Colnenne-David Caroline
email: caroline.colnenne@grignon.inra.fr

Doré Thierry
email: thierry.dore@agroparistech.fr

Designing innovative productive cropping systems with quantified and ambitious environmental goals

Keywords: Innovative cropping systems, environmental goals, ex ante assessment

Agriculture must face a number of very pressing environmental issues. We used the prototyping method to design three innovative cropping systems, each satisfying three ambitious goals simultaneously: (i) overcoming a major environmental constraint, which represents a major break regarding objectives to be reached in current cropping systems (differing between systems: a ban on all pesticides but with chemical N fertilizer permitted; reducing fossil energy consumption by 50%; or decreasing greenhouse gas emissions by 50%), (ii) meeting a wide range of environmental criteria, and (iii) maximizing yields, given the major constraint and environmental targets. A fourth cropping system was designed, in which the environmental and yield targets were achieved with no major constraint (PHEP system). The performances of these innovative cropping systems were compared to a conventional system in the Ile-de-France region. We used a three-step prototyping method: (1) new cropping systems were designed on the basis of scientific and expert knowledge, (2) these system prototypes were assessed with tools and a model (ex ante assessment) adjusted to the set of constraints and targets, with optimization by an iterative process until the criteria were satisfied and (3) evaluation in a long-term field experiment (ex post assessment), which is currently underway. We describe only the first two steps here, together with the results of the prototypes assessment with tools and a model. The pesticide, energy and greenhouse gas constraints were fulfilled. All these innovative systems satisfied environmental criteria in terms of nitrogen and phosphorus management, pesticide use, energy consumption and crop diversity. For the pesticidefree system, the soil organic matter indicator was lower than expected due to frequent plowing (every two years) and yields were 20 to 50% lower than for the PHEP system, depending on the crop considered. We focus our discussions on the design methodology and the availability of scientific knowledge and tools for projects of this type.

Introduction

Agriculturalists are faced with challenges relating to very pressing environmental and health issues, including the need to decrease pesticide use. In many countries, high levels of pesticides have frequently been found in rivers, lakes and groundwater [START_REF] Ifen | Les pesticides dans les eaux -données 2005[END_REF][START_REF] Schrack | Rémanence des pesticides dans les eaux issues de parcelles agricoles récemment converties à l'Agriculture Biologique[END_REF] . A second pressing environmental issue is the consumption of fossil fuel. Energy use has markedly increased over the last decade [START_REF] Höök | Validity of the fossil fuel production outlooks in the IPCC Emission Scenarios[END_REF] , and some scientists agree that oil availability will decline in the near future [START_REF] Jakobsson | How reasonable are oil production scenarios from public agencies?[END_REF] , leading to a sharp increase in oil prices [START_REF] Shafiee | When will fossil fuel reserves be diminished?[END_REF] . In this context, new ways of optimizing or reducing energy use have been proposed [START_REF] Mohr | Long term prediction of unconventional oil production[END_REF] .

Global warming is a third challenging environmental issue facing agriculture. About 12% of global greenhouse gas (GHG) emissions emanate from agricultural lands [START_REF] Smith | Agriculture[END_REF] , and this proportion is expected to rise in the future, due to increases in the amount of land used for agricultural purposes and the intensification of agricultural practices [START_REF] Galloway | Transformation of the nitrogen cycle: recent trends, questions, and potential solutions[END_REF] . Carbon (C) sequestration in the soil, through the return of crop residues, root deposition and organic amendments, may help to decrease GHG emissions [START_REF] Arrouays | Expertise scientifique collective "Contribution à la lutte contre l'effet de serre" Stocker du carbone dans les sols agricoles de France ? Collective Scientific Expert Report[END_REF] . Sustainable development is another pressing social issue. Sustainable agriculture must satisfy environmental criteria [START_REF] Spiertz | Nitrogen, sustainable agriculture and food security[END_REF] . The harmful impact of agriculture on the environment can be lessened by optimizing fertilization (N, P) and increasing crop diversity. Currently pesticide use and energy consumption should also be reduced [START_REF] Ifen | Les pesticides dans les eaux -données 2005[END_REF][START_REF] Schrack | Rémanence des pesticides dans les eaux issues de parcelles agricoles récemment converties à l'Agriculture Biologique[END_REF] , and soil fertility should be maintained. Since the 1950s, alternative crop management systems have been proposed [START_REF] Matson | Agriculture intensification and ecosystems properties[END_REF] and legislation and inspection services have controlled the use of inputs (pesticides, N fertilization). Finally, agricultural production must satisfy the food needs of a soaring world population [START_REF] Spiertz | Nitrogen, sustainable agriculture and food security[END_REF] . Global agricultural production currently feeds a population of approximately seven billion. Current projections suggest that the world population will have reached nine billion by 2050 12 . The resulting increase in the need for land for housing will reduce the amount of land available for agricultural purposes [START_REF] Bouman | Rice and Water[END_REF] . The availability of arable land per capita differs greatly between regions (e.g., between China and South America), and major cropping systems must take this scarcity into account. Foley [START_REF] Foley | Solutions for a cultivated planet[END_REF] have suggested that feeding a population of this size will be possible only if agricultural systems change, along with human eating habits.

Many studies in recent years have focused on the design and assessment of new cropping systems. New crop management strategies have been proposed to decrease pesticide use [START_REF] Aubertot | Pesticides, agriculture and the environment: reducing the use of pesticides and limiting their environmental impact[END_REF] , to decrease energy consumption [START_REF] Köpke | Ecological services of faba bean[END_REF] , or to enhance C sequestration through crop management practices [START_REF] Beheydt | N2O emissions from conventional and minimum-tilled soils[END_REF] . Energy use and greenhouse gas emissions have been calculated and assessed for different systems [START_REF] Kessel | Climate, duration, and N placement determine N2O emissions in reduced tillage systems: a meta-analysis[END_REF] . At the cropping system level, long-term trials also have been set up to investigate the effects of different technical operations, such as N fertilization, on soil physical and chemical properties [START_REF] Fu Weijun | Spatial variation of soil test phosphorus in a long-term grazed, experimental grassland field[END_REF] and on soil biology [START_REF] Heinze | Effects of fertilizer and spatial heterogeneity in soil pH on microbial, biomass indices in a long-term field trial of organic agriculture[END_REF] . Several studies have assessed the differences between organic, integrated and conventional cropping systems in terms of C sequestration [START_REF] Chirinda | Soil properties, crop production and greenhouse gas emissions from organic and inorganic fertilizer-based arable cropping systems[END_REF] , energy efficiency and use [START_REF] Tuomisto | Comparing energy balances, greenhouse gas balances and biodiversity impacts of contrasting farming systems with alternative land uses[END_REF] , profitability [START_REF] Shadbolt | Comparisons between organic & conventional pastoral dairy farming, systems: cost of production and profitability[END_REF] or productivity [START_REF] Ponti | The crop yield gap between organic and conventional agriculture[END_REF] . Some authors have analyzed the impact of different degrees of tillage on productivity [START_REF] Simonsen | Endogeic and anecic earthworm abundance in six Midwestern cropping systems[END_REF] or biological activity [START_REF] Riley | Soil structure, organic matter and earthworm activity in a comparison, of cropping systems with contrasting tillage, rotations, fertilizer, levels and manure use[END_REF] . Others have focused on the effect of cropping systems on biodiversity [START_REF] Snapp | Management intensity -not biodiversity -the driver of ecosystem services in a long-term row crop experiment[END_REF] , or have used life cycle assessment methods to analyze the sustainability of various farming systems [START_REF] Nemecek | Life cycle assessment of Swiss farming systems: I. Integrated and organic farming[END_REF] . In most of these cases, new cropping systems were designed by modifying a few agricultural practices targeting a single goal (e.g., no chemicals to be used in organic cropping systems; no ploughing to increase C sequestration; more legumes in the rotation to reduce energy consumption; and more inputs to enhance profitability), without considering the other dimensions of sustainability. Despite the evidence that the future of agriculture must address a wide range of issues, no study has designed innovative cropping systems with specific and quantitative objectives covering a broad range of issues.

The objective of our project was to design, by prototyping [START_REF] Vereijken | A methodological way of prototyping integrated and ecological arable farming systems (I/EAFS) in interaction with pilot farms[END_REF] , innovative cropping systems meeting three quantitative objectives: (i) to satisfy a major environmental constraint, which represents a major break regarding objectives to be reached in current cropping systems (the banning of pesticide use, reducing fossil energy consumption by 50%, or reducing gas emissions by 50%,; (ii) to satisfy a wide range of environmental criteria with specific quantitative targets, and (iii) to produce the maximum yield possible given the constraint and the environmental targets. The ultimate aim of this work is to improve arable cropping systems throughout northern Europe. Prototyping was developed to enable agronomists to design, test and improve more sustainable cropping systems [START_REF] Vereijken | A methodological way of prototyping integrated and ecological arable farming systems (I/EAFS) in interaction with pilot farms[END_REF] . With this approach, newly designed cropping systems could satisfy several of the issues mentioned above and contributed to identify the weaknesses of cropping systems. System prototypes were assessed with tools and a model (ex ante assessment), with discussion of their potential performances, before their assessment in a long-term field experiment (ex post assessment). We set up this long-term field experiment in 2008, and its results will be published in due course. We focus here on the prototyping and assessment of the cropping system prototypes. We discuss our results in terms of innovative design methodology, the innovation of agricultural practices, the availability of suitable tools, models and crop management, and the yields achievable.

