100
years of
SOIL
SCIENCE
past achievements
and future challenges

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Centennial
of the IUSS

Florence - Italy
May 19 - 21, 2024

www.centennialiuss2024.org

## Proposition for a prototype of MRV tool for cropland C stock change assessment at high resolution over large regions
# Cropland soil : CO₂ sequestration potential

## Environmental and societal challenges

* Climate change mitigation → remain below 1.5°C of temperature increase by 2100 (COP21, 4p1000 initiative),

* Soil health → improve soil fertility, water restitution to plant, reduce erosion ...

* Food security → increase crop resilience to extreme climatic events.

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Source : Illustrations Arbres et paysages 32

How to assess the impacts of those practices in terms of CO₂ emissions/soil organic carbon storage at the plot scale but over large areas?

→ **Need for a new generation of tools providing an exhaustive/objective vision of the effect of management on SOC stock changes adapted to different contexts of application (CAP, NDCs, carbon market...) → ORCaSa, ClieNfarms, MARVIC...EU projects**

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# Monitoring: How to objectively address C budget?

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CIRCASA, K. Paustian et al 2019, P. Smith et al 2020

C budget approach ⇒
accounting for C
input-output

⇒A **global** dynamic approach but which requires being able to quantify **all C fluxes** between the **plot** and its **environment** by **measurements** or via **modeling**.

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SPOT4/5 data

⇒ **Remote sensing** allows to follow the **dynamic** and **variability** of vegetation development.

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# SAFYE CO₂: Approach combining satellite and modeling for the spatial estimation of C budget

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## SAFYE-CO₂

### Satellite observations assimilation:

→ Reproduce the dynamics and development intensity of vegetation.

→ Implicit consideration of stress (N, water, etc), diseases and some practices.

### Very simple soil respiration approach:

→ At first, empirical T(°C) function to overcome the uncertainties of soil products and farmer's data.

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# Agricarbon-EO : processing chain with spatialized plant model
## SAFYE CO₂

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Source : T. Wijmer et al 2024

Consistent set of agro-environmental variables

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Realisation: A. Al Bitar, V. Antonenko, L. Arnaud
# Agricarbon-EO: processing chain with spatialized plant model
## SAFYE CO₂ + soil models

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→ Use parsimonious plant and soil models for spatialization. (Now AMG, next step RothC...)

→ Consider spatial heterogeneity of vegetation inside the plot.

→ Improve the carbon input estimation of the soil model.
Effect of cover crop compared to bare soil on Soil Organic Carbon (SOC) stock

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Biomass validation of cover crops

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SAFYE-CO2

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# Effect of cover crop compared to bare soil on Soil Organic Carbon (SOC) stock

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A. Al Bitar,

SAFYE-C02

Biomass validation of cover crops

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P10A P10A P02B P05A P04A P05A P06A P07A P08A P09A P09C P20A P12A P13A P14A P15A P16A P17A P18A P19A P20A P21A P22A P23A P24A P25A P26A P27A P28A P29A P30A P31A P32A P33A P34A P35A P36A P37A P38A P39A P40A P41A P42A P43A P44A P45A P46A P47A P48A P49A P50A P51A P52A P53A P54A P55A P56A P57A P58A P59A P60A

Simulations over a 2 years crops rotation.

2 scenarios:

1. **Real practice = Cover crop (CC) (buried or exported)** between the 2 main crops.

