Antonin Pépin

Marie Trydeman Knudsen

Kevin Morel

Philippe Jeanneret

Hayo M G Van Der Werf

Effect of farm type and functional unit on environmental impacts of organic vegetable farms

Keywords: life cycle assessment, agroecology, horticulture, organic farming, biodiversity, farmingsystem LCA

French organic vegetable farms are diverse, ranging from complex biodiversity-based systems with many vegetables to simple input-based systems with few vegetables, which suggests that their impacts on the environment may differ. We used life cycle assessment (LCA) to assess impacts of three contrasting farms: a microfarm (MF, high crop diversity and a low input level), a medium-sized farm specialised in sheltered production (SP, low crop diversity and a high input level), and a large farm specialised in open-field production (OP, intermediate input level and crop diversity). To manage the complexity of organic vegetable farms, we opted for a system LCA, based on farm inputs and output (i.e. « vegetables ») for a one-year period. Using functional units based on mass of output, area, and economic value, we analysed five impacts: climate change, cumulative energy demand, marine eutrophication, on-farm biodiversity, and the use of plastic. Farming-system LCA assessed environmental impacts of farms with different levels of agroecology, including complex systems with a large diversity of crops grown on small areas. The three functional units strongly influenced the ranking of the systems. Per ha, the systems differed greatly in climate change and energy demand: SP had the highest impacts, whereas OP had the lowest impacts. Per kg and per €, the systems differed much less in climate change and energy demand, and even ranked differently. OP used much less plastic but performed worse on biodiversity and yield. Despite its higher yield, SP performed no better than the other two farms for climate change, energy demand, and plastic use per kg and €. The impact on biodiversity contrasted with the other impacts, which highlighted the importance of semi-natural habitats. Quantification of plastic use echoed growing concerns about (micro-)plastic pollution in agricultural soils and landscapes, and the newly identified planetary boundary on novel entities.

Estimating nitrate leaching was difficult, and the Intergovernmental Panel on Climate Change (IPCC) Tier 1 model used to do so seemed unsatisfactory; thus, estimated marine eutrophication impacts had high uncertainty. Crop residues contributed greatly to marine eutrophication. In this perspective, models for estimating crop residues and nitrate leaching in a farming-system LCA approach need to be improved. In agroecological systems, semi-natural habitats are part of the farming system. The farming-system LCA approach requires clear rules for setting farm boundaries, which strongly influence impacts per ha and biodiversity impacts.

Introduction

French organic vegetable farms are diverse, ranging from complex biodiversity-based systems, with many vegetables, to simple input-based systems, with few vegetables. Beyond this conceptual dichotomy, [START_REF] Pépin | Conventionalised vs. agroecological practices on organic vegetable farms: Investigating the influence of farm structure in a bifurcation perspective[END_REF] described four types: microfarmers (high crop diversity and a low level of inputs), medium-sized market gardeners (high crop diversity and variable level of inputs), producers specialised in cultivation under shelter (low crop diversity and a high level of inputs), and large market gardeners specialised in open-field cultivation (low crop diversity and moderate input use). The heterogeneity of input use and farming practices, which can be interpreted as sign of bifurcation between "agroecological" and "conventionalised" organic farming [START_REF] Pépin | Conventionalised vs. agroecological practices on organic vegetable farms: Investigating the influence of farm structure in a bifurcation perspective[END_REF], may influence environmental impacts, but these have yet to be quantified. Organic farming claims to be environmentally sound, and conventionalisation of organic practices is seen as a threat to the identity of organic farming [START_REF] Darnhofer | Conventionalisation of organic farming practices: from structural criteria towards an assessment based on organic principles[END_REF]. Quantifying environmental impacts of different types of organic farms will inform the debate about the conventionalisation of organic farming.

Life cycle assessment (LCA) has been used to assess environmental impacts of agricultural products and systems for several years. LCA studies of vegetable production have been conducted at the crop scale for open-field (e.g. [START_REF] Abeliotis | Life cycle assessment of bean production in the Prespa National Park, Greece[END_REF] and sheltered production (e.g. [START_REF] Cellura | Life Cycle Assessment (LCA) of protected crops: an Italian case study[END_REF] and at the farm scale [START_REF] Adewale | Agricultural carbon footprint is farm specific: Case study of two organic farms[END_REF][START_REF] Markussen | Evaluating the Sustainability of a Small-Scale Low-Input Organic Vegetable Supply System in the United Kingdom[END_REF], for both conventional and organic farming, including studies that compared them [START_REF] Foteinis | Life cycle assessment of organic versus conventional agriculture. A case study of lettuce cultivation in Greece[END_REF] LCA is a method that quantifies a variety of potential environmental and health impacts and resource depletion issues that are associated with goods or services (i.e. multi-criteria assessment). The environmental impacts relevant to agricultural production include climate change, eutrophication, biodiversity decline, and energy demand, among others. The first three impacts correspond to planetary boundaries that have already been exceeded [START_REF] Rockström | Planetary Boundaries: Exploring the Safe Operating Space for Humanity[END_REF][START_REF] Steffen | Planetary boundaries: Guiding human development on a changing planet[END_REF]. Agriculture contributes greatly to climate change (IPCC, 2019a). The runoff and leaching of nitrogen (N) from agricultural soils are the main cause of marine eutrophication (Le [START_REF] Moal | Eutrophication: A new wine in an old bottle?[END_REF]. Biodiversity is rarely considered in LCA studies [START_REF] Knudsen | The importance of including soil carbon changes, ecotoxicity and biodiversity impacts in environmental life cycle assessments of organic and conventional milk in Western Europe[END_REF], although it is a key agri-environmental indicator [START_REF] Haas | Life Cycle Assessment Framework in Agriculture on the Farm Level[END_REF] and is high on the political agenda (United Nations Environment Programme, 2021a). Organic farming claims to enhance biodiversity (European Commission, 2007) and rely more on natural regulation, which makes biodiversity an important issue. Current methods for assessing impacts on biodiversity, reviewed by [START_REF] Curran | How Well Does LCA Model Land Use Impacts on Biodiversity?-A Comparison with Approaches from Ecology and Conservation[END_REF], include global approaches [START_REF] Chaudhary | Quantifying Land Use Impacts on Biodiversity: Combining Species-Area Models and Vulnerability Indicators[END_REF][START_REF] Chaudhary | Land Use Intensity-Specific Global Characterization Factors to Assess Product Biodiversity Footprints[END_REF][START_REF] Knudsen | Characterization factors for land use impacts on biodiversity in life cycle assessment based on direct measures of plant species richness in European farmland in the "Temperate Broadleaf and Mixed Forest" biome[END_REF], more detailed approaches [START_REF] Jeanneret | An expert system for integrating biodiversity into agricultural life-cycle assessment[END_REF], and attempts to combine both in a case study [START_REF] Bystricky | Potenzielle Umweltfolgen einer Umsetzung der Trinkwasserinitiative Potential Environmental Consequences of Implementing the Drinking-Water Initiative[END_REF].

Plastic pollution is an emerging concern worldwide, with for example a ban on certain single-use objects in the European Union (European Commission, 2019). The use of plastic in agriculture and the accumulation of microplastics in agricultural soil has been highlighted (United Nations Environment Programme, 2021b). Vegetable crop production, including in organic farming, is a major user of plastic, particularly as mulch and tunnels for several purposes (e.g. earlier production, higher yield, weed control, cleaner vegetables) [START_REF] Lamont | 3 -Plastic Mulches for the Production of Vegetable Crops[END_REF][START_REF] Lamont | Plastics: Modifying the Microclimate for the Production of Vegetable Crops[END_REF], which is a threat to long-term soil quality [START_REF] Steinmetz | Plastic mulching in agriculture. Trading short-term agronomic benefits for long-term soil degradation?[END_REF]. Massive use of plastic, especially in organic farming, has caused controversy [START_REF] Held | Organic Farming Has A Plastic Problem. One Solution Is Controversial[END_REF].

Plastic pollution contributes greatly to exceeding the planetary boundary for novel entities (i.e. "novel in a geological sense and that could have large-scale impacts that threaten the integrity of Earth system processes") [START_REF] Persson | Outside the Safe Operating Space of the Planetary Boundary for Novel Entities[END_REF]. However, there is no ready-made indicator in current LCA methods to assess plastic or microplastic pollution. Organic vegetable farms can be complex systems that combine a wide range of crops, intercrops, and non-crop biodiversity to help maintain the farming system's health and resistance to disturbance [START_REF] Morel | A conceptual framework for alternative farmers' strategic choices: the case of French organic market gardening microfarms[END_REF]. Within a field, farmers may grow several vegetables at the same time and that have different growing durations, which makes the spatial and temporal organization of crops complex [START_REF] Aubry | Les circuits courts en agriculture revisitent-ils l'organisation du travail dans l'exploitation ?[END_REF], with no clearly defined crop rotation [START_REF] Morel | A conceptual framework for alternative farmers' strategic choices: the case of French organic market gardening microfarms[END_REF]. A crop that often has high value, such as tomato, may be preceded and/or followed by several other crops on the same part of the field.

Generally, fertilisers are applied to feed the soil [START_REF] Fortier | Le jardinier maraîcher -Manuel d'agriculture biologique sur petite surface[END_REF] rather than directly to the crops.

Organic fertiliser such as compost may not be applied every year but instead every 2-4 years, which complicates the ability to allocate organic fertilisers to a given crop. Moreover, due to complex soil dynamics, allocating carbon dioxide (CO2) and nitrous oxide (N2O) emissions as well as nitrate (NO3) leaching to a given crop is challenging [START_REF] Goglio | Addressing crop interactions within cropping systems in LCA[END_REF]. Another challenge is that farmers often do not know the yield of each crop, as for many vegetables, small quantities can be harvested regularly over time. These challenges increase uncertainty in the allocation of inputs and estimates of outputs.

