Ezequiel Jorge-Smeding

Muriel Bonnet

Gilles Renand

Sébastien Taussat

Benoit Graulet

Isabelle Ortigues-Marty

Gonzalo Cantalapiedra-Hijar
email: gonzalo.cantalapiedra@inrae.fr

Common and diet-specific metabolic pathways underlying residual feed intake in fattening Charolais yearling bulls

Residual feed intake (RFI) is one of the preferred traits for feed efficiency animal breeding. However, RFI measurement is expensive and time-consuming and animal ranking may depend on the nature of the diets. We aimed to explore RFI plasma biomarkers and to unravel the underlying metabolic pathways in yearling bulls fed either a corn-silage diet rich in starch (corn diet) or a grass-silage diet rich in fiber (grass diet). Forty-eight extreme RFI animals (Low-RFI, n = 24, versus High-RFI, n = 24, balanced per diet) were selected from a population of 364 Charolais bulls and their plasma was subjected to a targeted LC-MS metabolomic approach together with classical metabolite and hormonal plasma analyses. Greater lean body mass and nitrogen use efficiency, and lower protein turnover were identified as common mechanisms underlying RFI irrespective of the diet. On the other hand, greater adiposity and plasma concentrations of branched-chain amino acids (BCAA) together with lower insulin sensitivity in High-RFI animals were only observed with corn diet. Conversely, greater plasma concentrations of BCAA and total triglycerides, but similar insulin concentrations were noted in efficient RFI cattle with grass diet. Our data suggest that there are diet-specific mechanisms explaining RFI differences in fattening Charolais yearling bulls.

In a growing world's human population the efficient use of natural resources becomes a crucial issue for sustainable livestock systems. In growing animals, animal feed efficiency can be defined as the animal ability to reach a market or adult BW with the least amount of feed intake [START_REF] Cantalapiedra-Hijar | Review: Biological determinants of between-animal variation in feed efficiency of growing beef cattle[END_REF] . Animal feed efficiency is quite variable across individuals [START_REF] Arthur | Residual feed intake in beef cattle[END_REF] and moderately heritable [START_REF] Rolfe | Genetic and phenotypic parameter estimates for feed intake and other traits in growing beef cattle, and opportunities for selection123[END_REF] , leading to the opportunity to genetically select animals for this trait with potential impacts on profitability and sustainability of beef production systems [START_REF] Hill | Feed Efficiency in the Beef Industry 1st edn[END_REF] . Among the metrics for evaluating animal feed efficiency, residual feed intake (RFI) is one of the more widely used trait within animal breeding, enabling us to identify individuals with lower feed intake but similar performances.

As for any other feed efficiency trait, RFI determination is expensive and time-consuming, needing at least 6 weeks of individual daily feed intake and body weight gain recording [START_REF] Manafiazar | Optimizing feed intake recording and feed efficiency estimation to increase the rate of genetic gain for feed efficiency in beef cattle[END_REF] . For this reason, research aimed at exploring biological markers of RFI has become of scientific interest in the last years as a way to find easier and cheaper strategies for identifying individuals with superior feed efficiency. Moreover, the discovery of biomarkers may help to gain deeper insights on the physiological mechanisms underlying individual variability in feed efficiency, which may contribute to better define genetic selection programs or precision feeding systems.

Metabolomics methodologies leading to high-throughput phenotyping have been used both to explore biomarkers and to unravel the metabolic mechanisms underlying individual variability of feed efficiency [START_REF] Meale | Exploration of biological markers of feed efficiency in young bulls[END_REF][START_REF] Goldansaz | Livestock metabolomics and the livestock metabolome: A systematic review[END_REF] . Targeted rather than untargeted metabolomic technologies are usually the best option when the identification of metabolic pathways and the mechanism associated to a particular condition is pursued [START_REF] Gowda | Biomarker discovery and translation in metabolomics[END_REF] . However, results have not been consistent across studies, and candidate plasma biomarkers still remain to be validated before being globally accepted and used [START_REF] Karisa | Plasma metabolites associated with residual feed intake and other productivity performance traits in beef cattle[END_REF][START_REF] Foroutan | Serum metabolite biomarkers for predicting residual feed intake (RFI) of young angus bulls[END_REF] . The lack of universally accepted biomarkers of RFI might be due to the difficulty of identifying metabolites driven by the inter-individual variability rather than by dietary treatments, the latter having a strong impact on plasma metabolome [START_REF] Meale | Exploration of biological markers of feed efficiency in young bulls[END_REF][START_REF] Zhang | Integrative hepatic metabolomics and proteomics reveal insights into the mechanism of different feed efficiency with high or low dietary forage levels in Holstein heifers[END_REF] . Moreover, the inconsistencies across studies could stem from the use of different types of diets since a potential RFI × Diet interaction has been evoked in a few works [START_REF] Durunna | Feed efficiency differences and reranking in beef steers fed grower and finisher diets[END_REF][START_REF] Lahart | The repeatability of feed intake and feed efficiency in beef cattle offered high-concentrate, grass silage and pasturebased diets[END_REF] . This could mean that biological mechanisms underlying RFI, and thus the associated biomarkers, might differ depending on the nature of the diet and finally to the type of absorbed nutrients. This is a crucial issue if RFI selection programs are to be applied in beef cattle systems where a wide variety of feeding conditions exists.

Therefore, the aim of this work was both exploring plasma biomarkers and unravelling key metabolic pathways that could explain the RFI differences in extreme purebred Charolais yearling bulls, the most representative beef cattle breed in France, assigned to two contrasting fattening diets, either a corn-silage diet rich in starch or a grass-silage diet rich in fiber. To the best of our knowledge, our study presents the largest experimental set up for exploring RFI biomarkers in fattening bulls through a targeted metabolomic approach.

Results

Animal performances. As previously reported [START_REF] Cantalapiedra-Hijar | Methionine-balanced diets improve cattle performance in fattening young bulls fed high-forage diets through changes in nitrogen metabolism[END_REF] , the RFI differences between Low and High-RFI animals averaged 1.48 kg DMI. Accordingly, the daily intake of organic matter, crude and metabolizable protein, neutral detergent fiber and net energy were also lower (-13 % on average, P < 0.01) for Low-versus High-RFI animals, irrespective of diet (Table 1). However, starch intake differed between RFI groups in a different way according to the diet (RFI × Diet; P < 0.01) as it was lower for Low-than High-RFI animals only in the corn silage diet (Table 1). Estimated net portal appearance of total volatile fatty acids, acetate, propionate and butyrate were lower (P < 0.01) for Low-than High-RFI animals in both diets. In agreement with what was observed for starch intake, the net portal appearance for glucose differed between RFI groups in a diet-dependent manner (RFI × Diet; P < 0.01) as it was lower for Low-than High-RFI animals only in the corn diet (Table 1).

Ultrasound subcutaneous fat depth measured at the end of the RFI test on three different anatomical regions was similar (P > 0.05) across diets and RFI groups (Table 2). Hot carcass body weight was higher in Low-versus High-RFI animals (+ 6%; P = 0.01) for both diets. In contrast, visceral fat mass (kg or % of body weight) was affected by an RFI × Diet interaction (P ≤ 0.07) with less visceral fat in Low-versus High-RFI animals in the corn diet but with similar levels with the grass diet.

Plasma concentrations of metabolites, hormones and vitamins. Among the classical plasma biochemical parameters here analyzed, only plasma urea concentration differed across RFI groups, being greater for High-versus Low-RFI animals (+ 21%; P = 0.01; Table 2). In relation to the diet effect, the corn diet promoted greater (P < 0.01) plasma concentrations of glucose, urea and β-hydroxbutyrate (BHB) compared to the grass diet. Differences across RFI groups on plasma insulin levels and the Revised Quantitative Insulin Sensitivity Check Index (RQUICKI) were diet-dependent (RFI × Diet; P ≤ 0.04) with lower values in efficient versus inef- ficient RFI animals only when they were fed the corn diet. As expected, higher insulin levels were found in corn versus grass-based diets (P < 0.01). No effect of diet or RFI group on plasma IGF-1 concentration was found (P > 0.05). Blood B vitamins concentrations here analyzed, vitamins B2 and B6 sums as well as ratios between vitamins, were not statistically different across RFI groups (P > 0.05; Table 2). However, the plasma concentrations of pyridoxal (PA) were significantly lower for animals fed grass than for those fed corn (-11%; P < 0.02). S1 online) by the MxP Quant 500 kit were effectively quantified (i.e. metabolite plasma concentration greater than the limit of detection in at least 80 % of the 48 plasma samples, Supplementary Table S2 online). The principal component analysis (PCA) performed on these 271 metabolites clearly discriminated individuals according to the diet they were fed on, but failed to discriminate individuals according to their RFI group (Fig. 1). According to partial least squares discriminant analysis (PLS-DA), no model for predicting the RFI group was obtained for the corn diet (Q 2 = -0.07, 1st component model; Supplementary Fig. S1 online) and only a poor predictive model was obtained for grass diets (Q 2 = 0.30, R 2 = 0.54 for 1st component model; Supplementary Fig. S1 online). Consequently, no RFI discriminant metabolites based on variable importance in projection (VIP) values could be proposed at this step. Detailed information on the 271 metabolites identification, their plasma concentrations and ANOVA statistics on experimental factors is available in the Supl. Table S2. ANOVA revealed that creatinine, β-alanine, trans-4-hydroxyproline, p-cresol sulphate and three specific triglycerides (TG) (TG17:0_34:1, TG16:0_33:1 and TG20:3_34:0) were increased while phosphatidylcholine (PC) ae 30:1 was decreased (FDR < 0.05) in Low-versus High-RFI bulls irrespectively of the diet (Table 3).

In contrast, the three BCAA (isoleucine, leucine, and valine) as well as PCA ae 38:2 were significantly affected by RFI (Table 3) but in a different way according to the type of diet (RFI × Diet; FDR < 0.05). The plasma concentration of the 3 individual BCAA was greater for High-versus Low-RFI in the corn diet but similar in the In order to gain a better understanding, the sums of key metabolic families as well as some ratios between metabolites were calculated a posteriori (Supplementary Table S2). Among them, only the sum of non-essential amino acids (NEAA) was significantly affected by RFI (FDR < 0.05) as it was greater for Low-versus High-RFI animals irrespective of the diet. In addition, only total BCAA were affected by the RFI × Diet interaction (FDR = 0.01), as total BCAA were greater for High-versus Low-RFI animals in corn diet, but lower in grass diet. Total triglycerides also tended to be affected by the RFI × Diet interaction (FDR = 0.06) as they were greater for Lowversus High-RFI animals only in the grass diet. 3. Blood plasma concentration of metabolites quantified through LC-MS and differing (FDR < 0.05) between extreme RFI young bulls (Low versus High RFI) fed either corn-or grass-silage diets. a,b: Means are compared only within each diet. Different letters means significant differences according to Tukey test (P < 0.05). 1: Plasma concentration (μM) are presented as mean and SEM. 2: Metabolite's names for these acronyms are available in Supplementary Table S1. Results from a PCA performed a posteriori only from those variables significantly different across RFI (Fig. 4) indicated that plasma concentration of insulin and BCAA, and visceral fat mass were positively correlated with RFI in the corn diet (PCA first component; 38.7%). In contrast, these same variable as well as plasma triglycerides showed a negative association with RFI in the grass diet (PCA first component; 42.6%) (Fig. 2).

