Johanna Fusco

Ricardo Villani

Michel Moulery

Tiziana Sabbatini

Leonith Hinojosa-Valencia

Claude Napoleone

Alberte Bondeau

Leonith Hinojosa

Land system dynamics in the Mediterranean basin across scales as relevant indicator for species diversity and local food systems Deliverable 1.1: Report on the comprehensive database building

Table of contents Introduction

Building a comprehensive and extensive spatial database on the various characteristics of land use, food production, socio-economy and many other dimensions of the Mediterranean basin was the first task of DIVERCROP project. The objective of this work package was to gather huge amounts of scattered data and information in a single harmonized framework, in order to provide the other work packages a solid base to perform in-depth analyses of Mediterranean land system structures and dynamics.

If many precise and interesting information are available for small parts of the Mediterranean basin, we chose to favor homogeneity by focusing on datasets covering the whole studied area. This choice intends providing a better control on the accuracy and the reliability of the results obtained after data processing, as well as building a common discourse on Mediterranean land system structures and dynamics. In order to ensure this homogenous coverage of the Mediterranean basin, global scale datasets with a fine resolution have been privileged.

Various types of data have been collected, gathered and harmonized in a postgres/postgis spatial database in order to fit each spatial resolution:

-Land use -Agricultural production systems -Population -Bio-physical information This report details all the aspects of the DIVERCROP database, including the characteristics of the original datasets and the various upscaling or downscaling processes required to harmonize them in a common spatial framework.

Database structure

Spatial resolution

After reviewing the various spatial and temporal resolutions of all available datasets including the whole extent of the Mediterranean basin, and the spatial resolutions chosen by previous Mediterranean land systems assessments (Malek, Verburg, 2017) we chose DIVERCROP database to be provided at three spatial resolutions, so that the user can choose the appropriate one following its needs (Figure 1, Table 1):

-2 km grid (2km x 2km cells): this grid covers the Mediterranean part of the countries as chosen by Malek and Verburg (2017) -5 arc-minutes grid (9.25 x 9.25 km at the equator, approximately 9 x 7 km for the Mediterranean basin): this grid covers the whole countries of the Mediterranean basin, and matches the grid built by the MAPSPAM program (see p7). -Aggregated data at regional scale: for the European part of the Mediterranean countries, we chose to aggregate data at Nuts2 scale1 ; for the non-European countries, we referred to the governorates' 2 administrative level.

Types of information gathered and original databases

The DIVERCROP database gathers various types of information from many different sources, mostly stemming from global-scale databases. The different types of variables and their original sources are listed in Table 2.

Temporal resolution

In order to allow the assessment of land system dynamics, two temporal spans have been chosen, according to the available data: 2005 and 2015, our intention being to add progressively other time spans in the database. Not all the information being available for these two dates, Table 3 indicates the time spans corresponding to each variable of the database. Detailed information on DIVERCROP database variables

Land use / Land cover

The Climate Change Initiative -Land Cover (CCI-LC) project delivers consistent global land cover maps at 300 m spatial resolution on an annual basis from 1992 to 2015. The typology, presented in Table 4, was defined using the Land Cover Classification System (LCCS) developed by the United Nations (UN) Food and Agriculture Organization (FAO), with the view to be as much as possible compatible with the GLC2000, GlobCover 2005 and 2009 products. In addition, the UNLCCS was found quite compatible with the Plant Functional Types (PFTs) used in climate models [RD.1]3 .

The 300 m maps have been upscaled at the three spatial resolutions of DIVERCROP database by calculating the percentage of each land cover variable present in each 2 km cell, 5 arc-minute cell and in each nuts-2. The upscaling process presented on Figure 2 for 2km grid is the same for the 5arcminute and nuts2 scale.

Agricultural production systems

Three sources of data have been chosen in order to describe Mediterranean agricultural production systems: MAPSPAM, Gridded Livestock of the World, and National Censuses.

MAPSPAM

The Spatial Production Allocation Model (SPAM) 2005 endeavors to disaggregate crop statistics identified at national and sub-national units for the year 2005 to 5 arc-minute grid cells while taking account of different farming practices. SPAM generates four major output variables: physical area, harvested area, production quantity and yield for each of 42 crops distinguished by four production systems (i.e., irrigated -high input, rainfed -high input, rainfed -low-input and rainfed -subsistence production). The allocation model uses a cross-entropy optimization approach informed by major inputs such as cropland surface, location of irrigated areas, crop suitability and potential yields, rural population densities, production systems characteristics and crop prices to disaggregate crop As the DIVERCROP 5 arc-minute grid has been chosen to match spatially MAPSPAM grid, transferring the information from one database to another did not require any preliminary treatment.

Upscaling the information from MAPSPAM grid to Nuts2 required an aggregation of the 5 arcminutes data, by summing crop surfaces for each Nuts2. This aggregation required to relate each 5 arc minutes pixel to the Nuts2 it belongs to. If attributing each pixel to a Nuts2 does not encounter any specific problem if the 5 arc-minute pixel is totally included in a Nuts2 polygon, a choice had to be made at the borders, as shown in Figure 3. We thus decided to consider each arc-minute pixel centroid's position in order to determine the correspondence between each 5 arc-minute pixel and each Nuts2. Downscaling 5 arc-minutes grid information to 2 km grid required some data processing as well. We chose to redistribute MAPSPAM's physical areas following the percentage of agricultural land use

CROP SURFACES CEREALS

• Wheat

• Rice • Maize • Barley • Pearl Millet • Small Millet • Sorghum • Other Cereals ROOTS & TUBERS • Potato • Sweet Potato • Other Roots PULSES • Bean • Chickpea • Cowpea • Lentil

OIL CROPS

• Soybean

• Groundnut • Sunflower • Rapeseed • Sesame Seed • Other Oil Crops SUGAR CROPS • Sugarcane • Sugarbeet FIBRES • Cotton • Other Fibre Crops STIMULATES • Tea • Tobacco FRUITS • Banana • Tropical Fruit • Temperate Fruit REST OF CROPS
contained in each 2 km pixel. The total agricultural land use for each 2km pixel has been first calculated by summing the values of the following land cover variables:

-Cropland rainfed -Cropland rainfed -Herbaceous cover -Cropland rainfed -Tree or shrub cover -Cropland irrigated or post-flooding -Mosaic cropland (>50%) / natural vegetation (tree/shrub/herbaceous cover) (<50%)

The downscaling process from 5 arc-minute MAPSMAM pixels to 2km pixels is described in Figure 4.

Livestock

FAO in collaboration with the Environmental Research Group Oxford (ERGO) developed the "Gridded Livestock of the World" (GLW). The GLW's main objective is to produce and distribute contemporary global distribution maps for the main species of livestock. These maps are created through the spatial disaggregation of sub-national statistical data based on empirical relationships with environmental variables in similar agro-ecological zones.

The data are produced in ESRI grid format at a spatial resolution of 3 minutes of arc (roughly 5 km at the equator) for cattle, buffalo, sheep, goats, pigs and poultry/chickens and are freely available for download via FAO's GeoNetwork data repository.

The Gridded Livestock of the World (GLW) database produced in 2007 provides modeled livestock densities of the world, adjusted to match official (FAOSTAT) national estimates for the reference year 2005, at a spatial resolution of 3 minutes of arc (about 5 x 5 km at the equator). 4

Table 6 presents the Gridded Livestock of the World variables available in DIVERCROP database (FAO, 2007).

National Censuses

DIVERCROP participant 9 gathered into DIVERCROP database the most relevant information from the national agricultural censuses, regarding number of holdings, cultivated areas and livestock. This task was carried out through a close and constant collaboration of Participants P1, P2, P5, P6, P7 and P8, particularly regarding data gathering and pre-processing phases, and in some cases (P1, P2, P7) regarding the furnishing of geographical datasets of the administrative-units.