Given these challenges related to the complexity of organic vegetable farms, it seems relevant to assess their environmental impacts using a farming-system LCA rather than a product LCA, which focuses on individual crops. Farming-system LCA approaches the farm as a whole that produces different products, which helps assess and compare farms and understand mechanisms that influence environmental impacts [START_REF] Goglio | Addressing crop interactions within cropping systems in LCA[END_REF]. In this perspective, all inputs and operations are estimated for the entire farm, and the output is the total production of vegetables.

The objectives of this study were to 1) assess environmental impacts of contrasting organic vegetable farms, 2) describe strengths and weaknesses of farming-system LCA, and 3) identify needs for research to better assess impacts of complex organic vegetable farms. To reach these objectives, we performed an LCA of three French organic vegetable farms, each being a specific case corresponding to a more general farm type, using farming-system LCA. Using functional units (FUs) based on mass of vegetables, area, and economic value, we analysed the climate change impact, cumulative energy demand, marine eutrophication impact, impact on biodiversity, and the use of plastic.

Materials and methods

Description of the farms

We chose three farms that participated in the survey of [START_REF] Pépin | Conventionalised vs. agroecological practices on organic vegetable farms: Investigating the influence of farm structure in a bifurcation perspective[END_REF] used to characterize the diversity of French organic vegetable farms. We chose farms that were among the most typical of their types. Because these farms were specific cases with individual characteristics, however, they should not be considered as completely representative. The farms were 1) a microfarm (MF, high crop diversity and a low input level), 2) a medium-sized farm specialised in sheltered production (SP, low crop diversity and a high input level), and 3) a large farm specialised in open-field production (OP, intermediate input level and crop diversity) (Table 1). MF was a recently established microfarm in the Brittany region that produced 35 types of vegetables (Fig. 1) in a 1200 m² tunnel and a 1600 m² open field (Fig. 2). The farmer was inspired by a French farming trend called "market gardening on living soil" (maraîchage sur sol vivant) that aims to protect and feed the soil -and its living organisms such as earthworms, bacteria and mycelia -by combining no-tillage and permanent cover of organic mulch and plants. Fertilisation consisted of compost of green waste, manure, and manure pellets to achieve long-term fertility and avoid short-term NO3 deficiency due to microbial activity. Reusable insect-proof netting was used to decrease insect problems, and copper sulphate was used against blight on tomatoes (the only crop that received a treatment). The selling outlets were vegetable boxes, a local market, shops, and restaurants in the nearby village.

SP, in the Provence-Alpes-Côte-d'Azur region, produced mainly tomato and cucumber in summer and lettuce in winter (Fig. 1), in 33 tunnels, for a total cultivated area of 19 840 m² ( Fig. 3). Sorghum cover crops were grown for 1-2 months each year in ca. 25% of the tunnels to add fresh biomass to the soil. Fertilisation consisted of industrial manure pellets and beet vinasse applied before each crop. Single-use plastic mulch was used against weeds. Purchased insects were released in the tunnels to control pests (Macrolophus spp., Chrysopa spp.) and for pollination (bumblebees).

The farmer sold the vegetables to wholesalers in France and Germany under the biodynamic label.

OP produced vegetables on 24 ha of open fields in Brittany (Fig. 4) following a four-year rotation: potato / rye followed by turnip / cabbages (cauliflower, green cabbage, savoy cabbage, Brussels sprouts, kale) / various vegetables (e.g. carrots, onions, squash) (Fig. 1). Fertilisation consisted of cow and poultry manure applied three out of every four years. Weeding was mechanical and, in carrot crops, thermal (natural gas). Reusable insect-proof netting was used to decrease insect problems on some vegetables, but overall pest and disease control was limited. The farmer sold the vegetables locally to organic stores and wholesalers, and at local markets.

Goal and scope definition

The aim of this study was to compare environmental impacts of three organic vegetable farms, each of which was a specific case that represented a more general farm type [START_REF] Pépin | Conventionalised vs. agroecological practices on organic vegetable farms: Investigating the influence of farm structure in a bifurcation perspective[END_REF]. We analysed the farms as a whole in a farming-system LCA: we considered the total annual production of vegetables and the total inputs, without specifying which input was used for which crop.

The impact categories considered were climate change (CC) with a 100-yr temporal horizon, marine eutrophication (ME), cumulative energy demand (CED), and on-farm biodiversity; use of plastic was also assessed. The impact assessment method used for CC and ME was ReCiPe 2016 (Huijbregts et al., 2016). We calculated CED following [START_REF] Frischknecht | Cumulative energy demand in LCA: the energy harvested approach[END_REF]. Biodiversity was assessed by adapting SALCA-BD [START_REF] Jeanneret | An expert system for integrating biodiversity into agricultural life-cycle assessment[END_REF] to vegetable production. SALCA-BD assesses potential impacts on terrestrial biodiversity of 11 indicator-species groups of land-use types (including semi-natural habitats) and management practices. Field-scale impact scores were aggregated at the farm scale.

For biodiversity scores, the contribution of each land-use type equalled the land-use type's intrinsic score weighted by the proportion of the farm area it occupied. Thus, a large or small contribution could be due to a high or low intrinsic score, respectively, or to the occupation of a large or small proportion of the farm, respectively, or both. Because of the weighting, the farm score was not a simple sum of the values for each land-use type, as for LCA indicators. For this reason, we calculated scores of the entire farm separately from the scores of the cultivated land alone. A higher score indicated higher biodiversity.

Plastic use was calculated by summing the mass of plastic used per year on the farm (Table Erreur ! Il n'y a pas de texte répondant à ce style dans ce document.2). The mass of materials that lasted several years was divided by their respective life span, which yielded a mean annual value. Plastic materials included tunnel covers and plastic components, (fert)irrigation pipes and drips, mulching sheets, insect-proof netting for pest protection, pots and trays for purchased and farm-grown seedlings, and plant support clips and strings.

Farming-system approach

As mentioned, given the complexity of organic vegetable farms, we opted for a system LCA based on farm inputs and output for a one-year period (Fig. 5).

Functional units

Agriculture has several functions, the first of which is to produce food. Product mass is often used as a FU to represent this function [START_REF] Schau | LCA studies of food products as background for environmental product declarations[END_REF]. Another function is to occupy land sustainably, for which an area-based FU was used. Last, agriculture has an economic function for farmers, and the economic value of products reflects their quality (van der [START_REF] Van Der Werf | Economic value as a functional unit for environmental labelling of food and other consumer products[END_REF]. In the present study, prices reflected value at the farm gate. An FU based on economic value is also a way to capture the heterogeneity of product mixtures among farms.

Except for impacts on biodiversity, which were expressed by a single score for the entire farm, impacts were expressed according to the following FUs:

-per ha of farmland occupied for one year, which included cultivated land and on-farm seminatural habitats (e.g. hedges, pastures, ruderal areas, spaces between tunnels), as they may provide regulating ecosystem services, but excluded off-farm land associated with production of inputs -per kg of vegetables produced during one year -per € of vegetables produced during one year, based on sales to the first buyer, who may or may not have been the final consumer

System boundary

We assessed impacts from the cradle to farm gate. The foreground system included field preparation, fertilisation, sowing and planting, weeding, pest and disease control, irrigation, harvesting, and onfarm storage (Fig. 6). The background system included the production of fertilisers, main materials, and energy used for production and on-farm storage. The construction phase of tunnels was included but not the production phase of tractors or pumps. Processes beyond the farm gate, such as transportation, packaging, retail, use, and end of life, were not included.

Estimation of emissions and biodiversity scores

For organic fertilisers, field emissions, including direct and indirect N2O, ammonia (NH3), and nitric oxide (NO) emissions, as well as NO3 leaching, were calculated according to EMEP (2019) and IPCC (2019b). N and ammonium contents of fertilisers were obtained from their commercial documents when available or from [START_REF] Avadí | Dataset of organic fertilisers' characteristics -French data[END_REF] and [START_REF] Koch | AGRIBALYSE®: Rapport Méthodologique-Volet Agriculture-Version 3.0[END_REF]. N supplied by crop residues and cover crops was calculated according to IPCC (2019b) using generic values. Emissions factors for NH3 emissions were obtained from the French data base Agribalyse® [START_REF] Koch | AGRIBALYSE®: Rapport Méthodologique-Volet Agriculture-Version 3.0[END_REF], which follows EMEP methodology (Supplementary Material). Impacts of producing manure and raw green waste, which were considered as waste from upstream systems, were attributed to the producers of livestock and green waste, according to Agribalyse® methodology. Impacts of producing commercial fertiliser and composts were estimated using [START_REF] Avadí | Screening LCA of French organic amendments and fertilisers[END_REF]. We adapted the green-waste composting process to divide environmental burdens between the producer of green waste (92.8%) and the production of compost (7.2%) using economic allocation based on the cost of composting and the price of compost. This type of allocation was suggested by [START_REF] Ekvall | Open-loop recycling: Criteria for allocation procedures[END_REF] and used by [START_REF] Christensen | Life-cycle greenhouse gas assessment of Community Supported Agriculture in California's Central Valley[END_REF] for similar composts. A variety of organic plant-protection substances, including biodynamic preparations, sexual confusion substances, plant-stimulation products and natural pesticides (Table 1), were used in small quantities. Life cycle inventories (LCIs) for these inputs and for purchased insects for biocontrol and pollination were not available; we thus excluded them, except for copper sulphate, which existed in the ecoinvent® data base. Seedling production on-and off-farm was included, but seed production was not, due to the lack of data for it and its minor impact. When seedling LCIs did not exist in the Agribalyse® data base, we used the LCI of the seedling of a similar crop after modifying the amount of natural gas consumed in a heated greenhouse nursery. Natural gas consumption was estimated using the Hortinergy tool (www.hortinergy.com), with data on period of the year, duration, and seedling density based on expert opinion. We included plastic use on the farm but not off of the farm (e.g. plastic trays for purchased seedlings). We excluded boxes for harvest and storage and nets for selling potatoes. Biogenic carbon (i.e. in the biomass produced) was excluded, as the biomass produced was consumed (vegetables) or decomposed (crop residues) in the short term.

