L Hossard

A Fadlaoui

E Ricote

H Belhouchette

Assessing the resilience of farming systems on the Saïss plain, Morocco

Keywords: Performances, farm types, diversification, climate change, water scarcity, participatory approach Highlights

A participatory approach was used to study farm resilience after a shock -Shocks differed according to farm type, but

We used a participatory approach with stakeholders on the Saïs plain of Morocco to design 5 possible future states and qualitatively assess the resilience of typical farm types (FTs) 6 experiencing major change. Our approach combined individual interviews of farmers and 7 stakeholders with participatory collective meetings to identify representative FTs and their 8 performance, project their evolutions and future performance in the face of change, and evaluate 9 their resilience. Performance, defined according to literature, interviews and meetings, included 10 different types of capital, income, yields, markets, support of public policies, and water access. 11 Four FTs were considered: highly irrigated market-gardening (FT1), rainfed cereals (FT2), 12 partly irrigated cereal-legumes (FT3), and mostly irrigated fruit-tree/market gardening (FT4). 13

The primary driver for FT2 and FT3 was climate change; for Ft1 and Ft4 it was limiting access 14 to water resources. Stakeholders designed more diversified systems for all FTs in relation to 15 those changes. Rankings of FT performance did not change between current and future states. 16

Performance did not evolve significantly, but FT4 was seen as the most resilient, and FT2 the 17 least. These qualitative results differ somewhat from other studies mobilizing quantitative 18 approaches, but they highlight the potential of local adaptation to limit the impacts of global 19 change on vulnerable agriculture. 20

Introduction 21

The Mediterranean region is expected to become a hotspot for the impacts of climate change, 22 rendering it particularly vulnerable to related global change (Giorgi and Lionello 2008). 23 Therefore, making agricultural food production systems resilient to climate and market shocks 24 is a major challenge [START_REF] Rivington | An integrated assessment approach to conduct analyses of climate change impacts 617 on whole-farm systems[END_REF]. Resilience can be defined as the capacity of a 25 system to buffer shocks while maintaining its structure and functions [START_REF] Walker | Resilience, adaptability 637 and transformability in social-ecological systems[END_REF]). In 26 the case of agricultural systems, resilience can be defined as their capacity to reorganize and 27 maintain interconnected functions and structures that are defined at different temporal and 28 spatial scales [START_REF] Walker | Resilience, adaptability 637 and transformability in social-ecological systems[END_REF]Souissi et al. 2018). This resilience is context-specific and 29 depends on three main characteristics: threats (e.g., climate change), vulnerability (i.e., 30 exposure due to agro-ecosystems and farmer characteristics), and reactive capacity (i.e., 31 capacity of farmers to adapt and recover) [START_REF] Altieri | Agroecology and the 525 design of climate change-resilient farming systems[END_REF]. This definition puts the farmer at 32 the heart of the assessment and improvement of resilience at field and farm levels. 33

In agro-ecosystems, resilience can be assessed at different levels: fields, farms (and connected 34 enterprises), and regions [START_REF] Peterson | Ways forward for resilience 605 research in agroecosystems[END_REF]. The determination of which level(s) to consider 35 is central to the assessment, because the various farm types will adapt differently (Reidsma et 36 al. 2010). 37 First, resilience is often assessed at the regional scale without considering farm diversity. Those 38 studies focus on socio-economic aspects, the role of stakeholders and their organizations, and 39 the resilience of the region or sector (e.g., [START_REF] Sinclair | Can resilience thinking 619 provide useful insights for those examining efforts to transform contemporary agriculture?[END_REF]. Such approaches are based on 40 surveys and trajectories, typically excluding bio-physical aspects. 41

Second, quantitative approaches are applied to study resilience at the farm scale, often relying 42 on bio-physical components [START_REF] Reidsma | Adaptation to climate change 613 and climate variability in European agriculture: the importance of farm level responses[END_REF], Souissi et al. 2018), or evolution of capital 43 and financial aspects [START_REF] Parsonson-Ensor | Exploratory research into the resilience of 602 farming systems during periods of hardship[END_REF]. Those studies are often limited in 44 terms of the inclusion of exogeneous factors, drivers (e.g., shocks) and the associated adaptation adaptation strategies is a difficult task, which may be particularly suited to local stakeholders 70 themselves, and thus calls for participatory research that can benefit from a simple tool. 71

In the current study, we designed a participatory approach aimed at analyzing the possible 72 futures of a diversity of farm types in a case study in Morocco. Several methods, such as 73 scenario exercises, have been proposed to study possible futures with stakeholders (e.g., 74 [START_REF] Delmotte | From stakeholders narratives to modelling plausible future agricultural systems. 551 Integrated assessment of scenarios for Camargue, Southern France[END_REF]. A scenario is characterized by the description of an initial situation and 75 the driving forces that induce a specific future [START_REF] Alcamo | Toward guidelines for environmental scenario anal-522 ysis[END_REF]. The causes of 76 change (the drivers) may be any combination of physical or ecological factors (e.g., climate 77 change), or social or economic factors (e.g., political, urban), depending on specific local issues 78 and contexts. Different scenarios can represent different impacts on agro-ecosystems (cropping 79 systems) and thus different futures with a range of performance at different spatial and temporal 80

scales. An important feature of the scenarios is their internal consistency. Cognitive maps can 81 be used to guarantee this consistency. A cognitive map is a graphical way of representing 82 interconnected concepts and is thus intended to graphically represent complex systems. It has 83 been widely used to elicit mental models (e.g., Mathevet et al. 2011;[START_REF] Gray | Fuzzy cognitive maps as representations of 572 mental models and group beliefs[END_REF], for farm 84 design [START_REF] Gouttenoire | Participatory modelling with farmers 567 groups to help them redesign their livestock farming systems[END_REF]) and scenario development [START_REF] Van Vliet | Linking stakeholders and modellers in 634 scenario studies: The use of Fuzzy Cognitive Maps as a communication and learning tool[END_REF]. As a flexible 85 and easy-to-understand tool that structures stakeholders' mental models [START_REF] Gray | Fuzzy cognitive maps as representations of 572 mental models and group beliefs[END_REF], 86 cognitive mapping together with written storylines can support collective thinking among 87 stakeholders. Although cognitive mapping has been used to study resilience in enterprises 88 [START_REF] Branco | Analysing determinants of small and medium-sized enterprise resilience using fuzzy 544 cognitive mapping[END_REF], urban systems [START_REF] Olazabal | Use of fuzzy cognitive maps to study urban resilience 600 and transformation[END_REF], and socio-ecological systems 89 [START_REF] Gray | Using fuzzy cognitive mapping as a participatory approach to analyze change, preferred 576 states, and perceived resilience of social-ecological systems[END_REF], to our knowledge it has never been used to study the resilience of farming 90

systems. 91

In this study, we mobilized stakeholders and cognitive mapping with two primary objectives: 92 to design possible futures of different farm types (FTs) under a major driver of change, and to 93 qualitatively assess the farms' resilience according to different FTs and their performance. 94

Study area 96

The Saïss plain covers 2,200 km 2 including 1,910 km 2 used for agriculture [START_REF] Fofack | Hybridation des règles d'accès à l'eau 560 souterraine dans le Saiss (Maroc) : entre anarchie et Léviathan[END_REF]. 97

