Julie Labatut

Vincent Ducrocq Referent

Gilles Brunschwig

Identification of the preferences of women farmers in terms of selection of their cows in India: typologies of women farmers Training Supervisors

Keywords:

Identification of the preferences of women farmers in terms of selection of their cows in India: typologies of women farmers Juliette Férial

Option AEST

2022

VetAgro Sup

Mémoire de fin d'études d'ingénieur

Introduction L'Inde est le premier producteur de lait depuis plus de 20 ans [START_REF] Vigne | Is Feeding the Indian Dairy Cow Sustainable? A Tentative Multiscalar Answer[END_REF]. La part de l'Inde dans la production mondiale de lait est de 16%, ce qui en fait le leader actuel du secteur laitier, avec une production de 132,4 millions de tonnes de lait. La production laitière du pays est en constante augmentation, avec un taux de croissance annuel d'environ 6,5 % [START_REF] Patel | Role of Women Gender in Livestock Sector: A Review[END_REF]. Cette activité fait partie intégrante de la culture et de la civilisation indiennes. L'élevage laitier joue un rôle très important dans l'amélioration de l'économie du pays. Le lait représente 2/3 de la valeur de la production animale : c'est le plus grand produit agricole du pays [START_REF] Sharma | Socio-Personal Profile of Women Cattle Owners in Arid Region of Rajasthan[END_REF]. Le secteur de l'élevage fournit une grande quantité de biens et de services en Inde. Il est en particulier une source de nourriture, d'engrais, de force de traction animale, de revenus monétaires et de fonctions sociales. Avec un troupeau moyen de moins de deux vaches, les petits exploitants en Inde produisent la plus grande partie du lait bovin (FAO, 2018). Cependant, l'émergence de conditions climatiques extrêmes dans les pays en développement en raison du changement climatique, ainsi que la croissance démographique, l'urbanisation et l'augmentation des revenus font de l'amélioration des animaux un enjeu majeur aujourd'hui pour continuer à subvenir aux besoins de la population.

Afin d'améliorer les animaux, il semble essentiel de connaître l'avis et les besoins des premiers concernés, à savoir les éleveuses, qui effectuent les tâches quotidiennes avec les vaches, et il s'avère que la répartition des tâches au sein des exploitations est genrée. En effet, la répartition des tâches entre les hommes et les femmes dans les exploitations agricoles est assez caractéristique et mérite que l'on s'y attarde. Les femmes jouent un rôle majeur dans les pratiques quotidiennes avec les vaches dans les élevages [START_REF] Sharma | Socio-Personal Profile of Women Cattle Owners in Arid Region of Rajasthan[END_REF]. L'ampleur de la participation des femmes à l'agriculture varie d'une région à l'autre, mais le secteur agricole est le plus grand employeur de femmes. Les femmes rurales représentent la force la plus importante dans l'économie des pays en développement, dont l'Inde. En effet, 4 sur 5 de toutes les femmes économiquement actives dans le pays sont employées dans le secteur agricole [START_REF] Swamikannan | Women Labour in Agriculture in India: Some Facets[END_REF]. Dans le secteur laitier, les femmes sont impliquées dans les activités quotidiennes avec les animaux, elles ont donc probablement des besoins particuliers concernant les vaches qu'elles élèvent. Cependant, malgré leur forte implication dans les tâches quotidiennes auprès des vaches, les éleveuses sont confrontées à des difficultés qui limitent fortement leur autonomisation, affectant ainsi directement ou indirectement la santé des ménages et l'amélioration des systèmes d'élevage dans le pays. Par exemple, le manque d'accès à l'information limite leur productivité et leur laisse un pouvoir de décision limité [START_REF] Lal | GENDER ISSUES: THE ROLE OF WOMEN IN AGRICULTURE SECTOR[END_REF]. Ainsi, il est difficile de savoir si elles ont un rôle dans les stratégies d'exploitation et d'élevage, si elles ont des objectifs précis, et dans quelle mesure elles sont impliquées.

La Bharatiya Agro Industries Foundation (BAIF) est une fondation de recherche et de développement engagée dans le développement durable. Elle agit pour le développement rural durable en générant des moyens de subsistance ruraux et en gérant les ressources naturelles. Parmi ses missions, la BAIF promeut l'élevage, en particulier l'élevage laitier comme une activité importante pour la subsistance durable dans les zones rurales et agit en parallèle pour l'autonomisation des femmes à travers les « Self Help Groups » (SHG) dans 9 états (https://baif.org.in/). Dans un contexte d'érosion génétique, l'un des principaux objectifs de la BAIF dans le développement du secteur de l'élevage est de participer à la conservation de la diversité génétique tout en améliorant le potentiel génétique du bétail dans le pays. Pour atteindre cet objectif, la stratégie de la BAIF consiste à élever des bovins locaux « non-descript » (ND) (croisements entre races indigènes) avec des taureaux nés dans le pays et de haute valeur génétique, puis à rétrocroiser ces croisements avec les races laitières indiennes pour produire une descendance supérieure. Ce programme couvre 80 000 villages dans 16 États de l'Inde. Parallèlement, la station centrale de recherche (CRS) de la BAIF à Uruli Kanchan, dans l'état du Maharashtra, maintient un troupeau de bovins et de buffles d'élite. Chaque jour, du sperme est collecté et congelé pour produire des animaux à plus forte production laitière, mais aussi pour conserver le germoplasme des animaux d'élite.

Le stage se déroule au sein de l'unité mixte de recherche GABI (Génétique animale et biologie intégrative) dont les orientations scientifiques visent à comprendre et exploiter la variabilité génétique des animaux pour analyser la construction des phénotypes, les interactions avec les écosystèmes microbiens et l'environnement au sens large, dans un contexte de transition agroécologique. Le stage est réalisé dans le cadre du projet "Scaling Genomic Selection Across the Indian Smallholder Dairy Sector" soutenu par la Fondation Bill and Melinda Gates et mené par la BAIF et aidé par deux université Australienne et INRAE (Département de Génétique Animale et Département Action, Transitions et Territoires). Il s'inscrit dans le cadre des activités d'un "laboratoire international associé" (LIA) regroupant ces différents partenaires et intitulé GIMIC pour Genetic IMprovement of Indian Cattle and buffaloes. L'objectif général du projet est d'intégrer les outils d'évaluation génomique dans le développement d'un programme de sélection multi-races et multi-traits pour les bovins laitiers indiens. Jusqu'à présent, les travaux du LIA se sont concentrés uniquement sur la production de lait. Le projet financé par la Fondation Bill et Melinda Gates vise à se concentrer sur un groupe qui a été peu étudié jusqu'à présent, alors qu'il est au coeur de la gestion des troupeaux en Inde : les femmes qui semblent faire le travail quotidien auprès des vaches.

L'objectif du stage est de répondre à la question suivante : Quelles sont les pratiques, les objectifs et les stratégies des éleveuses en termes de sélection de leurs vaches ? Existe-t-il une diversité des profils des éleveuses ? Si oui, selon quels critères ? Cette partie est liée à l'analyse systémique des exploitations agricoles en fonction des traits préférentiels des éleveuses, qui fait l'objet d'un second rapport rédigé par Laurane DESOUTTER, étudiante en dernière année d'école ingénieur ENSAT.

Pour répondre à cette question, des enquêtes de terrain ont été menées auprès d'éleveuses dans deux États indiens différents : Maharashtra et Jharkhand. Cette question n'ayant jamais été étudiée auparavant en Inde, il était impératif d'adopter une approche exploratoire, basée sur la qualité des entretiens plutôt que sur la quantité, afin de comprendre la place des femmes dans les élevages, dans les systèmes de décision et afin de comprendre leurs attentes, si elles en ont, en matière de sélection des vaches. Tout d'abord, afin de comprendre pourquoi un tel projet a été mis en place, il est important de s'intéresser aux raisons qui ont conduit à la volonté d'améliorer les animaux en Inde, puis de comprendre comment la sélection génomique des vaches est organisée en Inde et enfin d'analyser la place de la femme dans les élevages en Inde. La méthodologie de l'étude sera expliquée : d'abord l'identification des zones d'étude, puis l'élaboration de l'enquête, la réalisation des entretiens et enfin les choix d'analyse des données recueillies. Enfin, les résultats des enquêtes et une discussion sur les résultats et l'étude seront développés.

I.

Matériel et méthode

Identification de la zone d'étude

L'objectif du projet était de réaliser des entretiens avec des éleveuses dans différents états afin de comparer les situations selon les états sélectionnés, tout en sachant que les échantillons ne pouvaient pas être représentatifs puisque l'approche reste exploratoire. Deux facteurs majeurs devaient être pris en compte lors du choix du nombre d'États : le temps passé en Inde et la météo avec l'arrivée des moussons. Ainsi, en tenant compte de ces deux facteurs, le choix a été fait de mener les entretiens dans deux États : le Maharashtra et le Jharkhand, choisis par leur différence de climats, de richesse et par l'années d'implantation des travaux de la BAIF dans les états. Pour chaque état, des entretiens ont été menés avec des éleveuses des fermes partenaires de la BAIF. Nous avions imposé nos critères de sélection des fermes : une ou plusieurs races présentes sur la ferme (y compris les races " exotiques " et "indigènes"), différentes tailles de troupeaux, présence/absence de terres, différents statuts économiques, différentes castes, présence/absence d'autres animaux sur la ferme, appartenance ou non à une coopérative laitière.

Méthodologie de l'enquête

La présente étude est exploratoire car aucune étude dans la littérature combinant la sélection génétique des vaches et la place des femmes dans les exploitations agricoles en Inde n'a été trouvée. Ainsi, comme première étape du projet global, une enquête qualitative a été privilégiée. En effet, une étude quantitative, utilisant des questionnaires à choix multiples, aurait impliqué de mettre en place des catégories de pensée prédéfinies qui auraient pu être totalement opposées aux rationalités des femmes. Une enquête qualitative est également la seule méthode qui permet de comprendre les processus et les pratiques impliqués dans la prise de décision. L'objectif était donc de tenter de savoir s'il existait une diversité de modes de pensée chez les éleveuses à travers leurs pratiques et leurs attentes. C'est pourquoi la technique d'enquête par guide d'entretien (annexe 3) a été choisie pour comprendre la diversité des systèmes d'exploitation, notamment en identifiant les différentes pratiques des femmes, en réalisant une typologie des éleveuses par l'analyse statistique.

Le choix du nombre d'entretiens à réaliser par état a été longuement discuté. Il s'agissait de choisir entre privilégier la quantité pour augmenter la représentativité statistique ou réaliser des entretiens plus longs et privilégier la qualité. L'étude étant exploratoire, l'objectif était de privilégier la qualité et non la quantité des entretiens. Cela impliquait de passer plus de temps sur chaque ferme. Ainsi, aucun nombre d'entretiens n'a été fixé. Nous avons mené les entretiens en fonction du temps passé dans chaque état (4 semaines au Maharashtra, 3 semaines au Jharkhand), du temps passé par entretien et de la disponibilité des éleveuses et des traductrices. Au total, 29 entretiens ont été réalisés dans le Maharashtra et 17 dans le Jharkhand.

Afin de réaliser l'entretien dans un environnement féminin, ce qui pourrait mettre la femme plus à l'aise et ainsi faciliter la communication, parler ouvertement du sujet, et créer un environnement confortable et d'écoute, laissant moins de possibilités de réponse aux hommes pour les éleveuses, il a été choisi de faire appel à des traductrices plutôt que des traducteurs. La collecte des données s'est faite en binôme : pendant que l'une posait les questions, l'autre collectait les données en les inscrivant à la main sur une version papier du guide d'entretien. Cette technique s'est avérée être la plus efficace et la plus pratique.

Analyse des données

La description des données liées aux exploitation ; une Analyse en Composantes Principales (ACP) suivi d'une Classification Ascendante Hiérarchique (CAH) ; une Analyse en Composantes Multiples (ACM) suivie d'une Classification Hiérarchique sur Composante Principale (HCPC) ont été réalisées grâce au logiciel R. Le logiciel XMind a été utilisé pour la réalisation de cartes mentales.

Les classements des différentes caractéristiques des vaches ont été analysés en utilisant une comparaison par paire des variables. Cette méthode compare le rang de chaque variable à celui de chaque autre variable du classement. Cela donne un score à chaque variable, correspondant au nombre de fois où elle est classée avant les autres variables avec lesquelles elle est comparée. La signification des résultats a ensuite été testée à l'aide du test non paramétrique de Kruskal Wallis.

Hypothèses de recherche :

Pour répondre à la question de recherche suivante : « Quelles sont les pratiques, les objectifs et les stratégies des éleveuses en matière de sélection de leurs vaches ? », les hypothèses suivantes ont été définies :

• Il existe une différence significative en termes de préférences des éleveuses sur les caractéristiques des vaches dans les états du Maharashtra et du Jharkhand.

• Il existe une diversité dans les profils des éleveuses en fonction de leurs préférences pour les caractéristiques des vaches. • Les informations relatives à l'éleveuse, à savoir son niveau d'éducation, son âge, ses connaissances en matière d'élevage, son pouvoir de décision et les différentes activités qu'elle réalise au quotidien, influencent ses préférences en termes de traits de vaches.

II. Résultats

Après avoir décrit, selon les deux états, les données liées à l'exploitation (nombre de vaches, nombre de vaches « exotiques » nombre de vaches « indigènes », statuts économiques et familiaux des familles), les résultats sur les préférences des éleveuses chez leurs vaches ont été analysés.

Visualisation es préférences des éleveuses chez leurs vaches à travers deux questions ouvertes

Deux questions ouvertes ont été posées aux éleveuses : « quelle est votre vache préférée du troupeau et pourquoi ? » et « quelle est la vache que vous aimez le moins et pourquoi ? ». Deux critères principaux ressortent des préférences des femmes : le rendement laitier et le tempérament des vaches. Une vache docile et produisant beaucoup de lait est souvent préférées chez les éleveuses, tandis qu'une vache agressive produisant peu de lait est souvent la vache la moins appréciée du troupeau. Ces critères sont souvent accompagnés d'autres critères de préférence. L'objectif de la section suivante est de comprendre plus précisément les attentes des éleveuses à travers la hiérarchisation de différents critères.

2.2.

Classement des traits des vaches par ordre de préférence 2.2.1. Les préférences des éleveuses en termes d'apparence de la vache : structure générale du corps et l'esthétisme de la vache Les femmes ont classé 5 traits liés à la structure générale du corps de la vache selon leurs préférences (Tableau 4, Tableau 5). Ces cinq traits sont la musculature, la longueur, la hauteur, la largeur de la vache et la taille des pattes. Dans les deux États, les deux premiers critères préférés des femmes concernant la structure corporelle sont la musculature et la hauteur de la vache. Dans le Maharashtra, les éleveuses interrogées préfèrent la musculature à la taille de la vache et dans le Jharkhand, elles préfèrent la taille de la vache à sa musculature. Dans les deux États, la longueur, la largeur de la vache et la taille des jambes arrivent respectivement en troisième, quatrième et cinquième position. Le test de Dunn teste la signification des résultats de cette classification par paire. Dans les deux Etats, seules les différences entre la musculature et la taille des jambes et entre la musculature et la largeur de la vache sont significatives. Cela signifie que les éleveuses interrogées préfèrent significativement la musculature à la taille des pattes et à la largeur du corps de la vache (Annexe 4, Annexe 5).

Il a été demandé aux éleveuses de classer la couleur du pelage, la taille des poils et la présence/absence de cornes sur les vaches en fonction de leurs préférences. Dans le Maharashtra, la couleur du pelage est la plus importante, suivie par la présence/absence de cornes et la taille du poil (tableau 6). Le test de Dunn montre qu'il n'y a pas de résultat significatif pour le classement entre la présence/absence de cornes et la taille du poil dans le Maharashtra. Ainsi, pour les femmes interrogées dans le Maharashtra, seule la préférence de la couleur du pelage sur la présence/absence de cornes et la taille du poil peut être affirmée. Dans le Jharkhand, les éleveuses interrogées ont accordé plus d'importance à la présence/absence de cornes, suivie de la taille des poils, avant la couleur du pelage (tableau 7). Cependant, le test de Dunn ne montre aucune significativité entre les résultats. Ainsi, pour les femmes interrogées dans le Jharkhand, la préférence d'un critère par rapport à un autre ne peut être affirmée. L'annexe 6 détail les préférences des éleveuses en termes d'apparence des vaches.

Préférences des éleveuses en termes de 7 traits généraux chez leurs vaches

Les éleveuses interrogées ont été invitées à classer 7 traits généraux pour les vaches par ordre de préférence : le rendement laitier élevé, la fertilité, le tempérament, la longévité, la santé, le prix de marché de la vache et la structure corporelle. La méthode de classement par paire a été appliquée à ce classement pour les entretiens du Maharashtra (tableau 8) et du Jharkhand (tableau 9).

Dans les deux états, la méthode de classement par paire montre que le rendement laitier élevé est en première position par rapport aux autres critères. Le test de Dunn (annexe 7) montre que les femmes interrogées dans le Maharashtra préfèrent significativement le rendement laitier élevé au tempérament, à la fertilité, au prix du marché de la vache et à la longévité. Dans le Jharkhand, les femmes interrogées préfèrent significativement un rendement laitier élevé à tous les autres critères (annexe 8). Dans le Maharashtra, la santé apparait comme le deuxième critère préféré des éleveuses interrogées. Cependant, le test de Dunn montre que la santé est seulement significativement préférée à la fertilité et à la longévité. Dans le Jharkhand, la fertilité apparaît comme le deuxième critère préféré des femmes interrogées. Cependant, le test de Dunn montre que la préférence de la fertilité par rapport aux autres critères n'est pas significative.

Typologie des éleveuses en fonction de leurs préférences sur les traits généraux

Le classement par paire des 7 traits généraux démontre qu'il existe probablement des modes de pensée différents. Afin de décrire le jeu de données en identifiant les groupes d'individus et les liens entre les variables, ici les 7 traits généraux, une analyse en composantes principales (ACP) a été réalisée. Les ellipses de confiance des deux états sur le graphique des individus ne se recouvrent pas (Figure 7), justifiant le choix d'analyser les données en séparant les deux états. Ainsi, l'hypothèse d'une différence significative des résultats entre les deux états est vérifiée.

La CAH (Figure 9) détermine trois clusters d'individus classés selon leurs préférences sur ces traits généraux. Cette classification permet de déterminer des profils d'éleveuses en fonction de leurs préférences (Tableau 10). Le cluster 1 correspond majoritairement aux éleveuses de l'état du Maharashtra, qui valorisent la structure du corps et la santé et accordent peu ou pas d'importance à la fertilité, au tempérament et à la longévité. Le cluster 1 correspond à des éleveuses axées sur la fonction des vaches. Le cluster 2 regroupe seulement 6 individus qui semblent privilégier le tempérament et la fertilité, et accordent moins d'importance à un rendement laitier élevé. Le cluster 2 correspond aux éleveuses axées sur l'affection. Cela signifie qu'elles donnent la priorité au tempérament docile de leurs vaches, avant les autres critères. Ce type d'éleveuses peut être défini car c'est aussi l'impression qui ressort des entretiens : les éleveuses qui donnent la priorité au tempérament sont souvent celles qui sont les plus attachées sentimentalement à leurs vaches. Le cluster 3 correspond aux éleveuses de l'état de Jharkhand. Elles accordent plus d'importance à la longévité et, dans une moindre mesure, à un rendement laitier élevé. En revanche, elles n'accordent que peu ou pas d'importance à la structure corporelle et à la santé. Elles correspondent aux éleveuses axées sur la longévité et la production.

La conclusion de ces analyses est qu'il existe une diversité de profils d'éleveurs selon leurs préférences en matière de traits de leurs vaches. Cela permet d'établir une typologie des éleveuses interrogées, et cette typologie est aussi fonction des états. L'hypothèse selon laquelle "Il existe une diversité dans les profils des éleveuses selon leurs préférences pour les traits de leurs vaches" est vérifiée.