Agricultural Censuses are held in European nations every 10 years. The last agricultural census (2009/2010) was the first synchronized agricultural census across all EU-27 Member States. Although the official census data can be derived from several sources, no complete set of data with the necessary criteria was readily available, and as a consequence it had to be built.

The criteria for the database construction were the following: (1) to cover the Mediterranean areas of seven countries, namely Algeria (DZ), France (FR), Italy (IT), Malta (MT), Portugal (PT), Spain (ES) and Tunisia (TN); (2) to contain data referred to the most disaggregated level of administrative units for which data is available in each country; (3) to contain data referred to at least two time points, including the latest available data, in each country; (4) to contain data on number of farm holdings, on physical areas covered by the main annual and permanent crops and on livestock (number of heads); (5) to contain a primary key that allows joining the census and surveys database to a geographical dataset of administrative units covering the entire area; (6) to have an associated complete geographical dataset of administrative units.

Construction of the Construction of the Construction of the Construction of the Harmonized National Agricultural Censuses Database Harmonized National Agricultural Censuses Database Harmonized National Agricultural Censuses Database Harmonized National Agricultural Censuses Database

The official data source for homogeneous data on agricultural issues referred to the European part of the Mediterranean is Eurostat. Nevertheless, data could not be collected directly from Eurostat since the level of detail needed, namely the highest level of disaggregation of administrative units, was unavailable within the Eurostat databases. Indeed, Eurostat stores data aggregated at NUTS 2 level or higher, which was insufficient for the objectives of the Project. As for the two North African countries, a centralized source of data is not available, therefore, for both European and African countries it was necessary to gather data directly from each country's individual official sources. As a consequence of the above agricultural censuses data had to be collected directly from the local partners of the Project.

Although data from national agricultural censuses and surveys were submitted by local members of the Divercrop consortium directly to Participant 9 for their elaboration (except for data of Italy, which was gathered by Participant 9 itself), official web sites of the national statistics authorities were also consulted during the first screening phase of the data gathering activity. In Annex 1, the complete list of links to these web resources is presented. Data from the national agricultural censuses (France, Italy, Malta and Portugal) and from agricultural surveys (Algeria and Tunisia) were collected for several time points. In order to standardize administrative units within the area of interest of the Project, the European geocode standard for referencing the subdivisions of countries for statistical purposes was used, introducing adaptations of this standard where necessary (Algeria, Tunisia).

In most countries, data referred to the highest level of disaggregation of administrative units, namely LAU2, which corresponds to the municipality or equivalent. In fact, data of 4 out of 7 countries, namely, Italy (municipality), Malta (cities), Portugal (parishes) and Spain (municipalities) referred to the most disaggregated administrative level, which is defined by the European geocode standard as Local Administrative Unit Level 2 (LAU 2)5 while data of France needed aggregation at canton level, and the information provided by two countries (Algeria and Tunisia), contained in agricultural surveys was available at a higher level of geographical-administrative aggregation, namely Wilaya or governorates, as shown in Figure 5. Table 7 shows the hierarchy of the aggregators and the primary key in the relational database. Within the pre-processing phases, each Participant prepared the data keeping as many administrative levels, use for data aggregation (identifiers of NUTS levels) as possible. In such manner, the higher level of aggregation of data is the Country for all participant nations, while disaggregation is possible at LAU2 level for ES, IT, MT and PT, LAU 1 level for FR and Wilaya for DZ and TN (highlighted in grey in the table ).

Regarding the latter two countries, the only aggregator is the identifier of Wilaya. For the purpose of this Project, the Wilaya was assimilated to an intermediate level between NUTS 3 and NUTS 4, as it is also shown in Figure 5, nevertheless, to simplify the use of the Database, the Wilaya identifier was classified under NUTS 3 so as to avoid creating a further category in the hierarchy of administrative units levels.

Intermediate administrative levels are, as shown in Table 7, NUTS 2 (regions, autonomous communities) and NUTS 3 (provinces, departments).

Although Eurostat, in agreement with each EU-Member State, established the sub-divisions of NUTS and LAU levels and the corresponding codification systems, in all cases submission of statistical data carried non-standardized sets of LAU identifiers (which is obvious for Algeria and Tunisia, but should not be necessarily the case for the EU Member States).

Therefore, it was necessary to produce a unique set of codes referencing the most detailed Territorial Unit; this set of codes is referred to as "join_field", which is:

1) an unambiguous codification, generated by combining the identifier of the country (ISO ALPHA 2code) and the code of the most disaggregated Territorial Unit, provided by each country,

2) the field used for joining the data in the Agricultural Censuses Database with the geographical datasets of Territorial Units.

Regarding the point 2 above, it is worth pointing out that although Eurostat provides ready-to-use geographical datasets at LAU 2 level, carrying the official LAU 2 codes, as mentioned before, agricultural censuses data were submitted with codifications which do not comply with the EU standard. Furthermore, censuses data often referred to the local territorial units as they were in the year of the last agricultural census, and in some cases there were changes (i.e. between two censuses two or more municipalities may have been merged together) making it necessary the acquisition of the geography as it was in the year when the census was held. To be able to preserve the whole time-series without interruption in any single territorial unit, it was necessary to have all local territorial units matching throughout the time series, working on a case-by-case basis. This was done also through direct support from the Participants (this is particularly so in the case of Portugal).

For the above reasons, it was necessary to obtain also the geographical datasets of Local Territorial Units from each country, and then to transform the codification contained in these datasets so that it would match the "join_field" values in the Agricultural Censuses Database.

Finally, due to the inevitable diversity of sources of the geographical datasets, it was necessary to align all projections to fit the 3035 ETRS-LAEA European projection and to merge all datasets to obtain a unique coverage, which allows joining alphanumeric data from the Agricultural Censuses Database to the geographical dataset. In the following paragraphs, general features of the data obtained from all participant countries are presented along with the formats used by members of the Divercrop consortium for data submission.

DZ-Algeria

Data from national agricultural surveys (at Wilaya -governorat level) were available for 2012 through 2016, as spreadsheet. The Agricultural Censuses Database comprises data referred to the first ( 2012) and the last (2016) available surveys.

ES-Spain

Data at LAU 2 level were available for the latest two national agricultural censuses, 1999 and 2009, as SPSS files.

The case of Spain required specific elaborations (carried out by Participant 8) for data harmonization at municipal level since the census universe was considerably restricted in 2009 compared to the previous censuses in Spain. In fact, in the previous census (1999), the minimum threshold of Utilised Agricultural Area (UAA) for a farm to be registered was 0.1 ha while for the census of 2009 it has been raised to 1 ha (with exceptions in case of intensive orientations6 ). The result is that both sets of data are completely comparable, by means of the application of constraints that excluded from the 1999 census universe all data referred to farms falling outside the 2009 universe. The ready-to-use data available from INE (Spanish National Statistics Institute) do not allow to obtain a proper timeseries.

of utilised agricultural area (UAA).

Agricultural holdings with at least 0.2 ha of UAA used for fresh vegetables, melons and strawberries, flowers and ornamental plants (outdoors or under low protective cover) or irrigated fruit and berry plantations and citrus plantations, or nurseries, or woody crops under greenhouse.

-Agricultural holdings with at least 0.1 ha of UAA used for under-glass fresh vegetables, melons and strawberries. -Agricultural holdings with at least 0.1 ha of UAA used for under-glass flowers and ornamental plants.

-

Agricultural holdings with at least 0.5 ha of UAA used for tobacco. -Agricultural holdings with at least 0.5 ha of UAA used for hops. -Agricultural holdings with at least 0.5 ha of UAA used for cotton -Agricultural holdings with one or more livestock units and a total standard output equal to or above 0.75 of the economic size of holdings.

