- 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>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.
Up until now, the N-to-yield functions building and the spatialization of farm groups are realized outside of the **miraj** environment. However, the "nitrogen-yield" block and the use of spatialization probabilities are included in the **miraj** menu.

The AROPAj shell itself is made of scripts written in CShell, awk, sed, FORTRAN, and R codes. The general application is launched by the **miraj** command, thereby allowing the user to access nested menus. An overview of the file and program structure is presented in Figure 34.

## 11.2 Basic use and call of the model

**Key-points:**

Users, users' rights and the administrator

Some coding rules and links through the FADN, parameters, matrix elements and MP results

Exporting AROPAj outputs and spatialisation

Changing or adding blocks, parameters, outputs for graphics and tables, farm groups, MS

### 11.2.1 Parameter value transmission through AROPAj steps and files

Let us recall how a constraint is mathematically expressed (see the general optimization program in Section 4 and Table 2 in section 4.2 on page 25). Let us consider the *i*-constraint of the *k*-farmer's program labeled by its related dual value $\lambda_{ki}$, expressed as it follows:

$$ \lambda_{ki} : \sum_{j} E_{kij}(\theta_{k}, \phi) x_{kj} \leq Z_{ki}(\theta_{k}, \phi) $$

where $x_{kj}$ refers to the *j*-activity of the *k*-farmer's program, $z_{ki}$ refers to the RHS *i*-value, and $\phi$ and $\theta_{k}$ refer to general and to *k* dependent parameter vectors. The terms $E_{kij}$ and $Z_{ki}$ are matrix elements which depend on basic information provided by the estimation process (see Section 6).

For example, let us consider the element RRR used by the constraint s(j,k) of the crop block. This element depends on the *j*-crop yield (per hectare of *k*-farm's crop) which is labeled rjk(j,k) (see Section 4.2.1). <fnref n="11" /> The core of the modeling chain (referred to as aro.mgg) is written in a language close to FORTRAN and uses the vector form of parameters, which is rjk(j,k) in the case.

As for data, the FADN sourced parameters as well as all AROPAj parameters are fed into the model in a variety of ways. The IPCC sourced parameters, completed by data referring to feed and fertilization, are examples of non FADN sourced parameters. All parameters are clustered per AROPAj country<fnref n="12" /> into files labeled mgg.par. We base the AROPAj data feeding on the "on file line - one information" principle. When a parameter is referenced several times in the mgg.par data file, the due value that is ultimately retained in the LP matrix and solved program is the last occurrence in the file.

The typing of parameters is based on a mnemonic criterion (French at the beginning), on alphanumerical character parsimony, and obviously on the discriminating power of character as-

<footnote n="11">¹¹Yield values enter the LP matrix either directly from estimated values provided by the FADN or via nitrogen to yield functions (see Section 9.1.1). This latter case leads to using an intermediate function called by the element RRR.</footnote>

<footnote n="12">¹²See Section 5.1 on page 83 about farm group numbering.</footnote>
# 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
sembling. This leads to replacing numerical indexes by alphanumerical ones (especially for crops and animals, see Table 35 for correspondence between files and types). The correspondence between the matrix generator support (aro.mgg) using vectors and the data file (mgg.par) is realized via the intern.f FORTRAN subroutine. Generally, the 2-characters strings jj, jk, er and es, when located in columns 2-3, refer to crop data (and strings referring to crops appear in columns 4-5). The 2-characters strings ii, ik, er and es, when located in columns 4-5, refer to animal data (and animal categories appears in columns 2-3). Concentrated feed and raw feed are identified by 2-character strings a1, a1, a1, a1 and ag, when located in columns 4-5. Parameters referring to effluent categories are codes in the same way. The 5 animal sourced effluents are presented on Table 35. Urban and compost sludges as well as fermentation digestates will refer to added categories.

As an example, lines of the `mgg.par` referring to crops yields are of the type:

...

...

The FORTRAN code-lines referring to the yield example are summarized by something like:

```
if("1-character".eq.'r'.and."2-3-character".eq.'jj'.and. ...) rjk(j,k)=value
```

In the present numerical case, the FADN estimate of the 2-farm group soft wheat yield is 9.08665 (t/ha). This value aims at feeding the 2-farm group's LP matrix (for one or more member State of the EU). As another example, the following line refers to the raw energy contend in the 3-type concentrate intended to dairy cow of the 41-farm group (for one or more member State of the EU):

...

...

The use of this process is due to prior development of AROPAj as well as the pressing need for FORTRAN variables in AROPAj parameters for model coupling and for using AROPOAj as a subroutine of a broader program. This is the case when the AROPAj calibration is processed via computation tools (see Section 7). This technical process holds for any kind of constraints and for the objective function.

**Changing or adding GHG parameters: use of external data**

This is an example of linking structured external information to AROPAj parameters. Regular modifications occur either when we need to update the IPCC based parameters referring to the GHG block, or when AROPAj accounts for new Member States. It also occurs when parameters change in status, for instance when they become crop or animal indexed. Such a change may affect both the upstream IPCC sourced database (ipcc.dat.csv), the file code ipcc.awk and the kernel files (aro.mgg and intern.f).

**Changing parameters value: an example**

We consider the case of crop price change to be managed through the parameter datafile aro.mgg. We extract a series of 2009-FADN sourced farm groups'
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;
<table><caption>**Table 35.** Crops and animals typing in the parameter file *mgg.par* (2<sup>nd</sup> and 5<sup>th</sup> columns) and referred index in vectors used in the core file *aro.mgg* (1<sup>st</sup> and 4<sup>th</sup> columns); effluent categories below.</caption><thead><tr><th colspan="3">crops</th><th colspan="2">animals and bought feeds</th></tr></thead><tbody><tr><td>1</td><td>bd</td><td>durum wheat</td><td>2</td><td>ve 8-days slaughtered calves</td></tr><tr><td>6</td><td>bl</td><td>soft wheat</td><td>3</td><td>vg 2-months slaughtered calves</td></tr><tr><td>7</td><td>oh</td><td>barley</td><td>4</td><td>vm 6-months calves (field)</td></tr><tr><td>12</td><td>av</td><td>oats</td><td>5</td><td>vt calves reported on-farm to be young bulls</td></tr><tr><td>17</td><td>ca</td><td>other cereals</td><td>7</td><td>vb calves reported on-farm to be more 2-yr bulls</td></tr><tr><td>18</td><td>se</td><td>rye</td><td>8</td><td>nl female calves from dairy herd non reported on farm</td></tr><tr><td>19</td><td>rz</td><td>rice</td><td>9</td><td>nv female calves from breeding herd non reported on farm</td></tr><tr><td>20</td><td>ma</td><td>maize</td><td>10</td><td>fr female calves reported on farm</td></tr><tr><td>21</td><td>bt</td><td>sugar beet</td><td>11</td><td>jt male calves bought to be young bulls</td></tr><tr><td>22</td><td>ba</td><td>A-sugar</td><td>12</td><td>jb male calves bought to be bulls (&gt; 2 years)</td></tr><tr><td>23</td><td>bb</td><td>B-sugar</td><td>13</td><td>jl female calves bought to renew the dairy herd</td></tr><tr><td>24</td><td>bc</td><td>C-sugar</td><td>14</td><td>jv female calves bought to renew the breeding herd</td></tr><tr><td>25</td><td>tb</td><td>tobacco</td><td>21</td><td>te 18-months bulls</td></tr><tr><td>26</td><td>ct</td><td>cotton</td><td>23</td><td>me 1-yr males from "vb" or "jb" to be bulls</td></tr><tr><td>27</td><td>li</td><td>flax &amp; hemp</td><td>24</td><td>ma 1-yr males bought to be bulls</td></tr><tr><td>31</td><td>cz</td><td>rapeseed</td><td>25</td><td>fl 1-yr female on-farm dairy herd</td></tr><tr><td>32</td><td>tr</td><td>sunflower</td><td>26</td><td>fv 1-yr female on-farm breeding herd</td></tr><tr><td>35</td><td>sj</td><td>soja</td><td>27</td><td>hl 1-yr female bought for dairy herd</td></tr><tr><td>36</td><td>pr</td><td>proteins</td><td>28</td><td>hv 1-yr female bought for breeding herd</td></tr><tr><td>40</td><td>lg</td><td>field vegetables</td><td>31</td><td>be 2-yr males on-farm &gt; beef</td></tr><tr><td>41</td><td>fr</td><td>fallow</td><td>33</td><td>rl dairy heifers</td></tr><tr><td>42</td><td>gl</td><td>1988 set-aside</td><td>34</td><td>rv bred heifers</td></tr><tr><td>43-47</td><td>...</td><td>various set-aside*</td><td>35</td><td>vl dairy cows</td></tr><tr><td>49</td><td>ja</td><td>fix set-aside or forest</td><td>36</td><td>vv suckler cows</td></tr><tr><td>51-56</td><td>ms,...</td><td>various SRC**</td><td>41</td><td>ov sheep</td></tr><tr><td>61</td><td>pt</td><td>potatoes</td><td>45</td><td>cp goats</td></tr><tr><td>67</td><td>lf</td><td>fodder vegetable</td><td>51</td><td>po pigs</td></tr><tr><td>71</td><td>bf</td><td>fodderbeet</td><td>55</td><td>vo poultry</td></tr><tr><td>72</td><td>mf</td><td>fodder maize</td><td>ag bought raw feed</td><td></td></tr><tr><td>73</td><td>lz</td><td>alfalfa***</td><td>a1 simple energy-rich concentrated feed</td><td></td></tr><tr><td>74</td><td>sp</td><td>protein fodder</td><td>a2 simple protein-rich concentrated feed</td><td></td></tr><tr><td>81</td><td>sh</td><td>permanent meadows</td><td>a3 compound energy-rich concentrated feed</td><td></td></tr><tr><td>82</td><td>af</td><td>temporary grassland</td><td>a4 compound protein-rich concentrated feed</td><td></td></tr></tbody></table>

* refers to the 1992 *CAP* reform

** short rotation coppice such as miscanthus and poplars

*** for some *FADN* years only, not maintained

<table><caption>Effluent categories (animal-dependent or urban waste)</caption><tr><td>1</td><td>i1</td><td>swine &amp; bovine</td><td>slurry</td></tr><tr><td>2</td><td>i2</td><td>all except poultry</td><td>"solid" manure</td></tr><tr><td>3</td><td>i3</td><td>poultry</td><td>manure</td></tr><tr><td>4</td><td>i4</td><td>poultry</td><td>slurry</td></tr><tr><td>5</td><td>i5</td><td>poultry</td><td>droppings</td></tr><tr><td>11</td><td>cu</td><td>urban</td><td>compost</td></tr><tr><td>12</td><td>dv</td><td>urban</td><td>liquid</td></tr></table>
* **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
prices related to soft wheat in one of the EU country as follows. In addition we consider the relative price parameter allowing to changes all soft wheat individual prices.

...

<table><tr><td>pjjbl</td><td>: 1.</td></tr><tr><td>pjjbl 2</td><td>: 0.23000</td></tr><tr><td>pjjbl 3</td><td>: 0.15563</td></tr><tr><td>pjjbl 4</td><td>: 0.12505</td></tr><tr><td>pjjbl 9</td><td>: 0.12743</td></tr><tr><td>pjjbl10</td><td>: 0.17510</td></tr><tr><td>pjjbl11</td><td>: 0.21901</td></tr><tr><td>pjjbl13</td><td>: 0.23490</td></tr></table>

...

As explained above, parameter code refers to combinations of 1-, 2- or 5-character strings. In the present case, the first column character (p) refers to the price category among parameters, the 2-3 column characters jj means that information contained in the datafile line refers to crops, and the 4-5 column characters bl refers to soft wheat as detailed in Table 35. When no other indication exists, the parameter refers to the relative price which is 1. as default value. Reversely figures in columns 6-7 refer to the farm group numbering. It has to be noted that “1”, “1” and “01” are of equivalent interpretation. Nevertheless when dealing with other data files of mgg.par-like format, the user should select the “1” or “01” forms (this point refers to the scen.par file mentioned in Section 11.1.3). The parameter value should be registered in columns 10-25 (right from the “:” character) in standard FORTRAN-like format (namely f16.6).

When the model’s user wants to change all soft wheat prices in a common ratio (e.g. increasing by 50%), the value of the non-numbered parameter pjjbl has to be changed from 1. in 1.5, either in the last line of the date file containing this parameter or in an additional line registered as:

pjjbl : 1.5

We recall the rule of “last read value - stated value in the LP”.

### 11.2.2 Reporting

In addition to the GAMS files provided by the kernel file transformation, another GAMS file (report.gms) is produced with the aim of listing the LP comprehensive solution in terms of primal and dual variables. Based on other GAMS files and additional flags or descriptors (see the aro.dat file), this file is automatically built by a FORTRAN routine when the user launches AROPAj.

FORTRAN routines provide formatted output files designed for downstream treatment.

#### By default via minimal use of the AROPAj shell

The organization of calculus, as illustrated by Figure 34, and the organization of files, as illustrated by Figure 35, refers to the standard manner of using AROPAj. In this organization, most of user’s file names are indexed by the computer clock system in two ways.

Firstly, the names of general information files related to the simulation process management use the 12-digits index “YYMMDDhhmmss” (denoted by “$idm”). Since both trace files and the
<figure><img src="image_49.png" /><figcaption>**Figure 34.** Overview of the file and program structure related to the use of *AROPAj*.</figcaption></figure>
main table file use this format, the user can easily follow the process evolution over *AROPAj* steps and over *AROPAj* countries. This table file is named `tableau.$idm` when "$idm" accounts for the 12 digits.

The second way of indexing files uses the 8-digits index "DDhhmmss" format, which is also based on the system clock provided at the *AROPAj* country scale (denoted here by "$id"). Most of intermediate and final output files are renamed in accordance with this index. Standard comprehensive outputs provided by the *GAMS* solver (`sortie.gms`) are reported on files named `mggf.$id$.sortie`. Extracted results formatted through the *FORTRAN* routine (`scen.f`) are reported on files named `result$.$id$.$k`, $k$ denoting the farm group. The same principle is used for intermediate files used, for instance, in iterated *AROPAj* runs, when parameter values at the iteration $n$ depend on *LP* results provided at the iteration $n-1$.

Applications are developed to display aggregated and tabulated results at the farm group level as well as at the national level. The user accesses these files in accordance with the repertory hierarchy illustrated by Figure 35. On the upper level, results may be compiled in "txt" or "csv" format for "countries" for which *AROPAj* runs are realized. Additional two-dimensional indexation refers to run iterations that are conducted on a two-dimension grid (like when two parameter ranges are explored). The file name is of the type `table.compil.$scen.$n.$m.txt`.

### Changing output structure

The user always has the ability to change output files and their structure. The question here is not to discuss what can be done from default outputs freely exported (default outputs are easy to manage through personal computer tools). In the case of structural change upstream from *GAMS* files produced by the kernel, the reporting is automatically adjusted. In other words, the *LP* results are comprehensively displayed in the `output.gms`. The user may effectuate downstream change through his/her own shell script `scen.csh` in order to produce *FORTRAN* codes and related files. The *AROPAj* shell requests only to have a shell script file in due code. In order to fully benefit from all the applications around the *AROPAj* shell, we advise keeping basic structure of file and output formats.

### Output spatialization

The spatialization process can be use when the user created a file of the type `table.compil.$scen.$n.$m.txt`, and when the farm group location probabilities have been previously estimated. Let us recall that these probabilities are only computed for a few *AROPAj* versions and *MS* (Sweden and Finland are excluded, for the V2 version). The process implicitly calls for *R* code files which are included in the *AROPAj* shell. Attention should be paid to the output file size. Outputs are displayed in compressed "dbf" format files. These files are provided and indexed according to *FADN* region. File names are of the type `Arc.$scen.$reg.$n.$m.dbf`.

### 11.2.3 Inserting new items, linking parameters and outputs

#### Extending the *AROPAj* coverage

The coverage extension refers to different kinds of *AROPAj* enlargement, whether it be in terms of geography, crops, environmental issues, policy tools, or model applications.
Up until now, the N-to-yield functions building and the spatialization of farm groups are realized outside of the **miraj** environment. However, the "nitrogen-yield" block and the use of spatialization probabilities are included in the **miraj** menu.

The AROPAj shell itself is made of scripts written in CShell, awk, sed, FORTRAN, and R codes. The general application is launched by the **miraj** command, thereby allowing the user to access nested menus. An overview of the file and program structure is presented in Figure 34.

## 11.2 Basic use and call of the model

### Key-points:

* Users, users' rights and the administrator

* Some coding rules and links through the FADN, parameters, matrix elements and MP results

* Exporting AROPAj outputs and spatialisation

* Changing or adding blocks, parameters, outputs for graphics and tables, farm groups, MS

### 11.2.1 Parameter value transmission through AROPAj steps and files

Let us recall how a constraint is mathematically expressed (see the general optimization program in Section 4 and Table 2 in section 4.2 on page 27). Let us consider the *i*-constraint of the *k*-farmer's program labeled by its related dual value $\lambda_{ki}$, expressed as it follows:

$$ \lambda_{ki} : \sum_{j} E_{kij}(\theta_{k}, \phi) x_{kj} \leq Z_{ki}(\theta_{k}, \phi) $$

where $x_{kj}$ refers to the *j*-activity of the *k*-farmer's program, $z_{ki}$ refers to the RHS *i*-value, and $\phi$ and $\theta_{k}$ refer to general and to *k* dependent parameter vectors. The terms $E_{kij}$ and $Z_{ki}$ are matrix elements which depend on basic information provided by the estimation process (see Section 6).

For example, let us consider the element RRR used by the constraint s(j,k) of the crop block. This element depends on the *j*-crop yield (per hectare of *k*-farm's crop) which is labeled rjk(j,k) (see Section 4.2.1). <fnref n="11" /> The core of the modeling chain (referred to as aro.mgg) is written in a language close to FORTRAN and uses the vector form of parameters, which is rjk(j,k) in the case.

As for data, the FADN sourced parameters as well as all AROPAj parameters are fed into the model in a variety of ways. The IPCC sourced parameters, completed by data referring to feed and fertilization, are examples of non FADN sourced parameters. All parameters are clustered per AROPAj country<fnref n="12" /> into files labeled mgg.par. We base the AROPAj data feeding on the "on file line - one information" principle. When a parameter is referenced several times in the mgg.par data file, the due value that is ultimately retained in the LP matrix and solved program is the last occurrence in the file.

