Kaat Philippe
email: kaat.philippe@inrae.fr

Claire Chabanet

Sylvie Issanchou

Sandrine Monnery-Patris

Child eating behaviors, parental feeding practices and food shopping motivations during the COVID-19 lockdown in France: (how) did they change?

Keywords: child eating behavior, snacking, food parenting practices, BMI, boredom, stress

The COVID-19 pandemic caused France to impose a strict lockdown, affecting families' habits in many domains. This study evaluated possible changes in child eating behaviors, parental feeding practices, and parental motivations when buying food during the lockdown, compared to the period before the lockdown. Parents of 498 children aged 3-12 years (238 boys; M=7.32; SD=2.27) completed an online survey with items from validated questionnaires (e.g., CEDQ, CEBQ, HomeSTEAD). They reported on their (child's) current situation during the lockdown, and retrospectively on the period before the lockdown. Many parents reported changes in child eating behaviors, feeding practices, and food shopping motivations. When changes occurred, child appetite, food enjoyment, food responsiveness and emotional overeating significantly increased during the lockdown. Increased child boredom significantly predicted increased food responsiveness, emotional overeating and snack frequency in between meals. When parents changed their practices, they generally became more permissive: less rules, more soothing with food, more child autonomy. They bought pleasurable and sustainable foods more frequently, prepared more home-cooked meals and cooked more with the child. Level of education and increased stress level predicted changes in parental practices and motivations. This study provides insights in factors that can induce positive and negative changes in families' eating, feeding and cooking behaviors. This can stimulate future studies and interventions.

Introduction

At the end of 2019, the highly contagious coronavirus SARS-CoV-2 causing a severe acute respiratory syndrome (COVID-19) has sparked a pandemic. Many countries worldwide were affected by the spread of this virus, forcing governments to protect their inhabitants by imposing strict rules. In France, a strict first lockdown took place from March 17 until May 10, 2020. During this period, schools were closed, working from home was enforced except for some specific professional domains (e.g., working in hospital, in food shops). Leaving your home was allowed only under certain circumstances and only after filling in a special certificate. Valid reasons to leave your home, indicated on this certificate, were for example essential work, grocery shopping, medical reasons, urgent family matters or assistance to vulnerable people, and open-air physical activities (limited to one hour a day at a maximal distance of one kilometer from your home).

The lockdown forced people to adapt their everyday behaviors to the new situation, including their food-related behaviors. This particular situation stimulated many researchers to study the impact of the lockdown on eating behaviors. Most studies have been conducted with adolescents or adults. For example, Di Renzo and colleagues (2020) studied eating habits and lifestyles changes during the lockdown among the Italian population (aged between 12-86 years). Marty and colleagues (2021) studied how changes in French adults' food choice motives were related to changes in nutritional quality during the lockdown compared to the period before the lockdown. Pietrobelli and colleagues (2020) conducted a study in Italy on eating behavior with parents of children aged 6-18 years, but the sample was very small (N=41) and the children all had obesity.

The current study is original and complementary to these researches as it focused specifically on changes in children's eating behaviors and families' feeding practices during the lockdown, compared to the period before the lockdown.

Since schools were closed and most people had to work from home or were technically unemployed, many children and adults had to consume all their meals at home. Parents were consequently responsible for their child's food intake throughout the whole day, and this could be challenging in terms of time (additional meal planning, food shopping, food preparation), especially for those parents who were still working. The pandemic also faced some parents with changed accessibility and availability of foods and food insecurity, in particular those parents who were financially vulnerable [START_REF] Loopstra | Vulnerability to food insecurity since the COVID-19 lockdown[END_REF].

The psychological states (fear, depressive symptoms, stress, etc.) linked to the COVID-19 pandemic [START_REF] Jiao | Behavioral and emotional disorders in children during the COVID-19 epidemic[END_REF][START_REF] Jiao | Behavioral and emotional disorders in children during the COVID-19 epidemic[END_REF] possibly also affected children's and parents' eating behaviors and consequently also their motivations when buying foods. In fact, previous studies have shown that the experience of stress and negative emotions leads people to overeat and makes them reach for so-called "comfort foods", rich in sugar and calories [START_REF] Evers | Feeling bad or feeling good, does emotion affect your consumption of food? A meta-analysis of the experimental evidence[END_REF][START_REF] Michels | Stress, emotional eating behaviour and dietary patterns in children[END_REF]Rodriguez-Martin & Meule, 2015). Increased levels of boredom have previously also been associated with increased energy intake [START_REF] Moynihan | Eaten up by boredom: Consuming food to escape awareness of the bored self[END_REF].

Similarly, parents possibly adapted their parental feeding practices, i.e., the behavioral strategies to control what, how much, when, and where the child eats (Ventura & Birch, 2008), to this unseen situation. On the one hand, because of child-driven reasons: to meet the changed eating and emotional needs of their child at home. On the other hand, because of situation-driven or parent-driven reasons: changes in families' routines could for example affect the timing of meals or parents could have provided foods to entertain their children while working from home. As parental feeding practices have an important influence on child eating behavior [START_REF] Birch | Development of food preferences[END_REF], it is of importance to explore how these practices may have changed during the lockdown to obtain a more complete picture of the impact of the COVID-19 pandemic on the food domain. Moreover, young children are very dependent on their parents for food intake (e.g., [START_REF] Poti | Trends in energy intake among US children by eating location and food source, 1977-2006[END_REF]: what parents buy and their motivations when buying foods for their child influence children's eating behavior. [START_REF] Rigal | Links between maternal feeding practices and children's eating difficulties. Validation of French tools[END_REF]. It is thus important to differentiate their food shopping motivations from adults in general.

Therefore, this study's first goal was to evaluate possible changes in eating behaviors in children aged 3-12 years, in parental eating and cooking behaviors, in parental feeding practices, and also in parental motivations when shopping for food during the lockdown, compared to the period before the lockdown. The age range of 3-12 years was chosen because these children are still highly dependent on their caregivers for their food intake. Given the results of previous studies highlighting the impact of stress and of boredom on eating behaviors [START_REF] Evers | Feeling bad or feeling good, does emotion affect your consumption of food? A meta-analysis of the experimental evidence[END_REF][START_REF] Michels | Stress, emotional eating behaviour and dietary patterns in children[END_REF]Rodriguez-Martin & Meule, 2015;[START_REF] Moynihan | Eaten up by boredom: Consuming food to escape awareness of the bored self[END_REF], the second goal of this study was to explore possible links between, on the one side, changes in the child's level of boredom at home, changes in parental stress at home, and child and parental socio-demographic variables, and, on the other side the changes in children's and parental eating behaviors, practices and motivations for food shopping during the lockdown.

Method

Recruitment and ethics

An online questionnaire was used to obtain data for this study. Parents were recruited via an agency disposing of a panel of participants all over France. Prerequisites to participate were (1) having a child aged 3-12 years, and (2) no recent changes in the parent's or child's eating behaviors due to other reasons than a change of habits linked to the lockdown (e.g., following a new diet to lose weight, changed eating behaviors because of a medical treatment, changed eating behaviors because of religious reasons). The questionnaire was anonymous and on the first page of the questionnaire, parents were required to tick a box indicating that they understood and accepted the study information and data protection policy. The questionnaire was open for participation from the 30 th of April until the 10 th of May, 2020 (the end of the strict lockdown in France). Participants received a voucher of six euros for questionnaire completion. An ethical approval (n°20-686) was granted for this study by the Institutional Review Board (IRB00003888, IORG0003254, FWA00005831) of the French Institute of Medical Research and Health, and a study registration was done by the data protection service involved (CNRS).

Measures

Demographics

Parents were asked to report the sex of the child and his/her date of birth to ensure a correct calculation of the child's age and his/her normed body mass index' (BMI) z-score. Once these calculations were completed, the child's birth date was deleted to minimize information that could possibly help to identify the participants. Parents were also asked to report their own sex, age, relationship status, number of children in the household, level of education, type of housing, employment status before and during the lockdown, and their perception of their financial status. In addition, to describe the general eating habits of our sample during the lockdown, parents were asked to report the number of meals (breakfast, lunch, mid-afternoon snack, dinner) their child generally took at home on a weekly basis (ranging from 1-7) during the lockdown, and if they took more, less, or the same amount of meals with their child compared to the period before the lockdown.

Child eating behaviors

Appetite, Food enjoyment, Food pickiness

The Children's Eating Difficulties Questionnaire (CEDQ; [START_REF] Rigal | Links between maternal feeding practices and children's eating difficulties. Validation of French tools[END_REF] was used to measure the child's levels of appetite (three items; e.g., My child eats small quantities (even if the food is liked) (Reversed item)), food enjoyment (three items; e.g., My child looks forward to mealtimes), and food pickiness (three items; e.g., My child only eats a small variety of foods). Parents were asked to rate their agreement with each item on a five-point Likert-like scale (Strongly disagree, Disagree, Neither agree nor disagree, Agree, Strongly agree), according to their child's eating behavior during the lockdown, and retrospectively for the period before the lockdown. A score was calculated for each period. Scores were calculated in such way so higher scores indicated a higher appetite, a higher food enjoyment, and a higher level of food pickiness in the child.

