<figure><img src="image_73.png" /><figcaption>**Figure 57.** Greenhouse gas emissions for different levels of the mitigation tax (source Lungarska & Chakir, 2018)</figcaption></figure>

Figure 57 presents the obtained results in terms of greenhouse gas emissions and tax levels for the three climate scenarios. The tax impacts agricultural profits and, as expected, agricultural crops area is decreasing when taxation applies. Thus, the tax has both an impact on the intensive margin (lower emissions levels per hectare), and on the extensive margin (less area in agricultural activity). The effect on the extensive margin is given in figure 57 by the gap between the two emission curves with and without the land use change feedback effect.

Greenhouse gas emissions from French agriculture are to be reduced by 12% in 2028 comparing to 2013 levels. This objective is attained at 30€/tCO₂ eq. in the CTL case (40€/tCO₂ eq. when land use feedback is ignored); 90€/tCO₂ eq. in the A2 case (120€/tCO₂ eq. when land use feedback is ignored); and 90€/tCO₂ eq. in the B1 case (130€/tCO₂ eq. when land use feedback is ignored). We can thus conclude that climate change potentially would impact favorably agricultural profits, but can have negative impacts in terms of greenhouse gas emissions (and other pollutants associated with intensive agriculture such as nitrates), and can result in the loss of permanent grasslands and forests.
*EU* the European Union

*EU* expected utility

*Eurostat* is the statistical office of the *EU* situated in Luxembourg

*EUROTOOLS* research projet funded by the *EC* 5th FrameWork program

*E-V* refers to mean-variance models (“mean” or “esperance”)

*FADN* the farm accounting data network, based on national surveys managed by all *EU* member States, and managed bu the *EC*

*FAME* Fatty Acid Methyl Ester

*FAO* Food and Agriculture Organization of the United Nations

*FAOStat* the statistic division of the *FAO*

*farm group* refers to *AROPAj* clusters of *FADN* surveyed farms

*farm type* first refers to the *FADN* classification of the surveyed farms; it belongs to clustering criteria providing the *AROPAj* farm groups; it may be used instead of *farm group*

*FT* see *farm type* or *OTEX*

*GAMS* software used for implementation and solving of optimization programs

*GCE* generalized cross-entropy

*GCM* General Circulation Models or Global Climate Models

*GDAL* open source translator library for raster and vector geospatial data

*GDP* gross domestic product

*GENEDEC* acronym of one of research framework programmes granted by the *EC*, which supported many of *AROPAj* developments

*GeoMIRET* name of the *TIMES* model developed by *Ifpen* (acronym from “GEOgraphical Model Integration for (Renewable) Energies and Transport”)

*GHG* greenhouse gas, knowing that major agriculture sourced *GHG* are *N₂O* (nitrous oxide) and *CH₄* (methane)

*GIRAF* model of the European biofuel transformation sector

*GIS* geographical information system

*GLOBIOM* partial equilibrium model focusing on agriculture and forest land uses developed at *IIASA*

*GME* generalized maximum entropy

*GNU* Unix-like computer operating system intended to be Unix-compatible. The acronym stands for "GNU's Not Unix"

*GPL* The General Public License designed to deal with *GNU* products

*GTOPO30* a US geographical survey model used for *DEM*

*GWC* green waste compost

*GWP* global warming potential, defined for any GHG in terms of *CO₂* equivalent given the time horizon

*HVO* hydrotreated vegetable oil

*IEA-ETSAP* International Energy Agency - Energy Technology Systems Analysis Programme

*Ifpen* formerly *Ifp* as Institut FranÇais du PÂtrole, adding now new and renewable energy sources in its working area

*IFPRI* International Food Policy Research Institute (based in Washington)
# 13 Perspectives and further developments

**Key-point:**

Some challenging issues under study

This section deals with recently initiated or discussed research on *AROPAj* capabilities. That results from demand expressed by scientific or societal partners, or from researchers’ initiatives.

In 2015, the most important challenge involves the water issues of increasing and competing agricultural demand for water, and moreover, environmental issues and quality problems. The increase in demand would result from climate change and climate change adaptation, when agriculture will face water stress conditions more often and with greater intensity (see the caveats and the conclusions following the paper of Leclère et al. (2013)). Environmental issues related to water have been approached, mainly with regards to nitrates, and their analysis could be improved when accounting for water scarcity. Both qualitative and quantitative water issues will be key points in the future.

Questions about organic and “low input” farming systems are also very often adressed. There is a possible discrepancy between what consumers and policy makers expect for inputs and yields and what organic farmers are yielding. Indeed statistics are limited. We need to pay attention to the risky extrapolation of the favorable figures due to the management provided by pro-active, motivated and well-qualified bio-farmers to traditional growers and breeders converted in bio-farmers. Nevertheless, a prospective analysis demands that we be innovative in terms of scenarios and potential changes so that we evaluate even the optimistic of situation.

Many other issues are put on the table as well. *AROPAj* could be coupled with other bio-physical models to account for phosphorus, pesticides, and other major inputs that acts as major pollutants. At present, there is a large gap between what we require as information and data and what is provided. Moreover, it would be necessary to deal with other models than those already experimented.

Finally, we must develop a way to validate not only the *AROPAj* model, but also the entire modeling chains. This critical point is usually mentioned by articles’ readers and addressed by potential users. Nevertheless, we must remember that *AROPAj*, as a normative, economic and prospective model, cannot be validated through repeated observations in controlled conditions as it is done with physical, chemical or biological models. The calibration step is a part of the response to these issues. Publication of peer-reviewed papers and comparison of models are another part. Finally, the improvement of the model by including broader phenomena and processes allows us to “check with reality”. We present below some future extensions of the model.

## 13.1 Water issues

The adaptation of agriculture to climate change and growing demand for water by a range of users entails the question of water availability and pricing. A large part of renewable water (from rainfalls) used by agriculture circulates outside the market. Irrigation is and will become an even more pressing problem. But even in this case, costs related to water extraction (pumping and other investment or variable costs) remain higher than those related with the resource itself.
IIASA International Institute for Applied Systems Analysis (Austria)

IMPACT International Model for Policy Analysis of Agricultural Commodities and Trade (model conceived by IFPRI)

INRA Institut National de la Recherche Agronomique, becomes INRAE in 2020

INRAE Institut national de recherche pour l'agriculture, l'alimentation et l'environnement (see INRA)

INSEA "Integrated Sink Enhancement Assessment", a shared-cost action financed through the 6th Framework Program of the European Commission.

IPCC International Panel for Climate Change

JRC Joint Research Center (Ispra, Italy; Seville, Spain), related to the European Commission

LAI leaf area index

Linux OS supporting the AROPAj model use and development (from 2014)

LP linear programming, or linear program

LS least squares

LS livestock

LU (or LSU) livestock Unit: used for animal accounting based on FADN equivalence factors (the parameter iii(i) in AROPAj)

LUCAS refers to point land use data (Land Use/Cover Area frame Statistical Survey), developed by the JRC

MACC marginal abatement cost curve

MARKAL stands for "MARKet ALlocation computer model generator"

MARS unit of the JRC in charge of climate data

ME maximum entropy

MIP mixed integer programming

MNL refers to the "multinomial logit" statistical model

MODCOU hydrogeological model devoted to soil-aquifer transfers and related to the Seine river bassin

MP mathematical programming

MS Member States (of the EU)

N nitrogen

N₂O nitrous oxide

nexBtL a renewable diesel fuel production commercial process, see also BtL

NH₃ ammonia

NLP non linear programming

NO₃ nitrate

NUTS Nomenclature of Units for Territorial Statistics, refers to the European territorial classification

O₃ ozone

objective function refers to the function to be maximized (or minimized) in mathematical programming

OLS ordinary least squares

OM organic matter

OS operating system (AROPAj was developed under Unix until 2013, then Linux)

OTEX refers to the French "orientation technico-Â-Â¿Â½économique de l'exploitation" (see also FT)
An innovative mix of crop modeling and economic analysis is under progress with the aim of providing two-input yield response functions. These two inputs are nitrogen (through mineral fertilizers or manure sourced organic matter) and water. Water is taken into consideration as the command variable from the farmer's economic point of view. Thus, the producer may call for irrigation when rainfalls do not provide water in sufficient quantities in terms of the potential costs and benefits of additional water.

$$y(W,N) = (1 - e^{-\sigma(W_0 + W)}) (1 - e^{-\tau(N_0 + N)}) \phi$$

<figure><img src="image_74.png" /><figcaption>**Figure 58.** The yield function as annual response to water and nitrogen inputs; water and nitrogen refer to the total annual quantities including natural resource; the blue line refers to an isoquant in yields.</figcaption></figure>

The link between the crop model sourced functions and the economic analysis is complex. The demand for water (expressed in cubic meters or *mm* height per hectare, 1mm being equivalent to 10 cubic meters) is translated in water stress for the crop model simulation. For one period, the two-input function is illustrated by Figure 58. The method of response function building follows the method initiated by Godard et al. (2008) and expanded by the E. Galko's and D. LeclA¨re's PhD work. The two-input approach is much more complex to develop, but difficulties are compensated by database availability and increased computing capabilities. It is implemented and explored in the P. Humblot's PhD thesis (May 2016). Methodological aspects and implementation are described by Humblot et al. (2017).

A multi-scale assessment of water allocation is conducted in Reineix & Jayet (2025), exploiting the different levels of analysis afforded by the AROPAj model. The analysis explicitly accounts for resource scarcity. Building on a theoretical relationship embedded in the model, which characterizes the implicit value of water, an additional water shadow price is derived and applied to each irrigated crop within the irrigating farms of a region.

From a modeling standpoint, the study implements an iterative algorithm to determine, in a pragmatic manner, the convergent shadow prices endogenously associated with agricultural outputs and irrigation water (59).
* *OURSE* the energy demand model developed by *Ifpen*

* *OURSEurope* the energy demand model developed for Europe and derived from *OURSE*

* *PAM* refers to the “Partitioning Around Medoids” algorithm

* *PaSim* process-based grassland biogeochemical model
(https://www1.clermont.inra.fr/urep/modeles/pasim.htm)

* *PDIE* proteins digestible by the small intestine due to the energy provided by feed

* *PDIN* proteins digestible by the small intestine due to the nitrogen provided by feed

* *PET* potential evapo-transpiration

* *PMP* positive mathematical programming

* *POSTGreSQL* software for data management

* *POSTGIS* allows for interfacing *POSTgres* and geographical information system procedures

* *primal variable* refers to an explicit variable of a mathematical programming model

* *psql* query language interface for working with *POSTGreSQL*

* *R* a GNU programming language and software environment for statistical computing

* *RCA3* one of the Regional Climate Models (*RCM*)

* *RCM* regional climate models

* *RCP* representative concentration pathway, built by the *IPCC* for its 5th assessment report

* *RHS* right-hand side (stands for *LP*)

* *Roth-C* model to simulate change in organic matter stock on the long term (explanation about *RothC* and *Carbo-Pro*)

* *ROW* stands for “Rest of the World”

* *RUMINANT* a dynamic model simulating the process of ruminant digestion

* *sed* see *awk*

* *SFP* single farm payment

* *shadow price* refers to a mathematical programming constraint and to the optimal value of the limited resource related to this constraint (also shadow cost, implicit price or cost, dual value, Lagrange multiplier)

* *SimU* simulation unit (refers to spatial modeling tools)

* *SRES* special report on emissions scenarios, shared into 4 scenario families (i.e. *A-B × 1-2*) developed by the *IPCC* for its 4th report

* *SSP* stands for Shared Socio-economic Pathway (related to the *IPCC* *AR5*)

* *STICS* crop model, access *STICS*

* *TIMES* stands for “The Integrated *MARKAL-EFOM* System”

* *UNFCCC* United Nations Framework Convention on Climate Change: here access the *UNFCCC* site (provides numerous data on *GHG* emissions per country per year per sector)

* *UAA* utilized agricultural area

* *UFL* unit of energy content in fodder for dairy herds

* *UFV* unit of energy content in fodder for non dairy herds

* *Unix* OS under which *AROPAj* was formerly developed

* *UOR* urban organic residues

* *USS* urban sewage sludge

* *UZ* unsaturated zone (hydrogeology)

* *WLS* weighted least squares
<figure><img src="image_75.png" /><figcaption>**Figure 59.** Algorithm for calculating shadow costs related to on-farm crop use and water limit, see Reineix & Jayet (2025)</figcaption></figure>
Communication

Different elements are provided. In addition to scientific papers in peer-reviewed journals (see
a part of references below for scientific papers related to AROPAj), communicative papers have
also been developed. They are of different types, as developed below:

1. Lungarska, A., Brunelle, T., Chakir, R., Jayet, P.-A., Prudhomme, R., De Cara, S., & Bureau, J.-C. (2023). Halving mineral nitrogen use in European agriculture: Insights from multi-scale land-use models. *Applied Economic Perspectives and Policy*, 12

2. Gérard, M. & Jayet, P. (2023). European farmers' response to crop residue prices and implications for bioenergy policies. *Energy Policy*. https://doi.org/10.1016/j.enpol.2023.113561

3. Devaraju, N., Prudhomme, R., Lungarska, A., Wang, X., Yin, Z., de Noblet-Ducoudré, N., Chakir, R., Jayet, P.-A., Brunelle, T., Viovy, N., De Palma, A., Gonzalez, R., & Ciais, P. (2022). Quantifying the benefits of reducing synthetic nitrogen application policy on ecosystem carbon sequestration and biodiversity. *Scientific Reports* 2022 12:1, 12(1), 1–15

4. Prudhomme, R., Chakir, R., Lungarska, A., Brunelle, T., Devaraju, N., de Noblet, N., Jayet, P.-A., De Cara, S., & Bureau, J.-C. (2022). Food, climate and biodiversity: a trilemma of mineral nitrogen use in European agriculture. *Review of Agricultural, Food and Environmental Studies*

5. Gossiaux, E. & Jayet, P.-A. (2021). Exploring the potential for calories and bioenergy in france. *BE-Sustainable Magazine*, 12, 11–17. https://issuu.com/besustainablemagazine/docs/be-sustainable_magazine issue_12-april_2021/s/12150135

6. Bamière, L., Jayet, P.-A., Kahindo, S., & Martin, E. (2021). Carbon sequestration in french agricultural soils: a spatial economic evaluation. *Agricultural Economics*. doi: https://doi.org/10.1111/agec.12619

7. Isbasoiu, A., Jayet, P.-A., & De Cara, S. (2020). Increasing food production and mitigating agricultural greenhouse gas emissions in the european union: impacts of carbon pricing and calorie production targeting. *Environmental Economics and Policy Studies*, (pp. 32p). doi: https://doi.org/10.1007/s10018-020-00293-4

8. Barberis, D., Humblot, P., Chiadmi, I., Jayet, P.-A., Lungarska, A., & Ollier, M. (2020). Climate change and irrigation water: Should the north/south hierarchy of impacts on agricultural systems be reconsidered? *Environmental Modeling and Assessment*. doi: https://doi.org/10.1007/s10666-020-09724-8

9. Jayet, P.-A., Isbasoiu, A., & De Cara, S. (2020). Slaughter cattle to secure food calories and reduce agricultural greenhouse gas emissions? some prospective estimates for france. *Review of Agricultural, Food and Environmental Studies*, (pp. 24p). doi: https://doi.org/10.1007/s41130-020-00117-9

10. Ben Fradj, N., Jayet, P.-A., Rozakis, S., Georganta, E., & Jedrejek, A. (2020). Contribution of agricultural systems to the bioeconomy in poland: Integration of willow in the context of a stylised cap diversification. *Land Use Policy*, 99, 24p. doi: https://doi.org/10.1016/j.landusepol.2020.104797

11. Bayramoglu, B., Chakir, R., & Lungarska, A. (2020). Impacts of land use and climate change on freshwater ecosystems in france. *Environmental Modeling and Assessment*, 25, 147–172. doi: https://doi.org/10.1007/s10666-019-09673-x

12. Lungarska, A., Chakir, R., Ben Fradj, N., Jayet, P.-A., Bamière, L., Isbasoiu, A., Ollier, M., Gossiaux, E., Chiadmi, I., De Cara, S., & Kahindo, S. (2020). Approche intégrée des productions animales et végétales dans un modèle économique pour l’analyse des interactions agriculture – environnement. *Innovations Agronomiques*, 80, 69–85

13. Mosnier, C., Britz, W., Jullière, T., De Cara, S., Jayet, P.-A., Havlik, P., Franck, S., & Mosnier, A. (2019). Greenhouse gas abatement strategies and costs in dairy production – a comparison across bio-economic models. *Journal of Cleaner Production*, (236), 17589. doi: 10.1016/j.jclepro.2019.07.064

14. Jayet, P.-A., Barberis, D., Humblot, P., & Lungarska, A. (2018). Spatialisation de la demande en eau d'irrigation estimée par un modèle bioéconomique. *Revue internationale de géomatique*, 4(28), 485-503. doi: 10.3166/rig.2018.00064

15. Ben Fradj, N. & Jayet, P.-A. (2018). Optimal management of perennial energy crops by farming systems in france: A supply-side economic analysis. *Biomass and Bioenergy*, (116), 113-121. doi: 10.1016/j.biombioe.2018.06.003

16. De Cara, S., Henry, L., & Jayet, P.-A. (2018). Optimal coverage of an emission tax in the presence of monitoring, reporting, and verification costs. *Journal of Environmental Economics and Management*, 89, 1–23. doi: 10.1016/j.jeem.2018.03.001
## 13.2 Low input agriculture

The first attempt to analyze the performances of low input farming systems was carried out at a small watershed level, at which only a few organic farming systems currently exist. Economic performance was assessed through the standard criterion of gross margin. The environmental performance was judged through subsoil nitrogen losses. However, this kind of experimental approach suffers from a lack of reference points of comparison with traditional farming systems.

At this point, the first difficulties which arise concern the definition of organic farming, based on product labeling and farming practices. This indeterminacy of definition may be reflected in existing databases. Secondly, the European economic databases, such as the FADN, still provide too little information for parameter estimation.

As for climate change and other long-term issues, the use of models might provide very useful tools for overcoming the lack of experiments and observations. There is room and demand for the combination of validated bio-physical models and economic analysis, for instance, in the case of using large amount of organic matter as nitrogen intakes for crops. Such possibilities were tested by the Carbo-PRO and STICS based analyses (see Section 9.2.1). Nevertheless, in this case, organic sourced nitrogen acts as a supplement for mineral fertilizers. The analysis could be completed with simulations that exclude mineral nitrogen intakes, but bio-farming entails more than organic nitrogen sources. It refers also to other restrictive practices which need statistical surveying. Renewed databases and model based results remain a limiting but exciting factor for progress in this direction.

