PharmD Thomas Sinegre
email: tsinegre@chu-clermontferrand.fr

Mélissa Zlobecki

MD Eric Doré

Bruno Pereira

MD Victoria Grèze

MD Aurélien Lebreton

In vitro assessment of edoxaban anticoagulant effect in pediatric plasma

Keywords: Coagulation, Edoxaban, Pediatric, Thrombin generation Prothrombin time, APTT: Activated partial thromboplastin time, ETP: Endogenous thrombin potential, LMWH: Low molecular weight heparin, TGA: Thrombin generation assay, UFH: Unfractionated heparin, VKA: vitamin K antagonists, VTE: Venous thromboembolism

versus 76% [70-80], p<0.05 for 50, 150 and 300 ng/mL edoxaban, respectively).

Conclusions

Edoxaban in vitro effect are comparable in children and adults except in the <2year-old group.

Introduction

Anticoagulant therapy in pediatric patients remains an issue and safer therapies, such as direct oral anticoagulants could overcome the limitations of conventional anticoagulant treatments in this population. Edoxaban, a factor Xa inhibitor, is used for the prevention and treatment of venous thromboembolism. Due to its pharmacokinetic characteristics, edoxaban is a promising candidate molecule for children. This study compared edoxaban in vitro effect in children and adults.

Materials and Methods

Blood samples were prospectively collected from 87 adults and 97 children (n=12: <2 year-old; n=8: 2-4 year-old; n=9: 5-7 year-old; n=14: 8-9 year-old; n=10: 10-13 year-old; n=15: 14-15 year-old; and n=29: 16-18 year-old). Plasma samples were supplemented in vitro with edoxaban to a final concentration of 50, 150 or 300 ng/mL, and then edoxaban effect on prothrombin time (PT), activated partial thromboplastin time (aPTT), fibrinogen (Clauss assay), specific anti-factor Xa activity and thrombin generation assay (TGA) (with 5pM tissue factor and 4nM phospholipids) was evaluated.

Results

PT, aPTT, and specific anti-Xa activity exhibited similar dose-dependent responses to edoxaban in the different age groups. The reduction of thrombin peak, the most edoxaban-sensitive TGA parameter, was similar in adults and children, but for the youngest group (<2 year-old) where the peak value reduction (median [Q1-Q3]) was higher than in adults (51% [44-59] versus 40% [32-46], p<0.01; 74% [63-80] versus 65%

Introduction

The incidence of venous thromboembolism (VTE) in hospitalized children has strongly increased during the last decades, regardless of their age class. [START_REF] Raffini | Dramatic increase in venous thromboembolism in children's hospitals in the United States from 2001 to 2007[END_REF][START_REF] Boulet | Trends in venous thromboembolism-related hospitalizations, 1994-2009[END_REF] The most common anticoagulant therapies used in this population are multi-target compounds, such as unfractionated heparin (UFH), low molecular weight heparins (LMWH) and vitamin K antagonists (VKA). [3][START_REF] Young | How I treat pediatric venous thromboembolism[END_REF][START_REF] Monagle | Antithrombotic therapy in neonates and children: Antithrombotic Therapy and Prevention of Thrombosis, 9th ed: American College of Chest Physicians Evidence-Based Clinical Practice Guidelines[END_REF] However, their use is associated with important drawbacks, particularly in pediatric populations. VKA management requires strict monitoring [START_REF] Ageno | Oral anticoagulant therapy: Antithrombotic Therapy and Prevention of Thrombosis, 9th ed: American College of Chest Physicians Evidence-Based Clinical Practice Guidelines[END_REF] and the availability of a liquid formulation. UFH treatment requires injections and monitoring because of the variable pharmacokinetic profile. Similarly, LMWH are not available in oral formulation and require a regular follow-up in pediatric populations. Their effect cannot be easily reversed with protamine. 7,[START_REF] Malec | Treatment of Venous Thromboembolism in Pediatric Patients[END_REF] The concept of developmental hemostasis should be integrated when an anticoagulant is required in pediatric patients. Several studies have shown that vitamin K-dependent factors (factor II, VII, IX and X) and contact-phase factors tend to gradually increase from birth and reach adult values around 6 months of age.

Conversely, fibrinogen and factor V, VIII and XIII levels at birth are comparable with the adult levels. At birth, the levels of protein C, protein S and antithrombin are lower, whereas those of alpha-2-macroglobulin are higher compared with adults. [START_REF] Andrew | Development of the human coagulation system in the full-term infant[END_REF][START_REF] Attard | Developmental hemostasis: age-specific differences in the levels of hemostatic proteins[END_REF][START_REF] Monagle | Developmental haemostasis: secondary haemostasis[END_REF][START_REF] Andrew | Maturation of the hemostatic system during childhood[END_REF][START_REF] Toulon | Age dependency for coagulation parameters in paediatric populations. Results of a multicentre study aimed at defining the age-specific reference ranges[END_REF][START_REF] Kuhle | Developmental hemostasis: pro-and anticoagulant systems during childhood[END_REF][START_REF] Flanders | Pediatric reference intervals for seven common coagulation assays[END_REF][START_REF] Sosothikul | Pediatric reference values for molecular markers in hemostasis[END_REF] These proteins reach adult levels after few months or few years. Despite these differences in the concentration of coagulation factors, there is no evidence that children are more susceptible to coagulation abnormalities, such as bleeding or thrombosis. [START_REF] Monagle | Developmental haemostasis. Impact for clinical haemostasis laboratories[END_REF] However, the effects of anticoagulant drugs on hemostasis are affected by these developmental differences, [START_REF] Ignjatovic | The coagulation system in children: developmental and pathophysiological considerations[END_REF] and this could lead to variable and potentially dangerous responses in pediatric populations. Importantly, these pharmacodynamic changes could be overlooked because monitoring is done using tests based on adult data that do not take into account these developmental differences.

Edoxaban tosylate (edoxaban) is a direct oral anticoagulant that inhibits factor Xa. In adults, edoxaban is indicated for reducing the risk of stroke and systemic embolism in patients with non-valvular atrial fibrillation [START_REF] Giugliano | Edoxaban versus warfarin in patients with atrial fibrillation[END_REF] and for the prevention or treatment of deep vein thrombosis and pulmonary embolism after parenteral anticoagulants for 5-10 days. [START_REF] Investigators | Edoxaban versus warfarin for the treatment of symptomatic venous thromboembolism[END_REF] Edoxaban is currently not approved for anticoagulation in patients younger than 18 years of age. However, its oral administration route, interaction with few drugs and food, and antithrombin level-independent anti-Xa inhibition are interesting features for pediatric use.

Here, we compared the hemostatic response and coagulation assay results in plasma samples from adults and children (different age classes) spiked in vitro with specific concentrations of edoxaban.

Materials and Methods

Pediatric and adult populations

For this study, 87 adults (>18 years of age) and 97 children (≤18 years of age) were prospectively enrolled at Clermont-Ferrand University Hospital between January 2016 and November 2017. This study was approved by the local ethics committee (AU765, Sud-Est VI France). Exclusion criteria for adults and children were: ongoing antiplatelet or anticoagulant therapy, personal history of bleeding or VTE, and coagulation disorders [fibrinogen <2 g/L, prothrombin time (PT) >17s, activated partial thromboplastin time (aPTT) >50s]. Children were separated in different age groups to take into account the developmental coagulation changes: <2 years, 2-4 years, 5-7 years, 8-9 years, 10-13 years, 14-15 years and 16-18 years of age.

Blood sampling and plasma preparation

Blood was collected by venipuncture in 0.109 M citrate tubes (Beckton Dickinson, le Pont de Claix, France) after discarding the first few milliliters of blood. Corn trypsin inhibitor, an inhibitor of contact phase was not added because coagulation in the thrombin generation assay (TGA) was initiated by addition of 5pM tissue factor, a concentration that makes irrelevant the inhibition of contact phase. [START_REF] Van Veen | Corn trypsin inhibitor in fluorogenic thrombin-generation measurements is only necessary at low tissue factor concentrations and influences the relationship between factor VIII coagulant activity and thrombogram parameters[END_REF] Platelet-poor plasma (PPP) required for TGA was obtained by centrifuging the blood samples twice (2500 g at 20°C for 15min/each) with an intermediate decantation, according to the guidelines of the International Society on Thrombosis and Haemostasis (ISTH). [START_REF]Subcommittee on Control of Anticoagulation of the SSC of the ISTH. Towards a recommendation for the standardization of the measurement of plateletdependent thrombin generation[END_REF] Samples were stored at -80 °C until testing.

Then, frozen plasma samples were thawed in a water bath at 37 °C for 5 minutes.

Edoxaban stock solution (1.0 mg/ml in dimethyl sulfoxide) was diluted with phosphate buffered saline (PBS) to 5000 ng/ml, 15000 ng/ml and 30000 ng/mL working solutions that were added to the plasma samples to reach the target concentrations (50, 150 and 300 ng/mL) with a constant 1/100 dilution. An equivalent volume of PBS was added to baseline samples (0 ng/ml).

Coagulation assays

Coagulation assays were performed with a STA-R Max coagulometer (Stago, Asnièressur-Seine, France) using the following reagents (all from Stago): Neoplastin CI+ ® for PT, PTT-A ® for aPTT, and STA-Fibrinogen ® for fibrinogen (Clauss method). Edoxaban levels in plasma were measured using an anti-factor Xa activity assay (STA®-Liquid Anti-Xa, Stago) and the STA®-Edoxaban Calibrator and STA®-Edoxaban Control on a STA®-R analyzer (Stago).

