Unravelling the heterogeneity of farms irrigation practices on Mediterranean perennial agricultural systems for the optimization of water resource management

Unravelling the heterogeneity of farms irrigation practices on Mediterranean perennial agricultural systems for the optimization of water resource management | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Unravelling the heterogeneity of farms irrigation practices on Mediterranean perennial agricultural systems for the optimization of water resource management Rouault Pierre, Courault Dominique, Flamain Fabrice, Marta Debolini This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4580425/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract In the Mediterranean region, the quantity of water utilized for agricultural purposes ranges from 50 to 70%. Among the most water-demanding agricultural sectors are arboriculture and perennial crops. Orchards are particularly reliant on irrigation, a dependency that has been further intensified by climate change and the resulting reduction in water resources. This study aims to classify farms at the watershed scale according to their irrigation water consumption, and starting from this classification we aim to propose a method for estimating water consumption for irrigation at large scale and for heterogeneous land covers. The classification employed a variety of statistical methods to ensure robust results, including machine learning and regression approaches. Each method was applied independently, and the most common class allocation was retained. The study was conducted in the Ouvèze-Ventoux basin in south-eastern France, using data from various sources at both field and watershed scales. The data obtained from 21 farms provided accurate information on irrigation water usage, which was validated by data from the watershed's water manager. The benchmark analysis identified farms with high irrigation rates with 90% accuracy. Within these heavily irrigated orchards, a second benchmark identified heavily irrigated plots with 68% precision. Maps estimating water consumption were created at the watershed and municipal scales. The estimated total irrigation water use closely matched the actual consumption, with only a 14% deviation. This methodology offers an accessible estimation of water consumption at the watershed scale, without the need to rely on crop models. Moreover, the methodology accurately identifies areas with high irrigation demand based on actual irrigation practices. Perennial crops Water management Multivariate analysis Machine learning France Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Figure 9 Figure 10 Highlights Irrigation on perennial crops can be heterogeneous depending on farm habits. Machine learning methods are applied to unravelling the heterogeneity. Classification methods allow to estimate weakly and heavy water users. Farms and parcels typology allow to estimate water consumption at large scale. Highlighting the diversity of irrigation between orchards on heavily irrigated farms 1. Introduction The water dependency of various economic sectors in the Mediterranean region is steadily increasing (Réparaz 1993 ; Molle and Sanchis-Ibor 2019 ), and this is particularly evident in the agricultural sector, which is the most water consuming. Rising temperatures and more frequent dry periods are adversely affecting crop development (Masia et al. 2021 ; Fraga et al. 2016 ), particularly in the Mediterranean region, as it is considered an hotspot for climate change (Change (MedECC) et al. 2020; Tramblay 2022 ). The increasing temperatures and dry periods will affect the crop growing and attended yields (Lesk et al. 2016 ; Beillouin et al. 2020 ; Ortega-Farias et al. 2021 ; Nóia Júnior et al. 2023 ), causing relevant economic loss for farmers. The increase of water needs in agriculture in the Mediterranean climate can conduct to competition for resources use and lead to conflicts among different economic sectors (Mereu et al. 2016 ). This is particularly the case in summer, with the importance of mass tourism, often concentrated in the most productive crop-growing areas, (i.e. the coastal zones), thus centralising water requirements in the same place, with the diversity of private utilisation of water (especially pools and gardens) and the agriculture (Scoullos et al. 2002 ). Sustainable water management is therefore essential to maintain the region's economy and diversified agricultural production (Grujard 2003 ). Water management consortium/associations draw on and allocate water resources at catchment scale, and in France, these organisations apply water abstraction restrictions decided by political institutions during periods of drought, mainly decreasing the allocation of agricultural water resources (Riviere-Honegger 2008 ). Orchards are typical productions in the Mediterranean region and have a great economic and cultural interest to the region (Pardo 2005; Montanaro et al. 2017 ; Rodrigo-comino et al. 2018 ). However, perennial crops are one of the most affected by climate change and extremes events (Dalhaus et al. 2020 ). The increase in periods of drought impacts the trees productivity because of the lack of water (Kang et al. 2002 ; Moriana et al. 2003 ). Some studies show that this can also degrade the quality of the fruit in terms of its nutritional qualities or size (Romero et al. 2006 ; Rahmati et al. 2015 ). Moreover, fruit production is declining as a result of climate change, mainly because perennial crops are less adaptable to changes in temperature as well as to variations in CO2 concentration (Malhotra 2017 ; Chawla et al. 2021 ). Many authors have also shown that the variability of agricultural practices in Mediterranean orchards (presence or absence of grass in inter-row and on the row, plant density, variety, precocity of the production, pruning, etc.) have an impact on the water needs (Allen and Pereira 2009 ; Demestihas et al. 2017 ). If it is possible to manage the water needs at a field scale through local measurements or decision tools developed with precision agriculture (Loures et al. 2020 ), it is very difficult to effectively characterise the needs/consumption of water at the territorial scale due to the variability of practices in the landscape. Various studies relating to the assessment of water consumption were developed at plot scale. Volumes of water are estimated on each plot and extrapolated to the whole farm considering the sum of other plots. The water consumption of a field can be estimated by various methods, in particular by estimating plant transpiration using FAO-type formulas involving the calculation of a reference evapotranspiration (Allen 2000 ). Various factors such as the soil type and Available Water Content (AWC), climatic conditions, the topography, can be considered as they impact the water consumption (Vera et al. 2019 ; Zipori et al. 2020 ; Raluy et al. 2022 ). Other factors can also play a significant role: the available equipment and the habits and choices made by the farmer (Reynard et al. 2014 ; Schneider 2022 ). However, farmers behaviours, linked to the real or supposed needs in the specific context of his farm, influence the amount of water used sometimes more than other factors, creating variability between farms within the same region (Deffontaines and Petit 1985 ; Gadanakis et al. 2015 ; Calianno and Reynard 2016 ). It seems that this variability between farms due to the farmer’s behaviour is particularly marked and visible between historic farms (more than one generation of farmer), in areas where freshwater access is uncontested, and the resources are unpolluted and sufficiently abundant (Longo and York 2009 ) and where farmers are autonomous on their water management (Abdullaev and Mollinga 2010 ). An estimation of the water consumption is essential to manage water resources in a large area such as a watershed and provide reliable information on both crop water requirements in basin and on river management programmes (Vieillard-Coffre 2001 ). For these reasons, it is needed to characterise farmers' behaviour in terms of water use (Perrot and Landais 1993 ; Maton et al. 2005 ). In this sense, this study aims to propose a novel method to classify farms according to their water consumption for irrigation at watershed scale. Then, considering these characteristics, this study aims to estimate agricultural water consumption at large scale (municipality, watershed), based on different heterogenous database, implying the use of statistical machine learning tools. In this case, this study was particularly focus on orchards, because of the relevance that they have in the Mediterranean region, and also because of the variability in terms of irrigation behaviours for this kind of crops. 2. Materials and Methods 2.1 Overall methodology Analysing the data obtained from the water management institution at catchment scale (ASA), this study is based on the hypothesis that the farming practices applied in terms of irrigation management are related to some farms’ characteristics, determining their propensity to be heavily or weakly water consumers. This characterization can be the starting point to have an assessment of agricultural water consumption at large scale. Then, to refine the estimation, it is important to consider water applications that vary from one orchard field to another, particularly within farms that are heavily water consumers. The general methodology of this study is described in figure 1. First of all, at farm scale, (1) starting from available data acquired by the ASA and from farmers interviews, information about irrigation water consumption were collected at farm scale for 15 farms; then (2) the water consumptions of each farm was relied to a series of farms structural and biophysical characteristics, in order to understand which factors have an influence on irrigation behaviour (3) using eight statistical methods to classify farms in the territory; (4) the most occurrent statistical classification obtained was applied to the each farm in the area, in order to obtain a classification of all the farms in two main classes: the highest irrigators or the weakest. This first classification at farm level was one of the main inputs for the orchard fields scale analysis. For the heavily irrigated farms, the following workflow was applied: (1) irrigation water volumes applied were estimated on the field taking into account the accurate information from farmers interviews and orchards characteristics for 66 orchard fields; then (2) the estimated volumes of each field was used to rely them with some biophysical and geographical factors of the parcel particularly belonging to the heavily irrigated farms and finally (3) nine statistical classification methods were applied to each orchard fields which belong to heavily irrigated farms in the area and (4) the most occurrent statistical classification was applied to all the 279 orchard fields belonging to heavily irrigated farms , in order to have an estimation of the overall water consumption for irrigation at the watershed scale. The estimation obtained was compiled and compared with the available data from ASA at larger scale (municipalities and watershed scales), as a validation of the method. In particular, at municipal scale, the total volume corresponding to the sum of each terminal they monitored in all the municipality was used. These volumes were also compiled at watershed scale, in order to obtain a single amount of water consumed for the watershed. Fig.1 Overall scheme of the methodology. AWC: Available Water Content; ASA – ‘ Association Syndicale Autorisée’ : water managers at catchment scale; LPIS: Land Parcel Identification System; RGA - Recensement Général Agricole : French government data on crops grown in municipalities. Grey ovals refer to the sections of the material and methods describing these parts. 2.2 Description of the case study The Ouvèze-Ventoux region is a part of the Ouvèze river basin, in the South-East of France, a right tributary of the Rhône (Roux et al. 2019). The Ouvèze has a typical torrential flow regime, as it can be subject to heavy flooding as well as periods of very low flow, particularly in summer. The area is around 100km², delimited at North-East by the Ouvèze river, and at South-Est by the Mont Ventoux (map Fig 2). It includes four municipalities with a variety of landscapes: a wide plateau in the Entrechaux area (around 300m asl.) in the North-Ouest and a valley with a colder micro-climate and higher altitude (around 500m asl.) in Beaumont-du-Ventoux. The climate is characterised by rainfall of around 650mm/year, according to the Carpentras weather station, mainly concentrated in autumn-winter period, whereas recent years have been characterized by heavy summer droughts. Heavy rainfall can occur, with extremes events of up to 300mm in a single episode (maximal observation in Entrechaux 22/09/1992) (Piegay and Bravard 1997). The distribution of water for irrigation is managed by the ASA ( Association Syndicale Autorisée, Authorized Syndicate Association) Ouvèze-Ventoux. The managed areas of the ASA are mapped in red on figure 2 and cover 304ha in Beaumont-du-Ventoux, 467ha in Malaucène, 372 ha in Entrechaux and 211 ha in Le Crestet municipality (total 1354ha). Most of these surfaces are occupied by fruit trees (44% of the utilised agricultural areas – UAA) and vineyards (43% of the UAA - 83% of which are wine-growing vines and 17% table vines). Most of the orchards are cherry trees ( Prunus avium ), which is a very traditional production in the area (IGP “ Cerises des coteaux du Ventoux ”). However, in the last years they suffered different hazard related to climate change, with increasing water needs (around 540mm/year in the south of France, according to CABRL 2019) and recurrent diseases (Herrera and J 2015; Ali et al. 2017; Medda et al. 2022). In terms of water use, irrigation restrictions have been imposed during dry summers, reducing irrigation authorisations and even banning all irrigations systems, even water-saving drip irrigation (a so-called "crisis" state), as it was the case during the drought of 2022 (Préfecture de Vaucluse 2022). Figure 3 shows the relation between the water consumption for each year (data from ASA) and the rains: seasonal (April to September included) and annual from the Carpentras weather station (15Km-SSE). A large variability is observed which can appear sometimes correlated with the annual rainfall amount: for drier years (such as 2017 or 2022), higher consumptions are observed, but for most of the time this variability seems not related to rainfalls. The annual rainfall is not the only parameter impacting the ASA water consumption and the variability between annual and seasonal rainfall could affect the water reserved in the soil. The seasonal rainfall (between April and September) is also not completely correlate to the irrigation, even if the low level of seasonal rainfall could explain some years of heavy irrigation (2016, 2017 or 2019 for example). The taxation of the water consumption by ASA is referring to water terminals which are connected to one or more fields of a same consumer. According to the statements of water managers in the region, the water intake stations of the ASA are not very precise and show up to 30% error comparing to the real consumption, especially when the field is irrigated frequently but in small quantities (as in the case of a drip irrigation orchard or vineyard). Fig.2 Map of the Ouvèze-Ventoux watershed and ASA-OV managed areas and fields referenced in the LPIS. Fig.3 Water consumption in the ASA area in the watershed according to the ASA Ouvèze-Ventoux (mm): annual (in red) and seasonal (April-September included, in blue) rains from the Carpentras weather station (20Km SSO) from 2006 to 2022 2.3 Dataset description 2.3.1 Data on farms and fields’ structure and distribution Table 1 summarizes the available datasets for the Ouvèze-Ventoux watershed. All the data listed and described on the following paragraph have been used to characterize the farming systems of the study area, including all the topographical and farming practices information possibly related to the use of water. In order to have a comprehensive description of each cultivated parcels, data from the Land Parcel Identification System (LPIS, availed freely by the ‘geoservices’ [1] from IGN) was acquired. LPIS is a spatialized dataset on crops declared each year by farmers to received European subsidies. Considering the source, not all farms are declared, depending wherever or not they are asking for support. In particular, perennial crops are often lacking, because they are not submitted to subsidies. Usually, these crops are declared when they are cultivated in mixed farms, where there are also annual and/or herbaceous crops. Information on the location of the agricultural parcels and their crop are given, but no information on irrigation is furnished nether distinction among the different species of orchards, except for olive groves, table grapes, and vineyards. In this study, the last available LPIS data in the region (2020) was considered. This dataset represents 1721 agricultural fields, representing 48% of the total 3581 fields in the area, belonging to 65 farms. In terms of surface, they represent 1001ha, including 504 fields (302ha) of orchards in 2020. The orchard class in the LPIS do not include olive groves and truffle groves which are referenced differently. Considering that the LPIS does not cover all the farms in the area, the database was completed with the agricultural census (RGA - Recensement Général Agricole, partially freely available by the AGRESTE website [2] ). This database provides the information reported in table 2 at municipality scale only. In this dataset, irrigated surfaces are referenced but this information is not accessible freely and requires accreditation. In terms of topographical and soil data, this study is based on the digital elevation model and the pedological map of the study area. The digital elevation model was used to calculate the altitude, slope and exposition of the parcels. Soil data, especially about available water capacity (AWC) which is calculated according to the pedological characteristic of the soil (Cousin et al. 2022), have been derived from the soil map of the society managing the Provence canal (SCP [3] , Societé du canal de Provence) in 2012. The AWC values has been calculated for each farms’ field and then aggregated at farm level. In some cases, where the soil data were lacking, the information was completed with the Infosol unit map of AWC (Román Dobarco et al. 2019b, a; Romàn Dobarco et al. 2022) , available at France scale at a spatial resolution of 90m for the first 2m of soil (Román Dobarco et al. 2019b) [4] . In order to complete the description of the area with information on farming practices related to irrigation, a field surveys were carried out between 2019 and 2023, enquiring 21 farmers in the watershed area, 13 of which depending on the ASA for their water supplies, in farms which have a significant proportion of perennial crops (e.g. orchards or vineyards). During the interviews the following information were collected: location of all the farms’ fields (749 plots), land cover and crop type (with cultivar detail); for orchard crops, the spacing between trees and between rows, the type of drippers used, their flow and dispositions in the crop, and the irrigation schedule. Combining all the information listed, a geo-database including 3581 parcels was obtained which represent the whole agricultural areas in the watershed. Among this total, 710 parcels, corresponding to 393 ha (22% of the total agricultural surfaces), belong to the farms which were interviewed and for which more detailed information about farming practices were available. Tables 1: List of the available dataset Source Resolution Information type LPIS 2020 – Land parcel information system Data.gouv (French gouvernemental data) [5] Plot scale Crop typology (38 classes) - Farm to which the parcel belongs Surveys Farmers Plot scale (for 21 farms) Crops, cultural practices, trees implantation pattern, irrigation time RGA 2020 INSEE (partially confidential database) Municipal scale Crops typology and areas, including irrigated areas ASA water consumption ASA – Ouvèze-Ventoux Farm scale Municipal scale Watershed scale Water consumption taxed of 13 farms - Consumption at each water distribution terminal - Total water consumption provided by ASA (for each