## 13.3 Facilitating MP solutions involving interacting agents

The AROPAj model is based on individual mathematical programming LP which are solved independently of each other as long as individual activities and constraints are separated (in other words in case of “orthogonal” individual matrices). When considering that resources are shared by several agents, there is possibility to find the aggregated optimal solution of the aggregated mathematical program, for instance by trial-and-error process until dual values related to the resource constraints are equal. Nevertheless in many cases such processes are of quite high computation cost.

Software are now able to solve MP problems involving huge size matrices. Even when considering that computation time increases with matrix size much more than linearly, there is strong potential gain to solve directly this kind of mathematical programs. The challenge is to preserve the benefit of existing “matrix generator generator” and to keep operating applications developed around the AROPAj model. The locking point is the indexation of farm groups’ elements, which is reset for each sub-country.<fnref n="14" />

There is now the possibility to aggregate farm group matrices emanating from several EU countries, up to the EU at a whole. This is of interest when MP constraints involve activities from different farm groups. We define a specific *T*-block operating in the kernel of the model. (see Section 4.2 on page 27) The particular design of such constraints is that the related RHS elements are not farm group-indexed. Examples of this are targeted limits in agricultural production (e.g.

<footnote n="14">¹⁴Indeed the point arises when one EU member State is subdivided into two or more sub-countries, which is automatically performed in the farm group clustering step for countries with large sample size FADN database.</footnote>
17. Lungarska, A. & Chakir, R. (2018). Climate-induced Land Use Change in France: Impacts of Agricultural Adaptation and Climate Change Mitigation. *Ecological Economics*, 147, 134–154

18. Bounaffaa, M., Florio, A., Le Roux, X., & Jayet, P.-A. (2018). Economic and environmental analysis of maize inoculation by plant growth promoting rhizobacteria in the french rhône-alpes region. *Ecological Economics*, 146, 334–346. doi: 10.1016/j.ecolecon.2017.11.009

19. Jayet, P. A., Barberis, D., & Humblot, P. (2017b). The economic impact of climate change on agricultural water demand in france. In D. Kocasevic (Ed.), *Book of proceedings - VIII International Scientific Agriculture Symposium* (pp. 2032–2038).: AgroSym 2017

20. Chakir, R. & Lungarska, A. (2017b). Agricultural rent in land-use models: comparison of frequently used proxies. *Spatial Economic Analysis*, 12, 379–303. doi: 10.1080/17421772.2017.1273542

21. Humblot, P., Jayet, P.-A., & Petsakos, A. (2017). Farm-level bio-economic modeling of water and nitrogen use: Calibrating yield response functions with limited data. *Agricultural Systems*, 151, 47–60. doi: 10.1016/j.agsy.2016.11.006

22. Lungarska, A. & Jayet, P. A. (2016). Impact of spatial differentiation of nitrogen taxes on french farms' compliance costs. *Environmental and Resource Economics*. doi: 10.1007/s10640-016-0064-9

23. Assaiante, C., Jayet, P.-A., & Lantz, F. (2016). From raw material to biofuel demand: the agraf model. In A. R. E. Sabine Sauvage, José-Miguel Sánchez-Pérez (Ed.), *8th International Congress on Environmental Modelling and Software*. http://scholarsarchive.byu.edu/iemssconference/2016/Stream-A/15

24. Jayet, P. A., Aghajanzadeh-Darzi, P., & Petsakos, A. (2017a). Improvement of a bio-economic mathematical programming model in the case of on-farm source inputs and outputs. *Journal of Quantitative Economics*, 15, 489–508. doi: 10.1007/s40953-016-0058-z

25. Aghajanzadeh-Darzi, P., Martin, R., Laperche, S., & Jayet, P.-A. (2016). Climate change impacts on european agriculture revisited - adding the economic dimension of grasslands. *Regional Environmental Change*. doi: 10.1007/s10113-016-1018-z

26. 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*. doi: 10.1142/S2382624X16500211

27. 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. 10.1016/j.landusepol.2015.12.027

28. Jayet, P. A. & Petel, E. (2015). Economic valuation of the nitrogen content of urban organic residue by the agricultural sector. *Ecological Economics*, 120, 272–281

29. 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

30. 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

31. 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

32. Aghajanzadeh-Darzi, P. & Jayet, P. A. (2013). Impact économique du changement climatique sur l'alimentation animale et l'élevage dans l'union européenne. *Fourrages*, (215), 81–90

33. Ciaian, P., Espinosa, M., Gomez y Paloma, S., Heckelei, T., Langrell, S., Louichi, K., Sckokao, P., Thomas, A., & Vard, T. (2013). Farm level modelling of CAP: a methodological overview. Technical report, JRC Scientific and policy reports. doi:10.2791/86415

34. 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

35. 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

36. 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

37. Galko, E. & Jayet, P.-A. (2011). Economic and environmental effects of decoupled agricultural support in the eu. *Agricultural Economics*, 42, 605–618
minimal food-calorie amount), or environmental issue (e.g. maximal amount of emitted pollutants). There is a real gain by doing so when the problem of interest implies at least two kinds of not linearly correlated trans-farm group constraints. Otherwise, the standard individual *MP* should be better solved for all involved farm groups when parameterizing the “price” of the bounded resource. But even in this case, there is the advantage of an easier implementation of the “dual” approach, which directly provides the dual price of a bounded resource utilized by several economic agents (implicitly clearing a quota market).

A processing application is encoded in *FORTRAN* resetting automatically the list of suffices and the indexation of farm group-indexed parameters, independently from member-States involved in a given calculation. By doing so there is no need to change the kernel of the model (i.e. no required re-writing of basic blocks of *MP* activities and constraints). The counterpart is the need to manage the complete *MP* solution provided by *GAMS*. Nevertheless the *MP* result exploitation is facilitated by indexation specifying the sub-country and the farm group within the sub-country.

This application has been tested in solving the *MP* made of all *EU* farm groups in the case of three trans-group constraints. Solution is exhibited in reasonably required computer time and space limits on our multi-core workstation.

## 13.4 Anchoring the use of the STICS crop model and the calculation of probability farm group location to the core of AROPAj computing applications

The two aspects presented here are run-time costly, and consequently require friendly-user and repeatedly utilized applications which need to be carefully carried out.

### Building dose-response functions

The production of input-yield functions for a wide set of crops and farm groups in different climatic scenarios mobilizes computation resources when dealing with million runs of the STICS crop model. This is two-fold dimension problem referring first to databases and second to the use of the crop model by itself. Computing interfaces are developed to manage the different steps leading to STICS run management, yield function adjustment and selection in present time and in future climate context (see the section 13.1).

Applications made of *Python* encoded programs were tested for the V5-AROPAj version applied to France and the 2009-*FADN*. Refining and running the applications is conducted for the EU and *FADN*-years 2012-2018. Yield functions are provided as well as nitrogen-loss function, losses referring to the three pollutants $N_2O$, $NO_3$ and $NH_3$.

### Mapping farm groups at an infra-regional resolution level

The method presented in Section 8.3 is now tested for the whole of the *EU* and for the 6 *FADN*-years of the V5-AROPAj version. The link to the menu accessing the model use and surrounding computer applications is now validated (but reserved to the model's administrator). Applications are encoded in *CShell*, *R* and *GAMS* scripts.

Issues still pending refer to different kind of aspects:

* from the region list and to crop location priors in all *FADN*-year cases: a few regions are not properly referred to basic spatial information, e.g. 580-Canarias (Spain), 650-Açores e
38. Durandeau, S., Gabrielle, B., Godard, C., Jayet, P.-A., & Le Bas, C. (2010). Coupling biophysical and microeconomic models to assess the effect of mitigation measures on greenhouse gas emissions from agriculture. *Climatic Change*, 98, 51–73

39. De Cara, S. & Thomas, A. (2008). *Projections d'émissions/absorptions de gaz à effet de serre dans les secteurs forêt et agriculture aux horizons 2010 et 2020*. Technical report. 192 pp

40. Godard, C., Roger-Estrade, J., Jayet, P.-A., Brisson, N., & Le Bas, C. (2008). Use of available information at a european level to construct crop nitrogen response curves for the regions of the eu. *Agricultural Systems*, 97, 68–82

41. De Cara, S. & Jayet, P.-A. (2006). Mitigation of greenhouse gas emissions in EU agriculture: An assessment of the costs of reducing agricultural emissions and enhancing carbon sinks in agricultural soils. In *INSEA Report SSP1-CT-2003-503614-Final, European Commission, INSEA, IIASA, Laxenburg, Austria*

42. Jayet, P.-A. (2006). *Deliverable 4*. Technical report, GENEDEC project document

43. Bambière, L., Godard, C., Debove, E., De Cara, S., Jayet, P.-A., & Niang, B. (2005). Interface between agriculture and the environment: integrating yield response functions in an economic model of EU agriculture. In *Modelling agricultural policies: State of the Art and New Challenges, Proceedings of the 89th European Association of Agricultural Economists, Parma, Italy, Feb. 3-5, 2005*

44. Jayet, P.-A. & Labonne, J. (2005). Impact d'une réforme de la politique agricole commune par le découplage. *Economie et Prévision*, 167(1), 101–116

45. De Cara, S., Houzé, M., & Jayet, P.-A. (2005). Methane and nitrous oxide emissions from agriculture in the EU: a spatial assessment of sources and abatement costs. *Environmental and Resource Economics*, 32(4), 551–583

46. Jayet, P. A. (2002). Mathematical programming for the computation of multi-market equilibria: an attempt in modelling the french markets of cereals and livestock feeds. In S. Rozakis & J. Sourie (Eds.), *Comprehensive economic and spatial bio-energy modelling*, number 48 in Série A. Séminaires Méditerranéens (pp. 35–46).: CIHEAM

47. De Cara, S. & Jayet, P.-A. (2000a). Emissions of greenhouse gases from agriculture: the heterogeneity of abatement costs in France. *European Review of Agricultural Economics*, 75(4), 597–623

48. De Cara, S. & Jayet, P.-A. (2000b). Régulation de l'effet de serre d'origine agricole: Puits de carbone et instruments de second rang. *Economie et Prévision*, 143/144(2-3), 37–46

49. De Cara, S. & Jayet, P.-A. (1999). Evaluation et régulation de l'effet de serre d'origine agricole. *L'Actualité Economique*, 75(4), 597–623

50. Jayet, P.-A., Birfet, A., & Hofstetter, A. (1998). Forêt paysanne et politique agricole commune : une évaluation des impacts d'une incitation au reboisement. *Cahiers d'Economie et Sociologie Rurales*, 48, 6–35

**Manual** A description of part of the model building and file architecture was previously developed. It was partly open access, and will soon be entirely open access. These materials are mainly dedicated to *AROPAj* users.

**Electronic reports** Programs supported by the *AROPAj* model or which support it are available for *AROPAj* users. They reflect a major part of the development and problem solving over the years. Any research program is likely to supply model development opportunities. A series of “cahiers” / workbooks aggregates users’ and developers’ contributions over the years. In addition, technical reports may be provided for scientific partners, stakeholders and sponsors. This is the case for instance for the *GENEDEC* program from which strong *AROPAj* developments originate: Source: Synthetic material around *AROPAj* from the *GENEDEC* program

**Circular slides** A series of interlinked slides highlights different technical and informational aspects referenced in this paper. This series offers practical information about *AROPAj* use and its potential.
Madeira (Portugal), extra-European regions (205-Guadeloupe, 206-Martinique, 207-La Réunion), German "State-towns" (20-Hamburg, 40-Bremen, 110-Berlin)

* from the region list and to crop location priors for one FADN year in case of region merging in EC statistics: 610-Entre Douro e Minho/Beira litoral and 620-Tras-os-Montes/Beira interior merging into 620-Norte e Centro (Portugal, 2007); 761, 762 and 763 merging into 768-Dunántúl and 760, 765 and 766 merging into 768-Alföld (Hungary, 2012)

* from crop location priors to crop location posteriors: all regions in Germany, except 100-Saarland (10, 30, 50, 60, 70, 80, 90, 112, 113, 114, 116; all years); regions 10 and 20 merging into region 15 for 2018

* from crop location posteriors to farm group location: 204-Corse (France, all years), 221-Aosta (Italy, all years), all regions in the United Kingdom (411, 412, 413, 421, 431, 441; all years), 380-Ireland (all years), 100-Saarland (Germany, 2008-2012)

Given the region and the year, one step failure means that the farm group probability set is empty for this region and this year.

Managing some of these issues is still in progress in the encoded applications. At the present time, March 2024, between 24 and 30 regions are missing in terms of farm group location probabilities (see Table 1).

## 13.5 Organic matter, biogas and digestates, intermediate crops

The issues related to manure, crop residues, biogas production, and digestate recycling are being studied. In this series, the first issue focuses on volatile solids (VS) originating from manure when exchanges are possible between livestock farms and crop farms. The second issue involves considering manure and crop residues as raw materials for biogas production. Digestates, as by-products, are eligible for use as organic nitrogen by crops.

Simultaneously, cover crops are being introduced into the model. The module associated with this introduction requires substantial modifications currently undergoing testing. To this end, new activity blocks and constraints are being developed, along with their associated parameters.

The "biomethane" module will be presented in section 4.2.8. Beyond the inherent interest in renewable gas production, one can explore the competition between biofuels and biogas. Regarding biofuels in the model (as output from AROPAj results), it will be necessary to add "biojet" (a substitute for kerosene) to the other biofuels already present.

## 13.6 Intermediate crops

Intermediate energy crops are represented in the model as cover crops integrated into conventional crop rotations. The dedicated module is structured around two core components: (i) sets of "preceding-succeeding" crop sequences compatible with *Camelina sativa*, distinguishing between "summer" and "winter" regimes, with the option of seed production conditional on the maturity stage achieved at the end of the intercrop period; (ii) biophysical yield functions parameterized on the basis of cumulative growing degree days.

This module will be presented in section 4.2.9.
**Side events** Seminars dedicated to AROPAj developments and applications and to associated theoretical aspects in applied mathematics and economics are regularly organized. Three four-days seminars have been organized in Alps in 2010, 2013 and 2016 (consequently called “ski-minars”).

**How to create this document** The present document originates from an assembly of text files, figures and charts. Text files are of *LATEX* or *ORG* format, and edited through *Emacs*. Most of figures and charts originate from published papers and reports, and a few emanate from additional *R*, *Mathematica* or *CorelDraw* applications. The detailed block structure (see 4.2) is provided by a *CShell* script applied to the last version of the kernel (see the “inception date” mentioned at the end of 4.2 introduction).

The master document is compiled through *Emacs-ORG*.

**How to cite this document** as a citation and a reference included in the bibliography, as plain reference (updated 2020, June 30):

Jayet P.-A., Petsakos A., Chakir R., Lungarska A., De Cara S., Petel E., Humblot P., Godard C., Leclère D., Cantelaube P., Bourgeois C., Clodic M., Bambière L., Ben Fradj N., Aghajanzadeh-Darzi P., Dumollard G., Isbāşoiu A., Adrian J., Pilchak G., Bounaffaa M., Barberis D., Assaiante C., Ollier M., Henry L., Florio A., Chiadmi I., Gossiaux E., Ramirez R., Gérard M., and Reineix J., (2020). **The European agro-economic AROPAj model.** INRA - Economie Publique, 207p.

https://www6.versailles-grignon.inra.fr/economie_publique_eng/Research-work.

and encoded in *JabRef* or *Bibtex* as follows:

```
@book{Jayet2018,
  author = {Jayet, Pierre-Alain
  and Petsakos, Athanasios
  and Chakir, Raja
  and Lungarska, Anna
  and {De Cara}, St{\`{e}}phane
  and Petel, Elvire
  and Humblot, Pierre
  and Godard, Caroline
  and Lecl{\`{e}}re, David
  and Cantelaube, Pierre
  and Bourgeois, Cyril
  and Clodic, M\'elissa
  and Bami{\`{e}}re, Laure
  and {Ben Fradj}, Nosra
  and Aghajanzadeh-Darzi, Parisa
  and Dumollard, Gaspard
  and Isb\u{a}\c{s}oiu, Ancuta
  and Adrian, Juliette
  and Pilchak, Gregory
  and Bounaffaa, Myriam
  and Barberis, Delphine
  and Assaiante, Coline
  and Ollier, Maxime
  and Henry, Lo\"ic
  and Florio, Alessandro
  and Chidami, Ines
  and Gossiaux, Eva
  and Ramirez, Erica
  and G{\`{e}}rard, Maxence
```
# 14 Glossary, communication, references

## Glossary

* A2, A2H2, B1 refer to SRES (now RCP) proposed by the IPCC (AR4)

* ADEME the French Environment and Energy Management Agency

* AR4, AR5 Assessment Report (AR4 and AR5 standing respectively for the 4th and 5th IPCC reports)

* ArcGIS geographical information system software, formerly used in some cases

* AGRI4CAST refers to interpolated meteorological data provided by the JRC MARS unit.

