Task 1: Design of baseline, scenario and consistency check of various modelling tools
A first task is to collectively define a baseline scenario (i.e. a mid-term projection of the main exogenous drivers, such as climate, population, productivity) and two "shock" scenarios (i.e. policy actions that are treated as strong deviations from the baseline). The baseline and shock scenarios will make it possible to gauge the compatibility and complementarity of the different models developed by BASC under a range of conditions. Putting together a baseline scenario is complex since it is necessary to ensure consistency between a variety of parameters (e.g. resource growth, productivity and GDP). As decided in preliminary workshops, the baseline scenario will be based on a "business- as-usual" Shared Socio-economic Pathway (SSP) developed in the context of the IPCC (most likely the SSP2 scenario). For climate change, we will use CMIP5 climate projections that are consistent with the SSP, most likely based on the RCP 4.5 emissions pathway. This SSP2 scenario will be modified with additional scenarios exploring two different idealized "disruptive policy shocks". The first shock is a large-scale reduction of nitrogen fertilizers corresponding to European policies to reduce nitrogen pollution and potential increases in N fertilizer prices. The second shock is a large-scale expansion of bioenergy in Europe corresponding to policies that could be put in place to meet strong climate change mitigation targets set in the Paris Agreement on climate. These "shock" scenarios should be viewed as idealized extreme scenarios used to test the coupling between models.
1. Scenario 1: Baseline scenario + 50% reduction of nitrogen fertilizers in Europe
2. Scenario 2: Baseline scenario + large expansion of bioenergy in Europe. The extent and type of bioenergy deployment will be determined early in the project.
A list of input and output variables for each model, with their spatial dimension, units and formats has been put together (see the appendix). It shows that there are a number of obstacles and gaps that need to be overcome to develop a fully integrated suite of models. Issues include differences in spatial resolution and extent, as well as differences in temporal resolution. Making the modelling tools more consistent and building coherent "handshakes" between models requires both pragmatic approaches and methodological innovation. Software for disaggregation and aggregation (e.g., using enthropy methods) will be necessary to reconcile the framework used for trade, fiscal and environmental policies (MIRAGE model environment) with the representation of agricultural policy instruments (AROPAj and NLU) as far as spatial scale and entry inputs are concerned. The articulation of biophysical models (ORCHIDEE) with these models raises less technical problems, but experience in using ORCHIDEE and AROPAj together shows that there are several bottlenecks that need to be resolved. One is the articulation between different spatial and temporal scales. A second is the consistency with the animal and crop part of the models since preparatory work indicated a lack of consistency between modelled feedstuff production and consumption in the livestock sector across models, as well as lack of consistency with soil nutriments exports and manure/dejections between the crop and livestock sector.
Task 2: Supply response to the policy scenarios
In order to assess land use changes induced by the above defined policy scenarios, it is necessary to model the direct impact of the scenarios on agricultural yields and outputs, accounting for the biophysical drivers included in the baseline scenario, in particular with respect to climate change. While the supply response is not only driven by technical determinants (producers adjust the use of their variable inputs and the expected level of output to input and output prices), modeling the technology and in particular the change in response to the constraints imposed under the scenario is a crucial step. Hence, Task 2 includes the identification of the different crop and livestock production systems that would make it possible to cope best with the constraints imposed on EU agriculture. For this purpose, STIMUL will rely on the large set of knowledge developed by the "Agronomie Globale" team (large-scale agronomic databases, statistical models derived from meta-analyses, and simulations regarding the performances of crop systems at different scales). It is noteworthy that, in the current consortium, there are issues whose coverage is insufficient, in particular in the livestock sector and that some resources will need to be devoted to further analysis. A part of the solution could be the development of simulations using crop models (STICS, ORCHIDEE-CROP) to assess the potential production response to input changes. This task builds on previous work of BASC to specify the feedbacks between land use change and regional climate, and on the ongoing work on climate change impacts on biodiversity (see above).