The typing of parameters is based on a mnemonic criterion (French at the beginning), on alphanumerical character parsimony, and obviously on the discriminating power of character as-

<footnote n="11">¹¹Yield values enter the LP matrix either directly from estimated values provided by the FADN or via nitrogen to yield functions (see Section 9.1.1). This latter case leads to using an intermediate function called by the element RRR.</footnote>

<footnote n="12">¹²See Section 5.1 on page 88 about farm group numbering.</footnote>
MS inclusion is straightforward (no change in kernel files):

in particular, typology, parameter estimation and calibration remains unchanged.

Country data from the *FADN* is a prerequisite.

crops (or other agricultural activities):

introducing cotton, hoax or tobacco for instance requests *FADN* sourced information

For perennial crops, such as miscanthus, the user must calculate the discounted values of
(added) parameters

LP variables (activities and constraints, e.g. a new block):

changes have to be made in the kernel files (aro.mgg, intern.f, aroN.dat).

Related parameter estimates have to be added accordingly with the requisite format of the
*mgg.par* file.

It is also possible to call external data, such as *N* to yield functions. In this case, *lisscal.don*
files are read by the *intern.f* routine. Yield function parameters are used in each *AROPAj* run
as long as the user calls them through the shell script for that scenario (*scen.csh*). This allows
the user to change yield function parameters from one run to another (e.g. in case of climate
adaptation).

## Parameters valued through LP outputs

We have briefly exposed several cases relevant to projects and papers mentioned in previous
sections. The user must adequately prepare the *scen.csh* file in order to process the desired
application.

**Referencing GHG quotas** Instead of the “primal” approach based on *GHG* emission pricing (through tax), the “dual” approach entails using the emission quota shadow price. We must set these quotas, based on a reference situation without binding limits. To do so, we compute reference emissions and we transform these outputs in quota parameters (*qopes(k,l)*).

**Valuating the economic LT effect of OM on crop nutrient** This effect enters the model through the *aponlt(k)* parameter and lowers crops' *N*-requirement. This *FG* indexed parameter depends on annually renewed amounts of *OM* spread on arable lands. In the model, the available *OM*, be it on-farm sourced (manure) or imported (*UOR*), is optimally allocated to various crops and fodders with respect to regulatory limits. In other words, this amount per crop area is endogenously calculated by the model, thus impacting the *aponlt(k)* value.

**Fixing crop areas** In some cases there is an interest in constraining certain crop areas on order to, for instance, assess the part of different sources of autonomous adaptation in economic or environmental impacts. The user must set reference areas, i.e. the *xjk0(j,k)* parameters, which are in turn evaluated by the reference *AROPAj* outputs.

**Shadow prices substituting market prices** By adding new parameters, the user can easily process the iterated substitution of market prices by relevant shadow prices in the yield and *N*-input calculation. On the “crop output side”, new parameters (*rojk(j,k)*) refer to the dual values related to the *s(j,k)* constraints. We had not added yet the relevant parameters on the “*N* input
sembling. This leads to replacing numerical indexes by alphanumerical ones (especially for crops and animals, see Table 35 for correspondence between files and types). The correspondence between the matrix generator support (aro.mgg) using vectors and the data file (mgg.par) is realized via the intern.f FORTRAN subroutine. Generally, the 2-characters strings jj, jk, er and es, when located in columns 2-3, refer to crop data (and strings referring to crops appear in columns 4-5). The 2-characters strings ii, ik, er and es, when located in columns 4-5, refer to animal data (and animal categories appears in columns 2-3). Concentrated feed and raw feed are identified by 2-character strings a1, a1, a1, a1 and ag, when located in columns 4-5. Parameters referring to effluent categories are codes in the same way. The 5 animal sourced effluents are presented on Table 35. Urban and compost sludges as well as fermentation digestates will refer to added categories.

As an example, lines of the `mgg.par` referring to crops yields are of the type:

...  
rjjoh1 : 7.824480  
rjjpt1 : 36.363918  
rjjtb1 : 1.766110  
rjjbl2 : 9.086650  
rjjbt2 : 76.504700

...

The FORTRAN code-lines referring to the yield example are summarized by something like:

if("1-character".eq.'r'.and."2-3-character".eq.'jj'.and. ...) rjk(j,k)=value

In the present numerical case, the FADN estimate of the 2-farm group soft wheat yield is 9.08665 (t/ha). This value aims at feeding the 2-farm group's LP matrix (for one or more member State of the EU). As another example, the following line refers to the raw energy contend in the 3-type concentrate intended to dairy cow of the 41-farm group (for one or more member State of the EU):

...  
bvla341 : 4600.000000

...

The use of this process is due to prior development of AROPAj as well as the pressing need for FORTRAN variables in AROPAj parameters for model coupling and for using AROPOAj as a subroutine of a broader program. This is the case when the AROPAj calibration is processed via computation tools (see Section 7). This technical process holds for any kind of constraints and for the objective function.

**Changing or adding GHG parameters: use of external data**

This is an example of linking structured external information to AROPAj parameters. Regular modifications occur either when we need to update the IPCC based parameters referring to the GHG block, or when AROPAj accounts for new Member States. It also occurs when parameters change in status, for instance when they become crop or animal indexed. Such a change may affect both the upstream IPCC sourced database (ipcc.dat.csv), the file code ipcc.awk and the kernel files (aro.mgg and intern.f).

**Changing parameters value: an example**

We consider the case of crop price change to be managed through the parameter datafile aro.mgg. We extract a series of 2009-FADN sourced farm groups'
side", because there are very few cases of solely organic *N* input dedicated to a crop by a farm group.

**The MODANI model and equilibrium prices** This application is based on an iterated *tâtonnement* process that attempt to compute the converging prices associated with balanced markets. In this case, at each iteration, study-relevant market prices are changed in line with the excess of demand.

**The AROPAj calibration** The *AROPAj* calibration process is one of the primary instances in which *AROPAj* acts as a subroutine of a wider program. As explained above, the process is based on MonteCarlo and gradient algorithms, which require a large amount of *AROPAj* runs. Each run is iteratively based on re-evaluated parameters.

### 11.2.4 Starting and managing simulations

There are several prerequisites for starting *AROPAj* and calling a simulation scenario. First, a user account has to be created on the workstation that host *AROPAj*. In addition to the usual change of the “PATH” variable associated to the user’s account, some small changes have to be reported in the “login” scripts. The *AROPAj* administrator has to report the user’s account characteristics in the *AROPAj* shell scripts.

*AROPAj* simulations are launched through the *miraj* command script, thereby supplying nested “menus”. Initiating a simulation only requires a *scen.csh* script from the user.

The menu allows the user to undertake many operations. It also allows the administrator to undertake basic *AROPAj* steps such as farm group clustering, parameter estimation and calibration. The user can:

* access information files referring to scientific projects (named *cahier.txt*)

* successively or automatically initiate the different steps relating to the version choice and parameter complements, for different “*AROPAj* countries” covered by the selected version

* iterate the *AROPAj* runs as designed by the script file *scen.csh*

* aggregate the outputs at the country level

* tabulate the outputs at the country level

* export the output compilation in “txt/csv” format files for countries covered by simulations and for selected iterations (the user is free to select a subpart of iterations among all of them)

* undertake all the previous steps once the simulation is initiated; this entails selecting the version and the script *scen.csh*, and initiating the process for just one country

* display selected results through the spatialization process for *FADN* regions covered by the simulation when the spatialization is available; for this step, the user is free to select outputs and iterations among those scheduled by the script *scen.csh*.

During the computation process, as explained above, several files are reported in the working directory and in the subdirectories as shown on Figure 35. Process visualization is briefly developed in the Appendix A.
<table><caption>**Table 35.** Crops and animals typing in the parameter file *mgg.par* (2<sup>nd</sup> and 5<sup>th</sup> columns) and referred index in vectors used in the core file *aro.mgg* (1<sup>st</sup> and 4<sup>th</sup> columns); effluent categories below.</caption><thead><tr><th colspan="3">crops</th><th colspan="2">animals and bought feeds</th></tr></thead><tbody><tr><td>1</td><td>bd</td><td>durum wheat</td><td>2</td><td>ve 8-days slaughtered calves</td></tr><tr><td>6</td><td>bl</td><td>soft wheat</td><td>3</td><td>vg 2-months slaughtered calves</td></tr><tr><td>7</td><td>oh</td><td>barley</td><td>4</td><td>vm 6-months calves (field)</td></tr><tr><td>12</td><td>av</td><td>oats</td><td>5</td><td>vt calves reported on-farm to be young bulls</td></tr><tr><td>17</td><td>ca</td><td>other cereals</td><td>7</td><td>vb calves reported on-farm to be more 2-yr bulls</td></tr><tr><td>18</td><td>se</td><td>rye</td><td>8</td><td>nl female calves from dairy herd non reported on farm</td></tr><tr><td>19</td><td>rz</td><td>rice</td><td>9</td><td>nv female calves from breeding herd non reported on farm</td></tr><tr><td>20</td><td>ma</td><td>maize</td><td>10</td><td>fr female calves reported on farm</td></tr><tr><td>21</td><td>bt</td><td>sugar beet</td><td>11</td><td>jt male calves bought to be young bulls</td></tr><tr><td>22</td><td>ba</td><td>A-sugar</td><td>12</td><td>jb male calves bought to be bulls (&gt; 2 years)</td></tr><tr><td>23</td><td>bb</td><td>B-sugar</td><td>13</td><td>jl female calves bought to renew the dairy herd</td></tr><tr><td>24</td><td>bc</td><td>C-sugar</td><td>14</td><td>iv female calves bought to renew the breeding herd</td></tr><tr><td>25</td><td>tb</td><td>tobacco</td><td>21</td><td>te 18-months bulls</td></tr><tr><td>26</td><td>ct</td><td>cotton</td><td>23</td><td>me 1-yr males from "vb" or "jb" to be bulls</td></tr><tr><td>27</td><td>li</td><td>flax &amp; hemp</td><td>24</td><td>ma 1-yr males bought to be bulls</td></tr><tr><td>31</td><td>cz</td><td>rapeseed</td><td>25</td><td>fl 1-yr female on-farm dairy herd</td></tr><tr><td>32</td><td>tr</td><td>sunflower</td><td>26</td><td>fv 1-yr female on-farm breeding herd</td></tr><tr><td>35</td><td>sj</td><td>soja</td><td>27</td><td>hl 1-yr female bought for dairy herd</td></tr><tr><td>36</td><td>pr</td><td>proteins</td><td>28</td><td>hv 1-yr female bought for breeding herd</td></tr><tr><td>40</td><td>lg</td><td>field vegetables</td><td>31</td><td>be 2-yr males on-farm &gt; beef</td></tr><tr><td>41</td><td>fr</td><td>fallow</td><td>33</td><td>rl dairy heifers</td></tr><tr><td>42</td><td>gl</td><td>1988 set-aside</td><td>34</td><td>rv bred heifers</td></tr><tr><td>43-47</td><td>...</td><td>various set-aside*</td><td>35</td><td>vl dairy cows</td></tr><tr><td>49</td><td>ja</td><td>fix set-aside or forest</td><td>36</td><td>vv suckler cows</td></tr><tr><td>51-56</td><td>ms,...</td><td>various SRC**</td><td>41</td><td>ov sheep</td></tr><tr><td>61</td><td>pt</td><td>potatoes</td><td>45</td><td>cp goats</td></tr><tr><td>67</td><td>lf</td><td>fodder vegetable</td><td>51</td><td>po pigs</td></tr><tr><td>71</td><td>bf</td><td>fodderbeet</td><td>55</td><td>vo poultry</td></tr><tr><td>72</td><td>mf</td><td>fodder maize</td><td>ag bought raw feed</td><td></td></tr><tr><td>73</td><td>lz</td><td>alfalfa***</td><td>a1 simple energy-rich concentrated feed</td><td></td></tr><tr><td>74</td><td>sp</td><td>protein fodder</td><td>a2 simple protein-rich concentrated feed</td><td></td></tr><tr><td>81</td><td>sh</td><td>permanent meadows</td><td>a3 compound energy-rich concentrated feed</td><td></td></tr><tr><td>82</td><td>af</td><td>temporary grassland</td><td>a4 compound protein-rich concentrated feed</td><td></td></tr></tbody></table>

* refers to the 1992 *CAP* reform

** short rotation coppice such as miscanthus and poplars

*** for some *FADN* years only, not maintained

<table><caption>Effluent categories (animal-dependent, digestates or urban waste)</caption><tr><td>1</td><td>i1</td><td>swine & bovine</td><td>slurry</td></tr><tr><td>2</td><td>i2</td><td>all except poultry</td><td>"solid" manure</td></tr><tr><td>3</td><td>i3</td><td>poultry</td><td>manure</td></tr><tr><td>4</td><td>i4</td><td>poultry</td><td>slurry</td></tr><tr><td>5</td><td>i5</td><td>poultry</td><td>droppings</td></tr><tr><td>6</td><td></td><td>animal-sourced</td><td>digestate*</td></tr><tr><td>7</td><td></td><td>crop-sourced</td><td>digestate*</td></tr><tr><td>11</td><td>cu</td><td>urban</td><td>compost</td></tr><tr><td>12</td><td>dv</td><td>urban</td><td>liquid</td></tr></table>

* digestate-dependent parameters specifically managed
<figure><img src="image_49.png" /><figcaption>**Figure 35.** Overview of the file and program structure related to the use of *AROPAj*.</figcaption></figure>
prices related to soft wheat in one of the EU country as follows. In addition we consider the relative price parameter allowing to changes all soft wheat individual prices.

...

<table><tr><td>pjjbl</td><td>: 1.</td></tr><tr><td>pjjbl 2</td><td>: 0.23000</td></tr><tr><td>pjjbl 3</td><td>: 0.15563</td></tr><tr><td>pjjbl 4</td><td>: 0.12505</td></tr><tr><td>pjjbl 9</td><td>: 0.12743</td></tr><tr><td>pjjbl10</td><td>: 0.17510</td></tr><tr><td>pjjbl11</td><td>: 0.21901</td></tr><tr><td>pjjbl13</td><td>: 0.23490</td></tr></table>

...

As explained above, parameter code refers to combinations of 1-, 2- or 5-character strings. In the present case, the first column character (p) refers to the price category among parameters, the 2-3 column characters jj means that information contained in the datafile line refers to crops, and the 4-5 column characters bl refers to soft wheat as detailed in Table 35. When no other indication exists, the parameter refers to the relative price which is 1. as default value. Reversely figures in columns 6-7 refer to the farm group numbering. It has to be noted that “1”, “1” and “01” are of equivalent interpretation. Nevertheless when dealing with other data files of mgg.par-like format, the user should select the “1” or “01” forms (this point refers to the scen.par file mentioned in Section 11.1.3). The parameter value should be registered in columns 10-25 (right from the “:” character) in standard FORTRAN-like format (namely f16.6).

When the model's user wants to change all soft wheat prices in a common ratio (e.g. increasing by 50%), the value of the non-numbered parameter pjjbl has to be changed from 1. in 1.5, either in the last line of the date file containing this parameter or in an additional line registered as:

pjjbl : 1.5

We recall the rule of “last read value - stated value in the LP”.

### 11.2.2 Reporting

In addition to the GAMS files provided by the kernel file transformation, another GAMS file (report.gms) is produced with the aim of listing the LP comprehensive solution in terms of primal and dual variables. Based on other GAMS files and additional flags or descriptors (see the aro.dat file), this file is automatically built by a FORTRAN routine when the user launches AROPAj.

FORTRAN routines provide formatted output files designed for downstream treatment.

#### By default via minimal use of the AROPAj shell

The organization of calculus, as illustrated by Figure 34, and the organization of files, as illustrated by Figure 35, refers to the standard manner of using AROPAj. In this organization, most of user's file names are indexed by the computer clock system in two ways.

Firstly, the names of general information files related to the simulation process management use the 12-digits index “YYMMDDhhmmss” (denoted by “$idm”). Since both trace files and the
# 12 Applications

We explore some of the results and the applications of *AROPAj* capabilities for a wide range of issues. These findings mainly refer to published papers. We add a few results enlarging this range or providing renewed estimates across *AROPAj* versions.

## 12.1 Agricultural policy analysis

**Key-point:**

The *CAP* as a major driver and scenario provider

### Analysis of the 2003 CAP reform

A strong challenge for the model occurred with the 2003 *CAP* reform (namely the “Luxembourg agreement”) and its analysis through the European *GENEDEC* project. Technical information available in addition to published papers is collected in Jayet (2006) (see also Galko & Jayet (2011)). Policy tools introduced by the agreement were in large part implemented in the model as they were applied in to the EU-15 (see Section 4.2.4).

The first step was to assess the various levels of support coming from the former policy (“Agenda 2000”). This was used to calculate “historical” reference support per hectare and per farm (more precisely per farm group in *AROPAj*). This was used also because *MS* kept the right for a few years to (partially) recouple some supports. This is detailed in Jayet (2006). We recall here the policy tools explicitly taken into account in the analysis.

Full decoupling of beef production is one of a number of options that are available at the *MS* level. The other two options are either 1) to retain up to 100% of the suckler cow premium, 100% of the slaughter premium for calves and 40% of the slaughter premium for other animals (the choice of Belgium, Spain, France, Netherlands, Austria and Portugal), or 2) to retain up to 75% of the special beef premium and 100% of the slaughter premium (the choice of Denmark and Sweden). For crops, the intervention price remains unchanged. Apart from full decoupling, the options are to either retain up to 25% of the arable crop payments in the coupled form (independent of set aside payments, as France and Spain have chosen), or retain up to 40% of the durum wheat payment in a coupled form. For sheep, apart from full decoupling, the available option is to keep up to 50% of the ewe premium (the choice of Denmark, Spain and France). As of 2005 (2007 at the latest), the new milk premia can be part of the single farm payment. Mathematical programming models make it possible to compute the single farm payment, *SFP*, as well as compare farmers’ gross margins before and after the reform.

The computation of individual or regional decoupled payments in the *AROPAj* model is based on the results obtained using the Agenda 2000 policy as input. In the model, the single year of simulation is representative of the 3-year reference period. The prior *AROPAj* supports have been broken up according to different items related to possible decoupling combinations:

1. basic support for cereals, oilseeds and proteins

2. specific support for durum wheat

3. specific support for proteins
main table file use this format, the user can easily follow the process evolution over *AROPAj* steps and over *AROPAj* countries. This table file is named `tableau.$idm` when "$idm" accounts for the 12 digits.

The second way of indexing files uses the 8-digits index "DDhhmmss" format, which is also based on the system clock provided at the *AROPAj* country scale (denoted here by "$id"). Most of intermediate and final output files are renamed in accordance with this index. Standard comprehensive outputs provided by the *GAMS* solver (`sortie.gms`) are reported on files named `mggf.$id$.sortie`. Extracted results formatted through the *FORTRAN* routine (`scen.f`) are reported on files named `result$.$id$.$k`, $k$ denoting the farm group. The same principle is used for intermediate files used, for instance, in iterated *AROPAj* runs, when parameter values at the iteration $n$ depend on *LP* results provided at the iteration $n-1$.

