Increasing food production and mitigating agricultural greenhouse gas emissions in the European Union: impacts of carbon pricing and calorie production targeting

Keywords: greenhouse gas emissions, food production, carbon price, European Union, mathematical programming model JEL Classification: Q18, Q54

This study focuses on the links between food production and greenhouse gas emissions in the European Union. The analysis relies on two sets of simulations of AROPAj, a supply-side model of EU agriculture: (i) a carbon price affecting agricultural GHG emissions (from 0 to 200 EUR/tCO2eq), and (ii) a lower limit on the net quantity of food calories provided by EU agriculture (200 to 450 Mt soft wheat equivalent). The model is calibrated on six annual datasets 2007-2012. The results show that a moderate increase in the price of carbon would lead to an increase in total areas and outputs of crops. Animal production decreases over the explored range of carbon price. At 200 EUR/tCO2eq, the reduction in GHG emissions ranges from 25 to 35% depending on the year of calibration. The results also show that current net calorie production from food can be more than doubled, while simultaneously reducing GHG emissions by 10-15%. The compatibility between a reduction in GHG emissions and an increase in food calorie production relies on substantial changes in animal production and feed, which implies significant variations in grassland and fallow land. These effects are contrasted between the regions of the EU.

Introduction

One of the major challenges of the 21st century is to ensure an appropriate and viable food system (United Nations, 2015) while simultaneously reducing negative impacts on the environment [START_REF] Garnett | Where are the best opportunities for reducing greenhouse gas emissions in the food system (including the food chain)? Food Policy[END_REF]. Relationships between agriculture, climate change, and the environment are at the center of these debates in the scientific literature [START_REF] Foley | Solutions for a Cultivated Planet[END_REF][START_REF] Godfray | The challenge of feeding 9-10 billion people equitably and sustainably[END_REF][START_REF] Meijl | Comparing impacts of climate change and mitigation on global agriculture by 2050[END_REF][START_REF] Röös | Greedy or needy? Land use and climate impacts of food in 2050 under different livestock futures[END_REF][START_REF] Gregory | Climate change and food security[END_REF][START_REF] Ludi | Climate change, water and food security[END_REF][START_REF] Deering | Stepping up to the challenge -Six Issues facing global climate change and food security[END_REF][START_REF] Frank | Reducing greenhouse gas emissions in agriculture without compromising food security?[END_REF][START_REF] Devereux | Climate Change and Food Security[END_REF][START_REF] Beddington | Achieving food security in the face of climate change: Final report from the Commission on Sustainable Agriculture and Climate Change[END_REF][START_REF] Wilkes | National planning for GHG mitigation in agriculture: A guidance document[END_REF]. Agriculture is one of the productive activities most affected by climate change and, at the same time, must be an integral part of any strategy to mitigate global anthropogenic GHG emissions. To achieve the objective set by the Paris Agreement to limit global warming to 2°C, the analysis of the mutual relationship between climate change and agricultural production is of major interest. The dynamics of agricultural development are a result of the growing demand for food at the global level, with Europe being one of the main producers and suppliers of food globally. Therefore, a critical issue is to increase (or at least maintain) European agricultural production while preserving natural and environmental resources (European Commission, 2009, 2013, 2017;[START_REF] Hlpe | Food security and climate change. A report by the High Level Panel of Experts on Food Security and Nutrition of the Committee on World Food Security[END_REF].

Agriculture, through its activities, emits substantial quantities of methane (CH 4 ) and nitrous oxide (N 2 O) into the atmosphere, of which approximately 45% comes from enteric fermentation; 37% are from agricultural soils; 15% are linked to manure management; and, 3% are from rice cultivation, field burning of agricultural residues, and other sources. In 2017, the greenhouse gas (GHG) emissions from European agriculture were 440 MtCO 2 eq (European Environment Agency, 2019). Carbon pricing plays an essential and indispensable role in achieving substantial GHG emissions mitigation1 . The challenge lies in both addressing multiple environmental and social objectives and in fostering an effective reduction in the costs of obtaining them (World Bank and Ecofys, 2018;[START_REF] Aldy | The Promise and Problems of Pricing Carbon: Theory and Experience[END_REF]OECD, 2015;[START_REF] Vojtech | Policy Measures Addressing Agrienvironmental Issues[END_REF].

Beyond the overall reduction in GHG emissions that may be achieved at a certain emission price, the quantity and quality of total output as well as the distribution of impacts across farm types (e.g., crop vs. livestock production), land area allocation (food crops vs. feed crops vs. grassland) are also of great importance for policy design [START_REF] Leip | Evaluation of the livestock sector's contribution to the EU greenhouse gas emissions (GGELS)[END_REF][START_REF] Olesen | Consequences of climate change for European agricultural productivity, land use and policy[END_REF].

Given that livestock represents one of the major sources of emissions, and, at the same time, accounts for one-third of the protein in human food, climate mitigation policies involving livestock play an essential role. Animal rearing involves using 30% of the global land surface, land saving can be realized by increasing livestock productivity through feeding practices that require, among other things, an improved grassland management, less grazing, and better quality feeds. According to [START_REF] Gerber | Tackling climate change through livestock -A global assessment of emissions and mitigation opportunities[END_REF], the livestock sector must be seen as a solution to climate change, and its significant emissions can be reduced through mitigation measures that meet environmental objectives. At the same time, the livestock sector plays a key role in food security, by 2050 the demand for livestock products being projected to increase by 70%, which raises concerns about the impacts of a potential imbalance between this growth and the economic and environmental effects that may occur. The global-scale results obtained by [START_REF] Havlík | Climate change mitigation through livestock system transitions[END_REF] suggest that mitigation policies targeting emissions from land-use change are more effective than those targeting emissions from livestock only. [START_REF] Berners-Lee | Current global food production is sufficient to meet human nutritional needs in 2050 provided there is radical societal adaptation[END_REF] argue that industrialized meat and milk production accounts for 34% of global human calories but is highly inefficient in supplying energy, proteins, iron, and zinc, indispensable to humans and that it is incompatible with a sustainable food system.

According to [START_REF] West | Leverage points for improving global food security and the environment[END_REF], crops used for animal feed could produce a substantial gain in calories (approximately 70%) if they were intended for direct consumption instead of being used as animal feed to produce animal products, meat and milk.

The connection between mitigation of GHG emissions and food calorie target leads to the need for an assessment that accounts for the impact of GHG emission pricing on food production in the EU, the interactions between crop and livestock production activities, and the evaluation of existing Common Agricultural Policy (CAP) policy instruments. To address the trade-offs that may arise, FAO (2003bFAO ( , 2009) ) highlights the importance of the costs needed to achieve food production and climate change mitigation.

A quantitative evaluation of marginal abatement costs in the EU agricultural sector was conducted by De [START_REF] De Cara | Marginal abatement costs of greenhouse gas emissions from European agriculture, cost effectiveness, and the EU non-ETS burden sharing agreement[END_REF], underlining the effects of the EU burden-sharing agreement on this sector. A 10% EU GHG abatement target can be achieved at an emission price range of EUR 32-42/tCO 2 , showing that the agricultural sector may represent an important share of the reduction in a cost-effective way. [START_REF] Frank | Reducing greenhouse gas emissions in agriculture without compromising food security?[END_REF] highlight the substantial impacts that a global uniform carbon price can have on food security and its inequitable effects across sectors of the economy and regions. Thus, they show that food security is more strongly affected in countries that do not engage in mitigation actions, with the costs of agricultural production rising with inefficient mitigation.

According to [START_REF] Sonesson | Food production and emissions of greenhouse gases -An overview of the climate impact of different product groups[END_REF], a significant share of total GHG emissions at the global level is linked to the food chain, stressing that the choice of products (i.e. diets) represents one the main elements aimed at reducing the impact of food on climate change. [START_REF] Bajzelj | Importance of food-demand management for climate mitigation[END_REF] assess the environmental consequences of an increasing demand for food by 2050.

They emphasize the search for alternatives guaranteeing global food security without expanding crops or pastures and without increasing GHG emissions. In a reference scenario which considers a population level of 9.6 billion by 2050, they estimate that the average food consumption per capita would increase to 2710 kcal/day (including 470 kcal/day for livestock products). In such a scenario, if emission mitigation strategies are not implemented, an intensification of livestock and large-scale expansion of cropland would lead to an increase of about 77% in agricultural greenhouse gas emissions, due to the increase in food demand, the share of emissions from livestock and deforestation. Scenarios based on a healthy diet would have the effect of reducing cultivated areas by around 5%, pastures by around 25% and greenhouse gas emissions by around 45%, mainly linked to the reduction in herds. They point out that mitigation strategies could be based on economic incentives such as a carbon tax. The need for mitigation strategies aimed at balancing food production and GHG emissions is emphasized by [START_REF] Mcallister | Greenhouse gases in animal agriculture-Finding a balance between food production and emissions[END_REF], in order to make a growing global demand for food partly satisfied by livestock products compatible with the control of environmental impacts. According to [START_REF] Tilman | Global diets link environmentalsustainability and human health[END_REF], other factors are necessary for agriculture to become environmentally sustainable. The more efficient use of feed and pasture for animal production and of fertilizer or irrigation for crops would increase food production and decrease GHG emissions. With linear programming, van [START_REF] Van Kernebeek | Saving land to feed a growing population: consequences for consumption of crop and livestock products[END_REF] propose a model for optimizing land use including animal and plant production. They show that the optimal amount of dietary protein from animals in the human diet depends on both the size of the population and the relative share of land unsuitable for agricultural production.

Our study extends the vision of the connection between agricultural production and reducing GHG emissions in the European Union (EU) through two different analysis perspectives.

First, we use a price approach targeting a reduction of GHG emissions through the introduction of a carbon price. The second perspective constitutes a constraint approach, through the introduction of a minimum supply of food calorie constraint imposed on European agriculture as a whole. In addition, we take into consideration the economic context variability characterizing EU agriculture, based on six years (2007)(2008)(2009)(2010)(2011)(2012) marked by a strong variation in input and output prices. Long-run climate policies should ensure that the strongly changing agricultural economy is not disrupted beyond what pertains to the environmental target. The period from 2007-2012, on which our study is based and against which the model is calibrated due to data availability, shows how rapidly the economic context may change in terms of prices and productions.