* AROPAj acronym of the agro-bio-economic model ("Agriculture, recomposition de l'offre et politique agricole", "j" accounting for mnemonics)

* AROPAj version refers to one or several yearly FADN sample(s), characterized by specific farming systems clustering, parameter estimating and LPs calibrating

* ArTiX set of applications and database dedicated to STICS run management and designed to provide AROPAj with N and H₂O to yield functions

* awk is an interpreted programming language designed for data and text processing (sed is another programming language widely used by the AROPAj shell)

* BSA burden sharing agreement (refers to the European climate policy)

* BtL biomass to liquid (thermo-chemical process)

* CAP the Common Agricultural Policy (applying to all MS of the EU)

* CAPRI refers to the Common Agricultural Policy Regionalised Impact model, funded by several EU projects

* Carbo-PRO models the links between soil carbon storage and organic matter application, developed by INRA and the Veolia-VERI company

* CC climate change

* CE cross entropy

* CH₄ methane

* CLC the CORINE Land Cover database

* CMO Common Market Organization (refers to the CAP)

* COP stands for cereals, oilseeds and proteins crops

* DEM Digital Elevation Model

* DM dry matter

* dual variable refers to the implicit variable related to a constraint in a mathematical programming model (also: Lagrange multiplier, see also primal variable)

* EC the European Commission

* ECHAM5 one of the GCMs

* EFOM Energy Flow Optimization Model

* EPIC a crop model: http://blackland.tamu.edu/models/epic/

* ESDB the European Soil Database

* ESIM "European Simulation Model" (developed by the ERS-USDA and used by the Institute for Prospective Technological Studies, IPTS-Seville)

* ETS the (European) emission trading scheme
EU the European Union

EU expected utility

Eurostat is the statistical office of the EU situated in Luxembourg

EUROTOOLS research projet funded by the EC 5th FrameWork program

E-V refers to mean-variance models (“mean” or “esperance”)

FADN the farm accounting data network, based on national surveys managed by all EU member States, and managed bu the EC

FAME Fatty Acid Methyl Ester

FAO Food and Agriculture Organization of the United Nations

FAOStat the statistic division of the FAO

farm group refers to AROPAj clusters of FADN surveyed farms

farm type first refers to the FADN classification of the surveyed farms; it belongs to clustering criteria providing the AROPAj farm groups; it may be used instead of farm group

FT see farm type or OTEX

GAMS software used for implementation and solving of optimization programs

GCE generalized cross-entropy

GCM General Circulation Models or Global Climate Models

GDAL open source translator library for raster and vector geospatial data

GDP gross domestic product

GENEDEC acronym of one of research framework programmes granted by the EC, which supported many of AROPAj developments

GeoMIRET name of the TIMES model developed by Ifpen (acronym from “GEOgraphical Model Integration for (Renewable) Energies and Transport”)

GHG greenhouse gas, knowing that major agriculture sourced GHG are N₂O (nitrous oxide) and CH₄ (methane)

GIRAF model of the European biofuel transformation sector

GIS geographical information system

GLOBIOM partial equilibrium model focusing on agriculture and forest land uses developed at IIASA

GME generalized maximum entropy

GNU Unix-like computer operating system intended to be Unix-compatible. The acronym stands for "GNU's Not Unix"

GPL The General Public License designed to deal with GNU products

GTOPO30 a US geographical survey model used for DEM

GWC green waste compost

GWP global warming potential, defined for any GHG in terms of CO₂ equivalent given the time horizon

HVO hydrotreated vegetable oil

IEA-ETSAP International Energy Agency - Energy Technology Systems Analysis Programme

Ifpen formerly Ifp as Institut FranÇais du PÂtrole, adding now new and renewable energy sources in its working area

IFPRI International Food Policy Research Institute (based in Wahington)
References

Aghajanzadeh-Darzi, P. & Jayet, P. A. (2013). Impact économique du changement climatique sur l'alimentation animale et l'élevage dans l'union européenne. *Fourrages*, (215), 81-90.

Aghajanzadeh-Darzi, P., Martin, R., Laperche, S., & Jayet, P.-A. (2016). Climate change impacts on european agriculture revisited - adding the economic dimension of grasslands. *Regional Environmental Change*. doi: 10.1007/s10113-016-1018-z.

Arfini, F. (2001). *Mathematical Programming Models Employed in the Analysis of the Common Agriculture Policy*. INEA Working Papers 9.

Assaiante, C., Jayet, P.-A., & Lantz, F. (2016). From raw material to biofuel demand: the agraf model. In A. R. E. Sabine Sauvage, José-Miguel Sánchez-Pérez (Ed.), *8th International Congress on Environmental Modelling and Software*. http://scholarsarchive.byu.edu/iemssconference/2016/Stream-A/15.

Bamière, L., Godard, C., Debove, E., De Cara, S., Jayet, P.-A., & Niang, B. (2005). Interface between agriculture and the environment: integrating yield response functions in an economic model of EU agriculture. In *Modelling agricultural policies: State of the Art and New Challenges, Proceedings of the 89th European Association of Agricultural Economists, Parma, Italy, Feb. 3-5, 2005*.

Bamière, L., Jayet, P.-A., Kahindo, S., & Martin, E. (2021). Carbon sequestration in french agricultural soils: a spatial economic evaluation. *Agricultural Economics*. doi: https://doi.org/10.1111/agec.12619.

Barberis, D., Humblot, P., Chiadmi, I., Jayet, P.-A., Lungarska, A., & Ollier, M. (2020). Climate change and irrigation water: Should the north/south hierarchy of impacts on agricultural systems be reconsidered? *Environmental Modeling and Assessment*. doi: https://doi.org/10.1007/s10666-020-09724-8.

Bayramoglu, B., Chakir, R., & Lungarska, A. (2020). Impacts of land use and climate change on freshwater ecosystems in france. *Environmental Modeling and Assessment*, 25, 147-172. doi: https://doi.org/10.1007/s10666-019-09673-x.

Ben Fradj, N. & Jayet, P.-A. (2018). Optimal management of perennial energy crops by farming systems in france: A supply-side economic analysis. *Biomass and Bioenergy*, (116), 113-121. doi: 10.1016/j.biombioe.2018.06.003.

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. 10.1016/j.landusepol.2015.12.027.

Ben Fradj, N., Jayet, P.-A., Rozakis, S., Georganta, E., & Jedrejek, A. (2020). Contribution of agricultural systems to the bioeconomy in poland: Integration of willow in the context of a stylised cap diversification. *Land Use Policy*, 99, 24p. doi: https://doi.org/10.1016/j.landusepol.2020.104797.
IIASA International Institute for Applied Systems Analysis (Austria)

IMPACT International Model for Policy Analysis of Agricultural Commodities and Trade (model conceived by IFPRI)

INRA Institut National de la Recherche Agronomique, becomes INRAE in 2020

INRAE Institut national de recherche pour l'agriculture, l'alimentation et l'environnement (see INRA)

INSEA "Integrated Sink Enhancement Assessment", a shared-cost action financed through the 6th Framework Program of the European Commission.

IPCC International Panel for Climate Change

JRC Joint Research Center (Ispra, Italy; Seville, Spain), related to the European Commission

LAI leaf area index

Linux OS supporting the AROPAj model use and development (from 2014)

LP linear programming, or linear program

LS least squares

LS livestock

LU (or LSU) livestock Unit: used for animal accounting based on FADN equivalence factors (the parameter iii(i) in AROPAj)

LUCAS refers to point land use data (Land Use/Cover Area frame Statistical Survey), developed by the JRC

MACC marginal abatement cost curve

MARKAL stands for "MARKet ALlocation computer model generator"

MARS unit of the JRC in charge of climate data

ME maximum entropy

MIP mixed integer programming

MNL refers to the "multinomial logit" statistical model

MODCOU hydrogeological model devoted to soil-aquifer transfers and related to the Seine river bassin

MP mathematical programming

MS Member States (of the EU)

N nitrogen

N₂O nitrous oxide

nexBtL a renewable diesel fuel production commercial process, see also BtL

NH₃ ammonia

NLP non linear programming

NO₃ nitrate

NUTS Nomenclature of Units for Territorial Statistics, refers to the European territorial classification

O₃ ozone

objective function refers to the function to be maximized (or minimized) in mathematical programming

OLS ordinary least squares

OM organic matter

OS operating system (AROPAj was developed under Unix until 2013, then Linux)

OTEX refers to the French "orientation technico-Â-Â¿Â½économique de l'exploitation" (see also FT)
Bounaffaa, M., Florio, A., Le Roux, X., & Jayet, P.-A. (2018). Economic and environmental analysis of maize inoculation by plant growth promoting rhizobacteria in the french rhône-alpes region. *Ecological Economics*, 146, 334-346. doi: 10.1016/j.ecolecon.2017.11.009.

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.

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*. doi: 10.1142/S2382624X16500211.

Bourgeois, C. & Jayet, P. A. (2016). Regulation of relationships between heterogeneous farmers and an aquifer accounting for lag effects. *Australian Journal of Agricultural and Resource Economics*, 60(1), 39–59. doi: 10.1111/1467-8489.12102.

Brisson, N., Gary, C., Justes, E., Roche, R., Mary, B., Ripoche, D., Zimmer, D., Sierra, J., Bertuzzi, P., Burger, P., Bussière, F., Cabidoche, Y., Cellier, P., Debaeke, P., Gaudillère, J., Hénault, C., Maraux, F., Seguin, B., & Sinoquet, H. (2003). An overview of the crop model stics. *European Journal of Agronomy*, 18(3-4), 309–332. Modelling Cropping Systems: Science, Software and Applications.

Brisson, N. & Levrault, F. (2010). *Changement climatique, agriculture et for en France: simulation d'impacts sur les principales espèces. In: Livre vert de projet CLIMATOR (2007-2010)*. ADEME.

Brisson, N., Mary, B., Ripoche, D., Jeuffroy, M. H., Ruget, F., Nicoullaud, B., Gate, P., Devienne-Barret, F., Antonioletti, R., Durr, C., Richard, G., Beaudoin, N., Recous, S., Tayot, X., Plenet, D., Cellier, P., Machet, J.-M., Meynard, J.-M., & Delécolle, R. (1998). Stics: a generic model for the simulation of crops and their water and nitrogen balances. i. theory and parameterization applied to wheat and corn. *Agronomie*, 18(5-6), 311-346.

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.

Caputo, M. R. & Paris, Q. (2008). Comparative statics of the generalized maximum entropy estimator of the general linear model. *European Journal of Operational Research*, 185(1), 195-203.

Caurla, S., Delacote, P., Lecocq, F., Barthès, J., & Barkaoui, A. (2013). Combining an inter-sectoral carbon tax with sectoral mitigation policies: Impacts on the French forest sector. *Journal of Forest Economics*, 19(4), 450-461.

Chakir, R. (2009). Spatial downscaling of agricultural land-use data: An econometric approach using cross entropy. *Land Economics*, 85(2), 238-251.

Chakir, R. & Lungarska, A. (2017a). Agricultural rent in land-use models: comparison of frequently used proxies. *Spatial Economic Analysis*, 12(2-3), 279-303.
* *OURSE* the energy demand model developed by *Ifpen*

* *OURSEurope* the energy demand model developed for Europe and derived from *OURSE*

* *PAM* refers to the “Partitioning Around Medoids” algorithm

* *PaSim* process-based grassland biogeochemical model
(https://www1.clermont.inra.fr/urep/modeles/pasim.htm)

* *PDIE* proteins digestible by the small intestine due to the energy provided by feed

* *PDIN* proteins digestible by the small intestine due to the nitrogen provided by feed

* *PET* potential evapo-transpiration

* *PMP* positive mathematical programming

* *POSTGreSQL* software for data management

* *POSTGIS* allows for interfacing *POSTgres* and geographical information system procedures

* *primal variable* refers to an explicit variable of a mathematical programming model

* *psql* query language interface for working with *POSTGreSQL*

* *R* a GNU programming language and software environment for statistical computing

* *RCA3* one of the Regional Climate Models (*RCM*)

* *RCM* regional climate models

* *RCP* representative concentration pathway, built by the *IPCC* for its 5th assessment report

* *RHS* right-hand side (stands for *LP*)

* *Roth-C* model to simulate change in organic matter stock on the long term (explanation about *RothC* and *Carbo-Pro*)

* *ROW* stands for “Rest of the World”

* *RUMINANT* a dynamic model simulating the process of ruminant digestion

* *sed* see *awk*

* *SFP* single farm payment

* *shadow price* refers to a mathematical programming constraint and to the optimal value of the limited resource related to this constraint (also shadow cost, implicit price or cost, dual value, Lagrange multiplier)

* *SimU* simulation unit (refers to spatial modeling tools)

* *SRES* special report on emissions scenarios, shared into 4 scenario families (i.e. *A-B × 1-2*) developed by the *IPCC* for its 4th report

* *SSP* stands for Shared Socio-economic Pathway (related to the *IPCC* *AR5*)

* *STICS* crop model, access *STICS*

* *TIMES* stands for “The Integrated *MARKAL-EFOM* System”

* *UNFCCC* United Nations Framework Convention on Climate Change: here access the *UNFCCC* site (provides numerous data on *GHG* emissions per country per year per sector)

* *UAA* utilized agricultural area

* *UFL* unit of energy content in fodder for dairy herds

* *UFV* unit of energy content in fodder for non dairy herds

* *Unix* OS under which *AROPAj* was formerly developed

* *UOR* urban organic residues

* *USS* urban sewage sludge

* *UZ* unsaturated zone (hydrogeology)

* *WLS* weighted least squares
Chakir, R. & Lungarska, A. (2017b). Agricultural rent in land-use models: comparison of frequently used proxies. *Spatial Economic Analysis*, 12, 379–303. doi: 10.1080/17421772.2017.1273542.

Ciaian, P., Espinosa, M., Gomez y Paloma, S., Heckelei, T., Langrell, S., Louichi, K., Sckokao, P., Thomas, A., & Vard, T. (2013). *Farm level modelling of CAP: a methodological overview*. Technical report, JRC Scientific and policy reports. doi:10.2791/86415.

Clodic, M. (2013). *Modélisation de l'effre de biocarburants à l'échelle européenne, échanges intra-et extra-européens, effets croisés avec les marchés ptroliers et contrainte carbone*. PhD thesis, ABIES-AgroParisTech, dir.: P.-A. Jayet.

Day, R. H. (1963). On aggregating linear programming models of production. *Journal of Farm Economics*, 45(4), 797–813.

De Cara, S., Henry, L., & Jayet, P.-A. (2018). Optimal coverage of an emission tax in the presence of monitoring, reporting, and verification costs. *Journal of Environmental Economics and Management*, 89, 1–23. doi: 10.1016/j.jeem.2018.03.001.

De Cara, S., Houzé, M., & Jayet, P.-A. (2005). Methane and nitrous oxide emissions from agriculture in the EU: a spatial assessment of sources and abatement costs. *Environmental and Resource Economics*, 32(4), 551–583.

De Cara, S. & Jayet, P.-A. (1999). Evaluation et régulation de l'effet de serre d'origine agricole. *L'Actualité Economique*, 75(4), 597–623.

De Cara, S. & Jayet, P.-A. (2000a). Emissions of greenhouse gases from agriculture : the heterogeneity of abatement costs in france. *European Review of Agricultural Economics*, 75(4), 597–623.

De Cara, S. & Jayet, P.-A. (2000b). Régulation de l'effet de serre d'origine agricole : Puits de carbone et instruments de second rang. *Economie et Prévision*, 143/144(2-3), 37–46.

De Cara, S. & Jayet, P.-A. (2006). Mitigation of greenhouse gas emissions in EU agriculture: An assessment of the costs of reducing agricultural emissions and enhancing carbon sinks in agricultural soils. In *INSEA Report SSP1-CT-2003-503614-Final*, European Commission, INSEA, IIASA, Laxenburg, Austria.

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.

De Cara, S. & Thomas, A. (2008). *Projections d'émissions/absorptions de gaz à effet de serre dans les secteurs forêt et agriculture aux horizons 2010 et 2020*. Technical report. 192 pp.

Devaraju, N., Prudhomme, R., Lungarska, A., Wang, X., Yin, Z., de Noblet-Ducoudré, N., Chakir, R., Jayet, P.-A., Brunelle, T., Viovy, N., De Palma, A., Gonzalez, R., & Ciais, P. (2022). Quantifying the benefits of reducing synthetic nitrogen application policy on ecosystem carbon sequestration and biodiversity. *Scientific Reports* 2022 12:1, 12(1), 1–15.
Communication

Different elements are provided. In addition to scientific papers in peer-reviewed journals (see a portion of the references below for scientific papers using and related to AROPAj), communicative articles have also been developed. They are of different types, as developed below:

1. Jayet, P., Bamière, L., & Zuravel, O. (2025). Agroénergie et émissions de gaz à effet de serre dans l'union européenne. *Revue de l'Énergie*, 678, 13-28

2. Reineix, J. & Jayet, P.-A. (2025). Multi-scale analysis of water sharing based on bio-economic modeling. *Environmental Modeling & Assessment*

3. Ollier, M., Jayet, P.-A., & Humblot, P. (2024). An assessment of the distributional impacts of autonomous adaptation to climate change from european agriculture. *Ecological Economics*, 222, 108221

4. Ollier, M. & De Cara, S. (2024). Give and take: An analysis of the distributional consequences of emission tax-and-rebate schemes with an application to greenhouse gas emissions from european agriculture. *Ecological Economics*, 219, 108154

5. Lungarska, A., Brunelle, T., Chakir, R., Jayet, P.-A., Prudhomme, R., De Cara, S., & Bureau, J.-C. (2023). Halving mineral nitrogen use in European agriculture: Insights from multi-scale land-use models. *Applied Economic Perspectives and Policy*, 12

6. Gérard, M. & Jayet, P. (2023). European farmers' response to crop residue prices and implications for bioenergy policies. *Energy Policy*. https://doi.org/10.1016/j.enpol.2023.113561

7. Devaraju, N., P>rudhomme, R., Lungarska, A., Wang, X., Yin, Z., de Noblet-Ducoudré, N., Chakir, R., Jayet, P.-A., Brunelle, T., Viovy, N., De Palma, A., Gonzalez, R., & Ciais, P. (2022). Quantifying the benefits of reducing synthetic nitrogen application policy on ecosystem carbon sequestration and biodiversity. *Nature Scientific Reports* 2022 12:1, 12(1), 1-15

8. Prudhomme, R., Chakir, R., Lungarska, A., Brunelle, T., Devaraju, N., de Noblet, N., Jayet, P.-A., De Cara, S., & Bureau, J.-C. (2022). Food, climate and biodiversity: a trilemma of mineral nitrogen use in European agriculture. *Review of Agricultural, Food and Environmental Studies*

9. Gossiaux, E. & Jayet, P.-A. (2021). Exploring the potential for calories and bioenergy in france. *BE-Sustainable Magazine*, 12, 11-17. https://issuu.com/besustainablemagazine/docs/be-sustainable_magazine issue_12-april_2021/s/12150135

10. Bamière, L., Jayet, P.-A., Kahindo, S., & Martin, E. (2021). Carbon sequestration in french agricultural soils: a spatial economic evaluation. *Agricultural Economics*. doi: https://doi.org/10.1111/agec.12619

11. Isbasoiu, A., Jayet, P.-A., & De Cara, S. (2020). Increasing food production and mitigating agricultural greenhouse gas emissions in the european union: impacts of carbon pricing and calorie production targeting. *Environmental Economics and Policy Studies*, (pp. 32p). doi: https://doi.org/10.1007/s10018-020-00293-4

12. Barberis, D., Humblot, P., Chiadmi, I., Jayet, P.-A., Lungarska, A., & Ollier, M. (2020). Climate change and irrigation water: Should the north/south hierarchy of impacts on agricultural systems be reconsidered? *Environmental Modeling and Assessment*. doi: https://doi.org/10.1007/s10666-020-09724-8

13. Jayet, P.-A., Isbasoiu, A., & De Cara, S. (2020). Slaughter cattle to secure food calories and reduce agricultural greenhouse gas emissions? some prospective estimates for france. *Review of Agricultural, Food and Environmental Studies*, (pp. 24p). doi: https://doi.org/10.1007/s41130-020-00117-9

14. Ben Fradj, N., Jayet, P.-A., Rozakis, S., Georganta, E., & Jedrejek, A. (2020). Contribution of agricultural systems to the bioeconomy in poland: Integration of willow in the context of a stylised cap diversification. *Land Use Policy*, 99, 24p. doi: https://doi.org/10.1016/j.landusepol.2020.104797

15. Bayramoglu, B., Chakir, R., & Lungarska, A. (2020). Impacts of land use and climate change on freshwater ecosystems in france. *Environmental Modeling and Assessment*, 25, 147-172. doi: https://doi.org/10.1007/s10666-019-09673-x

16. Lungarska, A., Chakir, R., Ben Fradj, N., Jayet, P.-A., Bamière, L., Isbasoiu, A., Ollier, M., Gossiaux, E., Chiadmi, I., De Cara, S., & Kahindo, S. (2020). Approche intégrée des productions animales et végétales dans un modèle économique pour l'analyse des interactions agriculture - environnement. *Innovations Agronomiques*, 80, 69-85

17. Mosnier, C., Britz, W., Jullière, T., De Cara, S., Jayet, P.-A., Havlik, P., Franck, S., & Mosnier, A. (2019). Greenhouse gas abatement strategies and costs in dairy production – a comparison across bio-economic models. *Journal of Cleaner Production*, (236), 17589. doi: 10.1016/j.jclepro.2019.07.064
Durandeau, S., Gabrielle, B., Godard, C., Jayet, P.-A., & Le Bas, C. (2010). Coupling biophysical and microeconomic models to assess the effect of mitigation measures on greenhouse gas emissions from agriculture. *Climatic Change*, 98, 51–73.