Applications are developed to display aggregated and tabulated results at the farm group level as well as at the national level. The user accesses these files in accordance with the repertory hierarchy illustrated by Figure 35. On the upper level, results may be compiled in "txt" or "csv" format for "countries" for which *AROPAj* runs are realized. Additional two-dimensional indexation refers to run iterations that are conducted on a two-dimension grid (like when two parameter ranges are explored). The file name is of the type `table.compil.$scen.$n.$m.txt`.

### Changing output structure

The user always has the ability to change output files and their structure. The question here is not to discuss what can be done from default outputs freely exported (default outputs are easy to manage through personal computer tools). In the case of structural change upstream from *GAMS* files produced by the kernel, the reporting is automatically adjusted. In other words, the *LP* results are comprehensively displayed in the `output.gms`. The user may effectuate downstream change through his/her own shell script `scen.csh` in order to produce *FORTRAN* codes and related files. The *AROPAj* shell requests only to have a shell script file in due code. In order to fully benefit from all the applications around the *AROPAj* shell, we advise keeping basic structure of file and output formats.

### Output spatialization

The spatialization process can be use when the user created a file of the type `table.compil.$scen.$n.$m.txt`, and when the farm group location probabilities have been previously estimated. Let us recall that these probabilities are only computed for a few *AROPAj* versions and *MS* (Sweden and Finland are excluded, for the V2 version). The process implicitly calls for *R* code files which are included in the *AROPAj* shell. Attention should be paid to the output file size. Outputs are displayed in compressed "dbf" format files. These files are provided and indexed according to *FADN* region. File names are of the type `Arc.$scen.$reg.$n.$m.dbf`.

### 11.2.3 Inserting new items, linking parameters and outputs

#### Extending the *AROPAj* coverage

The coverage extension refers to different kinds of *AROPAj* enlargement, whether it be in terms of geography, crops, environmental issues, policy tools, or model applications.
4. set-aside support

5. extensification support related to livestock

6. support for milk

7. support for ovine, caprine, and generally for "small" herd

8. support for suckler cows

9. support for male bovine

10. support for slaughter calves

11. other slaughter supports;

12. other supports, possibly excluded from decoupling (taxes, sugar regimes)

The *AROPAj* modeling of the decoupling reform depends on the *FADN* regions and farm groups within regions. Thus, it is possible to compute the *SFP* for each farm group and to compute the unique regional entitlement. Some supports from the Agenda 2000 policy are currently not in the model. The main reason is the general lack of data. For example, the *FADN* does not supply information about product destination, and it does not distinguish between some types of crops, e.g., starch and food potatoes. Consequently, neither does the model. Nevertheless, the total European budget devoted to agricultural policy is well represented in *AROPAj*. The regionalisation option can be implemented without any difficulty in the *AROPAj* model when the regions covered by this option correspond to *FADN* regions upon which the *AROPAj* model is based (this is the case for Germany). Otherwise, some hypotheses on farm groups' location within a *FADN* region are necessary (the case for England).

The changes in the *CAP* related to the Luxembourg agreement are implemented in the model as explained in Table 10 on page 61. Values of policy tool parameters are provided by the previous step of reference estimates. We introduce a "full decoupling" scheme through the unique payment (*psuni*), which is estimated per *AROPAj* farm group.

We focus on two series of results which appear especially interesting. We consider three scenario changes between the “reference” and the full decoupling scheme. The scenarios are defined as follows:

*AG00* refers to the reference scenario, based on the calibrated 2001-*FADN AROPAj-V2* version

*AG15* only the livestock adjustment parameter is changed (from 0% in *AG00* to 15%)

*LX15* the Luxembourg agreement is implemented in the model, keeping the *LS* adjustment at the previous level

*FD15* there is no partial recoupling and the farm group *SFP* applies, keeping the *LS* adjustment at the previous level

**Allocation of agricultural lands** We present series of three-figure sets showing relative change in area allocated to (i) cereals (36), pastures (37), set-aside (38) and fallows (39). <fnref n="13" /> Changes in areas are computed at the *FADN* level. It is clear that they differ compared to the relatively slight average changes at the European level.

<footnote n="13">¹³Fallows are freely chosen by producers, meanwhile set-aside is requested by the *CAP*.</footnote>
MS inclusion is straightforward (no change in kernel files):

in particular, typology, parameter estimation and calibration remains unchanged.

Country data from the *FADN* is a prerequisite.

crops (or other agricultural activities):

introducing cotton, hoax or tobacco for instance requests *FADN* sourced information

For perennial crops, such as miscanthus, the user must calculate the discounted values of
(added) parameters

LP variables (activities and constraints, e.g. a new block):

changes have to be made in the kernel files (aro.mgg, intern.f, aroN.dat).

Related parameter estimates have to be added accordingly with the requisite format of the
*mgg.par* file.

It is also possible to call external data, such as *N* to yield functions. In this case, *lisscal.don*
files are read by the *intern.f* routine. Yield function parameters are used in each *AROPAj* run
as long as the user calls them through the shell script for that scenario (*scen.csh*). This allows
the user to change yield function parameters from one run to another (e.g. in case of climate
adaptation).

## Parameters valued through LP outputs

We have briefly exposed several cases relevant to projects and papers mentioned in previous
sections. The user must adequately prepare the *scen.csh* file in order to process the desired
application.

**Referencing GHG quotas** Instead of the “primal” approach based on *GHG* emission pricing (through tax), the “dual” approach entails using the emission quota shadow price. We must set these quotas, based on a reference situation without binding limits. To do so, we compute reference emissions and we transform these outputs in quota parameters (*qopes(k,l)*).

**Valuating the economic LT effect of OM on crop nutrient** This effect enters the model through the *aponlt(k)* parameter and lowers crops' *N*-requirement. This *FG* indexed parameter depends on annually renewed amounts of *OM* spread on arable lands. In the model, the available *OM*, be it on-farm sourced (manure) or imported (*UOR*), is optimally allocated to various crops and fodders with respect to regulatory limits. In other words, this amount per crop area is endogenously calculated by the model, thus impacting the *aponlt(k)* value.

**Fixing crop areas** In some cases there is an interest in constraining certain crop areas on order to, for instance, assess the part of different sources of autonomous adaptation in economic or environmental impacts. The user must set reference areas, i.e. the *xjk0(j,k)* parameters, which are in turn evaluated by the reference *AROPAj* outputs.

**Shadow prices substituting market prices** By adding new parameters, the user can easily process the iterated substitution of market prices by relevant shadow prices in the yield and *N*-input calculation. On the “crop output side”, new parameters (*rojk(j,k)*) refer to the dual values related to the *s(j,k)* constraints. We had not added yet the relevant parameters on the “*N* input
<figure><img src="image_50.png" /><figcaption>**Figure 36.** Variation in area devoted to cereals between the scenarios AG00, AG15, LX15 and FD15 (in % of the regional agricultural area).</figcaption></figure>
side", because there are very few cases of solely organic *N* input dedicated to a crop by a farm group.

**The MODANI model and equilibrium prices** This application is based on an iterated *tâtonnement* process that attempt to compute the converging prices associated with balanced markets. In this case, at each iteration, study-relevant market prices are changed in line with the excess of demand.

**The AROPAj calibration** The *AROPAj* calibration process is one of the primary instances in which *AROPAj* acts as a subroutine of a wider program. As explained above, the process is based on MonteCarlo and gradient algorithms, which require a large amount of *AROPAj* runs. Each run is iteratively based on re-evaluated parameters.

### 11.2.4 Starting and managing simulations

There are several prerequisites for starting *AROPAj* and calling a simulation scenario. First, a user account has to be created on the workstation that host *AROPAj*. In addition to the usual change of the “PATH” variable associated to the user’s account, some small changes have to be reported in the “login” scripts. The *AROPAj* administrator has to report the user’s account characteristics in the *AROPAj* shell scripts.

*AROPAj* simulations are launched through the *miraj* command script, thereby supplying nested “menus”. Initiating a simulation only requires a *scen.csh* script from the user.

The menu allows the user to undertake many operations. It also allows the administrator to undertake basic *AROPAj* steps such as farm group clustering, parameter estimation and calibration. The user can:

* access information files referring to scientific projects (named *cahier.txt*)

* successively or automatically initiate the different steps relating to the version choice and parameter complements, for different “*AROPAj* countries” covered by the selected version

* iterate the *AROPAj* runs as designed by the script file *scen.csh*

* aggregate the outputs at the country level

* tabulate the outputs at the country level

* export the output compilation in “txt/csv” format files for countries covered by simulations and for selected iterations (the user is free to select a subpart of iterations among all of them)

* undertake all the previous steps once the simulation is initiated; this entails selecting the version and the script *scen.csh*, and initiating the process for just one country

* display selected results through the spatialization process for *FADN* regions covered by the simulation when the spatialization is available; for this step, the user is free to select outputs and iterations among those scheduled by the script *scen.csh*.

During the computation process, as explained above, several files are reported in the working directory and in the subdirectories as shown on Figure 35. Process visualization is briefly developed in the Appendix A.
<figure><img src="image_51.png" /><figcaption>**Figure 37.** Variation in area devoted to pastures between the scenarios AG00, AG15, LX15 and FD15 (in % of the regional agricultural area).</figcaption></figure>
<figure><img src="image_50.png" /><figcaption>**Figure 35.** Overview of the file and program structure related to the use of *AROPAj*.</figcaption></figure>
<figure><img src="image_52.png" /><figcaption>**Figure 38.** Variation in area devoted to set-aside between the scenarios AG00, AG15, LX15 and FD15 (in % of the regional agricultural area).</figcaption></figure>
# 12 Applications

We explore some of the results and the applications of *AROPAj* capabilities for a wide range of issues. These findings mainly refer to published papers. We add a few results enlarging this range or providing renewed estimates across *AROPAj* versions.

## 12.1 Agricultural policy analysis

**Key-point:**

The *CAP* as a major driver and scenario provider

### Analysis of the 2003 CAP reform

A strong challenge for the model occurred with the 2003 *CAP* reform (namely the “Luxembourg agreement”) and its analysis through the European *GENEDEC* project. Technical information available in addition to published papers is collected in Jayet (2006) (see also Galko & Jayet (2011)). Policy tools introduced by the agreement were in large part implemented in the model as they were applied in to the EU-15 (see Section 4.2.4).

The first step was to assess the various levels of support coming from the former policy (“Agenda 2000”). This was used to calculate “historical” reference support per hectare and per farm (more precisely per farm group in *AROPAj*). This was used also because *MS* kept the right for a few years to (partially) recouple some supports. This is detailed in Jayet (2006). We recall here the policy tools explicitly taken into account in the analysis.

Full decoupling of beef production is one of a number of options that are available at the *MS* level. The other two options are either 1) to retain up to 100% of the suckler cow premium, 100% of the slaughter premium for calves and 40% of the slaughter premium for other animals (the choice of Belgium, Spain, France, Netherlands, Austria and Portugal), or 2) to retain up to 75% of the special beef premium and 100% of the slaughter premium (the choice of Denmark and Sweden). For crops, the intervention price remains unchanged. Apart from full decoupling, the options are to either retain up to 25% of the arable crop payments in the coupled form (independent of set aside payments, as France and Spain have chosen), or retain up to 40% of the durum wheat payment in a coupled form. For sheep, apart from full decoupling, the available option is to keep up to 50% of the ewe premium (the choice of Denmark, Spain and France). As of 2005 (2007 at the latest), the new milk premia can be part of the single farm payment. Mathematical programming models make it possible to compute the single farm payment, *SFP*, as well as compare farmers’ gross margins before and after the reform.

The computation of individual or regional decoupled payments in the *AROPAj* model is based on the results obtained using the Agenda 2000 policy as input. In the model, the single year of simulation is representative of the 3-year reference period. The prior *AROPAj* supports have been broken up according to different items related to possible decoupling combinations:

1. basic support for cereals, oilseeds and proteins

2. specific support for durum wheat

3. specific support for proteins
<figure><img src="image_53.png" /><figcaption>**Figure 39.** Variation in area devoted to fallows between the scenarios AG00, AG15, LX15 and FD15 (in % of the regional agricultural area).</figcaption></figure>
4. set-aside support

5. extensification support related to livestock

6. support for milk

7. support for ovine, caprine, and generally for "small" herd

8. support for suckler cows

9. support for male bovine

10. support for slaughter calves

11. other slaughter supports;

12. other supports, possibly excluded from decoupling (taxes, sugar regimes)

The *AROPAj* modeling of the decoupling reform depends on the *FADN* regions and farm groups within regions. Thus, it is possible to compute the *SFP* for each farm group and to compute the unique regional entitlement. Some supports from the Agenda 2000 policy are currently not in the model. The main reason is the general lack of data. For example, the *FADN* does not supply information about product destination, and it does not distinguish between some types of crops, e.g., starch and food potatoes. Consequently, neither does the model. Nevertheless, the total European budget devoted to agricultural policy is well represented in *AROPAj*. The regionalisation option can be implemented without any difficulty in the *AROPAj* model when the regions covered by this option correspond to *FADN* regions upon which the *AROPAj* model is based (this is the case for Germany). Otherwise, some hypotheses on farm groups' location within a *FADN* region are necessary (the case for England).

The changes in the *CAP* related to the Luxembourg agreement are implemented in the model as explained in Table 10 on page 65. Values of policy tool parameters are provided by the previous step of reference estimates. We introduce a "full decoupling" scheme through the unique payment (*psuni*), which is estimated per *AROPAj* farm group.

We focus on two series of results which appear especially interesting. We consider three scenario changes between the “reference” and the full decoupling scheme. The scenarios are defined as follows:

*AG00* refers to the reference scenario, based on the calibrated 2001-*FADN AROPAj-V2* version

*AG15* only the livestock adjustment parameter is changed (from 0% in *AG00* to 15%)

*LX15* the Luxembourg agreement is implemented in the model, keeping the *LS* adjustment at the previous level

*FD15* there is no partial recoupling and the farm group *SFP* applies, keeping the *LS* adjustment at the previous level

**Allocation of agricultural lands** We present series of three-figure sets showing relative change in area allocated to (i) cereals (36), pastures (37), set-aside (38) and fallows (39). <fnref n="13" /> Changes in areas are computed at the *FADN* level. It is clear that they differ compared to the relatively slight average changes at the European level.

<footnote n="13">¹³Fallows are freely chosen by producers, meanwhile set-aside is requested by the *CAP*.</footnote>
**Land shadow costs** The scenarios previously defined now apply for providing estimates of the land shadow cost. We introduce a slight change in the implementation of the scenario when the unique SFP payment is paid to the whole farm, as a lump-sum transfer. Considering this transfer with the 0-value is equivalent to suppressing the SFP ("no support" scenario). Since it is a "constant" element in the mathematical programming problem, the complete solution in case of "lump-sum support" should not differ from the FD scenario with the SFP, except for the shadow land cost. This is due to the fact that, when reporting the shadow cost from the LP output file, we take account of a unique additional term in the gross margin at the plot level. In the first case (FD), the SFP is added to each plot's gross margin, while in the other case ("lump-sum support"), the support stands for a "fix" term. Figures 40 show the changes in the land shadow cost among 4 scenarios, namely AG15, LX15, FD15 and no support.

**Managing new CAP tools: the case of milk market organization**

The *AROPAj* model was asked to provide insight on different policy changes with regards to existing milk quotas and to the European milk market. In this study initiated by the *EC*, various options were introduced in the model:

* add and modify a milk premium

* create a simple quota market

* create a double market (fixed quotas and market dedicated to out of quota quantities)

* suppress the *CMO* (no quota, no subsidy)

No results can be displayed here for confidentiality reasons. For our current purposes, we must
only focus on the method employed to introduce a quota market allowing for exchanges between
farming systems at different scales.

We put annual quotas into consideration, which is in line with the basic *AROPAj* period.
Introducing the rights to buyl or sell quotas is very simple in a *LP* model. We need to add:

* a parameter related to the quota exchange price (*pqlai*)

* a primal *LP* variable indexed on the farm group and related to the sale or purchase of milk quotas (*xolqm(k,l)*, considered as a "free" variable which can be positive or negative)

* a dual *LP* variable related to the quota demand excess (*qolqt*), which is of interest for aggregation at the scale desired by the user

The market clearing price at the desired scale is easily approximated by *AROPAj* runs realized
for series of *pqlai* prices valued in an adequate range. This “one dimension” problem has a unique
solution for each aggregation level (i.e. for each of the *MS* and for the *EU* as a whole).

The introduction of a “second market”, dedicated to out of quota milk production, can be easily
added to the milk quota market. Parameters and *LP* variables are listed in the *CAP* blocks, which
refer to “second market” in their description.

Post-2007 CAP

The *CAP* is a major economic framework shaping the European agriculture for many years
now. However, the decoupling scheme initiated in the 1990s is currently applied with less and less
<figure><img src="image_51.png" /><figcaption>**Figure 36.** Variation in area devoted to cereals between the scenarios AG00, AG15, LX15 and FD15 (in % of the regional agricultural area).</figcaption></figure>
<figure><img src="image_54.png" /><figcaption>**Figure 40.** Shadow costs of land within the scenarios AG15, LX15, FD15, and in the case of a “no support” policy, for land and activities covered by the AROPAj model.</figcaption></figure>
<figure><img src="image_52.png" /><figcaption>**Figure 37.** Variation in area devoted to pastures between the scenarios AG00, AG15, LX15 and FD15 (in % of the regional agricultural area).</figcaption></figure>
national- or product-specificities. The default setting in *AROPAj V5* policy tools refers to the full decoupling scheme with an updated quota system (no dairy product or sugar quota). In our model, we can easily reactivate any combination of *CAP* tools as necessary for future numerical explorations.

The *AROPAj* model has been updated with environmental policy tools to tackle rising environmental issues. Besides the *CAP* policy tools, additional economic regulation related to the environment and bio-products need to be taken into account. As a matter of fact, in addition to the *CAP* framework tools, the *AROPAj* model is including tools related to nitrogen pollutants, greenhouse gas emissions, climate change but also bio-fuel production and natural resource preservation. At last, *AROPAj* remains a supply-side model in which market prices are exogenous and parametrized as we should do in case of regulated prices.