year) DEM Geoportail 1m altitude Soil map SCP 1/25000 aggregated at Pedologic unit scale Pedological information (Dominant texture and Pedological names of first layers) AWC map 1 Link to the SCP soil map Pedologic unit scale AWC AWC map 2 Infosol INRAE unit Pixel size: 90m x 90m AWC estimated from modelling for the theorical 2 first meters of soil Table 2 Areas (ha) referred in the RGA data for main orchards and vineyards typologies in the four municipalities in the Ouvèze-Ventoux area. Total (ha) Irrigated (ha) Beaumont Crestet Entrechaux Malaucène Beaumont Crestet Entrechaux Malaucène Cherries 72 3 7 66 50 2 2 54 Apricots 34 3 18 55 20 0 11 38 Plums 44 6 2 33 30 6 1,5 25 Olive groves 5 10 11 24 1 1 0,6 11 Truffle tree groves 3 1 3 17 0,05 0 1 6 Others orchards 4 1 0,2 17 1 0 0,2 11 Table vineyards 17 19 30 26 13 13 15 9 Wine vineyards 82 86 212 200 8 0 6 18 2.3.2 Data on water consumption at farm and field scale In this study, the available information (provided by ASA) about water distribution and consumption on the study area, was used as dependent variable on the statistical modelling analysis, and as a validation for the overall consumption. Water resources for irrigation are managed by the ASA, which delivers water to each farmer via a pressure network and irrigation terminals. Each water access terminal can normally be linked to one or more fields of a same farm. Each farmer declares an irrigated surface and volumes are recorded on terminals. As mentioned before, the ASA estimates the sensor uncertainty at around 30%. This data is then summarised by the ASA to define a water consumption for each farm for the water taxation. The ASA provided the total consumption volumes for 13 of the 21 interviewed farms. The data about the farm consumption were calculated for 5 years (2016-2020) and adjusted for the total surface of the farm to estimate the average quantity of water applied in millimetres per year, assuming that the total farm area does not change from one year to another during the 5 years. Figure 4, shows the average water consumption per farm for the 13 farms from ASA information and 2 others (R3 and T1) for which the sum of irrigation was calculate (describe in 2 nd part of part 2.3.2). Two groups can be clearly identified: some farms consume more than 100mm per year while other consume less than 40mm. Dashed lines show the mean values of heavily irrigated (163.8mm) and weakly irrigated farms (12.9mm). According to this information, farms were separated in two groups: heavily irrigated, if they applied more than 100mm/year and weakly irrigated, if they applied less than 40mm/year. Fig.4 Water consumption according to the ASA data for the surveyed farms. Dashed red: average consumption of higher consumers (in red), Dashed blue: average consumption of lower consumers (in blue) Moreover, 2 more farms in the watershed were added not belonging to the territory managed by the ASA. These two farms have cherry orchards as only relevant irrigated crop. Volumes of water were estimated by the sum of the water use in each orchard’s plot, obtained according to some relevant characteristics assessed during interviews, namely: (1) the spacing between rows of trees (S rows in m) and between trees on a same row (S trees in m); (2) the type of irrigation equipment, namely r flow rate in litres per hour (Flow in L/h) and repartition in the field by the spacing between 2 drippers (S drip in m) or the number of drippers per trees (case of micro-sprinklers) (Ns in drippers/trees); (3) the irrigation schedule, which depends on the type of orchard cultivar, compiled as a number of hours of irrigation per year (t in hour/year). The assessment of the water quantity is thus made by following equation 1 or 2 according to the type of irrigation equipment. In the equation 1, relative to micro-sprinklers, the number of trees is computed according to the distances between rows and trees, considering a squared field of 1ha where 1m is lost on each side of the plot. In the Equation 2, referred to drip irrigation, the number of rows is computed for a squared field of 1ha where 1m is lost for the borders, this value divided by the spacing between rows is equivalent to the number of rows in this plot and, this value multiplied by 98m of row longer in the square plot (the 100m side of the square - 2m border) provide the length of piper’s tube in the field. These 2 equations give an estimation of the annual water consumption for an irrigated field of cherry orchard. However, this estimation does not reflect potential interannual variability. These equations were applied to 66 cherry orchard fields belonging to heavily irrigated farms and to the 68 cherry orchard field belonging to weakly irrigated farms. Figure 5, shows the high diversity in terms of water consumption for the orchards of heavily irrigated farms: we observed two main behaviours, corresponding to the two pics. Dashed lines show the mean value for each class. Fig.5 Density of plots by their water consumption estimated for the 66 orchards of heavily consumers farms, in red the threshold between the 2 groups of plots, in dashed green the mean values of each groups (110mm and 538mm). Considering the general distribution of the water consumption at field scale, the two most occurrent means values (110mm and 538mm) were applied at each orchard field scale to sum the overall water use at municipal and watershed scale. For the weakly irrigated farms, less variability was observed and the mean value of water consumption at field scale for cherry orchards was 26mm, used as reference value. For the remaining irrigated fields, not covered by orchards, we used as value of water amount yearly applied the quantity declared by farmers during the interviews and also by the local experts, in particular the ASA technicians. The yearly volumes declared are showed on table 3. Table 3 Irrigation apply according to the typology of perennial crops Crop Water volume per field (mm) Orchards heavily irrigated of heavily irrigated farm 538 Orchard weakly irrigated of heavily irrigated farm 110 Orchard of weakly irrigated farm 26 Olive groves 150 Truffle tree groves 40 Table vineyards 300 Wine vineyards 100 The two additional farms for which were estimated the water consumption (R3 and T1 in figure 4) considering the sum of each orchard consumption have respectively 173mm/year for the R3 farm and 224mm/year for the T1. These two farms are high consumers where the main crop is orchards which represent the biggest part of them area (86% for R3, 69% for T1). 2.4 Statistical approach In terms of statistical methods, for both the classification at farm and field level, a benchmark of classification models was applied, namely: Random Forest (RF), Support vector machine (SVM), Principal component analysis (PCA), Neural Network (NN), Naive Bayesian classification (NB), logistical regression (LR), K nearest neighbours (KNN), linear discriminant analysis (LDA) and Factor Analysis of Mixed Data (FAMD) (only for plot scale considering some non-quantitative variables). The list and description of the statistical methods applied are showed on Tab.4. The different statistical methods are applying in order to give more robustness to the modelling approach. We then considered as the correct class the one resulting on more of the models applied. The Pearson correlation coefficient (Pearson 1920) was estimate between each variables of our datasets and variables not significantly independent were not kept for the NB, kNN and NN classifications. Table 4. Statistical methods descriptions (default parameters are the default parameters in the R function used) Statistical approach Description Hyper-parameters applied Principal Component Analysis (PCA) Dimensionality reduction technique identifying the principal components, which are orthogonal linear combinations of the original variables (Gifi 1990; Husson et al. 2005) ncp: 5 Size of confidence ellipses: 95% Support Vector Machine Machine learning algorithm used for classification tasks which find the best hyperplane that separates different classes in the data space (Chen et al. 2004) Kernel: radial Regularization parameter: default Naive Bayesian Application of the Bayes theorem at a dataset of objects with independent characteristics to define classes (Rish 2001) None Random Forest Machine learning method used for classification and regression tasks. It constructs multiple decision trees during training and combines their predictions through voting (for classification) or averaging (for regression) to improve accuracy and robustness (Breiman 1996, 2001) Tree number,Predictor number per split, Leaf size : default k-Nearest Neighbor It predicts the class or value of a new data point by considering the majority class or average value of its k-nearest neighbor in the training data (Keller et al. 1985) k: 3 Logistical regression Binary classification, estimating the probability that a given input belongs to one of two classes based on predictor variables (Lee et al. 2006) Family: "binomial" Regulatization parameters: default Factor Analysis of Mixed Data Multivariate statistical technique used for analyzing datasets containing both quantitative and qualitative variables. It explores underlying structures and relationships between variables through dimensionality reduction and factor analysis (Escofier 1979; Kiers 1991) Number of dimensions kept (ncp): 5 Neural Network Training a model composed of interconnected nodes (neurons) to classify data based on patterns learned from input-output pairs (LeCun et al. 2015; Chollet 2017) 2 layers (5,5 neurons in farm's classification 5,6 in the plot's one) Linear Discriminant Analysis Finds directions that maximize class separation, projects data onto these directions, and predicts class membership based on the projected values (Friedman 1989) Regulatization parameters: default For each statistical method, a cross-validation was applied for the two training datasets (15 farms – 66 orchards) and the methods not providing a sufficient score were not considered. After applying each classification method independently for each holding, the allocation to the most common class was retained. Similarly, each classification was applied to each of the orchard fields of the heavily irrigated farms and the most common allocation was retained. 2.4.1 Farm scale classification For the farm classification, the characteristics selected had to meet two main criteria: they had to be accessible via the data available at watershed level, and they had to correspond to characteristics that have a strong influence on water consumption, since the differences observed between the consumption of heavily and weakly irrigated farms are very large (figure 4). Firstly, we assumed that a farm that will have a predominance of irrigated perennial crops (table vines/ orchards) will consume more water. This hypothesis is also supported by other previous studies which show that farms that do not have enough water will turn to less water-consuming crops (meadows, cereals, etc.) rather than apply deficit irrigation on heavily irrigated crops (Schuck and Green 2001; Gómez‐Limón and Riesgo 2004). Then, the characteristics of the farms tend to define their water consumption. In this study, the choice was to characterise farms by their soil via the average AWC (average of the average AWC of each field weighted by their area), considering that AWC is one of the major characteristics of a plot's water requirements (Pereira et al. 2015). On the other hand, the average altitude of the field is one of the main factors in the characteristics of the field and enables fields to be distinguished by their microclimate (temperatures/winds/rains) (Jacobsen et al. 1997; Sevruk 1997; Archer and Caldeira 2009). So, in this study was kept the average altitude of the farm (mean altitude of each plot weighted by their area) as an important factor of the decision of agricultural practices (Schoenly et al. 1996) Table 5 Used parameters for the farms’ classification Data Source Area of heavily irrigated crops (orchards + Table vineyards) LPIS Area of weakly irrigated crops (Cuve vineyards + other crops) LPIS Average available water capacity (AWC) of the farm SCP / Infosol Average altitude of the farm DEM 2.4.2 Field scale classification Based on the previous assessment, the classification was carried out at field scale for the orchard fields belonging to heavily irrigated farms. The variables used for the classification are listed on table 6. As previously, for this classification each variable was selected to describe the water needs of a crop in this field as a representative proxy of the irrigation need. Altitude was kept as a descriptor of the microclimatic cultural conditions. The percentage of pixels exposed to south was add as a description of the solar direct radiation which affect highly the climatic condition (Dufour 1887). For soil, the main drivers of water needs are relative to the AWC. The available water is affected by the quality of the soil from the texture (sandy soil or not) and by water losses, which are partly linked to runoff from the slope (Gaetano et al. 2017). The proximity of watercourse could also affect the available water and the characteristic of soil (Struyf et al. 2009). Finally, from the point of view of human labour, according to the farmers surveyed, the proximity of a field will have an impact on the frequency of visits by the farmer and therefore on whether or not water is supplied. Table 6 Variables for the orchard fields’ classification Data Source Altitude of the centroid of the plot DEM Average slope of the plot DEM Percentage of pixels exposed to the south DEM Dominant soil texture Donesol / pedological map AWC SCP - Infosol Distance to nearest watercourse IGN [6] Distance from the centre of the farm LPIS 2.5 Overall water consumption for irrigation at watershed scale The values of irrigated fields referenced in the table 2 are weighted considering the municipality and the crop type in the field according to the agricultural census data. In fact, the census indicates the percentage of irrigated surface area per municipality, so it was considered a ratio applied to the overall estimation. For the orchard species characterization, the values of water consumption applied at field scale (table 3) were calculated on cherry orchard for a large majority (76%), whereas apricot and other orchards can be considered to consume around 70% of the cherries, based on farmers surveys and irrigation manual (CABRL 2019). For this reason, a correction factor was applied based on the census data about the percentage of each crop and its rate of irrigation in the municipality. For the LPIS possible bias (not all plots are declared), we had to consider that these data are declarative and as previously indicated, in the study area they cover only 48% of the lands. To compare this value to the total irrigated area in the ASA perimeter, a factor was applied which assumes that the ratio of surface area allocated to each crop in the LPIS is representative to the ratio between surfaces areas allocated to each crop in the all watershed (referred in the table 7). To know the total agricultural area, in the case of this study, a general shapefile was created by the observations in the territory and the cadastral data. This map referred to all the agricultural areas in the territory. The ratio between this area and the area referenced in the LPIS (percentage of crops in the LPIS according to the totality of crops) is used to correct the missing part of the LPIS surfaces. The table 7 shows the percentage of represented area in the LPIS. Table 7 Representativity of the LPIS surface in comparison of all the parcels in the territory of ASA in each municipality Beaumont Crestet Entrechaux Malaucène LPIS area (ha) 119 44 98 185 Total agricultural area (ha) 164 110 210 254 Ratio of LPIS representativity (%) 73 40 47 73 3. Results 3.1 Farm typologies The accuracy evaluated from a cross validation with the 15 farms affiliation for the classifications of heavily or weakly irrigated farms shows a large variability among the methods (from 67 to 100% Fig. 6 ). The NB classification presents the lowest performance, the PCA the highest. The NB classification, which gave the worst results, was not considered. Without considering the NB classification, the average result of statistical methods is 90% of good identification on the training dataset. According to the Pearson correlation test, the average AWC of the farm is not statistically independent to other values (see Supplementary Material figure S.1). Since data independence is important in the case of the NB, NN and kNN classifications, this variable was be removed from the classifications. Figure 7 shows the map obtained from the farm classification, with the most common assignment among the statistical classifications (kNN, LDA, LR, NN, PCA, RF and SVM) for each farm. Most of the farms classified as heavily irrigated are located in the East of the watershed, in the Beaumont-du-Ventoux municipality. The large Entrechaux plateau most located at West and North-West includes a large proportion of weakly irrigated farms. This classification of the LPIS farms shows 50 farms (77%) classified as weakly irrigated and 15 farms (13%) classified as heavily irrigated. 3.2 Orchard fields classification The accuracy of the cross validation done at fields scale with the estimations of orchard’s consumption by the different statistical methods show a large variability (Fig. 8 ). These cross validations were applied at the 66 orchards plots in the training dataset where we have accurate surveys. The highest score is at 77% for SVM classification. With respectively 53% and 54% of good identification at cross validations, FAMD and NN classifications are the least able to identify parcels with high or low consumption. Results of these two methods were not kept for the final classification. Without these two methods the mean result of the cross validation is 68%. The correlation between each variable is show in figure S.2 in Supplementary material. In the analyse, the Pearson coefficient between each variable shows that the percentage of pixels with a South orientation is not statistically independent to other values. Since data independence is important in the case of the NB, NN and kNN classifications, this variable will be removed from these classifications. The map of the classified fields is presented Fig. 9 a. This map shows the 3 groups of orchards: two groups for heavily irrigated farms (red in previous section Fig. 7 ) including either heavily and weakly irrigated orchard fields. Figure 9 -b shows the repartition of the orchards from heavily irrigated farms with a higher proportion of less irrigated orchards (170 orchards) than more irrigated orchards (109). The third group of orchard classification (blue) concern orchards fields in the weakly irrigated farms. 