**No irrigation** When $\forall i \mathbb{I}(i) = 0$ (implying $\mathbb{I} = 0$), necessarily $\beta = 0$ (thanks to 15 and $\alpha + \beta + \gamma = 1$). There remains to set $\gamma_a$ values. We suggest to retain positive values increasing with absolute values of distorsion $\delta_a$ such as $\gamma_a = 1 - \frac{M-\mathbb{A}(a)}{M}e^{-|\delta_a|}$. Let us note that the subcase in which the region is homogeneous in elevation leads to $\gamma_a = 0$, thanks to Equations 11 and 13. This is compatible with the case below.

**Homogeneous elevation class** When the region is homogeneous in elevation class, there is nothing to loose to set $\gamma = 0$. In case of irrigation areas positively estimated by the FADN-AROPAj source, we suggest to retain a value reflecting the distorsion in irrigation between FADN and DEM sources for estimation, i.e. $\beta = 1 - \frac{M-\mathbb{I}}{M}e^{-|\delta_{irr}|}$. Let us note that when the part of irrigated area is close to 0 or to 1, the $\beta$ value is close to 0. This is a consequence of Equation 12 which implies that $\delta_{irr}$ tends to 0 when $\mathbb{I}$ is close to $M$).

**Mixed cases with areas splitted in irrigation and in elevation** Let us consider the case where irrigation information exists in FADN (correlatively AROPAj $S_{jk}^{irr}$ are estimated). Let us consider the system defined by Equation 14, Equations 15 (as many as a elevation classes) and $\alpha + \beta + \sum_a \gamma_a = 1$ which is linear in the weights parameters $\alpha, \beta$, and $\gamma_a$. Let us note that $\sum_a \|\mathbb{A}(a)\| = \|\mathbb{I}\|$, due to the fact that the a areas do not intersect. With some re-arrangements of the system, we get that $\beta \|\mathbb{I}\| = \beta \sum_a (\|\mathbb{A}(a)\|^2 / \|\mathbb{A}(a)\|)$. There is generally no solution except $\beta = 0$, which means that weights of our probability set (Equation 10) is based on redundant information (irrigated areas on the one hand and irrigated areas intersecting elevation classes on the other hand). Moreover, that leads to keep equal the $\gamma$ values, i.e. $\forall a: \gamma_a = \gamma$. We suggest to overcome this parameter under-determination by retaining the parameters as follows:

$$ \gamma = 1 - \alpha = 1 - \frac{M - \mathbb{I}}{M} e^{-|\delta_{irr}|} $$

In other words, we rank first irrigation before elevation, which is nevertheless taken into account through the part of k farm group area of elevation a ($U_{ak}$). The probability set becomes:

$$ T_{ik} = \alpha \sum_j P_{ij} \frac{S_{jk}}{\sum_{k'} S_{jk'}} + \gamma \sum_a \frac{U_{ak}}{\sum_{k'} U_{ak'}} \mathbb{I}_{A(a)}(i) \quad (16) $$

The distorsion term $\delta_{irr}$ comes from Equations 11 and 12: $\delta_{irr} = 1 - \frac{M \sum_{jk}^{irr}}{\mathbb{I} \sum_{jk}}$.

## 8.3 Application

User-friendly application is anchored to the general AROPAj-menu (“miraj”, see Appendix A), aiming first at managing the compilation of databases, precisely regarding:

1. FADN region list

2. CLC for Europe raster

3. Soils

4. Elevation

5. Slope

6. year-dependent climate
<table><caption>**Table 28.** N-loss i-emission function for crop j, i = {NO₃, N₂O, NH₃}: [eᵢ]ⱼ(N) = [αᵢ]ⱼ N + [βᵢ]ⱼ; β denotes natural nitrogen loss in kgN/ha/year.</caption><thead><tr><th>Crop</th><th>α<sub>N<sub>2</sub>O</sub></th><th>β<sub>N<sub>2</sub>O</sub></th><th>α<sub>NH<sub>3</sub></sub></th><th>β<sub>NH<sub>3</sub></sub></th><th>α<sub>NO<sub>3</sub></sub></th><th>β<sub>NO<sub>3</sub></sub></th></tr></thead><tbody><tr><td>hard wheat</td><td>0.0232</td><td>0.7106</td><td>0.0537</td><td>1.6084</td><td>0.1081</td><td>36.3959</td></tr><tr><td>soft wheat</td><td>0.0207</td><td>0.7923</td><td>0.0477</td><td>1.8510</td><td>0.0366</td><td>46.3375</td></tr><tr><td>sugar beet</td><td>0.0216</td><td>0.0526</td><td>0.1109</td><td>0.3511</td><td>0.1412</td><td>18.3436</td></tr><tr><td>rapeseed</td><td>0.0183</td><td>0.6038</td><td>0.0691</td><td>2.1157</td><td>0.1854</td><td>41.5223</td></tr><tr><td>corn</td><td>0.0214</td><td>0.0779</td><td>0.0810</td><td>0.2499</td><td>0.1998</td><td>28.3517</td></tr><tr><td>winter barley</td><td>0.0075</td><td>0.2624</td><td>0.0163</td><td>0.5724</td><td>0.0550</td><td>26.4918</td></tr><tr><td>potatoes</td><td>0.0202</td><td>0.0268</td><td>0.0469</td><td>0.0321</td><td>0.2552</td><td>2.6380</td></tr><tr><td>sunflower</td><td>0.0209</td><td>0.0105</td><td>0.0440</td><td>0.0560</td><td>0.1331</td><td>16.2587</td></tr></tbody></table>

source: Bourgeois et al. (2014)

the STICS crop model, as well as simulation results. In order to automatize such a process for the numerous crop-agent cases and scenarios, we developed an *informatic framework* named **ArTiX** (described in this section).

ArTiX designates both a *database* containing various types of data, and a *software* interfacing the database with the STICS FORTRAN code and the user:

**The ArTiX database.** It is a PostgreSQL database, containing data in the form of tables and relations. This data is geo-referenced with either an FADN region identifier or an identifier of another spatial distribution (vector or raster form), and describes:

* All the parameters required by the STICS model to describe its physical and technical environment (e.g., descriptors of soil texture, daily weather, etc.),

* All the STICS output relevant for further processing (e.g., crop yield, fertilizer application doses and dates, dates of phenological stages),

* Additional information concerning crop-agent cases (e.g., crop output selling price, etc.),

**The ArTiX software.** It is a software coded in Java, that allows the following functionalities:

* To perform simulations for a particular crop-agent case specified by the user. This includes automatic query of input information required by the STICS crop model, automatic generation of STICS input files, launching of STICS simulations and management of errors, and reading of output files from which results of interest are read and stored in the database.

* To allow the user to define a particular setup for the simulations to be done (e.g., scenarios for climate data, or crop management practices).

* To allow the user to query data of any type, contained in the database, via `psql` queries.

The overall concept is summarized in 28, illustrating the database, the java code, and the user interface.

## 9.2 Carbo-PRO
7. LUCAS

8. intersecting CLC and countries involved in the application

9. FADN-based AROPAj-dependent information related to farm group's area per crop group

The next steps of the application follow the algorithm described in the previous section, performing the calculations of crop location's priors and posteriors, and the geo-referenced probability sets of farm groups.

We implemented the calculation process for the V5 AROPAj version applied to most of EU-27 FADN regions and for the 6 years 2007-2012. This is illustrated for France and the 2009 FADN on Figures 27 providing the location probabilities of two of the farm groups in the Bourgogne region.

<figure><img src="image_29.png" /><figcaption>**Figure 27.** Location probabilities of farm groups “GT37” and “GT39” in the Bourgogne region (2009- V5 AROPAj version).</figcaption></figure>
<figure><img src="image_29.png" /><figcaption>**Figure 28.** Schematic overview of the *ArTiX* database and Java interface between the *AROPAj* and *STICS* models, and the user.</figcaption></figure>

**Key-points:**

* Improving the insertion of organic matter sourced nitrogen

* Applying the method to urban organic residue (UOR)

We develop a method for introducing organic *N* sources in the *AROPAj* model, thereby completing the *LP N*-block presented in Section 4.2.6. Manure is an important endogenous *AROPAj* activity related to livestock. Urban wastes are exogenous *N* sources. Agronomic long term effects have been introduced. This consideration can be attributed to the use of the *Carbo-PRO* model.

The method was applied to the French Ile-de-France context. It allowed to provide the value of the nitrogen sourced by *UOR* when considering the intensive effect of *N*-spreading at the plot level, the extensive effect of land cover change among crops at the farm-type level and the *N* allocation between farming system at the regional level (Jayet & Petel, 2015).

### 9.2.1 Organic nitrogen sources

The nitrogen block includes new forms of nitrogen input : urban organic residues (UOR). At present, parameters are available for two types of UOR : green waste compost (GWC) and urban sewage sludge (USS)<fnref n="8" />. At the plot scale, they are considered as any other source of organic matter (OM), i.e. livestock manure.

<footnote n="8">⁸This work emanated from the PRO-EXTERN project from the French Environment and Energy Management Agency (ADEME)</footnote>
# 9 Models linkage

The interest of linking the *AROPAj* economic model with biophysical models relies on the possibility of accounting for the impact of technical as well as environmental and physical variables (i.e. soil and climate) on the agricultural production. Moreover, linking *AROPAj* to biophysical models is important for understanding the environmental externalities. Regarding crop production, the general rationale is to introduce in the model a relation between crop production inputs (e.g. fertilizer applications) and crop production outputs (e. g. yields). The possible connecting points between the economic and the biophysical models are the farmer practices found in the accountings, such as irrigation, application of pesticides, and fertilizers. Among those practices, we chose nitrogen (*N*) fertilization because of (i) its key role in environmental policies in relation to non-point source pollution and greenhouse gas emissions, (ii) its traceability in accounts through fertilizers costs, and (iii) the wealth of usable information for our model on *N* effects on yield.

## 9.1 STICS

### Key-points:

* Introducing crop models and replacing constant yields and inputs by nitrogen to yield functions
* The integrated *ArTiX* database and applications

Economic modeling benefits from accessing biophysical information on the systems they represent. In the case of agricultural production, crop models are often associated with economic models, with the information being presented in a simplified form to fit the economic model's constraints. An important variable is the yield of the crop. This depends on environmental factors, for instance climate, as well as farmers' decisions (fertilizer and water use, technical management etc...). In the case of *AROPAj*, the generic crop model *STICS* (Brisson et al., 1998, 2003) was used to generate data linking the yield of major crops with the technical choices made at the farm level.

The genericity of the *STICS* crop model was one of the main reasons for choosing it among all the existing crop models. The potential crop model linked to *AROPAj* needed to simulate yield as well as environmental variables (such as nitrate fluxes) for all of the main crops simulated by the economic model across the *EU* territory. The *STICS* model simulates crop growth, development, and environmental impacts for the main European crops, in the various pedo-climatic conditions of the *EU*. Indeed, the main processes simulated by *STICS* are crop growth and development as well as water and nitrogen soil balances. A of this information is dynamically driven by data such as daily weather data and information regarding soil characteristics and management. The model is organized in several modules: three modules are dedicated to the simulation of plant aerial part (*LAI*, biomass and yield), three to the soil simulation (water and nitrogen balances, and root growth), and the last one additional module dedicated to the management of interactions between cropping techniques and soil-plant system (i.e. water or fertilizer supplies, or climatic environment of the plant). *STICS* is a deterministic model that simulates the soil-plant-weather system in supposedly homogenous physical and technical conditions, which corresponds to each crop of each
Integrating long term effects of organic matter spreading in the yield-to-nitrogen response function

An enhanced form of the response function has been developped in order to integrate the long-term effect of organic nitrogen spreading. Organic residue spreading has a short-term fertilization effect (content in nitrogen, phosphorus and potassium, impact on biological and pH properties of soil). It also has a long-term effect, increasing soil OM and so supporting soil properties (water retention, cation exchange capacity, nitrogen-to-plant availability). This leads to some extra nitrogen available to the plant, provided by the slow mineralization process of additional soil humus. OM spreading could then be considered as a double source of nitrogen for crops via short and long term effects.

The long term effect is assumed to modify the A parameter of the yield function, B potentially remaining unchanged (see the yield function and the parameters to which it refers on page 114). The difference between the function that integrates a long term effect ($Y^{LT}(N^m, N^o) = B - (B - A^{LT})e^{-\tau(N^m + \alpha^{ST}N^o)}$) and the initial short term function ($Y^{ST}(N^m, N^o) = B - (B - A^{ST})e^{-\tau(N^m + \alpha^{ST}N^o)}$), leads to the following:

$$Y^{LT}(N^m, N^o) - Y^{ST}(N^m, N^o) = (B - A^{ST}) \left(1 - \frac{B - A^{LT}}{B - A^{ST}}\right) e^{-\tau(N^m + \alpha^{ST}N^o)}$$

in which the term $(1 - \frac{B-A^{LT}}{B-A^{ST}})$ remains within the interval $[0, 1]$ and can be expressed as $e^{-\tau\alpha^{LT}N^o}$ (with $\alpha_l^{LT}$ being positive).

The exponential form of the Mitscherlich-Baule function suggests that the long term yield function can be thought of as a leftward translation of the short-term one. This effect can be translated as a decrease of the nitrogen needed to reach a given yield level, all other things being equal. Given the amount of organic nitrogen application, the mineral nitrogen gap between short term and long term leading to a given yield is constantly equal to $\Delta N = -\frac{1}{\tau}\log(\frac{B-A}{B-A^{LT}})$, as represented in Figure 29.

<figure><img src="image_30.png" /><figcaption>Figure 29. Long term effect of OM spreading on the N-to-yield response function.</figcaption></figure>

The nitrogen-to-yield function, which is generalized to L sources of organic nitrogen and which integrates both short and long term effects of organic matter spreading, is amended and as expressed by the following equation (with remarkably unchanged A and B values):

$$Y^{LT}(N^m, N_1^o, N_2^o, \dots, N_L^o) = B - (B - A^{ST})e^{-\tau(N^m + \sum_{l=1}^{L}(\alpha_l^{ST}N_l^o + \alpha_l^{LT}N_l^o))} \quad (18)$$
European farm type of the AROPAj model. Regarding the nitrogen sources, the STICS model was also able to account for both chemical fertilizers and on-farm produced manures.

### 9.1.1 N to yield functions

We decided to provide AROPAj with response functions $y(N)$ that describes crop yield level $y$ as a function of nitrogen input $N$. With such a response function, the production process is summarized in a simple function that focuses on the main limiting production factor ($N$) while also implicitly considering the other ones. No general consensus appears in the literature on the ideal shape of such a response curve (Makowski et al. (1999) cited in Godard et al. (2008)). However, the use of concave and strictly increasing curve is consistent from both an economic perspective (requirements for the estimation of a mathematical optimum), and an agronomic perspective (consistent with expected shape, and defined by easily interpretable parameters). Hence, we selected an exponential function, expressed as the following:

$$y(N) = B - (B - A)e^{-\tau N}$$

where $y$ is crop yield, $N$ is the nitrogen fertilizer amount, $A$, $B$, and $\tau$ are, respectively, the minimal yield, maximal yield and sensitivity to $N$ input.

To build up the nitrogen response curve, a two-step procedure was set up and repeated for each crop of each farm type. In the first step, STICS was run for each crop of each farm type using inputs derived from available databases and expert knowledge (see sections 3.2.2, 3.2.4 and 3.3.1). Each case was thus first characterized by weather conditions and by several combinations of soil and agronomic choices. To avoid too many combinations, the most representative soils and techniques were selected for each case studied: namely, 5 soil types, 3 sowing dates or earliness groups, 2 preceding crops and, given the crop, rainfall or irrigated option were selected for one case studied. This procedure yielded a set of 30 or 60 likely $N$-response curves.

In the second step, economic data (i.e. yields and price ratios extracted by AROPAj from the FADN, and from price databases) were used to select the relevant response curve so that a single $N$-response curve emerges with the required biophysical and economic properties. To do so, we first selected the curves for which the reference yield of the crop, given by the FADN database, was reached. Second, we selected the curve for which the slope of the tangent, at the point of the curve corresponding to the above mentioned reference yield value, was the closest to the price ratio (price of nitrogen/price of crop).

In the process, special attention was paid to livestock farms. For these farms, effluents from livestock are spread in addition to (or instead of) chemical nitrogen fertilizer. In this case, the STICS model needs additional data to simulate the appropriate $N$-response curves: first, a curve for the combined organic sources of nitrogen and second a curve of yield against chemical nitrogen only. Indeed, this additional organic nitrogen affects the crop yield but does not have to be represented on the X-axis of a curve showing yield response to chemical nitrogen fertilizer. Consequently, potential organic nitrogen available in each livestock farm type was estimated from the FADN. Used as STICS inputs (complementary $N$ source apart from fertilizer $N$), it enabled one to choose the optimal STICS input set in step 2. Another run of the STICS model, using the selected set of inputs (soil, agronomic choices) then generated the response curve to chemical fertilizer nitrogen
The $\alpha_j^{LT}$ coefficient, related to the long term effects of organic matter spreading, is obtained via the *Carbo-PRO* tool (see www.CARBO-PRO.fr and section 9.2.2).

<figure><img src="image_31.png" /><figcaption>**Figure 30.** Illustration of the procedure to get coefficients associated with long-term impact of organic residue from *Carbo-PRO*.</figcaption></figure>

Source: Jayet & Petel (2015)

**Modification of the optimization problem for UOR integration as organic nitrogen sources.**

At the regional scale, a constraint relative to the *UOR* availability is added. Let us focus on the constraints associated with nitrogen supplies ($N_{jk}^m$ and $N_{jkl}^o$) and extract:

- positivity constraints;

- the per area unit limit of organic matter spreading ($\bar{Q}$ not depending on the farmer $k$ neither on the crop $j$, in accordance with real regulation);

- the upper bound of available organic matter $M_{lk}$ referring to the $l$ organic source. This constraint only concerns *UOR* availability, since livestock manure quantities are endogenous to the model (related to livestock management) and are not included in organic matter
itself. The theoretical framework used for the inclusion of different nitrogen sources in the N-response equation was based on an equivalence rule allowing us to compare any N source to a reference chemical nitrogen fertilizer. Once the exponential shape had been selected for the N-response curve, the minimal and maximal yields were assumed to be independent of the N source, and N sources were then distinguished by the “rate of increase” of the function. Practically, the N-response curve was expressed using the following form:

$$y(\{N_i\}) = B - (B - A)e^{-\sum_i \tau_i N_i}$$

where y is the yield for a given crop on a given farm type, B and A represent the maximal and minimal yield of that crop, respectively, and $\tau_i$ represents the rate of increase of the yield response function to a nitrogen source i (e.g. manure, slurry etc.) the quantity of which is $N_i$.

### 9.1.2 N to emission functions

In addition to the amount of fertilizers, the STICS crop model provides us with the leaching associated to each type of soil and crop together with the climatic conditions and input application date. We derive the leaching functions from STICS outputs when nitrogen input varies within a reasonable range. Linear regression applied to STICS outputs allows us to relate N-losses and fertilizer amounts (through affine functions). Consequently, the emission function for pollutant i, related to crop j and farm group k, depends on the N-input through relation (17); $\alpha$ and $\beta$ are estimated for a given plant on a given farm group in the form:

$$[e_i]_{j,k}(N) = [\alpha_i]_{j,k} N + [\beta_i]_{j,k} ; \quad i = \{NO_3, N_2O, NH_3\}. \qquad (17)$$

It should be noted that we focus only on the N-losses at the soil-root level (i.e. $NO_3$) and on two gas emissions ($N_2O$ and $NH_3$). The time lag existing between the N-fertilizer application and the actual pollution is not taken into account.

The spatialization method provides the contribution of each farm group on each cell of the grid to the regional (FADN) agricultural activities and enables AROPAj outputs to be distributed over France. Once the AROPAj farm groups have been distributed, we aggregate the results in terms of emissions among other model outputs.

For instance, Table (28) represents the emissions for $NH_3$, $N_2O$ and $NH_3$ for the main crops analyzed in AROPAj in France

### 9.1.3 Artix

We couple the AROPAj model to the STICS crop model in order to estimate input-output relationships (see above sections) for particular crops and the economic agents considered in the AROPAj model. The combination of an economic agent and a crop is hereafter referred to as a **crop-agent case**. For a specific crop-agent case, we estimate the input-output relationships from several simulations of the STICS crop model, while using various sources of data defining its physical (soil, altitude, slope, climate) and technical (such as sowing date, precocity group of the cultivar used, use of irrigation or not, fertilization calendar, preceding crop etc.) environments. This process thus requires the manipulation of various types of geo-referenced data as inputs to
exchanges between farm types. This constraint allows for the transfer of UOR between farm-types. Optimal sharing is achieved when all farm-types' individual shadow prices are equal. If *l* refers to UOR, the regional amount of related organic matter *M*<sub>*lk*</sub> is an external parameter. If *l* refers to animal-sourced organic matter, the *M*<sub>*lk*</sub> amount refers to endogenous (bounded) animal activities (livestock and manure management).

Optimal UOR availability sharing is obtained via the following aggregated regional program:

$$
\begin{array}{l@{\qquad}l}
\max_{x_k, a_k, N_{jk}^m, N_{jkl}^o} & \sum_{k=1}^{K} \pi_k(x_k, a_k, N_{jk}^m, N_{jkl}^o) \\
\forall k \ \forall j: & F_k(x_k, a_k, N_{jk}^m, N_{jkl}^o) \le O \quad (\psi_k) \\
& N_{jk}^m \ge O \quad (\beta_{jk}^m) \\
\forall k \ \forall l \ \forall j: & N_{jkl}^o \ge O \quad (\beta_{jkl}^o) \\
& \sum_{l=1}^{L} N_{jkl}^o \le \bar{Q} \quad (\lambda_{jk}) \\
\forall l: & \sum_{k=1}^{K} \sum_{j=1}^{J} N_{jkl}^o \ a_{jk} \le \sum_{k=1}^{K} M_{lk} \quad (\xi_l)
\end{array}
\qquad (\mathcal{R})
$$

Implementation of the long-term effect in AROPAj simulations.

Thanks to the Carbo-PRO tool (see www.CARBO-PRO.fr and section 9.2.2), we obtain a long
term effect parameter ($\alpha_l^{LT}$) for each organic matter source in AROPAj. Integrating long term
extra-$N$ as a reduction of the nitrogen need of plants makes it difficult to keep the LP structure of
the model. Indeed, non linearity of organic matter $N_{o,l,j,k}$ enters the optimization program when
long term impact of repeated organic matter application is taken into account. Linearity is ensured
when optimal $N_{o,l,j,k}$ tends to converge toward the value for which long term effect is estimated. We
use the iterative computational process below, where the short term $N_{o,l,j,k}$ computed at a given
iteration is used to estimate long term effect for the following iteration:

1. The following two-step subprocess is implemented:

(a) Market prices replace shadow prices to solve the plot optimization programs (see section 4.3). Null-values are considered for additional long term impact of organic nitrogen.

(b) The (R) regional optimization program is solved, doing so for yields and nitrogen applications provided by the step (a).

2. Shadow prices required for pre-solving the plot optimization program are extracted from the previous calculus' step At the same time, quantities of organic matter spread are extracted, and average quantities of organic nitrogen are computed against land endowment.

3. The three-step subprocess is implemented:

(a) Shadow prices and long term organic nitrogen amount provided by the previous step are injected in the plot optimization program.

(b) The (R) regional optimization program is solved, doing so for yields and nitrogen applications provided by the step (a).