Errington, S. (1989). Estimating Enterprise Input-Output Coefficients From Regional Farm Data. *Journal of Agricultural Economics*, 40(1), 52–56.

Florio, A., Pommier, T., Gervaix, J., Bérard, A., & Le Roux, X. (2018). Soil C and N statuses determine the effect of maize inoculation by plant growth-promoting rhizobacteria on nitrifying and denitrifying communities. *Scientific reports*, 7(1), 8411.

Freund, R. J. (1956). The Introduction of Risk into a Programming Model. *Econometrica*, 24(3), 253–263.

Galko, E. & Jayet, P.-A. (2011). Economic and environmental effects of decoupled agricultural support in the eu. *Agricultural Economics*, 42, 605–618.

Godard, C., Roger-Estrade, J., Jayet, P.-A., Brisson, N., & Le Bas, C. (2008). Use of available information at a european level to construct crop nitrogen response curves for the regions of the eu. *Agricultural Systems*, 97, 68–82.

Golan, A., Judge, G., & Miller, D. (1996). Maximum entropy econometrics: Robust estimation with limited data. *New York, John Wiley eds.*

Golan, A., Judge, G., & Perloff, J. (1997). Estimation and inference with censored and ordered multinomial response data. *Journal of Econometrics*, 79(1), 23–51.

Gomez, E. (2002). *Modélisation intégrée du transfert de nitrate à l'échelle régionale dans un système hydrologique. Application au bassin de la Seine.* PhD thesis, Ecole Nationale Supérieure des Mines de Paris, France.

Gomez, E., Ledoux, E., Viennot, P., Mignolet, C., Benoit, M., Bornerand, C., Schott, C., Mary, B., Billen, G., Ducharne, A., et al. (2003). Un outil de modélisation intégrée du transfert des nitrates sur un système hydrologique: application au bassin de la seine. *La Houille Blanche*, (3), 38–45.

Good, I. J. (1963). Maximum entropy for hypothesis formulation, especially for multidimensional contingency tables. *The Annals of Mathematical Statistics*, Vol. 34, No. 3., 911–934.

Gossiaux, E. & Jayet, P.-A. (2021). Exploring the potential for calories and bioenergy in france. *BE-Sustainable Magazine*, 12, 11–17. https://issuu.com/besustainablemagazine/docs/besustainable_magazine issue_12--april_2021/s/12150135.

Gérard, M. & Jayet, P. (2023). European farmers’ response to crop residue prices and implications for bioenergy policies. *Energy Policy*. https://doi.org/10.1016/j.enpol.2023.113561.

Havlík, P., Schneider, U. A., Schmid, E., Böttcher, H., Fritz, S., Skalský, R., Aoki, K., De Cara, S., Kindermann, G., Kraxner, F., et al. (2011). Global land-use implications of first and second generation biofuel targets. *Energy Policy*, 39(10), 5690–5702.
18. Jayet, P.-A., Barberis, D., Humblot, P., & Lungarska, A. (2018). Spatialisation de la demande en eau d'irrigation estimée par un modèle bioéconomique. *Revue internationale de géomatique*, 4(28), 485-503. doi: 10.3166/rig.2018.00064

19. Ben Fradj, N. & Jayet, P.-A. (2018). Optimal management of perennial energy crops by farming systems in france: A supply-side economic analysis. *Biomass and Bioenergy*, (116), 113-121. doi: 10.1016/j.biombioe.2018.06.003

20. De Cara, S., Henry, L., & Jayet, P.-A. (2018). Optimal coverage of an emission tax in the presence of monitoring, reporting, and verification costs. *Journal of Environmental Economics and Management*, 89, 1-23. doi: 10.1016/j.jeem.2018.03.001

21. Lungarska, A. & Chakir, R. (2018). Climate-induced Land Use Change in France: Impacts of Agricultural Adaptation and Climate Change Mitigation. *Ecological Economics*, 147, 134-154

22. Bounaffaa, M., Florio, A., Le Roux, X., & Jayet, P.-A. (2018). Economic and environmental analysis of maize inoculation by plant growth promoting rhizobacteria in the french rhône-alpes region. *Ecological Economics*, 146, 334-346. doi: 10.1016/j.ecolecon.2017.11.009

23. Jayet, P. A., Barberis, D., & Humblot, P. (2017b). The economic impact of climate change on agricultural water demand in france. In D. Kocasevic (Ed.), *Book of proceedings - VIII International Scientific Agriculture Symposium* (pp. 2032-2038).: AgroSym 2017

24. Chakir, R. & Lungarska, A. (2017b). Agricultural rent in land-use models: comparison of frequently used proxies. *Spatial Economic Analysis*, 12, 379-303. doi: 10.1080/17421772.2017.1273542

25. Humblot, P., Jayet, P.-A., & Petsakos, A. (2017). Farm-level bio-economic modeling of water and nitrogen use: Calibrating yield response functions with limited data. *Agricultural Systems*, 151, 47-60. doi: 10.1016/j.agsy.2016.11.006

26. Lungarska, A. & Jayet, P. A. (2016). Impact of spatial differentiation of nitrogen taxes on french farms' compliance costs. *Environmental and Resource Economics*. doi: 10.1007/s10640-016-0064-9

27. Assaiante, C., Jayet, P.-A., & Lantz, F. (2016). From raw material to biofuel demand: the agraf model. In A. R. E. Sabine Sauvage, José-Miguel Sánchez-Pérez (Ed.), *8th International Congress on Environmental Modelling and Software*. http://scholarsarchive.byu.edu/iemssconference/2016/Stream-A/15

28. Jayet, P. A., Aghajanzadeh-Darzi, P., & Petsakos, A. (2017a). Improvement of a bio-economic mathematical programming model in the case of on-farm source inputs and outputs. *Journal of Quantitative Economics*, 15, 489-508. doi: 10.1007/s40953-016-0058-z

29. Aghajanzadeh-Darzi, P., Martin, R., Laperche, S., & Jayet, P.-A. (2016). Climate change impacts on european agriculture revisited - adding the economic dimension of grasslands. *Regional Environmental Change*. doi: 10.1007/s10113-016-1018-z

30. 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*. doi: 10.1142/S2382624X16500211

31. 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. doi: 10.1016/j.landusepol.2015.12.027

32. Jayet, P. A. & Petel, E. (2015). Economic valuation of the nitrogen content of urban organic residue by the agricultural sector. *Ecological Economics*, 120, 272-281

33. 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

34. 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

35. 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

36. Aghajanzadeh-Darzi, P. & Jayet, P. A. (2013). Impact économique du changement climatique sur l'alimentation animale et l'élevage dans l'union européenne. *Fourrages*, (215), 81-90

37. Ciaian, P., Espinosa, M., Gomez y Paloma, S., Heckelei, T., Langrell, S., Louichi, K., Sckokao, P., Thomas, A., & Vard, T. (2013). Farm level modelling of CAP: a methodological overview. Technical report, JRC Scientific and policy reports. doi:10.2791/86415

38. 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
Hazell, P. B. R. & Norton, R. D. (1986). *Mathematical Programming for Economic Analysis in Agriculture*. New York: MacMillan.

Heckelei, T. & Wolff, H. (2003). Estimation of constrained optimisation models for agricultural supply analysis based on generalised maximum entropy. *European Review of Agricultural Economics*, 30(1), 27–50.

Hénin, S. & Dupuis, M. (1945). *Essai de bilan de la matière organique du sol*, volume 15.

Herrero, M., Thornton, P., Notenbaert, A., Wood, S., Msangi, S., Freeman, H., Bossio, D., Dixon, J., Peters, M., Steeg, J., et al. (2010). Smart investments in sustainable food production: revisiting mixed crop-livestock systems.

Howitt, R. E. (1995). *Positive Mathematical Programming*. *American Journal of Agricultural Economics*, 77(2), 329–342.

Humblot, P., Jayet, P.-A., & Petsakos, A. (2017). Farm-level bio-economic modeling of water and nitrogen use: Calibrating yield response functions with limited data. *Agricultural Systems*, 151, 47–60. doi: 10.1016/j.agsy.2016.11.006.

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.

Isbasoiu, A., Jayet, P.-A., & De Cara, S. (2020). Increasing food production and mitigating agricultural greenhouse gas emissions in the european union: impacts of carbon pricing and calorie production targeting. *Environmental Economics and Policy Studies*, (pp. 32p). doi: https://doi.org/10.1007/s10018-020-00293-4.

Izaurralde, R., Williams, J., McGill, W., Rosenberg, N., & Quiroga Jakas, M. (2006). Simulating soil c dynamics with epic: Model description and testing against long-term data. *Ecological Modelling*, 192(3), 362–384.

Jayet, P. A. (2002). Mathematical programming for the computation of multi-market equilibria: an attempt in modelling the french markets of cereals and livestocks feeds. In S. Rozakis & J. Sourie (Eds.), *Comprehensive economic and spatial bio-energy modelling*, number 48 in Série A. Séminaires Méditerranéens (pp. 35–46).: CIHEAM.

Jayet, P.-A. (2006). *Deliverable 4*. Technical report, GENEDEC project document.

Jayet, P. A., Aghajanzadeh-Darzi, P., & Petsakos, A. (2017a). Improvement of a bio-economic mathematical programming model in the case of on-farm source inputs and outputs. *Journal of Quantitative Economics*, 15, 489–508. doi: 10.1007/s40953-016-0058-z.

Jayet, P. A., Barberis, D., & Humblot, P. (2017b). The economic impact of climate change on agricultural water demand in france. In D. Kocasevic (Ed.), *Book of proceedings - VIII International Scientific Agriculture Symposium* (pp. 2032–2038).: AgroSym 2017.
39. 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

40. 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

41. Galko, E. & Jayet, P.-A. (2011). Economic and environmental effects of decoupled agricultural support in the eu. *Agricultural Economics*, 42, 605-618

42. Durandeau, S., Gabrielle, B., Godard, C., Jayet, P.-A., & Le Bas, C. (2010). Coupling biophysical and microeconomic models to assess the effect of mitigation measures on greenhouse gas emissions from agriculture. *Climatic Change*, 98, 51-73

43. De Cara, S. & Thomas, A. (2008). *Projections d'émissions/absorptions de gaz à effet de serre dans les secteurs forêt et agriculture aux horizons 2010 et 2020*. Technical report. 192 pp

44. Godard, C., Roger-Estrade, J., Jayet, P.-A., Brisson, N., & Le Bas, C. (2008). Use of available information at a european level to construct crop nitrogen response curves for the regions of the eu. *Agricultural Systems*, 97, 68-82

45. De Cara, S. & Jayet, P.-A. (2006). Mitigation of greenhouse gas emissions in EU agriculture: An assessment of the costs of reducing agricultural emissions and enhancing carbon sinks in agricultural soils. In *INSEA Report SSP1-CT-2003-503614-Final*, European Commission, INSEA, IIASA, Laxenburg, Austria

46. Jayet, P.-A. (2006). *Deliverable 4*. Technical report, GENEDEC project document

47. Bamière, L., Godard, C., Debove, E., De Cara, S., Jayet, P.-A., & Niang, B. (2005). Interface between agriculture and the environment: integrating yield response functions in an economic model of EU agriculture. In *Modelling agricultural policies: State of the Art and New Challenges, Proceedings of the 89th European Association of Agricultural Economists*, Parma, Italy, Feb. 3-5, 2005

48. Jayet, P.-A. & Labonne, J. (2005). Impact d'une réforme de la politique agricole commune par le découplage. *Economie et Prévision*, 167(1), 101-116

49. De Cara, S., Houzé, M., & Jayet, P.-A. (2005). Methane and nitrous oxide emissions from agriculture in the EU: a spatial assessment of sources and abatement costs. *Environmental and Resource Economics*, 32(4), 551-583

50. Jayet, P. A. (2002). Mathematical programming for the computation of multi-market equilibria: an attempt in modelling the french markets of cereals and livestocks feeds. In S. Rozakis & J. Sourie (Eds.), *Comprehensive economic and spatial bio-energy modelling*, number 48 in Série A. Séminaires Méditerranéens (pp. 35-46).: CIHEAM

51. De Cara, S. & Jayet, P.-A. (2000a). Emissions of greenhouse gases from agriculture: the heterogeneity of abatement costs in france. *European Review of Agricultural Economics*, 75(4), 597-623

52. De Cara, S. & Jayet, P.-A. (2000b). Régulation de l'effet de serre d'origine agricole : Puits de carbone et instruments de second rang. *Economie et Prévision*, 143/144(2-3), 37-46

53. De Cara, S. & Jayet, P.-A. (1999). Evaluation et régulation de l'effet de serre d'origine agricole. *L'Actualité Economique*, 75(4), 597-623

54. Jayet, P.-A., Birfet, A., & Hofstetter, A. (1998). Forêt paysanne et politique agricole commune : une évaluation des impacts d'une incitation au reboisement. *Cahiers d'Economie et Sociologie Rurales*, 48, 6-35

**Manual** A description of part of the model building and file architecture was previously developed. It was partly open access, and will soon be entirely open access. These materials are mainly dedicated to AROPAj users.

**Electronic reports** Programs supported by the AROPAj model or which support it are available for AROPAj users. They reflect a major part of the development and problem solving over the years. Any research program is likely to supply model development opportunities. A series of “cahiers” / workbooks aggregates users’ and developers’ contributions over the years. In addition, technical reports may be provided for scientific partners, stakeholders and sponsors. This is the
Jayet, P.-A., Barberis, D., Humblot, P., & Lungarska, A. (2018). Spatialisation de la demande en eau d'irrigation estimée par un modèle bioéconomique. *Revue internationale de géomatique*, 4(28), 485-503. doi: 10.3166/rig.2018.00064.

Jayet, P.-A., Birfet, A., & Hofstetter, A. (1998). Forêt paysanne et politique agricole commune : une évaluation des impacts d'une incitation au reboisement. *Cahiers d'Economie et Sociologie Rurales*, 48, 6-35.

Jayet, P.-A., Isbasoiu, A., & De Cara, S. (2020). Slaughter cattle to secure food calories and reduce agricultural greenhouse gas emissions? some prospective estimates for france. *Review of Agricultural, Food and Environmental Studies*, (pp. 24p). doi: https://doi.org/10.1007/s41130-020-00117-9.

Jayet, P.-A. & Labonne, J. (2005). Impact d'une réforme de la politique agricole commune par le découplage. *Economie et Prévision*, 167(1), 101-116.

Jayet, P. A. & Lapierre, O. (1998). *La dynamique des systèmes*, chapter Modélisation des marchés de l'alimentation animale, (pp. 73-93). Hermès.

Jayet, P. A. & Petel, E. (2015). 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.

Jaynes, E. T. (1957). Information theory and statistical mechanics. *Physics Review*, 106, 620-630.

Kampas, A., Petsakos, A., & Rozakis, S. (2012). Price induced irrigation water saving: Unraveling conflicts and synergies between European agricultural and water policies for a Greek Water District. *Agricultural Systems*, 113, 28-38.

Kennedy, J. O. (1975). Using regression analysis to reduce aggregation bias in linear programming supply models. *The Australian Journal of Agricultural Economics*, 19(1), 11p.

Kjellström, E., Nikulin, G., Hansson, U., Strandberg, G., & Ullerstig, A. (2011). 21st century changes in the european climate: uncertainties derived from an ensemble of regional climate model simulations. *Tellus A*, 63(1), 24-40.

Leclère, D., de Noblet-Ducoudré, N., & Jayet, P.-A. (2014). Understanding the farm-scale response of european agriculture to climate change: evaluation of a coupled economic-crop model. *submitted*.

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.

Ledoux, E. (1980). *Modélisation intégrée des écoulements de surface et des écoulements souterrains sur un bassin hydrologique*. PhD thesis, Ecole des Mines de Paris.
case for instance for the *GENEDEC* program from which strong *AROPAj* developments originate:
Source: Synthetic material around *AROPAj* from the *GENEDEC* program

**Circular slides** A series of interlinked slides highlights different technical and informational aspects referenced in this paper. This series offers practical information about *AROPAj* use and its potential.

**Side events** Seminars dedicated to *AROPAj* developments and applications and to associated theoretical aspects in applied mathematics and economics are regularly organized. Three four-days seminars have been organized in Alps in 2010, 2013 and 2016 (consequently called “ski-minars”).

**How to create this document** The present document originates from an assembly of text files, figures and charts. Text files are of .LaTeX or .ORG format, and edited through Emacs. Most of figures and charts originate from published papers and reports, and a few emanate from additional *R*, *Mathematica* or *CorelDraw* applications. The detailed block structure (see 4.2) is provided by a *CShell* script applied to the last version of the kernel (see the “inception date” mentioned at the end of 4.2 introduction).

The master document is compiled through *Emacs-ORG*.