By using the European agro-economic AROPAj model, we analyze the compatibility of environmental objectives and food production, through two different approaches (pricing and binding), but with the same methodological framework. An accurate analysis of the GHG emission reduction and, implicitly, the marginal abatement cost curves are conducted in a separate study.

Thereby, the objectives of this study are twofold: (i) to assess the consequences of a carbon price introduction on the crop and livestock production in the EU, and (ii) to assess the effects of introducing a food production target on the EU agricultural GHG emissions.

In doing so, we consider the position of two decision-makers, one focused on the objective of food production and the other on direct emissions of N 2 O and CH 4 from agriculture.

Among the major results of our article, we show that a carbon price has a different impact on plant and animal production. A moderate increase in the price of carbon leads to an increase in crop production -especially cereals and oilseeds -, the magnitude of which varies between products and Member States. This increase concerns both the areas and the quantities of plant products, whether marketed or used as animal feed on farms. On the other hand, the production of milk and meat decreases with the price of carbon over the entire range explored. Then, calorie targets are introduced from 200 to 400Mt of soft wheat equivalent. For the respective values of 300, 350 and 400 Mt, the marginal costs associated with them vary from year to year in the ranges [20,36], [27, 52] and [36, 94] euros per tonne of soft wheat equivalent. These effects reflect complex substitutions in crops, grasslands and forages and are related to animal feed. The results show that the reduction in GHG emissions is compatible with the increase in food production for the benefit of agricultural products and to the detriment of livestock products. The impacts of pricing GHG emissions (price-based approach) or a food calorie production target (threshold-based approach) result in a sharp decrease in grass areas, partially offset by an increase in the area of crops sold but also by a significant increase in fallow areas.

The rest of this article is organized as follows: section 2 begins with a brief presentation of the AROPAj model, followed by a description of the two angles of analysis undertaken in the study, namely the price approach with pricing of GHG emissions and the constraint approach with the setting of a target for the production of food calories. The results obtained are examined in section 3. The discussion on the scope of the results and their political implications is highlighted in section 4. The concluding remarks are presented in section 5.

Methodological elements 2.1 General framework -AROPAj model

Our analysis is based on the use of a supply-side model capable of integrating both the economic and technical connections between the agricultural sector, climate, and GHG emissions as a tool for strategic decision-making. The European agro-economic model AROPAj is based on linear programming (LP). It aims to simulate the EU agricultural supply by taking into consideration the production derived from main crops and livestock3 . The model has been widely used in previous studies of agricultural and/or environmental policies [START_REF] De Cara | Methane and Nitrous Oxide Emissions from Agriculture in the EU: A Spatial Assessment of Sources and Abatement Costs[END_REF][START_REF] Galko | Economic and environmental effects of decoupled agricultural support in the EU[END_REF][START_REF] De Cara | Marginal abatement costs of greenhouse gas emissions from European agriculture, cost effectiveness, and the EU non-ETS burden sharing agreement[END_REF][START_REF] De Cara | Optimal coverage of an emission tax in the presence of monitoring, reporting, and verification costs[END_REF].

The model parameters are estimated from the annual Farm Accountancy Data Network (FADN), which allows using the model for all EU Member States. FADN possesses accounting information on approximately 80,000 agricultural holdings, totaling approximately 5 million farms in the EU. The model covers approximately 85% of the holdings, 90% the total utilized agriculturalarea (UAA) and the EU total agricultural production. 4 One of the strengths of the AROPAj model is its capacity of simultaneously incorporating crops, livestock, grassland, and feed (both on-farm and marketed feed).

To form a unit of the AROPAj model for a given year,a clustering method is employed, allowing us to group the farms in the FADN into representative farms5 . The number of farms modeled is reduced due to the protection of individual data, the statistical quality of the parameter estimation and calculation costs, while representing the great diversity of the EU agricultural sector. According to the years, AROPAj is declined in 1800 to 1950 representative farms. Each of them represents from a few tens to a few thousand real farms. Representativeness is determined by the weighting system proposed in the FADN, which associates a weight with each farm in the sample.

In the model, each representative farm k has the objective of maximizing the total gross margin π k : max

x k π(x k , θ k , φ ) s.t. x k ∈ A k (θ k , φ )
where x k represents the vector of endogenous activities depending, at the optimum, on specific parameters θ k and general parameters φ . Among many other activities, the vector x k include cropspecific variables such as areas, marketed quantities and on-farm used quantities. It also includes animal activities broken down into several categories (more detailed for cattle) and the production of meat and milk. The vector θ k refers to k-specific parameters and the vector φ refers to common parameters such as GHG price. The production set A k represents the combinations of values of the x k components respecting a set of inequalities expressed linearly against to components. Among the set of constraints, we mention quasi-fixed factor limits such as UAA and livestock,crop rotation constraints, animal feed requirements (met from on-farm produced cereals, purchased concentrates and forage or meadows) and the implementation of the Common Agricultural Policy instruments.

The calibration of the AROPAj model consists in re-estimating the values of a subset of parameters for which the preliminary values are considered fragile. It sequentially combines Monte Carlo-type random drawing methods and gradient methods. The principle consists in estimating the value of the parameters θ k which minimizes a criterion of distance between the LP solution x * k (θ k , φ 0 ), given the present-time economic context φ 0 , and the "observed" values corresponding to these variables (estimated directly from the FADN). In practice, the method involves a subset of 120 to 150 parameters mainly concerning animal feed (inputs and needs), and the criterion is the sum of the squares of the deviations concerning in particular the areas for crops and the numbers of different animal categories. It is applied for each representative farm and for each of the years of the FADN.

AROPAj includes 32 crop productions and 28 animal productions. Crop producing activities utilize a large part of the EU agricultural land, the crops being divided into three categories:

(i) crops that can be either sold or consumed on-farm (i.e., cereals), (ii) crops that can be only consumed on-farm (e.g., fodder and pastures), and (iii) crops intended for sale. As regards animal production, the model includes a large variety of animals (24 categories of cattle and 4 other categories: sheep, pigs, goats, and poultry).

Livestock may be adjusted within a chosen amplitude. The adjustment limit refers to a few animal categories. These categories are, separately, swine, poultry, sheep, goats and four bovine categories, namely dairy cows, non-dairy cows, bulls and oxen. Other animal categories included in the model referring to calves and young animal differentiated by age, sex, and dairy or non-dairy may freely adjust when accounting for the inter-age balance. Our results are derived from simulations based on a amplitude limit of +/-25% applying to FADN sourced estimates. For the i-animal categories concerned, this limit (α), called livestock adjustment ratio thereafter, is integrated into linear programming in the form

|L i -L i0 | ≤ αL i0 .
For ruminants (cattle, in particular), reducing GHG emissions involves the improvement of the efficiency of animal husbandry through the use of fodder and better feed formulation that can reduce the CH 4 generated during digestion, and the CH 4 and N 2 O produced by manure decomposition. Grasslands play an important role in mitigating GHG emissions and achieving food security, given the fact that meat and milk production depends on ruminants feeding. Climatic conditions, rainfall and temperature distribution, and soil characteristics are among the main factors influencing the grassland spatial distribution and productivity [START_REF] O'mara | The role of grasslands in food security and climate change[END_REF][START_REF] Huyghe | Grasslands and herbivore production in Europe and effects of common policies[END_REF]. CH 4 emissions depend on the number of animals and the composition of animal feeding.

CH 4 from enteric fermentation depends directly on animal feeding, which must meet all the requirements in terms of energy and proteins. This is achieved by including various types of feeds in the model: concentrated feeds, crop products, and raw feeds. A certain intake of energy and proteins is necessary for each animal species, depending on different factors, such as age, daily activity, physical condition, and potential production. In the model, animal feeding is endogenous and farmers have the choice to use either fodder feed from their own crops or purchased concentrates. The reduction in N 2 O emissions from agriculture is mainly the result of improving the efficiency of agricultural techniques related to manure application, storage, and management, as well as that of soil and crop use techniques [START_REF] Smith | Greenhouse gas mitigation in agriculture[END_REF][START_REF] Smith | How much land-based greenhouse gas mitigation can be achieved without compromising food security and environmental goals?[END_REF]).

In

Carbon price implementation

When a carbon tax is introduced, representative farms may behave in various ways to reduce their emissions, by reducing their number of animals or by changing area allocations among crops or modifying animal feeding. We introduce a pricing of GHG emissions weighted according to GHG Global Warming Potential, considering the direct emissions of N 2 O and CH 4 from agriculture. In AROPAj, the carbon price introduced ranges widely, from 0 to 10,000 A C/tCO 2 eq, in gradual steps. Simulations are conducted by using 200 values selected from this range, when the livestock adjustment ratio is of 25%. They are carried out for the six FADN years for which the AROPAj model operates.

To obtain a relatively broad view of the impacts of emission taxing on the production system and reach valid conclusions, we have decided to introduce a carbon price (expressed in A C/tCO 2 equivalent) ranging in the interval [0, 200], a relatively wide price range, but "realistic" at the same time. These prices are in line with those in previous studies [START_REF] De Cara | Methane and Nitrous Oxide Emissions from Agriculture in the EU: A Spatial Assessment of Sources and Abatement Costs[END_REF][START_REF] De Cara | Marginal abatement costs of greenhouse gas emissions from European agriculture, cost effectiveness, and the EU non-ETS burden sharing agreement[END_REF]. At the same time, a price higher than 100 A C/tCO 2 is relevant when referring to climate policies. In Sweden, the carbon tax represents the most powerful instrument of the Swedish climate policy since 1991 and is currently at 120 A C/tCO 2 eq (Adelphi, 2018).