(c) Go to step (2.) until a predefined number of iterations is reached or a predefined convergence threshold is attained.
<table><caption>**Table 28.** N-loss i-emission function for crop j, i = {NO₃, N₂O, NH₃}: [eᵢ]ⱼ(N) = [αᵢ]ⱼ N + [βᵢ]ⱼ; β denotes natural nitrogen loss in kgN/ha/year.</caption><thead><tr><th>Crop</th><th>α<sub>N<sub>2</sub>O</sub></th><th>β<sub>N<sub>2</sub>O</sub></th><th>α<sub>NH<sub>3</sub></sub></th><th>β<sub>NH<sub>3</sub></sub></th><th>α<sub>NO<sub>3</sub></sub></th><th>β<sub>NO<sub>3</sub></sub></th></tr></thead><tbody><tr><td>hard wheat</td><td>0.0232</td><td>0.7106</td><td>0.0537</td><td>1.6084</td><td>0.1081</td><td>36.3959</td></tr><tr><td>soft wheat</td><td>0.0207</td><td>0.7923</td><td>0.0477</td><td>1.8510</td><td>0.0366</td><td>46.3375</td></tr><tr><td>sugar beet</td><td>0.0216</td><td>0.0526</td><td>0.1109</td><td>0.3511</td><td>0.1412</td><td>18.3436</td></tr><tr><td>rapeseed</td><td>0.0183</td><td>0.6038</td><td>0.0691</td><td>2.1157</td><td>0.1854</td><td>41.5223</td></tr><tr><td>corn</td><td>0.0214</td><td>0.0779</td><td>0.0810</td><td>0.2499</td><td>0.1998</td><td>28.3517</td></tr><tr><td>winter barley</td><td>0.0075</td><td>0.2624</td><td>0.0163</td><td>0.5724</td><td>0.0550</td><td>26.4918</td></tr><tr><td>potatoes</td><td>0.0202</td><td>0.0268</td><td>0.0469</td><td>0.0321</td><td>0.2552</td><td>2.6380</td></tr><tr><td>sunflower</td><td>0.0209</td><td>0.0105</td><td>0.0440</td><td>0.0560</td><td>0.1331</td><td>16.2587</td></tr></tbody></table>

source: Bourgeois et al. (2014)

the STICS crop model, as well as simulation results. In order to automatize such a process for the numerous crop-agent cases and scenarios, we developed an *informatic framework* named **ArTiX** (described in this section).

ArTiX designates both a *database* containing various types of data, and a *software* interfacing the database with the STICS FORTRAN code and the user:

**The ArTiX database.** It is a PostgreSQL database, containing data in the form of tables and relations. This data is geo-referenced with either an FADN region identifier or an identifier of another spatial distribution (vector or raster form), and describes:

* All the parameters required by the STICS model to describe its physical and technical environment (e.g., descriptors of soil texture, daily weather, etc.),

* All the STICS output relevant for further processing (e.g., crop yield, fertilizer application doses and dates, dates of phenological stages),

* Additional information concerning crop-agent cases (e.g., crop output selling price, etc.),

**The ArTiX software.** It is a software coded in Java, that allows the following functionalities:

* To perform simulations for a particular crop-agent case specified by the user. This includes automatic query of input information required by the STICS crop model, automatic generation of STICS input files, launching of STICS simulations and management of errors, and reading of output files from which results of interest are read and stored in the database.

* To allow the user to define a particular setup for the simulations to be done (e.g., scenarios for climate data, or crop management practices).

* To allow the user to query data of any type, contained in the database, via psql queries.

The overall concept is summarized in 28, illustrating the database, the java code, and the user interface.

## 9.2 Carbo-PRO
### 9.2.2 Carbo-PRO

The *Carbo-PRO* tool, designed by *INRA* (Department of “Environnement et Grandes Cultures”) and the *Veolia* company, is used to get coefficients associated with the long term effects of organic matter spreading ($\alpha_l^{LT}$). More details on the long term effect are available in the section focusing on nitrogen. *Carbo-PRO* aims at computing the carbon stock in soil that is created by repeated organic residue application. It is based on the Henin-Dupuis model of soil carbon dynamics (Hénin & Dupuis, 1945) and on the *RothC* model that simulates the evolution of organic matter (Peltre et al., 2012). It also provides estimations of soil stability improvement, water retention, cation exchange capacity and quantity of nitrogen stocked in soil after many years of mineralization process.

We only use the nitrogen module of *Carbo-PRO*. The module is concerned with the process of nitrogen mineralization related to organic matter degradation that renders organic nitrogen available for plant. This extra soil-*N* is estimated as the product of soil nitrogen content and the K2 isohumic rate, or rate of mineralization of the soil-organic matter. More details on *Carbo-PRO* operation are available on www.CARBO-PRO.fr.

To get coefficients associated with long term effects of organic matter spreading, a spreading scenario has to be entered in *Carbo-PRO* with information regarding soil type, climate and spreading specifications (application rate and frequency, humic potential indicators, dry matter and organic matter content). The procedure is presented in Figure 30: For a given quantity of organic matter, *Carbo-PRO* provides the soil extra-*N* quantity. This extra-*N* at the end of the spreading period is obtained for different quantities of organic matter spread. A relation between extra-*N* and OM quantity at a given time can then be traced. As the relation is linear, the $\alpha_l^{LT}$ coefficient corresponds to the derivative of this function, related to the number of years of spreading.

The procedure has to be repeated for each source of organic nitrogen. We obtain one parameter for every type of organic matter. In the core of *AROPAj*, the extra-*N* is integrated as a reduction of the nitrogen need of plants (Needs - “long-term extra-*N*” ≤ Sum of $N_{m/o}$-inputs), requiring the iterative process described in section 4.3.

## 9.3 PaSim

**Key-points:**

* Overcoming of the lack of STICS sourced response functions for pastures and fodderries

* Rough replacement of fodder yields by PaSim in different climate change scenarios

The pasture simulation model (*PaSim*, (Riedo et al., 1998)) is a multi-year, process-based biogeochemical grassland model, which is based on the Hurley Pasture Model ((Thornley, 1998)). It simulates fluxes of carbon, nitrogen, water and energy at the soil-plant-animal-atmosphere interface for managed grasslands at the plot scale. Simulations may run over one or several years.

*PaSim* simulates grassland processes on a time step of 1/50th of a day. It provides detailed sub-daily dynamics and ensures energy budget stability. A fixed legume fraction in the grasslands is assumed in the *PaSim* model, in which vegetation is represented by mixed sward, when grasses and legumes co-exist. *PaSim* was developed to simulate the meat and milk production of cattle
<figure><img src="image_30.png" /><figcaption>**Figure 28.** Schematic overview of the *ArTiX* database and Java interface between the *AROPAj* and *STICS* models, and the user.</figcaption></figure>

**Key-points:**

- Improving the insertion of organic matter sourced nitrogen

- Applying the method to urban organic residue (*UOR*)

We develop a method for introducing organic *N* sources in the *AROPAj* model, thereby completing the *LP N*-block presented in Section 4.2.6. Manure is an important endogenous *AROPAj* activity related to livestock. Urban wastes are exogenous *N* sources. Agronomic long term effects have been introduced. This consideration can be attributed to the use of the *Carbo-PRO* model.

The method was applied to the French Ile-de-France context. It allowed to provide the value of the nitrogen sourced by *UOR* when considering the intensive effect of *N*-spreading at the plot level, the extensive effect of land cover change among crops at the farm-type level and the *N* allocation between farming system at the regional level (Jayet & Petel, 2015).

### 9.2.1 Organic nitrogen sources

The nitrogen block includes new forms of nitrogen input : urban organic residues (*UOR*). At present, parameters are available for two types of *UOR* : green waste compost (*GWC*) and urban sewage sludge (*USS*)<fnref n="8" />. At the plot scale, they are considered as any other source of organic matter (*OM*), i.e. livestock manure.

<footnote n="8">⁸This work emanated from the PRO-EXTERN project from the French Environment and Energy Management Agency (ADEME)</footnote>
in response to different climate and management conditions. Animal processes are limited to
the plot scale when animals are grazing at pasture (not in the barn). PaSim has been used
to simulate greenhouse gas fluxes over grasslands and to examine the role of grasslands in the
European greenhouse gas budget ((Schmid et al., 2001; Vuichard et al., 2007a,b)).

The method for the coupling of the *AROPAj* economic model and the *STICS* crop model (Brisson et al. (1998)), developed by Godard et al. (2008), is best adapted to major agricultural crops (cereals, oilseeds, etc.). Hence, it is not still appropriate for forage and grass plants, due to the lack of information in terms of productivity and prices. In order to obtain a reliable measurement of grass and fodder yields, we have coupled the *AROPAj* model and the *PaSim* grassland system simulation model. This allows us to better measure the grass yield and the interactions between climate, crop system and more generally the production intended for animal feed (see Aghajanzadeh-Darzi et al. (2016)).

To this end, the model was run as explained in Section 3.3.2.

The level of nitrogen input at the European scale for the *A2H2* far future scenario is given in figures in Table 29 <fnref n="9" />. As shown, the amount of nitrogen applied varies from 0 to 0.10 metric ton of *N* per hectare (t*N*/ha) in most European Union countries, except for Germany, Spain, Ireland, the United Kingdom and central-eastern regions of France, with a mean variation in nitrogen supply from 0.10 to 0.30 t*N*/ha.

The *PaSim* model provides the grass yield (in dry matter) for each pixel for the entire Europe at three levels of nitrogen intensity, which is different from the agricultural yield in the *AROPAj* model where yields are estimated in raw material. This generates a group of points, in which a *N*-yield response curve was statistically adjusted for each crop and *EU* region. These dose-response *N*-yield functions provided by the *PaSim* model replace the previously calibrated values in the *AROPAj* model for fodder beet, fodder maize, permanent grassland, temporary meadows and other grasslands (figures in Table 30).

9.4 MODCOU

Key-points:

How to couple *AROPAj* with a model dealing with subsoil losses of nitrogen at a fine spatial resolution level

Modeling experiments with the hydro-geological model *MODCOU*

In this section, we present a method of linking *AROPAj* with a hydrological model, *MODCOU*, in order to estimate the evolution of a pollutant concentration in aquifers when an environmental policy modifies the amount of fertilizer used by farmers.

The *MODCOU* application focuses on the Seine basin (95,560 km²) as well as three major aquifers with different geological characteristics: the Oligocene (sands and limestone), the Eocene (sands and limestone), and chalk (Cretaceous chalk), with a spatial resolution varying from 1 to 8 km (Gomez, 2002; Gomez et al., 2003; Ledoux et al., 2007).

<footnote n="9">⁹The *A2H2* and *B1H2* scenarios are among those proposed by the *IPCC* to explore far future impacts of climate change (for more explanation, see for instance: http://www.ipcc.ch/ipccreports/sres/emission/index.php?idp=98).</footnote>
Integrating long term effects of organic matter spreading in the yield-to-nitrogen response function

An enhanced form of the response function has been developped in order to integrate the long-term effect of organic nitrogen spreading. Organic residue spreading has a short-term fertilization effect (content in nitrogen, phosphorus and potassium, impact on biological and pH properties of soil). It also has a long-term effect, increasing soil OM and so supporting soil properties (water retention, cation exchange capacity, nitrogen-to-plant availability). This leads to some extra nitrogen available to the plant, provided by the slow mineralization process of additional soil humus. OM spreading could then be considered as a double source of nitrogen for crops via short and long term effects.

The long term effect is assumed to modify the A parameter of the yield function, B potentially remaining unchanged (see the yield function and the parameters to which it refers on page 119). The difference between the function that integrates a long term effect ($Y^{LT}(N^m, N^o) = B - (B - A^{LT})e^{-\tau(N^m + \alpha^{ST}N^o)}$) and the initial short term function ($Y^{ST}(N^m, N^o) = B - (B - A^{ST})e^{-\tau(N^m + \alpha^{ST}N^o)}$), leads to the following:

$$Y^{LT}(N^m, N^o) - Y^{ST}(N^m, N^o) = (B - A^{ST}) \left( 1 - \frac{B - A^{LT}}{B - A^{ST}} \right) e^{-\tau(N^m + \alpha^{ST}N^o)}$$

in which the term $(1 - \frac{B-A^{LT}}{B-A^{ST}})$ remains within the interval $[0, 1]$ and can be expressed as $e^{-\tau\alpha^{LT}N^o}$ (with $\alpha_l^{LT}$ being positive).

The exponential form of the Mitscherlich-Baule function suggests that the long term yield function can be thought of as a leftward translation of the short-term one. This effect can be translated as a decrease of the nitrogen needed to reach a given yield level, all other things being equal. Given the amount of organic nitrogen application, the mineral nitrogen gap between short term and long term leading to a given yield is constantly equal to $\Delta N = -\frac{1}{\tau}\log(\frac{B-A}{B-A^{LT}})$, as represented in Figure 29.

<figure><img src="image_31.png" /><figcaption>Figure 29. Long term effect of OM spreading on the N-to-yield response function.</figcaption></figure>

The nitrogen-to-yield function, which is generalized to L sources of organic nitrogen and which integrates both short and long term effects of organic matter spreading, is amended and as expressed by the following equation (with remarkably unchanged A and B values):

$$Y^{LT}(N^m, N_1^o, N_2^o, \dots, N_L^o) = B - (B - A^{ST})e^{-\tau(N^m + \sum_{l=1}^{L}(\alpha_l^{ST}N_l^o + \alpha_l^{LT}N_l^o))} \quad (18)$$
<figure><img src="image_32.png" /><figcaption>(a) Nitrogen input – Low intensity</figcaption></figure>

<figure><img src="image_33.png" /><figcaption>(b) Grass yield – Low nitrogen intensity</figcaption></figure>

<figure><img src="image_34.png" /><figcaption>(c) Nitrogen input – Medium intensity</figcaption></figure>

<figure><img src="image_35.png" /><figcaption>(d) Grass yield – Medium nitrogen intensity</figcaption></figure>

<figure><img src="image_36.png" /><figcaption>(e) Nitrogen input – High intensity</figcaption></figure>

<figure><img src="image_37.png" /><figcaption>(f) Grass yield – High nitrogen intensity</figcaption></figure>

**Table 29.** Results of the sensitivity analysis of fodder maize yields to nitrogen input in three French regions under two climate scenarios *A2H2* (left) and *B1H2* (right) over the far future 2070-2100 period.
The $\alpha_j^{LT}$ coefficient, related to the long term effects of organic matter spreading, is obtained via the *Carbo-PRO* tool (see www.CARBO-PRO.fr and section 9.2.2).

<figure><img src="image_32.png" /><figcaption>**Figure 30.** Illustration of the procedure to get coefficients associated with long-term impact of organic residue from *Carbo-PRO*.</figcaption></figure>

Source: Jayet & Petel (2015)

**Modification of the optimization problem for UOR integration as organic nitrogen sources.**

At the regional scale, a constraint relative to the *UOR* availability is added. Let us focus on the constraints associated with nitrogen supplies ($N_{jk}^m$ and $N_{jkl}^o$) and extract:

- positivity constraints;

- the per area unit limit of organic matter spreading ($\bar{Q}$ not depending on the farmer $k$ neither on the crop $j$, in accordance with real regulation);

- the upper bound of available organic matter $M_{lk}$ referring to the $l$ organic source. This constraint only concerns *UOR* availability, since livestock manure quantities are endogenous to the model (related to livestock management) and are not included in organic matter
<figure><img src="image_38.png" /><figcaption>(a) A2H2 Midi-Pyrénées</figcaption></figure>

<figure><img src="image_39.png" /><figcaption>(b) B1H2 Midi-Pyrénées</figcaption></figure>

<figure><img src="image_40.png" /><figcaption>(c) A2H2 Limousin</figcaption></figure>

<figure><img src="image_41.png" /><figcaption>(d) B1H2 Limousin</figcaption></figure>

<figure><img src="image_42.png" /><figcaption>(e) A2H2 Auvergne</figcaption></figure>

<figure><img src="image_43.png" /><figcaption>(f) B1H2 Auvergne</figcaption></figure>

<caption>**Table 30.** The results of the sensitivity analysis of fodder maize yields to nitrogen input in three French regions (Midi-Pyrénées, Limousin, Auvergne) under two climate scenarios *A2H2* (left) and *B1H2* (right) over the far future 2070-2100 period.</caption>
exchanges between farm types. This constraint allows for the transfer of UOR between farm-types. Optimal sharing is achieved when all farm-types' individual shadow prices are equal. If *l* refers to UOR, the regional amount of related organic matter *M*<sub>*lk*</sub> is an external parameter. If *l* refers to animal-sourced organic matter, the *M*<sub>*lk*</sub> amount refers to endogenous (bounded) animal activities (livestock and manure management).

Optimal UOR availability sharing is obtained via the following aggregated regional program:

$$
\begin{array}{l@{\qquad}l}
\max_{x_k, a_k, N_{jk}^m, N_{jkl}^o} & \displaystyle\sum_{k=1}^{K} \pi_k(x_k, a_k, N_{jk}^m, N_{jkl}^o) \\
\forall k \ \forall j: & F_k(x_k, a_k, N_{jk}^m, N_{jkl}^o) \le O \quad (\psi_k) \\
& \forall k \ \forall j: \ N_{jk}^m \ge O \quad (\beta_{jk}^m) \\
\text{s.t.} & \forall k \ \forall l \ \forall j: \ N_{jkl}^o \ge O \quad (\beta_{jkl}^o) \\
& \forall k \ \forall j: \ \sum_{l=1}^{L} N_{jkl}^o \le \bar{Q} \quad (\lambda_{jk}) \\
& \forall l: \ \sum_{k=1}^{K} \sum_{j=1}^{J} N_{jkl}^o \ a_{jk} \le \sum_{k=1}^{K} M_{lk} \quad (\xi_l)
\end{array}
\qquad (\mathcal{R})
$$

Implementation of the long-term effect in AROPAj simulations.

Thanks to the Carbo-PRO tool (see www.CARBO-PRO.fr and section 9.2.2), we obtain a long
term effect parameter ($\alpha_l^{LT}$) for each organic matter source in AROPAj. Integrating long term
extra-$N$ as a reduction of the nitrogen need of plants makes it difficult to keep the LP structure of
the model. Indeed, non linearity of organic matter $N_{o,l,j,k}$ enters the optimization program when
long term impact of repeated organic matter application is taken into account. Linearity is ensured
when optimal $N_{o,l,j,k}$ tends to converge toward the value for which long term effect is estimated. We
use the iterative computational process below, where the short term $N_{o,l,j,k}$ computed at a given
iteration is used to estimate long term effect for the following iteration:

1. The following two-step subprocess is implemented:

(a) Market prices replace shadow prices to solve the plot optimization programs (see section 4.3). Null-values are considered for additional long term impact of organic nitrogen.

(b) The (R) regional optimization program is solved, doing so for yields and nitrogen applications provided by the step (a).

2. Shadow prices required for pre-solving the plot optimization program are extracted from the previous calculus' step At the same time, quantities of organic matter spread are extracted, and average quantities of organic nitrogen are computed against land endowment.

3. The three-step subprocess is implemented:

(a) Shadow prices and long term organic nitrogen amount provided by the previous step are injected in the plot optimization program.

(b) The (R) regional optimization program is solved, doing so for yields and nitrogen applications provided by the step (a).

(c) Go to step (2.) until a predefined number of iterations is reached or a predefined convergence threshold is attained.
<figure><img src="image_44.png" /><figcaption>**Figure 31.** Schematic representation of the coupling methodology. The colors refer to the themes: green for agronomy, blue for hydrology and red for economics. Darker colors refer to models, and lighter colors to inputs/outputs. The method for building the tax scenarios is described in the box below. The information which underpins this work is depicted by the blue line, some *AROPAj* input data and the *MODCOU* initial conditions being given from previous works.</figcaption></figure>

Source: Bourgeois et al. (2016)
### 9.2.2 Carbo-PRO

The *Carbo-PRO* tool, designed by *INRA* (Department of “Environnement et Grandes Cultures”) and the *Veolia* company, is used to get coefficients associated with the long term effects of organic matter spreading ($\alpha_l^{LT}$). More details on the long term effect are available in the section focusing on nitrogen. *Carbo-PRO* aims at computing the carbon stock in soil that is created by repeated organic residue application. It is based on the Henin-Dupuis model of soil carbon dynamics (Hénin & Dupuis, 1945) and on the *RothC* model that simulates the evolution of organic matter (Peltre et al., 2012). It also provides estimations of soil stability improvement, water retention, cation exchange capacity and quantity of nitrogen stocked in soil after many years of mineralization process.

We only use the nitrogen module of *Carbo-PRO*. The module is concerned with the process of nitrogen mineralization related to organic matter degradation that renders organic nitrogen available for plant. This extra soil-*N* is estimated as the product of soil nitrogen content and the K2 isohumic rate, or rate of mineralization of the soil-organic matter. More details on *Carbo-PRO* operation are available on www.CARBO-PRO.fr.

To get coefficients associated with long term effects of organic matter spreading, a spreading scenario has to be entered in *Carbo-PRO* with information regarding soil type, climate and spreading specifications (application rate and frequency, humic potential indicators, dry matter and organic matter content). The procedure is presented in Figure 30: For a given quantity of organic matter, *Carbo-PRO* provides the soil extra-*N* quantity. This extra-*N* at the end of the spreading period is obtained for different quantities of organic matter spread. A relation between extra-*N* and OM quantity at a given time can then be traced. As the relation is linear, the $\alpha_l^{LT}$ coefficient corresponds to the derivative of this function, related to the number of years of spreading.

The procedure has to be repeated for each source of organic nitrogen. We obtain one parameter for every type of organic matter. In the core of *AROPAj*, the extra-*N* is integrated as a reduction of the nitrogen need of plants (Needs - “long-term extra-*N*” ≤ Sum of $N_{m/o}$-inputs), requiring the iterative process described in section 4.3.

## 9.3 PaSim

**Key-points:**

* Overcoming of the lack of STICS sourced response functions for pastures and fodderries

* Rough replacement of fodder yields by PaSim in different climate change scenarios

The pasture simulation model (*PaSim*, (Riedo et al., 1998)) is a multi-year, process-based biogeochemical grassland model, which is based on the Hurley Pasture Model ((Thornley, 1998)). It simulates fluxes of carbon, nitrogen, water and energy at the soil-plant-animal-atmosphere interface for managed grasslands at the plot scale. Simulations may run over one or several years.

*PaSim* simulates grassland processes on a time step of 1/50th of a day. It provides detailed sub-daily dynamics and ensures energy budget stability. A fixed legume fraction in the grasslands is assumed in the *PaSim* model, in which vegetation is represented by mixed sward, when grasses and legumes co-exist. *PaSim* was developed to simulate the meat and milk production of cattle
The use of the hydrological model *MODCOU* traces the nitrate transfer from the root zone toward the groundwaters of the Seine river basin. The hydrological model *MODCOU* (Ledoux, 1980; Ledoux et al., 1984, 1989) computes the daily water balance using climatic data such as rainfall and potential evapo-transpiration (*PET*), the water flow to and in the river network, and also the flow to, in, and between aquifer layers and the interactions between rivers and each layer of the free part of the aquifer. It is also able to compute the convective transfer of solute to, in, and between those layers.

Regarding the *MODCOU* model, the initial condition (in 2002) can affect the simulation over several decades due to transfer time through the unsaturated zone (*UZ*) and aquifer nitrogen accumulation. Thus, special attention was paid to obtaining realistic initial conditions for the *AROPAj-MODCOU* simulations. Initialization is based on the *MODCOU-STICS* simulations (as described in Ledoux et al. (2007) and in Viennot et al. (2007)), and is aimed at correctly reproducing the nitrate stock in the *UZ*, among other things, at the starting date of simulations (2002). In such simulations, *STICS* provides estimates of the nitrate flux entering in the *UZ* over the period 1971-2002 and reaching the aquifer from 2003. Moreover, *MODCOU-STICS* provides the *NO*<sub>3</sub> reference flux as the mean value of 2002-2004 fluxes (see Figure 31).

Coupling of *AROPAj* and *MODCOU* comes into play from the simulation starting date (2002). In this respect, we use the *AROPAj* spatialization process which geographically distributes *AROPAj* output at a fine level of resolution (see Section 8). The spatialization method provides the contribution of each farm group on each cell of the grid to the regional (*FADN*) agricultural activity. This enables any *AROPAj* output to be distributed over the geographical area, i.e., the Seine river basin. Soil-root nitrate losses related to fertilizer use are then distributed on a grid compatible with the spatial resolution of the hydrological model. Following the temporal tax path designed by the theoretical economic model exposed in Bourgeois & Jayet (2016), spatialized simulated *NO*<sub>3</sub> fluxes provided by the *AROPAj* model feed *MODCOU* by topsoil nitrate changes according to the formula:

$$NO_{3\text{input}}(\text{year}) = \text{Mean}[NO_{3\text{MODCOU-STICS}}(2001-2003)] \quad (19) \\ * \left[1 + \frac{NO_{3\text{AROPAj}}(\text{year}) - NO_{3\text{AROPAj}}(2002)}{NO_{3\text{AROPAj}}(2002)}\right]$$

For each year, the signal transmitted to *MODCOU* is based on the average emissions provided by the coupled physical models (*MODCOU-STICS*) for the period [2001,2003], and is augmented with the annual variation provided by *AROPAj* and then related to the temporal tax path.