Materials and methods

Design method

The method used for cropping system design was based on the prototyping approach [START_REF] Vereijken | A methodological way of prototyping integrated and ecological arable farming systems (I/EAFS) in interaction with pilot farms[END_REF][START_REF] Dogliotti | A method for exploring sustainable development options at farm scale: a case study for vegetable farms in South Uruguay[END_REF][START_REF] Wery | Les outils des agronomes pour l'évaluation et la conception de systèmes de culture. L'agronomie aujourd'hui[END_REF][START_REF] Lançon | An improved methodology for integrated crop management systems[END_REF] , which involves four major steps:

(i) Defining and ranking the constraints and targets;

(ii) Designing innovative cropping system prototypes on the basis of current knowledge;

(iii) Assessments of cropping system prototypes with tools and models adapted for the constraints and targets used, with improvement of the cropping systems (in terms of rotation or crop management aspects) by an iterative process, until satisfaction of the constraints or achievement of results considered the best possible; and

(iv) Assessment of the most promising cropping system candidates in a long-term field trial. This practical assessment is currently being carried out in a long-term field experiment, initiated in 2008.

Constraints and targets for innovative cropping systems

Four different cropping systems with quantified constraints (i.e., conditions that had to be fulfilled), environmental and yield targets, were designed for the agricultural conditions and principal crops of northern France. These constraints and targets were prioritized as follows: an environmental constraint had to be satisfied first; a set of environmental targets then had to be attained, and finally, yield had to be maximized. The quantitative levels of the constraints did not correspond to any regulations and reflected a major break to be reached in current cropping systems. Inclusion of the use of organic fertilizers (manure, compost, etc.), which are currently not readily available to many farmers in large areas of Western Europe, was not permitted in the design of the cropping systems.

The productive high environmental performance cropping system (PHEP).

No major environmental constraint was placed on this cropping system, which was designed to reach environmental targets. Eleven environmental indicators, according to the INDIGO® tool [START_REF] Bockstaller | Agri-environmental indicators to assess cropping and farming systems. A review[END_REF] , were used to assess the effects of the cropping system on groundwater pollution (nitrate and pesticides), crop diversity, energy use and soil quality (organic matter content and phosphorus concentration). To reach environmental goals, all these environmental indicators, calculated over an entire crop rotation sequence, had to have values higher than 7 (graduated scale from 1 to 10) [START_REF] Bockstaller | Agri-environmental indicators to assess cropping and farming systems. A review[END_REF] . This system was used as the reference system for comparisons with the other three systems.

No-pesticide cropping system (No-Pest).

This cropping system was subject to a specific pesticide constraint: no pesticide use was tolerated, even using substances (e.g. acetic acid) at levels usually considered acceptable in organic cropping systems. However, inorganic chemical fertilizers were allowed (these fertilizers are not permitted in organic farming systems). This system had to achieve the same environmental targets as the PHEP cropping system.

Low energy cropping system (L-EN).

This cropping system was subjected to a specific energy constraint: it had to have fossil fuel consumption levels no greater than half those of the PHEP cropping system. It had to reach the same environmental targets as the PHEP cropping system.

Low greenhouse gas emission cropping system (L-GHG).

This cropping system was subject to a specific constraint concerning greenhouse gas emissions: its greenhouse gas emissions had to be no more than half those of the PHEP cropping system by increasing carbon sequestration in the soil and decreasing N2O emissions. It had to meet the same environmental targets as the PHEP cropping system. For each cropping system, once the constraint had been satisfied and environmental targets had been reached, the combination of agricultural practices giving the highest yields was retained.

Design of the four innovative cropping systems

The innovative cropping systems were designed from published knowledge, quantitative data from field experiments and individual or group expertise provided by scientists, extension service staff and farmers. For each cropping system, one prototype, consisting of the species in the rotation and the combination of agricultural practices used, was designed.

If the constraints were not satisfied, the candidate was modified iteratively (changes to the crops in the rotation or agronomic practices) until they were. At the beginning of the process, a modification of a crop led to a multitude of changes; at the end, changes were only one at a time. When the constraints were satisfied, environmental targets were optimized by an iterative procedure until improvement was observed. Maximum achievable yields were then determined by experts knowledge or from trial results, for the various cropping systems. The candidate cropping systems selected for further assessment in a field experiment were, those with the best performances in terms of constraints, environmental targets and achievable yields.

We carefully selected agronomic strategies from previous publications, to satisfy the given constraints. Examples of such strategies are presented in Table 1. Current knowledge, based on conventional cropping systems, had to be adapted for innovative cropping systems, and it was necessary to combine strategies. Agronomic strategies were translated into decision rules (as described by Debaeke [START_REF] Debaeke | Iterative design and evaluation of rule-based cropping systems: methodology and case studies[END_REF] ) to meet the requirements of future cropping systems and to cope with the variability of weather and agronomic conditions.

Assessment with tools and a model and fine-tuning of innovative cropping system prototypes

During the design process, the cropping system prototypes were assessed with various tools and a model, to determine the best ways to satisfy the set of constraints and targets imposed. Direct and indirect non-renewable energy consumption was assessed with the INDIGO® tool (v. 1.9). Direct energy consumption concerned the fuel, lubricants and electricity used to power farm machinery and tractors. Indirect energy consumption concerned the energy used in the manufacture, formulation, packaging and maintenance of inputs, such as machinery, fertilizer or pesticides. The energy outputs of the cropping systems were calculated as the gross energy content of the harvested produce. Energy consumption was calculated on a per hectare basis, per tonne of crop product and per calorie produced, over a complete crop sequence.

C sequestration in the soil was assessed with (i) the Roth C 26.3 model 35 and (ii) the SIMEOS® tool (v.2010) based on the AMG model [START_REF] Andriulo | Modelling soil carbon dynamic with various cropping sequences on the rolling pampas[END_REF] . We used climatic data (i.e., monthly mean air temperature, monthly precipitation, and monthly open pan evaporation) from a meteorological station located in Grignon (Ile-de-France region, 30 km west of Paris). The soil characteristics (plow layer, 0-30 cm) used to drive simulations were as follows: clay content 20.6%, bulk density 1.4, initial C content 8 g/kg dry matter. The expected annual yields were estimated from experimental data obtained under the same conditions (i.e., Ile de France region) and adjusted by expert knowledge. These values were used to estimate the expected annual dry matter production of roots and stubble, as described by Van Groenigen et al. [START_REF] Van Groenigen | Soil C storage as affected by tillage and straw management: An assessment using field measurements and model predictions[END_REF] . Direct and indirect GHG emissions were estimated with the GES'TIM database [START_REF] Ges | Ministère de l'agriculture, de l'alimentation, de la pêche, de la ruralité et de l'aménagement du territoire[END_REF] . We focused on two main greenhouse gases: nitrous oxide (N2O) and carbon dioxide (CO2). Direct emissions included N2O emissions from N fertilizers, calculated with Intergovernmental Panel on Climate Change coefficients [START_REF]Intergovernmental Panel on Climate Change. Climate Change 2007. The physical science basis[END_REF] , and the CO2 produced by the combustion of fossil fuels by farm machinery; CO2 respired by soil organisms was not taken into account in calculations. Indirect emissions corresponded to the use of fossil energy in the manufacture and maintenance of farm inputs. GHG balances (C sequestration plus GHG emissions) were determined over periods of 25 and 50 years, in accordance with Intergovernmental Panel on Climate Change proposals [START_REF]Intergovernmental Panel on Climate Change. Climate Change 2007. The physical science basis[END_REF] and current knowledge of C sequestration kinetics in the soil.

In this investigation, any GHG entering the system is counted negatively whereas GHG leaving the system is counted positively. Therefore, the overall balance is a positive value if more greenhouse gases are emitted than sequestered in the system.

Environmental indicators, such as in the INDIGO® tool (v. 1.9), were used to assess the environmental effects of cropping system prototypes. Three indicators of nitrogen effects provided information about ammonia volatilization, nitrous oxide emissions into the air, and nitrate leaching into the groundwater. Four pesticide indicators were studied: three providing information about pesticide volatilization, pesticide runoff and pesticide leaching into groundwater and one taking the global effect of pesticides into account. The last four indicators used provided information about crop diversity, energy consumption, organic matter in the soil, and phosphorus management. Each indicator takes a value between 1 (worst) and 10 (best). For rotations of more than five crops, the crop diversity indicator was calculated from the coefficients of Leteinturier [START_REF] Leteinturier | Adaptation of a crop sequence indicator based on a land parcel management system[END_REF] . For example, values 0.5, 4.1 and 7.6 respectively correspond to a wheat monoculture, a wheat-maize rotation and a wheat-sunflower-spring barley-maize rotation.

All these tools and the model were chosen on the basis of their relevance for assessing compliance with constraints and environmental targets. In a comparison of the performance of nine soil organic C models, using different datasets from long-term experiments from different parts of the world, Smith [START_REF] Smith | A comparison of the performance of nine soil organic matter models using datasets from seven long-term experiments[END_REF] found that the RothC model was among those that performed best. Bockstaller [START_REF] Bockstaller | Comparison of methods to assess the sustainability of agricultural systems. A review[END_REF] analyzed four methods for assessing the sustainability of agricultural systems. They found that the INDIGO® tool was the most relevant for conditions corresponding to those used here. These tools and the model have been regularly used in different countries. For example, Roth C has been used by De Li Liu 43 and Cerri [START_REF] Cerri | Simulating SOC changes in 11 land use change chronosequences from the Brazilian Amazon with RothC and Century models[END_REF] , and INDIGO® has been used by Bockstaller [START_REF] Bockstaller | Comparison of methods to assess the sustainability of agricultural systems. A review[END_REF] .