Food responsiveness and Emotional overeating

The Children's Eating Behavior Questionnaire (CEBQ; Wardle, Guthrie, Sandreson, Rapoport, 2001) was used to measure the child's levels of food responsiveness (five items; e.g., My child is always asking for food), and emotional overeating (four items; e.g., My child eats more when anxious). Parents rated their agreement with each item on a five-point Likertlike scale (Never, Rarely, Sometimes, Often, Always), for both the period before and during the lockdown. For emotional overeating, we also added a sixth answer option: not applicable, as we were not sure if all children would have already presented all emotions (worried, annoyed, anxious, boredom) during the lockdown. Higher scores indicated higher food responsiveness and more emotional overeating.

Snacking frequency and Types of snacks

In France, the mid-afternoon snack ("goûter") is a common practice and is perceived as an additional meal beside breakfast, lunch and dinner, especially in children [START_REF] Francou | Le goûter en perte de vitesse et loin des recommandations[END_REF]. We therefore distinguished between the frequency of the mid-afternoon snack (which usually also includes a drink) and the frequency of other snacks/drinks in between meals. We clearly explained the difference between both types of snacking occasions to parents in the instructions of the questions. For the mid-afternoon snack, parents were asked to rate the child's frequency of this snacking occasion on a four-point scale (Less than once a week, 1-3 times per week, 4-6 times per week, Every day), for both the period before and during the lockdown. For other snacks/drinks, parents rated the frequency on a seven-point scale (Less than once a week, 1-3 times per week, 4-6 times per week, once per day, Twice a day, Three times a day, 4 or more times a day), also for both the period before and during the lockdown.

We gave examples of possible snacks/drinks (e.g., candy, piece of bread, fruit, compote, yoghurt, salty or sweet biscuits) to illustrate that any food and drink, except water, should be counted as a snack/drink. We asked parents as well about the types of foods their child usually consumed during snack times: "When your child has a mid-afternoon snack or a snack/drink in between meals, how often does (s)he consume the following types of foods and drinks?". The frequency of each type of food/drink (Table 4) was rated on a five-point-Likert scale (Never, Rarely, Sometimes, Often, Always), for both the period before and during the lockdown. The selection of the types of foods and drinks was based on the food groups presented in a French food consumption report (ANSES, 2017).

Child boredom

Parents were asked to report how often their child was bored at home on a five-point Likert scale (Never, Rarely, Sometimes, Often, Always), for both the period before and during the lockdown. Higher scores indicated higher levels of boredom at home.

Parental feeding practices

Parental feeding practices were derived from the Home Self-Administered Tool for Environmental Assessment of Activity and Diet Family Food Practices Survey (HomeSTEAD;[START_REF] Vaughn | Development of a comprehensive assessment of food parenting practices: The home self-administered tool for environmental assessment of activity and diet family food practices survey[END_REF]. This 86-item instrument captures five coercive control practices (CCP), seven autonomy support practices (ASP), and twelve structure practices (SP). We selected seven practices we thought to be susceptible for change during the lockdown: Soothing with food (CCP; four items; e.g., I give my child something to eat or drink when she or he is bored or worried, even if I know she or he is not hungry), Guided choices -when (ASP; three items; e.g., I let my child eat between meals whenever she or he wants), Guided choices -what (ASP; three items; e.g., I allow my child to choose what she or he has for snacks), Guided choice -amount (ASP; three items; e.g., During meals, I allow my child to decide when she or he has had enough to eat.), Rules and limits around unhealthy foods (SP; four items; e.g., I place limits on the sweet or salty snacks (candy, ice cream, cake, potato chips, tortilla chips) that my child eats), Meal setting (SP; three items; e.g., Do you limit snacking to designated places in your home?; I insist my child eats meals at the table.), and Atmosphere of meals (SP; three items; e.g., Dinner time is usually a pleasant time for the family). Parents rated their use of these practices on a fivepoint Likert scale (Never, Rarely, Sometimes, Often, Always), for both the period before and during the lockdown. Higher scores indicated the use of more soothing with foods, more child autonomy, more rules and limits, a stricter meal setting, and a more positive meal atmosphere.

The items were translated from English to French by several researchers of the team, and some questions were slightly modified; to adapt them to the French situation (e.g., midafternoon snack "goûter" vs. other snacks/drinks) or to be more uniform within the entire questionnaire (Appendix A).

One additional feeding practice "Feeding on a schedule" was selected for this study. This three-item dimension (e.g., During the week, do you make him/her eat at set times?) was retrieved from the Infant Feeding Questionnaire (IFQ; [START_REF] Baughcum | Maternal feeding practices and beliefs and their relationships to overweight in early childhood[END_REF] and has already been validated for the use in French samples (Monnery-Patris, [START_REF] Monnery-Patris | Development of a new questionnaire to assess the links between children's selfregulation of eating and related parental feeding practices[END_REF]. Parents rated their answers on a five-point Likert-like scale (Never, Rarely, Sometimes, Often, Always), for both the period before the lockdown and during the lockdown.

Higher scores indicated stricter times for eating.

Parental motivations for buying foods

Changes in parental motivations for buying foods were assessed using 19 items. Most of these items were retrieved from the Questionnaire relating to Parental Motivations when buying food for children [START_REF] Rigal | Links between maternal feeding practices and children's eating difficulties. Validation of French tools[END_REF]. This 17-item instrument captures six dimensions of parental motivations: convenience (e.g., easy to cook), weight-control (e.g., not too high in calories), natural-content (e.g., fresh), health-concern (e.g., high in vitamins), preference (e.g., adapted to children's taste), price (e.g., good price-quality). Originally, parents are asked to rate their agreement with each item: e.g.,"For my child, I am careful to buy food which are… easy to cook" on a five-point scale ranging from "very wrong for me" (1) to "very true for me" (5). For this study, we wanted to evaluate the changes in parental motivations (during vs. before the lockdown) in a direct way, so we reformulated all items to e.g., "Compared to the period before the lockdown, you buy and prepare foods for your child(ren) that are… easy to cook". Parents indicated a possible difference on a five-point scale (Much less often than before, A bit less often than before, As often as before, A bit more often than before, Much more often than before). The answers were rescored to -2, -1, 0, 1, 2 respectively so negative scores would indicate a decrease, zero no change, and positive scores an increase. Four original items were deleted because they were less relevant for this study, and the dimensions sustainability (three items, i.e., locally produced; seasonal products; biological), pleasure (one item: pleasurable), conservation (one item: easy to store for a longer period) and comfort (one item: comfort foods) were added.

Parental eating and cooking behaviors and stress level at home

Parents were asked to rate their own frequency of intake of a mid-afternoon snack and of other snacks/drinks in between meals using the same scales as for the children, also for both the period before and during the lockdown.

Parents were also asked to report how often they felt stressed or tensed at home on a fivepoint Likert scale (Never, Rarely, Sometimes, Often, Always), for both the period before and during the lockdown. Higher scores indicated higher levels of stress at home. Parents were also asked to report changes in their emotional eating, in the preparation of homemade dishes, in the preparation of comfort foods, and in the time they spent cooking with their child(ren). These changes were directly rated on a five-point scale (Much less than before, A bit less than before, As often as before, A bit more than before, Much more than before). The answers were rescored to -2, -1, 0, 1, 2 respectively so negative scores would indicate a decrease, zero no change, and positive scores an increase.

The questionnaire also contained three open questions to ask parents about their food-related experiences during the COVID-19 lockdown. The results of these questions are not be presented in this paper.

Anthropometric data for parent and child

As measuring and weighing participants was impossible for the researchers during the COVID-19 lockdown, parents were asked to self-report their current weight and height, and the weight and height of their child. Parents were encouraged to report recent child measurements carried out by health professionals from the child's medical health book. If no recent measures were available in this book, or if the measurements of height and weight were not carried out within a time span of two months, we asked them to measure and/or weigh their child at home. Parents' and children's BMI were calculated by dividing their weight (kg) by their height (m) squared. For children, normed BMI z-scores were calculated using WHO's (2006) international growth standards for children.

Statistical analyses

R version 3.6.1 (R Core Team, 2019) was used to clean and analyse the data.

Data cleaning

Questionnaires were excluded when the child was younger than 3 years or older than 12.9 years (n=4), when the child had an illness (different from food allergy) susceptible of influencing his/her eating (e.g., autism, thyroid disease; n=8), or when the child was born very premature (< 28 weeks of gestation; n=0). When information on age, sex, illness or prematurity was missing, these questionnaires were also excluded (n=20).

