Solveig Mendowski
email: s.mendowski@valorex.com

Pierre Noziere
email: pierre.noziere@inra.fr

Anne Ferlay
email: anne.ferlay@inrae.fr

Pauline Denis
email: pauline.denis@inrae.fr

Guillaume Chesneau
email: g.chesneau@valorex.com

Patrick Chapoutot
email: patrick.chapoutot@agroparistech.fr

Pierre Nozière
email: pierre.noziere@inrae.fr

Raw or technologically treated proteaginous seeds as alternatives to soybean meal for dairy cows: Comparative evaluation by meta-analysis of in situ and in vivo digestive parameters, nitrogen partition and dairy performance

Keywords: proteaginous seed, soybean meal, dairy cows, nitrogen

niveau recherche, publiés ou non, émanant des établissements d'enseignement et de recherche français ou étrangers, des laboratoires publics ou privés.

Introduction

Replacing soybean meal (SBM) by proteaginous seeds (PS) in dairy cow diets has been studied for many years in different parts of the world (e.g. Ingalls and McKirdy, 1974;Yu et al., 1999;[START_REF] Puhakka | Effects of replacing rapeseed meal with fava bean at 2 concentrate crude protein levels on feed intake, nutrient digestion, and milk production in cows fed grass silage-based diets[END_REF]. Compared to soybean meal, PS contain less crude protein (CP) (between 213 and 380 g/kg dry matter (DM) for PS versus 526 g/kg DM for SBM, INRA, 2018). In addition, proteins of raw PS are more degradable in the rumen (nitrogen ruminal degradability calculated with a particle outflow from the rumen of 0.06 h -1 (ED6_N) between 0.70 and 0.81 g/g versus 0.63 g/g for SBM, INRA 2018), decreasing their nutritive value for ruminants. To counteract this, heat-treatments of PS have been developed and have become a well-known solution to reduce the rumen degradability of PS proteins (Cros et al., 1991;Benchaar et al., 1992) and thus to improve their nutritive value.

Our first hypothesis was that raw seeds, compared to SBM, would lead to higher ruminal degradation of proteins and lower dairy performance. Our second hypothesis was that treated PS compared to raw seeds would lead to lower ruminal degradation of proteins and higher dairy performance. To validate these hypotheses, the literature was searched for published data on the digestion of raw or treated PS in dairy cow diets and a meta-analysis performed to highlight the trends and quantify their effects on nitrogen ruminal degradability, nitrogen intestinal true digestibility, ruminal parameters, nitrogen partitioning and milk production and composition.

Materials and methods

Selection of articles

Publications of interest were identified by searching Google Scholar, Science Direct, Web of Science and PubMed using the key words "ruminants AND (digestibility OR degradability OR milk production) AND (faba bean OR lupin OR pea)". Two databases were created, the first gathered in vivo results about the addition of raw or heat-treated PS in ruminant diets, and the second collected in situ results concerning seeds ruminal degradability and intestinal true digestibility. All these results were published between 1974 and 2018 (except studies of Mendowski et al., 2019 and2020). Two major axes were studied: comparisons between SBM and PS on the one hand, and between raw and treated PS on the other hand.

To be included in the in vivo database, publications were required to describe studies carried out on dairy cows, focused on replacement of all or part of the concentrate protein source by PS (faba bean, lupin or pea), and contain results on milk performance, nitrogen digestion (in the rumen or in the intestine measured using duodenal and fecal flows), or nitrogen partition between milk, feces and urines. To be included in the in situ database, publications had to contain results on nitrogen degradability in the rumen and/or nitrogen true digestibility in the intestine (measured using nylon bag in situ incubations) for faba bean, lupin or pea. In all cases, nitrogen was determined using Dumas or Kjeldahl methods. The steps involved in the selection of the publications are summarized in the Prisma flow diagram (Figure 1).

Creation of databases

Two file templates were created with Microsoft Excel software (Microsoft Corporation, Redmond, WA, USA, version 2016), for the in vivo and in situ experiments, respectively, as methodologies and results differed between the two types of publications. For each publication, an individual sub-file, which gathered all the information contained in the publication, was completed using the corresponding template (in vivo and/or in situ) to be as exhaustive as possible. If one publication contained both in vivo and in situ results, two individual sub-files (one per template) were completed for this publication. Once all individual sub-files were completed, both databases (in vivo and in situ) were created by compiling their corresponding individual sub-files. For each database, a homogenization of qualitative terms used for some data was necessary before coding, and some units were converted when required. Moreover, additional calculations useful for interpretation were performed when not directly reported in the publication. In the in vivo database, this concerned mainly the proportion of dietary CP provided by the tested experimental feed (i.e. the proportion of this feed in the diet × CP content of this feed/CP content of the diet). As the calculation of N in milk differed between publications, this variable was systematically recalculated as (milk yield × milk protein content / 6.38 / 0.95), assuming 6.38 g of N in 100 g of milk proteins and 95 g of protein N in 100 g of total milk N (DePeters and Cant, 1992;[START_REF] Spanghero | Critical analysis of N balance experiments with lactating cows[END_REF]. Nitrogen balance was systematically recalculated as N intake -(N in milk + N in feces + N in urine), using N in milk calculated with the abovedescribed formula. In the in situ database, the effective degradability of N (ED6_N) was calculated using an outflow rate of particles from the rumen fixed at 0.06 h -1 , to make all values from the different publications comparable. The ED6_N (g/g) was calculated according to [START_REF] Ørskov | The estimation of protein degradability in the rumen from incubation measurements weighted according to rate of passage[END_REF] as: ED6_N = a + (b × c) / (c + 0.06), with a the soluble fraction (g/g), b the degradable fraction (g/g) and c the rate of degradation of b (h -1 ); or by the "step by step method" from the different points of N degradation kinetics [START_REF] Kristensen | Estimation of the effective protein degradability in the rumen of cows using the nylon bag technique combined with the outflow rate[END_REF]. The content of dietary proteins entering the intestine (PIA6) and of dietary proteins truly digestible in the intestine (PDIA6) was calculated, respectively, as PIA6 = CP content of the seed × (1 -ED6_N) and PDIA6 = PIA6 × dr_N, with dr_N the intestinal true digestibility of N (in g/g) assimilated to the proportion of N apparently disappearing from mobile bags in the intestines [START_REF] Vérité | Révision du système des protéines digestibles dans l'intestin (PDI)[END_REF]INRA, 2018).

Finally, coding was carried out using an exhaustive inventory of all the experimental factors, taking into account the PS studied, and the possible technological treatments applied to the seed (e.g. extrusion, toasting...).

Treatment comparisons

For both in vivo and in situ data analyses, the PS studied were faba bean (Vicia faba), Treatments applied to PS were categorized according to their characteristics in order to compare technological effects on the seeds. The three categories were: heat treatments, i.e. heat but no pressure (which included cooking, i.e. boiling the seeds; steam cooking, i.e. heating the seeds with steam; toasting and roasting, i.e. heating the seeds by conduction with or without steam, respectively); thermo-mechanical treatments, i.e. heat and pressure (which included flaking, i.e. steam cooking combined with flattening; expansion, i.e. heating the seeds with hot air under high pressure; extrusion, i.e. forcing the seeds to pass through a die to induce self-heating and destructuring of the seed cells; autoclaving, i.e. cooking the seeds under high pressure); and other treatments (tanning, i.e. binding the amino function of proteins). Within the "heat treatment" category, roasting and toasting have been gathered as their description did not allow a proper distinction and their measured effects appeared similar. Seeds treated in this manner are described as "toasted/roasted" seeds in the rest of this article and analyzed together to increase the number of data within each category.

Calculations and statistical analyses

Statistical analyses were carried out using Minitab 17 software (Minitab Inc., USA, 2017).

For each comparison, effects of treatments were assessed using t-tests applied to the difference between the tested versus control treatments (as described above), for each measured or calculated variable of interest (listed in Table 3). The differences were considered significant when below the threshold P-value of 0.05 and a trend was considered at 0.05 < P-value < 0.10. When only one comparison was found, the P-value reported in the associated publication -when available -was indicated in the text.

Results

In situ degradation and protein values

The mean characteristics of all variables of interest from in situ database are summarized in Table 1. Lupin was the main PS represented, followed by faba bean, and pea. Direct comparisons between PS and SBM were scarce. Only six experiments from five publications simultaneously reported results for CP, ED6_N and dr_N required for PDIA6 calculation.

Raw or treated proteaginous seeds versus soybean meal

Results of in situ degradation of raw or treated PS compared to SBM are summarized in Table 4, and results on ED6_N are represented in Figure 2. The PS contained less CP than SBM (P < 0.001), with differences averaging -203, -147 and -248 g/kg DM for faba bean, lupin and pea, respectively. All the raw PS had higher ED6_N than SBM (+0.158, +0.181 and +0.151 g/g for faba bean, lupin and pea, respectively, P < 0.004, Figure 2). This led to lower PIA6 values (-121, -123 and -130 g/kg DM for faba bean, lupin and pea, respectively, P < 0.001). Compared to SBM, dr_N tended to decrease with raw faba bean (-0.273 g/g, P = 0.096), but not with lupin. Only one comparison was found for raw pea (Solanas et al., 2005), which concluded that PS were associated with significantly lower dr_N than SBM (-0.102 g/g, P < 0.050).

In situ data reporting direct comparisons of treated PS vs. SBM were scarce and variable between studies (Solanas et al., 2005;[START_REF] Aguilera | The degradability of legume seed meals in the rumen: effect of heat treatment[END_REF]Chapoutot et al., unpublished data). Briefly, extrusion of faba bean led to significantly lower ED6_N than SBM (-0.009 g/g, P < 0.001), and extrusion of PS led to lower PIA6, similar dr_N and lower PDIA6 than SBM (on average, -70 g/kg DM, -0.03 g/g and -62 g/kg DM for all PS) mainly due to the lower CP content of PS. Autoclaving faba bean led to similar ED6_N as SBM, but autoclaving lupin tended to lower ED6_N (-0.093 g/g, P = 0.066). The only comparison found for autoclaving pea led to a lower ED6_N (-0.153 g/g, [START_REF] Aguilera | The degradability of legume seed meals in the rumen: effect of heat treatment[END_REF]. Finally, roasting PS led to similar ED6_N and PIA6 as SBM.

Treated versus raw proteaginous seeds

The in situ degradation results for treated faba bean, lupin and pea compared to the same raw seeds are summarized in Table 5, and results for ED6_N are represented in Figure 3.

Treatments did not affect the CP content of PS, except for extrusion of faba bean and lupin (+15 and +10 g/kg DM, P = 0.009 and P = 0.020, n = 6 from 3 experiments and n = 3 from 2 experiment, respectively), which was an unexpected finding.

Extrusion of faba bean decreased ED6_N (-0.111 g/g, P = 0.001) and consequently increased PIA6 (+34 g/kg DM, P = 0.002), increased dr_N (+0.179 g/g, P = 0.002) and PDIA6 (+62 g/kg DM, P < 0.001). When faba bean was toasted or roasted, the ED6_N decreased by 0.093 g/g compared with raw faba bean (P = 0.005). No effect was observed on PIA6 with toasted/roasted faba bean, whereas dr_N was increased by 0.051 g/g (P < 0.001), and PDIA6 tended to be increased by 98.7 g/kg DM (P = 0.083, Table 5). Only one study comparing autoclaved to raw faba bean was found, which showed that ED6_N decreased by 0.224 g/g, and PIA6 consequently increased by 76.2 g/kg DM after autoclaving [START_REF] Aguilera | The degradability of legume seed meals in the rumen: effect of heat treatment[END_REF].