Current cropping system in the Ile-de-France region

The current system in the Ile-de-France region was defined on the basis of data collected in 2006 (Agreste [START_REF] Agreste | an agricultural statistical service of the French government[END_REF] ), the most recent data available at the initiation of this program. We defined the current cropping system in terms of agronomic practices and crop descriptions. This system was validated by various experts (farmers, extension service staff) with few adjustments in terms of types and numbers of crops in the rotation. This system was used as a reference for further comparisons.

Results

Design methodology step

The systems were designed over a six-month period, by a panel of about 15 experts. For each innovative cropping system, the first candidate characteristics were based on the current cropping system in the Ile-de-France region. The system was then optimized through an iterative process, which produced approximately 70 prototypes, to find the four most promising candidates. These candidates corresponded to the prototypes satisfying the constraint and environmental targets imposed and yielding the best results in the assessment. For example, for the PHEP system, the value of the crop diversity indicator was gradually increased from 5 to 7 during the fine-tuning of the system, with simultaneous improvement of the values of the other indicators. The first prototype was based on a 3-year rotation (winter oilseed rape, winter barley and winter wheat), currently used in the Ile-de-France region. In the best prototype, a winter legume and spring barley with a mustard catch crop were gradually introduced, leading to the following rotation: winter field beans (Vicia faba), winter wheat, winter oilseed rape, winter wheat and spring barley with a mustard catch crop. In the design process, we began by determining the crop rotation and then defined the crop management practices.

Description of the innovative cropping system prototypes

For each cropping system, we present only the most promising prototype. The systems are first described in terms of the crop rotation, crop management practices and yield targets. We then present the results of the final assessment with respect to constraints (i.e., pesticide use, energy consumption and greenhouse gas emissions) and, finally, we evaluate the systems in terms of environmental targets. The crop rotations and targeted yields are presented in Tables 2 and3.

The productive high environmental performance cropping system (PHEP)

The PHEP cropping system was designed with multiple environmental targets in mind and was based on the following agronomic strategies: (i) to reduce pesticide use, we increased crop diversity (four different crops instead of the three currently sown), (ii) to reduce the amount of N used and indirect energy consumption, we included at least one legume in the rotation, (iii) to decrease nitrogen leaching, a catch crop was always sown before the spring crop and N fertilization was forbidden during autumn and winter, (iv) to reduce direct energy consumption, plowing was allowed only once in the rotation, before the spring crop, (v) to reduce pesticide use and crop loss due to insects and diseases, highly resistant varieties were used, together with optimal sowing dates and densities and (vi) to stabilize or/and to enrich the soil organic matter (SOM) content of the soil, crop residues were not removed. As the system had to satisfy environmental targets requiring the use of fewer inputs, the target yields set were similar to those currently achieved with low-input cropping systems in the Ile-de-France region.

The no-pesticide cropping system (No-Pest)

Pesticide use was prohibited in the "No-Pest" cropping system. Therefore, this cropping system was designed as follows: (i) to break the cycles of some common soil-borne pathogens, we used a long rotation including a range of species (five different crops), with the alternate sowing of host and non-host plants, (ii) to reduce weed emergence from year to year, we sowed species with different sowing dates in spring and in winter successively, (iii) to decrease pest and disease pressure and damage, we used highly resistant varieties and species mixtures, and excluded crops highly susceptible to some enemies but with few non-chemical solutions, such as oilseed rape or potatoes, from the rotation (iv) to increase the competitiveness of the crop with respect to weeds, we sowed species with rapid shoot growth, such as hemp and triticale, (v) to maximize weed emergence before sowing, we used the stale seed-bed technique, (vi) to reduce weed emergence after sowing, plowing was carried out before each spring crop and (vii) we adapted sowing densities to make it possible to use mechanical weeding techniques and to decrease pathogen propagation. We used the following approaches to reach environmental targets: (i) to reduce nitrate leaching, catch crops were always sown before spring crops and the spreading of nitrogen fertilizer was allowed only in the spring, (ii) to decrease direct and indirect energy consumption, we decreased the number of plowing events and N fertilization was calculated according to yield objectives, and (iii) to stabilize SOM, crop residues were not removed. Yield targets were lower than those for the PHEP cropping system, because no pesticides were used. However, they were higher than those achieved in organic systems because chemical fertilizers were allowed, increasing flexibility in the management of crop nitrogen nutrition.

For the integration of these features, in accordance with current knowledge of pest and disease pressures in the Ile-de-France region, experts suggested yield potentials 30% lower than those for the PHEP system for cereals and 25% lower for field beans.

Low energy cropping system (L-EN)

The L-EN cropping system was designed, to have a much lower energy consumption than the PHEP cropping system, as follows: (i) to reduce indirect fuel consumption due to N fertilization, we included as many legumes as possible in the rotation (field beans as a main crop, clover as a catch crop, and a white clover-winter wheat mixture), and we used species or varieties with high N use efficiency (e.g., oats [START_REF] Riley | Economic and environmental optimization of nitrogen fertilizer recommendations for cereals in Norway[END_REF] ) and forms of mineral N fertilizers requiring less energy for their manufacture, (ii) to decrease direct fuel consumption, we omitted plowing, which is a very resource-intensive operation, and used a direct drilling system, and (iii) we decreased the amounts of mineral fertilizer (N, P, K) applied, implying a decrease in target yields. We also designed the L-EN cropping system along the same lines as the PHEP system, to achieve environmental targets for crop diversity, length of rotation, date of nitrogen spreading, and catch crop sowing. Target yields were 20% lower than for the PHEP cropping system, except for field beans.

Low greenhouse gas emission cropping system (L-GHG)

The L-GHG cropping system was designed to decrease greenhouse gas emissions by increasing C sequestration in the soil and decreasing N2O emissions.

C sequestration in the soil was increased by (i) including as many cereals as possible in the rotation, to ensure the production of large amounts of residues (i.e., maize, winter wheat, winter barley or triticale), (ii) maintaining continuous soil cover to increase the amounts of organic residues (i.e., cover or catch crops were always sown between main crops, and volunteers were left to grow after harvest), (iii) targeting high yields for the main and catch crops, to ensure the production of large amounts of residues, and (iv) excluding moldboard plowing, which increases C mineralization.

N2O emissions were reduced by (i) decreasing the amount of N fertilizer required at rotation scale [START_REF] Jeuffroy | Nitrous oxide emissions from crop rotations including wheat, oilseed rape and dry peas[END_REF] , and consequently direct emissions of N2O, by sowing legumes in the rotation (main and catch crops), (ii) improving and optimizing N fertilization practices according to climatic conditions, through the use of appropriate decision rules to prevent applications in conditions favoring N2O emissions, and (iii) sowing species with taproots to reduce soil compaction and N2O emissions.

The L-GHG cropping system was also designed according to the same principles as the PHEP system, to reach environmental targets for crop diversity, length of rotation, pesticide use, date of N spreading, and catch crop sowing.

Target yields were considered to be a compromise between the production of large amounts of C residues (i.e., high yields) and the decrease in N2O emissions (i.e., low N fertilization). Experts thought that potential yields would be similar to those achieved by the PHEP cropping system.

The current cropping system in the Ile-de-France region

This system is based on a cereal crop rotation, with five cereal crops over a six-year rotation (Table 2). In order to secure high yields, the agronomic practices were as follows: regular plowings, four times over a 6-year rotation. The amounts of N fertilizer exceed crop requirements, to prevent yield shortfalls in the event of unfavorable climatic conditions or unexpected nitrogen losses. Pesticides and growth regulators were used liberally to prevent diseases, weeds, pests and lodging (3 to 5 pesticides every year).

Cropping system prototypes assessments with tools and a model

Constraint assessment

Pesticide constraint in the No-Pest cropping system

This constraint was achieved by not applying pesticides in the No-Pest cropping system.

Energy constraint in the L-EN cropping system

Mean total fossil energy consumption (direct and indirect energy), calculated over a single rotation, was 4517 MJ ha -1 year -1 for the L-EN system and 8826 MJ ha -1 year -1 for the PHEP system (Figure 1). Chemicals, including N fertilizers, the largest component, accounted for 1271 MJ ha -1 year -1 (43% of total indirect energy consumption) in the L-EN system and 4345 MJ ha -1 year -1 (95% of total indirect energy consumption) in the PHEP system. The use of machinery for tillage, fertilization, harvesting, sowing and crop protection was the only component of direct energy consumption that was nearly halved in the L-EN system (2976 MJ ha -1 year -1 and 4228 MJ ha -1 year -1 for the L-EN and the PHEP systems, respectively). The difference between these two systems can be accounted for by the absence of tillage and the use of less N fertilizer in the L-EN cropping system.

When expressed in MJ ha -1 , the total fossil energy in the L-EN system is 49% lower than that in the PHEP system (Table 4). However, if expressed in MJ t -1 , the energy performance of the L-EN system is lower (i.e. difference between the PHEP and the L-EN systems of only 24% in term of total fossil energy per tonne of produce), because the target yield is about 20% lower. A similar reduction in energy use (about 29%) was observed for the calculation in kJ kcal -1 .