Preliminary analyses

Cronbach's alphas were calculated to test the psychometric properties of the measures used for evaluating child eating behaviors and parental feeding practices before and during the lockdown. When these alphas were too low (< 0.60), confirmatory factor analyses (CFA) with a SEM approach [START_REF] Bollen | A new incremental fit index for general structural equation models[END_REF][START_REF] Kaur | Confirmatory factor analysis of the child-feeding questionnaire among parents of adolescents[END_REF] were performed to gain more insights in the factor structures and to potentially optimize them. Acceptable Cronbach alphas were observed for all child eating behaviors (ranging between 0.79 and 0.87). For parental feeding practices, some Cronbach's alphas were acceptable (ranging between 0.63 and 0.81; for soothing with food, rules and limits around healthy food, atmosphere of meals), some were borderline acceptable (ranging between 0.52 and 0.57; for guided choices -when, and feeding on a schedule) and some were found lower (ranging between 0.31 and 0.41; for guided choiceswhat and amount, and meal setting). In contrast, the CFAs indicated acceptable factor loadings for all practices, except for guided choices -amount. One item was deleted for this dimension because the factor loading was very low. Details are available in Appendix A.

Primary analyses

Scores were calculated for each dimension by averaging the scores of the corresponding items, for the period of the lockdown, and for the period before the lockdown. For the dimensions emotional overeating and soothing with food, the answer option "not applicable" was coded as missing value. For emotional overeating, 22 parents responded with "not applicable" to all corresponding items, and for soothing with food, six parents responded with "not applicable" to all items. These parents thus did not report changes in this behavior/practice during the lockdown compared to before the lockdown. Proportions of individuals showing a change (scoreduring lockdown -scorebefore lockdown ≠ 0) were calculated for each child behavior and each parental feeding practice. For those children/ parents for whom changes were reported, paired-samples t-tests were conducted for each behavior/practice in order to compare mean scores of both periods (Mduring lockdown -Mbefore lockdown). Simple regressions were performed to study the effects of changes in level of child boredom at home, child age, child sex, and child z-BMI (as a continuous variable) on changes in child eating behaviors. Simple regressions were also used to study the effects of parental demographics (parent's sex, BMI, relationship status, level of education, work status during lockdown, perception of financial status) and changes in parental stress levels at home, on changes in parental feeding practices, changes in parental motivations for buying foods, and on changes in parental cooking behaviors. Whenever the results of these simple regressions indicated multiple significant predictors for a given dependent variable, we subsequently performed a multiple regression analysis to verify if the relations remained significant after controlling for the effects of the other predictors. In all regression analyses, the dependent variables only included the children/parents for whom changes in their behaviors, practices or motivations were reported. This approach was chosen since this study was specifically designed to focus on possible predictors of the observed changes, but also for statistical reasons (i.e., to meet the assumption of normality, and to maintain a homogenous variance). The significance level was set at p < 0.05 for all analyses. Our analytic plan was pre-specified in our study file and submitted to the ethical committee before the data were collected.

Results

Participants

A sample of 498 parents of children aged 3.0-12.3 years (47.8% boys; M age = 7.3; SD = 2.2) was retained for analyses after data cleaning. The demographics for the parents are presented in Table 1. According to parental reports of child weight and height, 8% of children aged 3.0-5.0 years had underweight (z-BMI < -2), 68% had a normal weight (-2 ≤ z-BMI < 1), 18% were at risk for overweight (1 ≤ z-BMI < 2), 5% had overweight (2 ≤ z-BMI < 3), and 1% had obesity (z-BMI > 3) (categories derived from WHO, 2006). Among the children aged 5.1-12.3 years, 6% had underweight (z-BMI < -2), 69% had a normal weight (-2 ≤ z-BMI < 1), 15% had overweight (1 ≤ z-BMI < 2), and 9% had obesity (z-BMI > 2) (categories derived from de Onis, Onyango, Borghi, [START_REF] De Onis | Development of a WHO growth reference for school-aged children and adolescents[END_REF]. During the lockdown, the children in this study took on average 6.8 breakfasts a week at home, 6.8 lunches, and 7.0 dinners. Fourteen percent of parents reported taking more breakfasts with their child during the lockdown than before, 85% reported no difference, and 1% of parents reported a decrease.

For lunch, 59% of parents reported an increase in lunches taken with their child, 37% no difference, and 3% a decrease. Forty-six percent of parents reported an increase in the number of mid-afternoon snacks taken with their child, 50% no difference, and 4% a decrease. For dinner, 14% of parents reported an increase in dinners taken with their child, 86% no difference, and 1% a decrease.

Children

Changes in child eating behaviors (during versus before lockdown)

Sixty percent of parents reported a change on at least one dimension of their child's eating behaviors during the lockdown compared to the period before the lockdown. When looking only at the children with changed behaviors, paired-samples t-tests resulted in a significant increase for all behaviors but food pickiness (Table 2). The highest increases in mean score were observed for emotional eating (+0.61) and for food responsiveness (+0.44). b Answer modalities ranged from do not agree at all (1) to do completely agree (5).

Significant results (p < 0.05) in bold. In this study, two types of snacking were studied: the mid-afternoon snack (perceived as a meal for children in France) and snacks/drinks in between meals. The frequency of the midafternoon snack increased in 15% of children (during versus before the lockdown), decreased in 9%, and did not change in 76% of children. The majority of children already had a daily mid-afternoon snack before the lockdown, and maintained this habit during the lockdown (Table 3). Parents reported an increase in snack frequency in between meals in 36% of children, a decrease in 4% of children, and no change in 60% of children.

Links with child boredom, age, sex, and z-BMI

Forty-five percent of parents reported no change in their child's level of boredom at home during the lockdown compared to the period before the lockdown, 53% reported an increase in level of boredom, and 2% a decrease. A paired-samples t-test performed on the scores of the children for whom changes were reported (n = 276) indicated a significant increase in mean score of level of boredom (+1.20, t(275) = 26.82, p < 0.001; Mbefore = 2.28, SDbefore = 0.67; Mduring = 3.48, SDduring = 0.70).

Simple regressions indicated that a higher increase in children's level of boredom at home (during vs. before lockdown) was significantly linked with a higher increase in emotional overeating, in food responsiveness and in snack frequency in between meals (Table 5). Simple regressions also indicated that child age, child sex and child z-BMI were not significant predictors for changes in child boredom levels, neither for changes in child (mid-afternoon) snack frequency, nor for changes in child eating behaviors, except for a significantly lower increase in food responsiveness in children with higher BMI z-scores (β = -0.07, t = -2.96, p < 0.001). The results of these regression analyses, significant and non-significant, can be found in Appendix B.1.

Table 5

Simple linear regression models with the changes in child eating behaviors (when change occurred) as dependent variables, and the change in child level of boredom as independent variable.

Change in

Parents

Changes in parental feeding practices

Sixty percent of parents reported at least one change in their feeding practices during lockdown compared to the period before the lockdown. When including only the parents who reported a change, paired-samples t-tests resulted in a significant increase in mean scores for soothing with food, guided choices -when, what and amount, and meal atmosphere. A significant decrease was observed for rules and limits around unhealthy foods, meal setting, and feeding on a schedule (Table 6). The highest increases in mean score were observed for soothing with food (+0.43) and guided choices -when (+0.36), the highest decrease was observed for feeding on a schedule (-0.40).

Table 6

Parental feeding practices: percentage of total sample of parents (N = 498) reporting a change (%), mean scores before and during the lockdown (M before and M during) for these parents with changed practice, standard deviations (SD), difference in mean scores (M difference = M during -M before), and paired-samples t-tests (t value and p value). a Higher scores for guided choice indicate higher levels of autonomy granted to the child.

Parental

b Meal setting refers to the place where the child eats, higher scores indicate stricter rules.

Changes in parental motivations for buying foods

Eighty-five percent of parents reported at least one change in their motivations to buy and prepare certain foods for their child(ren) during the lockdown compared to the period before the lockdown. For each motivation dimension, proportions of parents who reported no change, a decrease, or an increase are presented in Fig. 1. Highest increases in motivation were observed for buying pleasurable and sustainable foods. The highest decrease in motivation was observed for buying convenient foods.

Fig. 1 Proportions of parents who reported a decrease (-), no difference (=), and an increase (+) in their motivation to buy/ prepare certain foods for their child(ren).

Changes in parental eating and cooking behaviors

The frequency of the mid-afternoon snack increased in 35% of parents (during versus before the lockdown), decreased in 4%, and did not change in 61% of parents. Thirty-one percent of parents reported an increase in their snack frequency in between meals, 8% reported a decrease, and 62% no change. The frequencies of both snack occasions in parents before and during the lockdown are presented in Table 3. When asked if the lockdown and the accompanying emotions (e.g., boredom, stress, anxiety) induced parents to have more, the same or less desire to eat during the lockdown than before, 46% of parents answered that they felt more like eating than before, 41% of parents reported no change, and 14% of parents reported feeling less like eating than before.

When asked about the preparation of homemade dishes, 66% of parents reported preparing more homemade dishes than before, 30% reported no change, and 4% of parents reported preparing less homemade dishes. When asked about the preparation of comforting foods or recipes, 57% of parents reported preparing more comforting foods or recipes, 40% reported no change, and 3% reported preparing less. When asked about the time they spent cooking with their child(ren), 71% of parents reported spending more time cooking with their child(ren), 26% reported no change, and 2 % reported spending less time cooking together.

