The European Journal of Development Research

, Volume 28, Issue 4, pp 722–740 | Cite as

An Exploratory study of Dairying Intensification, Women’s Decision Making, and Time Use and Implications for Child Nutrition in Kenya

  • Jemimah Micere Njuki
  • Amanda Wyatt
  • Isabelle Baltenweck
  • Kathryn Yount
  • Clair Null
  • Usha Ramakrishnan
  • Aimee Webb Girard
  • Shreyas Sreenath
Original Article

Abstract

Dairy intensification as a development strategy is expected to improve household nutrition, yet the pathways by which this occurs are not well understood. This article examines how women’s time use and decision-making patterns related to dairy income and consumption are associated with dairy intensification, as a way of exploring the links between intensification and nutrition. Results from our mixed methods study conducted with households representing low, medium and high levels of dairy intensification in rural Kenya indicated that children in high-intensity households received more milk than children in medium-intensity households. While women seemed to be gaining control over evening milk sales decisions, men seemed to be increasingly controlling total dairy income, a trend countered by the increase in reported joint decision making. Women from medium-intensity households reported spending more time on dairy activities than women from high-intensity households. More research on how dairy interventions affect women is needed.

Keywords

Kenya dairy intensification child nutrition women’s decision making time use 

Abstract

L’intensification de la laiterie comme stratégie en matière de développement est censé d’améliorer la nutrition des ménages, mais comment ça se passe n’est pas encore bien compris. Cet article examine comment l’emploi du temps des femmes et leurs modelés de prise des décisions, en matière de revenu et consommation laitière, sont associes a l’intensification de la laiterie, et à travers ça on explore aussi les liens entre cette intensification et la nutrition. Les résultats de notre étude, conduit avec des ménages à bas, moyen, et hauts niveaux de intensification laitière au Kenya, démontrent que les enfants appartenant à des ménages en haute intensification reçoivent plus de lait que les enfants appartenant à des ménages à moyen intensification. Il parait que les femmes sont en train de gagner plus de contrôle sur les décisions de la vente du lait recueilli au but du jour; et que les hommes contrôlent de plus en plus le revenu laitier total. Cette tendance est cependant contrée par la croissance signalée dans la prise de décisions communes dans les ménages. Chez les ménages à moyenne intensification laitière, les femmes signalent qu’elles passent plus de temps en activités relationés à la laiterie, comparés aux femmes chez les ménages à haute intensification laitière. Il faut plus de recherche sur comment les interventions affectent les femmes.

Introduction

From a development perspective, the overarching aims of technologies and interventions designed to improve agricultural production are to improve food security, reduce poverty and improve nutrition of the world’s poor farmers while respecting the environment. Although there are a number of frameworks for describing the relationship between agriculture and human nutrition, at the household level there are three primary pathways by which nutrition could be affected. Herforth and Harris (forthcoming) describe these three pathways as follows:
  • Food Production: Increases in production can improve household access to and consumption of foods.

  • Agricultural Income: Marketing of agricultural products and income from wages can be used to purchase food or non-food items that affect overall well-being.

  • Women’s Empowerment: The empowerment of women in agriculture can affect household income, capacity to care for children and women’s own energy expenditure.

Haddad (2000) describes specific and generic pathways from agriculture to nutrition, complementary to the framework described by Herforth and Harris. The specific pathways by which agriculture could improve nutrition include the consumption of own produced food and livestock products and the effects of increased production on prices. Some of the generic pathways include income generation for producers and others in the value chain, time allocation effects, impacts on decision making, energy and nutrient expenditures of those involved in agriculture, and the health environment effects of agriculture production.

Addressing these pathways through agricultural interventions, as a group or in isolation, does not translate into automatic improvements in household nutrition. Indeed, the pathways are complex and there are many interactions among them. For example, Herforth and Harris (forthcoming) point out that there are critical points along the production and income pathways where who – men or women – has control over decisions and assets may have a critical influence on the direction of nutritional outcomes, particularly for mothers and young children in these households. There is increasing evidence that women’s active involvement in education, income-generating activities, and household income decisions and management contributes to improved child nutrition (Smith et al, 2003; FAO, 2011).

Keeping livestock, in particular, influences human nutritional and health status through numerous multiple-link causal chains (Randolph et al, 2007). Owning animals can increase the amount of animal protein available for consumption at the household level, and improve dietary intake of animal source foods (ASF). Increasing the productivity of animals owned can lead to higher product sales and increases in household income, leading to higher purchasing power for other foods and better health care by households. Animals owned are also hypothesized to provide traction and nutrient cycling services that increase food crop production, possibly increasing crop sales, household income and household food crop consumption. However, livestock can also introduce negative risks to human health and nutrition through a variety of linkages. Land allocated to livestock or the production of fodder for livestock could lead to a reduction in available cropland (Randolph et al, 2007), while workload associated with livestock production could impact negatively on caregiver income and on caregiver time and workload, with detrimental effects on young children in the household. While some evidence on these pathways exists, the analysis has often been done on single pathways without a comprehensive analysis of how these pathways influence nutrition outcomes.