The plain has fertile soils and benefits from groundwater aquifers (Berriane, 2002). The climate 98 is semi-arid, with rainfall ranging from 207 to 677 mm per year, for an average of 500 mm/year 99 (data 1980-2010). After a series of dry years, the 1980s marked a drastic reduction of rain-fed 100 crops and the development of irrigated agriculture, mainly vegetables (Quarouch et al. 2014). 101 This increase in irrigated crops (e.g., potato, onions, peach orchards, vineyards), which are also 102 associated with a high use of pesticides and chemical fertilizers [START_REF] Baccar | The determining 533 factors of farm sustainability in a context of growing agricultural intensification[END_REF], has led to 103 the overexploitation of the aquifer [START_REF] Quarouch | Eaux souterraines, 607 sources de dignité et ressources sociales : cas d'agriculteurs dans la plaine du Saïss au Maroc[END_REF]). In the process, the groundwater level 104 has dropped as much as 60 m at certain points (Agence de bassin hydraulique du Sebou, 2011). 105 106 2.2. Methodological framework 107

The general methodology followed in this work can be divided into three main steps. Each step 108 was aimed at qualifying one element of resilience, corresponding to the three components 109 suggested by Souissi et al. (2018): 1) characteristics and associated performance of the initial 110 system, 2) shock (from a specified cause), and 3) characteristics and associated performance of 111 the future system (equilibrium after the shock). 112

In each step the involvement of local and national stakeholders is a key issue. We interviewed 113 several farmers and local stakeholders in order to achieve the first step's objectives: to 114 characterize the initial situation (baseline) and the associated challenges and to select 115 representative FTs. The objectives of the second step, achieved through a collective workshop, 116 were to describe the structure and the functioning of the representative FTs, identify the drivers 117 of change in each case, and describe how they would adapt to the selected change in the future 118 scenario. In a second collective workshop, the objective of the third step was to assess the 119 performance of representative FTs, both current and future. 120

The identification and engagement of stakeholders involved in this study built on knowledge 121 from previous local studies [START_REF] Ameur | Specifying the differentiated 527 contribution of farmers to groundwater depletion in two irrigated areas in North Africa[END_REF][START_REF] Baccar | Shared environment, 530 diversity of pathways: dynamics of family farming in the Saïs Plain (Morocco)[END_REF][START_REF] Belhouchette | Impact assessment of climate change on farming systems in the South 627 Mediterranean area: a Tunisian case study[END_REF], associated project 122 partners (INRA-Morocco), and key interviews with individual sources. The goal was to involve 123 stakeholders that had expressed either concerns or courses of action (Appendix 1) on the 124 agricultural systems, as suggested by [START_REF] Van Den Belt | Public sector administration of ecological economics systems using mediated 632 modelling[END_REF]. 125

The identification and the involvement of stakeholders to be a part of this study were achieved 126 around two principles: 127

(i)
Strong farmer participation, especially in step 1. This was achieved by interviewing 128 21 farmers characterized as having distinct production objectives and farm 129 structures (access to resources, dominant crops, etc.). The main local challenges 130 were also identified by interviewing actors from extension services, statistics 131 services, and water management (5 interviews in total, each with 2 to 4 actors; Table 132 1). 133 (ii) Favor participation of stakeholders with similar or compatible interests (workshops 134 in steps 2 and 3), both among participants in general and between the participants in 135 the two different workshops. Overall, 30 participants from 20 institutions attended 136 the first workshop, and 16 from 11 attended the second (Table 1). These 137 stakeholders were from different types of organizations and levels (from provincial 138 to national). Except for two researchers, all the participants in the second workshop 139 also attended the first. A total of 53 people was involved in the entire process. The drivers to be studied were selected by stakeholders during the first collective workshop (6 157 hours). The project's objectives (future projections for 15 years hence, and the resilience of 158 Saïss farming systems) were introduced during this workshop. We then presented (1) a 159 summary of the SWOT analysis carried out during the individual interviews of stakeholders, 160

(2) a summary of farmers current constraints and projects collected during farmers' interviews, 161 and (3) a proposition of representative FTs to be studied during the workshops. After a short 162 discussion on these three diagnostic factors, we presented and illustrated definitions of the 163 terms, resilience and driver. The list of drivers of change (presented without indicating their 164 variation or type of impact, i.e., positive or negative) was based on a literature review and 165 interviews from the first step. This list included labor force, diseases, market access and 166 commercialization options, water resources, land access, prices of inputs, selling prices, and 167 climate changes. This list was finalized by stakeholders who were asked to individually choose, 168 justify and rank up to three drivers for each representative FT. Based on these choices and 169 rankings, the primary driver was selected for each FT in order to study the resilience and 170 changes in farm performance. 171

During this initial workshop, a phase of collective work was also organized, in order to facilitate 172 both knowledge sharing and dialogue. Groups of stakeholders were invited to characterize the 173 current state (structure, performance) of each FT, and the projection for its future state 174 following a shock instigated by the identified driver. This projected state envisions a new 175 equilibrium, once the changes have occurred. The characterizations were formalized using 176 cognitive maps to describe the overall context of the current, or projected FTs: their 177 environment (e.g., market prices, public policies), their resources (e.g., water, labor), their 178 current (or future) structure (e.g., farm size) and functions (e.g., cultivated cropping systems, 179 farm activities) that condition their current or future performance in terms of indicators such as 180 yield, income, and irrigation water use. The "future" state was projected on a 15-year time 181 horizon, suggested by the research team and validated by the stakeholders as coherent (i.e., 182 close enough to be realistic, and far enough to be creative). This horizon is considered a good 183 compromise between the evolution of socio-economic conditions which are too uncertain to 184 project beyond 15 years, and the effects of climate change which are hardly visible in less than 185 15 years (Souissi et al. 2018). 186

For each FT, two cognitive maps (CMs) were constructed by groups of stakeholders (4 groups 187 of 5-6 people, each considering one FT): one CM describing its current state, and another CM 188 describing its possible future state according to the driver previously chosen. At the end of the 189 workshop, a member of each group presented both CMs in order to explain the contextual 190 information chosen and the relationships (positive or negative) between context, farm, and 191 performance. 192 structure were introduced to all participants, explaining the "boxes" with the use of examples 194 (e.g., market price, environmental policies, etc.) and describing the links between boxes (e.g., 195 positive/negative relationship). Second, one facilitator gave a simple example of a CM for the 196 initial situation of FT1, using only commonly reported elements from the interviews. Third, the 197 The performance indicators to be quantified were selected from the literature (Table 2), with 206 regard to the issues faced by agricultural systems in arid zones. Based on the three pillars of 207 sustainability, it included eight indicators: land, labour, farmer income (Souissi et al. 2018), 208 yields (El Ansari et al. 2020), family assets [START_REF] Bar | Indicateurs de vulnérabilité, résilience, durabilité et 536 viabilité des systèmes d'activité au Lac Alaotra, Madagascar Définition des concepts[END_REF], market access and 209 commercialization [START_REF] Castel | Vulnérabilité, résilience et 546 capacité à se développer des exploitations agricoles familiales dans le périmètre irrigué de 547 Bittit (plaine du Saiss)[END_REF]), irrigation (El Ansari et al. 2020), and policies (Gameroff 210 and Pommier 2012) (Table 2). This list was consistent with results from farmer and stakeholder 211 interviews (1 st step) and the results of the first workshop (2 nd step). It was discussed and 212 validated at the beginning of the second workshop. 213

# Table 2 approximately here # 214

The semi-quantification of certain measures of performance was carried out in two steps. The descriptions of the agricultural systems by the different local stakeholders highlighted a 235 consensus on the primary issues. The main strengths were related to environmental and social 236 features (e.g. pedo-climatic conditions suitable for a diversity of crops, and qualified workers). 237