From this angle of analysis and, more precisely, in what we call the price approach, the study aims to discover the potential impacts of an emission tax on agricultural commodities brought to the market, with the model applied to very diverse economic conditions, given the strongly changing 2007-2012 prices of inputs and outputs. By using the AROPAj model, we assess the potential effects on crop and livestock production in the EU when a carbon price is introduced and analyze the trade-off between and within these type of productions at the European level and the environment, as results of policies targeting GHG emissions.

Implementation of food calorie target

The binding approach refers to the integration of a food calorie production target. For easier interpretation, the threshold introduced was based on calorie quantities expressed in tons of soft wheat equivalent (tsweq). Thus, the estimation of the food parameters was done by using the database provided by FAO (2003a). The calorie target is introduced as a bound affecting the net sum of calories emanating from marketed crops, milk, and meat related to sold animals, and from bought concentrated feed in all representative farms combined. The constraint binding all these representative farms was the calorie balance, including the calorie content of different crops and livestock productions (see Table 1). In the case of crop productions, the data were used in raw form. For animal productions, given that AROPAj takes into account live animals, it was necessary to convert meat into calories. We considered only exported or marketed animals and, thus, took into account the animals' lifetime, with the meat content weighted by referencing AROPAj units.

Calories used in animal feed are counted negatively in the net balance of calories produced by the system. Simulations are performed for the years 2007-2012 when the livestock adjustment ratio is of 25%. We needed to modify some calculations in our programming tools, as the target affects the European farming system as a whole. The kernel of the model was improved to integrate and parameterize this threshold. We started from the reference level and we increased the value of the target to the maximum level allowing the existence of a solution. In this constraint approach, the target varies from the unbounded case up to the feasibility limit. From a technical point of view, the indexation of the representative farms was modified automatically. All these steps were based on a sub-aggregation of AROPAj representative farms by country, and a re-indexation of representative farms that allows dealing with the solution directly. We did preliminary work dedicated to scriptwriting and prepared code to obtain the results, with very large files processed in reasonable time, which allowed us to obtain the desired results.

Mathematical programming models make it possible to estimate the implicit value of a resource, which is zero when it is not limiting and positive. If not, this will be used to evaluate the marginal value of calorie production.

Result

Carbon pricing impact

We focus on cereal area and production while distinguishing its marketed output and onfarm use as feed, oilseeds production and area, livestock, feed quantity, nitrogen fertilizer consumption, grasslands, and fallows.

Large differences between the results in each year reflect the strong variation of agricultural prices and climatic conditions during the period. A moderate carbon price leads to a strong variation in the abatement rate (from 10% to 16% when the price is EUR 50, and from 16% to 25% when the price is EUR 100). However, the estimated emissions exhibit a narrower spread (13 Mt CO 2 eq based on the interval [318,331] when the price is EUR 50, and 19 Mt based on [289,308] when the price is EUR 100). The spread of the abatement rate across the years ranges from 25% to 39%, with an emission spread of 34 Mt when the price is 200 A C/tCO 2 eq (see Table 2). The annual changes in emission prices should be explained by contrasting annual sets of agricultural input and output prices, when quasi-fixed factors (UAA and livestock) are relatively stable (i.e., when the initial livestock spread is less than 3% of the six-year average and the UAA spread is approximately 1% of the six-year average).

For the EU, the variation of the main crop and livestock productions when the carbon price changes, as well as the envelope curve drawn for each of these agricultural productions, are illustrated in Figure 1. For each year, a series of simulations to account for the GHG price change are performed while keeping global economic and climatic conditions constant, based on the conditions of each FADN year. An increase in the carbon price expectedly leads to a decrease in GHG emissions and impacts agricultural supply differently.

The marketed cereal production varies in each FADN year as the carbon price increases, exhibiting a notable peak often appearing in the retained carbon price interval (Figure 1c). The peak strongly changes in terms of cereal quantity and price limit from one year to the other.

The most significant result is that there is a peak in each of the six years (although it lies out of the price scope for three of them: 2007, 2011, and 2012). In 2009, for example, the marketed cereal production increases smoothly up to a price limit maximum (44 A C/tCO 2 ) and then strongly decreases when the carbon price exceeds this limit.

By extending the analysis to cereal area7 , we observe that, in each year, the peak's price limit here can differ substantially from the peak's price limit in the case of marketed production (see Figure 1a). This result combines the effect of the relative change in the use of production (on-farm vs market), the effect of the substantial change in the global price system and cross-price effect between feed sources, and the effect of the annual change in meteorological conditions.

Figure 1a also provides the envelope interval for the six years curves. Given the carbon price, when the six-year-based general conditions are assumed to occur with the same probability, the peak for cereal area is obtained at a price of around 160 A C/tCO 2 (see the blue curve, referring to the value averaged over the years). This is far above the price observed in the CO 2 market over the past years. The price level at which this change occurs depends on the year.

The key point here is that, on average, a peak in cereal land allocation and in the marketed part of production is obtained when the carbon price is around 150 A C/tCO 2 eq, when the livestock adjustment is substantial but moderate (+/-25%). The peak shifts toward a higher carbon price in the case of on-farm re-use of cereals for feed. This reflects the complex relationships between cropping and breeding activities through feed and different parts of feed, typically on-farm cereals (Figure 1d), fodders and meadows, and concentrated feed (Figure 1h). The cases of oleoproteins area and production (Figures 1f and1e) reinforce the statement that interference with animal feed represents one of the aspects of the trade-off between crop and animal productions.

As shown above, the EU harvested cereal production, representing the sum of marketed Animal production decreases with the carbon price, with an impact on the demand for feed (fodder, concentrated feed, and on-farm cereals). Therefore, in contrast to crop production, which increases, animal production continuously decreases when the carbon price increases. Figure 1g illustrates the decline in the EU meat supply (expressed in carcass weight equivalent, teqcw accounting for metric ton), with the same decreasing slope for each of the six years. Figure 1g reveals that the reduction of meat production is characterized by an average decrease of about 2

Mteqcw over the entire carbon price range. Marketed crops areas, and mainly cereals areas, resist almost homogenously across the entire European agricultural system when the carbon price increases, whereas meadows are strongly affected, due to their connection to animal productions, especially cattle. This would mainly affect the westernmost part of Europe as well as Central Europe. With the abandonment of grasslands being largely compensated by adoption of fallow land, the agricultural landscape could be substantially modified and differently so across different European regions. The analysis of the carbon price impact over the six years examined, highlights a form of robustness in the allocation of land across years, although the AROPAj typology is conducted completely independently in each year.

Food calorie target impact

We introduced different calorie quantity thresholds, ranging from 165 to 555 Mtsweq, and conducted the calculations for the six years against which the model is calibrated. As expected, a feasible solution of the mathematical programming model depends on the year. For a dual price of 0, the quantities of calories from 2007 to 2012 vary between 166 Mt and 227 Mt (see Table 3).

Applying an increasing threshold of calories leads to an increase in the dual price. EUR 180, EUR 210 per ton, respectively, for 2007[START_REF] Smith | Greenhouse gas mitigation in agriculture[END_REF][START_REF] Ludi | Climate change, water and food security[END_REF][START_REF] Vojtech | Policy Measures Addressing Agrienvironmental Issues[END_REF][START_REF] Garnett | Where are the best opportunities for reducing greenhouse gas emissions in the food system (including the food chain)? Food Policy[END_REF], and 2012. Differences between years reflect the heterogeneous economic and meteorological conditions prevailing each year in the period from 2007 to 2012. In Figure 7, we illustrate how the land sharing among the different groups of crops (on the y-axis) varies from the quantities of human calories provided by the EU farming system (on the x-axis) in the six years. The results differ significantly across the years, when considering the gaps between the unbounded case and the feasibility limit case on the one hand, and between years in terms of the quantity shift from left to right (on the x-axis) on the other hand. However, the trends in land allocation when changing the calorie quantity limit appear robust across the six years. It should be noted that the EU potential calorie limit estimated by the AROPAj model varies from 450 Mt up to 550 Mt, expressed in equivalent soft wheat over the six years, rising from 2.5 to 5 times the basic calibrated case level.

The gain in calorie production is mainly due to transfers from animal sources (milk and meat) toward cereals, oilseeds, and protein crops. Another key aspect emerges through changes q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q 200000 300000 400000 500000 0.00 0.05 0.10 0.15 0.20 0.25

Dual price (k€/t wheat eq) Food quantity threshold (thousands of tonnes soft wheat eq) Year in the animal diet by recalling that, in the model, on-farm cereals, concentrate feed, and pasture account for feeding.

We detail the analysis regarding livestock (see Figures 8 and9). In our simulations, livestock is allowed to be adjusted within a limit of +/-25% with respect to the basic case. Considering that unchanged prices may be in favor of some animal categories in the unbounded calorie threshold case, the gap of concerned livestock categories may reach 50% over the interval of calorie targets from the unbounded case to the upper limit case. We illustrate changes for two emblematic categories, namely, beef cows and milk cows. Although the number of beef cows varies regularly as the calorie target increases, the number of milk cows follows a different path of weaker amplitude and decreases irregularly. The milk quota system associated with guaranteed prices and premiums, applied in the period from 2007 to 2012 matters substantially.

The impact of introducing a calorie production target on total GHG emissions highlights a regular decrease of emissions as the calorie target increases (see Figure 10). Depending on the year, an increase of the food production target from 230 Mtsweq to 435 Mtsweq, would lead to a decrease in emissions ranging from 26 MtCO 2 eq (7%) to 75 MtCO 2 eq (20%).

Given the diversity of farming systems across the EU, we investigate the results at the regional level. To this end, we consider areas dedicated to straw cereals on the one hand and to permanent meadows on the other hand. Proportions of the area shared between these two categories are mapped for each of the six years and for two cases: the reference case and a calorie target of 435 million tons of soft wheat equivalent (Figure 11 refers to cereals and 12 to meadows).

Supporting the aggregated land sharing illustrated in Figure 7, there is no apparent difference between the regions across the EU in terms of land dedicated to straw cereals (Figure 11).