## 12.2 GHG emissions and abatement

**Key-points:**

*GHG* emission assessment initiating *AROPAj* evolution and supplying original analysis

### Estimation of marginal abatement cost curves (MACC)

In order to derive marginal abatement cost curves in this type of model, an emission price (expressed in €/tCO₂eq) is introduced in the objective function of the model. It affects each farmer's revenue according to the total amount of emissions emitted at the representative farm level. By construction, for a given emission tax *t*, emissions are such that the marginal loss of income due to an additional reduction equals *t* at the individual optimum for any farm *k*. By letting *t* vary in a given range, one thus obtains the quantity of abatement that each farmer is ready to supply at this price or, equivalently, the *MACC* pertaining to this particular farm. Note that, by using the same level of tax for all representative farms, one obtains the cost-effective repartition of abatement across farms. Several applications of the model have used this procedure to assess *MACC* at various levels of aggregation (De Cara & Jayet, 2000a; De Cara et al., 2005; De Cara & Jayet, 2011).

### Illustrative examples

Abatement costs are a fundamental determinant of the role that agriculture could play in meeting efficiently the EU commitment to reduce its GHG emissions. Two broad dimensions have been examined by De Cara et al. (2005): the magnitude of abatement costs in the agricultural sector and their heterogeneity.

Regarding the magnitude of abatements from agriculture for plausible carbon prices, the range is found to be substantial. Despite rather conservative assumptions – such as the fixed number of farms, fixed total area, fixed crop yield response to nitrogen, no adoption of specific abatement technology – an additional potential abatement of about 4% of 2001 agricultural emissions is obtained at a price of 20 €/tCO₂eq. The drop of abatement is of 8% at a price of 56 €/tCO₂eq. In the design of economic instruments, one faces an important trade-off between accuracy and observability. In this respect, the IPCC-based economic instruments, which we analysed, provide
<figure><img src="image_53.png" /><figcaption>**Figure 38.** Variation in area devoted to set-aside between the scenarios AG00, AG15, LX15 and FD15 (in % of the regional agricultural area).</figcaption></figure>
an interesting balance. On one hand, complex emission processes are only imperfectly captured because of the use of simplified relationships between activities and emissions. On the other hand, the IPCC framework is recognized as an international reference and is based on data that is relatively easy to collect. The study was based on a preliminary V2 version of AROPAj applied to 15 EU Member States (FADN 2002).

<figure><img src="image_55.png" /><figcaption>**Figure 41.** Abatement of GHG emissions at a price of 56€/tCO₂eq averaged at the FADN region level. Source: De Cara et al. (2005).</figcaption></figure>

According to this study, abatement cost heterogeneity comes from a variety of sources. The farm type approach captures some of these sources of heterogeneity in the results, such as those related to farms' size, crop yields and area allocation, total animal numbers, input use, and CAP support. Some are only captured at the country-level (e.g. IPCC emission factors). And, due to the lack of data, some of the sources of heterogeneity have been ignored (e.g. spatially disaggregated emission factors). Nevertheless, the results indicate a wide variability of abatement costs (Figure 41). This has two broad implications for policy purposes. First, the impacts of incentive-based instruments on income and environmental performances vary widely from one farmer to another. Second, the cost savings permitted by market-based instruments relative to uniform standards are substantial. This means that, if mitigation policies are to make use of quantity-based instruments, substantial savings can be drawn from tradable (or at least differentiated) emission allowances. Clearly, this also means that such policies will have to go beyond uniform or region-specific standards commonly used in agricultural policies (e.g. set-aside rate or regionally differentiated reference yields). At last, this underlines the importance of the inclusion of agriculture in the European Emission Trading Scheme (ETS).
<figure><img src="image_54.png" /><figcaption>**Figure 39.** Variation in area devoted to fallows between the scenarios AG00, AG15, LX15 and FD15 (in % of the regional agricultural area).</figcaption></figure>
According to De Cara & Jayet (2011), the assessment of marginal abatement costs (*MAC*) is key to the issue of cost-effectiveness. The study carried out a quantitative assessment of marginal abatement costs of *GHG* emissions from the European agriculture and analyzed the implications of the *EU* burden sharing agreement (*BSA*) for this sector. To do so, a generic specification of *MAC* curves was proposed and applied to the *V3* version of *AROPAj* (the *EU*-25 except Malta, *FADN* 2004, with 1307 farm groups).

Their findings are threefold. First, the heterogeneity of *MAC* across Member States stands out as an important feature. As the agreed targets under the *BSA* do not reflect the heterogeneity of agricultural *MAC* at the Member State level, the use of flexibility mechanisms may provide substantial cost-savings compared to the strict implementation of each individual country's target. Second, the equilibrium price range at which the *EU* 10% reduction target can be reached in European agriculture (32-42€/tCO₂eq) is in line with results from other analyses covering all non-*ETS* sectors and lower than that found in previous studies focusing on *EU* agriculture (56 €/tCO₂eq in De Cara et al. (2005) for an 8% reduction target). This suggests that the agricultural sector could play an important role in meeting the overall *EU* target in a cost-effective manner. Third, the use of the *BSA* targets as a basis for allocating allowances in a cap-and trade system for agricultural emissions may have important redistributive impacts.

For the actual simulations, the *IPCC* parameters for 27 *EU* Member States have been updated (FADN 2009). As well as the *IPCC* parameters, the prices of fertilizers have been updated.

**Using the MACC from AROPAj to assess the benefit from an emission tax when it is costly to administrate**

The magnitude and the heterogeneity of the abatement costs are of great concerns when the mitigation policy is costly to administrate. In the context of climate policy, these costs are due to the costly monitoring, reporting, and verification (MRV) procedures that ensure the compliance with the policy objective. Indeed, as soon as the policy involves MRV costs that are increasing with the number of agents, it may well be the case that including the whole agricultural sector within the mitigation policy could lead to greater MRV costs than the net benefits from the emissions abatted. Therefore, it can be better not to implement any policy than fully covering the sector to mitigate agricultural GHG emissions.

The work in De Cara et al. (2018) studies an instrument design that bypasses this issue. As is the case in some environmental policies (the EU-ETS or the CAP concerning the 'green' payment), some exemptions are granted for some firms, e.g. the small emitters. This paper explores the implications of such exemptions in the case of an emission tax, and in the presence of monitoring, reporting, and verification (MRV) costs. They develop an analytical framework capturing the trade-off between the cost-effectiveness of a broader tax base, and the savings on MRV costs enabled by a partial coverage. Second-best partial coverage is defined by a threshold value of some characteristic of the firms (e.g. size as measured by initial emissions) below which firms are exempted. It then allows to characterize the optimal threshold and discuss its welfare implication. The optimal threshold and the welfare implied greatly depend on the magnitude and the heterogeneity of the abatement costs, alongside with the magnitude and heterogeneity of the MRV costs and the marginal damage of emissions. Indeed, the more efficient are the bigger emitters in reducing their emissions, we will expect the coverage to be smaller and then save a significant
**Land shadow costs** The scenarios previously defined now apply for providing estimates of the land shadow cost. We introduce a slight change in the implementation of the scenario when the unique SFP payment is paid to the whole farm, as a lump-sum transfer. Considering this transfer with the 0-value is equivalent to suppressing the SFP ("no support" scenario). Since it is a "constant" element in the mathematical programming problem, the complete solution in case of "lump-sum support" should not differ from the FD scenario with the SFP, except for the shadow land cost. This is due to the fact that, when reporting the shadow cost from the LP output file, we take account of a unique additional term in the gross margin at the plot level. In the first case (FD), the SFP is added to each plot's gross margin, while in the other case ("lump-sum support"), the support stands for a "fix" term. Figures 40 show the changes in the land shadow cost among 4 scenarios, namely AG15, LX15, FD15 and no support.

## Managing new CAP tools: the case of milk market organization

The *AROPAj* model was asked to provide insight on different policy changes with regards to existing milk quotas and to the European milk market. In this study initiated by the *EC*, various options were introduced in the model:

* add and modify a milk premium

* create a simple quota market

* create a double market (fixed quotas and market dedicated to out of quota quantities)

* suppress the *CMO* (no quota, no subsidy)

No results can be displayed here for confidentiality reasons. For our current purposes, we must only focus on the method employed to introduce a quota market allowing for exchanges between farming systems at different scales.

We put annual quotas into consideration, which is in line with the basic *AROPAj* period. Introducing the rights to buy or sell quotas is very simple in a *LP* model. We need to add:

* a parameter related to the quota exchange price (*pqlai*)

* a primal *LP* variable indexed on the farm group and related to the sale or purchase of milk quotas (*xolqm(k,l)*, considered as a "free" variable which can be positive or negative)

* a dual *LP* variable related to the quota demand excess (*qolqt*), which is of interest for aggregation at the scale desired by the user

The market clearing price at the desired scale is easily approximated by *AROPAj* runs realized for series of *pqlai* prices valued in an adequate range. This "one dimension" problem has a unique solution for each aggregation level (i.e. for each of the *MS* and for the *EU* as a whole).

The introduction of a "second market", dedicated to out of quota milk production, can be easily added to the milk quota market. Parameters and *LP* variables are listed in the *CAP* blocks, which refer to "second market" in their description.

## Post-2007 CAP

The *CAP* is a major economic framework shaping the European agriculture for many years now. However, the decoupling scheme initiated in the 1990s is currently applied with less and less
amount of MRV costs on the smaller emitters exempted from any mitigation effort. However, in
practice there is not a monotonic relationship between the abatement cost and the size of the firms.

Then, the paper uses the MACC from AROPAj to assess the welfare implication of such a
scheme. The study is based on the version V.5. of AROPAj applied to the EU-27 with 1802 farm
groups. According to this simulation, the agricultural GHG emissions are rather concentrated,
since the top 25% emitters emit almost 80% of total emissions as depicted by the Lorenz curve in
Figure 42.

<figure><img src="image_56.png" /><figcaption>**Figure 42.** Lorenz curve of initial emissions. Source: De Cara et al. (2018)</figcaption></figure>

Thus, targeting only the top 25% emitters (those to the right of point A in Figure 42) saves the MRV costs associated with the remaining 75% of farmers, while still covering almost 80% of total agricultural emissions. Of course, the setting of the optimal coverage will all the more depend on whether the small emitters are less efficient at reducing their emissions than the large emitters. Therefore, the welfare implications of partially covering the agricultural sector within the climate policy is dependant on the heterogeneity of abatement costs and their distribution among agents. The more concentrated are the emissions, and the more efficient are the big emitters in mitigating their emissions, the smaller the coverage and the higher the welfare gains (for a given MRV costs level). Thus, they use the AROPAj model to assess the relationship between the abatement efficiency and the size of the farm. They find an abatement elasticity with farm size of 1.1 (which means that when the initial emissions of the farm increases by 1%, its abatement efficiency increases by 1.1%). This abatement cost elasticity is greater than the MRV cost elasticity. Thus, it ensures that the bigger the farms, the greater the net social benefit from its inclusion in
<figure><img src="image_55.png" /><figcaption>**Figure 40.** Shadow costs of land within the scenarios AG15, LX15, FD15, and in the case of a “no support” policy, for land and activities covered by the AROPAj model.</figcaption></figure>
the emission tax.

In their benchmark case (MRV costs increasing and concave per farm, averaging 275 €.farm⁻¹, or 2.50€.tCO₂⁻¹eq and marginal damage of GHG emissions set at 30€.tCO₂⁻¹eq), the top 10% of the bigger farms are covered (see Figure 43). It leads to a net social benefit of 124 € and abatement costs of about 184 € (to be compared with a sector-wide gross margin of about 140 billion €). Thus, their empirical application shows that including the agricultural sector within the European climate policy is not an all-or-nothing solution, since exempting the small emitters may provide significant savings on MRV costs compared to the full-coverage, while still incentivizing cost-effective reductions in emissions.

<figure><img src="image_57.png" /><figcaption>**Figure 43.** Total social benefit (solid curve) and its components in the benchmark configuration. The net social value of abatement is directly derived from the AROPAj simulation results. Notes: $\delta = 30 \text{ €/tCO}_2\text{eq}$, medium, increasing and concave per-farm MRV costs. Source: De Cara et al. (2018)</figcaption></figure>

In terms of mitigation policy design, this work could be extended in several directions. First, the analysis of an emission tax could be adapted to examine a cap-and-trade mechanism. Although the fundamental mechanisms at work would remain, this would require to take into account the costs related to the trading of allowances. Since these costs depend on the level of abatement, this would introduce a wedge between marginal abatement cost and the emission price. Second, the simple second-best approach developed here could be compared to a more complex mechanism design aimed at revealing individual information. The empirical model used in this paper could serve as a basis for quantifying the associated information rent. Third, the introduction of a partial
national- or product-specificities. The default setting in *AROPAj V5* policy tools refers to the full decoupling scheme with an updated quota system (no dairy product or sugar quota). In our model, we can easily reactivate any combination of *CAP* tools as necessary for future numerical explorations.

The *AROPAj* model has been updated with environmental policy tools to tackle rising environmental issues. Besides the *CAP* policy tools, additional economic regulation related to the environment and bio-products need to be taken into account. As a matter of fact, in addition to the *CAP* framework tools, the *AROPAj* model is including tools related to nitrogen pollutants, greenhouse gas emissions, climate change but also bio-fuel production and natural resource preservation. At last, *AROPAj* remains a supply-side model in which market prices are exogenous and parametrized as we should do in case of regulated prices.

## 12.2 GHG emissions and abatement

**Key-points:**

*GHG* emission assessment initiating *AROPAj* evolution and supplying original analysis

### Estimation of marginal abatement cost curves (MACC)

In order to derive marginal abatement cost curves in this type of model, an emission price (expressed in €/tCO₂eq) is introduced in the objective function of the model. It affects each farmer's revenue according to the total amount of emissions emitted at the representative farm level. By construction, for a given emission tax *t*, emissions are such that the marginal loss of income due to an additional reduction equals *t* at the individual optimum for any farm *k*. By letting *t* vary in a given range, one thus obtains the quantity of abatement that each farmer is ready to supply at this price or, equivalently, the *MACC* pertaining to this particular farm. Note that, by using the same level of tax for all representative farms, one obtains the cost-effective repartition of abatement across farms. Several applications of the model have used this procedure to assess *MACC* at various levels of aggregation (De Cara & Jayet, 2000a; De Cara et al., 2005; De Cara & Jayet, 2011).

### Illustrative examples

Abatement costs are a fundamental determinant of the role that agriculture could play in meeting efficiently the EU commitment to reduce its GHG emissions. Two broad dimensions have been examined by De Cara et al. (2005): the magnitude of abatement costs in the agricultural sector and their heterogeneity.

Regarding the magnitude of abatements from agriculture for plausible carbon prices, the range is found to be substantial. Despite rather conservative assumptions – such as the fixed number of farms, fixed total area, fixed crop yield response to nitrogen, no adoption of specific abatement technology – an additional potential abatement of about 4% of 2001 agricultural emissions is obtained at a price of 20 €/tCO₂eq. The drop of abatement is of 8% at a price of 56 €/tCO₂eq. In the design of economic instruments, one faces an important trade-off between accuracy and observability. In this respect, the IPCC-based economic instruments, which we analysed, provide
coverage might cause leakage effects, and/or induce strategic behavior from firms in response to
implementation of partial coverage. For instance, we might observe firms to bunch below the initial
emissions threshold in order to be exempted.

12.3 *N*-sourced pollutions

Key-points:

Environmental policies and policy mixes accounting for multiple activities, multiple agents,
multiple pollutants and widely diverse spatial conditions

Possible counterproductive effect of input taxation

The first empirical application of the coupling between *AROPA*<sub>j</sub> and *STICS* was to investigate the effect of a nitrogen tax on agricultural production in France under different policy scenarios (Jayet & Petsakos, 2013). Additionally to estimating yield response functions for every crop and farm-type combination, linear functions of nitrate losses at the root zone were also estimated, using the output from the nitrogen balance module in *STICS*. The model was able to reveal the uncertainty that characterizes second best instruments, such as input taxes, and showed that the *N*-tax could actually lead to the completely opposite results than those expected in theory.

More precisely, it was demonstrated that the tax can force a “crop substitution effect”, which
is caused by the change in the relative profitability of the different farming activities; as the N-tax
increases, and due to the different yield functions of each crop, the changes in crops’ gross margins
lead to an increase in area allocated to the most profitable ones, which, in turn, emit more nitrate
than the original crop. These paradoxical results indicate that the decrease in nitrogen use can be
sometimes overlapped by extensive margin changes that involve an increase in land allocated to
more profitable, but also more polluting crops.

This paradox is illustrated on Figure 44.

Joint pollutions

Bourgeois et al. (2014) assess the cost-effectiveness of a mixed policy in attempts to reduce the presence of three nitrogen pollutants: *NO*₃, *N*₂*O* and *NH*₃. The regulation of joint agricultural pollutants remains a challenge for applied economics and modelling techniques when pollutant emission factors differ strongly and depend on soils and farming practices. Thus far, policies have addressed these issues on a pollutant-by-pollutant basis. However, the land-use and crop reallocation induced by each policy (alone) may increase the regulation cost for other pollutants.

This paper improves the understanding of the relations between nitrogen pollutants and the
gains associated to regulating these different pollutants together by a combination of instruments.
An integrated economic and agronomic approach is proposed. *AROPA*<sub>j</sub> is combined with *STICS*
in order to capture the soil and crop heterogeneities in fertilizer response (in terms of yields and
emissions) as well as to compute nitrogen pollutions. The policy under review combines a tax
on nitrogen input and incentives that promote perennial crops (Miscanthus), which is assumed to
require low levels of input.
an interesting balance. On one hand, complex emission processes are only imperfectly captured because of the use of simplified relationships between activities and emissions. On the other hand, the IPCC framework is recognized as an international reference and is based on data that is relatively easy to collect. The study was based on a preliminary V2 version of AROPAj applied to 15 EU Member States (FADN 2002).

<figure><img src="image_56.png" /><figcaption>**Figure 41.** Abatement of GHG emissions at a price of 56€/tCO₂eq averaged at the FADN region level. Source: De Cara et al. (2005).</figcaption></figure>

According to this study, abatement cost heterogeneity comes from a variety of sources. The farm type approach captures some of these sources of heterogeneity in the results, such as those related to farms' size, crop yields and area allocation, total animal numbers, input use, and CAP support. Some are only captured at the country-level (e.g. IPCC emission factors). And, due to the lack of data, some of the sources of heterogeneity have been ignored (e.g. spatially disaggregated emission factors). Nevertheless, the results indicate a wide variability of abatement costs (Figure 41). This has two broad implications for policy purposes. First, the impacts of incentive-based instruments on income and environmental performances vary widely from one farmer to another. Second, the cost savings permitted by market-based instruments relative to uniform standards are substantial. This means that, if mitigation policies are to make use of quantity-based instruments, substantial savings can be drawn from tradable (or at least differentiated) emission allowances. Clearly, this also means that such policies will have to go beyond uniform or region-specific standards commonly used in agricultural policies (e.g. set-aside rate or regionally differentiated reference yields). At last, this underlines the importance of the inclusion of agriculture in the European Emission Trading Scheme (ETS).
<figure><img src="image_58.png" /><figcaption>**Figure 44.** N-Input tax impact on the input consumption and on the pollutant emission: a possible paradoxical effect (on the Seine river basin - SN). The tax represented on the x-axis corresponds to the percent increase in nitrogen price.</figcaption></figure>

A key point appears regarding the two kinds of pollutants, a soil pollutant ($NO_3$) and the atmospheric pollutants ($NH_3$ and $N_2O$). While there is no significant cost-efficiency gain to promote the land-use policy for gas pollutants, the policy mix strongly decreases the abatement cost for nitrate pollution.