Transport of inputs to the farms was not included except when it was included in ecoinvent® or Agribalyse® LCIs.

Using SALCA-BD adapted to vegetable production, farm-scale impact scores on biodiversity were calculated at two scopes: cultivated land alone, and both cultivated land and semi-natural habitats.

For SP and OP, the crops were assessed individually and considered as different habitats. For MF, since many vegetables were intercropped in the tunnel and open field, we used the SALCA-BD category "intercropped vegetables".

Life cycle inventory and data collection

The foreground system was based on the use of inputs and infrastructure as reported by the farmers.

LCI data for the inputs and infrastructure in the background system came from ecoinvent® 3.5 [START_REF] Wernet | The ecoinvent database version 3 (part I): overview and methodology[END_REF] in SimaPro® 9.0. Farming practice data were collected through interviews with the farmers. In the first part of the interview, we asked general questions to obtain an overview of the farm (e.g. farm map; crops grown; farmer's approach to fertilisation; weed, pest and disease control; irrigation). In the second part, we asked about practices at the farm or crop scale, depending on the farm's management. For example, most data for SP was at the crop scale (e.g. fertilisation, pest management), while the rest was at the farm scale (e.g. diesel, disposable drip pipes). Quantitative records of practices were used when available. Follow-up phone calls and e-mails allowed us to obtain missing or inaccurate data.

Production quantities, farm area, and turnover were key data as they were used for the FUs. Turnover, calculated from farmers' accounts, was considered reliable. Farm areas were calculated from the French Géoportail web mapping service (www.geoportail.gouv.fr). Because OP grew rye in the rotation, its area (4.2 ha for 1 year, i.e. 20% of its cultivated land) was subtracted from the farm area, and consequently, 20% of fertiliser input was subtracted. Other inputs (mainly diesel) used to produce rye were not included. SP and OP had recorded the quantities of vegetables sold at the farm and crop scales, respectively. MF practiced direct selling (i.e. boxes and markets), with nearly daily harvest of small quantities, which were not weighed. We estimated the total production by dividing the farm turnover by the mean price per kg of vegetables (estimated from the boxes). This estimate was doublechecked by multiplying the mean yields of organic vegetables by the area occupied by each, which yielded a difference of only 4%. Mean yields were provided by the farmer of MF, who estimated expected yields based on a variety of technical references.

Sensitivity analysis of farm area

We performed a sensitivity analysis of the farm area of MF by including semi-natural areas to differing degrees. The MF farmer owned 1.10 ha of land, but one field was not cultivated at the time of the survey (Fig. 7A). Excluding this field resulted in an area of 0.71 ha (Fig. 7Fig. 7B). With these boundaries, a large area was covered by pond banks that lay far from the farm's core. Excluding the furthest banks resulted in 0.46 ha (Fig. 7C). Considering only the closest semi-natural habitats resulted in an area of 0.34 ha (Fig. 7D), which was used as the reference area of MF when comparing it to the other farms.

The sensitivity analysis was performed for CC and biodiversity.

Results

Main input and output flows

The main input and output flows were expressed per ha of land occupied per year (Table 3Table 3). SP had the highest N fertiliser input (362 kg N/ha/yr), mainly from solid (90%) and liquid (10%) commercial fertilisers made with raw materials such as livestock manure, castor bean meal, bone meal, phosphate, or plant-based compost and waste. MF applied 141 kg N/ha/yr, which was provided mainly by slowrelease N fertilisers: composted cow manure (40% of N input), shredded green waste (30%), and compost of green waste (25%). OP had the lowest N input, with 96 kg N/ha/yr from livestock manure.

The direct energy used by SP (74 GJ/ha/yr) was composed mainly of electricity (72%), mainly for irrigation. The direct energy used by MF (48 GJ/ha/yr) was composed entirely of diesel, also mainly for irrigation. The direct energy used by OP (16 GJ/ha/yr) was composed of diesel for tractors (64%) and electricity for a storage refrigerator (33%). SP had the highest yield (109 t/ha/yr), followed by MF (43 t/ha/yr) and OP (11 t/ha/yr). MF had the highest mean vegetable price (2.83 €/kg), followed by SP (2.20 €/kg) and OP (1.90 €/kg).

emissions of NO, NO3, and N2O were nearly proportional to fertiliser N input. NH3 was also emitted after application of N fertiliser, but because fertiliser types varied, emissions were not proportional the amounts applied. CO2 emissions were caused by lime used to whitewash tunnels. For CC, the contribution of system components varied among farms. Field emissions contributed more for SP than for MF or OP (1.51, 0.68, and 0.52 t CO2 eq./ha, respectively). Fertiliser production also contributed more for SP than for MF or OP (2.17, 0.75, and 0 t CO2 eq./ha, respectively). Diesel contributed much more for MF than for SP or OP (3.70, 1.04, and 0.68 t CO2 eq./ha, respectively).

Conversely, plastic tunnels contributed more for SP than for MF or OP (4.49, 2.01, and 0 t CO2 eq./ha, respectively). Plastic production (other than for tunnels) also contributed more for SP than for MF or OP (1.31, 0.23, and 0.01 t CO2 eq./ha, respectively). Seedling production contributed much more for SP than for MF or OP (2.02, 0.15, and 0.06 t CO2 eq./ha, respectively).

For MF, diesel was the main contributor (49%) to CC, followed by tunnels (27%), fertiliser (10%), and field emissions (9%). For SP, tunnels were the main contributor (34%), followed by seedling production (15%, mainly for greenhouse heating), fertiliser (16%), field emissions (11%), and plastic (10%). For OP, diesel and field emissions were the two main contributors (54% and 34%, respectively).

For CED, the contributions were generally similar to those to CC, except that field emissions did not contribute; instead, seedlings (because of the use of peat) contributed more, particularly for MF (2% of CC vs. 23% of CED), as did electricity, particularly for SP, where it was used for irrigation (4% of CC vs. 33% of CED). Field emissions dominated ME for MF, SP, and OP (98%, 96%, and 96%, respectively), followed by a modest contribution of fertiliser production (2% for SP) and seedlings (4% for OP). Per kg of vegetables, MF the highest CC impact, followed by SP and OP (215,198, and 134 g CO2 eq./kg, respectively) (Fig. Erreur ! Il n'y a pas de texte répondant à ce style dans ce document.8). The ranking of MF, SP, and OP was the same for CED (4.5, 5.7, and 3.1 MJ/kg, respectively). Conversely, OP had the highest ME impact, followed by OP and SP (0.77, 0.35, and 0.35 g N eq./kg, respectively).

Per € of vegetables, SP had the highest CC impact, followed by MF and OP (90, 76, and 71 g CO2 eq./€, respectively) (Fig. Erreur ! Il n'y a pas de texte répondant à ce style dans ce document.8). SP also had the highest CED, followed by MF and OP (2.6, 1.6, and 1.6 MJ/€, respectively). OP had the highest ME impact, followed by SP and MF (0.40, 0.16, and 0.12 g N eq./€, respectively). Differences in the biodiversity score among the farms were when considering cultivated land alone (7.7, 6.0, and 7.2 for MF, SP, and OP, respectively) than when considering the entire farm, for which SP had the highest score (20.8), with a contribution of 82% from semi-natural habitats (especially ruderal areas (76% of the total)) and 18% from tunnels (Fig. 9). MF had a score of 16.4, with relatively equal contributions from cultivated land (40%) and semi-natural habitats (60%). OP had a score of 14.6, with cultivated land contributing 43% and semi-natural habitats contributing 57%, of which 40% of the total was due to hedges.

Plastic use

Depending on the FU, SP used 2-4 times as much plastic as MF (1129 and 299 kg/ha, 16.8 and 8.5 kg/t of vegetables, and 7.6 and 3.0 kg/k€ of vegetables, respectively), whereas OP used little plastic (2 kg/ha, 0.2 kg/t of vegetables, and 0.1 kg/k€ of vegetables) (Fig. 10). Plastic was used mainly in tunnels (60% of plastic use for MF and SP). For MF, insect-proof netting and pots and trays represented 17% and 12% of plastic use, respectively. For SP, plastic mulch in tunnels and disposable water pipes represented 28% and 9% of plastic use, respectively.

Effects of farm area on impacts per ha

In the sensitivity analysis of farm area, the cultivated area of 0.28 ha represented 25%, 39%, 61%, and 82% of the farm area when farm area equalled 1.10, 0.71, 0.46 and 0.34 ha, respectively. These four areas resulted in CC impacts of 2.3, 3.6, 5.6, and 7.5 t CO2 eq./ha, respectively (Fig. 11A), while biodiversity scores were 28.8, 24.3, 20.0, and 16.4, respectively (Fig. 11B). and the other part had two crops per year (tunnel). The CC impact of SP per ha was 10.6 times as high as that of OP. Major contributors to CC impact and CED included the use of diesel (MF) and electric (SP) pumps for irrigation, the tunnel structure (MF and SP), the use of plastic water pipes and mulch (SP), and seedling production in heated greenhouses (SP); these inputs were not used by OP. Impacts of tunnels were due mainly to their galvanized steel structures, which was assumed to last 20 years, and plastic covers, which were assumed to last 4-8 years, depending on the farm. Using the same tunnel longer would reduce impacts.