Dairy intensification is a particular agricultural strategy designed to alleviate poverty by increasing smallholder dairy income through improved dairy production. Typically, it combines multiple technologies, including but not limited to investment in higher-yielding and non-indigenous breeds of cows; improved cattle management and feeding systems such as use of stall-feeding systems and supplementary feeding; and animal health practices that include regular deworming and vaccinations (Staal et al, 2008). On the other hand, these technologies tend to increase farmers’ workload as the higher-yielding cows can be more demanding, and feeding systems shift from grazing to stall-feeding. Moreover, increase in income from dairy may not increase proportionally to the increase in input use, including labour. This increase in physical labour and demands on time affects those caring for and feeding the cows, who typically are women and girls (Kristjanson et al, 2010). Rural women comprise two-thirds of the world’s 600 million poor livestock keepers (Kristjanson et al, 2010). In addition, evidence broadly suggests that women have some influence over household allocation of dairy products for consumption and have some control over the sale of and income from dairy products (FAO, 2011). However, there is potential for this to change with dairy intensification as dairy enterprises become more commercialized.

This article looks at how dairy intensification influences the pathways between livestock production and household consumption and nutrition. This study was designed to explore the related pathways between dairy intensification and nutrition, paying close attention to the points along the food production, agricultural income and women’s empowerment pathways where the implications for women could be detrimental to maternal and child nutrition. The study was conducted in June–July 2010 in three villages in Rift Valley Province, Kenya, where most of Kenya’s dairy production is concentrated and the East Africa Dairy Development (EADD) project, a dairy development programme targeting smallholder farmers, had been implemented since 2008 (www.eadairy.org). In this next section, we explain the three pathways linking agriculture to nutrition in more detail, followed by a more detailed description of the EADD project in Kenya, and an analysis of the three pathways.

Pathways from Dairy Interventions to Nutritional Outcomes

Pathway 1: Food Production – Consumption of Cow’s Milk and Dietary Diversity

In the food production pathway, nutrition could be improved by the increases in agricultural production, which improves household access to and consumption of foods. In the case of dairy intensification, livestock can provide a regular supply of ASF, such as meat and milk. These foods tend to be energy dense, and serve as an excellent source of protein and micronutrients, such as Vitamin A, zinc and iron. A deficit of these micronutrients is linked to increased risks of anaemia, stunting, blindness, illness and even death (Black et al, 2008). Several studies have also shown positive associations between the consumption of ASF and outcomes such as physical growth and cognitive development during early childhood (Allen, 1993; Whaley et al, 2003; Long et al, 2011). Dairy, in particular, and its role in the nutrition of young children have been well studied (Dror and Allen, 2011; Allen, 2013). Cow’s milk is one of the earliest complementary foods introduced to infants in many cultures, including in Kenya. Cow’s milk is considered to be an energy-dense and high-quality protein, important in human nutrition, but potentially harmful to very young children (<6 months old). For infants older than 6 months of age, cow’s milk is recommended as an additive to traditional complementary foods that are not nutrient-dense, such as uji (smooth porridge made from grain flour such as maize flour or millet flour and water), to increase the nutrient quality (WHO, 2000).

Although there is some evidence that dairy intensification interventions increase dairy production and thereby reduce poverty, much less is known about the nutritional benefits to dairy farming household members (Berti et al, 2004; Randolph et al, 2007). Other authors have noted that, among the few studies on livestock interventions that have examined nutritional outcomes, it is challenging to find studies that robustly measure the nutritional impact of such interventions (Leroy and Frongillo, 2007). Studies on dairy interventions and dairy farming households have shown varying effects on household consumption and nutrition, but few have looked at the effects on young children in the households. One study in Kenya, after adjusting for child, household head and household characteristics, found that the ownership of cows was positively associated with child growth, specifically a reduced prevalence of stunting, but there was no association with wasting (Nicholson et al, 2003). A more recent study in Kenya that compared members of a dairy cooperative to non-members found that women and school-age children (5–14 years old) from member households consumed more cow’s milk than non-members (Walton et al, 2014). Another study from India found that preschool-age children (1–4 years old) in the highest dairy-producing households had higher energy and protein intakes, but the author did not assess or adjust for potential confounding (Begum, 1994).