The ED6_N was lower in extruded than raw lupin (-0.155 g/g, P < 0.001), whereas PIA6, dr_N and PDIA6 were all higher (+55 g/kg DM, P < 0.001, +0.324 g/g, P = 0.001 and +92 g/kg DM, P = 0.020, respectively). Toasting or roasting lupin reduced ED6_N (-0.161 g/g, P < 0.001), and increased PIA6 (+52.0 g/kg DM, P < 0.001) and dr_N (+0.018 g/g, P < 0.001), leading to an increase in PDIA6 (+93.1 g/kg DM, P = 0.040). Publications about the effects of other treatments on lupin were scarce. [START_REF] Aguilera | The degradability of legume seed meals in the rumen: effect of heat treatment[END_REF] showed that autoclaving lupin reduced ED6_N by 0.313 g/g, thus increasing PIA6 by 128 g/kg DM. Rodehutscord et al. (1999) observed that cooking or tanning lupin reduced ED6_N by 0.035 and 0.071 g/g, while PIA6 was increased by 10.8 and 18.9 g/kg DM, respectively.

When pea was treated by extrusion, the decrease in ED6_N was not significant compared to raw pea, but PIA6 was higher (+47 g/kg DM, P = 0.010). Solanas et al. (2005) observed an increase of 0.121 g/g in dr_N (P < 0.050) with extruded pea. Toasting pea reduced ED6_N (-0.190 g/g, P < 0.001) and increased PIA6 (+60 g/kg DM, P = 0.017) and dr_N (+0.033 g/g, P < 0.001, Table 5). The only comparison between autoclaved and raw pea concluded that autoclaving reduced ED6_N by 0.323 g/g and consequently increased PIA6 by 86 g/kg DM [START_REF] Aguilera | The degradability of legume seed meals in the rumen: effect of heat treatment[END_REF].

In vivo responses

Characteristics of cow milk production (days in milk and initial milk yield) and diets (nature and quantities of dietary forage, dry matter intake (DMI)) included in the in vivo database are summarized in Tables 2 and3. Among PS, faba bean and lupin were the most represented, particularly in comparisons with SBM. Out of the 112 diets included in the database, 72 were distributed ad libitum, 12 were distributed sub ad libitum (i.e. at more than 90% of previously measured ad libitum DM intake, so that changes in intake reflect changes in choice feed between treatments), 11 were distributed in a restricted manner, and 17 were distributed in an unspecified manner. Only diets distributed ad libitum or sub ad libitum were considered for DMI analyses. Most of the diets were based on maize silage. None of the publications comparing SBM and PS, or raw and treated PS, except Pereira et al. (2017) and Mendowski et al. (2019 and2020), indicated whether a methionine supplementation was applied, or provided the digestible methionine content of the diet.

Raw and treated proteaginous seeds versus soybean meal

Results of the in vivo effects of feeding raw or treated PS (faba bean, lupin or pea) instead of SBM are summarized in Tables 6 to 8. Replacement of SBM by raw or treated PS was made in iso-nitrogenous conditions (P > 0.100 for all seeds). Only one exception has been observed for comparison between SBM and extruded lupin: the CP content of the diet was 5 g/kg DM higher with extruded lupin than with SBM (P = 0.030). Moreover, in most cases, the proportion of dietary CP provided by the experimental concentrate (PS or SBM) did not differ between tested and control treatments, except for extruded faba bean (P = 0.039, Table 6) and extruded lupin (P = 0.060, Table 7) diets, which provided more dietary CP than SBM.

Replacement of SBM by PS in the diet did not affect DMI (measured on ad libitum or sub ad libitum diets), except for extruded lupin, which tended to decrease DMI by 1.4 kg/d compared to SBM (P = 0.093, Table 7).

Compared to SBM, including raw faba bean in dairy cow diets (Table 6) increased NH3 content (+16.7 mg/L, P = 0.039; Figure 4), decreased total volatile fatty acids (VFA) (-6.8 mmol/L, P = 0.036), and tended to increase acetate/propionate ratio and branched-chain VFA (BC-VFA) in ruminal fluid (+0.2 mol/mol, P = 0.052 and +0.2 mol/100 mol total VFA, P = 0.079, respectively). Raw faba bean also tended to decrease milk yield (-0.5 kg/d, P = 0.079), decrease milk protein content (MPC; -0.7 g/kg, P = 0.018), N secreted in milk (-4.8 g/d, P = 0.001), and the N in milk/N intake ratio (from 28.6 to 28.0 g/100 g, P = 0.026), but also decreased milk urea content (-30.0 mg/L, P = 0.084). With extruded faba bean, the N in milk/N intake ratio decreased (from 29.3 to 28.5 g/100 g, P = 0.047) and milk urea tended to decrease (-48.0 mg/L, P = 0.079) compared to SBM (Table 6). Few data were available with steam cooked faba bean, but MPC tended to decrease (-0.7 g/kg, P = 0.090, Table 6) compared to SBM.

Including raw lupin in the diet instead of SBM increased NH3 in ruminal fluid (+21.1 mg/L, P = 0.032; Figure 4) and decreased MPC (-1.2 g/kg, P < 0.001, Table 7). When extruded lupin was used instead of SBM, BC-VFA and MPC decreased (-0.2 mol/100 mol total VFA, P = 0.051 and -2.2 g/kg, P < 0.001, respectively), and milk urea tended to decrease (-67.0 mg/L, P = 0.085) (Table 7). Few data were available with roasted lupin, and no difference compared with SBM was observed on ruminal parameters, milk yield, milk fat and protein contents (Table 7). The only publication reporting replacement of SBM by tanned lupin concluded that milk yield, neither milk fat and protein contents nor milk N secretion were modified (Emile et al., 1991).

Replacing SBM by raw pea led to an increase in NH3 in ruminal fluid (+25.7 mg/L, P = 0.009; Figure 4), and tended to increase BC-VFA in the rumen (+0.2 mol/100 mol VFA) and to decrease N in milk (-6.1 g/d, P = 0.092, Table 8). Only one comparison was found about duodenal flow, and it showed that non-NH3 N and microbial N were numerically increased (P > 0.050, Khorasani et al., 2000), whereas non-NH3 non-microbial N (dietary + endogenous) was numerically decreased (P > 0.050, Khorasani et al., 2000). When pea was extruded, milk fat content (MFC) tended to decrease (-1.4 g/kg, P = 0.070), but N in milk tended to increase (+5.8 g/d, P = 0.090, Table 8 andFigure 6) compared to SBM. Petit et al. (1997) found that extrusion increased N excreted in urine and N balance (P < 0.050). When pea was flaked, milk urea was increased (+21.9 mg/L, P = 0.016, Table 8) compared to SBM. When pea was expanded and compared to SBM, no difference was observed on ruminal NH3, milk yield, MFC and MPC (Masoero et al., 2006).

Treated versus raw proteaginous seeds

The in vivo effects of replacing raw by treated faba bean, lupin and pea are summarized in Tables 9 to 11. Except for raw versus extruded lupin diets (+2.3 g CP/kg DM, P = 0.014, Table 10), comparisons between raw versus treated seeds were made in iso-nitrogenous conditions. Likewise, raw and treated seeds contributed to the same proportion of CP dietary content. Whatever the PS or the applied treatment, DMI was not changed between raw versus treated seeds (even when diets were distributed ad libitum and sub ad libitum), except for toasted or roasted faba bean (-0.1 kg/d, P = 0.092, Table 9).

For faba bean, BC-VFA were lower (-0.5 mol/100 mol total VFA, P = 0.001), as was MFC (-2.7 g/kg, P = 0.020), and N in milk tended to be higher (+4.1 g/j, P = 0.085, Table 9 and Figure 7) with extruded than with raw seeds. The only experiment reporting N duodenal flow documented an increase in both microbial and non-NH3 non-microbial flow (P < 0.050, Benchaar et al., 1994b). When faba bean was toasted or roasted, N intake tended to decrease (-7.9 g N/d, P = 0.088) as did MFC (-1.6 g/kg, P = 0.023, Table 9) compared to raw faba bean. Only one publication was found about the effects of tanning faba bean, which concluded that tanning increased MPC and N intake (P < 0.050, Pelletier and Bouchard, 1978).

Replacing raw lupin by extruded seeds decreased NH3 in ruminal fluid (-11.8 mg/L, P = 0.040, Figure 5), BC-VFA (-0.5 mol/100 mol VFA, P = 0.022) and MFC (-4.7 g/kg, P = 0.006), but increased N in milk (+4.0 g/j, P = 0.033, Table 10 andFigure 7); the only experiment with duodenal flow reported that there was an increase in both microbial and non-NH3 non-microbial N duodenal flow (P < 0.050, Benchaar et al., 1994a). No difference in in vivo results was observed when lupin was toasted or roasted compared to raw lupin.

Publications about the effects of expanding, tanning, autoclaving and cooking lupin were scarce. Pieper et al. (2006) concluded that expanding lupin led to better protection of CP, resulting in a better milk yield (P < 0.050), but no change in MFC and MPC. Emile et al. (1991) observed a higher milk yield but a lower MFC when lupin was tanned, with no difference in MPC.

For pea, few in vivo data were available about effects of extrusion, flaking, expanding and tanning (Table 11). Focant et al. (1990) concluded that extrusion led to a significant increase in microbial (P < 0.010) and a numerical increase in non-NH3 non-microbial N flow in the duodenum, a decrease in NH3 content from ruminal fluid (P < 0.001), but reported that steamflaking had no observed effects. No beneficial effect on milk production was observed with extrusion, and Petit et al. (1997) concluded that extrusion decreased N excreted in feces (P < 0.050), but increased N balance (P < 0.010). Masoero et al. (2006) concluded that expanding had no effects on intake and milk production. Finally, Pelletier and Bouchard (1978) concluded that tanning pea had no effect on milk production, but increased MPC (P < 0.050) and had no effect on N partitioning despite an increase in N intake (P < 0.050).

Discussion

The aims of this study were twofold: first, to make an inventory of the available data about the in situ degradation and the in vivo responses of dairy cows fed raw or treated faba bean, lupin and pea; and second, to quantify the main trends related to the substitution of SBM by PS, or the replacement of raw PS by treated PS, on N ruminal degradability and intestinal true digestibility, ruminal parameters, milk production and composition, and N partitioning.

In situ degradation

With all PS, the ED6_N was higher for raw seeds (on average 0.778 g/g) than for SBM (on average 0.613 g/g). In this analysis, the ED6_N values for PS were slightly lower than those averaged from larger data sets, and presented in feed tables by INRA (2018) and INRA-CIRAD-AFZ (2017), i.e. 0.82, 0.86 and 0.86 g/g for raw faba bean, lupin and pea, respectively. Most of the treatments significantly decreased the ED6_N: on average -0.100, -0.158 and -0.130 g/g with all treatments for faba bean, lupin and pea, respectively, with the strongest effect with autoclaving, followed by extrusion and finally toasting (Figure 3). The observed reductions in ED6_N confirm that heat and/or pressure treatments can protect N from ruminal degradation, which led on average to an increase of +43 g PIA6/kg DM for all PS and treatments. In this study, the ED6_N reductions observed with extrusion were slightly lower than those reported in INRA Feed Tables (2018). This could partly be due to differences in treatment modalities (duration, temperatures reached, intensity of pressure treatments, etc.), which are often poorly described in the literature. For similar treatments (extrusion or other heat treatments except toasting/roasting), the greatest reductions in ED6_N were observed for lupin, which appeared to be the seed the most reactive to treatment. This implies a higher increase in PIA6 (in g/kg DM) for lupin (+53, n = 56) than for faba bean (+18, n = 25); pea also appeared reactive (+58, n = 6), but there were less available data. This finding was also supported by intra-experiment comparisons: reductions in ED6_N were greater for lupin than for faba bean with autoclaving [START_REF] Aguilera | The degradability of legume seed meals in the rumen: effect of heat treatment[END_REF], extrusion [START_REF] Chapoutot | Influence of extrusion on nitrogen degradability of faba bean or lupine blends including urea[END_REF] and toasting (Goelema, 1999), and greater for lupin than for pea with extrusion [START_REF] Aufrère | Degradation in the rumen of lupin (Lupinus albus L.) and pea (Pisum sativum L.) seed proteins: Effect of heat treatment[END_REF] and toasting (Goelema, 1999;Goelema et al., 1998). Fewer data were available for dr_N than ED6_N, but when PS were heat-treated, dr_N increased significantly (on average for all treatments: +0.10, +0.12 and +0.04 g/g for faba bean, lupin and pea, respectively). This suggests that the protection of proteins against ruminal degradation is reversible in the abomasum, which allows the absorption of amino acids in the intestine. In addition, the flow of amino acids potentially absorbed is greater. Some treated PS have similar or lower ED6_N than SBM: autoclaved faba bean, lupin, or pea, and roasted lupin. However, due to their lower CP content, no treated PS reached a level of PIA6 comparable to SBM despite the effects of heat treatments: PIA6 values were on average 60 g/kg DM lower with treated PS than with SBM, and no direct comparison between treated PS and SBM was available for PDIA6.