2.4.

Typologie des éleveuses en fonction des données sociales L'objectif de cette partie est de vérifier l'hypothèse suivante : "Les informations relatives à l'éleveuse, à savoir son niveau d'éducation, son âge, ses connaissances en matière d'élevage, son pouvoir de décision et les différentes activités qu'elle réalise au quotidien, influencent ses préférences en matière de traits de vaches". En d'autres termes, l'objectif est de savoir si les préférences des femmes pour les caractéristiques de leurs vaches sont liées à des profils particuliers, ce qui pourrait aider à comprendre pourquoi certains critères sont préférés à d'autres.

À cette fin, une ACM (figure 10) suivie d'une HCPC (figure 11) ont été réalisés avec les variables qualitatives liées aux activités des éleveuses (cleaning_shed, cleaning_animals, chopping_feeding_prep, feeding, milking, health_care), leur implication dans la commercialisation du lait (selling_milk), leur implication dans le système de décision sur la race des vaches (breed_choice_implication), leur connaissance ou non des races des vaches dont elles s'occupent (breed_awareness). Deux variables quantitatives ont été ajoutées (woman_age, woman_education). Aussi, la variable "cluster_pref_trait" a été ajoutée. Cette variable correspond aux trois clusters déterminés dans la partie précédente, concernant les préférences des éleveuses en termes de caractéristiques générales des vaches. Cette variable permet de faire le lien entre la classification sur les préférences des éleveuses et la présente classification sur les différents profils des éleveuses.

Trois clusters ont été déterminés grâce à la classification (Figure 11 et tableau 11). Les clusters 1 et 2 se distinguent du cluster 3 par la réalisation des activités des éleveuses auprès des vaches. Le cluster 3 comprend des femmes qui ne font pratiquement aucun travail auprès des animaux. Il s'agit de femmes qui participent à des activités avec les vaches de temps en temps, mais pas de façon quotidienne. Ce cluster ne correspond pas à ce qui a été trouvé dans la littérature, à savoir des femmes qui effectuent des tâches quotidiennes avec les vaches. La participation quotidienne des femmes aux activités liées aux vaches doit donc être discutée : toutes les femmes des exploitations ne travaillent pas quotidiennement avec les vaches. Les clusters 1 et 2 diffèrent par les activités qu'elles exercent, leur connaissance de la race, leur implication dans la sélection de la race, leur niveau d'éducation et l'état auquel elles appartiennent. En termes d'activités, les éleveuses du cluster 1 sont plus impliquées dans la traite et la vente du lait que celles du cluster 2. Par ailleurs, la majorité des éleveuses du cluster 1 connaissent les races de vaches qui composent leur troupeau, contrairement aux éleveuses du cluster 2. Les éleveuses du cluster 1 sont également impliquées dans le choix de la race lors de la reproduction ou de l'achat de nouvelles vaches, ce qui n'est pas le cas des femmes du cluster 2. De plus, les éleveuses du cluster 1 sont peu ou pas éduquées, contrairement aux éleveuses du cluster 2. On peut expliquer ces résultats par le fait que les femmes les moins éduquées appartiennent à des fermes du Jharkhand, donc des fermes contenant de plus petits troupeaux, avec moins de production laitière, moins de lait à vendre, et surtout le lait n'étant pas la seule (voire pas la plus importante) source de revenue dans ces fermes (la vente de légumes sur le marché est souvent la source de revenus la plus importante). Ces femmes sont davantage impliquées dans la vente du lait. Tandis que dans les fermes du Maharashtra, là où les femmes sont plus éduquées mais ne connaissent pas les races, la vente du lait constitut souvent le principal revenu, donc ce sont les hommes qui s'en occupent et les femmes sont seulement des exécutantes, effectuant les taches quotidiennement auprès des vaches, mais sans être impliquées dans la vente. Ainsi, soit elles ont moins d'intérêt à connaitre la race puisqu'elles ne sont pas concernées par le business, soit tout simplement les hommes ne les incluent pas dans les décisions qui sont liées aux revenus (donc le choix des races).

Enfin, seules les éleveuses axées sur la longévité et la production sont représentés dans la présente classification : elles sont mieux représentées par le cluster 1, ce qui est cohérent avec le fait que ces éleveuses sont celles qui sont les plus impliquées dans la vente du lait.

III. Discussion

3.1.

Discussion sur la collecte des données

Biais de traduction

Les résultats de l'étude sont sujets à discussion, notamment en raison d'un biais de traduction. En effet, la traductrice qui a accompagné les enquêtrices dans les fermes du Maharashtra n'avait aucune connaissance en matière d'élevage ou d'agriculture. Ainsi, elle ne connaissait pas certains termes techniques liés à l'élevage. Il était nécessaire de lui expliquer les termes techniques et pour elle de les mémoriser. Cependant, parfois l'explication n'était pas suffisante, les termes restaient incompris, ce qui pouvait entraîner des erreurs de traduction. De plus, il arrivait que les éleveuses utilisent des termes très spécifiques dans la langue locale, que la traductrice ne connaissait pas non plus, rendant impossible la traduction de la langue locale vers l'anglais.

Questionnement sur la compréhension de certaines éleveuses sur certaines questions

Les résultats peuvent également être discutés car parfois les éleveuses n'ont pas compris les questions posées. Cela était particulièrement vrai pour les questions qui demandaient aux éleveuses de classer différents critères selon leurs préférences. On peut s'interroger sur leur compréhension des différents termes, parfois techniques, étant donné que certaines femmes ne sont pas du tout impliquées dans les décisions, et sont surtout des exécutantes, sans vraiment comprendre les stratégies d'élevage, qui sont plutôt le fruit de réflexions entre les hommes de la famille. C'est une des raisons pour lesquelles une enquête quantitative à choix multiples n'est peut-être pas une bonne solution, car il n'y aurait pas assez de temps pendant les entretiens pour vraiment expliquer les questions et comprendre les rationalités des éleveuses.

Suggestions pour le futur de l'étude : choix des traductrices

Le choix de traductrices ayant un bon niveau d'anglais et une bonne connaissance en élevage est impératif pour minimiser les biais dans la collecte des données. De plus, lors des entretiens avec les éleveuses, le choix de traductrices, et non de traducteurs, est recommandé car cela peut permettre aux éleveuses de se sentir plus à l'aise dans un environnement féminin. D'abord les entretiens tests avec un traducteur masculin, puis les entretiens de l'étude avec des traductrices ont montré qu'une traductrice peut faciliter la communication, permettre aux éleveuses de parler ouvertement du sujet, et créer un environnement confortable et d'écoute, laissant moins de possibilités aux hommes de répondre à la place des éleveuses.

3.3.

Recommandations pour la BAIF concernant les éleveuses L'objectif initial du projet dans le cadre duquel cette étude est réalisée est d'intégrer les éleveuses dans les études de sélection animale. À cette fin, le projet visait à incorporer un critère de sélection dans l'évaluation génomique des animaux dans le développement d'un programme de sélection multiraces et multi-traits pour l'élevage laitier indien. Cependant, cette étude a montré qu'il n'y a pas un critère préféré des femmes, mais plusieurs, selon le type d'éleveuses et l'état, et que ce n'est pas en incluant un critère de sélection des vaches que l'égalité des sexes s'améliorera, mais plutôt en informant les femmes sur les pratiques d'élevage et les soins à apporter aux vaches. Il est important de comprendre que c'est par la transmission de connaissances aux femmes, qui sont au centre des activités quotidiennes avec les vaches, que les fermes de vaches seront plus efficaces grâce à l'amélioration des pratiques d'élevage. La BAIF propose déjà des formations aux femmes, mais cet effort doit être poursuivi. Cependant, les résultats de l'analyse sociale montrent qu'il ne suffit pas de former les femmes, il faut aussi travailler sur leur place dans les processus de décision au sein de l'exploitation.

Conclusion

Pour rappel, l'objectif de ce projet était d'identifier les pratiques, les objectifs et les stratégies des éleveuses en termes de sélection des vaches en Inde, puisque ce sont généralement elles qui font le travail quotidien avec les vaches. Cette étude fait partie d'un projet plus large dont l'objectif est de déterminer le ou les critères de sélection des vaches afin d'améliorer les vaches selon les préférences des femmes. L'analyse bibliographique a démontré l'importance de la sélection génomique des animaux en Inde dans un contexte de croissance démographique, d'urbanisation, d'augmentation des revenus et de changement climatique. La recherche bibliographique a également permis de contextualiser la place des femmes éleveuses dans l'élevage indien, ainsi que leur place dans les systèmes de décision. L'étude réalisée est une étude exploratoire, jamais réalisée auparavant, basée sur la qualité des entretiens plutôt que sur la quantité. Pour la réalisation de cette étude, il a été nécessaire de se documenter sur la manière de mener une recherche en sciences sociales, afin de choisir la meilleure façon de mener les entretiens avec les éleveuses indiennes. L'étude a montré qu'il est difficile d'identifier des objectifs et des stratégies spécifiques pour les éleveuses car elles ont peu de pouvoir de décision sur l'exploitation. Cependant, l'étude montre qu'il existe une diversité de profils d'éleveuses avec des préférences différentes pour les vaches, et cette diversité s'explique également par les Etats dans lesquels se trouvent les exploitations, qui diffèrent notamment par leur richesse et leur climat. Les résultats tendent à montrer que le rendement laitier élevé reste le critère préféré des éleveuses, mais les préférences des autres critères qui suivent varient selon les profils. Par ailleurs, l'étude est allée plus loin en identifiant des profils d'éleveuses en fonction de leur niveau d'éducation, de leurs connaissances en matière d'élevage, de leur implication dans les systèmes de décision et des activités qu'elles réalisent avec les vaches. Les femmes impliquées dans les activités quotidiennes avec les vaches peuvent être seulement des exécutantes, sans pouvoir de décision dans l'exploitation et avec peu de connaissances sur les races, ou bien elles peuvent être plus impliquées dans les affaires liées à la production et à la vente de lait, les impliquant ainsi davantage dans les décisions, ces femmes sont aussi celles qui connaissent le mieux les races qui composent leur troupeau, et celles qui sont même impliquées dans le choix des races et ce sont elles qui correspondent aux éleveuses axées sur la longévité et la production. Ce point souligne l'importance d'intégrer les femmes dans les systèmes de prise de décision, tout en les formant aux techniques d'élevage, ce qui permettrait d'améliorer les pratiques d'élevage et donc d'améliorer la production laitière et l'adaptation des vaches à leur environnement qui évolue en raison du changement climatique. Les efforts pour apporter des connaissances aux femmes et les intégrer dans les systèmes de décision doivent être poursuivis car ils sont les principaux leviers pour impliquer plus de femmes dans la sélection des animaux, et ainsi se rapprocher de l'égalité des sexes dans les exploitations agricoles indiennes.

List of Figures

List of Tables

Introduction

India has been the largest country in terms of milk production for over 20 years [START_REF] Vigne | Is Feeding the Indian Dairy Cow Sustainable? A Tentative Multiscalar Answer[END_REF]. India's share of the world's milk production is 16%, making it the current leader in the dairy sector, with a yearly milk production of 132.4 million tons of milk. The country's milk production is growing steadily, with an annual growth rate of about 6.5% [START_REF] Patel | Role of Women Gender in Livestock Sector: A Review[END_REF]. This activity is a part and parcel of Indian culture and civilization. Dairy farming plays a very important contribution to the economy of the country. Milk represents 2/3 of the value of livestock output : it is the largest agricultural commodity in the country [START_REF] Sharma | Socio-Personal Profile of Women Cattle Owners in Arid Region of Rajasthan[END_REF]. The livestock sector provides a large quantity of goods and services in India. It is especially a source of food, fertilizer, fuel (in terms of animal traction), cash incomes and social functions. With an average herd size of less than two cows, smallholders in India produce the largest proportion of cattle milk (FAO, 2018). However, the emergence of extreme weather conditions in developing countries due to climate change, as well as population growth, urbanization and income growth make animal improvement a major issue today to continue to provide benefit to the population.

In order to improve animals' production, it is essential to know the opinion and the needs of the first concerned, namely the farmers, who carry out the daily tasks with the cows, and it turns out that the distribution of tasks within the farms is gendered. Indeed, the division of labor between men and women on Indian farms is quite specific and deserves attention. Women play a major role in the daily management of cows on the farms [START_REF] Sharma | Socio-Personal Profile of Women Cattle Owners in Arid Region of Rajasthan[END_REF]. The extent of women involvement in agriculture varies between regions, but the agricultural sector is the largest employer of women. Rural women represent the most important force in the economy of developing countries, including India. Indeed 4 out of 5 of all women economically active in the country are employed in the agriculture sector [START_REF] Swamikannan | Women Labour in Agriculture in India: Some Facets[END_REF]. In the dairy sector, women are involved in daily activities with animals, so we assume that they probably have particular needs regarding the cows. However, despite their strong involvement in daily tasks with cows, women face difficulties that severely limit their empowerment, thus directly or indirectly affecting household health and the improvement of livestock systems in the country. For example, the lack of access to information constraints their productivity and leave them with limited decision-making power [START_REF] Lal | GENDER ISSUES: THE ROLE OF WOMEN IN AGRICULTURE SECTOR[END_REF]. Thus, it is difficult to know if they have a role in the farming and breeding strategies, if they have specific goals or expectations, and to which extent.

The Bharatiya Agro Industries Foundation (BAIF) is a Research and Development foundation committed to sustainable development of smallholder farmers. It promotes for sustainable rural development through generating rural livelihoods and through better management of natural resources. Among its missions, BAIF promotes livestock husbandry, particularly dairy husbandry as an important activity for sustainable livelihood in rural areas and acts in parallel for women empowerment through 2478 Self Help Groups (SHG) in 9 states (https://baif.org.in/). In a context of genetic erosion, one of the main objectives of BAIF in the development of the livestock sector is to participate in the conservation of genetic diversity while improving the genetic potential of livestock in the country. For meeting this goal, the BAIF's strategy has been to breed local nondescript (ND) Bos Indicus (zebu) cattle with semen from home-born Bos Taurus bulls of high genetic merit and then back-crossing the crossbreds with the Indian dairy cattle breeds to produce a superior progeny. This programme covers 80 000 villages in 16 states of India.

At the same time, BAIF's Central Research Station (CRS) in Uruli Kanchan, Maharashtra, maintains an elite herd of cattle (pure Holstein, Jersey) and crossbred bulls with Bos Indicus (zebu) breeds as well as pure zebu and buffaloes bulls. Every day, semen is collected and frozen to produce animals with higher milk production, but also to conserve the germplasm of the elite animals.

The internship takes place at INRAE in the GABI Joint Research Unit (Animal Genetics and Integrative Biology) whose scientific orientations aim to understand and exploit the genetic variability of animals to analyse the construction of phenotypes, interactions with microbial ecosystems and the environment at large, in a context of agroecological transition. The internship is carried out within the framework of the project "Scaling Genomic Selection Across the Indian Smallholder Dairy Sector" supported by the Bill and Melinda Gates Foundation and led by BAIF with the help from two Australian universities and INRAE. Our part of the project brings together scientists from INRAE (Animal Genetics Department and Action, Transitions and Territories Department) and BAIF. It is also part of the activities of an "associated international laboratory" (LIA) grouping these different partners from INRAE and BAIF and entitled GIMIC (for Genetic IMprovement of Indian Cattle and buffaloes). The general objective of the Bill and Melinda Gates Foundation's project is to integrate genomic evaluation tools in the development of a multi-breed and multi-trait selection program for Indian dairy cattle. Until now, its work focused solely on milk production. It is now more ambitious, in particular by focussing on a group that has been rarely studied until now, even though it is at the heart of herd management in India: the women who seem to do the daily work with cows.

The objective of the internship is to tackle the following questions: What are the practices, objectives and strategies of women farmers in terms of selection of their cows? Is there a diversity of profiles among women farmers? If yes, what are the characteristics of their cows that they would like? This part is linked to the systemic analysis of a sample of farms in relation to the preferred traits among women farmers, which is the subject of a second report written by Laurane DESOUTTER, the other Master student.

To address this question, field surveys were conducted with women farmers in two different Indian states: Maharashtra, located in western India and Jharkhand, located in the north-east of India. Since this question seems to have never been studied before in India, it was imperative to adopt an exploratory approach, based on the quality of the interviews, rather than on the quantity, in order to understand the place of women in the breeding farms, in the decision-making systems and their expectations in terms of cow selection, if they have any.

First, in order to understand why such a project was set up, it is important to look at the reasons that led to the desire to improve animals in India, then to understand how selection of cows is organized in India. Secondly, the place of women in cattle breeding systems in India will be discussed in order to better understand the expected benefit of the project in which the internship is included. Then, the methodology of the study will be explained: first the choice of the areas studied, then the development of the survey, the handling of the interviews and finally the choices of analysis of the collected data. Finally, the results of the surveys and a general discussion on the findings will be developed.

I. Context 1.1. Selection of cows in developing countries

Importance of animal improvement in developing countries

The attention toward animal improvement in developing countries is the result of three main factors: population growth, urbanization and income growth. These three topics lead to a massive increase in the demand for animal-based food [START_REF] Delgado | Livestock to 2020: The Next Food Revolution[END_REF].

1.1.1.1.
Demographic growth India is the second most populous country in the world after China, and the Indian population is expected to continue to grow and surpass the Chinese one between 2025 and 2030, reaching 1.64 billion by 2050 (Figure 1) [START_REF] Gu | Major Trends in Population Growth Around the World[END_REF]. This increase of the Indian population leads to an increase in the demand for food, especially for milk and milk products.

Note: the solid color lines are estimates, whereas the dotted color lines are projections under the medium variant and color shadows are the 95% projection intervals. Source: United Nations. Drawn from the World Population Prospects 2019. 1.1.1.2

. Increased urbanization

Most urbanization is taking place in developing countries. By 2050, 90% of urbanization will occur in Africa and Asia. In India, the urban population will double by 2050, from 410 million to 814 million people will live in urban areas (United Nations department of economic and social affairs, 2014). This urbanization will go with a decrease in the availability of resources for the livestock sector and therefore the need for an adaptation of feeding strategies for dairy farmers living in urbanizing environments [START_REF] Reichenbach | Dairy feed efficiency and urbanization -A system approach in the rural-urban interface of Bengaluru, India[END_REF]. 1.1.1.3. Income growth The third main reason that leads to an interest in animal improvement is the income growth in developing countries. India is the first country with the highest estimated economic growth by 2050. Between 2007 and 2050, its the Growth Domestic Product (GDP) increase is estimated to be 8.5 US$ per year [START_REF] Hawksworth | The World in 2050, Beyond the BRICs: a broader look at emerging market growth prospects[END_REF]. As the standard of living in India increases, so does consumer demand. The livestock sector is one of the employment sectors that represents an important part of economic growth, and therefore represents a major challenge for the coming years. These three factors are, and will be even more so in the following years, the cause of a significant increase in consumer demand, especially for animal products. In India, this demand for food will mainly be translated into a demand for milk and dairy products which constitute the main source of proteins in a country where 40% of the population is vegetarian [START_REF] Frioux | L'avenir de la viande se joue en Inde[END_REF]. The dairy sector in India is therefore at the center of public health issues for improved nutrition. It also represents opportunities for income growth for those who need it most. The challenge today is to improve the animals, notably by increasing their productivity, in order to meet the needs of a population whose living conditions will change in the coming decades. 1.1.1.4. Climate change In addition to these three reasons, environmental criteria must imperatively be taken into consideration, in particular, the competition for resources such as water, food and fertilizers, but also the climate change. Indeed, animals will have to produce under more and more extreme and constraining climatic conditions and therefore they will have to adjust.