Links with changes in parental level of stress and parental demographics

Effects of changes in parental stress level on parental feeding practices

Forty-four percent of parents reported no change in their level of stress at home during the lockdown compared to the period before the lockdown. An increase in level of stress was reported by 42% of parents and a decrease by 14%. A paired-samples t-test performed on the scores of the parents with a change in their stress level (n = 280), indicated a significant increase in mean score of stress level with +0.59 (t(279) = 7.70, p < 0.001; Mbefore = 2.74, SDbefore = 0.86; Mduring = 3.33, SDduring = 0.93).

Simple regressions indicated that higher increases in stress level were linked with higher increases in guided choice -amount (more autonomy for the child to decide the amount of intake) (Table 7): on average, guided choice -amount increased during the lockdown (Table 6), and this increase was even higher if stress level increased. Also, on average, the meal time atmosphere quality improved during the lockdown (Table 6), but not for those parents who became more stressed at home (Table 7). More specifically, compared to the period before the lockdown, there was no improvement in meal atmosphere quality if parents' stress level increased by one unit, and there was a decrease in atmosphere quality if the stress level increased by more than one unit.

Table 7

Simple linear regression models with the changes in parental feeding practices (when change occurred) as dependent variables and the change in parental level of stress as independent variable.

Change in

Effects of parental demographics on changes in parental feeding practices

Some parental demographics were also identified as significant predictors of changes in parental feeding practices. Simple regressions indicated that the decrease in rules and limits around unhealthy foods (Table 6) was even larger among parents with a higher level of education (β = -0.08, t = -2.45, p = 0.02; see Appendix B.2). Feeding on schedule decreased on average (Table 6), but a lower decrease was observed in more educated parents (β = 0.11, t = 2.56, p = 0.01; see Appendix B.2). In other words, parents became more permissive regarding the times to eat, but to a lower extent among higher educated parents. Parental sex significantly predicted changes in guided choices -when (β = 0.22, t = 2.32, p = 0.02): mothers showed an increase in this practice and thus granted increased autonomy to the child in deciding when to eat, while fathers did not show such a change. Finally, a higher parental BMI predicted a significantly lower increase in meal atmosphere quality (β = -0.03, t = -2.47, p = 0.01). The results of all regression analyses, significant and non-significant, can be found in Appendix B.2.

Effects of parental demographics on changes in parental cooking behavior

Regarding parental cooking behaviors, simple regressions indicated that a higher level of education and a more comfortable perceived financial status predicted higher increases in time spent cooking with the child (Table 8). However, for level of education, this result became non-significant after adjustment for financial status in a multiple regression model (β = +0.05, t = 1.69, p = 0.09).

Table 8

Simple linear regression models with changes in cooking behaviors (when change occurred) as dependent variables and parental demographics as independent variables. 365 -0.00 0.08 -0.04 0.96 a No work refers to those parents who were at home without work; e.g., those who were technically unemployed due to the lockdown, parents on parental leave, students, etc.

Df

b Perceived financial status ranges from less to more comfortable. § No longer significant after adjustment for financial status (multiple regression). * Remains significant after adjustment for level of education (multiple regression).

20

Effects of parental demographics on changes in parental motivations for buying foods

Some parental demographics were also identified as significant predictors of changes in parental motivations for buying foods for their child(ren). Employment status during the lockdown significantly predicted changes in the motivation to buy convenient foods: parents who were working from home (β = -0.54, t = -3.18, p < 0.001) and parents who were at home without work (β = -0.41, t = -2.41, p = 0.02) showed a significant decrease in this motivation, while parents working outside the home showed no significant change in this motivation. In simple regressions, parental level of education (β = -0.11, t = -2.18, p = 0.03) and parent BMI (β = 0.03, t = 2.05, p = 0.04) also significantly predicted changes in the motivation for buying convenient foods. However, in a multiple regression including these three predictors (work status, level of education, parent BMI), only the effect of work status remained significant when adjusted for the effects of these other predictors.

Furthermore, in simple regressions, parents with a higher level of education showed a higher increase in the motivation to buy healthy foods (β = 0.13, t = 3.25, p < 0.001), foods linked to weight control (β = 0.12, t = 2.37, p = 0.02), comforting foods (β = 0.12, t = 2.28, p = 0.02), and sustainable foods (β = 0.17, t = 5.04, p < 0.001) than parents with a lower level of education. In a simple regression model, perceived financial status also significantly predicted changes in the motivation to buy foods related to weight control (β = 0.13, t = 2.08, p = 0.04), but in a multiple regression model, both the effects of level of education and financial status became non-significant after adjustment for each other's effect. Also, in simple regressions, parents with a more comfortable perceived financial status showed a higher increase in the motivation to buy sustainable foods (β = 0.14, t = 3.19, p < 0.001) and single parents showed a lower increase in this motivation (β = -0.37, t = -2.57, p = 0.01) compared to parents with a less comfortable financial status and parents with a partner. In a multiple regression, level of education and family situation ("single parent") remained significant predictors for sustainability after adjusting for each other's effects, but not financial status. Finally, parents with a higher BMI showed a lower increase in the motivation to buy foods that can easily be preserved ("conservation") (β = -0.04, t = -2.22, p = 0.03). The results of all regression analyses, significant and non-significant, can be found in Appendix B.3.

Discussion

This study wanted to evaluate possible changes in eating and feeding habits in families with young children during the COVID-19 lockdown in France, versus the period before the lockdown. The results showed that not all, but a majority of parents reported some changes in their child's eating behaviors, in their feeding practices, their food shopping motivations, and in their own eating and cooking behaviors. This clearly indicates that the lockdown had an important impact on families' eating and feeding habits at home. Children showed significant increases in "food approach" behaviors during the lockdown (behaviors involving a movement toward or a desire for foods: i.e. food enjoyment, emotional overeating, food responsiveness [START_REF] Vandeweghe | Food approach and food avoidance in young children: Relation with reward sensitivity and punishment sensitivity[END_REF]Webber, Cooke, Hill, & Wardle, 2010)). Children's snack frequency in between meals also increased significantly. Moreover, increases in emotional overeating, food responsiveness and snack frequency were predicted by an increase in child boredom at home: children may have tried to "fill up" their time with eating or found comfort and enjoyment in food during this unusual, monotonous period. In children, the literature related to bored-eating is scarce and the construct is often lumped together in questionnaires with emotional-and stress-eating (e.g., in CDEBQ, CEBQ). In this study, we also studied emotional overeating in a more general way with the CEBQ (four items studying overeating in response to both boredom, anxiety, annoyment, and worry). However, recent studies have indicated that bored-eating is viewed as a distinct construct by mothers, and may be a more common practice in children than emotional-or stress-eating. Therefore, the authors suggested that it may be of interest to present and to study bored-eating separately from other emotions [START_REF] Hayman | Low-income women's conceptualizations of emotional-and stress-eating[END_REF]Koball, Meers, Storfer-Isser, Domoff, & Musher-Eizenman, 2012). In adults, boredom has previously been found to increase the desire to eat unhealthily (e.g., [START_REF] Moynihan | Eaten up by boredom: Consuming food to escape awareness of the bored self[END_REF]. Similar to the results in adults, our results showed that increased boredom in children was strongly related to increased food responsiveness, increased emotional overeating and increased snack frequency. Our study thus showed that also in (young) children boredom can play a role in their desire for foods.

Moreover, even though the COVID-19 lockdown was an unusual situation, the increased manifestation of these food approach behaviors and their link with child boredom could be cause for concern. It suggests that these children did not merely rely on their internal cues of hunger and satiety when asking for foods/drinks (crucial for an optimal self-regulation of food intake); and ignoring internal cues could possibly make children overeat and induce weight gain if maintained for a long period [START_REF] Kral | Caloric compensation and eating in the absence of hunger in 5-to 12-y-old weightdiscordant siblings[END_REF][START_REF] Monnery-Patris | Development of a new questionnaire to assess the links between children's selfregulation of eating and related parental feeding practices[END_REF]. With age, research has shown that children rely less on their internal cues for their food intake (e.g., [START_REF] Fox | Relationship between Portion Size and Energy Intake among Infants and Toddlers: Evidence of Self-Regulation[END_REF]. It is therefore important to encourage children (and their caregivers) from a young age to listen to their inner sensations for food intake, and to maintain this even in more challenging situations. Parents and schools could play an important role in guiding children in using adaptive self-regulation strategies and in modeling these strategies. In both children and adults, several types of interventions such as mindfulness-based interventions and appetite awareness trainings have been proposed to increase awareness of hunger and satiety cues, with various levels of success (e.g., in adults: [START_REF] Alberts | des consommations alimentaires (Etude INCA3). Actualisation de la base de données des consommations alimentaires et de l'estimation des apports nutritionnels des individus vivant en France[END_REF][START_REF] Craighead | Appetite awareness training: A cognitive behavioral intervention for binge eating[END_REF][START_REF] Kristeller | Mindfulness-based eating awareness training for treating binge eating disorder: the conceptual foundation[END_REF]Van de Veer et al., 2012;in children: Bloom, Sharpe, Mullan, Zucker, 2013;[START_REF] Boutelle | Two novel treatments to reduce overeating in overweight children: A randomized controlled trial[END_REF][START_REF] Johnson | Improving preschoolers' self-regulation of energy intake[END_REF][START_REF] Johnson | Improving preschoolers' self-regulation of energy intake[END_REF][START_REF] Lumeng | Improving self-regulation for obesity prevention in head start: a randomized controlled trial[END_REF]. Some interventions were for example successful in the short term, but not in the long term [START_REF] Bloom | A pilot evaluation of appetiteawareness training in the treatment of childhood overweight and obesity: A preliminary investigation[END_REF]. Reigh and colleagues (2020) recently also suggested a technology-enhanced intervention for preschoolers, using an interactive character-based technology platform and educational materials for parents, to improve preschoolers' energy intake regulation and their knowledge related to hunger, fullness and digestion. In their pilot study, preschoolers' (N=33) knowledge increased significantly and boys' short-term energy compensation improved following a 4-week intervention.

