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Sommaire -- Avant-propos -- 1. Disponibilité et usage des Mafor en France -- Les gisements et l'utilisation des Mafor en France -- Le droit applicable aux Mafor -- La perception des Mafor et les politiques locales de gestion de leur utilisation -- Substituabilité aux engrais minéraux et déséquilibres régionaux -- Conclusion -- 2. Intérêts agronomiques des Mafor et impacts environnementaux associés -- Les principaux mécanismes déterminant la valeur agronomique des Mafor -- La valeur fertilisante des Mafor -- La valeur amendante organique des Mafor

Intro -- Le recyclage des résidus organiques - Regards sur une pratique agro-écologique -- Introduction -- Chapitre 1 - Mondes et représentations - Le point de vue du sociologue et de l'anthropologue -- Chapitre 2 - Réglementation et politiques publiques - Le point de vue du législateur et du politique -- Chapitre 3 - Du déchet au produit valorisable en agriculture - Le point de vue du technologue -- Chapitre 4 - Stocker, transporter et épandre les produits résiduaires organiques - Le point de vue du logisticien -- Chapitre 5 - La caractérisation des PRO pour éclairer les choix d'usage en agriculture - Le point de vue du biochimiste-agronome -- Chapitre 6 - Impacts des produits résiduaires organiques sur la teneur et le comportement des éléments-traces dans les sols - Le point de vue du géochimiste -- Chapitre 7 - L'intégration des PRO dans les systèmes de culture - Le point de vue de l'agronome -- Chapitre 8 - Péninsule de Dakar : recyclage en zone périurbaine -- Chapitre 9 - Mahajanga, Madagascar : gestion de la ressource en matière organique pour les cultures de légumes-feuilles -- Chapitre 10 - La Réunion : recyclage de PRO en milieu insulaire tropical -- Chapitre 11 - La plaine de Versailles : synergie potentielle entre ville et campagne pour le remplacement des engrais minéraux via le recyclage des ressources organiques urbaines -- Conclusion -- Planches couleurs -- Bibliographie -- Liste des auteurs.

AbstractOrganic waste products (OWP) application to crop lands makes possible nutrients recycling. However, it can result in long-term accumulation of trace elements (TE) in soils. The present study aimed at (i) assessing the impact of regular applications of urban composts and manure on the TE contents of topsoils and crops in a long-term field experiment, (ii) comparing the TE mass balances with the stock variations of TE in soils, and (iii) proposing a prospective evaluation of this practice, based on estimated soil safe threshold values and simulations of soil TE accumulation for 100 years. In the long-term field experiment, physico-chemical properties and TE contents (Cd, Cr, Cu, Hg, Ni, Pb and Zn) have been measured in OWP, soils, plants and leaching waters for the period 1998–2015, and used for mass balance calculations and long-term simulations of TE accumulations. The composts of green wastes and sludge (GWS) and of municipal solid waste (MSW) were the OWP with the largest TE contents, while the farmyard manure tended to have the lowest. Repeated application of OWP led to significant accumulation of Zn and Cu in the topsoil layer (not for Cr, Cd, Hg, Ni, Pb), especially with GWS, without overpassing calculated protective threshold values. No effect of repeated application of OWP has been observed on TE contents in grains (wheat, maize, barley). The positive mass balance has been dominated by the input flux of TE through OWP and resulted in the observed increases of soil stocks for Cu and Zn. Prospective simulation of soil content evolution until 2100 showed that soil content reached 0.4 mg Cd kg−1 soil (GWS, MSW), 38 mg Cu kg−1 soil (GWS) and 109 mg Zn kg−1 soil (GWS), which remained lower than protective threshold values.

The land use, land-use change and forestry (LULUCF) activities and crop management (CM) in Europe could be an important carbon sink through soil organic carbon (SOC) sequestration. Recently, the (EU decision 529/2013) requires European Union's member states to assess modalities to include greenhouse gas (GHG) emissions and removals resulting from activities relating to LULUCF and CM into the Union's (GHG) emissions reduction commitment and their national inventories reports (NIR). Tier 1, the commonly used method to estimate emissions for NIR, provides a framework for measuring SOC stocks changes. However, estimations have high uncertainty, especially in response to crop management at regional and specific national contexts. Understanding and quantifying this uncertainty with accurate confidence interval is crucial for reliably reporting and support decision-making and policies that aims to mitigate greenhouse gases through soil C storage. Here, we used the Tier 3 method, consisting of process-based modelling, to address the issue of uncertainty quantification at national scale in France. Specifically, we used 20 Long-term croplands experiments (LTE) in France with more than 100 treatments taking into account different agricultural practices such as tillage, organic amendment, inorganic fertilization, cover crops, etc. These LTE were carefully selected because they are well characterized with periodic SOC stocks monitoring overtime and covered a wide range of pedo-climatic conditions. We applied linear mixed effect model to statistically model, as a function of soil, climate and cropping system characteristics, the uncertainty resulting from applying this Tier 3 approach. The model was fitted on the dataset yielded by comparing the simulated (with the Century model V 4.5) to the observed SOC changes on the LTE at hand. This mixed effect model will then be used to derive uncertainty related to the simulation of SOC stocks changes of the French Soil Monitoring Network (FSMN) where only one measurement is done in ...