TGAs were performed using the CAT method [START_REF] Hemker | The calibrated automated thrombogram (CAT): a universal routine test for hyper-and hypocoagulability[END_REF] with a fluorometer (Fluoroscan Ascent, ThermoLab Systems, Franklin, USA) equipped with a dispenser. Coagulation was initiated with 5 pM tissue factor in the presence of 4 µM procoagulant phospholipids (PPP reagent ® , Stago). For each sample, calibration was performed with the Thrombin calibrator ® (Stago).

All plates (Immulon 2HB, Waltham, USA) were incubated at 37 °C for 10 minutes before adding the fluorogenic substrate and CaCl2 (FluCa-kit ® , Thrombinoscope BV). All tests were performed in duplicate with a <10% difference between endogenous thrombin potential (ETP) results. Raw data were analyzed using Thrombinoscope TM . For each assay, ETP (nM.min) and thrombin peak (nM) were the primary endpoints. The first represents the whole thrombin generated in a plasma sample, and the second the highest thrombin concentration that can be generated and that is particularly affected by edoxaban. Edoxaban effect on ETP and thrombin peak was also expressed as percent inhibition that corresponded for each patient to ( Statistical analysis was performed using the Prism software, version 6 (GraphPad software, Inc., La Jolla, USA). Tests were two-sided, with a type I error set at α = 0.05. Continuous data were presented as medians [Q1-Q3]. Independent groups were compared using ANOVA, or the Kruskal-Wallis test when the ANOVA conditions were not met (normality and homoscedasticity verified with the Bartlett test). Dependent groups were compared using ANOVA or the Friedman test, followed by the appropriate multiplecomparison post-hoc, Tukey-Kramer, or Dunn test. 1.

Results

Eighty

Routine coagulation and specific anti-Xa assays

Overall (pediatric and adult samples together), the impact of edoxaban on clotting times increased in a dose-dependent manner. PT (n=56) increased from 13.3s [12.7-13.9] to values showed a linear response to edoxaban, they were relatively insensitive to the increasing concentrations of edoxaban, as indicated by their 1.6-fold and 1.4-fold increase, respectively, from baseline to 300 ng/mL edoxaban. Conversely, edoxaban had no effect on fibrin clot formation (fibrinogen assay) (n=50) with fibrinogen concentrations of 3.2 g/L [2.7-4.0], 3.3 g/L [3.0-3.9], 3.3 g/L [2.9-3.9], and 3.2 g/L [2.7-3.9] for vehicle, 50, 150 and 300 ng/mL edoxaban, respectively. When these results were compared in the different age groups, PT, aPTT and fibrinogen concentration were comparable in the adult and pediatric populations before and after edoxaban addition and for all the drug concentrations (Table 2 and Figure 1; available online).

When edoxaban level in plasma samples was measured using a specific anti-factor Xa assay, the concentrations were comparable to those expected on the basis of the in vitro spiking, without any age-related difference (Figure 2; available online).

Thrombin generation assay

Overall (pediatric and adult samples together), edoxaban led to major changes in thrombin generation profile in a dose-dependent manner. The lag time, which corresponds to the time required to generate the first thrombin traces, gradually increased with edoxaban concentration. Lag time was sensitive to edoxaban anticoagulant effect, as indicated by the 3-fold increase for 300 ng/mL edoxaban compared with baseline. Similarly, time to peak significantly increased in a dose-dependent manner. ETP (maximum decrease of 40% at 300 ng/mL) and particularly thrombin peak (reduction by 80% at 300 ng/m) also were affected by edoxaban in a dose-dependent manner. The thrombin generation curve was altered by in vitro addition of edoxaban with a delay in the initiation phase, and a flattening of the curve leading to a plateau, especially from 150 ng/mL of edoxaban, due to the significant decrease in thrombin peak (Figure 3). ETP was significantly and linearly affected by edoxaban concentration, whereas the thrombin peak decreased sharply at 50 ng/mL (approximately by 50%) and then less markedly (Figure 4).

In the different age groups, at baseline, lag time ranged from 3.0min [2.7-3.7] 3).

In vitro addition of edoxaban, irrespectively of the concentration, did not differentially affect lag time and time to peak in the different age groups. Overall, for all age groups, including adults, lag time increased by 1.7-fold, 2.3-fold and 3.0-fold, and time to peak by 1.7-fold, 2.6-fold and 3.1-fold, upon addition of 50, 150 and 300 ng/mL edoxaban, respectively.

Lower ETP values during the first years of life were observed also after in vitro addition of edoxaban. Upon addition of 50 ng/mL edoxaban, ETP was 1296 nM.min [1144-1466] Thrombin peak also was significantly lower in the <2-year age group after in vitro addition of edoxaban (Table 3 and Figure 4).

Edoxaban-mediated thrombin-generation inhibition was relatively stable in the different age groups. The percentage of ETP inhibition varied between 8 and 13% according to the age group (p = 0.73) for 50 ng/mL, between 25 and 35% (p = 0.29) for 150 ng/mL, and between 37 and 54% (p = 0.08) for 300 ng/mL edoxaban. The percentage of thrombin peak inhibition by edoxaban was similar between adults and children, but for the <2-year age group where edoxaban-mediated inhibition was more pronounced than in adults 150 and 300 ng/mL edoxaban respectively) (Figure 5). These results indicate that edoxaban affects ETP evenly across the different age groups, but has a stronger inhibition effect on the thrombin peak in young children.

Discussion

The present study investigated for the first time the in vitro effect of edoxaban, a direct and reversible factor Xa inhibitor, in different pediatric age groups and in adults. PT, aPTT, and the specific anti-Xa anticoagulant activity showed a similar dose-dependent response to edoxaban in the different age groups. Inhibition of thrombin peak, the most edoxaban-sensitive TGA parameter, was similar between adults and children, except for the youngest age group (<2 years).

The recommended edoxaban dose in adults for prevention or treatment of deep vein thrombosis and pulmonary embolism (i.e., 60 mg/daily) should give a maximum plasma concentration between 152 and 302 ng/mL, and trough plasma concentrations that rarely exceed 50 ng/mL. [START_REF] Ogata | Clinical safety, tolerability, pharmacokinetics, and pharmacodynamics of the novel factor Xa inhibitor edoxaban in healthy volunteers[END_REF] Therefore, the plasma concentrations used in our study (i.e., 50, 150 and 300 ng/mL) were adequate to the expected exposure in patients treated with the usual dose. Our results showed similar coagulation times in the different age groups for the classical coagulation tests (PT, aPTT and fibrinogen assay). These data are consistent with previous findings, [START_REF] Andrew | Maturation of the hemostatic system during childhood[END_REF][START_REF] Flanders | Pediatric reference intervals for seven common coagulation assays[END_REF][START_REF] Sosothikul | Pediatric reference values for molecular markers in hemostasis[END_REF] although some studies found a variable lengthening according to age. [START_REF] Andrew | Development of the human coagulation system in the full-term infant[END_REF][START_REF] Toulon | Age dependency for coagulation parameters in paediatric populations. Results of a multicentre study aimed at defining the age-specific reference ranges[END_REF][START_REF] Kuhle | Developmental hemostasis: pro-and anticoagulant systems during childhood[END_REF] When we used conventional coagulation tests, edoxaban spiking led to PT increase in both adult and pediatric plasma samples in a concentrationdependent and linear manner, with low sensitivity (particularly at low concentrations). This effect could vary depending on the thromboplastin used. Similar results were obtained for aPTT. These data are comparable with edoxaban effects in adults [START_REF] Douxfils | Laboratory testing in patients treated with direct oral anticoagulants: a practical guide for clinicians[END_REF][START_REF] Morishima | Laboratory measurements of the oral direct factor Xa inhibitor edoxaban: comparison of prothrombin time, activated partial thromboplastin time, and thrombin generation assay[END_REF][START_REF] Samuelson | Laboratory Assessment of the Anticoagulant Activity of Direct Oral Anticoagulants: A Systematic Review[END_REF] Therefore, conventional assays, such as PT or aPTT, do not seem to be adapted to monitor the hemostasis changes during development. On the other hand, TGA is considered to be an innovative way to understand coagulation globally, especially when it is disturbed by complex changes that alter the coagulation balance, for instance in developmental hemostasis. Several studies indicate that TGA is a relevant tool to identify prothrombotic or hypocoagulable conditions [START_REF] Dieri | The thrombogram in rare inherited coagulation disorders: its relation to clinical bleeding[END_REF][START_REF] Castoldi | Thrombin generation tests[END_REF][START_REF] Hemker | Thrombin generation, a function test of the haemostatic-thrombotic system[END_REF][START_REF] Hron | Identification of patients at low risk for recurrent venous thromboembolism by measuring thrombin generation[END_REF] TGA allowed highlighting the baseline decrease in thrombin generation in pediatric plasma samples, particularly the lower ETP and thrombin peak in children younger than 8 years of age. These data are consistent with several studies that reported a marked reduction in thrombin generation in children, including an inverse correlation between age and ETP or thrombin peak. [START_REF] Monagle | Developmental haemostasis. Impact for clinical haemostasis laboratories[END_REF][START_REF] Haidl | Age-dependency of thrombin generation measured by means of calibrated automated thrombography (CAT)[END_REF][START_REF] Kremers | Low paediatric thrombin generation is caused by an attenuation of prothrombin conversion[END_REF][START_REF] Orhon | Evaluation of indirect microparticle activity and parameters of thrombin generation test in healthy infants[END_REF] Edoxaban inhibited thrombin generation in a dose-dependent manner. The thrombin peak appeared to be the most sensitive parameter with 50% inhibition with 50 ng/mL edoxaban, followed by a plateau at 150 ng/mL, while ETP inhibition was more linear. These findings are consistent with literature data on TGA in adults. [START_REF] Douxfils | Edoxaban: Impact on routine and specific coagulation assays. A practical laboratory guide[END_REF][START_REF] Samama | In vitro study of the anticoagulant effects of edoxaban and its effect on thrombin generation in comparison to fondaparinux[END_REF] ETP was inhibited by edoxaban uniformly in all age groups. Conversely thrombin peak was the most age-sensitive parameter. Indeed, whatever the concentration, edoxaban anticoagulant effect was significantly higher in the <2-year age group compared with adults. Therefore, in addition to basically generating less thrombin, these patients would be more sensitive to factor Xa inhibition than adults. Kremers et al. [START_REF] Kremers | Low paediatric thrombin generation is caused by an attenuation of prothrombin conversion[END_REF] showed in children a reduction in prothrombin conversion and a concomitant decrease in conversion rates with lower generation of prothrombinase complexes than in adults. Edoxaban may have a synergistic effect because it inhibits both free factor Xa and within the prothrombinase complex. These data should be taken into account for the definition of edoxaban doses in children.