The main weaknesses identified were related to social, economic, and technological features 238 (e.g., poor organization between farmers and between administrators, difficulty in 239 commercializing, and difficulty in attaining high yields). For the most part, social arguments 240 ranked at the top. The opportunities highlighted during the interviews were mainly economic 241 and technological (e.g., attractiveness for investors, new markets, and no-till farming); the 242 Although more regional in perspective, the description of agricultural system issues by local 245 stakeholders was mostly consistent with those of the interviewed farmers (step 1); both of these 246 groups emphasized commercialization and lack of workers as important weaknesses (Appendix 247 2). However, it should be noted that concerns about weaknesses in land ownership and yields 248 at the farm scale, emphasized by local stakeholders, were not highlighted by farmers. 249

Conversely, opportunities such as subsidies to increase drop irrigation, increase fruit 250 plantations, and develop farm structures for storage and transformation, were cited by 251 interviewed farmers, but not by the local stakeholders. 252 253 During the first workshop the research team presented the farm typology, with four FTs 254 differing in their production, size, proportion of irrigated land, labor and livestock (Table 3). 255 Two FTs are seen as intensive with significant mobilization of inputs (especially irrigation 256 water) and some cattle/sheep farming for the market or household consumption of meat or milk 257 (FT1 and FT4). These two FTs were currently considered as the most profitable. FT2 258 corresponded to a typical extensive farm, which produces dry cereals and livestock, particularly 259 sheep, intended for the market. FT2 was considered by local stakeholders as the less profitable. 260

Finally, FT3 corresponded to a moderately intensive mixed farm growing cash crops and raising 261 sheep for the market. 262 # Table 3 approximately here # 263 264

3.2.Selection of drivers 265

The individual list of drivers developed by local stakeholders in the first workshop was very 266 diverse, within and between FTs (Appendix 3). These drivers were later considered as possible 267 the interviews (Appendix 3): climate change (FT2 and FT3) and decrease in water resources 269 (especially for FT1 and FT4, and to a lesser extent for FT3), along with the weaknesses of 270 commercialization (all) and labor (especially for FT4-the largest-see Table 3). Based on 271 these individual rankings, one main driver was chosen for each FT: climate change was seen as 272 the main driver of change for FT2 and FT3, access to irrigation water was identified for FT1 273 and FT4. 274 In order to identify how the chosen drivers would affect the current FTs, the groups of 277 stakeholders built cognitive maps (together with written explanations) of the farm context 278 (explaining their state) for each FT, in both current and future states. The explaining factors 279 differed between FTs, and between current and future states (Figure 1). 280 281 Farm Type 1 is specialized in marked gardening and generates high income with highly 282 profitable crops (onions and potatoes), but this income varies with input and market prices 283 (Figure 1A). Intensive practices and short rotations have a negative impact on soils (organic 284 matter), and require significant irrigation, which impacts water resources already subject to 285 inefficient management and irregular rainfall. Poor management of water resources is 286 encouraged by the lack of controls on irrigation, government policies that subsidize its 287 extension (irrigation systems can be fully subsidized in some cases), and the negligible cost of 288

water. 289

The shock for FT1 is a decrease in the availability of groundwater irrigation (i.e., a drop in the 290 water table). This shock would involve large changes in the cropping system, with a reduction 291 of intensive market gardening in favor of more extensive practices and less input for crops such 292 drought-tolerant varieties (Figure 1B). Direct sowing would be implemented in order to 294 improve conservation of water and organic matter. Farmers could also add value with crop by-295 products (straw, pulp). Irrigation would decline from 75% of the surface area to only 45% (due 296 to the reduction in market gardening; Table 3). Farmers would have acquired the knowledge 297 needed to control irrigation (inspired by new technologies). 298 299 Farm Type 2 only grows rainfed cereals which demand few resources (labor, inputs). 300

Government policies providing subsidies for insurance, seeds and equipment have a positive 301 impact on these farms. Difficult access to adapted seeds, appropriate phytosanitary products 302 and their high prices have a direct negative impact on their production. The FT2 farms must 303 endure constraints related to storage of agricultural products and commercialization 304 (intermediaries, selling prices, etc.) which can also impact them negatively (Figure 1C). 305

The shock for FT2 is climate change characterized by varying amounts of rainfall 306 (droughts/excessive water) during the year and between years. Climate change would have a 307 direct impact on farms, as grain yields are driven primarily by rainfall. Droughts resulting from 308 climate change would also have a negative impact on livestock feed (less straw), requiring 309 farms to purchase supplementary feed. Nevertheless, the government might try to maintain 310 livestock production in the region by initiating new policies (livestock and milk subsidies, 311 supplementary feed subsidies). Therefore, two options for FT2 farms were envisioned for the 312 future (Figure 1B): (1) specialization towards subsidized livestock, and (2) system 313 diversification with rain-fed arboriculture (arbutus, olive, almond, fig tree) and 314 cereals/legumes/oilseed crop rotations (Table 3). The availability of more efficient 315 phytosanitary treatments and the adoption of new practices (such as direct sowing) would help 316 tackle the challenges of climate change. However, without access to the financing needed to 317 transform their system, some farmers might abandon agriculture. 318 319 # Figure 1 approximately here# 320 321 Farm Type 3 corresponds to family farming with less access to markets. The government 322 strategies seek to overcome related issues: (1) facilitate marketing for farmers (help reduce the 323 number of intermediaries and find new markets), (2) strengthen production capacity through 324 collective purchases of materials and organization of shared use), (3) help farmers with 325 decision-making (adoption of best practices), and (4) facilitate dialogue between farmers and 326 members of the agricultural sector, and strengthen professional organizations (Figure 1E). 327

Although the government tries to structure the market with mechanisms such as contracts and 328 labelling, the liberalization of agricultural markets has negative consequences for FT3: selling 329 prices are low and unstable (mainly for market gardening), thus limiting the valorization of 330 farm products. 331

In the case of FT3, the shock of climate change (characterized by rising temperatures, a decrease 332 and variability in rainfall, a shorter crop cycle and shifting cultivation areas) may affect farms 333 in a number of ways, including a decline in production that would lead to lower revenue (Figure 334 1F). In the projected future state, government policies would have a positive impact by 335 buffering the effects of climate change through a reorientation of strategies (adopting best 336 practices and reducing investment for adverse projects) and the strengthening of the multi-risk 337 climate insurance system introduced in 2011. Professional organizations could make farmers 338 aware of the consequences of climate change, and enable them to better adapt and gain access 339 to new technologies (irrigation, mechanization). The main response to climate change would 340 be the introduction of the rustic tree crops to the current cropping system (Table 3). 341 342 For FT4, the expansion of irrigated areas has a positive impact on farm income, enabling 343 cultivation of arboreal species that have high added value (Figure 1G). But current policies 344 funding the installation of new boreholes have a direct negative impact on water resources. This 345 policy favors less labor-intensive drip irrigation, leading to a decrease in hired labor, but at the 346 same time, the extension of cultivated land increases the demand for labor. The potential lack 347 of product valorization (refrigerated storage or sorting) has a negative impact on marketing and 348 subsequently on farmers' income. Thus, the potential benefit of increased production is 349 countered by lower prices and storage difficulties. 350 Like FT1, the shock for FT4 is the decline in groundwater resources, leading to a decrease in 351 irrigated areas and impacting rotations. In the projected future, rustic arboriculture species 352 (olive, almond) would replace some rosaceous species, and some market gardening areas would 353 convert to cereals and legumes (Table 3). The sector would experience an evolution in fruit 354 quality and traceability, and new market policies would be initiated to achieve better 355 organization of sales and product valorization. Production area would be less extensive (partly 356 compensated by increased yields through better technical management) but superior in quality: 357 farmers would maintain a good income even if production decreases (Figure 1H). 358 359 All FTs would diversify their crops (Table 3). Some would largely change orientation, such as 360 FT1 that would reduce the practice market-gardening. FT2 would diversify (legumes, 361 arboriculture), but remain rainfed. Rainfed legumes and arboriculture would be grown on all 362 farm types, even on FT4 where the tree cultivation would be divided between rosacea and rustic 363 species. The irrigated areas would not exceed half of the total agricultural area, and livestock 364 would not evolve over time. 365