Metabolic pathway analysis.

Metabolic pathway analysis revealed 7 and 16 metabolic pathways differing (FDR < 0.05) between RFI groups in the corn and grass diets, respectively (Table 4, Fig. 3). Among these metabolic pathways, pantothenate and CoA biosynthesis, and pyrimidine metabolism were shift-regulated between RFI groups in both diets. While pyrimidine metabolism was up-regulated in Low-RFI animals in both diets, pantothenate and CoA biosynthesis was down-regulated in the corn diet and up-regulated in the grass diet (Fig. 4). In the corn diet, most of metabolic pathways were associated with lower plasma concentration of metabolites in Low-RFI such as those involved in BCAA metabolism, pantothenate and CoA biosynthesis, lysine degradation and biotin metabolism (Fig. 4). The opposite trend was found for the grass diet, where most of the metabolites involved in the identified metabolic pathways had greater plasma concentrations for Low-RFI than High-RFI animals (Fig. 4).

Discussion

Before implementing genetic programs for improving animal feed efficiency several issues should be first addressed. One is related to the potential interaction between genetics and environment (G× E), such that animals with superior performance may not be the same across different environments. For instance, this may be a concern for improving feed efficiency through selection if the animal ranking changes with the quality of the offered diet, the latter differing highly across beef production systems. In this sense, several studies have pointed at a re-ranking of beef cattle in terms of RFI when energy-dense diets were shifted to more grass-based diets or vice versa [START_REF] Lahart | The repeatability of feed intake and feed efficiency in beef cattle offered high-concentrate, grass silage and pasturebased diets[END_REF][START_REF] Lawrence | Grazed grass herbage intake and performance of beef heifers with predetermined phenotypic residual feed intake classification[END_REF] . In this context, the aim of our study was to use targeted metabolite analysis to evaluate the biological mechanisms underlying RFI variation in beef cattle fed a classical energy-dense fattening diet based on corn silage (high-starch) versus a grass silage diet (high-fiber) . Our results suggest that both shared and diet-specific mechanisms underlying RFI may exist. As further discussed, some interactions between RFI and the type of diet for several plasma metabolites involved in protein and lipid metabolic pathways were observed. On the other hand, many common metabolites and pathways were found to diverge between RFI groups across the two diets suggesting that some features and process such as body composition, muscle protein degradation and N use efficiency are likely common determinants of RFI variations irrespective of the diet.

Among the few metabolites showing FDR significant differences across RFI groups, and irrespective of the diet, there were creatinine and trans-4-hydroxyproline (concentrations + 16 % for Low-versus High-RFI on average). These two metabolites reflect directly or indirectly changes in muscle body mass. Creatinine, is a breakdown product of creatinine phosphate in muscle [START_REF] Brosnan | Creatine metabolism and the urea cycle[END_REF] , and its plasma/blood concentration has been proposed and used as a proxy of muscle mass in ruminants [START_REF] Caldeira | The effect of body condition score on blood metabolites and hormonal profiles in ewes[END_REF] . The higher plasma values of creatinine in Low-RFI animals for both diets agree with previous reports in beef cattle [START_REF] Karisa | Plasma metabolites associated with residual feed intake and other productivity performance traits in beef cattle[END_REF][START_REF] Lawrence | Grazed grass herbage intake and performance of beef heifers with predetermined phenotypic residual feed intake classification[END_REF][START_REF] Richardson | Metabolic differences in angus steers divergently selected for residual feed intake[END_REF] indicating higher muscle body mass in efficient versus inefficient cattle. Likewise, trans-4-hydroxyproline, a major component of collagen, showed higher plasma concentration in efficient RFI animals. Plasma trans-4-hydroxyproline has been proposed as a biomarker of total collagen body mass [START_REF] Vázquez-Ortíz | Hydroxyproline measurement by HPLC: Improved method of total collagen determination in meat samples[END_REF] , and thus its plasma concentration might indirectly reflect differences in the amount of connective tissues associated to the musculoskeletal system. In this regard, plasma trans-4-hydroxyproline was found in greater concentration in beef steers with higher growth rate and lean mass [START_REF] Bruce | Effects of compensatory growth on protein metabolism and meat tenderness of beef steers[END_REF] . Additionally, β-alanine was also consistently higher in Low-RFI animals across the two diets. Even if it is considered to be at the crossroad of many metabolic pathways, higher β-alanine (i.e. a breakdown product from the muscle-rich dipeptides anserine and carnosine) in plasma of Low-RFI animals might also agree with greater body muscle mass. According to a recent review, RFI trait is negatively associated with lean body mass [START_REF] Cantalapiedra-Hijar | Review: Biological determinants of between-animal variation in feed efficiency of growing beef cattle[END_REF] and recent genetic studies demonstrated that carcass muscle content was negatively correlated to RFI in Charolais bulls [START_REF] Taussat | Relationship between feed efficiency and slaughter traits of French Charolais bulls[END_REF] . Taken together, the differences in plasma concentration across RFI groups for these three endogenous metabolites support a contrasted body composition across RFI groups whatever the diet is used. However, it is noteworthy to mention that these differences in body composition were not reflected by the real-time ultrasound echography in our conditions (i.e. late maturing breed). Our findings highlight the need for other proxies of body composition in Charolais yearling bulls to be used in the RFI model.

Because efficient RFI animals eat less protein (as here reported) but retain equal (or greater) amount of protein [START_REF] De Assis Lage | Relationship between feed efficiency indexes and performance, body measurements, digestibility, energy partitioning, and nitrogen partitioning in pre-weaning dairy heifers[END_REF] it could be expected that the N use efficiency (i.e. N retention/N intake) will be improved. This has been empirically confirmed in dairy cows with contrasted RFI values [START_REF] Liu | Relationship of residual feed intake and protein efficiency in lactating cows fed high-or low-protein diets[END_REF] . However, milk N secretion is easier to measure than N accretion in growing animals. In growing cattle, most studies have failed to prove this link through N balance measurements [START_REF] Carmona | Feed efficiency and nitrogen use rankings of Bos indicus steers differ on low and high protein diets[END_REF][START_REF] Silva | Grouping crossbred Holstein x Gyr heifers according to different feed efficiency indexes and its effects on energy and nitrogen partitioning, blood metabolic variables and gas exchanges[END_REF] . However, results from our team using a novel isotopic approach applied to this same experimental setup (n = 48) suggested that more efficient animals in terms of RFI also show higher N use efficiency compared to less efficient RFI animals [START_REF] Cantalapiedra-Hijar | Natural 15N abundance in specific amino acids indicates associations between transamination rates and residual feed intake in beef cattle[END_REF] . Data from the present study showed that plasma urea concentration, a confirmed biomarker of N use efficiency [START_REF] Kohn | Using blood urea nitrogen to predict nitrogen excretion and efficiency of nitrogen utilization in cattle, sheep, goats, horses, pigs, and rats[END_REF] , was decreased in more efficient animals in agreement with some reports [START_REF] Richardson | Metabolic differences in angus steers divergently selected for residual feed intake[END_REF][START_REF] De Assis Lage | Relationship between feed efficiency indexes and performance, body measurements, digestibility, energy partitioning, and nitrogen partitioning in pre-weaning dairy heifers[END_REF][START_REF] Lahann | Energy expenditure, urea kinetics, and body weight gain within a segregating resource family population[END_REF] but unlike others [START_REF] Meale | Exploration of biological markers of feed efficiency in young bulls[END_REF][START_REF] Clemmons | Serum metabolites associated with feed efficiency in black angus steers[END_REF] . Interestingly, a non-targeted metabolomics approach applied to the plasma of growing beef heifers revealed the urea cycle as a metabolic pathway underlying RFI differences [START_REF]Metabolomics reveals changes in urea cycle associated to residual feed intake in growing heifers[END_REF] . Furthermore, the metabolic pathways analysis identified the pyrimidine metabolism as the unique pathway shifting across RFI groups in the same way in both diets. Pyrimidine biosynthesis is intimately linked to the urea cycle pathway through carbamoyl phosphate and pyrimidine catabolism and it has been suggested to contribute to the N use efficiency in dairy cows [START_REF] Stentoft | Absorption and intermediary metabolism of purines and pyrimidines in lactating dairy cows[END_REF] .

Despite our observation that efficient RFI animals had a greater muscle mass, our results indicated a trend for a lower fractional protein degradation rate of skeletal muscle as indicated by the lower plasma 3-methylhistidine to creatinine ratio (FDR = 0.10; Supplementary Table S2). Plasma 3-methylhistidine to creatinine ratio has been Table 4. Identified metabolic pathways associated with RFI differences in young bulls fed either corn-or grass-silage diets. 1: Total number of metabolites theoretically considered by the KEGG database for identify metabolic pathways. 2: Metabolites effectively quantified in the current study (Figs. 3,4). 3: FDR > 0.05 is depicted by "-". 4: Topological analysis of impact of the current metabolic pathway. reported to be a useful index to evaluate myofibrillar protein degradation [START_REF] Nagasawa | Plasma N τ -methylhistidine concentration is a sensitive index of myofibrillar protein degradation during starvation in rats[END_REF] and creatinine allows normalizing by unit of muscle mass. Similar studies looking at this ratio in urine from beef cattle did not, however, find any difference and concluded that the muscle degradation rate was similar across RFI groups [START_REF] Richardson | Metabolic differences in angus steers divergently selected for residual feed intake[END_REF][START_REF] Castro Bulle | Growth, carcass quality, and protein and energy metabolism in beef cattle with different growth potentials and residual feed intakes[END_REF] . In contrast, evidence of lower protein turnover rate in efficient RFI animals have been reported in cattle [START_REF] Mcdonagh | Meat quality and the calpain system of feedlot steers following a single generation of divergent selection for residual feed intake[END_REF][START_REF] Elolimy | Residual feed intake in beef cattle is associated with differences in protein turnover and nutrient transporters in ruminal epithelium[END_REF] and swine [START_REF] Cruzen | Evidence of decreased muscle protein turnover in gilts selected for low residual feed intake[END_REF] using indirect molecular approaches. Similarly, Carvalho et al. [START_REF] Carvalho | Differentially expressed mRNAs, proteins and miRNAs associated to energy metabolism in skeletal muscle of beef cattle identified for low and high residual feed intake[END_REF] found greater muscle abundance of Heat Shock Protein β 1 (HSPB1) in skeletal muscle for Low-than High-RFI animals and suggested this difference may account for an increased actin and myosin degradation and protein breakdown, and therefore greater protein turnover in skeletal muscle of less efficient animals. Because of the energy cost of protein turnover, our results would agree with the fact that Low-RFI animals have a lower metabolic rate and energy expenditure compared to High-RFI animals [START_REF] Cantalapiedra-Hijar | Review: Biological determinants of between-animal variation in feed efficiency of growing beef cattle[END_REF] . Finally, the p-cresol concentrations were greater in Low-versus High-RFI animals regardless the diet. P-cresol is a rumen metabolite of tyrosine [START_REF] Martin | The origin of urinary aromatic compounds excreted by ruminants: 3. The metabolism of phenolic compounds to simple phenols[END_REF] which may further undergo a sulphation reaction by the host's cells into p-cresol sulphate [START_REF] Wikoff | Metabolomics analysis reveals large effects of gut microflora on mammalian blood metabolites[END_REF] . Higher plasma levels of p-cresol sulphate in efficient RFI animals may translate a higher rumen fermentation rate of feed proteins (tyrosine) because of their expected higher rumen retention time [START_REF] Cantalapiedra-Hijar | Review: Biological determinants of between-animal variation in feed efficiency of growing beef cattle[END_REF] .