None or almost none of the regions decrease the land dedicated to cereals. However, focusing on the land occupied by meadows reveals that regions differ significantly in terms of land sharing when calorie targets are ambitious. Some western and central European regions may suffer from a cattle decline revealed through a decrease in grassland (e.g., northwest France and southeast England, and south Germany and Austria). Knowing that fallows should replace grasslands (as shown in Figure 7), increasing the target of net calorie production may affect the activity in some rich agricultural regions. 4 Discussion for policy implications

Scope of results

Our methodological framework relies on the principle "simulate and average", unlike many other models, whose simulations assume the use of the average values of the parameters.

The analysis is conducted for six-years of diverse economic and meteorological contexts, as an example remarkably characterized by the doubling of cereal prices over the period. That does not prevent against the lack of all feedback effects that could enrich the analysis. The first type of feedback would be price changes resulting from market clearing, and the other would be climate feedback and technical progress induced by a large change in the European agricultural supply.

Nevertheless, the amplitude of observed economic changes for which the model accounts makes our analysis valuable. The important point is that results are provided in technically and economically viable (observed) conditions.

The six versions are each based on a classification of samples into representative farms carried out independently of each other. The same applies to the calibration of each version. The structure of the agricultural system represented appears stable, despite the biases still attributable to the quality of the FADN samples and despite the biases inherent in the AROPAj model. For example, the UAA represented by AROPAj, of the order of 131 Mha, offers an interannual relative standard deviation of 0.7 %, and the livestock, of 101 MLU, a standard deviation of 0.9 %. This structural stability does not prevent greater variability in the areas occupied by the different activities, from 2.1% for common wheat to 26.2% for soybeans (and 3.9% for all 9 cereals represented by the model, for an average surface of 64.4 Mha). This variability, partly accentuated because it is a mathematical programming model, mainly reflects the variability of prices and weather conditions. And still realistically, the production of net food calories and greenhouse gas emissions accentuate the effects of the economic and meteorological environment. In the calibration solution for the 6 years, the calorie production is on average 186.5 Mt common wheat equivalent, with a relative standard deviation of 10.6 %, and the total direct GHG emissions are estimated on average at 364.1Mt CO 2 with a standard deviation of 2.5 % (210.5 and 153.6 respectively for methane and nitrogen oxide, with 1.4 % and 5.2 % as standard deviations, respectively). The results used in our analysis are based on the hypothesis of the inter-age balance of the cattle herd with an adjustment of the animal capital varying in an interval fixed as a percentage of the initial capital. At zero carbon price and in the absence of any calorie production threshold, the livestock lag resulting from an adjustment rate of 25% is 1.3%.

In the EU, a carbon price of EUR 200 would reduce GHG emissions from 273 Mt to 239 Mt and increase the abatement rates from 25 % to 39 %, depending on the year, given that the annual base emissions also vary significantly (from 356 Mt to 391 Mt). By letting this price vary across the entire interval considered, [0,200], crop productions increase to some extent, and eventually decrease but much less than animal productions, which decline normally when the CO 2 price increases. This result is reinforced when we assign to the sector an increasing production of food calories. The food objective is achieved by reducing GHG emissions, to the detriment of animal production and by considerably modifying the mode of animal feeding. This translates into a sharp reduction in the area under grass, partially offset by an increase in the area under marketed crops, but also by a significant increase in the area under fallow. By striving to reach an ambitious goal of producing food calories, it could be more costly to maintain animal productions consuming plants than to suppress these productions, at least in certain regions, including regions dominated by animal husbandry. The regional analysis (mapping) carried out above (see Figures 11 and12) illustrates this result. If it appears possible, in the light of the results, to double the production of calories on average without upsetting the balance between animal and vegetable production too much, an additional effort still possible of around 50% (compared to reference production) very substantially alters this balance, and in all cases animal feed is the key to change.

Policy implications

We investigated the compatibility of two goals frequently supported by policy-makers, underlined in European Commission guidelines and boosted as general common objectives. The goals, namely, stabilizing or even increasing food production and decreasing agricultural impact on the environment are expressed in our analysis via the net supply of calories for the human diet on the one hand and via the abatement of GHG emissions on the other hand.

The margin offered to secure the production of calories in the EU is potentially significant, the net production of calories being able to be two and a half times higher while remaining within the framework of the economic and technical environment of the years 2007-2012. It is therefore technically and economically realistic. But to reach this level, one would have to expect major changes in terms of agricultural land use associated mainly with changes and especially with the decrease in the quantities of food ingested by a declining herd.

Even if it can always be argued that a significant change in agricultural production concerning 130 Mha would significantly impact the physical and economic environment in the absence of economic regulation, it should be noted that (i) with regard to the physical environment through the lens of climate change, the significant reduction in GHG emissions associated with the disruption of production resulting from simulations is both explained and realistic; (ii) with regard to the economic environment, in particular the reaction of agricultural prices to changes in European supply, it is important to remember that agricultural prices have been framed for several decades of agricultural policy, proving that political will would make an ambitious quantitative goal of producing food calories possible.

The evolution of food consumption under the effect of changes in consumer choices, accompanied or amplified by public health policies, is a determining factor for the future of the agricultural sector. It is also a determining factor in terms of human impact on the global environment, in particular the climate via agricultural GHG emissions. Even if our study only focuses on the supply of the agricultural sector alone and its direct GHG emissions, it intersects the field covered by three policies, namely food policy, agricultural policy, climate policy. We can assess, even briefly, some of the terms of social welfare taken into account by the public decision-makers in charge of these policies.

In this regard, let us specify the basic elements of the calculation. The European agricultural gross margin, from one year to another over the period 2007-2012, varies from EUR 126.0 to 165.8 billion, with an average of EUR 149.6 billion (relative standard deviation of 9.2%), including support for CAP size from EUR 40.8 to 44.6 billion (on average 43.4, with a standard deviation of 3.4%).

Consider the year 2012, to fix things. Under the technical and economic conditions of the year, it is mathematically possible to go from a production of food calories from 200 to 500 Mtsweq, which would result in a reduction in GHG emissions of 97 MtCO 2 , going from 367 to 270 MtCO 2 eq. The remaining emissions, valued at EUR 210 (which is the price of CO 2 allowing this reduction, all other things being equal), the "valuation" of emissions at the marginal cost of reduction is EUR 57 billion. By implementing a CO 2 pricing policy, the livestock adjustment ratio being 25%, the gross margin is reduced by EUR 66 billion. The marginal value of the last calorie produced, when producing 500 Mtsweq, is greater than 1000 A C/tsweq, about five times the price of common wheat paid to the producer in France in 2012, while the European gross margin goes from EUR 176 to 110 billion, and GHG emissions from 367 to 270 MtCO 2 . To lower GHG emissions by 97 MtCO 2 only by "taxing" GHG emissions (when no calorie production threshold applies), the price per tonne of CO 2 eq would be EUR 210, with a loss of EUR 65 billion in gross margin (from 175.6 to 110.6), and a tax proceeds of EUR 57 billion. In this case, the net cost (gross margin difference increased by the tax proceeds) would be EUR 8 billion, logically lower than the loss of gross margin of EUR 31 billion which would result from an obligation to produce 500 Mtsweq in net food calories.

At a more moderate level of dual food calorie price, close in soft wheat equivalent to the price of common wheat in French production, the potential for calorie production is estimated at 470 Mtsweq, and the associated fall in emissions of GHG to around 52 MtCO 2 eq. The corresponding decrease in gross margin is estimated at EUR 16 billion. Achieving the same level of reduction in GHG emissions of 52 MtCO 2 eq by carbon pricing would be obtained with a CO 2 -price of 81.5 A C/tCO 2 eq. At this price, GHG emissions are "valued" at EUR 25.7 billion (the carbon tax proceeds), with a drop in gross margin of EUR 27.5 billion, and a net social cost (margin differential increased by the differential of tax proceeds) of EUR 1.8 billion. From an environmental point of view, for a reduction in GHG emissions of 52 MtCO 2 (or approximately 15 % of emissions estimated for 2012), the difference in social cost between the "food calorie target" option and the "carbon price" option (the least expensive because it directly targets GHG emissions) is EUR 14 billion. The decline in livestock, all animal categories combined, is significant. It is 12% with the "food calorie target" option and 8.5% with the "carbon policy" option.

Table 4 summarizes the elements making it possible to compare the effects of a policy aimed at increasing the production of food calories and a policy of pricing GHG emissions leading to an equivalent reduction in GHG emissions. The calculations are made for two levels of calorie production, respectively 500 Mtsweq (close to the technically feasible maximum) and 470 Mtsweq, based on the year 2012.

Within the European Union, a policy aimed at reducing agricultural GHG emissions as a priority would therefore have a significant effect on animal production, just like a policy aimed at increasing net production of food calories. The public health policy is outside the scope of this study, it would therefore remain to assess what could be the social benefits of a human diet in balance with an agricultural offer evolving towards crop production while diverting somewhat from animal production . What is shown here is an example of the positive cross-effects that a policy can have on a domain other than its own. If positive results clearly emerge at European level, at local level the negative effects on agricultural activity could be very significant, in particular in regions where few alternatives to animal production exist. To mitigate these negative effects, among the political options that could emerge, in addition to promoting the quality of animal products (viable if prices rise substantially), we find the promotion of bio-energies from plants that are difficult to transform into human food. Switchgrass is a candidate plant to be transformed into liquid agrofuels, while other productions are the preferred substrates for anaerobic digestion plants.

Conclusion

Diet trends could be powerful drivers of change in the agricultural production sector. According to the European Public Health Association (2017), while European food consumption varies from country to country, most countries are trying to move towards a healthy diet that respects the environment. Although meat remains an important item in the food basket, the amount of meat consumed has decreased since the 1980s8 . If the average EU diet were to approach healthier levels, animal production and therefore pasture and concentrated food would be reduced and land would be freed up for agricultural production. Our analysis even shows that a significant part of the land could become fallow when animal production systems offer few prospects for reconversion. In addition, reducing food waste would further reduce pressure on the land, and work in progress shows that increased agricultural production could widen outlets for bio-energy. The evolution of the diet and the pressure exerted by securing the production of food calories or the wider outlets offered for the energy recovery of agricultural products and co-products have significant impacts on the environment. Greenhouse gas emissions from agriculture could decrease by 25 to 30% if the farming system were called upon to maximize the production of food calories.