In the second workshop the participants quantitatively assessed the eight types of performance 368 of current and future FTs, as determined with cognitive maps (Figure 1) and associated 369 characterization (Table 3). 370 FT4 shows the best performance (high yields, high income, landowner, and supported by 371 government policies) both now and in the future (Figure 2). Family assets is the only indicator 372 seen as low, because this FT has few livestock. The Political, Irrigation and Marketing 373 indicators were considered average for the current situation, but they all increased in the future, 374 with improved policy support, better irrigation practices, and better marketing, when compared 375 to the current situation. For this FT4, the participants foresee an improvement in irrigation 376 management, product quality and selling price. 377 For FT1 and FT3, only four indicators vary between the current and future situation. For both 378 types, the four changing indicators tend to reflect declining performance. For FT1, these four 379 indicators are Land, Family assets, Farmers' income, and Yields. The land indicator is 380 deteriorating due to land fragmentation. Yield would decrease because of climate change. 381 Farmers assets and farm income would decrease due to lower yields and less-profitable crops. 382 For FT3, the four indicators are Income, Labor, Irrigation, and Policies. The fall in income 383 would be due to the expected increase in production costs (phytosanitary products, water and 384 labor). The Labor indicator would decrease because of a decline in the availability of family 385 labor (rural exodus), generating additional costs for farms. 386

Fifteen years from now, FT2 (Figure 2), like FT1 and FT3, would anticipate lower performance 387 than today. Nevertheless, participants expected policies to continue to accompany FT2 through 388 subsidized equipment. 389

The performance indicator judged as the most important/critical remained stable for each FT 390 (boxed performance indicator in Figure 2; Appendix 4), which would argue for their resilience. 391

It is also the FT with the lowest overall change between current and future performance 393 indicators. Finally, FT1 and FT3 show four stable indicators of performance; FT2 and FT4 394 show three. While all FTs remained the same size (Table 3), most changed their crop strategies, 395 lowered their use of water resources (except for FT2 that is rain-fed only) and increased or 396 maintained hired labor (Table 3). 397 # Figure 2 approximately here # 398 399 4. Discussion 400

Based on our assessments of resilience, all FTs would be relatively resilient to the main driver 401 of change chosen by stakeholders (either climate change or decrease in availability of 402 irrigation). They would notably adapt to the future through diversification, re-introducing 403 rainfed crops and rustic species (especially among trees). Rainfed legumes would be included 404 in all FTs, contrary to the current situation on most farms. These modifications would be the 405 main adaptation to the increasing scarcity of water, either due to lack of water for irrigation, or 406 to climate change. Several previous studies have highlighted crop diversification as key to 407 improved resilience of farm structure and functions [START_REF] Reidsma | Regional crop modelling in 610 Europe: the impact of climatic conditions and farm characteristics on maize yields[END_REF][START_REF] Baccar | The determining 533 factors of farm sustainability in a context of growing agricultural intensification[END_REF]. 408

Diversification was seen as a way of spreading the risk and compensating for eventual losses. 409

But the participants of our study saw diversification as being limited to cropping systems, thus 410 excluding both livestock production and off-farm activities. In the first workshop, although feed 411 was highlighted as a possible future issue, the absence of participants or FTs specialized in 412 livestock may have biased the results. In the FTs characterized, the modest number of livestock 413 were seen more as a savings account than as a means of producing regular income (thus 414 differing from the results of [START_REF] Baccar | The determining 533 factors of farm sustainability in a context of growing agricultural intensification[END_REF]. This perspective was consistent; only one out 415 of the 21 farmers interviewed projected to increase his herd. Off-farm activities, cited by one 416 the opportunities or impacts, such as labor. By contrast, Lallau and Thibault (2005) highlighted 418 this extra-income as a crucial part of the farmer's strategy to build farm resilience. This factor 419 would require further study to assess the feasibility and willingness of farmers, as well as the 420 local opportunities for off-farm activities, and their impacts on the farm (e.g. seasonal labor). 421

Farm adaptation in Mediterranean irrigated agriculture inventoried by Harmanny and Malek 422 (2019) included strategies for changes in water management (irrigation techniques), other 423 technological developments (mechanization), and farm production practices (crop choices), but 424 few changes in sustainable resource management (e.g., groundcover to reduce erosion, use of 425 cover crop, etc.) or farm management (e.g., financial and administrative practices). In our study, 426 adaptation through changes in farm production practices was dominant and transversal to all 427 FTs; this is consistent with findings of [START_REF] Harmanny | Adaptations in irrigated agriculture in the 578 Mediterranean region: an overview and spatial analysis of implemented strategies[END_REF] who found it to be the most 428 frequent adaptation in the Mediterranean area. 429

The only FT showing overall better performance in the future than in the current situation is 430 FT4 (primarily arboreal). This could be due to the multi-variable indicators that were adopted 431 in this study. For instance, while yields would decrease, their quality and price would increase, 432 possibly even leading to higher global income. Although dependent on the current and future 433 state of irrigation, this type of farm is in a better position to adapt due to its initial capital, and 434 the assistance provided by government policies. The government's agricultural policies were 435 very often cited by the study participants as influencing or conditioning current and possibly 436 future systems and practices. This could be linked to the anticipated renewal of the "Green 437 Morocco Plan", launched in 2008 with two goals: develop a modern, productivity-oriented 438 agriculture with investors, and ensure solidarity-based support for small farms as a means of 439 alleviating poverty through increased agricultural income [START_REF] Marzin | Etude sur l'agriculture 587 familiale à petite échelle au Proche-Orient et Afrique du Nord[END_REF]. 440 recognized it as a useful tool to build a systemic vision of the specific context encountered by 443 different FTs, and to highlight differences between the FTs. We did, however, observe that all 444 CMs did not show the same degree of complexity (number of boxes and relationships). This 445 could be due to the diversity of participants in group (e.g. more representatives of professional 446 organizations in the FT3 group). Although the collective construction of a CM did not cause 447 major disagreement, it could represent a bias in the CM comparisons. Overriding this bias could 448 be achieved by pre-determining the group composition, but this would entail two particular 449 difficulties: (1) the uncertainty about who will participate in the workshop, and (2) a top-down 450 approach that might be questioned by some participants and limit creativity. The performance 451 quantification exposed one particular difficulty, when one group was confused during the 452 exercise of ranking the importance of a performance indicator and quantifying it. This issue 453 could have been avoided if the research team had allowed more time between the two exercises 454 and provided more assistance in the group work during this step. Finally, this approach enabled 455 participants to share their perceptions on the current and possible future systems. CMs and their 456 associated storylines highlighted the different contextual aspects characterizing the agricultural 457 systems. The perceptions of the current and future contexts differed somewhat among the 458 participants according to their institution, function, and scale of action. Knowledge sharing in 459 the workshops allowed participants to imagine a broader picture of performance, helping them 460 to anticipate the negative consequences of various drivers of change with respect to specific 461 vulnerabilities of the different FTs. 462