Besides common candidate biomarkers of RFI regardless of the diet, our data strongly suggest that there may exist diet-specific metabolic pathways underlying RFI. Interestingly, among the only 4 metabolites showing significant RFI × Diet interactions, three of them belonged to the family of BCAA (Ile, Leu and Val; FDR < 0.05). In this regard, a meta-analysis on genomic regions associated to RFI in beef cattle, mainly fed high-energy dense diets, concluded that the only and unique significant pathway underlying RFI differences was related to BCAA degradation [START_REF] Duarte | Genome-wide association studies pathway-based meta-analysis for residual feed intake in beef cattle[END_REF] . Similarly, Foroutan et al. [START_REF] Foroutan | Serum metabolite biomarkers for predicting residual feed intake (RFI) of young angus bulls[END_REF] recently reported greater plasma concentrations of BCAA for High-than Low-RFI Angus steers when they were fed on an energy-dense diet. Despite that the first step in BCAA degradation relies on transaminases using B6 vitamin as cofactor [START_REF] Aki | Transaminase of branched chain amino acids[END_REF] no changes in blood concentration of B vitamin across RFI groups were found in the present study. However, other relevant data potentially related to BCAA metabolism showed a similar RFI × Diet interaction (i.e. insulin, fat visceral mass) than those observed for BCAA. Results from a PCA only including the most significant biological variables explaining RFI differences (Fig. 4) showed that whereas visceral fat mass and plasma concentration of BCAA and insulin were positively correlated with RFI in the corn diet, all of them along with plasma triglycerides were negatively correlated with RFI in the grass diet. Therefore, cause and effect relationships between RFI, BCAA metabolism, and energy and lipid metabolism, remain unclear.

Our data strongly suggest there may exist diet-specific metabolic pathways underlying RFI. While greater amino acid absorption was estimated in less efficient RFI animals irrespective of the diet (i.e. higher theoretical MP intake), only greater net portal absorption of glucose was estimated in efficient RFI animals when fed the corn diet (Table 1). It seems, thus, that differences in insulin sensitivity associated with RFI would only appear when providing glucogenic and high starch diets. Indeed, the lowest RQUICKI score values observed for High-RFI animals fed the corn diet are indicative of lower insulin sensitivity in these animals [START_REF] Holtenius | A model to estimate insulin sensitivity in dairy cows[END_REF] . This agrees with previous reports [START_REF] Shafer | Insulin Sensitivity in Tropically Adapted Cattle with Divergent Residual Feed Intake[END_REF][START_REF] Mckenna | Residual feed intake phenotype and gender affect the expression of key genes of the lipogenesis pathway in subcutaneous adipose tissue of beef cattle[END_REF] showing that inefficient animals may require more insulin for the uptake of glucose by peripheral tissues compared to the efficient ones. Insulin together with BCAA concentrations, two cell-exogenous signals translating greater energy and AA availability, are known to jointly upregulate the mechanistic target of rapamycin (mTOR), which is a master regulator of cell growth and metabolism involved in protein and lipid synthesis [START_REF] Laplante | mTOR signaling at a glance[END_REF] . It is important to mention that the mTOR upregulation depends on the synergy of both nutrient signals (insulin and BCAA) [START_REF] Shimobayashi | Multiple amino acid sensing inputs to mTORC1[END_REF] and thus could explain the observed contrasted BCAA profile across RFI phenotypes depending on the nature of diet (glucogenic vs. ketogenic). Although the exact mechanisms linking BCAA plasma concentrations and insulin sensitivity are far from being well understood in ruminants, it seems reasonable to infer from human studies [START_REF] Gannon | BCAA metabolism and insulin sensitivity-Dysregulated by metabolic status?[END_REF] that this link is regulated through the mTOR pathway. In this sense, the mTOR pathway has been previously demonstrated to be upregulated in tissues from inefficient RFI beef cattle fed corn-based diets [START_REF] Elolimy | Residual feed intake in beef cattle is associated with differences in protein turnover and nutrient transporters in ruminal epithelium[END_REF] . Our data can be interpreted as the greater glucogenic nutrients and BCAA absorption in High-RFI animals fed the corn diet would have led to a chronic activation of the mTOR pathway and thus to a lower insulin sensitivity [START_REF] Wullschleger | TOR signaling in growth and metabolism[END_REF][START_REF] Asghari | High dietary intake of branched-chain amino acids is associated with an increased risk of insulin resistance in adults[END_REF] and higher BCAA catabolism. Catabolic intermediates of BCAA could have promoted lipogenesis in High-RFI animals fed the corn diet [START_REF] Salinas-Rubio | Emerging perspectives on branched-chain amino acid metabolism during adipocyte differentiation[END_REF] , and thus contributing with insulin to explain their higher visceral fat mass, when compared to Low-RFI animals. Taken together, our results lead us to believe a key role of BCAA and insulin sensitivity for explaining the diet-specific effects on the link between body composition and RFI phenotype.

Unlike what was observed in the corn-based diet, three specific features were noted for the grass-based diet when comparing extreme RFI groups: similar plasma insulin concentration (and likely insulin sensitivity as regarded by the RQUICKI index) and visceral fat mass across RFI groups, but greater plasma concentration of total triglycerides in more efficient animals. Few studies evaluated body composition in contrasted RFI animals fed high-fiber diets. However, among them none have reported significant differences in fat thickness and body condition score across RFI groups [START_REF] Lawrence | Grazed grass herbage intake and performance of beef heifers with predetermined phenotypic residual feed intake classification[END_REF][START_REF] Fitzsimons | Methane emissions, body composition, and rumen fermentation traits of beef heifers differing in residual feed intake[END_REF] unlike what it is usually observed with more energy-dense diets [START_REF] Basarab | Residual feed intake and body composition in young growing cattle[END_REF][START_REF] Fitzsimons | Feeding behavior, ruminal fermentation, and performance of pregnant beef cows differing in phenotypic residual feed intake offered grass silage[END_REF] . Previous results from our team also pointed at similar visceral fat mass and carcass fat score across divergent RFI animals fed high-fibre diets [START_REF] Meale | Exploration of biological markers of feed efficiency in young bulls[END_REF] which may support the concept of contrasted adiposity across RFI groups depending on the diet. Interestingly, Trujillo et al. [START_REF] Trujillo | Relationship of metabolic hormones, urea and body composition with feed efficiency in Angus heifers carrying different genetic markers under grazing condition[END_REF] observed that Low-RFI compared to High-RFI heifers had a greater proportion of body fat when animals were tested in grazing conditions. In an experiment with divergent RFI lines in pigs fed two contrasted diets (low vs. high fibre) a significant trend was observed for perirenal fat content to be higher in low versus high-RFI pigs when using high-fibre, high-fat diets whereas the opposite was observed with low-fibre, low-fat diets [START_REF] Gondret | Dietary energy sources affect the partition of body lipids and the hierarchy of energy metabolic pathways in growing pigs differing in feed efficiency[END_REF] .

Lastly, regarding the use of plasma metabolites to assist genetic selection, it is important to mention that those metabolites which were found to be affected by the RFI group in a diet-dependent manner should not be considered as universal biomarkers in genetic selection programs. However, several metabolites (e.g.: creatinine. trans-4-hydroxyproline, β-alanine, p-cresolsulphate) were found to be different across RFI groups regardless the diet. Therefore, we think these metabolites may be potential candidates for biomarker-assisted genetic selection strategies. Further studies across different breeds and diets are required in order to validate these candidate RFI biomarkers. It is noteworthy to say that, alongside with metabolomics, arising technologies are showing that miRNA and mRNA can be useful for feed efficiency biomarkers discovery in cattle [START_REF] De Lima | Potential biomarkers for feed efficiency-related traits in nelore cattle identified by co-expression network and integrative genomics analyses[END_REF] . In this sense, it could be imagined that future studies will combine metabolomics and genomic analysis, as complementary approaches rather than exclusive technologies to search biomarkers.

Conclusions

Although common metabolic pathways underlying RFI were found irrespective of the diet consumed, our data also suggests that there are diet-specific mechanisms explaining RFI differences in Charolais yearling bulls. Among shared mechanisms, we identified muscle mass, protein metabolism and the N use efficiency as the most important common drivers of RFI for both high-starch and high-fibre diets. Concerning diet-specific mechanisms underlying RFI we identified that the pathway linking BCAA catabolism and lipid metabolism was associated to RFI in a diet-dependent manner: when using the high-starch diet the contrasted insulin plasma concentration and sensitivity across RFI groups seemed be positively associated with BCAA plasma concentration and adiposity, while the opposite was observed in the grass diet. We speculate that the mTOR pathway could explain many of the RFI × Diet interactions here observed. More studies are warranted to confirm our results, especially in other breeds and production systems, and investigate the role of BCAA catabolism and mTOR pathways in the contrasted body composition observed across RFI groups when nutrient supply varies.

Methods

This study was carried out in compliance with the ARRIVE guidelines and the French legislation on animal care. All procedures were carried out in accordance with relevant guidelines and regulations, approved by the regional ethics committee (Auvergne-Rhône-Alpes, France) and subsequently validated by the French Ministry of Agriculture under the authorization number APAFIS#2930-2015111814299194v3.

Feed efficiency test and experimental diets.