Finally a “scenario” is defined as a set of time-ordered annual *AROPAj* inputs from date 0 up to a given horizon *T*. Any of the *t*-annual *AROPAj* runs provides an *NO*<sub>3</sub> lixiviated flux which is transferred to the groundwater by the dynamic hydrological model *MODCOU*. A scenario gathers different incentive instruments that can be implementable into the *AROPAj* model. It could be an input or emission tax and/or a subsidy to environmentally-friendly crop (existing in *AROPAj* like miscanthus, grassland). We can also modify the livestock adjustment. For the economic analysis, the *NO*<sub>3</sub> concentration in the aquifer is calculated as the annual median value for the entire chalk aquifer.
in response to different climate and management conditions. Animal processes are limited to
the plot scale when animals are grazing at pasture (not in the barn). PaSim has been used
to simulate greenhouse gas fluxes over grasslands and to examine the role of grasslands in the
European greenhouse gas budget ((Schmid et al., 2001; Vuichard et al., 2007a,b)).

The method for the coupling of the *AROPAj* economic model and the *STICS* crop model (Brisson et al. (1998)), developed by Godard et al. (2008), is best adapted to major agricultural crops (cereals, oilseeds, etc.). Hence, it is not still appropriate for forage and grass plants, due to the lack of information in terms of productivity and prices. In order to obtain a reliable measurement of grass and fodder yields, we have coupled the *AROPAj* model and the *PaSim* grassland system simulation model. This allows us to better measure the grass yield and the interactions between climate, crop system and more generally the production intended for animal feed (see Aghajanzadeh-Darzi et al. (2016)).

To this end, the model was run as explained in Section 3.3.2.

The level of nitrogen input at the European scale for the *A2H2* far future scenario is given in figures in Table 29 <fnref n="9" />. As shown, the amount of nitrogen applied varies from 0 to 0.10 metric ton of *N* per hectare (t*N*/ha) in most European Union countries, except for Germany, Spain, Ireland, the United Kingdom and central-eastern regions of France, with a mean variation in nitrogen supply from 0.10 to 0.30 t*N*/ha.

The *PaSim* model provides the grass yield (in dry matter) for each pixel for the entire Europe at three levels of nitrogen intensity, which is different from the agricultural yield in the *AROPAj* model where yields are estimated in raw material. This generates a group of points, in which a *N*-yield response curve was statistically adjusted for each crop and *EU* region. These dose-response *N*-yield functions provided by the *PaSim* model replace the previously calibrated values in the *AROPAj* model for fodder beet, fodder maize, permanent grassland, temporary meadows and other grasslands (figures in Table 30).

9.4 MODCOU

Key-points:

How to couple *AROPAj* with a model dealing with subsoil losses of nitrogen at a fine spatial resolution level

Modeling experiments with the hydro-geological model *MODCOU*

In this section, we present a method of linking *AROPAj* with a hydrological model, *MODCOU*, in order to estimate the evolution of a pollutant concentration in aquifers when an environmental policy modifies the amount of fertilizer used by farmers.

The *MODCOU* application focuses on the Seine basin (95,560 km²) as well as three major aquifers with different geological characteristics: the Oligocene (sands and limestone), the Eocene (sands and limestone), and chalk (Cretaceous chalk), with a spatial resolution varying from 1 to 8 km (Gomez, 2002; Gomez et al., 2003; Ledoux et al., 2007).

<footnote n="9">⁹The *A2H2* and *B1H2* scenarios are among those proposed by the *IPCC* to explore far future impacts of climate change (for more explanation, see for instance: http://www.ipcc.ch/ipccreports/sres/emission/index.php?idp=98).</footnote>
## 9.5 GLOBIOM

**Key-points:**

Attempts to integrate price and yield feedbacks related to clearing markets at a global scale

Focusing on crop yields and prices more than fertilizers and feed at a meso-economic scale

(countries or sets of countries within the EU)

The *GLOBIOM* model is a global, recursive and dynamic, partial equilibrium model designed to aid policy analysis of issues involving land use competition among the major land-based production sectors, particularly agriculture, forestry, livestock and bioenergy (Havlík et al. (2011)). Moreover, one of the main particularities of *GLOBIOM* is that it makes intensive use of biophysical data.

*GLOBIOM* is an optimization model based on the maximization of global welfare, defined as the sum of consumers' and producers' surpluses. For each output product defined in the model (i.e. each crop or forest product), a reference level is defined that is subject to an exogenous increase over time on the basis of population and gross domestic product (*GDP*) growth. Besides, the demand for a given product at each time step depends on the product's own price. Prices are homogeneous within each market regions and there about 30 market regions comprising the global scale. Interregional trade, subject to transportation costs and tariffs, is permitted between these regions.

The supply system is based on a detailed representation of land and land allocation. The world is divided into simulation units (*SimU*) that are homogenous in terms of agro-climatic potential (soil, climate). There are more than 200k land units worldwide when the model is run at its most detailed resolution. Land shares allocated to the different uses within each simulation unit are the main endogenous variables on the supply-side in *GLOBIOM*. Prior to this optimization process, crop yields are determined on the *SimU* level thanks to a biophysical model (in this case the crop model *EPIC*, Izaurralde et al. (2006)) fed with SimU-specific climate and soil data. These yields are then introduced as parameters in *GLOBIOM* (the approach is similar for the forestry sector). However, yields are not completely fixed in *GLOBIOM*, they are allowed to vary both exogenously (e.g. exogenous technological progress) and endogenously. As for these endogenous changes, within a given *SimU* and for a given crop, there can be switches between different production systems (namely subsistence, low input, high input, irrigated), yields corresponding to the different systems are all determined by the *EPIC* crop model. The parameters corresponding to the livestock sector are determined on the basis of input-output coefficients provided by the *RUMINANT* model (Herrero et al. (2010)) for ruminants and from a literature review concerning monogastrics.

The model is dynamic and is run on several time steps on the basis of an exogenous baseline (population, *GDP*, technological progress, climate change...). It is also recursive as land-use changes between two time steps are limited in their extent and therefore depends on the state at the previous time step.

There are several points that justify the linkage between *GLOBIOM* and *AROPAj*. At first, endogenous output prices from *GLOBIOM* can be fed as exogenous parameters into *AROPAj*. Then, land-use projections from *GLOBIOM* could be forced into *AROPAj* so as to investigate the impact on both output and non-land input markets. At last, comparing the results from both models may help to identify stylized facts.
<figure><img src="image_33.png" /><figcaption>(a) Nitrogen input – Low intensity</figcaption></figure>

<figure><img src="image_34.png" /><figcaption>(b) Grass yield – Low nitrogen intensity</figcaption></figure>

<figure><img src="image_35.png" /><figcaption>(c) Nitrogen input – Medium intensity</figcaption></figure>

<figure><img src="image_36.png" /><figcaption>(d) Grass yield – Medium nitrogen intensity</figcaption></figure>

<figure><img src="image_37.png" /><figcaption>(e) Nitrogen input – High intensity</figcaption></figure>

<figure><img src="image_38.png" /><figcaption>(f) Grass yield – High nitrogen intensity</figcaption></figure>

<caption>**Table 29.** Results of the sensitivity analysis of fodder maize yields to nitrogen input in three French regions under two climate scenarios *A2H2* (left) and *B1H2* (right) over the far future 2070-2100 period.</caption>
<figure><img src="image_45.png" /><figcaption>**Table 31.** Difference in European (a) and *ROW* (b) annualized *GHG* emissions (%) comparing scenarios *SSP1* to *SSP3* over time.</figcaption></figure>

The *AROPAj-GLOBIOM* forcing process relies primarily on the application of future yields and price trend variations in order to evaluate economic and environmental impacts on the *EU* agricultural system until 2050. To do so, two macroeconomic scenarios of Shared Socioeconomic Pathways (*SSP1* and *SSP3*), developed under the aegis of the *IPCC-AR5*, have been used.

The combination of the two following assumptions has been used: (i) yield and price variation estimated for two SSP scenarios from 2000 to 2050 and provided by the GLOBIOM model; and (ii) six levels of livestock adjustment, defining the possibility of farmers to adjust their livestock numbers, from 0% in 2000 to 75% in 2050 (by step of 15% every ten years)<fnref n="10" />.

The *AROPAj-GLOBIOM* forcing results show that, although the *SSP1* scenario represents a sustainable world with rapid technological progress towards more environmentally friendly processes, it would provide negative impacts on European grass cover and *GHG* emissions (3.4% in *SSP1* compared to *SSP3*) in the near future (until 2050), as a result of overall developments on both agricultural yields and on the demand side (i.e. increases in population and *GDP*). It seems that lower agriculture product prices in *SSP1* lead farmers to increase cropland so as to obtain higher revenues. In the rest of the world (*ROW*), the *SSP1* may help mitigate climate change and reach emission targets by reducing land use change emissions by 35% and net *GHG* emissions by 15% by 2050 compared to *SSP3* (see Figure 31).

## 9.6 GeoMIRET

### Key-points:

* Accounting for bio-energy production and bio-energy plants

* Benefiting from other modeling experiments at the French scale

<footnote n="10">¹⁰Aghajanzadeh-Darzi et al, (2015), Global change challenges for the EU agricultural sector: a multi-scale model analysis, submitted</footnote>
<figure><img src="image_39.png" /><figcaption>(a) A2H2 Midi-Pyrénées</figcaption></figure>

<figure><img src="image_40.png" /><figcaption>(b) B1H2 Midi-Pyrénées</figcaption></figure>

<figure><img src="image_41.png" /><figcaption>(c) A2H2 Limousin</figcaption></figure>

<figure><img src="image_42.png" /><figcaption>(d) B1H2 Limousin</figcaption></figure>

<figure><img src="image_43.png" /><figcaption>(e) A2H2 Auvergne</figcaption></figure>

<figure><img src="image_44.png" /><figcaption>(f) B1H2 Auvergne</figcaption></figure>

<caption>**Table 30.** The results of the sensitivity analysis of fodder maize yields to nitrogen input in three French regions (Midi-Pyrénées, Limousin, Auvergne) under two climate scenarios *A2H2* (left) and *B1H2* (right) over the far future 2070-2100 period.</caption>
We analyse the interactions between cellulosic biomass supply and the cellulosic biofuel demand through the coupling of *AROPAj* and the energy-transport system model *TIMES GeoMIRET*, developed by IFP Energies nouvelles (*Ifpen*). Under public policies, demand and resource availability constraints, *TIMES GeoMIRET* optimizes the French energy mix and determines the location of new biofuel facilities while taking into account all possible energy uses of biomass (heat, electricity or biofuels) and the location of biomass resource basins, refineries, first generation (1<sup>st</sup>G) existing ethanol facilities, harbours and imported biomass entries on the Belgian and German borders.

The *GeoMIRET* model is a partial equilibrium model applied to the French energy sector and built with *MARKAL/TIMES*, the techno-economic model paradigm developed by *IEA-ETSAP*. It is a long-term, multi-period, techno-economic planning model that covers the energy-transport system. Aiming at providing final energy services at minimum cost, the system encompasses all the steps from primary resources in place to the supply of the energy services demanded by energy consumers, through the chain of processes which transform, transport, distribute and convert energy into services. These processes are mainly driven by the reference demands for energy services (e.g., mobility for the transport sector), the supply of the resources (e.g., amount availability and/or price level), along with public policy constraints, which are provided as exogenous inputs to the model.

Technologies are described in *TIMES* by technical and economic parameters. The choice of the technology generation is conditioned by 4 components : energy service demands, primary resource potentials, policy settings, and description of the technologies set. The demand component includes French aggregated heat and electricity demands, mobility demands for short and long distance for passenger and freight transport and demands for exported products (oil products, electricity). The primary energy supply includes imported fossil energy (crude oil, coal, natural gas), imported bioproducts (palm oil, soybean oil and wood biomass) and locally-produced biomass (sugar beet, wheat, corn, rapeseed, sunflower, miscanthus, switchgrass, willow, forest wood, residues/straw). The energy technology component includes process-detailed oil refining, biofuels facilities (1<sup>st</sup>G ethanol and bio-diesel, Fatty Acid Methyl Ester (FAME) and Hydrotreated Vegetable Oil (HVO)), lignocellulosic ethanol and Biomass-to-Liquid (BtL or Diesel Fischer-Tropsch), electricity generation, preparation of fuels for transport at blending, and end-use technologies for road mobility (private passenger cars, public transport vehicles, light and heavy duty vehicles). The policy component includes a variety of micro measures (technology portfolios, vehicle fleet with different engine technologies), the national renewable energy action plan and broader policy targets (sectoral carbon tax, renewable energy and fuel quality directives).

*AROPAj* and *TIMES GeoMIRET* have different spatial distribution scales. While *AROPAj* results can be aggregated at the administrative regional scale, *TIMES GeoMIRET* gathers input data into administrative departments which are clustered into supplying-lignocellulosic biomass basins. These basins are defined as potential regions for supplying second generation (2<sup>nd</sup>G) biofuel facilities that correspond to homogeneous facilities in terms of the available biomass type and pedoclimatic conditions. Coupling the two models involves the systematization of the spatial disaggregation of *AROPAj* output to a suitable *TIMES GeoMIRET* spatial scale. We proceed therefore to overlay the *AROPAj* polygons with the administrative department boundaries, by using the Geographic Information System (ArcGIS).

The departmental biomass quantities were grouped in supply basins according to an unsuper-
<figure><img src="image_45.png" /><figcaption>**Figure 31.** Schematic representation of the coupling methodology. The colors refer to the themes: green for agronomy, blue for hydrology and red for economics. Darker colors refer to models, and lighter colors to inputs/outputs. The method for building the tax scenarios is described in the box below. The information which underpins this work is depicted by the blue line, some *AROPAj* input data and the *MODCOU* initial conditions being given from previous works.</figcaption></figure>

Source: Bourgeois et al. (2016)
vised classification algorithm, i.e. k-medoids algorithm (PAM). Algorithm inputs were represented by department centroids with longitude and latitude coordinates and agricultural and forest availabilities per department. Outputs were 9 partitions of departments, each of which fed biomass to a facility assumed to be probably located at the cluster barycentre or harbour site or 1<sup>st</sup>G ethanol site. Once the contiguous and homogeneous regions are defined, we calculate the supply distances between the barycentre of each biomass basin, refineries, 1<sup>st</sup>G ethanol facilities, harbours and entry points in order to estimate the costs of transport, by applying the great circle equation used to determine the straight-line distance between two points.

<figure><img src="image_46.png" /><figcaption>**Figure 32.** Geographical distribution of biomass resource basins, refineries, ethanol facilities, harbours and imported biomass entries according to different biomass supply scenarios.</figcaption></figure>

**9.7 OURSEurope**

Key-points:

Geographical location and vertical market integration accounting for biomass transformation into biofuels at the European scale

Spatialized three-sector multi-agents viewed through building an operating model cluster

Europe is the first biodiesel market at the global level, and similarly, *AROPA*<sub>j</sub> is a European agricultural supply model. Therefore, it is interesting to associate it with an agricultural demand model for energy purposes, in order to assess the feasibility and the potential of the biofuel sector in particular. Indeed, the willingness for energy independence, for a boost of the local economy and for a reduction in pollutant emissions has supported the European bioenergy development. Target levels of bio-fuel incorporation in fossil fuels are applicable in most European countries, despite the criticism regarding the environment, the budget constraints, and the arising conflicts on biofuels and the current economic crisis. In response to environmental criticism, eligibility requirements have also been established and the percentage of certified production is constantly increasing.

The objective of a coupling between *AROPA*<sub>j</sub> and a model simulating agricultural demand for biofuel production is to estimate the potential of domestic biofuels production, their profitability and the factors limiting their development as well as their environmental and economic impacts. A massive use of biofuels can indeed change the allocation of agricultural land and the profitability of
The use of the hydrological model *MODCOU* traces the nitrate transfer from the root zone toward the groundwaters of the Seine river basin. The hydrological model *MODCOU* (Ledoux, 1980; Ledoux et al., 1984, 1989) computes the daily water balance using climatic data such as rainfall and potential evapo-transpiration (*PET*), the water flow to and in the river network, and also the flow to, in, and between aquifer layers and the interactions between rivers and each layer of the free part of the aquifer. It is also able to compute the convective transfer of solute to, in, and between those layers.

Regarding the *MODCOU* model, the initial condition (in 2002) can affect the simulation over several decades due to transfer time through the unsaturated zone (*UZ*) and aquifer nitrogen accumulation. Thus, special attention was paid to obtaining realistic initial conditions for the *AROPAj-MODCOU* simulations. Initialization is based on the *MODCOU-STICS* simulations (as described in Ledoux et al. (2007) and in Viennot et al. (2007)), and is aimed at correctly reproducing the nitrate stock in the *UZ*, among other things, at the starting date of simulations (2002). In such simulations, *STICS* provides estimates of the nitrate flux entering in the *UZ* over the period 1971-2002 and reaching the aquifer from 2003. Moreover, *MODCOU-STICS* provides the *NO*<sub>3</sub> reference flux as the mean value of 2002-2004 fluxes (see Figure 31).

Coupling of *AROPAj* and *MODCOU* comes into play from the simulation starting date (2002). In this respect, we use the *AROPAj* spatialization process which geographically distributes *AROPAj* output at a fine level of resolution (see Section 8). The spatialization method provides the contribution of each farm group on each cell of the grid to the regional (*FADN*) agricultural activity. This enables any *AROPAj* output to be distributed over the geographical area, i.e., the Seine river basin. Soil-root nitrate losses related to fertilizer use are then distributed on a grid compatible with the spatial resolution of the hydrological model. Following the temporal tax path designed by the theoretical economic model exposed in Bourgeois & Jayet (2016), spatialized simulated *NO*<sub>3</sub> fluxes provided by the *AROPAj* model feed *MODCOU* by topsoil nitrate changes according to the formula:

$$NO_{3\text{input}}(\text{year}) = \text{Mean}[NO_{3\text{MODCOU-STICS}}(2001-2003)] \quad (19) \\ * \left[1 + \frac{NO_{3\text{AROPAj}}(\text{year}) - NO_{3\text{AROPAj}}(2002)}{NO_{3\text{AROPAj}}(2002)}\right]$$

For each year, the signal transmitted to *MODCOU* is based on the average emissions provided by the coupled physical models (*MODCOU-STICS*) for the period [2001,2003], and is augmented with the annual variation provided by *AROPAj* and then related to the temporal tax path.

Finally a “scenario” is defined as a set of time-ordered annual *AROPAj* inputs from date 0 up to a given horizon *T*. Any of the *t*-annual *AROPAj* runs provides an *NO*<sub>3</sub> lixiviated flux which is transferred to the groundwater by the dynamic hydrological model *MODCOU*. A scenario gathers different incentive instruments that can be implementable into the *AROPAj* model. It could be an input or emission tax and/or a subsidy to environmentally-friendly crop (existing in *AROPAj* like miscanthus, grassland). We can also modify the livestock adjustment. For the economic analysis, the *NO*<sub>3</sub> concentration in the aquifer is calculated as the annual median value for the entire chalk aquifer.
agents. To study the impacts of biofuels on the European agriculture, we have chosen to represent
the biofuel production chain, using *AROPA*<sub>j</sub> as initial link in providing the supply of raw materials.

To implement this approach, our choice was focused on the establishment of a linear mathemat-
ical programming model for the entire European biofuels industry. A linear programming model
allows us to determine the optimal solution to a problem according to the constraints scripted for a
given period and it functions as a decision aid for problems of a large scale. This method actually
allows us to use a very large number of variables and equations. Developing a global model is par-
ticularly complex and requires, given the variety of stakeholders in the sector, a relatively simplistic
representation. To preserve the delicacy of modeling various sectors involved in this industry, we
opted for a weak coupling of several sectoral models. Thus, in contrast to a strong coupling, we
maintain the independence of the models, which grants them the freedom to evolve.

The methodology here implemented entails coupling three models, each representing one of
the key sectors of the biofuels industry. The model of European agricultural supply, *AROPA*<sub>j</sub>,
simulates the domestic supply of raw materials. All or part of its production, as well as the
maximum imports of raw materials, is then used by the partial equilibrium model *GIRAF*. This
model simulates the European industrial production of different types of biofuels, depending on
the supply of domestic and international raw materials, as well as the demand for biofuels. The
European refining model *OURSEurope* simulates the production of European refinery products
and therefore the local demand for biofuels to meet the objectives of incorporation.

The *AROPA*<sub>j</sub> model was modified in order to introduce energy crops and crop by-products. The *GIRAF* model was created for the coupling because no existing model met the desired requirements. As for the model *OURSEurope*, it is a European version of the global refining *OURSE* model (developed by the *Ifpen*), and it was created specifically for coupling. In addition, the geographic dimension has been implemented in the three models to spatialize all represented agents (1307 farm-type groups of the *V3-AROPA*<sub>j</sub> version, 288 biofuel producers and 109 refiners) as well as to highlight the exchanges between them. This allows, in particular, the identification of the European bio-fuel production and consumption areas.

<figure><img src="image_47.png" /><figcaption>**Figure 33.** Applying the AGRAF model to evaluate impacts of blending rates of biofuel in gazoline and gasoil consumed in Europe.</figcaption></figure>

Source: Assaiante et al. (2016)
## 9.5 GLOBIOM

**Key-points:**

Attempts to integrate price and yield feedbacks related to clearing markets at a global scale

Focusing on crop yields and prices more than fertilizers and feed at a meso-economic scale

(countries or sets of countries within the EU)

The *GLOBIOM* model is a global, recursive and dynamic, partial equilibrium model designed to aid policy analysis of issues involving land use competition among the major land-based production sectors, particularly agriculture, forestry, livestock and bioenergy (Havlík et al. (2011)). Moreover, one of the main particularities of *GLOBIOM* is that it makes intensive use of biophysical data.

*GLOBIOM* is an optimization model based on the maximization of global welfare, defined as the sum of consumers' and producers' surpluses. For each output product defined in the model (i.e. each crop or forest product), a reference level is defined that is subject to an exogenous increase over time on the basis of population and gross domestic product (*GDP*) growth. Besides, the demand for a given product at each time step depends on the product's own price. Prices are homogeneous within each market regions and there about 30 market regions comprising the global scale. Interregional trade, subject to transportation costs and tariffs, is permitted between these regions.

The supply system is based on a detailed representation of land and land allocation. The world is divided into simulation units (*SimU*) that are homogenous in terms of agro-climatic potential (soil, climate). There are more than 200k land units worldwide when the model is run at its most detailed resolution. Land shares allocated to the different uses within each simulation unit are the main endogenous variables on the supply-side in *GLOBIOM*. Prior to this optimization process, crop yields are determined on the *SimU* level thanks to a biophysical model (in this case the crop model *EPIC*, Izaurralde et al. (2006)) fed with SimU-specific climate and soil data. These yields are then introduced as parameters in *GLOBIOM* (the approach is similar for the forestry sector). However, yields are not completely fixed in *GLOBIOM*, they are allowed to vary both exogenously (e.g. exogenous technological progress) and endogenously. As for these endogenous changes, within a given *SimU* and for a given crop, there can be switches between different production systems (namely subsistence, low input, high input, irrigated), yields corresponding to the different systems are all determined by the *EPIC* crop model. The parameters corresponding to the livestock sector are determined on the basis of input-output coefficients provided by the *RUMINANT* model (Herrero et al. (2010)) for ruminants and from a literature review concerning monogastrics.

The model is dynamic and is run on several time steps on the basis of an exogenous baseline (population, *GDP*, technological progress, climate change...). It is also recursive as land-use changes between two time steps are limited in their extent and therefore depends on the state at the previous time step.