2. **Baseline = Bare soil (BS) between the 2 main crops.**

$$ \Rightarrow \Delta\text{SOC}_{\text{stock}} = \text{SOC}_{\text{stock\_final}}(\text{CC}) - \text{SOC}_{\text{stock\_final}}(\text{BS}) $$
# Study site : Piacenza, Italy → Analysis of transposability of the approach

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Vegezzi

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# Study site : Piacenza, Italy → Analysis of transposability of the approach

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Vegezzi

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<table><thead><tr><th>2021</th><th>2022</th><th>2023</th><th>2024</th></tr></thead><tbody><tr><td>Tomato</td><td>Wheat</td><td>Silage<br>maize</td><td>Mixed cover<br>crop<br>Tomato</td></tr></tbody></table>

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100
years of
SOIL
SCIENCE
soil research
and future challenges

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# Dry above ground biomass maps at the end of cycle

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## GLAI Assimilation :

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(DAM: "Dry Above Mass")
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! Falsely
detects
vegetation due
to unfiltered
clouds !

2023

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(DAM: "Dry
Above Mass")
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100
years of
SOIL
SCIENCE
soil research
and future challenges

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# Dry above ground biomass maps at the end of cycle

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## Biomass Validation:

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Work in
progress..

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Work in
progress..

µDAM
(t/ha)

20
10
5
0

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(DAM: "Dry
Above Mass")

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ESU

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# Carbon input maps and its uncertainties for each year

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# Variation of ΔSOC<sub>stock</sub> estimated by AMG over 2 years

<figure><img src="image_62.png" /><figcaption>ΔSOC<sub>stock</sub> over 2 years on Vegezzi:</figcaption></figure>

<figure><img src="image_63.png" /><figcaption>ΔSOC<sub>stock</sub> over 2 years for all 5 plots pixels:</figcaption></figure>

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## AgriCarbonEO v2

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+ AMG

→ Allows to evaluate:

1. The effect of **contrasting practices** on variations in carbon stocks in different soil pools.

2. Over **short periods** (<5ans).
# Variation of ΔSOCstock estimated by ACEO v2 over 5 years

## T31TCJ Sentinel 2 Tile

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⇒ Use LUCAS data for soil properties in AMG.

⇒ Set uniform initial carbon stock at 46 tC/ha for the 30cm top soil.

⇒ Use french RPG for crop rotation.

⇒ To detect cover crop : at least 75% of the plot with NDVI>0.4 during january-march (usually faba bean and phacelia).

⇒ Assume straw incorporation and no organic amendment (realistic for the region).
# Variation of ΔSOC<sub>stock</sub> estimated by ACEO v2 over 5 years

T31TCJ Sentinel 2 Tile

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Villeneuve farm

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# Analyses of ΔSOC<sub>Stock</sub> per crop rotations (gC/m<sup>2</sup>) per pixel

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Source: T. Wijmer in preparation
**Objective:** Quantify the components of the C budget at high resolution and large scale

→ Use of an operational processing chain with new soil module (AMG) taking into account the spatial variability of biomass production/restitution (+ uncertainties) and soil properties.

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Source: Elisa Bruni, Marvic Webinar 7

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MARVIC
MRV for carbon farming
**Objective:** Quantify the components of the C budget at high resolution and large scale

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→ Use of an operational processing chain with new soil module (AMG) taking into account the spatial variability of biomass production/restitution (+ uncertainties) and soil properties.

→ Study of transposability of the chain (validated in France on biomass, yield, flux) in Italy.

→ Assessment of the impact of different practices on variations in soil organic carbon stocks.
1. Evaluate the reduction of uncertainties in the initialization of the soil model using the **Rock Eval method**

1. Evaluate the overall approach against in situ △ SOC<sub>Stock</sub>/fluxes measurements and compare the performances of AMG with other soil models

<figure><img src="image_75.png" /><figcaption>ICOS flux sites</figcaption></figure>

<figure><img src="image_76.png" /><figcaption>Nataïs plots</figcaption></figure>

<figure><img src="image_77.png" /><figcaption>ClieNFarms plots</figcaption></figure>

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Source : Elisa Bruni, Marvic
Webinar 7

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MARVIC
MRV for carbon farming

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Future perspective : when mature, using digital soil map of soil organic carbon from remote sensing as an input.
1. Evaluate the reduction of uncertainties in the initialization of the soil model using the Rock Eval method