This study is part of a large program aiming to explore plasma biomarkers of RFI in Charolais beef cattle. Details on the feed efficiency test, experimental diets and basic data on animal performances as DM intake, average daily gain and feed efficiency ranking have been previously reported [START_REF] Cantalapiedra-Hijar | Natural 15N abundance in specific amino acids indicates associations between transamination rates and residual feed intake in beef cattle[END_REF] . Briefly, this study used 364 pure bred Charolais young bulls (380 ± 58 kg body weight) tested in 3 different experimental farms. The feed efficiency trials lasted on average 210 ± 16 d (mean ± SD) and were conducted between 2015 and 2018 in 7 independent cohorts (farms × period) hosting between 48 and 63 young bulls each one. Animals were housed indoors in pens of 5 to 8 animals of similar body weight and evenly allocated to either a high-starch corn silage diet (corn diet) or a high-fibre grass silage diet (grass diet) as previously detailed [START_REF] Cantalapiedra-Hijar | Natural 15N abundance in specific amino acids indicates associations between transamination rates and residual feed intake in beef cattle[END_REF] . Diets were similar between farms and were formulated with a forage to concentrate ratio close to 65:35, providing a minimum of around 1.50 Mcal NE /kg DM, and meeting the recommended metabolizable protein to net energy ratio according to the INRA feeding system [START_REF]L'alimentation des Ruminants[END_REF] . Feed were offered in two daily meals as totalmixed rations. The individual dry matter intake (DMI) was recorded daily using automatic recording troughs (Biocontrol, Rakkestad, Norway). The body weight was determined fortnightly at 0200 pm. The average daily gain (ADG) of each animal was calculated as the slope of body weight regressed on time. Ultrasound echography (Easi-Scan, BCF Technology Ltd., Vienne, France; equipped with a linear probe) was conducted in three different anatomical regions at end of the RFI test as previously described [START_REF] Cantalapiedra-Hijar | Methionine-balanced diets improve cattle performance in fattening young bulls fed high-forage diets through changes in nitrogen metabolism[END_REF] . Distances analyzed from each anatomical place were the skin (D0) and back fat thickness (D1) that include both subcutaneous tissues and skin. The thickness of subcutaneous adipose tissue and connective tissues was calculated as D1-D0. The RFI was calculated as the difference between the actual and predicted DMI. Predicted DMI was calculated based on the ADG, the mean metabolic body weight and the effect of the contemporary group [START_REF] Taussat | Relationship between feed efficiency and slaughter traits of French Charolais bulls[END_REF] , the latter defined at the level of the pen within each cohort. No significant effects were found for the back fat depth to predict DMI variation beyond the effects of mean metabolic body weight and ADG and thus this variable was not included in the final RFI model.

Blood sampling.

Because the ultimate objective of our research program was to explore RFI biomarkers to assist genetic selection programs, blood was not sampled at the end of the RFI test to be able in field conditions to identify and select future sires long before slaughters. Therefore, blood samples were obtained from each animal one month before the end of each RFI test, which corresponds to an average age of 17.2 months (± 0.51), blood samples were obtained from each animal to explore RFI biomarkers. Blood samples (9 mL) were taken by coccygeal venipuncture using heparinized tubes (BD Vacutainer, Plymouth, UK) before the meal distribution in the morning. Samples were immediately centrifuged (2500 × g, 15 min, 4 • C), and the plasma was harvested and stored at -80 • C until analysis.

To increase the chance of finding RFI biomarkers, analyses were conducted only from 48 extreme animals in terms of RFI values (12 Low-RFI and 12 High-RFI per diet) as usually performed in studies aiming at exploring biomarkers [START_REF] Karisa | Plasma metabolites associated with residual feed intake and other productivity performance traits in beef cattle[END_REF][START_REF] Artegoitia | Rumen fluid metabolomics analysis associated with feed efficiency on crossbred steers[END_REF] . Moreover, to minimize the strong cohort effect on plasma metabolome [START_REF] Meale | Exploration of biological markers of feed efficiency in young bulls[END_REF] , the 48 extreme RFI animals were selected from 3 out 7 cohorts while maximizing the RFI differences across RFI groups. Our selection was balanced for the effects of diet, cohort and RFI group and so included 4 animals per diet × cohort × RFI group condition. As previously reported [START_REF] Cantalapiedra-Hijar | Natural 15N abundance in specific amino acids indicates associations between transamination rates and residual feed intake in beef cattle[END_REF] , the RFI differences between Low and High-RFI animals averaged 1.48 kg DMI (-0.71 vs. 0.78 kg DMI of RFI in corn diet for Low and High-RFI respectively, -0.78 versus 0.77 kg DMI of RFI in grass diet for Low and High-RFI , respectively).

Animal performances at the slaughterhouse. Animals from the same cohort were slaughtered in the same commercial slaughterhouse when the pen they belonged to approached an average body weight of 720 kg and corresponding to a target market carcass weight of around 430 kg. This corresponded to 22 animals slaughtered right at the end of the RFI test, mostly those fed the corn diet, and the remaining 26 slaughtered several days thereafter (50 ± 14 d) when they reached the targeted body weight. The number of animals within each cohort slaughtered at each time was completely balanced for the RFI groups. Thus, performances at the slaughterhouse were compared at similar body weight and no corrections were performed for differences in age or fattening length. Weights of hot carcass and visceral fat mass (kidney, pelvic and mesenteric fat together) were recorded for each animal.

Plasma concentrations of metabolites, hormones and vitamins. Blood plasma samples were subjected to spectrophotometrical quantification of glucose (glucose oxidase method), urea (glutamate dehydrogenase method), non-esterified fatty acids (NEFA, acyl-CoA synthase method) and BHB (D-β-hydroxbutyratedehydrogenase method). Determinations were carried out with commercial kits (Thermo Scientific References # 981379, #984325 and #981818 for glucose, BHB and urea, respectively and Sobioda Reference #W1W434-91795 for NEFA) using an autosampler spectrophotometer (Arena 20XT, Thermo Fisher Scientific, Cergy Pontoise, France). Intra-and inter-assay coefficients of variation were 1.4 and 3.1% for glucose, 5.9 and 8.5% for urea, 2.1 and 3.0% for NEFA, 4.5 and 5.5% for BHB. Plasma insulin (mean intra-assay coefficients of variation were 6.9% for 5.88 µUI/mL and 1.8% for 36.7 µUI/mL; Porcine Insulin RIA, MI-PI-12K, Merck KGaA, Darmstad, Germany) and IGF-1 (mean intra-and inter-assay coefficients of variation were 8.8% and 11.5%, respectively for 60.5 ng/mL; IGF-I RIA-CT, Ref IGF-R22, DiaSorin, Saluggia, Italy) were determined using radioimmunoassays. Blood vitamin B2 (flavin adenine dinucleotide (FAD) and riboflavin), and plasma vitamin B6 (pyridoxal-5'-phosphate (P5P) and pyridoxal), were quantified using the ClinRep Complete kit according to manufacturer's recommendations (Recipe, Munich, Germany) and extracts were analyzed by UPLC (Waters, Milford, MA, USA) as previously detailed by Meale et al. [START_REF] Meale | Exploration of biological markers of feed efficiency in young bulls[END_REF] . Intra-and inter-assay coefficients of variation determined using a reference bovine blood sample analysed repeatedly for 3 years were 3.3 and 17.5% for FAD, 3.3 and 13.4% for riboflavin, 1.0 and 6.1% for P5P, and 2.7 and 6.4% for pyridoxal.

A useful and easy-to-determine index of insulin sensitivity (RQUICKI index) proposed by Holtenius and Holtenius [START_REF] Holtenius | A model to estimate insulin sensitivity in dairy cows[END_REF] was estimated. In brief, it was calculated based on plasma concentrations of glucose, insulin and NEFA according to Eq. ( 1). Increased values of RQUICKI are indicative of increased insulin sensitivity.

Additionnaly, several ratios between vitamins concentrations reflecting the vitamin activation levels were also determined for vitamins B2 and B6 to state their role in feed efficiency as already explored for vitamin D vitamers and diabetes [START_REF] Ahmed | Vitamin D3 metabolite ratio as an indicator of vitamin D status and its association with diabetes complications[END_REF] .

Targeted metabolomic analysis and data acquisition. Frozen plasma samples were thawed overnight, centrifuged and the supernatant subjected to metabolomic profiling analysis at the core lab of Biocrates Life Science AG (Innsbruck, Austria). A targeted metabolomic approach was carried out using a commercially available kit (MxP Quant 500 kit, Biocrates Life Sciences, Innsbruck, Austria) for the quantification up to 630 metabolites belonging to 26 metabolites classes after derivatization by flow injection analysis-tandem mass spectrometry (FIA-MS/MS) in the case of lipids and hexoses, while small molecules were quantified by liquid chromatography-tandem mass spectrometry (LC-MS/MS). Both FIA-MS/MS and LC-MS/MS were performed using a 5500 QTRAP instrument (AB Sciex, Darmstadt, Germany) with an electrospray ionization (ESI) source. Data were quantified using a specific mass spectrometry software (Sciex Analyst) and further analysed using the Biocrates MetIDQTM. A complete list of analysed metabolites is available in Suppl. Table S1.

A cleaning data procedure was performed on raw data in order to exclude metabolites with missing values or concentration below the limit of detection (LOD). Metabolites were excluded when values were under the LOD in ≥ 20% of measured samples within each experimental group (RFI × Diet combination). In case of missing values among the retained metabolites, imputed values were added by replacing values below LOD by values between LOD and LOD/2 using a logspline imputation method [START_REF] Beaudet | Logspline density estimation for censored data[END_REF] . Finally, the imputed database ( µmol/L concentration data) was transformed by log 2 in order to avoid heteroscedasticity and any skewed distributions of data [START_REF] Di Guida | Non-targeted UHPLC-MS metabolomic data processing methods: A comparative investigation of normalisation, missing value imputation, transformation and scaling[END_REF] .

Calculations and statistical analysis. Intakes of organic matter, crude protein, neutral detergent fibre and starch were calculated on the basis of the observed DMI and analysed chemical composition of diets as reported in Cantalapiedra-Hijar et al. [START_REF] Cantalapiedra-Hijar | Natural 15N abundance in specific amino acids indicates associations between transamination rates and residual feed intake in beef cattle[END_REF] . Diets were characterized by the INRA feeding system [START_REF]L'alimentation des Ruminants[END_REF] from analysed chemical composition and observed animal feeding level. Net energy intake and metabolizable protein intake were estimated, as well as rumen fermentation parameters (rumen fermentable OM and NDF) and starch digestibility (starch digested in the small intestine) as they are needed to estimate the net portal appearance of total volatile fatty acids, acetate, propionate, butyrate and glucose according to equations proposed by Loncke et al. [START_REF] Loncke | Empirical prediction of net portal appearance of volatile fatty acids, glucose, and their secondary metabolites (β-hydroxybutyrate, lactate) from dietary characteristics in ruminants: A meta-analysis approach[END_REF] .

Prior to statistical analysis, all data -including cleaned metabolomic data set-were adjusted for the effect of the cohort by using a linear model with cohort as the only fixed effect. Adjustment consisted of adding up the average metabolite concentration from the whole population (i.e. intercept) to the individual residuals obtained in that model. Because the obtained residuals represent the variation of each observation from the average of its respective cohort, the between-cohort variation was in this way removed from raw values. Then, nutrient intakes, performance data and classical metabolic parameters were analysed by ANOVA considering diet, RFI group and their interaction as fixed effects. For performance, classical metabolites, hormones and vitamins, the analyses were performed using the R software (R development Core Team, 2015) and significance was set at P ≤ 0.05, and tendency was declared at 0.05 < FDR ≤ 0.10. For metabolomic data, the same univariate analyses were performed using the MetaboAnalyst software [START_REF] Chong | Using MetaboAnalyst 4.0 for comprehensive and integrative metabolomics data analysis[END_REF] (https:// www. metab oanal yst. ca/). The obtained raw-P values were adjusted by the Benjamini-Hochberg 65 false discovery rate (FDR) correction as previously suggested [START_REF] Vinaixa | A guideline to univariate statistical analysis for LC/MS-based untargeted metabolomics-derived data[END_REF] . Significance threshold was set at FDR ≤ 0.05, and tendency was declared at 0.05 < FDR ≤ 0.10. We aimed to compare how Low-and High-RFI metabolite profiles contrasted over the two diets. Therefore, when a significant RFI × Diet interaction was detected, pairwise comparisons performed by Tukey's honestly significance difference were only applied within each diet (sliced comparisons) as previously reported [START_REF] Chong | Using MetaboAnalyst 4.0 for comprehensive and integrative metabolomics data analysis[END_REF][START_REF] Acevedo-Acevedo | Metabolomics revealed the influence of breast cancer on lymphatic endothelial cell metabolism, metabolic crosstalk, and lymphangiogenic signaling in co-culture[END_REF] . Given the relatively small change in the plasma metabolite concentration expected across individuals fed the same diet but differing in RFI, no restrictive threshold fold-change cut-off was applied when interpreting FDR significant metabolites [START_REF] Foroutan | Serum metabolite biomarkers for predicting residual feed intake (RFI) of young angus bulls[END_REF][START_REF] Wang | Metabolomics analyses in high-low feed efficient dairy cows reveal novel biochemical mechanisms and predictive biomarkers[END_REF] .