The greenhouse gas constraint in the L-GHG cropping system

C sequestration was assessed for the optimized prototypes of the PHEP and the L-GHG systems, for the mean soil organic matter content in the Ile de France region (1.6%). Both the Roth C model and the SIMEOS® tool predicted that C would be sequestered throughout the study period, from the start, in both cropping systems. The highest values were obtained with the L-GHG system over a 50-year period, for both assessment tools (Table 5). For both systems, total C sequestration was higher during the first 25-year period than during the second 25-year period. When expressed in t CO2-eq ha -1 , C sequestration values were systematically higher with the Roth C model than with the SIMEOS® tool.

Nevertheless, after 25 and 50 years, the differences between the L-GHG and the PHEP systems calculated with the Roth C model and the SIMEOS® tool were similar if the results were expressed in relative values.

Direct and indirect greenhouse gas emissions were calculated with the GES'TIM database, over one rotation period, for the L-GHG and the PHEP cropping systems (Figure 2). Mean total greenhouse gas emissions were 1104 kg CO2-eq ha -1 year -1 and 1273 kg CO2-eq ha -1 year -1 for the L-GHG and the PHEP cropping systems, respectively. Direct and indirect greenhouse gas emissions accounted for similar proportions of total emissions: 48% and 52% for direct greenhouse gas emissions for the L-GHG and PHEP systems, respectively (Table 6). Chemical fertilizers caused both direct and indirect greenhouse gas emissions. They represented 76% and 73% of total greenhouse gas emissions for the L-GHG and the PHEP systems, respectively. Soil cultivation, accounting for 19% and 23% of total greenhouse gas emissions for the L-GHG and the PHEP systems, respectively, was the second most important component of these emissions.

When results were expressed per ha, total greenhouse gas emissions were 13% lower in the L-GHG system than in the PHEP system. When expressed per tonne of produce, the larger decrease (22%) may be accounted for by the higher yields, calculated at rotation scale, of the L-GHG system than of the PHEP system.

In terms of the overall balance of GHG emissions (Tables 78), GHG values were negative for the L-GHG system, except for the 50-year period with the SIMEOS® tool. All GHG balance values were lower for the L-GHG system than for the PHEP system. The difference in GHG balance between the two systems increased over time, and was greater with the Roth C model, which gave decreases in GHG emission of 51% for the 25-year period and 76% for the 50-year period.

Assessment of environmental targets

The results of assessments of environmental targets with the INDIGO® tool are shown in Figure 3. For all optimized cropping systems, all 11 indicators had values of at least 7 (i.e., environmental criteria were satisfied), except for the organic matter indicator for the No-Pest cropping system (OMI = 5.7). The large number of species (more than three in each rotation) and the small quantities of pesticides sprayed on crops (0 to 2 pesticides used per crop), the systematic restitution of residues and the optimization of tillage and fertilization management (i.e., the small number of ploughing operations, optimizing P and N fertilization in terms of both the amounts applied and the timing of applications), resulted in high values for the indicators for crop diversity, pesticide use, soil organic matter, phosphorus, nitrogen and fossil fuel, respectively.

The low value of the organic matter indicator for the No-Pest cropping system (5.7) could be accounted for by both the large number of plowing operations (alternate years), encouraging mineralization, and the lower yields, resulting in smaller amounts of C residues.

Discussion

Design and assessment of innovative cropping systems

The main challenge of this study was to design innovative cropping systems. Our approach is original in the multiplicity of purposes assigned to these systems (i.e., association of one major constraint with environmental and yield targets). In most previous studies, these issues have been analyzed separately. For example, Zentner [START_REF] Zentner | Effects of tillage method and crop rotation on non-renewable energy use efficiency for a thin Black Chernozem in the Canadian Prairies[END_REF] and Gefland 49 studied energy efficiency, whereas Nemecek [START_REF] Nemecek | Life cycle assessment of Swiss farming systems: I. Integrated and organic farming[END_REF] used life cycle assessment methods to evaluate environmental criteria, Nowacki [START_REF] Nowacki | Comparison of profitability of potato cultivation in organic and, integrated farming systems[END_REF] studied profitability, and Chikowo 51 studied new cropping systems with a lower reliance on pesticides. However, several other studies are currently investigating system sustainability including assessments of several different criteria [START_REF] Blazy | A methodological framework that accounts for farm diversity in the prototyping of crop management systems. Application to banana-based systems in Guadeloupe[END_REF] , or numerous environmental parameters [START_REF] Tuomisto | Comparing energy balances, greenhouse gas balances and biodiversity impacts of contrasting farming systems with alternative land uses[END_REF] . In our project, we combined one major constraint with environmental and yield targets, reflecting the multifunctionality of agriculture. Furthermore, there was a clear, particular hierarchy throughout the design process. In most published experiments, environmental consequences are assessed only during the assessment of technical innovations in the trials, or environmental goals exist but are not quantified at the start of the study. In our work, satisfying the major constraints and the precise environmental targets were major aims, which became the conditions determining yield, with target yields set as high as possible under the conditions concerned. In addition, the clear definition of the constraint (i.e., reducing energy consumption or GHG emissions by 50%) and the environmental targets (i.e., having a value of at least 7 for all INDIGO environmental indicators) was also original. The quantitative levels of the constraints did not correspond to any regulations (i.e., these constraints reflected a major break with the regulations). However these innovative cropping systems were considered as research tools which enabled to identify the most relevant agronomic practices combinations which could be used in more restrictive legislative contexts. The level quantifications of the constraints and environmental targets were very useful during the design process which required calculations.

Before the assessment of the prototypes in a long-term field trial, candidate systems were assessed and improved in an iterative process until the constraints were satisfied and environmental performance with respect to targets was optimized. This theoretical process of improvement has rarely been reported in previous studies. Cropping systems are usually assessed or compared in systems defined on the basis of the main standardized characteristics, essentially relating to one major aspect: e.g., organic versus conventional systems 53,54,24, , no-tillage versus conventional tillage [START_REF] Moreno | Rainfed crop energy balance of different farming systems and crop rotations in a semi-arid environment: Results of a long-term trial[END_REF][START_REF] Feiza | Sustainable tillage: results from long-term field experiments on Cambisol[END_REF] , or integrated versus conventional systems [START_REF] Nowacki | Comparison of profitability of potato cultivation in organic and, integrated farming systems[END_REF][START_REF] Baturo | Effect of organic system on spring barley stem base health in, comparison with integrated and conventional farming[END_REF] . Quantitative data for environmental criteria [START_REF] Riley | Soil structure, organic matter and earthworm activity in a comparison, of cropping systems with contrasting tillage, rotations, fertilizer, levels and manure use[END_REF] , yield [START_REF] Klimekova | Comparison of yields and qualitative characters of spring barley grown, after three preceding crops in an organic farming system in the years, 2003-2008[END_REF] or economic performance [START_REF] Liebman | Agronomic and economic performance characteristics of conventional and low-external-input cropping systems in the Central Corn Belt[END_REF][START_REF] Cavigelli | Long-term economic performance of organic and conventional field crops in the mid-Atlantic region[END_REF] were therefore recorded in experiments. The results of these comparisons can be used to compare the impact of different systems, but not to identify all solutions for their improvement. Even though inductive reasoning can bring about some conclusions in regards to general principles, another round of conception and evaluation is required, to strengthen cropping systems. Our prototyping approach is totally different. Innovative systems were assessed by modeling until they satisfied specific constraints and were optimized in terms of specific environmental targets. A field trial was then set up to determine whether each of the selected prototypes could satisfy its multiple constraints and targets. In this case, the various environmental targets were included in the agricultural strategies from the start of the design process, facilitating identification of the weaknesses of the system and making it possible to propose solutions for improvement before undertaking field trials. After the assessment of these innovative cropping systems in a field trial, their costs and economic performances will be calculated in different economic scenarios, to determine the likelihood of their being adopted by farmers.

Our approach required a large panel of experts (scientists, farmers, and extension service staff) to design and to support prototypes throughout the design process (i.e., from the first to the last candidates). This was necessary because (i) the best crop management system may not correspond simply to the sum of individual agricultural practices, but may instead involve a set of agronomic strategies and their interactions, and (ii) a breadth of agro-ecological knowledge is required to identify sets of agricultural rules likely to be responsive to such strict constraints and environmental targets.

Moreover, this approach provided a more realistic view of cropping systems, making the adoption of the proposed innovations more likely [START_REF] Gal | How does research address the design of innovative agricultural production systems at the farm level?[END_REF] . However, the field trial assessment step is still absolutely necessary because some innovative agronomic practices, not currently used in cropping systems, have never been evaluated by experts.

During the design process, about 15 experts attended individual sessions or group meetings, to provide knowledge unavailable from published work. The definition of crop rotations and agronomic practices took about six months, and a further 18 months were required for the writing of the decision rules. Published studies involving design processes have differed considerably in the number of experts involved and the time spent by individual experts, depending on the availability of the experts and the difficulties encountered in achieving the goals assigned to systems [START_REF] Blazy | A methodological framework that accounts for farm diversity in the prototyping of crop management systems. Application to banana-based systems in Guadeloupe[END_REF][START_REF] Stoorvogel | Participatory research for systems analysis: Prototyping for a Costa Rican banana plantation[END_REF] .