For profitable livestock production, adaptation of livestock to environmental challenges is therefore increasingly urgent. Current global warming and extreme weather events can have increasingly severe effects on livestock productivity and welfare. Heat stress has been shown to cause a 5 to10% decrease in milk production and a decrease in conception rate [START_REF] Berman | Invited Review: Are Adaptions Present to Support Dairy Cattle Productivity in Warm Climates?[END_REF]. It is therefore necessary to increase the production of animals with constant resources. To do so, it is necessary to find a balance between adaptation and productivity, especially in developing countries, where environmental constraints due to climate change are and will be even more severe than in temperate countries [START_REF] Ducrocq | Genomics for Ruminants in Developing Countries: From Principles to Practice[END_REF].

One way to increase animal productivity is to use genetic and/or genomic improvement techniques. These techniques have allowed developed countries to significantly increase genetic resources for several decades.

Animal selection in India

1.1.2.1. Focus on BAIF selection programs BAIF is a leading Indian NGO for agricultural development, which has been providing sustainable livelihoods to smallholder dairy farmers in India for more than 50 years (https://baif.org.in/). One of the ways in which this NGO has done this has been by promoting the improvement of "non-descript" low-yielding cattle and buffaloes through the use of artificial insemination (AI) with superior bulls using advanced frozen semen technology. BAIF introduced the crossbreeding of local indigenous Bos indicus (zebu) with exotic Bos taurus bulls of Holstein breeds and Jersey in India. Today, these crosses contribute to over 50% of the country's milk production. In 2016, BAIF's semen stations produced 12.5 million doses of semen from:

-Purebred (Bos taurus) Holstein and Jersey bulls born from BAIF bull mothers' herd or from imported embryos; -Indigenous purebred Bos Indicus bulls of breeds with high milk yields (mainly Gir and Sahiwal); but also bulls of draught breeds, especially in order to maintain their gene pool; -Crossbred bulls with 50-75% exotic (Holstein and Jersey) blood.

BAIF employs over 4,500 artificial insemination technicians (AITs), each covering 12-15 villages. In total, 12 states in India are covered by BAIF's work, representing over four million small farms with herds of one or two cows [START_REF] Ducrocq | Genomics for Ruminants in Developing Countries: From Principles to Practice[END_REF].

The use of genomic tools

There is no organized genetic selection in India, except by poorly controlled (not optimal) crosses with imported Bos taurus semen. Genomic selection is the use of molecular marker information to estimate the genetic value of animals [START_REF] Meuwissen | Prediction of Total Genetic Value Using Genome-Wide Dense Marker Maps[END_REF]. In developed countries, genomic selection has been the official evaluation of breeding stock since 2009 [START_REF] Boichard | Genomic selection in French dairy cattle[END_REF].

In India, unlike progeny testing, genomic selection would eliminate the need for national registration of cow pedigrees and milk recording of very large numbers of cows, the data to be collected is limited to cows that are genotyped. The use of genomic tools allows a better characterization of local populations and comparison with selected populations. Genomic information makes it possible to measure genetic relationship between animals, even with unknown pedigrees. It is based on a subset of the total population, called "reference population" that is both phenotyped and genotyped. It is thanks to this reference population that other animals of the population can then be evaluated and selected [START_REF] Ducrocq | Genomics for Ruminants in Developing Countries: From Principles to Practice[END_REF].

Despite the lower cost of national infrastructure for genomic selection compared to genetic selection (such as progeny testing), the use of genomic tools in developing countries remains limited for four main reasons. First, access to low-cost phenotype recording and data management tools remains limited in developing countries. Second, climatic conditions such as high heat, diseaserelated health conditions and poor nutrition of animals in developing countries decrease the efficiency of the use of genomic tools. Third, it is essential to pay attention to fitness traits. Finally, performing genomic analyses requires a high level of expertise in data management, quantitative genetics and modelling and data processing [START_REF] Ducrocq | Genomics for Ruminants in Developing Countries: From Principles to Practice[END_REF].

The place of women in cattle breeding systems

The livestock sector is an important source of goods and services for the society by constituting an important source of employment. However, there is a gender difference in the distribution of tasks on farms. Women represent the main labor force on the farms, they do the vast majority of the work dealing with cattle. Their activities with animals are diverse.

Rural women at the center of cows breeding activities in India

According to a report of World Bank (1991), 86 % of the total rural women are working for various agricultural operations [START_REF] Lahoti | Role of Women in Dairy Farming[END_REF]. They play an important role in the agricultural sector and the rural economy. The ILRI (International Livestock Research Institute) 2019 annual report indicates that "some 2/3 of the developing world's hundreds of millions of poor livestock keepers are women, not men" (ILRI, 2019). Agriculture is increasingly becoming a women activity. The 2020-2021 annual report of the department of Agriculture, Cooperation and Farmers Welfare of the Ministry of Agriculture and Farmer's Welfare indicates that the agricultural sector employs 80% of all economically active women, which include 33% of agricultural labour force and 48% of selfemployed farmers (Ministry of Agriculture and Farmer's Welfare, Government of India).

About 75 million women are involved in dairying in India, against 15 million men [START_REF] Lahoti | Role of Women in Dairy Farming[END_REF]. In rural India, cattle and buffalo rearing has traditionally been under the responsibility of women. The government of India report indicates that 85 % of rural women are engaged in livestock production [START_REF] Sarma | Women Dairy Farmers and Decision Making Pattern in Sonitpur District of Assam[END_REF]. In developing countries, women are generally involved in the dayto-day care of animals, which is a time-consuming, energy-intensive, and physically demanding work. However, the amount of time women spend on livestock is often underestimated in labor statistics (FAO, 2018). India's first place as the world's leading milk producer, as well as the significant increase in the availability of milk per capita in India, is largely the result of the work of rural women. The credit for this leading position goes to these largely illiterate women [START_REF] Patel | Role of Women Gender in Livestock Sector: A Review[END_REF].

Gender differences in the distribution of farm activities

In developing countries, and in India in particular, women and men do not perform the same tasks on farms. Women are mostly involved in tasks related to the animals, but depending on the publications, the list of these tasks differs. The tasks that are done mostly by women are feeding, cleaning and milking of dairy animals, care of young animals, and giving medication [START_REF] Sharma | Socio-Personal Profile of Women Cattle Owners in Arid Region of Rajasthan[END_REF]. But it is also mentioned that they sometimes perform other tasks in livestock management : Care of new-born calf, weaning and management of calf, maintenance of the farm records, cleaning the animal sheds, maintenance of the animal shed, compost making, cleaning the tools, etc. They are also involved in tasks related to livestock feeding such as feed preparation, enrichment of dry fodder, soaking of concentrates, preparing concentrate mixture, storage of concentrates, harvesting fodder crops, carrying the harvested fodder, feeding young calves, watering the livestock. Moreover, women often participate in breeding the livestock by carrying animals for Artificial Insemination (AI) service centre (but this is not the case at BAIF, where AI is performed on the farm), help during parturition, caring during pregnancy, doing the castration of male calves. Regarding the livestock health care, women often take care of sick animals, vaccinate the animals, groom, clean and bath cows and/or buffalos, they also diagnose common diseases, dispose infected litter material, deworm and purchase vet medicine. Finally, women participate in processing and marketing activities by purchasing feeds and concentrates, purchasing equipment, purchasing and sailing animals, marketing milk and milk products, maintening of accounts and financial records, getting involved in banking process, processing and preparing milk products and getting involved at the dairy cooperative [START_REF] Lahoti | Role of Women in Dairy Farming[END_REF]. However, the paper does not specify whether women perform these tasks related to processing and marketing regularly or only in a few cases, such as on farms where they have more control and influence. The men's place is quite different from that of women. In fact, in villages near larger cities, the men generally work in the city, while the women stay on the farms to take care of the animals and the household chores [START_REF] Rangnekar | A Study on Women in Dairy Production[END_REF]. While rural women are involved in production activities, men participate to tasks related to marketing such as selling milk, bringing added value products from milk, purchase and sale of animals. Activities related to finance, such as keeping farm records and obtaining loans or credit from banks, are also performed by men in most cases. Salesrelated activities are therefore predominantly carried out by men: for example, 83% of men are involved in selling livestock, compared to 13% of women [START_REF] Dudi | Contribution and Issues of Women in Livestock Sector of India-A Review[END_REF]. Thus, the distribution of tasks related to livestock is quite gendered, with women doing the vast majority of activities with animals.

The place of women in farms decision making process

The role of rural women in agriculture is still neglected. Despite their strong involvement in livestock activities and their major role in Indian's agricultural economy, women face difficulties in having a voice in household decision-making processes. Women absence from decision making process, either inside or outside home is one of the main obstacles in women empowerment in agriculture sector [START_REF] Lal | GENDER ISSUES: THE ROLE OF WOMEN IN AGRICULTURE SECTOR[END_REF]. Rural women's involvement in decision making differs according to the activities of dairy production. They may take decisions on their own, or they may not be involved in decision-making systems at all. The J. SARMA and S. PAYENG's study [START_REF] Sarma | Women Dairy Farmers and Decision Making Pattern in Sonitpur District of Assam[END_REF] is one of the few articles that discusses the decision-making power of women on farms in India. It assesses women's decision making in Sonitpur (India) in different categories: feeding, breeding, management, health care and milk processing. They collected information from the women farmers on who made the decision in each of these activities. The authors indicate that women participate in decision-making processes regarding food-related activities, either independently or jointly with their husband. They show that in terms of breeding activities, women are rarely involved in the decision-making process. For example, they are not really involved in decisions concerning the choice of AI vs natural mating, or whether or not to carry out a pregnancy diagnosis. This is a time-consuming activity that would prevent them from carrying out all the other activities they need to do within the household and with the livestock on the farm. In terms of management, women are mostly the ones who take decisions alone for activities such as weaning calves, keeping animals in the open or in the barn, milking time and number of milking. As for the number of animals, these are decisions made jointly by the wife and the husband. Regarding health care, many families are unaware of the importance of vaccinations. However, 30.7% answered that the decisions concerning vaccination were taken by the man only. For the treatment of sick animals, decisions are made either by the man alone, or by consulting the wife, or by consulting the wife and the family. Regarding processing of milk, activities related to milk processing are mainly the domain of women, as they are generally indoor activities [START_REF] Sarma | Women Dairy Farmers and Decision Making Pattern in Sonitpur District of Assam[END_REF]. Thus, women participate very little to decisions concerning breeding activities and animal care, decisions being taken mostly by their husband alone or collectively with the family. for increasing women's decision-making power, one of the major points to be improved for women is access to information. Indeed, access to technical knowledge would increase their participation in decision-making in the dairy sector [START_REF] Sarma | Women Dairy Farmers and Decision Making Pattern in Sonitpur District of Assam[END_REF]. Moreover, improving women's decision making could greatly improve women's empowerment within their households and communities, also allowing them to have some control over income, becoming aware about their personal right and freedom, improving their position in the family, and in general, being more confident in their capabilities [START_REF] Ikemoto | Women's Empowerment through Small-Scale Dairy Farming in Selected Areas of Bangladesh[END_REF].

The decision-making power of women in dairy cooperatives is also often very limited or nonexistent. Indeed, the leaders of cooperatives and producer associations are generally men, so women are underrepresented in the agricultural sector, impacting the benefits offered to rural women. Thus, when agricultural cooperatives are predominantly run by men, women are unlikely to be interested in the activities of these groups. The National Dairy Development Board (NDDB) of India (https://www.nddb.coop/) aims to improve milk production in the country. The NDDB shows the negative effects of gender imbalance in farmers' associations and cooperatives. In 1995, the NDDB initiated a dairy cooperative leadership program for women farmers. The NDDB provided training on hygiene and animal health, but also on leadership, responsibilities and members' rights, for example (FAO, 2018). The BAIF development research foundation is also involved in a "women centric development" through various actions with women, in particular with the aim of improving their decision-making power, but these activities seem independent of milk production or calf and cow care (https://child.baif.org.in/what-we-do/Women-Centric-Development).

Women's lack of access to some resources

Even though women are the main actors in animal activities, there are significant inequalities between men and women in terms of access to some resources such as technology, credit, information, inputs and services. This is likely due to gender inequalities in ownership of productive assets including land and livestock [START_REF] Patel | Role of Women Gender in Livestock Sector: A Review[END_REF]. In developing countries, women are often unable to own land on their own or to obtain land titles or bank credit. Nor can women control the household income generated by their work with livestock, due to prevailing social norms (ILRI, 2019).

There is a need for training women to remove technological knowledge gap in livestock systems [START_REF] Kathiriya | Training Needs of Dairy Farming Women and Constraints Faced by Rural Women: A Case Study of Gujarat[END_REF]. There is no doubt that training systematically contributes to improved knowledge and skills, which in turn leads to more efficient work. As women are the majority of the workforce in the livestock sector, it is obvious that training would improve their production potential. Indeed, training could provide them with technical knowledge in livestock production. Many extension programs are designed specifically for training women beneficiaries [START_REF] Dudi | Contribution and Issues of Women in Livestock Sector of India-A Review[END_REF]. A study [START_REF] Kathiriya | Training Needs of Dairy Farming Women and Constraints Faced by Rural Women: A Case Study of Gujarat[END_REF] shows that the majority of women need training (1) on animal health and disease control precaution, (2) on animal milk production, (3) on animal hygiene and management, (4) on common disease of milking animals and (5)on feeding of livestock during pregnancy.

Conclusion

In India, the livestock production is largely in the hands of women who perform the majority of daily tasks with animals. Even though women are the most present and active in the livestock sector, they are still very much in the background, facing numerous difficulties that strongly limit their empowerment. Indeed, their lack of access to resources such as training, technology, credit or access to land prevents them from fully realizing their potential in the field. The main barrier to this is a social one, where traditional systems remain ingrained in people's minds, preventing women from gaining empowerment. Indeed, gender bias persists in India. This, may be due to paternalistic thinking and to women's own attitudes as they are conditioned by the social culture. Thus, it results in an underestimation of the value of their work. In addition, their reduced place in decision-making systems within the farms but also within the dairy cooperatives has a strong impact on the improvement of the livestock sector. Differences between men and women's social roles and responsibilities can have large impacts on how livestock projects are implemented and maintained.

The more than significant role of women in daily activities with animals suggests that they probably have very specific breeding goals and strategies, particularly in terms of selecting their animals. Women are an interesting and resourceful group to learn more about cow selection expectations in India. To date, no scientific article has been found about women's preferences in terms of selecting their cows in India. So, it is important to understand what their preferences are in terms of selecting their cows. For this, it is essential to understand who these women are, to describe the diversity of profiles that could explain the diversity of their expectations in terms of selection of their cows. Thus, the project "Scaling Genomic Selection Across the Indian Smallholder Dairy Sector" in which the internship is part, supported by INRAE and BAIF, aims to identify one or more criteria of interest to women farmers for the selection of their cows.

Research hypothesis

Based on this literature review, the following research question was formulated: What are the practices, objectives and strategies of women farmers in terms of selecting their cows? To answer this question, the following assumptions were defined:

-There is a significant difference in terms of preferences of women farmers about the cows' most important traits across the states of Maharashtra and Jharkhand. -There is a diversity of profiles of women farmers according to their preferences for cow traits.

-Characteristics related to women farmers, namely their level of education, their age, their knowledge of animal husbandry, their decision-making power and the different activities they perform on a daily basis, influence their preferences in terms of cow traits.

To answer this question and test these assumptions, exploratory interviews were conducted with women farmers in India. The materials and methods section describes in detail the methodology used to achieve these goals.

II. Methodology The objective of the project was to carry out interviews with women farmers in different states in order to compare situations according to the selected states, while knowing that the samples could not be representative all of these states since the approach remains exploratory (2.3.1. Number of interviews conducted). Before leaving for India, the aim was to conduct the interviews in two or even three states. These were to be determined with the BAIF members in India. Once we arrived in India, a brainstorming meeting with BAIF members which suggested to conduct the interviews in 3 states, spending about 3 weeks in each state. However, there were two major factors to consider when choosing the number of states: Time frame :

o We could only spend three months in India. The first month was devoted to discussions with BAIF members, literature review, the development of an interview guide, and reflection on the data collection technique, leaving only two other months devoted to data collection on the field. o We had to set the planned number of interviews per state, which determines the time spent in each state, and thus, by extension, the number of states.

Weather:

o The rainy season, (beginning in mid-June, varying from state to state) when women farmers are no longer available for interviews because they take advantage of the rains to work on the fields. The rainy season also complicates travel in the countryside, making it difficult to reach the farms.

Thus, taking the time and weather factor into consideration, the choice was made to conduct the interviews in two states, not three, spending between 3 and 4 weeks in each.

Choice of the states

In order to carry out a typology of farms, the objective was to carry out interviews in two states differing by the initial time and level of implementation of BAIF in the state, their climate and their economic status. Maharashtra and Jharkhand were the two states chosen for the study.

Implementation of the BAIF

BAIF's crossbreeding work is not carried out in all Indian states. In the states where the crossbreeding work is being carried out, the project did not start at the same time, obviously influencing the type of breed and crossbreed present in the states, and consequently, the breeding objectives and strategies on the farms. The first works of the BAIF were initiated in the state of Maharashtra in 1988. The BAIF's works started 17 years later in Jharkhand, in 2005. Thus, in Jharkhand, BAIF has been present for a much shorter period of time than in Maharashtra, so differences in breeding objectives are likely to result. Similarly, perhaps women's knowledge of cow selection is more important in Maharashtra than in Jharkhand.

Climates

Differences in climatic conditions between states can lead to diversity in breeding practices, but also in the choice of breeds, which are not all adapted to the same climatic conditions. Thus, the climate can influence the expectations of women farmers in terms of genetic selection of their cows.

Maharashtra and Jharkhand have quite different climates: the climate of central Maharashtra is rather tropical semi-arid (steppe), while Jharkhand is rather characterised by tropical monsoons (Appendix 1).

Economic status and share of the livestock sector in India's GDP

The economic situation of the states in which the interviews were conducted is likely to have an impact on the farming systems, expectations and goals of women farmers, but also on the place of women in the activities and decision-making system on the farm. Maharashtra has the highest Gross State Domestic Product (GSDP) among the 33 states and union territories of India (Appendix 2). With a GSDP of 2 632 792 Rupees (33026 $US) in 2018-2019, its share in India's GDP is 13.94%. Among the 33 states and union territories of India, Jharkhand is the 19th state with a GSDP of 297,204 Rupees, contributing only 1.57% of the Indian GDP (GDP of Indian states -StatisticsTimes.com). The share of the livestock sector in the economy of the two Indian states is different. In Maharashtra, the Agriculture, Forestry and Fishing sector accounts for 10.97% of GDP, of which 2.58% is the livestock sector, after the crops sector, which accounts for 6.79% of Maharashtra's GDP. In Jharkhand, the Agriculture, Forestry and Fisheries sector accounts for 17.36% of GDP, of which 4.67% corresponds to the Livestock sector, after the Crop sector, which accounts for 8.50% of the GDP of Jharkhand (GDP of Indian states -StatisticsTimes.com).

Logisitcs and organization

The last selection criterion was related to logistics and facilities. It was comparatively easier to travel to these two states to conduct the interviews because of the good relationships with BAIF members had in the respective centres and the arrival of the rainy seasons, which did not disturb travelling as much in these two states, compared to other states where the rainy season already started.

Choice of farms

For each state, interviews were conducted with women farmers from farms workink with BAIF in Ahmadnagar and Nashik districts in Maharashtra and Ranchi, Khunti and Lohardaga districts in Jharkhand. For each state, the BAIF staff of the CDCs (Cattle Developpement Centers) chose the farms where the interviews could be conducted. However, we had also required our own criteria for selecting these farms. The criteria for selecting farms were: one or more breeds present on the farm (including 'exotic' and 'indigenous' breeds), different herd sizes, presence/absence of land, different economic status, different castes, presence/absence of other animals on the farm, membership or not of a dairy cooperative.