3.3 Assessment of total water consumption at watershed and municipality scale To validate the estimations of the model at large scale (municipalities, watershed), the different estimated consumption (Table 3 section 2.4.1 ) was considered according to ponderations applied to municipalities values (described in sections 2.5 ). The sum of these estimations for each field in the ASA managed territory (map Fig. 1 ) at the watershed scale is 697 723 m 3 for 2020. The ASA Ouvèze-Ventoux gives an annual water consumption for the watershed of 596 121 m 3 in 2020. This value could appear quite comparable with the estimated value (14% difference overestimated). According to the ASA, the margin of error for their value is estimated at a potential underestimate of 30%. The ASA's consumption is therefore between 596 121 and 774 957 m 3 . The value obtained of 697 723 is therefore well within this margin. Table 8 , shows the results of the estimation of the water consumption for each crop in the ASA managed area for each municipality. Figure 10 compares the values obtained for the estimated quantity of water per crop and per municipality with the ASA values for the year 2020. In comparison, water consumption is overestimated in Beaumont-du-Ventoux (+ 200 000 m³), relatively well estimated in Crestet and Entrechaux, and underestimated in Malaucène (-100 000 m 3 ). The consumption of truffle tree and olive groves is negligible in all municipality compared with the consumption and surface areas allocated to vineyards and other orchards. The main sources of errors could be link to the estimation of water irrigation of each crop even if the corrections were applied. In Beaumont, a large proportion of the irrigation water is used for orchards, whereas in Crestet and Entrechaux, most of the water is used for table wines, which cover most of the surface area. In addition, it should be noted that the orchards of weakly irrigation farms, although representing considerable surface areas, are not major sources of water consumption, unlike the orchards of heavily irrigated farms. This distinction can be seen particularly in Beaumont and Malaucène, where the orchards of heavily irrigated farms represent the main source of consumption in the municipality. The overestimation in Beaumont could be link to the value of 456 mm which is estimated apply to this category which could be overestimated. Table 8 Consumption estimated for each crops of each municipality’s ASA managed area Villages Crops Area (m2) Average consumption (mm) Consumption (m 3 ) Beaumont-du-Ventoux Olive groves 11861 150 1779 Wine vineyard 208897 10 2089 Orchard heavily irrigated - farms heavily irrigated 531290 456 242268 Orchard heavily irrigated - farms weakly irrigated 434554 93 40414 Orchard of weakly irrigated farms 86209 22 1897 Table vineyard 94520 300 28356 Crestet Olive groves 48744 19,5 951 Wine vineyard 669822 0 0 Orchard of weakly irrigated farms 88541 14 1240 Table vineyard 240497 300 72149 Entrechaux Olive groves 5110 150 767 Truffle tree groves 47061 19 886 Wine vineyard 1322449 3 3967 Orchard of weakly irrigated farms 197309 22 4341 Table vineyard 306355 300 91906 Malaucène Olive groves 64715 150 9707 Truffle tree groves 44235 14 619 Wine vineyard 498554 89 44371 Orchard heavily irrigated - farms heavily irrigated 254349 346 88005 Orchard heavily irrigated farms weakly irrigated 545065 71 38700 Orchard of weakly irrigated farms 430275 17 7315 Table vineyard 148113 108 15996 4. Discussion 4.1 Water need assessment The farm classification (Fig. 7 ) has outlined different practices according to the location and the main crop of the farm. The cross-validation of the statistical methods gives a rather high percentage of correct identification (Fig. 6 ). However, this result must be qualified because the cross-validation was carried out on a small data set (15 farms). A high proportion of heavily irrigated farms is located in the municipality of Beaumont-du-Ventoux, and indeed most of the farms in this area have mainly orchards, which consume the highest amount of water according to the ASA data. The high density of heavily irrigated farms in the eastern part of the watershed (Beaumont-du-Ventoux) could introduce a proximity bias for nearby farms. These nearby farms may differ socially in ways not considered in this study. It is generally recognized that classifications aimed at grouping irrigation decisions and individual choices have significant limitations due to the unpredictability of human decisions (Maton et al. 2005 ). This unpredictability may account for some potential biases in the study. Conversely, the low proportion of heavily irrigated farms in areas such as Entrechaux or Crestet could mask farms that tend to be heavily irrigated. Thus, farms that are socially but not geographically isolated may be misclassified by the model. A social explanation for the variability could be considered, but would require personal information on all farmers in a region (Bublot 1969 ). Different habits of farmers, especially related to the age of the irrigation manager and whether the farm is a family farm or not, could affect the decision and modality of an irrigation event, as observed in similar cases by Azizi Khalkheili and Zamani ( 2009 ) or Wang et al. ( 2013 ). Another social parameter that could affect the classification could be whether the farmer has access to technical advice on irrigation management, as also mentioned by Genius et al. ( 2014 ). All of these characteristics affect the amount of water applied by the farmer, but are difficult to access on a large scale. Other agricultural practices, not considered here, related to farmers' choices, can have an impact on a crop's water use. In particular, the use of fertilizer irrigation can have a significant impact on the amount of water applied to the plot, as fertilizer requires additional water to be applied. However, this information is not available at the farm level. The estimation of water consumption at the crop and municipality level (Table 8 ) shows a high diversity of consumption for the same crop in different municipalities. For example, the heavily irrigated orchards of heavily irrigated farms have an estimated irrigation of 456 mm in Beaumont and 346 mm in Malaucène according to the different weighting. These variations strongly affect the total consumption of the whole commune (Fig. 10 ). This variability seems relevant and apply a less important irrigation in the scale of a commune which have effectively less needs due to their physical and environmental characteristics. Regarding the recommendation of estimation given in other similar Mediterranean regions (Memento irrigation book (CABRL 2019 )), the values vary from 180 to 350 mm/year of irrigation for cherry orchards. This possible overestimation of the applied quantities could be one of the main reasons for the overestimation of the consumption in Beaumont (Fig. 10 ). 4.3 Methodological discussion The relevance of the heterogeneity of farmers' (Schuck and Green 2001 ; Gómez-Limón and Riesgo 2004 ; Wang and Cai 2009 ) or farming communities' (Wang et al. 2013 ) behavior in the irrigation decision has been analyzed in several recent papers. These articles are based on the importance of irrigation water from an economic point of view and try to find a relationship between the productive value of the water input, especially with respect to the soil characteristics of the regions studied (especially the avialable water content), and its cost. Moreover, the quantification of irrigation has also been related to field characteristics (Leenhardt et al. 2004 ; Ali et al. 2017 ; Vera et al. 2019 ), but these studies require modeling with soil water characteristics (Soil Water Potential, Available Water Content or Soil Water Content), which are parameters that are difficult to access accurately when applying a large-scale model. This study confirms the importance of considering both levels of analysis: both farm and plot characteristics are essential in estimating water use to obtain a coherent assessment of water use at the watershed scale, especially in the case of fruit tree crops. Few studies show an estimation of water use at the watershed scale. The most common studies are based on the estimation of crop water requirements by biophysical methods at different scales with crop models (Brisson et al. 2003 ; Mancosu et al. 2016 ; Akoko et al. 2021 ). These models can also be based on some remote sensing information to be applied at larger scales (Olioso et al. 1999 ; Duchemin et al. 2006 ; Courault et al. 2010 , 2021 ) or to identify plot characteristics (Abubakar et al. 2022 ). However, few studies focus on large areas dominated by perennial crops due to the difficulty of identifying characteristics of this type of heterogeneous land cover, although some techniques are being evaluated (El Hajj et al. 2018 ; Rouault et al. 2024 ). The few studies that consider this type of crop are usually based on field surveys or work in close contact with farmers, coupled with modeling (Kpadonou et al. 2015 ; Naulleau 2021 ). This study proposes a method based on heterogeneous spatial and statistical databases that are available and can be applied at a large scale. It is based on a demonstration of the variability of irrigation practices in orchards, which are the main field consumers of water in this region. Until now, to our knowledge, no study has addressed the variability of orchard irrigation on crucial aspects of farmers in the Mediterranean region, and the modeling available at scales large enough to be useful to water managers was based on modeling water requirements rather than real irrigation. This study proposes a method to estimate the real water consumption of a Mediterranean watershed by taking better account of farmers' behavior and by proposing a typology that can be applied to all farms in the watershed, starting from a small number of surveys. 5. Conclusion A variety of methods for classifying farms and fields at the small watershed scale, with a dominant presence of orchards, have been proposed based on spatialized databases and surveys. These methods are employed to assess the water use for irrigation. The estimation of water consumption at the watershed scale represents a preliminary step in the process of establishing the capability to ascertain the water consumption of different parts of a small Mediterranean watershed. This is achieved by compartmentalizing orchard crops into different categories. The consideration of farm typology as a proxy for farmers' irrigation decisions is a useful approach for describing orchard irrigation practices and identifying locations with high irrigation water consumption in a watershed. Distinguishing between heavily and weakly irrigated farms appears to reflect the distribution of farms in the area. The presence or absence of a majority of water-consuming perennial crops, as well as altitude and whether or not the farm has access to significant water resources in the soil, appear to be useful in identifying heavily water-consuming farms. This analysis also highlighted the diversity of irrigation practices between these high-consumption farms. Without information on the cultivated species, it is challenging to classify these plots as heavily or weakly irrigated. However, the physical characteristics of the field, including altitude, slope, exposure, AWC, and proximity to the watercourse, as well as the distance of the plot from the heart of the farm, appear to be parameters that can be statistically estimated to determine whether the plot belongs to one category or the other. In conclusion, this study demonstrates the potential to estimate the water needs of a small watershed with a dominant orchard crop using a combination of statistical methodologies. This approach involves first classifying farms according to their water consumption, which can be readily extrapolated to other environmental and agronomical contexts. Declarations Author Contribution PR has developped all the statistical analysis, data collection and first draft writing;DC has review the first draft, supervised the data analysis and methodological conceptualization and she managed the related projects;FF has developped the filed interviews, data acquisition;MD has reviewed and finalized the paper draft, supervised the methodological developpement.All authors reviewed the manuscript. Acknowledgement This study was funded by the PACA region and a project with the King Abdullah University of Science and Technology in Saudi Arabia. The authors thank the surveyed farmers and the ASA president, who have kindly provided important data for this study. The authors would also like to thank the colleagues who contributed their expertise to the processing and acquisition of the data, in particular Mr Samuel Buis for his advice and Mr Davide Martinetti for data acquisition.Access to some confidential data (Recensement Général de l’Agriculture 2020: https://doi.org/10.34724/CASD.39.4411.V1), on which is based this work, has been made possible within a secure environment offered by CASD – Centre d’accès sécurisé aux données (Ref. 10.34724/CASD) References Abdullaev I, Mollinga PP (2010) The Socio-Technical Aspects of Water Management: Emerging Trends at Grass Roots Level in Uzbekistan. Water 2:85–100. https://doi.org/10.3390/w2010085 Abubakar M, Chanzy A, Pouget G et al (2022) Detection of Irrigated Permanent Grasslands with Sentinel-2 Based on Temporal Patterns of the Leaf Area Index (LAI). Remote Sens 14:3056. https://doi.org/10.3390/rs14133056 Akoko G, Le TH, Gomi T, Kato T (2021) A Review of SWAT Model Application in Africa. 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Geoderma 344:14–30. https://doi.org/10.1016/j.geoderma.2019.02.036 Romàn Dobarco M, Bourennane H, Arrouays D et al (2022) Réservoir utile des sols de la France métropolitaine Román Dobarco M, Cousin I, Le Bas C, Martin MP (2019b) Pedotransfer functions for predicting available water capacity in French soils, their applicability domain and associated uncertainty. Geoderma 336:81–95. https://doi.org/10.1016/j.geoderma.2018.08.022 Romero P, Navarro JM, Pérez-Pérez J et al (2006) Deficit irrigation and rootstock: their effects on water relations, vegetative development, yield, fruit quality and mineral nutrition of Clemenules mandarin. Tree Physiol 26:1537–1548. https://doi.org/10.1093/treephys/26.12.1537 Rouault P, Courault D, Flamain F et al (2024) High-resolution satellite imagery to assess orchard characteristics impacting water use. Agric Water Manage 295:108763. https://doi.org/10.1016/j.agwat.2024.108763 Roux J-P, Beltra S, Menetrier F et al (2019) L’ouvèze Schneider L (2022) « Faire avec » ou contourner les quotas d’eau. étude du comportement d’agriculteurs dans le sud-ouest de la France Schoenly K, Cohen JE, Heong KL et al (1996) Food web dynamics of irrigated rice fields at five elevations in Luzon, Philippines. Bull Entomol Res 86:451–466. https://doi.org/10.1017/S0007485300035033 Schuck EC, Green GP (2001) Field attributes, water pricing, and irrigation technology adoption. J Soil Water Conserv 56:293–298 Scoullos M, Malotidi V, Spirou S, Constantianos V (2002) Gestion Intégrée des ressources en eau en Mediterranée Sevruk B (1997) Regional dependancy of precipitation-altitude relationship in the Swiss Alps. Clim Change 36:355–369. https://doi.org/10.1023/A:1005302626066 Struyf E, Opdekamp W, Backx H et al (2009) Vegetation and proximity to the river control amorphous silica storage in a riparian wetland (Biebrza National Park, Poland). Biogeosciences 6:623–631. https://doi.org/10.5194/bg-6-623-2009 Tramblay Y (2022) Rapport du GIEC: focus sur la Méditerranée | Site Web IRD. https://www.ird.fr/rapport-du-giec-focus-sur-la-mediterranee . Accessed 2 May 2023 Vera J, Conejero W, Conesa MR, Ruiz-Sánchez MC (2019) Irrigation Factor Approach Based on Soil Water Content: A Nectarine Orchard Case Study. Water 11:589. https://doi.org/10.3390/w11030589 Vieillard-Coffre S (2001) Gestion de l’eau et bassin versant. De l’évidente simplicité d’un découpage naturel à sa complexe mise en pratique. Hérodote 102:139–156. https://doi.org/10.3917/her.102.0139 Wang D, Cai X (2009) Irrigation Scheduling—Role of Weather Forecasting and Farmers’ Behavior. J Water Resour Plan Manag 135:364–372. https://doi.org/10.1061/(ASCE)0733- 9496(2009)135:5(364) Wang X, Otto IM, Yu L (2013) How physical and social factors affect village-level irrigation: An institutional analysis of water governance in northern China. Agric Water Manage 119:10–18. https://doi.org/10.1016/j.agwat.2012.12.007 Zipori I, Erel R, Yermiyahu U et al (2020) Sustainable Management of Olive Orchard Nutrition: A Review. Agriculture 10:11. https://doi.org/10.3390/agriculture10010011 Footnotes https://geoservices.ign.fr/rpg https://agreste.agriculture.gouv.fr/agreste-web/disaron/!searchurl/4545f1a9-afe6-4c86-a141-693f2c72d550!1b69a349-ca8f-4353-82bb-4c00c502412c!729f399f-53c3-4952-9971-4753794a7c1b!c6be0c43-70a0-4666-853f-80de38a08ec7!0c593aed-b1d0-476e-9359-12d6347d8243!b125c6dc-13b7-4260-9abd-6e9321b2b963!fec0e278-6655-4c48-ac47-aab6d8847e15/search/ https://canaldeprovence.com/ https://entrepot.recherche.data.gouv.fr/dataset.xhtml?persistentId=doi: 10.15454/9IRARJ https://www.data.gouv.fr/fr/datasets/registre-parcellaire-graphique-rpg-contours-des-parcelles-et-ilots-culturaux-et-leur-groupe-de-cultures-majoritaire/ https://geoservices.ign.fr/bdtopo Additional Declarations No competing interests reported. Supplementary Files Supplementarymaterial.docx Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-4580425","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":320912997,"identity":"eb170112-4481-4feb-bb44-e6552b3dd4c6","order_by":0,"name":"Rouault Pierre","email":"","orcid":"","institution":"UMR 1114 EMMAH INRAE, Avignon University","correspondingAuthor":false,"prefix":"","firstName":"Rouault","middleName":"","lastName":"Pierre","suffix":""},{"id":320912998,"identity":"ff695873-0516-4c35-9c83-c96f9caf3467","order_by":1,"name":"Courault Dominique","email":"","orcid":"","institution":"UMR 1114 EMMAH INRAE, Avignon University","correspondingAuthor":false,"prefix":"","firstName":"Courault","middleName":"","lastName":"Dominique","suffix":""},{"id":320912999,"identity":"9c0bcfdf-d57a-405c-8d11-71c96c251ac3","order_by":2,"name":"Flamain Fabrice","email":"","orcid":"","institution":"UMR 1114 EMMAH INRAE, Avignon University","correspondingAuthor":false,"prefix":"","firstName":"Flamain","middleName":"","lastName":"Fabrice","suffix":""},{"id":320913000,"identity":"f8fa9f4a-956a-43df-959c-71e0931cc4d4","order_by":3,"name":"Marta Debolini","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA30lEQVRIiWNgGAWjYPACZgYDBuaGAwwGNhB+An7ljA0QLYwgLWlQLfj1ILQAOYcZCFqj2372+YMfDNZy5uyNjYcLCs7L67YffsDw8AduLWZn0g0bexjSjS17DjYcnmFw23DbmTQDvA4zO5DG2MDDcDhxw43EhsM8BrcTzG7w4PeL2flnjI1/GA7XQ7WcI0LLjTTGZqAtCQYQLQeI0fKMcbaMQbrhTpBfeAySwX45kJCGz2FpDB/fVFjLm7M3H/7M88dO3uz44YcPf9jg1gIBBmj8A4Q0jIJRMApGwSjADwByR1WrqJYY4AAAAABJRU5ErkJggg==","orcid":"","institution":"CMCC Foundation, Euro-Mediterranean Center on Climate Change","correspondingAuthor":true,"prefix":"","firstName":"Marta","middleName":"","lastName":"Debolini","suffix":""}],"badges":[],"createdAt":"2024-06-14 07:55:12","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4580425/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4580425/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":59482762,"identity":"562e9fc4-233e-4887-9b30-a30dd330a631","added_by":"auto","created_at":"2024-07-02 10:21:40","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":86687,"visible":true,"origin":"","legend":"\u003cp\u003eOverall scheme of the methodology. AWC: Available Water Content; ASA – ‘\u003cem\u003eAssociation Syndicale Autorisée’\u003c/em\u003e: water managers at catchment scale; LPIS: Land Parcel Identification System; RGA - \u003cem\u003eRecensement Général Agricole\u003c/em\u003e: French government data on crops grown in municipalities. Grey ovals refer to the sections of the material and methods describing these parts.\u003c/p\u003e","description":"","filename":"Picture1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4580425/v1/c4ae899d45f685e043bb28df.jpg"},{"id":59482202,"identity":"0d34cf4d-6e5b-47d9-ab4e-4855962e5aea","added_by":"auto","created_at":"2024-07-02 10:13:40","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":313372,"visible":true,"origin":"","legend":"\u003cp\u003eMap of the Ouvèze-Ventoux watershed and ASA-OV managed areas and fields referenced in the LPIS.\u003c/p\u003e","description":"","filename":"Picture2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4580425/v1/a6eed12ba594cc13f1bfa490.jpg"},{"id":59482199,"identity":"261f6fa2-e3f2-4a6d-acf5-4f0a960d4413","added_by":"auto","created_at":"2024-07-02 10:13:40","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":84778,"visible":true,"origin":"","legend":"\u003cp\u003eWater consumption in the ASA area in the watershed according to the ASA Ouvèze-Ventoux (mm): annual (in red) and seasonal (April-September included, in blue) rains from the Carpentras weather station (20Km SSO) from 2006 to 2022\u003c/p\u003e","description":"","filename":"Picture3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4580425/v1/c1b62f0cd1c4ba3994f046c4.jpg"},{"id":59482207,"identity":"4a73e548-81e6-44a6-bc07-d2bad90a517d","added_by":"auto","created_at":"2024-07-02 10:13:40","extension":"jpg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":79912,"visible":true,"origin":"","legend":"\u003cp\u003eWater consumption according to the ASA data for the surveyed farms. Dashed red: average consumption of higher consumers (in red), Dashed blue: average consumption of lower consumers (in blue)\u003c/p\u003e","description":"","filename":"Picture4.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4580425/v1/d2809a4d7a85f22751248d38.jpg"},{"id":59482760,"identity":"10225333-4419-449c-85c7-3366d4b715da","added_by":"auto","created_at":"2024-07-02 10:21:40","extension":"jpg","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":49438,"visible":true,"origin":"","legend":"\u003cp\u003eDensity of plots by their water consumption estimated for the 66 orchards of heavily consumers farms, in red the threshold between the 2 groups of plots, in dashed green the mean values of each groups (110mm and 538mm).