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UID:pretalx-soil-health-now-2026-PDE8EG@pretalx.earthmonitor.org
DTSTART;TZID=Europe/Athens:20261208T151500
DTEND;TZID=Europe/Athens:20261208T153000
DESCRIPTION:Global agriculture faces the challenge of increasing productivi
 ty while maintaining environmental sustainability. Agricultural system mod
 els support climate-smart land management\; however\, accurately predictin
 g soil organic carbon (SOC) density changes and indexing soil health remai
 ns difficult because of the complex interactions among soils\, climate\, l
 and use\, and management practices. Emerging technologies such as Artifici
 al Intelligence (AI)\, including remote sensing\, drones\, and spectroscop
 y\, provide opportunities for real-time monitoring and decision-making des
 pite having several challenges.\nThis study advances the MODASYS (Model fo
 r Decision-Making in Agri-Environmental Systems) framework\, facilitating 
 the assessment of nutrient and land-management strategies aligned with the
  Climate Action Plan. By integrating high-resolution georeferenced soil da
 tasets with the Land Parcel Identification System (LPIS). For this\, SoilG
 rids (Global Soil Map) and the National Soil Database of Ireland were harm
 onised using ArcGIS and Multiple Imputation by Chained Equations (MICE)\, 
 supported by advanced statistical and Machine Learning approaches\, to gen
 erate a robust and spatially consistent soil dataset and map. The resultin
 g Soil Health Indices (SHIs) benchmark observed (O) SOC against Typical (T
 ) values\, which remain limited for organo-mineral and organic soils\, rev
 ealing substantial spatial variability across land uses and soil types in 
 Ireland.\nBy integrating LPIS-mapped datasets with the MODASYS API\, the f
 ramework enables high-resolution simulations of soil and climate-driven va
 riables to predict SHIs and quantify carbon footprints\, facilitating the 
 precise delineation of optimal carbon-management zones. Simulations from a
 n Irish dairy farm case study using nine land-use scenarios showed that\, 
 although the highest SHI (O/T SOC = 3.8) was predicted in grasslands\, mix
 ed farming systems incorporating agroforestry and reduced nitrogen inputs 
 achieved high SHI values (0.94) while simultaneously lowering carbon footp
 rints and maintaining profitability through reduced reliance on synthetic 
 fertilisers. In contrast\, a New Zealand case study with twelve crop-field
  sequences predicted declining O/T SOC ratios (from 1.20 to 1.12 over five
  years) despite fields remaining within a “High” SHI category\, highli
 ghting the limitations of relative indices in detecting absolute carbon de
 pletion.\nThe MODASYS framework demonstrates the power of integrated soil-
 health modelling for strategic agricultural planning\, carbon farming\, an
 d climate resilience. Future iterations will incorporate AI-driven digital
  twin methodologies\, evolving the model into a dynamic platform for predi
 ctive soil management and sustainable intensification in the face of escal
 ating climate risks.
DTSTAMP:20260825T193620Z
LOCATION:Amphitheater II
SUMMARY:Toward a Digital Twin for Agriculture: Soil Health and Carbon Asses
 sment with MODASYS - Mohammad Ibrahim Khalil
URL:https://pretalx.earthmonitor.org/soil-health-now-2026/talk/PDE8EG/
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