Per kg of vegetables, MF and SP had a similar CC impact, while that of MF was 1.6 times as high as that of OP. This difference was much smaller than for the CC impact per ha because OP had a lower total yield than MF. The higher productivity of SP gave it a similar or slightly lower CC impact per kg despite using more inputs per ha; however, for CED, SP had higher impact than MF per kg. SP relied more on direct (diesel and electricity) and indirect (plastic and seedlings) energy than MF and OP. Per €, the highest CC impact (SP) was 1.3 times as high as the lowest CC impact (OP). MF practiced direct selling of several vegetables, including those with high value (e.g. tomatoes, mixed greens), for a mean price of 2.83 €/kg. SP sold in a long supply chain, which dilutes the value among more stakeholders.

Nevertheless, SP's high-value vegetables (e.g. tomatoes, strawberries, lettuces) under a biodynamic label sold for a mean price of 2.20 €/kg. OP produced mainly less valuable vegetables (e.g. potatoes, cabbages, turnips, carrots) that were sold in a short supply chain for a mean price of 1.90 €/kg.

Marine eutrophication

For ME, the ranking of the farms depended on the FU, with OP having the lowest impact per ha but the highest impact per kg and €. NO3 leaching, which is the main contributor to ME, was estimated using proportions of fertiliser and crop residue N (IPCC, 2019b) and ignoring N output. The farms' yields differed greatly, which suggests that their levels of crop N output did as well. The type of fertilisers also differed among the farms, because some mineralise faster (e.g. commercial fertiliser used by SP, poultry manure used by OP) than others (e.g. shredded green waste used by MF), which results in differing rates of N release in the soil. According to [START_REF] Qasim | The potential importance of soil denitrification as a major N loss pathway in intensive greenhouse vegetable production systems[END_REF], incorporating straw into a greenhouse soil tended to reduce NO3 leaching by stimulating denitrification. The Agence de l'eau

Seine Normandie (2018) found low NO3 leaching under vegetable crops grown with "market gardening on living soil" principles, as on MF. The high C:N ratio of the fertilisers used by MF enhances the activity of soil microbes and immobilises NO3 [START_REF] Kirchmann | Possibilities for Reducing Nitrate Leaching from Agricultural Land[END_REF], and these fertilisers increase water retention [START_REF] Zemánek | Evaluation of compost influence on soil water retention[END_REF], which decreases leaching. Furthermore, soil sequestration of N decreases NO3 leaching [START_REF] Knudsen | The importance of including soil carbon changes, ecotoxicity and biodiversity impacts in environmental life cycle assessments of organic and conventional milk in Western Europe[END_REF] and depends on fertiliser properties. NO3 leaching may also depend on whether fertiliser is applied in a greenhouse (i.e. a controlled water supply) or an open field (i.e.

rainfall) [START_REF] Koch | AGRIBALYSE®: Rapport Méthodologique-Volet Agriculture-Version 3.0[END_REF]. The IPCC Tier 1 emission factor we used to estimate NO3 leaching is rudimentary and easy to apply in a farming-system LCA, but it seems too coarse given the variety of the farms' fertilisation strategies. Consequently, estimated ME impacts had high uncertainty, which calls for considering fertiliser properties and soil sequestration to improve estimates of NO3 leaching.

4.1.3. Plastic use SP used the most plastic, particularly to cover its 33 tunnels. MF also covered its tunnel in plastic, but used less, for two reasons: 1) only some of the cultivated land was under shelter, whereas all was under shelter on SP, and 2) the plastic lifetime was 8 years for MF and 4 for SP. The smaller tunnel area of MF allowed the farmer to repair plastic when damaged. In south-eastern France, where SP was located, plastic on small farms similar to MF had a lifetime of 6-7 years (Oriane Mertz, Agribio 84, pers.

comm.); thus, the climate may influence this practice, along with the effect of the farming system. SP also used more plastic for mulching than MF and OP. On SP, all crops were mulched with single-use plastic, whereas on MF, straw mulch, manual weed control, and reusable plastic mulch were combined.

Plastic use is not an LCA indicator, and to our knowledge it has not been included before in an environmental assessment of vegetable production. In our study, it revealed major differences among systems. Plastic use in agriculture is a growing concern (United Nations Environment Programme, 2021b). Plastic mulch is a major source of microplastics [START_REF] Bläsing | Plastics in soil: Analytical methods and possible sources[END_REF][START_REF] Campanale | Microplastics pollution in the terrestrial environments: Poorly known diffuse sources and implications for plants[END_REF] as it is thin and hard to remove from the soil [START_REF] Qi | Behavior of microplastics and plastic film residues in the soil environment: A critical review[END_REF]. Microplastics may have detrimental effects on plant growth [START_REF] Liu | Effects of plastic mulch film residues on soil-microbe-plant systems under different soil pH conditions[END_REF], soil properties [START_REF] Zhang | Plastic pollution in croplands threatens long-term food security[END_REF], and the fitness of soil bacteria and earthworms [START_REF] Jiang | Toxicological effects of polystyrene microplastics on earthworm (Eisenia fetida)[END_REF], and can be found in fruit and vegetables at worrying concentrations [START_REF] Conti | Micro-and nano-plastics in edible fruit and vegetables. The first diet risks assessment for the general population[END_REF]. An alternative to single-use plastic mulch that SP used the year after the survey is biodegradable plastic mulch, which is a common substitution approach [START_REF] Hill | Conceptual framework for the transition from conventional to sustainable agriculture[END_REF]. Its benefits remain uncertain, as some studies conclude that it has no noxious effects on soil organisms [START_REF] Sforzini | Application of Biotests for the Determination of Soil Ecotoxicity after Exposure to Biodegradable Plastics[END_REF], while others state that single-use and biodegradable plastic mulch have the same effects on earthworms [START_REF] Ding | Effect thresholds for the earthworm Eisenia fetida: Toxicity comparison between conventional and biodegradable microplastics[END_REF][START_REF] Kumar | Microplastics as pollutants in agricultural soils[END_REF].

Plastic use included all types of items, from thin single-use items (e.g. mulch, drip tape) to long-lasting items (e.g. hard pipes). All types of plastic, regardless of their life span, can generate microplastics because the breakdown process starts on the surface. However, plastic used on the soil is more likely to be a source of soil microplastics (United Nations Environment Programme, 2021b). We included onfarm plastic but excluded up-stream plastic and products unintentionally contaminated with plastic (e.g. compost). Considering these sources of plastic would improve the indicator.

Biodiversity

Assessing biodiversity on the cultivated land alone or on the entire farm gave contrasting results, which highlighted the importance of a farm's semi-natural habitats for biodiversity [START_REF] Chiron | Effects of non-cropped landscape diversity on spatial dynamics of farmland birds in intensive farming systems[END_REF][START_REF] Jeanneret | An increase in food production in Europe could dramatically affect farmland biodiversity[END_REF][START_REF] Rischen | Biodiversity in agricultural landscapes: different non-crop habitats increase diversity of ground-dwelling beetles (Coleoptera) but support different communities[END_REF]. On SP, the cultivated land yielded a low biodiversity score, which was offset by the high proportion of ruderal area (i.e. spaces between tunnels that are left to ruderal organisms). On OP, fields were generally surrounded by a ruderal strip or hedge. As its fields were large, its proportion of semi-natural habitat was lower, which yielded a lower biodiversity score at the whole-farm scale. On MF, the cultivated land yielded a biodiversity score similar to those of the other systems. Out of a maximum score of 45 in SALCA-BD for semi-natural habitats such as hedges, biodiversity-friendly managed grasslands and pastures can reach a score of 25 [START_REF] Lüscher | Biodiversity assessment in LCA: a validation at field and farm scale in eight European regions[END_REF], which was the case for the SP grassland. Such scores are far higher than those of the vegetable fields studied here [START_REF]D Crop production and agricultural soils[END_REF](4)(5)(6)(7)(8).

Consequently, for all farms, semi-natural habitats obviously contributed more to the biodiversity score than cultivated land. This result is in line with ecological studies that concluded that semi-natural habitats were important for spiders (e.g. Šálek et al., 2018), carabid beetles (e.g. [START_REF] Knapp | Even the Smallest Non-Crop Habitat Islands Could Be Beneficial: Distribution of Carabid Beetles and Spiders in Agricultural Landscape[END_REF], butterflies (e.g. [START_REF] Dover | Linear features and butterflies: The importance of green lanes[END_REF], birds (e.g. [START_REF] Billeter | Indicators for biodiversity in agricultural landscapes: A pan-European study[END_REF], and vascular plants (e.g. [START_REF] Billeter | Indicators for biodiversity in agricultural landscapes: A pan-European study[END_REF]. The benefits of small farms for biodiversity are also acknowledged by [START_REF] Ricciardi | Higher yields and more biodiversity on smaller farms[END_REF], since the fields of smaller farms have a higher perimeter:area ratio than those of larger farms. Smaller farms are also more likely to create heterogeneous landscapes.

SALCA-BD analysed impacts of land-use type, farmer practices, and elements of spatial organisation of the farms. Other biodiversity assessment methods [START_REF] Chaudhary | Land Use Intensity-Specific Global Characterization Factors to Assess Product Biodiversity Footprints[END_REF][START_REF] Knudsen | Characterization factors for land use impacts on biodiversity in life cycle assessment based on direct measures of plant species richness in European farmland in the "Temperate Broadleaf and Mixed Forest" biome[END_REF][START_REF] Koellner | Assessment of land use impacts on the natural environment -Part 2: Generic characterization factors for local species diversity in central Europe[END_REF][START_REF] Mueller | Comparing direct land use impacts on biodiversity of conventional and organic milk-based on a Swedish case study[END_REF] quantify impacts on biodiversity based on land-use classes and the distinction between organic and conventional farming. These methods are not adapted for assessing organic farms that have the same land use (arable land) but different farming practices.

Ranking and farm-specific effects

Considering the different impacts and FUs, a clear ranking of the farms did not emerge. OP had the lowest impacts, except for CED per € and for ME per kg and per €, and it was not best for biodiversity.