**How to cite this document** as a citation and a reference included in the bibliography,
as plain reference (updated 2023, August 28):

Jayet P.-A., Petsakos A., Chakir R., Lungarska A., De Cara S., Petel E., Humblot P., Godard C., Leclère D., Cantelaube P., Bourgeois C., Clodic M., Bamière L., Ben Fradj N., Aghajanzadeh-Darzi P., Dumollard G., Isbăşoiu A., Adrian J., Pilchak G., Bounaffaa M., Barberis D., Assaiante C., Ollier M., Henry L., Florio A., Chiadmi I., Gossiaux E., Ramirez R., Gérard M., Reineix J., Zuravel, O., Baldi, L., and Weng, M., (2020). **The European agro-economic AROPAj model.** INRAE - PSAE, 213p.

doi:10.17180/nxw3-3537

and encoded in *JabRef* or *Bibtex* as follows:

```
@book{Jayet2018,
author = {Jayet, Pierre-Alain
and Petsakos, Athanasios
and Chakir, Raja
and Lungarska, Anna
and {De Cara}, St{\`{e}}phane
and Petel, Elvire
and Humblot, Pierre
and Godard, Caroline
and Lecl{\`{e}}re, David
and Cantelaube, Pierre
and Bourgeois, Cyril
and Clodic, M\'elissa
and Bami{\`{e}}re, Laure
and {Ben Fradj}, Nosra
and Aghajanzadeh-Darzi, Parisa
and Dumollard, Gaspard
and Isb\u{a}\c{s}oiu, Ancuta
and Adrian, Juliette
and Pilchak, Gregory
and Bounaffaa, Myriam
```
Ledoux, E., Girard, G., de Marsily, G., Villeneuve, J., & Deschenes, J. (1989). Spatially distributed modelling: conceptual approach, coupling surface water and groundwater, 1989. *Unsaturated flow in hydrologic modelling–theory and practice*. NATO ASI ser. CNorwell, Massachusett: Kluwer academic, (pp. 435–454).

Ledoux, E., Girard, G., & Villeneuve, J. (1984). Proposition d'un modèle couplé pour la simulation conjointe des écoulements de surface et des écoulements souterrains sur un bassin hydrologique. *La houille blanche*, (1-2), 101–120.

Ledoux, E., Gomez, E., Monget, J., Viavattene, C., Viennot, P., Ducharne, A., Benoit, M., Mignolet, C., Schott, C., & Mary, B. (2007). Agriculture and groundwater nitrate contamination in the seine basin. the stics-modcou modelling chain. *Science of the Total Environment*, 375(1-3), 33–47.

Leip, A., Weiss, F., Wassenaar, T., Perez, I., Fellmann, T., Loudjani, P., Tubiello, F., Grandgirard, D., Monni, S., & Biala, K. (2010). *Evaluation of the livestock sector's contribution to the EU greenhouse gas emissions (GGELS)*. Technical report, European Commission, Joint Research Centre (JRC).

Léon, Y., Peeters, L., Quinqu, M., & Surry, Y. (1999). The Use of Maximum Entropy to Estimate Input-Output Coefficients From Regional Farm Accounting Data. *Journal of Agricultural Economics*, 50(3), 425–439.

Lobianco, A., Delacote, P., Caurla, S., & Barkaoui, A. (2016). Accounting for active management and risk attitude in forest sector models. *Environmental Modeling & Assessment*, 21, 391–405.

Louhichi, K., Jacquet, F., & Butault, J. P. (2012). Estimating input allocation from heterogeneous data sources: A comparison of alternative estimation approaches. *Agricultural Economics Review*, 13(2), 83–102.

Lungarska, A., Brunelle, T., Chakir, R., Jayet, P.-A., Prudhomme, R., De Cara, S., & Bureau, J.-C. (2023). Halving mineral nitrogen use in European agriculture: Insights from multi-scale land-use models. *Applied Economic Perspectives and Policy*, 12.

Lungarska, A. & Chakir, R. (2018). Climate-induced Land Use Change in France: Impacts of Agricultural Adaptation and Climate Change Mitigation. *Ecological Economics*, 147, 134–154.

Lungarska, A., Chakir, R., Ben Fradj, N., Jayet, P.-A., Bamière, L., Isbasoiu, A., Ollier, M., Gosiaux, E., Chiadmi, I., De Cara, S., & Kahindo, S. (2020). Approche intégrée des productions animales et végétales dans un modèle économique pour l'analyse des interactions agriculture – environnement. *Innovations Agronomiques*, 80, 69–85.

Lungarska, A. & Jayet, P. A. (2016). Impact of spatial differentiation of nitrogen taxes on french farms' compliance costs. *Environmental and Resource Economics*. doi: 10.1007/s10640-016-0064-9.

Makowski, D., Wallach, D., & Meynard, J. (1999). Models of yield, grain protein, and residual mineral nitrogen responses to applied nitrogen for winter wheat. *Agronomy Journal*, 91(3), 377–385.
Markowitz, H. M. (1952). Portfolio Selection. *The Journal of Finance*, 7(1), 77-91.

McCarl, B. A. & Sreen, T. H. (1980). Price Endogenous Mathematical Programming as a Tool for Sector Analysis. *American Journal of Agricultural Economics*, 62(1), 87-102.

Mills, G., Buse, A., Gimeno, B., Bermejo, V., Holland, M., Emberson, L., & Pleijel, H. (2007). A synthesis of aot40-based response functions and critical levels of ozone for agricultural and horticultural crops. *Atmospheric Environment*, 41(41), 2630-2643.

Mosnier, C., Britz, W., Jullière, T., De Cara, S., Jayet, P.-A., Havlik, P., Franck, S., & Mosnier, A. (2019). Greenhouse gas abatement strategies and costs in dairy production – a comparison across bio-economic models. *Journal of Cleaner Production*, (236), 17589. doi: 10.1016/j.jclepro.2019.07.064.

Olesen, J. & Bindi, M. (2002). Consequences of climate change for european agricultural productivity, land use and policy. *European Journal of Agronomy*, 16(4), 239-262.

Peltre, C., Christensen, B., Dragon, S., Icard, C., Kätterer, T., & Houot, S. (2012). RothC simulation of carbon accumulation in soil after repeated application of widely different organic amendments. *Soil Biology and Biochemistry*, 52(2012), 49-60.

Penman, J., D. K., Galbally, I., Hiraishi, T., Nyenzy, B., Emmanul, S., Buendia, L., Hoppaus, R., Martinsen, T., Meijer, J., Miwa, K., & Tanabe, K., Eds. (2001). *Good Practice Guidance and Uncertainty Management in National Greenhouse Gas Inventories*. Intergovernmental Panel on Climate Change (IPCC). Japan: Institute for Global Environmental Strategies (IGES).

Prudhomme, R., Chakir, R., Lungarska, A., Brunelle, T., Devaraju, N., de Noblet, N., Jayet, P.-A., De Cara, S., & Bureau, J.-C. (2022). Food, climate and biodiversity: a trilemma of mineral nitrogen use in European agriculture. *Review of Agricultural, Food and Environmental Studies*.

Reidsma, P., Ewert, F., Oude Lansink, A., & Leemans, R. (2009). Vulnerability and adaptation of european farmers: a multi-level analysis of yield and income responses to climate variability. *Regional Environmental Change*.

Riedo, M., Grub, A., Rosset, M., & Fuhrer, J. (1998). A pasture simulation model for dry matter production, and fluxes of carbon, nitrogen, water and energy. *Ecological Modelling*, 105, 141-183.

Rötter, R. & van de Geijn, S. (1999). Climate change effects on plant growth, crop yield and livestock. *Climatic Change*, 43(4), 651-681.

Schmid, M., Neftel, A., Riedo, M., & Fuhrer, J. (2001). Process-based modelling of nitrous oxide emissions from different nitrogen sources in mown grassland. *Nutrient Cycling in Agroecosystems*, 60, 177-187.

Shannon, C. E. (1948). A mathematical theory of communication. *Bell System Technical Journal*, 27, 379-423.

Soussana, J. & Lüscher, A. (2007). Temperate grasslands and global atmospheric change: a review. *Grass and Forage Science*, 62(2), 127-134.
References

Aghajanzadeh-Darzi, P. & Jayet, P. A. (2013). Impact économique du changement climatique sur l'alimentation animale et l'élevage dans l'union européenne. *Fourrages*, (215), 81-90.

Aghajanzadeh-Darzi, P., Martin, R., Laperche, S., & Jayet, P.-A. (2016). Climate change impacts on european agriculture revisited - adding the economic dimension of grasslands. *Regional Environmental Change*. doi: 10.1007/s10113-016-1018-z.

Arfini, F. (2001). *Mathematical Programming Models Employed in the Analysis of the Common Agriculture Policy*. INEA Working Papers 9.

Assaiante, C., Jayet, P.-A., & Lantz, F. (2016). From raw material to biofuel demand: the agraf model. In A. R. E. Sabine Sauvage, José-Miguel Sánchez-Pérez (Ed.), *8th International Congress on Environmental Modelling and Software*. http://scholarsarchive.byu.edu/iemssconference/2016/Stream-A/15.

Bamière, L., Godard, C., Debove, E., De Cara, S., Jayet, P.-A., & Niang, B. (2005). Interface between agriculture and the environment: integrating yield response functions in an economic model of EU agriculture. In *Modelling agricultural policies: State of the Art and New Challenges, Proceedings of the 89th European Association of Agricultural Economists, Parma, Italy, Feb. 3-5, 2005*.

Bamière, L., Jayet, P.-A., Kahindo, S., & Martin, E. (2021). Carbon sequestration in french agricultural soils: a spatial economic evaluation. *Agricultural Economics*. doi: https://doi.org/10.1111/agec.12619.

Barberis, D., Humblot, P., Chiadmi, I., Jayet, P.-A., Lungarska, A., & Ollier, M. (2020). Climate change and irrigation water: Should the north/south hierarchy of impacts on agricultural systems be reconsidered? *Environmental Modeling and Assessment*. doi: https://doi.org/10.1007/s10666-020-09724-8.

Bayramoglu, B., Chakir, R., & Lungarska, A. (2020). Impacts of land use and climate change on freshwater ecosystems in france. *Environmental Modeling and Assessment*, 25, 147-172. doi: https://doi.org/10.1007/s10666-019-09673-x.

Ben Fradj, N. & Jayet, P.-A. (2018). Optimal management of perennial energy crops by farming systems in france: A supply-side economic analysis. *Biomass and Bioenergy*, (116), 113-121. doi: 10.1016/j.biombioe.2018.06.003.

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. 10.1016/j.landusepol.2015.12.027.

Ben Fradj, N., Jayet, P.-A., Rozakis, S., Georganta, E., & Jedrejek, A. (2020). Contribution of agricultural systems to the bioeconomy in poland: Integration of willow in the context of a stylised cap diversification. *Land Use Policy*, 99, 24p. doi: https://doi.org/10.1016/j.landusepol.2020.104797.
Thornley, J. (1998). *Grassland dynamics - An ecosystem simulation model*. CAB International.

Vacheron, J., Desbrosses, G., Bouffaud, M.-L., Touraine, B., Moënne-Loccoz, Y., Muller, D., Legendre, L., Wisniewski-Dyé, F., & Prigent-Combaret, C. (2013). Plant growth-promoting rhizobacteria and root system functioning. *Frontiers in Plant Science*, 4(September), 1–19.

Viennot, P., Monget, J., Ledoux, E., & Schott, C. (2007). *Modélisation de la pollution nitrique des aquifères du bassin de la Seine : intégration des bases de données actualisées des pratiques agricoles, validation des simulations sur la période 1970-2004, simulations prospectives de mesures agro-environnementales*. rapport r070209pvie, 50 p., Ecole des mines de Paris, Centre de Géosciences.

Vuichard, N., Ciais, P., Viovy, N., & Soussana, J. (2007a). Estimating the greenhouse gas fluxes of european grasslands with a process-based model: 2. simulations at the continental level. *Global Biogeochemical Cycles*, 21.

Vuichard, N., Soussana, J., Ciais, P., Viovy, N., Ammann, C., Calanca, P., Clifton-Brown, J., Fuhrer, J., Jones, M., & Martin, C. (2007b). Estimating the greenhouse gas fluxes of european grasslands with a process-based model: 1. model evaluation from in situ measurements. *Global Biogeochemical Cycles*, 21.
Bounaffaa, M., Florio, A., Le Roux, X., & Jayet, P.-A. (2018). Economic and environmental analysis of maize inoculation by plant growth promoting rhizobacteria in the french rhône-alpes region. *Ecological Economics*, 146, 334-346. doi: 10.1016/j.ecolecon.2017.11.009.

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.

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*. doi: 10.1142/S2382624X16500211.

Bourgeois, C. & Jayet, P. A. (2016). Regulation of relationships between heterogeneous farmers and an aquifer accounting for lag effects. *Australian Journal of Agricultural and Resource Economics*, 60(1), 39–59. doi: 10.1111/1467-8489.12102.

Brisson, N., Gary, C., Justes, E., Roche, R., Mary, B., Ripoche, D., Zimmer, D., Sierra, J., Bertuzzi, P., Burger, P., Bussière, F., Cabidoche, Y., Cellier, P., Debaeke, P., Gaudillère, J., Hénault, C., Maraux, F., Seguin, B., & Sinoquet, H. (2003). An overview of the crop model stics. *European Journal of Agronomy*, 18(3-4), 309–332. Modelling Cropping Systems: Science, Software and Applications.

Brisson, N. & Levrault, F. (2010). *Changement climatique, agriculture et for en France: simulation d'impacts sur les principales espèces. In: Livre vert de projet CLIMATOR (2007-2010)*. ADEME.

Brisson, N., Mary, B., Ripoche, D., Jeuffroy, M. H., Ruget, F., Nicoullaud, B., Gate, P., Devienne-Barret, F., Antonioletti, R., Durr, C., Richard, G., Beaudoin, N., Recous, S., Tayot, X., Plenet, D., Cellier, P., Machet, J.-M., Meynard, J.-M., & Delécolle, R. (1998). Stics: a generic model for the simulation of crops and their water and nitrogen balances. i. theory and parameterization applied to wheat and corn. *Agronomie*, 18(5-6), 311-346.

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.

Caputo, M. R. & Paris, Q. (2008). Comparative statics of the generalized maximum entropy estimator of the general linear model. *European Journal of Operational Research*, 185(1), 195-203.

Caurla, S., Delacote, P., Lecocq, F., Barthès, J., & Barkaoui, A. (2013). Combining an inter-sectoral carbon tax with sectoral mitigation policies: Impacts on the French forest sector. *Journal of Forest Economics*, 19(4), 450-461.

Chakir, R. (2009). Spatial downscaling of agricultural land-use data: An econometric approach using cross entropy. *Land Economics*, 85(2), 238-251.

Chakir, R. & Lungarska, A. (2017a). Agricultural rent in land-use models: comparison of frequently used proxies. *Spatial Economic Analysis*, 12(2-3), 279-303.
# Appendix

## A User's call for the model

The user is supposed to have an account on the workstation dedicated to the model. Some slight preliminary operations are required before the model's use, properly speaking, regarding "login" and "path" command files and internal access rights. The user should be also preferably familiar with basic Linux-shell commands.

A few screen capture schemes detail how the user accedes to the AROPAj based shell leading to simulations and outputs exploitation. The following figures illustrate how it runs through screen copies from the user's point of view.

In order to initiate a new simulation:

<figure><img src="image_70.png" /></figure>

1. After the execution of the "miraj" command, select a project (ask the administrator for the project list).

<figure><img src="image_71.png" /></figure>

2. Select "s" for initiating the simulation process.

<figure><img src="image_72.png" /></figure>

3. Select a version of the AROPAj model ("2009" for the current version).

<figure><img src="image_73.png" /></figure>

4. Select the name of one of your csh shell simulation script (without ".csh"). If you don't remember the name of the script, type any letter and Miraj will propose the available scripts.

...

Launching simulations asked by some complex scenario design may lead to run times of several hours. At the moment (2015), one hundred simulations for a complete European cover (e.g. 27 Member States and more than 1800 unit LPs) lasts around half an hour when the workstation is
Chakir, R. & Lungarska, A. (2017b). Agricultural rent in land-use models: comparison of frequently used proxies. *Spatial Economic Analysis*, 12, 379–303. doi: 10.1080/17421772.2017.1273542.

Ciaian, P., Espinosa, M., Gomez y Paloma, S., Heckelei, T., Langrell, S., Louichi, K., Sckokao, P., Thomas, A., & Vard, T. (2013). *Farm level modelling of CAP: a methodological overview*. Technical report, JRC Scientific and policy reports. doi:10.2791/86415.

Clodic, M. (2013). *Modélisation de l'effre de biocarburants à l'échelle européenne, échanges intra-et extra-européens, effets croisés avec les marchés ptroliers et contrainte carbone*. PhD thesis, ABIES-AgroParisTech, dir.: P.-A. Jayet.

Day, R. H. (1963). On aggregating linear programming models of production. *Journal of Farm Economics*, 45(4), 797–813.

De Cara, S., Henry, L., & Jayet, P.-A. (2018). Optimal coverage of an emission tax in the presence of monitoring, reporting, and verification costs. *Journal of Environmental Economics and Management*, 89, 1–23. doi: 10.1016/j.jeem.2018.03.001.

De Cara, S., Houzé, M., & Jayet, P.-A. (2005). Methane and nitrous oxide emissions from agriculture in the EU: a spatial assessment of sources and abatement costs. *Environmental and Resource Economics*, 32(4), 551–583.

De Cara, S. & Jayet, P.-A. (1999). Evaluation et régulation de l'effet de serre d'origine agricole. *L'Actualité Economique*, 75(4), 597–623.

De Cara, S. & Jayet, P.-A. (2000a). Emissions of greenhouse gases from agriculture : the heterogeneity of abatement costs in france. *European Review of Agricultural Economics*, 75(4), 597–623.

De Cara, S. & Jayet, P.-A. (2000b). Régulation de l'effet de serre d'origine agricole : Puits de carbone et instruments de second rang. *Economie et Prévision*, 143/144(2-3), 37–46.

De Cara, S. & Jayet, P.-A. (2006). Mitigation of greenhouse gas emissions in EU agriculture: An assessment of the costs of reducing agricultural emissions and enhancing carbon sinks in agricultural soils. In *INSEA Report SSP1-CT-2003-503614-Final*, European Commission, INSEA, IIASA, Laxenburg, Austria.

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.

De Cara, S. & Thomas, A. (2008). *Projections d'émissions/absorptions de gaz à effet de serre dans les secteurs forêt et agriculture aux horizons 2010 et 2020*. Technical report. 192 pp.