For instance, for a target of 20% abatement, the mixed policy decreases the $NO_3$ abatement cost by 0.8 percentage points (from 2.2% to 1.4%) and does not significantly change the $N_2O$ abatement cost. The explanation lies in the fact that, unlike gas pollutants, $NO_3$ is very soil-crop dependent and the land reallocation in favour of miscanthus leads to higher additional abatements than those obtained with the N-tax alone. Thus, the land use induced by N-tax may be relatively far from the optimal one.

However, for $N_2O$ and $NH_3$ emissions, the abatement gain obtained from a mixed policy is low because the land use induced by N-tax is relatively close to the optimal one for gas pollutants. On the basis of the parsimony criterion, the fertilizer tax would be therefore preferred.

Another advantage of a mixed policy is the possibility of choosing the combination of instruments that brings the largest abatement for a given agricultural income loss and maximizes the abatement for a given revenue. In particular, for a given net tax receipt (tax receipt minus crop subsidies) between 0.6 and 0.8 M€, the abatement range is between 7 and 35% in the case of $NO_3$ and between 16 and 50% in the case of $N_2O$ in France. This is illustrated by Figures 36.

Moreover, because soils, crops and emissions are different in each region considered, we provide an analysis by river basin. These differences are reflected in the abatement cost curves. Also, we demonstrate that the mixed policy, especially the land-use policy, should be adapted per river basin in order to improve the cost-effectiveness of regulation.
According to De Cara & Jayet (2011), the assessment of marginal abatement costs (*MAC*) is key to the issue of cost-effectiveness. The study carried out a quantitative assessment of marginal abatement costs of *GHG* emissions from the European agriculture and analyzed the implications of the *EU* burden sharing agreement (*BSA*) for this sector. To do so, a generic specification of *MAC* curves was proposed and applied to the *V3* version of *AROPAj* (the *EU*-25 except Malta, *FADN* 2004, with 1307 farm groups).

Their findings are threefold. First, the heterogeneity of *MAC* across Member States stands out as an important feature. As the agreed targets under the *BSA* do not reflect the heterogeneity of agricultural *MAC* at the Member State level, the use of flexibility mechanisms may provide substantial cost-savings compared to the strict implementation of each individual country's target. Second, the equilibrium price range at which the *EU* 10% reduction target can be reached in European agriculture (32-42€/tCO₂eq) is in line with results from other analyses covering all non-*ETS* sectors and lower than that found in previous studies focusing on *EU* agriculture (56 €/tCO₂eq in De Cara et al. (2005) for an 8% reduction target). This suggests that the agricultural sector could play an important role in meeting the overall *EU* target in a cost-effective manner. Third, the use of the *BSA* targets as a basis for allocating allowances in a cap-and trade system for agricultural emissions may have important redistributive impacts.

For the actual simulations, the *IPCC* parameters for 27 *EU* Member States have been updated (FADN 2009). As well as the *IPCC* parameters, the prices of fertilizers have been updated.

## Using the MACC from AROPAj to assess the benefit from an emission tax when it is costly to administrate

The magnitude and the heterogeneity of the abatement costs are of great concerns when the mitigation policy is costly to administrate. In the context of climate policy, these costs are due to the costly monitoring, reporting, and verification (MRV) procedures that ensure the compliance with the policy objective. Indeed, as soon as the policy involves MRV costs that are increasing with the number of agents, it may well be the case that including the whole agricultural sector within the mitigation policy could lead to greater MRV costs than the net benefits from the emissions abatted. Therefore, it can be better not to implement any policy than fully covering the sector to mitigate agricultural GHG emissions.

The work in De Cara et al. (2018) studies an instrument design that bypasses this issue. As is the case in some environmental policies (the EU-ETS or the CAP concerning the 'green' payment), some exemptions are granted for some firms, e.g. the small emitters. This paper explores the implications of such exemptions in the case of an emission tax, and in the presence of monitoring, reporting, and verification (MRV) costs. They develop an analytical framework capturing the trade-off between the cost-effectiveness of a broader tax base, and the savings on MRV costs enabled by a partial coverage. Second-best partial coverage is defined by a threshold value of some characteristic of the firms (e.g. size as measured by initial emissions) below which firms are exempted. It then allows to characterize the optimal threshold and discuss its welfare implication. The optimal threshold and the welfare implied greatly depend on the magnitude and the heterogeneity of the abatement costs, alongside with the magnitude and heterogeneity of the MRV costs and the marginal damage of emissions. Indeed, the more efficient are the bigger emitters in reducing their emissions, we will expect the coverage to be smaller and then save a significant
<figure><img src="image_59.png" /><figcaption>**Table 36.** Abatement costs to *N*-losses under *NO*₃ and *N*₂*O* at the French level (source: Bourgeois et al. (2014)).</figcaption></figure>

Effect of spatial differentiation of nitrogen taxes

Lungarska & Jayet (2016) assess the impacts of the spatial differentiation of input-based ni-
trogen tax on farmers’ gross margins. In theory, this differentiation should lower the compliance
costs but the empirical studies on the matter report different magnitudes for the associated eco-
nomic gains. The assessment’s methodology combines a statistical model for nitrate pollution of
French water bodies and the *AROPAj* model. Thus, that accounts for the heterogeneity of French
farms and the disparities in the spatial distribution of water pollution. A major contribution to
the literature on tax differentiation is the inclusion of tax on livestock units as a source of animal
manure. The results obtained allow to estimate the potential efficiency gains associated with the
spatial differentiation of pollution taxes.

It is argued that the only economically viable input-based taxation scheme addressing nitrogen pollution from agriculture is spatially differentiated, since the estimated losses for firm-specific and water body-specific pollution fees equal respectively 5.8% and 32.5% of the gross margin, whereas the uniform policies studied (at the river-basin district and national level) result in substantial economic losses and the discontinuation of agricultural activity. Scenarios with fine-scale spatial differentiation are associated with lower tax rates in areas subject to lower environmental pressure which, in turns, results in lower compliance costs for the farmers. Such policies are better targeted and avoid needless restriction on agricultural activity. This is illustrated by maps in figure 45.

As in previous studies based on the *AROPAj* model and focused on nitrate pollution and input-based taxation, an adverse effect of the taxation is observed in some cases (see also 12.3 on page 165). This adverse effect is due to a crop switching process resulting from farmers’ adaptation to taxation since newly adopted crops require less nitrogen but are also less efficient in fixing it and thus, ultimately, are leading to more nitrate pollution than in the business-as-usual case. Even if the extent of this adverse is reported to be rather limited, there are numerous cases of it because of the fine-scale resolution of the study. However, results suggest that input-based taxation should be accompanied by additional regulation limiting the crop switching process.
amount of MRV costs on the smaller emitters exempted from any mitigation effort. However, in
practice there is not a monotonic relationship between the abatement cost and the size of the firms.

Then, the paper uses the MACC from AROPAj to assess the welfare implication of such a
scheme. The study is based on the version V.5. of AROPAj applied to the EU-27 with 1802 farm
groups. According to this simulation, the agricultural GHG emissions are rather concentrated,
since the top 25% emitters emit almost 80% of total emissions as depicted by the Lorenz curve in
Figure 42.

<figure><img src="image_57.png" /><figcaption>**Figure 42.** Lorenz curve of initial emissions. Source: De Cara et al. (2018)</figcaption></figure>

Thus, targeting only the top 25% emitters (those to the right of point A in Figure 42) saves the MRV costs associated with the remaining 75% of farmers, while still covering almost 80% of total agricultural emissions. Of course, the setting of the optimal coverage will all the more depend on whether the small emitters are less efficient at reducing their emissions than the large emitters. Therefore, the welfare implications of partially covering the agricultural sector within the climate policy is dependant on the heterogeneity of abatement costs and their distribution among agents. The more concentrated are the emissions, and the more efficient are the big emitters in mitigating their emissions, the smaller the coverage and the higher the welfare gains (for a given MRV costs level). Thus, they use the AROPAj model to assess the relationship between the abatement efficiency and the size of the farm. They find an abatement elasticity with farm size of 1.1 (which means that when the initial emissions of the farm increases by 1%, its abatement efficiency increases by 1.1%). This abatement cost elasticity is greater than the MRV cost elasticity. Thus, it ensures that the bigger the farms, the greater the net social benefit from its inclusion in
<figure><img src="image_60.png" /><figcaption>**Figure 45.** Geographical distribution and rates, in € 1000 per livestock unit (on the left) and per ton of mineral fertilizing nitrogen (on the right), for the "Tax hydrosectors" scenario (source: Lungarska & Jayet (2016)).</figcaption></figure>

## 12.4 Biomass provision from perennial crops

**Key-points:**

- Biomass and bio-fuel related issues as yet open questions

### Land use impact of perennial crops introduction

The impacts of producing cellulosic biofuels from dedicated energy crops influence agricultural and environmental outputs at local, regional and national scales. Thus, we attempt to assess the trade-offs between food, feed and (bio)fuel production by focusing on the interactions between the cultivation of lignocellulosic biomass, i.e. miscanthus, and its joint implications on agricultural activities and non-CO₂ emissions, i.e. CH₄ and N₂O at the European scale. To this end, miscanthus, which is a annually harvested perennial crop, was integrated into AROPAj. Introducing this crop in a one-year period model requires the development of a two-step procedure to feed the model with appropriate information. The first step involves selecting a continuous time-yield function, which is calibrated and adjusted to the yield of traditional crops. The second step is a computation step based on a Faustmann dynamic approach aiming at the estimation of the rotation duration, the average yield and the discounted annual costs.

The average estimated yield of miscanthus among AROPAj farm-groups is typically about 23 tDM/ha. Yields between 20 and 30 tDM/ha are very frequent and the highest and lowest yields are 35 and 5 tDM/ha respectively. In order to test the sensitivity of AROPAj results to possible variations in miscanthus yield, yield scenarios were evaluated. They correspond to the introduction of miscanthus with 10 levels of possible yield potential. We proceed to a homogeneous reduction of miscanthus yield from 0 to 100% by 10% intervals over all farm groups. The rate of reduced potential is denoted by $\psi$. This potential reduction is applied at the plot level after the Faustmann optimizing step. We assume that farmers first follow advice in terms of perennial crop management, and then observe yield reduction. Because of the vivid "Food vs Fuel" debates, we assume that the
the emission tax.

In their benchmark case (MRV costs increasing and concave per farm, averaging 275 €.farm⁻¹, or 2.50€.tCO₂⁻¹eq and marginal damage of GHG emissions set at 30€.tCO₂⁻¹eq), the top 10% of the bigger farms are covered (see Figure 43). It leads to a net social benefit of 124 € and abatement costs of about 184 € (to be compared with a sector-wide gross margin of about 140 billion €). Thus, their empirical application shows that including the agricultural sector within the European climate policy is not an all-or-nothing solution, since exempting the small emitters may provide significant savings on MRV costs compared to the full-coverage, while still incentivizing cost-effective reductions in emissions.

<figure><img src="image_58.png" /><figcaption>**Figure 43.** Total social benefit (solid curve) and its components in the benchmark configuration. The net social value of abatement is directly derived from the AROPAj simulation results. Notes: $\delta = 30 \text{ €/tCO}_2\text{eq}$, medium, increasing and concave per-farm MRV costs. Source: De Cara et al. (2018)</figcaption></figure>

In terms of mitigation policy design, this work could be extended in several directions. First, the analysis of an emission tax could be adapted to examine a cap-and-trade mechanism. Although the fundamental mechanisms at work would remain, this would require to take into account the costs related to the trading of allowances. Since these costs depend on the level of abatement, this would introduce a wedge between marginal abatement cost and the emission price. Second, the simple second-best approach developed here could be compared to a more complex mechanism design aimed at revealing individual information. The empirical model used in this paper could serve as a basis for quantifying the associated information rent. Third, the introduction of a partial
part of the farm group's UAA devoted to miscanthus does not exceed 20%.

Table 37 shows that arable land and grassland are reduced at the same rate when miscanthus is progressively introduced. Areas devoted to oilseeds and cereals decrease by 20% and 18% respectively. When the miscanthus potential increases ($\psi$ up to 1), grasslands are slowly but continuously reduced up to 11% (full potential). This is also the case for fallow land, from a 0.5% decrease when the potential is low, to 21% when it is high. The decrease in cropland and grassland areas leads to a decrease in animal and feed production. Indeed, the quantity of on-farm consumed cereals decreases by 4% and marketed concentrates increase by 3% when miscanthus potential is 100%. With a maximum of potential, the computation of the livestock units per hectare of grassland shows an increase of 10%. Given these results, it is evident that a 12% decrease in grassland leads to an intensification of animal rearing.

<table><caption>**Table 37.** Share of land among major groups of agricultural activities (% UAA) when miscanthus yield potential $\psi$ is increasing - estimates provided by the AROPAj model for the EU-15. (Ben Fradj et al. (2016)).</caption><thead><tr><th rowspan="2">$\psi$ =</th><th colspan="1">without</th><th colspan="9">with miscanthus</th></tr><tr><th>0</th><th>0.2</th><th>0.3</th><th>0.4</th><th>0.5</th><th>0.6</th><th>0.7</th><th>0.8</th><th>0.9</th><th>1</th></tr></thead><tbody><tr><td>miscanthus</td><td>0</td><td>0.0</td><td>0.3</td><td>1.4</td><td>4.7</td><td>8.8</td><td>10.8</td><td>13.1</td><td>14.0</td><td>15.3</td></tr><tr><td>cereals</td><td>38.7</td><td>38.7</td><td>38.6</td><td>38.1</td><td>36.6</td><td>34.7</td><td>33.8</td><td>32.7</td><td>32.4</td><td>31.8</td></tr><tr><td>sugarbeet</td><td>3.0</td><td>3.0</td><td>3.0</td><td>3.0</td><td>3.0</td><td>2.8</td><td>2.8</td><td>2.8</td><td>2.7</td><td>2.7</td></tr><tr><td>oilseeds, proteins</td><td>6.0</td><td>6.0</td><td>6.0</td><td>5.9</td><td>5.7</td><td>5.3</td><td>5.1</td><td>5.0</td><td>4.9</td><td>4.9</td></tr><tr><td>potatoes</td><td>3.6</td><td>3.6</td><td>3.6</td><td>3.6</td><td>3.5</td><td>3.4</td><td>3.3</td><td>3.3</td><td>3.2</td><td>3.1</td></tr><tr><td>fodders</td><td>11.0</td><td>11.0</td><td>11.0</td><td>11.0</td><td>10.5</td><td>10.4</td><td>10.1</td><td>9.9</td><td>9.8</td><td>9.6</td></tr><tr><td>fallow</td><td>10.2</td><td>10.2</td><td>10.2</td><td>9.7</td><td>9.5</td><td>8.8</td><td>8.5</td><td>8.3</td><td>8.2</td><td>8.1</td></tr><tr><td>grassland</td><td>27.4</td><td>27.4</td><td>27.3</td><td>27.2</td><td>26.6</td><td>25.8</td><td>25.5</td><td>24.9</td><td>24.7</td><td>24.5</td></tr></tbody></table>

Introducing an environmentally-friendly crop has positive effects on GHG emission abatement. The replacement of crops characterized by high nitrogen ($N$) consumption (e.g. cereals) by miscanthus, which requires low $N$ input, is certainly advantageous for the abatement of $N$-pollutants, among them $N_2O$. Results show that the introduction of miscanthus leads to a significant reduction in $N_2O$ losses. The figures rise to 10% when miscanthus reaches its full potential. With regard to $CH_4$ emissions, the increase in purchased concentrates leads to a reduction, but this reduction (of 2%) remains low because of the intensification of animal rearing.

## Sensitivity analysis

In another study, we address the question of biomass uncertainty in continuous time for a perennial resource, in our case miscanthus, in order to analyse the sensitivity of the supply of this crop in France (Ben Fradj & Jayet (2018)). By applying two Faustmann models, we derive harvesting rules to deal with the optimal rotation age and the value of miscanthus when its growth function is accounted for in a deterministic way as well as when it is governed by a stochastic process. To manage the random yield of miscanthus, we develop a simple stochastic model in which the yield process is based on a popular distribution, the so-called beta distribution (see Figure 46). Because of its versatility, this distribution has been used to model a variety of uncertainties. It is bounded on both sides and so it is often used for representing processes with natural lower and upper limits while it has the flexibility to accommodate both positively and negatively skewed data. The AROPAj model is used to highlight the large scale impact of annual yield randomization.

This study provides an analysis of one factor, i.e. yield, which comes into play when the farmer is
coverage might cause leakage effects, and/or induce strategic behavior from firms in response to
implementation of partial coverage. For instance, we might observe firms to bunch below the initial
emissions threshold in order to be exempted.

Providing the MACC within French national strategy at different time-horizons

For the French National Low Carbon Strategy, various analyses have been carried out using *AROPAj*. Varying the *CO<sub>2</sub>* price on the sole scope of France allowed the *MACC* to be plotted at the static calibration years.

Introducing prospective modelling within the *AROPAj* framework is also possible managing assumptions. For France Strategy report on agricultural abatement costs (**to be incl**), a set of input and output commodity prices was modeled for years 2021, 2030 and 2050. *AROPAj*, being a partial equilibrium model, allows for a prospective analysis with such an approach. Regulatory changes, in the form of the common agricultural policy and its subsidy policies, have also been taken into account for years 2030 and 2050. Also to account for cheptel variability in time, a key parameter has been increased for the modeled years, namely *dstkk* referring to livestock middle run adjustment, the possible change of a cheptel size at a farm-gate level. Evolving from 0.1% at the calibration year to 30% in 2050, the evolution of *dstkk* introduces flexibility in the prospective analysis although it is a very sensitive parameter as shown on Figure 44. Such implementations allow the construction of a baseline scenario.