In this study we evaluated resilience in a simple way in order to stimulate stakeholders' thinking 463 and knowledge sharing when projecting future farming conditions that anticipate the potential 464 negative effects of climate change (Giorgi and Lionello, 2008). This led to three major limits. 465

The first is the compensation seen in the projected evolution of the various chosen indicators 466 cognitive maps allowed us to highlight the differences between current and future states. 469 Second, we distinguished key resilience characteristics related to robustness, adaptability, and 470 transformability (Meuwissen et al. 2019). Our adoption of the scale of FTs was an advantage. 471

It allowed us to distinguish each FT's specific challenges together with the management of a 472 shock characterized by the chosen driver (climate change or water table decrease). Thus, 473 specified and general resilience, as defined by Meuwissen et al. (2019), were combined with 474 future options corresponding to current structures. Third, although the future can be shaped by 475 stress and shocks from interacting social, economic, institutional and environmental factors 476 (Meuwissen et al. 2019), we limited the assessment to one shock. Even though all the adaptation 477 strategies included legumes, adaptation would also depend on other factors such as selling price 478 or changes in the market. Those different types of drivers were cited by local stakeholders and 479 have been highlighted in previous studies concerning the same region [START_REF] Baccar | Shared environment, 530 diversity of pathways: dynamics of family farming in the Saïs Plain (Morocco)[END_REF]), but 480 our method was not designed to combine several drivers of change for an overall assessment. 481 482 Finally, a key point we did not consider is the transition, questioning in detail the adaptive 483 capacities and learning processes of farmers. Adaptation capacities can be assessed using the 484 adaptive capacity index [START_REF] Swanson | Indicators of adaptive capacity to climate 629 change for agriculture in the Prairie region of Canada[END_REF] to consider six determinants: economic 485 resources, technology, information and skills, infrastructure, institutions, and equity (Smit et al. 486 2001). Using this index, [START_REF] Grasso | Mediterranean agriculture under climate change: adaptive 570 capacity, adaptation, and ethics[END_REF] assessed the adaptive capacity in different 487 areas of the Mediterranean. They concluded that Morocco exhibited a low adaptive capacity, 488 performing poorly on all six determinants when compared to other countries. This differs from 489 the relatively stable performances projected by our study's stakeholders in their assessment of 490 the future, which viewed farmers as being able to adapt. This could be due to the different 491 scale. Their study was on national scale, whereas we focused on a small region. Our region of 493 study might be less vulnerable than others, such as those close to the sea with salinity issues 494 [START_REF] Faysse | 557 Participatory analysis for adaptation to climate change in Mediterranean agricultural systems: 558 possible choices in process design[END_REF]). This highlights the potential effects of both scaling down and focusing on 495 indicators of performance that have specific importance to local stakeholders. 496 497

Conclusion 498

This study focused on small and medium farms, and participants in the study showed that these 499 farms had the capacity to adapt. This was an expression of strong resilience. However, we did 500 not fully examine all dimensions of resilience. To complement our study, it would be useful to 501 make a complete diagnosis of the vulnerabilities, risks, and adaptation capacity. Although all 502 these dimensions were present in our methods and results, they were highly integrated and 503 cannot be evaluated as individual components. Another possibility would be to enlarge our 504 study by including large farms. Although their number is limited, they exert significant pressure 505 on (water) resources through intensive agriculture, such as orchard farming or market 506 gardening. Diagnosing the regional resilience would require integrating all types of farms and 507 other sectors, which would implicate a wider arena including livestock experts, and related 508 industries. 509 510

Acknowledgments 511

This study was carried out within the SEMI-ARID project (ARIMNET2 research program) 512 founded by the French Agency of Research (ANR) and MESRSFC (Morocco). We thank all 513 farmers and stakeholders who kindly participated in this study and shared their views on the 514 current and possible future state of agricultural in this highly vulnerable region. 515 Step 2 (Workshop 1)

Step 3 (Workshop 2) Step 1

  three facilitators observed the groups' work and intervened when necessary to clarify the 198 process of CM construction. 199 2.2.3. Step 3: Semi-quantification of the indicators of FT performance 200 There were three principal objectives in the second collective workshop (5 hours): (1) to 201 validate the cognitive map results, (2) to detail the description of future FTs according to 202 different elements (crops and their distribution, farm acreage, percentage of irrigated land, 203 family or external workforce, type and number of livestock), and (3) to describe performance 204 of current and future FTs (according to the previously chosen drivers). 205

  First, 215 stakeholders individually chose the three most relevant indicators of performance for each FT. 216 This had two main objectives: 1) familiarize stakeholders with the research team's definition 217 or weakness for each FT). Stakeholders were then divided into groups (2 groups of 6-7 peoples, 219 each group assessing two FTs) to quantify the eight performance indicators for current and 220 future FTs, together with written explanations. A member of each group presented these results 221 at the end of the workshop. The quantification of each indicator ranged between 1 (very 222 low/poor) to 5 (very high/good). During this quantification step, one group of stakeholders was 223 confused about the written explanation of indicator quantification. Therefore, we present the 224 results of their oral restitution, complemented with discussions from both workshops. 225 Resilience of each FT was qualitatively estimated based on those results: (1) the evolution of 226 the most relevant indicator, (2) the number of indicators with no change, and (3) the sum of the 227 differences between the current and future FT for each indicator (expressed as absolute in order 228 to avoid compensations). We thus obtained three indicators of resilience for each FT. Characterization of the initial situation 232 Based on the series of interviews, we summarized the characteristics of the initial situation in a 233 SWOT diagram (Appendix 2), which was presented and validated during the first workshop. 234

  275 3.3. Cognitive maps and future farms 276

  Selection of drivers of change and characterization of current and future FTs 156

	145	including available resources and constraints; (3) the different institutions, structures, and
	146	people involved in the agricultural development of the Saïs region. These three categories of
	147	information were assembled from literature, knowledge among the local research partners, and
	148	interviews with farmers and stakeholders. The general characteristics of the local agricultural
	149	system were described using SWOT analysis (Strengths, Weaknesses, Opportunities and
	150	Threats). The initial selection of representative FTs was based on previous studies in the area.
	151	Baccar et al. (2017) interviewed 40 farmers in 2014. El Ansari et al. (2020) interviewed 286
	152	farmers in 2015. A selection of representative farms was updated and completed with
	153	interviews, which were also aimed at gathering information on farmers' current constraints and
	154	future projects.
	155
		2.2.2. Step 2:
	140
	141	# Table 1 approximately here #
	142	2.2.1. Step 1: Characterization of current situation, including choice of FTs

Table 1 .

 1 Overview of participants in the interviews and workshops *Involvement was solicited by sending official invitations, followed by individual phone calls. Res: Research; CC: Crop Collector; Adm: administrations; Adv: Advisors.

Table 2 .