An alternative way of assessing how dairy intensification influences nutrition is to measure household and individual dietary diversity, or the number of foods or food groups consumed. Dietary diversity is a measure of food consumption that reflects household access to a variety of foods, and is also a proxy for the nutrient adequacy of the diet of individuals (FAO, 2008). A number of studies, using a variety of measures, have shown dietary diversity to be positively associated with young children’s nutritional status and growth (Ruel, 2003; Arimond and Ruel, 2004). After exclusive breastfeeding for the child’s first 6 months, a diverse diet of energy- and nutrient-dense foods becomes an important indicator of a child’s nutritional status. Dietary diversity is part of the recommendations for complementary feeding of breastfeeding children 6–23 months (Dewey, 2003). Studies have shown that dietary diversity may be a particularly important indicator for growth among non-breastfeeding children (Arimond and Ruel, 2004). Dietary diversity can be a good proxy for describing changes in diet quality and is therefore a practical measure of how an agricultural intervention, like dairy intensification, impacts household food consumption patterns.

Pathway 2: Agricultural Income – Dairy Income and Household Decision Making

The second pathway by which agriculture is linked to human nutrition is through income from agricultural activities. Increased dairy production should logically increase household income that could be spent on purchasing a variety of nutritious foods. Households, however, have a number of options for spending dairy income such as foods (of varying nutritional quality), health care, dairy investments and education. In addition, there is an underlying assumption in this pathway that households understand what foods are nutritious and appropriate to purchase and eat and that nutritious foods are available and affordable. For many smallholders, dairy income, compared to income from crops, can be a steadier source of income: cows produce milk daily and farmers can sell it, compared to most crops that follow a seasonal harvesting calendar. As households increase dairy production, one of the trade‐offs they may face is keeping the milk for household consumption or selling the milk for income. Dairy farmers face a crucial opportunity cost when trading milk consumption for milk sales or vice versa. The perceived value of milk as a food source in relation to the market value of milk as a cash source is influenced by dietary patterns and the cultural significance of certain foods. Previous studies among the Kalenjin found that milk had an important role in the rural diet that had not changed with increased commercialization of agriculture (Huss-Ashmore, 1996).

In the study area, milking occurred twice a day (morning and evening). Among the Kalenjin, the morning milk was traditionally the domain of males and was generally sold, while the evening milk was traditionally the domain of women and reserved for household consumption (Curry, 1996). Other studies in Kenya have reported this to be the case before the commercialization of the dairy market (Meinzen‐Dick et al, 2010). While the dairying household can be viewed as a single economic actor, its dairying decision outcomes are often negotiated through intrahousehold roles (Agarwal, 1997). Therefore, gender dynamics play an integral role in the final decisions a household makes in the consumption and production of and expenditure on milk, based on the various gender designations within dairying systems and individual households. In the case of the Kalenjin, studies have reported that although men traditionally inherited and controlled livestock, women had considerable influence on livestock management (Von Bulow, 1992; Huss-Ashmore, 1996).

Pathway 3: Women’s Empowerment – Women’s Roles in Dairy and Links to Childcare Practices

The third pathway by which agriculture is linked to human nutrition is through women’s empowerment, and more precisely, through women’s time allocation and workload (Gillespie et al, 2012). The amount of time women devote to agricultural activities influences their ability to manage household responsibilities, including the care, feeding and health of young children. Women’s expenditure of energy to complete tasks has a direct effect on their own nutritional status, and influences child nutrition during pregnancy and while breastfeeding.

In the 1990s, UNICEF took the lead in recognizing the importance of caregiving practices as an important determinant of child nutritional status, in addition to access to food and health (Engle et al, 2000). In this study, we focused on infant and young child feeding (IYCF) practices, one of six aspects of caregiving practices identified by UNICEF (Engle et al, 1997). Suboptimal feeding practices in a child’s first two years of life can increase the risks of morbidity, mortality, and poor growth and development (Black et al, 2008). One of the most crucial IYCF practices is exclusive breastfeeding until a child reaches 6 months of age (Bhutta et al, 2008). Public health practitioners have made strides in raising awareness among mothers about this guidance, but in practice many mothers still do not follow the recommendation. According to data from the 2008–2009 Demographic and Health Survey in Kenya, only around 32 per cent of infants had been exclusively breastfed throughout their first 6 months (UNICEF, 2012).

Many qualitative studies have shown that mothers still practice mixed feeding – breastfeeding, but introducing weaning foods and animal milks before the child reaches 6 months of age (Gewa et al, 2011; Kimani-Murage et al, 2011). An infant’s gastrointestinal, renal and neuro-physiological systems are not mature enough to process foods or liquids other than breast milk before 6 months. Furthermore, starchy foods, like uji, a maize-based porridge that is a common first food in Kenya, as well as cow’s milk, can cause diarrhoea and contribute to iron deficiency (Akre, 1989; Dewey, 2003). The reasons why mothers do not practise exclusive breastfeeding are varied and context-specific, but may include, for example, constraints on time, personal preferences and lack of awareness. For example, studies from Kenya have shown that mothers perceive an association between their own food insecurity or inadequacy and their capacity to produce sufficient breast milk (Gewa et al, 2011; Kimani-Murage et al, 2011; Nor et al, 2011; Webb Girard et al, 2012).