In vivo responses

The PS seem to have a satisfying palatability for dairy cows as, in most cases, DMI was not reduced compared to diets containing SBM (the majority of experiments were conducted with diets distributed ad libitum).

According to the observed in situ results, a lower valorization of proteins by dairy cows might be expected when SBM is replaced by PS in their diet, and reciprocally a better valuation of proteins with treated PS compared to raw PS. Nevertheless, the expected effects of raw or treated PS were not systematically observed in vivo. This could be explained by the fact that differences between treatments on in vivo measured N flows were lower than those observed on N balance, which was mainly due to balance default (as a result of methodological limits) in dairy cows [START_REF] Spanghero | Critical analysis of N balance experiments with lactating cows[END_REF]. It should also be noted that only Pereira et al. (2017) and Mendowski et al. (2019Mendowski et al. ( , 2020) ) explicitly reported on supplementation of metabolizable methionine. Thus, in all other studies, the diets containing faba bean, lupin and pea (particularly when extruded) could have provided a lower supply of digestible methionine than current recommendations (2.3% of PDI, INRA, 2018) for MPC and proteins secreted in milk. However, this may have been partly offset by the fact that comparisons between PS and SBM were performed with iso-CP diets (PS or SBM providing on average 39 g/100 g of dietary CP). Another explanation of differences between expected and observed effects when SBM is substituted by raw or treated PS in vivo could be related to the nature of the substitution within-experiments. Indeed, while in some publications, diets have been formulated to be iso-energetic, in others, PS diets provide more energy than SBM diets (the NEL / CP ratio being higher for PS than for SBM). Thus, responses of dairy cows may also be partly related to a higher energy intake with PS diets than with SBM diets. Due to scarce available data, it has not been possible to split data between iso-energetic and non-isoenergetic supplementations for the analysis. Given the average values of 20.1 kg for DM intake, 165 g/kg DM for dietary CP content, and 39% of dietary CP for substitution, the increase in energy intake may reach 4.2 Mcal NEL, that may contribute to 2.6 g N in milk/d according to INRA (2018).

Effects of substitution of soybean meal by raw proteaginous seeds

Even if the effects on protein degradation in the rumen appeared to follow a general trend when SBM was replaced by raw PS (Vander Pol et al., 2009;Cherif et al., 2018;Mendowski et al., 2019), the effects of these substitutions on milk production and composition were highly variable.

An evaluation of general trends provides a more global picture of the effects of dietary substitutions. When dairy cows were fed raw PS compared to SBM, ruminal NH3 content increased (on average with all PS: +20 mg/L, n = 15), and the higher the proportion of CP provided by raw PS, the higher the NH3 content in the rumen (as illustrated by the average within-experiment response law, Figure 8). The BC-VFA molar proportion tended to increase with faba bean compared to SBM (on average with all PS: +0.16 mol/100 mol total VFA, n = 11). The N in milk/N intake ratio decreased significantly with faba bean and numerically with lupin and pea (on average with all PS: -0.6 g/g, n = 29). The MPC decreased significantly with faba bean and lupin (on average -1.0 g/kg), but not with pea. Milk yield and N in milk did not change with lupin or pea (only N in milk tended to decrease with pea) but decreased with raw faba bean by 0.5 kg/d and 5 g/d, respectively. Taken together, these results are consistent with in situ observations: the increase in ruminal NH3, and the lower MPC and N in milk reflecting higher ruminal N degradation and the induced lower PDIA6 value. Our first hypothesis, which was that raw seeds compared to soybean meal would lead to lower dairy performance was verified with faba bean, and numerically the same trend was observed with lupin and pea, although it did not reach statistical significance. The N partitioning between urine and feces was not modified when SBM was replaced by raw PS, but very few data were available.

The attenuation observed between in vivo and in situ responses could partly be explained by the risk of over-estimating the ED6_N of PS due to particle loss during incubation when using the nylon bag technique, which has been reported to be high with PS (Michalet-Doreau and Cerneau, 1992). Consequently, PDI values of PS may have been underestimated, which would imply a higher gap in dairy performance between SBM and PS.

The low content of digestible methionine of PS (1.48, 1.44 and 1.72 g/100 PDI for faba bean, lupin and pea, respectively, INRA 2018) should be noted. As very few publications, except for example Mendowski et al. (2019Mendowski et al. ( , 2020)), reported supplementation with metabolizable methionine, diets containing PS could have had lower than recommended levels of digestible methionine, which could impair dairy performance, in particular MPC. This is consistent with what was obtained by Pereira et al. (2017), who observed that adding rumen-protected Lys and Met to a diet containing field pea increased MPC compared to the same diet with no AA supplement. In addition, Joch and Kudrna (2020) compared diets containing SBM in which either 0%, 30% or 50% of SBM was replaced by raw lupin (all diets containing protected Met) and observed no difference on MPC between diets: supplementing in protected Met may have prevented a decrease of MPC with lupin compared to SBM.

Effects of treatments on proteaginous seeds

Only a few in vivo measures varied significantly following PS treatments. As observed previously for the comparison between raw PS and SBM, the effects on protein degradation in the rumen appeared to follow a general trend when raw PS was replaced by heat-treated PS (Focant et al., 1990;Benchaar et al., 1992;Mendowski et al., 2019), but the effects of these substitutions on milk production and composition were highly variable.

Both raw and treated PS provided on average 29 g/100 g (from 14 to 53g/100 g) of CP in dairy cow diets. With extrusion there was a global decrease in ruminal NH3, which was significant for lupin (-12 mg/L) and numerical for faba bean and pea (on average -17 mg/L, n = 7 with faba bean and pea), suggesting extrusion protected the proteins against ruminal degradation. This is consistent with observations on ED6_N with extrusion. With other treatments than extrusion, the effects depended on seed and treatment. For toasted/roasted faba bean, there was a decrease in NH3 in ruminal fluid (the change was not statistically significant, but only two comparisons were found), whereas for toasting/roasting lupin and flaking pea the opposite was observed with an increase in NH3 in ruminal fluid (only few data available). The main significant zootechnical result on nitrogen utilization concerned extrusion of faba bean and lupin, which led to an increase in N in milk (as also illustrated by the within-experiment response law, Figure 9). However, some heat treatments of faba bean (extrusion and toasting/roasting) and lupin (extrusion) appeared to decrease MFC, and most of the other treatments followed the same trend. This could be explained by a greater availability of fat after extrusion of lupin compared to raw lupin [START_REF] Chilliard | Milk fatty acids in dairy cows fed whole crude linseed, extruded linseed, or linseed oil, and their relationship with methane output[END_REF], leading to an inhibition of de novo synthesis of fatty acids in the mammary gland by dietary polyunsaturated fatty acids or rumen biohydrogenation intermediates; [START_REF] Shingfield | Role of trans fatty acids in the nutritional regulation of mammary lipogenesis in ruminants[END_REF].

For extruded faba bean, the five available comparisons concerned 10:90 linseed:faba bean blends (Mendowski et al., 2019 and2020), in which fat could have been released from the linseed during extrusion. Moreover, faba bean has a high content of starch (44 % on a DM basis), and starch-rich diets have been shown to potentially induce milk fat depression in dairy cows [START_REF] Bauman | Nutritional regulation of milk fat synthesis[END_REF]. Consequently, the decrease in MFC observed with faba bean could result both from the release of polyunsaturated fatty acids from linseed and the faba bean seed itself, and its high starch content.

Effects of substitution of soybean meal by treated proteaginous seeds

At similar dietary CP content, diets containing treated PS rather than SBM had nonsignificantly higher NH3 rumen content and milk yield. The MPC was significantly lower with steam cooked faba bean and extruded lupin, and most other treated PS followed the same trend, so that N in milk tended to be lower than with SBM, but differences were small (-2 g N/day). Urinary or fecal N excretion and N balance values obtained with treated PS were comparable to those measured with SBM. The MFC tended to be lower with treated PS than with SBM, but differences were small (on average -1.2 g/kg, n = 16) and non-significant, except for a trend with extruded pea. When treated PS were compared to SBM either no change in values, or lower MFC and MPC values were observed, as reported with steamcooked faba bean (-0.7 g/kg protein content, P = 0.090), extruded lupin (-2.2 g/kg protein content, P < 0.001) and extruded pea (-1.4 g/kg fat content, P = 0.070 and -5.8 g/d N in milk, P = 0.090). Heat treatments improved the nutritional value of PS, but still did not allow the seeds to reach the value of SBM. At iso-CP comparisons, this can be explained by the lower PDI content of PS compared to SBM, and also by the lack of digestible methionine contained in PS compared to SBM.

Conclusions

For some treatment comparisons, very few (if any) data were available for dairy cows, and results concerning N partitioning in particular were scarce. However, despite the limited amounts of available data and the great diversity of feeding practices, some general trends appear from this quantitative review. Proteins from raw PS were more degradable in the rumen than proteins from SBM, as revealed both by in situ studies and by in vivo ruminal NH3 measurements (which is in accordance to our first hypothesis). This ruminal N degradability was lowered by heat treatments (confirming our second hypothesis), but even so the amount of PIA6 did not reach levels obtained with SBM because of the differences in CP content. Moreover, very few results on dr_N were available. Evaluations of in vivo dairy performance indicated that MPC and milk N secretion were decreased with raw PS compared to SBM (thereby confirming our first hypothesis). In contrast, no major effect was observed when PS were treated compared to raw PS (not supporting our second hypothesis), except for a decrease in MFC, especially for extruded faba bean and lupin. Finally, at similar CP content, N transfer as protein in milk remained lower with treated PS than with SBM, but differences were small. When SBM was replaced by raw PS, or when raw PS were replaced by treated PS, the observed variability of responses in milk production and composition could partly be explained by the variations specific to each study, and particularly the dietary level of digestible methionine. Moreover, when PS were heat-treated, technological processes were most often not fully described, which led to uncertainty about the intensity of the process the seeds may have undergone. A better description of these heat or pressure treatments would permit a better understanding of their effects on PS, and consequently the effect on dairy performance when these seeds are fed to dairy cows. This work revealed the very limited number of publications testing raw and treated PS in dairy cow diets, and how important it would be to perform more analytical feeding trials to improve both understanding of processes involved in N protection, and prevision of their nutritional interest as alternatives to SBM in dairy cow diets. 94 44.0 13.5 16.1 75.7 (1) standard deviation (1) difference between proteaginous seed (PS) and soybean meal (SBM) 674

(2) standard error of the difference 675 (1) difference between treated proteaginous seed (PS) raw PS 678 (2) standard error of the difference 679 1) difference between proteaginous seed (PS) and soybean meal (SBM) 687

(2) standard error of the difference 688

(3) branched-chain VFA (isobutyrate and isovalerate) 689 (1) difference between treated proteaginous seed (PS) and raw PS 701

(2) standard error of the difference 702 (3) (1) difference between treated proteaginous seed (PS) and raw PS 705 (2) standard error of the difference 706

  lupin (mainly Lupinus albus, and Lupinus angustifolius, considered without distinction because the two experiments with Lupinus angustifolius gave similar results to those with Lupinus albus) and pea (Pisum sativum). Only the following comparisons were considered in the present work (tested versus control): raw PS versus SBM; treated PS versus SBM; and treated versus raw for the same PS. This led to a selection from the in vivo and in situ databases, respectively, of 32 (representing 41 experiments) and 36 articles plus a nonpublished study, corresponding to the in situ measurements on the 11 experimental concentrates used in Mendowski et al. (2019 and 2020) by Chapoutot et al. (personal communication): 1 soybean meal, 7 faba bean-linseed blends (90:10) (raw, extruded at low or high temperature, with or without reducing sugars or an enzymatic cocktail), and 3 lupinlinseed blends (90:10) (raw or extruded at low or high temperature. The ED6_N and the dr_N of these feeds were determined according to Michalet-Doreau et al. (1987) and Theodoridou et al. (2010), respectively, with N content in the residues measured using Dumas method for ED6_N and Kjeldahl method for dr_N. Main results of this study were that extrusion of faba bean and lupin blends reduced ED6_N by 16 to 23 points, and increased dr_N by between 20 and 30 points (depending on extrusion modalities) compared to raw blends. Extruded faba bean had similar ED6_N to SBM, and dr_N of SBM was intermediate between raw and extruded PS.