However, OP had a much lower yield than MF and SP (75% and 90% lower, respectively), which required more land to produce the same quantity of vegetables. Although the three farms are typical of the variety of such farming systems, farm-specific effects cannot be ignored. For example, MF used a diesel pump for irrigation, which contributed strongly to its CC impact. MF tried to limit input use, whereas some microfarms inspired by "bio-intensive" practices may use commercial fertilisers or plastic mulch intensively. On MF, the tunnel had large impacts, but some microfarms, particularly in southern France, do not use tunnels. On SP, ruderal areas between tunnels occupied a large proportion of the farm, but farms similar to SP use glasshouses or multispan greenhouses rather than tunnels, without inter-tunnel areas. Microfarms, often inspired by permaculture design methods, include seminatural areas (e.g. hedges, ponds, woodland) [START_REF] Morel | Permaculture[END_REF]) that would increase their biodiversity score. OP reduced its use of plastic close to zero, but some farms that grow vegetables on large open fields such as OP use plastic mulch or small plastic "caterpillar" tunnels.

According to the biotechnical index [START_REF] Pépin | Conventionalised vs. agroecological practices on organic vegetable farms: Investigating the influence of farm structure in a bifurcation perspective[END_REF], the level of agroecology of the farm was highest for MF (0.73), intermediate for OP (0.47), and lowest for SP (0.07). Per ha, SP had the highest CC and ME impacts, and the highest CED and plastic use, which is in line with its low biotechnical index, which corresponds to intensive use of inputs. However, MF (high biotechnical index) did not have lower impacts per ha than OP (intermediate biotechnical index). For MF, the tunnel and diesel, used mainly used for irrigating, contributed 75% of CC and 60% of CED, but tunnels and irrigation were not considered in the biotechnical index, which appears to be a methodological oversight. Because their total yields differed, the farms ranked differently when expressing impacts per kg and per € than per ha. The ranking per kg and per ha did not correlate with the biotechnical index, which ignores the yield.

Microfarming is often promoted as a solution to produce food with lower environmental impacts, but the LCA results in this case study suggest that this benefit is not obvious. However, microfarms may be a good compromise by having higher yields than large open-field farms and lower impacts per ha, and promoting biodiversity by having a high ratio of semi-natural habitats to cultivated land and diversified crops.

Advantages and disadvantages of the farming-system approach

The farming-system LCA approach was able to estimate several environmental impacts of complex farms by considering the system as a whole, without modelling every single crop. It also compared farms and identified hotspots (i.e. the main contributors to impacts). For example, in unheated greenhouse production, we found fertilisers (including compost), the greenhouse structure, and heated seedling production to be major contributors to CC, which confirms other results in the literature [START_REF] Boulard | Environmental impact of greenhouse tomato production in France[END_REF][START_REF] Cellura | Life Cycle Assessment (LCA) of protected crops: an Italian case study[END_REF][START_REF] Martinez-Blanco | Assessment of tomato Mediterranean production in open-field and standard multi-tunnel greenhouse, with compost or mineral fertilizers, from an agricultural and environmental standpoint[END_REF]. The FUs identified differences in environmental impacts and eco-efficiency. Expressing impacts per kg and per unit of economic value are two ways to relate impacts to products. The mass-based FU considered production but introduced a bias when comparing farms that produced vegetables with different characteristics and value. In contrast, the value-based FU can compare any vegetables.

The farming-system approach followed the rationale of agroecology, in which inputs are farm-oriented rather than crop-oriented (e.g. fertilising the soil rather than the crop [START_REF] Gliessman | We need to "feed" the soil[END_REF]). On MF, the goal of fertilisation was to have a fertile soil that was rich in organic matter and soil organisms. In a product LCA approach, MF's tunnel would be allocated to the vegetables grown in tunnels and not to

those grown on open fields. However, it is difficult for a microfarm in this region to earn a sufficient

turnover with only open fields, which means that the vegetables require a tunnel. On OP, rye production in the crop rotation had the main functions of producing rye and reducing pest and disease pressure. Because the latter function influences the (non-)use of inputs for vegetable production, it would make sense to include rye impacts in vegetable LCAs, with the challenge of allocating impacts between the two functions.

Some vegetables may have higher impacts than others due to specific needs (e.g. seedlings, fertiliser, water, pest control), lower yields, and/or longer cropping periods, but the farming-system approach cannot identify such "hotspot" vegetables. Identifying specific operations that have high impact requires detailed information about farmer practices. For example, knowing total diesel consumption does not provide information about how it was used for individual operations.

Comparison to similar studies

CC impacts of the farms studied are consistent with the few studies of similar systems (Table 4Table 4). CC impact of a small-scale organic farm in Washington, USA, [START_REF] Adewale | Identifying hotspots in the carbon footprint of a small scale organic vegetable farm[END_REF] was 1.7-2.7 t CO2 eq./ha/yr and 45-623 g CO2 eq./kg, depending on the vegetable. Irrigation contributed strongly to CED, as for MF and SP. The greenhouse contributed 7-10% to the CC impact of vegetables produced under shelter. When assessing a small and a large organic farm, Adewale et al. ( 2019) estimated a CC impact of 7.1 and 3.4 t CO2 eq./ha/yr, respectively. For onion and winter squash, they estimated 188 and 276 g CO2 eq./kg, respectively, for the small farm and 50 and 68 g CO2 eq./kg, respectively, for the large farm. [START_REF] Christensen | Life-cycle greenhouse gas assessment of Community Supported Agriculture in California's Central Valley[END_REF] studied community-supported vegetable farms in California, USA, and estimated CC impacts of 1.72-6.69 kg CO2 eq./kg and 1.4-6.3 t CO2 eq./ha/yr. These farms had very low yields (534-949 kg/ha/yr), which explains their very high CC impact per kg. [START_REF] Cellura | Life Cycle Assessment (LCA) of protected crops: an Italian case study[END_REF] estimated a CC impact of 740 g CO2 eq./kg for conventional tomatoes produced in an unheated tunnel in Italy. These impacts are higher than those for SP, partly because of a wider scope that included packaging and transport, and a shorter tunnel life span. [START_REF] He | Environmental impact assessment of organic and conventional tomato production in urban greenhouses of Beijing city, China[END_REF] estimated a CC impact for that specialise in sheltered production have small areas; thus, uncultivated land may represent a much higher proportion of the area than that on a larger farm. The sensitivity analysis of farm area showed large differences in CC per ha and yielded a different ranking of the farms for biodiversity, depending on the area considered for MF. It is therefore important to establish clear rules for defining farm area, in particular when comparing farms of different types and sizes. Consequently, results with area-based FUs must be interpreted cautiously. It is reasonable to consider semi-natural areas as part of the farming system, as they provide regulating ecosystem services.

Methodological concerns when assessing organic vegetable farms

Estimates of NO3 leaching influence the ME impact greatly. On agroecological farms that use organic fertiliser with high stability and slow mineralisation, the IPCC Tier 1 equation for NO3 leaching (IPCC, 2019b) we used seems inappropriate, although it was easy to use. This calls for improving modelling of NO3 leaching in a farming-system approach for systems that use organic fertilisers.

N added to the soil by crop residues was estimated using a generic coefficient for all vegetables, although the N content of residues differs among vegetables. N2O emissions from crop residues and cover crops represented 5-12% of farm N2O emissions and 1-2% of CC impacts. Crop residues and cover crops caused 18-28% of NO3 leaching and 17-27% of ME impacts. These results suggest that improving estimates of crop residues is a priority for estimating ME impacts but not CC impacts, which agrees with [START_REF] Akkal-Corfini | Fate of Nitrogen from Artichoke (Cynara cardunculus L. var. scolymus (L.)) Crop Residues: A Review and Lysimeter Study[END_REF], who observed a large contribution of crop residues to NO3 leaching from artichoke and cauliflower.

Biocontrol, particularly using macro-organisms against pests or for pollination, is commonly used in organic vegetable production. Producing them requires infrastructure, feed, heat, and rapid transportation, but to our knowledge, their impacts are not known or no data are available. Studies that mention the use of insects for pest control or pollination excluded their impacts [START_REF] Cellura | Life Cycle Assessment (LCA) of protected crops: an Italian case study[END_REF]. Estimating impacts of these insects would improve estimates of impacts of organic vegetable production, as the biocontrol market is growing rapidly.

Conclusion

Farming-system LCA assesses environmental impacts of farms that have different levels of agroecology, including complex systems with a wide variety of crops grown on small areas. The three FUs strongly influenced the ranking of the systems. Depending on the FU and the impact, each farm ranked first, second, or third. Per ha, differences in the CC impact and CED among the systems were large. SP had the highest impacts, whereas OP had the lowest impacts, which correlated with the intensity of input use. Per kg and per €, differences in the CC impact and CED among the systems were much smaller. OP had a lower CC impact and CED per kg, but not per €. OP used much less plastic, but had a lower biodiversity score and total yield. Despite its high total yield, SP did not perform well for CC impact, CED, or plastic use per kg or per €.

Our results show that the biotechnical index developed by [START_REF] Pépin | Conventionalised vs. agroecological practices on organic vegetable farms: Investigating the influence of farm structure in a bifurcation perspective[END_REF] did not correlate with LCA-based estimates of environmental impacts. Including the use of greenhouses and irrigation infrastructure in the index would likely improve estimates of the CC impact and CED. Microfarms may be a good compromise, however, by having higher yields than large open-field farms and lower impacts per ha, and promoting biodiversity with a high ratio of semi-natural habitats to cultivated land and diversified crops. Although we selected farms that were typical of three farming systems, their potential farm-specific effects cannot be ignored. Farming-system LCA was able to assess farms using a relatively moderate amount data, and it can compare contrasting farming systems and identify hotspots within them.