Women typically have many roles within the home. They may be responsible for purchasing and preparing foods, taking care of household duties, and serving as primary caregivers to their children. As has been stated previously in this article, women in agricultural households often have an important role in agricultural production and marketing. These multiple demands may conflict with childcare activities, and as such, impact child nutrition (Montagne et al, 1998). To cope with time constraints, women might delegate certain childcare activities to others – such as older siblings in the household – or perform tasks at the same time, potentially decreasing the quality of childcare and feeding practices, and child nutrition (Engle, 1991).

Study Context

The EADD Project

The EADD project started in Kenya, Rwanda and Uganda in 2008. EADD is funded by The Bill and Melinda Gates Foundation and is implemented by Heifer International, in partnership with the International Livestock Research Institute, TechnoServe, African Breeders Services and the World Agroforestry Centre. The project’s goal is to transform the lives of 1 million people, or 179 000 families, by doubling household dairy income in 10 years through integrated interventions in dairy production, market access and knowledge application. The project is designed to alleviate two main constraints that dairy smallholders in East Africa face: low availability of affordable and high-quality inputs and services, and cash constraints to these inputs and services. In Kenya, the EADD model is built around the establishment of hubs, which serve as community-based milk bulking and collection centres, with or without facilities that keep the milk chilled for transportation. At the hub, farmers can access inputs and services, like agro-vet products and services, on credit, based on their milk deliveries. The credit system operates like a ‘check-off’ system, and thus when farmers do not have cash, the cost of the services is deducted from their monthly dairy earnings at the hub. In addition, some hubs may link farmers to other financial service associations to access savings and lending services. Most project activities target both small groups of 15–30 farmers for training, and larger groups of more than 500 farmers who own a dairy cooler and/or manage a milk collection centre. Other stated goals during the project’s first phase (2008–2013) were to reduce poverty and increase food security. It is important to note that improving child nutrition was not a stated goal of EADD and the project did not include any ongoing nutrition activities, such as education targeted towards farmers. Gender was not mainstreamed in the initial design of EADD, although early in the first phase, gender issues were identified and project partners developed a gender-integration strategy for the project (Mutinda and Baltenweck, 2013).

Study Sites

Kenya has one of the largest dairy industries in East Africa, with smallholders contributing up to 80 per cent of national milk production and 75 per cent of marketed milk (Ngigi, 2005). Estimates suggest that there are between 1 million and 1.8 million smallholder dairy households, which translates to 35 per cent of rural households and 26 per cent of Kenyan households overall (TechnoServe Kenya, 2008). Most of Kenya’s dairy production is concentrated in the Rift Valley where 53 per cent of Kenya’s dairy cattle are located and milk is distributed to milk deficit areas or Nairobi and other urban centres (TechnoServe Kenya, 2008).

The study was conducted in three villages in Buret and Kipkelion districts in Rift Valley Province, Kenya, where EADD had activities at the time. The main system of agriculture in this area is subsistence farming and mixed crop/livestock. The majority ethnic group in this area is the Kalenjin. Traditionally, most Kalenjin were semi-nomadic pastoralists. In the study area, Kalenjins started adopting maize as a subsistence and cash crop and settled into permanent homesteads in the 1930s (Borgerhoff Mulder, 1989). Towards the last years of colonialism in Kenya, tea farming was introduced and today tea farming in this area is constituted by a mix of large corporate tea estates and smallholder farms. At the time of this study, Kalenjins in the study area tended to have farms that included dairy and crops, such as tea, maize, sorghum and millet. Cattle remain symbols of wealth and status and are prized by households as their most valuable resource (Huss-Ashmore, 1996).

Methods

Overview of Fieldwork

The study team included a multidisciplinary team with expertise in human nutrition, economics, anthropology and public health. Fieldwork occurred between June and August 2010. Shreenath et al (2011) provides a comprehensive summary of the original study.

Qualitative Data Collection and Analysis

Separate focus group discussions (FGDs) were done with male farmers, female farmers and female farmers with at least one child below 5 years old (referred to as the ‘Under 5’ group). Participants were asked to report the current, daily milk production of their highest‐yielding cow, and were then subdivided into groups representing three levels of dairy intensification. We defined the three groups as households without cows or whose cows have not produced any milk in the past 30 days (‘low-intensity’ group); households whose best cow produces up to 6 litres/day (‘medium intensity,’ the EADD target group); and households whose best cow produces more than 6 litres/day (‘high-intensity’ group). These groups represented increasing levels of dairy intensification. There was some variation by site, but generally the range of daily milk production reported by the participants fell into these categories (Table 1).
Table 1

Range of daily milk production of highest-yielding cow (in litres) reported by participants in focus group discussions, by intensification level and site

 

Cheborgea

Kebenet

Kipkelion

 

Men

Women

Under 5s

Men

Women

Under 5s

Men

Women

Under 5s

Low

0

0

0

0

0

0

0

0

0

Medium

0.1–6

1.5–5

1–5

1–9

1–3

2–9

3–9

3–6

1–6

High

>7

>6

>6

>10

>4

>10

>7

>7

aIn Cheborge, the high-intensity production group was divided into two to accommodate the size of the group for the discussion. The range reported for one group was 7–10 litres and the range for the second group was more than 11 litres.