Achievement of multiple constraint and targets

For all innovative systems, the constraints were satisfied with no consequences for other environmental components, except for the organic matter indicator of the No-Pest system. In this case, regular tillage combined with the restitution of only small amounts of organic matter had an adverse effect on soil environmental characteristics (indicator value for soil organic matter of 5.7, according to the INDIGO® tool). Within this system, it did not appear to be possible to satisfy both the constraint and this environmental target with the available non-chemical techniques for pest control. Moreover, this was only possible with the available techniques by reducing yield targets with respect to those of current regional systems (Agreste [START_REF] Agreste | an agricultural statistical service of the French government[END_REF] ). Nevertheless, progress in integrated pest management is being made, and new techniques may make it possible to improve environmental and yield performances. In the design of the L-EN system, we managed various agricultural processes, decreasing both direct energy consumption (due to tillage, for example) and indirect energy consumption (due to the use of mineral fertilizers). We halved fossil energy consumption by greatly decreasing N fertilizer inputs, which was associated with a 20% yield loss. However, the energy performance of the L-EN system was expressed relative to that of the PHEP system, which also had a relatively low level of fossil fuel consumption with respect to current practices in Ile-de-France. The total energy consumption of the L-EN system was about 35% that of the current system in Ile-de-France (Agreste [START_REF] Agreste | an agricultural statistical service of the French government[END_REF] ), when energy was expressed in MJ ha -1 (Table 4). For both the L-GHG and the PHEP systems, decreases in pesticide were taken into account by considering the maximum achievable yields to be similar to those of current low-input systems in Ile-de-France (Agreste [START_REF] Agreste | an agricultural statistical service of the French government[END_REF] ), and much lower than those of conventional systems in the region (Agreste [START_REF] Agreste | an agricultural statistical service of the French government[END_REF] ). Achievable yields for the L-GHG and the PHEP systems were considered to be 13% and 21% lower, respectively, than those of the current system in Ile-de-France. However, considering all the innovative systems together, it would appear to be possible to satisfy such ambitious constraints and environmental targets at the expense of only relatively small yield losses.

Available knowledge and current techniques suggested that it would not be possible to overcome all constraints in a single cropping system, because the agronomic practices used in one innovative system were incompatible with the constraints imposed on others. Plowing, one of the most effective practices against weeds used in the No-Pest system, is incompatible with large decrease in fossil energy consumption and the increase in C sequestration achieved with the L-EN and L-GHG systems, respectively. The large decrease in N fertilizer levels of the L-EN system is not compatible with the achievable yields defined for the PHEP and the L-GHG systems. Winter wheat sowing was delayed to avoid pest pressure in the No-Pest system, whereas it was brought forward in the direct drilling conditions of the L-EN and the L-GHG systems. This pattern was already evident during the design of the L-GHG system (i.e., GHG emission processes were managed in hierarchical order). Consequently, the development of a system without pesticides, with ambitious constraints in terms of GHG emissions and fossil fuel use, and with other environmental and yield targets, will require further progress in agronomic knowledge. For example, a better understanding of the interactions between cash and cover crops in terms of cooperative and competitive effects might allow the introduction of a living cover crop during cash crop growth in the L-GHG system [START_REF] Shili-Touzi | Is it possible to manage competition between wheat and alfalfa grown as a service plant in an intercropping system in temperate conditions? Agriculture[END_REF] . The field trial assessment again proves essential to gain a better understanding of these interactions.

Improving the design process

In the design of the L-GHG system, we had to rank the secondary objectives (C sequestration had to be enhanced first, and then N2O emissions had to be reduced) to satisfy the GHG constraint. In this case, several practices had effects on both processes involved: no-tillage increased C sequestration in the soil but increased N2O emissions; ample N fertilizer applications were required to obtain high yields and, thus, abundant C residues, but this also generated more N2O emissions. We decided to promote C sequestration, because N2O emission assessments were highly uncertain due to the lack of published data about N2O emissions, for field bean residues for example (IPPC [START_REF]Intergovernmental Panel on Climate Change. Climate Change 2007. The physical science basis[END_REF] ), and the variability of results due to differences in soil and climatic conditions [START_REF] Janzen | Towards a revised coefficient for estimating N2O emissions from legumes[END_REF] . Nevertheless, knowledge about the effects of cropping systems on N2O emissions is increasing, and it should be possible to improve the adjustment of cropping systems in the future.

The cropping systems assessment required tools and models adapted to the set of objectives and convenient to use during the iterative optimization process. Some approximations were used, due to the lack of data. In the L-GHG system, the coefficient defined by the Intergovernmental Panel on Climate Change (Tier 1) was used to calculate N2O release from the amount of N applied, rather than using different values for different soil and climatic conditions [START_REF] Janzen | Towards a revised coefficient for estimating N2O emissions from legumes[END_REF] .

There were also uncertainties in the assessment of C sequestration. The two simulations provided similar ranks for the PHEP and the L-GHG systems, but the SIMEOS® tool and the Roth C model gave different quantitative results. One difference between these tools relates to tillage. In the SIMEOS® tool, mineralization mechanisms in the tilled layer are modified by tillage [START_REF] Andriulo | Modelling soil carbon dynamic with various cropping sequences on the rolling pampas[END_REF] . In the Roth C model, tillage is taken into account only indirectly, through the C inputs to the soil from crop biomass, which are affected by tillage practices [START_REF] De | Simulation of soil organic carbon dynamics under different pasture managements using the RothC carbon model[END_REF] . Further uncertainties arise from the lack of root biomass data. Furthermore, these tools partially take into account changes in soil organic matter content, which greatly influence N transformation mechanisms in the soil. Anyway, the difference in the values generated by these models suggested that neither may accurately predict actual performances of these contrasting cropping systems. If cropping systems assessment is to be considered relevant, it must make use of tools that are regularly updated, including: (i) "new" machinery, such as harrows, hoes or direct-sowing drills with adjustable parameters in terms of energy consumption and GHG emissions, and (ii) new more forms of pesticides or fertilizers. It may also be useful to improve systems assessments with crop models, but many parameters are unavailable for marginal crops (such as hemp or flax), for crop mixtures (e.g., cereal and legume combinations), or species mixtures used as catch crops (e.g., the association of spring oats, mustard and clover).

Several economic and social aspects were not taken into account during this design process, which focused on the cropping system rather than the farm scale. Nevertheless, we excluded some crops that are not grown by farmers in Ilede-France due to the lack of a market (i.e., with low economic performances), but we included others for which the market is poorly developed in this area (e.g., lucerne and hemp). We did not take the organization of farm work into account either, although the identification and quantification of pest pressures in the field, to decrease pesticide use, is known to be time-consuming. From a technical point of view, we assumed that all farms owned the specific machinery used in the innovative systems, such as direct-sowing drills or mechanical weeding tools. For all these reasons, further investigations of the most relevant innovative cropping systems identified in a field trial assessment are needed, to forecast their possible application in an area.

Toward field assessment of the innovative cropping system prototypes

The innovative systems prototypes designed here were based on different hypotheses about soil and climate effects, because the true impact on crops and soil are unknown. For example we assumed that agricultural practices and climatic conditions would not affect crop emergence. In our conditions, crops are not affected by direct drilling because this technique is widely used in France (http://agriculture-de-conservation.com/). However, conservation tillage and mulch tillage practices remain largely empiric [START_REF] Alletto | Two-dimensional spatial variation of soil physical properties in two tillage systems[END_REF] , and additional knowledge is required for the optimization of these techniques, to make it possible to achieve good results in terms of sowing management and target yields. We also assumed that rainfall would provide enough water throughout the year in northern France to allow catch crop and main crop emergence, but this may no longer be the case if the climate changes radically. We assumed that new equilibria would appear with certain practices, allowing inputs to be reduced. In the L-GHG system, the practice of leaving crop residues on the soil should decrease weed emergence, making it possible to decrease the amount of herbicide used, and the absence of plowing should lead to the maintenance or increase in size of ground beetle populations, making molluscicide use unnecessary. The achievable yield was determined on the basis of several assumptions. In the L-GHG system, we promoted C sequestration, which is highly dependent on cereal yields and difficult to estimate in the case of no-tillage systems, because the presence of excessive amounts of crop residues may decrease emergence. The chemical properties of the soil associated with different tillage practices are also poorly characterized [START_REF] Alletto | Two-dimensional spatial variation of soil physical properties in two tillage systems[END_REF] . In this system, the effects on water availability of sowing cover crops every year also should be analyzed. In the L-EN system, the mixture of winter wheat and white clover might also decrease cereal yields. In the No-Pest system, yields were defined by approximation to organic systems, in which no mineral fertilizers are permitted. In this system, late winter cereal sowing might lead to higher levels of damping-off and plant death during winter, decreasing yields to a greater extent than anticipated. These are just a few of the uncertainties remaining about the real performance of the systems we have devised. For these reasons, the field assessment of these system prototypes, which is currently being carried out (the experiment started in 2008), is absolutely necessary.

Finally, it should be stressed that approaches of this type could be used in many agronomic situations, with a diversity of challenges, provided that sufficient knowledge is available for the development of innovative strategies. In our opinion, this approach meets the need expressed by Foley [START_REF] Foley | Solutions for a cultivated planet[END_REF] to "search for practical solutions" for a cultivated planet. L-GHG -IdF IdF -53% -47%

Tables

Table 7: Greenhouse gas balances (t CO2-eq ha -1 ), for different cropping systems, determined with the SIMEOS® tool (2010) and the ROTH C model (V.26-3), for a current soil in the Ile-de-France region (SOM = 1.6%) and the GES'TIM [START_REF] Van Groenigen | Soil C storage as affected by tillage and straw management: An assessment using field measurements and model predictions[END_REF] database, for 25-year and 50-year periods. The GHG balance is positive when the amount of carbon emitted in greenhouse gases exceeds that sequestered. Cropping systems: PHEP (productive high environmental performance), L-GHG (low greenhouse gas emissions) and IdF (current system in Ile-de-France region).

