.Current research increasingly centers on the N2O/(N2O+N2) product ratio, offering valuable insights into the efficiency of nitrogen transformations and the potential for N2O emissions. Quantifying both gases, however, poses challenges that demand specialized techniques. Leveraging isotopic methods, such as the introduction of enriched NO3− and monitoring 15N labelled denitrification products, proves instrumental in unravelling N2O sources and facilitating emission mitigation strategies.This study aims to contribute to this knowledge by measuring N2O and N2 and identifying their sources using a 15N tracer. Soil samples were collected from a 0-15cm depth at Grabenegg, an agricultural site in Austria. Two treatments were applied, with 15NH414NO3 for treatment one and 14NH415NO3 for treatment two, both at a rate of 100 kg N/ha (equivalent to 150 kg N/ha when expressed as 100 mg N/kg soil). The incubation experiment spanned 10 days in 850ml glass jars at 60% WFPS, involving seven sampling days. Soil analyses included ammonium and nitrate content through colorimetric methods, pH determination, and 15N analysis using an Isotope Ratio Mass Spectrometer (IRMS) following an adjusted Brooks microdiffusion.Gas samples extracted from the jars over a two-hour period underwent analysis for CO2, CH4, and N2O content using a Picarro G5102-i isotopic and gas concentration analyzer. Integration with N tracing models yielded crucial insights into the connections between substrates and N transformation products, shedding light on the impacts of synthetic fertilizer and enabling the quantification of transformation rates.