Semi‐structured FGD guides were developed to generate discussion around the various pathways through which dairy intensification affects nutrition. A total of 27 FGDs were conducted with an average of 12 people in each group, with approximately 324 people participating across all three sites. At the end of each discussion, all participants were invited to a large group session where facilitators summarized what the groups had discussed and answered questions about human and animal health. All FGDs were conducted in local languages with trained facilitators. In the field, analyses of the FGDs focused on field notes of illustrative quotes related to the three pathways. Qualitative data reported in the current study are based on the field notes.

Quantitative Data Collection and Analysis

The basic sampling units for the household survey were households with young children (0–60 months) residing in the three study sites representing three levels of dairy intensification as defined previously. In each site, 15 GPS coordinates were randomly generated, within a 5-km radius from the EADD hub, using OziExplorer mapping software. Once the study team located the point, the nearest household was screened for the presence of a child under 5 years old and milk production based on the defined intensification levels. If the household did not qualify based on screening criteria and/or the quota had been met for the specific intensification level, the team asked the household to refer them to a nearby household that might meet the criteria. Owing to time constraints, if no one was home at the household nearest the coordinates, the study team went to the nearest household. If the 15 points were exhausted and the 10 household quota per intensification level was not met, 5 new, random points were generated and households were identified using the method as described.

Ninety-four households completed the questionnaire. Two questionnaires were discarded because upon review it was determined that (1) the respondent did not meet the selection criteria and (2) the respondent was unable to answer the majority of questions concerning the index child, leaving a sample size of 92. The household questionnaire consisted of two parts. Part A was designed to be administered to the head of household or primary caretaker of the index child, and Part B was to be administered to the primary caretaker of the index child. Questions focused on household demographics, household income and investments, dairy production and inputs, dairy consumption, dietary diversity, food security, and time allocation of the primary caretaker. Trained field assistants administered the questionnaire in the local language, which took around 1.5 hours to complete.

Each day, the field team reviewed the questionnaires to ensure that all appropriate fields were complete and understood by all members. The data were entered in an Access database. Following fieldwork, more data cleaning occurred using Excel and quantitative data cleaning and analyses were performed with SAS software (version 9.1; SAS Institute, Inc., Cary, NC, USA).

Variable Creation

For household diets, our indicator was the Food Consumption Score (FCS). The FCS was created by grouping the individual food items into seven food groups and summing the consumption frequencies within each group: (1) main staples (maize, maize flour, millet and other cereals); (2) pulses; (3) vegetables; (4) fruit; (5) meat and fish; (6) milk; and (7) oil according to methodology described by the World Food Programme (WFP, 2008). The FCS methodology includes two additional food groups – sugar and condiments – which were not included in our questionnaire. The FCS was constructed from the data collected on the number of times individual food items were consumed in the household seven days before the study date.

For diets of young children, we used the individual dietary diversity score (IDDS). Dietary diversity data were collected using a 24‐hour recall of foods consumed by the index child, administered to the primary caretaker following standard methodology (Swindale and Bilinsky, 2006). The IDDS is calculated based on the number of different food groups consumed out of eight: grains, roots or tubers; vitamin A-rich plant foods; other fruits and vegetables; meat, poultry, fish or seafood; eggs; pulses, legumes or nuts; milk and milk products; and foods cooked in oil or fat. Dietary diversity is useful for showing how agricultural interventions affect the composition of diets.

An ASF consumption score was calculated for both households and index children. The household ASF consumption score was based on the household’s score for the ‘meat and fish’ food group, excluding the ‘milk’ food group given the relatively high consumption of dairy observed in our sample. The child’s ASF consumption score was based on the sum of two food groups – ‘flesh’ and ‘eggs’ – and excluded the ‘dairy’ food group for the previously mentioned reason.

To assess childcare and feeding practices, we looked at responses on five IYCF practices, which align with current, age-specific feeding recommendations for young children from the WHO (2010). Currently breastfeeding was determined by an affirmative response to the question: ‘Are you still breastfeeding?’