The results of our study further showed that when feeding practices were adapted, there was a significant trend to more permissive, child-centered and pleasure-oriented practices: parents reported less rules and limits, more soothing with food and gave more autonomy to the child in deciding when, what, how much and where to eat. Regarding the types of foods offered during snacking, we also observed increased intake of so-called "comfort foods". The theory of division of autonomy states that parents should be mainly responsible for what, when and where the child eats, but the child for the amount of food eaten [START_REF] Satter | The feeding relationship: problems and interventions[END_REF]Vaughn et al., 2015). Here, we could thus argue that parents may have become a bit too permissive regarding the types of foods offered during the lockdown, there was also a significant decrease in structure of the meals (timing of meals, place). By contrast, the increases in guided choices (i.e., more child autonomy) may indicate that parents had the opportunity to listen better to children's needs and demands, and to respond to them in a more responsive way (even though we are aware that these child demands were not only based on children's internal cues, as discussed above). Interestingly, our results also showed that parental level of stress played a role in changes in parental feeding practices during the lockdown: higher increases in stress predicted higher increases in giving autonomy to the child regarding the amount to eat, and no improvement in meal atmosphere quality (in contrast to parents with no increases in stress). Furthermore, parents showed many changes in their motivations when buying foods for their children. Highest increases in motivations were observed for buying pleasurable foods, sustainable foods, natural foods and healthy foods. These findings are in accordance with the findings of a French survey that was carried out by Ipsos during the lockdown in April 2020 for L'Observatoire E.Leclerc des nouvelles consommations: they found that French consumers aged 16-75 years turned more to products of French origin (45%), fresh products (37%) or products from short circuits (37%). Sixty-three percent of consumers claimed that they consumed more local products in order to support the local economy during the lockdown. For the parents in our study, pleasure also became an important motivation, and this is in line with the observed increases in snack frequency in both parents and children, increased emotional eating in both, and the increase in the preparation of comforting foods/recipes during the lockdown. From a cultural point of view, family meals in France were already known to be strongly pleasure-oriented [START_REF] Lhuissier | Who still eats three meals a day? Findings from a quantitative survey in the Paris area[END_REF], and the lockdown seemed to have reinforced this. Convenience became less important for many parents, which can be supported by their reported increase in the preparation of home-cooked meals and their increase in time cooking with their children. Di Renzo and colleagues (2020) also observed this increase in homemade recipes during the lockdown in Italy.

In the present study, parental motivations for buying foods for their child(ren), changes in parental feeding practices and parental cooking behaviors were significantly predicted by parental characteristics. We observed that especially a higher level of education was linked to some more favorable changes in behaviors: for example, maintaining to eat at set times, buying more sustainable and healthy foods, more cooking with the child, preparing more homemade dishes (marginal effect: p = 0.06). These results may imply that it is of interest to take into account parental level of education when planning interventions to improve parental feeding behaviors. Parents with different levels of education may experience different barriers and facilitators for changing their behaviors. It seems that, during the lockdown, increased time at home could have played a role in facilitating cooking with the child, preparing homemade dishes and buying more local, sustainable foods, but more particularly for parents with higher levels of education. Previous studies have already shown that parental education level is linked to differences in parental feeding practices and in parental motivations when buying foods for their child. For instance, parents with lower levels of education tend to be less concerned by health and more concerned by children's preferences when buying foods [START_REF] Rigal | Food portion at ages 8-11 and obesogeny: The amount of food given to children varies with the mother's education and the child's appetite arousal[END_REF], they serve larger portion sizes [START_REF] Hébel | Nouvelles données sur les déterminants des quantités consommées[END_REF]Rigal et al., 2019) and are less likely to restrict their child's intake of unhealthy foods (Wijtzes, Jansen, Jansen, Jaddoe, Hofman, Raat, 2013).

The COVID-19 pandemic has changed our habits in many ways during the lockdown, but even after months, we have not gone back to the situation "before the pandemic". As we are still reshaping some of our habits, we suggest that future research and policy makers also focus on the implications for the food domain in all its facets, this by also taking into account possible facilitators and barriers linked to people's socio-demographic characteristics.

We acknowledge that there were several limitations to this study. First, parental practices and behaviors were self-reported in this study and may be subject to social desirability bias even though the questionnaires were anonymous. The children's eating behaviors and level of boredom were also parent-reported and thus reflected the parent's perception. Second, the data obtained about the period before the lockdown was reported retrospectively, possibly leading to a recall bias that can threaten the internal validity of our study (Delgado-Rodriguez & Llorca, 2014;Hassan et al., 2005). Yet, recall accuracy diminishes with increasing time gap, and as the time gap in this study was very small (max. eight weeks), we think the recall bias was limited here. Here, we also want to note that we did not define "the period before the lockdown" for the parents. It is therefore possible that parents interpreted this period in different ways (more or less broad) and thus responded differently based on their own interpretation, with possible corresponding effects on our results. We hope, however, that the differential interpretations would be limited because of the high contrast between the two periods parents needed to report on: the "normal" life and related general habits right before the lockdown versus those during the lockdown.

Meanwhile, this study also has several strengths. To our knowledge, it is the only study that looked in a more systemic way at changes in families' food habits during the COVID-19 lockdown, including eating and cooking behaviors, parental feeding practices and parental motivations when buying foods for the family. Other studies tend to focus uniquely on adults or on children. Our sample may not be entirely representative of the national population in France: there was for example a relatively small sample of parents with a low level of education (33.5% in our sample compared to approximately 55% in the French population [START_REF] Insee | France, portrait social, édition 2016[END_REF]), and the majority of our participants were female (71.7%). However, we managed to recruit parents with diverse profiles, also in terms of work status, perceived financial situation, relationship status, and BMI categories (of both children and adults) that were very close to representativeness in the French population [START_REF] Argouarc'h | Les niveaux de vie en 2016. La prime d'activité soutients l'évolution du niveau de vie des plus modestes[END_REF]Verdot, Torres, Salanave, Deschamps, 2017). This enabled us to obtain a broad idea of the changes in eating and feeding habits in young children and their parents in France, and of the parental characteristics that were linked to these changes.

Conclusion and perspectives

This study provided unique insights into how a drastic change in habits is accompanied by changes in eating and feeding habits both on parent and child level. The unusual situation drove some parents to turn a blind eye to the usual feeding rules, and to privilege enjoyment and comfort at home. Changes in child boredom and parental stress were found to influence eating and feeding behaviors, and some parental characteristics were identified as possible barriers and facilitators for eating, feeding and cooking behaviors. These insights could be useful for future studies and interventions, and could be of interest to policy makers.

Qualitative studies that reflect the experiences of parents and children during the lockdown could also be interesting to complement our results. They could provide us, for example, with more insights into reasons why eating behaviors, feeding practices and food shopping motivations have changed or not, and if the lockdown and the accompanying changes have had an impact on families' food habits on a longer term and why.

Table 1 .

 1 Demographics for parents.

	Demographic

Table 2

 2 Child eating behaviors: percentage of total sample of parents (N = 498) reporting a change for their child (%), mean scores before and during the lockdown (M before and M during) for these children with changed behaviors, standard deviations (SD), difference in mean scores (M difference = M during -M before), and paired-samples t-tests (t value and p value).

	Child eating behavior	%	M (SD)	M (SD)	M	t	p
			before	during	difference
	Emotional overeating a	31	2.43 (0.74) 3.05 (0.91)	0.61	12.43	<0.001
	Food responsiveness a	45	2.46 (0.70) 2.90 (0.93)	0.44	11.49	<0.001
	Food enjoyment b	28	2.69 (0.58) 2.96 (0.86)	0.27	3.87	<0.001
	Appetite b	33	2.18 (0.76) 2.30 (0.93)	0.12	1.98	0.049
	Food pickiness b	20	2.97 (0.89) 2.85 (1.01)	-0.12	-1.41	0.162
	a Answer modalities ranged from never (1) to always (5).