Task 3: Direct impacts of policies on land use and production in Europe
This task includes the modeling of land allocation in Europe among its different uses that results from the baseline and shock scenarios. Land use changes will be based on the technical relations modeled in Task 2, the changing price environment, and the different drivers embedded in the baseline and shock scenarios. Agricultural product supply that is driven by profitability and relative prices will play a key role in these scenarios. The relative prices result from the matching of supply and demand responses in Europe to the exogenous shocks introduced in the "shock" scenarios. This matching results from changes in consumption and production that balances supply and demand at the European level, i.e. with exogenous world prices.
The AROPAj model provides results in agricultural land allocation among series of crops and fodders or grasslands associated to the different scenarios. Scenarios include EU Common Agricultural Policy (CAP) and environmental policies impacting the agricultural sector, such as policies promoting bioenergy. In the latter case, perennial bioenergy crops may be introduced in the model. Production changes result from land allocation (extensive margin effect) and from change in marginal productivity of the land in case of price change. Livestock may be directly affected by meat and milk prices as well as by change of marketed feed price and by change of implicit value of on-farm sourced feed.
Econometric models developed within UMR Economie Publique will be used in this task. These models provide results of the impacts of different policy and climate change scenarios on land use (Chakir and Parent, 2009; Chakir and Le Gallo, 2013). Those models were developed at the French scale and are now extended to the EU scale within the TRUSTEE-ERANET project in collaboration with researchers from CESAER-INRA-DIJON. The econometric models include land rents and pedoclimatic variables as input and provide the distribution of land use for agriculture/forest/urban under different scenarios: Climate Change, public policy, price shocks.
Task 4: Global price vector, feedbacks, net displacement factor and indirect land use changes
The modeling of global economic effects makes it possible to take into account changes in world prices when developing scenarios for Europe. The distortion of agricultural, food and energy component of world prices results in a feedback on both supply and demand. At the world level, this requires the simulation of a new equilibrium characterized by prices, trade, production, consumption and therefore land use. The indirect consequences on land use (iLUC for indirect Land Use Changes) will therefore be added (or subtracted) to the direct effects obtained under T3. This step requires a comprehensive representation of the new equilibrium, and hence the calculation of changes in demand, trade and production. The combination of MIRAGE and NLU would therefore make it possible to add the "indirect" dimension through global effects, with consequences in terms of land use changes. It will make it possible to obtain a spatial representation of the changes in land use both between agriculture, forestry and other uses as well as between the different agricultural uses and land intensification. To represent land-changes mechanisms as explicitly as possible, indirect consequences will also be decomposed among production changes (through international trade, changes in intermediary and final demand) and yield changes (land vs non-land inputs substitution).
Task 5: Indicators and impact assessment
The consequences of dLUC and iLUC on European land use that results from the baseline and shock scenarios will be assessed using a set of biophysical, economic and environmental indicators. This will be combined with the impacts of climate change based on one or more coherent sets of climate projections (see above).
On the economic side, indicators will include use of agricultural inputs (e.g., fertilizers and pesticides), agricultural outputs, trade and global welfare (equivalent variation/consumer surplus). Changes in greenhouse gases will be matched to LUC at a disaggregated level (based on the NLU disaggregation). Changes in water pollution will be modelled based on the input use based on the "water" module of the AROPAj model.
The ORCHIDEE model will be use to evaluate impacts of land use and climate change on carbon and water cycles at national and European scales, potential for carbon storage, as well as impacts on forest productivity, crop and pasture productivity. Biodiversity and ecosystem impacts of land use and climate change will be examined at several levels including the level of habitats (≈ land cover change), plant functional types (from ORCHIDEE) and species diversity including birds, local scale species richness, species turnover, biodiversity intactness (difference between modified and "natural" system) and large scale species richness.
An additional effort to assess the impact of the baseline and shock scenario on local pollution will be carried out at the national scale (France) using land use change projections from spatial econometrics (Ay et al. 2014). This will provide a much more rapid means of interaction between land use change and impacts models compared to using the full chain of more mechanistic economic models outlined in Tasks 1-4. This will play a key role in identifying the difficulties to overcome, as well as possibilities in terms of model interactions. It will also provide an evaluation of these couplings at fine spatial resolutions and make it possible to use detailed data for France.