Breastfeeding was considered to be exclusive breastfeeding if the primary caretaker indicated that she did not begin giving the child any liquids or foods other than breast milk before 6 months. Early introduction of water was estimated by the response to a question regarding the age the child was first given water. If the response was before six months, the variable was coded as yes. Early introduction of cow’s milk was estimated by the response to a question about the age the child was first given cow’s milk. Like water, if the response was before six months, the variable was coded as yes. Finally, age-appropriate dietary diversity was estimated by the standard methodology described earlier.

An ordered probit analysis was done with the level of intensification – low, medium and high – as the dependent variable to determine what influences intensification.

Results

Characteristics of surveyed household members are reported in Table 2. The majority of households were headed by a male whose ethnicity was Kalenjin and was involved in agriculture. Of the surveyed households, 13 per cent had a child below 6 months old (n=12), 13 per cent had a child 6–11 months (n=12), 27 per cent had a child 12–23 months (n=25), and 47 per cent had a child 24–60 months (n=43). High-intensity households reported owning on average more household assets, agricultural assets, farm land and cows than medium- and low-intensity households. These are variables commonly associated with wealth (Table 3).
Table 2

Socio-demographic characteristics of key household members, n=92

Variables

Overall

Household head

 Age, in years, mean±SD

40.7±11.7

 Years of education, mean±SD

10.0±4.5

 Literate, % (n)

90.0% (80)

 Female-headed households, % (n)

9.8% (9)

 Ethnicity is Kalenjin, % (n)

90.2% (83)

 Primary activity is not agriculture, % (n)

36.7% (33)

Primary caretaker of index child

 Age, in years, mean±SD

31.4±9.3

 Years of education, mean±SD

8.6±3.7

 Primary activity is not agriculture, % (n)

9.8% (9)

Index child

 Age, in months, mean±SD

22.3±15.6

 Male children, % (n)

46.7% (43)

Table 3

Socio-demographic characteristics of households, by level of intensification, n=92

Variables, mean±SD

Low (n=30)

Medium (n=31)

High (n=31)

Household size

5.3±2.1

6.3±2.2

6.3±1.9

Children under 16 years old

2.9±1.9

3.3±1.7

3.6±1.5

Household assets owned out of six

1.9±1.4

2.0±0.9

2.6±1.0

Agricultural assets owned out of four

1.4±0.7

2.1±1.1

2.4±0.9

Acres of farm land owned

1.2±1.6

3.1±3.4

5.5±9.6

Total cows currently owned

4.7±4.1

5.5±5.4

The six household assets were cooker/gas stove, radio, television, mobile phone, motorcycle and bicycle; the four agricultural assets were: hoe, spade, plough and sprayer pump.

An analysis of determinants of intensification using an ordered probit showed that land was a key determinant of whether farmers had intensified dairy production. Farmers in the low-intensity category were likely to have less land, while farmers in the high-intensity category were likely to have more land (Table 4). Although we hypothesized that income may determine level of intensification, this was found to be insignificant. The relationship between income and intensification is complex, as higher income could be an effect of intensification rather than a determinant of intensification.
Table 4

Ordered probit analysis of the determinants of dairy intensification

 

Low

Medium

High

 

Marginal effects (ME)

SE

P>|z|

Marginal effects (ME)

SE

P>|z|

Marginal effects (ME)

SE

P>|z|

Age of head of household in years

0.001

0.008

0.876

0.000

0.001

0.878

−0.001

0.009

0.876

Sex of head of household (male =1)

0.160

0.166

0.334

0.024

0.033

0.463

−0.184

0.189

0.331

Years of education

−0.008

0.012

0.499

−0.001

0.002

0.569

0.009

0.014

0.499

HH primary activity (agriculture =1, other=0)

0.008

0.014

0.566

0.001

0.002

0.607

−0.009

0.016

0.565

Household size (number)

−0.024

0.028

0.400

−0.004

0.005

0.504

0.027

0.033

0.399

Farming experience (years)

0.000

0.008

0.972

0.000

0.001

0.972

0.000

0.009

0.972

Farm size

−0.038

0.019

0.045**

−0.006

0.007

0.393

0.044

0.023

0.058*

Ln (total income)

0.021

0.052

0.689

0.003

0.009

0.714

−0.024

0.060

0.690

Pseudo R2

0.0639

Prob> Chi2

0.2228

LR chi2 (8)

10.64

Number of observations

76

*, ** significant at 10% and 5% respectively.

Pathway 1: Food Production – Consumption of Cow’s Milk and Dietary Diversity

Is Increased Milk Production Associated with Increased Consumption of Milk by Young Children?