Table 3

 3 Frequency of mid-afternoon snacks and of snacks/drinks in between meals for all children and all parents (N = 498), before and during the lockdown.Concerning the types of foods consumed by the children during (mid-afternoon) snack occasions, 66% of parents reported at least one change in consumption during the lockdown versus before. When studying only the children with a change in their consumption, pairedsamples t-tests resulted in a statistically significant increase in mean scores (Mduring lockdown -Mbefore lockdown) for candy/chocolate, fruit juices, sodas, chips/salty biscuits, ice creams, pastries/cake/sweet cookies, cream dessert, milks, yoghurt/cheese/quark, fresh and dried fruits, and nuts. A significant decrease in the consumption of compote/fruits in syrup was observed (Table4).

	Children	Parents
	before (%)	during (%)	before (%)	during (%)

Table 4

 4 Snacking frequency: percentage of total sample of parents (N = 498) reporting a change for their child (%), mean scores before and during the lockdown (M before and M during) for these children with

	Types of food/drinks consumed	%	M (SD)	M (SD)	M	t	p
	during (mid-afternoon) snacks		before	during	difference
	Candy, chocolate	26	2.57 (0.86) 3.47 (0.98)	0.89	9.26 <0.001
	Fruit juice	22	2.36 (1.01) 3.09 (1.10)	0.73	7.53 <0.001
	Soda	11	2.13 (0.83) 3.02 (0.99)	0.89	7.24 <0.001
	Chips, salty biscuits	13	2.33 (1.06) 3.17 (1.06)	0.83	6.47 <0.001
	Ice cream	27	2.20 (0.71) 2.66 (1.14)	0.58	5.68 <0.001
	Pastries, cake, sweet cookies	30	2.97 (0.95) 3.48 (1.09)	0.52	4.76 <0.001
	Cream dessert	15	2.20 (0.94) 2.80 (1.13)	0.61	4.35 <0.001
	Milks	19	2.53 (1.00) 3.06 (1.26)	0.54	4.02 <0.001
	Yoghurt, cheese, quark	21	2.39 (1.00) 2.90 (1.16)	0.50	3.95 <0.001
	Fresh and dried fruits	23	2.63 (1.00) 3.00 (1.15)	0.37	3.29	0.001
	Nuts	10	2.23 (0.88) 2.69 (1.15)	0.46	2.68	0.010
	Bread	28	2.70 (0.91) 2.92 (1.16)	0.22	1.96	0.052
	Sandwich, pizza, savory pies	4	2.58 (0.69) 3.05 (1.08)	0.47	1.69	0.108
	Cheese	11	2.43 (0.95) 2.66 (1.18)	0.23	1.29	0.204
	Cereals, cereal bars	22	2.42 (0.86) 2.52 (1.11)	0.10	0.82	0.414
	Compote, fruits in syrup	25	3.26 (1.11) 2.97 (1.20)	-0.29	-2.24	0.027
	Answer modalities ranged from never (1) to always (5).
	Significant results (p < 0.05) in bold.

changed behaviors, standard deviations (SD), difference in mean scores (M difference = M during -M before), and paired-samples t-tests (t value and p value).

Estimate Std. Error t p More homemade dishes

	Level of education	347 0.07	0.04	1.87	0.06
	No work a [ref working outside]	346 0.16	0.12	1.41	0.16
	Working from home [ref working outside] 346 0.18	0.12	1.50	0.13
	Financial status b	344 0.03	0.04	0.75	0.46
	Single parent [ref couple]	347 -0.20	0.13	-1.51	0.13
	Parent BMI	347 0.01	0.01	1.27	0.20
	Parent sex [ref men]	347 0.03	0.09	0.32	0.75
	More time spent cooking with child
	Level of education	365 0.06	0.03	2.11	0.04 §
	No work a [ref working outside]	364 0.07	0.10	0.71	0.48
	Working from home [ref working outside] 364 0.03	0.10	0.27	0.79
	Financial status b	362 0.09	0.04	2.34	0.02*
	Single parent [ref couple]	365 -0.14	0.11	-1.28	0.20
	Parent BMI	365 0.00	0.01	-0.67	0.50
	Parent sex [ref men]

Acknowledgements

The authors would like to thank the independent ethical committee Inserm for accepting to review their study protocol in a very limited time span. They also thank the participants for their interest in the study.

Funding sources

This work was supported by the European Union's horizon 2020 research and innovation program (Marie Sklodowska-Curie grant agreement No 764985: EDULIA project); and by a grant from the Conseil Régional Bourgogne, Franche-Comte (PARI grant).

Authors Contributions

KP, SI and SM-P conceptualized the study. KP and CC conducted all analyses. KP wrote a first version of the manuscript, thereafter all authors contributed to editing the manuscript and they all approved the final article. van de Veer, E., van Herpen, E., & van Trijp, H. (2012)


M Bossoukpe

E Faye

O Ndiaye

S Diatta

O Diatta

A A Diouf

M Dendoncker

M H Assouma

S Taugourdeau

Low-cost drones help measure tree characteristics in the Sahelian savanna

Lowcost UAV can be used in Sahelian savanna to evaluate tree morphology variables.

Introduction

Savanna is defined is a mixed woodland-grassland ecosystem, where trees are often sparse with low ground cover. Two plant communities thus coexist: a herbaceous cover with either annual or perennial species and a woody layer with trees and shrubs (Hiernaux and Le Houérou, 2006). Savanna ecosystems account for 50% of the African continental area (Olson et al., 2001). The Sahelian Acacia savanna type covers around 3,042,417 km², i.e., 10% of the African continent (Olson et al., 2001). The Sahel is a semi-arid region between the Sahara desert and the Guinean coastal rainforest extending from Senegal in the west to Djibouti in the east. The Sahelian bioclimatic region is generally defined by annual rainfall of between 200 mm and 700 mm. Rain falls during the wet season (from June to October), with high temporal variability (Nicholson, 2013).

Woody vegetation in the Sahelian savanna ecosystem, especially trees, provides numerous ecosystem services. As some species bear leaves during the dry season (from November to May), they provide fodder for animals when the grass layer is depleted (Assouma et al., 2018) . Trees and shrubs have a longer vegetative period than herbaceous plants, making them an important contributor to the carbon, nutrient and water cycles. Their deep rooting system enhances water and nutrient uptake and largely contributes to evapotranspiration (Sinare and Gordon, 2015). In addition, the nutrients uptaken benefit all plants through litter deposits (Sinare and Gordon, 2015). Moreover, by providing shade, the trees create microclimates that enhance the productivity of the grass layer in Sahel (Grouzis and Akpo, 2006). They are also used by the local population for food, housing construction, energy and medicines.

In the 1970s and 1980s, woody vegetation was severely affected by droughts. Many authors reported a decrease in density and in species richness (Gonzalez et al., 2012;Herrmann and Tappan, 2013), whereas some remote sensing studies reported large-scale greening of the Sahel (Dardel et al., 2014;Olsson et al., 2005). An increase in woody cover seems to have been one of the reasons for the greening [START_REF] Anchang | Trends in woody and herbaceous vegetation in the Savannas of West Africa[END_REF]Brandt et al., 2015).

Field measurements, such as tree height, crown diameter, trunk circumference and species identification are thus required to document these changes and analyse their consequences for ecosystem services. However, field inventories are time consuming and require a substantial workforce. Furthermore, a sampling protocol needs to be established to study woody vegetation, especially the minimum sampling area required.

UAV is a new technology that could provide indirect and low-cost measurement protocols for woody communities. UAVs can produce very high definition maps of the vegetation in an area of several hectares in a few minutes. 3D vegetation models can be built by combining numerous images with multiple view angles using RGB photogrammetry (Grenzdörffer et al., 2008). Photogrammetry is a technique that uses several pictures of the same object to produce 3-dimensional representations of it. The processes use different viewpoints of the images to create a 3D model of the object. This process is called 'structure from motion' (SfM). During a UAV flight, many overlapping pictures can be taken. A photogrammetric software can then be used to build a digital surface model (DSM) and a digital terrain model (DEM). Using these 3D models makes it possible to compute a canopy height model (CHM) as the difference between the DSM and the DTM. With this technique, UAVs can be used to measure the structural metrics of woody plants, such as height, crown area, volume (Mayr et al., 2018;Sarron et al., 2018b;Surový et al., 2018) or for spatial patterns and species distributions (Wu et al., 2021). One interesting aspect of 3D models based on photogrammetry is that they can be computed using a simple RGB camera and do not require specific sensors such as NIR or LIDAR. For this reason, as commercial UAVs have recently become widely available and inexpensive, they can be very useful. These low-cost UAVs could easily be used in developing countries in the Sahel region.

In this context, the purpose of this study was to assess the characteristics of tree communities in the Sahelian savanna ecosystem by comparing field measurements and measurements made using low-cost UAV-based products.

Material and methods

The overall approach of the study is schematised in Figure 1.