FR-France

Data at LAU 1 level was submitted in spreadsheets referred to the latest two national agricultural censuses, 2000 and 2010. An agricultural census previous to that of the year 2000 exists, but data structure is different to the point that it is impossible to elaborate a time-series including that early census. Data acquisition, carried out by Participant 1, was supported by a public grant overseen by the French National Research Agency (ANR) as part of the "Investissements d'Avenir" program (reference: ANR-10-EQPX-17 -Centre d'accès sécurisé aux données -CASD).

It is worth pointing out that the French data were collected at canton level, which is an aggregation of municipality clusters (groups of neighbouring municipalities). This was necessary in order to keep the confidentiality of farms. In fact, the French authority for statistics does not disclose information at municipal level if in a given municipality one of the following conditions is met:

-there are less than 3 holdings; -a single farm covers more than 85% of the total Utilized Agricultural Area of the municipality; -the Standard Output of a single farm exceeds 85% of the total Standard Output of the farms within the municipality.

According to results of the data pre-elaboration phase, carried out by Participant 1, in the Mediterranean area of France a total number of 1,027 municipalities met at least one of the above conditions; this figure corresponds to 12% of the total number of municipalities of the area (8,571 municipalities). Therefore, while aggregations of data means reducing the geographical resolution of the data, on the other hand, keeping the information at municipality level would have led to a massive loss of information.

IT-Italy

Data at LAU 2 level, matching the 2010 list of Italian municipalities was available ready-to-use from the ISTAT (Italian National Institute for Statistics) website for the latest four national agricultural censuses, 1982, 1990, 2000 and 2010.

MT-Malta

Data at LAU 2 level from agricultural censuses 2001 and 2010 were collected by Participant 7 and elaborated into a ready-to-use geographical database. Only general information on areas (UAA, arable lands, set-aside, vineyards) were available for 2001 while a more detailed set of data for 2010. An important voice in the national agricultural census of Malta are areas dedicated to kitchen garden (12% of the total arable lands and an area equivalent to that dedicated to permanent crops), so the variable kitchen gardens was introduced for the case of Malta only.

PT-Portugal

Data at LAU 2 level from the latest three national agricultural censuses, namely 1989, 1999, 2009. Data gathering and elaboration was carried out by Participant 2. This was necessary particularly because INE-Statistics Portugal provides data at LAU 2 level only referred to 2009.

TN-Tunisia

Data from national agricultural surveys (at Wilaya -governorat level) were available for 1995 (hardcopy) and 2005 (html). Both sets of data were transferred to spreadsheet by Participant 9 before their inclusion in the database.

Table 8 shows, in relation with each Divercrop country, the years for which national agricultural datasets were incorporated into the database. Variable codes were defined similarly to another georeferenced database used in the Project (Mapspam). Therefore physical crop areas are composed of the letter a followed by the abbreviated name of the crop/crop type/land use (the latter case refers to UAA and TAA). A difference with the Mapspam codification consists in the use of the letter r to indicate irrigated areas and of the letter s to indicate non irrigated areas. The absence of any of these two letters in the physical areas codes indicates total values (irrigated+rainfed areas).

Figure 6 shows an elaboration of the geographically explicit data from the Harmonized Agricultural Censuses Database at the most disaggregated level of the administrative units for the area of interest of the Project. 10). • Max.

• Average TERRAIN SLOPE • Min.

• Max.

• Average

As 5 arc-minutes data matched spatially DIVERCROP 5 arc-minutes grid, no data processing was required to transfer this data from one grid to another. The aggregation at nuts2 level was performed by calculating the averages of each variable for each nuts2 (see Figure 3). For the 2km database, 30 arc-seconds WORLDCLIM data has been considered. Each 2km pixel was thus given the average value of each 30 arc-seconds pixel contained (considering that each 30 ac-seconds pixel is represented by its centroid, as shown in Figure 3).

Soil type and quality

Soils

Soil data for DIVERCROP database stem from the 30 arc-seconds Harmonized World Soil Database. They include a lot of pedological variables (soil types) and an assessment of soil qualities for crop production. On the basis of soil parameters provided by HWSD seven key soil qualities important for crop production have been derived, namely: nutrient availability, nutrient retention capacity, rooting conditions, oxygen availability to roots, excess salts, toxicities, and workability. Soil qualities are related to the agricultural use of the soil and more specifically to specific crop requirements and tolerances. For the illustration of soil qualities, maize was selected as reference crop because of its global importance and wide geographical distribution. Remember that classes are qualitative not quantitative. Only classes 1 to 4 are corresponding to an assessment of soil limitations for plant growth. Class 1 is generally rated between 80 and 100% of the growth potential, class 2 between 60 and 80%, class 3 between 40 and 60%, and class 4 less than 40% 8 .

In DIVERCROP database, each 2km and 5 arc-minutes pixel and each nuts2 was given the most represented soil quality index, and the percentage of each type of soils it contains.

Landscan data

Sensing, Oak Ridge National Laboratory's LandScan™ is the community standard for global population distribution. At approximately 1 km resolution (30" X 30"), LandScan is the finest resolution global population distribution data available and represents an ambient population (average over 24 hours). The LandScan algorithm, an R&D 100 Award Winner, uses spatial data and imagery analysis technologies and a multi-variable dasymetric modeling approach to disaggregate census counts within an administrative boundary. Since no single population distribution model can account for the differences in spatial data availability, quality, scale, and accuracy as well as the differences in cultural settlement practices, LandScan population distribution models are tailored to match the data conditions and geographical nature of each individual country and region. 10 In DIVERCROP database, each 2km, 5 arc minute pixel and each nuts 2 was given the average value of LANDSCAN 30 arc-seconds unit.

Hyde data

The HYDE database is developed under the authority of the Netherlands Environmental Assessment Agency. HYDE presents (gridded) time series of population and land use for the last 12,000 years. It also presents various other indicators such as GDP, value added, livestock, agricultural areas and yields, private consumption, greenhouse gas emissions and industrial production data, but only for the last century.11 Historical population, cropland and pasture statistics are combined with satellite information and specific allocation algorithms (which change over time) to create spatially explicit maps, which are fully consistent on a 5′ longitude/ latitude grid resolution, and cover the period 10,000 bc to ad 2000 (Goldewijk et al, 2011).

In DIVERCROP database, only 5 arc-minute and nuts2 levels were given the HYDE model data. As 5 arc-minutes HYDE and DIVERCROP grids matched spatially, no data processing was required to transfer the data. Each nuts2 was given the average value of the HYDE 5 arc-minutes pixels it contains (referring to the centroids of each 5 arc-minute pixels, as explained in Figure 3).

Protected areas

The World Database on Protected Areas (WDPA) 12 is the most comprehensive global database on terrestrial and marine protected areas. It is a joint project between the United Nations Environment Programme (UNEP) and the International Union for Conservation of Nature (IUCN), managed by UNEP World Conservation Monitoring Centre (UNEP-WCMC). ProtectedPlanet.net is the online interface for the WDPA. ProtectedPlanet.net lets you discover the protected areas of the world through exploring the maps and intuitive searching, feeding you information from the WDPA, photos from Panoramio and text descriptions from Wikipedia. 13 In DIVERCROP database, each 2km and 5 arc-minutes pixel was given the attributes of the protected area they intersect (Table 13).