In this sample, high-intensity households reported giving more cow’s milk to young children than both medium- and low-intensity households. In the survey, mothers from the low-intensity and medium-intensity households reported that the median amount of cow’s milk given to the index child was 0.5 cups, while mothers from high-intensity households reported a median amount of 1 cup (Table 5). The median amount of cow’s milk mothers reported giving to index children at least 12 months old was higher for the medium- and high-intensity households than the low-intensity households. A higher proportion of index children 6–60 months from the low-intensity households had not consumed any fresh milk in the previous 24 hours compared to children from the medium- and high-intensity households.
Table 5

Daily milk consumption quantities and patterns for index children 6–60 months, by level of intensification, n=80

Variables

Low (n=25)

Medium (n=28)

High (n=27)

Proportion of children consuming no fresh milk, % (n)

12.5% (10)

10.7% (3)

3.7% (1)

Proportion of children consuming up to 0.5 cups, % (n)

6.3% (5)

15.0% (12)

8.8% (7)

Proportion of children consuming at least 1 cup, % (n)

12.5% (10)

16.3% (13)

23.8% (19)

Index child’s fresh milk consumption, in cups, (median, IQR)

0.5 (0, 1.0)

0.5 (0.5, 1.0)

1.0 (0.5, 2.0)

By age

Low (n=6)

Medium (n=4)

High (n=2)

Index children 6–12 months, fresh milk consumption

1.0 (0, 1.0)

0.25 (0, 1.25)

0.5 (0.5, 0.5)

 

Low (n=6)

Medium (n=10)

High (n=9)

Index children 12–23 months, fresh milk consumption

0.5 (0.5, 1.0)

0.75 (0.5, 1.0)

1.0 (1.0, 1.5)

 

Low (n=13)

Medium (n=14)

High (n=16)

Index children >24 months, fresh milk consumption

0.0 (0, 1.0)

0.75 (0.5, 1.0)

1.5 (0.75, 2.0)

One theme that emerged from the FGDs was the belief that cow’s milk promoted health and nutrition among children, and cow’s milk was preferentially distributed to children. One woman from the high-intensity production group explained it this way:

Facilitator: ‘We have lunch and you told me that you eat ugali, milk, and vegetables. What amount of milk do you use for lunch?’

Response: ‘It will depend if the milk is there, if it is not there, you only give to the children’.

Farmers described how cow’s milk prevented malnutrition in children and consumption provided positive benefits. A female farmer from a medium-intensity production group said:

We want the children to have beautiful and handsome faces – that’s why we give milk.

How Are Household Diets and Diets of Young Children Associated with Intensification?

The diversity of household diets improved with level of intensification, while no differences were observed in dietary diversity of young children (Table 6). Dietary diversity typically increases with age, which might explain why dietary diversity of children did not change much, even as household diets became more diverse with higher levels of intensification. About one-third of index children overall had consumed ASF, excluding dairy, in the previous day, with little difference observed across intensification. Household consumption of ASF, excluding dairy because it was so common in this sample, also increased with intensification (Table 6).
Table 6

Dietary characteristics for households and index children, by level of intensification

Variables

Low (n=30)

Medium (n=31)

High (n=31)

Household food consumption score (FCS), mean±SD

32.8±6.7

36.9±4.8

38.1±4.5

Household animal source food (ASF) score, excluding milk

1.7±0.9

2.7±2.3

3.5±2.2

Individual dietary diversity score (IDDS) for the index child, n=80

5.0±1.2

4.7±1.2

5.6±0.9

IDDS met minimum requirements for age, % (n)

87.5% (21)

67.9% (19)

88.5% (23)

ASF, excluding milk, was consumed by index child, % (n)

32.0% (8)

32.1% (9)

37.0% (10)

Pathway 2: Dairy Income and Household Decision Making

Is the Proportion of Milk Kept for Household Consumption Associated with Intensification?

High-intensity households were keeping a slightly smaller proportion of their milk for consumption than the medium-intensity households. High-intensity production households also reported producing more milk and were keeping about 50 per cent or an average of 5 litres per day for household consumption (Table 7). The medium-intensity households reported producing and consuming less milk than high-intensity households, but the proportion they were keeping for household consumption was slightly higher – 60 per cent. Given the significantly higher volumes produced by high-intensity households, it is not surprising that they kept a smaller proportion for home consumption (while still consuming more milk at home). Most of the dairying households reported some evening milk production (n=59), but a higher proportion of high-intensity households were selling the evening milk compared to the medium-intensity households. Households that were selling evening milk were not selling large amounts. The medium-intensity households (n=2) were selling an average of 0.7 litres/evening while the high-intensity households (n=8) were selling 1.7 litres/evening.
Table 7

Total and evening only household milk production and consumption, in litres

Variables

Medium

High

Total daily milk production and consumption (n=62), mean±SD

 Daily household milk production, in litres

3.1±1.3

10.8±5.8

 Daily household milk consumption, in litres

1.8±1.2

4.9±1.9

 Proportion of daily milk produced kept for household consumption

0.6±0.3

0.5±0.2

Evening-only milk production and consumption (n=59), mean±SD

 Evening-only milk production

1.3±0.6

3.8±1.5

 Evening-only milk kept for consumption

1.3±0.6

3.4±1.5

 Proportion of households keeping all of evening milk for consumption, % (n)

93% (26)

74% (23)

How Are Intrahousehold Decision-Making Patterns Associated with Intensification?