Fig. 1 .Fig 3 .Fig 4 .Fig 5 .Fig 6 .Fig 7 .

 134567 Fig. 1. Prisma flow diagram 707

Table 1 :

 1 Descriptive statistics of the main variables of interest in the in situ database concerning proteaginous seeds and soybean meal whatever the treatment.

		n Mean sd 1	Min Max
	CP content (g/kg DM)
	Faba bean	57	308	45	246	471
	Lupin	111 361	42	283	513
	Pea	21	248	17	215	268
	Soybean meal	18	503	18	478	541
	ED6_N (g/g)
	Faba bean	63 0.720 0.134 0.434 0.937
	Lupin	112 0.683 0.154 0.370 0.954
	Pea	41 0.676 0.129 0.428 0.910
	Soybean meal	14 0.600 0.087 0.400 0.769
	dr_N (g/g)
	Faba bean	27 0.889 0.082 0.642 0.982
	Lupin	24 0.899 0.130 0.500 0.969
	Pea	16 0.959 0.029 0.870 0.992
	Soybean meal	5 0.970 0.14 0.956 0.987
	PIA6 (g/kg DM)
	Faba bean	45	66	42	19	173
	Lupin	94	113	60	17	353
	Pea	19	80	36	29	153
	Soybean meal	16	208	92	120	499
	PDIA6 (g/kg DM)
	Faba bean	11	58	51	14	170
	Lupin	7	91	55	14	171
	Pea	4	89	40	48	132
	Soybean meal	1	122	-	-	-
	1 standard deviation

Table 2 :

 2 Mean characteristics of variables of interest analyzed in the in vivo database, 662 concerning diets including proteaginous seeds (PS) and soybean meal (SBM) whatever the 663

	664	treatment.
			n Mean sd (1) Min Max
		CP in diet (g/kg) DM
		Faba bean	23 166	13	140	186
		Lupin	20 173	18	145	204
		Pea	15 153	9	134	169
		Soybean meal	20 161	15	138	203
		CP provided by SBM or PS (g/100 g dietary CP)
		Faba bean	19 39.7 12.3 13.6 53.4
		Lupin	10 38.9 10.9 22.0 50.0
		Pea	4 38.3 11.9 22.9 47.7
		Soybean meal	10 37.8	6.8	26.8 49.5
		DMI (kg/d)
		Faba bean	27 20.7	3.0	16.8 26.0
		Lupin	30 20.1	2.0	15.4 23.9
		Pea	16 19.7	5.1	8.1	26.3
		Soybean meal	28 20.0	4.2	7.8	25.9
		Ruminal NH3 (mg/L)
		Faba bean	20 172.4 42.4 110.5 268.4
		Lupin	7 165.1 31.0 125.0 194.2
		Pea	10 147.4 48.9 54.2 223.4
		Soybean meal	12 131.4 35.9 38.4 168.6
		Ruminal total VFA (mmol/L)
		Faba bean	20 105.7 15.6 74.0 129.5
		Lupin	7 106.4 5.2 101.0 114.1
		Pea	7 100.1 11.7 86.2 121.0
		Soybean meal	11 104.1 10.6 83.4 123.0
		Ruminal acetate/propionate (mol/mol)
		Faba bean	20 3.3	0.8	1.7	4.9
		Lupin	7	2.9	0.1	2.7	3.1
		Pea	7	3.6	0.5	2.3	4.5
		Soybean meal	11 2.8	0.6	1.6	3.7
		Ruminal BC-VFA (2) (% mol of total VFA)
		Faba bean	15 2.4	0.8	0.9	3.6
		Lupin	7	2.7	0.1	2.3	3.5
		Pea	4	2.3	0.8	1.8	2.9
		Soybean meal	10 2.2	0.6	1.0	3.4
		Rumen protein balance (g/kg DMI)
		Faba bean	10 18.8 21.7 -22.6 51.0
		Lupin	2	5.0	28.3 -15.0 25.1
		Pea	1 48.8	-	-	-
		Soybean meal	1 47.0	-	-	-

Table 3 :

 3 Description of cow milk production and diets in the in vivo database.

	n Mean sd (1) Min Max

Table 4 :

 4 In situ parameters and protein value of raw or treated proteaginous seeds (PS) 672

	673	compared to soybean meal (SBM).
		PS	Treatment	Variable (unit)	n Mean	Mean	Δ PS -	SE (2)	P-
						SBM	PS	SBM (1)	Δ	value
		Faba	Raw	CP (g/kg DM)	10 500	297	-203	9.4	<0.001
		bean		ED6_N (g/g)	10 0.629 0.787 0.153 0.023 <0.001
				PIA6 (g/kg DM) 10 185	64	-121	7.4	<0.001
				dr_N (g/g)	2 0.956 0.684 -0.273 0.042 0.096
				PDIA6 (g/kg DM)	2	156	24	-131	4.8	0.023
			Extrusion	CP (g/kg DM)	5	521	299	-222	7.0	<0.001
				ED6_N (g/g)	5 0.687 0.678 -0.009 0.090 <0.001
				PIA6 (g/kg DM)	5	163	96	-67	3.4	<0.001
				dr_N (g/g)	5 0.956 0.915 -0.041 0.013 0.038
				PDIA6 (g/kg DM)	5	156	88	-68	4.0	<0.001
			Autoclaving CP (g/kg DM)	1	478	341	-138	-	-
				ED6_N (g/g)	3 0.543 0.549 0.006 0.045 0.905
				PIA6 (g/kg DM)	1	201	171	-29	-	-
		Lupin	Raw	CP (g/kg DM)	10 503	356	-147	17.7 <0.001
				ED6_N (g/g)	9 0.593 0.773 0.181 0.044 0.003
				PIA6 (g/kg DM)	9	206	83	-123	21.6 <0.001
				dr_N(g/g)	2 0.964 0.752 -0.213 0.070 0.201
				PDIA6 (g/kg	1	156	16	-139	-	-
				DM)
			Extrusion	CP (g/kg DM)	4	515	367	-147	27.6	0.013
				ED6_N (g/g)	3 0.644 0.700 0.557 0.074 0.529
				PIA6 (g/kg DM)	3	185	111	-74	23.6	0.089
				dr_N (g/g)	3 0.961 0.961 -0.001 0.008 0.940
				PDIA6 (g/kg	2	156	109	-47	2.9	0.039
				DM)
			Autoclaving CP (g/kg DM)	1	478	407	-71	-	-
				ED6_N (g/g)	3 0.543 0.450 -0.093 0.025 0.066
				PIA6 (g/kg DM)	1	200	229	29	-	-
			Roasting	CP (g/kg DM)	2	525	362	-163	60.0	0.224
				ED6_N (g/g)	2 0.512 0.537 0.025 0.112 0.860
				PIA6 (g/kg DM)	2	258	168	-90	86.9	0.487
		Pea	Raw	CP (g/kg DM)	6	492	244	-248	11.2 <0.001
				ED6_N (g/g)	5 0.617 0.769 0.151 0.021 0.002
				PIA6 (g/kg DM)	5	187	57	-130	8.0	<0.001
				dr_N (g/g)	1 0.972 0.870 -0.102	-	-
			Extrusion	dr_N (g/g)	1 0.972 0.991 0.019	-	-
			Autoclaving CP (g/kg DM)	1	478	268	-211	-	-
				ED6_N (g/g)	1 0.561 0.428 -0.153	-	-
				PIA6 (g/kg DM)	1	201	153	-48	-	-

Table 5 :

 5 In situ parameters and protein value of raw proteaginous seeds (PS) compared to 676

	677	treated PS
		PS	Treatment	Variable (unit)	n Mean	Mean	Δ Treated	SE (2)	P-
						raw	treated	-raw (1)	Δ	value
		Faba Extrusion	CP (g/kg DM)	13	307	322	15	4.9	0.009
		bean		ED6_N (g/g)	14 0.876	0.765	-0.111	0.025 0.001
				PIA6 (g/kg DM)	12	33	67	34	8.7	0.002
				dr_N (g/g)	7 0.735	0.913	0.179	0.034 0.002
				PDIA6 (g/kg DM)	6	24	87	62	5.2	<0.001
			Autoclaving CP (g/kg DM)	1	341	341	0	-	-
				ED6_N (g/g)	1 0.721	0.497	-0.224	-	-
				PIA6 (g/kg DM)	1	95	171	76	-	-
			Toasting/	CP (g/kg DM)	13	310	311	1	1.5	0.703
			roasting	ED6_N (g/g)	33 0.737	0.644	-0.093	0.031 0.005
				PIA6 (g/kg DM)	12	124	121	-3	20.7	0.879
				dr_N (g/g)	12 0.868	0.919	0.051	0.007 <0.001
				PDIA6 (g/kg DM)	2	59	157	99	12.9	0.083
		Lupin Extrusion	CP (g/kg DM)	20	372	382	10	3.9	0.020
				ED6_N (g/g)	19 0.931	0.776	-0.155	0.026 <0.001
				PIA6 (g/kg DM)	16	28	83	55	11.1 <0.001
				dr_N (g/g)	6 0.622	0.946	0.324	0.047 0.001
				PDIA6 (g/kg DM)	2	16	109	92	2.9	0.020
			Autoclaving CP (g/kg DM)	1	407	407	0	-	-
				ED6_N (g/g)	1 0.750	0.437	-0.313	-	-
				PIA6 (g/kg DM)	1	102	229	128	-	-
			Toasting/	CP (g/kg DM)	37	355	357	3	1.8	0.169
			roasting	ED6_N (g/g)	47 0.773	0.611	-0.161	0.016 <0.001
				PIA6 (g/kg DM)	37	83	135	52	6.9	<0.001
				dr_N (g/g)	12 0.938	0.956	0.018	0.001 <0.001
				PDIA6 (g/kg DM)	2	72	165	93	5.9	0.040
			Cooking	CP (g/kg DM)	1	302	304	2	-	-
				ED6_N (g/g)	1 0.938	0.903	-0.035	-	-
				PIA6 (g/kg DM)	1	19	30	11	-	-
			Tanning	CP (g/kg DM)	1	302	283	-19	-	-
				ED6_N (g/g)	1 0.938	0.867	-0.071	-	-
				PIA6 (g/kg DM)	1	19	38	19	-	-
		Pea	Extrusion	CP (g/kg MS)	4	237	237	0	2.5	0.904
				ED6N (g/g)	7 0.782	0.708	-0.074	0.046 0.159
				PIA6 (g/kg DM)	3	29	76	47	4.8	0.010
				dr_N (g/g)	1 0.870	0.991	0.121	-	-
			Autoclaving CP (g/kg DM)	1	268	268	0	-	-
				ED6_N (g/g)	1 0.751	0.428	-0.323	-	-
				PIA6 (g/kg DM)	1	67	153	86	-	-
			Toasting	CP (g/kg DM)	2	261	261	0	0	-
				ED6_N (g/g)	11 0.783	0.593	-0.190	0.027 <0.001
				PIA6 (g/kg DM)	2	66	126	60	10.3	0.017
				dr_N (g/g)	11 0.932	0.964	0.033	0.004 <0.001