List of tables

List of figures

List of appendices

Figure 1 -

 1 Figure 1 -The three scales and spatial units of DIVERCROP database

Figure 2 -

 2 Figure 2 -Upscaling 300m land cover information to 2km grid

Figure 3 -

 3 Figure 3 -Matching 5 arc-minutes grid and nuts 2 with 5 arc-minutes pixels centroids

  , aggregated to all crops, food and non-food TECHNOLOGIES • all technologies together, ie complete crop • irrigated portion of crop • rainfed high inputs portion of crop • rainfed low inputs portion of crop • rainfed subsistence portion of crop • rainfed portion of crop (= TA -TI, or TH + TL + TS)

Figure 4 -

 4 Figure 4 -Downscaling MAPSPAM information from 5 arc-minute pixels to 2km pixels

Figure 5 -

 5 Figure 5 -Hierarchy of the European Nomenclature of Territorial Units (left) and correspondence between the Territorial Unit levels and data availability, by country (right)

Figure 6 -

 6 Figure 6 -Number of heads of cows in y2 (y3 in DZ and MT).Bio-physical

Figure 1 -

 1 Figure 1 -The three scales and spatial units of DIVERCROP database ....................................................................................... Figure 2 -Upscaling 300m land cover information to 2km grid ................................................................................................. Figure 3 -Matching 5 arc-minutes grid and nuts 2 with 5 arc-minutes pixels centroids ........................................................... Figure 4 -Downscaling MAPSPAM information from 5 arc-minute pixels to 2km pixels........................................................... Figure 5 -Hierarchy of the European Nomenclature of Territorial Units (left) and correspondence between the Territorial Unit levels and data availability, by country (right) ................................................................................................................. Figure 6 -Number of heads of cows in y2 (y3 in DZ and MT). .................................................................................................

Appendix 1 -

 1 List of the national statistics authorities of Divercrop Countries ........................................................................ Appendix 2 -Variables of the Agricultural Censuses Database. Orange background: 4 countries, light green background: countries, dark green background: 6 countries. ......................................................................................................................

Table 1 -Spatial parameters of DIVERCROP multiscale database

 1

		WKID WKID WKID WKID	COORDINATE COORDINATE COORDINATE COORDINATE SYSTEM SYSTEM SYSTEM SYSTEM	PROJECTION PROJECTION PROJECTION PROJECTION	LINEAR LINEAR LINEAR LINEAR UNIT UNIT UNIT UNIT	E E E EXTENT XTENT XTENT XTENT
	2 KM GRID	3035	GCS ETRS 1989	Lambert Equal Area Azimuthal	Meter
	5 ARC-
	MIN GRID
	NUTS 2 SPATIAL	4326	GCS WGS 1984		Degree
	SCALE

Table 2 -Variables of DIVERCROP database and their sources

 2

	VARIABLE VARIABLE VARIABLE VARIABLE	ORIGINAL DATABASE ORIGINAL DATABASE ORIGINAL DATABASE ORIGINAL DATABASE	ORIGINAL ORIGINAL ORIGINAL ORIGINAL DATA LINK DATA LINK DATA LINK DATA LINK
	LAND USE / LAND COVER	ESA/CCI database	https://maps.elie.ucl.ac.be/CCI/viewer/
	CROP PRODUCTION SYSTEMS	MAPSPAM	http://mapspam.info/
	NUMBER OF HOLDINGS, CULTIVATED AREAS & LIVESTOCK	National censuses	See Annex 1 and the section "Construction of the Harmonized Census Database" in page 10.
	LIVESTOCK	Gridded Livestock of the world	http://www.fao.org/ag/againfo/resources/en/glw/ho me.html
	TERRAIN	Global Digital Elevation Model	https://webmap.ornl.gov/ogc/wcsdown.jsp?dg_id=100 03_1
	BIOCLIMATIC	WORLDCLIM	http://worldclim.org/version2
	SOILS TYPES & QUALITY	Harmonized world soil database

http://webarchive.iiasa.ac.at/Research/LUC/External-World-soil-database/HTML/SoilQuality.html?sb=10 POPULATION LANDSCAN https://landscan.ornl.gov/ RURAL & URBAN POPULATION HYDE http://themasites.pbl.nl/tridion/en/themasites/hyde/i ndex.html PROTECTED AREAS World database on protected areas https://protectedplanet.net/

Table 3 -Temporal resolutions on DIVERCROP database

 3

	VARIABLE VARIABLE VARIABLE VARIABLE	2005 2005 2005 2005	2015 2015 2015 2015
	LAND USE / LAND COVER	x	x
	CROP PRODUCTION SYSTEMS	x
	NUMBER OF HOLDINGS,	Various time spans
	CULTIVATED AREAS &	following the available data.
	LIVESTOCK	See National censuses part.
	LIVESTOCK	x
	BIOCLIMATIC	Averages for years 1970 -2000
	POPULATION	x	x

Table 4 -Land use / land cover variables in DIVERCROP database

 4

  Sichra et al, 2016). Table5presents the MAPSPAM variables available in DIVERCROP database.

	statistics (Wood-
		LAND USE/LAND COVER VARIABLES LAND USE/LAND COVER VARIABLES LAND USE/LAND COVER VARIABLES LAND USE/LAND COVER VARIABLES
	•	Cropland rainfed
	•	Cropland rainfed -Herbaceous cover
	•	Cropland rainfed -Tree or shrub cover
	•	Cropland irrigated or post-flooding
	•	Mosaic cropland (>50%) / natural vegetation (tree/shrub/herbaceous cover) (<50%)
	•	Mosaic natural vegetation (tree/shrub/herbaceous cover) (>50%) / cropland (<50%)
	•	Tree cover broadleaved evergreen closed to open (>15%)
	•	Tree cover broadleaved deciduous closed to open (>15%)
	•	Tree cover broadleaved deciduous closed (>40%)
	•	Tree cover broadleaved deciduous open (15-40%)
	•	Tree cover needleleaved evergreen closed to open (>15%)
	•	Tree cover needleleaved evergreen closed (>40%)
	•	Tree cover needleleaved evergreen open (15-40%)
	•	Tree cover needleleaved deciduous closed to open (>15%)
	•	Tree cover needleleaved deciduous closed (>40%)
	•	Tree cover needleleaved deciduous open (15-40%)
	•	Tree cover mixed leaf type (broadleaved and needleleaved)
	•	Mosaic tree and shrub (>50%) / herbaceous cover (<50%)
	•	Mosaic herbaceous cover (>50%) / tree and shrub (<50%)
	•	Shrubland
	•	Shrubland evergreen
	•	Shrubland deciduous
	•	Grassland
	•	Sparse vegetation (tree/shrub/herbaceous cover) (<15%)
	•	Sparse tree (<15%)
	•	Sparse shrub (<15%)
	•	Sparse herbaceous cover (<15%)
	•	Tree cover flooded fresh or brakish water
	•	Tree cover flooded saline water
	•	Shrub or herbaceous cover flooded fresh/saline/brakish water
	•	Urban areas
	•	Bare areas
	•	Consolidated bare areas
	•	Unconsolidated bare areas
	•	Water bodies

Table 5 -MAPSPAM variables available in DIVERCROP database

 5

Table 6 -Livestock variables in DIVERCROP database

 6

  The Harmonized National Agricultural Censuses Database was built on an open source objectrelational database management system (PostgeSQL), and it consists in a product that can be easily integrated into the Comprehensive Database, foreseen as output of WP1. The database 'divercrop_census_db' contains one single table element (dz_es_fr_it_mt_pt_tn_census) exportable from Postgres into the csv format (readable by most worksheets software) using the following SQL string: COPY dz_es_fr_it_mt_pt_tn_census to 'C:\dz_es_fr_it_mt_pt_tn_census.csv' delimiters':' CSV HEADER;

	LIVESTOCK LIVESTOCK LIVESTOCK LIVESTOCK
	• Cattle
	• Goats
	• Sheep
	• Poultry
	• Pigs

Table 7 -Primary key and aggregators of the Agricultural Censuses Database, corresponding to different levels of the hierarchy of administrative units

 7

	Country	country	nuts2	nuts3 (including equivalent Wilaya)	lau1	nuts3_lau2 (LAU 2)	Primary key: join_field (showing number of administrative units)
	DZ-Algeria	X	N.A.	X			48
	ES-Spain	X	X	X		X	8,096
	FR-France	X			X		1,163
	IT-Italy	X	X	X		X	8,092
	MT-Malta	X	N.A.	N.A.		X	68
	PT-Portugal	X	X	X		X	4,077
	TN-Tunisia	X	N.A.	X			24
	Total number of administrative units				21,568
	in bold: names of the aggregator fields in the database
	D D D Da a a ata insertion ta insertion ta insertion ta insertion into the Harmonized Agricultural Census Database into the Harmonized Agricultural Census Database into the Harmonized Agricultural Census Database into the Harmonized Agricultural Census Database

Table 8 -Years for which national agricultural datasets were elaborated. Column headings indicate the year codification in the database (y0 through y4).