There are several points that justify the linkage between *GLOBIOM* and *AROPAj*. At first, endogenous output prices from *GLOBIOM* can be fed as exogenous parameters into *AROPAj*. Then, land-use projections from *GLOBIOM* could be forced into *AROPAj* so as to investigate the impact on both output and non-land input markets. At last, comparing the results from both models may help to identify stylized facts.
The approach adopted is a partial equilibrium modeling type, so in theory the prices of products considered (oil products, biofuels, etc.) do not affect other products of the economy. Taking into account the spatiality, combined with horizontal dimensions (products, actors) and vertical ones (from upstream to downstream activities), gives a certain advantages to this methodology. The modeling is dedicated to the European zone and is highly parameterized in order to adapt to different types of scenarios, both technical and environmental, as well as economic or political. This allows to test different hypotheses and to analyze their impacts on the production of biofuels (quantity, nature, environmental impacts, co-production and location), associated costs (investment, transportation, raw materials) and the exchanges between agents of the industry. Both conventional (pure vegetable oil, biodiesel, NexBtL and first generation bioethanol) and unconventional (BtL, second generation bioethanol) biofuel production are modeled. For each process, technical, environmental and economic data can be changed. However, due to a lack of access to reliable data on biofuels produced from seaweeds, we have chosen not to represent this production. The modeling is operational and requires that each step be accurately monitored to ensure the proper integration of the parameters in the simulation (Clodic, 2013).

As illustrative results, Figure 33 shows how incorporating bio-fuels in the European fuel demand may impact the overall cost for the raw biomass transformers and the refiners.
<figure><img src="image_46.png" /><figcaption>**Table 31.** Difference in European (a) and *ROW* (b) annualized *GHG* emissions (%) comparing scenarios *SSP1* to *SSP3* over time.</figcaption></figure>

The *AROPAj-GLOBIOM* forcing process relies primarily on the application of future yields and price trend variations in order to evaluate economic and environmental impacts on the *EU* agricultural system until 2050. To do so, two macroeconomic scenarios of Shared Socioeconomic Pathways (*SSP1* and *SSP3*), developed under the aegis of the *IPCC-AR5*, have been used.

The combination of the two following assumptions has been used: (i) yield and price variation estimated for two *SSP* scenarios from 2000 to 2050 and provided by the *GLOBIOM* model; and (ii) six levels of livestock adjustment, defining the possibility of farmers to adjust their livestock numbers, from 0% in 2000 to 75% in 2050 (by step of 15% every ten years)<fnref n="10" />.

The *AROPAj-GLOBIOM* forcing results show that, although the *SSP1* scenario represents a sustainable world with rapid technological progress towards more environmentally friendly processes, it would provide negative impacts on European grass cover and *GHG* emissions (3.4% in *SSP1* compared to *SSP3*) in the near future (until 2050), as a result of overall developments on both agricultural yields and on the demand side (i.e. increases in population and *GDP*). It seems that lower agriculture product prices in *SSP1* lead farmers to increase cropland so as to obtain higher revenues. In the rest of the world (*ROW*), the *SSP1* may help mitigate climate change and reach emission targets by reducing land use change emissions by 35% and net *GHG* emissions by 15% by 2050 compared to *SSP3* (see Figure 31).

## 9.6 GeoMIRET

### Key-points:

* Accounting for bio-energy production and bio-energy plants

* Benefiting from other modeling experiments at the French scale

<footnote n="10">¹⁰Aghajanzadeh-Darzi et al, (2015), Global change challenges for the EU agricultural sector: a multi-scale model analysis, submitted</footnote>
# 10 Model evolution

## 10.1 The model over time

**Key-points:**

* Relating published papers with different *AROPAj* versions and *FADN* databases

* From *CAP* oriented analysis toward environmental issues

* From private company support toward European Universities and institutional public support

* Competing for *EC* FrameWork program and tender support (*EUROTOOLS*, *GENEDEC*, *INSEA*, specific milk market reform, *CCTAME*, *AnimalChange*, *FoodSecure*)

The articles and reports published over the years in relation to the *AROPAj* model refer to different versions of the model. Differences stem mainly from the *FADN* data and from the state of the *EU* enlargement. In addition, series of new blocks and shell were developed regularly. This is mainly due to the change in the area of focus: the model building started with *CAP* analysis and it evolved toward the study of relationship between agriculture and the environment. Nevertheless, we keep the principles of the model intact.

One of the major driving forces of the model is its modular and generic conception. Another driver lies in the evolution of computing capacities and facilities. Thus, we have anticipated the need to overcome computation limits and to enlarge modeling capacities when new scientific problems arise.

The foundations of the model are derived from an early 1990s assessement of the *CAP* reform impacts in France and the UK, which was comissioned by an international grain trader. A major inflexion occurred in the mid 1990s when climate change impacts and GHG emissions emerged in the field of economic analysis. Two drivers significantly impact the model evolution: (i) European contacts acting as precursors, through researchers and MSc or PhD students from Universities of Bologna and Parma, and Universidad Politécnica de Madrid; (ii) French programs under the control of environmental agencies and ministries. Successful bids (partnership or coordination) in response to *EC* research calls (*EUROTOOLS*, *GENEDEC*, *INSEA*, *CC-TAME*, *AnimalChange*) strongly boosted the development of the model, in addition to more specific responses to calls for tenders and national incentives. High development and the support of multidisciplinary approaches facilitated the trend from analysis focusing on the *CAP* and economic criteria, to an analysis focusing on the environmental impacts and external effects regulation.

Appendices B and C provide the list of contributors over time and the list of projects which contributed to funding for *AROPAj*.

## 10.2 Version list

**Key-points:**

* From the *EU*-15 to the *EU*-27

* *FADN* sourced information, from 1997 untill 2009

* Toward a unified version of the model kernel
We analyse the interactions between cellulosic biomass supply and the cellulosic biofuel demand through the coupling of *AROPAj* and the energy-transport system model *TIMES GeoMIRET*, developed by IFP Energies nouvelles (*Ifpen*). Under public policies, demand and resource availability constraints, *TIMES GeoMIRET* optimizes the French energy mix and determines the location of new biofuel facilities while taking into account all possible energy uses of biomass (heat, electricity or biofuels) and the location of biomass resource basins, refineries, first generation (1<sup>st</sup>G) existing ethanol facilities, harbours and imported biomass entries on the Belgian and German borders.

The *GeoMIRET* model is a partial equilibrium model applied to the French energy sector and built with *MARKAL/TIMES*, the techno-economic model paradigm developed by *IEA-ETSAP*. It is a long-term, multi-period, techno-economic planning model that covers the energy-transport system. Aiming at providing final energy services at minimum cost, the system encompasses all the steps from primary resources in place to the supply of the energy services demanded by energy consumers, through the chain of processes which transform, transport, distribute and convert energy into services. These processes are mainly driven by the reference demands for energy services (e.g., mobility for the transport sector), the supply of the resources (e.g., amount availability and/or price level), along with public policy constraints, which are provided as exogenous inputs to the model.

Technologies are described in *TIMES* by technical and economic parameters. The choice of the technology generation is conditioned by 4 components : energy service demands, primary resource potentials, policy settings, and description of the technologies set. The demand component includes French aggregated heat and electricity demands, mobility demands for short and long distance for passenger and freight transport and demands for exported products (oil products, electricity). The primary energy supply includes imported fossil energy (crude oil, coal, natural gas), imported bioproducts (palm oil, soybean oil and wood biomass) and locally-produced biomass (sugar beet, wheat, corn, rapeseed, sunflower, miscanthus, switchgrass, willow, forest wood, residues/straw). The energy technology component includes process-detailed oil refining, biofuels facilities (1<sup>st</sup>G ethanol and bio-diesel, Fatty Acid Methyl Ester (FAME) and Hydrotreated Vegetable Oil (HVO)), lignocellulosic ethanol and Biomass-to-Liquid (BtL or Diesel Fischer-Tropsch), electricity generation, preparation of fuels for transport at blending, and end-use technologies for road mobility (private passenger cars, public transport vehicles, light and heavy duty vehicles). The policy component includes a variety of micro measures (technology portfolios, vehicle fleet with different engine technologies), the national renewable energy action plan and broader policy targets (sectoral carbon tax, renewable energy and fuel quality directives).

*AROPAj* and *TIMES GeoMIRET* have different spatial distribution scales. While *AROPAj* results can be aggregated at the administrative regional scale, *TIMES GeoMIRET* gathers input data into administrative departments which are clustered into supplying-lignocellulosic biomass basins. These basins are defined as potential regions for supplying second generation (2<sup>nd</sup>G) biofuel facilities that correspond to homogeneous facilities in terms of the available biomass type and pedoclimatic conditions. Coupling the two models involves the systematization of the spatial disaggregation of *AROPAj* output to a suitable *TIMES GeoMIRET* spatial scale. We proceed therefore to overlay the *AROPAj* polygons with the administrative department boundaries, by using the Geographic Information System (ArcGIS).

The departmental biomass quantities were grouped in supply basins according to an unsuper-
The European coverage was achieved by the model with the EU-12 EUROTOOLS program. The GENEDEC program allowed us to extend the coverage to the EU-15 and the EU-25 (except Malta, when the FADN was still provisional). The EU-27 coverage was realized at the end of the AnimalChange program (see Table 1 on page 9). When we intend to extend the time coverage (till 2011, as the last FADN year available in accordance with the FoodSecure program), change in EC FADN farm types and individual farm sampling requires to renew all AROPAj building steps.

We were forced to deal with delays in accessing FADN data as well as delays in validation of FADN data by the EC statistical services. It must be mentioned how fortunate we are to access the FADN, since its counterpart is an individual project that treat data only by providing anonymous parameter estimates.

A version of the model essentially refers to a specific annual FADN survey. Such a version is characterized by the typology (i.e. farm groups) and estimated parameter files (mgg.par described hereafter in section 11.1.3). At present, there are no, properly speaking, different versions related to the kernel of the model, in the sense that operating routine improvement does not impede running of previous FADN versions and calculations.

<table><caption>**Table 32.** The versions of the AROPAj model linked to the added blocks, process and applications (maintained/updated and used, 2020).</caption><thead><tr><th></th><th>V1<br>2001</th><th>V2<br>2005</th><th>V3<br>2007</th><th>V4-test<br>2012</th><th>V5<br>2014</th><th>V5<br>2017</th><th>V5<br>2020</th></tr></thead><tbody><tr><td>Typology</td><td>1997</td><td>2002</td><td>2004</td><td>2009</td><td>200(7 8 9)<br>2014</td><td>20(07 - 12)<br>2017</td><td>20(13 - 17)<br>2020</td></tr><tr><td>Irrigation</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Estimate</td><td>1997</td><td>2002</td><td>2004</td><td>2009</td><td>200(7 8 9)<br>2014</td><td>20(07 - 12)<br>2017</td><td>20(13 - 17)<br>2020</td></tr><tr><td>Entropy</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Kernel</td><td>Unix</td><td>Unix</td><td>Unix</td><td>Unix/Linux</td><td>Linux</td><td>Linux</td><td>Linux</td></tr><tr><td>GHG em.</td><td>IPCC-1996</td><td>IPCC-1996</td><td>IPCC-1996</td><td></td><td>IPCC-2010</td><td>IPCC-2010</td><td>IPCC-2016</td></tr><tr><td>N-yield</td><td></td><td>2011</td><td></td><td></td><td>-<br>2015</td><td>-<br>2018</td><td>-</td></tr><tr><td>Water-N-yield</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Spatial.</td><td></td><td>2013</td><td></td><td></td><td>2015</td><td>2018</td><td>-</td></tr><tr><td></td><td>EU-12</td><td>EU-15</td><td>EU-25</td><td>EU-27</td><td>EU-27</td><td>EU-27</td><td>EU-28</td></tr></tbody></table>

Due to the continual growth of the model core and the development of applications, there is significant evolution in the production of results. Model versions differ according to:

* the geographical coverage within the EU, referring to the FADN year

* the method for farming system clustering (e.g. through the classification method, the used criteria and the selected metric)

* the method for parameter estimation (including or not the GME method)

* the activation of added endogenous variables (e.g. yields and N-fertilizers when using N to yield functions)

* the economic and environmental outputs (e.g. N-losses functions associated to N to yield functions)

* the accuracy of results (e.g. in line with the GHG emission block)
vised classification algorithm, i.e. k-medoids algorithm (PAM). Algorithm inputs were represented by department centroids with longitude and latitude coordinates and agricultural and forest availabilities per department. Outputs were 9 partitions of departments, each of which fed biomass to a facility assumed to be probably located at the cluster barycentre or harbour site or 1<sup>st</sup>G ethanol site. Once the contiguous and homogeneous regions are defined, we calculate the supply distances between the barycentre of each biomass basin, refineries, 1<sup>st</sup>G ethanol facilities, harbours and entry points in order to estimate the costs of transport, by applying the great circle equation used to determine the straight-line distance between two points.

<figure><img src="image_47.png" /><figcaption>**Figure 32.** Geographical distribution of biomass resource basins, refineries, ethanol facilities, harbours and imported biomass entries according to different biomass supply scenarios.</figcaption></figure>

**9.7 OURSEurope**

Key-points:

Geographical location and vertical market integration accounting for biomass transformation into biofuels at the European scale

Spatialized three-sector multi-agents viewed through building an operating model cluster

Europe is the first biodiesel market at the global level, and similarly, *AROPA*<sub>j</sub> is a European agricultural supply model. Therefore, it is interesting to associate it with an agricultural demand model for energy purposes, in order to assess the feasibility and the potential of the biofuel sector in particular. Indeed, the willingness for energy independence, for a boost of the local economy and for a reduction in pollutant emissions has supported the European bioenergy development. Target levels of bio-fuel incorporation in fossil fuels are applicable in most European countries, despite the criticism regarding the environment, the budget constraints, and the arising conflicts on biofuels and the current economic crisis. In response to environmental criticism, eligibility requirements have also been established and the percentage of certified production is constantly increasing.

The objective of a coupling between *AROPA*<sub>j</sub> and a model simulating agricultural demand for biofuel production is to estimate the potential of domestic biofuels production, their profitability and the factors limiting their development as well as their environmental and economic impacts. A massive use of biofuels can indeed change the allocation of agricultural land and the profitability of
* the geographical downscaling of results (i.e. through the "spatialization process")

Table 32 displays how versions differ according to these items. Major parts of the model construction evolve over time. The structural changes of the model may affect results in terms of type and accuracy. Typology was improved when irrigation was taken into account. Parameter estimation is dramatically changed when the *GME* method is used. The *N* to yield functions allow us to account for the intensive margin effect at the plot level. In addition to these "internal" improvements, the spatialization process allows for new model applications and potential linkages with other models. Finally, let us recall that the evolution of computer capabilities strongly impacts the development of the different stages of model building and applications.

## 10.3 Adding blocks, programs and tools

**Key-points:**

- Implementing the calibration process

- Developments and modules related to some versions, ready to be expanded

- About *GHG* emissions, spatialization, dose-response functions, model coupling

The use of additional blocks, programs and applications is in large part autonomous from the model kernel. There is always the possibility of using any version of the model. At the same time activation of some of these additional blocks or applications may not be allowed under some model versions. The *LP* calibration process is a quite specific but emblematic case of external programs linked to the kernel.

### LP calibration

Calibration refers to one of the most important steps of the *AROPAj* building, in terms of complexity as well as computation time. The shell dedicated to this step acts as a super routine calling the *AROPAj* kernel as a subroutine. This shell encapsulates the kernel for managing iterated *AROPAj* runs. As explained in Section 7, the calibration algorithm determines how the *AROPAj* parameters have to change from one run to the next. In this case, the shell is composed of external code components (e.g. *FORTRAN* code, mainly). Additional information, requested for the calculation of the calibration criterion, enters the process through *FADN* sourced parameters. The process runs at the *FG* level and provides partly re-estimated parameters.

### GHG emission assessment

Our standard process of estimating *GHG* emissions is based on *IPCC* guidelines as well as national emission factors that impact emission inventories. We need to compute multiple series of national (possibly regional) parameters, separate from the core parameter set and external to the *AROPAj* estimation and calibration steps. This process entails adding a block to the kernel. Related parameter values are then formatted in line with *AROPAj* basic parameters.

Another possible way of assessing *N*₂*O* emission types is to activate the *N*-block. This requires that *N* to yield functions are available and that *N*-losses are regressed against *N*-inputs (see below).

### N to yield functions

This additional block requires the availability of calibrated *N* to yield functions. When such a function is available for a farm group and for a crop, *FADN* sourced yield and input values (at
agents. To study the impacts of biofuels on the European agriculture, we have chosen to represent
the biofuel production chain, using *AROPA*<sub>j</sub> as initial link in providing the supply of raw materials.

To implement this approach, our choice was focused on the establishment of a linear mathemat-
ical programming model for the entire European biofuels industry. A linear programming model
allows us to determine the optimal solution to a problem according to the constraints scripted for a
given period and it functions as a decision aid for problems of a large scale. This method actually
allows us to use a very large number of variables and equations. Developing a global model is par-
ticularly complex and requires, given the variety of stakeholders in the sector, a relatively simplistic
representation. To preserve the delicacy of modeling various sectors involved in this industry, we
opted for a weak coupling of several sectoral models. Thus, in contrast to a strong coupling, we
maintain the independence of the models, which grants them the freedom to evolve.

The methodology here implemented entails coupling three models, each representing one of
the key sectors of the biofuels industry. The model of European agricultural supply, *AROPA*<sub>j</sub>,
simulates the domestic supply of raw materials. All or part of its production, as well as the
maximum imports of raw materials, is then used by the partial equilibrium model *GIRAF*. This
model simulates the European industrial production of different types of biofuels, depending on
the supply of domestic and international raw materials, as well as the demand for biofuels. The
European refining model *OURSEurope* simulates the production of European refinery products
and therefore the local demand for biofuels to meet the objectives of incorporation.

The *AROPA*<sub>j</sub> model was modified in order to introduce energy crops and crop by-products. The *GIRAF* model was created for the coupling because no existing model met the desired requirements. As for the model *OURSEurope*, it is a European version of the global refining *OURSE* model (developed by the *Ifpen*), and it was created specifically for coupling. In addition, the geographic dimension has been implemented in the three models to spatialize all represented agents (1307 farm-type groups of the *V3-AROPA*<sub>j</sub> version, 288 biofuel producers and 109 refiners) as well as to highlight the exchanges between them. This allows, in particular, the identification of the European bio-fuel production and consumption areas.

<figure><img src="image_48.png" /><figcaption>**Figure 33.** Applying the *AGRAF* model to evaluate impacts of blending rates of biofuel in gazoline and gasoil consumed in Europe.</figcaption></figure>

Source: Assaiante et al. (2016)
the plot level) are replaced by computed values. In such case, the computed yield and N-input depend on the price ratio. By default, the model calls for market prices. There is need to change the kernel.

When *N* to yield and *N* to *N*-loss functions exist, there exists the possibility of assessing different *N*-losses at the farm and wider levels, namely *N*₂O, *NO*₃ and *NH*₃ (what the *STICS* crop model allows for estimation). It must be noted here that, regarding *N*₂O, results may vary drastically from those provided by the *IPCC* method.

### Organic *N* and *LT* effect

Accounting for *LT* effect assessment of organic *N* spreading in *AROPAj* requires the availability of *N* to yield functions. This kind of effect affects the *N* amount requested by a crop to reach a given yield. The magnitude of the *LT* effect depends on the average quantity of organic *N* spreading at the farm level. The computation requires the use of an external routine and the iteration of *AROPAj* runs leading to the convergence of *AROPAj* outputs. This process concerns two kinds of parameters, some of them provided by the *Carbo-PRO* model, and others re-estimated according to iterative *AROPAj* runs. The *LP* case study shows fast convergence of the iterative process.

### The replacement of market prices by shadow prices

This process may be implemented when activating the nitrogen block in line with *N* to yield functions. Mathematically, shadow prices linked to the use of crop production should replace market prices for maximizing the gross margin at the plot scale in some cases. This occurs when, at the optimum, the crop production is entirely re-used on-farm (e.g. for livestock feeding), or when no *N*-input comes from the market (e.g. manure sourced). In practice, the problem is solved by iterated *AROPAj* runs with shadow prices replacing the prices used at the previous iteration for the yield function derivative. The process quickly converge in most of *AROPAj* LPs, except for in a few cases where solutions have periodic forms over iterations, with slight amplitudes.

### Spatialisation of farm groups and downscaling of *AROPAj* outputs

There exist the possibility of *FG* locating, in terms of probabilistic distributed contribution of the agricultural activities over a geographical grid. This is obtained through the spatialization module (see Section 8) available for the *V2* version, and is in progress for the *V5* version. Once contributions of all *FGs* within a region are computed, there is no difficulty in displaying any *AROPAj* output related to this region at a fine resolution level. The size of the database and the overlapping data used to pose problems for our computing capacities, specifically at the first step of the spatialization process (e.g. organize data for spatial econometrics). Currently, most of the spatialization steps are performed on our workstation thanks to open-source *GNU* softwares, except for the *GCE* procedure which calls for *GAMS*.

The combination of these blocks and tools is displayed on Table 33.

## 10.4 Comparison tables and *AROPAj* sourced biases

**Key-points:**

* Farm group representativity and *FADN* based allocation of agricultural lands over the years and the *AROPAj* versions

* Calibration results and land allocation bias
The approach adopted is a partial equilibrium modeling type, so in theory the prices of products considered (oil products, biofuels, etc.) do not affect other products of the economy. Taking into account the spatiality, combined with horizontal dimensions (products, actors) and vertical ones (from upstream to downstream activities), gives a certain advantages to this methodology. The modeling is dedicated to the European zone and is highly parameterized in order to adapt to different types of scenarios, both technical and environmental, as well as economic or political. This allows to test different hypotheses and to analyze their impacts on the production of biofuels (quantity, nature, environmental impacts, co-production and location), associated costs (investment, transportation, raw materials) and the exchanges between agents of the industry. Both conventional (pure vegetable oil, biodiesel, NexBtL and first generation bioethanol) and unconventional (BtL, second generation bioethanol) biofuel production are modeled. For each process, technical, environmental and economic data can be changed. However, due to a lack of access to reliable data on biofuels produced from seaweeds, we have chosen not to represent this production. The modeling is operational and requires that each step be accurately monitored to ensure the proper integration of the parameters in the simulation (Clodic, 2013).

As illustrative results, Figure 33 shows how incorporating bio-fuels in the European fuel demand may impact the overall cost for the raw biomass transformers and the refiners.
<table><caption>**Table 33.** Impacted AROPAj steps and files when adding blocks or managing applications.</caption><thead><tr><th></th><th>Database</th><th>clustering /<br>sample farms</th><th>estimation &<br>calibration</th><th>LP<br>design</th><th>FG<br>spatialisation</th></tr></thead><tbody><tr><td>base</td><td>FADN</td><td>farm groups<br>(FG)</td><td>basic<br>parameters</td><td>kernel</td><td></td></tr><tr><td>FG's LP<br>calibration</td><td></td><td></td><td>re-estimated<br>parameters</td><td>external<br>routine</td><td></td></tr><tr><td>GHG<br>emissions</td><td>IPCC & nat.<br>inventories</td><td>regionally<br>distributed</td><td>added<br>parameters</td><td>GHG<br>block</td><td></td></tr><tr><td>N to yield<br>functions</td><td>STICS - relat.<br>requirement</td><td>added parameters related to<br>FG & FADN sourced data</td><td></td><td>internal<br>subroutine</td><td></td></tr><tr><td>N to N-loss<br>functions</td><td></td><td>parameters related to<br>N to yield function calibration</td><td></td><td></td><td></td></tr><tr><td>LT effect of<br>organic N</td><td></td><td>added & re-evaluated<br>parameters / N to yield funct.</td><td></td><td>external<br>routine</td><td></td></tr><tr><td>shadow prices<br>convergence</td><td></td><td></td><td>re-evaluated<br>parameters</td><td>external<br>routine</td><td></td></tr><tr><td>outputs<br>downscaling</td><td>CLC & other<br>physical data</td><td>probabilistic<br>FG location</td><td></td><td></td><td>external<br>program</td></tr></tbody></table>

From the real world up to the stylized calibrated representation of farming systems, there are successive gaps leading to an aggregation bias. Upstream from the AROPAj filter, we have to account for:

* the European surveys (with possible truncation, regarding very large farms as well as small systems managed by part-time farmers),

* their statistical treatment by the EC services (with possible difficulties due to heterogeneous quality of national surveys),

* the clustering of sample farms leading to FADN categories (FT, economic size, altitude), which strongly impacts the AROPAj FG building, even in terms of excluded farming systems (e.g. ones for which FT refers to wine or horticulture).