Devaraju, N., P>rudhomme, R., Lungarska, A., Wang, X., Yin, Z., de Noblet-Ducoudré, N., Chakir, R., Jayet, P.-A., Brunelle, T., Viovy, N., De Palma, A., Gonzalez, R., & Ciais, P. (2022). Quantifying the benefits of reducing synthetic nitrogen application policy on ecosystem carbon sequestration and biodiversity. *Nature Scientific Reports* 2022 12:1, 12(1), 1–15.
...

```
Sélection des options PAC sous la forme "IP1+IP2+IP3..." ou "0"
où IP1 est un indice de type novpac (1 2 ... 13 ...) (ex: 13+1)

choix --> (.../sinon x/r) 0
```

5. Fill with "0" as a choice for CAP options.

```
Incorporation dans le fichier des paramètres (mgg.par) de :
modpac (1) refscop (10) lisscal (100) externe (1000)
Les fichiers modpac.par (voir opération "3") et externe.par sont
supposés pré-exister (pour les version et pays demandés)
Le format d entrée : somme des puissances de 10 (ex: 1001)

choix --> (.../sinon 0/x/r) 1000
```

6. Fill with "1000" to choose the parameters file. Note the simulation identifier (12 digits). Output file names will refer to this identifier and to farm group index.

```
détruire les fichiers créés lors d'une simulation précédente
... si non, conserve les exécutables pour gain de temps compil.

choix --> (y/n/x/r) n

Identifiant ensemble de simulations : 150522145458

>>>> Opération sur tableau.150522145458
```

7. Usually fill with "n". Fill with "y" to delete files from previous simulation. Note the simulation identifier (12 digits).

```
Envoi en boucle sur tous pays de l opération à venir ?

choix --> (y/n/x) n
```

8. Fill with "n" (fill with "y" to start a loop).

```
Choirx du pays (4 lettres) parmi :
belg bgra cypr czek dani deul deu2 deu3 ella esp1 esp2 esto frai
ita3 ltua luxe lvat mlta nede osto pol1 pol2 pol3 pol4 port rou1

choix --> (a4/sinon x) |
```

9. Choose the AROPAj country or sub-country, identified by a 4-character sequence.

```
Choirx opération (il) parmi :
p opération sur commande propre avec "pays" comme paramètre
0 (base)
3 (modpac)
4 (validation)
5 (initialisation)
6 (simulation série)
7 (agrégation résultats)
8 (tabulation résultats)
n ("nettoyage fichiers")

choix --> (./r/x) |
```

10. Initiate the simulation process by calling successively "p", "0", "3", "4", and "5" for a country. The process can be stopped anytime to check the first LPs solving (fill with "x" as many times as necessary to leave the Miraj menu). You may complete the steps on the order "p", "0", "3", "4", "5", ("6" in case of iterated simulations ), "7", and "8".

<caption>Figure 55. Launching the call for AROPAj (1).</caption>
Durandeau, S., Gabrielle, B., Godard, C., Jayet, P.-A., & Le Bas, C. (2010). Coupling biophysical and microeconomic models to assess the effect of mitigation measures on greenhouse gas emissions from agriculture. *Climatic Change*, 98, 51–73.

Errington, S. (1989). Estimating Enterprise Input-Output Coefficients From Regional Farm Data. *Journal of Agricultural Economics*, 40(1), 52–56.

Florio, A., Pommier, T., Gervaix, J., Bérard, A., & Le Roux, X. (2018). Soil C and N statuses determine the effect of maize inoculation by plant growth-promoting rhizobacteria on nitrifying and denitrifying communities. *Scientific reports*, 7(1), 8411.

Freund, R. J. (1956). The Introduction of Risk into a Programming Model. *Econometrica*, 24(3), 253–263.

Galko, E. & Jayet, P.-A. (2011). Economic and environmental effects of decoupled agricultural support in the eu. *Agricultural Economics*, 42, 605–618.

Godard, C., Roger-Estrade, J., Jayet, P.-A., Brisson, N., & Le Bas, C. (2008). Use of available information at a european level to construct crop nitrogen response curves for the regions of the eu. *Agricultural Systems*, 97, 68–82.

Golan, A., Judge, G., & Miller, D. (1996). Maximum entropy econometrics: Robust estimation with limited data. *New York, John Wiley eds.*

Golan, A., Judge, G., & Perloff, J. (1997). Estimation and inference with censored and ordered multinomial response data. *Journal of Econometrics*, 79(1), 23–51.

Gomez, E. (2002). *Modélisation intégrée du transfert de nitrate à l'échelle régionale dans un système hydrologique. Application au bassin de la Seine.* PhD thesis, Ecole Nationale Supérieure des Mines de Paris, France.

Gomez, E., Ledoux, E., Viennot, P., Mignolet, C., Benoit, M., Bornerand, C., Schott, C., Mary, B., Billen, G., Ducharne, A., et al. (2003). Un outil de modélisation intégrée du transfert des nitrates sur un système hydrologique: application au bassin de la seine. *La Houille Blanche*, (3), 38–45.

Good, I. J. (1963). Maximum entropy for hypothesis formulation, especially for multidimensional contingency tables. *The Annals of Mathematical Statistics*, Vol. 34, No. 3., 911–934.

Gossiaux, E. & Jayet, P.-A. (2021). Exploring the potential for calories and bioenergy in france. *BE-Sustainable Magazine*, 12, 11–17. https://issuu.com/besustainablemagazine/docs/besustainable_magazine issue_12--april_2021/s/12150135.

Gérard, M. & Jayet, P. (2023). European farmers’ response to crop residue prices and implications for bioenergy policies. *Energy Policy*. https://doi.org/10.1016/j.enpol.2023.113561.

Havlík, P., Schneider, U. A., Schmid, E., Böttcher, H., Fritz, S., Skalský, R., Aoki, K., De Cara, S., Kindermann, G., Kraxner, F., et al. (2011). Global land-use implications of first and second generation biofuel targets. *Energy Policy*, 39(10), 5690–5702.
In order to complete a simulation that has already been initiated :

```
choix de la version "modèle+aropaj"
-----------------------------------------------------------------------
Opération en cours : indiquer identifiant 12 chiffres (tableau)
Sinon choix parmi :
    600 : UE-15 (février 2001)
    700 : UE-15 (juin 2005)
    800 : UE-24 (aout 2007)
20?? : UE-27 (novembre 2014 -3 années RICA 2007-8-9)
    (indiquer un nombre < 109 si identifiant oublie)
-----------------------------------------------------------------------
choix --> (700../2009/sinon x) 00000
## ###########################################
>>> ATTENTION : choix de 12 chiffres parmi: <<
150319161521
150407152450
150407162456
150522145458
## ###########################################
```

Repeat steps **1.** and **2.**. Then, select the simulation identifier (usually the last one). If you don't remember the identifier, type any number set and Miraj will propose the available identifiers.

Go directly to step **8.**, **9.** and **10.**, to continue the simulation process.

Once out of the Miraj menu (fill with "x" as many times as necessary), you can follow the simulation process through the table identified by the 12-digit identifier. In this table, a "country" (or a "sub-country" when country splitting) refers to a 8-digit identifier.

<table><caption>Tableau des opérations cree selon l'indexe date : 150522145458 - indicatif version : 2009<br/>- commande integree : simuGES<br/>- selection modpac : 0<br/>- selection fichier : 1000</caption><thead><tr><th>PAYS</th><th>nbgrp</th><th>base<br/>(0)</th><th>modpac<br/>(3)</th><th>comm. ex.<br/>(p)</th><th>selection(4)</th><th>initialisat.<br/>(5)</th><th>identif.</th><th>simulation<br/>(6)</th><th>agrég res<br/>(7)</th><th>tabula res<br/>(8)</th><th>nettoyage</th></tr></thead><tbody><tr><td>belg</td><td>29</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>bgra</td><td>42</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>cypr</td><td>7</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>czek</td><td>37</td><td>0 052214</td><td>3 052214</td><td>p 052214</td><td>4 052214</td><td>5 052215</td><td>22150059</td><td></td><td></td><td></td><td></td></tr><tr><td>dani</td><td>26</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr></tbody></table>

Experimented users may execute all simulations through the automatic simulation process by selecting "a" from the **2. menu** (see Figure 55).

<caption>**Figure 56.** Launching the call for AROPAj (2).</caption>
Hazell, P. B. R. & Norton, R. D. (1986). *Mathematical Programming for Economic Analysis in Agriculture*. New York: MacMillan.

Heckelei, T. & Wolff, H. (2003). Estimation of constrained optimisation models for agricultural supply analysis based on generalised maximum entropy. *European Review of Agricultural Economics*, 30(1), 27–50.

Hénin, S. & Dupuis, M. (1945). *Essai de bilan de la matière organique du sol*, volume 15.

Herrero, M., Thornton, P., Notenbaert, A., Wood, S., Msangi, S., Freeman, H., Bossio, D., Dixon, J., Peters, M., Steeg, J., et al. (2010). Smart investments in sustainable food production: revisiting mixed crop-livestock systems.

Howitt, R. E. (1995). *Positive Mathematical Programming*. *American Journal of Agricultural Economics*, 77(2), 329–342.

Humblot, P., Jayet, P.-A., & Petsakos, A. (2017). Farm-level bio-economic modeling of water and nitrogen use: Calibrating yield response functions with limited data. *Agricultural Systems*, 151, 47–60. doi: 10.1016/j.agsy.2016.11.006.

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.

Isbasoiu, A., Jayet, P.-A., & De Cara, S. (2020). Increasing food production and mitigating agricultural greenhouse gas emissions in the european union: impacts of carbon pricing and calorie production targeting. *Environmental Economics and Policy Studies*, (pp. 32p). doi: https://doi.org/10.1007/s10018-020-00293-4.

Izaurralde, R., Williams, J., McGill, W., Rosenberg, N., & Quiroga Jakas, M. (2006). Simulating soil c dynamics with epic: Model description and testing against long-term data. *Ecological Modelling*, 192(3), 362–384.

Jayet, P., Bamière, L., & Zuravel, O. (2025). Agroénergie et émissions de gaz à effet de serre dans l'union européenne. *Revue de l'Énergie*, 678, 13–28.

Jayet, P. A. (2002). Mathematical programming for the computation of multi-market equilibria: an attempt in modelling the french markets of cereals and livestock feeds. In S. Rozakis & J. Sourie (Eds.), *Comprehensive economic and spatial bio-energy modelling*, number 48 in Série A. Séminaires Méditerranéens (pp. 35–46).: CIHEAM.

Jayet, P.-A. (2006). *Deliverable 4*. Technical report, GENEDEC project document.

Jayet, P. A., Aghajanzadeh-Darzi, P., & Petsakos, A. (2017a). Improvement of a bio-economic mathematical programming model in the case of on-farm source inputs and outputs. *Journal of Quantitative Economics*, 15, 489–508. doi: 10.1007/s40953-016-0058-z.

Jayet, P. A., Barberis, D., & Humblot, P. (2017b). The economic impact of climate change on agricultural water demand in France. In D. Kocasevic (Ed.), *Book of proceedings - VIII International Scientific Agriculture Symposium* (pp. 2032–2038).: AgroSym 2017.
moderately loaded.

In case of computation problems, the user is kindly advised to check first her/his own files (writing format, inadequate blank lines in *scen.(par var gar)* files - see section 11.1.3, **FORTRAN** code in *scen.f* or *Cshell* code in *scen.csh*). Problems may be identified thanks to information tracking provided on files of type name *sortie.*\*. Secondly, some *LPs* may be "infeasible" due to inadequate values of parameters (e.g. in case of too restrictive quotas). Thirdly, as a researchers' model surely not sufficiently developed in line with the rules of the computation art, *AROPAj* encounters many problems that often PhD students are able to invent.

## B Contributors to AROPAj and to related publications

The list mentions contributors who used at least partially the model and who directly or indirectly contributed to its development, as *PhD* or *MSc* students and engineers or researchers hired on permanent positions (*PP*) or on short or middle term contracts (*EHC*).

* Annie Hofstetter (PP; 1990-1997)

* Philippe Bontems (MSc; 1990, 1992)

* Nathalie Taverdet-Popiolek (PhD; 1990-1993)

* Abdel Aloualiti (EHC; 1991)

* Frédérique Lefaudeux (MSc, EHC; 1991-1992)

* Vincent Thuillier (MSc; 1991)

* Jérôme Adda (MSc; 1991)

* Sophie Chavanne (MSc; 1992)

* Joël Mathurin (MSc, PhD; 1992, 1994-1997)

* Alessandra Schiavina (MSc; 1993)

* Franck Leclerq-Blondel (EHC; 1994)

* Gilles Le Moguédec (MSc, EHC; 1995-1996)

* Carolina Chaya (PhD; 1995-1997)

* Roberta Silvagni (MSc; 1996)

* Nicolas Godfroy (MSc; 1996)

* Alain Birfet (EHC; 1996-1997)

* Stéphane De Cara (MSc, PhD, PP; 1997-2000, occasional, 2015-)

* Solène Maugars (MSc; 1998)

* Eleonora Marzocchi (MSc, EHC; 1998-2000)

* Kais Abbes (MSc; 1999)

* Michele Donati (MSc, EHC; 2000)

* Guillaume Gruère (MSc; 2000)

* Caroline Godard (PhD; 2002-2005)

* Laure Bamière (MsC, PP; 2002-2005, occasional, 2017-)

* Julien Labonne (EHC; 2003)

* Martin Houzé (MSc; 2003-2004)

* Bineta Niang (MSc, EHC; 2003)
Jayet, P.-A., Barberis, D., Humblot, P., & Lungarska, A. (2018). Spatialisation de la demande en eau d'irrigation estimée par un modèle bioéconomique. *Revue internationale de géomatique*, 4(28), 485-503. doi: 10.3166/rig.2018.00064.

Jayet, P.-A., Birfet, A., & Hofstetter, A. (1998). Forêt paysanne et politique agricole commune : une évaluation des impacts d'une incitation au reboisement. *Cahiers d'Economie et Sociologie Rurales*, 48, 6-35.

Jayet, P.-A., Isbasoiu, A., & De Cara, S. (2020). Slaughter cattle to secure food calories and reduce agricultural greenhouse gas emissions? some prospective estimates for france. *Review of Agricultural, Food and Environmental Studies*, (pp. 24p). doi: https://doi.org/10.1007/s41130-020-00117-9.

Jayet, P.-A. & Labonne, J. (2005). Impact d'une réforme de la politique agricole commune par le découplage. *Economie et Prévision*, 167(1), 101-116.

Jayet, P. A. & Lapierre, O. (1998). *La dynamique des systèmes*, chapter Modélisation des marchés de l'alimentation animale, (pp. 73-93). Hermès.

Jayet, P. A. & Petel, E. (2015). 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.

Jaynes, E. T. (1957). Information theory and statistical mechanics. *Physics Review*, 106, 620-630.

Kampas, A., Petsakos, A., & Rozakis, S. (2012). Price induced irrigation water saving: Unraveling conflicts and synergies between European agricultural and water policies for a Greek Water District. *Agricultural Systems*, 113, 28-38.

Kennedy, J. O. (1975). Using regression analysis to reduce aggregation bias in linear programming supply models. *The Australian Journal of Agricultural Economics*, 19(1), 11p.

Kjellström, E., Nikulin, G., Hansson, U., Strandberg, G., & Ullerstig, A. (2011). 21st century changes in the european climate: uncertainties derived from an ensemble of regional climate model simulations. *Tellus A*, 63(1), 24-40.

Leclère, D., de Noblet-Ducoudré, N., & Jayet, P.-A. (2014). Understanding the farm-scale response of european agriculture to climate change: evaluation of a coupled economic-crop model. *submitted*.

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.

Ledoux, E. (1980). *Modélisation intégrée des écoulements de surface et des écoulements souterrains sur un bassin hydrologique*. PhD thesis, Ecole des Mines de Paris.
* Mathieu Godet (MSc; 2004)

* Elodie Debove-Galko (PhD; 2004-2007)

* Edouard Baranger (MSc, EHC; 2005-2006)

* Raja Chakir (PP; 2005-2006, occasional, 2016-)

* Nathalie Novello (EHC; 2006-2008)

* Fanny Guillet (MSc; 2006)

* Sophie Durandeau (EHC; 2006-2007)

* Marta Piñies (MSc; 2006)

* Paul Zakharov (EHC; 2006-2010)

* Agathe Rouaix (MSc; 2007)

* Bernd Kuepker (MSc; 2007)

* Melissa Clodic (MSc, PhD; 2007-2013)

* Cloé Garnache (MSc; 2007)

* Basile Grosdidier (EHC; 2007)

* Marie Génin (MSc; 2008)

* Pierre Cantelaube (EHC; 2008)

* Audrey Pollard (MSc; 2008)

* Cyril Bourgeois (PhD; 2008-2011)

* David Leclère (MSc, PhD; 2008-2013)

* Athanasios Petsakos (MSc, EHC; 2009-2010, 2012-2014)

* Nosra Ben Fradj (MSc, PhD, EHC; 2009-2013)

* Delphine Leconte-Demarsy (EHC; 2010)

* Parisa Aghajanzadeh-Darzi (MSc, PhD; 2010-2013, 2015-2016)

* Paola Clerino (EHC; 2011)

* Joao-Pedro Domingues (MSc; 2011)

* Pierre Humblot (EHC, PhD, own company; 2011-2016, 2017-2018)

* Myrsini Papadaki (MSc; 2012)

* Anna Lungarska (MSc, PhD, EHC; 2012-2016, 2017-)

* Sara Yavari (MSc; 2013)

* Elvire Petel (EHC; 2013-2015)

* Gaspard Dumollard (PhD; 2014-2016)

* Gregory Pilchak (MSc; 2015)

* Ancuta Isbasoiu (EHC, PhD; 2015-2019)

* Juliette Adrian (EHC; 2015-2016)

* Clément Servant (EHC, supervised by S. De Cara; 2015)

* Myriam Bounaffaa (EHC; 2015-2017)

* Gabrielle Despres (EHC; 2015-2016)

* Coline Assaiante (EHC; 2015-2016)

* Delphine Barberis (EHC; 2016-2017)

* Loïc Henry (MSc, PhD, supervised by S. De Cara; 2016-2020)

* Maxime Ollier (MSc, EHC, PhD; 2017-2018, 2018-2021)
Ledoux, E., Girard, G., de Marsily, G., Villeneuve, J., & Deschenes, J. (1989). Spatially distributed modelling: conceptual approach, coupling surface water and groundwater, 1989. *Unsaturated flow in hydrologic modelling–theory and practice*. NATO ASI ser. CNorwell, Massachusett: Kluwer academic, (pp. 435–454).

Ledoux, E., Girard, G., & Villeneuve, J. (1984). Proposition d'un modèle couplé pour la simulation conjointe des écoulements de surface et des écoulements souterrains sur un bassin hydrologique. *La houille blanche*, (1-2), 101–120.