Metabolomic data was additionally submitted to multivariate analyses. These included both clustering analysis by principal component analysis and classification models assessment by partial-least square discriminant analysis. The quality of prediction from PLS-DA models were evaluated through the Q 2 criteria. In addition, after univariate analysis, PCA analyses were performed a posteriori in order to explore the associations between RFI, and those classical and metabolomic variables with the greater significant differences across RFI groups within each diet. Finally, in order to gain deeper insights on mechanisms underlying RFI differences, metabolic pathways analysis, based on the Bos taurus KEGG database (https:// www. genome. jp/), were performed combining the Global test and a topology analysis based on betweenness centrality [START_REF] Chong | Using MetaboAnalyst 4.0 for comprehensive and integrative metabolomics data analysis[END_REF] . In brief, Global test determines whether a set of metabolites that participate in a specific metabolic pathway present or not a global trend when comparing two treatments. Finally, it gives a global P value for the given metabolic pathway after the evaluation of the effect of treatment on each metabolite [START_REF] Goeman | A global test for groups of genes: Testing association with a clinical outcome[END_REF] . In addition, betweenness centrality estimates how frequently a node (a given compound) is on the shortest pathways between every pair of compounds for detecting bottlenecks in a network. In other words, it gives an idea of how well interconnected (short biochemical connections) are the compounds measured for each given metabolic pathway [START_REF] Chong | Using MetaboAnalyst 4.0 for comprehensive and integrative metabolomics data analysis[END_REF] . Significant enrichment of metabolic pathway was set at FDR ≤ 0.05, and only metabolic pathways with at least two metabolites quantified in the current data set were further considered for discussion purposes.

Figure 1 .

 1 Figure 1. Score plots for principal components analysis (PCA) comparing Low-RFI versus High-RFI animals fed corn-or grass-silage diets based on all quantified metabolites

Figure 2 .Figure 3 .

 23 Figure 2. Loading plots of principal component analysis conducted a posteriori according to variables highly involved in shared and diet-dependent metabolic pathways underlying RFI variation in corn-(a) and grasssilage (b) diets. TG_n denote triglycerides with increasing saturations (0, 1, or more for TG_0, TG_1 and TG_2, respectively); t-4-OH-Prol: trans-4-hydroxyproline; 3MH/creatinine: 3-methylhistidine to creatinine ratio; 1-MH: 1-methylhistidine, 3-MH: 3-,methylhistidine; SDMA: symmetric dimethyl arginine; ADMA: asymmetric dimethyl arginine; PCaa: total diacyl-phosphatidylcholine, PCae: total acyl-ethyl-phosphatidylcholines

Figure 4 .

 4 Figure 4. Effectively detected and quantified metabolites involved in metabolic pathways differing between RFI groups fed the corn (a) or the grass (b) diet. Metabolic pathways references are stated next to the correspondent panel. Significant differences (Tukey's test < 0.05) between Low versus High-RFI within the given diet are depicted with *, whenever RFI × Diet interaction significant effect (FDR < 0.05 in the ANOVA analysis). Cells' color code depict metabolites concentrations for Low-versus High-RFI animals (red: increased, blue: decreased)

  (mg/dL)] + log[NEFA (mmol/L)] + log[insulin (µU/mL)]

Table 1 .

 1 Nutrients intake and absorption in high versus low residual feed intake (RFI) young bulls fed corn-or grass-silage diets. a,b: Means are compared only within each diet. Different letters means significant differences according to Tukey test (P < 0.05). 1: Measures based on DMI and diet composition, OM Organic matter intake, CP Crude protein, MP Metabolizable protein, NDF Neutral detergent fibre, NE Net energy. 2: Estimated according to Loncke et al.[START_REF] Loncke | Empirical prediction of net portal appearance of volatile fatty acids, glucose, and their secondary metabolites (β-hydroxybutyrate, lactate) from dietary characteristics in ruminants: A meta-analysis approach[END_REF] .

		Corn		Grass			P-value
									RFI
		High-RFI Low-RFI High-RFI Low-RFI	SEM	RFI	Diet	× Diet
	a. Measured nutrient intakes 1
	OM (kg/day)	9.98	8.55	8.77	7.96	0.330	< 0.01 < 0.01	0.20
	CP (kg/day)	1.53	1.30	1.34	1.21	0.051	< 0.01 < 0.01	0.20
	NDF (kg/day)	3.49	2.99	4.67	4.24	0.142	< 0.01 < 0.01	0.72
	Starch (kg/day)	3.14 a	2.69 b	0.39 a	0.35 a	0.081	< 0.01 < 0.01 < 0.01
	b. Estimated nutrient intake and absorption
	NE (Mcal/day)	17.0	14.6	14.9	13.5	0.57	< 0.01 < 0.01	0.20
	MP (kg/day)	0.90	0.77	0.77	0.70	0.032	< 0.01 < 0.01	0.20
	Net portal appearance (mmol/h kg BW) 2
	Total Volatil fatty acids	1371	1175	1376	1247	49.0	< 0.01	0.27	0.34
	Acetate	928	795	993	900	34.2	< 0.01 < 0.01	0.42
	Propionate	325	278	301	273	11.2	< 0.01	0.08	0.26
	Butyrate	73	62	55	50	2.3	< 0.01 < 0.01	0.13
	Glucose	-0.046 b	-0.054 a	-0.097 a	-0.098 a	0.0005 < 0.01 < 0.01 < 0.01

Metabolomic profiling and multivariate analysis. Only 271 out of 630 metabolites assayed (Supplementary Table

Table 2 . Animal performances and plasma biochemical parameters in High versus Low residual feed intake (RFI) young bulls fed corn-or grass-silage diets. a,b: Means are compared only within each diet. Different letters means significant differences according to Tukey test (P < 0.05). 1: Body weight at the begining and the end of the RFI test. 2: Kindey, pelvic and mesenteric fat. 3: NEFA Non-esterified fatty acid, BHBA β -hydroxybutyrate, IGF-1 Insulin growth factor-1; RQUICKI index. Corn Grass SEM P-value High-RFI Low-RFI High-RFI Low-RFI RFI Diet RFI × Diet a. Performance and carcass traits

 2

	Initial BW 1 (kg)	390	397	372	367	13.2	0.38	0.26 0.81
	Final BW 1 (kg)	730	725	650	698	19.1	0.26	0.01 0.19
	Final back fat depth (mm)
	12-13th rib	2.98	2.68	2.71	2.54	0.331 0.32	0.39 0.77
	Lumbar	3.59	3.03	2.93	2.90	0.330 0.21	0.10 0.26
	Gluteal	3.36	3.28	3.77	3.19	0.732 0.53	0.75 0.64
	Hot carcass weight (kg)	431	444	410	449	10.3	0.01	0.41 0.20
	Visceral fat 2 (kg)	7.13 a	5.28 b	6.79 a	7.89 a	0.661 0.57	0.09 0.03
	Visceral fat 2 (%)	0.96 a	0.73 b	1.00 a	1.10 a	0.083 0.34	0.01 0.07
	b. Metabolites and hormones
	Urea (g/L)	0.21	0.18	0.15	0.12	0.010 0.01 < 0.01 0.82
	NEFA (mol/L)	0.121	0.121	0.122	0.153	0.0182 0.40	0.36 0.37
	BHB (mol/L)	0.344	0.311	0.238	0.241	0.0201 0.45 < 0.01 0.36
	Glucose (g/L)	0.77	0.79	0.66	0.71	0.031 0.29 < 0.01 0.51
	Insulin ( µIU/ml)	30.7 a	20.7 b	13.4 a	15.7 a	3.03	0.19 < 0.01 0.04
	IGF-1 (ng/mL)	456	430	398	444	25.4	0.70	0.38 0.16
	RQUICKI	0.28 b	0.33 a	0.36 a	0.31 a	0.021 0.79	0.16 0.02
	c. Vitamins
	FAD ( µmol/L)	0.22	0.22	0.22	0.22	0.009 0.64	0.99 0.58
	Ribo ( µmol/L)	0.02	0.02	0.02	0.02	0.002 0.84	0.40 0.51
	B2 total ( µmol/L)	0.24	0.23	0.24	0.24	0.010 0.70	0.87 0.69
	FAD:B2 total	0.94	0.93	0.93	0.93	0.042 0.73	0.31 0.37
	Ribo:B2 total	0.06	0.07	0.07	0.07	0.013 0.73	0.31 0.37
	FAD:Ribo	15.0	14.0	14.0	14.2	1.21	0.68	0.60 0.50
	P5P ( µmol/L)	0.38	0.39	0.38	0.33	0.030 0.49	0.13 0.14
	PA ( µmol/L)	0.028	0.029	0.025	0.027	0.0021 0.29	0.02 0.70
	B6 total ( µmol/L)	0.41	0.42	0.40	0.36	0.030 0.55	0.11 0.16
	P5P:B6 total	0.93	0.93	0.94	0.93	0.004 0.28	0.79 0.13
	PA:B6 total	0.07	0.07	0.06	0.07	0.004 0.28	0.79 0.13
	P5P:PA	13.7	14.4	15.8	13.2	0.12	0.33	0.63 0.09

Corn 1 Grass 1 SEM RFI Diet RFI × Diet High-RFI Low-RFI High-RFI Low-RFI

	Leu 2	133 a	106 b	104 a	116 a	15.4	0.36	0.07 0.01
	Val	234 a	205 b	192 b	217 a	21.6	0.90	0.04 0.01
	Ile	128 a	110 b	110 a	124 a	14.1	0.75	0.65 0.04
	PC ae C38:2	2.83 a	2.49 a	3.18 b	3.89 a	0.441	0.72 < 0.01 0.03
	Creatinine	183	218	152	185	22.9	< 0.01 < 0.01 0.79
	Trans-4-Hydroxyproline	37.1	44.0	38.6	42.7	5.42	0.05	1.00 0.53
	β-Alanine	2.03	2.60	1.91	2.38	0.501	0.05	0.32 0.80
	p-Cresolsulphate	60.4	68.1	48.2	56.2	8.23	0.05 < 0.01 0.78
	PC ae C30:1	0.83	0.60	1.27	1.01	0.256	0.04 < 0.01 0.54
	TG17:0_34:1	0.55	0.51	0.37	0.53	0.130	0.04	0.16 0.14
	TG16:0_33:1	0.50	0.49	0.43	0.60	0.118	0.05	0.92 0.13
	TG20:3_34:0	0.24	0.26	0.23	0.25	0.110	0.05 < 0.01 0.78