\u003c/p\u003e","description":"","filename":"Picture5.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4580425/v1/97c02a43def315c3963df029.jpg"},{"id":59483373,"identity":"75892927-29e3-426b-ba4a-8d1ad27f8dcc","added_by":"auto","created_at":"2024-07-02 10:29:40","extension":"jpg","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":59792,"visible":true,"origin":"","legend":"\u003cp\u003eResult of the cross validation of each statistical method applied to the farms training dataset. This statistical benchmark is based of Random Forest (RF), Support vector machine (SVM), Principal component analysis (PCA), Neural Network (NN), Naive Bayesian classification (NB), logistical regression (LR), K nearest neighbours (KNN) and linear discriminant analysis (LDA)\u003c/p\u003e","description":"","filename":"Picture6.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4580425/v1/a8cdfbe1d65a4b4875a17b0c.jpg"},{"id":59482764,"identity":"2914e5e8-6e3d-43d2-b9b9-9fb02dcf79da","added_by":"auto","created_at":"2024-07-02 10:21:40","extension":"jpg","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":169008,"visible":true,"origin":"","legend":"\u003cp\u003eMap of farms estimated typology based on the water consumption used for crop irrigation in the Ouvèze-Ventoux watershed\u003c/p\u003e","description":"","filename":"Picture7.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4580425/v1/c2cea2b887396825c4109e36.jpg"},{"id":59483374,"identity":"1e47db41-c82d-4c76-820d-395192ce4c0c","added_by":"auto","created_at":"2024-07-02 10:29:40","extension":"jpg","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":41077,"visible":true,"origin":"","legend":"\u003cp\u003eResult of the cross validation of each statistical method applied at our plot’s dataset of training. This statistical benchmark is based of Random Forest (RF), Support vector machine (SVM), Principal component analysis (PCA), Neural Network (NN), Naive Bayesian classification (NB), logistical regression (LR), K nearest neighbours (KNN), linear discriminant analysis (LDA) and Factor Analysis of Mixed Data (FAMD)\u003c/p\u003e","description":"","filename":"Picture8.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4580425/v1/e7b5a1d3d89939507c592e1c.jpg"},{"id":59482761,"identity":"d07f1dce-f7ce-4549-867d-f4d04d800fa6","added_by":"auto","created_at":"2024-07-02 10:21:40","extension":"jpg","order_by":9,"title":"Figure 9","display":"","copyAsset":false,"role":"figure","size":53479,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003ea\u003c/strong\u003e-Map of the classification of orchard fields in the Ouvèze-Ventoux watershed with three categories: Orchards of weakly irrigated farms and two categories of heavily irrigated farms which have been classified according to the class most regularly awarded by the statistical methods used (kNN, LDA, LA, NB, PCA, RF and SVM) \u0026nbsp;\u003cstrong\u003eb\u003c/strong\u003e-Distribution of orchard fields classification results for the three categories of orchards according to the first classification (weakly or heavily irrigated farms) and the second one (weakly or heavily irrigated orchard field from heavily irrigated farms)\u003c/p\u003e","description":"","filename":"Picture9.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4580425/v1/ab4150741224114330d62034.jpg"},{"id":59482205,"identity":"576cc786-f4e7-4083-8c40-ec7bd3b48371","added_by":"auto","created_at":"2024-07-02 10:13:40","extension":"jpg","order_by":10,"title":"Figure 10","display":"","copyAsset":false,"role":"figure","size":28616,"visible":true,"origin":"","legend":"\u003cp\u003eComparison of the four municipalities with the estimated water consumption for the different crops and the total consumption according to the ASA for each municipality\u003c/p\u003e","description":"","filename":"Picture10.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4580425/v1/5c112c1d628196f16a7208bc.jpg"},{"id":77429281,"identity":"f83f1468-6130-4187-ae36-7a39eb59fdf3","added_by":"auto","created_at":"2025-02-28 13:46:59","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2185506,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4580425/v1/dffef7e0-be66-43bc-9cbf-5390826663c2.pdf"},{"id":59482200,"identity":"743e5326-caee-45df-bd3c-68badcdcfc22","added_by":"auto","created_at":"2024-07-02 10:13:40","extension":"docx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":39536,"visible":true,"origin":"","legend":"","description":"","filename":"Supplementarymaterial.docx","url":"https://assets-eu.researchsquare.com/files/rs-4580425/v1/48d1c5c94332cac7b5321fc7.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Unravelling the heterogeneity of farms irrigation practices on Mediterranean perennial agricultural systems for the optimization of water resource management","fulltext":[{"header":"Highlights","content":"\u003cul\u003e\n \u003cli\u003eIrrigation on perennial crops can be heterogeneous depending on farm habits.\u0026nbsp;\u003c/li\u003e\n \u003cli\u003eMachine learning methods are applied to unravelling the heterogeneity.\u0026nbsp;\u003c/li\u003e\n \u003cli\u003eClassification methods allow to estimate weakly and heavy water users.\u003c/li\u003e\n \u003cli\u003eFarms and parcels typology allow to estimate water consumption at large scale.\u003c/li\u003e\n \u003cli\u003eHighlighting the diversity of irrigation between orchards on heavily irrigated farms\u003c/li\u003e\n\u003c/ul\u003e"},{"header":"1. Introduction","content":"\u003cp\u003eThe water dependency of various economic sectors in the Mediterranean region is steadily increasing (R\u0026eacute;paraz \u003cspan citationid=\"CR72\" class=\"CitationRef\"\u003e1993\u003c/span\u003e; Molle and Sanchis-Ibor \u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e2019\u003c/span\u003e), and this is particularly evident in the agricultural sector, which is the most water consuming. Rising temperatures and more frequent dry periods are adversely affecting crop development (Masia et al. \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Fraga et al. \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2016\u003c/span\u003e), particularly in the Mediterranean region, as it is considered an hotspot for climate change (Change (MedECC) et al. 2020; Tramblay \u003cspan citationid=\"CR89\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). The increasing temperatures and dry periods will affect the crop growing and attended yields (Lesk et al. \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Beillouin et al. \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Ortega-Farias et al. \u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; N\u0026oacute;ia J\u0026uacute;nior et al. \u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e2023\u003c/span\u003e), causing relevant economic loss for farmers.\u003c/p\u003e \u003cp\u003eThe increase of water needs in agriculture in the Mediterranean climate can conduct to competition for resources use and lead to conflicts among different economic sectors (Mereu et al. \u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). This is particularly the case in summer, with the importance of mass tourism, often concentrated in the most productive crop-growing areas, (i.e. the coastal zones), thus centralising water requirements in the same place, with the diversity of private utilisation of water (especially pools and gardens) and the agriculture (Scoullos et al. \u003cspan citationid=\"CR86\" class=\"CitationRef\"\u003e2002\u003c/span\u003e). Sustainable water management is therefore essential to maintain the region's economy and diversified agricultural production (Grujard \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2003\u003c/span\u003e). Water management consortium/associations draw on and allocate water resources at catchment scale, and in France, these organisations apply water abstraction restrictions decided by political institutions during periods of drought, mainly decreasing the allocation of agricultural water resources (Riviere-Honegger \u003cspan citationid=\"CR75\" class=\"CitationRef\"\u003e2008\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eOrchards are typical productions in the Mediterranean region and have a great economic and cultural interest to the region (Pardo 2005; Montanaro et al. \u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Rodrigo-comino et al. \u003cspan citationid=\"CR76\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). However, perennial crops are one of the most affected by climate change and extremes events (Dalhaus et al. \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). The increase in periods of drought impacts the trees productivity because of the lack of water (Kang et al. \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2002\u003c/span\u003e; Moriana et al. \u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e2003\u003c/span\u003e). Some studies show that this can also degrade the quality of the fruit in terms of its nutritional qualities or size (Romero et al. \u003cspan citationid=\"CR80\" class=\"CitationRef\"\u003e2006\u003c/span\u003e; Rahmati et al. \u003cspan citationid=\"CR70\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). Moreover, fruit production is declining as a result of climate change, mainly because perennial crops are less adaptable to changes in temperature as well as to variations in CO2 concentration (Malhotra \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Chawla et al. \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Many authors have also shown that the variability of agricultural practices in Mediterranean orchards (presence or absence of grass in inter-row and on the row, plant density, variety, precocity of the production, pruning, etc.) have an impact on the water needs (Allen and Pereira \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2009\u003c/span\u003e; Demestihas et al. \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). If it is possible to manage the water needs at a field scale through local measurements or decision tools developed with precision agriculture (Loures et al. \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2020\u003c/span\u003e), it is very difficult to effectively characterise the needs/consumption of water at the territorial scale due to the variability of practices in the landscape.\u003c/p\u003e \u003cp\u003eVarious studies relating to the assessment of water consumption were developed at plot scale. Volumes of water are estimated on each plot and extrapolated to the whole farm considering the sum of other plots. The water consumption of a field can be estimated by various methods, in particular by estimating plant transpiration using FAO-type formulas involving the calculation of a reference evapotranspiration (Allen \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2000\u003c/span\u003e). Various factors such as the soil type and Available Water Content (AWC), climatic conditions, the topography, can be considered as they impact the water consumption (Vera et al. \u003cspan citationid=\"CR90\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Zipori et al. \u003cspan citationid=\"CR94\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Raluy et al. \u003cspan citationid=\"CR71\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Other factors can also play a significant role: the available equipment and the habits and choices made by the farmer (Reynard et al. \u003cspan citationid=\"CR73\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Schneider \u003cspan citationid=\"CR83\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). However, farmers behaviours, linked to the real or supposed needs in the specific context of his farm, influence the amount of water used sometimes more than other factors, creating variability between farms within the same region (Deffontaines and Petit \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e1985\u003c/span\u003e; Gadanakis et al. \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Calianno and Reynard \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). It seems that this variability between farms due to the farmer\u0026rsquo;s behaviour is particularly marked and visible between historic farms (more than one generation of farmer), in areas where freshwater access is uncontested, and the resources are unpolluted and sufficiently abundant (Longo and York \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2009\u003c/span\u003e) and where farmers are autonomous on their water management (Abdullaev and Mollinga \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2010\u003c/span\u003e). An estimation of the water consumption is essential to manage water resources in a large area such as a watershed and provide reliable information on both crop water requirements in basin and on river management programmes (Vieillard-Coffre \u003cspan citationid=\"CR91\" class=\"CitationRef\"\u003e2001\u003c/span\u003e). For these reasons, it is needed to characterise farmers' behaviour in terms of water use (Perrot and Landais \u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e1993\u003c/span\u003e; Maton et al. \u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e2005\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eIn this sense, this study aims to propose a novel method to classify farms according to their water consumption for irrigation at watershed scale. Then, considering these characteristics, this study aims to estimate agricultural water consumption at large scale (municipality, watershed), based on different heterogenous database, implying the use of statistical machine learning tools. In this case, this study was particularly focus on orchards, because of the relevance that they have in the Mediterranean region, and also because of the variability in terms of irrigation behaviours for this kind of crops.\u003c/p\u003e"},{"header":"2. Materials and Methods","content":"\u003ch3\u003e2.1 Overall methodology\u003c/h3\u003e\n\u003cp\u003eAnalysing the data obtained from the water management institution at catchment scale (ASA), this study is based on the hypothesis that the farming practices applied in terms of irrigation management are related to some farms\u0026rsquo; characteristics, determining their propensity to be heavily or weakly water consumers. This characterization can be the starting point to have an assessment of agricultural water consumption at large scale. Then, to refine the estimation, it is important to consider water applications that vary from one orchard field to another, particularly within farms that are heavily water consumers. The general methodology of this study is described in figure 1. First of all, at farm scale, (1) starting from available data acquired by the ASA and from farmers interviews, information about irrigation water consumption were collected at farm scale for 15 farms; then (2) the water consumptions of each farm was relied to a series of farms structural and biophysical characteristics, in order to understand which factors have an influence on irrigation behaviour (3) using eight statistical methods to classify farms in the territory; (4) the most occurrent statistical classification obtained was applied to the each farm in the area, in order to obtain a classification of all the farms \u0026nbsp;in two main classes: \u0026nbsp;the highest irrigators or the weakest.\u003c/p\u003e\n\u003cp\u003eThis first classification at farm level was one of the main inputs for the orchard fields scale analysis. For the heavily irrigated farms, the following workflow was applied: (1) \u0026nbsp;irrigation water volumes applied were estimated on the field taking into account the accurate information from farmers interviews and orchards characteristics for 66 orchard fields; then (2) the estimated volumes of each field was used to rely them with some biophysical and geographical factors of the parcel particularly belonging to the heavily irrigated farms and finally (3) nine statistical classification methods were applied to each orchard fields which belong to heavily irrigated farms in the area and (4) the most occurrent statistical classification was applied to all the 279 orchard fields belonging to heavily irrigated farms , in order to have an estimation of the overall water consumption for irrigation at the watershed scale.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe estimation obtained was compiled and compared with the available data from ASA at larger scale (municipalities and watershed scales), as a validation of the method. In particular, at municipal scale, the total volume corresponding to the sum of each terminal they monitored in all the municipality was used. These volumes were also compiled at watershed scale, in order to obtain a single amount of water consumed for the watershed. \u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFig.1\u003c/strong\u003e Overall scheme of the methodology. AWC: Available Water Content; ASA \u0026ndash; \u0026lsquo;\u003cem\u003eAssociation Syndicale Autoris\u0026eacute;e\u0026rsquo;\u003c/em\u003e: water managers at catchment scale; LPIS: Land Parcel Identification System; RGA - \u003cem\u003eRecensement G\u0026eacute;n\u0026eacute;ral Agricole\u003c/em\u003e: French government data on crops grown in municipalities. Grey ovals refer to the sections of the material and methods describing these parts.\u003c/p\u003e\n\u003ch3\u003e2.2 Description of the case study\u003c/h3\u003e\n\u003cp\u003eThe Ouv\u0026egrave;ze-Ventoux region is a part of the Ouv\u0026egrave;ze river basin, in the South-East of France, a right tributary of the Rh\u0026ocirc;ne (Roux et al. 2019). The Ouv\u0026egrave;ze has a typical torrential flow regime, as it can be subject to heavy flooding as well as periods of very low flow, particularly in summer. The area is around 100km\u0026sup2;, delimited at North-East by the Ouv\u0026egrave;ze river, and at South-Est by the Mont Ventoux (map Fig 2). It includes four municipalities with a variety of landscapes: a wide plateau in the Entrechaux area (around 300m asl.) in the North-Ouest and a valley with a colder micro-climate and higher altitude (around 500m asl.) in Beaumont-du-Ventoux. The climate is characterised by rainfall of around 650mm/year, according to the Carpentras weather station, mainly concentrated in autumn-winter period, whereas recent years have been characterized by heavy summer droughts. Heavy rainfall can occur, with extremes events of up to 300mm in a single episode (maximal observation in Entrechaux 22/09/1992) (Piegay and Bravard 1997).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe distribution of water for irrigation is managed by the ASA (\u003cem\u003eAssociation Syndicale Autoris\u0026eacute;e,\u0026nbsp;\u003c/em\u003eAuthorized Syndicate Association) Ouv\u0026egrave;ze-Ventoux. The managed areas of the ASA are mapped in red on figure 2 and cover 304ha in Beaumont-du-Ventoux, 467ha in Malauc\u0026egrave;ne,\u0026nbsp;372 ha in Entrechaux and 211 ha in Le Crestet municipality (total 1354ha). Most of these surfaces are occupied by fruit trees (44% of the utilised agricultural areas \u0026ndash; UAA) and vineyards (43% of the UAA - 83% of which are wine-growing vines and 17% table vines). Most of the orchards are cherry trees (\u003cem\u003ePrunus\u003c/em\u003e\u003cem\u003e\u0026nbsp;\u003c/em\u003e\u003cem\u003eavium\u003c/em\u003e), which is a very traditional production in the area (IGP \u0026ldquo;\u003cem\u003eCerises des coteaux du Ventoux\u003c/em\u003e\u0026rdquo;). However, in the last years they suffered different hazard related to climate change, with increasing water needs (around 540mm/year in the south of France, according to\u0026nbsp;CABRL 2019)\u0026nbsp;and recurrent diseases\u0026nbsp;(Herrera and J 2015; Ali et al. 2017; Medda et al. 2022). In terms of water use, irrigation restrictions have been imposed during dry summers, reducing irrigation authorisations and even banning all irrigations systems, even water-saving drip irrigation (a so-called \u0026quot;crisis\u0026quot; state), as it was the case during the drought of 2022\u0026nbsp;(Pr\u0026eacute;fecture de Vaucluse 2022).