Nomadic farms could not be included in the sample because they were not part of the farms included in the BAIF programs. On the other hand, visiting farms run solely by women was one of the objectives, but this proved to be too complicated to find. Tables 1 and2 below describe the characteristics of the 46 farms visited according to these selection criteria.

Development of the survey

The present study is an exploratory one, since no study in the literature that combined genetic selection of cows and the place of women on farms in India was found. Thus, as a first step of the global project, a qualitative survey was favoured. Indeed, a quantitative study, using multiple choices questionnaires, would have implied to set up predefined categories of thinking which might have been totally opposite to women rationalities. A qualitative survey is also the only method that allows understanding the processes and practices involved in decision-making. The objective was therefore to try to find out whether there was a diversity of ways of thinking among women farmers through their practices and expectations. This is why the interview guide survey technique (Appendix 3) was chosen to understand the diversity of farming systems, in particular by identifying the different practices of women and by carrying out a typology of women farmers through statistical analysis.

Selection of the assumptions

The purpose of the interview guide was to help to understand the practices and objectives of the women farmers regarding the selection of their cows. In order to do this, it was crutial to understand the system as a whole, i.e. not only focus on questions about the preferred traits of the farmers, but to have an overview of who the interviewed women is and what her practices are, but also of the characteristics of her farm and how the farm is managed. Indeed, two assumptions emerged before and during the development of the interview guide. Hypothesis 1 was that information related to the female farmer, i.e. her level of education, her knowledge of animal husbandry, her family situation, her decision-making power and the different activities she carries out on a daily basis, influence her expectations in terms of cow selection on the farm. Hypothesis 2 was that farming practices, feeding and cow health are also related to the expectations of female farmers in terms of cow selection. These two hypotheses are the subject of two different reports. The present report attempts to verify hypothesis 1, while Laurane DESOUTTER's (ENSAT) report concerns the validation of hypothesis 2.

Survey development

The development of the survey started with the help of examples found in the literature, notably in the reports of previous INRAE-BAIF trainees, who also went to India to conduct interviews with farmers (Jacquot 2006;[START_REF] Lassalle | Saurashtra, Gujarat, India). Maps of India[END_REF]. Consulting scientific articles [START_REF] Martin-Collado | Analyzing the heterogeneity of farmers' preferences for improvements in dairy cow traits using farmer typologies[END_REF][START_REF] Chandan | Dairy Cattle Welfare in India: A Review[END_REF][START_REF] Chawala | Farmer-preferred traits in smallholder dairy farming systems in Tanzania[END_REF][START_REF] Strandén | Genomic selection strategies for breeding adaptation and production in dairy cattle under climate change[END_REF] on cattle genetic selection and farmers' preferred traits in their animals also helped to define the different points to be addressed regarding the preferences of women farmers.

The survey of this study consists of 10 main parts. All parts of the survey were designed to identify potential diversities or homogeneities in order to subsequently develop typologies of farms and women farmers, based on women's expectations in terms of animal selection.

The first part is about information about the woman interviewed. This part makes it possible, on the one hand, to create a link with the woman, to show that we are interested in her, her daily life, her education and her family and, on the other hand, to understand what her place on the farm is, what her activities are with the cows and her other daily activities, etc. This first part is a very important part because it allows the investigators to collect information that will allow to define the typologies of women farmers, according to their expectations about the cows (hypothesis 1). The second part is data collection about the crops grown on the farm. The objective of this part is to understand the farmers' way of thinking, their objectives, in relation to the farm's livestock activity. This part was developed to partially address hypothesis 2, but also to identify the woman's level of knowledge about crops, which may potentially be related to her level of knowledge about livestock data (hypothesis 1), if we look at her overall approach to the farm. The third part is about cow feeding practices. This part aims to identify the level of knowledge of the woman on cow feeding practices, in connection with hypothesis 1, and to understand the diversity of breeding practices and objectives as a whole, in connection with hypothesis 2. The fourth part is about the preferred traits of women in cows. It aims to characterize the general characteristics of the herd (number of cows, number of calves, breed(s)), as well as to identify the traits preferred by women about their cows. These selection criteria are divided into 5 parts: general traits, phenotypic traits, production traits, reproductive traits and other traits. A ranking system of the different traits in each sub-section was set up to make data collection and analysis easier. The fifth part deals withpractices related to herd health. As with parts 1, 2 and 3, this part aims to find out if there is a link between health care and the expectations of women farmers. Parts 6, 7 and 8 correspond respectively to milk management, marketing and cow reproduction. The objective of these parts is to have a global view of the overall farm strategies, to understand what their objectives are in terms of herd management and products from the farm's livestock activity. These parts will provide answers to hypothesis 1 in the sense that the woman's knowledge of these parts will be linked to her expectations. They will also provide answers to hypothesis 2, by providing information on the system as a whole. Finally, part 9 corresponds to the women's future plans for the farm or herd. This part also questions their desire for training to improve their husbandry practices. Finally, this part contains questions related to BAIF, to know whether the woman knows the organization and is satisfied about its services. This last part will help to understand the objectives and strategies of the women farmers, their place in the decision-making systems and their motivations.

In addition, in each section, after almost every question, we asked the question "why" in order to get more details about the reasons for each choice made by the woman. In addition, all parts included questions related to the decision-making system. These questions ("who decides about this?") allow the investigators to understand who owns the decision-making power in the choices to be made on the farm (e.g., choice of breeds, choice of feed composition, choice in the sale of milk, choice in the care of the animals, etc.). These questions refer to hypothesis 1, which states that decision-making power has an impact on women's expectations in terms of selecting their cows.

The survey also included a section for observations (farm structure, equipment, who is there during the interview, who answers mainly, who answers sometimes) as well as a section with two questions concerning the caste and economic status of the family, which were only asked to the accompanying persons (milk recorder, milk supervisor, translator).

Finally, at the beginning of each part, open questions were asked to make the interview more spontaneous. If the woman could not answer these open questions, we asked her other questions to clarify the question and help her answer. These open questions are important because they do not guide the women's responses. Thus, it was possible to obtain spontaneous answers, reflecting what came to mind first. Through these open questions, it was possible to understand their reasoning for selecting animals. The rest of the questions in each section allowed them to elaborate on their answers, looking for more details around these open questions.

Survey testing

A test phase of the survey was conducted in Kopargaon district (Maharashtra). A total of five farms were visited during this test phase. This first test phase allowed the investigators to see what farms were like in India and to put them in a real situation for the first time.

The following Table 3 summarizes the problems encountered and the solutions considered following this field test phase:

Problems encountered with the survey

Solutions considered

No answer or short answers to the open questions Redefine questions, further contextualize open questions.

Stressed and uncomfortable women

Learn the interview questions so the investigators don't focus too much on their written support, make the interview open-ended. This means staying longer, putting more emphasis on observation and dialogue.

Following this test phase, the open questions were rewritten to make them more contextual. Also, another way of conducting the interviews was discussed, but abandoned : in order for the woman to feel more comfortable, to answer more confidently, and not to feel stressed by the "formal" interview, and so that the men do not stay for the entire interview, the question was raised of conducting longer interviews without an interview guide. Reading books and articles on social science research [START_REF] Beaud | L'usage de l'entretien en sciences sociales. Plaidoyer pour l' « entretien ethnographique[END_REF][START_REF] Marchive | Introduction. Les pratiques de l'enquête ethnologique[END_REF]Michelat G. 2014) also allowed investigators to reflect on the advantages and limitations of this type of survey. This would have led to a change in the way data was collected, by forgetting the interview guide but learning the questions to ask, so as to base the exchange on observation (for example, by attending the woman's tasks with the cows) and dialogue, rather than on precise data collection. This survey technique would have meant staying longer on the farms, even going twice in one day to a same farm, so as not to take up too much of the woman's time at once. However, this idea was abandoned because it assumed that the women had a lot of time to spare, which was not the case. This idea also implied to take back all the information after each interview, but in this way a lot of precise and useful information would have been lost for the data analysis. It was therefore decided to maintain the survey technique with an interview guide.

In total, after having reworked the interview guide several times with colleagues from the BAIF and my INRAE supervisors, Julie Labatut and Vincent Ducrocq, 10 versions of the questionnaire were developed, each version having been improved to give the following version. The interview guide presented in appendix 3 corresponds to version 10, composed of 119 questions.

Interview methodology 2.3.1. Numbers of interviews conducted

The choice of the number of interviews to be carried out per state was discussed for a long time. The choice was between favouring quantity for increasing statistical representativeness or conducting longer interviews and favouring quality. At first, BAIF partners had expected us to conduct 3 or 4 interviews per day. However, as the study is exploratory, the objective was to focus on the quality and not the quantity of interviews. This implied to spend more time on each farm. Thus, no number of interviews was set a priori. We conducted the interviews according to the time spent in each state (4 weeks in Maharashtra, 3 weeks in Jharkhand), the time spent per interview and the availability of the farmers and translators. In total, 29 interviews were conducted in Maharashtra and 17 in Jharkhand.

Choice of translators

During the "test interviews," many men were here: husbands, sons, fathers-in-law, cousins, and even neighbours. It was complicated to be alone with the women farmers. These men, knowing the answers to some of the questions, often interrupted the women to answer for them, or when women did answer, their answers were often influenced by the answers of the men around. On some farms, women became shy, looking at their husbands or fathers-in-law to see if they were giving the "wrong answer". This biased the interview because the objective was to learn about the decision-making process and the level of knowledge of farm management by the women. Because the purpose of the study required spontaneous responses from women farmers, it was highly preferable that men do not attend the interviews. A female-only interview environment was necessary. However, it was complicated to ask the men to leave, as the arrival of foreigners on their farms is a very rare event, so it was natural that they would be curious to attend our interviews. This is why we thought of having a female translator rather than a male translator to do the interview in a female environment, which could make the woman more comfortable and thus facilitate communication, talk openly about the topic, and create a comfortable and listening environment, leaving less opportunity for men to answer for the women farmers.

The selection criteria for the translator were therefore a woman with a good level of English, a good level of the local language (Maharathi in Maharashtra and Indie in Jharkand) and knowledge of livestock. This last selection criterion appeared only during our fieldwork in the first state (Maharashtra). Indeed, the first woman translator hired had no knowledge of livestock at all, making it very difficult to understand and to translate precise notions related to livestock. In these cases, we asked the milk recorder to translate for the translator. The choice of the second translator, the one who accompanied us to Jharkhand, was therefore based on her knowledge of animal husbandry. Indeed, she had recently joined BAIF and had a master's degree in rural management. The translation was thus more efficient.

Data collection

Data collection was done in pairs between Laurane, the other master student and myself: while one asked the questions, the other collected the data by handwriting them on a paper version of the interview guide. This technique proved to be the most efficient and practical. We tried to collect the data via a data collection software called "EPICollect", available on cell phones. The advantage of EPICollect was that all the data collected is directly recorded in a spreadsheet, avoiding the transcrsiption of data after the interviews. It is sufficient to enter each question of the interview guide into the software before starting the interview phase. However, the test phase on EPICollect showed that this data collection technique was not adapted to our survey because the handling of the application on the phone was not easy and the data collection depended on our cell phones, for which the battery is not adapted to the high Indian temperatures.

The recording of the interviews was also discussed. Indeed, we had considered recording the interviews in order to be able to take back verbatims from the women farmers, which would have been translated by a colleague from the BAIF. However, we also abandoned this idea after the test phase of the interviews because the women farmers were sometimes intimidated by the recorder, making the interview very formal. Indeed, the recorder can be a source of stress but this is exactly what we did not want.

Running interviews

Four weeks were dedicated to the interviews in Maharashtra, and three weeks in Jharkhand. The interviews took place mostly during the morning, with a driving time ranging from 1 to 2 hours to reach the farms. On average, the interview, the observation of the herd and the breeding facilities lasted 1h35. We were systematically at least 4 people to go to the farms: the translator, the milk supervisor, Laurane and myself. Sometimes, the milk recorder also accompanied us. Each time, the milk recorder and/or the milk supervisor acted as an intermediary between the woman farmers and the investigator. During the interviews, we were alone with the farmer and the translator. The milk supervisor and the milk recording technician were out of the way, often chatting with the men on the farm. In this way, we were able to have a female environment to make the woman farmer feel comfortable, as we wished.

Most often, two interviews were conducted per day, on farms close to each other, to optimize travel time. Indeed, it was complicated to realize more than two interviews per day because it was necessary to let the translators work on their side (the two translators had other activities during their days), it was also necessary that we return to eat at the hotel at the beginning of the afternoon and finally, the strong heats especially in a period of heat wave in which we were, were hard for the bodies of the Indians as for ours.

The questions were systematically asked in English to the translator, who then translated the question into the local language to the herder. The translator would then translate the farmer's answer into English. As soon as a male member of the family intervened to answer in place of the female translator, he was systematically reminded that our study was based solely on the responses of women, and that the responses of men would not be taken into consideration. When the "women's preferred traits" section was about to be discussed, we systematically asked to get closer to the cows so that the women farmers could have a visual support by showing us the characteristics of their cows to support their comments. This also helped us to clarify things if they did not understand our questions, using their cows as examples.

How to analyse the data

Database organization

Each interview was transcribed into a file in order to have all of them in a digital version (ease of access). The interviews were named by a number followed by a letter M for the interviews conducted in Maharashtra or J for those conducted in Jharkhand. The responses to the interview guide were transcribed onto an Excel spreadsheet, allowing all responses to be stored in one place. This Excel spreadsheet was adjusted to create a usable database. The Excel spreadsheet consists of several tabs defined according to the different parts of the questionnaire.

Data analysis

Two mind maps were created using XMind software to visualize the diversity of responses to two open-ended questions: "What is your favorite cow and why?" and "What is your least favorite cow and why?". The rankings of the different cow traits were analyzed using a pairwise comparison of the variables. This method compares the rank of each variable to that of each other variable in the ranking. This gives a score to each variable, corresponding to the number of times it is ranked before the other variables with which it is compared. This method allows to get the average rank of each variable, when compared individually to the others. This method also determines the percentage of times each variable was ranked ahead of every other variable. These pairwise comparisons of variables are in tabular form. The significance of the results was then tested using the non-parametric Kruskal Wallis test.

In order to identify groups of female farmers' preferences, a principal component analysis (PCA) on the ranking of general traits (7 variables), followed by a Hierarchic Clustering Analysis (HCA) were performed using the R software. Similarly, in order to determine groups of farmers according to the recorded social data, a Multiple Component Analysis (MCA) followed by a Hierarchic Clustering on Principle Component (HCPC) were performed.

III. Results

Characterization of farms samples per state

As a reminder, 29 farms were visited in Maharashtra and 17 in Jharkhand. The description and analysis of the samples by state allows us to understand the differences in farming systems between Maharashtra and Jharkhand but also to better understand the different practices of women with animals and their objectives and strategies. It is important to re-emphasize that the samples of farms visited are not fully representative of all the farms in Maharashtra and Jharkhand. Indeed, there is a diversity of farming systems and a sample of 46 farms only represents a part of this diversity.

Herds characteristics in Maharashtra and Jharkhand

Number of cows in the herds

The herds in Maharashtra are characterized by a high number of cows compared to those in Jharkhand. The farms visited in Jharkhand have an average of 4 cows (4.24 ± 2.73) while the herds considered in Maharashtra consist of an average of 13 cows (12.97 ± 9.66) (Figure 2). The farms considered in Maharashtra are more diverse in terms of number of cows per herd. Indeed, the sample ranges from 2 to 45 cows per herd. While the farms visited in Jharkhand have herds of 2 to 13 cows. As the Indian average is less than 3 cattle per farm (Aubron C, 2016), the samples from Maharashtra and Jharkhand are far above this average.

Proportion of exotic and indigene breeds in the herds

The data presented in this section 3.1.1.2. are based on the observation of the interviewers on the farms and the information given by the milk supervisors, and not based on the answers of the women farmers, because as it will be mentioned in section 3.5, the women do not always know the breeds that make up their herds.

In this study the two exotic crossbreds encountered are Holstein Friesians (HF) crossbred and Jerseys crossbred. HFs originate from the Netherlands and Jerseys originate from the British island of Jersey. Indeed, in the present study, when breeds are called "exotic", it does not mean that the cows have pure Holstein or pure Jersey blood (which is very rare in India), but that they are Holstein crossbred or Jersey crossbred with other breeds, especially indigenous ones. The indigenous breeds are those originating from India.

The sample of farms visited included indigenous breeds such as Khillar, Gir, Sahiwal, buffalos but also "Non Descript" (ND), which are cows from undetermined crosses of two or more indigenous breeds.

There are differences across farms and states in the composition of the herds. The farms visited in Maharashtra and Jharkhand generally had more exotic crossbreds cows than indigenous breeds and ND crossbreds, but in different proportions (Figures 3 and4). The farms visited from Maharashtra had an average of 12 (12.52 ± 9.20) exotic crossbred cows and one (1.10 ± 1.12) indigenous and/or ND cow, while the farms visited from Jharkhand have an average of 4 (3.71 ± 2.99) exotic crossbred cows and one (0.65 ± 1.54) indigenous and/or ND cow. In the majority of the farms visited, the presence of indigenous and ND cows is used to provide milk for family consumption. Milk from indigenous and ND cows is considered to have more fat and have more nutrition quality. Families keep this type of milk for feeding their children, for tea and for various transformed products such as paneer (Indian soft cheese), ghee (clarified butter), yogurt, etc. The larger the herds on the farms visited in Maharashtra, the more the herds consisted of many cows of exotic crosses, especially HF, with one or no native cow.

In the literature, no data was found regarding the number of exotic, native and ND cows in the herds for the states of Maharashtra and Jharkhand. However, one study explains that 73% of the Indian cattle population is made up of indigenous and ND cows, while exotic and crossbred cows represent 26% of the Indian cattle population ('73% of The Indian Cattle Population Is Indigenous and Non-Descript', 2022). In the present study, exotic crossbreds in the farms visited from Maharashtra account for 96.5% of the herds, while indigenous and ND breeds account for 3.5% of the herds. In the farms visited from Jharkhand, the exotic crossbreds account for 87.5% of the cows in the herds and the indigenous and ND breeds account for 12.5% of the cows in the herds. In both states, exotic crossbred cows make up for 95% of the herds, while native breed and ND cows make up 5% of the herds. Thus, the farms visited for this study do not match the percentages of exotic and native breeds of cattle in India, but for a sample of this small size, it is not so different. This may be because the cattle population in India also considers draught cattle herds, in addition to dairy cattle herds. Cattle for draught are predominantly cows of indigenous and ND breeds, which could explain the high proportion of indigenous and ND cows in the Indian cattle population. This finding underscores the fact that exotic breeds are primarily selected for dairy production. 3.1.2. General characteristics of the farms in Maharashtra and Jharkhand

family status

Two types of families owned the farms visited: nuclear families and joint families. A nuclear family is a household composed of two married, heterosexual parents and their legal children, while a joint family is a group of descendants living together with their husband and offspring in a single house. [START_REF] Bansal Sb | A study to compare various aspects of members of joint and nuclear family[END_REF].

In total, out of 46 farms visited in the two states combined, 35 belong to joint families and 11 belong to nuclear families. Of the 35 joint families, 27 are in the sample of farms visited in Maharashtra and 8 are in the sample of farms visited in Jharkhand. Of the 11 nuclear families, 9 are in the sample of farms visited in Jharkhand and 3 in Maharashtra.

According to 2011 Census data, 70% of Indian households are nuclear families. Joint families represent only 20% of Indian households, and the remaining 10% correspond to single members. Indeed, in India, the joint family system has existed for a very long time but is giving way to the nuclear family system. However, the increase in nuclear families is mostly taking place in cities, where employment opportunities lead to a lack of living space in housing, making it difficult to accommodate all members of a joint family in a single house in the city [START_REF] Bansal Sb | A study to compare various aspects of members of joint and nuclear family[END_REF]. In rural areas, this problem is much less prevalent, making joint families still possible, especially on farms, where work in the fields and with animals requires family labor. This is the reason why there is a high proportion of joint families in the sample of farms visited for the study.

economic status

The families belonged to two different economic statuses according to their position on the poverty line. The Indian poverty line is defined according to the World Bank Poverty Line which defines extreme poverty as living on less than $1.90 a day (in 2011 international dollars) [START_REF] Gaur | Poverty Measurement in India: a Status Update[END_REF].