The impact on on-farm biodiversity, which highlighted the importance of semi-natural habitats, contrasted with the other impacts. The quantification of plastic use echoes growing concerns about (micro-)plastic pollution in agricultural soils and landscapes, and the newly identified planetary boundary for novel entities. Estimating NO3 leaching accurately was difficult, and IPCC Tier 1 modelling seemed inappropriate; consequently, estimates of the ME impact had high uncertainty. Because crop residues contributed greatly to this impact, models that estimate NO3 leaching in a farming-system LCA need to be improved. In agroecological systems, semi-natural habitats are important for natural regulation, as they are part of the farming system. The farming-system LCA approach requires clear rules for setting farm boundaries, which strongly influence impacts per ha and biodiversity impacts.

Fig. 1 .

 1 Fig. 1. Distribution of crops produced (fresh mass) by the three types of farms: microfarm (MF), sheltered production (SP), open-field production (OP)

Fig. 2 .

 2 Fig. 2. (A) Satellite image (source: www.geoportail.gouv.fr) and (B, C) photographs (source: the authors) of the microfarm. The red line indicates the farm's boundary.

Fig. 3 .

 3 Fig.3. (A) Satellite image (source: www.geoportail.gouv.fr) and (B, C, D) photographs (source: the authors) of the sheltered production farm. The red line indicates the farm's boundary.

Fig. 4 .

 4 Fig. 4. (A) Satellite image (source: www.geoportail.gouv.fr) and (B, C) photographs (source: the authors) of the open-field production farm. The red lines indicate the farm's boundary.

Fig. 5 .

 5 Fig. 5. System Life Cycle Assessment applied to a microfarm. The colours represent botanical families, each column is a month, and each line is a vegetable bed of 43 m² (1200 m²/28 beds).

Fig. 6 .

 6 Fig. 6. Diagram of material flows of the three farm types (microfarm (MF), sheltered production (SP), and open-field production (OP)) and their system boundaries. Materials were common to all three types, except for those noted (triangles).

Fig. 7 .

 7 Fig. 7. Estimates of the area of the microfarm using different boundaries: (A) 1.10 ha, (B) 0.71 ha, (C) 0.46 ha, and (D) 0.34 ha. The field identified with the red spot was not cultivated at the time of the survey.

Fig.

  Fig. Erreur ! Il n'y a pas de texte répondant à ce style dans ce document.8. Impacts per ha of farmland during one year, per kg of vegetables, and per € of vegetables; and contributions of inputs and field emissions for the microfarm (MF), sheltered production farm (SP), and open-field production farm (OP)

Fig. 9 .

 9 Fig. 9. On-farm biodiversity scores (higher = better for biodiversity) and contributions of land-use types for the three types of farms (microfarm (MF), sheltered production (SP), and open-field production (OP)) for the entire farm (cultivated land (blue bars) and semi-natural habitats (green bars)) and for the cultivated land alone

Fig. 10 .

 10 Fig. 10. On-farm plastic use per ha of farmland, per t of vegetables, and per k€, and contributions of plastic uses for the three types of farms: microfarm (MF), sheltered production (SP), and open-field production (OP).

Fig

  Fig. 11. (A) Climate change impact expressed per ha of farmland and (B) biodiversity scores for four scenarios for the microfarm (MF) (for areas of 0.34, 0.46, 0.71, and 1.10 ha), sheltered production farm (SP), and open-field production farm (OP). SNH: semi-natural habitats.

Table 1 . Characteristics of three French organic vegetable farms studied Characteristic Farm type

 1

		Microfarm	Sheltered production	Open-field production
	Farm area	0.34 -1.1 ha	3.2 ha	21.3 ha
	Cultivated vegetable area	0.28 ha	2.0 ha	17.5 ha
	Open-field vegetable area	0.16 ha	0 ha	17.5 ha
	Sheltered vegetable area	0.12 ha	2.0 ha	0 ha
	Labour (full-time equivalent)	1.3	5.0	4.3
	Number of tractors	1	3	3
	Number of vegetable crops	35	6	20
	Main vegetable crops	Many	Tomato, cucumber, lettuce, strawberry	Potato, cabbage, carrot, squash, onion
	Fertilisation	Mainly compost of green waste + manure, green manure	Commercial fertiliser + green manure	Cattle + poultry manure + green manure
	Tillage	Shallow tillage or no-tillage	Deep non-inversion tillage	Deep non-inversion tillage
	Weed control	Organic mulch or reusable plastic mulch	Single-use plastic mulch	Mechanical weeding + thermal weeding on carrot
	Pest and disease control	Mainly natural biocontrol	Purchased biocontrol	None + insect-proof netting on turnip and radish
	Seeds and seedlings	Some seeds and seedlings self-produced	Seeds and seedlings purchased	Some seedlings self-produced
	Food supply chain	Direct selling, locally	Long supply chain, to France and Germany	Direct selling + short supply chain, locally
	Turnover	33 000 €	475 000 €	380 000 €
	Year of creation	2017	1987	1992
	Organic since the beginning	Yes	No, since 2005	No, since 1997
	Region of France	North-west	South-east	North-west
	Biotechnical Index 1	0.73	0.07	0.47
	Socio-economic Index 2	1	0	0.88

Quantities of plastic used per year (kg) by the three farm types (microfarm (MF), sheltered production (SP), and open-field production (OP))

	Type of plastic	MF	SP	OP
	Polypropylene	22.6	0.0	2.1
	Polyethylene	7.1	1618.5	0.0
	Polystyrene	11.8	38.9	0.0
	Polyester resin	2.2	72.0	0.0
	Phenolic resin	0.0	0.7	0.0
	Polyvinylchloride	5.3	57.5	21.0
	Ethylene vinyl acetate	52.3	1728.5	0.0
	Polylactide	0.0	135.8	0.0
	Nylon	0.0	0.0	11.9
	Total	101.3 3651.9 35.0

Table Erreur ! Il n'y a pas de texte répondant à ce style dans ce document.2.

Table 3 . Main annual inputs and output flows of the three farms expressed per ha of total cultivated land

 3 Per ha, SP had the highest CC impact, followed byMF and OP (13.3, 7.5, and 1.3 t CO2 eq./ha, respectively) (Fig. Erreur ! Il n'y a pas de texte répondant à ce style dans ce document.8). The ranking of SP, MF, and OP was the same for CED (387, 157, and 29 GJ/ha, respectively) and ME (23.2, 12.2, and 7.2 kg N respectively).

	Type	Item	Unit	Microfarm	Sheltered production	Open-field production
	Inputs	Fertiliser	kg N/ha/yr	141	362	96
		Electricity	GJ/ha/yr	0	53.4	5.1
		Diesel	GJ/ha/yr	48.4	20.3	10.0
		Natural gas	GJ/ha/yr	0	0	0.4
		Irrigation water	m 3 /ha/yr	4622	4111	0
		Plastic	kg/ha/yr	363	1821	1
						Cabbage: 3903
				0 (self-	Tomato: 3355	Onion: 2857
				production of	Cucumber: 3097	Leek: 1029
		Seedlings	no./ha/yr	seedlings and direct sowing)	Lettuce: 119 323 Strawberry: 4788	Swiss chard: 263 Squash:143
				Potting soil: 5357	Celery: 3327	Potato: 286 kg/ha/yr
				kg/ha/yr	Fennel: 6397	+ self-production of
						seedlings and direct sowing
					Wettable sulphur: 0.6 kg/ha/yr
					Neem oil: 1.8 L/ha/yr
		Chemicals	-	Copper sulphate: 0.47 kg/ha/yr 36 kg/ha/yr Iron phosphate:	Plant-based biostimulant: 0.8 L/ha/yr Horn silica (501): 20 g/ha/yr doses/ha/yr Sex pheromone: 180	-
					Prepared horn manure (500P):
					140 g/ha/yr
					Macrolophus spp. (box of 1000
		Purchased insects	no./ha/yr	-	insects): 6 10 000 insects): 28 Chrysoperla carnea (box of	-
					Bumblebee hives: 17
		Shading paint	kg/ha/yr	0	590	0
		Plastic tunnel	ha/ha/yr	0.43	1	0
	Output	Vegetables	t/ha/yr	43	109	11
		Price	€/kg	2.83	2.20	1.90
	Emissions	N2O	kg/ha/yr	2.75	7.30	1.73
		NH3	kg/ha/yr	7.22	16.90	9.39
		NO	kg/ha/yr	5.69	13.92	3.54
		NO3	kg/ha/yr	214	531	131
		CO2	kg/ha/yr	0	259	0

The biotechnical index estimates the extent to which organic practices were agroecological (towards 1) or conventionalised (towards 0). Values are from[START_REF] Pépin | Conventionalised vs. agroecological practices on organic vegetable farms: Investigating the influence of farm structure in a bifurcation perspective[END_REF].

The socio-economic index estimates the extent to which the socio-economic context was territorially embedded (towards 1) or connected to the global market (towards 0). Values are from[START_REF] Pépin | Conventionalised vs. agroecological practices on organic vegetable farms: Investigating the influence of farm structure in a bifurcation perspective[END_REF].

Acknowledgements

The authors thank the farmers who shared their time to provide us with valuable data. The research was supported by the Centre Technique Interprofessionnel des Fruits et Légumes (CTIFL) and the Association Nationale Recherche Technologie (ANRT).

with compost and mineral fertilisers. Tomatoes produced in a heated greenhouse had CC impacts 10-50 times as high as those of vegetables produced in unheated tunnels, and heating and lighting contributed 97% of the impact [START_REF] Williams | Determining the environmental burdens and resource use in the production of agricultural and horticultural commodities[END_REF]. In open-field production in Oregon, USA, which is likely similar to OP, Venkat (2012) estimated a CC impact of 409 and 268 g CO2 eq./kg for organic broccoli and lettuce, respectively. As a comparison to other organic crops, [START_REF] Nitschelm | Life cycle assessment data of French organic agricultural products[END_REF] estimated a mean CC impact for 106 cereals and legumes (e.g. spring and winter barley, spring and winter wheat, winter pea, fava bean) of 0.8 ±0.2 and 258 ±112 g CO2 eq./kg, respectively.