Greenhouse gas balances (t CO2-eq ha -1 ), Assessment period Figure 2: Cropping system prototypes assessment with GES'TIM database: Mean total greenhouse gas emissions (expressed in kg CO2-eq ha -1 year -1 ) calculated over one rotation period for the different cropping systems, and total greenhouse gas emissions for each crop of the various cropping systems (i.ghg: indirect greenhouse gas -d.ghg: direct greenhouse gas-w: winter -s: spring). Cropping systems: PHEP (productive high environmental performance), L-GHG (low greenhouse gas emissions) and IdF (current system in Ile-de-France region).

FiguresFigure 1 :

 1 Figures

Figure 3 :

 3 Figure 3: Environmental criteria assessment of the different cropping system prototypes with the INDIGO® tool (v.1.9). Cropping systems: PHEP (productive high environmental performance), L-GHG (low greenhouse gas emissions), L-EN (low energy), No-Pest (No pesticides) and IdF (current system in Ile-de-France region). Minimum values to satisfy environmental targets.

Table 1 :

 1 Examples of published agronomic strategies and associated practices for achieving specific goals

	Specific goal	Agronomic management strategies	Example of agronomic practices
	To reduce pesticide use:	To avoid the coincidence of the pest,	Modification of sowing date, earlier for oilseed
	(Munier-Jolain 65 ; Aubertot 15 ;	disease and weed contamination periods	rape, later for winter wheat
	Chikowo 51 )	and sensitive stages of crop
		development
		To reduce the density of pests	Use of mechanical weeding
			Use of Trichogramma parasitoid wasps against
			Ostrinia nubilalis on maize
		To reduce the impacts of pests, diseases	Decrease in sowing density and N fertilization to
		and weeds on crops	decrease shoot biomass
			Choice of varieties with the highest resistance
			Maximization of competition against weeds, by
			sowing winter oilseed rape very early (mid-August
			in the Ile-de-France region)
		To decrease the pool of pathogenic	Lengthening of the crop rotation
		fungi in the soil, diseases and weeds	Sowing of a wide range of crops, to decrease the
			pool of fungi in the soil
			Maximization of the use of stale seed beds to
			increase weed emergence before sowing
		To maintain beneficial insects	Shallow plowing to maintain populations of carabid
			beetles (slug predators)
	To reduce fossil fuel energy	Direct energy:	Reducing or eliminating the use of deep plowing
	use: (Tonitto 66 ; Deick 67 ;	To decrease the use of the most power-
	Nemecked 68 ; XueLi 70 ;	consuming farm machinery
	Gelfand 49 )	Indirect energy:	Introduction of large numbers of legumes and
		To decrease chemical N fertilization	species with the highest N use efficiency into the
			crop rotation
	To enhance C sequestration in	To decrease organic matter	Avoidance of plowing, replaced by direct drilling
	the soil:	mineralization	(conversion to no-tillage practices)
	(Arrouays 9 ; Freibauer 71 ;
	Mosier 72 ; Beheydt 17 ; Smith 7 ;	To allow high levels of C accumulation,	Sowing of maize and cereals in rotation
	Lehuger 73 )	depending on the nature of the crop
		residues
	To reduce N2O emissions:	To decrease anaerobic conditions, to	Decreasing compaction and soil moisture content
	(Rochette 63 ; Pelster 74 )	decrease denitrification mechanisms	without deep tillage
		To decrease the amount of available	Optimization of N fertilization, according to soil
		mineral N in the soil	nitrogen content
			Increasing the N use efficiency of crops
	To decrease nitrogen leaching To decrease soil nitrate content during	Sowing of catch crops
		autumn and winter	Sowing of oilseed rape after legumes
			Banning of N fertilization during autumn and
			winter

Table 2 :

 2 Crop rotations of the innovative cropping systems designed, and a current cropping system, defined by experts 705 on the basis of data from the Ile-de-France region in 2006 (Agreste[START_REF] Agreste | an agricultural statistical service of the French government[END_REF]

	706
	Cropping system	Crop rotation
	Productive high environmental performance	Winter field bean -Winter wheat -Winter oilseed rape -Winter
	(PHEP)	wheat -(with mustard as a catch crop) -Spring barley
	Without pesticide (No-Pest)	Triticale -(species mixture as a catch crop) Maize -Winter wheat
		-(species mixture as a catch crop) Spring field bean -Winter wheat
		-(species mixture as a catch crop) -Hemp
	Low energy use: (L-EN)	Winter field bean -Winter wheat -Winter oil flax -Winter wheat-
		white clover mixture -(white clover as a catch crop) Spring oat
	Low greenhouse gas emissions (L-GHG).	Triticale -(frost-sensitive species mixture as a catch crop) -Spring
		field bean -Winter oilseed rape (volunteers) -Winter wheat -
		(legumes as a cover crop) -Winter barley -(legume-oat mixture as
		catch crop) Maize
	Ile-de-France (IdF)	Winter oilseed rape -Winter wheat -Winter barley -Maize -
		Winter wheat -Winter wheat
	707
	708

Table 3 :

 3 Targeted yields (t ha -1 ) for different species sown in the innovative cropping systems and comparison with current yields. Cropping systems: PHEP (productive high environmental performance), No-Pest (no pesticide use), L-EN (low energy use), L-GHG (low greenhouse gas emissions) and IdF (current system in Ile-de-France region).

	Cropping systems	PHEP	No-Pest	L-EN	L-GHG	IdF
	/ Crops (t ha -1 )					Mean from
						1998 to 2007
						(Agreste)
	Spring barley	6.2	-	-	-
	Winter barley	-	4.6	-	6.6	6.6
	Spring field bean	-	4.7	-	4.7
	Winter field bean	3.4	-	3.4
	Hemp	-	8	-	-
	Maize	-	7.3	-	9.1	9.1
	Winter oil flax	-	-	1.8	-
	Winter oilseed rape	3.1	-	-	3.1	3.4
	Triticale	-	4.2	-	6.1
	Winter wheat	7.9	5.5	6.3	7.9	8.0

Table 4 :

 4 Comparisons of total fossil energy consumption, expressed in MJ ha -1 , MJ t -1 and kJ jCal -1 (INDIGO® tool, v.1.9), between cropping systems. Cropping systems: PHEP (productive high environmental performance), L-EN (low energy use) and IdF (current system in Ile-de-France region).

		Total fossil energy	Total fossil energy	Total fossil energy
		consumption (MJ ha -1 )	consumption (MJ t -1 )	consumption (kJ kCal -1 )
	L-EN -PHEP	-49%	-24%	-29%
	PHEP
	PHEP -IdF	-31%	-21%	-30%
	IdF
	L-EN -IdF	-65%	-40%	-50%
	IdF

Table 5 :

 5 C sequestration (t CO2-eq ha -1 ) simulated over 25-year and 50-year periods for the different cropping systems with the ROTH C model (V.26-3) and the SIMEOS® tool (2010), for mean soil organic matter content in the Ile-de-France region (1.6%. SOM = 123.3 t CO2-eq ha -1 ). Cropping systems: PHEP (productive high environmental performance), L-GHG (low greenhouse gas emissions) and IdF (current system in Ile-de-France region).

	C sequestration (t CO2-eq ha -1 )	Assessment period	Assessment period
			25 years	50 years
	Cropping systems	Tool or model
	PHEP	SIMEOS® tool	15.1	19.7
	PHEP	ROTH C model	85.2	116.0
	L-GHG	SIMEOS® tool	32.0	40.5
	L-GHG	ROTH C model	102.9	147.8
	IdF	SIMEOS® tool	17.4	22.1
	IdF	ROTH C model	87.1	129.2
	(L-GHG -PHEP)
	PHEP SOC	SIMEOS® tool	12%	15%
	(L-GHG -PHEP)
	PHEP SOC	ROTH C model	9%	13%

Table 6 :

 6 Comparison of total simulated greenhouse gas emissions, expressed in t CO2-eq ha -1 and t CO2-eqt -1 , between cropping systems. Cropping systems: PHEP (productive high environmental performance), L-GHG (low greenhouse gas emissions) and IdF (current system in Ile-de-France region).

		Total greenhouse gas	Total greenhouse gas
		emissions (t CO2-eq ha -1 )	emissions (t CO2-eq t -1 )
	L-GHG -PHEP	-13%	-22%
	PHEP
	PHEP -IdF	-46%	-32%
	IdF

Table 8 :

 8 Greenhouse gas balance ratio of the various cropping systems, expressed in t CO2-eq ha-1 , simulated with the ROTH C (V.26-3) model over two different periods (25 and 50 years). Cropping systems: PHEP (productive high environmental performance), L-GHG (low greenhouse gas emissions) and IdF (current system in Ile-de-France region).

		Assessment period
	25 years	50 years

Acknowledgments:

We thank the experts who took part in the design process, Marie-Hélène Jeuffroy, Jane Jonhson, Laurence Guichard, Marie Gosme and David Makowski for their useful comments on a previous version of the paper, Suzette Tanis-Plant for linguistic help, and Alain Bone for assistance with bibliographic research.