Pathway TC 1 Hits 2 Corn Grass 1 FDR 3 Impact 4 FDR Impact

	β-Alanine metabolism	21	5	-	-	0.01	0.5
	Valine, leucine and isoleucine degradation	40	3	0.01	0.00	-	-
	Valine, leucine and isoleucine biosynthesis	8	4	0.03	0.00	-	-
	Pyrimidine metabolism	38	2	0.03	0.00	0.03	0.00
	Purine metabolism	66	1	-	-	0.01	0.00
	Propanoate metabolism	23	1	0.03	0.00	-	-
	Porphyrin and chlorophyll metabolism	30	2	-	-	0.01	0.00
	Pantothenate and CoA biosynthesis	19	4	0.03	0.02	0.01	0.02
	Nitrogen metabolism	6	2	-	-	0.01	0.00
	Lysine degradation	25	2	0.05	0.14	-	-
	Histidine metabolism	16	6	-	-	0.01	0.36
	Glyoxylate and dicarboxylate metabolism	32	5	-	-	0.01	0.17
	Glycerophospholipid metabolism	36	1	-	-	0.01	0.03
	Glutathione metabolism	28	4	-	-	0.01	0.11
	D-Glutamine and D-glutamate metabolism	5	2	-	-	0.01	1.00
	Butanoate metabolism	15	1	-	-	0.01	0.00
	Biotin metabolism	10	1	0.05	0.00	-	-
	Arginine biosynthesis	14	6	-	-	0.01	0.48
	Arginine and proline metabolism	38	5	-	-	0.03	0.39
	Aminoacyl-tRNA biosynthesis	48	20	-	-	0.03	0.17
	Alanine, aspartate and glutamate metabo-lism	28	5	-	-	0.01	0.53

Scientific Reports | (2021) 11:24346 | https://doi.org/10.1038/s41598-021-03678-x

Scientific Reports | (2021) 11:24346 | https://doi.org/10.1038/s41598-021-03678-x

Acknowledgements

Authors wish to thank APISGENE for their financial support of this project, which forms part of the larger national program BEEFALIM 2020. We also thank Marine Gauthier and Martine Tourret and Arnaud Delavaud (INRAE-UMRH) for help with the laboratory analyses and Clément Fossaert (IDELE) and the "Chambres d' Agriculture" from Pays de Loire, Bretagne and Saône-et-Loire (France) for their great support in conducting the feed efficiency test, blood samplings and measurements. Finally, we also thank Eduardo Rico for his great advises on lipid metabolism data presentation and interpretation.

Author contributions

Competing interests

The authors declare no competing interests.


The 2008 map of consolidated rural areas in the Brazilian Legal Amazon state of Mato Grosso: accuracy assessment and implications for the environmental regularization of rural properties

Keywords: Brazilian Amazon, Rural Environmental Registry, Environmental governance, remote sensing

Accurate land use mapping in the Amazon is crucial to support the implementation of environmental policies. The new Brazilian Forest Code published in 2012 defines a "consolidated rural area" (CRA) as an area within a rural property and occupied by humans prior to July 22, 2008. Environmental infractions that occurred before that date are granted amnesty and landowners are exempted from the obligation to restore the degraded areas. To assess the compliance of rural property owners with the environmental legislation, the Brazilian government created the Rural Environmental Registry (CAR), for mandatory electronic self-declaration of all rural private properties. The successful implementation of the CAR relies on the accurate mapping of CRA before the reference date of July 2008. The objective of the present paper is to assess the accuracy of the 2008 map of consolidated rural areas (CRA2008) that is the result of a fruitful collaboration between a non-governmental organization (NGO), a state administration and academic institutions. The CRA2008 map was produced by photo-interpretation of high resolution remote sensing images and validated after comparison with 90 aerial photographs. The validation metrics (overall accuracy = 0.926, Kappa index = 0.851 and F-score = 0.924) confirmed the reliability of the CRA2008 map. Most errors identified were due to imprecision at the edges between CRA and native vegetation because of different spatial resolutions of the CRA2008 and the aerial photographs. Consequently, the results were slightly lower (although still high; Kappa = 0.835) in the Amazon biome where landscapes are more fragmented. Finally, in a context of severe criticisms expressed by national government leaders towards Brazilian research institutions and NGOs, our results clearly demonstrate the benefits of combining institutional efforts and of working in synergy to produce relevant

Introduction

Despite the rapid and significant reduction in deforestation in the Brazilian Amazon in the late 2000s (from 27,800 km 2 in 2004 to 4,600 km 2 in 2012), deforestation remains a hot topic as proven by the official statistics provided by the Brazilian National Institute for Space Research (INPE) pointing to a regular increase since 2012 (7,900 km 2 in 2018) [START_REF]Taxas de desmatamento na Amazônia[END_REF]. In this context, the federal government created the Environmental Regularization Program (PRA) to assess the compliance of private rural property owners with environmental legislation [START_REF] Roitman | Rural Environmental Registry: An innovative model for land-use and environmental policies[END_REF] and hence better control of land use dynamics in the Amazon. But to be efficient, the PRA depends on accurate mapping of open areas, named "consolidated rural areas" (CRA), in July 2008, the date of reference because the PRA has suspended all sanctions for environmental infractions that occurred before that date.

In the case of the Brazilian Amazon state of Mato Grosso, which is regularly in the spotlight because of its high deforestation rates (1,685 km 2 in 2019, i.e. 17.26% of total deforestation in the Amazon), the non-governmental organization (NGO) Instituto Centro de Vida (ICV), in partnership with the Mato Grosso Secretary of State for Environment (SEMA-MT), was asked to map consolidated rural areas at a scale of 1: 25,000 based on visual interpretation of very high resolution remote sensing images. However, even though it has already been publicly released and is currently being used by the SEMA-MT to check private property owners are in compliance with the environmental legislation, the map has not yet been validated.

In line with the skepticism recently expressed by the Brazilian president regarding the legitimacy of official deforestation statistics for the Amazon [START_REF] Escobar | Brazilian president attacks deforestation data[END_REF] (exacerbated by the international media exposure of the multiplication of forest fires in the Brazilian Amazon in August 2019 and the resulting diplomatic incidents), the lack of validation statistics leaves room for criticism by stakeholders with diverging viewpoints and interests in territorial planning in the Amazon.

As a consequence, there was an urgent need to assess the quality of the 2008 map of consolidated rural areas in Mato Grosso to limit the risks of misinterpretations, erroneous conclusions and controversial decisions while guaranteeing its use by SEMA-MT analysts to efficiently support the implementation of environmental public policies.

The objective of this paper is thus to present the 2008 official map of consolidated rural areas in Mato Grosso and to assess its quality. For this purpose, we brought together a complementary team including the map producers (the NGO Instituto Centro de Vida) and users (SEMA-MT) accompanied by academic institutions (Federal University of Mato Grosso and University of Rennes, in France) to implement the validation.

Study area, Definitions and Data

Study area

The state of Mato Grosso is located in the southern part of the Brazilian Amazon (Figure 1). It plays an important role in the Brazilian economy thanks to a very efficient agricultural sector specialized in the production of beef and important commodities including soybean, maize and cotton. The agribusiness began to emerge in Mato Grosso in the 1970s when the military government supported the colonization of the Amazon by private capital [START_REF] Arvor | Combining socioeconomic development with environmental governance in the Brazilian Amazon: the Mato Grosso agricultural frontier at a tipping point[END_REF]. However, the expansion of agricultural land resulted in the direct or indirect clearing of large areas of Brazilian savannas (Cerrado) and Amazon forests [START_REF] Gollnow | Propertylevel direct and indirect deforestation for soybean production in the Amazon region of Mato Grosso, Brazil[END_REF] and it became urgent to promote a new development model to decouple deforestation from agricultural production [START_REF] Macedo | Decoupling of deforestation and soy production in the southern Amazon during the late 2000s[END_REF].

In this context, Mato Grosso is also at the forefront in the implementation of an environmental governance model to control deforestation and improve land use sustainability [START_REF] Galford | Prospects for land-use sustainability on the agricultural frontier of the brazilian amazon[END_REF][START_REF] Tritsch | Transition in environmental governance in the Brazilian Amazon: emergence of a new pattern of socio-economic development and deforestation[END_REF][START_REF] Arvor | Land use sustainability on the south-eastern amazon agricultural frontier: Recent progress and the challenges ahead[END_REF][START_REF] Milhorance | Emerging hybrid governance to foster lowemission rural development in the Amazon frontier[END_REF]. In the 2000s, major efforts by public administrations through the implementation of efficient command-and-control policies [START_REF] Nepstad | Slowing Amazon deforestation through public policy and interventions in beef and soy supply chains[END_REF] and by the private sector through innovative market-oriented policies (i.e. Soy and Beef Moratorium [START_REF] Gibbs | Brazils Soy Moratorium[END_REF][START_REF] Silva | Soy Moratorium in Mato Grosso: Deforestation undermines the agreement[END_REF][START_REF] Kastens | Soy moratorium impacts on soybean and deforestation dynamics in Mato Grosso, brazil[END_REF]) led to a reduction of deforestation rates and a better compliance with the Brazilian Forest Code. This code is a major policy tool to control deforestation in Brazil that defines sub-areas in private rural properties including (1) the Permanently Protected Areas (PPAs), along water bodies and on sloping ground, and (2) Legal Reserve Areas (LRAs), corresponding to areas in which native vegetation is to be preserved, i.e., 80% of the entire property area in the Legal Amazon, 35% in Cerrado areas located in the Legal Amazon and 20% elsewhere [START_REF] Arvor | Combining socioeconomic development with environmental governance in the Brazilian Amazon: the Mato Grosso agricultural frontier at a tipping point[END_REF].

Although the Forest Code has long been recognized as indispensable to control deforestation, the way it was implemented has been severely criticized, which then led to its revision [START_REF] Stickler | Defending public interests in private lands: compliance, costs and potential environmental consequences of the brazilian forest code in Mato Grosso[END_REF][START_REF] Tollefson | Brazil set to cut forest protection[END_REF]. After years of debate, in 2012, a new Forest Code was implemented under the Federal Native Vegetation Protection Law N o 12.651 [START_REF] Taniwaki | The Native Vegetation Protection Law of Brazil and the challenge for first-order stream conservation[END_REF]. This new environmental legislation has long been the subject of controversy among experts, civil society and scientists for supporting the economic interests of the agribusiness sector that defends the full use of land to the detriment of the protection of the environmental function of rural properties, especially the preservation of native vegetation [START_REF] Soares-Filho | Cracking brazils forest code[END_REF][START_REF] Felippe | O conceito de área rural consolidada no código florestal de 2012: principais controvérsias[END_REF]. Indeed, the new Forest Code defines the concept of "consolidated rural area" as an area within a rural property and characterized by human occupation, i.e. buildings and/or agricultural activities, prior to July 22, 2008. This date corresponds to the publication of decree N o 6.514 that defines environmental infractions and corresponding administrative sanctions [START_REF] Felippe | O conceito de área rural consolidada no código florestal de 2012: principais controvérsias[END_REF]. The Environmental Regularization Program (PRA) then suspended all administrative fines for environmental infractions committed before that date, thus granting amnesty to those responsible for illegal deforestation and exempting landowners from the obligation to restore degraded areas, disregarding Decree N o 3.179 published in 1999 that defined sanctions for exploitation in Legal Reserve Areas. All illegal clearing and environmental damage that took place after July 22, 2008 have to be restored by the landowners according to decree N o 1.031 published on June 2, 2017.