In total, 30 families out of 46 belong to the APL (Above Poverty Line) status and 16 belong to the BPL (Below Poverty Line) status. Among the 17 farms visited in Jharkhand, 15 (88.2%) belong to BPL status and 2 (11.8%) to APL status. Of the 29 farms visited in Maharashtra, only one (3.5%) is in BPL status, the other 28 (96.5%) are APL. This difference is explained by the difference in wealth of the two states. As mentioned in the methodology, Maharashtra is one of the richest states in India with a share of 13.94% in India's GDP, while Jharkhand is one of the poorest, with a share of 1.57% in India's GDP. (GDP of Indian states -StatisticsTimes.com). World Bank data show that 15% of the rural population is considered "poor", i.e., belonging to BPL status, while 85% belong to APL status (povertydata.worldbank.org). No data by state was found.

Visualization of women's preferences in their cows through open questions

In order to understand the way of thinking, expectations and objectives of women farmers in terms of selecting their cows, it is necessary to obtain spontaneous answers first, not guided by closed questions. For this reason, the questionnaire was constructed as follows: at the beginning of each part, open questions were asked before asking more closed questions to obtain clarification on the women's responses to the open questions. Open questions about their favourite and least favourite cow(s) in the herd were asked before asking them to rank different traits, which will be the subject of section 3.3. Beforehand, the interviewers always asked the woman farmer to show them her herd, so that she could show her favourite and least favourite cow(s). This also allowed the woman farmers to use what she saw on her cows to support her answers.

The favorite cow

When asked "Which is your favourite cow and why?" many women were confused and began by saying that they did not have a favourite cow, but that they loved all their cows. The interviewer then asked the question differently: "If tomorrow you had to keep only one cow in your herd, which one would it be and why?". Then, to this question, the women generally ended up with an answer.

The responses of the 46 women farmers are summarized in Figure 5. The answers to this question are quite diversified even if two criteria stand out from the others: high milk yield and the cow's temperament. Indeed, 22 women mentioned the docile temperament as the reason for choosing their favorite cow in the herd. These women interested in the docile temperament of their cows are also often emotionally bonded to their cows: out of 22 women who answered "docile", 8 specified their emotional bonding to their cow, often because the cow corresponded to the "mother of the herd", i.e. the first cow obtained, which thus gave the first calf. In addition, 19 women mentioned the criterion of high milk yield to justify their choice of their favourite cow in the herd. Some added milk quality criteria such as high fat and Solid Non Fat (SNF) and milk thickness, temperament (docile), fertility or appearance of the cow (strong, high, dehorned, brown colour). The rest of the responses involve a much smaller number of women per response. The thickness of the milk is of interest to three women, and in all three cases, they add the importance of the color of the cow in the preference criteria. It is possible that for them, the thickness of the milk is directly linked to the color of the cow, and thus to its breed. However, the preferred color mentioned is different for these three women: red (corresponding to a Gir), white and black (corresponding to a HF) and brown (corresponding to a Jersey). Two women mentioned the feed efficiency criterion, i.e., the amount of feed needed to obtain one liter of milk. Two women mentioned the "genetic background" of the preferred cow, one by mentioning the high percentage of HF breed and the other by mentioning the origin of the BAIF cow. Another mentioned the good environmental adaptation of her favourite cow, which is a Sahiwal. Finally, 4 women maintained that they had no favourite cow, stating that they loved all their cows.

The least favorite cow

After the question about the favorite cow, the interviewer asked the following question: "Which cow do you like the least and why?" Since most women answered that they liked all their cows, the interviewer rephrased the question as "If tomorrow you had to give one cow in your herd (not as a gift), which one would it be and why?" Despite this rephrasing, 26 women did not respond because they did not really have any cows they disliked, arguing that if there was one that the family did not like, then they would sell it immediately. The responses are summarized in Figure 6.

Regarding the other answers, 8 women mentioned the criterion of aggressive temperament as a reason why they did not like a particular cow. This criterion corresponds to one of the criteria also mentioned in the question on the preferred cow: 4 of the 8 women also mentioned the criterion temperament (docile) in their answer to the question about their favourite cow. Thus, cow temperament seems to be an important criterion for women farmers. In addition, 4 farmers cited low milk yield as a criterion for choosing their least favourite cow. Of these, one added fertility problems. Thus, as with the question about the preferred cow, the milk yield of the cow seems to be a criterion favored by the women farmers. Two women mentioned poor feed efficiency of the cow, but these were not the same women who had mentioned this criterion in the question about the preferred cow. Two women mentioned health problems (legs problems and mastitis), 3 women mentioned the age of the cow, and one woman from Maharashtra mentioned the white color to justify her choice of her least favourite cow, which is quite surprising given that, according to the women farmers from Maharashtra, the market price of the cow in Maharashtra be higher if the cow is whiter in its coat. This choice of white color to justify her choice of least favourite cow may therefore simply be due to the fact that she recognizes the cow she likes least by its color, and the problem may rather come from another criterion like the aggressive temperament or a low milk production from this cow, which is a white cow. Thus, two main criteria emerge from women's preferences: milk yield and cow temperament. These criteria are often accompanied by other preference criteria. The objective of the following section is to understand more precisely the expectations of women farmers through the ranking of different criteria.

Preference ranking of cows' traits

After the open questions described in section 3.2, women were asked to rank criteria in order of preference, from most to least preferred. Five rankings were asked to them. Two rankings are about the physical aspect of the animal: « overall body traits » and « aesthetic », one ranking is for reproductive traits, one is for milk production traits and one is for general traits. Each ranking was analysed using the pairwise comparison method for each variable. In this study, only the results on the rankings on the physical aspect and on the general traits will be presented because rankings on production related traits and reproduction related traits were often biased, as many women didn't know some terms such as "early peak yield", "food efficient ratio", "days in lactation", "calving interval". Even if we explained the terms to them, it was sometimes complicated for them to understand these terms. This calls into question the relevance of including the analysis of these rankings in this study. A score is given to each variable, corresponding to the number of times it is ranked before the other variables with which it is compared. The average rank of each variable is specified. For each pair comparison, the percentage of occurrences when the criterion came before the other criterion with which it is compared is mentioned. Pairwise analyses were performed independently in the two states.

3.3.1. Women's preferences in terms of cow appearance: overall body structure and aesthetic

Pair wise ranking of overall body traits

Women ranked 5 traits related to the overall cow body structure according to their preferences (Table 4, Table 5). The 5 traits are muscularity, lenght size, height size, width size and leg size. In both states, the top two preferred criteria for women regarding body structure are muscularity and height. In Maharashtra, interviewed women prefer muscularity over height and in Jharkhand, they prefer height over muscularity. In both states, length, width and legs size are placed third, fourth and fifth respectively. The Dunn test tests the significance of the results of this pairwise classification. In both states, only the differences between muscularity and legs size and between muscularity and width are significant. This means that the women interviewed significantly prefer muscularity over legs size and width of the cow's body (Appendix 4, Appendix 5). Note: The percentages correspond to the proportion of times the criterion is ranked before the other criterion with which it is compared. The stars correspond to the significance of the preference result and "ns" means "not significant" (Dunn's test). Note: The percentages correspond to the proportion of times the criterion is ranked before the other criterion with which it is compared. The stars correspond to the significance of the preference result and "ns" means "not significant" (Dunn's test).

Pair wise ranking of aesthetic traits

Women were asked to rank coat color, hair size, and presence/absence of horns on cows according to their preferences. In Maharashtra, coat color is most important, followed by horn presence/absence and hair size (Table 6). Dunn's test shows that there is no significant result for the ranking between presence/absence of horns and hair size in Maharashtra. Thus, for the interviewed women in Maharashtra, only the preference of coat color over the presence/absence of horns and hair size can be asserted. The previous section shows that the preferred color of the women farmers interviewed in Maharashtra is black and white, with more bench than black. In Jharkhand, interviewed women gave more importance to the presence/absence of horns, followed by hair size, before coat color (Table 7). However, Dunn's test shows no significance between the results. Thus, for the women interviewed in Jharkhand, the preference of one criterion over another cannot be stated. Note: The percentages correspond to the proportion of times the criterion is ranked before the other criterion with which it is compared. The stars correspond to the significance of the preference result and "ns" means "not significant" (Dunn's test). Note: The percentages correspond to the proportion of times the criterion is ranked before the other criterion with which it is compared. The stars correspond to the significance of the preference result and "ns" means "not significant" (Dunn's test).

Description of the preferences linked to the body structure and aesthetic

In addition to the rankings among the different criteria, the farmers were asked to specify their preferences for each criterion (Appendix 6).

In Maharashtra, 4 out of 5 women interviewed (82.76%) prefer the Black and White colors for their cows. These colors correspond to the pattern of HF cows. This means that the majority of the interviewed women in Maharashtra prefer this breed to other breeds, probably for reasons related to higher milk yield. This hypothesis will be verified in the part concerning the classification of general traits (Part 3.3.2.). Indeed, 28 out of 29 women interviewed keep HF cows in their herds. In Jharkhand, the women interviewed prefer Black and White (41.18%) or Black (41.18%). As in Maharashtra, the Black and White coat color corresponds to the HF breed. Indeed, in Jharkhand, 15 out of 17 women interviewed raise HF cows. Regarding the preference for the black color, this may also correspond to HF cows, with more black than white because when the interviewer asked them if they preferred "black coat with white patches" or "white coat with black patches", most of them answered "black coat with white patches". This may also be due to the price of the cows on the market in this state. Indeed, some women said that the more black the cow is, the more expensive she is on the market. However, this was the opposite case in Maharashtra, where HF cows with more white were more valued in the market. This is related to beliefs that the higher proportion of white (Maharashtra) or black (Jharkhand) means higher milk yield, from what some women said during the interviews.

The vast majority of women interviewed preferred dehorned cows (96.55% in Maharashtra and 100% in Jharkhand). This choice is related to the risk of injury to conspecifics and humans. Thus, exotic cows (HF and Jersey) are more valuable in the market if they are already dehorned. However, some women prefer indigenous cows (Khillar, Sahiwal, ND) with horns for aesthetic reasons. In fact, women said that indigenous cows look better with horns, and are therefore worth more in the market with horns than without.

Regarding hair coat size, all the women interviewed preferred short hair. Indeed, none of the breeds we saw during the interviews had long hair.

Regarding muscularity, 71.43% of the women interviewed in Maharashtra and 100% of the women interviewed in Jharkhand responded that they preferred strong cows. Indeed, strong cows mean for them that they are healthy cows, which produce more milk.

Women interviewed generally prefer a medium body width (52% in Maharashtra; 64.71% in Jharkhand). In Maharashtra, they tend to prefer a long body length (58.2%), while in Jharkhand, they prefer a medium length (76.47%). In both states, women interviewed preferred a medium body height (51.7% in Maharashtra;58.82% in Jharkhand). Some women justified this choice by saying that tall cows were less manageable. This was also reflected in their preference for medium leg size (62% in Maharashtra; 58.82% in Jharkhand).

Details on udders were also requested. More than half of the women interviewed in Maharashtra (58.52%) and Jharkhand (58.82%) prefer large udders as they believe this means that the cow produces larger quantities of milk. Regarding teat length, women interviewed in Maharashtra prefer long teats (65.52%), while women in Jharkhand tend to prefer medium sized teats (70.59%). This difference in teat size preference may be due to the milking technique. Indeed, 65.5% of the farms visited in Maharashtra use a milking machine for milking, while all farms visited in Jharkhand milk by hand. Some women in Maharashtra said that milking machines are not suitable for short teats. On the other hand, some women in Jharkhand said that hand milking is more difficult when the teats are long or short, so they prefer a medium size. Finally, almost all women interviewed preferred low teat resistance (86.21% in Maharashtra; 100% in Jharkhand) for ease of milking, whether with the milking machine or hand milking.

Women's preferences in terms of general traits

The women interviewed were asked to rank 7 general criteria for cows according to their preference. The 7 criteria are milk yield, fertility, temperament, longevity, health, market price of the cow and body structure. The pairwise ranking method was applied to this ranking for the Maharashtra (Table 8) and Jharkhand (Table 9) interviews.

In both states, the pair ranking method shows that high milk yield is in first position compared to all other criteria. Dunn's test (Appendix 7) shows that the women interviewed in Maharashtra significantly prefer high milk yield over temperament, fertility, market price of the cow and longevity. In contrast, their preference for high milk yield over health and body structure is not significant. In Jharkhand, the women interviewed significantly preferred high milk yield over all other criteria (Appendix 8).

In Maharashtra, health is the second most preferred criterion of the women interviewed. However, Dunn's test shows that health is only significantly preferred to fertility and longevity. In Jharkhand, fertility appears to be the second most preferred criterion of the women interviewed. However, Dunn's test shows that the preference of fertility over the other criteria is not significant. The third preferred criterion of women farmers in Maharashtra seems to be body structure, however, Dunn's test shows that body structure is only significantly preferred over fertility and longevity. In Jharkhand, the third most preferred criterion of the women interviewed appears to be the body structure, however this preference over the other criteria is not significant. Note: The percentages correspond to the proportion of times the criterion is ranked before the other criterion with which it is compared. The stars correspond to the significance of the preference result and "ns" means "not significant" (Dunn's test). Note: The percentages correspond to the proportion of times the criterion is ranked before the other criterion with which it is compared. The stars correspond to the significance of the preference result and "ns" means "not significant" (Dunn's test).

3.4. Typology of women farmers according to their preferences on general traits

The pair wise ranking of the 7 general traits demonstrates that there are probably different ways of thinking.

A first hypothesis is that there is a difference in ways of thinking between the two states Maharashtra and Jharkhand. Indeed, as the two states are very different in terms of climate, Gross State Domestic Product (GSDP) and livestock system, there is probably a significant difference between women's preferences in terms of the general traits of their cows, and this is what we will try to demonstrate. A second hypothesis is that there are several ways of thinking among the women farmers interviewed and that they can be defined thanks to the classification of their preferences on the general traits, described in the part 3.3.2. The objective of this part 3.4. is to try to determine these groups.

In order to describe the dataset by identifying groups of individuals and links between variables, here the 7 general traits, a Principal Component Analysis (PCA) was performed.

The confidence ellipses of the two states on the graph of individuals do not overlap (Figure 7). This justifies the choice to analyse the data separating the two states. Thus, the hypothesis of the existence of a significant difference between results in the two states is verified.

The correlation circle (Figure 8) shows that the positive correlation between variables means that either farmers tend to prefer these criteria at the same time or tend to like these criteria less at the same time.

Dimension 1 with contribution of 28.66%, suggests that longevity and high milk yield are positively correlated, which means that women farmers tend to pay attention to the criteria "longevity" and "high milk yield". On the other hand, these same farmers tend to pay less attention to the criteria "body structure" and "health" when they like the criteria "longevity" and "high milk yield", or conversely, the farmers who like the criteria "body structure" and "health" tend to like less the criteria "longevity" and "high milk yield". This result seems surprising because longevity is related to health. This may be explained either by the fact that women who value health do not care about the longevity of their cows because they sell them first; or by the fact that women who value longevity do not have health problems with their cows. Dimension 2 with a contribution of 19.09%, shows that the variable "market price" is inversely correlated with the variable "fertility" and, to a lesser extent, with the variable "body structure". This means that women farmers who value the market price of cows do not value the body structure of the cow and its fertility, but perhaps more the temperament of the cow. This seems surprising because during the interviews, some women explained that the market price of cows depends on their appearance (muscularity, etc). It can be assumed that the women either misunderstood the term "body structure" during the interview, or that many of them are not aware of the criteria for determining the market price of the cow, and therefore do not give importance to body structure, or that other criteria seemed more important to them, such as temperament. Dimension 3 moderately contrasts the temperament and high milk yield variables. Thus, if women farmers tend to give importance on the temperament of their cows, then they place little or no importance on high milk yield, and vice versa. These two criteria are the two criteria that emerged in the analysis of the open-ended questions: "what is your favorite cow and why?" and "what is your least favorite cow and why?". Thus, the PCA provides the information that farmers who like high milk yield are not the same as those who like temperament and, moreover, those who like high milk yield tend not to give importance to temperament and vice versa. This information allows to understand that there are distinct profiles of farmers. The realization of the Hierarchic Clustering Analysis (HCA) makes it possible to verify this.

The HCA (Figure 9) determine three clusters of individuals classified according to their preferences on these general traits. This classification makes it possible to determine profiles of women farmers according to their preferences (Table 10).

Cluster 1 corresponds to women farmers from the state of Maharashtra, who either value body structure and health and place little or no importance on fertility, temperament and longevity. The details of the rankings made by the farmers belonging to this class allow to assert that this class corresponds to the farmers having as preferred criteria the structure of the body and/or the health, and giving little or no importance to fertility temperament and longevity. Cluster 1 corresponds to functionally focused farmers. Cluster 2 groups only 6 individuals, who seem to favor temperament and fertility, and place less importance on high milk yield. Cluster 2 corresponds to affection focused farmers. This means that they give priority to the docile temperament of their cows, before the other criteria. This type of farmers can be defined as it is also the impression that came out of the interviews: the farmers who prioritize temperament are often those who are the most sentimentally bonded to their cows. Cluster 3 corresponds to women farmers from the state of Jharkhand. They give more importance to longevity and, to a lesser extent, to high milk yield. However, they attach little or no importance to body structure and health. Presumably, the reason they place less emphasis on health is that their cows are more resilient and get less sick than cows on farms in the other two clusters. However, the data on cow diseases show that women in cluster 3 have slightly more cow diseases than those in cluster 2 and slightly less than those in cluster 1. Thus, it cannot be said that cows in cluster 3 are more resistant than those in the other two clusters. Cluster 3 corresponds to longevity and production focused farmers.

The conclusion of these analyses is that there is a diversity of farmers profiles according to their preferences in terms of their cows' traits. This makes it possible to establish a typology of interviewed women farmers, and this typology is also a function of the states. The hypothesis that "There is diversity in the profiles of women farmers according to their preferences for cow traits" is verified. The women interviewed in Maharashtra are more functionally focused farmers, the women interviewed in Jharkhand are more longevity and production focused farmers, and some of the women interviewed in Jharkhand and Maharashtra are affection focused farmers. The objective of this part is to verify the hypothesis "Information related to the farmers, namely her level of education, her age, her knowledge of animal husbandry, her decision-making power and the different activities she performs on a daily basis, influence her preferences in terms of cow traits". In other words, the objective is to find out if women's preferences for their cows' traits are related to particular women's profiles, which could help to understand why certain criteria are preferred over others.

Most represented state

Maharashtra

Maharashtra and Jharkhand

Jharkhand

Typology of women farmers

Functionality focused farmers

Affection focused farmers

Longevity and production focused farmers

For this purpose, a Multiple Correspondence Analysis (Figure 10) followed by a Hierarchic Clustering on Principle Component (Figure 11) were conducted with the qualitative variables concerning their involvement in the activities with the cows on the farm (cleaning_shed, cleaning_animals, chopping_feeding_prep, feeding, milking, health_care), their involvement in the milk marketing (selling_milk), their involvement in the cow breed decision system (breed_choice_implication), their knowledge or not of the breeds of the cows they look after (breed_awareness). Two quantitatives variables were added regarding the woman age (woman_age) and the woman education (woman_education). In addition, the variable "cluster_pref_trait" was added. This variable corresponds to the three clusters determined in the previous section, concerning the preferences of women farmers in terms of general traits of the cows. This variable makes it possible to find the link between the classification on women's preferences and the present classification on the different profiles of women farmers. 30.98% of the inertia is explained by dimensions 1 and 2 of the MCA. The MCA shows that there are significant differences between the two states of Maharashtra and Jharkhand because the confidence ellipses do not overlap on the graph of individuals in the MCA (Appendix 9). Thus, the hypothesis of a significant difference in results between the two states is verified once again.