Obviously, as for any model, the AROPAj model results do not perfectly fit with observations (here viewed through the FADN). The LP design, the FG typology and the estimation and calibration phases all contributed to the distortion of the raw FADN estimates of production and allocated area among crops. In addition, comparison is made more difficult when our LP objective does not correspond exactly to a FADN variable.

In order to illustrate the quality of the calibration phase, we present histograms that show the distribution of FGs' square root of sum of squared deviations between FADN based informations and computed terms (see Figures 11, 12, 13, 14, 15, 16). Regarding the possible FADN sourced bias, the figures displayed in Table 21 on page 86 show significant sources of bias. Bias may occur because of the AROPAj typology (see the UAA deviation between the V4 and V5s versions for Estonia or Romania, among others EC countries). Bias also occurs because of the FADN itself, as is evident when comparing the national UAA among the years 2007, 2008 and 2009, all of which were estimated through a common algorithm of FADN-farms clustering (e.g. for Italy and Poland, between 2007 and 2008 or 2009).
# 10 Model evolution

## 10.1 The model over time

**Key-points:**

* Relating published papers with different *AROPAj* versions and *FADN* databases

* From *CAP* oriented analysis toward environmental issues

* From private company support toward European Universities and institutional public support

* Competing for *EC* FrameWork program and tender support (*EUROTOOLS*, *GENEDEC*, *INSEA*, specific milk market reform, *CCTAME*, *AnimalChange*, *FoodSecure*)

The articles and reports published over the years in relation to the *AROPAj* model refer to different versions of the model. Differences stem mainly from the *FADN* data and from the state of the *EU* enlargement. In addition, series of new blocks and shell were developed regularly. This is mainly due to the change in the area of focus: the model building started with *CAP* analysis and it evolved toward the study of relationship between agriculture and the environment. Nevertheless, we keep the principles of the model intact.

One of the major driving forces of the model is its modular and generic conception. Another driver lies in the evolution of computing capacities and facilities. Thus, we have anticipated the need to overcome computation limits and to enlarge modeling capacities when new scientific problems arise.

The foundations of the model are derived from an early 1990s assessement of the *CAP* reform impacts in France and the UK, which was comissioned by an international grain trader. A major inflexion occurred in the mid 1990s when climate change impacts and GHG emissions emerged in the field of economic analysis. Two drivers significantly impact the model evolution: (i) European contacts acting as precursors, through researchers and MSc or PhD students from Universities of Bologna and Parma, and Universidad Politécnica de Madrid; (ii) French programs under the control of environmental agencies and ministries. Successful bids (partnership or coordination) in response to *EC* research calls (*EUROTOOLS*, *GENEDEC*, *INSEA*, *CC-TAME*, *AnimalChange*) strongly boosted the development of the model, in addition to more specific responses to calls for tenders and national incentives. High development and the support of multidisciplinary approaches facilitated the trend from analysis focusing on the *CAP* and economic criteria, to an analysis focusing on the environmental impacts and external effects regulation.

Appendices B and C provide the list of contributors over time and the list or projects which contributed to funding for *AROPAj*.

## 10.2 Version list

**Key-points:**

* From the *EU*-15 to the *EU*-28

* *FADN* sourced information, from 1997 untill 2018

* Toward a unified version of the model kernel
Stated as a quasi-fix factor, the UAA is not optimally re-computed at any level in AROPAj (from the FG to the EU). However, calibration impacts the land allocation within the UAA, as illustrated by Tables referring to often used V2 and V3 versions, and to the 2007 to 2009 V5 ones.

<table><caption>**Table 34.** Comparison of land allocation between FADN estimates and AROPAj calibrated outputs, aggregated at the member State level and for different AROPAj versions (millions hectares) - (AROPAj runs were realized on March 2015).</caption><thead><tr><th rowspan="2">MS</th><th rowspan="2">Land use</th><th colspan="2">V2</th><th colspan="2">V3</th><th colspan="2">V5-2007</th><th colspan="2">V5-2008</th><th colspan="2">V5-2009</th></tr><tr><th>FADN</th><th>calibr.</th><th>FADN</th><th>calibr.</th><th>FADN</th><th>calibr.</th><th>FADN</th><th>calibr.</th><th>FADN</th><th>calibr.</th></tr></thead><tbody><tr><td>bel</td><td>cereals</td><td>0,306</td><td>0,332</td><td>0,303</td><td>0,321</td><td>0,333</td><td>0,313</td><td>0,379</td><td>0,382</td><td>0,381</td><td>0,379</td></tr><tr><td>bel</td><td>oilseeds & proteins</td><td>0,006</td><td>0,003</td><td>0,005</td><td>0,006</td><td>0,041</td><td>0,041</td><td>0,044</td><td>0,041</td><td>0,047</td><td>0,054</td></tr><tr><td>bel</td><td>roots & indus.</td><td>0,156</td><td>0,161</td><td>0,171</td><td>0,190</td><td>0,161</td><td>0,155</td><td>0,141</td><td>0,146</td><td>0,153</td><td>0,151</td></tr><tr><td>bel</td><td>pastures</td><td>0,689</td><td>0,560</td><td>0,574</td><td>0,420</td><td>0,611</td><td>0,648</td><td>0,615</td><td>0,621</td><td>0,626</td><td>0,671</td></tr><tr><td>bel</td><td>fodders</td><td>0,254</td><td>0,255</td><td>0,271</td><td>0,291</td><td>0,175</td><td>0,171</td><td>0,196</td><td>0,182</td><td>0,202</td><td>0,151</td></tr><tr><td>bel</td><td>fallows & set-aside</td><td>0,021</td><td>0,121</td><td>0,015</td><td>0,111</td><td>0,012</td><td>0,004</td><td>0,005</td><td>0,007</td><td>0,002</td><td>0,005</td></tr><tr><td>bgr</td><td>cereals</td><td></td><td></td><td></td><td></td><td>1,637</td><td>1,720</td><td>1,548</td><td>1,712</td><td>1,733</td><td>1,820</td></tr><tr><td>bgr</td><td>oilseeds & proteins</td><td></td><td></td><td></td><td></td><td>0,660</td><td>0,670</td><td>0,702</td><td>0,680</td><td>0,756</td><td>0,743</td></tr><tr><td>bgr</td><td>roots & indus.</td><td></td><td></td><td></td><td></td><td>0,016</td><td>0,039</td><td>0,015</td><td>0,043</td><td>0,027</td><td>0,054</td></tr><tr><td>bgr</td><td>pastures</td><td></td><td></td><td></td><td></td><td>0,344</td><td>0,223</td><td>0,390</td><td>0,202</td><td>0,309</td><td>0,247</td></tr><tr><td>bgr</td><td>fodders</td><td></td><td></td><td></td><td></td><td>0,065</td><td>0,020</td><td>0,051</td><td>0,011</td><td>0,055</td><td>0,012</td></tr><tr><td>bgr</td><td>fallows & set-aside</td><td></td><td></td><td></td><td></td><td>0,029</td><td>0,080</td><td>0,075</td><td>0,132</td><td>0,105</td><td>0,109</td></tr><tr><td>cyp</td><td>cereals</td><td></td><td></td><td>0,087</td><td>0,072</td><td>0,042</td><td>0,054</td><td>0,083</td><td>0,004</td><td>0,060</td><td>0,055</td></tr><tr><td>cyp</td><td>oilseeds & proteins</td><td></td><td></td><td>0,000</td><td>0,000</td><td>0,003</td><td>0,010</td><td>0,006</td><td>0,008</td><td>0,006</td><td>0,010</td></tr><tr><td>cyp</td><td>roots & indus.</td><td></td><td></td><td>0,004</td><td>0,011</td><td>0,004</td><td>0,008</td><td>0,004</td><td>0,012</td><td>0,003</td><td>0,008</td></tr><tr><td>cyp</td><td>pastures</td><td></td><td></td><td>0,026</td><td>0,027</td><td>0,010</td><td>0,001</td><td>0,004</td><td>0,000</td><td>0,005</td><td>0,021</td></tr><tr><td>cyp</td><td>fodders</td><td></td><td></td><td>0,014</td><td>0,017</td><td>0,038</td><td>0,001</td><td>0,022</td><td>0,001</td><td>0,031</td><td>0,001</td></tr><tr><td>cyp</td><td>fallows & set-aside</td><td></td><td></td><td>0,006</td><td>0,009</td><td>0,009</td><td>0,034</td><td>0,006</td><td>0,100</td><td>0,007</td><td>0,017</td></tr><tr><td>cze</td><td>cereals</td><td></td><td></td><td>1,664</td><td>1,795</td><td>1,618</td><td>1,795</td><td>1,510</td><td>1,693</td><td>1,511</td><td>1,633</td></tr><tr><td>cze</td><td>oilseeds & proteins</td><td></td><td></td><td>0,319</td><td>0,357</td><td>0,392</td><td>0,434</td><td>0,377</td><td>0,431</td><td>0,422</td><td>0,425</td></tr><tr><td>cze</td><td>roots & indus.</td><td></td><td></td><td>0,141</td><td>0,204</td><td>0,103</td><td>0,076</td><td>0,088</td><td>0,070</td><td>0,101</td><td>0,058</td></tr><tr><td>cze</td><td>pastures</td><td></td><td></td><td>1,037</td><td>0,693</td><td>0,782</td><td>0,665</td><td>0,815</td><td>0,732</td><td>0,823</td><td>0,785</td></tr><tr><td>cze</td><td>fodders</td><td></td><td></td><td>0,249</td><td>0,229</td><td>0,376</td><td>0,205</td><td>0,358</td><td>0,167</td><td>0,354</td><td>0,277</td></tr><tr><td>cze</td><td>fallows & set-aside</td><td></td><td></td><td>0,020</td><td>0,153</td><td>0,006</td><td>0,101</td><td>0,005</td><td>0,060</td><td>0,012</td><td>0,044</td></tr><tr><td>dan</td><td>cereals</td><td>1,530</td><td>1,416</td><td>1,485</td><td>1,591</td><td>1,455</td><td>1,879</td><td>1,518</td><td>1,633</td><td>1,553</td><td>1,705</td></tr><tr><td>dan</td><td>oilseeds & proteins</td><td>0,081</td><td>0,039</td><td>0,133</td><td>0,139</td><td>0,195</td><td>0,286</td><td>0,189</td><td>0,226</td><td>0,174</td><td>0,224</td></tr><tr><td>dan</td><td>roots & indus.</td><td>0,106</td><td>0,103</td><td>0,094</td><td>0,107</td><td>0,088</td><td>0,122</td><td>0,086</td><td>0,087</td><td>0,078</td><td>0,078</td></tr><tr><td>dan</td><td>pastures</td><td>0,187</td><td>0,246</td><td>0,164</td><td>0,172</td><td>0,389</td><td>0,018</td><td>0,424</td><td>0,400</td><td>0,500</td><td>0,325</td></tr><tr><td>dan</td><td>fodders</td><td>0,320</td><td>0,257</td><td>0,335</td><td>0,177</td><td>0,198</td><td>0,140</td><td>0,214</td><td>0,098</td><td>0,216</td><td>0,217</td></tr><tr><td>dan</td><td>fallows & set-aside</td><td>0,212</td><td>0,376</td><td>0,185</td><td>0,210</td><td>0,160</td><td>0,042</td><td>0,089</td><td>0,077</td><td>0,068</td><td>0,041</td></tr><tr><td>deu</td><td>cereals</td><td>6,461</td><td>5,667</td><td>6,717</td><td>7,523</td><td>6,488</td><td>7,191</td><td>6,823</td><td>7,511</td><td>6,683</td><td>7,218</td></tr><tr><td>deu</td><td>oilseeds & proteins</td><td>1,011</td><td>0,838</td><td>1,189</td><td>1,178</td><td>1,511</td><td>1,807</td><td>1,627</td><td>1,841</td><td>1,735</td><td>1,867</td></tr><tr><td>deu</td><td>roots & indus.</td><td>0,681</td><td>0,902</td><td>0,724</td><td>0,905</td><td>0,679</td><td>0,671</td><td>0,632</td><td>0,507</td><td>0,648</td><td>0,731</td></tr><tr><td>deu</td><td>pastures</td><td>4,452</td><td>3,560</td><td>4,568</td><td>2,864</td><td>3,861</td><td>4,425</td><td>3,873</td><td>4,376</td><td>3,931</td><td>4,484</td></tr><tr><td>deu</td><td>fodders</td><td>1,278</td><td>0,698</td><td>1,387</td><td>0,590</td><td>1,755</td><td>0,600</td><td>2,021</td><td>0,896</td><td>2,157</td><td>0,935</td></tr><tr><td>deu</td><td>fallows & set-aside</td><td>0,845</td><td>3,063</td><td>0,829</td><td>2,354</td><td>0,636</td><td>0,238</td><td>0,328</td><td>0,175</td><td>0,257</td><td>0,175</td></tr><tr><td>ell</td><td>cereals</td><td>0,876</td><td>0,824</td><td>0,966</td><td>0,991</td><td>1,383</td><td>1,544</td><td>1,499</td><td>1,631</td><td>1,470</td><td>1,593</td></tr><tr><td>ell</td><td>oilseeds & proteins</td><td>0,022</td><td>0,019</td><td>0,012</td><td>0,021</td><td>0,084</td><td>0,115</td><td>0,085</td><td>0,116</td><td>0,105</td><td>0,144</td></tr><tr><td>ell</td><td>roots & indus.</td><td>0,053</td><td>0,044</td><td>0,331</td><td>0,317</td><td>0,381</td><td>0,368</td><td>0,383</td><td>0,370</td><td>0,345</td><td>0,336</td></tr><tr><td>ell</td><td>pastures</td><td>0,156</td><td>0,138</td><td>0,181</td><td>0,071</td><td>0,058</td><td>0,066</td><td>0,059</td><td>0,078</td><td>0,059</td><td>0,103</td></tr><tr><td>ell</td><td>fodders</td><td>0,061</td><td>0,159</td><td>0,078</td><td>0,185</td><td>0,245</td><td>0,147</td><td>0,263</td><td>0,171</td><td>0,261</td><td>0,166</td></tr><tr><td>ell</td><td>fallows & set-aside</td><td>0,052</td><td>0,035</td><td>0,083</td><td>0,066</td><td>0,205</td><td>0,116</td><td>0,155</td><td>0,077</td><td>0,183</td><td>0,081</td></tr><tr><td>esp</td><td>cereals</td><td>5,769</td><td>5,948</td><td>5,942</td><td>6,999</td><td>6,917</td><td>8,162</td><td>7,032</td><td>8,073</td><td>6,521</td><td>7,863</td></tr><tr><td>esp</td><td>oilseeds & proteins</td><td>0,628</td><td>0,292</td><td>0,582</td><td>0,664</td><td>0,996</td><td>1,083</td><td>0,900</td><td>1,046</td><td>0,956</td><td>0,825</td></tr><tr><td>esp</td><td>roots & indus.</td><td>0,193</td><td>0,454</td><td>0,299</td><td>0,376</td><td>0,269</td><td>0,208</td><td>0,244</td><td>0,224</td><td>0,242</td><td>0,273</td></tr><tr><td>esp</td><td>pastures</td><td>2,463</td><td>1,825</td><td>2,228</td><td>1,293</td><td>2,718</td><td>2,193</td><td>2,372</td><td>2,157</td><td>2,815</td><td>2,437</td></tr><tr><td>esp</td><td>fodders</td><td>0,434</td><td>1,933</td><td>0,373</td><td>1,180</td><td>0,657</td><td>0,981</td><td>0,754</td><td>0,822</td><td>0,941</td><td>1,051</td></tr><tr><td>esp</td><td>fallows & set-aside</td><td>1,842</td><td>0,878</td><td>1,909</td><td>0,821</td><td>2,009</td><td>0,939</td><td>1,706</td><td>0,686</td><td>1,778</td><td>0,805</td></tr><tr><td>est</td><td>cereals</td><td></td><td></td><td>0,232</td><td>0,419</td><td>0,301</td><td>0,381</td><td>0,325</td><td>0,390</td><td>0,336</td><td>0,369</td></tr><tr><td>est</td><td>oilseeds & proteins</td><td></td><td></td><td>0,039</td><td>0,046</td><td>0,091</td><td>0,105</td><td>0,081</td><td>0,080</td><td>0,085</td><td>0,096</td></tr><tr><td>est</td><td>roots & indus.</td><td></td><td></td><td>0,006</td><td>0,053</td><td>0,005</td><td>0,038</td><td>0,004</td><td>0,035</td><td>0,004</td><td>0,026</td></tr><tr><td>est</td><td>pastures</td><td></td><td></td><td>0,163</td><td>0,009</td><td>0,410</td><td>0,292</td><td>0,435</td><td>0,331</td><td>0,424</td><td>0,382</td></tr><tr><td>est</td><td>fodders</td><td></td><td></td><td>0,189</td><td>0,091</td><td>0,010</td><td>0,007</td><td>0,008</td><td>0,004</td><td>0,013</td><td>0,006</td></tr><tr><td>est</td><td>fallows & set-aside</td><td></td><td></td><td>0,040</td><td>0,051</td><td>0,020</td><td>0,014</td><td>0,016</td><td>0,027</td><td>0,024</td><td>0,006</td></tr><tr><td>fra</td><td>cereals</td><td>9,251</td><td>10,956</td><td>9,283</td><td>10,916</td><td>9,120</td><td>9,987</td><td>9,650</td><td>10,249</td><td>9,486</td><td>10,155</td></tr><tr><td>fra</td><td>oilseeds & proteins</td><td>1,770</td><td>1,925</td><td>1,902</td><td>1,788</td><td>2,704</td><td>2,789</td><td>2,543</td><td>2,767</td><td>2,679</td><td>2,772</td></tr><tr><td>fra</td><td>roots & indus.</td><td>0,571</td><td>0,507</td><td>0,585</td><td>0,910</td><td>0,585</td><td>0,504</td><td>0,559</td><td>0,502</td><td>0,578</td><td>0,456</td></tr><tr><td>fra</td><td>pastures</td><td>5,964</td><td>4,986</td><td>6,092</td><td>4,696</td><td>9,975</td><td>8,662</td><td>10,087</td><td>8,812</td><td>10,176</td><td>8,949</td></tr><tr><td>fra</td><td>fodders</td><td>5,318</td><td>2,735</td><td>5,431</td><td>3,870</td><td>1,529</td><td>1,195</td><td>1,593</td><td>1,298</td><td>1,610</td><td>1,420</td></tr></tbody></table>

continued on next page -
The European coverage was achieved by the model with the EU-12 EUROTOOLS program. The GENEDEC program allowed us to extend the coverage to the EU-15 and the EU-25 (except Malta, when the FADN was still provisional). The EU-27 coverage was realized at the end of the AnimalChange program (see Table 1 on page 11). When we intend to extend the time coverage (till 2011, as the last FADN year available in accordance with the FoodSecure program), change in EC FADN farm types and individual farm sampling requires to renew all AROPAj building steps.

We were forced to deal with delays in accessing FADN data as well as delays in validation of FADN data by the EC statistical services. It must be mentioned how fortunate we are to access the FADN, since its counterpart is an individual project that treat data only by providing anonymous parameter estimates.

A version of the model essentially refers to a specific annual FADN survey. Such a version is characterized by the typology (i.e. farm groups) and estimated parameter files (mgg.par described hereafter in section 11.1.3). At present, there are no, properly speaking, different versions related to the kernel of the model, in the sense that operating routine improvement does not impede running of previous FADN versions and calculations.

<table><caption>**Table 32.** The versions of the AROPAj model linked to the added blocks, process and applications (maintained/updated and used, 2020).</caption><thead><tr><th></th><th>V1<br>2001</th><th>V2<br>2005</th><th>V3<br>2007</th><th>V4-test<br>2012</th><th>V5<br>2014</th><th>V5<br>2017</th><th>V5<br>2020</th></tr></thead><tbody><tr><td>Typology</td><td>1997</td><td>2002</td><td>2004</td><td>2009</td><td>200(7 8 9)<br>2014</td><td>20(07 - 12)<br>2017</td><td>20(13 - 17)<br>2020</td></tr><tr><td>Irrigation</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Estimate</td><td>1997</td><td>2002</td><td>2004</td><td>2009</td><td>200(7 8 9)<br>2014</td><td>20(07 - 12)<br>2017</td><td>20(13 - 17)<br>2020</td></tr><tr><td>Entropy</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Kernel</td><td>Unix</td><td>Unix</td><td>Unix</td><td>Unix/Linux</td><td>Linux</td><td>Linux</td><td>Linux</td></tr><tr><td>GHG em.</td><td>IPCC-1996</td><td>IPCC-1996</td><td>IPCC-1996</td><td></td><td>IPCC-2010</td><td>IPCC-2010</td><td>IPCC-2016</td></tr><tr><td>N-yield</td><td></td><td>2011</td><td></td><td></td><td>-<br>2015</td><td>-<br>2018</td><td>-</td></tr><tr><td>Water-N-yield</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Spatial.</td><td></td><td>2013</td><td></td><td></td><td>2015</td><td>2018</td><td>-</td></tr><tr><td></td><td>EU-12</td><td>EU-15</td><td>EU-25</td><td>EU-27</td><td>EU-27</td><td>EU-27</td><td>EU-28</td></tr></tbody></table>

Due to the continual growth of the model core and the development of applications, there is significant evolution in the production of results. Model versions differ according to:

* the geographical coverage within the EU, referring to the FADN year

* the method for farming system clustering (e.g. through the classification method, the used criteria and the selected metric)

* the method for parameter estimation (including or not the GME method)

* the activation of added endogenous variables (e.g. yields and N-fertilizers when using N to yield functions)

* the economic and environmental outputs (e.g. N-losses functions associated to N to yield functions)

* the accuracy of results (e.g. in line with the GHG emission block)
- continued from previous page