1. Evaluate the overall approach against in situ Δ SOC<sub>Stock</sub>/fluxes measurements and compare the performances of AMG with other soil models

<figure><img src="image_81.png" /><figcaption>ICOS flux sites</figcaption></figure>

<figure><img src="image_82.png" /><figcaption>Nataïs plots</figcaption></figure>

<figure><img src="image_83.png" /><figcaption>ClieNFarms plots</figcaption></figure>

## Uncertainties in SOC models

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Source: Elisa Bruni, Marvic Webinar 7

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MARVIC
MRV for carbon farming

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Future perspective: when mature, using digital soil map of soil organic carbon from remote sensing as an input.
1. Evaluate the reduction of uncertainties in the initialization of the soil model using the Rock Eval method

1. Evaluate the overall approach against in situ Δ SOC<sub>Stock</sub>/fluxes measurements and compare the performances of AMG with other soil models

<figure><img src="image_87.png" /><figcaption>ICOS flux sites</figcaption></figure>

<figure><img src="image_88.png" /><figcaption>Nataïs plots</figcaption></figure>

<figure><img src="image_89.png" /><figcaption>ClieNFarms plots</figcaption></figure>

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Source: Elisa Bruni, Marvic Webinar 7

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MARVIC
MRV for carbon farming

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Future perspective: when mature/accurate enough, using **digital soil map** of soil organic carbon from remote sensing as an input.
Thank you for your attention !

Ainhoa IHASUSTA : ainhoa.ihasusta@inrae.fr

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# Annexes

Ainhoa IHASUSTA : ainhoa.ihasusta@inrae.fr
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# AMG forced SAFYE CO₂ biomass

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# AMG forced SAFYE CO₂ biomass

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Safye-CO₂

Crop

Remote sensing

Weather

$C_{\text{input}}$ returned to the soil

Safye-CO₂

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$$C_{\text{ag}} = C_{\text{content}} \cdot \text{DAM} \cdot (1 - \text{HI}) \cdot \text{Pse}$$

$$C_{\text{re}} = C_{\text{content}} \cdot \text{DBM}$$

$$\text{BG}_{\text{F-Depth}} = 1 - \beta^{\text{Depth}}$$

$$C_{\text{BG}} = \text{BG}_{\text{F-Depth}} \cdot C_{\text{re}}$$

Farmer's data

$$C_{\text{OA}} = \text{dose} \cdot C_{\text{content}}$$

$$C_{\text{input}} = C_{\text{ag}} + C_{\text{BG}} + C_{\text{OA}}$$

ACEO

<figure><img src="image_100.png" /></figure>

ock variations
(0-30cm)
# Propagation of biomass uncertainties of SAFYE CO₂ into the Cinput estimation

## Safye-CO₂

<figure><img src="image_101.png" /></figure>

$$C_{ag} = C_{content} \cdot \text{DAM} \cdot (1 - \text{HI}) \cdot P_{se} = a \cdot \text{DAM}$$

$$C_{re} = C_{content} \cdot \text{DBM}$$

$$\text{BG}_{\text{F-Depth}} = 1 - \beta^{\text{Depth}}$$

$$C_{BG} = \text{BG}_{\text{F-Depth}} \cdot C_{re} = b \cdot \text{DBM}$$

<figure><img src="image_102.png" /></figure>

$$C_{OA} = \text{dose} \cdot C_{content}$$

$$C_{input} = C_{ag} + C_{BG} + C_{OA}$$

<figure><img src="image_103.png" /></figure>

$$\sigma(C_{input}) = \sigma(C_{ag})^2 + \sigma(C_{bg})^2 + \sigma(C_{OA})^2 + 2ab \cdot \text{cov}(\text{DAM}, \text{DBM})$$

$$\text{Avec : } \sigma(C_{ag})^2 = a^2 \sigma(\text{DAM})^2 \text{ et } \sigma(C_{bg})^2 = b^2 \sigma(\text{DBM})^2$$