One of the limitations of this study is the impact of alpha2-macroglobulin, the concentration of which is higher in neonates. Ignjatovic et al. [START_REF] Ignjatovic | Thrombin generation: the functional role of alpha-2-macroglobulin and influence of developmental haemostasis[END_REF] showed that in most methods used to measure ETP, the substrates could be cleaved also by alpha2-macroglobulin-bound thrombin, which is inactive in vivo, and this could induce a bias in the comparison of adults and children. To overcome this limitation we used the method described by Hemker in which the alpha2-macroglobulin-bound thrombin fraction is estimated and subtracted from the signal. [START_REF] Hemker | The contribution of alpha(2)-macroglobulin thrombin to the endogenous thrombin potential[END_REF] Only children and adults not requiring anticoagulant therapy were included in our study. Edoxaban effect should also be studied in patients with potentially prothrombotic disorders who are likely to receive anticoagulant therapy. An in vivo clinical study is needed to evaluate edoxaban efficacy and safety, particularly in children younger than 2 years. The number of samples for the different age groups, especially for the youngest children, was small. This is explained by the limited availability of volunteers who matched the inclusion criteria (healthy subject, without anticoagulant therapy), and the difficulty of obtaining blood samples from young children and in sufficient volume for the different tests. Nevertheless, edoxaban response was different in <2-yar-old children and it is known that coagulation changes are more important close to birth. A study specifically on this age group would be interesting.

Edoxaban anti-factor Xa activity accurately reflects plasma levels in both children and adults, and its in vitro effects are similar at all ages, with the exception of children younger than 2 years of age in whom the anticoagulant effect is increased.

  1-(ETP in the presence of edoxaban divided by the patient's baseline ETP)) x100. Statistical analysis Sample size was estimated according to (i) the CONSORT 2010 statement, extension to randomized pilot and feasibility trials and (ii) Cohen's recommendations, which define effect-size bounds as follows: small (ES: 0.2), medium (ES: 0.5) and large (ES: 0.8, "grossly perceptible and therefore large"). More precisely, an effect size greater than 1.2 (accordind to pilot data) could be highlighted for a two-sided type I error at 0.001 (correction due to multiple comparisons) and a statistical power greater than 80%, with at least 75 adults and 15 children by group. Effect-size of the difference between children and adults are expressed by coefficient of Hedges g [95% confidence interval].

  -seven adults and 97 children (<2 years of age: n=12; 2-4 years: n=8; 5-7 years: n=9; 8-9 years: n=14; 10-13 years: n=10; 14-15 years: n=15; and 16-18 years: n=29) were included in the study. Their demographic characteristics are summarized in Table

  14.2s[13.5-15.3], 17.0s[16.4-18.5] and 21.7s [20.1-23.0] for vehicle, 50, 150 and 300 ng/mL edoxaban, respectively, and aPTT (n=59) from 35.5s [32.6-39.8] to 38.8s [34.7-43.4], 44.2s [39.5-50.3] and 50.3s [43.7-56.1], respectively. Although, the PT and aPTT

  in adults, 979 nM.min [760-1099] in the <2-year (p<0.001, g=-1.7[-2.5;-0.8] compared with adults), 976 nM.min [878-1239] in the 2 to 4-year (p<0.05, g=-1.3[-2.1;-0.3]), and 964 nM.min [873-1083] in the 5 to 7-year age group (p<0.01, g=-1.5[-2.3;-0.6]). After addition of 150 ng/mL edoxaban, ETP was 1108 nM.min [933-1225] in adults, 750 nM.min [496-946] in the <2-year (p<0.001, g=-1.6[-2.4;-0.8]), 815 nM.min [585-1027] in the 2 to 4-year (p<0.05, g=-1.2[-2.1;-0.3]), and 861 nM.min [761-949] in the 5 to 7-year age group (p<0.05, g=-1.1[-1.8;-0.2]). Finally, upon addition of 300 ng/mL edoxaban, ETP values ranged from 928 nM.min [692-1079] in adults to 572 nM.min [333-735] in the <2-year (p<0.001, g=-1.5[-2.2;-0.7]), 660 nM.min [490-823] in the 2 to 4-year (p<0.05, g=-1.1[-2.0;-0.2]), and 702 nM.min [611-814] in the 5 to 7-year age group (p=0.23, g=-0.8[-1.6;-0.1]).

(

  51% [44-59] vs 40% [32-46], p<0.01, g=1.0[0.3;1.7]; 74% [63-80] vs 65% [58-70], p<0.05, g=0.4[0.2;1.1]; and 84% [73-88] vs 76% [70-80], p<0.05, g=0.8[0.1;1.4], for 50,

Table 3 :FiguresFigure 1 :

 31 Figures

Figure 2 :

 2 Figure 2: Relationship between edoxaban concentrations and specific anti-factor Xa

Figure 3 :

 3 Figure 3: Effect of increasing concentrations of edoxaban on thrombin generation

Figure 4 :B

 4 Figure 4: Relationship between edoxaban concentrations and thrombin generation

Figure 5 :

 5 Figure 5: Percentage of thrombin peak inhibition by increasing edoxaban

Acknowledgements

Authors sincerely thank the technical staff of the Hemostasis Department (University hospital of Clermont-Ferrand) and Elisabetta Andermarcher for English editing.

This study was funded by a grant from Octapharma


Davide Rizzo

Olivier Therond

Romain Lardy

Clément Murgue

Delphine Leenhardt
email: delphine.burger-leenhardt@inra.fr

A rapid, spatially explicit approach to describe cropping systems dynamics at the regional scale

INTRODUCTION

Agricultural practices and crop performances significantly affect and depend on environmental, social and technical contexts [START_REF] Ramankutty | The global distribution of cultivable lands: current patterns and sensitivity to possible climate change[END_REF][START_REF] Ray | Recent patterns of crop yield growth and stagnation[END_REF][START_REF] Van Vliet | Manifestations and underlying drivers of agricultural land use change in Europe[END_REF]. For example, water availability and irrigation technologies allow crop diversification and increase potential yields, but at the same time, the way farmers use water for crop management may determine the state of resources and possibly lead to conflicts with other water users, especially during drought (European Environment Agency 2012; [START_REF] Murgue | Toward integrated water and agricultural land management: Participatory design of agricultural landscapes[END_REF].

A specificity of agricultural practices is their great spatial and temporal variability. Indeed, farmers make their decisions, both strategic (e.g., crop rotation, irrigated versus rainfed crop production, cultivar earliness) and tactical (e.g., starting/stopping irrigation and its dose), according to the biophysical environment -particularly the spatiotemporal conditions of the soil, weather and plants -as well as the socio-economic context (e.g. prices, regulations). In addition, some management practices, such as irrigation or fertilization, are complex combinations of technical operations at successive dates (e.g. water or fertilizer applications).