 8 , variables are classified according to the number of Countries for which they are available in a given time point. Only variables available in at least 4 Countries are shown, for the complete set of variables, with indications of their availability per country and per year, please see Appendix 2.In the table, values available in 4 Countries are highlighted in orange, those available in 5 Countries are shown with a light-green background, while the background is dark green when variables are available for 6 countries.

		y0	y1	y2	y3	y4
	DZ				2012	2016
	ES			1999	2009
	FR			2000	2010
	IT	1982	1990	2000	2010
	MT			2001	2010
	PT		1989	1999	2009
	TN		1995	2005
	In Table 9

Table 10 -Terrain variables in DIVERCROP database Bioclimatic

 10

	Bioclimatic values stem from the Worldclim database (Fick, Hijmans, 2017), available in both 5 arc-
	minutes and 30 arc-seconds. Worldclim bioclimatic variables are an average for the years 1970-2000.
	Bioclimatic variables are derived from the monthly temperature and rainfall values in order to
	generate more biologically meaningful variables. The bioclimatic variables represent annual trends
	(e.g., mean annual temperature, annual precipitation) seasonality (e.g., annual range in temperature
	and precipitation) and extreme or limiting environmental factors (e.g., temperature of the coldest and
	warmest month, and precipitation of the wet and dry quarters). A quarter is a period of three months
	(1/4 of the year). 7

7 Source : http://worldclim.org

ELEVATION

•

Min.

Table 11 -Bioclimatic variables in DIVERCROP database

 11

Table 12 -Soils variables in DIVERCROP database

 12 Table 12 lists all the soil types and soil quality types available in DIVERCROP database. For extended information about soil quality index elaboration, see Nachtergaele et al, 2009. Population DIVERCROP database integrates two population data for 2005 and 2015 each: LANDSCAN 9 and HYDE models.

	BIOCLIMATIC
	TEMPERATURE (Average for years 1970 to 2000)	PRECIPITATION (Average for years 1970 to
			2000)
	•	Annual Mean Temp.
	•	Mean Diurnal Range (Mean of monthly (max temp -min	•	Annual Precip.
		temp))	•	Precip. of Wettest Month
	•	Isothermality (BIO2/BIO7) (* 100)	•	Precip. of Driest Month
	•	Temp. seasonality (stand. dev. *100)	•	Precip. Seasonality (Coef. of Variation)
	•	Max Temp. of Warmest Month	•	Precip. of Wettest Quarter
	•	Min Temp. of Coldest Month	•	Precip. of Driest Quarter
	•	Temp. Annual Range (BIO5-BIO6)	•	Precip. of Warmest Quarter
	•	Mean Temp. of Wettest Quarter	•	Precip. of Coldest Quarter
	•	Mean Temp. of Driest Quarter
	•	Mean Temp. of Warmest Quarter
	•	Mean Temp. of Coldest Quarter

8 Source : http://webarchive.iiasa.ac.at/Research/LUC/External-World-soil-database/HTML/SoilQuality.html?sb=10

Table 13 -Attributes of the protected areas in DIVERCROP database

 13

	13 Source : https://protectedplanet.net/

Nachtergaele, F.,

van Velthuizen, H., Verelst L. (2008)

. Harmonized World Soil Database Version 1.1 FAO/IIASA/ISRIC/ISS-CAS/JRC (2009). Harmonized World Soil Database (version 1.1). FAO, Rome, Italy and IIASA, Laxenburg, Austria.

Table 1 -

 1 Spatial parameters of DIVERCROP multiscale database .............................................................................................. Table 2 -Variables of DIVERCROP database and their sources .................................................................................................. Table 3 -Temporal resolutions of DIVERCROP database ........................................................................................................... Table 4 -Land use / land cover variables in DIVERCROP database ............................................................................................ Table 5 -MAPSPAM variables available in DIVERCROP database .............................................................................................. Table 6 -Livestock variables in DIVERCROP database ............................................................................................................. Table 7 -Primary key and aggregators of the Agricultural Censuses Database, corresponding to different levels of the hierarchy of administrative units ............................................................................................................................................. Table 8 -Years for which national agricultural datasets were elaborated. Column headings indicate the year codification in the database (y0 through y4)................................................................................................................................................... Table 9 -Variables of the Agricultural Censuses Database available for most Divercrop countries. backgrounds show: countries (orange), 5 countries (light green), 6 countries (dark green). .................................................................................. Table 10 -Terrain variables in DIVERCROP database ............................................................................................................... Table 11 -Bioclimatic variables in DIVERCROP database ......................................................................................................... Table 12 -Soils variables in DIVERCROP database ................................................................................................................... Table 13 -Attributes of the protected areas in DIVERCROP database ....................................................................................

Spatial data on nuts2 borders downloaded on Eurostat website : https://ec.europa.eu/eurostat/web/main/home

Spatial data on administrative borders for non European countries downloaded on http://www.diva-gis.org/

Source: Land Cover CCI PRODUCT USER GUIDE VERSION 2.0 (2017) https://maps.elie.ucl.ac.be/CCI/viewer/download/ESACCI-LC-Ph2-PUGv2_2.0.pdf

Source: http://www.fao.org/ag/againfo/resources/en/glw/home.html

To meet the demand for statistics at a local level, Eurostat maintains a system of Local Administrative Units (LAUs) compatible with NUTS. These LAUs are the building blocks of the NUTS, and comprise the municipalities and communes of the European Union. (http://ec.europa.eu/eurostat/web/nuts/local-administrative-units)

More specifically, the farms are eligible when falling in one of the following independent criteria: Agricultural holdings with at least 1 ha

This product was made utilizing the LandScan (insert dataset year)™ High Resolution global Population Data Set copyrighted by UT-Battelle, LLC, operator of Oak Ridge National Laboratory under Contract No. DE-AC05-00OR22725 with the United States Department of Energy. The United States Government has certain rights in this Data Set. Neither UT-BATTELLE, LLC NOR THE UNITED STATES DEPARTMENT OF ENERGY, NOR ANY OF THEIR EMPLOYEES, MAKES ANY WARRANTY, EXPRESS OR IMPLIED, OR ASSUMES ANY LEGAL LIABILITY OR RESPONSIBILITY FOR THE ACCURACY, COMPLETENESS, OR USEFULNESS OF THE DATA SET.