On the other hand, the new Forest Code also gave birth to the environmental regularization concept and led to the creation of the Rural Environmental Registry (CAR). The CAR is a mandatory registry for electronic self-declaration of all rural private properties in the Brazilian territory aimed at enabling assessment of their owners' compliance with the environmental legislation [START_REF] Roitman | Rural Environmental Registry: An innovative model for land-use and environmental policies[END_REF]. Landowners identified as not being in full compliance with the law shall be subject to sanctions and prevented from accessing services such as public agricultural loans [START_REF] Roitman | Rural Environmental Registry: An innovative model for land-use and environmental policies[END_REF].

In order to ensure the efficient implementation of the CAR, one of the major challenges consisted in mapping consolidated rural areas in Mato Grosso on the reference date (July 2008), essential information for the creation and analysis of the registries. But fine resolution mapping of these areas is difficult due to the huge siez of Mato Grosso state (906,000 km 2 ), which includes three different biomes (Rainforest, Cerrado and Pantanal) and because the "consolidated rural areas" include many sub-classes (e.g. buildings, pastures, cropland) that make automatic remote sensing-based classification difficult.

Definition of consolidated rural areas

The concept of consolidated rural area is defined in federal law N o 12.651 of May 25, 2012 as the area of a rural property under human occupation before July 22, 2008. In this definition of consolidated rural areas, the term rural property means urban areas are not included in the analysis. In addition, the term "human occupation" refers both to buildings and agricultural activities, including fallow land. Conversely, areas that were converted before July 22, 2008 and then abandoned or are undergoing of natural regeneration are not included in what is defined as consolidated rural areas. It is worth noting that distinguishing between a fallow and an abandoned area is based on duration, i.e. a fallow area remains a fallow for five years at most. Finally, degraded areas (e.g. burned areas), explored areas (e.g. timber logging) and natural pastures (especially present in the Pantanal and Cerrado biomes) are not included in what is defined as consolidated rural areas.

Data

Two datasets were used in this study: [START_REF]Taxas de desmatamento na Amazônia[END_REF] the 2008 map of consolidated rural areas in Mato Grosso, hereafter CRA2008, and (2) 90 geometrically corrected aerial photographs with corresponding maps of consolidated rural areas for validation purposes, hereafter REF2008.

The CRA2008 map was produced between 2016 and 2017 by ICV and then reviewed by analysts from the department of Geoinformation and Environmental Monitoring Coordination (CGMA) at SEMA-MT. Mapping consolidated rural areas was a complex task that required taking into consideration both the land use observed circa 2008 and changes that occurred in the following years to confirm its continued use. For this reason, and with the aim of achieving the high degree of accuracy required for maps designed for the implementation of public policies, mapping was entirely based on the visual interpretation and digitization of satellite imagery, particularly SPOT-5 images at 2.5 m spatial resolution acquired between 2007 and 2009. For areas covered by images acquired in 2009, Landsat-5 images (at 30 m spatial resolution) were used to confirm that cleared areas had already been in use in 2008. In addition, Landsat-8 and RapidEye (5 m spatial resolution) were used to check for any changes in land use in areas of interest, thereby confirming or rejecting the consolidated rural area status.

The methodological framework to map consolidated rural areas followed a technical note published by SEMA-MT on the interpretation of satellite images [START_REF] Sema-Mt | Satellite image interpretation methodology for the definition of consolidated areas in Mato Grosso state[END_REF]. This technical note presents the main conceptual, legal and methodological characteristics that define a consolidated rural area, exemplifying how these areas can be observed in remote sensing images over the Amazon, Cerrado and Pantanal biomes. It thus seeks to standardize the interpretation of the class and its identification in satellite imagery in order to guide SEMA-MT analysts and environmental project developers [START_REF] Sema-Mt | Satellite image interpretation methodology for the definition of consolidated areas in Mato Grosso state[END_REF]. As a result, a 1:25,000 continuous basis was obtained for the entire state of Mato Grosso where consolidated rural areas were estimated to account for 29.2 million ha in 2008, i.e. 32% of total area of the state (Figure 1). The data can be accessed at the following address cited in [START_REF] Icv | Map of consolidated rural areas -CRA[END_REF].

The REF2008 maps were produced using the same visual approach but using 90 aerial photographs at 60 cm spatial resolution. These photographs are archived on the SEMA-MT database and were acquired between July 11 and July 15, 2008 along a 3,686 km flight line originally designed to capture information about deforestation and forest degradation (Figure 1). A Nikon D200 camera with a 10.2 Mp resolution aboard an airplane was used. In this study, these images were considered as random samples to draw the REF2008 data for the purpose of validation. A total of 90 photographs were selected across the three biomes that comprise Mato Grosso, i.e. 57 photos taken in the Amazon, 26 in the Cerrado and 7 in the Pantanal biome (Figure 1). A total area of 294.09 km 2 (3.26 km 2 in average per aerial photograph) was then used for validation, including 142.65 km 2 mapped as consolidated rural area (48.5% of the validation area).

Method used for the validation of the 2008 map of consolidated rural areas

The CRA2008 map is intended to provide statistically accurate estimates. Given the size of Mato Grosso State (906,000 km2), identifying the potential errors to assess the spatial variability of the map's quality appears to be indispensable.

Statistical analysis

The validation procedure was based on regular sampling of 100 points in each aerial photograph (i.e. one point per 175 m approximately). The following information was then retrieved automatically for each sample point: 1) the classification labels in the CRA2008 and the REF2008 maps and 2) the type of biome. Based on this information, we computed confusion matrices and derived the statistical metrics commonly used in remote sensing applications: overall accuracy (OA), user's accuracy (UA), producer's accuracy (PA), the F-score and the Kappa index (K) [START_REF] Congalton | A review of assessing the accuracy of classifications of remotely sensed data[END_REF]. These indices were calculated for each aerial photograph and then aggregated at biome level (e.g. Amazon, Cerrado and Pantanal) and for Mato Grosso as a whole.

Error analysis

We performed a qualitative analysis of erroneously classified polygons to qualify the sources of errors. In particular, we wanted to distinguish real classification errors by the photo-interpreters from precision errors resulting from data limitations mainly related to different acquisition dates and/or the different spatial resolutions between CRA2008 and REF2008 maps. In the following quantitative analysis, we then computed the distances between the wrongly classified points and the closest edge separating native vegetation from a consolidated rural area in the REF2008 data set. In this way, we analyzed the proportion of pixels that were wrongly classified due to uncertainties in the manual delineation of polygons in CRA2008 and REF2008 by different operators using different images (2.5 m to 30 m for CRA2008 and 60 cm spatial resolution for REF2008). In addition, we extracted polygons of wrongly classified areas, i.e. the areas labelled as consolidated rural areas in REF2008 but not in CRA2008, and vice versa. We then computed their size and estimated the proportion of small polygons (here considered as less than 1 hectare as suggested by [23] and [START_REF] Sannier | Suitability of Global Forest Change data to report forest cover estimates at national level in Gabon[END_REF] for forest patches), considering that a high proportion of small polygons points to a high proportion of precision errors due to different spatial resolutions of CRA2008 and REF2008.

Landscape analysis

In the case of a large proportion of errors located at the edges between areas of consolidated rural areas and native vegetation, i.e. precision errors, one would expect the landscape structure to affect the validation metrics. Indeed, fragmented landscapes have longer edges and are thus more likely to be affected by precision errors. We thus calculated the accumulated edge length of polygons classified as consolidated rural areas in REF2008 (edges corresponding to the border of the photographs were discarded) for each of the 90 aerial photographs in order to compute the correlation with the corresponding Kappa index.

Results

Statistical validation of the CRA2008 map

Figure 2 shows some examples of aerial photographs and the corresponding mapping of consolidated rural areas as classified in the CRA2008 and REF2008 maps. They illustrate the overal satisfactory match between the CRA2008 map and the REF2008 dataset. This preliminary visual impression was confirmed statistically by the validation metrics (Table 1). The overall accuracy (OA = 0.926), Kappa index (K = 0.851) and F-score (F -score = 0.924) for entire Mato Grosso were good and validate the high accuracy of the CRA2008 map. This accuracy was well balanced across biomes, with slightly higher values (K = 0.893) in the Pantanal and lower values (K = 0.835) in the Amazon. The F-scores were also very high (> 0.90) in all cases but the fact that producer accuracies were consistently higher than user accuracies indicated a slight tendency toward commission errors, i.e. overestimation of CRA areas. This is visually confirmed by the examples in Figure 2 where blue areas predominate over red areas. At the level of the aerial photographs, the median values across the 90 images are high for all validation metrics. Nonetheless, it is worth noting that four images have very low indices (Kappa < 0.5) and thus deserve to be addressed in detail. These images are shown in Figure 3 to illustrate the different types of errors. In image ID = 8447, there is no overlap between the consolidated rural areas in CRA2008 and REF2008 so the Kappa is null. The large red area refers to a vegetated area in which the presence of a house indicates human activities, thus justifying its classification as consolidated rural area in REF2008. However, this open area was not visible in the corresponding 2.5 m SPOT image used to produce the CRA2008 map and was consequently omitted. In image ID = 8301, the error is due to the fact that the corresponding SPOT image was acquired in August 2007. At that date, the field was burned meaning the area had been cleared in 2007. However, it was not classified as consolidated rural area because it did not fit the definition of CRA (see section 2.2). In images ID = 7242 and ID = 7909, the CRA2008 map overestimated the consolidated rural areas by identifying large polygons (in blue) that were not visible in the reference aerial photographs. In the first case (ID = 7242), the large blue polygon corresponds to natural field areas in the Cerrado biome that were confused with consolidated rural areas in the 2.5 m SPOT image. In the second case (ID = 7909), the blue area refers to an area under regeneration in July 2007 that was misinterpreted as consolidated rural area by the photo-interpreter.