Study sites

All the measurements were carried out at the ISRA (Senegalese Agricultural Research Institute) Dahra Djoloff zootechnical centre (coordinates:15.3665, -15.398935 UTM 28N), which receives an average 400 mm of rain during the three months of the wet season between August and September. This research station covers 6.800 hectares, with 4.800 hectares of natural savanna and 2.000 hectares of Senegalia senegal(L.) Britton plantations for the production of gum arabic (Ndiaye et al., 2014;Ndiaye et al., 2015). The plantations were established in the savanna area belonging to the station. The main savanna tree species in the study area are Balanites aegyptiaca (L.) Del., Vachellia tortilis (Forssk.) Galasso & Banfi, Senegalia senegal(L.) Britton, Vachellia nilotica (L.) P.J.H.Hurter & Mabb., Vachellia seyal (Delile) P.J.H.Hurter and Boscia senegalensis (Pers.) Lam. ex Poir. The soils at the study sites are sandy and sandy-clay with a succession of dunes and low-lying hilly areas.

Twenty-four one-hectare plots on the station were selected. The plots were a subset of plots that were sampled in the studies by Raynal (1964) and Ndiaye et al. (2015). The initial selection was made to represent the spatial variability of the vegetation at the station.

UAV and flight plans

A DJI® spark UAV (DJI, DJI Inc., Shenzhen, China) equipped with the native RGB (Red-Green-Blue) camera of the DJI spark was used in the study (1/2.3" CMOS® sensor with an effective resolution of 12 Mpx). The lens of the camera has a field of view of 81.9° 25 mm (35 mm format equivalent) and a shooting range of 2 m to ∞ (f/2.6). Litchi applications (i.e., flyover mission) were used for the flight plans (https://flylitchi.com). For each plot, one flight with six 100-m transects each separated by 20 m was programmed at an altitude of 80 m and a speed of 5 m.s-1. Pictures were taken at two-second intervals throughout the flight in autofocus mode. The camera was set at an angle of 80°.

The frontal overlap was around 90% and the side overlap around 80%. All flights took place at the end of the rainy season (between the 1 st and the 15 th of October 2018), at the peak of grass growth.

Image processing

The images taken during each flight were processed using a PiX4D mapper (Pix4D SA, Lausanne, Switzerland). 3D mapping is the basic parameter proposed in the software. For each plot, an orthophotograph, a digital surface model, and a digital elevation model were computed and exported in GeoTIFF format. These different raster images were then further processed using ArcGIS 10.3 (ESRI, Redlands, CA, USA). The difference between the digital surface model and the digital elevation model provided the canopy height model (CHM).

From the orthophotograph, and in each plot, 10 trees were selected and their crowns were manually delineated to calculate the area covered. The 10 trees were selected based on their crown areas to cover the range of crown areas present in the plots. The geotag and the position on the image were recorded. When a tree was not found on the ground, we selected another tree on the ground in its place.

The crown limits obtained (i.e., crown masks in shapefiles) were then used to extract the tree height from the CHM. For each tree, the computed UAV variables were the crown area, average height, minimum and maximum heights, and the red, green and blue reflectance across the crown limit. The colour contrasts between the different images were not corrected.

Field measurements

Field measurements on trees were carried out in December 2018. First, the trees previously selected were identified using the GPS coordinates extracted from the orthophotograph of the plots. Measurements of the trees were then taken in the field. The measured variables were the maximum height of the tree (using a clinometer), the diameter of the tree crown in the north-south direction (NSD) and in the west-east direction (WED). All woody species were identified at the species and genus levels and their tree crown area was calculated using the following formula assuming that the crown was a circle:

= ((NSD + /4)² x π

Data analysis

Tree height and crown area For tree height and crown area, a linear regression model was built between field and UAV variables. The model residuals were analysed according to the species, the genus, the plot, the crown area, the maximum height, and the crown area to maximum height ratio using ANOVA.

The aim of the ANOVA was to identify ways of improving predictions. The linear model was built using 66% of the dataset, randomly selected. Model performance was then assessed using the remaining 33% of samples. A bootstrap technique was used to assess the predictive performance of the model. The root mean square error (RMSE) was computed from predicted and measured variables. RMSE was computed for the 100 replicates and the average value was used as the accuracy metric of the prediction model.

Species identification.

The main objective of this analysis was to check the potential of UAV outputs to identify the species and genus of the trees in the study area. The UAV variables used were crown area, tree height (mean crown height and maximum height) and the area to height ratio obtained from the 3D model. The colour (i.e., RGB) values of the trees were also extracted from the orthophotograph. The extracted values were then used to calculate three normalized difference indices considering colours two by two, and using the following equation (C1 is the value for colour 1 and C2 the value for colour 2).

1 2 = ( 1 -2) 1 + 2
The calculated indices were (i) between green and red (NGRDI), (ii) between blue and green (NBGRI), and (iii) between blue and red (NBRDI). We performed a random forest analysis (Breiman, 2001;Breiman et al., 1984) to predict the species and the genus of each tree. The V The input variables in the random forest were the different colour indices and the variables obtained from the 3D model. We used the Random Forest package in Rcore software. We obtained a confusion matrix from the random forest. We also extracted the importance of each input variable of the random forest. All analyses were carried out using R 3.23. (R Core,

2013)

Results

The field inventory showed that six woody species were present in the study site: Vachellia tortilis, Senegalia senegal, Vachellia nilotica, Vachellia seyal, Balanites aegyptiaca and Boscia senegalensis. The full list of available metrics for the 239 trees can be found in appendix 1.

Tree height

The height of the assessed woody plants ranged from 0.37 m to 13.20 m (average 5.57 m).

Figure 2 shows the correlation between the maximum tree height measured with the UAV and in the field. This relationship was significant at P < 0.01 and with an R² of 0.83. A residual analysis of the model showed that all the tested parameters (species genus, area, area/height ratio (AH) and plot) had a significant effect on the residuals. However, the effect was quite low for all the parameters (R² ranging from 0.023 to 0.041) with the exception of the plot parameter. The plot parameter explained 40% of the variability of the model residuals (R² = 0.40). When the plot was included in the model between field height and UAV height measurements as a nested factor, R² rose to 0.92. The root mean square error (RMSE) was 0.95 m.

Tree crown area

The woody crown area measured in the field ranged from 1.1 m² to 213.7 m² (average 29.03 m²). The relationship between field and UAV measurements was significant (P < 0.01) and showed a high R² (0.93) (Figure 3). Table 1 lists the results of the residuals analysis. The plot had the most effect on the residuals; the R² was 0.14 for the model explaining the residual variability from the different plots. The area/height ratio also had on effect on the residuals.

The root mean square error mean obtained from the bootstrap was 7.46 m².

Species identification

Of the 239 trees whose contours were available, 132 were B. aegyptiaca, 54 V. tortilis, 29 S senegal, 10 B. senegalensis, 9 V. nilotica and 5 V. seyal.

Discussion

Low-cost UAVs can be used to assess the dendrometric parameters of woody species in the savanna

Our results show that low-cost UAVs can successfully assess certain characteristics of the woody vegetation in the Sahelian savanna. Indeed, strong relationships were found between tree characteristics measured in the field and those obtained from the UAV. For tree height, we found a very good correlation with no more than 1m difference between the field measurement and the UAV measurements. The difference between field and UAV measurements was not necessarily due to inaccuracies produced by the UAV. The clinometer is not the most accurate of field methods for estimating height (Williams et al., 1994). Similar results for estimated tree height were found in the literature for different types of tree communities: tropical forests, orchards, plantations (Sarron et al., 2018b). We assume that UAV is sufficiently accurate compared with the other methods. The tree crown area estimated in the field was largely explained by the area measured on the orthophotograph (Figure 3). The area in the field was obtained by considering that the tree crown is circular.

Conversely, in the UAV measurement, the border of the tree crown was manually drawn on the orthophotograph. It can be assumed that the area obtained with a UAV would be more accurate for a tree with an unbalanced crown.

Our results also showed that UAV metrics can discriminate between the four species present in the study area. Woody vegetation diversity was quite low at the study site where the woody species displayed very distinct features in terms of shape (only four species were represented by more than 10 individuals in our sample). It would thus be interesting to test UAVs on more diverse species. Two criteria were mainly used for woody species discrimination, NGRDI and the area/height ratio. These two variables are indicators of the morphology of a tree. "Acacia" species generally have a larger crown area height ratio than other species. Indeed, "Acacia" species have a more elongated structure than other species.

The NGRDI index was also higher for Acacia than for the other species. One objective of future research will be to distinguish the species in the "Acacia" genus. "Acacia" species are legumes and generally have greener leaves than other species. Our acquisition flights were only performed during the wet season, when almost all species had green leaves. As many species lose their leaves during the dry season, further flight tests should be made to ensure that species recognition works correctly whatever the time of year.

Limits of commercial UAVs

We analysed the residuals of the model to estimate the height and crown area. Our goal was to identify potential sources of error to see how the model could be improved. The ANOVA of the residuals showed that they differed between plots in both models. When the plot was added as a nested factor for height and crown area, we found a significant increase in R².