The land use, land-use change and forestry (LULUCF) activities and crop management (CM) in Europe could be an important carbon sink through soil organic carbon (SOC) sequestration. Recently, the (EU decision 529/2013) requires European Union's member states to assess modalities to include greenhouse gas (GHG) emissions and removals resulting from activities relating to LULUCF and CM into the Union's (GHG) emissions reduction commitment and their national inventories reports (NIR). Tier 1, the commonly used method to estimate emissions for NIR, provides a framework for measuring SOC stocks changes. However, estimations have high uncertainty, especially in response to crop management at regional and specific national contexts. Understanding and quantifying this uncertainty with accurate confidence interval is crucial for reliably reporting and support decision-making and policies that aims to mitigate greenhouse gases through soil C storage. Here, we used the Tier 3 method, consisting of process-based modelling, to address the issue of uncertainty quantification at national scale in France. Specifically, we used 20 Long-term croplands experiments (LTE) in France with more than 100 treatments taking into account different agricultural practices such as tillage, organic amendment, inorganic fertilization, cover crops, etc. These LTE were carefully selected because they are well characterized with periodic SOC stocks monitoring overtime and covered a wide range of pedo-climatic conditions. We applied linear mixed effect model to statistically model, as a function of soil, climate and cropping system characteristics, the uncertainty resulting from applying this Tier 3 approach. The model was fitted on the dataset yielded by comparing the simulated (with the Century model V 4.5) to the observed SOC changes on the LTE at hand. This mixed effect model will then be used to derive uncertainty related to the simulation of SOC stocks changes of the French Soil Monitoring Network (FSMN) where only one measurement is done in 16 Km regular grid. These simulations on the grid will be in turn used for NIR. Preliminary results suggest that the model do not adequately simulate SOC stocks levels but succeeds at capturing SOC changes due to management, despite the fact that the model does not explicitly simulate some management such as tillage. This is probably due to inappropriate model parametrization especially for crops and thus Cinput in the French context and/or model initialization.

The land use, land-use change and forestry (LULUCF) activities and crop management (CM) in Europe could be an important carbon sink through soil organic carbon (SOC) sequestration. Recently, the (EU decision 529/2013) requires European Union's member states to assess modalities to include greenhouse gas (GHG) emissions and removals resulting from activities relating to LULUCF and CM into the Union's (GHG) emissions reduction commitment and their national inventories reports (NIR). Tier 1, the commonly used method to estimate emissions for NIR, provides a framework for measuring SOC stocks changes. However, estimations have high uncertainty, especially in response to crop management at regional and specific national contexts. Understanding and quantifying this uncertainty with accurate confidence interval is crucial for reliably reporting and support decision-making and policies that aims to mitigate greenhouse gases through soil C storage. Here, we used the Tier 3 method, consisting of process-based modelling, to address the issue of uncertainty quantification at national scale in France. Specifically, we used 20 Long-term croplands experiments (LTE) in France with more than 100 treatments taking into account different agricultural practices such as tillage, organic amendment, inorganic fertilization, cover crops, etc. These LTE were carefully selected because they are well characterized with periodic SOC stocks monitoring overtime and covered a wide range of pedo-climatic conditions. We applied linear mixed effect model to statistically model, as a function of soil, climate and cropping system characteristics, the uncertainty resulting from applying this Tier 3 approach. The model was fitted on the dataset yielded by comparing the simulated (with the Century model V 4.5) to the observed SOC changes on the LTE at hand. This mixed effect model will then be used to derive uncertainty related to the simulation of SOC stocks changes of the French Soil Monitoring Network (FSMN) where only one measurement is done in 16 Km regular grid. These simulations on the grid will be in turn used for NIR. Preliminary results suggest that the model do not adequately simulate SOC stocks levels but succeeds at capturing SOC changes due to management, despite the fact that the model does not explicitly simulate some management such as tillage. This is probably due to inappropriate model parametrization especially for crops and thus Cinput in the French context and/or model initialization.