Agricultural practices, and specifically their spatial distribution and their dynamics, can affect natural resources, causing environmental issues, such as water scarcity or water pollution (e.g. [START_REF] Glavan | Spatial variation of crop rotations and their impacts on provisioning ecosystem services on the river Drava alluvial plain[END_REF][START_REF] Allain | Integrated assessment of four strategies for solving water imbalance in an agricultural landscape[END_REF]. To address these issues, land managers need spatially explicit information about agricultural practices (e.g. [START_REF] Yunju | Fertilizer use patterns in Yunnan Province, China: Implications for agricultural and environmental policy[END_REF]. Several authors (e.g., [START_REF] Deffontaines | Analyse du paysage et étude régionale des systèmes de production agricole[END_REF][START_REF] Moss | Interdisciplinarity, landscape ecology and theTransformation of Agricultural Landscapes[END_REF][START_REF] Scherr | Biodiversity conservation and agricultural sustainability: towards a new paradigm of "ecoagriculture" landscapes[END_REF][START_REF] Leenhardt | Describing and locating cropping systems on a regional scale. A review[END_REF][START_REF] Boiffin | Agronomie, espace, territoire : travailler « pour et sur » le développement territorial, un enjeu pour l'agronomie[END_REF]) repeatedly mention the need to improve methods to describe agricultural practices at the regional scale, i.e. on areas where exhaustive surveys of farms are impossible (typically around 100 km² and above). A consistent approach would be useful for exploring land management practices and integrating them at various spatial and temporal scales. This would help formalize the knowledge that farmers and other land managers use to make their decisions [START_REF] Sarangi | A decision support system for soil and water conservation measures on agricultural watersheds[END_REF][START_REF] Macé | Time scales as a factor in decisionmaking by French farmers on weed management in annual crops[END_REF][START_REF] Mccown | A cognitive systems framework to inform delivery of analytic support for farmers' intuitive management under seasonal climatic variability[END_REF]. To be reliable, this approach should obtain knowledge about all components of "cropping systems".

A cropping system is defined for a uniformly managed spatial unit as a sequence of crops and the management system for each crop in the sequence. The crop management system is the set of technical operations applied to the crop, for all management practices: soil tillage, cultivar choice, fertilization, weed and pest management, irrigation, etc. [START_REF] Sebillotte | Système de culture, un concept opératoire pour les agronomes[END_REF]). Describing the spatial distribution and the dynamics of cropping systems at the regional scale to address natural resource management issues needs to consider (i) biophysical and socio-economic conditions driving their spatial distribution, hereafter called location factors [START_REF] Clavel | Integrating expert knowledge and quantitative information for mapping cropping systems[END_REF][START_REF] Temme | Mapping and modelling of changes in agricultural intensity in Europe[END_REF]; and (ii) the decision rules, which determine the nature and timing of each technical operation.

Describing the spatial distribution of cropping systems at the regional scale remains a major scientific challenge [START_REF] Therond | Methodology to translate policy assessment problems into scenarios: the example of the SEAMLESS integrated framework[END_REF][START_REF] Leenhardt | Describing and locating cropping systems on a regional scale. A review[END_REF][START_REF] Rizzo | Farming systems designing landscapes: land management units at the interface between agronomy and geography[END_REF][START_REF] Murgue | Hybridizing local and generic information to model cropping system spatial distribution in an agricultural landscape[END_REF]. Recent improvements in satellite imagery, the increased availability of harmonized censuses and surveys at large scales have contributed to the development of spatially explicit cropland databases. For example, remote-sensing-based databases [START_REF] Bégué | Agricultural Systems Studies Using Remote Sensing[END_REF]Waldner et al. 2015a) and periodic surveys such as the European Land Parcel Identification System (LPIS) [START_REF] Sagris | Land Parcel Identification System conceptual model: development of geoinfo community conceptual model[END_REF], the LUCAS database [START_REF] Temme | Mapping and modelling of changes in agricultural intensity in Europe[END_REF], the French TerUti database [START_REF] Xiao | Modeling the spatial distribution of crop sequences at a large regional scale using land-cover survey data: A case from France[END_REF], and the Farm Accountancy Data Network [START_REF] Vitali | Classification of Italian Farms in the FADN Database Combining Climate and Structural Information[END_REF] provide improved support for describing and modeling crop sequences.

The generic character of these databases allows using them for many purposes in different domains and at different levels of analysis [START_REF] Murgue | Hybridizing local and generic information to model cropping system spatial distribution in an agricultural landscape[END_REF]. However, they have a major limitation: the static perspective adopted for crop management systems is not appropriate for analyzing the dynamic interactions between technical operations, weather conditions and natural resource conditions [START_REF] Ruiz-Martinez | Indicators of agricultural intensity and intensification: a review of the literature[END_REF][START_REF] Murgue | Hybridizing local and generic information to model cropping system spatial distribution in an agricultural landscape[END_REF]. Studies based on these data have addressed the crop management component of cropping systems only loosely [START_REF] Martínez-Casasnovas | Mapping multiyear cropping patterns in small irrigation districts from time-series analysis of Landsat TM images[END_REF][START_REF] Mignolet | Spatial dynamics of farming practices in the Seine basin: Methods for agronomic approaches on a regional scale[END_REF][START_REF] Steinmann | Spatio-temporal analysis of crop rotations and crop sequence patterns in Northern Germany: potential implications on plant health and crop protection[END_REF][START_REF] Sahajpal | Identifying representative crop rotation patterns and grassland loss in the US Western Corn Belt[END_REF][START_REF] Kollas | Crop rotation modelling-A European model intercomparison[END_REF], for example representing average practices [START_REF] Therond | Using a cropping system model at regional scale: Low-data approaches for crop management information and model calibration[END_REF] or fixed-calendar scheduling [START_REF] Dupas | Integrating local knowledge and biophysical modeling to assess nitrate losses from cropping systems in drinking water protection areas[END_REF].

Overall, we identify three major obstacles that limit a complete description of cropping systems with such databases. First, available land cover maps provide insufficient spatial continuity and temporal depth to properly describe crop sequences [START_REF] Fuchs | A high-resolution and harmonized model approach for reconstructing and analysing historic land changes in Europe[END_REF][START_REF] Xiao | Modeling the spatial distribution of crop sequences at a large regional scale using land-cover survey data: A case from France[END_REF]. Second, a lack of information about crop management exists; it is generally addressed by using land cover as a rough proxy (e.g., [START_REF] Verburg | From land cover change to land function dynamics: A major challenge to improve land characterization[END_REF][START_REF] Houet | Improving the simulation of fineresolution landscape changes by coupling top-down and bottom-up land use and cover changes rules[END_REF][START_REF] Zimmermann | Assessing land-use history for reporting on cropland dynamics-A comparison between the Land-Parcel Identification System and traditional inter-annual approaches[END_REF]. Third, only a few methods can represent the dynamic adaptation of crop management to pedoclimatic conditions [START_REF] Houet | Exploring subtle land use and land cover changes: a framework for future landscape studies[END_REF][START_REF] Hutchings | A model for simulating the timelines of field operations at a European scale for use in complex dynamic models[END_REF][START_REF] Constantin | The soil-crop models STICS and AqYield predict yield and soil water content for irrigated crops equally well with limited data[END_REF]. The most advanced are based on crop management decision rules formalized with classic IF [Indicator] [Operator] [Threshold] THEN [Action1] ELSE [Action2] rules [START_REF] Bergez | MODERATO: an object-oriented decision tool for designing maize irrigation schedules[END_REF][START_REF] Mccown | A cognitive systems framework to inform delivery of analytic support for farmers' intuitive management under seasonal climatic variability[END_REF].

Formalizing decision rules and coupling them with crop models enables simulating dynamics of crop management systems and their effects on the soil-crop system dynamics and possibly on water resources. For example, irrigation decision rules influence water withdrawals, which modify the water resource [START_REF] Clavel | A Modelling Solution for Developing and Evaluating Agricultural Land-Use Scenarios in Water Scarcity Contexts[END_REF][START_REF] Murgue | Fine spatio-temporal simulation of cropping and farming systems effects on irrigation withdrawal dynamics within a river basin[END_REF]. The challenge lies in describing decision rules associated with cropping systems within a region in a way that accounts for the potentially great variability in individual decision-making processes [START_REF] Nesme | Are agronomic models useful for studying farmers' fertilisation practices?[END_REF][START_REF] Leenhardt | Describing and locating cropping systems on a regional scale. A review[END_REF].

To describe the spatial variability and dynamics of cropping systems, it is critical to enrich the information provided by generic data sources with local knowledge obtained mainly through interviews, surveys or local databases [START_REF] Marie | Intérêts et limites des statistiques agricoles pour l'étude des structures spatiales produites par l'agriculture en Basse-Normandie[END_REF]. Local knowledge is crucial to capture information related to farmers' decisions, i.e. location factors and crop management decision rules. This knowledge is largely implicit, however, and consequently difficult to access [START_REF] Aubry | Modelling decision-making processes for annual crop management[END_REF][START_REF] Tress | From tacit to explicit knowledge in integrative and participatory research[END_REF][START_REF] Toffolini | Indicators used by farmers to design agricultural systems: a survey[END_REF][START_REF] Murgue | Hybridizing local and generic information to model cropping system spatial distribution in an agricultural landscape[END_REF]. Interviews with farmers remain the main approach for obtaining this knowledge [START_REF] Marie | Intérêts et limites des statistiques agricoles pour l'étude des structures spatiales produites par l'agriculture en Basse-Normandie[END_REF][START_REF] Debolini | Mapping local spatial knowledge in the assessment of agricultural systems: A case study on the provision of agricultural services[END_REF][START_REF] O'keeffe | The use of semi-structured interviews for the characterisation of farmer irrigation practices[END_REF]. However, an exhaustive survey of all farmers in a region can easily become excessively time-consuming (see examples in [START_REF] Maton | Modelling the days which are agronomically suitable for sowing maize[END_REF][START_REF] Schaller | Combining farmers' decision rules and landscape stochastic regularities for landscape modelling[END_REF].