Miao Zhang

Michel Haritopoulos

Asoke K Nandi

Fetal ECG Subspace Estimation Based on Cyclostationarity

Keywords: Fetal Electrocardiogram, Fetal Heart Rate, Cyclostationarity, Cyclic Coherence, Blind Source Separation, Multidimensional Independent Component Analysis

In this paper, we propose a strategy to estimate a Fetal Electrocardiogram (FECG) subspace from a set of mixed ECG recordings from the thoracic and abdominal electrodes attached on a pregnant woman. The ECGs from an expectant woman contain FECG that can provide valuable information for fetal health monitoring, such as the fetal heart rate (FHR). After applying blind source separation (BSS) methods to mixed ECG, independent components are obtained. The main purpose of this paper is to classify an FECG group from all of these components which can be classified as FECG, MECG and noise according to the features of signals. Inspired by the concept of multidimensional independent component analysis (MICA) and to automate the classification task, we propose a procedure based on cyclostationarity of FECGs, in particular, an integrated Cyclic Coherence. The method is validated on real world DaISy dataset and the results are promising.

I. INTRODUCTION

Fetal Electrocardiogram (FECG) is an important tool to indicate the health state of a fetus. Non-invasive FECG measurement can be utilized to prevent diseases and complications caused by heart defects before a baby is delivered. Basically, FECG is obtained from electrodes attached on the mother's skin. When mixed ECGs signals are sampled via several electrodes from thoracic and abdominal regions, some separating algorithms are used to extract FECGs from the mixed ECGs. This is not an easy task because voltage amplitude of an FECG is very low compared to a maternal ECG (MECG). Moreover, other noise cannot be neglected. They may come from mother, such as Electromyogram (EMG) or respiration. And they may also be from instruments and power grid, such as baseline wandering and thermal noise, etc.

Research on FECG extraction methods are very popular in recent decades and a variety of processing techniques emerged. They can be classified on different categories according to their principle, such as blind source separation (BSS) [START_REF] Zarzoso | Maternal and foetal ECG separation using blind source separation density function[END_REF] [24] [START_REF] Nandi | Fourth-order cumulant based blind source separation[END_REF] methods, adaptive filter [START_REF] Outram | Techniques for optimal enhancement and feature extraction of fetal electrocardiogram[END_REF], wavelet transform [START_REF] Ye | Application of wavelet analysis in detection of fetal ECG[END_REF] [START_REF] Vigneron | Fetal electrocardiogram extraction based on non-stationary ICA and wavelet denoising[END_REF], artificial intelligence techniques [START_REF] Kezi Selva | Interference Cancellation in FECG using Artificial Intelligence Techniques[END_REF], etc. Adaptive filtering based approaches, such as Bayesian filter [START_REF] Behar | A Bayesian Filtering Framework for accurate extracting of the non-invasive FECG morphology[END_REF], Kalman filter [START_REF] Niknazar | Fetal ECG Extraction by Extended State Kalman Filtering Based on Single-Channel Recordings[END_REF] (specifically for linear model assumption), extract FECG through a training with the help of MECG and recordings from maternal chest as cancellation reference signals. Wavelet transform [START_REF] Ye | Application of wavelet analysis in detection of fetal ECG[END_REF] is utilized to find MECG with noise. Then FECG is obtained by removing this noisy MECG from the mixed signals. Wavelet transform is also a good pre-processing tool for denoising [START_REF] Vigneron | Fetal electrocardiogram extraction based on non-stationary ICA and wavelet denoising[END_REF] before FECG is separated. The key point for wavelet transform is to choose a suitable mother wavelet with good position and scale parameters. Artificial intelligence, such as adaptive linear neural network [START_REF] Amin | Separation of fetal electrocardiography (ECG) from composite ECG using adaptive linear neural network for fetal monitoring[END_REF], needs sufficient training of weights to obtain MECG and some distinctive noise such as baseline wondering, which are later subtracted from abdominal ECG to get FECG. The training need much time, generally.

BSS methods, such as principal component analysis (PCA), independent component analysis (ICA), singular value decomposition (SVD) [START_REF] Kanjilal | Fetal ECG extraction from singlechannel maternal ECG using singular value decomposition[END_REF], separate mixed signals without or using little prior information. ICA [11] [22] [23] is a separation algorithm that is based on higher-order statistics. After optimizing, signals with maximum statistic properties are the components that we need. After separated, FECG, MECG and noise can be displayed by different time series. It is necessary to find out which ones are in an FECG group for a further diagnosing of fetus heart state. For an FECG subspace estimation, our paper focuses on the separation via ICA BSS algorithms and makes use of the concept of multidimensional ICA (MICA) [START_REF] Cardoso | Multidimensional independent component analysis[END_REF].

In Section II, the basic BSS model used for FECG estimation as well as the concept of MICA are presented. Next, in Section III, we introduce a criterion based on the cyclostationary properties of an FECG to figure out whether an independent component can be classified in an FECG group or not. We propose our new criterion and procedure for an automatic classification in Section IV. The experiments and results are displayed in Section V. Finally, a conclusion is presented in Section VI.

II. SEPARATION MODEL OF BSS AND MULTIDIMENSIONAL ICA

A. Blind Source Separation and ICA

BSS was first proposed in [START_REF] Jutten | Blind separation of sources, part I: An adaptive algorithm based on neuromimetic architecture[END_REF] and [START_REF] Jutten | Blind separation of sources, part II: Problems statement[END_REF] by Jutten et al. It can be described as obtaining a set of "pure" or source signals from a set of observations with little priori knowledge or information and few assumptions. In general, BSS is described as a linear, time invariant and instantaneous model.

Let us consider a set of M observation signals 𝐱(𝑡) = [𝑥 1 (𝑡) ⋯ 𝑥 𝑀 (𝑡)] 𝑇 ; and a set of N unknown source signals 𝐬(𝑡) = [𝑠 1 (𝑡) ⋯ 𝑠 𝑁 (𝑡)] 𝑇 , 𝑁 ≤ 𝑀, where t denotes a time index and T is a transpose operator. Thus, the linear instantaneous BSS model can be stated as:

 𝐱(𝑡) = 𝐀𝐬(𝑡) (1)
where A is an unknown 𝑀 × 𝑁 mixing matrix and it should be full column rank as 𝑀 ≥ 𝑁.

In addition, as a basic condition of ICA, it is usually assumed source signals are mutually statistically independent and there is at most one Gaussian distribution source in 𝐬(𝑡).

B. MICA

Author of [START_REF] Cardoso | Multidimensional independent component analysis[END_REF] generalized the notion of ICA to that of multidimensional ICA (also referred to as independent subspace analysis); to do this, the starting point was rewriting model of eq. ( 1) as a sum of M 1-dimensional independent vectors 𝑥 1 , … , 𝑥 𝑀 , transforming the original multiplicative model into an additive one. Thus, instead of separating independent components (ICs), the MICA concept aims at separating independent subspaces. The main advantage of MICA over ICA methods is that ICs of the same group may be dependent to each other but independent from ICs grouped in different estimated subspaces. For bioelectric signals processing, the MICA decomposition can be performed by a two steps procedure: first, estimate ICs from the observed signals using an ICA algorithm and, then, group together those which are part of the same multidimensional component (in this work, the criterion for automatic classification is based on the inherent cyclostationary properties of the ECG signal and will be detailed in Section IV).

A theoretical study on the uniqueness of the MICA problem but limited to the case where the estimated subspaces have the same size is given in [START_REF] Theis | Multidimensional independent component analysis using characteristic funtions[END_REF]. Also, some applications of the MICA concept in the biomedical research field exist in the literature. For example, in [START_REF] Castells | Multidimensional ICA for the Separation of Atrial and Ventricular Activities from Single Lead ECGs in Paroxysmal Atrial Fibrillation Episodes[END_REF], authors aimed at classifying the estimated independent components into ventricular activity (VA) and atrial activity (AA) subspaces using data from a single ECG electrode and in [START_REF] Camargo-Olivares | The Maternal Abdominal ECG as Input to MICA in the Fetal ECG Extraction Problem[END_REF] the MECG component is estimated from abdominal recordings and cleaned from any FECG contribution before being considered as an extra input to MICA together with recordings from electrodes positioned at the mother's thoracic region.

III. FECG CLASSIFICATION CRITERION BASED ON CYCLOSTATIONARITY

A. Cyclostationary Property

From the waveforms of FECGs and MECGs, we can assume that the heartbeats of fetuses and mothers are both repeated, which means they have their own relative stable periods. Although they are not strictly periodic, they are cyclostationary signals. Signal with cyclostationarity can be extracted directly by only knowing the cyclic frequency as a prior information [START_REF] Capdessus | Extraction of a cyclostationary source using a new cost function without pre-whitening[END_REF]. In [START_REF] Haritopoulos | Foetal PQRST extraction from ECG recordings using cyclostationarity-based source separation method[END_REF], this assumption is demonstrated to be valid and promising for extracting fetal PQRST wave. And it also gives an a priori estimation of the fundamental cyclic frequency of the fetus.

In the following of this paper, a classification criterion based on cyclostationarity is introduced. It will tell which of the independent components that are separated via ICA belong to an FECG subspace. Hence, the remaining estimated components should be grouped into MECG and noise subspaces.

B. Cyclic Coherence Criterion

In order to measure the correlation degree between cyclostationary signals as well as themselves, we use Cyclic Coherence (CC) [START_REF] Hurd | An investigation of periodically correlated stochastic processes[END_REF]. It is a form of simple coherence [START_REF] Max | Méthodes et techniques de traitement du signal et application aux mesures physiques[END_REF] in frequency domain. Suppose 𝑥(𝑡) denotes a signal in time domain. The transform of 𝑥(𝑡) is 𝑋(𝑓), where f is a frequency variable. Thus, the CC has the form below:

 𝐶 𝑥 𝛼 (𝑓) = 𝐸[𝑋(𝑓)𝑋 * (𝑓-𝛼)] (𝐸[|𝑋(𝑓)| 2 ]𝐸[|𝑋(𝑓-𝛼)| 2 ]) 1 2 ⁄  
In this formula, the CC criterion is normalized, so the value of CC 𝐶 𝑥 𝛼 (𝑓) ∈ [-1,1]. The more close to 1 the |CC| is, the more strong coherent between components of signal 𝑥(𝑡) at frequency f and 𝑓 -𝛼 is.