Conversely, seeking to reduce greenhouse gas emissions by pricing GHG emissions will obviously have significant impacts on the production of agriculture and livestock. Up to a high level of this price -let's keep 200 euros per tonne of GHG in carbon dioxide equivalent -plant production increases or decreases slightly while animal production (mainly meat, then milk) decreases significantly. Our results show that here again animal feed plays a key role, while pricing greenhouse gas emissions impacts the consumption of synthetic nitrogen fertilizers in a direction and in a different amplitude than what is obtained in seeking to increase the production of calories.

The importance of livestock in the adjustment of agricultural production systems is also verified with GHG emissions, since, up to C 200 per tonne of CO2 equivalent, the reduction in methane emissions is twice as high as reduction of nitrous oxide emissions.

Increasing European production of food calories would result in an increase in plant production and a decrease in animal production in significant proportions, with a significant reduction in European agricultural GHG emissions. Putting a price on agricultural GHG emissions obviously leads to a reduction in GHG emissions, by affecting the production of livestock systems while allowing crop production to be maintained, at least as long as the price of GHG remains below EUR 200 per tonne of CO 2 -equivalent. The objective of increasing calorie production and the objective of reducing GHG emissions are therefore compatible, one appearing as a co-benefit of the other.

Our results are one contribution among others to the multi-criteria evaluation of different components of public policies. We can illustrate the entanglement and the complexity of the political choice through the criteria that the AROPAj model makes it possible to assess, by focusing on the gross agricultural margin and some criteria that can be associated with the challenges of public health and environment.

Let us retain the emblematic value of EUR 100 per tonne of CO2-equivalent that we would apply to methane and nitrous oxide emissions, in a scenario where the number of animals in the main categories of livestock can vary over the interval from -25% to + 25% of the initial value. Based on 2012 FADN data, the fall in emissions is 16% (-59MtCO 2 eq), the value of the remaining emissions is EUR 30.8 billion (tax revenue), the fall in agricultural gross margin is 19% (-33.3 billion) and the loss of gross margin less tax revenue is EUR 2.5 billion. The associated impacts are an increase in the net production of food calories by 1%, a fall in livestock by 9.8%, a drop in area devoted to permanent meadows by 44%, and a decrease in the consumption of synthetic fertilizers by 3.3 %. The reductions in GHG emissions are respectively 9.3% for N 2 O and 20.5% for CH 4 . Let us now consider, with the same range of variation in livestock, the level of food calories corresponding to a drop in GHG emissions of 16%, a level estimated at 480 million tonnes of common wheat in calorie equivalent (+ 240%). In return for this production effort, the drop in gross margin is estimated at 18 billion euros (-10.3%). The herd decreases by 13.7% and the area under permanent meadows by 53%. The consumption of synthetic fertilizers increased by 9.7%.

For a given level of GHG emissions, the two options lead to very contrasting values on the criteria of agricultural gross margin, production of food calories, animal production, allocation of land and consumption of synthetic fertilizers. These different criteria, among others, should be taken into account to integrate the impacts on health and the environment into public choices.

The interest of the economic model of European agriculture used in the analysis is to be able to estimate these criteria under realistic economic and technical conditions. Although costly in computing, the analysis will be enriched by determining the frontier of what European agriculture can offer in terms of production of food calories, production of bio-energies, GHG emissions and consumption of synthetic fertilizers.

  On the production side, we focus on cereals. Let us recall that, in 2013 (European Commission, 2014), one-third of the EU agricultural area was cultivated with cereals, whose value represented one-eighth of the total value of EU agricultural products. The annual EU cereal production varied significantly in time, between 266 and 321 Mt over 2007-2012, as a result of both the economic and meteorological context, peaking in 2008 2 .

  the model, emissions are obtained according to the Intergovernmental Panel on Climate Change (IPCC) Guidelines (IPCC, 2006), allowing inter-country comparisons. In particular, the model relies on country-specific activity data and emission factors. The IPCC parameters for each EU Member State can be found in the respective National Report of GHG Inventories, submitted on a yearly basis to the United Nations Framework Convention on Climate Change. AROPAj relies on the following agricultural emission sources: N 2 O emissions from agricultural soils and manure management, and CH 4 emissions from manure management, enteric fermentation, and rice cultivation (refining and updating the results of De Cara et al. (2005)). N 2 O emissions from agricultural soils are subdivided into: (i) direct emissions: use of synthetic fertilizers, manure application, biological N fixation, crop residues and animal production; (ii) indirect emissions: atmospheric deposition, and leaching and run-off. Our calculations depend on these 11 emission sources, which are directly associated with the IPCC data. Our results are based on the most recent version of the model calibrated against six sets of annual data (FADN data for the period from 2007 to 2012). Each FADN year refers to a specific farm clustering into representative farms 6 , which allows the representation of six economic situations of European agriculture. The six-year period on which our study is based is very diverse, with agricultural and energy prices that exhibit strong variations.

  Figure 1. Results from the AROPAj model version based on the six FADN years (2007-2012).

  Figure 1 continued. Results from the AROPAj model version based on the six FADN years (2007-2012).

  and on-farm used cereal productions, exhibited a strong variation during 2007-2012, with a peak in 2008. If we introduce a carbon tax, the harvested cereal production increases until a certain price level, after which it starts to decrease.For 2007[START_REF] Ludi | Climate change, water and food security[END_REF]For , and 2010, this , this price level lies in the range [125 A C, 180 A C] (see Figure 1b). For 2008, 2011 and 2012, this change occurs outside the price scope.

Figure 2

 2 Figure2shows the effects of introducing a GHG emission tax of 200 A C/tCO 2 on marketed feed, marketed cereals, and on-farm used cereals in the EU for a livestock adjustment of 25% from 2007-2012. The quantity of on-farm used cereals, regardless of the carbon tax level, which had a positive but insignificant influence, peaked in 2008 (77 Mt when there is no tax and 109 Mt for a EUR 200 tax), after which it fell sharply to more than two-thirds in 2012 (24 Mt when there is no tax and 26 Mt for a EUR 200 tax). In contrast, the quantities of marketed cereals and marketed feed exhibited a large decrease in 2009, after which they started to increase again. This can be explained by the fluctuation of global cereal prices (with a large decline in 2009 as a result of the 2008 crisis), a drop in animal numbers, and the water scarcity in 2008.Changes in the area dedicated to major crops vs carbon price are illustrated in Figure3for the six years examined(2007)(2008)(2009)(2010)(2011)(2012). A tax increase implies a decrease in grasslands and fodders area in the EU and a strong increase in fallows area. Even if land allocations differ across years, major crops resist carbon pricing to some extent, when fodders and more strongly permanent meadows are dramatically affected. Animal production suffers from strong penalties on fermentation-generated CH 4 emission involving ruminants, as well as manure-generated N 2 O emission involving cattle as a whole.The spatial distribution of the areas cultivated with different crops is illustrated by the maps of area proportions among all crops considered in AROPAj, for different emission tax levels. Figures4 and 5illustrate the proportion of the areas cultivated with cereals, as well as the permanent meadows, reported to the AROPAj UAA. other cereals.

Figure 2 .

 2 Figure 2. Marketed feed vs Marketed cereals vs On-farm used cereals (expressed in Mt), for the no tax situation and for a EUR 200 emission tax.

Figure 3 .

 3 Figure 3. Trends in the areas of major crops vs CO 2 price in the EU (2007-2012 FADN years -AROPAj).

Figure 4 .

 4 Figure 4. Proportion of total straw cereals area in the EU (2007-2012) for two carbon tax values: EUR 0 (on the left) and EUR 200 (on the right).

Figure 5 .

 5 Figure 5. Proportion of permanent meadows area in the EU (2007-2012) for two carbon tax values: EUR 0 (on the left) and EUR 200 (on the right).

Figure 6 .

 6 Figure 6. Net quantity of food vs dual price.

Figure 7 .Figure 8 .

 78 Figure 7. Trends in areas of major crops by calorie quantity threshold

Figure 9 .Figure 10 .

 910 Figure9. Trends in the number of beef cows as the calorie threshold increases.

Figure 11 .

 11 Figure 11. Proportion of total straw cereals area in the EU (2007-2012) for no threshold (on the left), and after the introduction of a threshold of 435 Mtsweq (on the right).

Figure 12 .

 12 Figure 12. Proportion of permanent meadows area in the EU (2007-2012) for no threshold (on the left), and after the introduction of a threshold of 435 Mtsweq (on the right).

Table 1 .

 1 Calorie content of products exported from farms(FAO, 2003a); the content is weighted by the life duration of animals in each category (in years), as estimated for the AROPAj model.

	Crops	Calorie content	Animal category	Calorie content Meat content
		[kcal/100g]		[kcal/100g]	[ton/animal/year]
	oats	385	two-year-old males on-farm	250	0.48
	durum wheat	334	female calves from dairy herd*	250	0.27
	soft wheat	334	female calves from breeding herd* 250	0.27
	maize	356	18-month-old bulls	250	0.4
	other cereals	340	8-day old slaughtered calves	250	0.1
	barley	332	two-month-old slaughtered calves	250	0.25
	rye	319	dairy cows	250	0.036
	rice	362	six-month-old calves (field)	250	0.27
	A-sugar	70	suckler cows	250	0.080
	B-sugar	70	goats	210	0.012
	C-sugar	70	sheep	210	0.012
	sugar beet	70	pigs	220	0.26
	field vegetables 40	poultry	200	7.5
	proteins	80	milk**	61
	potatoes	67
	soy	335
	protein fodder	387
	rapeseed	387	concentrated feed	350
	sunflower	387	raw feed	80
	* non reported on farm;
	** distinct category, as animal product

Table 2 .

 2 EU aggregate values (initial emission level, emissions, and abatement level and rate), for each of the six years(2007)(2008)(2009)(2010)(2011)(2012) and for different emission tax levels EUR 0, 50, 100, and 200.