10 Source : https://landscan.ornl.

gov/ 11 Source : http://themasites.pbl.nl/tridion/en/

themasites/hyde/ 12 UNEP-WCMC and IUCN (2018), Protected Planet: [The World Database on Protected Areas (WDPA)/The Global Database on Protected Areas Management Effectiveness (GD-PAME)] [On-line], [01/2018], Cambridge, UK: UNEP-WCMC and IUCN. Available at: www.protectedplanet.net.

total area under fruit plantation, olive, vineyards olive, non irrigated X a_olive_y3 olive X X X X X X a_fruit_olive_vine_y3 total area under fruit plantation, olive, vineyards X X X X X a_fruit_palm_y3 fruit palms X a_permcrops_y3 permanent crops X X X a_grape_s_y3 grapes, non irrigated X a_grape_r_y3 grapes, irrigated X X a_grape_y3 grapes X X X a_viney_r_y3 vineyards, irrigated X X X a_viney_y3 vineyards X X X X X X a_uaa_y3 UAA X X X X X X a_meadow_y3 meadows X X X X a_uaa_r_y3 UAA, irrigated X X a_taa_y3 TAA X X X X a_kitchgard_y3 kitchen gardens X Year 4 = 2016 (DZ)


Hajar Ghannam

Cyrille Bazin

Adil Chahboun

Mireille Turmine

Control of the growth electrodeposited Zinc oxide onto FTO glass

In this work, zinc oxide (ZnO) was directly electrodeposited onto Fluorine-doped tin oxide (FTO). The physical and chemical heterogeneity of FTO have contributed to important and exploitable results. In fact, the doping of tin dioxide (SnO2) with fluorine distorts its rutile crystal lattice. This distortion leads to contraction mechanical constraints on the network contributing to a rough surface morphology. Moreover, the nano-roughness of FTO surface impedes the epitaxial growth of ZnO nanorods. However, this roughness combined with the chemical heterogeneity of the FTO surface leads to favored growth sites. This can be explained by the presence of fluorine atoms, into the SnO2 network, with a stronger electronegativity which attracts the ZnO nuclei. The accumulation of many nuclei around the same electronegative spot contributes to the development of nanoflower-like structures or tilted nanorods. In such case, the non-polar facets of ZnO are exposed and the electrodeposited film is highly hydrophobic. The growth of ZnO onto FTO was studied by varying four main parameters of the synthesis: time of electrodeposition, temperature, concentration of Zn 2+ precursor and concentration of KCl. The influence of those parameters on the shape, size, growth mechanism, and density of the ZnO electrodeposit was discussed. Thus, when the concentration of Zn 2+ precursor increases from 0.1 to 5 mM, the nanorods' size decreases but the density of these nanostructure increases leading to their organization in nanoflowers. However, the increase of KCl concentration from 0.5 to 4 M causes a change of the ZnO nanostructures shape from hexagonal nanorod arrays to nanopencil arrays and also a significant decrease of density accompanied with a significant increase of ZnO nanostructures size whose diameter varies from 90 to 200 nm. Furthermore, the time of electrodeposition is a key parameter influencing the height of ZnO nanorods. Finally, a good crystallization of ZnO is observed at high temperature (about 80°C).

Introduction

Zinc oxide (ZnO) is a low cost and non-toxic material. It has a large band gap (3.3 eV) absorbing UV rays hence its application as an anti-UV coating 1 . It is used, also, in the absorber layer for photovoltaic applications 2 . The structural property of ZnO thin films has a major influence on the observed change of its electrical, optical, photocatalytic and wettability properties.

3-12

These properties can be improved or weakened according to the nanostructures size. In fact, when the nano-roughness of ZnO film decreases, conductivity and photocatalytic activity decrease, on the other hand the transmittance increases. 13, 14 Moreover, the nanostructures influence the wettability behavior of a surface, playing an amplifier role. A hydrophobic surface becomes super-hydrophobic when nano-roughness increases and vice versa. [START_REF] Ghannam | International Renewable and Sustainable Energy Conference (IRSEC)[END_REF] Therefore, the study of ZnO nanostructures is very important, since many properties depend on their shape and/or size. ZnO nanostructures can be grown in forms of nanoplates, nanoflowers, nanorods, nanotubes [START_REF] Kıcır | [END_REF] , etc. ZnO nanorod is one of the more interesting form for photovoltaic application thanks to the large surface area. 17 The geometric features of ZnO nanorods depend on synthesis technique and deposition condition. Electrochemical technique is the most efficient method for tuning structural properties.

18

Several studies were carried out in aim to control the growth of ZnO using electrochemical method by varying the concentration and nature of supporting electrolyte, or the concentration of the precursors (Zn 2+ and OH -). Skompska et al. 19 have presented an extensive review about electrodeposition of ZnO nanorod arrays on transparent conducting substrates. Tena-Zaera et al. 20 have studied the effect of supporting electrolyte concentration (potassium chloride) on the ZnO nanostructure electrodeposited onto Fluorine-doped Tin Oxide (FTO) first covered by a seed layer of ZnO nanocrystallite. They showed that the increase of KCl concentration causes a rise of ZnO deposition efficiency. Furthermore, for a high concentration of KCl (>1M) a lateral growth of ZnO is favored. Elias et al. 21 have investigated the effect of the anion nature of the supporting electrolyte on the features of the ZnO nanostructures, showing that the change of anion nature leads to a difference in adsorption behavior onto ZnO nanostructures, causing a variation on OHproduction rate and therefore a modification of ZnO nanostructure sizes. In fact, several studies have been focused on the effect of the concentration of Zn 2+ precursor on ZnO nanostructure growth, finding that the concentration of Zn 2+ precursor is the major parameter influencing the ZnO geometric dimensions; the size of ZnO nanostructures increases when concentration of Zn 2+ precursor rises. 22 Other parameters, as time and temperature of the electrodeposition, were studied.

23, 24

In this work, we have explored the effect of electrosynthesis parameters on the ZnO growth on FTO. Contrarily to the majority of the published studies, we made the choice to electrodeposit the ZnO thin layer directly on FTO surface in order to simplify the operating mode and to benefit from the surface nature of the FTO. Most often, only one characteristic Please do not adjust margins Please do not adjust margins is sought such as maximum of hydrophobicity 25 and few studies have considered the variations of the experimental conditions on the morphology of ZnO nanostructures directly deposited on the FTO. Xu et al. 26 have examined the effect of some additives in order to control the ZnO nanostructures growth and especially they sought the better conditions to obtain compact epitaxial growth of ZnO nanostructures. In our case, we provided four main parameters: the concentration of Zn 2+ precursor, concentration of supporting electrolyte KCl, time and temperature of electrodeposition. The variations of these key parameters modified the morphology of ZnO nanostructures and the growth mechanism.

ARTICLE

Experimental

ZnO was successfully electrochemically deposited by using three-electrode classical cell. FTO glass (from Solems, 80 nm thickness), a saturated calomel electrode (SCE) and a Zinc rod was used as working electrode (WE), reference electrode (RE) and counter electrode (CE), respectively. FTO was cleaned prior any use with acetone and ethanol, for 10 min each, in an ultrasonic bath, afterwards it was rinsed with distilled water.

To deposit ZnO, the three electrodes (CE, WE and RE) were vertically immersed in an aqueous electrolyte containing ZnCl 2 (MERCK, purity>95%) as a source of Zn 2+ , gaseous dioxygen (O 2 ) as OH -precursor and KCl (VWR International, purity 99.0-100.5%) was used as supporting electrolyte to increase the electrical conductivity of the solution. The temperature of the solution was kept constant using a cryostat bath.