Rapid surveys can be limited to easily observable features [START_REF] Biarnès | Identifying indicators of the spatial variation of agricultural practices by a tree partitioning method: The case of weed control practices in a vine growing catchment[END_REF] or performed on stratified samples based on pre-defined farm typologies [START_REF] Joannon | Reducing runoff by managing crop location at the catchment level, considering agronomic constraints at farm level[END_REF][START_REF] Murgue | Hybridizing local and generic information to model cropping system spatial distribution in an agricultural landscape[END_REF]. Despite these simplifications, collecting farm survey data to describe the spatial distribution and dynamics of cropping systems remains time consuming. For example, [START_REF] Murgue | Hybridizing local and generic information to model cropping system spatial distribution in an agricultural landscape[END_REF] interviewed 27 farmers to describe their decision rules in detail (indicators and thresholds) for maize and other regionally relevant crops in an 840 km² watershed (downstream area of the Aveyron River, southwestern France). Each interview lasted approximately half a day. In total, the interviews took more than 13 working days, plus the processing time to formalize all crop management system decision rules (≈2 months) [START_REF] Hipolito | Distribution spatiale et caractérisation des systèmes de culture dans le territoire irrigué à l'aval de la rivière Aveyron[END_REF]). Although the study described spatial distribution of cropping systems with location and decision rules, the process was resource-consuming and therefore would be difficult to repeat for multiple and large study areas.

This article presents a simple, rapid approach to describe spatial distribution of current cropping systems at the regional scale in a way that considers their dynamic dimensions and thus can be used for simulation purposes. It builds on the study by [START_REF] Murgue | Hybridizing local and generic information to model cropping system spatial distribution in an agricultural landscape[END_REF], which modeled irrigation practices at the watershed scale. The approach we adopted to describe the cropping systems and their spatial distribution is a low-data method (sensu [START_REF] Therond | Using a cropping system model at regional scale: Low-data approaches for crop management information and model calibration[END_REF], i.e. based on expert knowledge and easy-to-collect regional crop management information. Expertise collection rested on the "key informant" approach originally described by [START_REF] Tremblay | The Key Informant Technique: A Nonethnographic Application[END_REF] where key informants are considered as experts having a perception grounded in theory, yet attained as a result of deep understanding, practice and interaction with the subject matter [START_REF] Twongyirwe | Revisiting the drivers of deforestation in the tropics: Insights from local and key informant perceptions in western Uganda[END_REF]. We hypothesized that key informants, in our case local extension agents, have sufficient knowledge to describe location factors and decision rules of cropping systems at the regional scale and identify their most relevant characteristics at the field scale. We evaluated this approach in two large areas in southwestern France, first in the Tarn River area and then in the Adour River area. To assess the reliability of our approach to model the cropping systems and their spatial distribution, we used a multi-agent, spatially explicit model that simulates their effects on water withdrawals, and then compared the simulated water withdrawals to observed ones.

MATERIALS AND METHODS

Case studies and available data

Focused on the interactions between irrigation and water-deficit issues, this study explored irrigation-management practices in two study areas in the Adour-Garonne River basin in southwestern France (116 000 km², Midi-Pyrénées region), which experiences a chronic imbalance between water resources and water needs during the low-water period, which occurs mainly during summer [START_REF] Debril | Les difficultés associées à la gestion quantitative de l'eau et à la mise en oeuvre de la réforme des volumes prélevables : le cas du bassin Adour-Garonne[END_REF][START_REF] Mazzega | Critical multi-level governance issues of integrated modelling: An example of low-water management in the Adour-Garonne basin (France)[END_REF]. The first area (2 952 km²) is located in the downstream area of the Tarn River, one of the main tributaries of the Garonne River. This area is influenced by a Mediterranean climate, with high temperatures in summer and heavy rainfall events in autumn and spring. Of note, this area is contiguous with the Aveyron watershed, the above mentioned case study by [START_REF] Murgue | Hybridizing local and generic information to model cropping system spatial distribution in an agricultural landscape[END_REF]. The second area (1 446 km²) is located in the upstream area of the Adour River, which drains the northern slopes of the French Pyrenees. Recent statistics highlight that it has the highest irrigation water withdrawals in the Adour River basin (OEBA 2015). Its climate is oceanic, with relatively warm and humid weather in summer and heavy rainfall events in winter. The two main soil types in each study area are (i) alluvial and hydromorphic loamy silt soils, mainly associated with fluvial terraces, and (ii) calcareous clayey soils, which predominate elsewhere (Fig. 1 A andB). As for any area in France, generic databases are available to map the main attributes of the study areas [START_REF] Murgue | Hybridizing local and generic information to model cropping system spatial distribution in an agricultural landscape[END_REF]. For example, the "Land Parcel Identification Systems" (LPIS) is a georeferenced database to be developed by each member state of the European Union to control annual Common Agricultural Policy (CAP) subsidies. It stores the land cover information (nature and area) of each CAP islet of each farmer concerned by these subsidies [START_REF] Inan | Data model for the collaboration between land administration systems and agricultural land parcel identification systems[END_REF]. We used the French LPIS database (for 2006-2012) to (i) map the main annual crops and spatial distribution of fields (ASP 2012), (ii) retrace crop sequences at the field scale, and (iii) identify fields that were declared as irrigated at least once from 2007-2009. Of note, LPIS no longer collected irrigation-related data after 2009. The French LPIS mapping unit is a "farmer's block" or "Common Agricultural Policy islet", which corresponds to one or more aggregated contiguous agricultural land parcels [START_REF] Sagris | Land Parcel Identification System conceptual model: development of geoinfo community conceptual model[END_REF][START_REF] Murgue | Hybridizing local and generic information to model cropping system spatial distribution in an agricultural landscape[END_REF]. We also used the CORINE Land Cover database for 2012 (EEA 2015) to complete the land-use map for orchards, typically underrepresented in LPIS, and to map non-agricultural land cover (i.e., forest and urban). Representing 55% of the total area, arable land dominates in both study areas, but includes different percentages of irrigated area: 28% in the Tarn area and 39% in the Adour area (Table 1). Additional information about cropland composition was obtained from the general agricultural census performed by the French Ministry of Agriculture [START_REF] Agreste | RGA -Recensement Général Agricole (general agricultural census[END_REF] We obtained climate data from the SAFRAN database [START_REF] Vidal | A 50-year high-resolution atmospheric reanalysis over France with the Safran system[END_REF]) and soil types from the French Geographic Soil Database at a scale of 1:1 000 000, which represents the French portion of the European Geographic Soil Database [START_REF] King | Soil Mapping and Soil Monitoring: State of Progress and Use in France[END_REF][START_REF] Antoni | The French information system on soils: a decision support system for soil inventory, monitoring and management[END_REF].

Finally, we used water withdrawal data provided by water users through an annual mandatory report to the Adour-Garonne Water Agency (AEAG 2015). For each water user, these data provide the total amount of water withdrawn at each "withdrawal point". The locations of only a few points are known precisely; most are approximated as the centroid of the municipality containing the withdrawal point. Since the boundaries of the two study areas did not overlap with municipality boundaries, we multiplied the reported withdrawn water volumes by the percentage of the municipal area located within each watershed, to estimate water withdrawals in each study area.

Key informant interviews

To identify key informants, namely experts with knowledge about the spatial distribution and management of the main irrigated crops, we first interviewed shortly the managers of the agro-environmental department of the extension service of each area (30mn each). They designated the technicians of agricultural extension services who could serve as key informants regarding the main crops of the studied areas (Table 2). We conducted four semistructured face-to-face interviews, lasting approximately 2.5 hours each, to deal with the two irrigated crop classes for which the most water for irrigation is withdrawn: maize and orchards. Three of the four interviews involved two key informants at the same time. We designed the interview script to focus on the question: "How is irrigation managed for the main crops in the study area?" The interview was composed of four steps: (i) identify the main crops in the target area, (ii) create a list of land use classes for each crop (e.g., for maize: rainfed, irrigated, silage, seed), (iii) describe water consumption (i.e., volumes per period) for the main crops irrigated within irrigated crop classes and (iv) describe indicators and thresholds necessary to define IF-THEN crop management decision rules and location factors used to define location rules.

Interviews were supported by intermediate objects to (i) summarize existing knowledge to highlight and help fill information gaps and (ii) mediate between the researchers and local key informants [START_REF] Vinck | Les objets intermédiaires dans les réseaux de coopération scientifique. Contribution à la prise en compte des objets dans les dynamiques sociales[END_REF][START_REF] Buller | The lively process of interdisciplinarity[END_REF]. The rationale was to facilitate elicitation of decision rules that underlie crop management practices. Finally, these intermediate objects were used to obtain easy-to-formalize, spatially explicit answers, since they provide a framework to pre-process key informants' discourses into an input format for the simulation model.