Task 6: Linking with and supporting similar initiatives at the international level
Participants in this project are involved in coordination and in contributions to several international programs that include development and use of similar types of integrated modelling. This includes coordination and participation in activities within in Future Earth (e.g., bioDISCOVERY and iLEAPs projects, Future Earth modelling "clusters"); initiatives to develop ties between the climate and biodiversity scenarios and modelling communities supported by the Convention on Biological Diversity (CBD), IPBES (Intergovernmental Platform on Biodiversity and Ecosystem Services) and UNESCO; and activities to support IPCC assessments including SSP development and use for impact studies as well as collaboration with AgMIP (Agricultural Model Intercomparison Project) and EMF (Energy Modelling Forum - Stanford University).
Al-Riffai, P., Dimaranan, B. and Laborde, D. (2010). Global Trade an Environmental Impact Study of the EU Biofuels Mandate. , International Food Policy Research Institute (IFPRI).
Alkemade, R., Oorschot, M., Miles, L., Nellemann, C., Bakkenes, M. and Brink, B. ten (2009). GLOBIO3: A framework to investigate options for reducing global terrestrial biodiversity loss. Ecosystems 12: 374–390.
Ay, J.-S., R. Chakir, L. Doyen, F. Jiguet and P. Leadley (2014). Integrated models, scenarios and dynamics of climate, land use and common birds. Climatic Change 126.1-2 , pp. 13–30.
Ay, J.-S.; Chakir, R. & Le Gallo, J. (2015), 'Aggregated versus individual land-use models: Modeling spatial autocorrelation to increase predictive accuracy', revised and resubmitted to Environmental Modelling and Assessment.
Ben Fradj, N., Jayet, P. A., & Aghajanzadeh-Darzi, P. (2016). Competition between food, feed, and (bio)fuel: a supply-side model based assessment at the European scale. Land Use Policy, 52, 195-205.
Brunelle, T., Dumas, P., Souty, F., Dorin, B. and Nadaud, F. (2015), Evaluating the impact of rising fertilizer prices on crop yields. Agricultural Economics. doi : 10.1111/agec.12161.
Brunelle, T., Dumas, P., and Souty, F. (2014). The impact of globalization on food and agriculture : The case of the diet convergence. The Journal of Environment & Development.
Bourgeois, C., Habets, F., Jayet, P.-A., & Viennot, P. (2016). Estimating the marginal social value of agriculturally-driven nitrate concentrations in an aquifer: a combined theoretical-applied approach. Water Economics and Policy. accepted
Bourgeois, C., Ben Fradj, N., & Jayet, P.-A. (2014). How cost-effective is a mixed policy targeting the management of three agricultural N-pollutants? Environmental Modeling and Assessment, 19(5), 389-405.
Cantelaube, P., Jayet, P.-A., Carré, F., Zakharov, P., & Bamps, C. (2012). Geographical downscaling of outputs provided by an economic farm model calibrated at the regional level. Land Use Policy, 29, 35-44.
Chakir, R. (2009) "Spatial downscaling of Agricultural Land Use Data : An econometric approach using cross-entropy", Land Economics, 85(2), 238-251.
Chakir, R. and Le Gallo, J. (2013) : "Predicting land use allocation in France : a spatial panel data analysis", Ecological Economics, vol 92, 114–125.
Chakir, R. et Vermont, B. (2013): "Analyse des changements d'allocation des sols et des émissions de gaz à effet de serre liées au développement des biocarburants en France". Rapport d'une étude financée par l'ADEME. Rapport final, 72 pages.
Chakir, R., De Cara, S. and Vermont, B. (2011) :"Emissions de gaz à effet de serre dues à l’agriculture et aux usages des sols en France : une analyse spatiale", Économie et Statistique; N 444-445.
Chakir, R., Parent, O (2009) "Determinants of land use changes : a spatial multinomial probit approach". Papers in Regional Science, 88(2).
Chakir, R. & Lungarska, A. (2016), 'Agricultural rent in land use models: Comparison of frequently used proxies', under revision.