Ledoux, E., Gomez, E., Monget, J., Viavattene, C., Viennot, P., Ducharne, A., Benoit, M., Mignolet, C., Schott, C., & Mary, B. (2007). Agriculture and groundwater nitrate contamination in the seine basin. the stics-modcou modelling chain. *Science of the Total Environment*, 375(1-3), 33–47.

Leip, A., Weiss, F., Wassenaar, T., Perez, I., Fellmann, T., Loudjani, P., Tubiello, F., Grandgirard, D., Monni, S., & Biala, K. (2010). *Evaluation of the livestock sector's contribution to the EU greenhouse gas emissions (GGELS)*. Technical report, European Commission, Joint Research Centre (JRC).

Léon, Y., Peeters, L., Quinqu, M., & Surry, Y. (1999). The Use of Maximum Entropy to Estimate Input-Output Coefficients From Regional Farm Accounting Data. *Journal of Agricultural Economics*, 50(3), 425–439.

Lobianco, A., Delacote, P., Caurla, S., & Barkaoui, A. (2016). Accounting for active management and risk attitude in forest sector models. *Environmental Modeling & Assessment*, 21, 391–405.

Louhichi, K., Jacquet, F., & Butault, J. P. (2012). Estimating input allocation from heterogeneous data sources: A comparison of alternative estimation approaches. *Agricultural Economics Review*, 13(2), 83–102.

Lungarska, A., Brunelle, T., Chakir, R., Jayet, P.-A., Prudhomme, R., De Cara, S., & Bureau, J.-C. (2023). Halving mineral nitrogen use in European agriculture: Insights from multi-scale land-use models. *Applied Economic Perspectives and Policy*, 12.

Lungarska, A. & Chakir, R. (2018). Climate-induced Land Use Change in France: Impacts of Agricultural Adaptation and Climate Change Mitigation. *Ecological Economics*, 147, 134–154.

Lungarska, A., Chakir, R., Ben Fradj, N., Jayet, P.-A., Bamière, L., Isbasoiu, A., Ollier, M., Gosiaux, E., Chiadmi, I., De Cara, S., & Kahindo, S. (2020). Approche intégrée des productions animales et végétales dans un modèle économique pour l'analyse des interactions agriculture – environnement. *Innovations Agronomiques*, 80, 69–85.

Lungarska, A. & Jayet, P. A. (2016). Impact of spatial differentiation of nitrogen taxes on french farms' compliance costs. *Environmental and Resource Economics*. doi: 10.1007/s10640-016-0064-9.

Makowski, D., Wallach, D., & Meynard, J. (1999). Models of yield, grain protein, and residual mineral nitrogen responses to applied nitrogen for winter wheat. *Agronomy Journal*, 91(3), 377–385.
* Monia El-Akkari (PhD; 2018-2020, supervised by B. Gabrielle and J.L. Drouet)

* Salomé Kahindo (MSc, supervised by L. Bamière, 2018; 2019-)

* Jeffrey Norville (EHC, supervised by V. Martinet; 2018-2019)

* Eleni Georganta (MSc ERASMUS, 2019)

* Panagiota Arampatzi (MSc ERASMUS, 2019)

* Amal Azizi (PostDoc, 2019)

* Inès Chiadmi (EHC, PhD co-supervised by S. Van Passel; 2019-2021, 2021-2024)

* Eva Gossiaux (MSc, 2019-2020)

* Adrien Coiffard (MSc, 2020)

* Erica Ramirez (MSc, EHC, 2020-2021)

* Anaïs Kanellos (MSc, EHC, 2021)

* Maxence Gérard (MSc, EHC, 2021)

* Julie Reineix (MSc, EHC, PhD co-supervised by A. Ducharne; 2021, 2021-2024)

* Théodore Fontenaille (MSc, supervised by Ministère de la Transition Ecologique, 2022)

* Lisa Baldi (visiting PhD Univ. Parma, 2023)

* Olga Zuravel (EHC, 2023-2024)

* Mingzhu Weng (MSc, 2023)

## C Funded projects and supports benefiting AROPAj

Projects that follow contributed to the development of AROPAj. Obviously fundings that were obtained were generally only partly devoted to the model itself.

1. Société Louis-Dreyfus, 1990-1992 (initiates AROPAj - France & UK)

2. Unigrains, 1990-1993, (PhD - theory-oriented)

3. AIP Inra "modélisation du secteur agricole", 1991-1993 (coll. Y. Le Roux, A. Hofstetter)

4. Inra Support, 1991-1993 (PhD co-fin. Inra-Edf N. Taverdet)

5. Commissariat Général du Plan & Direction de la Prévision, 1991-1992, convention d'étude CGP n°11/1191, prestation DP 910127, (coll. J.C. Bureau, Y. Le Roux)

6. AIP Inra "Politique Agricole Commune", 1992-1994 (MS strategies, coll. E. Giraud-Héraud, Y. Le Roux, A. Schiavina; extensification of agricultural productions, coll. J.P. Butault, A. Barkaoui - ESR Nancy, J. Mathurin)

7. Commissariat Général du Plan & Direction de la Prévision, 1992-1993, convention d'étude CGP n°6/1992, prestation DP 920060

8. Bologna University, 1993-1996 (AROPAj Italy, coll. A. Schiavina, 3 alloc. COMETT)

9. Confédération Paysanne, 1993-1995 (funding for internships, coll. G. Le Moguédec)

10. AIP Inra "Nouvelles fonctions de l'agriculture", 1994-1996 (Interactions between agricultural policies and aforestation incentives, coll. ESR Nancy, A. Birfet, A. Hofstetter)

11. Demeter, 1995

12. Funding from MinAgri, 1994-1997 (PhD Ing. Agron. J. Mathurin)

13. AIP Inra "ALIMAN", 1995-1997 (modeling of cereals and feed markets, coupling AROPAj - industrial feed formulation, coll. Y. Dronne ESR Rennes, O. Lapierre INAPG et CEREOPA)
Markowitz, H. M. (1952). Portfolio Selection. *The Journal of Finance*, 7(1), 77-91.

McCarl, B. A. & Sreen, T. H. (1980). Price Endogenous Mathematical Programming as a Tool for Sector Analysis. *American Journal of Agricultural Economics*, 62(1), 87-102.

Mills, G., Buse, A., Gimeno, B., Bermejo, V., Holland, M., Emberson, L., & Pleijel, H. (2007). A synthesis of aot40-based response functions and critical levels of ozone for agricultural and horticultural crops. *Atmospheric Environment*, 41(41), 2630-2643.

Mosnier, C., Britz, W., Jullière, T., De Cara, S., Jayet, P.-A., Havlik, P., Franck, S., & Mosnier, A. (2019). Greenhouse gas abatement strategies and costs in dairy production – a comparison across bio-economic models. *Journal of Cleaner Production*, (236), 17589. doi: 10.1016/j.jclepro.2019.07.064.

Olesen, J. & Bindi, M. (2002). Consequences of climate change for european agricultural productivity, land use and policy. *European Journal of Agronomy*, 16(4), 239-262.

Ollier, M. & De Cara, S. (2024). Give and take: An analysis of the distributional consequences of emission tax-and-rebate schemes with an application to greenhouse gas emissions from european agriculture. *Ecological Economics*, 219, 108154.

Ollier, M., Jayet, P.-A., & Humblot, P. (2024). An assessment of the distributional impacts of autonomous adaptation to climate change from european agriculture. *Ecological Economics*, 222, 108221.

Peltre, C., Christensen, B., Dragon, S., Icard, C., Kätterer, T., & Houot, S. (2012). RothC simulation of carbon accumulation in soil after repeated application of widely different organic amendments. *Soil Biology and Biochemistry*, 52(2012), 49-60.

Penman, J., D. K., Galbally, I., Hiraishi, T., Nyenzy, B., Emmanul, S., Buendia, L., Hoppaus, R., Martinsen, T., Meijer, J., Miwa, K., & Tanabe, K., Eds. (2001). *Good Practice Guidance and Uncertainty Management in National Greenhouse Gas Inventories*. Intergovernmental Panel on Climate Change (IPCC). Japan: Institute for Global Environmental Strategies (IGES).

Prudhomme, R., Chakir, R., Lungarska, A., Brunelle, T., Devaraju, N., de Noblet, N., Jayet, P.-A., De Cara, S., & Bureau, J.-C. (2022). Food, climate and biodiversity: a trilemma of mineral nitrogen use in European agriculture. *Review of Agricultural, Food and Environmental Studies*.

Reidsma, P., Ewert, F., Oude Lansink, A., & Leemans, R. (2009). Vulnerability and adaptation of european farmers: a multi-level analysis of yield and income responses to climate variability. *Regional Environmental Change*.

Reineix, J. & Jayet, P.-A. (2025). Multi-scale analysis of water sharing based on bio-economic modeling. *Environmental Modeling & Assessment*.

Riedo, M., Grub, A., Rosset, M., & Fuhrer, J. (1998). A pasture simulation model for dry matter production, and fluxes of carbon, nitrogen, water and energy. *Ecological Modelling*, 105, 141-183.
14. UP Madrid, 1995-1997, (PhD co-supervising with Université de Bourgogne, C. Chaya)

15. Contrat cadre Inra-Inia (Spain), 1996, support of the previous project

16. Sido, 1996-1998 (coll. Y. Dronne, O. Lapierre - module "feed" and partial equilibrium approach, MODANI-AROPAj)

17. Mission Interministérielle sur l'Effet de Serre, 1996-1997 (initiating GHG emission studies), lettre de commande n° 14/96.

18. Bologna University, 1999, grant Leonardo

19. Partenaire (initiateur) programme EUROTOOLS, FAIR5 CT97-3403, 1998-2000 (AROPAj-UE-12)

20. GICC (MinEnvir & MIES), 1999-2001 (S. De Cara, coll. Inra Avignon related to the STICS model)

21. GICC (MinEnvir), 2001-2003 (initiating of spatialisation, coll. Soil Sciences Inra Orléans)

22. DG AGRI (Com. Europ.), 2001-2002 (AROPAj EU-15, reforming the milk quota system viewed through in AROPAj)

23. GICC (MinEnvir), 2002-2004 (initiating the coupling AROPAj-STICS, coll. Inra CSE-Avignon & Infosol-Orléans)

24. Ademe, 2002-2005 (PhD grant, C. Godard, coupling STICS-AROPAj)

25. Direction de la Prévision, 2002-2003 (AROPAj and decoupling in the CAP)

26. STREP FP5 GENEDEC-502184, 2004-2007 (coordinating 10 teams in 6 MS, support and exchange PhD, coll. JRC and Universities, Parma, Madrid, Reading, Rethymno, Verona, Dublin, FAL)

27. Inra-MinAgri, 2004-2007 (funding of a PhD, E. Galko)

28. Strep FP5 INSEA, 2004-2006 (partner, Iiasa coordinating)

29. GICC-2 (ministère de l'environnement), 2004-2006 (GHG emissions, climate change, regulation & mitigation, initiating analysis of adaptation, coll. LSCE, N. de Noblet)

30. ADD-DST, 2005-2008 (impact of soil compaction, coll. F. Guillet, J. Roger-Estrade, G. Richard, Inra-Orléans)

31. PIREN-Seine, 2007-2011 (PIREN phase V, coll. ParisVI-Sisyphe, Mines-Géosciences, Inra Mirecourt; initiating water issues analysis with AROPAj, water and nitrogen pollutants, coupling STICS-MODCOU-AROPAj)

32. Ifp, 2007-2011 (coll. Ifp, PhD grant, M. Clodic, AROPAj EU-25 & bio-fuels)

33. Ademe-CEA, 2008-2011 (Climate change, European agriculture and mitigation - adaptation, PhD grant, D. Leclère)

34. R2DS (Ile-de-France region), 2009-2012 (PhD grant, C. Bourgeois, aquifer dynamics & joined pollutions)

35. "Feturol" (INRA, Ifp), 2009-2012 (PhD grant, N. Ben Fradj, AROPAj & perennial crops dedicated to bio-fuels & environmental impacts)

36. Eau-dyssée (CNRS-"ec2co"), 2009, 2010, (grant dedicated to model coupling, coll. F. Habets Sisyphe-MinesParisTech)

37. CC-TAME FP7-KKBE-2010-4 (partner, coll. S. De Cara, AROPAj & GHG-MACs, initiating the AROPAj version EU-27)

38. ANR-VALIDATE, 2008-2011 (grasslands, livestock & climate change)
Rötter, R. & van de Geijn, S. (1999). Climate change effects on plant growth, crop yield and livestock. *Climatic Change*, 43(4), 651–681.

Schmid, M., Neftel, A., Riedo, M., & Fuhrer, J. (2001). Process-based modelling of nitrous oxide emissions from different nitrogen sources in mown grassland. *Nutrient Cycling in Agroecosystems*, 60, 177–187.

Shannon, C. E. (1948). A mathematical theory of communication. *Bell System Technical Journal*, 27, 379–423.

Soussana, J. & Lüscher, A. (2007). Temperate grasslands and global atmospheric change: a review. *Grass and Forage Science*, 62(2), 127–134.

Souty, F., Brunelle, T., Dumas, P., Dorin, B., Ciais, P., Crassous, R., Müller, C., & 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. *Geoscientific Model Development*, 5(5), 1297–1322.

Thornley, J. (1998). *Grassland dynamics - An ecosystem simulation model*. CAB International.

Vacheron, J., Desbrosses, G., Bouffaud, M.-L., Touraine, B., Moënne-Loccoz, Y., Muller, D., Legendre, L., Wisniewski-Dyé, F., & Prigent-Combaret, C. (2013). Plant growth-promoting rhizobacteria and root system functioning. *Frontiers in Plant Science*, 4(September), 1–19.

Viennot, P., Monget, J., Ledoux, E., & Schott, C. (2007). *Modélisation de la pollution nitrique des aquifères du bassin de la Seine : intégration des bases de données actualisées des pratiques agricoles, validation des simulations sur la période 1970-2004, simulations prospectives de mesures agro-environnementales*. rapport r070209pvie, 50 p., Ecole des mines de Paris, Centre de Géosciences.

Vuichard, N., Ciais, P., Viovy, N., & Soussana, J. (2007a). Estimating the greenhouse gas fluxes of european grasslands with a process-based model: 2. simulations at the continental level. *Global Biogeochemical Cycles*, 21.

Vuichard, N., Soussana, J., Ciais, P., Viovy, N., Ammann, C., Calanca, P., Clifton-Brown, J., Fuhrer, J., Jones, M., & Martin, C. (2007b). Estimating the greenhouse gas fluxes of european grasslands with a process-based model: 1. model evaluation from in situ measurements. *Global Biogeochemical Cycles*, 21.
39. ANR-VULNOZ, 2008-2012 (ozone impacts on agricultural, coll. D. Leconte, P. Clerino, P. Humblot, coll. Inra-EGC, LSCE)

40. ANIMALCHANGE FP7, 2011-2014 (coord. J.F. Soussana, PhD grant co-funded by ADEME, P. Aghajanzadeh-Darzi, climate change & livestock)

41. PIREN-Seine phase VI, 2011-2015 (coll. Sisyphe Univ. ParisVI & MinesParisTech, policy analysis & directives dedicated to the environment)

42. ANR-ORACLE, 2011-2016 (land use, water, agriculture, grasslands and forest, coll. INRA-LEF, LSCE, Mines-Géosciences, PhD funding for A. Lungarska)

43. Ademe "Pro-Extern", 2011-2014 (coll. Veolia, Ineris, Inra-EGC, analysis of UOR spreading and nitrogen valuation)

44. MESR, ENS PhD funding (P. Humblot, 2012-2016)

45. ANR-AZODURE, 2013-2017 (coord. Univ. Lyon 1, EHC funding / M. Bounaffaa)

46. FoodSecure FP7, (Inra partner, 2013-2016)

47. ABIES, PhD funding (G. Dumollard, 2013-2016)

48. BASC (Labex, coll. Univ Paris-sud Orsay, LSCE, 2014-2016, 2017)

49. "Futurol" (INRA), 2015-2016 (raw matter for bio-fuels in France, N. Ben Fradj, coll. UMR EcoSys)

50. PIREN-Seine phase VII, 2016-2019 (coll. Metis Univ. ParisVI & MinesParisTech)

51. ASTREA (Ile-de-France region), 2016-2018 (PhD grant, A. Isbasoiu)

52. LabEx BASC-STIMUL (2016-2018, supervised by R. Chakir)

53. Institut de Convergence CLAND (2018-2023)

54. LabEx BASC-APISMAL, 2018-2019, supervised by V. Martinet)

55. Meta-programme ACCAF & Chaire Economie du Climat (2018-2021)

56. Ademe - APR GRAINE, 2018-2022 (supervised by D. Lorne, Ifpen)

57. ANR-Diet+, 2018-2021 (supervised by S. Marette)

58. Tender JRC-Seville (2019)

59. ANR-PPR 2020 (FAST project supervised by J. Subervie) (2021-2023)

60. Belmont project BLUEGEM (supervised by A. Ducharne) (2021-2024)

61. Ministère de la Transition Ecologique (conj. Min Agric., 2022)

62. TotalEnergies (2023)
# Appendix

## A User's call for the model

The user is supposed to have an account on the workstation dedicated to the model. Some slight preliminary operations are required before the model's use, properly speaking, regarding "login" and "path" command files and internal access rights. The user should be also preferably familiar with basic Linux-shell commands.

A few screen capture schemes detail how the user accedes to the AROPAj based shell leading to simulations and outputs exploitation. The following figures illustrate how it runs through screen copies from the user's point of view.

In order to initiate a new simulation:

<figure><img src="image_76.png" /></figure>

1. After the execution of the "miraj" command, select a project (ask the administrator for the project list).

<figure><img src="image_77.png" /></figure>

2. Select "s" for initiating the simulation process.

<figure><img src="image_78.png" /></figure>

3. Select a version of the AROPAj model ("2009" for the current version).

<figure><img src="image_79.png" /></figure>

4. Select the name of one of your csh shell simulation script (without ".csh"). If you don't remember the name of the script, type any letter and Miraj will propose the available scripts.

...