IV. INTEGRATED CYCLIC COHERENCE AND AUTOMATIC CLASSIFICATION PROCEDURE

The 𝐶 𝑥 𝛼 (𝑓) is a bivariate function of which variables are 𝛼 and f, respectively. We can plot it as an image and the colour of each pixel stands for the value at a specific point (𝑓 0 , 𝛼 0 ). In order to make CC more reasonable to use for classification and according to the property of CC, we present a criterion named integrated Cyclic Coherence (iCC) which can be defined as follows:

 𝑖𝐶𝐶(𝛼) = ∑ 𝐶 𝑥 𝛼 (𝑓) 𝑓∈[𝑓 𝑚𝑖𝑛 ,𝑓 𝑚𝑎𝑥 ] , 𝛼 ∈ [𝛼 𝑚𝑖𝑛 , 𝛼 𝑚𝑎𝑥 ] 
where [𝑓 𝑚𝑖𝑛 , 𝑓 𝑚𝑎𝑥 ] and [𝛼 𝑚𝑖𝑛 , 𝛼 𝑚𝑎𝑥 ] are ranges of f and 𝛼, respectively. They are rough ranges that can be determined according to the physical truths of FECG and MECG. The choice of these ranges will be described in the experiments and results section.

The strategy of classifying the FECG group from a set of separated components from the mixed ECGs is:

1) The starting point is a set of raw ECG recordings which are a mixture of fetal and maternal ECGs. A BSS algorithm is used to separate these into independent components (IC).

2) For each independent component, both the cyclic coherence and the integrated cyclic coherence are calculated.

3) The fundamental cyclic frequency of FECG can be found either from plots of CC on a plane of cyclic frequency vs. frequency, as can be seen from Fig. 3-a), or from plots of (𝑚𝑎𝑥(𝑖𝐶𝐶) -𝑖𝐶𝐶) vs. cyclic frequency, as can be seen from Fig. 4.

4) The independent components are considered to be FECGs if the kurtosis values of their corresponding iCCs are above a threshold value.

In the next section, three BSS algorithms are applied to real world data in order to test the criterion we proposed.

V. EXPERIMENTS AND RESULTS

In this section, we use JADE [START_REF] Cardoso | Blind beamforming for non-Gaussian signals[END_REF], SOBI [START_REF] Belouchrani | A Blind Source Separation Technique Using Second-Order Statistics[END_REF] and FastICA [START_REF] Hyvarinen | Fast and robust fixed-point algorithms for independent component analysis[END_REF] as the BSS methods to separate raw ECG. The well-known DaISy database [START_REF] Leuven | Database for the Identification of Systems at[END_REF] is tested in this paper. The signals in DaISy is a real world data that came from eight electrodes attached on a pregnant woman. The dataset signals are displayed in Fig. 1. All signals have a 500Hz sampling rate with a total sampling time of 5 sec. The first five channel (from top to bottom) recordings come from abdominal regions and the other three from thoracic regions. All signals are sampled simultaneously. We first test the proposed strategy under the JADE method. Fig. 2 shows the eight independent components extracted by using the JADE algorithm. Then the corresponding CCs are calculated (displayed in Fig. 3). Abscissa and ordinate stand for the frequency f and the cyclic frequency 𝛼, respectively. We choose a wide range of f between 1~120Hz with the interval of 1Hz. And also we choose the range of 𝛼 = 1~10Hz in which fetal and maternal heart rate are both located. For a fixed f, 𝛼 varies within its range by the step of 0.2Hz. Thus, using this resolution, one can compute the CC of 50 different 𝛼 values. From the features of these images, one can classify eight ICs into three groups: {IC3, IC5}, {IC2, IC6, IC7, IC8} and {IC1, IC4}.

It can be seen very clearly that any signal that belongs to the 2-dimensional subspace {IC3, IC5} has large correlation according to the value of CC when 𝛼 = 4.5Hz. It indicates this is an FECG subspace compared to the 4-dimentional subspace {IC2, IC6, IC7, IC8} which is the MECG subspace because they have high CCs when 𝛼 = 2.7Hz. The remaining subspace {IC1, IC4} is a 2-dimensional noise subspace. The conclusion can be drawn reasonably through the figures. In order to make a classification by calculating the criterion rather than visual inspecting, iCC of each IC is computed. Table I depicts the kurtosis and the standard deviation (STD) of iCCs for every IC and also the subspace in which each IC should be classified. F, M and N stand for the FECG subspace, the MECG subspace and the noise subspace, respectively. For instance, IC1 is classified into the noise subspace while IC3 is classified as FECG. In our paper, we focus on the kurtosis of iCC to classify the FECG subspace. It is obvious that a high threshold of kurtosis (e.g. kurtosis ≥ 30) is reasonable to divide ICs into the FECG group from other groups. In the previous work [START_REF] Haritopoulos | Cyclostationarity-Based Estimation of the Foetus Subspace Dimension from ECG Recordings[END_REF], the authors point out that an independent component can be classified into a noise subspace when STD of iCC is low enough (e.g. threshold is 1). The classification of maternal (M), fetal (F) and noise (N) subspace in Table I is completed based on this paper and the previous work [START_REF] Haritopoulos | Cyclostationarity-Based Estimation of the Foetus Subspace Dimension from ECG Recordings[END_REF]. To explain the above phenomenon, we plot the (𝑚𝑎𝑥(𝑖𝐶𝐶) -𝑖𝐶𝐶) in Fig. 4. The vertical coordinate stands for 𝛼 while the horizontal ordinate stands for (𝑚𝑎𝑥(𝑖𝐶𝐶) -𝑖𝐶𝐶). IC3 (yellow) and IC5 (red) have null points at 4.5Hz, so they belong to FECG subspace, which has the same conclusion according to Fig. 3-a). It is sure enough to classify IC2, 4, 6, 7, 8 into MECG subspace due to null points at 2.7Hz. IC1 (cyan), however, has a same null point. We still regard it as a noise because from the line chart, it is a bit "smooth" with low STD and low kurtosis, which indicates that there is very little information or has a weak feature. This is very reasonable if we look back at Fig. 2. IC1 and IC4 are noisy and can only provide little ECG features (regardless of FECG or MECG features). These conclusions can also be verified as reasonable through plots of ICs separated by these two algorithms. See Fig. 5 and Fig. 6. In this paper, we propose an integrated Cyclic Coherence as a criterion to classify FECG subspace automatically. We also present a complete procedure to implement this process. High kurtosis of iCC is the key threshold to divide FECG from other ICs. This method is tested with three different BSS algorithms on the real world DaISy database. The results are very promising. In combination with the previous work [START_REF] Haritopoulos | Cyclostationarity-Based Estimation of the Foetus Subspace Dimension from ECG Recordings[END_REF], we can now divide ICs into FECG, noise and MECG groups. Next, we will focus on robustness of our method.

Fig. 1

 1 Fig. 1 Eight channel recordings of a pregment woman from DaISy database

  Fig. 3 Cyclic Coherence of eight independent components from JADE

Fig. 4 (

 4 Fig.4(max(iCC)-iCC) of independent components from JADE

Fig. 5 Fig. 6

 56 Fig. 5 Independent components extracted by SOBI from DaISy

Table I .

 I Kurtosis and Standard Deviation of integrated Cyclic Coherence of eight independent components from JADE. F, M and N are classification results for subspaces of FECG, MECG and noise, respectively

	IC	1	2	3	4	5	6	7	8
	Kurtosis	7.27 16.57 36.11 -0.26 34.24 24.88 15.20 25.75
	Subspace	N	M	F	N	F	M	M	M
	STD	0.71 3.78 5.53 0.62 2.53 4.64 4.80 2.08

Table II .

 II Kurtosis and Standard Deviation of integrated Cyclic Coherence of eight independent components from SOBI. F, M and N are classification results for subspaces of FECG, MECG and noise, respectively

	IC	1	2	3	4	5	6	7	8
	Kurtosis 31.99 22.33 32.32 -0.50 15.23 22.82 15.93 20.19
	Subspace	F	M	F	N	M	M	M	M
	STD	3.31 2.01 2.45 0.58 1.49 1.78 1.71 5.96

Table III .

 III Kurtosis and Standard Deviation of integrated Cyclic Coherence of eight independent components from FastICA. F, M and N are classification results for subspaces of FECG, MECG and noise, respectively

	IC	1	2	3	4	5	6	7	8
	Kurtosis 24.32 17.87 37.67 15.51 33.66 0.05 26.52 6.93
	Subspace	M	M	F	M	F	N	M	N
	STD	4.79 5.35 5.51 3.53 2.35 0.75 1.84 0.76
	Table II and Table III display the kurtosis and STD utilizing
	SOBI and FastICA. From kurtosis and STD, FECG subspace,
	MECG subspace and noise subspace for them are listed
	respectively. SOBI: {IC1, IC3}, {IC2, IC5, IC6, IC7, IC8},
	{IC4}; FastICA: {IC3, IC5}, {IC1, IC2, IC4, IC7}, {IC6, IC8}.