Individual 10 (the 10th interview) was referenced as an illustrative individual. Indeed, it turns out that it is an outlier: the majority of the answers of this farmer were "NA" because she did not answer.

During this interview, the woman farmers was restrained, her husband was present, and she did not dare answer the questions; she often looked at her husband. The interviewers were not able to obtain all the desired answers to the questions asked.

After having put individual 10 as an illustrative individual, three clusters were determined using to the classification (Figure 11). To determine if a modality participates in the creation of the cluster, the Mod/Cla value must be greater than the Global value (Appendix 10). The three clusters are summarized in Table 11. Cluster 1 and 2 differ from cluster 3 in the extent to which women carry out cow-rearing activities. In fact, Cluster 3 includes women farmers who do almost no work with animals. These are women who are involved in activities with the cows from time to time, but not on a daily basis. On the farms in cluster 3, it is mostly the men in the family (husband, brother-in-law, father-in-law, son) who take care of the cows on a daily basis. Such farms were found in both states. This cluster does not correspond to what has been found in the literature, namely, women who perform daily tasks with cows. The daily participation of women in cow-related activities must therefore be discussed: not all women on farms work with cows on a daily basis.

Cluster 1 (16 individuals) and Cluster 2 (21 individuals) differ in the activities that the women farmers perform, their breed awareness, their involvement in breed selection, their education level and the state to which they belong. In terms of activities, both clusters include women farmers who do the cleaning and health care. However, the women farmers in cluster 1 are more involved in milking and selling milk than the cluster 2.

Regarding the choice of breeds and their knowledge of breeds, the majority of women farmers in cluster 1 know the cow breeds that make up their herd. Indeed, among the 11% of women interviewed in the total sample who know the breeds of their cows, 8.7% belong to the cluster 1. Women farmers from cluster 1 are also involved in the choice of breed when breeding or buying new cows. Indeed, among the 21.74% of women who are involved in breed choice, 17.39% of them belong to cluster 1. While the women farmers in cluster 2 do not know the breeds that make up their cow herd and are also not involved in the choice of breeds. Moreover, women farmers in Cluster 1 are poorly educated or not educated at all, while women farmers in Cluster 2 are mostly educated, sometimes up to grade 10 or 12.

Thus, it can be said that the more knowledgeable and involved the women interviewed are in the breeds, the less educated they are. But when we say "educated", we are not necessarily talking about studies related to breeding, but studies in general. The lack of education for women who know more about the breeds of their cows and are more involved in this choice may seem surprising, but it can be explained by the fact that these farms correspond to smaller farms, especially from Jharkhand, where the business is not only done through the sale of milk, but also through the sale of vegetables on the market, unlike the farms in Maharashtra (cluster 2), where the herds are larger, the quantities of milk to be sold are greater, the milk selling is the main source of income for the family, and therefore men are more involved in this business than women. Thus, Jharkhand farms are farms where women are more involved in the business related to cows and selling milk: out of 34.78% of the total sample of women involved in selling milk, 28.26% of them belong to cluster 1 (farms belonging mostly to Jharkhand). Thus, it can be assumed that less educated women know more about their breeds and are more involved in the choice of breeds because they are more involved in the whole business of selling the products (especially milk) from the work they do with the cows, especially because the main source of income does not necessarily come from the milk sold. In contrast, in Maharashtra farms, where women are more educated but do not know the breeds, selling milk is often the main income, so it is the men who are involved in selling milk and the women are only "doers", doing daily tasks with the cows, but not involved in selling milk. So, either they have less interest in knowing the breed since they are not involved in the business, or the men simply do not include them in the decisions that are related to income (i.e. the choice of breeds).

Finally, only the longevity and production focused farmers are represented in the present classification on female-related social data. Thus, the longevity and production focused farmers are better represented by cluster 1 of the present classification, which correspond to the fact that these women are the ones most involved in selling milk.

To summarize, the women in cluster 1 are mostly women from Jharkhand, involved in daily activities with animals, especially in activities related to milk (milking and selling milk). These are women who are involved in the choice of breed when breeding or buying cows. According to their criteria of preference in cows, they are rather focused on the milk production of their cows. However, these women are poorly educated but have learned about animal husbandry from their parents, who are themselves farmers, or after they married by being included in the decision-making systems, probably due to their greater involvement in activities related to the production and sale of milk. The women in cluster 2 are mostly from Maharashtra and are also involved in daily activities with the cows but are less involved in activities related to milk production. They are not aware of the breed of their cows, nor are they involved in the choice of breeds. However, they have more education than the women in cluster 1. These women are mostly "doers", not very involved in the decision-making system or in production, but they are very involved in the daily tasks with the cows for their wellbeing. Finally, the women in cluster 3 correspond to women who are only occasionally involved in cow-related activities; they are an aid to others who are more involved in the activities.

We also notice that the age of the woman is not a modality that allows to build clusters, it has less or no importance in the creation of profiles. The desire of women farmers to have more information about livestock management was also surveyed. When asked "Would you like to have access to more information about livestock breeding?", 43 women answered "yes", 2 women answered "no" and one woman was confused and did not know how to answer. This clearly shows that there is a real desire among women to obtain more information to gain knowledge about livestock management. They were also asked what topics they wanted more information on. The different responses from the women farmers are summarized in Figure 12.

19 women mentioned that they wanted to learn about cow health care. Indeed, on most of the farms visited, cows suffer from diseases that farmers have difficulty in eliminating, such as mastitis, foot and mouth disease (FMD), cold-related diseases in winter and difficulties in adapting to high heat in summer. Some farms implement strategies to avoid certain diseases (vaccination against FMD, cleaning teats before milking, cleaning the cow to avoid ticks, etc.), but women farmers seem to want to receive more knowledge on techniques to avoid diseases, which directly impact milk yields. There is also a strong desire for knowledge on cow feeding practices: 14 women mentioned that they wanted to receive information on how to feed their cows, how to adapt the proportions of concentrates to the cows according to their production (lactating cows, dry cows, pregnant cows). 9 women wanted information on cow management in general. Finally, some women wanted more information on milk production, milking management, shed management, farm equipment to improve cow welfare and about breeds to select for good milk yield.

Women were also asked how they would prefer to receive this information (Figure 13). The answers show that 15 women want to receive knowledge through videos, 13 women want to do trainings, 7 women want to receive information directly from the artificial insemination technician. To a lesser extent, some women wanted to receive knowledge through the internet, farmers groups or books. "DK" means "don't know" ; "NA" means "not applicable". "DK" means "don't know" ; "NA" means "not applicable".

IV. Discussion The results of the study are open to discussion, particularly because of a translation bias. Indeed, the translator who accompanied the interviewers to the farms in Maharashtra did not have any knowledge of livestock or agriculture. Thus, she did not know some technical terms related to livestock. It was necessary to explain the technical terms to her, and for her to memorize them. However, sometimes the explanation was not enough, the term remained misunderstood, which could lead to translation errors. In addition, sometimes the farmers used very specific terms in the local language, which the translator did not know either, making it impossible to translate from the local language to English. Thus, the problems of understanding were in both directions: from the interviewer to the translator and from the farmers to the translator, making translation complicated. Some questions must have been misunderstood by the translator, and therefore wrongly asked to the farmers, so the answers may have been biased. In addition, some of the farmers' answers must have been misunderstood by the translator. For example, sometimes, when asked "what do you like about your cows?", the farmers answered with several sentences, for more than a minute, and the translator translated it with only two words -"look good". The investigators knew that the farmers had probably given more details, which the translator had probably not understood. Sometimes there was also a bias because of the translator's limited level of English. Indeed, she did not understand certain words in English, which were not necessarily related to the vocabulary of the livestock. Thus, it is imperative to select a translator with knowledge of livestock and a good English level. Therefore, for the interviews in Jharkhand, it was specified to select a translator with a good level in English and livestock knowledge. The translator in Jharkhand had studied rural management and started to work with BAIF. It is also possible that Indians could conduct the interviews, but several difficulties remain: they would have to have knowledge of animal husbandry, they would have to be women in order to maintain the female environment, and there would have to be no problems related to the different castes between the interviewers and the farmers, a problem that does not arise when foreigners conduct the interviews. In addition, I suppose that interviewees often take more time to explain things to foreigners, because they generally assume that foreigners are less familiar with the country's practices.

4.1.2. Questioning women's understanding of some questions

The results can also be debated because sometimes farmers did not understand the questions asked. This was especially true for questions that required the farmers to rank different criteria according to their preferences. In some cases, some farmers ranked the criteria in ascending order as indictated by the translator. Either the farmers did not understand the different terms of the ranking mentioned by the translator, or they did not understand the fact that the criteria had to be ranked in order of preference, or it was the result of chance and therefore it is indeed their order of preference. However, we can question their understanding of the different, sometimes technical, terms given that some women are not at all involved in the decisions, and are mainly doers, without really understanding the breeding strategies, which are rather the result of considerations between the men of the family, especially when it is a joint family (husband, son, father-in-law, uncle, brother-in-law). This is one of the reasons why a quantitative survey with multiple choice might not be a good solution, as there would not be enough time during the interviews to really explain the questions and understand the rationalities of the farmers.

Length of the survey

At the end of some interviews, the interviewers felt that the women were bored and sometimes did not want to answer anymore and ended up leaving to do other household or cow work before the interview was over. This was problematic, as the last questions were either not answered or were answered without thought by some of the women. Thus, the length of the survey should be limited to keep the attention of the women throughout the interview, and to ensure that all answers are well thought. To avoid this, interviewers sometimes suggested that the farmer take a break during the interview. In this way, the farmers could do a few waiting tasks and then return. The break also cut the interview in half and prevented the farmers from getting bored. 4.2. Suggestions for future surveys

Choice of translators

The choice of translators with a good level of English and knowledge of livestock is essential to minimize bias in data collection. In addition, when interviewing women farmers, the choice of female translators is recommended because it may allow the women farmers to feel more comfortable in a female environment. First the test interviews with a male translator, and then the study interviews with female translators showed that a woman translator can facilitate communication, allow the women farmers to speak openly about the topic, and create a comfortable and listening environment, leaving less opportunity for men to answer for the women farmers.

Collecting the opinions of men and women simultaneously

During the surveys, we thought about how to conduct the interviews for the rest of the project. Since the men were curious about what the interviewers were asking the women, perhaps conducting small interviews with the men first would be a way to get their permission to do the same with the women alone later. One way to do this is to ask the men questions that the interviewers know they will not be able to answer and that the women will know. This will help them understand that it is essential that the women be interviewed alone. It will also give information on men's preferences, which is unknown too in literature.

Legitimacy of qualitative surveys

Before launching large-scale quantitative surveys, it is essential to conduct qualitative exploratory surveys. This is part of the report-building process for the Bill and Melinda Gates Foundation project. The results obtained in this survey would not have been possible with a quantitative survey. If a quantitative survey had been launched without a qualitative survey, the quantitative survey would not have been validated, since the results of the qualitative survey showed that women have very little say in the selection of breeds, and most women farmers (89%) do not even know the breed of their cows. Thus, quantitative surveys would not have been validated since they would not have demonstrated these two main results, which are very important for understanding the place of women in the decision-making system, and thus indirectly, for understanding why they have such preferences in their cows.

Recommendation for BAIF with women farmers

The initial objective of the project under which this study is being carried out is to include women farmers into animal selection studies. To this end, the project aimed at incorporating a selection criterion important for women farmers in the genomic evaluation of animals in the development of a multi-breed, multi-trait selection program for Indian dairy farming. However, this study has shown that there is not one preferred criterion for women, but several, depending on the type of women farmers and the state, and that it is not by including such a selection criterion that gender equality will improve, but rather by informing women about breeding practices and the care to be given to cows. Indeed, most women want to receive information and even trainings to better understand their daily work with cows. It is important to understand that it is through the transmission of knowledge to women, who are at the center of daily activities with cows, that Indian cattle farms will be more efficient, through improved breeding practices. Thus, certain diseases can be avoided, feed efficiency can be increased and milk yields can be improved. BAIF already provides such training to women, but this effort should receive proper attention and be developed further.

However, the results of the social analysis show that it is not enough to train women, it is also necessary to work on their place in the decision-making processes on the farm. Indeed, the results showed that educated women are not the ones who know the breeds of their cows, nor the ones involved in the selection of breeds, especially because they are not involved in the decisions related to the sale of milk. Therefore, they have less interest in knowing the breed since they are not involved in the business. If women were more involved in the decision-making process, then they would be more interested in the breeds that make up their herds for example.

Finally, the results also show that BAIF should perhaps be better known among women farmers. Indeed, 52.17% of the women farmers interviewed did not know about BAIF, mainly because they did not attend the data collection by the AIT when he came to the farm. Thus, many of the women interviewed did not even know the AIT even though he/she comes to the farm regularly. Although BAIF acts for women empowerment, especially through Self Help Groups, BAIF members should also involve women more when making recommendations for livestock practices.

Conclusion

As a reminder, the objective of this project was to identify the practices, objectives and strategies of women farmers in terms of cow selection in India, since they are generally the ones who do the day-to-day work with the cows. This study is part of a larger project whose objective is to determine cow selection criterion(s) to improve cows according to women's preferences.

The literature review demonstrated the importance of genomic selection of animals in India in a context of population growth, urbanization, increased income and climate change. The bibliographic research also allowed to contextualize the place of women farmers in Indian livestock production, as well as their place in decision-making systems. This research phase has also already shown the importance of training women in animal husbandry to improve daily practices.

The study carried out is an exploratory study, never before carried out, based on the quality of the interviews rather than on the quantity. For the realization of this study, it was necessary to read up on how to conduct social science research, in order to choose the best way to conduct the interviews with women farmers. This exploratory approach showed that the survey methodology is difficult to grasp and that it needs to be improved for the rest of the project.

The study showed that it is difficult to identify specific goals and strategies for women farmers since they have little decision-making power on the farm. However, the study shows that there is a diversity of profiles of women farmers with different cow preferences, and this diversity is also explained by the state in which the farms are, which especially differ by their wealth and their climate. The results tend to show that high milk yield is still the preferred criterion of women farmers, but the preferences of the other criteria that follow vary according to the profiles. In addition, the study went further to identify profiles of women farmers based on their levels of education, their livestock knowledge, their involvement in decision-making systems, and the activities they perform with cows. Women involved in the daily activities with the cows may be only "doers", with no decision-making power on the farm and little knowledge of the breeds, or they may be more involved in the business related to the production and sale of milk, thus involving them more in the decisions, these women are also the ones who know the most about the breeds that make up their herd, and the ones who are even involved in the choice of breeds and they are the ones who correspond to longevity and production focused farmers. This point highlights the imperative of integrating women into decision-making systems, while training them in livestock skills, thus allowing for the improvement of livestock practices and therefore the improvement of milk production and the adaptation of cows to their environment which is evolving due to climate change. Efforts to provide knowledge to women and to integrate them into decision-making systems must be pursued because they are the main levers to involve more women in animal selection, and thus get closer to gender equality in Indian farms.

2. 5 .

 5 Attentes des éleveuses en termes d'accès à l'information sur l'élevage Le désir des éleveuses d'avoir plus d'informations sur la gestion du bétail a également été sondé. 43 éleveuses sur 46 ont affirmé vouloir recevoir plus d'informations concernant les pratiques d'élevage. Cela montre clairement qu'il existe un réel désir chez les femmes d'obtenir plus d'informations pour acquérir des connaissances sur la gestion des troupeaux.

4. 1 . 3 .

 13 Length of the survey ...................................................................................................................... 4.2. Suggestions for future surveys ............................................................................................................. 4.2.1. Choice of translators ...................................................................................................................... 4.2.2. Collecting the opinions of men and women simultaneously ......................................................... 4.2.3. Legitimacy of qualitative surveys .................................................................................................. 4.3. Recommendation for BAIF with women farmers ................................................................................ Conclusion ....................................................................................................................................................... References ....................................................................................................................................................... List of Appendices ........................................................................................................................................... Appendices ......................................................................................................................................................

Figure 1 :

 1 Figure 1: Population growth for selected populous countries, 1950-2100 / Croissance de la population pour certains pays à forte population, 1950-2100 ....................................................................................................Figure 2: Boxplot of the number of cows per farm visited according to the two states Maharashtra and Jharkhand / Boxplot du nombre de vaches par ferme visitée selon les deux états Maharashtra et Jharkhand Figure 3: Boxplot of the number of exotic cows per farm visited according to the two states Maharashtra and Jharkhand / Boxplot du nombre de vaches exotiques par ferme visitée selon les deux états Maharashtra et Jharkhand .........................................................................................................................................................Figure 4: Boxplot of the number of indigenous and ND cows per farm visited according to the two states Maharashtra and Jharkhand / Boxplot du nombre de vaches indigènes et ND par ferme visitée selon les deux états Maharashtra et Jharkhand ........................................................................................................................Figure 5: Mind Map of the 46 answers to the question "What is your favorite cow and why?" / Mind Map des 46 réponses à la question "Quelle est votre vache préférée et pourquoi ?" ...............................................Figure 6: Mind Map of the 46 answers to the question "Which cow do you like the least and why?"/ Mind Map des 46 réponses à la question "Quelle vache aimez-vous le moins et pourquoi ?" .................................Figure 7: Graph of the individuals of the PCA on the preferences of the farmers, with ellipses of confidence for the two states / Graphique des individus de l'ACP sur les préférences des éleveuses, avec ellipses de confiance pour les deux états ...........................................................................................................................Figure 8: Graph of the circle of correlation of the variables " general traits " of the PCA / Graphique du cercle de corrélation des variables " traits généraux " de l'ACP ......................................................................Figure 9: Factorial map of the HCA on farmers' preferences / Plan factoriel de la CAH sur les préférences des éleveuses ...................................................................................................................................................Figure 10: Gaph of the variables of the MCA on social data / Graphique des variables de l'ACM sur les données sociales ..............................................................................................................................................Figure 11: Factorial map of the HCPC on social data / Plan factoriel de l'HCPC sur les données sociales ...Figure 12: Graph of topics about which women farmers want more information (Maharashtra and Jharkhand) / Graphique des sujets sur lesquels les éleveuses veulent plus d'informations (Maharashtra et Jharkhand) ...Figure 13: Graph of means of transmitting information on cow breeding desired by women farmers / Graphique des moyens de transmission des informations sur l'élevage des vaches souhaités par les éleveuses. .........................................................................................................................................................................