The electrodeposition of ZnO is generally realized by an elevation of the local pH, due to the reduction of the oxygen in order to provide hydroxides around the surface. Then, the formation of metal oxide is made by chemical reaction between the native hydroxides (OH -) and the metal cations present in the solution, according to the following reactions under static-potential (E = -1V/SCE):

O 2 + 4e + 2 H 2 O → 4 OH - Zn 2+ + 2 OH -→ ZnO + H 2 O
This can be summed up as

Zn 2+ + ½ O 2 + 2e → ZnO
The quantity of ZnO deposited on the cathode was estimated from the total electric charge, Q (in Coulombs). The latter was calculated from the integration of the chronoamperometric curve (Fig. 1). From the Faraday's law, one can write

݉ = ொ×ெ ௭×ி (eq.1)
In which, m is the mass of ZnO electrodeposited on the WE (FTO), F (= 96500 C mol -1 ) is the Faraday constant, z is the number of transferred electrons per ion which is equal to 2, and M is the molar mass of ZnO (81.38 g mol -1 ).

The morphology of the electrosynthesized ZnO thin films was analyzed by field emission scanning electron microscope (SEM-FEG) ULTRA 55 ZEISS. The crystal structure of ZnO thin films was investigated by Empyrean Panalytical X-ray diffraction (XRD).

Results and discussion

To study the ZnO growth on FTO substrate, four main parameters (concentration of Zinc precursor, of the supporting electrolyte KCl, time and temperature of electrodeposition) were changed during the ZnO electrosynthesis.

Effect of ZnCl 2 concentration on ZnO nanorods growth onto FTO

Several aqueous solutions containing the same KCl concentration (0.1 M) but different concentrations of ZnCl 2 (between 0.1 to 5 mM) were prepared. All these solutions were saturated with oxygen by bubbling. The electrodeposition of ZnO on FTO was performed at 80°C during 1000s at -1 V/SCE. The SEM pictures (Fig. 2) show that at low ZnCl 2 concentration (< 0.5 mM), the nanorods of ZnO are gathered in 2-3 nanorods. For high concentrations of ZnCl 2 (> 0.5 mM), the number of amassed nanorods increases leading to a nanoflower shape. Therefore, the density of nanocolumns increases when the concentration of ZnCl 2 increases. The polarity of underlying surface plays a main role on the apparition regions of ZnO nanostructures, as illustrated by Lee et al. 27 and Perillat-Merceroz et al. 28 who generated a selective growth of ZnO by controlling the surface polarity. In our case, this underlying surface polarity can explain the observed ZnO growth. The difference in electronegativity between FTO atoms generates some more welcoming regions for ZnO deposition.

Hence, due to its polarity, the first nucleus of ZnO choose fluorine atom to be linked to the surface through its Zn-polar facet. The number of nuclei linked to the surface depends on the zinc precursor concentration; a high concentration of Zn precursor gives rise to a high density of ZnO nuclei around the same region contributing after growth process to nanoflower form (Fig. 3). Please do not adjust margins

ARTICLE

Please do not adjust margins

Contrariwise the density, the height and width of ZnO nanorods decrease when the concentration of ZnCl 2 increases. Thus, when the ZnCl 2 concentration increases from 0.1 to 5mM, we found a diminution in height and width (Fig. 4). This reduction in size of ZnO nanorods is accompanied by mass loss (Table 1). The mass of ZnO deposited on FTO increases when concentration of ZnCl 2 varies from 0.1 to 1 mM and decreases for concentration of ZnCl 2 higher than 1mM (Table 1). This reduction of mass and size can be justified by the inequality between diffusion coefficient of molecular oxygen and Zn 2+ . 29,30 The diffusion coefficient of molecular oxygen is more important than Zn 2+ what makes the production of OH -ions faster than the transport of Zn 2+ . The OH -ions produced at the electrode diffuse towards the bulk solution through the ZnO nanostructures and adsorb on nanorod tips. The adsorption phenomenon of OH - ions weakens the polarity of ZnO and therefore its growth. Moreover, we have to notice that our results are different from those presented by Elias et al. 22 This is mainly related to the surface polarity. The growth of ZnO on the rough FTO surface probably begins by the appearance of nuclei of ZnO on the anfractuosities due to the surface roughness. Those first nuclei adopt the same inclination angle than FTO nano-roughness hence the appearance of inclined nanorods of ZnO. Ichinose et al. 31 had discussed the influence of surface roughness on the ZnO orientation. Moreover, ZnO growth along c axis is mainly related to its crystal structure. The Wurtzite crystal structure Please do not adjust margins Please do not adjust margins Please do not adjust margins

Please do not adjust margins

The stability of the structure is then enhanced by binding the latter with the Zn-polar plane (0001 ത ) of new ZnO mesh. As a function of time, stability / instability process of ZnO polar facets contributes to a repetition of ZnO bonding along c axis at the atomic-scale leading to long nanorods at nanoscale (Fig. 7). The growth rate of ZnO is mainly related to the plane polarity of growth direction. In fact, the deposition speed of ZnO oriented along Zn-polar plane is 1.5 times higher than ZnO oriented along O-polar plane. [32][33][34][35] The growth rate of ZnO in our case is relatively slow due to the O-polar plane (0001) which is responsible for growth along c axis.

Effect of KCl concentration on ZnO nanorods growth on FTO

The effect of KCl concentration was carried out at constant temperature (80°C), time (1000s) and potential (-1V/SCE) of electrodeposition. The concentration of Zn 2+ precursor is kept constant for all samples (2.5mM) and the KCl concentration varied from 0.1 to 0.5M in the electrolyte solution saturated with oxygen. SEM images (Fig. 8) and estimated mass results (Table 3) show that the concentration of the electrolyte support, KCl, plays an inhibitor role on the ZnO electrodeposition when present in excess in the solution. The mass of ZnO electrodeposited on FTO and the density of ZnO nanorods decrease for a concentration of KCl higher than 0.5M. Besides, the size of ZnO nanorods shows a significant growth at the level of the width and the height keeping the preferential direction of the growth according to c axis (Fig. 9). On the one hand, the presence of KCl excess prevents ZnO nuclei from binding to the surface. This excess contributes to adsorption of K + and Cl -ions on the surface forming a barrier which impedes ZnO from depositing and weakens the polarity of the surface. Also, the adsorption of Cl -and K + on the surface causes a decrease of OH around the surface contributing to a decrease of ZnO deposition rate. However, the first ZnO nuclei which appear near the surface bind to it and form a very attractive center for ZnO deposit (Fig. 10). Effect of KCl concentration on ZnO growth on FTO covered by nanocrystalline ZnO buffer layer has been reported by Tena-Zaera et al. 20 finding that the increase of KCl concentration mainly enhanced the longitudinal and lateral growth for a concentration of KCl higher than 1M. This result is also observed in our case. Tena-Zaera et al. 20 found also that an increase of KCl concentration causes an increase of ZnO deposition efficiency. This result is observed for a concentration lower than 0.5M. But high concentrations of KCl (>0.5M) cause a decrease of ZnO deposition efficiency.

Structurally, two main forms of ZnO nanorods can be distinguished according to the KCl concentration. The first one is ZnO nanorod arrays observed for a KCl concentration lower than 0.5M. For this form, ZnO nanorods retain the hexagonal form of ZnO with a flat end. The second one is ZnO nanopencil arrays observed for a KCl concentration higher than 0.5M. This form is generated by adsorption of K + ions on the O-polar plane of ZnO which weakens Please do not adjust margins Please do not adjust margins polarity of the plane (001) and prevents the natural growth of ZnO.

The adsorption effect is more important on the edges than on the center of ZnO nanorods. Xu et al. 26 reported the effect of the adsorption of compounds other than KCl on the apparition of nanopencil arrays of ZnO.

Temperature effect on ZnO nanorods growth on FTO

From the same electrolyte solution saturated with oxygen made of ZnCl 2 (2.5mM) and KCl (0.1M), a series of sample was synthetized at the same potential (-1V/ SCE) and time (1000s) of electrodeposition but at various temperatures in the range of 30 to 80°C. The temperature plays an important role on the ZnO formation. A precipitation of zinc hydroxide is promoted after oxygen reduction. The zinc hydroxide is thermodynamically unstable, a high temperature promotes their dehydration and therefore the ZnO formation.