The land use, land-use change and forestry (LULUCF) activities and crop management (CM) in Europe could be an important carbon sink through soil organic carbon (SOC) sequestration. Recently, the (EU decision 529/2013) requires European Union's member states to assess modalities to include greenhouse gas (GHG) emissions and removals resulting from activities relating to LULUCF and CM into the Union's (GHG) emissions reduction commitment and their national inventories reports (NIR). Tier 1, the commonly used method to estimate emissions for NIR, provides a framework for measuring SOC stocks changes. However, estimations have high uncertainty, especially in response to crop management at regional and specific national contexts. Understanding and quantifying this uncertainty with accurate confidence interval is crucial for reliably reporting and support decision-making and policies that aims to mitigate greenhouse gases through soil C storage. Here, we used the Tier 3 method, consisting of process-based modelling, to address the issue of uncertainty quantification at national scale in France. Specifically, we used 20 Long-term croplands experiments (LTE) in France with more than 100 treatments taking into account different agricultural practices such as tillage, organic amendment, inorganic fertilization, cover crops, etc. These LTE were carefully selected because they are well characterized with periodic SOC stocks monitoring overtime and covered a wide range of pedo-climatic conditions. We applied linear mixed effect model to statistically model, as a function of soil, climate and cropping system characteristics, the uncertainty resulting from applying this Tier 3 approach. The model was fitted on the dataset yielded by comparing the simulated (with the Century model V 4.5) to the observed SOC changes on the LTE at hand. This mixed effect model will then be used to derive uncertainty related to the simulation of SOC stocks changes of the French Soil Monitoring Network (FSMN) where only one measurement is done in 16 Km regular grid. These simulations on the grid will be in turn used for NIR. Preliminary results suggest that the model do not adequately simulate SOC stocks levels but succeeds at capturing SOC changes due to management, despite the fact that the model does not explicitly simulate some management such as tillage. This is probably due to inappropriate model parametrization especially for crops and thus Cinput in the French context and/or model initialization.

The land use, land-use change and forestry (LULUCF) activities and crop management (CM) in Europe could be an important carbon sink through soil organic carbon (SOC) sequestration. Recently, the (EU decision 529/2013) requires European Union's member states to assess modalities to include greenhouse gas (GHG) emissions and removals resulting from activities relating to LULUCF and CM into the Union's (GHG) emissions reduction commitment and their national inventories reports (NIR). Tier 1, the commonly used method to estimate emissions for NIR, provides a framework for measuring SOC stocks changes. However, estimations have high uncertainty, especially in response to crop management at regional and specific national contexts. Understanding and quantifying this uncertainty with accurate confidence interval is crucial for reliably reporting and support decision-making and policies that aims to mitigate greenhouse gases through soil C storage. Here, we used the Tier 3 method, consisting of process-based modelling, to address the issue of uncertainty quantification at national scale in France. Specifically, we used 20 Long-term croplands experiments (LTE) in France with more than 100 treatments taking into account different agricultural practices such as tillage, organic amendment, inorganic fertilization, cover crops, etc. These LTE were carefully selected because they are well characterized with periodic SOC stocks monitoring overtime and covered a wide range of pedo-climatic conditions. We applied linear mixed effect model to statistically model, as a function of soil, climate and cropping system characteristics, the uncertainty resulting from applying this Tier 3 approach. The model was fitted on the dataset yielded by comparing the simulated (with the Century model V 4.5) to the observed SOC changes on the LTE at hand. This mixed effect model will then be used to derive uncertainty related to the simulation of SOC stocks changes of the French Soil Monitoring Network (FSMN) where only one measurement is done in 16 Km regular grid. These simulations on the grid will be in turn used for NIR. Preliminary results suggest that the model do not adequately simulate SOC stocks levels but succeeds at capturing SOC changes due to management, despite the fact that the model does not explicitly simulate some management such as tillage. This is probably due to inappropriate model parametrization especially for crops and thus Cinput in the French context and/or model initialization.

The land use, land-use change and forestry (LULUCF) activities and crop management (CM) in Europe could be an important carbon sink through soil organic carbon (SOC) sequestration. Recently, the (EU decision 529/2013) requires European Union's member states to assess modalities to include greenhouse gas (GHG) emissions and removals resulting from activities relating to LULUCF and CM into the Union's (GHG) emissions reduction commitment and their national inventories reports (NIR). Tier 1, the commonly used method to estimate emissions for NIR, provides a framework for measuring SOC stocks changes. However, estimations have high uncertainty, especially in response to crop management at regional and specific national contexts. Understanding and quantifying this uncertainty with accurate confidence interval is crucial for reliably reporting and support decision-making and policies that aims to mitigate greenhouse gases through soil C storage. Here, we used the Tier 3 method, consisting of process-based modelling, to address the issue of uncertainty quantification at national scale in France. Specifically, we used 20 Long-term croplands experiments (LTE) in France with more than 100 treatments taking into account different agricultural practices such as tillage, organic amendment, inorganic fertilization, cover crops, etc. These LTE were carefully selected because they are well characterized with periodic SOC stocks monitoring overtime and covered a wide range of pedo-climatic conditions. We applied linear mixed effect model to statistically model, as a function of soil, climate and cropping system characteristics, the uncertainty resulting from applying this Tier 3 approach. The model was fitted on the dataset yielded by comparing the simulated (with the Century model V 4.5) to the observed SOC changes on the LTE at hand. This mixed effect model will then be used to derive uncertainty related to the simulation of SOC stocks changes of the French Soil Monitoring Network (FSMN) where only one measurement is done in 16 Km regular grid. These simulations on the grid will be in turn used for NIR. Preliminary results suggest that the model do not adequately simulate SOC stocks levels but succeeds at capturing SOC changes due to management, despite the fact that the model does not explicitly simulate some management such as tillage. This is probably due to inappropriate model parametrization especially for crops and thus Cinput in the French context and/or model initialization.