Figure 2 :

 2 Figure 1: Population growth for selected populous countries, 1950-2100 / Croissance de la population pour certains pays à forte population, 1950-2100 ....................................................................................................Figure 2: Boxplot of the number of cows per farm visited according to the two states Maharashtra and Jharkhand / Boxplot du nombre de vaches par ferme visitée selon les deux états Maharashtra et Jharkhand Figure 3: Boxplot of the number of exotic cows per farm visited according to the two states Maharashtra and Jharkhand / Boxplot du nombre de vaches exotiques par ferme visitée selon les deux états Maharashtra et Jharkhand .........................................................................................................................................................Figure 4: Boxplot of the number of indigenous and ND cows per farm visited according to the two states Maharashtra and Jharkhand / Boxplot du nombre de vaches indigènes et ND par ferme visitée selon les deux états Maharashtra et Jharkhand ........................................................................................................................Figure 5: Mind Map of the 46 answers to the question "What is your favorite cow and why?" / Mind Map des 46 réponses à la question "Quelle est votre vache préférée et pourquoi ?" ...............................................Figure 6: Mind Map of the 46 answers to the question "Which cow do you like the least and why?"/ Mind Map des 46 réponses à la question "Quelle vache aimez-vous le moins et pourquoi ?" .................................Figure 7: Graph of the individuals of the PCA on the preferences of the farmers, with ellipses of confidence for the two states / Graphique des individus de l'ACP sur les préférences des éleveuses, avec ellipses de confiance pour les deux états ...........................................................................................................................Figure 8: Graph of the circle of correlation of the variables " general traits " of the PCA / Graphique du cercle de corrélation des variables " traits généraux " de l'ACP ......................................................................Figure 9: Factorial map of the HCA on farmers' preferences / Plan factoriel de la CAH sur les préférences des éleveuses ...................................................................................................................................................Figure 10: Gaph of the variables of the MCA on social data / Graphique des variables de l'ACM sur les données sociales ..............................................................................................................................................Figure 11: Factorial map of the HCPC on social data / Plan factoriel de l'HCPC sur les données sociales ...Figure 12: Graph of topics about which women farmers want more information (Maharashtra and Jharkhand) / Graphique des sujets sur lesquels les éleveuses veulent plus d'informations (Maharashtra et Jharkhand) ...Figure 13: Graph of means of transmitting information on cow breeding desired by women farmers / Graphique des moyens de transmission des informations sur l'élevage des vaches souhaités par les éleveuses. .........................................................................................................................................................................

Figure 4 :

 4 Figure 1: Population growth for selected populous countries, 1950-2100 / Croissance de la population pour certains pays à forte population, 1950-2100 ....................................................................................................Figure 2: Boxplot of the number of cows per farm visited according to the two states Maharashtra and Jharkhand / Boxplot du nombre de vaches par ferme visitée selon les deux états Maharashtra et Jharkhand Figure 3: Boxplot of the number of exotic cows per farm visited according to the two states Maharashtra and Jharkhand / Boxplot du nombre de vaches exotiques par ferme visitée selon les deux états Maharashtra et Jharkhand .........................................................................................................................................................Figure 4: Boxplot of the number of indigenous and ND cows per farm visited according to the two states Maharashtra and Jharkhand / Boxplot du nombre de vaches indigènes et ND par ferme visitée selon les deux états Maharashtra et Jharkhand ........................................................................................................................Figure 5: Mind Map of the 46 answers to the question "What is your favorite cow and why?" / Mind Map des 46 réponses à la question "Quelle est votre vache préférée et pourquoi ?" ...............................................Figure 6: Mind Map of the 46 answers to the question "Which cow do you like the least and why?"/ Mind Map des 46 réponses à la question "Quelle vache aimez-vous le moins et pourquoi ?" .................................Figure 7: Graph of the individuals of the PCA on the preferences of the farmers, with ellipses of confidence for the two states / Graphique des individus de l'ACP sur les préférences des éleveuses, avec ellipses de confiance pour les deux états ...........................................................................................................................Figure 8: Graph of the circle of correlation of the variables " general traits " of the PCA / Graphique du cercle de corrélation des variables " traits généraux " de l'ACP ......................................................................Figure 9: Factorial map of the HCA on farmers' preferences / Plan factoriel de la CAH sur les préférences des éleveuses ...................................................................................................................................................Figure 10: Gaph of the variables of the MCA on social data / Graphique des variables de l'ACM sur les données sociales ..............................................................................................................................................Figure 11: Factorial map of the HCPC on social data / Plan factoriel de l'HCPC sur les données sociales ...Figure 12: Graph of topics about which women farmers want more information (Maharashtra and Jharkhand) / Graphique des sujets sur lesquels les éleveuses veulent plus d'informations (Maharashtra et Jharkhand) ...Figure 13: Graph of means of transmitting information on cow breeding desired by women farmers / Graphique des moyens de transmission des informations sur l'élevage des vaches souhaités par les éleveuses. .........................................................................................................................................................................

Figure 5 :

 5 Figure 1: Population growth for selected populous countries, 1950-2100 / Croissance de la population pour certains pays à forte population, 1950-2100 ....................................................................................................Figure 2: Boxplot of the number of cows per farm visited according to the two states Maharashtra and Jharkhand / Boxplot du nombre de vaches par ferme visitée selon les deux états Maharashtra et Jharkhand Figure 3: Boxplot of the number of exotic cows per farm visited according to the two states Maharashtra and Jharkhand / Boxplot du nombre de vaches exotiques par ferme visitée selon les deux états Maharashtra et Jharkhand .........................................................................................................................................................Figure 4: Boxplot of the number of indigenous and ND cows per farm visited according to the two states Maharashtra and Jharkhand / Boxplot du nombre de vaches indigènes et ND par ferme visitée selon les deux états Maharashtra et Jharkhand ........................................................................................................................Figure 5: Mind Map of the 46 answers to the question "What is your favorite cow and why?" / Mind Map des 46 réponses à la question "Quelle est votre vache préférée et pourquoi ?" ...............................................Figure 6: Mind Map of the 46 answers to the question "Which cow do you like the least and why?"/ Mind Map des 46 réponses à la question "Quelle vache aimez-vous le moins et pourquoi ?" .................................Figure 7: Graph of the individuals of the PCA on the preferences of the farmers, with ellipses of confidence for the two states / Graphique des individus de l'ACP sur les préférences des éleveuses, avec ellipses de confiance pour les deux états ...........................................................................................................................Figure 8: Graph of the circle of correlation of the variables " general traits " of the PCA / Graphique du cercle de corrélation des variables " traits généraux " de l'ACP ......................................................................Figure 9: Factorial map of the HCA on farmers' preferences / Plan factoriel de la CAH sur les préférences des éleveuses ...................................................................................................................................................Figure 10: Gaph of the variables of the MCA on social data / Graphique des variables de l'ACM sur les données sociales ..............................................................................................................................................Figure 11: Factorial map of the HCPC on social data / Plan factoriel de l'HCPC sur les données sociales ...Figure 12: Graph of topics about which women farmers want more information (Maharashtra and Jharkhand) / Graphique des sujets sur lesquels les éleveuses veulent plus d'informations (Maharashtra et Jharkhand) ...Figure 13: Graph of means of transmitting information on cow breeding desired by women farmers / Graphique des moyens de transmission des informations sur l'élevage des vaches souhaités par les éleveuses. .........................................................................................................................................................................

Figure 6 :

 6 Figure 1: Population growth for selected populous countries, 1950-2100 / Croissance de la population pour certains pays à forte population, 1950-2100 ....................................................................................................Figure 2: Boxplot of the number of cows per farm visited according to the two states Maharashtra and Jharkhand / Boxplot du nombre de vaches par ferme visitée selon les deux états Maharashtra et Jharkhand Figure 3: Boxplot of the number of exotic cows per farm visited according to the two states Maharashtra and Jharkhand / Boxplot du nombre de vaches exotiques par ferme visitée selon les deux états Maharashtra et Jharkhand .........................................................................................................................................................Figure 4: Boxplot of the number of indigenous and ND cows per farm visited according to the two states Maharashtra and Jharkhand / Boxplot du nombre de vaches indigènes et ND par ferme visitée selon les deux états Maharashtra et Jharkhand ........................................................................................................................Figure 5: Mind Map of the 46 answers to the question "What is your favorite cow and why?" / Mind Map des 46 réponses à la question "Quelle est votre vache préférée et pourquoi ?" ...............................................Figure 6: Mind Map of the 46 answers to the question "Which cow do you like the least and why?"/ Mind Map des 46 réponses à la question "Quelle vache aimez-vous le moins et pourquoi ?" .................................Figure 7: Graph of the individuals of the PCA on the preferences of the farmers, with ellipses of confidence for the two states / Graphique des individus de l'ACP sur les préférences des éleveuses, avec ellipses de confiance pour les deux états ...........................................................................................................................Figure 8: Graph of the circle of correlation of the variables " general traits " of the PCA / Graphique du cercle de corrélation des variables " traits généraux " de l'ACP ......................................................................Figure 9: Factorial map of the HCA on farmers' preferences / Plan factoriel de la CAH sur les préférences des éleveuses ...................................................................................................................................................Figure 10: Gaph of the variables of the MCA on social data / Graphique des variables de l'ACM sur les données sociales ..............................................................................................................................................Figure 11: Factorial map of the HCPC on social data / Plan factoriel de l'HCPC sur les données sociales ...Figure 12: Graph of topics about which women farmers want more information (Maharashtra and Jharkhand) / Graphique des sujets sur lesquels les éleveuses veulent plus d'informations (Maharashtra et Jharkhand) ...Figure 13: Graph of means of transmitting information on cow breeding desired by women farmers / Graphique des moyens de transmission des informations sur l'élevage des vaches souhaités par les éleveuses. .........................................................................................................................................................................

Figure 7 :

 7 Figure 1: Population growth for selected populous countries, 1950-2100 / Croissance de la population pour certains pays à forte population, 1950-2100 ....................................................................................................Figure 2: Boxplot of the number of cows per farm visited according to the two states Maharashtra and Jharkhand / Boxplot du nombre de vaches par ferme visitée selon les deux états Maharashtra et Jharkhand Figure 3: Boxplot of the number of exotic cows per farm visited according to the two states Maharashtra and Jharkhand / Boxplot du nombre de vaches exotiques par ferme visitée selon les deux états Maharashtra et Jharkhand .........................................................................................................................................................Figure 4: Boxplot of the number of indigenous and ND cows per farm visited according to the two states Maharashtra and Jharkhand / Boxplot du nombre de vaches indigènes et ND par ferme visitée selon les deux états Maharashtra et Jharkhand ........................................................................................................................Figure 5: Mind Map of the 46 answers to the question "What is your favorite cow and why?" / Mind Map des 46 réponses à la question "Quelle est votre vache préférée et pourquoi ?" ...............................................Figure 6: Mind Map of the 46 answers to the question "Which cow do you like the least and why?"/ Mind Map des 46 réponses à la question "Quelle vache aimez-vous le moins et pourquoi ?" .................................Figure 7: Graph of the individuals of the PCA on the preferences of the farmers, with ellipses of confidence for the two states / Graphique des individus de l'ACP sur les préférences des éleveuses, avec ellipses de confiance pour les deux états ...........................................................................................................................Figure 8: Graph of the circle of correlation of the variables " general traits " of the PCA / Graphique du cercle de corrélation des variables " traits généraux " de l'ACP ......................................................................Figure 9: Factorial map of the HCA on farmers' preferences / Plan factoriel de la CAH sur les préférences des éleveuses ...................................................................................................................................................Figure 10: Gaph of the variables of the MCA on social data / Graphique des variables de l'ACM sur les données sociales ..............................................................................................................................................Figure 11: Factorial map of the HCPC on social data / Plan factoriel de l'HCPC sur les données sociales ...Figure 12: Graph of topics about which women farmers want more information (Maharashtra and Jharkhand) / Graphique des sujets sur lesquels les éleveuses veulent plus d'informations (Maharashtra et Jharkhand) ...Figure 13: Graph of means of transmitting information on cow breeding desired by women farmers / Graphique des moyens de transmission des informations sur l'élevage des vaches souhaités par les éleveuses. .........................................................................................................................................................................

Figure 8 :

 8 Figure 1: Population growth for selected populous countries, 1950-2100 / Croissance de la population pour certains pays à forte population, 1950-2100 ....................................................................................................Figure 2: Boxplot of the number of cows per farm visited according to the two states Maharashtra and Jharkhand / Boxplot du nombre de vaches par ferme visitée selon les deux états Maharashtra et Jharkhand Figure 3: Boxplot of the number of exotic cows per farm visited according to the two states Maharashtra and Jharkhand / Boxplot du nombre de vaches exotiques par ferme visitée selon les deux états Maharashtra et Jharkhand .........................................................................................................................................................Figure 4: Boxplot of the number of indigenous and ND cows per farm visited according to the two states Maharashtra and Jharkhand / Boxplot du nombre de vaches indigènes et ND par ferme visitée selon les deux états Maharashtra et Jharkhand ........................................................................................................................Figure 5: Mind Map of the 46 answers to the question "What is your favorite cow and why?" / Mind Map des 46 réponses à la question "Quelle est votre vache préférée et pourquoi ?" ...............................................Figure 6: Mind Map of the 46 answers to the question "Which cow do you like the least and why?"/ Mind Map des 46 réponses à la question "Quelle vache aimez-vous le moins et pourquoi ?" .................................Figure 7: Graph of the individuals of the PCA on the preferences of the farmers, with ellipses of confidence for the two states / Graphique des individus de l'ACP sur les préférences des éleveuses, avec ellipses de confiance pour les deux états ...........................................................................................................................Figure 8: Graph of the circle of correlation of the variables " general traits " of the PCA / Graphique du cercle de corrélation des variables " traits généraux " de l'ACP ......................................................................Figure 9: Factorial map of the HCA on farmers' preferences / Plan factoriel de la CAH sur les préférences des éleveuses ...................................................................................................................................................Figure 10: Gaph of the variables of the MCA on social data / Graphique des variables de l'ACM sur les données sociales ..............................................................................................................................................Figure 11: Factorial map of the HCPC on social data / Plan factoriel de l'HCPC sur les données sociales ...Figure 12: Graph of topics about which women farmers want more information (Maharashtra and Jharkhand) / Graphique des sujets sur lesquels les éleveuses veulent plus d'informations (Maharashtra et Jharkhand) ...Figure 13: Graph of means of transmitting information on cow breeding desired by women farmers / Graphique des moyens de transmission des informations sur l'élevage des vaches souhaités par les éleveuses. .........................................................................................................................................................................

Figure 9 :

 9 Figure 1: Population growth for selected populous countries, 1950-2100 / Croissance de la population pour certains pays à forte population, 1950-2100 ....................................................................................................Figure 2: Boxplot of the number of cows per farm visited according to the two states Maharashtra and Jharkhand / Boxplot du nombre de vaches par ferme visitée selon les deux états Maharashtra et Jharkhand Figure 3: Boxplot of the number of exotic cows per farm visited according to the two states Maharashtra and Jharkhand / Boxplot du nombre de vaches exotiques par ferme visitée selon les deux états Maharashtra et Jharkhand .........................................................................................................................................................Figure 4: Boxplot of the number of indigenous and ND cows per farm visited according to the two states Maharashtra and Jharkhand / Boxplot du nombre de vaches indigènes et ND par ferme visitée selon les deux états Maharashtra et Jharkhand ........................................................................................................................Figure 5: Mind Map of the 46 answers to the question "What is your favorite cow and why?" / Mind Map des 46 réponses à la question "Quelle est votre vache préférée et pourquoi ?" ...............................................Figure 6: Mind Map of the 46 answers to the question "Which cow do you like the least and why?"/ Mind Map des 46 réponses à la question "Quelle vache aimez-vous le moins et pourquoi ?" .................................Figure 7: Graph of the individuals of the PCA on the preferences of the farmers, with ellipses of confidence for the two states / Graphique des individus de l'ACP sur les préférences des éleveuses, avec ellipses de confiance pour les deux états ...........................................................................................................................Figure 8: Graph of the circle of correlation of the variables " general traits " of the PCA / Graphique du cercle de corrélation des variables " traits généraux " de l'ACP ......................................................................Figure 9: Factorial map of the HCA on farmers' preferences / Plan factoriel de la CAH sur les préférences des éleveuses ...................................................................................................................................................Figure 10: Gaph of the variables of the MCA on social data / Graphique des variables de l'ACM sur les données sociales ..............................................................................................................................................Figure 11: Factorial map of the HCPC on social data / Plan factoriel de l'HCPC sur les données sociales ...Figure 12: Graph of topics about which women farmers want more information (Maharashtra and Jharkhand) / Graphique des sujets sur lesquels les éleveuses veulent plus d'informations (Maharashtra et Jharkhand) ...Figure 13: Graph of means of transmitting information on cow breeding desired by women farmers / Graphique des moyens de transmission des informations sur l'élevage des vaches souhaités par les éleveuses. .........................................................................................................................................................................

Figure 10 :

 10 Figure 1: Population growth for selected populous countries, 1950-2100 / Croissance de la population pour certains pays à forte population, 1950-2100 ....................................................................................................Figure 2: Boxplot of the number of cows per farm visited according to the two states Maharashtra and Jharkhand / Boxplot du nombre de vaches par ferme visitée selon les deux états Maharashtra et Jharkhand Figure 3: Boxplot of the number of exotic cows per farm visited according to the two states Maharashtra and Jharkhand / Boxplot du nombre de vaches exotiques par ferme visitée selon les deux états Maharashtra et Jharkhand .........................................................................................................................................................Figure 4: Boxplot of the number of indigenous and ND cows per farm visited according to the two states Maharashtra and Jharkhand / Boxplot du nombre de vaches indigènes et ND par ferme visitée selon les deux états Maharashtra et Jharkhand ........................................................................................................................Figure 5: Mind Map of the 46 answers to the question "What is your favorite cow and why?" / Mind Map des 46 réponses à la question "Quelle est votre vache préférée et pourquoi ?" ...............................................Figure 6: Mind Map of the 46 answers to the question "Which cow do you like the least and why?"/ Mind Map des 46 réponses à la question "Quelle vache aimez-vous le moins et pourquoi ?" .................................Figure 7: Graph of the individuals of the PCA on the preferences of the farmers, with ellipses of confidence for the two states / Graphique des individus de l'ACP sur les préférences des éleveuses, avec ellipses de confiance pour les deux états ...........................................................................................................................Figure 8: Graph of the circle of correlation of the variables " general traits " of the PCA / Graphique du cercle de corrélation des variables " traits généraux " de l'ACP ......................................................................Figure 9: Factorial map of the HCA on farmers' preferences / Plan factoriel de la CAH sur les préférences des éleveuses ...................................................................................................................................................Figure 10: Gaph of the variables of the MCA on social data / Graphique des variables de l'ACM sur les données sociales ..............................................................................................................................................Figure 11: Factorial map of the HCPC on social data / Plan factoriel de l'HCPC sur les données sociales ...Figure 12: Graph of topics about which women farmers want more information (Maharashtra and Jharkhand) / Graphique des sujets sur lesquels les éleveuses veulent plus d'informations (Maharashtra et Jharkhand) ...Figure 13: Graph of means of transmitting information on cow breeding desired by women farmers / Graphique des moyens de transmission des informations sur l'élevage des vaches souhaités par les éleveuses. .........................................................................................................................................................................

Figure 11 :

 11 Figure 1: Population growth for selected populous countries, 1950-2100 / Croissance de la population pour certains pays à forte population, 1950-2100 ....................................................................................................Figure 2: Boxplot of the number of cows per farm visited according to the two states Maharashtra and Jharkhand / Boxplot du nombre de vaches par ferme visitée selon les deux états Maharashtra et Jharkhand Figure 3: Boxplot of the number of exotic cows per farm visited according to the two states Maharashtra and Jharkhand / Boxplot du nombre de vaches exotiques par ferme visitée selon les deux états Maharashtra et Jharkhand .........................................................................................................................................................Figure 4: Boxplot of the number of indigenous and ND cows per farm visited according to the two states Maharashtra and Jharkhand / Boxplot du nombre de vaches indigènes et ND par ferme visitée selon les deux états Maharashtra et Jharkhand ........................................................................................................................Figure 5: Mind Map of the 46 answers to the question "What is your favorite cow and why?" / Mind Map des 46 réponses à la question "Quelle est votre vache préférée et pourquoi ?" ...............................................Figure 6: Mind Map of the 46 answers to the question "Which cow do you like the least and why?"/ Mind Map des 46 réponses à la question "Quelle vache aimez-vous le moins et pourquoi ?" .................................Figure 7: Graph of the individuals of the PCA on the preferences of the farmers, with ellipses of confidence for the two states / Graphique des individus de l'ACP sur les préférences des éleveuses, avec ellipses de confiance pour les deux états ...........................................................................................................................Figure 8: Graph of the circle of correlation of the variables " general traits " of the PCA / Graphique du cercle de corrélation des variables " traits généraux " de l'ACP ......................................................................Figure 9: Factorial map of the HCA on farmers' preferences / Plan factoriel de la CAH sur les préférences des éleveuses ...................................................................................................................................................Figure 10: Gaph of the variables of the MCA on social data / Graphique des variables de l'ACM sur les données sociales ..............................................................................................................................................Figure 11: Factorial map of the HCPC on social data / Plan factoriel de l'HCPC sur les données sociales ...Figure 12: Graph of topics about which women farmers want more information (Maharashtra and Jharkhand) / Graphique des sujets sur lesquels les éleveuses veulent plus d'informations (Maharashtra et Jharkhand) ...Figure 13: Graph of means of transmitting information on cow breeding desired by women farmers / Graphique des moyens de transmission des informations sur l'élevage des vaches souhaités par les éleveuses. .........................................................................................................................................................................