These differences between plots may have originated in the 3D mapping in the Pix4D software. In fact, the exact position of the images (geotagging) is required for photogrammetric analysis and the software used the geotag of the image produced by the UAV. These coordinates come with a number of inaccuracies. Consequently, one of the first steps in the process is to recalibrate the position of the images using the key point on the ground. One characteristic of Dji Spark is that the GPS coordinates are only recorded up to 4 decimal points, unlike higher UAV standards used by the same company (Mavic, Phantom or Matrice 600 for instance). This low accuracy increased the repositioning process, and it can be assumed that some differences in recalibration for each plot added some differences to the size referential. To correct these differences, a similar object between plots, such as a car, could be used to recalibrate the 3D model. This problem could also be reduced by using a UAV with higher positioning accuracy, such as the Dji mavic pro (8 decimals), which was used, for example, in the study by Sarron et al. (2018a). Low geotag resolution is also problematic for the accuracy of positioning objects in the field. This is one of the reasons why some trees could not be found in the field. Furthermore, with the problem of positioning accuracy, some flight planning applications could not be used with the Dji spark (Pix4D capture) and, in this study, we only identified the Litchi application as a solution. Another drawback is that flight planning cannot be carried out directly in the field. Dji spark has only one 12Mpx captor, so the orthophotograph produced with an 80 m flight had a resolution of around 4 cm. Some commercial UAVs now have higher resolution cameras and could be used instead to produce higher-resolution maps. Of course, higher resolution includes longer calculation times and heavier images. In each case, it needs to be decided whether a more precise map is more important than ease of use. However, this work showed that a commercial UAV equipped with RGB camera can be used to estimate tree communities. Of course, higher spectral resolution captors could be worthwhile, perhaps using different variables from the ones we used. Indeed, some indices calculated from NIR and thermal variables provide information about the physiological stage of the tree (evaluation of photosynthesis, hydrological status of the tree).

Future outlook for the use of UAV in the Sahelian savanna

UAV is currently a hot topic in forestry research. Many studies have been published on the use of UAV to estimate the morphological features of trees using structure from motion processes in plantations (dos Santos et al., 2020), drylands (Cunliffe et al., 2016) or in natural savanna (Mayr et al., 2018). UAV is also used to identity tree species mainly using a multispectral camera. (Kolarik et al., 2020;Oldeland et al., 2017) In these studies, the trees are mostly isolated like in our study. Our study confirmed that UAV can also be used for Sahelian tree communities where the woody cover is around 5%.

We suppose this approach could be limited in denser savanna such as the Guinean savanna with more 40% of woody cover, but other approaches based on UAV can be used in dense savanna or forest (Bourgoin et al., 2020).

In the present study, we used the cheapest UAV with GPS navigation available at the time, the DJI Spark system (around 500€). Few studies on UAV in forest science have used the Spark (Anifantis et al., 2019). We also used a user-friendly structure from motion software PiX4D mapper with the basic 3D map parameter, showing that UAV can be used on the woody community at very low cost without specialised knowledge of image analysis. UAV could thus be a tool for forester research and agencies in the Sahel region.

UAVs have other drawbacks. The maximum flight time of a low-cost drone is between 15 min (For the Dji Spark) and 30 min (for the Dji Mavic or Parrot Anafi), so the area covered by the UAV is limited to a few tens of hectares, so cheap UAVs cannot be used to monitor large areas. Another drawback is the calculation time needed to produce 3D maps (around 6 or 7 hours for each map on a basic computer). Further, regulations on the use of UAV may be restrictive.

Nevertheless, simple UAVs, like the one we used in the present study could be a useful tool for forestry researchers and agents in all Sahelian countries and would be very useful for future research on woody communities in the following cases:

-to study the spatial distribution of trees (distance between trees, pattern of distribution).

-to assess woody biomass (tree and leaf biomass) and density on a scale of 1 to 20 ha.

They could also be an intermediate stage between field measurements and satellite measurements and could be used to produce maps of tree variables on a large scale.

-to monitor tree communities in the savanna. Figure 4: Importance of the different variables in the random forest prediction of species (the importance is expressed by the mean decrease in accuracy as a percentage). In the random forest process, the predictor variables were not used in each of the 500 regression trees. The mean decrease in accuracy was calculated by comparing the quality of the regression trees using a variable and without the variable. The variable with the highest mean decrease in accuracy in % is the variable that was the most important.

Figure 1 :

 1 Figure 1: Methodological approach used in this work. The study was conducted on 24 plots. The white boxes show the workflow of the UAV images. The black boxes represent the tree measurements made on the ground. Details of the methodology can be found in the material and methods section.

Figure 2 :

 2 Figure 2: Correlation between tree height measured from the UAV and tree height measured in the field. The grey level of the dots represents the species. The black line represents the model between the two variables. and the dotted line represents the 1:1 line

  Table2presents the confusion matrix obtained from the random forest computed at species level. The overall error of the random forest for species identification was 20.08%. During the classification, 124 individuals of B. aegyptiaca were correctly identified by the random forest model, while two were identified as V. tortilis and four as S. senegal (6% of errors in the classification). Forty-three Due to the small number of individual V. seyal, the random forest did not create a class for them, consequently all the individuals were wrongly classified. Figure4shows the importance of the variables in the classification. The normalized green-red difference index (NGRDI) was the most important variable for species classification, followed by the area to height ratio. Table3lists the confusion matrix obtained from the random forest at genus level. All the Acacia (Vachellia and Senegalia) species were clustered together and the overall error was 13.81%. Among the 97 Acacia trees, 75 "Acacia" were correctly classified and 22 wrongly classified (23% of errors), 124 B. aegyptica were in the right class (with eight in the wrong classes) and seven out of 10 B. senegalensis were correctly classified.

	V. tortilis individuals were correctly classified, while the other 11 were classified as B.
	aegyptica (20% of errors). Nine out of 10 B. senegalensis were classified correctly. The tenth
	was classified as S. senegal (10% of errors). Thirteen out of 29 S. senegal were classified

correctly. The other nine were classified as V. tortilis, B. aegyptica and Boscia senegalensis (55% of errors). Only two V. nilotica trees were classified in the right category. The other seven were classified as S. senegal, V. tortilis, B. aegyptica and Boscia senegalensis (78% of errors).

Table 1

 1 Analysis of the residuals of the model for height and crown area. The impact of the species, the genus, the plot, the crown area, the height of the tree and the area to height ratio were tested. The two first columns represent the analysis in the model of tree height and the two last the crown area for each, the p value and the R² of the model between the residuals and the tested factors are presented

		Height (m)		Area (m 2 )
		pvalue	R²	pvalue	R²
	Species	8E-03	0.04	0.4	0
	Genus	2E-03	0.04	0.2	0.01
	Plot	2.E-16	0.40	5E-05	0.14
	Area	8.E-03	0.02	/	/
	Maximum height	/	/	0.2	0
	AH ratio	4E-03	0.03	5E-05	0.07

Table 2 :

 2 Confusion matrix of the random forest for the prediction of the species from UAV variables. The rows show the species of the individual trees that were identified on the ground and the columns show the species predicted by the random forest based on UAV input. The last column gives the percentage of individuals that were classified in the correct category.

								Class
		Vachellia	Acacia	Vachellia	Vachellia	Balanites	Boscia
								error in
		nilotica	senegal	seyal	tortilis	aegyptiaca	senegalensis
								%r
	Vachellia nilotica (9)	2	2	0	2	1	2	78
	Senegalia senegal (29)	0	13	0	7	8	1	55
	Vachellia seyal (5)	0	0	0	0	5	0	100
	Vachellia tortilis (54)	0	0	0	43	11	0	20
	Balanites aegyptiaca (132)	1	4	0	2	124	1	6
	Boscia senegalensis (10)	0	1	0	0	0	9	10

Table 3 :

 3 Confusion matrix of the random forest for the prediction of the genus from UAV variables. The rows show the genus of the individual trees that were identified on the ground and the columns show the genus predicted by the random forest based on UAV input. The last column gives the percentage of individuals that were classified in the correct category. The genus Senegalia and Vachellia (Old Acacia genus) were put together.

		Senegalia	and
		Vachellia (97)	Balanites(132)	Boscia(10)	Class error in %
	Senegalia	and
	Vachellia (97)		75	20	2	23
	Balanites(132)		8	124	0	6
	Boscia(10)		2	1	7	30

Acknowledgement

We thank Peter Biggins and Daphne Goodfellow for correcting the English.

CONCLUSION

The goal of this study was to estimate tree parameters using a low-cost UAV in a Sahelian savanna. Our results show that tree height and crown area in the savanna can be easily assessed from UAV images using structure from motion techniques. These results confirm the results of previous studies on the use of the UAV to study isolated trees in other types of ecosystem.

In this work, we used the cheapest UAV available with GPS and use structure from motion software without changing the mapping parameter, thus confirming that UAV may be a cheap and user friendly tool for forest studies in drylands.

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