<table><thead><tr><th rowspan="2">MS</th><th rowspan="2">Land use</th><th colspan="2">V2</th><th colspan="2">V3</th><th colspan="2">V5-2007</th><th colspan="2">V5-2008</th><th colspan="2">V5-2009</th></tr><tr><th>FADN</th><th>calibr.</th><th>FADN</th><th>calibr.</th><th>FADN</th><th>calibr.</th><th>FADN</th><th>calibr.</th><th>FADN</th><th>calibr.</th></tr></thead><tbody><tr><td>fra</td><td>fallows & set-aside</td><td>1,167</td><td>2,933</td><td>1,028</td><td>2,141</td><td>1,152</td><td>1,928</td><td>0,776</td><td>1,580</td><td>0,589</td><td>1,366</td></tr><tr><td>gbr</td><td>cereals</td><td>3,689</td><td>3,660</td><td>3,431</td><td>3,765</td><td>2,991</td><td>3,527</td><td>3,296</td><td>3,423</td><td>3,227</td><td>3,412</td></tr><tr><td>gbr</td><td>oilseeds & proteins</td><td>0,439</td><td>0,491</td><td>0,567</td><td>0,664</td><td>0,856</td><td>1,084</td><td>0,764</td><td>0,878</td><td>0,840</td><td>0,938</td></tr><tr><td>gbr</td><td>roots & indus.</td><td>0,395</td><td>0,302</td><td>0,349</td><td>0,385</td><td>0,254</td><td>0,225</td><td>0,266</td><td>0,366</td><td>0,258</td><td>0,332</td></tr><tr><td>gbr</td><td>pastures</td><td>4,852</td><td>4,922</td><td>4,078</td><td>3,471</td><td>5,437</td><td>5,052</td><td>5,476</td><td>5,334</td><td>5,691</td><td>5,554</td></tr><tr><td>gbr</td><td>fodders</td><td>1,663</td><td>1,098</td><td>1,364</td><td>1,067</td><td>0,225</td><td>0,166</td><td>0,240</td><td>0,135</td><td>0,235</td><td>0,151</td></tr><tr><td>gbr</td><td>fallows & set-aside</td><td>0,609</td><td>1,175</td><td>0,547</td><td>0,984</td><td>0,563</td><td>0,272</td><td>0,281</td><td>0,187</td><td>0,354</td><td>0,219</td></tr><tr><td>hun</td><td>cereals</td><td></td><td></td><td>2,309</td><td>2,683</td><td>2,360</td><td>2,762</td><td>2,355</td><td>2,708</td><td>2,356</td><td>2,662</td></tr><tr><td>hun</td><td>oilseeds & proteins</td><td></td><td></td><td>0,495</td><td>0,393</td><td>0,752</td><td>0,691</td><td>0,797</td><td>0,757</td><td>0,790</td><td>0,702</td></tr><tr><td>hun</td><td>roots & indus.</td><td></td><td></td><td>0,075</td><td>0,074</td><td>0,055</td><td>0,042</td><td>0,018</td><td>0,033</td><td>0,024</td><td>0,037</td></tr><tr><td>hun</td><td>pastures</td><td></td><td></td><td>0,393</td><td>0,082</td><td>0,218</td><td>0,094</td><td>0,227</td><td>0,097</td><td>0,234</td><td>0,153</td></tr><tr><td>hun</td><td>fodders</td><td></td><td></td><td>0,151</td><td>0,153</td><td>0,293</td><td>0,090</td><td>0,296</td><td>0,095</td><td>0,295</td><td>0,140</td></tr><tr><td>hun</td><td>fallows & set-aside</td><td></td><td></td><td>0,053</td><td>0,091</td><td>0,047</td><td>0,044</td><td>0,033</td><td>0,036</td><td>0,040</td><td>0,043</td></tr><tr><td>irl</td><td>cereals</td><td>0,304</td><td>0,312</td><td>0,350</td><td>0,411</td><td>0,259</td><td>0,301</td><td>0,298</td><td>0,297</td><td>0,306</td><td>0,322</td></tr><tr><td>irl</td><td>oilseeds & proteins</td><td>0,001</td><td>0,001</td><td>0,001</td><td>0,002</td><td>0,008</td><td>0,017</td><td>0,006</td><td>0,013</td><td>0,007</td><td>0,008</td></tr><tr><td>irl</td><td>roots & indus.</td><td>0,039</td><td>0,036</td><td>0,047</td><td>0,070</td><td>0,014</td><td>0,016</td><td>0,010</td><td>0,015</td><td>0,006</td><td>0,007</td></tr><tr><td>irl</td><td>pastures</td><td>2,531</td><td>2,272</td><td>2,380</td><td>2,049</td><td>3,634</td><td>3,359</td><td>3,696</td><td>3,484</td><td>3,807</td><td>3,577</td></tr><tr><td>irl</td><td>fodders</td><td>0,289</td><td>0,330</td><td>0,269</td><td>0,395</td><td>0,047</td><td>0,028</td><td>0,050</td><td>0,014</td><td>0,059</td><td>0,033</td></tr><tr><td>irl</td><td>fallows & set-aside</td><td>0,035</td><td>0,247</td><td>0,041</td><td>0,161</td><td>0,031</td><td>0,271</td><td>0,011</td><td>0,249</td><td>0,011</td><td>0,249</td></tr><tr><td>ita</td><td>cereals</td><td>3,417</td><td>4,163</td><td>3,646</td><td>4,358</td><td>3,658</td><td>4,515</td><td>3,618</td><td>4,625</td><td>3,403</td><td>4,822</td></tr><tr><td>ita</td><td>oilseeds & proteins</td><td>0,264</td><td>0,260</td><td>0,222</td><td>0,186</td><td>0,570</td><td>0,675</td><td>0,511</td><td>0,529</td><td>0,590</td><td>0,620</td></tr><tr><td>ita</td><td>roots & indus.</td><td>0,244</td><td>0,161</td><td>0,239</td><td>0,290</td><td>0,178</td><td>0,155</td><td>0,157</td><td>0,170</td><td>0,146</td><td>0,173</td></tr><tr><td>ita</td><td>pastures</td><td>2,891</td><td>1,243</td><td>0,434</td><td>0,384</td><td>4,115</td><td>3,197</td><td>0,060</td><td>0,260</td><td>0,122</td><td>0,548</td></tr><tr><td>ita</td><td>fodders</td><td>0,488</td><td>0,777</td><td>3,338</td><td>2,303</td><td>0,233</td><td>0,247</td><td>3,217</td><td>1,288</td><td>3,152</td><td>0,911</td></tr><tr><td>ita</td><td>fallows & set-aside</td><td>0,248</td><td>0,947</td><td>0,247</td><td>0,578</td><td>0,355</td><td>0,320</td><td>0,285</td><td>0,977</td><td>0,571</td><td>0,907</td></tr><tr><td>ltu</td><td>cereals</td><td></td><td></td><td>0,687</td><td>0,833</td><td>0,905</td><td>1,032</td><td>0,931</td><td>1,021</td><td>0,889</td><td>0,949</td></tr><tr><td>ltu</td><td>oilseeds & proteins</td><td></td><td></td><td>0,097</td><td>0,119</td><td>0,199</td><td>0,211</td><td>0,184</td><td>0,186</td><td>0,197</td><td>0,214</td></tr><tr><td>ltu</td><td>roots & indus.</td><td></td><td></td><td>0,053</td><td>0,118</td><td>0,037</td><td>0,059</td><td>0,029</td><td>0,049</td><td>0,030</td><td>0,047</td></tr><tr><td>ltu</td><td>pastures</td><td></td><td></td><td>0,063</td><td>0,065</td><td>0,642</td><td>0,572</td><td>0,635</td><td>0,534</td><td>0,641</td><td>0,617</td></tr><tr><td>ltu</td><td>fodders</td><td></td><td></td><td>0,394</td><td>0,306</td><td>0,030</td><td>0,008</td><td>0,021</td><td>0,015</td><td>0,021</td><td>0,005</td></tr><tr><td>ltu</td><td>fallows & set-aside</td><td></td><td></td><td>0,177</td><td>0,028</td><td>0,094</td><td>0,025</td><td>0,090</td><td>0,085</td><td>0,090</td><td>0,037</td></tr><tr><td>lux</td><td>cereals</td><td>0,030</td><td>0,034</td><td>0,027</td><td>0,021</td><td>0,029</td><td>0,023</td><td>0,030</td><td>0,025</td><td>0,030</td><td>0,023</td></tr><tr><td>lux</td><td>oilseeds & proteins</td><td>0,003</td><td>0,005</td><td>0,004</td><td>0,005</td><td>0,005</td><td>0,005</td><td>0,005</td><td>0,005</td><td>0,005</td><td>0,005</td></tr><tr><td>lux</td><td>roots & indus.</td><td>0,001</td><td>0,004</td><td>0,001</td><td>0,002</td><td>0,001</td><td>0,001</td><td>0,001</td><td>0,002</td><td>0,001</td><td>0,001</td></tr><tr><td>lux</td><td>pastures</td><td>0,081</td><td>0,064</td><td>0,077</td><td>0,067</td><td>0,063</td><td>0,073</td><td>0,062</td><td>0,076</td><td>0,062</td><td>0,083</td></tr><tr><td>lux</td><td>fodders</td><td>0,013</td><td>0,008</td><td>0,014</td><td>0,021</td><td>0,027</td><td>0,019</td><td>0,027</td><td>0,015</td><td>0,029</td><td>0,010</td></tr><tr><td>lux</td><td>fallows & set-aside</td><td>0,002</td><td>0,014</td><td>0,001</td><td>0,008</td><td>0,001</td><td>0,004</td><td>0,000</td><td>0,003</td><td>0,000</td><td>0,004</td></tr><tr><td>lva</td><td>cereals</td><td></td><td></td><td>0,409</td><td>0,599</td><td>0,485</td><td>0,592</td><td>0,495</td><td>0,614</td><td>0,492</td><td>0,555</td></tr><tr><td>lva</td><td>oilseeds & proteins</td><td></td><td></td><td>0,066</td><td>0,061</td><td>0,103</td><td>0,121</td><td>0,087</td><td>0,108</td><td>0,093</td><td>0,111</td></tr><tr><td>lva</td><td>roots & indus.</td><td></td><td></td><td>0,034</td><td>0,080</td><td>0,021</td><td>0,054</td><td>0,017</td><td>0,038</td><td>0,017</td><td>0,051</td></tr><tr><td>lva</td><td>pastures</td><td></td><td></td><td>0,133</td><td>0,096</td><td>0,405</td><td>0,346</td><td>0,426</td><td>0,299</td><td>0,439</td><td>0,420</td></tr><tr><td>lva</td><td>fodders</td><td></td><td></td><td>0,175</td><td>0,147</td><td>0,018</td><td>0,023</td><td>0,015</td><td>0,029</td><td>0,014</td><td>0,029</td></tr><tr><td>lva</td><td>fallows & set-aside</td><td></td><td></td><td>0,182</td><td>0,017</td><td>0,109</td><td>0,006</td><td>0,116</td><td>0,069</td><td>0,122</td><td>0,012</td></tr><tr><td>mlt</td><td>cereals</td><td></td><td></td><td></td><td></td><td>0,000</td><td>0,000</td><td>0,000</td><td>0,000</td><td>0,000</td><td>0,000</td></tr><tr><td>mlt</td><td>oilseeds & proteins</td><td></td><td></td><td></td><td></td><td>0,000</td><td>0,000</td><td>0,000</td><td>0,000</td><td>0,000</td><td>0,000</td></tr><tr><td>mlt</td><td>roots & indus.</td><td></td><td></td><td></td><td></td><td>0,000</td><td>0,000</td><td>0,000</td><td>0,000</td><td>0,000</td><td>0,000</td></tr><tr><td>mlt</td><td>pastures</td><td></td><td></td><td></td><td></td><td>0,000</td><td>0,000</td><td>0,000</td><td>0,000</td><td>0,000</td><td>0,000</td></tr><tr><td>mlt</td><td>fodders</td><td></td><td></td><td></td><td></td><td>0,001</td><td>0,000</td><td>0,002</td><td>0,000</td><td>0,001</td><td>0,000</td></tr><tr><td>mlt</td><td>fallows & set-aside</td><td></td><td></td><td></td><td></td><td>0,000</td><td>0,001</td><td>0,000</td><td>0,001</td><td>0,000</td><td>0,001</td></tr><tr><td>ned</td><td>cereals</td><td>0,209</td><td>0,217</td><td>0,168</td><td>0,187</td><td>0,195</td><td>0,211</td><td>0,204</td><td>0,279</td><td>0,203</td><td>0,183</td></tr><tr><td>ned</td><td>oilseeds & proteins</td><td>0,002</td><td>0,011</td><td>0,003</td><td>0,006</td><td>0,074</td><td>0,072</td><td>0,068</td><td>0,077</td><td>0,069</td><td>0,083</td></tr><tr><td>ned</td><td>roots & indus.</td><td>0,335</td><td>0,258</td><td>0,234</td><td>0,195</td><td>0,224</td><td>0,264</td><td>0,219</td><td>0,205</td><td>0,223</td><td>0,213</td></tr><tr><td>ned</td><td>pastures</td><td>0,676</td><td>0,756</td><td>0,588</td><td>0,661</td><td>0,978</td><td>0,284</td><td>0,982</td><td>0,904</td><td>0,963</td><td>1,194</td></tr><tr><td>ned</td><td>fodders</td><td>0,240</td><td>0,122</td><td>0,209</td><td>0,114</td><td>0,229</td><td>0,024</td><td>0,245</td><td>0,003</td><td>0,252</td><td>0,004</td></tr><tr><td>ned</td><td>fallows & set-aside</td><td>0,049</td><td>0,147</td><td>0,031</td><td>0,072</td><td>0,017</td><td>0,860</td><td>0,011</td><td>0,260</td><td>0,008</td><td>0,041</td></tr><tr><td>ost</td><td>cereals</td><td>0,675</td><td>0,743</td><td>0,732</td><td>0,788</td><td>0,705</td><td>0,835</td><td>0,736</td><td>0,832</td><td>0,737</td><td>0,802</td></tr><tr><td>ost</td><td>oilseeds & proteins</td><td>0,086</td><td>0,041</td><td>0,082</td><td>0,084</td><td>0,123</td><td>0,090</td><td>0,122</td><td>0,109</td><td>0,119</td><td>0,095</td></tr><tr><td>ost</td><td>roots & indus.</td><td>0,069</td><td>0,120</td><td>0,065</td><td>0,090</td><td>0,063</td><td>0,083</td><td>0,064</td><td>0,094</td><td>0,064</td><td>0,070</td></tr><tr><td>ost</td><td>pastures</td><td>0,797</td><td>0,665</td><td>0,811</td><td>0,702</td><td>0,619</td><td>0,571</td><td>0,634</td><td>0,610</td><td>0,636</td><td>0,662</td></tr><tr><td>ost</td><td>fodders</td><td>0,074</td><td>0,043</td><td>0,084</td><td>0,081</td><td>0,213</td><td>0,161</td><td>0,213</td><td>0,130</td><td>0,200</td><td>0,106</td></tr><tr><td>ost</td><td>fallows & set-aside</td><td>0,086</td><td>0,176</td><td>0,080</td><td>0,107</td><td>0,056</td><td>0,040</td><td>0,038</td><td>0,033</td><td>0,035</td><td>0,056</td></tr><tr><td>pol</td><td>cereals</td><td>0,000</td><td>0,000</td><td>4,830</td><td>5,267</td><td>7,361</td><td>7,815</td><td>7,974</td><td>8,476</td><td>7,934</td><td>8,426</td></tr><tr><td>pol</td><td>oilseeds & proteins</td><td>0,000</td><td>0,000</td><td>0,555</td><td>0,561</td><td>1,313</td><td>1,448</td><td>1,278</td><td>1,420</td><td>1,316</td><td>1,487</td></tr><tr><td>pol</td><td>roots & indus.</td><td>0,000</td><td>0,000</td><td>0,591</td><td>0,753</td><td>0,649</td><td>0,882</td><td>0,577</td><td>0,597</td><td>0,592</td><td>0,629</td></tr><tr><td>pol</td><td>pastures</td><td>0,000</td><td>0,000</td><td>0,248</td><td>0,055</td><td>2,590</td><td>2,201</td><td>2,663</td><td>2,281</td><td>2,752</td><td>2,298</td></tr><tr><td>pol</td><td>fodders</td><td>0,000</td><td>0,000</td><td>1,474</td><td>1,043</td><td>0,556</td><td>0,126</td><td>0,638</td><td>0,369</td><td>0,652</td><td>0,375</td></tr><tr><td>pol</td><td>fallows & set-aside</td><td>0,000</td><td>0,000</td><td>0,008</td><td>0,026</td><td>0,125</td><td>0,122</td><td>0,124</td><td>0,111</td><td>0,127</td><td>0,159</td></tr><tr><td>por</td><td>cereals</td><td>0,390</td><td>0,577</td><td>0,312</td><td>0,502</td><td>0,212</td><td>0,566</td><td>0,279</td><td>0,564</td><td>0,247</td><td>0,565</td></tr><tr><td>por</td><td>oilseeds & proteins</td><td>0,021</td><td>0,020</td><td>0,016</td><td>0,014</td><td>0,042</td><td>0,048</td><td>0,043</td><td>0,051</td><td>0,044</td><td>0,072</td></tr><tr><td>por</td><td>roots & indus.</td><td>0,033</td><td>0,201</td><td>0,024</td><td>0,054</td><td>0,020</td><td>0,016</td><td>0,019</td><td>0,019</td><td>0,021</td><td>0,020</td></tr></tbody></table>

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* the geographical downscaling of results (i.e. through the "spatialization process")

Table 32 displays how versions differ according to these items. Major parts of the model construction evolve over time. The structural changes of the model may affect results in terms of type and accuracy. Typology was improved when irrigation was taken into account. Parameter estimation is dramatically changed when the *GME* method is used. The *N* to yield functions allow us to account for the intensive margin effect at the plot level. In addition to these "internal" improvements, the spatialization process allows for new model applications and potential linkages with other models. Finally, let us recall that the evolution of computer capabilities strongly impacts the development of the different stages of model building and applications.

## 10.3 Adding blocks, programs and tools

**Key-points:**

- Implementing the calibration process

- Developments and modules related to some versions, ready to be expanded

- About *GHG* emissions, spatialization, dose-response functions, model coupling

The use of additional blocks, programs and applications is in large part autonomous from the model kernel. There is always the possibility of using any version of the model. At the same time activation of some of these additional blocks or applications may not be allowed under some model versions. The *LP* calibration process is a quite specific but emblematic case of external programs linked to the kernel.

### LP calibration

Calibration refers to one of the most important steps of the *AROPAj* building, in terms of complexity as well as computation time. The shell dedicated to this step acts as a super routine calling the *AROPAj* kernel as a subroutine. This shell encapsulates the kernel for managing iterated *AROPAj* runs. As explained in Section 7, the calibration algorithm determines how the *AROPAj* parameters have to change from one run to the next. In this case, the shell is composed of external code components (e.g. *FORTRAN* code, mainly). Additional information, requested for the calculation of the calibration criterion, enters the process through *FADN* sourced parameters. The process runs at the *FG* level and provides partly re-estimated parameters.

### GHG emission assessment

Our standard process of estimating *GHG* emissions is based on *IPCC* guidelines as well as national emission factors that impact emission inventories. We need to compute multiple series of national (possibly regional) parameters, separate from the core parameter set and external to the *AROPAj* estimation and calibration steps. This process entails adding a block to the kernel. Related parameter values are then formatted in line with *AROPAj* basic parameters.

Another possible way of assessing *N*₂*O* emission types is to activate the *N*-block. This requires that *N* to yield functions are available and that *N*-losses are regressed against *N*-inputs (see below).

### N to yield functions

This additional block requires the availability of calibrated *N* to yield functions. When such a function is available for a farm group and for a crop, *FADN* sourced yield and input values (at
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<table><thead><tr><th rowspan="2">MS</th><th rowspan="2">Land use</th><th colspan="2">V2</th><th colspan="2">V3</th><th colspan="2">V5-2007</th><th colspan="2">V5-2008</th><th colspan="2">V5-2009</th></tr><tr><th>FADN</th><th>calibr.</th><th>FADN</th><th>calibr.</th><th>FADN</th><th>calibr.</th><th>FADN</th><th>calibr.</th><th>FADN</th><th>calibr.</th></tr></thead><tbody><tr><td>por</td><td>pastures</td><td>0,501</td><td>0,497</td><td>0,424</td><td>0,250</td><td>0,693</td><td>0,665</td><td>0,842</td><td>0,892</td><td>0,916</td><td>0,589</td></tr><tr><td>por</td><td>fodders</td><td>0,211</td><td>0,222</td><td>0,238</td><td>0,197</td><td>0,349</td><td>0,275</td><td>0,396</td><td>0,247</td><td>0,317</td><td>0,248</td></tr><tr><td>por</td><td>fallows & set-aside</td><td>0,833</td><td>0,473</td><td>0,703</td><td>0,374</td><td>0,715</td><td>0,462</td><td>0,689</td><td>0,493</td><td>0,579</td><td>0,630</td></tr><tr><td>rou</td><td>cereals</td><td></td><td></td><td></td><td></td><td>4,538</td><td>5,386</td><td>5,947</td><td>7,180</td><td>5,997</td><td>7,160</td></tr><tr><td>rou</td><td>oilseeds & proteins</td><td></td><td></td><td></td><td></td><td>1,348</td><td>1,029</td><td>1,469</td><td>1,528</td><td>1,532</td><td>1,586</td></tr><tr><td>rou</td><td>roots & indus.</td><td></td><td></td><td></td><td></td><td>0,133</td><td>0,345</td><td>0,180</td><td>0,499</td><td>0,145</td><td>0,356</td></tr><tr><td>rou</td><td>pastures</td><td></td><td></td><td></td><td></td><td>1,278</td><td>0,855</td><td>1,365</td><td>0,553</td><td>1,768</td><td>0,712</td></tr><tr><td>rou</td><td>fodders</td><td></td><td></td><td></td><td></td><td>0,539</td><td>0,040</td><td>0,722</td><td>0,112</td><td>0,655</td><td>0,177</td></tr><tr><td>rou</td><td>fallows & set-aside</td><td></td><td></td><td></td><td></td><td>0,080</td><td>0,166</td><td>0,417</td><td>0,227</td><td>0,086</td><td>0,190</td></tr><tr><td>suo</td><td>cereals</td><td>0,953</td><td>1,070</td><td>1,043</td><td>1,193</td><td>1,031</td><td>1,177</td><td>1,116</td><td>1,201</td><td>1,092</td><td>1,200</td></tr><tr><td>suo</td><td>oilseeds & proteins</td><td>0,067</td><td>0,002</td><td>0,086</td><td>0,086</td><td>0,115</td><td>0,127</td><td>0,096</td><td>0,114</td><td>0,119</td><td>0,147</td></tr><tr><td>suo</td><td>roots & indus.</td><td>0,067</td><td>0,127</td><td>0,062</td><td>0,117</td><td>0,046</td><td>0,053</td><td>0,049</td><td>0,062</td><td>0,056</td><td>0,067</td></tr><tr><td>suo</td><td>pastures</td><td>0,007</td><td>0,010</td><td>0,002</td><td>0,001</td><td>0,629</td><td>0,614</td><td>0,646</td><td>0,660</td><td>0,661</td><td>0,618</td></tr><tr><td>suo</td><td>fodders</td><td>0,611</td><td>0,655</td><td>0,600</td><td>0,555</td><td>0,016</td><td>0,005</td><td>0,014</td><td>0,003</td><td>0,017</td><td>0,000</td></tr><tr><td>suo</td><td>fallows & set-aside</td><td>0,183</td><td>0,025</td><td>0,185</td><td>0,026</td><td>0,188</td><td>0,048</td><td>0,163</td><td>0,043</td><td>0,165</td><td>0,078</td></tr><tr><td>sve</td><td>cereals</td><td>1,057</td><td>1,022</td><td>1,105</td><td>1,242</td><td>0,979</td><td>1,023</td><td>1,061</td><td>1,197</td><td>1,038</td><td>0,930</td></tr><tr><td>sve</td><td>oilseeds & proteins</td><td>0,055</td><td>0,054</td><td>0,089</td><td>0,087</td><td>0,153</td><td>0,175</td><td>0,144</td><td>0,178</td><td>0,143</td><td>0,122</td></tr><tr><td>sve</td><td>roots & indus.</td><td>0,090</td><td>0,158</td><td>0,090</td><td>0,086</td><td>0,069</td><td>0,108</td><td>0,069</td><td>0,047</td><td>0,067</td><td>0,092</td></tr><tr><td>sve</td><td>pastures</td><td>0,386</td><td>0,726</td><td>0,328</td><td>0,442</td><td>1,072</td><td>1,131</td><td>1,067</td><td>0,966</td><td>1,118</td><td>1,215</td></tr><tr><td>sve</td><td>fodders</td><td>0,829</td><td>0,325</td><td>0,721</td><td>0,378</td><td>0,030</td><td>0,012</td><td>0,033</td><td>0,034</td><td>0,042</td><td>0,034</td></tr><tr><td>sve</td><td>fallows & set-aside</td><td>0,223</td><td>0,353</td><td>0,227</td><td>0,325</td><td>0,179</td><td>0,034</td><td>0,097</td><td>0,050</td><td>0,088</td><td>0,104</td></tr><tr><td>svk</td><td>cereals</td><td></td><td></td><td>0,707</td><td>1,038</td><td>0,774</td><td>0,955</td><td>0,769</td><td>0,874</td><td>0,748</td><td>0,857</td></tr><tr><td>svk</td><td>oilseeds & proteins</td><td></td><td></td><td>0,178</td><td>0,239</td><td>0,280</td><td>0,392</td><td>0,286</td><td>0,383</td><td>0,298</td><td>0,361</td></tr><tr><td>svk</td><td>roots & indus.</td><td></td><td></td><td>0,041</td><td>0,086</td><td>0,026</td><td>0,032</td><td>0,021</td><td>0,026</td><td>0,027</td><td>0,013</td></tr><tr><td>svk</td><td>pastures</td><td></td><td></td><td>0,597</td><td>0,205</td><td>0,588</td><td>0,352</td><td>0,575</td><td>0,441</td><td>0,540</td><td>0,475</td></tr><tr><td>svk</td><td>fodders</td><td></td><td></td><td>0,068</td><td>0,116</td><td>0,236</td><td>0,119</td><td>0,223</td><td>0,123</td><td>0,229</td><td>0,120</td></tr><tr><td>svk</td><td>fallows & set-aside</td><td></td><td></td><td>0,170</td><td>0,077</td><td>0,021</td><td>0,076</td><td>0,025</td><td>0,051</td><td>0,012</td><td>0,027</td></tr><tr><td>svn</td><td>cereals</td><td></td><td></td><td>0,060</td><td>0,096</td><td>0,077</td><td>0,096</td><td>0,085</td><td>0,097</td><td>0,085</td><td>0,091</td></tr><tr><td>svn</td><td>oilseeds & proteins</td><td></td><td></td><td>0,001</td><td>0,000</td><td>0,008</td><td>0,007</td><td>0,006</td><td>0,006</td><td>0,006</td><td>0,003</td></tr><tr><td>svn</td><td>roots & indus.</td><td></td><td></td><td>0,009</td><td>0,012</td><td>0,004</td><td>0,002</td><td>0,004</td><td>0,005</td><td>0,003</td><td>0,011</td></tr><tr><td>svn</td><td>pastures</td><td></td><td></td><td>0,311</td><td>0,253</td><td>0,312</td><td>0,290</td><td>0,309</td><td>0,274</td><td>0,316</td><td>0,286</td></tr><tr><td>svn</td><td>fodders</td><td></td><td></td><td>0,049</td><td>0,032</td><td>0,035</td><td>0,035</td><td>0,030</td><td>0,030</td><td>0,028</td><td>0,027</td></tr><tr><td>svn</td><td>fallows & set-aside</td><td></td><td></td><td>0,001</td><td>0,038</td><td>0,001</td><td>0,007</td><td>0,000</td><td>0,022</td><td>0,000</td><td>0,019</td></tr></tbody></table>

Former versions exhibit some significant deviations between FADN estimates and calibrated results, with some countries showing larger discrepancies than others. This could be due to difficulties in FADN land use identification (with likely misinterpreted land use codes on the FADN data warehouse). On the whole, cereals and set-aside are overestimated and pastures are underestimated, even though fodders partly compensate the underestimated pastures in several countries. The fifth version is able to minimize the discrepancy between FADN estimates and calibrated results due to progressions in the typology, cost estimates, and the calibration process itself. The calibration process could be even further improved with the ability to achieve even more iterated runs by our computing system.
the plot level) are replaced by computed values. In such case, the computed yield and N-input depend on the price ratio. By default, the model calls for market prices. There is need to change the kernel.