UA

Error analysis

Although some real classification errors exist as illustrated in Figure 3, Figure 2 shows that most discrepancies between the two maps are actually located close to the edges between consolidated rural areas and native vegetation. Considering that the spatial resolution of a remote sensing image should be 1/2 to 3/4 the size of the smaller objects of interest in the scene [START_REF] Marceau | Remote sensing and the measurement of geographical entities in a forested environment. 1. the scale and spatial aggregation problem[END_REF], we can consider that wrongly classified points located less than 10 m from an edge are due to inaccuracy resulting from the fact that both CRA2008 and REF2008 maps were produced by visual interpretation but the images used had different spatial resolutions. Figure 4 presents the proportion of wrongly classified points depending on distance from the closest edge. The results show that 25% of erroneously classified points are located at less than 10 m from an edge (58% of wrongly classified points are located less than 50 m from the closest edge and 90% less than 180 m from the closest edge). If these points (< 10m) were considered as correctly classified, then the validation metrics would increase significantly, OA would increase from 0.926 to 0.945 and Kappa from 0.851 to 0.890. In addition, our analysis of wrongly classified polygons indicates that 95.46% of omitted polygons (in red areas in Figures 2 and3) are less than 1 ha in size. Similarly, 90.03% of overestimated polygons (blue areas) are less than 1 ha; it should be recalled that 1 ha is considered to be the minimum mapping unit for forested areas [23].

Landscape analysis

In order to check if the landscape structure explains the spatial variability of accuracy metrics, we plotted the edge length measured from REF2008 in each aerial photograph against the corresponding Kappa index (Figure 5). We removed the four images with very low Kappa values identified in Figure 3 because they are specific types of errors. Our results indicate a significant (p -value = 1.659e -06) relationship of R 2 = 0.23, evidence that the CRA2008 map is less reliable for fragmented landscapes. Considering that the mean edge length measured in the aerial photographs of Pantanal, Cerrado and Amazon biomes were 6,966 m, 9,641 m and 10,304 m, respectively, we conclude that the more fragmented landscapes observed in aerial photographs in the Amazon biome explain why the accuracy metrics are slightly lower (although still high) in this biome as shown in Table 1.

Discussion

Accurate mapping of consolidated rural areas in 2008 is very important to ensure the correct application of the CAR by state administrations of Mato Grosso, including SEMA-MT. The use of 2.5 m spatial resolution SPOT-5 images proved to be appropriate to achieve high levels of accuracy. Although image acquisition dates may differ from the reference date (July 2008) because it was technically impossible to obtain completely cloud-free coverage of Mato Grosso in this specific period, the procedure made it possible to confirm the status of consolidated rural areas using complementary Landsat-5 images acquired around July 2008.

The methodological approach based on visual interpretation was very effective given the accuracy revealed by the validation, and we consider it was the best way to proceed in order to produce a single map of a unique but complex land use class. In addition, visual interpretation is the method used by the technicians who design the environmental regularization projects in which SEMA-MT analysts are using the CRA2008 map as a reference. However, in the future, we may consider testing classifiers, e.g. support vector machine [START_REF] Mountrakis | Support vector machines in remote sensing: A review[END_REF], random forest [START_REF] Belgiu | Random forest in remote sensing: A review of applications and future directions[END_REF] or deep learning [START_REF] Zhu | Deep learning in remote sensing: a review[END_REF] tools, perhaps associated with Geographic Object-Based Image Analysis (GEOBIA) [START_REF] Blaschke | Object based image analysis for remote sensing[END_REF][START_REF] Blaschke | Geographic object-based image analysis -towards a new paradigm[END_REF].

Yet, the major concern to date regards the concrete use of the CRA2008 map for the implementation of present and future land use policies in the Southern Amazon. In this regard, it is worth noting that the CRA2008 map of consolidated rural areas is already being used by SEMA-MT technicians as a reference to implement the (1) Rural Environmental Registry (CAR) and (2) to issue Provisional Authorizations for the Rural Activity Operation (APF) [START_REF] Sema-Mt | SEMA emite automaticamente autorização provisória para áreas de uso consolidado[END_REF]. Regarding the CAR, between August 23, 2018 and September 2019, 5,502 rural properties were analyzed, representing 9.3% of all registry entries uploaded by landowners and still waiting to be analyzed. Regarding APF, since October 2019, authorizations have started to be issued automatically based on the CRA2008 map. This authorization was introduced to allow rural producers to continue their agricultural activities (crop or livestock production) normally during the period SEMA-MT needs to adjust its licensing procedure to the changes in the new Brazilian Forest Code.

More importantly from our point-of-view, we wish to emphasize how the CRA2008 map contributes to the emergence of a new governance model in Mato Grosso. Indeed, while the search for compromise between competing development (e.g. incentives to private colonization) and environmental policies (e.g. Forest Code) has long been the rule and subject of disagreements between local stakeholders in Mato Grosso, significant efforts have been carried out during the last decades to promote integrated policies that would ensure both environmental preservation and socio-economic development [START_REF] Arvor | Combining socioeconomic development with environmental governance in the Brazilian Amazon: the Mato Grosso agricultural frontier at a tipping point[END_REF]. The emergence of such integrated policies has been driven either by bottom-up strategies (such as the Sorriso Vivo project whose objective was to turn local rural properties from Sorriso municipality compliant with the environmental legislation by promoting forest restoration [START_REF] Arvor | Développement, crises et adaptation des territoires du soja au mato grosso: lexemple de sorriso[END_REF]) or by top-down market-oriented strategies (such as the Soy moratorium implemented to prohibit farmers who grew soy on deforested areas to sell their production [START_REF] Nepstad | Slowing Amazon deforestation through public policy and interventions in beef and soy supply chains[END_REF]). At regional level, state administrations are also working in close collaboration with NGOs, private companies, farmers union, etc to promote a new governance model. As a major example, the Produce, Conserve, Include (PCI) program intends to increase agricultural efficiency while preserving native vegetation and restoring degraded areas [START_REF] Milhorance | Emerging hybrid governance to foster lowemission rural development in the Amazon frontier[END_REF]. Besides supporting all these initiatives, the CRA2008 map will also serve as a basis for the monitoring and quantification of regenerating and secondary forests (i.e. consolidated rural areas later observed as woody vegetation or forests), a fundamental information for the implementation of REDD (Reducing Emissions from Deforestation and forest Degradation) policies.

Yet, beyond the intentions, the concrete implementation of these laudable efforts is often called into question because the underlying motivations for changing the environmental governance model are still dubious, i.e. economic considerations or real environmental concerns [START_REF] De Mello-Théry | Climate change and public policies in the brazilian amazon state of mato grosso: Perceptions and challenges[END_REF]. In order to clarify that point, it is necessary to make accurate data publicly available and debatable in order to avoid future criticisms from stakeholders with diverging interests. In this regard, the production of the CRA2008 map is part of an innovative strategy since, differently from other deforestation maps and because it serves as a basis for regularization, landowners are entitled to challenge it, if they have doubts, or if errors are identified. A group of technicians with proven connections to agribusiness, indeed severely criticized the quality of the CRA2008 map. But, out of a total of 5,502 properties analyzed to date, SEMA-MT has only received 131 (2.3%) official requests to check for inconsistencies in the interpretation of consolidated rural areas. Most errors were related to precision errors that may be particularly significant in the case of small properties. Obviously, there were also few classification errors due to confusion between consolidated and natural areas (e.g. cerrado and wetland) or degraded areas (such as burned areas or regenerating areas) in which case, manual editing is provided to improve the product. By fostering debate about the CRA2008 map, Mato Grosso state administrations hope to ensure its better acceptance and thus ease the environmental regularization process in Mato Grosso.

Conclusion

Here we have presented and assessed the quality of the 2008 map of consolidated rural areas in Mato Grosso, a reference map widely used by state administrations to deal with the demands for environmental regularization of rural properties. It serves as a basis for the implementation of public policy instruments that aim to ensure that agricultural commodities produced in Mato Grosso and exported worldwide have not been produced in recently deforested areas. This is a very important information since the environmental regularization of rural properties is necessary to ensure Brazil achieves its objectives to reduce greenhouse gas emissions as it may also impact future international economic agreements (e.g. the European Union-Mercosur Free Trade Agreement [START_REF] Afionis | The environment as a strategic priority in the european union-brazil partnership: is the EU behaving as a normative power or soft imperialist?[END_REF][START_REF] Kehoe | Inclusion, transparency, and enforcement: How the EU-mercosur trade agreement fails the sustainability test[END_REF]). Nonetheless, the efficiency of such policies to contain deforestation remains to be proven. For example, Azevedo et al. [START_REF] Azevedo | Limits of brazil's forest code as a means to end illegal deforestation[END_REF] produced evidence that registered properties originally showed lower deforestation rates than non-registered ones, but that these differences tended to diminish over time.

At a time when the political debate about the development models to be promoted in the Amazon is the center of attention in the global media, the validation of spatial information data appears essential to limit controversial analyses and this paper is a contribution in that direction. Yet, its importance is also maximized by recent criticisms directed at non-governmental organizations and research institutions, since this study is the result of a fruitful collaboration between a Brazilian non-governmental organization, a state administration and academic institutions. It thus provides evidence for the interest of combining institutional efforts and working in synergy to produce relevant information with the final objective of ensuring the implementation of environmental public policies and consequently improving land use sustainability in the Amazon.

Figure 1 :

 1 Figure 1: Location of the Brazilian Amazon state of Mato Grosso. Red crosses refer to the 90 aerial photographs used for validation. Two of these photographs are shown on the left side with corresponding reference mapping of consolidated rural areas.

Figure 2 :

 2 Figure 2: Examples of aerial photographs and mapping of consolidated rural areas (CRA) as classified in CRA2008 and REF2008. The green areas are classified as CRA in both products, thus representing correctly classified areas. The blue areas correspond to commission errors where areas are classified as consolidated rural area in CRA2008 but not in REF2008. The red areas identify omission errors when areas are classified as CRA in REF2008 but not in CRA2008. The white crosses represent the sample points used for validation.

Figure 3 :

 3 Figure 3: Examples of four aerial photographs with important classification errors. The two top photographs show cases of significant underestimation of consolidated rural areas (large red areas). The two bottom photographs show cases of overestimation (large blue areas).

Figure 4 :

 4 Figure 4: Proportion of wrongly classified sample points depending on the distance to the closest edge separating consolidated rural areas and native vegetation.

Figure 5 :

 5 Figure 5: Relationship between edge length (in meters) of REF2008 and Kappa index measured on the corresponding aerial photographs.

Table 1 :

 1

		PA F-score OA Kappa	N
	Aerial photograph 0.920 0.969	0.924	0.945 0.873	90
	Pantanal	0.940 0.966	0.953	0.947 0.893	644
	Cerrado	0.916 0.946	0.930	0.937 0.873 2,746
	Amazon	0.891 0.946	0.918	0.917 0.835 5,570
	Mato Grosso	0.902 0.948	0.924	0.926 0.851 8,960

Validation statistics of the CRA class at different spatial levels. UA: User Accuracy, PA: Producer Accuracy and OA: Overall Accuracy. For aerial photographs, values correspond to median values obtained for the N = 90 photographs. For the other lines, the statistics were computed based on the number of validation points indicated in column N .

Acknowledgements

The authors wish to thank (1) the Climate and Land Use Alliance (CLUA), (2) the State Government of Mato Grosso, (3) the French National Centre for Space Studies (CNES) which funded the CASTAFIOR project (grant agreement 168591), (4) the European Union which funded the H2020-MSCA-RISE-2015 ODYSSEA project (Project Reference: 691053) and ( 5) the French National Centre for Scientific research which funded the SCOLTEL International Emerging Action.