Soil carbon evolution all over the world over for the past 40 years | 2pm PT Tues, Dec 5

[Grigory Bronevetsky]

Modeling Talks

Soil carbon evolution all over the world over for the past 40 years José Padarian Campusano, University of Sydney Tuesday, Dec 5 | 2pm PT Meet | Youtube Stream Hi all, The presentation will be via Meet and all questions will be addressed there. If you cannot attend live, the event will be recorded and can be found afterward at https://sites.google.com/modelingtalks.org/entry/soil-carbon-evolution-all-over-the-world-over-for-the-past-40-years Abstract: Soils are under threat globally, with declining soil productivity and soil condition in many places. As a key indicator of soil functioning, soil organic carbon (SOC) is crucial for ensuring food, soil, water and energy security, together with biodiversity protection. While there are global efforts to map SOC stock and status, SOC is a dynamic soil property and can change rapidly as a function of land management and land use. Here, we introduce a semi-mechanistic model to monitor SOC stocks at a global scale, underpinned by one of the largest worldwide soil database to date. Our model generates a SOC stock baseline using machine learning methods, which is then propagated through time by keeping track of annual landcover changes obtained from remote sensing products with loss and gain dynamics dependent on temperature and precipitation, which finally define the magnitude, rate and direction of the SOC changes. We will share what this monitoring system enable us to do in terms of global SOC stock accounting, how it relates to soil productivity and its contribution in the context of green house gas emissions. We will also discuss the future improvements necessary to turn this project into the soil monitoring system needed to secure Earth's soils. Bio: José is a Soil Scientist currently working as a Postdoctoral Research Associate in the School of Life and Environmental Sciences and Sydney Institute of Agriculture (University of Sydney). José is particularly interested in spatio-temporal soil modelling and soil spectroscopy from the regional to the global scale, and on how to use machine learning methods to tackle the methodological challenges associated with them. He leads the application of deep learning in soil sciences, developing new modelling frameworks for digital soil mapping and soil spectroscopy. Besides the interest on improving the accuracy of soil models, he is also interested in the interpretability of these usually considered "black-boxes", privacy-preserving training methods and on how to improve the connectivity/transportability between global and local-scale models. More information on previous and future talks: https://sites.google.com/modelingtalks.org/entry/home

[Grigory Bronevetsky]

Video Recording: https://youtu.be/22kvDsUhjYI

Slides: https://drive.google.com/file/d/1Y1SEcDY6JWE2Fz5QNEazbMlk_Y3fsLhp/view?usp=sharing

Summary:

• Soils

  • Store water and solutes

  • Healthy spoils critical for agriculture and societal health

• Soil security framework

  • Use soil indicators and utility graphs

    • Water capacity

    • Salinization 

  • Key indicator: Soil organic carbon (SOC)

  • Many initiatives

    • GlobalSoilMap.net: bottom->up

    • ISRIC: top->bottom

    • FAO, 4Pour1000

• Challenge: traditional approaches are static but soil properties change dynamically

  • E.g. SOC loss .04%-1.2%/year

  • Expansion of agriculture/forestry is threatening soils

  • Critical to integrate dynamic landcover changes in SOC assessment 

• Approach: SOC Monitoring at global scale

  • Baseline map

  • Landcover tracking: Where, when, change type, duration of change persistence

• Baseline methodology: digital soil mapping

  • Based on covariates: 

    • Soil properties

    • Climate

    • Organisms/landcover

    • Topography

    • Parent material/geology

    • Age

    • Geographic location

  • Trained ML model (Cubist) on dataset

• Dynamics: Landcover tracking

  • MODIS MCF12Q1, 500m resolution

  • IGBP classification scheme

  • SOC amount: Depends on regional ecology and change in land cover type

  • Rate of change: depends on temperature and precipitation

  • Shape of change: gaining/losing SOC

  • Landcover tracking via Google Earth Engine

• Key findings

  • Evolution maps of various regions of how changes in landcover types drive changes in SOC

  • Annual losses:

    • 1.9Pg SOC / year (topsoil): 20% larger than annual production-based emission in US in 2018

    • Global total : 700-800 Pg

    • Tropic and sub-tropic : ~50% of global loss

  • Soil productivity:

    • Critical SOC limits:

      • 1.1% tropical: 11 Mha/year

      • 2% tropical: 6 Mha/year

      • Soil on these lands is very hard to recover (limited resource)

• Towards a soil early warning system

  • 500m-30m Landsat

  • Baseline for 1985

• Future improvements:

  • Climate change effect, larger analytic window will make it possible to capture effect more clearly

  • Within-class changes

    • Different crops

    • Different management

    • Requires more soil data

  • Scenario analysis

    • Inform management and policy

    • Improve soil condition

    • Secure soils