 2 Type and definition of performance indicators

	Indicators of performance	Relevance		Reference
	Land: status, size, successor
	Labor: number of employees, price	Indicators of natural, social and	Soussi et al.
	Income: prices of inputs and their	financial capital		2018
	variation, products type, price and
	price variation
	Yields: efficiency of inputs, crop management	Most current agronomical indicator to assess farm performance	El Ansari et al. 2020
	Family assets: liquid assets,
	insurance, livestock (and possibility	Essential for farm equilibrium Bar et al. 2011
	to sell)
	Market commercialization: intermediaries, access and processing, labels	Cited as highly important factors during interviews (and according to farmers workshop 1); major constraints	Castel et al. 2014
	Irrigation: water access, type of irrigation, management	Very important for some farm types; Allows to intensify the agricultural production	El Ansari et al. 2020
		Agriculture	is	highly
	Policies: subsidies, minimum salary, export regulations	subsidized and supported by the Moroccan government; public support can be seen as	Gameroff and Pommier, 2012
		favoring resilience

characteristics of the agricultural region; (2) the description of current representative FTs,

market conditions) (see Appendix 2 for details).

compensated by an increase in yield. Tracking those compensations through storylines in the

region. Global Planet Change, 63 (2-3), 90-104.

Table 3. Main characteristics of the four current FTs (current situation), based on El [START_REF] Ansari | Trade-553 Offs between Sustainability Indicators in Response to the Production Choices of Different 554 Farm Household Types in Drylands[END_REF] and [START_REF] Baccar | Shared environment, 530 diversity of pathways: dynamics of family farming in the Saïs Plain (Morocco)[END_REF], and the future FTs achieving equilibrium (future situation), as projected by stakeholders during the workshops. UAA: Utilized Agricultural Area; ↓ and ↑ highlights decrease/increase as compared to current situation


M Sahihi

Y Ghayeb

An investigation of molecular dynamics simulation and molecular docking: Interaction of citrus flavonoids and bovine β-lactoglobulin in focus

Keywords:

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

Introduction

Citrus fruits have a determining role in human health. Due to the delicious flavors and healthy benefits of these fruits, they play a major role in human diet. Their flavonoids are actually among the main components of these fruits. Flavonoids have the same skeleton but have different classifications that are generated upon the modifications of the nucleus of these compounds.

Flavonoids display antioxidant [START_REF] Vanacker | Flavonoids can replace alpha-tocopherol as an antioxidant[END_REF], [START_REF] Yu | Antioxidant activity of citrus limonoids, flavonoids, and coumarins[END_REF] and anti-carcinogenic activity [START_REF] Kawaii | Quantitation of flavonoid constituents in citrus fruits[END_REF], [START_REF] Pszczola | Natural colors: pigments of imagination[END_REF], [START_REF] Cooray | Interaction of the breast cancer resistance protein with plant polyphenols[END_REF]. They are mainly detected in human serum and urine. It is thus clear that the study of bioactivity of flavonoids is essential to achieve a full biological significance. Below is a description of citrus flavonoid: Naringenin (Fig. 1a) is the predominant flavonoid in grapefruit. Its antioxidant activity on angiotensin II-treated VSMCs and its role in cell growth have been investigated previously [START_REF] Xu | Naringenin inhibits angiotensin II-induced vascular smooth muscle cells proliferation and migration and decreases neointimal hyperplasia in balloon injured rat carotid arteries through suppressing oxidative stress[END_REF], [START_REF] Waheed | Naringenin inhibits the growth of Dictyostelium and MDCK-derived cysts in a polycystin-2 (TRPP2)-dependent manner[END_REF]. Hesperetin (Fig. 1b) is another citrus flavonoid specifically found in peel. It has a protective role against lead-induced damage in rats and cardiac remodeling induced by pressure overload [START_REF] Deng | Hesperetin protects against cardiac remodelling induced by pressure overload in mice[END_REF], [START_REF] Wang | Antioxidative effects of hesperetin against lead acetate-induced oxidative stress in rats[END_REF]. Nobiletin (Fig. 1c), isolated from citrus peels, like in tangerine. Nobiletin has anti-inflammatory effects and inhibits cartilage degradation [START_REF] Yoshigai | Citrus nobiletin suppresses inducible nitric oxide synthase gene expression in interleukin-1β-treated hepatocytes[END_REF], [START_REF] Henrotin | Nutraceuticals: do they represent a new era in the management of osteoarthritis? A narrative review from the lessons taken with five products[END_REF].

Tangeretin (Fig. 1d) is found in tangerine and other citrus peels, inhibits agonist-induced integrin, αIIbβ3 inside-out and outside-in signaling, intracellular calcium mobilization, and granule secretion [START_REF] Vaiyapuri | Tangeretin regulates platelet function through inhibition of phosphoinositide 3-kinase and cyclic nucleotide signaling[END_REF]. The results of Chong et al. suggest that tangeretin can be appropriate for chemosensitization of cancerous cells which lack the ability of DSB-repair [START_REF] Chong | Tangeretin sensitizes SGS1-deficient cells by inducing DNA damage[END_REF].

The delivery of flavonoids and their metabolites are poorly understood. The interaction of these flavonoids and a transport protein such as bovine β-lactoglobulin (BLG) could be a valuable factor to control their transport to biological sites.

Lipocalins are small proteins with the ability to bind and transfer small hydrophobic ligands [START_REF] Flower | The lipocalin protein family: structural and sequence overview[END_REF], [START_REF] Schlehuber | Lipocalins in drug discovery: from natural ligandbinding proteins to 'anticalins[END_REF]. The core structure of these proteins consists of an eight stranded antiparallel βbarrel that defines an internal cavity for binding the ligands. Bovine β-lactoglobulin (BLG) is the major whey protein, with several genetic variants [START_REF] Sawyer | The core lipocalin, bovine β-lactoglobulin[END_REF], [START_REF] Dong | Infrared and circular dichroism spectroscopic characterization of structural differences between β-lactoglobulin A and B[END_REF]. It consists of 162 amino acids with about 18.4 kDa of molecular weight [START_REF] Brownlow | Bovine [beta]-lactoglobulin at 1.8 Å resolution-still an enigmatic lipocalin[END_REF]. The high affinity of BLG to hydrophobic ligands such as steroids, fatty acids, retinoids, vitamin D, and cholesterol, has been verified [START_REF] Kontopidis | The ligand-binding site of bovine [beta]lactoglobulin: evidence for a function?[END_REF], [START_REF] Kontopidis | beta]-Lactoglobulin: binding properties, structure, and function[END_REF], [START_REF] Wu | β-Lactoglobulin binds palmitate within its central cavity[END_REF].

Unfortunately, the clinical application of flavonoids is limited due to their poor water solubility and low bioavailability caused by their hydrophobic ring structure. Besides, an important physicochemical property of BLG is its ability to bind in-vitro several physiologically relevant ligands. Therefore, BLG can serve as a potential transport and depot protein, especially for poor water-soluble molecules. Bohin et al. [START_REF] Bohin | Efficacy of food proteins as carriers for flavonoids[END_REF] have confirmed the efficacy of milk proteins as carriers for flavonoids. Also, finding novel bioactive ligand molecules of BLG is of biotechnological and pharmacological importance. This contributes to deeper understanding of molecular recognition properties of this lipocalin. However, BLG exhibits significant resistance against both gastric and simulated duodenal digestions. Therefore, it seems a proper nominee for the safe delivery and protection of pH sensitive drugs in stomach. Moreover, the high stability of BLG at acidic condition guarantee the low delivery of hydrophobic ligands in acidic condition of stomach.