\u003c/p\u003e\n\u003cp\u003eFigure 3 shows the relation between the water consumption for each year (data from ASA) and the rains: seasonal (April to September included) and annual from the Carpentras weather station (15Km-SSE). A large variability is observed which can appear sometimes correlated with the annual rainfall amount: for drier years (such as 2017 or 2022), higher consumptions are observed, but for most of the time this variability seems not related to rainfalls. The annual rainfall is not the only parameter impacting the ASA water consumption and the variability between annual and seasonal rainfall could affect the water reserved in the soil. The seasonal rainfall (between April and September) is also not completely correlate to the irrigation, even if the low level of seasonal rainfall could explain some years of heavy irrigation (2016, 2017 or 2019 for example).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe taxation of the water consumption by ASA is referring to water terminals which are connected to one or more fields of a same consumer. According to the statements of water managers in the region, the water intake stations of the ASA are not very precise and show up to 30% error comparing to the real consumption, especially when the field is irrigated frequently but in small quantities (as in the case of a drip irrigation orchard or vineyard).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFig.2\u003c/strong\u003e Map of the Ouv\u0026egrave;ze-Ventoux watershed and ASA-OV managed areas and fields referenced in the LPIS.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFig.3\u003c/strong\u003e Water consumption in the ASA area in the watershed according to the ASA Ouv\u0026egrave;ze-Ventoux (mm): annual (in red) and seasonal (April-September included, in blue) rains from the Carpentras weather station (20Km SSO) from 2006 to 2022\u003c/p\u003e\n\u003ch3\u003e2.3 Dataset description\u003c/h3\u003e\n\u003ch4\u003e2.3.1 Data on farms and fields\u0026rsquo; structure and distribution\u003c/h4\u003e\n\u003cp\u003eTable 1 summarizes the available datasets for the Ouv\u0026egrave;ze-Ventoux watershed. All the data listed and described on the following paragraph have been used to characterize the farming systems of the study area, including all the topographical and farming practices information possibly related to the use of water.\u003c/p\u003e\n\u003cp\u003eIn order to have a comprehensive description of each cultivated parcels, data from the Land Parcel Identification System (LPIS, availed freely by the \u0026lsquo;geoservices\u0026rsquo;\u003ca href=\"#_ftn1\" name=\"_ftnref1\" title=\"\"\u003e[1]\u003c/a\u003e from IGN) was acquired. LPIS is a spatialized dataset on crops declared each year by farmers to received European subsidies. Considering the source, not all farms are declared, depending wherever or not they are asking for support. In particular, perennial crops are often lacking, because they are not submitted to subsidies. Usually, these crops are declared when they are cultivated in mixed farms, where there are also annual and/or herbaceous crops. \u0026nbsp;Information on the location of the agricultural parcels and their crop are given, but no information on irrigation is furnished nether distinction among the different species of orchards, except for olive groves, table grapes, and vineyards. In this study, the last available LPIS data in the region (2020) was considered. This dataset represents 1721 agricultural fields, representing 48% of the total 3581 fields in the area, belonging to 65 farms. In terms of surface, they represent 1001ha, including 504 fields (302ha) of orchards in 2020. The orchard class in the LPIS do not include olive groves and truffle groves which are referenced differently. Considering that the LPIS does not cover all the farms in the area, the database was completed with the agricultural census (RGA - Recensement G\u0026eacute;n\u0026eacute;ral Agricole, partially freely available by the AGRESTE website\u003ca href=\"#_ftn2\" name=\"_ftnref2\" title=\"\"\u003e[2]\u003c/a\u003e). This database provides the information reported in table 2 at municipality scale only. In this dataset, irrigated surfaces are referenced but this information is not accessible freely and requires accreditation.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eIn terms of topographical and soil data, this study is based on the digital elevation model and the pedological map of the study area. The digital elevation model was used to calculate the altitude, slope and exposition of the parcels. Soil data, especially about available water capacity (AWC) which is calculated according to the pedological characteristic of the soil\u0026nbsp;(Cousin et al. 2022), have been derived from the soil map of the society managing the Provence canal (SCP\u003ca href=\"#_ftn3\" name=\"_ftnref3\" title=\"\"\u003e[3]\u003c/a\u003e , Societ\u0026eacute; du canal de Provence) in 2012. The AWC values has been calculated for each farms\u0026rsquo; field and then aggregated at farm level. In some cases, where the soil data were lacking, the information was completed with the Infosol unit map of AWC (Rom\u0026aacute;n Dobarco et al. 2019b, a; Rom\u0026agrave;n Dobarco et al. 2022) , available at France scale at a spatial resolution of 90m for the first 2m of soil (Rom\u0026aacute;n Dobarco et al. 2019b)\u003ca href=\"#_ftn4\" name=\"_ftnref4\" title=\"\"\u003e[4]\u003c/a\u003e.\u003c/p\u003e\n\u003cp\u003eIn order to complete the description of the area with information on farming practices related to irrigation, a field surveys were carried out between 2019 and 2023, enquiring 21 farmers in the watershed area, 13 of which depending on the ASA for their water supplies, in farms which have a significant proportion of perennial crops (e.g. orchards or vineyards). During the interviews the following information were collected: \u0026nbsp;location of all the farms\u0026rsquo; fields (749 plots), land cover and crop type (with cultivar detail); for orchard crops, the spacing between trees and between rows, the type of drippers used, their flow and dispositions in the crop, and the irrigation schedule.\u003c/p\u003e\n\u003cp\u003eCombining all the information listed, a geo-database including 3581 parcels was obtained which represent the whole agricultural areas in the watershed. Among this total, 710 parcels, corresponding to 393 ha (22% of the total agricultural surfaces), belong to the farms which were interviewed and for which more detailed information about farming practices were available.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTables 1:\u003c/strong\u003e List of the available dataset\u0026nbsp;\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"100%\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"14.141414141414142%\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"40.4040404040404%\"\u003e\n \u003cp\u003e\u003cstrong\u003eSource\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.21212121212121%\"\u003e\n \u003cp\u003e\u003cstrong\u003eResolution\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.242424242424242%\"\u003e\n \u003cp\u003e\u003cstrong\u003eInformation type\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"14.141414141414142%\"\u003e\n \u003cp\u003eLPIS 2020 \u0026ndash; Land parcel information system\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"40.4040404040404%\"\u003e\n \u003cp\u003eData.gouv (French gouvernemental data) \u003ca href=\"#_ftn5\" name=\"_ftnref5\" title=\"\"\u003e[5]\u003c/a\u003e\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.21212121212121%\"\u003e\n \u003cp\u003ePlot scale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.242424242424242%\"\u003e\n \u003cp\u003eCrop typology (38 classes)\u003c/p\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003cp\u003eFarm to which the parcel belongs\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"14.141414141414142%\"\u003e\n \u003cp\u003eSurveys\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"40.4040404040404%\"\u003e\n \u003cp\u003eFarmers\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.21212121212121%\"\u003e\n \u003cp\u003ePlot scale (for 21 farms)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.242424242424242%\"\u003e\n \u003cp\u003eCrops, cultural practices, trees implantation pattern, irrigation time\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"14.141414141414142%\"\u003e\n \u003cp\u003eRGA 2020\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"40.4040404040404%\"\u003e\n \u003cp\u003eINSEE (partially confidential database)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.21212121212121%\"\u003e\n \u003cp\u003eMunicipal scale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.242424242424242%\"\u003e\n \u003cp\u003eCrops typology and areas, including irrigated areas\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"14.141414141414142%\"\u003e\n \u003cp\u003eASA water consumption\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"40.4040404040404%\"\u003e\n \u003cp\u003eASA \u0026ndash; Ouv\u0026egrave;ze-Ventoux\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.21212121212121%\"\u003e\n \u003cp\u003eFarm scale\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eMunicipal scale\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eWatershed scale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.242424242424242%\"\u003e\n \u003cp\u003eWater consumption taxed of 13 farms\u003c/p\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003cp\u003eConsumption at each water distribution terminal\u003c/p\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003cp\u003eTotal water consumption provided by ASA (for each year)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"14.141414141414142%\"\u003e\n \u003cp\u003eDEM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"40.4040404040404%\"\u003e\n \u003cp\u003eGeoportail\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.21212121212121%\"\u003e\n \u003cp\u003e1m\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.242424242424242%\"\u003e\n \u003cp\u003ealtitude\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"14.141414141414142%\"\u003e\n \u003cp\u003eSoil map\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"40.4040404040404%\"\u003e\n \u003cp\u003eSCP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.21212121212121%\"\u003e\n \u003cp\u003e1/25000 aggregated at Pedologic unit scale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.242424242424242%\"\u003e\n \u003cp\u003ePedological information (Dominant texture and Pedological names of first layers)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"14.141414141414142%\"\u003e\n \u003cp\u003eAWC map 1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"40.4040404040404%\"\u003e\n \u003cp\u003eLink to the SCP soil map\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.21212121212121%\"\u003e\n \u003cp\u003ePedologic unit scale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.242424242424242%\"\u003e\n \u003cp\u003eAWC\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"14.141414141414142%\"\u003e\n \u003cp\u003eAWC map 2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"40.4040404040404%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eInfosol INRAE unit\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.21212121212121%\"\u003e\n \u003cp\u003ePixel size: 90m x 90m\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.242424242424242%\"\u003e\n \u003cp\u003eAWC estimated from modelling for the theorical 2 first meters of soil\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003eTable 2\u003c/strong\u003e Areas (ha) referred in the RGA data for main orchards and vineyards typologies in the four municipalities in the Ouv\u0026egrave;ze-Ventoux area.\u0026nbsp;\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"100%\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"15.306122448979592%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"40.816326530612244%\" colspan=\"4\"\u003e\n \u003cp\u003e\u003cstrong\u003eTotal (ha)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"43.87755102040816%\" colspan=\"4\"\u003e\n \u003cp\u003e\u003cstrong\u003eIrrigated (ha)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"15.625%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"9.375%\"\u003e\n \u003cp\u003e\u003cstrong\u003eBeaumont\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.375%\"\u003e\n \u003cp\u003e\u003cstrong\u003eCrestet\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.458333333333334%\"\u003e\n \u003cp\u003e\u003cstrong\u003eEntrechaux\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.416666666666666%\"\u003e\n \u003cp\u003e\u003cstrong\u003eMalauc\u0026egrave;ne\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.416666666666666%\"\u003e\n \u003cp\u003e\u003cstrong\u003eBeaumont\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.375%\"\u003e\n \u003cp\u003e\u003cstrong\u003eCrestet\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.5%\"\u003e\n \u003cp\u003e\u003cstrong\u003eEntrechaux\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.458333333333334%\"\u003e\n \u003cp\u003e\u003cstrong\u003eMalauc\u0026egrave;ne\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"15.625%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003eCherries\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.375%\"\u003e\n \u003cp\u003e72\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.375%\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.458333333333334%\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.416666666666666%\"\u003e\n \u003cp\u003e66\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.416666666666666%\"\u003e\n \u003cp\u003e50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.375%\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.5%\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.458333333333334%\"\u003e\n \u003cp\u003e54\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"15.625%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003eApricots\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.375%\"\u003e\n \u003cp\u003e34\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.375%\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.458333333333334%\"\u003e\n \u003cp\u003e18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.416666666666666%\"\u003e\n \u003cp\u003e55\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.416666666666666%\"\u003e\n \u003cp\u003e20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.375%\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.5%\"\u003e\n \u003cp\u003e11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.458333333333334%\"\u003e\n \u003cp\u003e38\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"15.625%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003ePlums\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.375%\"\u003e\n \u003cp\u003e44\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.375%\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.458333333333334%\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.416666666666666%\"\u003e\n \u003cp\u003e33\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.416666666666666%\"\u003e\n \u003cp\u003e30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.375%\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.5%\"\u003e\n \u003cp\u003e1,5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.458333333333334%\"\u003e\n \u003cp\u003e25\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"15.625%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003eOlive groves\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.375%\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.375%\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.458333333333334%\"\u003e\n \u003cp\u003e11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.416666666666666%\"\u003e\n \u003cp\u003e24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.416666666666666%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.375%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.5%\"\u003e\n \u003cp\u003e0,6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.458333333333334%\"\u003e\n \u003cp\u003e11\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"15.625%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003eTruffle tree groves\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.375%\" valign=\"bottom\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.375%\" valign=\"bottom\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.458333333333334%\" valign=\"bottom\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.416666666666666%\" valign=\"bottom\"\u003e\n \u003cp\u003e17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.416666666666666%\" valign=\"bottom\"\u003e\n \u003cp\u003e0,05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.375%\" valign=\"bottom\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.5%\" valign=\"bottom\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.458333333333334%\" valign=\"bottom\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"15.625%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003eOthers orchards\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.375%\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.375%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.458333333333334%\"\u003e\n \u003cp\u003e0,2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.416666666666666%\"\u003e\n \u003cp\u003e17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.416666666666666%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.375%\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.5%\"\u003e\n \u003cp\u003e0,2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.458333333333334%\"\u003e\n \u003cp\u003e11\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"15.625%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003eTable vineyards\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.375%\"\u003e\n \u003cp\u003e17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.375%\"\u003e\n \u003cp\u003e19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.458333333333334%\"\u003e\n \u003cp\u003e30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.416666666666666%\"\u003e\n \u003cp\u003e26\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.416666666666666%\"\u003e\n \u003cp\u003e13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.375%\"\u003e\n \u003cp\u003e13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.5%\"\u003e\n \u003cp\u003e15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.458333333333334%\"\u003e\n \u003cp\u003e9\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"15.625%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003eWine vineyards\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.375%\"\u003e\n \u003cp\u003e82\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.375%\"\u003e\n \u003cp\u003e86\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.458333333333334%\"\u003e\n \u003cp\u003e212\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.416666666666666%\"\u003e\n \u003cp\u003e200\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.416666666666666%\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.375%\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.5%\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.458333333333334%\"\u003e\n \u003cp\u003e18\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003ch4\u003e2.3.2 Data on water consumption at farm and field scale\u003c/h4\u003e\n\u003cp\u003eIn this study, the available information (provided by ASA) about water distribution and consumption on the study area, was used as dependent variable on the statistical modelling analysis, and as a validation for the overall consumption. Water resources for irrigation are managed by the ASA, which delivers water to each farmer via a pressure network and irrigation terminals. \u0026nbsp;Each water access terminal can normally be linked to one or more fields of a same farm. Each farmer declares an irrigated surface and volumes are recorded on terminals. As mentioned before, the ASA estimates the sensor uncertainty at around 30%. This data is then summarised by the ASA to define a water consumption for each farm for the water taxation. The ASA provided the total consumption volumes for 13 of the 21 interviewed farms. \u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe data about the farm consumption were calculated for 5 years (2016-2020) and adjusted for the total surface of the farm to estimate the average quantity of water applied in millimetres per year, assuming that the total farm area does not change from one year to another during the 5 years.