When *N* to yield and *N* to *N*-loss functions exist, there exists the possibility of assessing different *N*-losses at the farm and wider levels, namely *N*₂O, *NO*₃ and *NH*₃ (what the *STICS* crop model allows for estimation). It must be noted here that, regarding *N*₂O, results may vary drastically from those provided by the *IPCC* method.

### Organic *N* and *LT* effect

Accounting for *LT* effect assessment of organic *N* spreading in *AROPAj* requires the availability of *N* to yield functions. This kind of effect affects the *N* amount requested by a crop to reach a given yield. The magnitude of the *LT* effect depends on the average quantity of organic *N* spreading at the farm level. The computation requires the use of an external routine and the iteration of *AROPAj* runs leading to the convergence of *AROPAj* outputs. This process concerns two kinds of parameters, some of them provided by the *Carbo-PRO* model, and others re-estimated according to iterative *AROPAj* runs. The *LP* case study shows fast convergence of the iterative process.

### The replacement of market prices by shadow prices

This process may be implemented when activating the nitrogen block in line with *N* to yield functions. Mathematically, shadow prices linked to the use of crop production should replace market prices for maximizing the gross margin at the plot scale in some cases. This occurs when, at the optimum, the crop production is entirely re-used on-farm (e.g. for livestock feeding), or when no *N*-input comes from the market (e.g. manure sourced). In practice, the problem is solved by iterated *AROPAj* runs with shadow prices replacing the prices used at the previous iteration for the yield function derivative. The process quickly converge in most of *AROPAj* LPs, except for in a few cases where solutions have periodic forms over iterations, with slight amplitudes.

### Spatialisation of farm groups and downscaling of *AROPAj* outputs

There exist the possibility of *FG* locating, in terms of probabilistic distributed contribution of the agricultural activities over a geographical grid. This is obtained through the spatialization module (see Section 8) available for the *V2* version, and is in progress for the *V5* version. Once contributions of all *FGs* within a region are computed, there is no difficulty in displaying any *AROPAj* output related to this region at a fine resolution level. The size of the database and the overlapping data used to pose problems for our computing capacities, specifically at the first step of the spatialization process (e.g. organize data for spatial econometrics). Currently, most of the spatialization steps are performed on our workstation thanks to open-source *GNU* softwares, except for the *GCE* procedure which calls for *GAMS*.

The combination of these blocks and tools is displayed on Table 33.

## 10.4 Comparison tables and *AROPAj* sourced biases

**Key-points:**

* Farm group representativity and *FADN* based allocation of agricultural lands over the years and the *AROPAj* versions

* Calibration results and land allocation bias
# 11 Computing tools

## 11.1 Hardware and software

**Key-points:**

* From Unix to Linux

* Preferably GNU GPL applications, the FORTRAN compiler, and the GAMS software

* From the MGG kernel to enlarged MGG code transformed into GAMS code

* AROPAj file architecture

### 11.1.1 Operating system, software and runtime

Until 2013, all computing tool implementation were implemented within the Unix operating system. Now the AROPAj model core and applications are used via the Linux OS. These softwares are quite convenient for long term secured applications launched by batch programs.

Over the time, the model has been developed and implemented on multi-core workstations. Protection and security tools are managed by the INRA "Direction des Services Informatiques". Thus, databases, including the FADN, are protected and secure. This final point is of high interest, the ability to manage datasets provided by public institutions is of paramount importance.

The running workstation is of a 16-core class. In observing the progression from the first working tool to the 2014 working tool, we see a computer core scale example of "Moore's Law." In other words, what was once a binding time limit became much less binding (e.g. for implementation of the calibrating process). Multi-core systems multiply the gain in runtime, so what required several weeks of runtime 10 years ago now only needs a couple of hours.

### 11.1.2 Software and runtime

Year after year, we have chosen to use GNU softwares in the improvement of the AROPAj model, not only for their relatively low financial costs, but also as a means of facilitating the maintenance and evolution of our programs and applications.

The starting command and the processes calling for major applications are written in csh CShell language. Intermediate commands and applications are written in FORTRAN or CShell languages.

The core of the AROPAj model is written in FORTRAN, even if the basic file aro.mgg remains coded in the former MGG code, close to FORTRAN. It is currently written and used regardless of the former MGG software. Programs that transform the core files and deliver the GAMS code for MP solving combine CShell and FORTRAN processing tools.

Basic statistical calculations are now realized through R code programs. The R language is also used for spatialization modules, which deliver maps. The POSTGreSQL system is used for data processing and management, especially in the case of STICS based applications. Major batch interfaces between databases and the STICS crop model are made of R code programs. In addition, some processing tools are now written in Python code.

The GAMS software is used for cumbersome optimization calculations. That applies for AROPAj simulations as well as for GME and GCE estimating programs. The GAMS code is
<table><caption>**Table 33.** Impacted AROPAj steps and files when adding blocks or managing applications.</caption><thead><tr><th></th><th>Database</th><th>clustering /<br>sample farms</th><th>estimation &<br>calibration</th><th>LP<br>design</th><th>FG<br>spatialisation</th></tr></thead><tbody><tr><td>base</td><td>FADN</td><td>farm groups<br>(FG)</td><td>basic<br>parameters</td><td>kernel</td><td></td></tr><tr><td>FG's LP<br>calibration</td><td></td><td></td><td>re-estimated<br>parameters</td><td>external<br>routine</td><td></td></tr><tr><td>GHG<br>emissions</td><td>IPCC & nat.<br>inventories</td><td>regionally<br>distributed</td><td>added<br>parameters</td><td>GHG<br>block</td><td></td></tr><tr><td>N to yield<br>functions</td><td>STICS - relat.<br>requirement</td><td>added parameters related to<br>FG & FADN sourced data</td><td></td><td>internal<br>subroutine</td><td></td></tr><tr><td>N to N-loss<br>functions</td><td></td><td>parameters related to<br>N to yield function calibration</td><td></td><td></td><td></td></tr><tr><td>LT effect of<br>organic N</td><td></td><td>added & re-evaluated<br>parameters / N to yield funct.</td><td></td><td>external<br>routine</td><td></td></tr><tr><td>shadow prices<br>convergence</td><td></td><td></td><td>re-evaluated<br>parameters</td><td>external<br>routine</td><td></td></tr><tr><td>outputs<br>downscaling</td><td>CLC & other<br>physical data</td><td>probabilistic<br>FG location</td><td></td><td></td><td>external<br>program</td></tr></tbody></table>

From the real world up to the stylized calibrated representation of farming systems, there are successive gaps leading to an aggregation bias. Upstream from the AROPAj filter, we have to account for:

* the European surveys (with possible truncation, regarding very large farms as well as small systems managed by part-time farmers),

* their statistical treatment by the EC services (with possible difficulties due to heterogeneous quality of national surveys),

* the clustering of sample farms leading to FADN categories (FT, economic size, altitude), which strongly impacts the AROPAj FG building, even in terms of excluded farming systems (e.g. ones for which FT refers to wine or horticulture).

Obviously, as for any model, the AROPAj model results do not perfectly fit with observations (here viewed through the FADN). The LP design, the FG typology and the estimation and calibration phases all contributed to the distortion of the raw FADN estimates of production and allocated area among crops. In addition, comparison is made more difficult when our LP objective does not correspond exactly to a FADN variable.

In order to illustrate the quality of the calibration phase, we present histograms that show the distribution of FGs' square root of sum of squared deviations between FADN based informations and computed terms (see Figures 11, 12, 13, 14, 15, 16). Regarding the possible FADN sourced bias, the figures displayed in Table 21 on page 91 show significant sources of bias. Bias may occur because of the AROPAj typology (see the UAA deviation between the V4 and V5s versions for Estonia or Romania, among others EC countries). Bias also occurs because of the FADN itself, as is evident when comparing the national UAA among the years 2007, 2008 and 2009, all of which were estimated through a common algorithm of FADN-farms clustering (e.g. for Italy and Poland, between 2007 and 2008 or 2009).
generated for *AROPAj* from the kernel files described hereafter (Section 11.1.3). This is automatically done when the user calls the model for simulation runs.

Some specific applications may call for other languages and softwares. For example, we used *Mathematica* for time based scenario development. This occurs when applying the Faustmann rule to the calculation of the optimal perennial crop rotation over years. These softwares are used upstream from *AROPAj* simulations, in an external way, without direct coupling to *AROPAj*. In addition, *AROPAj* outputs may be formatted for treatment by usual office softwares.

At last, we combine some of the previously mentioned GNU languages to feed information documents into LATEX format. This is the case with some of the elements of this paper, such as the list of parameters and LP variables.

### 11.1.3 The AROPAj files and shell

The core of the *AROPAj* model is essentially composed of 4 files, as required by the use of *MGG* and *SCICONIC* software. We updated these files and transform them into *GAMS* language via *Unix CShell* and *FORTRAN* programs. This transformation is automatically processed when the user calls the model via the *AROPAj* call shell. These files are:

*   **aro.mgg**, which designs and describes the basic linear structure of *AROPAj* by listing and defining activities, constraints and the objective function, as well as the link to the model parameters. The coding language is similar to *FORTRAN*. This file allows us to build the linear programming matrix associated to any parameter set. We keep this method for matrix generation operational;

*   **intern.f**, which codes the relationship between parameters (in **mgg.par** format) and the matrix generator **aro.mgg**. This file is made of **FORTRAN** instructions;

*   **aro.dat**, which is useful for writing reports and naming primal and dual variables. It also plays a role in flagging variables to activate and deactivate them;

*   **mgg.par**, which is made of the comprehensive list of parameters that is read by **intern.f** and which is required by **aro.mgg** to design the matrix. This is a *text* format file made of fixed format lines referring to the type of information (parameter category, e.g. *rjj* for crop yield), the characteristics inside the category (e.g. *bl* for soft wheat), the farm group number when appropriate, and the parameter value. When information exists on several lines of the file, the operating value retained for the matrix is the last one.

The model kernel can be included in a calculation chain designed by the user. To do that, the user has to combine the core with external files with an eye to her/his goals (see the next section). One important illustrative case is the calibrating process (see Section 7).

The usual way to implement a series of simulations embedded in a *scenario* is to select and design the following files ("scen" is of user's choice):

*   **scen.par**, which identifies the parameters of interest from those listed in the **mgg.par** file (the **scen.par** file keeps the **mgg.par** format);

*   **scen.var** which focuses on *LP* (primal and dual) variables of interest for the *scenario*. The format is designed to allow the *LP* outputs to become entries of the calculation program;
Stated as a quasi-fix factor, the UAA is not optimally re-computed at any level in AROPAj (from the FG to the EU). However, calibration impacts the land allocation within the UAA, as illustrated by Tables referring to often used V2 and V3 versions, and to the 2007 to 2009 V5 ones.

<table><caption>**Table 34.** Comparison of land allocation between FADN estimates and AROPAj calibrated outputs, aggregated at the member State level and for different AROPAj versions (millions hectares) - (AROPAj runs were realized on March 2015).</caption><thead><tr><th rowspan="2">MS</th><th rowspan="2">Land use</th><th colspan="2">V2</th><th colspan="2">V3</th><th colspan="2">V5-2007</th><th colspan="2">V5-2008</th><th colspan="2">V5-2009</th></tr><tr><th>FADN</th><th>calibr.</th><th>FADN</th><th>calibr.</th><th>FADN</th><th>calibr.</th><th>FADN</th><th>calibr.</th><th>FADN</th><th>calibr.</th></tr></thead><tbody><tr><td>bel</td><td>cereals</td><td>0,306</td><td>0,332</td><td>0,303</td><td>0,321</td><td>0,333</td><td>0,313</td><td>0,379</td><td>0,382</td><td>0,381</td><td>0,379</td></tr><tr><td>bel</td><td>oilseeds & proteins</td><td>0,006</td><td>0,003</td><td>0,005</td><td>0,006</td><td>0,041</td><td>0,041</td><td>0,044</td><td>0,041</td><td>0,047</td><td>0,054</td></tr><tr><td>bel</td><td>roots & indus.</td><td>0,156</td><td>0,161</td><td>0,171</td><td>0,190</td><td>0,161</td><td>0,155</td><td>0,141</td><td>0,146</td><td>0,153</td><td>0,151</td></tr><tr><td>bel</td><td>pastures</td><td>0,689</td><td>0,560</td><td>0,574</td><td>0,420</td><td>0,611</td><td>0,648</td><td>0,615</td><td>0,621</td><td>0,626</td><td>0,671</td></tr><tr><td>bel</td><td>fodders</td><td>0,254</td><td>0,255</td><td>0,271</td><td>0,291</td><td>0,175</td><td>0,171</td><td>0,196</td><td>0,182</td><td>0,202</td><td>0,151</td></tr><tr><td>bel</td><td>fallows & set-aside</td><td>0,021</td><td>0,121</td><td>0,015</td><td>0,111</td><td>0,012</td><td>0,004</td><td>0,005</td><td>0,007</td><td>0,002</td><td>0,005</td></tr><tr><td>bgr</td><td>cereals</td><td></td><td></td><td></td><td></td><td>1,637</td><td>1,720</td><td>1,548</td><td>1,712</td><td>1,733</td><td>1,820</td></tr><tr><td>bgr</td><td>oilseeds & proteins</td><td></td><td></td><td></td><td></td><td>0,660</td><td>0,670</td><td>0,702</td><td>0,680</td><td>0,756</td><td>0,743</td></tr><tr><td>bgr</td><td>roots & indus.</td><td></td><td></td><td></td><td></td><td>0,016</td><td>0,039</td><td>0,015</td><td>0,043</td><td>0,027</td><td>0,054</td></tr><tr><td>bgr</td><td>pastures</td><td></td><td></td><td></td><td></td><td>0,344</td><td>0,223</td><td>0,390</td><td>0,202</td><td>0,309</td><td>0,247</td></tr><tr><td>bgr</td><td>fodders</td><td></td><td></td><td></td><td></td><td>0,065</td><td>0,020</td><td>0,051</td><td>0,011</td><td>0,055</td><td>0,012</td></tr><tr><td>bgr</td><td>fallows & set-aside</td><td></td><td></td><td></td><td></td><td>0,029</td><td>0,080</td><td>0,075</td><td>0,132</td><td>0,105</td><td>0,109</td></tr><tr><td>cyp</td><td>cereals</td><td></td><td></td><td>0,087</td><td>0,072</td><td>0,042</td><td>0,054</td><td>0,083</td><td>0,004</td><td>0,060</td><td>0,055</td></tr><tr><td>cyp</td><td>oilseeds & proteins</td><td></td><td></td><td>0,000</td><td>0,000</td><td>0,003</td><td>0,010</td><td>0,006</td><td>0,008</td><td>0,006</td><td>0,010</td></tr><tr><td>cyp</td><td>roots & indus.</td><td></td><td></td><td>0,004</td><td>0,011</td><td>0,004</td><td>0,008</td><td>0,004</td><td>0,012</td><td>0,003</td><td>0,008</td></tr><tr><td>cyp</td><td>pastures</td><td></td><td></td><td>0,026</td><td>0,027</td><td>0,010</td><td>0,001</td><td>0,004</td><td>0,000</td><td>0,005</td><td>0,021</td></tr><tr><td>cyp</td><td>fodders</td><td></td><td></td><td>0,014</td><td>0,017</td><td>0,038</td><td>0,001</td><td>0,022</td><td>0,001</td><td>0,031</td><td>0,001</td></tr><tr><td>cyp</td><td>fallows & set-aside</td><td></td><td></td><td>0,006</td><td>0,009</td><td>0,009</td><td>0,034</td><td>0,006</td><td>0,100</td><td>0,007</td><td>0,017</td></tr><tr><td>cze</td><td>cereals</td><td></td><td></td><td>1,664</td><td>1,795</td><td>1,618</td><td>1,795</td><td>1,510</td><td>1,693</td><td>1,511</td><td>1,633</td></tr><tr><td>cze</td><td>oilseeds & proteins</td><td></td><td></td><td>0,319</td><td>0,357</td><td>0,392</td><td>0,434</td><td>0,377</td><td>0,431</td><td>0,422</td><td>0,425</td></tr><tr><td>cze</td><td>roots & indus.</td><td></td><td></td><td>0,141</td><td>0,204</td><td>0,103</td><td>0,076</td><td>0,088</td><td>0,070</td><td>0,101</td><td>0,058</td></tr><tr><td>cze</td><td>pastures</td><td></td><td></td><td>1,037</td><td>0,693</td><td>0,782</td><td>0,665</td><td>0,815</td><td>0,732</td><td>0,823</td><td>0,785</td></tr><tr><td>cze</td><td>fodders</td><td></td><td></td><td>0,249</td><td>0,229</td><td>0,376</td><td>0,205</td><td>0,358</td><td>0,167</td><td>0,354</td><td>0,277</td></tr><tr><td>cze</td><td>fallows & set-aside</td><td></td><td></td><td>0,020</td><td>0,153</td><td>0,006</td><td>0,101</td><td>0,005</td><td>0,060</td><td>0,012</td><td>0,044</td></tr><tr><td>dan</td><td>cereals</td><td>1,530</td><td>1,416</td><td>1,485</td><td>1,591</td><td>1,455</td><td>1,879</td><td>1,518</td><td>1,633</td><td>1,553</td><td>1,705</td></tr><tr><td>dan</td><td>oilseeds & proteins</td><td>0,081</td><td>0,039</td><td>0,133</td><td>0,139</td><td>0,195</td><td>0,286</td><td>0,189</td><td>0,226</td><td>0,174</td><td>0,224</td></tr><tr><td>dan</td><td>roots & indus.</td><td>0,106</td><td>0,103</td><td>0,094</td><td>0,107</td><td>0,088</td><td>0,122</td><td>0,086</td><td>0,087</td><td>0,078</td><td>0,078</td></tr><tr><td>dan</td><td>pastures</td><td>0,187</td><td>0,246</td><td>0,164</td><td>0,172</td><td>0,389</td><td>0,018</td><td>0,424</td><td>0,400</td><td>0,500</td><td>0,325</td></tr><tr><td>dan</td><td>fodders</td><td>0,320</td><td>0,257</td><td>0,335</td><td>0,177</td><td>0,198</td><td>0,140</td><td>0,214</td><td>0,098</td><td>0,216</td><td>0,217</td></tr><tr><td>dan</td><td>fallows & set-aside</td><td>0,212</td><td>0,376</td><td>0,185</td><td>0,210</td><td>0,160</td><td>0,042</td><td>0,089</td><td>0,077</td><td>0,068</td><td>0,041</td></tr><tr><td>deu</td><td>cereals</td><td>6,461</td><td>5,667</td><td>6,717</td><td>7,523</td><td>6,488</td><td>7,191</td><td>6,823</td><td>7,511</td><td>6,683</td><td>7,218</td></tr><tr><td>deu</td><td>oilseeds & proteins</td><td>1,011</td><td>0,838</td><td>1,189</td><td>1,178</td><td>1,511</td><td>1,807</td><td>1,627</td><td>1,841</td><td>1,735</td><td>1,867</td></tr><tr><td>deu</td><td>roots & indus.</td><td>0,681</td><td>0,902</td><td>0,724</td><td>0,905</td><td>0,679</td><td>0,671</td><td>0,632</td><td>0,507</td><td>0,648</td><td>0,731</td></tr><tr><td>deu</td><td>pastures</td><td>4,452</td><td>3,560</td><td>4,568</td><td>2,864</td><td>3,861</td><td>4,425</td><td>3,873</td><td>4,376</td><td>3,931</td><td>4,484</td></tr><tr><td>deu</td><td>fodders</td><td>1,278</td><td>0,698</td><td>1,387</td><td>0,590</td><td>1,755</td><td>0,600</td><td>2,021</td><td>0,896</td><td>2,157</td><td>0,935</td></tr><tr><td>deu</td><td>fallows & set-aside</td><td>0,845</td><td>3,063</td><td>0,829</td><td>2,354</td><td>0,636</td><td>0,238</td><td>0,328</td><td>0,175</td><td>0,257</td><td>0,175</td></tr><tr><td>ell</td><td>cereals</td><td>0,876</td><td>0,824</td><td>0,966</td><td>0,991</td><td>1,383</td><td>1,544</td><td>1,499</td><td>1,631</td><td>1,470</td><td>1,593</td></tr><tr><td>ell</td><td>oilseeds & proteins</td><td>0,022</td><td>0,019</td><td>0,012</td><td>0,021</td><td>0,084</td><td>0,115</td><td>0,085</td><td>0,116</td><td>0,105</td><td>0,144</td></tr><tr><td>ell</td><td>roots & indus.</td><td>0,053</td><td>0,044</td><td>0,331</td><td>0,317</td><td>0,381</td><td>0,368</td><td>0,383</td><td>0,370</td><td>0,345</td><td>0,336</td></tr><tr><td>ell</td><td>pastures</td><td>0,156</td><td>0,138</td><td>0,181</td><td>0,071</td><td>0,058</td><td>0,066</td><td>0,059</td><td>0,078</td><td>0,059</td><td>0,103</td></tr><tr><td>ell</td><td>fodders</td><td>0,061</td><td>0,159</td><td>0,078</td><td>0,185</td><td>0,245</td><td>0,147</td><td>0,263</td><td>0,171</td><td>0,261</td><td>0,166</td></tr><tr><td>ell</td><td>fallows & set-aside</td><td>0,052</td><td>0,035</td><td>0,083</td><td>0,066</td><td>0,205</td><td>0,116</td><td>0,155</td><td>0,077</td><td>0,183</td><td>0,081</td></tr><tr><td>esp</td><td>cereals</td><td>5,769</td><td>5,948</td><td>5,942</td><td>6,999</td><td>6,917</td><td>8,162</td><td>7,032</td><td>8,073</td><td>6,521</td><td>7,863</td></tr><tr><td>esp</td><td>oilseeds & proteins</td><td>0,628</td><td>0,292</td><td>0,582</td><td>0,664</td><td>0,996</td><td>1,083</td><td>0,900</td><td>1,046</td><td>0,956</td><td>0,825</td></tr><tr><td>esp</td><td>roots & indus.</td><td>0,193</td><td>0,454</td><td>0,299</td><td>0,376</td><td>0,269</td><td>0,208</td><td>0,244</td><td>0,224</td><td>0,242</td><td>0,273</td></tr><tr><td>esp</td><td>pastures</td><td>2,463</td><td>1,825</td><td>2,228</td><td>1,293</td><td>2,718</td><td>2,193</td><td>2,372</td><td>2,157</td><td>2,815</td><td>2,437</td></tr><tr><td>esp</td><td>fodders</td><td>0,434</td><td>1,933</td><td>0,373</td><td>1,180</td><td>0,657</td><td>0,981</td><td>0,754</td><td>0,822</td><td>0,941</td><td>1,051</td></tr><tr><td>esp</td><td>fallows & set-aside</td><td>1,842</td><td>0,878</td><td>1,909</td><td>0,821</td><td>2,009</td><td>0,939</td><td>1,706</td><td>0,686</td><td>1,778</td><td>0,805</td></tr><tr><td>est</td><td>cereals</td><td></td><td></td><td>0,232</td><td>0,419</td><td>0,301</td><td>0,381</td><td>0,325</td><td>0,390</td><td>0,336</td><td>0,369</td></tr><tr><td>est</td><td>oilseeds & proteins</td><td></td><td></td><td>0,039</td><td>0,046</td><td>0,091</td><td>0,105</td><td>0,081</td><td>0,080</td><td>0,085</td><td>0,096</td></tr><tr><td>est</td><td>roots & indus.</td><td></td><td></td><td>0,006</td><td>0,053</td><td>0,005</td><td>0,038</td><td>0,004</td><td>0,035</td><td>0,004</td><td>0,026</td></tr><tr><td>est</td><td>pastures</td><td></td><td></td><td>0,163</td><td>0,009</td><td>0,410</td><td>0,292</td><td>0,435</td><td>0,331</td><td>0,424</td><td>0,382</td></tr><tr><td>est</td><td>fodders</td><td></td><td></td><td>0,189</td><td>0,091</td><td>0,010</td><td>0,007</td><td>0,008</td><td>0,004</td><td>0,013</td><td>0,006</td></tr><tr><td>est</td><td>fallows & set-aside</td><td></td><td></td><td>0,040</td><td>0,051</td><td>0,020</td><td>0,014</td><td>0,016</td><td>0,027</td><td>0,024</td><td>0,006</td></tr><tr><td>fra</td><td>cereals</td><td>9,251</td><td>10,956</td><td>9,283</td><td>10,916</td><td>9,120</td><td>9,987</td><td>9,650</td><td>10,249</td><td>9,486</td><td>10,155</td></tr><tr><td>fra</td><td>oilseeds & proteins</td><td>1,770</td><td>1,925</td><td>1,902</td><td>1,788</td><td>2,704</td><td>2,789</td><td>2,543</td><td>2,767</td><td>2,679</td><td>2,772</td></tr><tr><td>fra</td><td>roots & indus.</td><td>0,571</td><td>0,507</td><td>0,585</td><td>0,910</td><td>0,585</td><td>0,504</td><td>0,559</td><td>0,502</td><td>0,578</td><td>0,456</td></tr><tr><td>fra</td><td>pastures</td><td>5,964</td><td>4,986</td><td>6,092</td><td>4,696</td><td>9,975</td><td>8,662</td><td>10,087</td><td>8,812</td><td>10,176</td><td>8,949</td></tr><tr><td>fra</td><td>fodders</td><td>5,318</td><td>2,735</td><td>5,431</td><td>3,870</td><td>1,529</td><td>1,195</td><td>1,593</td><td>1,298</td><td>1,610</td><td>1,420</td></tr></tbody></table>