	Carbon price (A C)		2007 2008 2009 2010 2011 2012
	0	Initial emission level [MtCO 2 eq] 391.3 355.5 366.2 387.0 367.0 366.6
		Emissions [MtCO 2 eq]	327.2 320.0 318.1 323.3 331.2 331.0
	50	Abatement [MtCO 2 eq]	64.1	35.6	48.2	63.8	35.8	35.7
		Abatement rate (%)	16%	10%	13%	16%	10%	10%
		Emissions [MtCO 2 eq]	294.0 296.8 289.4 291.4 307.7 307.8
	100	Abatement [MtCO 2 eq]	97.3	58.8	76.9	95.6	59.3	58.9
		Abatement rate (%)	25%	17%	21%	25%	16%	16%
		Emissions [MtCO 2 eq]	239.1 260.5 239.0 240.0 267.2 273.4
	200	Abatement [MtCO 2 eq]	152.2	95.0 127.3 147.5	99.8	93.2
		Abatement rate (%)	39%	27%	35%	38%	27%	25%

  If we introduce the different targets of calorie quantities of 300, 350, and 400 Mtsweq, the dual price varies in the ranges [20 A C,36 A C], [27 A C,52 A C], and [36 A C,94 A C], respectively. The dual price rises to amaximum value corresponding to the maximum production thresholds, beyond which the solution obtained is no longer feasible. Figure6illustrates the net quantity of food, as the dual price rises to EUR 250. These dual prices can be compared to marketed soft wheat prices, which, for French

representative farms in cereal regions, were, on average, EUR 170, EUR 140, EUR 110, EUR 170,

Table 3 .

 3 Calorie quantities (Mt soft wheat equivalent) and other calorie indicators for the six FADN years(2007)(2008)(2009)(2010)(2011)(2012).

	FADN year	2007 2008 2009 2010 2011 2012
	unconstrained estimate (Mtsweq*)	166	227	219	183	218	204
	feasibility limit (Mtsweq)	450	530	550	500	505	505
	dual price limit (A C/tsweq)	1920 1244 1889 1284 1525 1974
	dual price for 300 Mtsweq threshold	25	26	20	25	29	36
	dual price for 350 Mtsweq threshold	52	32	27	30	34	44
	dual price for 400 Mtsweq threshold	94	49	36	51	72	83
	reference soft wheat price** (A C/t)	170	140	110	170	180	210
	limit/reference ratio	2.6	3.8	5	2.9	2.8	2.4
	* Million tons of soft wheat equivalent.

** reference price: average of soft wheat prices in the French Centre region.

Table 4 .

 4 Targeting calorie production vs pricing GHG emissions given first the calorie target and second the level of GHG emissions (computations for 2012, in millions of tonnes of CO 2 equivalent) for two calorie targets (in millions of tonnes of soft wheat equivalent); quantity surplus estimated in billion A C; livestock deviation estimated in % of the average.

	calorie target

There are two main ways to introduce carbon pricing: carbon taxes and cap-and-trade systems. Both of these carbon pricing instruments have been presented as an important factor in incentivizing the mitigation of GHG emissions and promoting investment in low emission technologies and practices(OECD and WBG,

2015;[START_REF] Kossoy | Carbon pricing watch 2015 : an advance brief from the state and trends of carbon pricing 2015 report[END_REF] The Grantham Research Institute, 2011).

World Bank data https://data.worldbank.org

An entire technical presentation of the model is available at https://www6.versaillesgrignon.inra.fr/economie publique/Media/fichiers/ArticlAROPAj

 4 For a detailed description of FADN data, see: http://ec.europa.eu/agriculture/

rica/5 Four key variables are used: FADN-defined type of farming (12 TF), altitude (3 levels), irrigation (share of area) and economic size (10 classes).

For each of the years, the model is delineated through more than 1,700 and up to more than 1,900 representative farms.

In AROPAj, the cereal area includes the main cereal crops: durum wheat, soft wheat, barley, maize, oats, rye, and

https://www.insee.fr/fr/statistiques


Vianney Lapotre

Purushotham Murugappa

Guy Gogniat

Amer Baghdadi

Michael Hübner

Jean-Philippe Diguet

Keywords: Wireless communication, turbo codes, ASIP, Dynamic configuration

The multiplication of wireless communication standards is introducing the need of flexible and reconfigurable multi-standard baseband receivers. In this context, multiprocessor turbo decoders have been recently developed in order to support the increasing flexibility and throughput requirements of emerging applications. However these solutions do not sufficiently address reconfiguration performance issues, which can be a limiting factor in the future. This work presents the design of a reconfigurable multiprocessor architecture for turbo decoding achieving very fast reconfiguration without compromising the decoding performances.

A dynamically reconfigurable multi-ASIP architecture for multi-standard and multi-mode turbo decoding

I. INTRODUCTION
A channel coding technique is typically associated to a variety of parameters and configuration options (frame size, communication channel, signal-to-noise ratio, etc). Among channel coding techniques, turbo codes are frequently adopted in the recent wireless standards to reach a very low bit error rate (BER). Furthermore, the high throughput requirement of emerging services imposes the efficient exploitation of different parallelism levels of the underlying algorithms such as sub-block parallelism [START_REF] Muller | Parallelism efficiency in convolutional turbo decoding[END_REF] or shuffled decoding [START_REF] Zhang | Shuffled iterative decoding[END_REF] techniques.

The introduction of contention-free interleavers in recent communication standards, such as WiMAX and LTE, enables high-throughput implementations presented in [START_REF] Wong | Reconfigurable turbo decoder with parallel architecture for 3GPP LTE system[END_REF]- [START_REF] May | A 150Mbit/s 3GPP LTE turbo code decoder[END_REF]. These architectures propose to use multiple Soft-Input Soft-Output (SISO) decoders to reach the high throughput requirement of emerging and future standards. These turbo decoders offer certain degrees of flexibility to adapt for instance the number of SISO decoders, the turbo code mode, i.e. Single Binary turbo Code (SBTC) or Double Binary turbo Code (DBTC), or the frame size. However, these efforts do not present any configuration infrastructures associated to these architectures in order to support fast and efficient dynamic configuration switches. Recently, Application Specific Instruction-set Processor (ASIP) solutions have been investigated in order to offer architectures providing good tradeoffs in terms of flexibility, throughput and power dissipation. In [START_REF] Muller | Asip-based multiprocessor soc design for simple and double binary turbo decoding[END_REF], a flexible and high performance ASIP model for turbo decoding was proposed, which can be configured to support all single and double binary turbo codes up to eight states. The architecture uses shuffled decoding with frame sub-blocking. Afterwards, optimizations on the proposed ASIP, called DecASIP, have been added in [START_REF] Murugappa | A flexible high throughput multi-asip architecture for ldpc and turbo decoding[END_REF]. In [START_REF] Brehm | A scalable multi-asip architecture for standard compliant trellis decoding[END_REF], the authors introduce the FlexiTreP ASIP in a multi-ASIP architecture for turbo decoding to reach the 150 Mbps throughput requirement of LTE. The FlexiTreP ASIP supports both SBTC and DBTC for various standards and it is configured through an interleaver memory, a program memory and the Dynamically Reconfigurable Channel Code Control. In [START_REF] Kunze | A "multi-user" approach towards a channel decoder for convolutional, turbo and ldpc codes[END_REF], the authors propose a reconfigurable multiprocessor approach in order to decode multiple data streams in parallel. However, the configuration process of the platform is not described. A mixed XML/SystemC simulation model of the platform has been implemented to reach a maximum throughput V. Lapotrre, G. Gogniat and J.-P. Diguet are with Univ. Bretagne Sud, UMR6285, Lab-STICC, France. e-mail: firstname.lastname@univ-ubs.fr P. Murugappa and A. Baghdadi are with Telecom Bretagne, UMR6285, Lab-STICC, France. e-mail: firstname.lastname@telecom-bretagne.eu M. Hübner is with Rurh-Universität, ESIT, Germany. e-mail: michael.huebner@rub.de Manuscript received MonthX XX, 2013; revised MonthX XX, 20XX.

of 86Mbps, which does not satisfy the throughput requirement of recent communication standards. Furthermore, the latency aspect and the scalability of the configuration process for a higher number of processing elements are not discussed. In fact, previous works provide an efficient way to reach the high performance requirement of emerging standards. However, the dynamic reconfiguration aspect of these platforms is superficially addressed. Among the few works which consider this issue, we can cite the recent architecture presented in [START_REF] Condo | Vlsi implementation of a multimode turbo/ldpc decoder architecture[END_REF], where solutions for the reconfiguration management of the Network-on-Chip (NoC) based multiprocessor turbo/lowdensity parity-check (LDPC) decoder architecture presented in [START_REF]A network-on-chip-based turbo/ldpc decoder architecture[END_REF] were proposed. Up to 35 processing elements (PEs) and up to 8 configuration buses have been implemented. However, the proposed solution does not guarantee that the configuration process can be masked by the current decoding task. Then, stopping the current processing to configure the new configuration is unavoidable and leads to a decoding quality loss in terms of BER. To leverage these issues, this paper presents a novel dynamically reconfigurable turbo decoder providing an efficient and high speed configuration process. The rest of this paper is organized as follows. Section II gives more insights about the motivation of this work. Section III introduces the considered multi-ASIP architecture implementing the RDecASIP processor. Section IV presents the flexibility features added to the initial architecture. Section V presents the implementation results. Finally, section VI concludes the paper.

II. MOTIVATION

When a turbo decoder is designed to support several communication standards, the decoder behavior has to be adapted in order to respect the application requirements and to take into account the communication channel quality. In future systems, simultaneous applications dealing with multiple communication standards have to be considered. In this scenario, the turbo decoder deals with input frames that have to be decoded for multiple applications that use different communication standards or modes. Each application is associated with specific throughput and BER objectives. Moreover, considering a mobile terminal, the configuration associated to an application has to be adapted temporally depending on the communication channel quality evolution. Consequently, each frame received by the turbo decoder is associated to a specific configuration, which takes into account the application requirements and the channel quality. In order to avoid extra delays between two frames associated with different configurations, the configuration process for a frame (i.e. computing and loading the new configuration) can be performed during the processing of the current frame. Thus, the Maximum Configuration Latency (MCL) for a frame k ensuring a null extra delay between two frames is evaluated using Equation [START_REF] Muller | Parallelism efficiency in convolutional turbo decoding[END_REF].