Launching simulations asked by some complex scenario design may lead to run times of several hours. At the moment (2015), one hundred simulations for a complete European cover (e.g. 27 Member States and more than 1800 unit LPs) lasts around half an hour when the workstation is
...

```
Sélection des options PAC sous la forme "IP1+IP2+IP3..." ou "0"
où IP1 est un indice de type novpac (1 2 ... 13 ...) (ex: 13+1)

choix --> (.../sinon x/r) 0
```

5. Fill with "0" as a choice for CAP options.

```
Incorporation dans le fichier des paramètres (mgg.par) de :
modpac (1) refscop (10) lisscal (100) externe (1000)
Les fichiers modpac.par (voir opération "3") et externe.par sont
supposés pré-exister (pour les version et pays demandés)
Le format d entrée : somme des puissances de 10 (ex: 1001)

choix --> (.../sinon 0/x/r) 1000
```

6. Fill with "1000" to choose the parameters file. Note the simulation identifier (12 digits). Output file names will refer to this identifier and to farm group index.

```
détruire les fichiers créés lors d'une simulation précédente
... si non, conserve les exécutables pour gain de temps compil.

choix --> (y/n/x/r) n

Identifiant ensemble de simulations : 150522145458

>>>> Opération sur tableau.150522145458
```

7. Usually fill with "n". Fill with "y" to delete files from previous simulation. Note the simulation identifier (12 digits).

```
Envoi en boucle sur tous pays de l opération à venir ?

choix --> (y/n/x) n
```

8. Fill with "n" (fill with "y" to start a loop).

```
Choirx du pays (4 lettres) parmi :
belg bgra cypr czek dani deul deu2 deu3 ella esp1 esp2 esto frai
ita3 ltua luxe lvat mlta nede osto pol1 pol2 pol3 pol4 port rou1

choix --> (a4/sinon x) |
```

9. Choose the AROPAj country or sub-country, identified by a 4-character sequence.

```
Choirx opération (il) parmi :
p opération sur commande propre avec "pays" comme paramètre
0 (base)
3 (modpac)
4 (validation)
5 (initialisation)
6 (simulation série)
7 (agrégation résultats)
8 (tabulation résultats)
n ("nettoyage fichiers")

choix --> (./r/x) |
```

10. Initiate the simulation process by calling successively "p", "0", "3", "4", and "5" for a country. The process can be stopped anytime to check the first LPs solving (fill with "x" as many times as necessary to leave the Miraj menu). You may complete the steps on the order "p", "0", "3", "4", "5", ("6" in case of iterated simulations ), "7", and "8".

<caption>**Figure 60.** Launching the call for AROPAj (1).</caption>
In order to complete a simulation that has already been initiated :

```
choix de la version "modèle+aropaj"
-----------------------------------------------------------------------
Opération en cours : indiquer identifiant 12 chiffres (tableau)
Sinon choix parmi :
    600 : UE-15 (février 2001)
    700 : UE-15 (juin 2005)
    800 : UE-24 (aout 2007)
20?? : UE-27 (novembre 2014 -3 années RICA 2007-8-9)
    (indiquer un nombre < 109 si identifiant oublie)
-----------------------------------------------------------------------
choix --> (700../2009/sinon x) 00000
## ###########################################
>>> ATTENTION : choix de 12 chiffres parmi: <<
150319161521
150407152450
150407162456
150522145458
## ###########################################
```

Repeat steps **1.** and **2.**. Then, select the simulation identifier (usually the last one). If you don't remember the identifier, type any number set and Miraj will propose the available identifiers.

Go directly to step **8.**, **9.** and **10.**, to continue the simulation process.

Once out of the Miraj menu (fill with "x" as many times as necessary), you can follow the simulation process through the table identified by the 12-digit identifier. In this table, a "country" (or a "sub-country" when country splitting) refers to a 8-digit identifier.

<table><caption>Tableau des opérations cree selon l'indexe date : 150522145458 - indicatif version : 2009<br/>- commande integree : simuGES<br/>- selection modpac : 0<br/>- selection fichier : 1000</caption><thead><tr><th>PAYS</th><th>nbgrp</th><th>base<br/>(0)</th><th>modpac<br/>(3)</th><th>comm. ex.<br/>(p)</th><th>selection(4)</th><th>initialisat.<br/>(5)</th><th>identif.</th><th>simulation<br/>(6)</th><th>agrég res<br/>(7)</th><th>tabula res<br/>(8)</th><th>nettoyage</th></tr></thead><tbody><tr><td>belg</td><td>29</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>bgra</td><td>42</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>cypr</td><td>7</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>czek</td><td>37</td><td>0 052214</td><td>3 052214</td><td>p 052214</td><td>4 052214</td><td>5 052215</td><td>22150059</td><td></td><td></td><td></td><td></td></tr><tr><td>dani</td><td>26</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr></tbody></table>

Experimented users may execute all simulations through the automatic simulation process by selecting "a" from the **2. menu** (see Figure 60).

<caption>**Figure 61.** Launching the call for AROPAj (2).</caption>
moderately loaded.

In case of computation problems, the user is kindly advised to check first her/his own files (writing format, inadequate blank lines in *scen.(par var gar)* files - see section 11.1.3, **FORTRAN** code in *scen.f* or *Cshell* code in *scen.csh*). Problems may be identified thanks to information tracking provided on files of type name *sortie.*\*. Secondly, some *LPs* may be "infeasible" due to inadequate values of parameters (e.g. in case of too restrictive quotas). Thirdly, as a researchers' model surely not sufficiently developed in line with the rules of the computation art, *AROPAj* encounters many problems that often PhD students are able to invent.

# B Contributors to AROPAj and to related publications

The list mentions contributors who used at least partially the model and who directly or indirectly contributed to its development, as *PhD* or *MSc* students and engineers or researchers hired on permanent positions (*PP*) or on short or middle term contracts (*EHC*).

* Annie Hofstetter (PP; 1990-1997)

* Philippe Bontems (MSc; 1990, 1992)

* Nathalie Taverdet-Popiolek (PhD; 1990-1993)

* Abdel Aloualiti (EHC; 1991)

* Frédérique Lefaudeux (MSc, EHC; 1991-1992)

* Vincent Thuillier (MSc; 1991)

* Jérôme Adda (MSc; 1991)

* Sophie Chavanne (MSc; 1992)

* Joël Mathurin (MSc, PhD; 1992, 1994-1997)

* Alessandra Schiavina (MSc; 1993)

* Franck Leclerq-Blondel (EHC; 1994)

* Gilles Le Moguédec (MSc, EHC; 1995-1996)

* Carolina Chaya (PhD; 1995-1997)

* Roberta Silvagni (MSc; 1996)

* Nicolas Godfroy (MSc; 1996)

* Alain Birfet (EHC; 1996-1997)

* Stéphane De Cara (MSc, PhD, PP; 1997-2000, occasional, 2015-)

* Solène Maugars (MSc; 1998)

* Eleonora Marzocchi (MSc, EHC; 1998-2000)

* Kais Abbes (MSc; 1999)

* Michele Donati (MSc, EHC; 2000)

* Guillaume Gruère (MSc; 2000)

* Caroline Godard (PhD; 2002-2005)

* Laure Bambière (MsC, PP; 2002-2005, occasional, 2017-)

* Julien Labonne (EHC; 2003)

* Martin Houzé (MSc; 2003-2004)

* Bineta Niang (MSc, EHC; 2003)
* Mathieu Godet (MSc; 2004)

* Elodie Debove-Galko (PhD; 2004-2007)

* Edouard Baranger (MSc, EHC; 2005-2006)

* Raja Chakir (PP; 2005-2006, occasional, 2016-)

* Nathalie Novello (EHC; 2006-2008)

* Fanny Guillet (MSc; 2006)

* Sophie Durandeau (EHC; 2006-2007)

* Marta Piñies (MSc; 2006)

* Paul Zakharov (EHC; 2006-2010)

* Agathe Rouaix (MSc; 2007)

* Bernd Kuepker (MSc; 2007)

* Melissa Clodic (MSc, PhD; 2007-2013)

* Cloé Garnache (MSc; 2007)

* Basile Grosdidier (EHC; 2007)

* Marie Génin (MSc; 2008)

* Pierre Cantelaube (EHC; 2008)

* Audrey Pollard (MSc; 2008)

* Cyril Bourgeois (PhD; 2008-2011)

* David Leclère (MSc, PhD; 2008-2013)

* Athanasios Petsakos (MSc, EHC; 2009-2010, 2012-2014)

* Nosra Ben Fradj (MSc, PhD, EHC; 2009-2013)

* Delphine Leconte-Demarsy (EHC; 2010)

* Parisa Aghajanzadeh-Darzi (MSc, PhD; 2010-2013, 2015-2016)

* Paola Clerino (EHC; 2011)

* Joao-Pedro Domingues (MSc; 2011)

* Pierre Humblot (EHC, PhD, own company; 2011-2016, 2017-2018)

* Myrsini Papadaki (MSc; 2012)

* Anna Lungarska (MSc, PhD, EHC, PP; 2012-2016, 2017-)

* Sara Yavari (MSc; 2013)

* Elvire Petel (EHC; 2013-2015)

* Gaspard Dumollard (PhD; 2014-2016)

* Gregory Pilchak (MSc; 2015)

* Ancuta Isbasoiu (EHC, PhD; 2015-2019)

* Juliette Adrian (EHC; 2015-2016)

* Clément Servant (EHC, supervised by S. De Cara; 2015)

* Myriam Bounaffaa (EHC; 2015-2017)

* Gabrielle Despres (EHC; 2015-2016)

* Coline Assaiante (EHC; 2015-2016)

* Delphine Barberis (EHC; 2016-2017)

* Loïc Henry (MSc, PhD, supervised by S. De Cara; 2016-2020)

* Maxime Ollier (MSc, EHC, PhD; 2017-2018, 2018-2021)
* Monia El-Akkari (PhD; 2018-2020, supervised by B. Gabrielle and J.L. Drouet)

* Salomé Kahindo (MSc, supervised by L. Bamière, 2018; 2019-)

* Jeffrey Norville (EHC, supervised by V. Martinet; 2018-2019)

* Eleni Georganta (MSc ERASMUS, 2019)

* Panagiota Arampatzi (MSc ERASMUS, 2019)

* Amal Azizi (PostDoc, 2019)

* Inès Chiadmi (EHC, PhD co-supervised by S. Van Passel; 2019-2021, 2021-2024)

* Eva Gossiaux (MSc, 2019-2020)

* Adrien Coiffard (MSc, 2020)

* Erica Ramirez (MSc, EHC, 2020-2021)

* Anaïs Kanellos (MSc, EHC, 2021)

* Maxence Gérard (MSc, EHC, 2021)

* Julie Reineix (MSc, EHC, PhD co-supervised by A. Ducharne; 2021, 2021-2024)

* Théodore Fontenaille (MSc, supervised by Ministère de la Transition Ecologique, 2022)

* Lisa Baldi (visiting PhD Univ. Parma, 2023)

* Olga Zuravel (EHC, 2023-2024)

* Mingzhu Weng (MSc, 2023)

* Robin Nizou (MSc, 2024)

* Yasmine Derbal (EHC, 2024-2025)

* Eleonore Piaia (EHC, 2025)

## C Funded projects and supports benefiting AROPAj

Projects that follow contributed to the development of AROPAj. Obviously fundings that were obtained were generally only partly devoted to the model itself.

1. Société Louis-Dreyfus, 1990-1992 (initiates AROPAj - France & UK)

2. Unigrains, 1990-1993, (PhD - theory-oriented)

3. AIP Inra "modélisation du secteur agricole", 1991-1993 (coll. Y. Le Roux, A. Hofstetter)

4. Inra Support, 1991-1993 (PhD co-fin. Inra-Edf N. Taverdet)

5. Commissariat Général du Plan & Direction de la Prévision, 1991-1992, convention d'étude CGP n°11/1191, prestation DP 910127, (coll. J.C. Bureau, Y. Le Roux)

6. AIP Inra "Politique Agricole Commune", 1992-1994 (MS strategies, coll. E. Giraud-Héraud, Y. Le Roux, A. Schiavina; extensification of agricultural productions, coll. J.P. Butault, A. Barkaoui - ESR Nancy, J. Mathurin)

7. Commissariat Général du Plan & Direction de la Prévision, 1992-1993, convention d'étude CGP n°6/1992, prestation DP 920060

8. Bologna University, 1993-1996 (AROPAj Italy, coll. A. Schiavina, 3 alloc. COMETT)

9. Confédération Paysanne, 1993-1995 (funding for internships, coll. G. Le Moguédec)

10. AIP Inra "Nouvelles fonctions de l'agriculture", 1994-1996 (Interactions between agricultural policies and aforestation incentives, coll. ESR Nancy, A. Birfet, A. Hofstetter)

11. Demeter, 1995
12. Funding from MinAgri, 1994-1997 (PhD Ing. Agron. J. Mathurin)

13. AIP Inra "ALIMAN", 1995-1997 (modeling of cereals and feed markets, coupling *AROPAj* - industrial feed formulation, coll. Y. Dronne ESR Rennes, O. Lapierre INAPG et CEREOPA)

14. UP Madrid, 1995-1997, (PhD co-supervising with Université de Bourgogne, C. Chaya)

15. Contrat cadre Inra-Inia (Spain), 1996, support of the previous project

16. Sido, 1996-1998 (coll. Y. Dronne, O. Lapierre - module "feed" and partial equilibrium approach, *MODANI-AROPAj*)

17. Mission Interministérielle sur l'Effet de Serre, 1996-1997 (initiating GHG emission studies), lettre de commande n° 14/96.

18. Bologna University, 1999, grant Leonardo

19. Partenaire (initiateur) programme EUROTOOLS, FAIR5 CT97-3403, 1998-2000 (*AROPAj-UE-12*)

20. GICC (MinEnvir & MIES), 1999-2001 (S. De Cara, coll. Inra Avignon related to the STICS model)

21. GICC (MinEnvir), 2001-2003 (initiating of spatialisation, coll. Soil Sciences Inra Orléans)

22. DG AGRI (Com. Europ.), 2001-2002 (*AROPAj EU-15*, reforming the milk quota system viewed through in *AROPAj*)

23. GICC (MinEnvir), 2002-2004 (initiating the coupling *AROPAj-STICS*, coll. Inra CSE-Avignon & Infosol-Orléans)

24. Ademe, 2002-2005 (PhD grant, C. Godard, coupling STICS-*AROPAj*)

25. Direction de la Prévision, 2002-2003 (*AROPAj* and decoupling in the *CAP*)

26. STREP FP5 GENEDEC-502184, 2004-2007 (coordinating 10 teams in 6 MS, support and exchange PhD, coll. *JRC* and Universities, Parma, Madrid, Reading, Rethymno, Verona, Dublin, FAL)

27. Inra-MinAgri, 2004-2007 (funding of a PhD, E. Galko)

28. Strep FP5 INSEA, 2004-2006 (partner, Iiasa coordinating)

29. GICC-2 (ministère de l'environnement), 2004-2006 (*GHG emissions, climate change, regulation & mitigation, initiating analysis of adaptation, coll. LSCE, N. de Noblet*)

30. ADD-DST, 2005-2008 (impact of soil compaction, coll. F. Guillet, J. Roger-Estrade, G. Richard, Inra-Orléans)

31. PIREN-Seine, 2007-2011 (PIREN phase V, coll. ParisVI-Sisyphe, Mines-Géosciences, Inra Mirecourt; initiating water issues analysis with *AROPAj*, water and nitrogen pollutants, coupling *STICS-MODCOU-AROPAj*)

32. Ifp, 2007-2011 (coll. Ifp, PhD grant, M. Clodic, *AROPAj EU-25* & bio-fuels)

33. Ademe-CEA, 2008-2011 (Climate change, European agriculture and mitigation - adaptation, PhD grant, D. Leclère)

34. R2DS (Ile-de-France region), 2009-2012 (PhD grant, C. Bourgeois, aquifer dynamics & joined pollutions)

35. "Feturol" (INRA, Ifp), 2009-2012 (PhD grant, N. Ben Fradj, *AROPAj* & perennial crops dedicated to bio-fuels & environmental impacts)

36. Eau-dyssée (CNRS-"ec2co"), 2009, 2010, (grant dedicated to model coupling, coll. F. Habets Sisyphe-MinesParisTech)
37. CC-TAME FP7-KKBE-2010-4 (partner, coll. S. De Cara, AROPAj & GHG-MACs, initiating the AROPAj version EU-27)

38. ANR-VALIDATE, 2008-2011 (grasslands, livestock & climate change)

39. ANR-VULNOZ, 2008-2012 (ozone impacts on agricultural, coll. D. Leconte, P. Clerino, P. Humblot, coll. Inra-EGC, LSCE)

40. ANIMALCHANGE FP7, 2011-2014 (coord. J.F. Soussana, PhD grant co-funded by ADEME, P. Aghajanzadeh-Darzi, climate change & livestock)

41. PIREN-Seine phase VI, 2011-2015 (coll. Sisyphe Univ. ParisVI & MinesParisTech, policy analysis & directives dedicated to the environment)

42. ANR-ORACLE, 2011-2016 (land use, water, agriculture, grasslands and forest, coll. INRA-LEF, LSCE, Mines-Géosciences, PhD funding for A. Lungarska)

43. Ademe "Pro-Extern", 2011-2014 (coll. Veolia, Ineris, Inra-EGC, analysis of UOR spreading and nitrogen valuation)

44. MESR, ENS PhD funding (P. Humblot, 2012-2016)

45. ANR-AZODURE, 2013-2017 (coord. Univ. Lyon 1, EHC funding / M. Bounaffaa)

46. FoodSecure FP7, (Inra partner, 2013-2016)

47. ABIES, PhD funding (G. Dumollard, 2013-2016)

48. BASC (Labex, coll. Univ Paris-sud Orsay, LSCE, 2014-2016, 2017)

49. "Feturol" (INRA), 2015-2016 (raw matter for bio-fuels in France, N. Ben Fradj, coll. UMR EcoSys)

50. PIREN-Seine phase VII, 2016-2019 (coll. Metis Univ. ParisVI & MinesParisTech)

51. ASTREA (Ile-de-France region), 2016-2018 (PhD grant, A. Isbasoiu)

52. LabEx BASC-STIMUL (2016-2018, supervised by R. Chakir)

53. Institut de Convergence CLAND (2018-2023)

54. LabEx BASC-APISMAL, 2018-2019, supervised by V. Martinet)

55. Meta-programme ACCAF & Chaire Economie du Climat (2018-2021)

56. BIOC4M (Ademe - APR GRAINE), 2018-2022 (supervised by D. Lorne, Ifpen)

57. ANR-Diet+, 2018-2021 (supervised by S. Marette)

58. Tender JRC-Seville (2019)

59. ANR-PPR 2020 (FAST project supervised by J. Subervie) (2021-2023, 2025)

60. Belmont project BLUEGEM (supervised by A. Ducharne) (2021-2024)

61. Ministère de la Transition Ecologique (conj. Min Agric., 2022)

62. TotalEnergies (supervised by L. Bamière) (2023-2025)

63. FranceStratégie (supervised by Stéphane De cara) (2024)

64. CarboBio (Ademe - APR GRAINE), 2025-2027 (supervised by D. Lorne, Ifpen)