De Cara, S., Goussebaile, A., Grateau, R., Levert, F., Quemener, J. and Vermont, B. (2012). Revue critique des études évaluant l’effet des changements d’affectation des sols sur les bilans environnementaux des biocarburants. , ADEME.
De Cara, S. & Jayet, P.-A. (2011). Marginal abatement costs of greenhouse gas emissions from European agriculture, cost effectiveness, and the EU non-ETS burden sharing agreement. Ecological Economics, 70, 1680-1690.
Galko, E. & Jayet, P.-A. (2011). Economic and environmental effects of decoupled agricultural support in the EU. Agricultural Economics, 42, 605-618.
Hertel, T., Steinbuks, J. and Baldos, U. (2013). Competition for land in the global bioeconomy. Agricultural Economics 44: 129–138.
Humblot, P., Leconte-Demarsy, D., Clerino, P., Szopa, S., Castell, J.-F., & Jayet, P.-A. (2013). Assessment of ozone impacts on farming systems: A bio-economic modeling approach applied to the widely diverse French case. Ecological Economics, 85, 50-58
Jayet, P. A. & Petel, E (2016) Economic valuation of the nitrogen content of urban organic residue by the agricultural sector. Ecological Economics, 120, 272-281.
Jayet, P.-A. & Petsakos, A. (2013). Evaluating the efficiency of a uniform N-input tax under different policy scenarios at different scales. Environmental Modelling and Assessment, 18, 57-72
Laborde, D. and Valin, H. (2012). Modeling land-use changes in a global CGE: assessing the EU biofuel mandates with the Mirage-BioF model. Climate Change Economics 3.
Lambin, E. F. and Meyfroidt, P. (2011). Inaugural article: Global land use change, economic globalization, and the looming land scarcity. Proceedings of the National Academy of Sciences 108: 3465–3472.
Leclère, D., Jayet, P.-A., & de Noblet Ducoudré, N. (2013). Farm-level autonomous adaptation of European agricultural supply to climate change. Ecological Economics, 87, 1-14.
May, R. M. (2000). Species-area relations in tropical forests. Science 290: 2084–2086.
Myers, N., Mittermeier, R. A., Mittermeier, C. G., Fonseca, G. A. B. da and Kent, J. (2000). Biodiversity hotspots for conservation priorities. Nature 403: 853–858.
Phalan, B., Onial, M., Balmford, A. and Green, R. E. (2011). Reconciling food production and biodiversity conservation: Land sharing and land sparing compared. Science 333: 1289–1291.
Plevin, R. J., O’Hare, M., Jones, A. D., Torn, M. S. and Gibbs, H. K. (2010). Greenhouse gas emissions from biofuels’ indirect land use change are uncertain but may be much greater than previously estimated. Environmental Science & Technology 44: 8015–8021.
Plevin, R. J.; Beckman, J.; Golub, A. A.; Witcover, J. & O'Hare, M. (2015). Carbon accounting and economic model uncertainty of emissions from biofuels-induced land use change Environmental Science & Technology, 49, 2656–2664
Searchinger, T., Heimlich, R., Houghton, R. A., Dong, F., Elobeid, A., Fabiosa, J., Tokgoz, S., Hayes, D. and Yu, T.-H. (2008). Use of U.S. croplands for biofuels increases greenhouse gases through emissions from land-use change. Science 319: 1238–1240.
Souty, F., Brunelle, T., Dumas, P., Dorin, B., Ciais, P., Crassous, R., Muller, C., and Bondeau, A. (2012). The Nexus Land-Use model version 1.0, an approach articulating biophysical potentials and economic dynamics to model competition for land-use, Geosci. Model Dev
Sutton, M., Howard, C. and Erisman, J. (eds) (2011a). The European Nitrogen Assessment: Sources, Effects and Policy Perspectives. Cambridge University Press. See also Sutton, M. A., Oenema, O.,
Erisman, J. W., Leip, A., Grinsven, H. van and Winiwarter, W. (2011b). Too much of a good thing. Nature 472: 159–161.
Ce site utilise des cookies afin de vous proposer des vidéos, des boutons de partage, des remontées de contenus de plateformes sociales et des contenus animés et interactifs.