The intermediate objects were a map, a table and several diagrams. The map was used at the beginning of the interview to clarify watershed boundaries (Fig. 1 A andB), which differ from those of the administrative unit at which the interviewees work: the Département also called NUTS 3 (Eurostat 2015). The table listed the main crops identified from analyzing census data (for 2010) and LPIS and CORINE Land Cover maps (both for 2012). The diagrams (Fig. 2) were designed to help interviewees depict management practices for each main irrigated crop in the region. They were structured around a simplified calendar that explicitly required specifying indicators and threshold values that farmers are expected to use to start or stop sowing, irrigation and harvest. To identify cropping systems associated with each crop, we asked key informants if the values identified for standard management of the crop could vary in space or according to location factors (e.g. crop sequence, soil type, irrigation equipment). For example, key informants indicated that winter wheat can be irrigated if it occurs in the same sequence as maize, otherwise it is rainfed. We also asked key informants to describe an average year and then identify possible adaptations to infrequent conditions (e.g., humid or dry seasons). During the process, we invited key informants to rely on their own field experience of crop management practices rather than on the advice they usually give to farmers. These face-to-face interviews were supplemented with phone interviews to obtain missing information about specific crops (e.g., legume, pastures, vineyards -see Table 2). Phone interviews (approximately 20 minutes each) began with the same short introduction as the full interview, which was followed by a short list of questions about crop management decision rules and expected irrigation volumes for a standard cropping season. The interviewer recorded answers on the intermediate objects (tables and diagrams) described previously.

Both types of interviews were transcribed into a text editor in preparation for analysis. We processed the local information by grouping all details provided for each crop and cropping system during the interviews to address possible differences among key informants, especially those with different fields of competence, and to formalize and encode a first draft of the decision rules.

Altogether, from interviews to the first draft of decision rules, the key informant approach required approximately 260 working hours for the two study areas.

Modeling cropping systems and their spatial distribution

Based on the information collected from interviews, we modeled spatial distributions of cropping systems in each study area in three steps. First, we addressed the two components of cropping systems, namely the crop sequences (step 1) and the decision rules of crop management (step 2). Then, we coupled the dynamic decision rules with the location factors (step 3).

Step 1. Describing crop sequences using real data

Our objective was to define sequence types to infer irrigation management rules. First, we processed the 2006-2012 LPIS data (simplifying the crop labels slightly to perform an initial grouping). This allowed us to identify the real crop sequences observed in all farmers' blocks (see details in [START_REF] Leenhardt | Quelle représentation des systèmes de culture pour la gestion de l'eau sur un grand territoire ?[END_REF]. Since this list of sequences was too large to manage easily, we created a typology of crop sequences useful for dealing with crop management decision rules. For this, each type of crop sequence was defined by the proportion of each crop in the duration of the sequence, regardless of their order. For example, Maize-Maize-Maize-Soybean-Wheat-Maize belongs to the sequence type "maize, rotation", in which maize occurs more often than any of the other crops.

Second, we adapted the generic LPIS crop classes using the information from interviews about main local crops. We aggregated crop classes covering small areas (e.g., different types of set-aside land) or those with similar water use and irrigation practices (e.g., grain legumes and protein crops). We disaggregated crop classes into new sub-classes when key informants indicated the need to differentiate irrigation management practices (e.g. silage maize vs. grain maize).

Step 2. Modeling crop management systems using the information retrieved Dynamics and locations of crop management systems were modeled using rules developed from information collected during interviews. We used information about indicators and their threshold values to formalize sowing, irrigation, and harvest management rules for each crop. We used information about location factors to develop location rules. For example, the crop sequence type was identified as a location factor: wheat is irrigated only if it occurs in a sequence including maize, but not if it occurs in a rainfed sequence type (e.g., cereal-sunflower). The location rules were then used to assign a crop management system (described as a set of decision rules) to each combination of [crop sequence type] × [soil] × [irrigation equipment].

Step 3. Defining the spatial distribution of cropping systems

Cropping systems were assigned to each land parcel in each farmer's block according to the following procedure. The soil type, crop sequence type, irrigation status and irrigation equipment of land parcels were characterized using GIS. The dominant agricultural soil type of each farmer's block from the 1:1 000 000 soil database was assigned to each of its land parcels. If the original LPIS crop class observed on a given land parcel was divided into new classes during step 2, one of these new classes was assigned to the land parcel according to location factors such as the soil type, crop sequence or crop area of the farm it belonged to, while respecting, for each study area, the proportions of these new classes observed in the 2010 agricultural census [START_REF] Agreste | RGA -Recensement Général Agricole (general agricultural census[END_REF]. We assigned the crop sequence of each land parcel to a crop sequence type (defined in step 1) using the Dice percentage similarity index [START_REF] Dice | Measures of the Amount of Ecologic Association Between Species[END_REF]. Irrigation status of a land parcel was determined using 2007-2009 LPIS data. We assumed that land parcels were potentially irrigated, i.e. equipped for irrigation, if the farmer's block they belonged to was irrigated at least once during those years. Crops that can be managed either with or without irrigation were assumed to be potentially irrigated only if they occurred in a farmer's block equipped for irrigation. Due to a lack of local information about the relative frequencies of types of irrigation equipment and the location factors that influence them, we assigned the equipment used most in the area (travelling gun sprinkler) to each potentially irrigated farmer's block.

Finally, a crop management system was assigned to each land parcel in each farmer's block in which the crop occurred according to the observed combination of [crop sequence type] ×

[soil] × [irrigation equipment], using the table constructed in step 3.

Evaluating the approach

Evaluating our approach was challenging: because it was developed to compensate for the relative lack of cropping system data, it used all the data available to formalize and spatialize the cropping systems, leaving no data for classic "observed vs. simulated" evaluation. We therefore evaluated the approach indirectly. We used the cropping system information retrieved from our approach to parameterize a model able to simulate irrigation withdrawals and compared simulated withdrawals to withdrawal data obtained from the Adour-Garonne Water Agency (AEAG). Since AEAG data were annual data only, we also verified that, for each cropping system, the simulated withdrawal dynamics were consistent with key informants' descriptions of irrigation starting and ending dates. Irrigation withdrawals were chosen as an evaluation indicator because the simplified survey of crop management practices focused on irrigation and only secondarily on sowing and harvesting. The model used was chosen for its ability to account for all components of the cropping system: the observed crop sequences retrieved from the generic data sources, the decision rules and the location factors identified from the key informant interviews. This indirect way to evaluate the reliability of a modeled spatial distribution of cropping systems was previously used by [START_REF] Murgue | Hybridizing local and generic information to model cropping system spatial distribution in an agricultural landscape[END_REF].

For simulating water withdrawals, we used the agricultural module of the MAELIA multiagent and spatially explicit simulation platform [START_REF] Gaudou | The MAELIA multi-agent platform for integrated assessment of low-water management issues[END_REF][START_REF] Therond | Integrated modelling of social-ecological systems: The MAELIA high-resolution multi-agent platform to deal with water scarcity problems[END_REF][START_REF] Mazzega | Critical multi-level governance issues of integrated modelling: An example of low-water management in the Adour-Garonne basin (France)[END_REF]; http://maelia-platform.inra.fr/). This agricultural module, fully described by [START_REF] Murgue | Fine spatio-temporal simulation of cropping and farming systems effects on irrigation withdrawal dynamics within a river basin[END_REF], combines a soil-crop model and a farmer agent model. The former predicts daily soil water balance based on rainfall, evapotranspiration, soil water content and crop development stage [START_REF] Constantin | The soil-crop models STICS and AqYield predict yield and soil water content for irrigated crops equally well with limited data[END_REF]. The latter applies farmers' decisions using crop management decision rules. Virtual farmer agents check, every day and for each land parcel, whether thresholds encoded in management rules have been reached or exceeded and, if so, perform the corresponding technical operation(s). Note that since the hydrological and normative modules of MAELIA were deactivated, virtual farming agents have access to unlimited water resources.

The modeled cropping systems, i.e. observed crop sequences purposely simplified (step 1), along with the decision rules (step 2) and the location rules (step 3), were used to parameterize the agricultural module of MAELIA. Through the multi-agent structure of MAELIA, this module (soil-crop and farmer agent models) was run independently for each land parcel in each farmer's block on each farm in each study area. During a simulation, the model reads through the crop sequence each year to find the crop grown, then associates the crop with its management system (i.e., the set of IF-THEN rules) as a function of the crop sequence type, soil and irrigation equipment, based on the table constructed for this purpose. The simulation ran from 2003, an extreme drought season, to 2012, thus covering the period for which LPIS data were available to reconstruct crop sequences (i.e., 2006-2012). Crop sequences observed from 2006-2012 were replicated to cover the previous years without LPIS data (2003-2005).

Given the many fields simulated (Table 1) and their multiple interactions, our first objective was to reproduce the interannual dynamics of total withdrawal recorded in the AEAG database.

RESULTS

Cropping systems

Twenty-three crops out of the 29 recorded in the LPIS are cultivated in the two study areas (Fig. 3 and4, columns 1 and 2). For the Tarn area, we identified 21 330 crop sequences, which were grouped into 22 sequence types (Table 3). For the Adour area, LPIS analysis identified 7 163 crop sequences, which were grouped into 18 sequence types (Table 4). Figure 5 and Table 7 report the percentages and location factors used to determine the "new" crop classes in both areas, i.e. those different from the original LPIS. The original LPIS "maize" class was first divided into silage maize and grain maize according to a decision tree based on the percentages of maize and pasture area in the farm agricultural area (Table 5).

Next, the grain maize class was divided into two classes of earliness depending on the crop sequence and the soil in the land parcel (Fig. 5). The "other cereals" and "orchards" LPIS classes were also divided into new classes according to crop sequence and soil type location factors. For example, in the Tarn area, kiwifruit orchards were randomly located on 25% of the land parcels grown continuously in orchards (according to LPIS) and situated on alluvial soils. This approach resulted in 18 crop classes, 14 of which can be irrigated, in the Tarn area (Fig. 3, column 4) and 14 crop classes, 11 of which can be irrigated, in the Adour area (Fig. 4, column 4). 4.