continued on next page -
* **scen.f** which codes (in FORTRAN) the *scenario* and manages the writing of the final output (by default close to *csv* format); final outputs may become entry variables of analytic functions of parameters of interest (belonging to the **scen.par** list) and **LP** outputs (belonging to the **scen.var** list). This kind of program may be used to insert *AROPAj* as a subroutine of a wider model, when *AROPAj* parameters are viewed as variables of the global model (e.g. the *AROPAj* calibrating process);

* **scen.gar**, which is a list of 8-character chains that refer to final outputs. These outputs are delivered per farm group for each "country" to which the *scenario* applies. Each of **scen.gar** entry must be linked to a *FORTRAN* instruction in **scen.f**, which designs a formatted output as a part of the output file (*result$ID$k*, see below Section 11.2.2);

* **scen.ini**, which, depending on the **scen.f** programming file, allows us to select a series of sub-scenarios from the *scenario* class. A typical example is when a user wants to examine *AROPAj* results while changing one, two or series of parameters within a predefined range of values;

* **scen.don**, which is optional and refers to any external data which is possibly required by the *scenario* (e.g. when nitrogen-yield functions depend on climate change);

* **scen.csh**, which organizes the linkage between the kernel files (and more precisely the *AROPAj* shell Unix scripts allowing the user to call *AROPAj*) and the previous *scenario* files. The *AROPAj* shell allows the user to follow the calculation steps in real time, and it provides identifying numbers, automatically based on the computer clock, which relate to the output files. All other files named **scen** may be built and/or copied (by default "cloned" files) and transformed through **scen.csh** by the user.

The user may utilize other files as inputs or outputs, and these files may be built, called and transformed with the support of the **scen.csh** script as well as of the **scen.f** routine. Some processing rules apply when building from **scen.f** and **scen.csh**. These mainly entail format consistency, (possibly forbidden) comment lines, and consistency with predefined *FORTRAN* variables.

Different steps of the *AROPAj* building are performed upstream by the *AROPAj* administrator, namely:

* the *AROPAj* shell conception and the development of codes (*CShell*, *FORTRAN*, *R*), widely enlarged by users' contribution

* the *AROPAj* shell implementation (**miraj** is the starting command launched by the administrator and by users)

* the modeled farm design and the *LP* matrix generating conception

* the secure transformation of *FADN* databases

* the farm group typology and the *FADN* farms clustering

* the parameter estimation step which aims at feeding the *AROPAj* databases (**mgg.par**)

* the re-estimation of parameters (calibration process)

* the building of "*AROPAj* country" parameter databases, i.e. the **mgg.par** files which contain all farm group information referring to the *FADN*-year based scenario

* the conception of simulation and *AROPAj* run management
- continued from previous page

<table><thead><tr><th rowspan="2">MS</th><th rowspan="2">Land use</th><th colspan="2">V2</th><th colspan="2">V3</th><th colspan="2">V5-2007</th><th colspan="2">V5-2008</th><th colspan="2">V5-2009</th></tr><tr><th>FADN</th><th>calibr.</th><th>FADN</th><th>calibr.</th><th>FADN</th><th>calibr.</th><th>FADN</th><th>calibr.</th><th>FADN</th><th>calibr.</th></tr></thead><tbody><tr><td>fra</td><td>fallows & set-aside</td><td>1,167</td><td>2,933</td><td>1,028</td><td>2,141</td><td>1,152</td><td>1,928</td><td>0,776</td><td>1,580</td><td>0,589</td><td>1,366</td></tr><tr><td>gbr</td><td>cereals</td><td>3,689</td><td>3,660</td><td>3,431</td><td>3,765</td><td>2,991</td><td>3,527</td><td>3,296</td><td>3,423</td><td>3,227</td><td>3,412</td></tr><tr><td>gbr</td><td>oilseeds & proteins</td><td>0,439</td><td>0,491</td><td>0,567</td><td>0,664</td><td>0,856</td><td>1,084</td><td>0,764</td><td>0,878</td><td>0,840</td><td>0,938</td></tr><tr><td>gbr</td><td>roots & indus.</td><td>0,395</td><td>0,302</td><td>0,349</td><td>0,385</td><td>0,254</td><td>0,225</td><td>0,266</td><td>0,366</td><td>0,258</td><td>0,332</td></tr><tr><td>gbr</td><td>pastures</td><td>4,852</td><td>4,922</td><td>4,078</td><td>3,471</td><td>5,437</td><td>5,052</td><td>5,476</td><td>5,334</td><td>5,691</td><td>5,554</td></tr><tr><td>gbr</td><td>fodders</td><td>1,663</td><td>1,098</td><td>1,364</td><td>1,067</td><td>0,225</td><td>0,166</td><td>0,240</td><td>0,135</td><td>0,235</td><td>0,151</td></tr><tr><td>gbr</td><td>fallows & set-aside</td><td>0,609</td><td>1,175</td><td>0,547</td><td>0,984</td><td>0,563</td><td>0,272</td><td>0,281</td><td>0,187</td><td>0,354</td><td>0,219</td></tr><tr><td>hun</td><td>cereals</td><td></td><td></td><td>2,309</td><td>2,683</td><td>2,360</td><td>2,762</td><td>2,355</td><td>2,708</td><td>2,356</td><td>2,662</td></tr><tr><td>hun</td><td>oilseeds & proteins</td><td></td><td></td><td>0,495</td><td>0,393</td><td>0,752</td><td>0,691</td><td>0,797</td><td>0,757</td><td>0,790</td><td>0,702</td></tr><tr><td>hun</td><td>roots & indus.</td><td></td><td></td><td>0,075</td><td>0,074</td><td>0,055</td><td>0,042</td><td>0,018</td><td>0,033</td><td>0,024</td><td>0,037</td></tr><tr><td>hun</td><td>pastures</td><td></td><td></td><td>0,393</td><td>0,082</td><td>0,218</td><td>0,094</td><td>0,227</td><td>0,097</td><td>0,234</td><td>0,153</td></tr><tr><td>hun</td><td>fodders</td><td></td><td></td><td>0,151</td><td>0,153</td><td>0,293</td><td>0,090</td><td>0,296</td><td>0,095</td><td>0,295</td><td>0,140</td></tr><tr><td>hun</td><td>fallows & set-aside</td><td></td><td></td><td>0,053</td><td>0,091</td><td>0,047</td><td>0,044</td><td>0,033</td><td>0,036</td><td>0,040</td><td>0,043</td></tr><tr><td>irl</td><td>cereals</td><td>0,304</td><td>0,312</td><td>0,350</td><td>0,411</td><td>0,259</td><td>0,301</td><td>0,298</td><td>0,297</td><td>0,306</td><td>0,322</td></tr><tr><td>irl</td><td>oilseeds & proteins</td><td>0,001</td><td>0,001</td><td>0,001</td><td>0,002</td><td>0,008</td><td>0,017</td><td>0,006</td><td>0,013</td><td>0,007</td><td>0,008</td></tr><tr><td>irl</td><td>roots & indus.</td><td>0,039</td><td>0,036</td><td>0,047</td><td>0,070</td><td>0,014</td><td>0,016</td><td>0,010</td><td>0,015</td><td>0,006</td><td>0,007</td></tr><tr><td>irl</td><td>pastures</td><td>2,531</td><td>2,272</td><td>2,380</td><td>2,049</td><td>3,634</td><td>3,359</td><td>3,696</td><td>3,484</td><td>3,807</td><td>3,577</td></tr><tr><td>irl</td><td>fodders</td><td>0,289</td><td>0,330</td><td>0,269</td><td>0,395</td><td>0,047</td><td>0,028</td><td>0,050</td><td>0,014</td><td>0,059</td><td>0,033</td></tr><tr><td>irl</td><td>fallows & set-aside</td><td>0,035</td><td>0,247</td><td>0,041</td><td>0,161</td><td>0,031</td><td>0,271</td><td>0,011</td><td>0,249</td><td>0,011</td><td>0,249</td></tr><tr><td>ita</td><td>cereals</td><td>3,417</td><td>4,163</td><td>3,646</td><td>4,358</td><td>3,658</td><td>4,515</td><td>3,618</td><td>4,625</td><td>3,403</td><td>4,822</td></tr><tr><td>ita</td><td>oilseeds & proteins</td><td>0,264</td><td>0,260</td><td>0,222</td><td>0,186</td><td>0,570</td><td>0,675</td><td>0,511</td><td>0,529</td><td>0,590</td><td>0,620</td></tr><tr><td>ita</td><td>roots & indus.</td><td>0,244</td><td>0,161</td><td>0,239</td><td>0,290</td><td>0,178</td><td>0,155</td><td>0,157</td><td>0,170</td><td>0,146</td><td>0,173</td></tr><tr><td>ita</td><td>pastures</td><td>2,891</td><td>1,243</td><td>0,434</td><td>0,384</td><td>4,115</td><td>3,197</td><td>0,060</td><td>0,260</td><td>0,122</td><td>0,548</td></tr><tr><td>ita</td><td>fodders</td><td>0,488</td><td>0,777</td><td>3,338</td><td>2,303</td><td>0,233</td><td>0,247</td><td>3,217</td><td>1,288</td><td>3,152</td><td>0,911</td></tr><tr><td>ita</td><td>fallows & set-aside</td><td>0,248</td><td>0,947</td><td>0,247</td><td>0,578</td><td>0,355</td><td>0,320</td><td>0,285</td><td>0,977</td><td>0,571</td><td>0,907</td></tr><tr><td>ltu</td><td>cereals</td><td></td><td></td><td>0,687</td><td>0,833</td><td>0,905</td><td>1,032</td><td>0,931</td><td>1,021</td><td>0,889</td><td>0,949</td></tr><tr><td>ltu</td><td>oilseeds & proteins</td><td></td><td></td><td>0,097</td><td>0,119</td><td>0,199</td><td>0,211</td><td>0,184</td><td>0,186</td><td>0,197</td><td>0,214</td></tr><tr><td>ltu</td><td>roots & indus.</td><td></td><td></td><td>0,053</td><td>0,118</td><td>0,037</td><td>0,059</td><td>0,029</td><td>0,049</td><td>0,030</td><td>0,047</td></tr><tr><td>ltu</td><td>pastures</td><td></td><td></td><td>0,063</td><td>0,065</td><td>0,642</td><td>0,572</td><td>0,635</td><td>0,534</td><td>0,641</td><td>0,617</td></tr><tr><td>ltu</td><td>fodders</td><td></td><td></td><td>0,394</td><td>0,306</td><td>0,030</td><td>0,008</td><td>0,021</td><td>0,015</td><td>0,021</td><td>0,005</td></tr><tr><td>ltu</td><td>fallows & set-aside</td><td></td><td></td><td>0,177</td><td>0,028</td><td>0,094</td><td>0,025</td><td>0,090</td><td>0,085</td><td>0,090</td><td>0,037</td></tr><tr><td>lux</td><td>cereals</td><td>0,030</td><td>0,034</td><td>0,027</td><td>0,021</td><td>0,029</td><td>0,023</td><td>0,030</td><td>0,025</td><td>0,030</td><td>0,023</td></tr><tr><td>lux</td><td>oilseeds & proteins</td><td>0,003</td><td>0,005</td><td>0,004</td><td>0,005</td><td>0,005</td><td>0,005</td><td>0,005</td><td>0,005</td><td>0,005</td><td>0,005</td></tr><tr><td>lux</td><td>roots & indus.</td><td>0,001</td><td>0,004</td><td>0,001</td><td>0,002</td><td>0,001</td><td>0,001</td><td>0,001</td><td>0,002</td><td>0,001</td><td>0,001</td></tr><tr><td>lux</td><td>pastures</td><td>0,081</td><td>0,064</td><td>0,077</td><td>0,067</td><td>0,063</td><td>0,073</td><td>0,062</td><td>0,076</td><td>0,062</td><td>0,083</td></tr><tr><td>lux</td><td>fodders</td><td>0,013</td><td>0,008</td><td>0,014</td><td>0,021</td><td>0,027</td><td>0,019</td><td>0,027</td><td>0,015</td><td>0,029</td><td>0,010</td></tr><tr><td>lux</td><td>fallows & set-aside</td><td>0,002</td><td>0,014</td><td>0,001</td><td>0,008</td><td>0,001</td><td>0,004</td><td>0,000</td><td>0,003</td><td>0,000</td><td>0,004</td></tr><tr><td>lva</td><td>cereals</td><td></td><td></td><td>0,409</td><td>0,599</td><td>0,485</td><td>0,592</td><td>0,495</td><td>0,614</td><td>0,492</td><td>0,555</td></tr><tr><td>lva</td><td>oilseeds & proteins</td><td></td><td></td><td>0,066</td><td>0,061</td><td>0,103</td><td>0,121</td><td>0,087</td><td>0,108</td><td>0,093</td><td>0,111</td></tr><tr><td>lva</td><td>roots & indus.</td><td></td><td></td><td>0,034</td><td>0,080</td><td>0,021</td><td>0,054</td><td>0,017</td><td>0,038</td><td>0,017</td><td>0,051</td></tr><tr><td>lva</td><td>pastures</td><td></td><td></td><td>0,133</td><td>0,096</td><td>0,405</td><td>0,346</td><td>0,426</td><td>0,299</td><td>0,439</td><td>0,420</td></tr><tr><td>lva</td><td>fodders</td><td></td><td></td><td>0,175</td><td>0,147</td><td>0,018</td><td>0,023</td><td>0,015</td><td>0,029</td><td>0,014</td><td>0,029</td></tr><tr><td>lva</td><td>fallows & set-aside</td><td></td><td></td><td>0,182</td><td>0,017</td><td>0,109</td><td>0,006</td><td>0,116</td><td>0,069</td><td>0,122</td><td>0,012</td></tr><tr><td>mlt</td><td>cereals</td><td></td><td></td><td></td><td></td><td>0,000</td><td>0,000</td><td>0,000</td><td>0,000</td><td>0,000</td><td>0,000</td></tr><tr><td>mlt</td><td>oilseeds & proteins</td><td></td><td></td><td></td><td></td><td>0,000</td><td>0,000</td><td>0,000</td><td>0,000</td><td>0,000</td><td>0,000</td></tr><tr><td>mlt</td><td>roots & indus.</td><td></td><td></td><td></td><td></td><td>0,000</td><td>0,000</td><td>0,000</td><td>0,000</td><td>0,000</td><td>0,000</td></tr><tr><td>mlt</td><td>pastures</td><td></td><td></td><td></td><td></td><td>0,000</td><td>0,000</td><td>0,000</td><td>0,000</td><td>0,000</td><td>0,000</td></tr><tr><td>mlt</td><td>fodders</td><td></td><td></td><td></td><td></td><td>0,001</td><td>0,000</td><td>0,002</td><td>0,000</td><td>0,001</td><td>0,000</td></tr><tr><td>mlt</td><td>fallows & set-aside</td><td></td><td></td><td></td><td></td><td>0,000</td><td>0,001</td><td>0,000</td><td>0,001</td><td>0,000</td><td>0,001</td></tr><tr><td>ned</td><td>cereals</td><td>0,209</td><td>0,217</td><td>0,168</td><td>0,187</td><td>0,195</td><td>0,211</td><td>0,204</td><td>0,279</td><td>0,203</td><td>0,183</td></tr><tr><td>ned</td><td>oilseeds & proteins</td><td>0,002</td><td>0,011</td><td>0,003</td><td>0,006</td><td>0,074</td><td>0,072</td><td>0,068</td><td>0,077</td><td>0,069</td><td>0,083</td></tr><tr><td>ned</td><td>roots & indus.</td><td>0,335</td><td>0,258</td><td>0,234</td><td>0,195</td><td>0,224</td><td>0,264</td><td>0,219</td><td>0,205</td><td>0,223</td><td>0,213</td></tr><tr><td>ned</td><td>pastures</td><td>0,676</td><td>0,756</td><td>0,588</td><td>0,661</td><td>0,978</td><td>0,284</td><td>0,982</td><td>0,904</td><td>0,963</td><td>1,194</td></tr><tr><td>ned</td><td>fodders</td><td>0,240</td><td>0,122</td><td>0,209</td><td>0,114</td><td>0,229</td><td>0,024</td><td>0,245</td><td>0,003</td><td>0,252</td><td>0,004</td></tr><tr><td>ned</td><td>fallows & set-aside</td><td>0,049</td><td>0,147</td><td>0,031</td><td>0,072</td><td>0,017</td><td>0,860</td><td>0,011</td><td>0,260</td><td>0,008</td><td>0,041</td></tr><tr><td>ost</td><td>cereals</td><td>0,675</td><td>0,743</td><td>0,732</td><td>0,788</td><td>0,705</td><td>0,835</td><td>0,736</td><td>0,832</td><td>0,737</td><td>0,802</td></tr><tr><td>ost</td><td>oilseeds & proteins</td><td>0,086</td><td>0,041</td><td>0,082</td><td>0,084</td><td>0,123</td><td>0,090</td><td>0,122</td><td>0,109</td><td>0,119</td><td>0,095</td></tr><tr><td>ost</td><td>roots & indus.</td><td>0,069</td><td>0,120</td><td>0,065</td><td>0,090</td><td>0,063</td><td>0,083</td><td>0,064</td><td>0,094</td><td>0,064</td><td>0,070</td></tr><tr><td>ost</td><td>pastures</td><td>0,797</td><td>0,665</td><td>0,811</td><td>0,702</td><td>0,619</td><td>0,571</td><td>0,634</td><td>0,610</td><td>0,636</td><td>0,662</td></tr><tr><td>ost</td><td>fodders</td><td>0,074</td><td>0,043</td><td>0,084</td><td>0,081</td><td>0,213</td><td>0,161</td><td>0,213</td><td>0,130</td><td>0,200</td><td>0,106</td></tr><tr><td>ost</td><td>fallows & set-aside</td><td>0,086</td><td>0,176</td><td>0,080</td><td>0,107</td><td>0,056</td><td>0,040</td><td>0,038</td><td>0,033</td><td>0,035</td><td>0,056</td></tr><tr><td>pol</td><td>cereals</td><td>0,000</td><td>0,000</td><td>4,830</td><td>5,267</td><td>7,361</td><td>7,815</td><td>7,974</td><td>8,476</td><td>7,934</td><td>8,426</td></tr><tr><td>pol</td><td>oilseeds & proteins</td><td>0,000</td><td>0,000</td><td>0,555</td><td>0,561</td><td>1,313</td><td>1,448</td><td>1,278</td><td>1,420</td><td>1,316</td><td>1,487</td></tr><tr><td>pol</td><td>roots & indus.</td><td>0,000</td><td>0,000</td><td>0,591</td><td>0,753</td><td>0,649</td><td>0,882</td><td>0,577</td><td>0,597</td><td>0,592</td><td>0,629</td></tr><tr><td>pol</td><td>pastures</td><td>0,000</td><td>0,000</td><td>0,248</td><td>0,055</td><td>2,590</td><td>2,201</td><td>2,663</td><td>2,281</td><td>2,752</td><td>2,298</td></tr><tr><td>pol</td><td>fodders</td><td>0,000</td><td>0,000</td><td>1,474</td><td>1,043</td><td>0,556</td><td>0,126</td><td>0,638</td><td>0,369</td><td>0,652</td><td>0,375</td></tr><tr><td>pol</td><td>fallows & set-aside</td><td>0,000</td><td>0,000</td><td>0,008</td><td>0,026</td><td>0,125</td><td>0,122</td><td>0,124</td><td>0,111</td><td>0,127</td><td>0,159</td></tr><tr><td>por</td><td>cereals</td><td>0,390</td><td>0,577</td><td>0,312</td><td>0,502</td><td>0,212</td><td>0,566</td><td>0,279</td><td>0,564</td><td>0,247</td><td>0,565</td></tr><tr><td>por</td><td>oilseeds & proteins</td><td>0,021</td><td>0,020</td><td>0,016</td><td>0,014</td><td>0,042</td><td>0,048</td><td>0,043</td><td>0,051</td><td>0,044</td><td>0,072</td></tr><tr><td>por</td><td>roots & indus.</td><td>0,033</td><td>0,201</td><td>0,024</td><td>0,054</td><td>0,020</td><td>0,016</td><td>0,019</td><td>0,019</td><td>0,021</td><td>0,020</td></tr></tbody></table>

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<figure><img src="image_48.png" /><figcaption>**Figure 34.** Overview of the file and program structure related to the use of *AROPAj*.</figcaption></figure>