The farm area and production area

In a product LCA, the area of the field or greenhouse is usually used for an area-based FU. In a farmingsystem LCA, the farm area is used, but farms often have uncultivated semi-natural areas. On microfarms, farmers may leave land uncultivated due to a lack of time or labour, or to enhance biodiversity and/or regulating ecosystem services [START_REF] Morel | Permaculture[END_REF]. On farms with tunnels, areas between tunnels are rarely considered in yields or for an area-based FU. Microfarms and small farms


Hugo Deléglise

Agnès Bégué

Roberto Interdonato

Elodie Maître D'hôtel

Mathieu Roche

Maguelonne Teisseire

Mining news articles dealing with Food Security

Keywords: Food security, sentiment analysis, spatiotemporal analysis, term discrimination, text mining, word embedding

Food security is a major concern in West Africa, particularly in Burkina Faso, which has been the epicenter of a humanitarian crisis since the beginning of this century. Early warning systems for food insecurity and famines rely mainly on numerical data for their analyses, whereas textual data, which are more complex to process, are rarely used. To this end, we propose an original and dedicated pipeline that combines different textual analysis approaches (e.g., word embedding, sentiment analysis, and discrimination calculation) to obtain an explanatory model evaluated on real-world and large-scale data. The results of our analyses have proven how our approach provides significant results that offer distinct and complementary qualitative information on the food security theme and its spatial and temporal characteristics.

Introduction

Hunger remains a major problem in many parts of the world. Although a large scale and permanent solution to this situation is far from being achieved, steady progress was made in the first 15 years of this century. Among West African countries, Burkina Faso is in one of the most severe situations, with an undernourishment prevalence of 21.3 % from 2015-2017 [START_REF] Fao | ECA: Addressing the threat from climate variability and extremes for food security and nutrition[END_REF]. Burkina Faso is also one of the countries most affected by the phenomenon commonly known as the "triple burden of malnutrition", characterized by the coexistence of overnutrition, undernutrition and micronutrient deficiencies in the population.

Following several food crises in the 1970s and 1980s in different regions of the world, several food security alert and monitoring systems (FSMSs) were created by governmental organizations and NGOs. The objective of these systems, which are still very active today, is to prevent food crises and to help countries plan food aid programs to optimize their food production and distribution channels. In this study, we examine the ability of text mining methods to extract and analyze the qualitative information used as proxies for the national and regional food situation and its evolution over the last ten years in Burkina Faso from a corpus of newspapers from the country. The aim is to provide explainable indicators complementary to the automatic predictions of food security scores, i.e., as the ones obtained in our previous works based on the application of machine learning approaches on heterogeneous [START_REF] Deléglise | Food security prediction from heterogeneous data combining machine and deep learning methods[END_REF] and textual [2] data.

However, the difficulties associated with the implementation of text-mining approaches are linked to the structural complexity of textual data and are the subject of a large number of studies. We now detail the approaches proposed in the scientific literature to address these difficulties. In the domain of agriculture, which is closely related to food security, information extraction from textual data is a topic that has been attracting increasing interest [START_REF] Drury | A survey of the applications of text mining for agriculture[END_REF]. In this field, several studies have focused on sentiment analysis [START_REF] Surjandari | Text mining of twitter data for public sentiment analysis of staple foods price changes[END_REF], named entity extraction (i.e., places, dates or individuals related to agriculture) [START_REF] Malarkodi | Named entity recognition for the agricultural domain[END_REF], etc.

The originality and methodological contributions of this work are presented at 3 levels: (1) its multidisciplinary aspect involving the combination of approaches based on text mining (e.g., word embedding and sentiment analysis) for the analysis of food security, which has been little studied from this perspective; (2) spatiotemporal analysis based on the content of French texts; and (3) extension of discrimination measures to address spatiotemporal data. The usefulness of this approach is to propose an explanatory framework complementary to the outputs of the predictive models usually applied to other types of data (e.g., digital data and satellite images). While we focus on the study case of Burkina Faso, the proposed method is generic and can be applied to any other area in the world. Section 2 presents the proposed approach. Section 3 outlines and discusses the information extracted from a dedicated corpus in French.

Proposed approach

Text mining approaches

Studies use text mining methods to extract information on food securityrelated events from newspapers which proposes a framework for automatic detection of food crises [START_REF] Xiao | Food safety event detection based on multi-feature fusion[END_REF]. Their method consists of extracting the most characteristic vocabulary (keywords) by tf-idf (term frequency-inverse document frequency) for each article [START_REF] Salton | Term-weighting approaches in automatic text retrieval[END_REF], which is a method of weighting characteristic terms of texts, and then extracting the named entities with a Bi-LSTM-CNN-CRF framework [START_REF] Yu | Named Entity Recognition with Deep Learning[END_REF]. A weight is associated with each keyword according to its semantic similarity (by Word2vec) with the terms of the article title. Each article, through a set of weighted keywords and associated named entities, is classified by single-pass clustering [START_REF] Papka | On-line new event detection using single pass clustering[END_REF]. The ability of tf-idf to extract relevant and specific vocabulary from newspaper articles has also been demonstrated [START_REF] Ao | News keywords extraction algorithm based on textrank and classified tf-idf[END_REF]. In this context, some text-mining methods are integrated in our pipeline: Word2vec. Word2vec (w2v) [START_REF] Mikolov | Efficient estimation of word representations in vector space[END_REF] is a family of automatic language processing models for word embedding, i.e., the transformation of terms and texts into vectors. W2v is based on two-layer neural networks and aims at learning vector representations of terms in texts so that terms that share similar contexts (i.e., are often surrounded by the same terms) are represented by close numerical vectors. In our study, a CBOW (continuous bag of words) architecture is used (preferred to the skip-gram architecture, which requires more execution time while sometimes offering less satisfactory performance for processing newspaper articles [START_REF] Jang | Word2vec convolutional neural networks for classification of news articles and tweets[END_REF]). CBOW aims at predicting the appearance of a term by using as proxies the terms that are close to it in the text. The model is trained on a large training corpus (French Wikipedia, in our study) by traversing each term and its neighbors and obtaining a set of feature vectors that represent each term in the text as the output. Term polarity. The polarity of a term is a criterion that indicates whether it is positive, negative or neutral [START_REF] Szabolcsi | Positive polarity -negative polarity[END_REF]9]. In our context, the average polarity of texts dealing with food security can give us relevant information about their worrisome or even alarming character. There are currently few methods for performing sentiment analysis on French texts. To evaluate the negativity of a term, we use the French version of the sentiment analysis model VADER (Valence Aware Dictionary and Sentiment Reasoner) implemented by the Python package vaderSentiment-fr 6 . This model is based on a lexicon of 7500 terms classified as positive or negative and on contextual rules that can modify the valence of the terms (e.g., the use of negation, punctuation, capitalization, and adverbs). This model was chosen because it has a good compromise between its simplicity of implementation and execution time and its classification performance, performing better than many existing methods, some of which are based on the use of machine learning [START_REF] Gilbert | Vader: A parsimonious rule-based model for sentiment analysis of social media text[END_REF]. tf-idf. To evaluate the discrimination of the terms of an article, we use the concept of tf-idf (term frequency-inverse document frequency) [START_REF] Salton | Term-weighting approaches in automatic text retrieval[END_REF], which measures to what extent a term is characteristic of a text by evaluating its relevance and its singularity. Its principle is based on a formula in which two values, tf (term frequency) and idf (inverse document frequency), are multiplied together. tf corresponds to the frequency of a term in a text, and it therefore increases when a term is frequent in the text. idf measures the importance of a term according to its distribution in all the texts studied rather than based on its frequency in a particular text.

Our food security pipeline

The objective of our pipeline is to perform a spatiotemporal analysis of food security based on the terminology of this domain linked to the textual proxies we define. In this framework, we propose an original and dedicated pipeline that combines different textual analysis approaches (e.g., word embedding, sentiment analysis, and discrimination calculation). To this end, we present in this section the methodology deployed to obtain a spatial and temporal explanatory context of the Burkinabe food situation from the corpus of newspapers studied. Fig. 1 summarizes the analysis plan. For this a first general lexicon on food security is therefore used to detect articles of interest, we name this lexicon "GLEX" (Generalist LEXicon). Then, two other more detailed lexicons are used to detect the expressions of "food security" and "crisis" themes used and thus obtain a more qualitative view of the content of the articles. We call these two detailed lexicons on food security and on crises "F LEX" (Food LEXicon) and "CLEX" (Crises LEXicon), respectively. These lexicons are freely available [START_REF] Deléglise | Lexiques en français sur la sécurité alimentaire et les crises[END_REF].

First, we present step (1) of selecting relevant articles. For this, we compute by w2v the semantic similarity between each article and the generalist lexicon GLEX, used as a basis to identify articles on the theme "food security". The principle is to consider an article as dealing with food security if its semantic similarity with GLEX by w2v is higher than a threshold x (chosen and validated in the Appendix document [START_REF] Deléglise | Automatic extraction of food security knowledge from newspaper articles -Appendix[END_REF]). This aims to detect the articles of interest to focus the analyses.

Second, we establish in step (2) the textual proxies of food security on the selected articles. To this end, we perform the following operations:

-We keep for the selected articles their w2v score calculated during step (1), which quantifies their degree of connection with the food security theme and constitutes a proxy of this domain.