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eFigure 4, shows the average water consumption per farm for the 13 farms from ASA information and 2 others (R3 and T1) for which the sum of irrigation was calculate (describe in 2\u003csup\u003end\u003c/sup\u003e part of part 2.3.2). Two groups can be clearly identified: some farms consume more than 100mm per year while other consume less than 40mm. Dashed lines show the mean values of heavily irrigated (163.8mm) and weakly irrigated farms (12.9mm). According to this information, farms were separated in two groups: heavily irrigated, if they applied more than 100mm/year and weakly irrigated, if they applied less than 40mm/year.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFig.4\u003c/strong\u003e Water consumption according to the ASA data for the surveyed farms. Dashed red: average consumption of higher consumers (in red), Dashed blue: average consumption of lower consumers (in blue)\u003c/p\u003e\n\u003cp\u003eMoreover, 2 more farms in the watershed were added not belonging to the territory managed by the ASA. These two farms have cherry orchards as only relevant irrigated crop. Volumes of water were estimated by the sum of the water use in each orchard\u0026rsquo;s plot, obtained according to some relevant characteristics assessed during interviews, namely: (1) the spacing between rows of trees (S\u003csub\u003erows\u0026nbsp;\u003c/sub\u003ein m) and between trees on a same row (S\u003csub\u003etrees\u0026nbsp;\u003c/sub\u003ein m); (2) the type of irrigation equipment, namely r flow rate in litres per hour (Flow in L/h) and repartition in the field by the spacing between 2 drippers (S\u003csub\u003edrip\u003c/sub\u003e in m) or the number of drippers per trees (case of micro-sprinklers) (Ns in drippers/trees); (3) the irrigation schedule, which depends on the type of orchard cultivar, compiled as a number of hours of irrigation per year (t in hour/year). The assessment of the water quantity is thus made by following equation 1 or 2 according to the type of irrigation equipment. In the equation 1, relative to micro-sprinklers, the number of trees is computed according to the distances between rows and trees, considering a squared field of 1ha where 1m is lost on each side of the plot. In the Equation 2, referred to drip irrigation, the number of rows is computed for a squared field of 1ha where 1m is lost for the borders, this value divided by the spacing between rows is equivalent to the number of rows in this plot and, this value multiplied by 98m of row longer in the square plot (the 100m side of the square - 2m border) provide the length of piper\u0026rsquo;s tube in the field.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cimg 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nSzL0ATGoUQy+VIb57esWYodhdtFRPasLaqdNS9APpEDFkBHuc1+5XgaNnPEwb4izAoXtm/BrLjlyDQOuhr8K4UYsz9uHcLTNMU6ruk+FVahbd+ifHxQKHZiT5LE/gFXjw8isGdjezxsONuiCxEQgWsJNE1j/4qXKjOV9izLrOed3SZk91tLtVL45xOwivlmbd+Sdr7oMLajHwFTNkz00PtxHCGKEEhqMHYwXW8AR8lWpNEzaJoGZcECj5ns8fHhKiYOo1EK4gzDgJPB8MfGfUltEVi3CkcBdqOmI2ms8sJWptZVHiKNjoJzDnZ+mOW5cpp9fETA+J5THmz4Oh/dFQEROI3Axg73VNrpkYOqojeP691vnUZDBe1O4FOkHVamyDEE7vFFUUBl8RJnWUZvcEwtc5HxCh1rw7d/VGmaYh9QW1YIAVYLmtlDCPYaBWH/cHjn0asOVg4aA5AtfBrZS0D+HNYjN3gYoItgbfhlWbKB8P4LIWBpNWbobQT0e5IkgU9ACME2fIWPbomACJxGILLzrZS7RdqxGTPWItlfFRzWgMynqr8l1UrFdOvDCXyKtMOp7cNhvVC9pmk4en4huZKIgAjcj4C16q23birtGEswlTXI736Lpejilwm8bpBHH5bG6jtBxOj/qZ/CnZqstoiACOxFYCrtzjk7SW/n13e/tVcrlM9XE3hd2r+62aq8CIiACBxBAF70djlMCAEzdwis6xpLZlD67reOaJTy/DoCkvave2SqsAiIwCcSwD4fdj8uK/DcKcvuVYVm7H7rE+moTucSkLSfy1uliYAIiIAIiMDBBCTtBwNW9iIgAiIgAiJwLgFJ+7m8VZoIiIAIiIAIHExA0n4wYGUvAiIgAiIgAucSkLSfy1uliYAIiIAIiMDBBCTtBwOeZI+jYCfBuwU0TcON/3bLVBmJgAiIgAh8DwFJ+6nPqus6bio5jqM9nRaH4WLXXmxGO44jtunFrr1LFfXeY70Ntw+ypSylUrgIiIAIiMBdCUjaT32yeZ7zhNmqqux5MzjqZhxHBlL4o0Nuohpjj3pslMFblHmG6EIEREAEROBHCEjaT33QkeLaM2F5zCtPxuNR9DwzgIf18oK17/ueJ0DjJBttlEs4975wztmtUe7dWLVOBERgCwFJ+xZKu8VZl3acIIcTckMIPL6dw/elenRdZ0+DDiFUVSVpX8J1p3BYa2gKulPT1BYREIGXCUjaX0b3SsJI2tu25aERbdtmWQaDPM6dK8sSF/ZA22mpfd/zVFnKufee19MkDCnL8uQBH06plxTxEbx50TQNX6E3s1JyERCB2xCQtJ/6KO1cO1zkYFqHDPO8ebrR5XmeJMm6Gx3Olbf5YMT/tGHOORSEmMMweO/p5RdCsH58jDkbuFRW3/fOuSRJeHp9CAFbbS8lUfifCKx3+/6UlSKLgAjchoCk/dRHefTKNzTGlhLpNw31TdPYufmmacqyLIrCSrtzDvYA9/gg89nAJYhFUcAqUJZlkvz/ZfPen2wtWKrht4cnSTIMw7e3QvUXARHYl8D/f233zVe5LRGwursU553waF173/dZlmHMbeXcOTe12NOVDxVIkgQJ6dkXQpgNfFphOhAgZtd1mGt4mlARVgjYCZ2VaLolAiLwawQk7fd/4jCA13VtJ2Uxrx813kp73/ccZ3Nl3WxglMns12hSfxzHLMtmYypwOwHvvbYn2o5LMUXgdwhI2s971lyxdtoF21bXdWS5pWwzDvzqaZC3I3UM1kMIK4FRo5hPCAHWflsQM4wC9fVPBLClwZ+SKLIIiMAvEJC03/8p21E7XdOfjtqXbO9/NcjbWQCy1qidKF6+wJKKl5MroQiIwI0JSNpv/HD/27S+7+mWb1V2dq69KAo7BV4UBVzt/OMDUrOBuDX9186pW689Gz5NpZAtBLz30WYGW1IpjgiIwC8QkLRf9pQx6ooWhu1em8hDvixLeM9ZmaelnUZ1xIELXrT6bjZwqdp2YZ6dApCH/BKx7eGyxm9npZgi8GsEJO2XPfE0Tdu2xakwl6wEK4qCq9XPpKB17e/TxokD7+ejHERABG5JQNJ+2WPNsuxa92YcPXdyr0K70e3ywpWPzy5ZKRMREIH7EZC0X/ZMMXhd32nussqp4M8mkGXZdFuCz66yaicCInAeAUn7eaxnS3LOwb8M+7XhLDhMS0P7cYL7MAzYsRWZZFk2DAOWorVty/lvu3J9trgfDwSxaB3g1zHp+z5N06+rtiosAiJwGgFJ+2mo/ymIa88o7W3bFo/POI4Q+zzPsTq8LMuqqnjGF3Z2w6ANDu3wSmPCf0r6vS92c/7IMRBr9799t5yyLO1yg997wmqxCIjAEwKS9ieAjrg9jiPOfUmSxDln15pzP3AMxFE6pD2EADMsDnFpmqZ7fHjgyp1s+8MwTJuDk2mIaOnRoA9EH4Jom52nJ+QuZbsxvG3bqe5u9BxsmiY6G3C20CzL4IA5e1eBIiACIiBp/5R3YBgGO5rsug7WdYzRoWdFUZRl2bZtXdfOOYzp8RWnvd1p/tV7zxX5OIYuz/Opak6fH7ScHaYQgp2nKIriUO9F9NtsPWE/Z1djWmGEIOHTpeqyxi8BVLgIiAAJSNqJ4uKLaKE5pBoT7RyiccQJ4Yd6NU0Ds/N0mHtxk94unuo+1cuVvElpNg5308NdHjtrLSjgyc4BZ0Y4VT+bMwNtbTfqOnPmo2Ru0UVd17bfEN3VVxEQARH4716ioiACn0wA6r5xvI6GcLIjUmjuzMP2DsOQpqn3fnx8sizDoLlt2yRJYAIZhiFJEm6Jv3GeG+runLMH1bPc2QsU9HS6Ic/zpwaA2fwVKAIi8DsEJO3nPWsojf61BJ7SH8cxfXw4hl5Pgol22Dm6rosGuFVV2cls51xkq8ddqCykvaqqNE2ZqiiKp+qLGmIgzoTr1cYhOnZGZjY+KrYRxWwOChQBEfgFApL2X3jKX9xGqO/2UTsG3EviVxQFx984/8bOu2dZxrtJkrRti2F9WZZQ6KZpbPwVrDj/5k+jdq6VWMkWPhYrEXRLBERABGSQ1zvw0QRemGtHkqVW0cyOZQX2KwbZ3HkXMl8URfv4ZFmGWfmlnG34C3PtIYQtpntZ4y1nXYuACCwR0Kh9iYzCLyZAXUc9rF4u1QzW+yUbeN/30HJMrkNNMUzHMgRrvS+KIssyhNC0vsUUP63nRk862gmW6i9r/NJzV7gIiEBEQNIeAbnn16qqrC9Y1EhIVxR47dfX1rVzsxra1W0rYCTnqTyc3sYyhCiJ7VjY+Xub4ez1y+vaUfmVNetN09gjd2dLV6AIiIAIyCB/q3egqioeojpdHs0VXNM2f6C0TyupEOfcxpl+sRIBEfhxAhq13+QFgLUZFuPZTc0iD7KbNPtnmgHLwZYZgZ9BooaKgAgsEpC0L6L5rhuQ9rqurXP4OI44VAbrx2BzhnG+rmtYgDFkx4E0uAWH8CRJ7rcHznc9U1vbpmnsOj17S9ciIAIiEBGQtEdAvvhr27ZZlqVpymlj7z32qMc0M8PzPEd4nudd12HBGFqePz7DMCDJ1LD/xYC+ueqyxn/z01PdReBsApL2s4kfXV7btmmaQpLt4i5rkLfX0R5t9la0wcvRNVf+SwRkjV8io3AREIFZApL2WSzfHUhJlrR/94P8X+1ljf8fCf0vAiKwicATacdxZFgdxPzspl11XWPf0KXFuEyli0MJVFWFRdiYX8cqKRjeMS7HZumoQ57n1tJuDfI8Mx4GeblkH/rUNmbuvbfPa2MqRRMBEbgHAXpEwQVqS6OeSHsIAa5VdM7COWOctcWJmfbwzS2lKs7uBDCwQzeLZ8Dj0PckSeAZl6Zp3/d4oHZAj1TYMh1btSCEW7vsXltl+CcCeHB/SqLIIiACtyGQZRlWx2DfKqu/S23cKu1Ij5227App7P/F7TmXilH4txCIBvTfUu0b1xPekTduoJomAiKwnYD3fouN/G/SXj0+1tOqfHy2V+tOMa2RBMNc+y8Gwd/VXrboGyv/Xai311bW+O2sFFMEPpYADN7QCHsuM82oWZbNbkkStagoCrsldnSXX59LO2bTQwiYfB3HkdIOey9t9cxUFyIgAnsRkDV+L5LKRwSuIgBdt0Z1DJ8QjmuMrNZH5DDI257BUoueSzvKw6Q7nKoo7TgXaylrrqrafeNrdHNWmof9WFYqplsi8BUEZI3/isekSorAOgGKJqJx5B1Z17m4aSk3uDkv3bXhW6Uds+ysVlVVG4+O9t6ve1l3XffCNlufMJSh+dra4Xktm7Z9z3T9GoGyLLcY317LXKlEQATOIcDTPSgQ+LumxqMa69Luvc+ybGOFt0q7956j5KIoyrKkzx5s9XmeQ26xfWnbtqgB9jvDdd/3PN4KnndcO7dyLhlbYpOnaRpCGIYhz3OcpY2cMV9A92/vPa5xwRVE4ziWZZmmKU7SDCE459I07boOMVH/sizxJNaNJKyhLkRgXwIrB8GxoLZtp+/nOI54k5Mk4YoJJNn9FmuiCxEQgVkC0aidcfzjw68r0o4/Z8Z8erFV2m1nASJqx+JN02DDLCyXwlIrSD42J8d1mqZIVVUVrfRPrfpoA1zxkbwsSySPysXoHzXBdAB6A0VR9H1vV28757BBOqradZ2N6b3vH58sy8ZxHIaBtX0KVBFEYC8COOh9JTeeETCV9qIosCQVW1PYCLvfWqmhbomACHAN+RSFnYPHNf9UsQ4ZSf6q65sOdYXZmUP2EAJ+GqJaDsOQpilc6tj16LqOFbXz39YPMEkS64hHewUvkINNXpYlOxb4+bM52EJDCFmWWSeFEAKT9H1vF/Jh1M52YQ7Ce6/jtshEF2cSeGqNd85howL+laF68LWh5Qy2MevCs+OtM4GoLBH4XgL0hIe0ccaW1jWM7Pm3TGnHnzMFMUq+BOT5qH0pZRTeNA0nBZ1zMH1jsRxiWouEf3wwsN440c7kcNTnSvrplL8tFEMWVKCua5RFxFbXZ0dI4zhWVcUuC/LRvyJwDoEt1ngMCPhzgIqh+8/+Lnrn+CnZ/dY5KFSKCPwCAY6K32/sbtLO1bd1XVMLi6JomgZb5zjnoP1N06RpijEEWgIFXW8MJvgREwN9GBKcc9HWPCgUd7n5dtd1HJRT44dh4Lml1iqAmuBW1JNYr6TuisBeBHBK75bcpj8H6LwyLaQdfya732IpuhABEXiTwPRv+eUMd5P2LMvyPE+SJM9zDqnhqgaV5aan1rEOigs/uPU2YKYcmePgUQxKaG9ncluo9x61wm4AiANLOzbjZVW997aLwM3zeYoa89eFCJxAwPqjrBc3/TmY1W+6udD9hctT37m1XjfdFQER2E5g+re8PW0Ucx9px0R7lPUnfN3SafiEeqoOIhARyPPcOrhEd+3X6c8BJtdnDfK737I10bUIiMCHENhH2jGBZ0e9n9A8eB9E05CfUDHVQQTWCeDVpTavR55KO4z59JWz8+u731qvm+6KgAhcQmAfab+k6ipUBO5KYOocutJSbDIRReCkFfZ+4I4O9qjGvW5FReurCIjA5QQk7Zc/AlVABF4kUJYlXEmSJMmyzBqouL8TnEWsAWD3Wy/WXslEQAQOIyBpPwytMhYBERABERCBKwhI2q+grjJFQAREQARE4DACkvbD0CpjERABERABEbiCgKT9CuoqUwREQAREQAQOIyBpPwytMhYBERABERCBKwhI2q+grjJFQAREQARE4DACkvbD0CpjERABERABEbiCgKT9CuoqUwREQAREQAQOIyBpPwytMhYBERABERCBKwhI2q+grjJFQAREQARE4DACkvbD0CpjERABERABEbiCwH8AOTb2uBUDohEAAAAASUVORK5CYII=\"\u003e\u003c/p\u003e\n\u003cp\u003eThese 2 equations give an estimation of the annual water consumption for an irrigated field of cherry orchard. However, this estimation does not reflect potential interannual variability. These equations were applied to 66 cherry orchard fields belonging to heavily irrigated farms and to the 68 cherry orchard field belonging to weakly irrigated farms. Figure 5, shows the high diversity in terms of water consumption for the orchards of heavily irrigated farms: we observed two main behaviours, corresponding to the two pics. Dashed lines show the mean value for each class.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFig.5\u003c/strong\u003e Density of plots by their water consumption estimated for the 66 orchards of heavily consumers farms, in red the threshold between the 2 groups of plots, in dashed green the mean values of each groups (110mm and 538mm).\u003c/p\u003e\n\u003cp\u003eConsidering the general distribution of the water consumption at field scale, the two most occurrent means values (110mm and 538mm) were applied at each orchard field scale to sum the overall water use at municipal and watershed scale.\u003c/p\u003e\n\u003cp\u003eFor the weakly irrigated farms, less variability was observed and the mean value of water consumption at field scale for cherry orchards was 26mm, used as reference value.\u003c/p\u003e\n\u003cp\u003eFor the remaining irrigated fields, not covered by orchards, we used as value of water amount yearly applied the quantity declared by farmers during the interviews and also by the local experts, in particular the ASA technicians. The yearly volumes declared are showed on table 3. \u0026nbsp;\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 3\u003c/strong\u003e Irrigation apply according to the typology of perennial crops\u003c/p\u003e\n\u003cdiv\u003e\n \u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"65.55023923444976%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eCrop\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"34.44976076555024%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eWater volume per field (mm)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"65.55023923444976%\" valign=\"top\"\u003e\n \u003cp\u003eOrchards heavily irrigated of heavily irrigated farm\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"34.44976076555024%\" valign=\"top\"\u003e\n \u003cp\u003e538\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"65.55023923444976%\" valign=\"top\"\u003e\n \u003cp\u003eOrchard weakly irrigated of heavily irrigated farm\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"34.44976076555024%\" valign=\"top\"\u003e\n \u003cp\u003e110\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"65.55023923444976%\" valign=\"top\"\u003e\n \u003cp\u003eOrchard of weakly irrigated farm\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"34.44976076555024%\" valign=\"top\"\u003e\n \u003cp\u003e26\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"65.55023923444976%\" valign=\"top\"\u003e\n \u003cp\u003eOlive groves\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"34.44976076555024%\" valign=\"top\"\u003e\n \u003cp\u003e150\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"65.55023923444976%\" valign=\"top\"\u003e\n \u003cp\u003eTruffle tree groves\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"34.44976076555024%\" valign=\"top\"\u003e\n \u003cp\u003e40\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"65.55023923444976%\" valign=\"top\"\u003e\n \u003cp\u003eTable vineyards\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"34.44976076555024%\" valign=\"top\"\u003e\n \u003cp\u003e300\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"65.55023923444976%\" valign=\"top\"\u003e\n \u003cp\u003eWine vineyards\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"34.44976076555024%\" valign=\"top\"\u003e\n \u003cp\u003e100\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003e\u0026nbsp;The two additional farms for which were estimated the water consumption (R3 and T1 in figure 4) considering the sum of each orchard consumption have respectively 173mm/year for the R3 farm and 224mm/year for the T1. These two farms are high consumers where the main crop is orchards which represent the biggest part of them area (86% for R3, 69% for T1).\u003c/p\u003e\n\u003ch3\u003e2.4 Statistical approach\u003c/h3\u003e\n\u003cp\u003eIn terms of statistical methods, for both the classification at farm and field level, a benchmark of classification models was applied, namely: Random Forest (RF), Support vector machine (SVM), Principal component analysis (PCA), Neural Network (NN), Naive Bayesian classification (NB), logistical regression (LR), K nearest neighbours (KNN), linear discriminant analysis (LDA) and Factor Analysis of Mixed Data (FAMD) (only for plot scale considering some non-quantitative variables). The list and description of the statistical methods applied are showed on Tab.4. The different statistical methods are applying in order to give more robustness to the modelling approach. We then considered as the correct class the one resulting on more of the models applied. The Pearson correlation coefficient (Pearson 1920) was estimate between each variables of our datasets and variables not significantly independent were not kept for the NB, kNN and NN classifications.\u003c/p\u003e\n\u003cp\u003eTable 4. Statistical methods descriptions (default parameters are the default parameters in the R function used)\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"572\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"14.86013986013986%\"\u003e\n \u003cp\u003e\u003cstrong\u003eStatistical approach\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"59.44055944055944%\"\u003e\n \u003cp\u003e\u003cstrong\u003eDescription\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.6993006993007%\"\u003e\n \u003cp\u003e\u003cstrong\u003eHyper-parameters applied\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"14.86013986013986%\"\u003e\n \u003cp\u003ePrincipal Component Analysis (PCA)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"59.44055944055944%\"\u003e\n \u003cp\u003eDimensionality reduction technique identifying the principal components, which are orthogonal linear combinations of the original variables\u0026nbsp;(Gifi 1990; Husson et al. 2005)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.6993006993007%\"\u003e\n \u003cp\u003encp: 5\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eSize of confidence ellipses: 95%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"14.86013986013986%\"\u003e\n \u003cp\u003eSupport Vector Machine\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"59.44055944055944%\"\u003e\n \u003cp\u003eMachine learning algorithm used for classification tasks which find the best hyperplane that separates different classes in the data space\u0026nbsp;(Chen et al. 2004)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.6993006993007%\"\u003e\n \u003cp\u003eKernel: radial\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eRegularization parameter: default\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"14.86013986013986%\"\u003e\n \u003cp\u003eNaive Bayesian\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"59.44055944055944%\"\u003e\n \u003cp\u003eApplication of the Bayes theorem at a dataset of objects with independent characteristics to define classes\u0026nbsp;(Rish 2001)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.6993006993007%\"\u003e\n \u003cp\u003eNone\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"14.86013986013986%\"\u003e\n \u003cp\u003eRandom Forest\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"59.44055944055944%\"\u003e\n \u003cp\u003eMachine learning method used for classification and regression tasks. It constructs multiple decision trees during training and combines their predictions through voting (for classification) or averaging (for regression) to improve accuracy and robustness\u0026nbsp;(Breiman 1996, 2001)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.6993006993007%\"\u003e\n \u003cp\u003eTree number,Predictor number per split, Leaf size :\u003c/p\u003e\n \u003cp\u003edefault\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"14.86013986013986%\"\u003e\n \u003cp\u003ek-Nearest Neighbor\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"59.44055944055944%\"\u003e\n \u003cp\u003eIt predicts the class or value of a new data point by considering the majority class or average value of its k-nearest neighbor in the training data\u0026nbsp;(Keller et al. 1985)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.6993006993007%\"\u003e\n \u003cp\u003ek: 3\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"14.86013986013986%\"\u003e\n \u003cp\u003eLogistical regression\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"59.44055944055944%\"\u003e\n \u003cp\u003eBinary classification, estimating the probability that a given input belongs to one of two classes based on predictor variables\u0026nbsp;(Lee et al. 2006)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.6993006993007%\"\u003e\n \u003cp\u003eFamily: \u0026quot;binomial\u0026quot;\u003c/p\u003e\n \u003cp\u003eRegulatization parameters: default\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"14.86013986013986%\"\u003e\n \u003cp\u003eFactor Analysis of Mixed Data\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"59.44055944055944%\"\u003e\n \u003cp\u003eMultivariate statistical technique used for analyzing datasets containing both quantitative and qualitative variables. It explores underlying structures and relationships between variables through dimensionality reduction and factor analysis\u0026nbsp;(Escofier 1979; Kiers 1991)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.6993006993007%\"\u003e\n \u003cp\u003eNumber of dimensions kept (ncp): 5\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"14.86013986013986%\"\u003e\n \u003cp\u003eNeural Network\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"59.44055944055944%\"\u003e\n \u003cp\u003eTraining a model composed of interconnected nodes (neurons) to classify data based on patterns learned from input-output pairs\u0026nbsp;(LeCun et al. 2015; Chollet 2017)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.6993006993007%\"\u003e\n \u003cp\u003e2 layers (5,5 neurons in farm\u0026apos;s classification 5,6 in the plot\u0026apos;s one)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"14.86013986013986%\"\u003e\n \u003cp\u003eLinear Discriminant Analysis\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"59.44055944055944%\"\u003e\n \u003cp\u003eFinds directions that maximize class separation, projects data onto these directions, and predicts class membership based on the projected values\u0026nbsp;(Friedman 1989)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.6993006993007%\"\u003e\n \u003cp\u003eRegulatization parameters: default\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eFor each statistical method, a cross-validation was applied for the two training datasets (15 farms \u0026ndash; 66 orchards) and the methods not providing a sufficient score were not considered.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eAfter applying each classification method independently for each holding, the allocation to the most common class was retained. Similarly, each classification was applied to each of the orchard fields of the heavily irrigated farms and the most common allocation was retained.\u003c/p\u003e\n\u003cp\u003e2.4.1 Farm scale classification\u003c/p\u003e\n\u003cp\u003eFor the farm classification, the characteristics selected had to meet two main criteria: they had to be accessible via the data available at watershed level, and they had to correspond to characteristics that have a strong influence on water consumption, since the differences observed between the consumption of\u0026nbsp;heavily\u0026nbsp;and weakly irrigated farms are very large (figure 4).