Table 6 :

 6 Effects of replacing soybean meal (SBM) by raw or treated faba bean seed on in vivo 680

	681	parameters in dairy cows
		Treatment Variable (unit)	n	Mean	Mean	∆ PS -	SE (2)	P-
					SBM	PS	SBM (1)	∆	value
		Raw	Diet CP (g/kg DM)	8	159	159	0	0.4 0.718
			CP provided by SBM or PS	4	35	41	6	2.8 0.104
			(g/100 g CP)
			DM intake (kg/d)	9	20.7	20.7	0.0	0.08 0.876
			NH3 (mg/L)	8	144.9	161.6	16.7	6.59 0.039
			Total VFA (mmol/L)	8	105.4	98.6	-6.8	2.61 0.036
			Acetate/propionate (mol/mol)	8	2.7	2.9	0.2	0.10 0.052
			BC-VFA (3) (mol/100 mol	5	2.3	2.5	0.2	0.10 0.079
			VFA)
			Milk yield (kg/d)	10	26.8	26.3	-0.5	0.23 0.079
			Milk fat (g/kg)	10	33.3	33.9	0.6	0.67 0.424
			Milk protein (g/kg)	10	33.2	32.6	-0.7	0.23 0.018
			Milk urea (mg/L)	2	217.0	187.0	-30.0	4.00 0.084
			N intake (g/d)	9	522.8	519.8	-3.0	3.13 0.370
			N milk (g/d)	10	146.4	141.6	-4.8	1.00 0.001
			N urine (g/d)	4	182.6	179.5	-3.1	4.96 0.572
			N feces (g/d)	4	197.9	194.4	-3.5	2.79 0.296
			N balance (g/d)	4	2.9	10.1	7.2	3.12 0.104
			N milk/N intake (g/100 g)	9	28.6	28.0	-0.7	0.24 0.026
		Extrusion Diet CP (g/kg DM)	2	144	144	0	4.3 0.953
			CP provided by SBM or PS	2	40	48	8	0.5 0.039
			(g/100 g CP)
			DM intake (kg/d)	2	21.6	21.8	0.3	0.11 0.256
			NH3 (mg/L)	2	112.0	131.0	19.0	5.00 0.164
			Total VFA (mmol/L)	2	101.0	102.0	1.0	7.00 0.910
			Acetate/propionate (mol/mol)	2	3.1	3.1	0.08	0.17 0.713
			BC-VFA (3) (mol/100 mol	2	2.6	2.2	-0.4	0.13 0.183
			VFA)
			Milk yield (kg/d)	2	29.7	30.2	0.5	1.30 0.778
			Milk fat (g/kg)	2	31.6	29.7	-1.8	1.16 0.359
			Milk protein (g/kg)	2	30.6	29.1	-1.5	0.26 0.107
			Milk urea (mg/L)	2	177.0	129.0	-48.0	6.00 0.079
			N intake (g/d)	2	499.9	506.5	6.6	19.70 0.794
			N milk (g/d)	2	146.5	144.1	-2.4	5.28 0.729
			N urine (g/d)	2	148.5	155.7	7.2	4.28 0.343
			N feces (g/d)	2	184.7	180.6	-4.1	0.80 0.123
			N balance (g/d)	2	20.3	26.1	5.8	11.00 0.688
			N milk/N intake (g/100 g)	2	29.3	28.5	-0.9	0.06 0.047
		Steam	Milk yield (kg/d)	2	21.2	21.3	0.1	0.08 0.425
		cooking	Milk fat (g/kg)	2	38.4	37.5	-0.9	1.20 0.590
			Milk protein (g/kg)	2	34.5	33.8	-0.7	0.10 0.090
			Milk urea (mg/L)	2	309.7	281.9	-27.8 11.40 0.246
			N milk (g/d)	2	120.4	119.6	-0.8	0.83 0.500

Table 7 :

 7 Effects of replacing soybean meal (SBM) by raw or treated lupin seed on in vivo 685

	686	parameters in dairy cows.
		Treatment Variable (unit)	n	Mean	Mean	∆ PS -	SE (2)	P-
					SBM	PS	SBM (1)	∆	value
		Raw	Diet CP (g/kg DM)	9	176	173	-3	3.1	0.389
			CP provided by SBM or PS	4	41	40	-2	3.5	0.656
			(g/100 g CP)
			DM intake (kg/d)	11	20.3	19.9	-0.4	0.33 0.296
			NH3 (mg/L)	2	144.3	165.4	21.1	1.07 0.032
			Total VFA (mmol/L)	2	111.0	106.1	-5.0	1.05 0.133
			Acetate/propionate (mol/mol) 2	2.9	2.9	0.03	0.07 0.706
			BC-VFA (3) (mol/100 mol	2	2.4	2.5	0.0	0.13 0.852
			VFA)
			Milk yield (kg/d)	13	28.5	28.8	0.3	0.35 0.400
			Milk fat (g/kg)	13	36.5	37.1	0.6	0.53 0.266
			Milk protein (g/kg)	13	31.2	30.0	-1.2	0.20 <0.001
			Milk urea (mg/L)	3	325.0	303.7	-21.3 17.20 0.340
			N intake (g/d)	11	577.7	574.6	-3.1	11.90 0.799
			N milk (g/d)	13	146.2	142.6	-3.5	2.11 0.119
			N urine (g/d)	1	139.0	132.0	-7.2	-	-
			N feces (g/d)	1	171.0	148.0	-22.6	-	-
			N balance (g/d)	1	40.0	36.0	-4.1	-	-
			N milk/N intake (g/100 g)	11	26.2	26.0	-0.2	0.38 0.683
		Extrusion Diet CP (g/kg DM)	3	145	150	5	0.8	0.030
			CP provided by SBM or PS	2	40	43	3	0.3	0.060
			(g/100 g CP)
			DM intake (kg/d)	3	20.1	18.7	-1.4	0.45 0.093
			NH3 (mg/L)	2	120.0	129.0	9.0	4.00 0.266
			Total VFA (mmol/L)	2	108.0	101.0	-7.0	0	-
			Acetate/propionate (mol/mol) 2	2.7	2.9	0.2	0.12 0.337
			BC-VFA (3) (mol/100 mol	2	2.5	2.3	-0.2	0.02 0.051
			VFA)
			Milk yield (kg/d)	4	29.2	30.7	1.5	0.72 0.135
			Milk fat (g/kg)	4	34.4	31.2	-3.2	1.47 0.119
			Milk protein (g/kg)	4	30.5	28.4	-2.2	0.07 <0.001
			Milk urea (mg/L)	2	178.0	111.0	-67.0	9.00 0.085
			N intake (g/d)	3	503.2	484.3	-18.9 16.80 0.378
			N milk (g/d)	4	146.1	143.2	-2.9	2.52 0.339
			N urine (g/d)	2	139.3	125.5	-13.8	4.55 0.202
			N feces (g/d)	2	170.5	158.4	-12.2	6.02 0.293
			N balance (g/d)	2	39.6	37.7	-2.0	8.35 0.854
			N milk/N intake (g/100 g)	3	30.8	31.2	0.5	0.36 0.310
		Roasting DM intake (kg/d)	2	23.7	22.5	-1.2	0.34 0.169
			NH3 (mg/L)	1	168.6	194.2	25.6	-	-
			Total VFA (mmol/L)	1	114.0	114.1	0.1	-	-
			Acetate/propionate (mol/mol) 1	3.1	2.9	-0.2	-	-

Table 8 :

 8 Effects of replacing soybean meal (SBM) by raw or treated pea seed on in vivo 690

	691	parameters in dairy cows.
		Treatment Variable (unit)	n	Mean	Mean PS ∆ PS -	SE (2) ∆	P-value
					SBM		SBM (1)
		Raw	Diet CP (g/kg DM)	9	159	157	-2	4.7	0.759
			CP provided by SBM or PS	4	36	38	2	8.03	0. 799
			(g/100 g CP)
			DM intake (kg/d)	9	22.4	22.2	-0.2	0.34	0.612
			NH3 (mg/L)	5	108.1	133.8	25.7	5.43	0.009
			Total VFA (mmol/L)	4	102.9	107.1	4.2	1.79	0.101
			Acetate/propionate (mol/mol)	4	3.0	3.2	0.2	0.08	0.113
			BC-VFA (3) (mol/100 mol VFA)	4	2.1	2.2	0.2	0.06	0.085
			Rumen protein balance (g/kg	1	47.0	48.8	1.8	-	-
			DMI)
			Non-NH3 N duodenal flow (g/d)	1	383.3	401.7	18.4	-	-
			Microbial N duodenal flow (g/d)	1	221.4	254.1	32.7	-	-
			Non-NH3 non-microbial N	1	161.9	147.6	-14.3	-	-
			duodenal flow (g/d)
			Milk yield (kg/d)	9	32.0	30.9	-1.1	0.64	0.140
			Milk fat (g/kg)	9	36.8	37.1	0.3	0.50	0.563
			Milk protein (g/kg)	9	31.4	31.2	-0.2	0.12	0.169
			Milk urea (mg/L)	5	243.3	238.1	-5.2	27.70	0.861
			N intake (g/d)	9	569.0	563.3	-5.7	17.00	0.744
			N milk (g/d)	9	162.3	156.2	-6.1	3.2	0.092
			N urine (g/d)	5	207.4	211.8	4.4	12.60	0.745
			N feces (g/d)	5	175.4	181.0	5.6	8.82	0.560
			N balance (g/d)	5	38.4	36.2	-2.3	8.86	0.809
			N milk/N intake (g/100 g)	9	28.6	27.7	-0.9	0.51	0.120
		Extrusion Diet CP (g/kg DM)	2	161	154	-7	7.9	0.530
			DM intake (kg/d)	2	20.7	21.4	0.7	0.50	0.395
			NH3 (mg/L)	1	143.8	136.3	-7.4	-	-
			Milk yield (kg/d)	2	34.1	34.5	0.5	0.65	0.614
			Milk fat (g/kg)	2	37.1	35.7	-1.4	0.15	0.070
			Milk protein (g/kg)	2	31.8	32.2	0.4	0.60	0.626
			Milk urea (mg/L)	1	186.2	234.2	48.1	-	-
			N intake (g/d)	2	540.9	585.7	44.8	37.20	0.441
			N milk (g/d)	2	176.5	182.3	5.8	0.83	0.090
			N urine (g/d)	1	148.0	180.0	32.0	-	-
			N feces (g/d)	1	196.0	181.0	-15.0	-	-
			N balance (g/d)	1	9.3	67.7	58.4	-	-
			N milk/N intake (g/100 g)	2	32.6	31.2	-1.4	1.81	0.582
		Flaking	Milk yield (kg/d)	2	23.0	22.6	-0.4	0.24	0.364
			Milk fat (g/kg)	2	36.6	35.6	-1.0	0.55	0.307
			Milk protein (g/kg)	2	34.3	33.0	-1.3	0.30	0.144
			Milk urea (mg/L)	2	290.4	312.3	21.9	0.55	0.016
			N milk (g/d)	2	143.5	132.8	-10.7	2.47	0.144
		Expansion Diet CP (g/kg DM)	1	159	159	1	-	-

