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.

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.

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.

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.

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.