-We compute the negativity rate of the articles we propose as a proxy, i.e., the frequency of the negative terms in each article (Formula 1), to obtain information on the alarming nature of the articles' content.

N eg(art) = nb terms_neg (art) nb terms (art) ( 1
)
where Neg is the negativity rate of an art article, and nbterms_neg and nbterms represent the number of negative terms and the number of terms of an art article, respectively, based on the French version of the VADER model (Valence Aware Dictionary and Sentiment Reasoner).

The hypothesis assumes that articles published during periods and in areas of food insecurity are associated with more negative valences than in a context of food sufficiency. An article is considered to be negative if its negativity rate is greater than 0.1 (the threshold validation methodology is detailed in the Appendix document [START_REF] Deléglise | Automatic extraction of food security knowledge from newspaper articles -Appendix[END_REF]). -We study the most used vocabulary in articles related to food security to detect whether the vocabulary adopted is consistent with the trends and crises that have affected food security in the country and thus to have a more explanatory perspective of the data. To accomplish this, we calculate for each article the frequency of 119 expressions from the two detailed lexicons F LEX and CLEX.

Third, we describe step (3) of global, regional and annual analysis of the proxies defined in step (2). To take into account the spatiotemporal aspect of food security, the proxies presented are then aggregated at different granularities to perform targeted analyses at the global, regional and annual levels and thus be able to visualize the trends and food crises that have affected the country over the last decade. The proxies are aggregated at three levels: Global level: this level of analysis provides a general view of the characteristics of the country's food situation between 2009 and 2018 and can be used as a comparison for targeted analyses (regional and annual). The proportions of articles dealing with food security and negative articles are calculated over the entire corpus. We consider the average frequency of occurrence of each term in the detailed F LEX and CLEX lexicons across all articles in the corpus.

Regional level: this level of analysis aims to provide a representation of the food situation and characteristics at the regional level. We illustrate our analyses with three regions: the Centre, Hauts-Bassins and Sahel regions. These three regions were chosen because they are among the most frequently cited in the articles in the corpus and are associated with distinct health situations [START_REF] Wfp | Burkina Faso : Analyse Globale de la Vulnérabilité[END_REF]. Our approach consists of considering an article as associated with a region if a locality of the region is mentioned at the beginning of the article (i.e., in the title or in the first sentence of the article). The proportions of articles dealing with food security and negative articles were calculated for each of the 3 regions. To extract the characteristic regional vocabulary, we compute the tf-idf of each term of the F LEX and CLEX lexicons on the articles of each considered region. In our context, tf-idf allows us to highlight the expressions of food security and crises that are frequent in the articles related to a certain region and that are more specifically used in the articles of the region (i.e., more than for the other articles).

Annual level: this level of analysis provides annual characteristics of the food situation in Burkina Faso and tracks its evolution from 2009 to 2018. Each article is associated with its year of publication, which is extracted in the metadata linked to the article. This proposal, called the T IR (Tf-Idf ratio), is based on the concept of tf-idf and proves to be more suitable in our context, allowing us to distinguish rare and year-specific expressions more than tf-idf. More precisely, we first compute for each expression of the lexicons F LEX and CLEX the tf-idf of the expression on average on the articles of the year (Formula 2); then, in a second step, we compute the ratio of this tf-idf by the tf-idf of the expression on average on the articles of other years (T IR ratio, (Formula 3)).

T F -IDF moy (t, A y ) = art∈Ay T F -IDF (t, art) N y ( 2
)
where T F -IDFmoy is the average tf-idf of the term "t" on the articles "art" belonging to the set Ay of the articles of year y; we note Ny is the cardinality of this set.

T IR(t, A

y ) = T F -IDF moy (t, A y ) T F -IDF moy (t, A z ) ( 3
)
where T IR is the ratio of the tf-idf of the term "t" averaged over the articles belonging to the set Ay of articles in year y to the tf-idf of the term "t" averaged over the articles belonging to the set Az of articles in different years of year y. 3 Experiments

Corpus of newspapers

Currently, the main Burkinabe newspapers have their own news website on which they publish their articles. For the creation of our newspaper corpus, we turned to two Burkinabe newspapers whose websites allow for good data accessibility: Burkina24 and LeFaso. These newspapers are among the most read newspapers in the country and have a large number of articles on various topics online. We extracted a total of 22856 articles between 2009 and 2018 (5595 for Burkina24 and 17261 for LeFaso), a period during which food security has undergone significant variations and several crises. The articles were filtered and then lemmatized with the Python package Spacy 7 .

Results

Regional Analysis. We focus here on certain regions and observe whether the food security proxies aggregated over these regions are associated with the known regional food situation. The three regions studied are the Centre, Hauts-Bassins and Sahel regions. In Table 1, we see that the Hauts-Bassins region, which is the least food insecure of the regions presented, is associated with the lowest proportions of "food security" theme articles and negative articles. Conversely, the Sahel region, which is experiencing the most severe situation, has the highest proportions of "food security" themed articles and negative articles, which are both significantly higher than those at the national level. These data are consistent with expectations: the more an area is plagued by food insecurity and/or crises, the more articles will mention these topics and be negative. In Fig. 2, we see that for the Hauts-Bassins region, the least poor, the most important expressions of the lexical field of food security are neutral (e.g., "riz" (rice), "agriculture" (agriculture), and "campagne agricole" (agricultural campaign)), whereas in the Sahel region, the expressions of food security are more negative (e.g., "malnutrition" (malnutrition) and "crise alimentaire" (food crisis)). Regarding the expressions of the theme of crises, the word clouds highlight concerns that are characteristic of each region. For example, the expression "inondation" (flood ) is the most important in the Centre region, which is plagued by this problem, whereas the expression "foncier" (land ) is the most important in the Hauts-Bassins region, where land management is a major problem.

Table 1. Comparison of the percentage of articles on the theme of "food security" (FS) and the percentage of negative articles for the three regions of the Centre, Hauts-Bassins, and Sahel and for Burkina Faso (BF).

Centre Hauts-Bassins Sahel BF % of FS theme articles 6. Annual analysis. In this section, we examine whether food security proxies point over time to elements that are consistent, nuanced, or even contradictory to observations and events that have taken place over the past decade that may have affected food security. Namely, there has been a decline in food security since 2013 as well as events negatively impacting food security (e.g., flooding, drought, and conflict). The objective here is to determine the annual characteristics and evolution of the Burkinabe food situation through the food security proxies considered. We compute for each expression in the F LEX and CLEX lexicons the tf-idf of the expression averaged over the articles of the year, as well as the ratio T IR that we proposed.

Finally we analyze the evolution of the food security and crisis vocabularies used in the articles as a function of time (see Fig. 3a). In Fig. 4 (a), which represents the evolution of the proportion of negative articles by year from 2009 to 2018, we see a trend for negative articles to decrease in proportion. This may seem counterintuitive, and it may be explained by a certain freedom of the press that has tended to decline over the last decade (see Fig. 4 (b)). Fig. 3b shows the evolution of the tf-idf of 5 expressions on average on the articles of each year between 2009 and 2018. We can see an upward trend in the tf-idf of the terms "sécurité alimentaire" (food security) and "malnutrition" (malnutrition), which have been increasingly used over the last decade. Moreover, some peaks correspond to the year of occurrence of events that took place over the period: the tf-idf of the expression "sécheresse" (drought) was the highest in 2012, which experienced a severe drought. The tf-idf for "conflits" (conflict) and "déplacement" (displacement) peaks in 2013, when conflicts in the Sahel led to the displacement of people from Sahelian countries bordering Burkina Faso.

Conclusion and Future work

In this study, we examined the ability of text mining methods to extract spatial and temporal thematic information on food security from newspaper articles by examining the context of Burkina Faso.

We proposed, combined and extended, with adapted text mining methods (the Word2vec lexical embedding model, the VADER sentiment analysis model and the tf-idf term importance weighting method) three types of proxies defined on a set of articles, allowing us to obtain distinct and complementary information on the food security theme. This type of approach and the associated results can be exploited as complementary information to the outputs of predictive models (i.e., based on machine and deep learning). Indeed, machine and deep learning models applied to other types of data (e.g., digital data and satellite images) have strong predictive power but often lack explicability and interpretability. These models can then be validated, nuanced or explained by qualitative information from textual data that could make sense to domain experts and advance their understanding of complex food security phenomena.

To improve the thematic search in a finer way than with the word embedding applied in this work with w2v, technologies based on BERT (bidirectional encoder representations from transformers) and trained models for French, such as CamemBERT or FlauBERT, could also be integrated [START_REF] Le | Flaubert: Unsupervised language model pretraining for french[END_REF].

Fig. 1 .

 1 Fig. 1. General illustration of the analysis plan. The main steps are numbered: (1) selection of articles of interest; (2) computation of textual proxies on these articles; (3) global, spatial and temporal analysis; and (4) visualization.

Fig. 2 .

 2 Fig. 2. Word clouds of expressions from the F LEX (FS expressions) and CLEX (Crises expressions) lexicons, based on "food security" theme articles related to three regions (Centre, Hauts-Bassins and Sahel). The size of the terms is proportional to their average tf-idf.

Acknowledgments.

  This work was supported by the French National Research Agency under the Investments for the Future Program #DigitAg, referred to as ANR-16-CONV-0004.

Fig. 3 .

 3 Fig. 3. (a) Change in the proportion (in percentage) of "food security" (FS) theme articles from 2009 to 2018 on the corpus studied. (b) Evolution of the average tf-idf of 5 expressions from the two detailed lexicons F LEX and CLEX between 2009 and 2018.

Fig. 4 .

 4 Fig. 4. Changes from 2009 to 2018 in the proportion (in percent) of negative articles per year among articles on the theme of "food security" (a) and Burkina Faso in the press freedom index (Reporters Without Borders 9 ) (b).

https://pypi.org/project/vaderSentiment-fr/