Table 9 :

 9 Effects of replacing raw by treated faba bean seed on in vivo parameters in dairy 695

	696	cows.
		Treatment Variable (unit)	n Mean	Mean	∆ Treated	SE (2)	P-
					raw	treated	-raw (1)	∆	value
		Extrusion Diet CP (g/kg DM)	6	160	161	1	1.6	0.430
			CP provided by faba bean (g/100	6	24	24	0	0.3	0.223
			g CP)
			DM intake (kg/d)	6 21.3	21.5	0.2	0.17 0.248
			NH3 (mg/L)	6 168.3	160.0	-8.3	9.37 0.418
			Total VFA (mmol/L)	6 109.5	112.9	3.4	2.25 0.191
			Acetate/propionate (mol/mol)	6	3.1	3.2	0.1	0.09 0.449
			BC-VFA (3) (mol/100 mol VFA)	6	2.6	2.1	-0.5	0.06 0.001
			Rumen protein balance (g/kg	1	-3.5	-22.6	-19.2	-	-
			DMI)
			Non-NH3 N duodenal flow (g/d) 1 365.5	408.7	43.2	-	-
			Microbial N duodenal flow (g/d) 1 194.2	216.2	22.0	-	-
			Non-NH3 non-microbial N	1 172.1	192.6	20.5	-	-
			duodenal flow (g/d)
			Milk yield (kg/d)	5 30.2	31.2	1.0	0.48 0.106
			Milk fat (g/kg)	5 38.0	35.2	-2.7	0.73 0.020
			Milk protein (g/kg)	5 30.2	30.1	-0.2	0.11 0.211
			Milk urea (mg/L)	5 213.2	213.6	0.4	7.81 0.962
			N intake (g/d)	6 541.9	549.0	7.0	7.43 0.388
			N milk (g/d)	5 150.4	154.4	4.1	1.78 0.085
			N urine (g/d)	5 175.6	175.5	-0.1	3.08 0.977
			N feces (g/d)	5 213.8	209.9	-3.9	3.66 0.344
			N balance (g/d)	5 43.1	60.6	17.5	11.50 0.204
			N milk/N intake (g/100 g)	5 26.1	26.4	0.3	0.29 0.360
		Toasting/ Diet CP (g/kg DM)	4	181	179	-2	1.5	0.267
		roasting	CP provided by faba bean (g/100	4	28	25	-3	2.1	0.310
			g CP)
			DM intake (kg/d)	4 21.4	21.3	-0.1	0.04 0.092
			NH3 (mg/L)	2 227.7	209.5	-18.2	9.71 0.312
			Total VFA (mmol/L)	2 117.0	98.0	-19.0	9.00 0.282
			Acetate/propionate (mol/mol)	2	4.1	4.9	0.8	0.33 0.262
			Rumen protein balance (g/kg	4 29.8	23.7	-6.1	2.87 0.126
			DMI)
			Non-NH3 N duodenal flow (g/d) 2 99.2	97.5	-1.8	0.59 0.205
			Milk yield (kg/d)	4 27.0	27.4	0.4	0.30 0.275
			Milk fat (g/kg)	4 46.1	44.5	-1.6	0.37 0.023
			Milk protein (g/kg)	4 35.6	35.4	-0.2	0.37 0.630
			Milk urea (mg/L)	4 234.2	238.6	4.4	10.50 0.707
			N intake (g/d)	6 455.0	447.0	-7.9	3.74 0.088
			N milk (g/d)	4 155.5	157.6	2.1	2.17 0.397
			N feces (g/d)	2 34.6	35.2	0.6	1.60 0.772
			N milk/N intake (g/100 g)	4 25.3	26.1	0.8	0.41 0.160
		Tanning	Milk yield (kg/d)	1 20.4	19.8	-0.6	-	-

  branched-chain VFA (isobutyrate and isovalerate) 703

	N feces (g/d)	1	141.0	148.0	7.0	-	-
	N balance (g/d)	1	-3.8	13.3	17.2	-	-
	N milk/N intake (g/100	1	27.7	27.2	-0.4	-	-
	g)

Acknowledgments

This work was conducted as part of the PROLEVAL project, supported by Valorex (Combourtillé, France) and funded by bpifrance (Paris, France). The authors thank Jenny Grice (Bieuzy Les Eaux, France) for checking and amending the English text.

23 0.28 0.04 0.22 0.34 (1) standard deviation 665 (2) branched-chain VFA (isobutyrate and isovalerate) 666 (3) (1) difference between proteaginous seed (PS) and soybean meal (SBM) 692

(2) standard error of the difference 693 (3) (1) difference between treated proteaginous seed (PS) and raw PS 697

(2) standard error of the difference 698

(3) branched-chain VFA (isobutyrate and isovalerate) 699


Raphaël Paut
email: raphael.paut@inrae.fr

Rodolphe Sabatier

Arnaud Dufils

Marc Tchamitchian

How to reconcile short-term and long-term objectives in mixed farms? A dynamic model application to mixed fruit tree -vegetable systems

Keywords: crop diversification, horticulture, mixed horticultural farms, dynamic modeling

Mixed fruit tree and vegetable (MFV) farms correspond to the joint production of fruit trees and vegetable crops. In temperate regions, this emerging farming system is a potentially attractive land use to foster the diversification of agricultural systems. However, these systems combine two productive and labor-intensive enterprises (orchard and market gardening) and in practice, the system feasibility depends on the availability and need for labor. Long-term yield predictions are needed to assess the economic profitability of such innovative farming systems.

A dynamic bio-economic model of MFV systems was developed to assess its main properties over time. This simple state-control model is based on yields, biological dynamics, workload requirements and economic profitability of each farming enterprise. Due to the lack of data from long-term experiments, the model was parametrized on data from specialized systems and calibrated with expert knowledge. The implementation of the model made it possible to simulate management strategies for vegetable crops and fruit trees, and to assess their impact on the long-term system dynamics. Results show that specialized market gardening enterprise has an overall higher profitability per hectare than specialized fruit trees. Nevertheless, mixed systems composed of roughly equivalent shares of fruit trees and vegetable crops make it possible to decrease workload constraints while maintaining a high level of profitability. In

Introduction

The productive and environmental challenges that agriculture is facing have led to the emergence of new farming models based on agro-ecological paradigms [START_REF] Altieri | Linking ecologists and traditional farmers in the search for sustainable agriculture[END_REF][START_REF] Wezel | Agroecological practices for sustainable agriculture[END_REF]. These models are built on a high agrobiodiversity, combining cultivated and natural elements within the agroecosystem. One of these, known as Mixed Fruit tree-Vegetable system (MFV), which corresponds to the joint production of fruit trees and vegetables is attracting a growing interest in Europe, especially among new farmers [START_REF] Léger | Agroforesterie maraîchère : un choix stratégique pour garantir une durabilité en transition agroécologique ? Expériences issues du projet SMART[END_REF][START_REF] Warlop | The smart project: a focus on fruit trees and vegetables agroforestry systems in France[END_REF].

In addition to being highly diversified (up to 20-30 crop species), these systems combine two productive and highly labor-intensive enterprises: orchard and market gardening. This represents a diversification strategy that goes further than increasing the number of crops, since it adds a functional diversity provided by fruit trees. These farming systems, which were still extremely rare at the beginning of this century, are currently undergoing a strong development, in line with the current trend of the establishment of small organic farms, generally created by people without any previous family farming ties [START_REF] Léger | Agroforestry market gardening: a strategic choice to improve sustainability in agroecological transition?[END_REF].

The existing literature on MFV deals mainly with their conception [START_REF] Sieffert | Co-design of innovative periurban horticultural agroforestry systems: case study of a pilot farm in the south of France[END_REF][START_REF] Warlop | Vertical project : designing fruit agroforestry systems for a renewed horticulture[END_REF], or consists of detailed descriptive analyses [START_REF] Léger | Agroforestry market gardening: a strategic choice to improve sustainability in agroecological transition?[END_REF][START_REF] Pantera | Agroforestry for high value tree systems in Europe[END_REF]. While this work is important, it leaves farmers somewhat lacking regarding the management of these systems, as the interactions between agro-ecological processes and management actions are not taken into account. However, the management of MFV is particularly challenging for two main reasons. The first reason is linked to the great complexity of these systems, since they add up the intrinsic difficulties of two very demanding systems -orchard and market gardening -to which are added the difficulties linked to the interaction between these two systems. It involves, even more than for specialized production systems, a need for prioritization between objectives and trade-offs between constraints [START_REF] Navarrete | Crop management, labour organization, and marketing: three key issues for improving sustainability in organic vegetable farming[END_REF]. The second limitation is related to the differences in temporal dynamics between crop types within such systems combining perennials and annuals.

The simultaneous management of short-term market gardening and long-term orchards, with their own dynamics, can be very complex and jeopardize the long-term viability of the farm. Following van der Werf et al. (2007) we argue that one of the key factors in the success of mixed systems, compared to specialized ones, is the appropriate use of operational resources and more specifically of labor. In practice, trade-offs between short term and longterm objectives imply that farmers have to make compromises in the allocation of working time to each enterprise. Thus, each enterprise may end up being managed in sub-optimal conditions depending on working time needs and supply.

Being able to study various scenarios of resource use, especially labor, and characterizing their consequences on the system properties is therefore paramount. In the specific case of the MFV, since the adoption dynamic is relatively recent, empirical data on mature systems are still very scarce and the modelling approach can overcome constraints of data availability. The existing literature on dynamic models of mixed tree-crop systems focuses mainly on process-based models, like tree-soil-crop interactions [START_REF] Dupraz | Hi-sAFe: A 3D Agroforestry Model for Integrating Dynamic Tree-Crop Interactions[END_REF][START_REF] Dupraz | Synthesis of the Silvoarable Agroforestry For Europe project[END_REF][START_REF] Van Noordwijk | WaNuLCAS, a model of water, nutrient and light capture in agroforestry systems[END_REF]. Some models that integrate socio-economic components mainly deal with timber-based systems [START_REF] Graves | Farm-SAFE: The process of developing a plot-and farm-scale model of arable, forestry, and silvoarable economics[END_REF][START_REF] Graves | Development and application of bio-economic modelling to compare silvoarable, arable, and forestry systems in three European countries[END_REF] and do not consider perennials with annual production such as fruit trees.

In the present study, we built a simple dynamic model of a mixed fruit tree-vegetable farming system making it possible to assess its main properties through time. This model is then implemented to explore different management strategies in MFV farming systems. The ultimate goal of our model is to dynamically predict the long-term evolution of an MFV system under conflicting workload conditions between fruit tree and vegetable enterprises. Running the model under different configurations and settings allows addressing the following questions:

• How do farmers' choices on crop rotation and allocation of their working time impact the system dynamics in the long term?

• How can farmers make the most of the complementarities in terms of working time in an MFV system? • How can short-and long-term profitability objectives be reconciled in MFV systems management?

Materials and methods

Qualitative description of the model

To address our study objectives, we define a discrete time state-control model of a mixed orchard-market gardening system at the scale of a one-hectare farm. The model (Fig. 1) aimed to represent the main biological dynamics while being simple enough to remain understandable. It is based on two sub-models: market gardening and orchard models that are described in the following sections ( §2.3 and §2.4). It runs at a yearly time step over a period of 20 years. We analyze the dynamics of the states of fruit trees (X[0]) and the net present value, which is the discounted cumulative annual gross margin (X [START_REF] Tichit | A viability model to assess the sustainability of mixed herds under climatic uncertainty[END_REF]). The dynamics are steered by two control variables: (i) the crop rotation (U[0]) which represents the choice of tree species and vegetable crops grown each year; (ii) the workload allocation (U [START_REF] Tichit | A viability model to assess the sustainability of mixed herds under climatic uncertainty[END_REF]) which refers to the farmer's prioritization of labor between orchard and market gardening.