Table 3. Crop sequence types for the Tarn area (first column). Each sequence is described by the dominant crop classes, its duration (each crop representing one year), and the frequency with which each crop occurs. For example, "Maize, rot" (rot = rotation) is a 6-year sequence with maize (silage or grain, early or late variety) in 3 out of 6 years, soybean, wheat and another cereal. For monocropping (mc) and perennial production, the crop is only listed in the second column. Note that crops in a sequence that can be irrigated or rainfed are managed differently. Crop names come from the Land Parcel Identification System (original label, or labeled after the initial grouping -see Fig. 3). Table 4. Crop sequence types for the Adour area (first column). Each sequence is described by the simplified dominant crop classes, its duration (each crop representing one year), and the frequency with which each crop occurs. For example, "Maize, rot" (rot=rotation) is a 5year sequence with maize (silage or grain, early or late variety) in 3 out of 5 years, soybean and a small grain cereal crop. For monocropping (mc) and perennial production, the crop is only listed in the second column (Crop 1). Since the grouping of crops in the Adour area was relatively simple, we used the labels of the simplified crop classes (Fig. 4) to name the crops (except for maize). ). Here, "maize" is only grain maize. This procedure was applied to one area each in the Adour and Tarn watersheds, France.

Sequence

Based on the irrigated and rainfed areas of each crop derived from LPIS irrigation data for 2007-2009 (Fig. 6), maize was the most extensively irrigated crop in both areas. For the Adour area, we estimated that ≈70% of maize area was irrigated, whereas the key informants estimated that 75% was irrigated. For the Tarn area, we estimated that 93% and 82% of late and early maize varieties, respectively, were irrigated, whereas the key informants estimated that 100% of each was irrigated (Fig. 6). As for location factors of cropping systems, the key informants mainly emphasized soil water-holding capacity (i.e., the greater the clay content, the higher and less frequent the irrigation) and irrigation equipment (e.g., pivot irrigation has lower doses and higher frequency than sprinkler irrigation).

Simulated water withdrawals at the regional scale

Median annual withdrawals for 2003-2012 reported by AEAG were 39.7 and 42.7 hm³ for the Adour and Tarn areas, respectively, while MAELIA predicted 37.7 and 40.4 hm³ (Fig. 7), respectively (under-predicted by 5%). The model simulated well withdrawal dynamics among years (Fig. 7). Predictions for the Tarn area ranged from 16% below (in 2011) to 11% above (in 2009) observations. Predictions were less accurate for the Adour area, especially for 2003-2005; for 2006-2012, they ranged from 14% below to 11% above observations. Less accurate predictions in the Adour area for 2003-2005 may have been influenced by the lack of LPIS crop data before 2006 and by the quality of the AEAG data for withdrawn volumes. Notably, AEAG data for the Adour area in the earlier years were mostly estimates based on farm irrigated area, while those in the later years were mostly measurements. This factor could be the most important, since the AEAG obtained measured withdrawals from an earlier date in the Tarn area, for which predictions were more accurate.

Discussion

In this article, we present a method to model the current cropping systems and their spatial distribution at the regional scale. Similar approaches for the same objective, based on a few local key informants, were used to describe the influence of agriculture on landscape management [START_REF] Galli | Characterisation of Agri-Landscape Systems at a Regional Level: A Case Study in Northern Tuscany[END_REF]) and to identify strengths and weaknesses of extension services [START_REF] Debolini | Mapping local spatial knowledge in the assessment of agricultural systems: A case study on the provision of agricultural services[END_REF]. The discussion below addresses the (i) strengths, (ii) limits, (iii) replicability and genericity and (iv) evaluation of our approach to describe cropping systems at the regional scale.

Strengths.

Our approach, based on generic data sources, key informant interviews and decision modeling, allowed us to describe current cropping systems at the regional scale, not only as a static picture but as dynamic systems. The generic data sources allowed the description of crop sequences. Key informants allowed us to directly access average farmers' strategies: we assumed that extension agents would have a wide view of crop management systems and could identify average decision rules and potential local and individual adaptations. Their integrated vision of the study area helped us to avoid the interpretation difficulties that occur in detailed surveys of agricultural management practices performed at the farm level: great heterogeneity among farmers and bias resulting from complying with regulations or from small samples [START_REF] Schott | Les « carnets de plaine » des agriculteurs : une source d'information sur l'usage des pesticides à l'échelle de bassins versants ?[END_REF]. The ability to obtain extensive coverage of the study areas from only a few interviews was facilitated by the fact that French extension services are generally organized to operate with relatively few agents. In this regard, the help of the two managers was crucial. The design of targeted intermediate objects (maps, tables and diagrams) also helped structuring the interviews to obtain the missing data in the two study areas. When evaluating this approach with MAELIA's agricultural module, we observed a similar ability to simulate water withdrawals as in the previous study in the nearby Aveyron watershed [START_REF] Murgue | Hybridizing local and generic information to model cropping system spatial distribution in an agricultural landscape[END_REF]. Our approach, using local knowledge of key informants to formalize location and crop management rules, performs as well as the previous approach: the same order of magnitude of differences between simulated water withdrawals and AEAG withdrawals in the present study as in [START_REF] Murgue | Hybridizing local and generic information to model cropping system spatial distribution in an agricultural landscape[END_REF]. Besides, our simplified approach is more efficient since it enabled us to describe crop management systems at the regional scale on six times as many fields as in the Aveyron study [START_REF] Murgue | Hybridizing local and generic information to model cropping system spatial distribution in an agricultural landscape[END_REF], in approximately one-fourth of the time required in this previous study.

Limits. The limits of our approach are closely related to the quality of the generic data and the local knowledge used.

First, the irrigated status of land parcels appears as probably poorly estimated, as revealed by the underestimates of simulated water withdrawals. Indeed, we used 2007-2009 LPIS data to determine whether land parcels could be irrigated, which might have biased estimates of irrigation status before and after this period. In this perspective, it would be reasonable to update these data using specific sources, such as recent multi-sensor, highresolution time series of satellite images being evaluated for identifying irrigated crops in our study region (Waldner et al. 2015b).

Second, the allocation of irrigation equipment to irrigated land parcels should be improved by collecting information about relative frequencies and distributions of types of irrigation equipment. The type of irrigation equipment strongly influences irrigation volume per ha. Discussions with irrigation experts indicate that the increasing use of pivot irrigation tends to increase irrigation intensity. Methods to obtain irrigation equipment information should be developed.

Third, our crop classification was developed to discriminate crops based on their irrigation use, but it may be oversimplified. We ignored irrigated crops that covered small areas. For example, we gathered in a unique "seed production" class the main seed crop (maize) and many other seed crops found in the Tarn area (e.g. carrots, onions).

Fourth, the estimation of crop management could certainly be improved. On the one hand, we simplified the description of rainfed crops because we primarily wanted to reproduce water withdrawals correctly. On the other hand, crop management of irrigated crops, although detailed, depended on the experience of key informants in local crop management practices.

Fifth, the collected data is dependent on the key informants. In our case, the selected key informants ranged from a highly experienced extension agent, who had worked in the same area for approximately 20 years, to a recently recruited agent. In general, the longer their experience in local farming, the greater their ability to describe crop management decision rules at the regional scale, though their answers appeared to be adapted to average pedoclimatic conditions. Conversely, the shorter their experience, the more they tended to refer to extension service bulletins, i.e. recommendations instead of actual practices. The key informants with less experience tended to provide detailed descriptions of farmers' decision rules; however, the information was restricted to the most recent years. Ultimately, we had to find a balance between the reliable experience of the former and the high detail of the latter. In this regard, the intermediate objects used during the interviews (maps, tables and diagrams) allowed to frame the respondents answers, so as to enable their comparability and following encoding.

Genericity and replicability. Our approach of describing cropping systems at the regional scale was applied to the Tarn and Adour watersheds in response to specific irrigation issues.