We have previously studied the interaction of three polyphenol flavonoids (quercetin, quercitrin and rutin) and BLG [START_REF] Sahihi | The interaction of polyphenol flavonoids with β-lactoglobulin: molecular docking and molecular dynamics simulation studies[END_REF]. In the present study, however, we examined the interaction and protein conformation of BLG with hesperetin, naringenin, tangeretin and nobiletin, using molecular docking and molecular dynamics simulation studies. Finding new biologically active ligands to bind BLG is very important, and not only can contribute to the better understanding of the molecular properties of this protein, but also might pave the way for future studies about the transport of drugs to biological sites.

Experiment

Molecular docking

All of the docked conformations of flavonoid-BLG complexes were generated through using ArgusLab 4.0.1 molecular docking program [START_REF] Thompson | ArgusLab 4.0[END_REF]. Comparison of the results of ArgusLab and Genetic Optimization for Ligand Docking (GOLD) showed a little difference in their docking accuracies [START_REF] Thompson | ArgusLab 4.0[END_REF], [START_REF] Thompson | molecular docking using ArgusLab: an efficient shape-based search algorithm and the AScore scoring function[END_REF], [START_REF] Nikolic | QSAR study of imidazoline antihypertensive drugs[END_REF]. Also, the accuracy of ArgusLab was checked by redocking the dodecyl sulfate to BLG. After docking the dodecyl sulfate back into BLG, the RMSD value between docked dodecyl sulfate and reference dodecyl sulfate was 2.4 Å and the interactions between docked dodecyl sulfate and BLG were similar to those of original modes, suggesting that ArgusLab is suitable for docking of ligands to BLG.

The known crystal structure of BLG (PDB ID: 3NPO) was taken from the RSCB protein data bank (www.rcsb.org) and was prepared in pH=7.5. The R-value of this file was 0.216, a sufficient capacity for further studies. The water molecules in the used pdb file are not structural; thus, the ArgusLab suite was used to remove them. Actually, Becke three-parameter Lee-Yang-Parr (B3LYP) hybrid density functional theory with the basis set of 6-31 g** was employed to optimize the flavonoids structures, using the GAMESS quantum chemistry software (USA) [START_REF] Schmidt | General atomic and molecular electronic structure system[END_REF].

The blind docking approach was used and the whole protein was taken as a potential binding site. Also, Ascore scoring method in ArgusLab using high precision, 0.4 Å grid resolution, and maximum 200 candidate poses were employed for docking calculations. During all docking process, the structure of BLG kept rigid and all the torsional bonds of ligands were set free (flexible docking). Ascore scoring function ranked the docked conformations on the basis of their free binding energy. The lowest free binding energy conformation of each flavonoid-BLG complex was left for further studies.

Molecular dynamics simulation

The lowest free binding energy conformation of each complex was considered as the initial conformation for the MD studies. All MD studies were carried out using GROMACS 4.5.5 (University of Groningen, Netherlands) package [START_REF] Berendsen | GROMACS: a messagepassing parallel molecular dynamics implementation[END_REF], [START_REF] Lindah | GROMACS 3.0: a package for molecular simulation and trajectory analysis[END_REF] and GROMOS96 43a1 force field [START_REF] Van Gunsteren | Biomolecular Simulation: The GROMOS96 Manual and User Guide[END_REF], [START_REF] Van Gunsteren | Encyclopedia of Computational Chemistry[END_REF]. The Dundee PRODRG2.5 server was used to generate the topology parameters of flavonoid [START_REF] Schuttelkopf | PRODRG: a tool for high-throughput crystallography of protein-ligand complexes[END_REF]. The partial atomic charges of flavonoids were calculated using GAMESS quantum chemistry software (USA) [START_REF] Schmidt | General atomic and molecular electronic structure system[END_REF] at the level of HF/6-31 g**. The complex was located in the cubic box with the periodic boundary conditions. The box volume was 294.14 nm 3 (6.6504×6.6504×6.6504 nm 3 ) and the minimum distance between protein surface and the box was 1.0 nm. The box filled with extended simple point charge (SPC), water molecules [START_REF] Berendsen | Intermolecular Forces[END_REF], and the solvated systems were neutralized by adding 8 sodium ions (Na + ). Energy minimization was done through using the steepest descent method for 8 ps. Then, the system was equilibrated for 40 ps at the temperature of 300 K. Finally, a 10 ns MD simulation was carried out at 1 bar and 300 K. Berendsen thermostat [START_REF] Berendsen | Molecular dynamics with coupling to an external bath[END_REF] at 300 K, the particle mesh Ewald (PME) method [START_REF] Darden | Particle mesh Ewald: an N log(N) method for Ewald sums in large systems[END_REF], [START_REF] Essmann | A smooth particle mesh Ewald method[END_REF] for long range electrostatics, and a 7 Å cut off for van der Walls interactions and Coulomb interactions were used. The equation of motions was integrated by the leap-frog algorithm with the 2 fs time step. The atomic coordinates were recorded to the trajectory file every 0.5 ps for later analysis. Finally, an all-bond constrain was used to keep the ligand from drifting in MD.

Results and discussion

Molecular docking studies

BLG has three potential binding sites, with the internal cavity as the main binding site for the hydrophobic ligands [START_REF] Roufik | Thermodynamics of binding interactions between bovine β-lactoglobulin A and the antihypertensive peptide β-Lg f142-148[END_REF]. Previous studies on the interaction of polyphenol flavonoids and BLG

show that quercetin and quercitrin bind in this cavity [START_REF] Sahihi | The interaction of polyphenol flavonoids with β-lactoglobulin: molecular docking and molecular dynamics simulation studies[END_REF]. In the present study, the binding mode of citrus flavonoids, i.e. hesperetin, naringenin, tangeretin and nobiletin, at the active site of BLG was examined. The docking results revealed that hesperetin, naringenin, tangeretin and nobiletin bind in the internal cavity of BLG (Fig. 2). Fig. 3 shows the interactions of flavonoids within the internal cavity of BLG. This binding site is considerably hydrophobic, and naringenin is in hydrophobic interaction with Val (41), Val (43), Pro [START_REF] Renard | Dissertation Etude de I'agr égation et de la g élification des prot éines globulaires: application 'a la beta-Lactoglobuline[END_REF], Leu (39), Leu (58), Met (107), Ile (56), and Ile (84). However, despite the main role of hydrophobic interactions, this site focused on some hydrogen-bond interactions where the -OH groups of naringenin interact with oxygen of Pro [START_REF] Renard | Dissertation Etude de I'agr égation et de la g élification des prot éines globulaires: application 'a la beta-Lactoglobuline[END_REF] and nitrogen of Asn (90). In addition, the π-π stacking interaction with Phe (105) and dipolar interactions with Asn (90) and Gln (120) could stabilize the naringenin in its binding site. Fig. 3b shows that hesperetin is in hydrophobic interaction with Ile (12), Ile (56), Leu (46), Leu (54), Val [START_REF] Schlehuber | Lipocalins in drug discovery: from natural ligandbinding proteins to 'anticalins[END_REF], Val (41), Val (43), Val (92), and Met (107). However, the -OH of hesperetin is in hydrogen-bond interaction with oxygen of Val (41). The π-π stacking interaction with Phe (105) and dipolar interactions with Gln (120) can also stabilize the hesperetin in its binding site. Fig. 3c shows that tangeretin is in hydrophobic interaction with Ile (71), Ile (56), Leu (39), Leu (58), Pro [START_REF] Renard | Dissertation Etude de I'agr égation et de la g élification des prot éines globulaires: application 'a la beta-Lactoglobuline[END_REF], Val (41), and Met (107). At the same time, the -OCH3 of tangeretin are in hydrogen-bond interactions with nitrogen of Lys (69). The π-π stacking interaction with Phe (105) and dipolar interactions with Lys (69) can stabilize the tangeretin in its binding site. Finally, Fig. 3d indicates that nobiletin is in hydrophobic interaction with Asn (90), Ile (71), Ile (56), Ile (84), Leu (39), Leu (46), Val (41), Val (43), and Met (107). However, the -OCH3 of nobiletin is in hydrogen-bond interaction with nitrogen of Asn (90). Furthermore, the π-π stacking interaction with Phe (105) and dipolar interactions with Gln (120), Lys (60), and Lys (69) can stabilize hesperetin. The near residues for each flavonoids are listed in Table 1.