A bio-economic analysis

A variety of conceptual models of mixed tree and crop farming systems economics have been developed depending on specific contexts and analysis objectives [START_REF] Luedeling | Field-scale modeling of tree-crop interactions: Challenges and development needs[END_REF]. When comparing perennial and arable systems, while the economic results of vegetables occur on an annual basis, timber revenues from trees generally occur once, many years after system establishment [START_REF] Graves | Development and application of bio-economic modelling to compare silvoarable, arable, and forestry systems in three European countries[END_REF]. The profitability of such mixed systems relative to pure arable agriculture and forestry can be determined by comparing their net present value (NPV), calculated from cost-benefit analysis by discounting future benefits and costs [START_REF] Graves | Farm-SAFE: The process of developing a plot-and farm-scale model of arable, forestry, and silvoarable economics[END_REF][START_REF] Graves | Development and use of a framework for characterising computer models of silvoarable economics[END_REF][START_REF] Van Der Werf | Yield-SAFE: A parameter-sparse, process-based dynamic model for predicting resource capture, growth, and production in agroforestry systems[END_REF]. We thus developed a bio-economic model, at a one-hectare scale, of mixed fruit tree-vegetable systems. The economic value of each enterprise was calculated using the annual gross margin (GM) calculated as the revenue (R) minus production costs (including supply costs, labor, machinery) associated with the enterprise (C), Eq. ( 1). The fixed costs of structures are therefore not taken into account.

= R -C

(1)

Economic data was retrieved from the Regional Chamber of Agriculture of Provence-Alpes-Côte d'Azur region. All detailed data for the model are available in Appendix A.

Market gardening sub-model

The market gardening sub-model consists of an intra-annual rotation of vegetable crops, where each crop represents an equivalent 1/ , being the total number of crops. Each vegetable crop is defined by a gross margin ( ) and a workload requirement ( ).

Depending on the workload allocation control ( [START_REF] Tichit | A viability model to assess the sustainability of mixed herds under climatic uncertainty[END_REF]), workload requirement for vegetable crops may or may not be fulfilled. The amount of working time dedicated to vegetables ( ) is therefore the ratio between crop workload requirements ( ) and the time actually allocated ( ):

= (2)
Ultimately, the annual gross margin for vegetables ) depends on the theoretical vegetable gross margin ) multiplied by the workload actually available for vegetable crops (Eq. ( 3)).

) = 1 ) ! "# (3)
Note at this stage that the gross margin equations (Eqs. 3 above and 4 below) contain

the assumption that yields are proportional to the percentage of required workload expended on the crop. In order to select contrasting market gardening strategies, we simulated all possible combinations of 5 vegetable crops. Based on these simulations, we chose three contrasting

Orchard sub-model

The orchard sub-model is more complex because, due to the perennial nature of fruit species, it is essential to take into account their inter-annual dynamics. We are not aware of any research that has explored fruit tree dynamics growing in sub-optimal conditions. We therefore used data from pure crop systems, which we calibrated according to experts' knowledge to express their dynamics with a suboptimal management. The orchard sub-model was calibrated in a three-hour collective workshop in February 2020, bringing together 10 fruit tree experts (7 fruit tree technicians and 3 researchers. The production dynamics of each fruit tree species were characterized according to three scenarios: (i) optimal investment, corresponding to the systematic prioritization of trees in the system management, so that they develop optimally; (ii) minimal investment, where only essential operations such as irrigation and training are carried out; (iii) the intermediate scenario stands for an in between situation where tree operations are not done perfectly or are staggered in time. These three scenarios differ in the amount of labour time that the farmer can allocate to the fruit-growing enterprise. This made it possible to characterize fruit tree growth as a function of workload allocation. Some experts were also consulted individually afterwards to refine estimates.

The resulting fruit tree dynamics were fitted using the Nonlinear Least Squares method [START_REF] Bates | Nonlinear regression analysis: Its applications[END_REF]. Note that the fitted curve has been assumed to have a sigmoidal shape (detailed in Appendix B). Similar to vegetables, the annual gross margin of trees $% ) was modeled as:

$% ) = 1 & $ ) & ) ' ) ( &"# (4)
where & is the total number of fruit tree species in the rotation, $ ) is the gross margin of fruit trees, & is the workload allocation dedicated to fruit trees and ' ) is the annual tree development coefficient.

As for vegetable crops, we simulated all possible combinations of at least three fruit tree species and we chose three different management strategies (labelled A, B and C respectively in Fig. 2b): (1) a strategy with low labor-demand and low-payoff; (2) an intermediate labor-intensive and payoff strategy; (3) highly labor-intensive, high-payoff strategy.

Performance indicators and simulations

Several indicators were computed for system performance: the annual gross margin (AGM), the Net Present Value (NPV) and a workload constraint indicator. The annual gross margin (€/ha per year) restates the system profitability into an annual basis (Eq. 5). It was calculated as the sum of the total gross margin from orchard (FT) and market gardening (MG).

The AGM allows calculating an inter-annual average and standard deviation of gross margin, which will be used in the study of the dynamics of the system profitability.

AGM t) = FT t) + MG t) (5)
The net present value (NPV, in € ha -1 ) of each enterprise was then defined using Eq. ( 6), as the sum of annual gross margin AGM(t) over a period of T years, discounted by the discount rate (r). The analysis was performed with a 4% discount rate.

NPV = AGM t) 1 + ) 3 3"4 3"5 (6)
The last indicator was workload constraint, which was expressed as the yearly relative workload in excess of the workload required by both orchard and market gardening enterprises.

Model outputs are presented with a yearly time step, but within each year the model takes into account the labor needs and supply at a monthly scale. The workload constraint indicator then reflects the number of adaptations that farmers could make when managing the system:

6 7 8 9 7 : = 1 % 1 - 1 2 < ) ) + & ) & ) = 4 # (7)
where ) is the workload effectively allocated to vegetables at year ) and ) is the workload requirement for vegetables at year ).

In order to study the temporal dynamics of mixed systems, we modeled the combination of three market gardening crop rotation types and three fruit tree portfolios (labelled respectively A, B and C in Fig. 2). The combination of these three strategies per enterprise generates nine scenarios. In each scenario, the two controls presented in Fig. 1,[0] and [START_REF] Tichit | A viability model to assess the sustainability of mixed herds under climatic uncertainty[END_REF] can vary: [0], the relative area of fruit trees over vegetables is a continuous variable that ranges between 0.1 and 0.9. [START_REF] Tichit | A viability model to assess the sustainability of mixed herds under climatic uncertainty[END_REF], the allocation of working time is a continuous variable that ranges between 0 and 1 (i.e. between a systematic prioritization of working time towards market gardening to fruit trees).

As a first step, we simulated 10,000 random tree and crop portfolios in which the crop rotation and the controls described above are chosen randomly. In a second step, due to computation limitations, we modelled the nine contrasted scenarios described above, screening in detail controls [0] and [START_REF] Tichit | A viability model to assess the sustainability of mixed herds under climatic uncertainty[END_REF] with 30 steps each. All computations were performed with Python 3.8.1 (www.python.org/), graphical outputs were produced alternatively with Python and R software 3.6.0 (https://cran.r-project.org). Model source code is available in Appendix C.

Results

Fruit tree dynamics

The collective experts' knowledge sharing workshop, completed with additional expert interviews, made it possible to describe six main species of fruit trees most cultivated in the South-East of France: Apple (Malus domestica), Peach (Prunus persica), Pear (Pyrus communis L.), Apricot (Prunus armeniaca), Cherry (Prunus avium) and Prune (Prunus domestica). The dynamics (Fig. 3) illustrate the potential yield of each fruit trees according to three levels of workload allocation: (i) optimal investment; (ii) minimal investment; (iii) the intermediate scenario. At this stage, two types of fruit trees can be distinguished: those that require a high investment to produce (Fig. 3c,d,f), and the ones that are less sensitive to a lower maintenance and that can produce in the long term despite suboptimal management (Fig. 3a,b,e). Fruit tree species also differ in the timing of management and therefore in the dates on which workload conflicts may arise. These characteristics are important features to take into account in the design of an MFV system and will greatly influence the dynamics of the system in the long run.

Crop rotation and workload allocation

As a first step, we modelled 10,000 random portfolios of 10 crops. Each portfolio is a random combination of fruit tree and vegetable enterprises, and a random workload allocation between both enterprises. From these simulations, inter-annual average and standard deviation of gross margin were calculated (Fig. 4). At this stage, the average annual gross margin does not show clear trends, but we can observe that standard deviation of AGM is mainly driven by the relative share of each enterprise in the crop rotation. The workload constraint indicator provides an indication of the ratio between the working time required by a given crop rotation and the work time available. This reflects the notion of feasibility (or unfeasibility) of a crop rotation, and whether adjustments are needed in work organization over the duration of trees. The measurement of workload constraints for the 10,000 random portfolios shows that the indicator is > 0 for 87.2% of the scenarios, which implies that adaptations are necessary for all these scenarios (Fig. 5). The number of adaptations needed increased with the prioritization of one or another of the crops. This indicates that lower constraints on working time are found for intermediate strategies, balancing trees and vegetables within the system. Furthermore, workload constraints decrease with the relative share of fruit in the crop rotation, highlighting the existence of a trade-off between AGM standard deviation and workload constraint.

Trade-offs between short-term and-long term objectives

To deepen the analysis of trade-offs between short-term and long-term objectives, we investigated the combination of the two means of control (workload allocation and share of each enterprise) on system dynamics, in contrasted situations. Three distinct vegetable crop rotations and three distinct fruit tree portfolios were selected (labelled respectively A, B and C in Fig. 2). The combination of these three strategies per enterprise generates nine scenarios (Fig. 6). The relation between the workload allocation and the relative share of each enterprise in the crop rotation shows that overall a better average annual gross margin (AGM) is obtained with a higher proportion of vegetables (x-axis) and higher workload allocated to vegetables (yaxis) (Fig. 6a). Nevertheless, for intensive market gardening (vegs = C), it seems that a combination of a significant part of fruit trees with an allocation of working time towards fruit trees brings satisfactory results. The workload constraint indicator provides additional information (Fig. 6b). For low-intensity vegetable crop rotation (Vegs = A), the overall trend is that high NPVs are associated with low workload constraints. However, for intermediate and These results provide a first insight into what different combinations of controls can achieve under very distinct scenarios. However, it is important to further analyze these characteristics, particularly their evolution over time. Over twenty years, we analyzed the average AGM as a function of the share of each enterprise in the crop rotation (Fig. 7a). To compare different values of the control [0], we plotted the net present value (NPV) difference between three values of [0]: 0.1, 0.5, and 0.9. The difference between these three [0] values is given pair by pair (Fig. 7b,c,d).

The results show that crop rotations including a larger share of vegetable crops lead systematically to a better short-term productivity. However, the long-term profitability shows mixed features. In Fig. 7b, for instance, if we consider the difference between strategies promoting vegetables (FTmin) and intermediate strategies (FTinterm), we can observe two tipping points. First, the gap between both strategies is compensated from the fifth year onwards, and FTinterm becomes more effective on an annual basis. Second, the NPV over the entire period indicates that the FTinterm strategy is generally more profitable (second switchover at 10 years after planting). In Fig. 7c, the comparison between FTmin and FTmax shows that both strategies have a similar NPV over 20 years, but FTmin is more profitable in the first 6 years. Finally, comparing FTinterm and FTmax (Fig. 7d) shows that the intermediate strategy is always more profitable than a strategy promoting fruit trees. This indicates that in the long term, a strategy favoring intermediate share of fruit trees can be more desirable than a strategy promoting one or another enterprise. However, strategies implying large shares of fruit trees require being able to cope with several successive years of low (or even negative) ANPV, either by accepting a lower income in the first years, or through a larger starting capital. This underlines the importance of analyzing not only the overall NPV but also its dynamics through time, as it is likely to fluctuate significantly.