Figure 12 :

 12 Figure 1: Population growth for selected populous countries, 1950-2100 / Croissance de la population pour certains pays à forte population, 1950-2100 ....................................................................................................Figure 2: Boxplot of the number of cows per farm visited according to the two states Maharashtra and Jharkhand / Boxplot du nombre de vaches par ferme visitée selon les deux états Maharashtra et Jharkhand Figure 3: Boxplot of the number of exotic cows per farm visited according to the two states Maharashtra and Jharkhand / Boxplot du nombre de vaches exotiques par ferme visitée selon les deux états Maharashtra et Jharkhand .........................................................................................................................................................Figure 4: Boxplot of the number of indigenous and ND cows per farm visited according to the two states Maharashtra and Jharkhand / Boxplot du nombre de vaches indigènes et ND par ferme visitée selon les deux états Maharashtra et Jharkhand ........................................................................................................................Figure 5: Mind Map of the 46 answers to the question "What is your favorite cow and why?" / Mind Map des 46 réponses à la question "Quelle est votre vache préférée et pourquoi ?" ...............................................Figure 6: Mind Map of the 46 answers to the question "Which cow do you like the least and why?"/ Mind Map des 46 réponses à la question "Quelle vache aimez-vous le moins et pourquoi ?" .................................Figure 7: Graph of the individuals of the PCA on the preferences of the farmers, with ellipses of confidence for the two states / Graphique des individus de l'ACP sur les préférences des éleveuses, avec ellipses de confiance pour les deux états ...........................................................................................................................Figure 8: Graph of the circle of correlation of the variables " general traits " of the PCA / Graphique du cercle de corrélation des variables " traits généraux " de l'ACP ......................................................................Figure 9: Factorial map of the HCA on farmers' preferences / Plan factoriel de la CAH sur les préférences des éleveuses ...................................................................................................................................................Figure 10: Gaph of the variables of the MCA on social data / Graphique des variables de l'ACM sur les données sociales ..............................................................................................................................................Figure 11: Factorial map of the HCPC on social data / Plan factoriel de l'HCPC sur les données sociales ...Figure 12: Graph of topics about which women farmers want more information (Maharashtra and Jharkhand) / Graphique des sujets sur lesquels les éleveuses veulent plus d'informations (Maharashtra et Jharkhand) ...Figure 13: Graph of means of transmitting information on cow breeding desired by women farmers / Graphique des moyens de transmission des informations sur l'élevage des vaches souhaités par les éleveuses. .........................................................................................................................................................................

Figure 13 :

 13 Figure 1: Population growth for selected populous countries, 1950-2100 / Croissance de la population pour certains pays à forte population, 1950-2100 ....................................................................................................Figure 2: Boxplot of the number of cows per farm visited according to the two states Maharashtra and Jharkhand / Boxplot du nombre de vaches par ferme visitée selon les deux états Maharashtra et Jharkhand Figure 3: Boxplot of the number of exotic cows per farm visited according to the two states Maharashtra and Jharkhand / Boxplot du nombre de vaches exotiques par ferme visitée selon les deux états Maharashtra et Jharkhand .........................................................................................................................................................Figure 4: Boxplot of the number of indigenous and ND cows per farm visited according to the two states Maharashtra and Jharkhand / Boxplot du nombre de vaches indigènes et ND par ferme visitée selon les deux états Maharashtra et Jharkhand ........................................................................................................................Figure 5: Mind Map of the 46 answers to the question "What is your favorite cow and why?" / Mind Map des 46 réponses à la question "Quelle est votre vache préférée et pourquoi ?" ...............................................Figure 6: Mind Map of the 46 answers to the question "Which cow do you like the least and why?"/ Mind Map des 46 réponses à la question "Quelle vache aimez-vous le moins et pourquoi ?" .................................Figure 7: Graph of the individuals of the PCA on the preferences of the farmers, with ellipses of confidence for the two states / Graphique des individus de l'ACP sur les préférences des éleveuses, avec ellipses de confiance pour les deux états ...........................................................................................................................Figure 8: Graph of the circle of correlation of the variables " general traits " of the PCA / Graphique du cercle de corrélation des variables " traits généraux " de l'ACP ......................................................................Figure 9: Factorial map of the HCA on farmers' preferences / Plan factoriel de la CAH sur les préférences des éleveuses ...................................................................................................................................................Figure 10: Gaph of the variables of the MCA on social data / Graphique des variables de l'ACM sur les données sociales ..............................................................................................................................................Figure 11: Factorial map of the HCPC on social data / Plan factoriel de l'HCPC sur les données sociales ...Figure 12: Graph of topics about which women farmers want more information (Maharashtra and Jharkhand) / Graphique des sujets sur lesquels les éleveuses veulent plus d'informations (Maharashtra et Jharkhand) ...Figure 13: Graph of means of transmitting information on cow breeding desired by women farmers / Graphique des moyens de transmission des informations sur l'élevage des vaches souhaités par les éleveuses. .........................................................................................................................................................................

  ......................................................................................... 30 Table 4: Preference ranking for overall body traits in Maharashtra / Classement des préférences pour l'ensemble des caractéristiques corporelles dans le Maharashtra .................................................................... 40 Table 5: Preference ranking for overall body traits in Jharkhand / Classement des préférences pour l'ensemble des caractéristiques corporelles dans le Jharkhand ........................................................................ 40 Table 6: Preference ranking for aesthetic traits in Maharashtra / Classement des préférences pour les traits esthétiques dans le Maharashtra ...................................................................................................................... 41 Table 7: Preference ranking for aesthetic traits in Jharkhand / Classement des préférences pour les traits esthétiques dans le Maharashtra ...................................................................................................................... 41 Table 8: Preference ranking for general traits in Maharashtra / Classement des préférences pour les traits généraux dans le Maharashtra ......................................................................................................................... 43 Table 9: Preference ranking for general traits in Jharkhand / Classement des préférences pour les traits généraux dans le Jharkhand ............................................................................................................................. 43 Table 10: Summary of the three clusters according to general traits preferences / Résumé des trois groupes selon les préférences de traits généraux .......................................................................................................... 45 Table 11: Summary of the three clusters according to social data about the women farmers / Résumé des trois clusters selon les données sociales des éleveuses ............................................................................................ 49

Figure 1 :

 1 Figure 1: Population growth for selected populous countries, 1950-2100 / Croissance de la population pour certains pays à forte population, 1950-2100

2. 1 .

 1 Identification of the study areas 2.1.1. Choice of the number of states

Figure 2 :

 2 Figure 2: Boxplot of the number of cows per farm visited according to the two states Maharashtra and Jharkhand / Boxplot du nombre de vaches par ferme visitée selon les deux états Maharashtra et Jharkhand Note: Boxplots represent median (solid lines), first and third quartiles (contained in the boxes), dispersion (dashed line) and outliers (open points).

Figure 3 :

 3 Figure 3: Boxplot of the number of exotic cows per farm visited according to the two states Maharashtra and Jharkhand / Boxplot du nombre de vaches exotiques par ferme visitée selon les deux états Maharashtra et Jharkhand Note: Boxplots represent median (solid lines), first and third quartiles (contained in the boxes), dispersion (dashed line) and outliers (open points).

Figure 4 :

 4 Figure 4: Boxplot of the number of indigenous and ND cows per farm visited according to the two states Maharashtra and Jharkhand / Boxplot du nombre de vaches indigènes et ND par ferme visitée selon les deux états Maharashtra et Jharkhand Note: Boxplots represent median (solid lines), first and third quartiles (contained in the boxes), dispersion (dashed line) and outliers (open points).

Figure 5 :

 5 Figure 5: Mind Map of the 46 answers to the question "What is your favorite cow and why?" / Mind Map des 46 réponses à la question "Quelle est votre vache préférée et pourquoi ?"

Figure 6 :

 6 Figure 6: Mind Map of the 46 answers to the question "Which cow do you like the least and why?"/ Mind Map des 46 réponses à la question "Quelle vache aimez-vous le moins et pourquoi ?"

Figure 7 :

 7 Figure 7: Graph of the individuals of the PCA on the preferences of the farmers, with ellipses of confidence for the two states / Graphique des individus de l'ACP sur les préférences des éleveuses, avec ellipses de confiance pour les deux états

Figure 10 :

 10 Figure 10: Gaph of the variables of the MCA on social data / Graphique des variables de l'ACM sur les données sociales

Figure 12 :

 12 Figure12: Graph of topics about which women farmers want more information (Maharashtra and Jharkhand) / Graphique des sujets sur lesquels les éleveuses veulent plus d'informations(Maharashtra et Jharkhand)

  Figure 13: Graph of means of transmitting information on cow breeding desired by women farmers / Graphique des moyens de transmission des informations sur l'élevage des vaches souhaités par leséleveuses.

4. 1 .

 1 Discussion on data collection 4.1.1. Translation bias

  3.1.1. Herds characteristics in Maharashtra and Jharkhand ..................................................................... 3.1.2. General characteristics of the farms in Maharashtra and Jharkhand ............................................. 3.2. Visualization of women's preferences in their cows through open questions ...................................... 3.2.1. The favorite cow ............................................................................................................................ 3.2.2. The least favorite cow .................................................................................................................... 3.3. Preference ranking of cows' traits ........................................................................................................ 3.3.1. Women's preferences in terms of cow appearance: overall body structure and aesthetic ............. 3.3.2. Women's preferences in terms of general traits ............................................................................ 3.4. Typology of women farmers according to their preferences on general traits ..................................... 3.5. Typology of women farmers according to the social data .................................................................... 3.6. Expectations of women farmers in terms of access to information about livestock ............................. IV. Discussion ................................................................................................................................................. 4.1. Discussion on data collection ............................................................................................................... 4.1.1. Translation bias.............................................................................................................................. 4.1.2. Questioning women's understanding of some questions ...............................................................

Table 1 :

 1 Description of the quantitative variables chosen for the selection of farms / Description des variables quantitatives retenues pour la sélection des exploitations agricoles ................................................................ 27

Table 2 :

 2 Description of the qualitative variables chosen for the selection of farms / Description des variables qualitatives retenues pour la sélection des exploitations agricoles .................................................................. 28

Table 3 :

 3 Problems encountered during the survey testing and solutions considered / Problèmes rencontrés lors de l'essai de l'enquête et solutions envisagées .

Table 1 :

 1 Description of the quantitative variables chosen for the selection of farms / Description des variables quantitatives retenues pour la sélection des exploitations agricoles

	Variable	mean	sd	IQR 0% 25% 50% 75% 100% n NA
	General_acres_nb	5.80	7.11 4.13 0	2	4.25 6.13 40	44 2
	Nb_cows	9.41	8.97 8.00 1	3	7.5	11.00 45	46 0
	Nb_breed	1.72	0.62 1	1	1	2	2	3	46 0
	Nb_exotic	9.26	8.63 7.00 0	4	8.0	11.00 47	46 0
	Nb_indigene_ND	0.93	1.32 1.00 0	0	1.0	1.00 6	46 0
	Nb_pers_implied_livestock 2.85	1.40 1.75 1	2	2.0	3.75 7	46 0

Table 2 :

 2 Description of the qualitative variables chosen for the selection of farms / Description des variables qualitatives retenues pour la sélection des exploitations agricoles

		States	Economic_status		Cast
		Maharashtra	Jharkhand	APL	BPL	general	OBC	SC	ST
	Numbers	29	17	30	16	29	15	1	1
	%	63.04	36.96	65.22	34.78	63.04	32.61	2.17 2.17
		Family_status	Coop_membership	Other_animals
		Joint_family Nuclear_family	yes	no	yes	no
	Numbers	35	11	41	5		26	20
	%	76.09	23.91	89.13	10.87	56.52	43.48
	APL (Above Poverty Line); BPL (Below Poverty Line); OBC (Other Backward Classes); SC (Schedule Cast); ST (Schedule Tribe).

Table 3 :

 3 Problems encountered during the survey testing and solutions considered / Problèmes rencontrés lors de l'essai de l'enquête et solutions envisagées

Table 4 :

 4 Preference ranking for overall body traits in Maharashtra / Classement des préférences pour l'ensemble des caractéristiques corporelles dans le Maharashtra

	Maharashtra Muscularity Lenght_size	Height_size	Width_size	Legs_size Score Rank
			musclularity	musclularity	musclularity	musclularity
			70%	63%	74%	74%
	Muscularity	-	ns	ns	*	*	4	1
				height size	Lenght size	lenght size
				67%	61%	61%
	Lenght_size		-	ns	ns	ns	2	3
					height size	height size
					71%	54%
	Height_size			-	ns	ns	3	2
						width size
						61%
	Width_size				-	ns	1	4
	Legs_size					-	0	5

Table 5 :

 5 Preference ranking for overall body traits in Jharkhand / Classement des préférences pour l'ensemble des caractéristiques corporelles dans le Jharkhand

	Jharkhand	Muscularity Lenght_size Height_size Width_size	Leg_size	Score Rank
			muscularity	height size	muscularity	muscularity
			71%	53%	82%	82%
	Muscularity	-	ns	ns	*	*	3	2
				height size	lenght size	lenght size
				53%	71%	53%
	Lenght_size		-	ns	ns	ns	2	3
					height size	height size
					76%	76%
	Height_size			-	ns	ns	4	1
						width size
						53%
	Width_size				-	ns	1	4
	Leg_size					-	0	5

Table 6 :

 6 Preference ranking for aesthetic traits in Maharashtra / Classement des préférences pour les traits esthétiques dans le Maharashtra

	Maharashtra	Coat_color Hair_coat_size	Horns	Score	Rank
				Coat color
			Coat color 96%	78%
	Coat_color	-	****	***	2	1
				Horns
				52%
	Hair_coat_size		-	ns	0	3
	Horns			-	1	2

Table 7 :

 7 Preference ranking for aesthetic traits in Jharkhand / Classement des préférences pour les traits esthétiques dans le Maharashtra

	Jharkhand	Coat_color Hair_coat_size	Horns	Score	Rank
			Hair coat size	horns
			53%	53%
	Coat_color	-	ns	ns	0	3
				horns
				53%
	Hair_coat_size		-	ns	1	2
	Horns			-	2	1

Table 8 :

 8 Preference ranking for general traits in Maharashtra / Classement des préférences pour les traits généraux dans le Maharashtra

	Maharashtra	HMY	Fert	Temp	Long	Health	MP	BS	Score Rank
			HMY	HMY	HMY	HMY	HMY	HMY
	High_Milk_Yield		83%	79%	90%	62%	83%	55%
	(HMY)	-	****	*	****	ns	**	ns	6	1
				Temp	Fert	Health	Fert	BS
				62%	55%	83%	59%	69%
	Fertility (Fert)		-	ns	ns	**	ns	*	2	5
					Temp	Health	Temp	BS
	Temperament				79%	72%	59%	59%
	(Temp)			-	ns	ns	ns	ns	3	4
						Health	MP	BS
						86%	72%	76%
	Longevity (Long)				-	****	ns	***	0	7
							Health	Health
							72%	55%
	Health					-	ns	ns	5	2
								BS
	Market_price							69%
	(MP)					-	-	ns	1	6
	Body structure (BS)							-	4	3

Table 9 :

 9 Preference ranking for general traits in Jharkhand / Classement des préférences pour les traits généraux dans le Jharkhand

	Jharkhand	HMY	Fert	Temp	Long	Health	MP	BS	Score Rank
			Milk	Milk	Milk	Milk	Milk	Milk
	High_Milk_Yield		94%	94%	88%	100%	100%	94%
	(HMY)	-	*	****	***	****	****	***	6	1
				Fert	Fert	Fert	Fert	Fert
				71%	59%	71%	82%	59%
	Fertility (Fert)		-	ns	ns	ns	ns	ns	5	2
					Long	Health	MP	BS
	Temperament				65%	59%	53%	53%
	(Temp)			-	ns	ns	ns	ns	0	7
						Long	Long	BS
						53%	65%	53%
	Longevity (Long)				-	ns	ns	ns	3	4
							Health	BS
							65%	53%
	Health					-	ns	ns	2	5
								BS
	Market_price							71%
	(MP)					-	-	ns	1	6
	Body_structure
	(BS)							-	4	3

Table 10 :

 10 Summary of the three clusters according to general traits preferences / Résumé des trois groupes selon les préférences de traits généraux

		Cluster 1	Cluster 2	Cluster 3
	Preferred traits	Body structure	Fertility	Longevity
		Health	Temperament	High milk yield
	Least preferred traits	Fertility	High milk yield	Health
		Longevity		Body Structure
		Temperament

Table 11 :

 11 Summary of the three clusters according to social data about the women farmers / Résumé des trois clusters selon les données sociales des éleveuses

		Cluster 1	Cluster 2	Cluster 3
	Activities	Selling_milk, Milking,	Cleaning_animals,	No
		Cleanng_animals, Health_care	Health_care,
			Chopping_feeding_prep
	Breed awareness	Yes	No	Yes and no
	Breed choice	Yes	No	Yes and no
	implication
	Education level	Less or not educated	Educated	Educated (-)
	Cluster on prefered	Longevity and production
	traits	focused farmers
	State	Jharkhand	Maharashtra	Both

Acknowledgements

My first thanks go to Dr. Vincent Ducrocq and Dr. Julie Labatut for having trusted me for this study, for their advice and support.

List of Appendices

Context:

Nice to meet you and thank you for welcoming us to your home. We are two French students, Juliette and Laurane, working on a study about women's daily practices, and objectives in dairy cattle about their animal selection. This study is initiated by BAIF in India and a French research institute in agriculture: INRAE. We would like to ask you questions about your daily activities with the cows and your preferences. it's ok if you don't know the answer to our questions. There is no good or bad answer. The interview should last 1h30, is it ok for you? If men are there: "Hello, our study is about women's work on farms, we would like to be in the presence of only the woman for the interview, otherwise, our study will not be valid. Thank you for your understanding."

Rainy season

Winter season

Summer season

Other comments: What is the order of crops on the same land ? • Based on the pedigree • Based on the progeny performance • Linked to the bull's mother milk production

ABSTRACT

The livestock sector provides a large amount of goods and services in India such as milk and dairy products. Population growth, urbanization, increasing incomes, and climate change make cow breeding a major challenge to meet the needs of the population. However, in order to improve the animals, it seems essential to focus on the opinions and needs of the primary stakeholders, namely the women farmers who perform the daily tasks with the animals. The objective of the exploratory study is to identify the preferences of women farmers in terms of selecting their cows. To this end, interviews were conducted with women farmers in Maharashtra and Jharkhand to identify potential heterogeneities between the profiles. This study shows, on the one hand, that there is a difference between the preferences of women farmers in two Indian states that differ in wealth and climate; and on the other hand, that there are typologies of women farmers according to their preferences in terms of cow selection and according to their profile (level of education, involvement in activities with cows, involvement in the choice of breeds, etc.). Finally, recommendations for the future of the study and for BAIF are developed. Training of women farmers on livestock and their inclusion in decision-making systems are the main levers to increase the participation of women farmers in animal selection and thus move closer to gender equality on Indian farms.