The binding constant (Ka), free binding energy (∆G°), and the structural volume (V) of flavonoids are listed in Table 2. The comparison of binding energies shows that -OCH3 groups interfere negatively with binding of flavonoids to BLG. The results reveal how the substitution of -OH groups with -OCH3 groups causes an increase in the volume of flavonoids, while their binding constant decreases and the structural volume of flavonoids increases. It is to be added that as the structural volume of the ligand increases, the orientation of the ligand in its binding site gets inappropriate, and this can affect the fitting of ligand in the binding site. The inappropriate orientation of ligand leads to the reduction of both hydrophobic interactions and the stability of BLG-flavonoid complexes. Similar results have been observed in the interaction of rutin and BLG [START_REF] Sahihi | The interaction of polyphenol flavonoids with β-lactoglobulin: molecular docking and molecular dynamics simulation studies[END_REF].

MD simulation studies

The root mean square deviation (RMSD) of the backbone of BLG was obtained to examine the stability of trajectories for unliganded BLG and BLG-flavonoids complexes (Fig. 4). A glance at the analysis of Fig. 4 shows that the RMSD of various systems reached equilibrium and fluctuated around the mean value at various times. These times were about 1100, 3500, 3600, 4000 and 5500 ps, respectively for BLG-tangeretin, unliganded BLG, BLG-hesperetin, BLG-nobiletin and BLG-naringenin complex. The radius of gyration (Rg) for the BLG and BLG-flavonoid complexes were also specified and plotted versus simulation time to examine the protein compactness (Fig. 5). The results showed that the Rg of all systems stabilizes after about 2500 ps, indicating that the MD simulation equilibrated after 2500 ps. The initial value of Rg for all systems was about 1.44 nm, which approves earlier experimental results [START_REF] Renard | Dissertation Etude de I'agr égation et de la g élification des prot éines globulaires: application 'a la beta-Lactoglobuline[END_REF]. Also, Fig. 5 shows that the Rg value of BLG does not depend upon the complexation with flavonoids. This indicates that the environment of BLG did not change during its interaction with flavonoids. In addition, the amount of secondary structure components of unliganded BLG and BLGflavonoid complexes revealed that the conformation of BLG does not change during flavonoids complexation (Fig. 7).

Summary

Based on our molecular docking results, tangeretin, naringenin, hesperetin and nobiletin bind in the internal cavity of BLG. Among these flavonoids, naringenin has the lowest structural volume and its free binding energy is more negative than others. Therefore, the docking results represent that the hydrophobic interactions have a dominant role in the BLG-flavonoid complex stability.

MD simulation showed the stabilization of BLG and BLG-ligand complexes to be around 2500 ps.

Besides, BLG did not exhibit conformational changes during flavonoids binding. Also, the profiles of atomic fluctuations showed the rigidity of ligand binding site during the simulation. Despite to this fact that we have investigated the binding interaction of these flavonoids with BLG at physiological pH, it should be mentioned that pH does not have substantial effect on the binding affinity of BLG [START_REF] Riihimäki | Binding of phenolic compounds and their derivatives to bovine and reindeer β-lactoglobulin[END_REF], therefore, it can be concluded that our obtained results certified the safe transferring of these flavonoids from stomach.

Fig. 1 .

 1 Fig. 1. Chemical structure of citrus flavonoids. (a) Naringenin, (b) hesperetin, (c) tangeretin and (d) nobiletin.

Fig. 2 .Fig. 3 .

 23 Fig. 2. Superposition of the docking poses of naringenin (blue), hesperetin (green), tangeretin (yellow) and nobiletin (red). (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.)

Fig. 4 .

 4 Fig. 4. Time dependence of RMSD. RMSD values for unliganded BLG and BLG-flavonoid complexes.

Fig. 5 .

 5 Fig. 5. Time evolution of the radius of gyration (Rg) during 10000 ps of MD simulation of BLG and BLG-flavonoids complexes.

Fig. 6

 6 Fig. 6 shows the changes in the total solvent accessible surface (SAS) of protein during the 10 ns simulation time. The variation of SAS represents that the trends for unliganded BLG and BLGflavonoid complexes are similar to the variation of their Rg. This similarity validates the accuracy of MD simulation results.

Fig. 6 .

 6 Fig. 6. Time evolution of the total solvent accessible surface (SAS) during 10000 ps of MD simulation of BLG and BLG-flavonoids complexes.

Fig. 7 .

 7 Fig. 7. Amount of the secondary structure components of BLG-hesperetin complex. (The results for unliganded BLG and other BLG-flavonoid complexes are not shown.)

Fig. 8 .

 8 Fig. 8. The RMSF values of non-liganded BLG and BLG-flavonoids complexes were plotted against residue numbers.

Fig. 9 .

 9 Fig. 9. Superposition of the average structure from the MD of naringenin (blue), hesperetin (green), tangeretin (yellow) and nobiletin (red). (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.)

Table 1 .

 1 Residues within 3 Ǻ distance from each flavonoid.

	Flavonoid Residues
		Ala (73), Asn (90), Gln (120), Ile (12), Ile (56), Ile (71), Ile (84), Leu (39), Leu (46), Leu
	Naringenin	(54), Leu (58), Leu (103), Leu (122), Met (107), Phe (82), Phe (105), Pro (38), Val (15),
		Val (41), Val (43), Val (92), Val (94)
	Hesperetin	Ala (73), Gln (120), Ile (12), Ile (56), Ile (71), Ile (84), Leu (46), Leu (54), Leu (103), Leu (122), Met (107), Phe (82), Phe (105), Val (15), Val (41), Val (43), Val (92), Val (94)
	Tangeretin	Ile (56), Ile (71), Ile (84), Leu (39), Leu (46), Leu (54), Leu (58), Leu (103), Leu (122), Lys (69), Met (107), Phe (105), Pro (38), Val (41), Val (43), Val (92)

Nobiletin

Asn (90), Gln (120), Ile (56), Ile (71), Ile (84), Leu (39), Leu (46), Leu (54), Leu (

Table 2 .

 2 Docking summary of BLG with flavonoids.

	Flavonoid	Ka (M -1 )	∆G° (kcal mol -1 )	V (Å 3 )
	Naringenin	1.004×10 8	-10.91	727.25
	Hesperetin	4.542×10 7	-10.44	804.38
	Tangeretin	5.141×10 6	-9.15	1011.95
	Nobiletin	6.550×10 5	-7.93	1080.08

Acknowledgments

The financial support of the Research Council of University of Isfahan is gratefully acknowledged.

The researchers would also like to deeply acknowledge their discussion with Professor A.K.

Bordbar.

Conflict of interest

None.