Discussion

Model strengths and limitations

Mixed fruit tree-vegetable systems, which correspond to the combination of fruit trees and vegetable crops within a farming system, are currently undergoing strong development in France and other parts of Europe. However, since this is a relatively recent dynamic, empirical data on these systems are very rare and biological processes are still very uncertain [START_REF] Imbert | Estimating population dynamics parameters of cabbage pests in temperate mixed apple tree-cabbage plots compared to control vegetable plots[END_REF][START_REF] Lauri | Agroforestry for fruit trees in Europe and Mediterranean North Africa[END_REF]. In the present study, we built a simple model of mixed fruit tree vegetable (MFV) system to assess its main properties through time. The approach presented in this study has been to combine in a dynamic way quantitative elements from both fruit trees and vegetables with qualitative and quantitative elements from expert knowledge to develop a model of MFV systems at a one-hectare scale. This model was dynamically implemented to (i) explore different management strategies in MFV systems and (ii) to dynamically predict longterm fruit tree and vegetable crops dynamics under competitive conditions on workload between both enterprises. This is, to our knowledge, the first attempt to model dynamics of MFV systems. Existing dynamic bio-economic model applications of mixed trees and crops systems do not consider the existence of two annually productive plants [START_REF] Van Der Werf | Yield-SAFE: A parameter-sparse, process-based dynamic model for predicting resource capture, growth, and production in agroforestry systems[END_REF][START_REF] Van Noordwijk | WaNuLCAS, a model of water, nutrient and light capture in agroforestry systems[END_REF] and they also rarely take into account labor constraints linked to the presence of other enterprises (but see [START_REF] Graves | Farm-SAFE: The process of developing a plot-and farm-scale model of arable, forestry, and silvoarable economics[END_REF]. However, these models take better account of interactions for resources other than labor (mainly biological interactions for water, nutrients and light). A model including all these interactions could be even more relevant, although relatively complex to implement in a purely deterministic way.

The use of a dynamic analytical framework applied to MFV systems made it possible to highlight the relation between different dimensions of system performance. The simulation of various characteristic scenarios also allows to show the relationships between the share of each enterprise in the crop rotation (fruit trees and vegetables), the workload allocation and the bio-economic performance of the system. Our results indicate that specialized market gardening enterprise has an overall better short-and long-term profitability per hectare (but not necessarily per working hour) than specialized fruit trees. Nevertheless, mixed systems composed of roughly equivalent shares of fruit trees and vegetable crops make it possible to decrease workload constraints while maintaining a high level of profitability. In addition, these intermediate situations can lead to better profitability in the long term, but require addressing both short-and long-term objectives, and the ability to cope with several consecutive years of lower income. The issue of uncertainty into long-term performance projections of agroforestry systems has recently been highlighted in tropical fruit-based agroforestry systems [START_REF] Do | Decision analysis of agroforestry options reveals adoption risks for resource-poor farmers[END_REF] and seems to be a recurring obstacle to the adoption of such systems.

Due to the simplicity of its individual components, the overall model was still manageable in terms of complexity and number of inputs required. This deliberately simple model makes it possible to model a large panel of situations. It has nevertheless some limitations that deserve to be discussed in more details. A first limit of our approach is that the model does not take into account some important but unknown tree-crop biological interactions. In mixed fruit tree -vegetable systems, crops can be organized in intercropping or agroforestry design, where crops have biological interactions with each other [START_REF] Paut | Modelling crop diversification and association effects in agricultural systems[END_REF]. These interactions may lead to a different overall yield and profitability, but may also influence the yields of both vegetables and fruit trees in a dynamic way [START_REF] Burgess | Modelling agroforestry systems[END_REF][START_REF] Paul | Agroforestry versus farm mosaic systems -Comparing land-use efficiency, economic returns and risks under climate change effects[END_REF]. In view of the current state of the literature on these systems, such components have not been included in the analysis, but it would be relevant to integrate them for further development of our model [START_REF] Van Vooren | Greening and producing: An economic assessment framework for integrating trees in cropping systems[END_REF].

Another limitation to our approach is that the model was deterministic and did not capture the uncertainty on parameter values. In fruit production, in particular, some key elements such as alternate bearing or climatic factors can affect inter-annual yield variability [START_REF] Beattie | Production variability in Apple crops[END_REF]. These sources of uncertainty are all the more justified in MFV systems as there is very little knowledge about the development of fruit trees under suboptimal management conditions. The introduction of stochasticity on key parameters (e.g., fruit tree yields) would be an approach to overcome these limitations. In this perspective, dynamic stochastic modelling approaches are very useful for characterizing the management flexibility of a system in a highly fluctuating and uncertain environment [START_REF] Accatino | Robustness and management adaptability in tropical rangelands: a viability-based assessment under the non-equilibrium paradigm[END_REF][START_REF] Do | Decision analysis of agroforestry options reveals adoption risks for resource-poor farmers[END_REF][START_REF] Sabatier | Grazing in an uncertain environment: Modeling the trade-off between production and robustness[END_REF].

Implications and perspectives

Our approach makes it possible to quantify dynamically the annual profitability of mixed fruit tree-vegetable farming systems. Combined with the characterization of implications in terms of labor constraints at different times of the year, it allowed highlighting determining factors of success at the scale of a one-hectare plot. The integration of farm fixed costs may in turn be used for a global farm analysis (García de [START_REF] García De Jalón | Modelling and valuing the environmental impacts of arable, forestry and agroforestry systems: a case study[END_REF][START_REF] Graves | Farm-SAFE: The process of developing a plot-and farm-scale model of arable, forestry, and silvoarable economics[END_REF][START_REF] Graves | Development and application of bio-economic modelling to compare silvoarable, arable, and forestry systems in three European countries[END_REF].

Finally, as with other integrated systems, an important aspect that would require further investigations is the dynamic and adaptive system management [START_REF] Bell | Integrated crop-livestock systems in Australian agriculture: Trends, drivers and implications[END_REF][START_REF] Hendrickson | Principles of integrated agricultural systems: Introduction to processes and definition[END_REF]. In our approach, we simulated scenarios where controls were defined as strategies for the entire cycle duration. However, it has been shown that adaptive management is a crucial factor to integrate in the analysis of agroecological systems [START_REF] Catalogna | Diversity of experimentation by farmers engaged in agroecology[END_REF]. In MFV systems, trees are planted and fixed in the system but vegetable crop rotation may vary over time. The management needs to be adapted to structural and functional changes that these systems undergo over time [START_REF] Léger | Agroforesterie maraîchère : un choix stratégique pour garantir une durabilité en transition agroécologique ? Expériences issues du projet SMART[END_REF]. Trade-offs that relate to the immediate provision of the production service (in our case, market gardening) may be to the detriment of other services in the future (e.g., fruit production). These trade-offs over time have a decisive impact on the biological dynamics of the agroecosystem. Many natural processes, such as those affecting tree training, occur at such a rate that several years may pass before significant effects are perceived by the farmer [START_REF] Lauri | Agroforestry for fruit trees in Europe and Mediterranean North Africa[END_REF][START_REF] Lauri | Fruit-trees in Agroforestry systems -Review and prospects for the temperate and mediterranean zones[END_REF]. The consequences of these trade-offs are then borne with a time lag. A useful method to capture the evolution of a dynamically managed system is viability theory [START_REF] Aubin | Viability theory[END_REF]. This mathematical framework makes it possible to study the evolution of dynamical systems under constraints on the system states and controls. Its recent applications to natural resource management have proved to be particularly relevant on livestock systems [START_REF] Sabatier | Assessing both ecological and engineering resilience of a steppe agroecosystem using the viability theory[END_REF][START_REF] Tichit | A viability model to assess the sustainability of mixed herds under climatic uncertainty[END_REF] or sylviculture [START_REF] Domenech | Forest Conservation and CO2 Emissions: A Viable Approach[END_REF] and would be particularly relevant for improving our approach.

Conclusion

Our model was a first attempt to analyze mixed fruit tree vegetable (MFV) systems.

We developed a dynamic bio-economic model to investigate the consequences of farmers' management decisions in terms of crop rotation and workload allocation on the dynamics of MFV systems. Using this dynamic approach based on states and controls, we simulated management strategies for both market gardening and orchard, and assessed their impact on the long-term dynamics of the system. The results made it possible to illustrate the consequences of different types of trade-offs between management objectives when making decisions in the steering of these systems, taking into account uncertainty linked to the lack of knowledge about these processes. These results highlight that the integration of fruit trees into a market gardening system can maintain a high level of profitability, while at the same time being a driver of flexibility. However, MFV systems require coping with several years of lower income due to the delay of fruit production. Further research will now be needed to better understand the processes underlying MFV systems and their dynamics in different management configurations.

Fig. 1 .

 1 Fig. 1. Conceptual representation of the model of joint fruit tree -vegetable systems. Blue round boxes stand for the state variables, yellow boxes stand for the control variables and cloud shapes stand for the performance indicators.

  management strategies: (1) a low labor-intensive and low-paying strategy; (2) an intermediate labor-intensive and paying strategy; (3) highly labor-intensive, high-paying strategy. These crop rotations are labelled A, B and C respectively in Fig. 2a. On this preliminary result, it is interesting to note that the two Pareto fronts intersect: A(a) > A(b) but C(a) < C(b). Increasing profitability in fruit growing is less time-consuming than increasing profitability in market gardening.

Fig. 2 .

 2 Fig. 2. Gross margin as a function of workload for all possible portfolios of (a) 5 vegetable crops and (b) at least 3 fruit tree species.

Fig. 3 .

 3 Fig. 3. Expert knowledge-based characterization of the dynamics of six important fruit trees over time according to three contrasted scenarios: (1) optimal investment; (2) moderate investment and (3) minimum investment (Cf. §2.4).

Fig. 4 .

 4 Fig. 4. Model-derived average annual gross margin and its inter-annual standard deviation from both orchard and market gardening enterprises, relative share of each enterprise in the crop rotation.FT: fruit trees; AGM: Annual Gross Margin.

Fig. 5 .

 5 Fig. 5. Workload constraint indicator as a function of (left) workload allocation between both enterprises; (right) standard deviation of annual gross margin. The blue curve displays the smoothed conditional mean. The color gradient represents the relative area of fruit trees in the crop rotation. WL: workload; AGM: annual gross margin.

Fig. 6 .

 6 Fig. 6. Model-derived (a) average annual gross margin and (b) workload constraint indicator, according to nine scenarios combining fruit trees and vegetable crops. Trees: Fruit trees portfolio. Vegs: vegetable crop rotation. A, B, C respectively refer to extensive, intermediate and intensive crop rotation. Each scenario was modeled with controls of 30 steps each. WL: workload. AGM: annual gross margin.

Fig. 7 .

 7 Fig. 7. (1) Evolution of the annual gross margin in the scenario combining intermediate vegetables scenario (Vegs = B, corresponding to an intermediate labor-and remunerationintensive strategy) and intensive fruit trees scenario (Trees = C, corresponding to a high laborand remuneration-intensive strategy) according to the relative share of each enterprise in the crop rotation (U[0]). Three contrasted values of U[0], 0.1 (▲), 0.5 ( ) and 0.9 ( ) are compared pair by pair: (2) FTmin-FTinterm; (3) FTmin-FTmax and (4) FTinterm-FTmax. AGM: Annual Gross Margin. NPV: Net Present Value.

Acknowledgments

The present work was carried out with the support of the Fondation de France (grant n°00064844) which is hereby thanked. We also thank Pierre-Eric Lauri, François Warlop, Solène Borne, Thierry Girard, Laurent Poulet, Guilhem Severac, Hugues Reynold, Julia Frezel and Emmanuelle Filleron for their contribution to the calibration workshop and their valuable advice. We would especially like to thank reviewer 1 for his/her very comprehensive and constructive review.