<figure><img src="image_59.png" /><figcaption>**Figure 44.** Sensitivity analysis of the French MACC to the livestock adjustment parameter dstkk: the different MACC are plotted with a color going from red (very low) to green (high). At a relative abatement of 40%, a difference of 30% in ‘dstkk’ implies a difference of 50€/tCO2eq in marginal abatement cost.</figcaption></figure>

The second step is to model the national emission reduction path in terms of relative objectives,
in the report for years 2030 and 2050. At this stage and after simulation it is easy to compute the
CO2eq price, t*, that allows the aimed CO2 reduction mentioned at years 2030 and 2050. Finally,
one can linearly – or using more advanced methods – interpolate the variable of interest on t*.
With this approach, it has been possible to model the decarbonization path for France (Figure
45), key insights of the two principal structural levers, transition costs and efforts, plot spatial
heterogeneity at the region and farm-gate level.
<figure><img src="image_61.png" /><figcaption>**Figure 46.** Miscanthus yield distribution: an example of generated yield sample.</figcaption></figure>

given the possibility of adopting miscanthus. We highlight the differences between the deterministic yield expectations and stochastic yield expectations of miscanthus in terms of profitability, land-use allocation and N-losses. Results show that planting this crop requires an important investment which will be returned as of the 4th year in the deterministic case and as of the 10th year in the uncertain case. Concerning the economically optimal rotation age, it is about 16 years in the deterministic case. The introduction of a random factor in the growth function delays the optimal rotation for 5 years, up to 21 years.

In addition to the long-term profitability problem, the farmer has to deal with questions related to land use change. Favourable yield potential incites the farmer to adopt miscanthus despite the high degree of uncertainty. In fact, introducing yield uncertainty delays the adoption of miscanthus until its potential is sufficiently high to motivate the farmer's decision. More specifically, in the deterministic case, miscanthus is adopted when 30% of the yield potential is reached. In the stochastic case, this occurs when 60% of the yield potential is reached.

Simulations show a strong decrease in the food-crop areas when miscanthus is introduced. When the miscanthus yield reaches its full potential, cereals' areas decrease by 25% in the deterministic case, and by 7% in the stochastic one. At the same time, grassland decreases by 13% and 7.5%, in the certain and uncertain cases, respectively. Moreover, abandoned areas decrease to 28% in the first case, and to 14% in the second one. These changes in land use have a substantial effect on the N-input demand and N-losses. Indeed, we notice a decrease of fertilizer demand by 22% in the deterministic case and by 13% in the stochastic case when the miscanthus yield reaches its full potential. This decrease in N-demand leads to a 26% reduction in nitrate losses in the first case and 13% in the second one.

Through a sensitivity analysis, we notice that yields, renewal cycle costs and the discount rate interact with yield randomization and significantly affect the future profitability of miscanthus.
<figure><img src="image_60.png" /><figcaption>**Figure 45.** Decomposition of the emission reduction trajectory following the projection of the national emissions of each emission category from the calibration year to 2050 through the two major structural levers: Enteric fermentation of non-dairy bovine and direct emissions of synthetic fertilizers.</figcaption></figure>

## 12.3 *N*-sourced pollutions

**Key-points:**

* Environmental policies and policy mixes accounting for multiple activities, multiple agents, multiple pollutants and widely diverse spatial conditions

### Possible counterproductive effect of input taxation

The first empirical application of the coupling between *AROPA*<sub>j</sub> and *STICS* was to investigate the effect of a nitrogen tax on agricultural production in France under different policy scenarios (Jayet & Petsakos, 2013). Additionally to estimating yield response functions for every crop and farm-type combination, linear functions of nitrate losses at the root zone were also estimated, using the output from the nitrogen balance module in *STICS*. The model was able to reveal the uncertainty that characterizes second best instruments, such as input taxes, and showed that the *N*-tax could actually lead to the completely opposite results than those expected in theory.

More precisely, it was demonstrated that the tax can force a “crop substitution effect”, which is caused by the change in the relative profitability of the different farming activities; as the *N*-tax increases, and due to the different yield functions of each crop, the changes in crops’ gross margins lead to an increase in area allocated to the most profitable ones, which, in turn, emit more nitrate than the original crop. These paradoxical results indicate that the decrease in nitrogen use can be sometimes overlapped by extensive margin changes that involve an increase in land allocated to more profitable, but also more polluting crops.

This paradox is illustrated on Figure 46.

### Joint pollutions

Bourgeois et al. (2014) assess the cost-effectiveness of a mixed policy in attempts to reduce the
## 12.5 Bio-economy oriented studies

**Key-points:**

Opportunities in complex bio-economic model based analysis combining all *AROPAj* modules

Farming systems negatively impact by tropospheric ozone

Complex impacts in promoting rhizobacteria

### Ozone impacts

The *AROPAj* model was used in a study aiming at estimating the potential detrimental impacts of surface ozone (*O*₃) on French crops in the context of climate change (Humblot et al. (2013)). Surface ozone is an air pollutant which causes damage to leaves and reduces photosynthesis efficiency. Its atmospheric concentration is predicted to increase in the future and could significantly impair agricultural productions.

<figure><img src="image_62.png" /><figcaption>**Figure 47.** *AROPAj* yield response function (blue) and the impact of ozone (red), in this case a 10% decrease in yield. The economical optimum is affected and the nitrogen quantites are not the same after the impact of ozone. (source Humblot et al. (2013))</figcaption></figure>

Since the impact of ozone depends on the considered crop, the developed methodology had to take this heterogeneity into account. To do so, the authors used a literature review compiled by Mills et al. (2007), who performed regression analyses on more than 700 articles and 19 crops to extract the coefficients of impact as a function of the ozone concentration.

The data for future ozone levels in 2030 were taken from climatic studies and represented in a 50km per 50km grid. The amount of ozone was calculated as the sum of daily values for the three months preceding the harvest date of the crop, as estimated by the *STICS* model, enabling for a fine consideration on both spatial and temporal variability in ozone concentrations. Then for each crop and each cell of the grid, the ozone impact coefficients were applied to the yield response function as presented in Figure 47.

The impacts on gross margin are significant and can reach more than 5% in some regions based on the worst case climate change scenario, as presented in Figure 48. The authors also observed a shift towards the use of more ozone resistant crops such as barley for which acreage increases to the detriment of sensitive crops such as wheat.
<figure><img src="image_61.png" /><figcaption>**Figure 46.** N-Input tax impact on the input consumption and on the pollutant emission: a possible paradoxical effect (on the Seine river basin - SN). The tax represented on the x-axis corresponds to the percent increase in nitrogen price.</figcaption></figure>

presence of three nitrogen pollutants: *NO*₃, *N*₂*O* and *NH*₃. The regulation of joint agricultural pollutants remains a challenge for applied economics and modelling techniques when pollutant emission factors differ strongly and depend on soils and farming practices. Thus far, policies have addressed these issues on a pollutant-by-pollutant basis. However, the land-use and crop reallocation induced by each policy (alone) may increase the regulation cost for other pollutants.

This paper improves the understanding of the relations between nitrogen pollutants and the gains associated to regulating these different pollutants together by a combination of instruments. An integrated economic and agronomic approach is proposed. *AROPAj* is combined with *STICS* in order to capture the soil and crop heterogeneities in fertilizer response (in terms of yields and emissions) as well as to compute nitrogen pollutions. The policy under review combines a tax on nitrogen input and incentives that promote perennial crops (Miscanthus), which is assumed to require low levels of input.

A key point appears regarding the two kinds of pollutants, a soil pollutant (*NO*₃) and the atmospheric pollutants (*NH*₃ and *N*₂*O*). While there is no significant cost-efficiency gain to promote the land-use policy for gas pollutants, the policy mix strongly decreases the abatement cost for nitrate pollution.

For instance, for a target of 20 % abatement, the mixed policy decreases the *NO*₃ abatement cost by 0.8 percentage points (from 2.2% to 1.4%) and does not significantly change the *N*₂*O* abatement cost. The explanation lies in the fact that, unlike gas pollutants, *NO*₃ is very soil-crop dependent and the land reallocation in favour of miscanthus leads to higher additional abatements than those obtained with the N-tax alone. Thus, the land use induced by N-tax may be relatively far from the optimal one.
<figure><img src="image_63.png" /><figcaption>**Figure 48.** Losses in agricultural gross margin (in %) in France according to the worst case climate scenario. (source Humblot et al. (2013))</figcaption></figure>

Impacts of crop inoculation by plant growth promoting rhizobacteria

Bounaffaa et al. (2018) assessed the economic and environmental impacts of crop inoculation by plant growth promoting rhizobacteria (PGPR) in the Rhône-Alpes region. The inoculation practice with these beneficial bacteria, including the genus Azospirillum, has been shown to sustainably enhance cereal growth (Vacheron et al., 2013) and has attracted a lot of attention over the past 40 years. However, effects on crop yield, including maize, are still uncertain. As a result, no economic analysis of the effect of inoculation by Azospirillum has been conducted so far. Moreover, no previous studies have addressed the possible environmental effect of such inoculation, in particular regarding GHG emissions.

For the economic and environmental analysis, we used a combination of AROPAj and outputs generated by the crop model STICS. The main proxies used to assess the effect on farmers profit were the possible influence of the PGPR on maize yield and its inoculation cost. Since neither previous reports nor our field trials results provided clear and consistent inoculation effect on maize yield, we created a range of randomized realistic scenarios of the possible effect of inoculation on yield using the dose

N

-response function generated by STICS. The environmental analysis of inoculation based on nitrous oxide N₂O emissions from cropland soil was assessed firstly through field trials. Our results show that N₂O production was stimulated by inoculation up to > 80% at sites with high C content, but was slightly decreased by inoculation down to 8% at sites where C content was lower (Florio et al., 2018). These results were used to elaborate the scenarios for the possible effects of inoculation of soil N₂O emission according to soil type.

In the case of a low soil C content (50c) and for a given set of parameters (N0 and τ) for the
However, for N₂O and NH₃ emissions, the abatement gain obtained from a mixed policy is low because the land use induced by N-tax is relatively close to the optimal one for gas pollutants. On the basis of the parsimony criterion, the fertilizer tax would be therefore preferred.

Another advantage of a mixed policy is the possibility of choosing the combination of instru-
ments that brings the largest abatement for a given agricultural income loss and maximizes the
abatement for a given revenue. In particular, for a given net tax receipt (tax receipt minus crop
subsidies) between 0.6 and 0.8 M€, the abatement range is between 7 and 35% in the case of NO₃
and between 16 and 50% in the case of N₂O in France. This is illustrated by Figures 36.

<figure><img src="image_62.png" /><figcaption>**Table 36.** Abatement costs to N-losses under NO₃ and N₂O at the French level (source: Bourgeois et al. (2014)).</figcaption></figure>

Moreover, because soils, crops and emissions are different in each region considered, we provide
an analysis by river basin. These differences are reflected in the abatement cost curves. Also,
we demonstrate that the mixed policy, especially the land-use policy, should be adapted per river
basin in order to improve the cost-effectiveness of regulation.

Effect of spatial differentiation of nitrogen taxes

Lungarska & Jayet (2016) assess the impacts of the spatial differentiation of input-based ni-
trogen tax on farmers’ gross margins. In theory, this differentiation should lower the compliance
costs but the empirical studies on the matter report different magnitudes for the associated eco-
nomic gains. The assessment’s methodology combines a statistical model for nitrate pollution of
French water bodies and the AROPAj model. Thus, that accounts for the heterogeneity of French
farms and the disparities in the spatial distribution of water pollution. A major contribution to
the literature on tax differentiation is the inclusion of tax on livestock units as a source of animal
manure. The results obtained allow to estimate the potential efficiency gains associated with the
spatial differentiation of pollution taxes.

It is argued that the only economically viable input-based taxation scheme addressing nitrogen pollution from agriculture is spatially differentiated, since the estimated losses for firm-specific and water body-specific pollution fees equal respectively 5.8% and 32.5% of the gross margin, whereas the uniform policies studied (at the river-basin district and national level) result in substantial economic losses and the discontinuation of agricultural activity. Scenarios with fine-scale spatial differentiation are associated with lower tax rates in areas subject to lower environmental pressure
dose-response function (50a), N₂O emission was reduced (green line, 50c) at low fertilization rates. In contrast, N₂O emission increased in response to inoculation in the case of a high soil C content (50d).

<figure><img src="image_64.png" /><figcaption>**Figure 49.** Changes in yield (a, b) and N₂O emission (c, d) in response to maize inoculation for farmland with low (a, c) and high (b, d) soil carbon content. (source Bounaffaa et al. (2018))</figcaption></figure>

Compared to the benchmark (red line, 50), gross margin increased on average for maize when the inoculation cost was low, it was close to neutral for a cost of €20/ha, and it dramatically declined for higher costs (up to €20/ha). Similar results were obtained on average for marketed maize production, maize on-farm use and fertilizer demand (50) but not for maize area, for which density functions strongly differed with different inoculation costs. In all cases, synthetic fertilizer demand decreased, leading to a decrease in GHG emissions. Furthermore, improved emission abatement in low C content soils was reversed and deteriorated when soil C content increases. In this case, private economic benefits in farming systems were offset by increasing GHG emissions.

## 12.6 Adaptation to climate change

**Key-points:**

* Combining the crop model capability of refining technical cropping routes and the optimal agricultural land allocation provided by *AROPAj*

* Using *AROPAj* to feed spatial econometrical analysis of climate induced land use change

### Autonomous adaptation of farming systems

Leclère et al. (2013) investigated the impacts of climate change on main European crops, the
which, in turns, results in lower compliance costs for the farmers. Such policies are better targeted
and avoid needless restriction on agricultural activity. This is illustrated by maps in figure 47.

<figure><img src="image_63.png" /><figcaption>**Figure 47.** Geographical distribution and rates, in € 1000 per livestock unit (on the left) and per ton of mineral fertilizing nitrogen (on the right), for the "Tax hydrosectors" scenario (source: Lungarska & Jayet (2016)).</figcaption></figure>

As in previous studies based on the AROPAj model and focused on nitrate pollution and input-based taxation, an adverse effect of the taxation is observed in some cases (see also 12.3 on page 171). This adverse effect is due to a crop switching process resulting from farmers’ adaptation to taxation since newly adopted crops require less nitrogen but are also less efficient in fixing it and thus, ultimately, are leading to more nitrate pollution than in the business-as-usual case. Even if the extent of this adverse is reported to be rather limited, there are numerous cases of it because of the fine-scale resolution of the study. However, results suggest that input-based taxation should be accompanied by additional regulation limiting the crop switching process.

Halving mineral nitrogen use as part of EU Green Deal policies

With the EU Green Deal proposal, a set of objectives have been given to EU agricultural
namely the reduction of nitrogen losses from the sector by 50%. In Lungarska et al. (2023) we
model a policy halving the use of mineral nitrogen by EU farmers and study its effects in terms of
i) agricultural production and profitability; ii) organic nitrogen use; iii) greenhouse gas emissions;
and iv) land use among others. Furthermore, in Devaraju et al. (2022) we explore the impacts
on soil carbon and biodiversity. Prudhomme et al. (2022) offers a synthesis of this two works and
proposes a cost-benefit analysis of the policy in light of our findings.

AROPAj supply-side simulations are jointly presented with those from the partial equilibrium model Nexus Land Use (NLU, Souty et al., 2012 and provide an agent-level glimpse, aggregated at regional, national, and EU scales. These model runs build on the capacity of the agents to adjust their crop mix and animal production as well as the amount of fertilizers applied on field through the nitrogen-water dose response functions defined before. Moreover, combined with an econometric land use shares model as in Lungarska & Chakir (2018), the results have been enriched with the evolution in forestry, urban, and other land use. This renders them interesting for studying soil carbon and biodiversity as mentioned before.
<figure><img src="image_65.png" /><figcaption>**Figure 50.** AROPAj based probability density function for gross margin, maize area, marketed maize production, maize on-farm use, fertilizer demand and GHG emissions, at the region level. Blue lines correspond to four inoculation costs, from 0€ (dark blue) to 60 €/ha (light blue). The red line represents the non-inoculated maize control case. (source Bounaffaa et al. (2018))</figcaption></figure>
<figure><img src="image_64.png" /><figcaption>**Figure 48.** Crops, pastures, and fallow land shares for the baseline and half N policy (Lungarska et al., 2023)</figcaption></figure>

Figure 48 provides the results in terms of land allocation between crops, pastures, and fallow land as well as the crop mix. The 50% reduction is very significant in magnitude and impacts strongly the profitability of agricultural activity. Since there are semi-fixed costs of putting land into production (seeds, pest control, etc.), if these are not covered by the revenues the plot is set aside as fallow land. We can see that fallow land increases more than threefold at the expenses of cropland with some conversion also into pastures. Among different crops, the most impacted are oilcrops (decrease in acreage), legumes and cereals with an increase in percentages for the latter.

In terms of fertilizer use per ha, figure 49 provides two plots distinguishing the effects of the policy among EU member states and mineral and organic nitrogen. The results show important reduction in per ha use of mineral N for countries with high initial values such as Ireland, Sweden, Austria, Germany, Greece, France and others. As for organic N, its per ha use increases mainly driven by the reduction in the cropland acreage (overall organic N use remains stable with animal numbers being slightly impacted by the policy).

## 12.4 Biomass provision from perennial crops

**Key-points:**

* Biomass and bio-fuel related issues as yet open questions

### Land use impact of perennial crops introduction

The impacts of producing cellulosic biofuels from dedicated energy crops influence agricultural and environmental outputs at local, regional and national scales. Thus, we attempt to assess the
potential autonomous adaptation of farmers, and the final impacts on European agricultural supply. This is the first study to estimate in a consistent manner what would be the impacts of climate on main European crops and the gross margin and production levels of European farmers, while accounting for a certain type of adaptations. These autonomous adaptations cover actions that rely on routine farmer's knowledge of how climate influences production activities, such as shifting sowing date by up to 3 weeks, using a cultivar of lower precocity, altering the area allocated to various crops, or applying irrigation to previsouly rainfed crops.

The methodology builds on the *AROPAj* model (version V2) coupling to the *STICS* crop through *N*-to-yield dose-response functions on the *ArTiX* plateform (see section 9.1 and 9.1.3).

First, we generated a first set of dose-response functions using present climate information at the scale of EU-15, as simulated by a regional climate model (RCA3, Kjellström et al. (2011)) driven by simulations from the ECHAM5 global climate model for the time period 1976-2005. The effects of using this set of dose-response functions, as compared to using the initial yield and *N* rates contained in FADN data (i.e. no coupling), as well as to using dose-response functions simulated with a more accurate source of weather data for present climate was evaluated in an article under review Leclère et al. (2014).