M CL(k) = NP revF rame(k). F rameSize(k -1).Rc(k -1) T hroughput(k -1) (1)
where k is the k th received frame, NP revF rame(k) is the number of consecutive frames decoded with the same configuration that precedes the frame k, F rameSize(k -1) is the (k -1) th frame size in bits, T hroughput(k -1) is the throughput requirement associated with the (k -1) th data frame and Rc(k -1) is the code rate associated with (k -1) th data frame. MCL, FrameSize and Throughput are expressed in seconds, bits and bits/s respectively. The maximum configuration latency critically decreases with high throughput targeted by emerging and future wireless communication standards. It will reach latencies around few microseconds in LTEadvanced standard that can provide a throughput higher than 1Gbps. Thus, emerging and future high throughput multi-mode and multistandard architectures will have to deal with maximum configuration latencies around few microseconds. That is why, in order to face this challenge, this paper brings contributions providing an efficient and high speed dynamic configuration of a multi-mode and multi-standard turbo decoder.

III. UDEC ARCHITECTURE

The proposed dynamic reconfigurable UDec turbo decoder architecture is shown in Fig. 1. It consists of two rows of RDecASIPs [START_REF] Lapotre | Optimizations for an Efficient Reconfiguration of an ASIP-Based Turbo Decoder[END_REF] interconnected via two butterfly topology NoCs. Each row corresponds to a component decoder. In the example of Fig. 1, four ASIPs are organized in 2 component decoders respectively built with 2 ASIPs. Within each component decoder the ASIPs are connected by two 44-bit buses for boundary state metrics exchange (not shown in Fig. 1). The RDecASIP implements the Max-Log MAP algorithm [START_REF] Robertson | A comparison of optimal and sub-optimal map decoding algorithms operating in the log domain[END_REF]. It supports both single and double binary convolutional turbo codes. Moreover, sliding window technique [START_REF] Schurgers | Memory optimization of map turbo decoder algorithms[END_REF], [START_REF] Benedetto | Soft-output decoding algorithms in iterative decoding of turbo codes[END_REF] is used. Large frames are processed by dividing the frame into N windows each with a maximum size of 64 symbols. Each ASIP can manage a maximum of 12 windows. Each ASIP can be configured through a 26x12 configuration memory. The configuration memory contains all parameters required to perform the initialization of the ASIP. Since the RDecASIP is designed to work in a multi-ASIP architecture as described in [START_REF] Murugappa | A flexible high throughput multi-asip architecture for ldpc and turbo decoding[END_REF], it requires several parameters to deal with a subblock of the data frame and several parameters to configure the ASIP mode. Concerning the subblock partitioning, each ASIP is configured with the size and the number of windows it has to decode. Furthermore, the last window size can be different so it corresponds to an additional parameter. In a single binary turbo code mode, the address of the tail bits in memory, the size and the number of windows for the tail bits have to be configured. Parameters for the ASIP mode correspond to the location of the ASIP in the architecture, the number of ASIPs required, the parameter which defines if the current ASIP is in charge of tail bits or not, the target standard (3rd Generation Partnership Project -Long Term Evolution (3GPP-LTE), WIMAX, or Digital Video Broadcasting -Return Channel via Satellite (DVB-RCS)) and the scaling factor for extrinsic information. Finally, some seed values are necessary for interleaving address generation in order to exchange information over the NoC that connects the ASIPs of each decoder component. All these parameters are required for a configuration of an ASIP within the platform. The entire configuration load represents 253 bits. Moreover, RDecASIP is associated with 3 memory banks of size 24x256 used to store the input channel Log-Likelihood Ratio (LLR) values. There are also 3 banks of size 30x256 used for extrinsic information storing. Each ASIP is further equipped with two 88x32 memories, which hold 11bit state metric values and a 16x128 program memory that stores a unified program for turbo decoding modes [START_REF] Lapotre | Optimizations for an Efficient Reconfiguration of an ASIP-Based Turbo Decoder[END_REF]. To increase the use of parallelism degree, shuffled decoding is used by employing multiple SISO decoders grouped into natural and interleaved domains. Finally, the extrinsic information generated is sent to the other domain through the Butterfly NoC interconnection.

The platform is dynamically configured through a dedicated busbased communication infrastructure shown in Fig. 1 that consists in a pipeline unidirectional bus implementing incremental burst, multicast and broadcast mechanisms. It can be split in three functional blocks: Master Interface (MI), Slave Interface (SI), and Selector. Each configuration memory is connected to the bus through a SI. The configuration manager deals with the configuration generation which is based on internal decisions and external information and commands. The MI provides an interface allowing the connection of the configuration manager to the bus. The SI provides an interface between the bus and the configuration memory. The Selector provides a simple and efficient solution to select, at run-time, RDecASIPs that are targeted by the next configuration data. This solution allows the transfer of a data into the configuration memory with a latency of 5 clock cycles. Moreover, thanks to the pipeline nature of the transfers, the configuration infrastructure is able to provide one data per clock cycle to the destination. The rest of the paper focuses on the reconfiguration process of the proposed multi-ASIP architecture through methods which bring more flexibility and more efficiency to the UDec platform. A throughput analysis of the UDec platform considering a stopping-free dynamic reconfiguration can be found in [START_REF] Lapotre | Stopping-free dynamic configuration of a multi-asip turbo decoder[END_REF].

IV. FLEXIBLE UDEC ARCHITECTURE

This section presents techniques that we propose in order to increase the dynamic configuration ability of the UDec Architecture. In a multi-mode and multi-standard context, the requirements in terms of throughput and BER evolve dynamically. Thus, depending on these requirements, the number of active ASIPs have to be adapted at run-time. Moreover, in order to deal with hot spot and potentially faulty cores management for the UDec architecture, the location of the activated ASIPs has to be dynamically defined. In the initial UDec architecture, the number of ASIPs used for a given configuration is fixed at design time and is equal to the total number of implemented cores. Sections IV-A and IV-B present solutions in order to bring this new flexibility.

A. Ring buses adaptation

The ring buses consist of direct connections between the ASIPs allowing to exchange boundary state metrics. So, when the number and the location of the selected ASIPs dynamically evolve, the loop connections between the last and the first selected ASIPs have to be adapted. Fig. 2 shows different examples of the ring buses adaptation when 4 ASIPs are implemented in each component decoder. Fig. 2a shows the case where two ASIPs are selected to perform the decoding task. The location of the first ASIP has been shifted from RDecASIP 0 to RDecASIP 1. Fig. 2b shows the case where three ASIPs are selected and the location of the first ASIP has been shifted from RDecASIP 0 to RDecASIP 2. In this case, the last ASIP of the component decoder is the RDecASIP 0 and the RDecASIP 1 has to be bypassed. The configuration of the ring buses is done using two parameters. The number of selected ASIPs (NumASIPs) in each component decoder reflecting the level of sub-block parallelism and the shift value (ASIPShift) determining which ASIP is the first selected ASIP. Considering the configuration example presented in Fig. 2a, N umASIP s = 2 and ASIP Shif t = 1 while N umASIP s = 3 and ASIP Shif t = 2 for the example of Fig. 2b. Based on these two parameters, a selection vector is computed. Each bit of this vector corresponds to one RDecASIP of the component decoder. The same selection vector is used for the two component decoders of the platform creating a symmetry. The selection vector drives multiplexers that determine the ring buses configuration as shown in Fig. 2c where control signals are presented in dotted lines considering one ring bus for clarity reasons.This proposed Ring buses adaptation requires the implementation of few additional logic gates and multiplexers as illustrated in Fig. 2c. Very limited area overhead is implied. For the example of Fig. 2c with 4 RDecASIPs in each component decoder, the area overhead related to the two corresponding Ring buses is 0.00229mm 2 in 65nm CMOS technology. This overhead represents 0.3% of the core area of the 4 RDecASIPs (4 x 0.187mm 2 [START_REF] Lapotre | Optimizations for an Efficient Reconfiguration of an ASIP-Based Turbo Decoder[END_REF]).

B. Butterfly topology NoCs adaptation

The extrinsic information transfers through the NoC are also impacted when the location of the selected ASIPs changes dynamically. Indeed, the routing information for the transfer is computed by the network interface associated with each ASIP depending on a global address of the symbol generated by the ASIP. Fig. 3 illustrates the routing principle for the considered Butterfly topology NoC. This topology allows a unique path in the network between each pair of nodes (source to destination). For each router, there are two inputs and two outputs. A single bit is used in a router to select the appropriate output: 0 for the first output, 1 for the second. In the proposed architecture, the routing information has to be adapted depending on the location of the ASIPs determined by the ASIPShift value The routing information is generated from a global address (G@) generated by the ASIP. As an example, Table I shows the routing information corresponding to the configurations for 2 selected ASIPs for both SBTC and DBTC modes. In this example the ASIPShift value is equal to 0. When two RDecASIPs are selected in each component decoder, the frame which has to be decoded is split and each RDecASIP is associated with half of the frame. Thus two cases are met as shown in Table I. When the global address generated by the ASIP is lower than the frame size (FS) divided by two, the extrinsic information has to be sent to the first ASIP of the second component decoder while it has to be sent to the second ASIP when G@ is between F S/2 and F S. Considering an architecture configuration implementing eight RDecASIPs, i.e. four in each component decoder, three bits are necessary to route the information through three routers. The last bit of the routing information depends on the turbo code mode. In DBTC mode a message is split, so one half is sent with the last stage route to 0 and the other half with the last stage route to 1. That is why the routing information is presented in the form "00 & 0 and 1" in Table I. In SBTC mode, the route of the last stage is determined by the LSB of the global address. That is why the routing information is presented in the form "00 & 0 or 1" in Table I. If the global address is odd, the route of the last stage is 1 while it is 0 when the global address is even.