However, it has a generic character. First, it uses data that is relevant beyond these two study areas. The LPIS database, from which we obtained descriptions of crop sequences, is increasingly used to map crop patterns and dynamics in France [START_REF] Durpoix | Intérêts de l'analyse territorialisée des parcellaires des exploitations agricoles concernées par une aire d'alimentation de captage[END_REF][START_REF] Rizzo | Miscanthus spatial location as seen by farmers: A machine learning approach to model real criteria[END_REF][START_REF] Bouty | Are crop sequence evolutions influenced by farm territory dynamics? 5[END_REF][START_REF] Levavasseur | RPG Explorer: A new tool to ease the analysis of agricultural landscape dynamics with the Land Parcel Identification System[END_REF]) and elsewhere in Europe [START_REF] Leteinturier | Adaptation of a crop sequence indicator based on a land parcel management system[END_REF][START_REF] Levin | Applying parcel-specific land-use data for improved monitoring of seminatural grassland in Denmark[END_REF][START_REF] Zimmermann | Assessing land-use history for reporting on cropland dynamics-A comparison between the Land-Parcel Identification System and traditional inter-annual approaches[END_REF]. Second, the local information was integrated using intermediate objects (e.g., diagrams) and then formalized as IF-THEN rules for management decisions and cropping system location. Combining diagrams and IF-THEN rules defined a methodological framework that can be adapted easily to study other areas and crop management practices besides irrigation, as well as to account for various annual and perennial crops. We found that the intermediate objects prepared for the interviews (scripts and diagrams) could be adapted rapidly from the first area (Tarn) to the second (Adour), demonstrating their genericity. Formalizing the knowledge that underpins the intermediate objects used for key informant interviews may eventually facilitate social learning among farmers, extension agents and scientists, at least by providing focal points for communication and bridging gaps among them [START_REF] Jakku | A conceptual framework for guiding the participatory development of agricultural decision support systems[END_REF][START_REF] Houllier | Les sciences participatives en France[END_REF][START_REF] Moonen | From Action Research to Action Learning -Ecosystem services assessment as a learning platform for students, local land users and researchers[END_REF]. More generally, we assume the key informant approach can be repeated anywhere, and also extended to the collection of other data, e.g. of crop rotations and several technical operations, where or when generic database are not available, as did [START_REF] Mignolet | Spatial dynamics of agricultural practices on a basin territory: a retrospective study to implement models simulating nitrate flow. The case of the Seine basin[END_REF]. Though, its main limitation, as in other participatory approaches, is the difficulty in identifying the best key informants. One way to evaluate their representativeness is by characterizing the local web of actors and the socio-technical system [START_REF] Mathevet | Contexts and dependencies in the ComMod processes[END_REF]. We limited our key informant approach to extension agents, but other local actors have knowledge about crop management practices that complements farmers' knowledge. During the interviews, managers of local water-user associations, who are also farmers, or extension agents working for private or crop-specific technical institutes, were often mentioned as able to provide relevant knowledge to describe cropping systems and irrigation practices at the regional scale.

Evaluation. More complete evaluation of our approach would have been based on an independent dataset of observed farmers' practices. This dataset would have included all (or nearly all) farms within the region to address the high heterogeneity of farmers' strategies. A dataset corresponding to a sample cannot represent the entire population and is thus insufficient for complete evaluation. However, no exhaustive dataset was available. The lack of adequate data is a major obstacle in evaluating model outputs, as often reported for model evaluation at the regional scale (e.g. [START_REF] Leenhardt | Simulating farming practices within a region using a stochastic bio-decisional model: Application to irrigated maize in south-western France[END_REF][START_REF] Temme | Mapping and modelling of changes in agricultural intensity in Europe[END_REF]Waldner et al. 2015a). Due to the lack of independent data on observed farmers' practices, we could not evaluate our approach directly; thus, we used a simulation model to assess its ability to provide consistent estimates of water withdrawals. We used AEAG water withdrawal data as a validation dataset despite its weakness in spatial coverage, locations of withdrawal points and how withdrawn volumes were surveyed over time. The differences between observed and simulated water withdrawals were due in part to these inaccurate observations, but also likely due to underestimates of irrigation of orchards and vegetables by the model, particularly in the Tarn area, because the soil-crop model in MAELIA was not sufficiently parameterized to represent these two crops correctly. Another option to evaluate our approach, despite the lack of observed cropping system data, could have been to involve farmers (or other local experts not previously involved) and verify whether the simulated dates for the main technical operations (sowing, harvest) were consistent with their field knowledge. This would have required summarizing the temporal distribution of the corresponding technical operations for each combination of [crop sequence type] × [soil] × [irrigation equipment] for each crop into diagrams similar to those used during the interviews (Fig. 2). These alternative ways to assess our approach, however, are mainly qualitative and rely on either the quality of farmers' expertise or the availability of new experts.

Conclusions

Describing cropping systems at the regional scale requires integrating generic data sources with formalized local expert knowledge. The major challenge concerns the data on crop management practices due to the high spatial and temporal heterogeneity of farmers' decision making. We evaluated the contribution of a key informant approach to simplify the survey and modeling of crop management practices. Comparison of our approach to that in a previous study [START_REF] Murgue | Hybridizing local and generic information to model cropping system spatial distribution in an agricultural landscape[END_REF]) indicated that we needed only one-fourth the time to collect and process the relevant information. Our simplified approach involved only 12 interviewees compared to the sample of 27 farmer interviews (and several focus groups) in the previous study. The gain in efficiency was even greater because our study areas were a total of six times as large as that in the previous study. Implementing the key informant approach allowed us to model irrigation decisions and their spatial distributions and, through dynamic simulations, to predict annual amounts and interannual dynamics of irrigation water withdrawals relatively well. One main innovation of our method was to use knowledge from generic databases when interacting with key informants and ad-hoc intermediate objects to help them focus on missing information. In particular, use of diagrams reduced interview duration and processing time. These intermediate objects are easy to reuse in new study areas. This approach was applied to two large areas and can easily be adapted to other areas where LPIS data are available. Although we focused on irrigation management practices in areas with water scarcity issues, this approach is sufficiently generic to be applied to areas, and can be easily adapted to focus on other management practices and crops. Thus, our approach can be considered a rapid and inexpensive tool to inform decisions and policy making to address agri-environmental issues that require modeling of the spatial distribution of cropping systems at the regional scale.

Figure 1 .

 1 Figure 1. Locations and characteristics of the study areas (A) Adour and (B) Tarn, which is closer to the Aveyron study area of Murgue et al. (2016) (gray). Non-agricultural areas (white) on the soil maps represent the following CORINE Land Cover classes for 2012: urban, woods, wetlands, and water bodies.

Figure 2 .

 2 Figure 2. Example of a diagram used during key informant interviews. Interviewees were asked (A) to specify the crop class and (B) to quantify irrigation scheduling (number of days between two applications) during the most relevant periods. Then, after (C) a quick reminder of the modeling approach, they were asked to list (D) indicators (e.g., rainfall, soil moisture) and the associated thresholds farmers use to manage irrigation during (E) the irrigation season. Similarly, the timeline was used to frame (F) the scheduling of sowing and harvesting, for which interviewees were asked to set (G) indicators and thresholds underpinning the decision rules. Source: English translation of the original in French.

Figure 3 .Figure 4 .

 34 Figure 3. Definition of the crop classes at the regional scale in the Tarn area. Crop classes are irrigated (••), seldom irrigated (•) and exclusively rainfed (°). Absent crops from the original LPIS list were not reported. The second column shows the original Land Parcel Identification

Figure 5 .

 5 Figure 5. Procedure used to develop detailed crop classes from original Land Parcel Identification System (LPIS) classes, as specified by key informants (cf. step 1 of the crop system modeling). The diagram presents the original LPIS class (col. 1), the location factors (col. 2) and percentages (col. 3) used to divide LPIS classes, and possibly recombine them, into new crop classes (col. 4). Here, "maize" is only grain maize. This procedure was applied to one area each in the Adour and Tarn watersheds, France.

Fig. 6 .

 6 Fig. 6. Median rainfed and irrigated crop areas (ha) for the period simulated (2003-2012) in the two study areas. Values in parentheses indicate the percentage of total crop area that was irrigated. Maize is divided into silage (maizeS), early (maizeE) and late (maizeL) varieties, in addition to seed production.

Fig. 7 .

 7 Fig. 7. Comparison of water withdrawals for irrigation declared to the water agency by irrigating farmers and predicted by the agricultural module of the MAELIA platform.

Table 1 .

 1 and freely available at the Local Administrative Unit 2 level, corresponding to a French municipality(Eurostat 2015). Main characteristics of the two study areas (for 2012). Source: Land Parcel Identification System (LPIS) and CORINE Land Cover data. The number of crop fields was obtained from LPIS data after processing (see details in[START_REF] Leenhardt | Quelle représentation des systèmes de culture pour la gestion de l'eau sur un grand territoire ?[END_REF]

	Characteristic

Table 2 .

 2 Description of key informants (by expected expertise and study area) and interviews (by type, number of people interviewed and duration).

	Key informants				Interviews
	Expected expertise	Tarn Adour	Telephone	Face-to-face	Duration
	Maize production and irrigation practices	2	2		2	320 min
	Orchards and drip-irrigation practices	2	-		1	120 min
	Cash crops	1	-		1	140 min
	Vineyards	1	-	1		20 min
	Seed production and legumes	1	-	1		20 min
	Pastures and alfalfa	1	-	1		20 min
	Total	8	2	3	4	640 min

Table 5 .

 5 Distribution of the Land Parcel Identification System maize class into grain and silage classes based on location factors (percentages of maize and pasture in the farm area). 414 This model was used for both the Tarn and Adour areas.

	Location factors	Distribution of the LPIS maize class into grain and silage classes
	% of maize in farm agricultural area	% of pasture in farm agricultural area	Grain maize	Silage maize
	< 30%	-	75%	25%
		< 10%	100%	0%
	> 30%	10-35%	50%	50%
		> 35%	100%	0%

Acknowledgments

This study is part of the SIMULTEAU project, funded by the French Ministry of Agriculture as part of the CASDAR program, and received additional funding from the French National Office for Water and Aquatic Environment (ONEMA). The authors thank the interviewees from the extension services and the other local experts who generously contributed to the description and formalization of cropping systems for the Tarn and Adour areas. Members of the SIMULTEAU project are also acknowledged for their useful comments and improvements on the method. Finally, we thank Pierre Casel and Denis Baudet (INRA UMR AGIR) for their help in retrieving and processing the LPIS and geographic data.