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GPP

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$$GPP = R_g \cdot \epsilon_c f_T(T_a) \cdot f_w(WC) \cdot \text{ELUE} \cdot \text{APAR} \cdot \text{SR}_{10},$$
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Daily estimation :

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$$GPP = R_g \cdot \epsilon_c f_T(T_a) \cdot f_w(WC) \cdot \text{ELUE} \cdot \text{APAR} \cdot \text{SR}_{10},$$

$$ \begin{cases} R_{\text{auto}} = R_{\text{maint}} + R_{\text{grow}}, & \text{where} \\ R_{\text{maint}} = R_{10} \cdot Q_{10}^{-0.1 \cdot (T-10)} \cdot \text{SR}_{10} \\ R_{\text{grow}} = (1 - Yg) \cdot (GPP - R_{\text{maint}}) \end{cases} $$

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$$GPP = R_g \cdot \epsilon_c f_T(T_a) \cdot f_w(WC) \cdot \text{ELUE} \cdot \text{APAR} \cdot SR_{10},$$

$$ \begin{cases} R_{\text{auto}} = R_{\text{maint}} + R_{\text{grow}}, & \text{where} \\ R_{\text{maint}} = R_{10} \cdot Q_{10}^{-0.1 \cdot (T-10)} \cdot SR_{10} \\ R_{\text{grow}} = (1 - Yg) \cdot (GPP - R_{\text{maint}}) \end{cases} $$

$$ \begin{cases} \Delta\text{DAM} = \frac{\text{NPP}}{C_{\text{veg}}} \cdot (1 - \text{PRT\_R}) & \text{ (+\checkmark) } (\Sigma\checkmark = \text{SMT}) \\ \Delta\text{DLM} = \Delta\text{DAM} \cdot (\text{PRT\_L}) & \text{ (-\checkmark) } (\Sigma\checkmark = \text{SMT}) \\ \Delta\text{DBM} = \frac{\text{NPP}}{C_{\text{veg}}} \cdot (\text{PRT\_R}), & \text{ (\Sigma\textasciitilde) } (\Sigma\textasciitilde = \text{SMT}) \end{cases} $$
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$$GPP = R_g \cdot \epsilon_c f_T(T_a) \cdot f_w(WC) \cdot \text{ELUE} \cdot \text{APAR} \cdot \text{SR}_{10},$$

$$ \begin{cases} R_{\text{auto}} = R_{\text{maint}} + R_{\text{grow}}, & \text{where} \\ R_{\text{maint}} = R_{10} \cdot Q_{10}^{-0.1 \cdot (T-10)} \cdot \text{SR}_{10} \\ R_{\text{grow}} = (1 - Y_g) \cdot (\text{GPP} - R_{\text{maint}}) \end{cases} $$

$$ \begin{cases} \Delta \text{DAM} = \frac{\text{NPP}}{C_{\text{veg}}} \cdot (1 - \text{PRT\_R}) \quad (\Sigma \checkmark = \text{SMT}) \\ \Delta \text{DLM} = \Delta \text{DAM} \cdot (\text{PRT\_L}) \quad (\Sigma \checkmark = \text{SMT}) \\ \Delta \text{DBM} = \frac{\text{NPP}}{C_{\text{veg}}} \cdot (\text{PRT\_R}), \quad (\Sigma \checkmark = \text{SMT}) \end{cases} $$

$$ \begin{cases} \Delta \text{GLAI}^{+} = \text{DLM} \cdot \text{SLA} \\ \Delta \text{GLAI}^{-} = \text{GLAI} \cdot (\text{SMT} - \text{Sen}_a) \cdot \text{Sen}_b^{-1}, \end{cases} $$

**Assimilated variable**
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# Dry above ground biomass uncertainties maps at the end of cycle

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(DAM: "Dry Above Mass")