In a second step we simulated four new sets of dose-response functions under future climate with the STICS crop model using the same source of climate data (RCA3 regional climate model driven by ECHAM5 global climate model) for the time period 2071-2100. These four sets of dose-response functions differed in terms of the assumed radiative forcing level (SRES scenario A2 or B1) as well as by virtue of wether or not farmers used an alternative sowing date or cultivar. These four sets thus govern the effects of autonomous farmers' adaptation. An additional scenario, which permits for the possible land reallocation in the AROPAj model, further allowed us to estimate the impacts of crop substitution. This was achieved by introducing additional parameters (xjk0(j,k), see 4.2.1) referring to the land allocation provided by a reference scenario, and additional constraints (fx1(j,k), fx2(j,k), see 4.2.3).

Overall, we found results similar to previous literature for climate change impacts without adaptation: aggregated impacts over Europe range from -13% to +15% depending on the crop, with high spatial variability and Northern (resp. Southern) Europe being more positively (resp. negatively) impacted. We found that autonomous adaptation - and in particular adjustments in field scale management practices - could have large beneficial impact: for instance, accounting for them would change the impact of climate change on aggregated European cereal production from moderately positive (+5-13%) to highly positive (+53-55%). This would, however, entail the high cost of a sharp increase in water consumption, in particular because autonomous adaptation increases crop yields without increasing their water-use efficiency. Our results show that autonomous adaptation is very important to account for when estimating climate change impacts, and is likely to trigger price feedbacks from markets as well as high pressure on water resources.

## Grass monetary value and its sensitivity to climate change

Grassland (e.g. Soussana & Lüscher, 2007) and livestock (e.g. Leip et al., 2010) systems have an essential role in both ecosystem services and agricultural economy. Livestock (Rötter & van de Geijn, 1999) as well as grassland (Brisson & Levrault, 2010) are impacted by climate change and the overall impacts are region-dependent (Olesen & Bindi, 2002). Implementation of practices
<figure><img src="image_65.png" /><figcaption>**Figure 49.** Mineral and organic nitrogen use per ha of arable land for the baseline and half N policy (Lungarska et al., 2023)</figcaption></figure>

trade-offs between food, feed and (bio)fuel production by focusing on the interactions between the cultivation of lignocellulosic biomass, i.e. miscanthus, and its joint implications on agricultural activities and non-CO₂ emissions, i.e. CH₄ and N₂O at the European scale. To this end, miscanthus, which is a annually harvested perennial crop, was integrated into *AROPAj*. Introducing this crop in a one-year period model requires the development of a two-step procedure to feed the model with appropriate information. The first step involves selecting a continuous time-yield function, which is calibrated and adjusted to the yield of traditional crops. The second step is a computation step based on a Faustmann dynamic approach aiming at the estimation of the rotation duration, the average yield and the discounted annual costs.

The average estimated yield of miscanthus among *AROPAj* farm-groups is typically about 23 tDM/ha. Yields between 20 and 30 tDM/ha are very frequent and the highest and lowest yields are 35 and 5 tDM/ha respectively. In order to test the sensitivity of *AROPAj* results to possible variations in miscanthus yield, yield scenarios were evaluated. They correspond to the introduction of miscanthus with 10 levels of possible yield potential. We proceed to a homogeneous reduction of miscanthus yield from 0 to 100% by 10% intervals over all farm groups. The rate of reduced potential is denoted by $\psi$. This potential reduction is applied at the plot level after the Faustmann optimizing step. We assume that farmers first follow advice in terms of perennial crop management, and then observe yield reduction. Because of the vivid "Food vs Fuel" debates, we assume that the part of the farm group's *UAA* devoted to miscanthus does not exceed 20%.

Table 37 shows that arable land and grassland are reduced at the same rate when miscanthus is progressively introduced. Areas devoted to oilseeds and cereals decrease by 20% and 18% respec-
for climate change adaptation and mitigation could reduce climate change damage to agricultural systems (e.g. Reidsma et al., 2009; Leclère et al., 2013) and also open up the possibility of increasing the resilience of agricultural systems.

A large proportion of produced forage and grass is not marketed and therefore their economic impacts are not considered in farm costs. To overcome this issue, the economic value of grass production has been calculated, across fifteen member states of the European Union (EU-15), using the AROPAj agro-economic, which then has been coupled with PaSim biogeochemical grassland model to address both economic and environmental evaluation of European farming systems, taking into consideration the potential future impacts caused by climate change till 2100 (See section 9.3).

The shadow price of five types of grass and fodders implemented in AROPAj has been calculated. Our results show that grass shadow value depend significantly on climatic trends (See Table 38). According to our results, grass cover increase to a greater extent under B1H2 scenario, which may rely on a small increase in atmospheric CO2, a slight increase in the temperature, along with precipitation levels substantially higher than A2H2 scenario (Leclère et al., 2013). However, applying farm-scale adaptation practices (A2H2-A and B1H2-A) resulted in an important increase in cropland cover, at the expense of grassland (see Fig. 51). As a result of these changes, grass shadow prices will increase more significantly under the two adaptation scenarios due to the potential decrease in grassland area (see Table 38).

<table><caption>**Table 38.** Economic value of five varieties of grass under different climatic scenarios (expressed in €/t) and the relative shadow price difference (expressed in %) between two future adaptation scenarios (source:Aghajanzadeh-Darzi et al. (2016)).</caption><thead><tr><th rowspan="2"></th><th colspan="5">Shadow prices (€/t)</th><th colspan="2">Europe</th></tr><tr><th>CTL</th><th>A2H2</th><th>A2H2-A</th><th>B1H2</th><th>B1H2-A</th><th>A2H2-A</th><th>B1H2-A</th></tr></thead><tbody><tr><td>Beef forage</td><td>3.9</td><td>4.0</td><td>4.0</td><td>3.8</td><td>3.9</td><td>0.6</td><td>2.6</td></tr><tr><td>Maize forage</td><td>19.0</td><td>18.9</td><td>19.5</td><td>17.6</td><td>18.5</td><td>3.0</td><td>5.4</td></tr><tr><td>Permanent grassland</td><td>14.3</td><td>14.1</td><td>15.0</td><td>13.0</td><td>14.2</td><td>6.8</td><td>9.8</td></tr><tr><td>Temporary grassland</td><td>7.7</td><td>7.7</td><td>8.2</td><td>6.9</td><td>7.5</td><td>5.7</td><td>8.5</td></tr><tr><td>Other meadows</td><td>10.2</td><td>9.8</td><td>10.3</td><td>8.4</td><td>9.1</td><td>5.2</td><td>8.5</td></tr></tbody></table>

## Adaptation and mitigation of climate change in agriculture: accounting for possible land use changes

Following the study by Leclère et al. (2013), we go a step further in the possible adaptation measures to climate change. Since climate change is going to modify future agriculture incomes, it will also impact the returns to agricultural land and the spatial equilibrium between land based economic sectors such as agriculture and forestry. In order to account for these effects, we developed a methodology where the results of two sector-specific models (AROPAj for agriculture and FFSM++ for forestry) are used into a spatial econometric land use shares model (Chakir & Lungarska, 2017a). We investigate then the effects of climate change adaptation of farmers on land use along with the effects of a mitigation policy (tax on greenhouse gas emissions from agriculture). We find that the mitigation costs for farmers are lower when the land use feedback is accounted for (Lungarska & Chakir, 2018).

The land use shares model is based on the hypothesis that landowners maximize the income of their land or, in other words, their land rent. We model the following land uses: i) agriculture and pasture, ii) forest, iii) urban, and iv) other land uses. For each land use we have an explanatory
tively. When the miscanthus potential increases ($\psi$ up to 1), grasslands are slowly but continuously reduced up to 11% (full potential). This is also the case for fallow land, from a 0.5% decrease when the potential is low, to 21% when it is high. The decrease in cropland and grassland areas leads to a decrease in animal and feed production. Indeed, the quantity of on-farm consumed cereals decreases by 4% and marketed concentrates increase by 3% when miscanthus potential is 100%. With a maximum of potential, the computation of the livestock units per hectare of grassland shows an increase of 10%. Given these results, it is evident that a 12% decrease in grassland leads to an intensification of animal rearing.

<table><caption>**Table 37.** Share of land among major groups of agricultural activities (% UAA) when miscanthus yield potential $\psi$ is increasing - estimates provided by the AROPAj model for the EU-15. (Ben Fradj et al. (2016)).</caption><thead><tr><th rowspan="2">$\psi$ =</th><th colspan="1">without</th><th colspan="9">with miscanthus</th></tr><tr><th>0</th><th>0.2</th><th>0.3</th><th>0.4</th><th>0.5</th><th>0.6</th><th>0.7</th><th>0.8</th><th>0.9</th><th>1</th></tr></thead><tbody><tr><th>miscanthus</th><td>0</td><td>0.0</td><td>0.3</td><td>1.4</td><td>4.7</td><td>8.8</td><td>10.8</td><td>13.1</td><td>14.0</td><td>15.3</td></tr><tr><th>cereals</th><td>38.7</td><td>38.7</td><td>38.6</td><td>38.1</td><td>36.6</td><td>34.7</td><td>33.8</td><td>32.7</td><td>32.4</td><td>31.8</td></tr><tr><th>sugarbeet</th><td>3.0</td><td>3.0</td><td>3.0</td><td>3.0</td><td>3.0</td><td>2.8</td><td>2.8</td><td>2.8</td><td>2.7</td><td>2.7</td></tr><tr><th>oilseeds, proteins</th><td>6.0</td><td>6.0</td><td>6.0</td><td>5.9</td><td>5.7</td><td>5.3</td><td>5.1</td><td>5.0</td><td>4.9</td><td>4.9</td></tr><tr><th>potatoes</th><td>3.6</td><td>3.6</td><td>3.6</td><td>3.6</td><td>3.5</td><td>3.4</td><td>3.3</td><td>3.3</td><td>3.2</td><td>3.1</td></tr><tr><th>fodders</th><td>11.0</td><td>11.0</td><td>11.0</td><td>11.0</td><td>10.5</td><td>10.4</td><td>10.1</td><td>9.9</td><td>9.8</td><td>9.6</td></tr><tr><th>fallows</th><td>10.2</td><td>10.2</td><td>10.2</td><td>9.7</td><td>9.5</td><td>8.8</td><td>8.5</td><td>8.3</td><td>8.2</td><td>8.1</td></tr><tr><th>grassland</th><td>27.4</td><td>27.4</td><td>27.3</td><td>27.2</td><td>26.6</td><td>25.8</td><td>25.5</td><td>24.9</td><td>24.7</td><td>24.5</td></tr></tbody></table>

Introducing an environmentally-friendly crop has positive effects on GHG emission abatement. The replacement of crops characterized by high nitrogen ($N$) consumption (e.g. cereals) by miscanthus, which requires low $N$ input, is certainly advantageous for the abatement of $N$-pollutants, among them $N_2O$. Results show that the introduction of miscanthus leads to a significant reduction in $N_2O$ losses. The figures rise to 10% when miscanthus reaches its full potential. With regard to $CH_4$ emissions, the increase in purchased concentrates leads to a reduction, but this reduction (of 2%) remains low because of the intensification of animal rearing.

## Sensitivity analysis

In another study, we address the question of biomass uncertainty in continuous time for a perennial resource, in our case miscanthus, in order to analyse the sensitivity of the supply of this crop in France (Ben Fradj & Jayet (2018)). By applying two Faustmann models, we derive harvesting rules to deal with the optimal rotation age and the value of miscanthus when its growth function is accounted for in a deterministic way as well as when it is governed by a stochastic process. To manage the random yield of miscanthus, we develop a simple stochastic model in which the yield process is based on a popular distribution, the so-called beta distribution (see Figure 50). Because of its versatility, this distribution has been used to model a variety of uncertainties. It is bounded on both sides and so it is often used for representing processes with natural lower and upper limits while it has the flexibility to accommodate both positively and negatively skewed data. The AROPAj model is used to highlight the large scale impact of annual yield randomization.

This study provides an analysis of one factor, i.e. yield, which comes into play when the farmer is given the possibility of adopting miscanthus. We highlight the differences between the deterministic yield expectations and stochastic yield expectations of miscanthus in terms of profitability, land-use allocation and $N$-losses. Results show that planting this crop requires an important investment
<figure><img src="image_66.png" /><figcaption>**Figure 51.** Relative variation in land covered by grass (expressed as a %) across the EU under future scenarios compared to the present (CTL). a. A2H2, b. B1H2, c. A2H2-A and d. B1H2-A (source:Aghajanzadeh-Darzi et al. (2016)).</figcaption></figure>
<figure><img src="image_66.png" /><figcaption>**Figure 50.** Miscanthus yield distribution: an example of generated yield sample.</figcaption></figure>

which will be returned as of the 4^th^ year in the deterministic case and as of the 10^th^ year in the uncertain case. Concerning the economically optimal rotation age, it is about 16 years in the deterministic case. The introduction of a random factor in the growth function delays the optimal rotation for 5 years, up to 21 years.

In addition to the long-term profitability problem, the farmer has to deal with questions related to land use change. Favourable yield potential incites the farmer to adopt miscanthus despite the high degree of uncertainty. In fact, introducing yield uncertainty delays the adoption of miscanthus until its potential is sufficiently high to motivate the farmer's decision. More specifically, in the deterministic case, miscanthus is adopted when 30% of the yield potential is reached. In the stochastic case, this occurs when 60% of the yield potential is reached.

Simulations show a strong decrease in the food-crop areas when miscanthus is introduced. When the miscanthus yield reaches its full potential, cereals' areas decrease by 25% in the deterministic case, and by 7% in the stochastic one. At the same time, grassland decreases by 13% and 7.5%, in the certain and uncertain cases, respectively. Moreover, abandoned areas decrease to 28% in the first case, and to 14% in the second one. These changes in land use have a substantial effect on the N-input demand and N-losses. Indeed, we notice a decrease of fertilizer demand by 22% in the deterministic case and by 13% in the stochastic case when the miscanthus yield reaches its full potential. This decrease in N-demand leads to a 26% reduction in nitrate losses in the first case and 13% in the second one.

Through a sensitivity analysis, we notice that yields, renewal cycle costs and the discount rate interact with yield randomization and significantly affect the future profitability of miscanthus.

**12.5 Bio-economy oriented studies**

**Key-points:**

* Opportunities in complex bio-economic model based analysis combining all AROPAj modules

* Farming systems negatively impact by troposheric ozone

* Complex impacts in promoting rhizobacteria
<figure><img src="image_67.png" /><figcaption>**Figure 52.** Land use change for the three climate change scenarios and for different greenhouse gas tax levels (source Lungarska & Chakir, 2018)</figcaption></figure>

variable or variables to proxy their respective land rent. Agricultural land and pasture are approximated by the land shadow price estimated by AROPAj. Since we cannot specifically distinguish land rent for crops and land rent for pastures, in the application to climate change of this methodology, we model crops and pasture as one land use in the econometric part, and distinguish them later on the basis of their respective share in the production mix of farmers. Returns to forestry are obtained by the partial-equilibrium forestry model FFSM++ (Lobianco et al., 2016; Caurla et al., 2013). The proxy for the urban rent is the population density and incomes.

The land use shares model is also accounting for the spatial spillovers through the error terms and also through the explanatory variables. We simulate the effects of climate change by modifying the land rents for each of the modeled land uses. For the urban land use, we use the projected demographic evolution by IIASA downscaled to France based on the French national statistical institute (INSEE) projections. The impact of climate change on forestry returns has been captured through two indicators: tree growth and tree mortality. Also, forest manager are allowed to change tree species if another specie is more profitable under future climate conditions.

Since we are building on the results from Leclère et al. (2013), we simulate the A2 and B1 climate change scenarios and the baseline, CTL. We also introduce a tax on greenhouse gas emissions with values from 0 to 200€/tCO₂ eq. Figure 52 presents the land use changes resulting from climate change and/or the mitigation policy. Under current climatic conditions (CTL scenario), the mitigation tax results in an increase in forest and pastures area at the expense of agricultural crops. Agricultural crop area is nevertheless increasing under both climate change scenarios although this tendency is somewhat restrained when greenhouse gas emissions taxes are applied.

Figure 53 presents the obtained results in terms of greenhouse gas emissions and tax levels for the three climate scenarios. The tax impacts agricultural profits and, as expected, agricultural crops area is decreasing when taxation applies. Thus, the tax has both an impact on the intensive margin (lower emissions levels per hectare), and on the extensive margin (less area in agricultural activity). The effect on the extensive margin is given in figure 53 by the gap between the two emission curves with and without the land use change feedback effect.

Greenhouse gas emissions from French agriculture are to be reduced by 12% in 2028 comparing to 2013 levels. This objective is attained at 30€/tCO₂ eq. in the CTL case (40€/tCO₂ eq. when land use feedback is ignored); 90€/tCO₂ eq. in the A2 case (120€/tCO₂ eq. when land use feedback is ignored); and 90€/tCO₂ eq. in the B1 case (130€/tCO₂ eq. when land use feedback is ignored). We can thus conclude that climate change potentially would impact favorably agricultural
## Ozone impacts

The AROPAj model was used in a study aiming at estimating the potential detrimental impacts of surface ozone ($O_3$) on French crops in the context of climate change (Humblot et al. (2013)). Surface ozone is an air pollutant which causes damage to leaves and reduces photosynthesis efficiency. Its atmospheric concentration is predicted to increase in the future and could significantly impair agricultural productions.

<figure><img src="image_67.png" /><figcaption>**Figure 51.** AROPAj yield response function (blue) and the impact of ozone (red), in this case a 10% decrease in yield. The economical optimum is affected and the nitrogen quantites are not the same after the impact of ozone. (source Humblot et al. (2013))</figcaption></figure>

<figure><img src="image_68.png" /><figcaption>**Figure 52.** Losses in agricultural gross margin (in %) in France according to the worst case climate scenario. (source Humblot et al. (2013))</figcaption></figure>

Since the impact of ozone depends on the considered crop, the developed methodology had to take this heterogeneity into account. To do so, the authors used a literature review compiled by Mills et al. (2007), who performed regression analyses on more than 700 articles and 19 crops to extract the coefficients of impact as a function of the ozone concentration.
<figure><img src="image_68.png" /><figcaption>**Figure 53.** Greenhouse gas emissions for different levels of the mitigation tax (source Lungarska & Chakir, 2018)</figcaption></figure>

profits, but can have negative impacts in terms of greenhouse gas emissions (and other pollutants associated with intensive agriculture such as nitrates), and can result in the loss of permanent grasslands and forests.
The data for future ozone levels in 2030 were taken from climatic studies and represented in a 50km per 50km grid. The amount of ozone was calculated as the sum of daily values for the three months preceding the harvest date of the crop, as estimated by the STICS model, enabling for a fine consideration on both spatial and temporal variability in ozone concentrations. Then for each crop and each cell of the grid, the ozone impact coefficients were applied to the yield response function as presented in Figure 51.