\u003c/p\u003e\n\u003cp\u003eFirstly, we assumed that a farm that will have a predominance of irrigated perennial crops (table vines/ orchards) will consume more water. This hypothesis is also supported by other previous studies which show that farms that do not have enough water will turn to less water-consuming crops (meadows, cereals, etc.) rather than apply deficit irrigation on heavily irrigated crops (Schuck and Green 2001; G\u0026oacute;mez‐Lim\u0026oacute;n and Riesgo 2004). Then, the characteristics of the farms tend to define their water consumption. In this study, the choice was to characterise farms by their soil via the average AWC (average of the average AWC of each field weighted by their area), considering that AWC is one of the major characteristics of a plot\u0026apos;s water requirements (Pereira et al. 2015). On the other hand, the average altitude of the field is one of the main factors in the characteristics of the field and enables fields to be distinguished by their microclimate (temperatures/winds/rains) (Jacobsen et al. 1997; Sevruk 1997; Archer and Caldeira 2009). So, in this study was kept the average altitude of the farm (mean altitude of each plot weighted by their area) as an important factor of the decision of agricultural practices (Schoenly et al. 1996)\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 5\u0026nbsp;\u003c/strong\u003eUsed parameters for the farms\u0026rsquo; classification\u003c/p\u003e\n\u003cdiv\u003e\n \u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"76%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eData\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eSource\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"76%\" valign=\"top\"\u003e\n \u003cp\u003eArea of heavily irrigated crops (orchards + Table vineyards)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24%\" valign=\"top\"\u003e\n \u003cp\u003eLPIS\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"76%\" valign=\"top\"\u003e\n \u003cp\u003eArea of weakly irrigated crops (Cuve vineyards + other crops)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24%\" valign=\"top\"\u003e\n \u003cp\u003eLPIS\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"76%\" valign=\"top\"\u003e\n \u003cp\u003eAverage available water capacity (AWC) of the farm\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24%\" valign=\"top\"\u003e\n \u003cp\u003eSCP / Infosol\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"76%\" valign=\"top\"\u003e\n \u003cp\u003eAverage altitude of the farm\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24%\" valign=\"top\"\u003e\n \u003cp\u003eDEM\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003e2.4.2 Field scale classification\u003c/p\u003e\n\u003cp\u003eBased on the previous assessment, the classification was carried out at field scale for the orchard fields belonging to heavily irrigated farms. The variables used for the classification are listed on table 6.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eAs previously, for this classification each variable was selected to describe the water needs of a crop in this field as a representative proxy of the irrigation need. Altitude was kept as a descriptor of the microclimatic cultural conditions. The percentage of pixels exposed to south was add as a description of the solar direct radiation which affect highly the climatic condition (Dufour 1887). For soil, the main drivers of water needs are relative to the AWC. The available water is affected by the quality of the soil from the texture (sandy soil or not) and by water losses, which are partly linked to runoff from the slope (Gaetano et al. 2017). The proximity of watercourse could also affect the available water and the characteristic of soil (Struyf et al. 2009). Finally, from the point of view of human labour, according to the farmers surveyed, the proximity of a field will have an impact on the frequency of visits by the farmer and therefore on whether or not water is supplied.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 6\u003c/strong\u003e Variables for the orchard fields\u0026rsquo; classification\u003c/p\u003e\n\u003cdiv\u003e\n \u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"61.881188118811885%\" valign=\"top\"\u003e\n \u003cp\u003eData\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"38.118811881188115%\" valign=\"top\"\u003e\n \u003cp\u003eSource\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"61.881188118811885%\" valign=\"top\"\u003e\n \u003cp\u003eAltitude of the centroid of the plot\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"38.118811881188115%\" valign=\"top\"\u003e\n \u003cp\u003eDEM\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"61.881188118811885%\" valign=\"top\"\u003e\n \u003cp\u003eAverage slope of the plot\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"38.118811881188115%\" valign=\"top\"\u003e\n \u003cp\u003eDEM\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"61.881188118811885%\" valign=\"top\"\u003e\n \u003cp\u003ePercentage of pixels exposed to the south\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"38.118811881188115%\" valign=\"top\"\u003e\n \u003cp\u003eDEM\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"61.881188118811885%\" valign=\"top\"\u003e\n \u003cp\u003eDominant soil texture\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"38.118811881188115%\" valign=\"top\"\u003e\n \u003cp\u003eDonesol / pedological map\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"61.881188118811885%\" valign=\"top\"\u003e\n \u003cp\u003eAWC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"38.118811881188115%\" valign=\"top\"\u003e\n \u003cp\u003eSCP - Infosol\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"61.881188118811885%\" valign=\"top\"\u003e\n \u003cp\u003eDistance to nearest watercourse\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"38.118811881188115%\" valign=\"top\"\u003e\n \u003cp\u003eIGN\u003ca href=\"#_ftn6\" name=\"_ftnref6\" title=\"\"\u003e[6]\u003c/a\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"61.881188118811885%\" valign=\"top\"\u003e\n \u003cp\u003eDistance from the centre of the farm\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"38.118811881188115%\" valign=\"top\"\u003e\n \u003cp\u003eLPIS\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003ch3\u003e2.5 Overall water consumption for irrigation at watershed scale\u003c/h3\u003e\n\u003cp\u003eThe values of irrigated fields referenced in the table 2 are weighted considering the municipality and the crop type in the field according to the agricultural census data. In fact, the census indicates the percentage of irrigated surface area per municipality, so it was considered a ratio applied to the overall estimation. For the orchard species characterization, the values of water consumption applied at field scale (table 3) were calculated on cherry orchard for a large majority (76%), whereas apricot and other orchards can be considered to consume around 70% of the cherries, based on farmers surveys and irrigation manual \u0026nbsp;(CABRL 2019). For this reason, a correction factor was applied based on the census data about the percentage of each crop and its rate of irrigation in the municipality.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eFor the LPIS possible bias (not all plots are declared), we had to consider that these data are declarative and as previously indicated, in the study area they cover only 48% of the lands. To compare this value to the total irrigated area in the ASA perimeter, a factor was applied which assumes that the ratio of surface area allocated to each crop in the LPIS is representative to the ratio between surfaces areas allocated to each crop in the all watershed (referred in the table 7). To know the total agricultural area, in the case of this study, a general shapefile was created by the observations in the territory and the cadastral data. This map referred to all the agricultural areas in the territory. The ratio between this area and the area referenced in the LPIS (percentage of crops in the LPIS according to the totality of crops) is used to correct the missing part of the LPIS surfaces. The table 7 shows the percentage of represented area in the LPIS.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 7\u003c/strong\u003e Representativity of the LPIS surface in comparison of all the parcels in the territory of ASA in each municipality\u0026nbsp;\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"20%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20%\"\u003e\n \u003cp\u003e\u003cstrong\u003eBeaumont\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20%\"\u003e\n \u003cp\u003e\u003cstrong\u003eCrestet\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20%\"\u003e\n \u003cp\u003e\u003cstrong\u003eEntrechaux\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20%\"\u003e\n \u003cp\u003e\u003cstrong\u003eMalauc\u0026egrave;ne\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20%\"\u003e\n \u003cp\u003e\u003cstrong\u003eLPIS area (ha)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20%\"\u003e\n \u003cp\u003e119\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20%\"\u003e\n \u003cp\u003e44\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20%\"\u003e\n \u003cp\u003e98\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20%\"\u003e\n \u003cp\u003e185\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20%\"\u003e\n \u003cp\u003e\u003cstrong\u003eTotal agricultural area (ha)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20%\"\u003e\n \u003cp\u003e164\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20%\"\u003e\n \u003cp\u003e110\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20%\"\u003e\n \u003cp\u003e210\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20%\"\u003e\n \u003cp\u003e254\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20%\"\u003e\n \u003cp\u003e\u003cstrong\u003eRatio of LPIS representativity (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20%\"\u003e\n \u003cp\u003e73\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20%\"\u003e\n \u003cp\u003e40\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20%\"\u003e\n \u003cp\u003e47\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20%\"\u003e\n \u003cp\u003e73\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e"},{"header":"3. Results","content":"\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003e3.1 Farm typologies\u003c/h2\u003e \u003cp\u003eThe accuracy evaluated from a cross validation with the 15 farms affiliation for the classifications of heavily or weakly irrigated farms shows a large variability among the methods (from 67 to 100% Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e). The NB classification presents the lowest performance, the PCA the highest. The NB classification, which gave the worst results, was not considered. Without considering the NB classification, the average result of statistical methods is 90% of good identification on the training dataset.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eAccording to the Pearson correlation test, the average AWC of the farm is not statistically independent to other values (see Supplementary Material figure S.1). Since data independence is important in the case of the NB, NN and kNN classifications, this variable was be removed from the classifications.\u003c/p\u003e \u003cp\u003eFigure \u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003e shows the map obtained from the farm classification, with the most common assignment among the statistical classifications (kNN, LDA, LR, NN, PCA, RF and SVM) for each farm. Most of the farms classified as heavily irrigated are located in the East of the watershed, in the Beaumont-du-Ventoux municipality. The large Entrechaux plateau most located at West and North-West includes a large proportion of weakly irrigated farms. This classification of the LPIS farms shows 50 farms (77%) classified as weakly irrigated and 15 farms (13%) classified as heavily irrigated.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003e3.2 Orchard fields classification\u003c/h2\u003e \u003cp\u003eThe accuracy of the cross validation done at fields scale with the estimations of orchard\u0026rsquo;s consumption by the different statistical methods show a large variability (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003e). These cross validations were applied at the 66 orchards plots in the training dataset where we have accurate surveys. The highest score is at 77% for SVM classification. With respectively 53% and 54% of good identification at cross validations, FAMD and NN classifications are the least able to identify parcels with high or low consumption. Results of these two methods were not kept for the final classification. Without these two methods the mean result of the cross validation is 68%.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe correlation between each variable is show in figure S.2 in Supplementary material. In the analyse, the Pearson coefficient between each variable shows that the percentage of pixels with a South orientation is not statistically independent to other values. Since data independence is important in the case of the NB, NN and kNN classifications, this variable will be removed from these classifications.\u003c/p\u003e \u003cp\u003eThe map of the classified fields is presented Fig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e9\u003c/span\u003ea. This map shows the 3 groups of orchards: two groups for heavily irrigated farms (red in previous section Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003e) including either heavily and weakly irrigated orchard fields. Figure\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e9\u003c/span\u003e-b shows the repartition of the orchards from heavily irrigated farms with a higher proportion of less irrigated orchards (170 orchards) than more irrigated orchards (109). The third group of orchard classification (blue) concern orchards fields in the weakly irrigated farms.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003e3.3 Assessment of total water consumption at watershed and municipality scale\u003c/h2\u003e \u003cp\u003eTo validate the estimations of the model at large scale (municipalities, watershed), the different estimated consumption (Table \u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e3\u003c/span\u003e section \u003cspan refid=\"Sec9\" class=\"InternalRef\"\u003e2.4.1\u003c/span\u003e) was considered according to ponderations applied to municipalities values (described in sections \u003cspan refid=\"Sec11\" class=\"InternalRef\"\u003e2.5\u003c/span\u003e). The sum of these estimations for each field in the ASA managed territory (map Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e) at the watershed scale is 697 723 m\u003csup\u003e3\u003c/sup\u003e for 2020.\u003c/p\u003e \u003cp\u003eThe ASA Ouv\u0026egrave;ze-Ventoux gives an annual water consumption for the watershed of 596 121 m\u003csup\u003e3\u003c/sup\u003e in 2020. This value could appear quite comparable with the estimated value (14% difference overestimated). According to the ASA, the margin of error for their value is estimated at a potential underestimate of 30%. The ASA's consumption is therefore between 596 121 and 774 957 m\u003csup\u003e3\u003c/sup\u003e. The value obtained of 697 723 is therefore well within this margin.\u003c/p\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab7\" class=\"InternalRef\"\u003e8\u003c/span\u003e, shows the results of the estimation of the water consumption for each crop in the ASA managed area for each municipality. Figure\u0026nbsp;\u003cspan refid=\"Fig10\" class=\"InternalRef\"\u003e10\u003c/span\u003e compares the values obtained for the estimated quantity of water per crop and per municipality with the ASA values for the year 2020. In comparison, water consumption is overestimated in Beaumont-du-Ventoux (+\u0026thinsp;200 000 m\u0026sup3;), relatively well estimated in Crestet and Entrechaux, and underestimated in Malauc\u0026egrave;ne (-100 000 m\u003csup\u003e3\u003c/sup\u003e). The consumption of truffle tree and olive groves is negligible in all municipality compared with the consumption and surface areas allocated to vineyards and other orchards. The main sources of errors could be link to the estimation of water irrigation of each crop even if the corrections were applied. In Beaumont, a large proportion of the irrigation water is used for orchards, whereas in Crestet and Entrechaux, most of the water is used for table wines, which cover most of the surface area. In addition, it should be noted that the orchards of weakly irrigation farms, although representing considerable surface areas, are not major sources of water consumption, unlike the orchards of heavily irrigated farms. This distinction can be seen particularly in Beaumont and Malauc\u0026egrave;ne, where the orchards of heavily irrigated farms represent the main source of consumption in the municipality. The overestimation in Beaumont could be link to the value of 456 mm which is estimated apply to this category which could be overestimated.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab7\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 8\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eConsumption estimated for each crops of each municipality\u0026rsquo;s ASA managed area\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVillages\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCrops\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eArea (m2)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eAverage consumption (mm)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eConsumption (m\u003csup\u003e3\u003c/sup\u003e)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"5\" rowspan=\"6\"\u003e \u003cp\u003eBeaumont-du-Ventoux\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOlive groves\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e11861\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e150\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1779\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eWine vineyard\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e208897\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2089\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOrchard heavily irrigated - farms heavily irrigated\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e531290\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e456\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e242268\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOrchard heavily irrigated - farms weakly irrigated\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e434554\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e93\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e40414\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOrchard of weakly irrigated farms\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e86209\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1897\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTable vineyard\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e94520\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e300\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e28356\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003eCrestet\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOlive groves\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e48744\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e19,5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e951\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eWine vineyard\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e669822\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOrchard of weakly irrigated farms\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e88541\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1240\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTable vineyard\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e240497\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e300\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e72149\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"4\" rowspan=\"5\"\u003e \u003cp\u003eEntrechaux\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOlive groves\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e5110\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e150\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e767\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTruffle tree groves\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e47061\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e886\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eWine vineyard\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1322449\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e3967\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOrchard of weakly irrigated farms\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e197309\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e4341\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTable vineyard\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e306355\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e300\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e91906\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"6\" rowspan=\"7\"\u003e \u003cp\u003eMalauc\u0026egrave;ne\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOlive groves\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e64715\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e150\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e9707\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTruffle tree groves\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e44235\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e619\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eWine vineyard\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e498554\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e89\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e44371\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOrchard heavily irrigated - farms heavily irrigated\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e254349\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e346\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e88005\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOrchard heavily irrigated farms weakly irrigated\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e545065\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e71\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e38700\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOrchard of weakly irrigated farms\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e430275\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e7315\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTable vineyard\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e148113\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e108\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e15996\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"4. Discussion","content":"\u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003e4.1 Water need assessment\u003c/h2\u003e \u003cp\u003eThe farm classification (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003e) has outlined different practices according to the location and the main crop of the farm. The cross-validation of the statistical methods gives a rather high percentage of correct identification (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e). However, this result must be qualified because the cross-validation was carried out on a small data set (15 farms). A high proportion of heavily irrigated farms is located in the municipality of Beaumont-du-Ventoux, and indeed most of the farms in this area have mainly orchards, which consume the highest amount of water according to the ASA data.\u003c/p\u003e \u003cp\u003eThe high density of heavily irrigated farms in the eastern part of the watershed (Beaumont-du-Ventoux) could introduce a proximity bias for nearby farms. These nearby farms may differ socially in ways not considered in this study. It is generally recognized that classifications aimed at grouping irrigation decisions and individual choices have significant limitations due to the unpredictability of human decisions (Maton et al. \u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e2005\u003c/span\u003e). This unpredictability may account for some potential biases in the study. Conversely, the low proportion of heavily irrigated farms in areas such as Entrechaux or Crestet could mask farms that tend to be heavily irrigated. Thus, farms that are socially but not geographically isolated may be misclassified by the model.\u003c/p\u003e \u003cp\u003eA social explanation for the variability could be considered, but would require personal information on all farmers in a region (Bublot \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e1969\u003c/span\u003e). Different habits of farmers, especially related to the age of the irrigation manager and whether the farm is a family farm or not, could affect the decision and modality of an irrigation event, as observed in similar cases by Azizi Khalkheili and Zamani (\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2009\u003c/span\u003e) or Wang et al. (\u003cspan citationid=\"CR93\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). Another social parameter that could affect the classification could be whether the farmer has access to technical advice on irrigation management, as also mentioned by Genius et al. (\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). All of these characteristics affect the amount of water applied by the farmer, but are difficult to access on a large scale.\u003c/p\u003e \u003cp\u003eOther agricultural practices, not considered here, related to farmers' choices, can have an impact on a crop's water use. In particular, the use of fertilizer irrigation can have a significant impact on the amount of water applied to the plot, as fertilizer requires additional water to be applied. However, this information is not available at the farm level.\u003c/p\u003e \u003cp\u003eThe estimation of water consumption at the crop and municipality level (Table\u0026nbsp;\u003cspan refid=\"Tab7\" class=\"InternalRef\"\u003e8\u003c/span\u003e) shows a high diversity of consumption for the same crop in different municipalities. For example, the heavily irrigated orchards of heavily irrigated farms have an estimated irrigation of 456 mm in Beaumont and 346 mm in Malauc\u0026egrave;ne according to the different weighting. These variations strongly affect the total consumption of the whole commune (Fig.\u0026nbsp;\u003cspan refid=\"Fig10\" class=\"InternalRef\"\u003e10\u003c/span\u003e). This variability seems relevant and apply a less important irrigation in the scale of a commune which have effectively less needs due to their physical and environmental characteristics. Regarding the recommendation of estimation given in other similar Mediterranean regions (Memento irrigation book (CABRL \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2019\u003c/span\u003e)), the values vary from 180 to 350 mm/year of irrigation for cherry orchards. This possible overestimation of the applied quantities could be one of the main reasons for the overestimation of the consumption in Beaumont (Fig.\u0026nbsp;\u003cspan refid=\"Fig10\" class=\"InternalRef\"\u003e10\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec18\" class=\"Section2\"\u003e \u003ch2\u003e4.3 Methodological discussion\u003c/h2\u003e \u003cp\u003eThe relevance of the heterogeneity of farmers' (Schuck and Green \u003cspan citationid=\"CR85\" class=\"CitationRef\"\u003e2001\u003c/span\u003e; G\u0026oacute;mez-Lim\u0026oacute;n and Riesgo \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2004\u003c/span\u003e; Wang and Cai \u003cspan citationid=\"CR92\" class=\"CitationRef\"\u003e2009\u003c/span\u003e) or farming communities' (Wang et al. \u003cspan citationid=\"CR93\" class=\"CitationRef\"\u003e2013\u003c/span\u003e) behavior in the irrigation decision has been analyzed in several recent papers. These articles are based on the importance of irrigation water from an economic point of view and try to find a relationship between the productive value of the water input, especially with respect to the soil characteristics of the regions studied (especially the avialable water content), and its cost. Moreover, the quantification of irrigation has also been related to field characteristics (Leenhardt et al. \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2004\u003c/span\u003e; Ali et al. \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Vera et al. \u003cspan citationid=\"CR90\" class=\"CitationRef\"\u003e2019\u003c/span\u003e), but these studies require modeling with soil water characteristics (Soil Water Potential, Available Water Content or Soil Water Content), which are parameters that are difficult to access accurately when applying a large-scale model. This study confirms the importance of considering both levels of analysis: both farm and plot characteristics are essential in estimating water use to obtain a coherent assessment of water use at the watershed scale, especially in the case of fruit tree crops.\u003c/p\u003e \u003cp\u003eFew studies show an estimation of water use at the watershed scale. The most common studies are based on the estimation of crop water requirements by biophysical methods at different scales with crop models (Brisson et al. \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2003\u003c/span\u003e; Mancosu et al. \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Akoko et al. \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). These models can also be based on some remote sensing information to be applied at larger scales (Olioso et al. \u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e1999\u003c/span\u003e; Duchemin et al. \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2006\u003c/span\u003e; Courault et al. \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2010\u003c/span\u003e, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) or to identify plot characteristics (Abubakar et al. \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). However, few studies focus on large areas dominated by perennial crops due to the difficulty of identifying characteristics of this type of heterogeneous land cover, although some techniques are being evaluated (El Hajj et al. \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Rouault et al. \u003cspan citationid=\"CR81\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). The few studies that consider this type of crop are usually based on field surveys or work in close contact with farmers, coupled with modeling (Kpadonou et al. \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Naulleau \u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). This study proposes a method based on heterogeneous spatial and statistical databases that are available and can be applied at a large scale. It is based on a demonstration of the variability of irrigation practices in orchards, which are the main field consumers of water in this region. Until now, to our knowledge, no study has addressed the variability of orchard irrigation on crucial aspects of farmers in the Mediterranean region, and the modeling available at scales large enough to be useful to water managers was based on modeling water requirements rather than real irrigation. This study proposes a method to estimate the real water consumption of a Mediterranean watershed by taking better account of farmers' behavior and by proposing a typology that can be applied to all farms in the watershed, starting from a small number of surveys.\u003c/p\u003e \u003c/div\u003e"},{"header":"5. Conclusion","content":"\u003cp\u003eA variety of methods for classifying farms and fields at the small watershed scale, with a dominant presence of orchards, have been proposed based on spatialized databases and surveys. These methods are employed to assess the water use for irrigation. The estimation of water consumption at the watershed scale represents a preliminary step in the process of establishing the capability to ascertain the water consumption of different parts of a small Mediterranean watershed. This is achieved by compartmentalizing orchard crops into different categories. The consideration of farm typology as a proxy for farmers' irrigation decisions is a useful approach for describing orchard irrigation practices and identifying locations with high irrigation water consumption in a watershed. Distinguishing between heavily and weakly irrigated farms appears to reflect the distribution of farms in the area. The presence or absence of a majority of water-consuming perennial crops, as well as altitude and whether or not the farm has access to significant water resources in the soil, appear to be useful in identifying heavily water-consuming farms. This analysis also highlighted the diversity of irrigation practices between these high-consumption farms. Without information on the cultivated species, it is challenging to classify these plots as heavily or weakly irrigated. However, the physical characteristics of the field, including altitude, slope, exposure, AWC, and proximity to the watercourse, as well as the distance of the plot from the heart of the farm, appear to be parameters that can be statistically estimated to determine whether the plot belongs to one category or the other. In conclusion, this study demonstrates the potential to estimate the water needs of a small watershed with a dominant orchard crop using a combination of statistical methodologies. This approach involves first classifying farms according to their water consumption, which can be readily extrapolated to other environmental and agronomical contexts.\u003c/p\u003e"},{"header":"Declarations","content":"\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003ePR has developped all the statistical analysis, data collection and first draft writing;DC has review the first draft, supervised the data analysis and methodological conceptualization and she managed the related projects;FF has developped the filed interviews, data acquisition;MD has reviewed and finalized the paper draft, supervised the methodological developpement.All authors reviewed the manuscript.\u003c/p\u003e\u003ch2\u003eAcknowledgement\u003c/h2\u003e\u003cp\u003eThis study was funded by the PACA region and a project with the King Abdullah University of Science and Technology in Saudi Arabia. The authors thank the surveyed farmers and the ASA president, who have kindly provided important data for this study. The authors would also like to thank the colleagues who contributed their expertise to the processing and acquisition of the data, in particular Mr Samuel Buis for his advice and Mr Davide Martinetti for data acquisition.Access to some confidential data (Recensement G\u0026eacute;n\u0026eacute;ral de l\u0026rsquo;Agriculture 2020: https://doi.org/10.34724/CASD.39.4411.V1), on which is based this work, has been made possible within a secure environment offered by CASD \u0026ndash; Centre d\u0026rsquo;acc\u0026egrave;s s\u0026eacute;curis\u0026eacute; aux donn\u0026eacute;es (Ref. 10.34724/CASD)\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eAbdullaev I, Mollinga PP (2010) The Socio-Technical Aspects of Water Management: Emerging Trends at Grass Roots Level in Uzbekistan. 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Agriculture 10:11. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3390/agriculture10010011\u003c/span\u003e\u003cspan address=\"10.3390/agriculture10010011\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"},{"header":"Footnotes","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003e \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://geoservices.ign.fr/rpg\u003c/span\u003e\u003cspan address=\"https://geoservices.ign.fr/rpg\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003e \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://agreste.agriculture.gouv.fr/agreste-web/disaron/!searchurl/4545f1a9-afe6-4c86-a141-693f2c72d550!1b69a349-ca8f-4353-82bb-4c00c502412c!729f399f-53c3-4952-9971-4753794a7c1b!c6be0c43-70a0-4666-853f-80de38a08ec7!0c593aed-b1d0-476e-9359-12d6347d8243!b125c6dc-13b7-4260-9abd-6e9321b2b963!fec0e278-6655-4c48-ac47-aab6d8847e15/search/\u003c/span\u003e\u003cspan address=\"https://agreste.agriculture.gouv.fr/agreste-web/disaron/!searchurl/4545f1a9-afe6-4c86-a141-693f2c72d550!1b69a349-ca8f-4353-82bb-4c00c502412c!729f399f-53c3-4952-9971-4753794a7c1b!c6be0c43-70a0-4666-853f-80de38a08ec7!0c593aed-b1d0-476e-9359-12d6347d8243!b125c6dc-13b7-4260-9abd-6e9321b2b963!fec0e278-6655-4c48-ac47-aab6d8847e15/search/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003e \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://canaldeprovence.com/\u003c/span\u003e\u003cspan address=\"https://canaldeprovence.com/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003e https://entrepot.recherche.data.gouv.fr/dataset.xhtml?persistentId=doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.15454/9IRARJ\u003c/span\u003e\u003cspan address=\"10.15454/9IRARJ\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003e \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.data.gouv.fr/fr/datasets/registre-parcellaire-graphique-rpg-contours-des-parcelles-et-ilots-culturaux-et-leur-groupe-de-cultures-majoritaire/\u003c/span\u003e\u003cspan address=\"https://www.data.gouv.fr/fr/datasets/registre-parcellaire-graphique-rpg-contours-des-parcelles-et-ilots-culturaux-et-leur-groupe-de-cultures-majoritaire/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003e \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://geoservices.ign.fr/bdtopo\u003c/span\u003e\u003cspan address=\"https://geoservices.ign.fr/bdtopo\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Perennial crops, Water management, Multivariate analysis, Machine learning, France","lastPublishedDoi":"10.21203/rs.3.rs-4580425/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4580425/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eIn the Mediterranean region, the quantity of water utilized for agricultural purposes ranges from 50 to 70%. Among the most water-demanding agricultural sectors are arboriculture and perennial crops. Orchards are particularly reliant on irrigation, a dependency that has been further intensified by climate change and the resulting reduction in water resources. This study aims to classify farms at the watershed scale according to their irrigation water consumption, and starting from this classification we aim to propose a method for estimating water consumption for irrigation at large scale and for heterogeneous land covers. The classification employed a variety of statistical methods to ensure robust results, including machine learning and regression approaches. Each method was applied independently, and the most common class allocation was retained. The study was conducted in the Ouvèze-Ventoux basin in south-eastern France, using data from various sources at both field and watershed scales. The data obtained from 21 farms provided accurate information on irrigation water usage, which was validated by data from the watershed's water manager. The benchmark analysis identified farms with high irrigation rates with 90% accuracy. Within these heavily irrigated orchards, a second benchmark identified heavily irrigated plots with 68% precision. Maps estimating water consumption were created at the watershed and municipal scales. The estimated total irrigation water use closely matched the actual consumption, with only a 14% deviation. This methodology offers an accessible estimation of water consumption at the watershed scale, without the need to rely on crop models. Moreover, the methodology accurately identifies areas with high irrigation demand based on actual irrigation practices.\u003c/p\u003e","manuscriptTitle":"Unravelling the heterogeneity of farms irrigation practices on Mediterranean perennial agricultural systems for the optimization of water resource management","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-07-02 10:13:35","doi":"10.21203/rs.3.rs-4580425/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"13bc6ba1-c11d-4cfd-902f-3df239b7ce13","owner":[],"postedDate":"July 2nd, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-02-28T13:38:44+00:00","versionOfRecord":[],"versionCreatedAt":"2024-07-02 10:13:35","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-4580425","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4580425","identity":"rs-4580425","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}