The extrinsic information has to be stored in the extrinsic memory associated with each RDecASIP. Thus a local address (Local @) is computed depending on the global address computed by the emitter RDecASIP and the level of sub-block parallelism as shown in Table I. In DBTC mode, one complete extrinsic information for one symbol is stored in each memory address. Thus depending on the level of sub-block parallelism, the extrinsic information is spread over the extrinsic memories of each RDecASIP from the local address 0 to F S N umASIP s where N umASIP s is the number of the selected RDecASIPs in each component decoder. In SBTC mode, extrinsic information for two symbols is stored in each memory address. Thus depending on the level of sub-block parallelism, the extrinsic information is spread over the extrinsic memories of each RDecASIP from the local address 0 to F S/N umASIP s 2 . When the ASIPShift value differs from 0, the routing information can be computed as shown in Fig. 4. To obtain the final routing information taking into account the ASIPShift value, the initial routing information is first added with 2 × ASIP Shif t (since each router has two output ports) and then, a modulo operator is used in order to ensure a loop-like configuration where RDecASIP 0 and RDecASIP 4 can logically follow the RDecASIP 3 and RDecASIP 7 respectively.

These flexible parameters are introduced in the 26-bit width configuration memory associated with the platform controller. It also DBTC mode SBTC mode Routing info.

Local @ Routing info.

Local @ 0 ≤ G@ < F S 2 00 & 0 and 1 G@ 00 & 0 or 1 contains some parameters which are used to spread the input symbols over the input memories of each activated ASIP. Considering all configuration memories of the platform, a configuration represents 286 bits per activated ASIP plus 182 bits for the platform configuration memory.

G@ 2 F S 2 ≤ G@ < F S 01 & 0 and 1 G@ -F S 2 01 & 0 or 1 (G@-F S/2) 2

V. IMPLEMENTATION RESULTS

In order to evaluate the configuration latency of the proposed architecture, a hardware prototype of the configuration infrastructure has been developed on a Xilinx XUPV5 platform implementing a Virtex 5 LX110T field-Programmable Gate Array (FPGA). Configuration latencies were evaluated for several numbers of RDecASIPs. For this purpose, a MicroBlaze processor connected to the configuration infrastructure through a Xilinx Fast Simplex Link (FSL) is used to generate and transfer the configuration information and the design frequency is set to 125 MHz. Table II shows the configuration transfer latencies in nanoseconds and in number of clock cycles for different numbers of RDecASIPs being reconfigured. Moreover, estimations for an 1pplication-Specific Integrated Circuit (ASIC) implementation targeting 65nm CMOS technology at 500 MHz are provided. The maximum speedup of 12 is reached thanks to a higher clock frequency and by removing the FSL link between the processor and the configuration infrastructure. Results show that up to 128 RDecASIP can be configured in 9960ns and 830ns for FPGA and ASIC implementation respectively.

In the recent related work proposed in [START_REF] Condo | Vlsi implementation of a multimode turbo/ldpc decoder architecture[END_REF], where the configuration infrastructure consists of several buses each connected to a group of 4 PEs, up to 8 buses have been implemented to configure 35 PEs. However, the way the buses are driven is not described in details in [START_REF] Condo | Vlsi implementation of a multimode turbo/ldpc decoder architecture[END_REF] but the management of the 8 buses in parallel should increase the complexity of the configuration manager used to load new configurations. Results of Table III of each bus is set to 200 MHz. Moreover, in order to estimate the maximum configuration latency of the architecture presented in [START_REF] Condo | Vlsi implementation of a multimode turbo/ldpc decoder architecture[END_REF],

we assume that a one clock cycle latency is necessary for each configuration data that has to be transfered. Moreover, the maximum configuration latency is computed considering the maximum number of PEs and the maximum configuration load per PE (i.e. 1771 memory lines in [START_REF] Condo | Vlsi implementation of a multimode turbo/ldpc decoder architecture[END_REF]). The high maximum configuration load per PE of [START_REF] Condo | Vlsi implementation of a multimode turbo/ldpc decoder architecture[END_REF] comes from the necessity to load interleaving tables in each PE while this information is generated at run-time in the RDecASIP processor. Compared with [START_REF] Condo | Vlsi implementation of a multimode turbo/ldpc decoder architecture[END_REF], the proposed solution guarantees configuration latency below 10µs and 1µs in FPGA and ASIC implementation respectively while 44µs are necessary to configure 35 PEs in the ASIC implementation presented in [START_REF] Condo | Vlsi implementation of a multimode turbo/ldpc decoder architecture[END_REF] using a multibuses approach.

Both works presented in [START_REF] Murugappa | A flexible high throughput multi-asip architecture for ldpc and turbo decoding[END_REF] and [START_REF] Condo | Vlsi implementation of a multimode turbo/ldpc decoder architecture[END_REF] support TC and LDPC (Low-Density Parity-Check) codes. In LDPC mode, data exchange can be performed through a global ring bus that connects all the ASIPs of the platform as presented in [START_REF] Murugappa | A flexible high throughput multi-asip architecture for ldpc and turbo decoding[END_REF]. It is worth noting that methods proposed in this paper are fully compatible with the integration of LDPC mode. In particular, the ring buses adaptation method presented in Subsection IV-A can be adapted for LDPC mode by adding a link between both groups of ASIPs of the two component decoders in order to provide a global ring bus. At the processor level, the configuration and program memories can be organized in order to take advantage of broadcast mechanism provided by the configuration infrastructure since the major part of configuration parameters for LDPC mode is identical for all selected ASIPs. Indeed, the main configuration parameters for LDPC mode concern the parity check matrix configuration and content, which are identical for all ASIPs.

VI. CONCLUSION

The multiplication of communication standards leads to complex scenarios where the configuration process becomes a key point in order to guarantee high performances. In this context, this work tackles the dynamic configuration issues of multiprocessor platform for turbo decoding in order to respect hard constraints imposed by emerging multi-mode and multi-standard scenario. The configuration process of the entire platform implementing 128 RDecASIPs can be performed in less than 10µs with FPGA implementation. Moreover, ASIC synthesis results considering up to 128 RDecASIPs have demonstrated that a configuration transfer latency below 1µs is reachable providing an efficient solution in order to support dynamic configuration in the multi-mode and multi-standard scenario.

Fig. 2 .

 2 Fig. 2. Ring buses dynamic adaptation examples and architecture

Fig. 3 .

 3 Fig. 3. Butterfly topology routing principle and the level of sub-block parallelism determined by the number of selected RDecASIPs for the configuration in each component decoder (NumASIPs).The routing information is generated from a global address (G@) generated by the ASIP. As an example, TableIshows the routing information corresponding to the configurations for 2 selected ASIPs for both SBTC and DBTC modes. In this example the ASIPShift value is equal to 0. When two RDecASIPs are selected in each component decoder, the frame which has to be decoded is split and each RDecASIP is associated with half of the frame. Thus two cases are met as shown in TableI. When the global address generated by the ASIP is lower than the frame size (FS) divided by two, the extrinsic information has to be sent to the first ASIP of the second component decoder while it has to be sent to the second ASIP when G@ is between F S/2 and F S. Considering an architecture configuration implementing eight RDecASIPs, i.e. four in each component decoder, three bits are necessary to route the information through three routers. The last bit of the routing information depends on the turbo code mode. In DBTC mode a message is split, so one half is sent with the last stage route to 0 and the other half with the last stage route to 1. That is why the routing information is presented in the form "00 & 0 and 1" in TableI. In SBTC mode, the route of the last stage is determined by the LSB of the global address. That is why the routing information is presented in the form "00 & 0 or 1" in TableI. If the global address is odd, the route of the last stage is 1 while it is 0 when the global address is even.The extrinsic information has to be stored in the extrinsic memory associated with each RDecASIP. Thus a local address (Local @) is computed depending on the global address computed by the emitter RDecASIP and the level of sub-block parallelism as shown in TableI. In DBTC mode, one complete extrinsic information for one symbol is stored in each memory address. Thus depending on the level of sub-block parallelism, the extrinsic information is spread over the extrinsic memories of each RDecASIP from the local address 0 to

TABLE I ROUTING

 I INFORMATION FOR A 2 SELECTED ASIPS CONFIGURATION

	RDecASIP	G@	Routing information generation	+	modulo	Final Routing information
		Frame Size (FS)			Number of RDecASIPs
					implemented in each
		NumASIPs			component decoder
		2 x ASIPShift				NI TX
	Fig. 4. Complete routing information generator
	Nb.		Transfer latency
	ASIPs	FPGA 1 125MHz	ASIC 2 500MHz
		in ns	in clock cycles	in ns	in clock cycles
	4	1 032	129	86	43
	8	1 320	165	110	55
	16	1 896	237	158	79
	32	3 048	381	254	127
	64	5 352	669	446	223
	128	9 960	1245	830	415
			TABLE II
	ESTIMATED CONFIGURATION TRANSFER TIME IN ns FOR AN ASIC

IMPLEMENTATION ( 1 MEASURED -2 ESTIMATED)

  compare the proposed solution with both initial work and the work presented in[START_REF] Condo | Vlsi implementation of a multimode turbo/ldpc decoder architecture[END_REF] where the clock frequency MEASURED -2 ESTIMATED -3 TC=TURBO CODES)

		Initial	Proposed solution	[13]
		UDec [10]	FPGA	ASIC (65nm)	ASIC (90nm)
	Supported codes	TC 3 LDPC	TC 3		TC 3 LDPC
	Configuration infrastructure	not available	bus-based	bus-based
	bus frequency	-	125 MHz	500 MHz	200 MHz
	Number of buses	-	1		8
	Number of PEs	-	128		35
	Max. config. latency	-	9 960 ns 1	830 ns 2	44 275 ns 2
			TABLE III
	CONFIGURATION ARCHITECTURE AND LATENCY COMPARISON
	( 1