The impacts on gross margin are significant and can reach more than 5% in some regions based on the worst case climate change scenario, as presented in Figure 52. The authors also observed a shift towards the use of more ozone resistant crops such as barley for which acreage increases to the detriment of sensitive crops such as wheat.

## Impacts of crop inoculation by plant growth promoting rhizobacteria

Bounaffaa et al. (2018) assessed the economic and environmental impacts of crop inoculation by plant growth promoting rhizobacteria (PGPR) in the Rhône-Alpes region. The inoculation practice with these beneficial bacteria, including the genus Azospirillum, has been shown to sustainably enhance cereal growth (Vacheron et al., 2013) and has attracted a lot of attention over the past 40 years. However, effects on crop yield, including maize, are still uncertain. As a result, no economic analysis of the effect of inoculation by Azospirillum has been conducted so far. Moreover, no previous studies have addressed the possible environmental effect of such inoculation, in particular regarding GHG emissions.

For the economic and environmental analysis, we used a combination of AROPAj and outputs generated by the crop model STICS. The main proxies used to assess the effect on farmers profit were the possible influence of the PGPR on maize yield and its inoculation cost. Since neither previous reports nor our field trials results provided clear and consistent inoculation effect on maize yield, we created a range of randomized realistic scenarios of the possible effect of inoculation on yield using the dose N-response function generated by STICS. The environmental analysis of inoculation based on nitrous oxide $N_2O$ emissions from cropland soil was assessed firstly through field trials. Our results show that $N_2O$ production was stimulated by inoculation up to > 80% at sites with high C content, but was slightly decreased by inoculation down to 8% at sites where C content was lower (Florio et al., 2018). These results were used to elaborate the scenarios for the possible effects of inoculation of soil $N_2O$ emission according to soil type.

In the case of a low soil C content (54c) and for a given set of parameters ($N_0$ and $τ$) for the dose-response function (54a), $N_2O$ emission was reduced (green line, 54c) at low fertilization rates. In contrast, $N_2O$ emission increased in response to inoculation in the case of a high soil C content (54d).

Compared to the benchmark (red line, 54), gross margin increased on average for maize when the inoculation cost was low, it was close to neutral for a cost of €20/ha, and it dramatically declined for higher costs (up to €20/ha). Similar results were obtained on average for marketed maize production, maize on-farm use and fertilizer demand (54) but not for maize area, for which density functions strongly differed with different inoculation costs. In all cases, synthetic fertilizer demand decreased, leading to a decrease in GHG emissions. Furthermore, improved emission abatement in low C content soils was reversed and deteriorated when soil C content increases. In this case, private economic benefits in farming systems were offset by increasing GHG emissions.
# 13 Perspectives and further developments

**Key-point:**

Some challenging issues under study

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

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

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

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

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

## 13.1 Water issues

The adaptation of agriculture to climate change and growing demand for water by a range of users entails the question of water availability and pricing. A large part of renewable water (from rainfalls) used by agriculture circulates outside the market. Irrigation is and will become an even more pressing problem. But even in this case, costs related to water extraction (pumping and other investment or variable costs) remain higher than those related with the resource itself.
<figure><img src="image_69.png" /><figcaption>**Figure 53.** Changes in yield (a, b) and N₂O emission (c, d) in response to maize inoculation for farmland with low (a, c) and high (b, d) soil carbon content. (source Bounaffaa et al. (2018))</figcaption></figure>

## 12.6 Adaptation to climate change

**Key-points:**

Combining the crop model capability of refining technical cropping routes and the optimal agricultural land allocation provided by *AROPAj*

Using *AROPAj* to feed spatial econometrical analysis of climate induced land use change

### Autonomous adaptation of farming systems

Leclère et al. (2013) investigated the impacts of climate change on main European crops, the potential autonomous adaptation of farmers, and the final impacts on European agricultural supply. This is the first study to estimate in a consistent manner what would be the impacts of climate on main European crops and the gross margin and production levels of European farmers, while accounting for a certain type of adaptations. These autonomous adaptations cover actions that rely on routine farmer's knowledge of how climate influences production activities, such as shifting sowing date by up to 3 weeks, using a cultivar of lower precocity, altering the area allocated to various crops, or applying irrigation to previsouly rainfed crops.

The methodology builds on the *AROPAj* model (version V2) coupling to the *STICS* crop through N-to-yield dose-response functions on the *ArTiX* plateform (see section 9.1 and 9.1.3).

First, we generated a first set of dose-response functions using present climate information at the scale of EU-15, as simulated by a regional climate model (RCA3, Kjellström et al. (2011)) driven by simulations from the ECHAM5 global climate model for the time period 1976-2005.
An innovative mix of crop modeling and economic analysis is under progress with the aim of providing two-input yield response functions. These two inputs are nitrogen (through mineral fertilizers or manure sourced organic matter) and water. Water is taken into consideration as the command variable from the farmer's economic point of view. Thus, the producer may call for irrigation when rainfalls do not provide water in sufficient quantities in terms of the potential costs and benefits of additional water.

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

<figure><img src="image_69.png" /><figcaption>**Figure 54.** The yield function as annual response to water and nitrogen inputs; water and nitrogen refer to the total annual quantities including natural resource; the blue line refers to an isoquant in yields.</figcaption></figure>

The link between the crop model sourced functions and the economic analysis is complex. The demand for water (expressed in cubic meters or *mm* height per hectare, 1mm being equivalent to 10 cubic meters) is translated in water stress for the crop model simulation. For one period, the two-input function is illustrated by Figure 54. The method of response function building follows the method initiated by Godard et al. (2008) and expanded by the E. Galko's and D. Leclère's PhD work. The two-input approach is much more complex to develop, but difficulties are compensated by database availability and increased computing capabilities. It is implemented and explored in the P. Humblot's PhD thesis (May 2016). Methodological aspects and implementation are described by Humblot et al. (2017).

## 13.2 Low input agriculture

The first attempt to analyze the performances of low input farming systems was carried out at a small watershed level, at which only a few organic farming systems currently exist. Economic performance was assessed through the standard criterion of gross margin. The environmental performance was judged through subsoil nitrogen losses. However, this kind of experimental approach suffers from a lack of reference points of comparison with traditional farming systems.
<figure><img src="image_70.png" /><figcaption>**Figure 54.** AROPAj based probability density function for gross margin, maize area, marketed maize production, maize on-farm use, fertilizer demand and GHG emissions, at the region level. Blue lines correspond to four inoculation costs, from 0€ (dark blue) to 60 €/ha (light blue). The red line represents the non-inoculated maize control case. (source Bounaffaa et al. (2018))</figcaption></figure>
At this point, the first difficulties which arise concern the definition of organic farming, based on product labeling and farming practices. This indeterminacy of definition may be reflected in existing databases. Secondly, the European economic databases, such as the FADN, still provide too little information for parameter estimation.

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

### 13.3 Facilitating MP solutions involving interacting agents

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

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

There is now the possibility to aggregate farm group matrices emanating from several *EU* countries, up to the *EU* at a whole. This is of interest when *MP* constraints involve activities from different farm groups. We define a specific *T*-block operating in the kernel of the model. (see Section 4.2 on page 25) The particular design of such constraints is that the related *RHS* elements are not farm group-indexed. Examples of this are targeted limits in agricultural production (e.g. minimal food-calorie amount), or environmental issue (e.g. maximal amount of emitted pollutants). There is a real gain by doing so when the problem of interest implies at least two kinds of not linearly correlated trans-farm group constraints. Otherwise, the standard individual *MP* should be better solved for all involved farm groups when parameterizing the “price” of the bounded resource. But even in this case, there is the advantage of an easier implementation of the “dual” approach, which directly provides the dual price of a bounded resource utilized by several economic agents (implicitly clearing a quota market).

<footnote n="14">¹⁴Indeed the point arises when one *EU* member State is subdivided into two or more sub-countries, which is automatically performed in the farm group clustering step for countries with large sample size *FADN* database.</footnote>
The effects of using this set of dose-response functions, as compared to using the initial yield and *N* rates contained in FADN data (i.e. no coupling), as well as to using dose-response functions simulated with a more accurate source of weather data for present climate was evaluated in an article under review Leclère et al. (2014).

In a second step we simulated four new sets of dose-response functions under future climate with the STICS crop model using the same source of climate data (RCA3 regional climate model driven by ECHAM5 global climate model) for the time period 2071-2100. These four sets of dose-response functions differed in terms of the assumed radiative forcing level (SRES scenario A2 or B1) as well as by virtue of wether or not farmers used an alternative sowing date or cultivar. These four sets thus govern the effects of autonomous farmers' adaptation. An additional scenario, which permits for the possible land reallocation in the AROPAj model, further allowed us to estimate the impacts of crop substitution. This was achieved by introducing additional parameters (xjk0(j,k), see 4.2.1) referring to the land allocation provided by a reference scenario, and additional constraints (fx1(j,k), fx2(j,k), see 4.2.3).

Overall, we found results similar to previous literature for climate change impacts without adaptation: aggregated impacts over Europe range from -13% to +15% depending on the crop, with high spatial variability and Northern (resp. Southern) Europe being more positively (resp. negatively) impacted. We found that autonomous adaptation - and in particular adjustments in field scale management practices - could have large beneficial impact: for instance, accounting for them would change the impact of climate change on aggregated European cereal production from moderately positive (+5-13%) to highly positive (+53-55%). This would, however, entail the high cost of a sharp increase in water consumption, in particular because autonomous adaptation increases crop yields without increasing their water-use efficiency. Our results show that autonomous adaptation is very important to account for when estimating climate change impacts, and is likely to trigger price feedbacks from markets as well as high pressure on water resources.

## Grass monetary value and its sensitivity to climate change

Grassland (e.g. Soussana & Lüscher, 2007) and livestock (e.g. Leip et al., 2010) systems have a essential role in both ecosystem services and agricultural economy. Livestock (Rötter & van de Geijn, 1999) as well as grassland (Brisson & Levrault, 2010) are impacted by climate change and the overall impacts are region-dependent (Olesen & Bindi, 2002). Implementation of practices for climate change adaptation and mitigation could reduce climate change damage to agricultural systems (e.g. Reidsma et al., 2009; Leclère et al., 2013) and also open up the possibility of increasing the resilience of agricultural systems.

A large proportion of produced forage and grass is not marketed and therefore their economic impacts are not considered in farm costs. To overcome this issue, the economic value of grass production has been calculated, across fifteen member states of the European Union (EU-15), using the AROPAj agro-economic, which then has been coupled with PaSim biogeochemical grassland model to address both economic and environmental evaluation of European farming systems, taking into consideration the potential future impacts caused by climate change till 2100 (See section 9.3).

The shadow price of five types of grass and fodders implemented in AROPAj has been calculated. Our results show that grass shadow value depend significantly on climatic trends (See Table 38). According to our results, grass cover increase to a greater extent under B1H2 scenario,
A processing application is encoded in FORTRAN reseting automatically the list of suffices and the indexation of farm group-indexed parameters, independently from member-States involved in a given calculation. By doing so there is no need to change the kernel of the model (i.e. no required re-writing of basic blocks of MP activities and constraints). The counterpart is the need to manage the complete MP solution provided by GAMS. Nevertheless the MP result exploitation is facilitated by indexation specifying the sub-country and the farm group within the sub-country.

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

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

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

### Building dose-response functions

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

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

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

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

Issues still pending refer to different kind of aspects:

* from the region list and to crop location priors in all *FADN*-year cases: a few regions are not properly referred to basic spatial information, e.g. 580-Canarias (Spain), 650-Açores e Madeira (Portugal), extra-European regions (205-Guadeloupe, 206-Martinique, 207-La Réunion), German "State-towns" (20-Hamburg, 40-Bremen, 110-Berlin)

* from the region list and to crop location priors for one *FADN* year in case of region merging in *EC* statistics: 610-Entre Douro e Minho/Beira litoral and 620-Tras-os-Montes/Beira interior merging into 620-Norte e Centro (Portugal, 2007); 761, 762 and 763 merging into 768-Dunántúl and 760, 765 and 766 merging into 768-Alföld (Hungary, 2012)
which may rely on a small increase in atmospheric CO₂, a slight increase in the temperature, along with precipitation levels substantially higher than A2H2 scenario (Leclère et al., 2013). However, applying farm-scale adaptation practices (A2H2-A and B1H2-A) resulted in an important increase in cropland cover, at the expense of grassland (see Fig. 55). As a result of these changes, grass shadow prices will increase more significantly under the two adaptation scenarios due to the potential decrease in grassland area (see Table 38).

<table><caption>**Table 38.** Economic value of five varieties of grass under different climatic scenarios (expressed in €/t) and the relative shadow price difference (expressed in %) between two future adaptation scenarios (source:Aghajanzadeh-Darzi et al. (2016)).</caption><thead><tr><th rowspan="2"></th><th colspan="5">Shadow prices (€/t)</th><th colspan="2">Europe</th></tr><tr><th>CTL</th><th>A2H2</th><th>A2H2-A</th><th>B1H2</th><th>B1H2-A</th><th>A2H2-A</th><th>B1H2-A</th></tr></thead><tbody><tr><td>Beef forage</td><td>3.9</td><td>4.0</td><td>4.0</td><td>3.8</td><td>3.9</td><td>0.6</td><td>2.6</td></tr><tr><td>Maize forage</td><td>19.0</td><td>18.9</td><td>19.5</td><td>17.6</td><td>18.5</td><td>3.0</td><td>5.4</td></tr><tr><td>Permanent grassland</td><td>14.3</td><td>14.1</td><td>15.0</td><td>13.0</td><td>14.2</td><td>6.8</td><td>9.8</td></tr><tr><td>Temporary grassland</td><td>7.7</td><td>7.7</td><td>8.2</td><td>6.9</td><td>7.5</td><td>5.7</td><td>8.5</td></tr><tr><td>Other meadows</td><td>10.2</td><td>9.8</td><td>10.3</td><td>8.4</td><td>9.1</td><td>5.2</td><td>8.5</td></tr></tbody></table>

<figure><img src="image_71.png" /><figcaption>**Figure 55.** Relative variation in land covered by grass (expressed as a %) across the EU under future scenarios compared to the present (CTL) a. A2H2, b. B1H2, c. A2H2-A and d. B1H2-A (source:Aghajanzadeh-Darzi et al. (2016)).</figcaption></figure>

## Adaptation and mitigation of climate change in agriculture: accounting for possible land use changes

Following the study by Leclère et al. (2013), we go a step further in the possible adaptation measures to climate change. Since climate change is going to modify future agriculture incomes,
* from crop location priors to crop location posteriors: all regions in Germany, except 100-Saarland (10, 30, 50, 60, 70, 80, 90, 112, 113, 114, 116; all years); regions 10 and 20 merging into region 15 for 2018

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

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

Managing some of these issues is still in progress in the encoded applications. At the present time, March 2018, between 24 and 30 regions are missing in terms of farm group location probabilities (see Table 1).

## 13.5 Organic matter, biogas, digestates

Two problems are under study involving manure, crop residues, biogas production and digestate recycling. The first problem is devoted to the volatile solid (VS) sourced from manure when possibility of exchanges exists between breeding farms and plant growing farms. The second problem leads to consider manure and crop residues as raw matter (and further intercropping productions) to be transformed into biogas. Digestates as by-products are eligible to be part of organic Nitrogen utilized by crops. A new complex block included in the model is in progress.
<figure><img src="image_72.png" /><figcaption>**Figure 56.** Land use change for the three climate change scenarios and for different greenhouse gas tax levels (source Lungarska & Chakir, 2018)</figcaption></figure>

it will also impact the returns to agricultural land and the spatial equilibrium between land based economic sectors such as agriculture and forestry. In order to account for these effects, we developed a methodology where the results of two sector-specific models (AROPAj for agriculture and FFSM++ for forestry) are used into a spatial econometric land use shares model (Chakir & Lungarska, 2017a). We investigate then the effects of climate change adaptation of farmers on land use along with the effects of a mitigation policy (tax on greenhouse gas emissions from agriculture). We find that the mitigation costs for farmers are lower when the land use feedback is accounted for (Lungarska & Chakir, 2018).

The land use shares model is based on the hypothesis that landowners maximize the income of their land or, in other words, their land rent. We model the following land uses: i) agriculture and pasture, ii) forest, iii) urban, and iv) other land uses. For each land use we have an explanatory variable or variables to proxy their respective land rent. Agricultural land and pasture are approximated by the land shadow price estimated by AROPAj. Since we cannot specifically distinguish land rent for crops and land rent for pastures, in the application to climate change of this methodology, we model crops and pasture as one land use in the econometric part, and distinguish them later on the basis of their respective share in the production mix of farmers. Returns to forestry are obtained by the partial-equilibrium forestry model FFSM++ (Lobianco et al., 2016; Caurla et al., 2013). The proxy for the urban rent is the population density and incomes.

The land use shares model is also accounting for the spatial spillovers through the error terms and also through the explanatory variables. We simulate the effects of climate change by modifying the land rents for each of the modeled land uses. For the urban land use, we use the projected demographic evolution by IIASA downscaled to France based on the French national statistical institute (INSEE) projections. The impact of climate change on forestry returns has been captured through two indicators: tree growth and tree mortality. Also, forest manager are allowed to change tree species if another specie is more profitable under future climate conditions.

Since we are building on the results from Leclère et al. (2013), we simulate the A2 and B1 climate change scenarios and the baseline, CTL. We also introduce a tax on greenhouse gas emissions with values from 0 to 200€/tCO₂ eq. Figure 56 presents the land use changes resulting from climate change and/or the mitigation policy. Under current climatic conditions (CTL scenario), the mitigation tax results in an increase in forest and pastures area at the expense of agricultural crops. Agricultural crop area is nevertheless increasing under both climate change scenarios although this tendency is somewhat restrained when greenhouse gas emissions taxes are applied.
# 14 Glossary, communication, references

## Glossary

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

* ADEME the French Environment and Energy Management Agency

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

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

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

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

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

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

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

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

* BtL biomass to liquid (thermo-chemical process)

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

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

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

* CC climate change

* CE cross entropy

* CH₄ methane

* CLC the CORINE Land Cover database

* CMO Common Market Organization (refers to the CAP)

* COP stands for cereals, oilseeds and proteins crops

* DEM Digital Elevation Model

* DM dry matter

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

* EC the European Commission

* ECHAM5 one of the GCMs

* EFOM Energy Flow Optimization Model

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

* ESDB the European Soil Database

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

* ETS the (European) emission trading scheme