The mass of ZnO electrodeposited on FTO surface increases with the temperature (Table 4). The rise in temperature leads also to an increase of ion mobility which promotes the ZnO deposition. The SEM images (Fig. 11) show the temperature effect on ZnO growth on FTO. As a function of temperature, the thin film of ZnO changes its appearance from a smooth thin film to nanostructure thin film, through randomly structures. For temperatures between 40 to 50°C, ZnO growths on the surface as small grains having a rough wall. For temperature between 50 to 70°C, ZnO nanorods begin to appear, but with a rough wall. For temperature equal to 80°C, the hexagonal nanorods of ZnO grows with smooth walls and marked crystalline plans. Please do not adjust margins Please do not adjust margins Moreover, the peaks at 2θ values of 27° and 52° correspond to FTO crystal structure (Fig. 12). These results agreed with several works which have studied the temperature effect on crystallization of material 23, 24 finding that high temperatures give rise to a good crystallization of the material. Moreover, in our case, we can notice that the intensities of (100) and (101) planes are higher than this of (002). This is related to the surface roughness and especially to the inclination of ZnO nanorods revealing the most hydrophobic planes as mentioned by Chang et al.

Conclusion

The electrodeposition in one step of ZnO nanorods on FTO is carried out under different conditions. The effect of four main parameters (concentration of Zn Moreover, the temperature is an important parameter regarding the transfer of matter in the electrolyte and the appearance of ZnO nanorods with accented crystalline planes (Fig. 13 (c)). Finally, the concentration of KCl influences the nanorods density. A high concentration gives rise to a low density of nanorods. However, a high temperature increases the conductivity of the solution and promotes the dehydration of zinc hydroxide guaranteeing the electrochemical deposition of ZnO (Fig. 13 (d)).

Conficts of interest

There are no conflicts to declare.

Journal Name 2 |

 2 J. Name., 2018, 00, 1-3 This journal is © The Royal Society of Chemistry 20xx

Fig. 1

 1 Fig.1The chronoamperograms for the preparation of ZnO nanostructure on FTO at -1.0 V/SCE during 7000s in a O2 saturated aqueous medium of 2.5 mM ZnCl2 and 0.1 M KCl.

Fig. 2

 2 Fig. 2 SEM images of ZnO nanorods (plan and cross-sectional views) obtained in different ZnCl2 concentrations, at 80°C, during 1000 s, at E=-1V/SCE, in 0.1M KCl.

2+

Journal Name 4 |

 4 J. Name., 2018, 00, 1-3 This journal is © The Royal Society of Chemistry 20xx

Fig. 4

 4 Fig.4 Mean values of nanorod dimensions as a function of ZnCl2 concentration, black squares for the width (W) and blue circles for the height (H).

Fig. 3 5

 35 Fig. 3 Schematic illustration of ZnO growth mechanism for different concentrations of ZnCl2.

Fig. 5

 5 Fig. 5 SEM images of ZnO nanorods (plan and cross-sectional views) for different electrodeposition times, at 80°C, [ZnCl2]=2.5mM, [KCl]=0.1M and E=-1V/SCE

Fig. 6

 6 Fig. 6 Mean values of nanorod dimensions as a function of time, black squares for the width (W) and blue circles for the height (H).

Fig. 7 7

 77 Fig. 7 Schematic illustration of ZnO growth mechanism for different synthesis times.

Fig. 8 8 |

 88 Fig. 8 SEM images of ZnO nanorods (plan and cross-sectional view) for different KCl concentrations. T=80°C, [ZnCl2]=2.5mM, time=1000s and E=-1V/SCE.

Fig. 9

 9 Fig. 9 Mean values of nanorod dimensions as a function of KCl concentration, black square for the width (W) and blue circles for the height (H).

Fig. 10

 10 Fig. 10 Schematic illustration of ZnO growth mechanism for different concentrations of KCl.

Fig. 11

 11 Fig. 11 SEM images of ZnO nanorods (plan and cross-sectional view) for different temperatures. Time=1000s, [ZnCl2]=2.5mM, [KCl]=0.1M and E=-1V/SCE.

36

 36

Fig. 12

 12 Fig. 12 XRD patterns of ZnO synthesized at 40, 60, and 80°C

Table 1

 1 Elias et al. have studied the effect of concentration of Zn 2+ precursor on ZnO growth on FTO first covered by a buffer layer of ZnO finding that the size of ZnO nanorods increases with the concentration of Zn 2+ precursor in the solution. The coating of FTO surface by smooth ZnO layer before depositing the ZnO nanorods leads to a surface polarity equilibrium forming a homogeneous and well-dispersed deposit of ZnO nanorods. Estimated values of mass for electrodeposited ZnO nanorods according to the concentration of zinc precursor.

	[ZnCl2] /(mM)	Charge density (/C cm -2 )	m(ZnO) (/µg)
	0.1	0.136	57
	0.5	0.197	83
	1	0.199	84
	2	0.165	70
	3	0.160	67
	4	0.150	63
	5	0.145	61

3.

2 Time effect on ZnO nanorods growth on FTO Several

  ZnO samples were prepared from solution of 0.1M KCl and 2.5mM ZnCl 2 saturated with oxygen by bubbling. The electrodeposition of ZnO is realized at 80°C, constant potential (-1V/ SCE) and for an electrodeposition time varying between 0 and 10000s. Fig. 5 depicts some SEM images of different ZnO growths on FTO surface as a function of the electrodeposition time. The dimension values of ZnO nanorods estimated from SEM images are gathered in Fig. 6. As shown on Fig. 5, the density of ZnO nanorods increases until time of 2000s. While, the density remains almost constant (≈ 20 nanorods/µm

	Time (/s)	Charge density (/C cm -2 )	m (ZnO) (/µg)
	500	0.048	20
	1000	0.1447	61
	2000	0.181	76
	3000	0.273	115
	4000	0.379	160
	5000	0.479	202
	6000	0.600	253
	7000	0.700	295
	8000	0.825	348
	9000	0.943	398
	10000	1.070	451

2

) for a deposition time higher than 2000s. If we focused on the nanostructures size, we can notice that the width increases in the first moment and then remains constant (around of 78nm) from an electrodeposition time of 3000s. Regarding the height variation of ZnO nanorods, a monotonous increase over the time was observed, it goes from 300nm for 500s to 1100nm for 10000s. Moreover, as seen on Table

2

, the mass of electrodeposited ZnO on FTO increases as a function of time.

Table 2

 2 Estimated values of mass of electrodeposited ZnO nanorods as a function of time

Table 3

 3 Estimated values of mass for ZnO nanorods electrodeposited for different support electrolyte concentrations.

	[KCl] (/M)	Charge density (/C cm -2 )	m(ZnO) (/µg)
	0.1	0.144	61
	0.5	0.154	65
	1	0.138	58
	2	0.130	55
	3	0.126	53
	4	0.097	41

Table 4

 4 Estimated values of mass for ZnO nanorods electrodeposited at various temperatures

	T (/°C)	Charge density (/C cm -2 )	m(ZnO) (/µg)
	30	0.032	13
	40	0.106	45
	50	0.137	58
	60	0.138	58
	70	0.181	76
	80	0.204	86

Published on 12 September 2018. Downloaded by Sorbonne Université on 9/12/2018 3:17:03 PM. View Article Online

Published on 12 September 2018. Downloaded by Sorbonne Université on 9/12/2018 3:17:03 PM. View Article Online DOI: 10.1039/C8CE01223G

Acknowledgements

The authors thank the support of the Erasmus+ programme of the European Union, especially for the funding of H.G.