En savoir plus
A propos des cookies
Qu’est-ce qu’un « cookie » ?
Un "cookie" est une suite d'informations, généralement de petite taille et identifié par un nom, qui peut être transmis à votre navigateur par un site web sur lequel vous vous connectez. Votre navigateur web le conservera pendant une certaine durée, et le renverra au serveur web chaque fois que vous vous y re-connecterez.
Différents types de cookies sont déposés sur les sites :
Cookies strictement nécessaires au bon fonctionnement du site
Cookies déposés par des sites tiers pour améliorer l’interactivité du site, pour collecter des statistiques
Les différents types de cookies déposés sur ce site
Cookies strictement nécessaires au site pour fonctionner
Ces cookies permettent aux services principaux du site de fonctionner de manière optimale. Vous pouvez techniquement les bloquer en utilisant les paramètres de votre navigateur mais votre expérience sur le site risque d’être dégradée.
Par ailleurs, vous avez la possibilité de vous opposer à l’utilisation des traceurs de mesure d’audience strictement nécessaires au fonctionnement et aux opérations d’administration courante du site web dans la fenêtre de gestion des cookies accessible via le lien situé dans le pied de page du site.
Cookies techniques
Nom du cookie
Finalité
Durée de conservation
Cookies de sessions CAS et PHP
Identifiants de connexion, sécurisation de session
Session
Tarteaucitron
Sauvegarde vos choix en matière de consentement des cookies
12 mois
Cookies de mesure d’audience (AT Internet)
Nom du cookie
Finalité
Durée de conservation
atid
Tracer le parcours du visiteur afin d’établir les statistiques de visites.
13 mois
atuserid
Stocker l'ID anonyme du visiteur qui se lance dès la première visite du site
13 mois
atidvisitor
Recenser les numsites (identifiants unique d'un site) vus par le visiteur et stockage des identifiants du visiteur.
13 mois
À propos de l’outil de mesure d’audience AT Internet :
L’outil de mesure d’audience Analytics d’AT Internet est déployé sur ce site afin d’obtenir des informations sur la navigation des visiteurs et d’en améliorer l’usage.
L‘autorité française de protection des données (CNIL) a accordé une exemption au cookie Web Analytics d’AT Internet. Cet outil est ainsi dispensé du recueil du consentement de l’internaute en ce qui concerne le dépôt des cookies analytics. Cependant vous pouvez refuser le dépôt de ces cookies via le panneau de gestion des cookies.
À savoir :
Les données collectées ne sont pas recoupées avec d’autres traitements
Le cookie déposé sert uniquement à la production de statistiques anonymes
Le cookie ne permet pas de suivre la navigation de l’internaute sur d’autres sites.
Cookies tiers destinés à améliorer l’interactivité du site
Ce site s’appuie sur certains services fournis par des tiers qui permettent :
de proposer des contenus interactifs ;
d’améliorer la convivialité et de faciliter le partage de contenu sur les réseaux sociaux ;
de visionner directement sur notre site des vidéos et présentations animées ;
de protéger les entrées des formulaires contre les robots ;
de surveiller les performances du site.
Ces tiers collecteront et utiliseront vos données de navigation pour des finalités qui leur sont propres.
Accepter ou refuser les cookies : comment faire ?
Lorsque vous débutez votre navigation sur un site eZpublish, l’apparition du bandeau « cookies » vous permet d’accepter ou de refuser tous les cookies que nous utilisons. Ce bandeau s’affichera tant que vous n’aurez pas effectué de choix même si vous naviguez sur une autre page du site.
Vous pouvez modifier vos choix à tout moment en cliquant sur le lien « Gestion des cookies ».
Vous pouvez gérer ces cookies au niveau de votre navigateur. Voici les procédures à suivre :
Pour obtenir plus d’informations concernant les cookies que nous utilisons, vous pouvez vous adresser au Déléguée Informatique et Libertés de INRAE par email à cil-dpo@inrae.fr ou par courrier à :
INRAE 24, chemin de Borde Rouge –Auzeville – CS52627 31326 Castanet Tolosan cedex - France