All respondents were asked a series of questions about who in the household makes agricultural decisions. The same questions were asked of both respondents who completed the questionnaire, but because of inconsistency in data collection we were left with a very small sample size for this series of questions. Despite the small sample size, 50 per cent of households (n=7) reported that it was the woman (‘spouse’) who decides how much morning milk to keep for household consumption and 38 per cent of households (n=3) reported that it was the woman who makes the same decision for the evening milk (Table 8). For morning milk sales decision making, a higher percentage of high-intensity households than medium-intensity households reported that the decision was made by the male head (30 versus 25 per cent) and a smaller percentage of high-intensity households than medium-intensity households reported joint decision making (20 versus 25 per cent). These findings could suggest that women may lose some influence over morning milk decisions with intensification. As households intensify and become more commercialized, with higher amounts of milk going to the market, gender roles in decision making can change. A different trend was observed with evening milk decisions where women appeared to gain greater influence with intensification (Table 8). These results should, however, be interpreted with caution owing to the small sample size. Although women seemed to have more control of the evening milk and income from the milk, overall men had greater control and decision making over dairy income for the advanced intensification-level households that sold more milk. There was, however, also more joint decision making in high-intensity households compared to medium-intensity households (35 versus 17 per cent), (Table 9). Joint decision making is, however, complex as it is difficult to know whether each spouse has the same voice in the decision, or whether one spouse may just have consulted the other on the decision. Studies hoping to gain a more nuanced understanding of joint decision making should use a decision-making scale that ranges from unilateral/singular decision by one spouse to equal voice and agreement on the decision by both husband and wife.
Table 8

Household decision-making patterns in regards to how much milk is kept for household consumption, by level of intensification

Decision-maker, % (n)

Morning milk

Evening milk

 

Medium (n=4)

High (n=10)

Medium (n=4)

High (n=4)

 Head

25% (1)

30% (3)

50% (2)

 Spouse

50% (2)

50% (5)

25% (1)

50% (2)

 Joint

25% (1)

20% (2)

25% (1)

50% (2)

Table 9

Household member who manages income from dairy, as reported by respondent B, n=47

Decision-maker, % (n)

Medium (n=24)

High (n=23)

Head of household

50% (12)

34.8% (8)

 Male head of household

33.3% (8)

30.4% (7)

 Female head of household

16.7% (4)

4.3% (1)

Spouse (all female)

33.3% (8)

30.4% (7)

Joint

16.7% (4)

34.8% (8)

How Is the Role of Dairy Income Associated with Dairy Intensification?

Low-intensity households reported fewer sources of income compared to medium- and high-intensity households. The most frequently mentioned source of income by low-intensity households was wages (73 per cent), followed by crop sales (47 per cent) (data not shown). For medium- and high-intensity households, the most frequently mentioned source of income was milk sales (71 and 90 per cent, respectively) (data not shown). This is expected as the medium- and high-intensity households would have more cows, higher-yielding cows and also a higher market orientation than low-intensity households. These findings confirm those of a study by Nicholson et al (2004) that found that the number of dairy cows owned had a large and statistically significant impact on household cash income; each cow owned increased income by at least 53 per cent of the mean total income of households without dairy cows.

Across the 88 households that reported cash income, crop sales contributed to nearly half of the monthly household income, followed by wages and then other sources of income. Differences between the high- and medium-intensity households are illustrated on the right side of Figure 1.
Figure 1

Contribution of different sources of income to total monthly income across the entire sample (left) and among medium- and high-intensity households (right), n=88.

Pathway 3: Women’s Involvement in Dairy and Links to Childcare Practices

Is Maternal Workload Associated with Intensification?

Eighty-four per cent (n=26) of primary caretakers in the medium-intensity households reported spending time on dairy activities compared to 48 per cent of primary caretakers in the high-intensity households (Table 10). Women from the medium-intensity households reported spending more time on dairy activities than women from the high-intensity households. A slightly higher percentage of women from high-intensity households reported hiring labour to help with dairy activities than women from medium-intensity households, implying that – with higher levels of intensification – households are able to hire labour for dairy activities. The time women reported spending on other income-generating activities and childcare activities did not differ much by level of intensification. These findings suggest that while dairy workload may be higher for advanced-intensity households, these households compensate by hiring additional labour. A study in coastal Kenya by Nicholson et al (2004) found that in households with more cattle, labour allocation to cattle by household members decreased and labour requirements for dairy cows were met primarily by an increase in hired labour. In contrast, Mullins et al (1996) found that increases in income and consumption of milk with dairy intensification were often achieved at the expense of more work for women.
Table 10

Allocation of time and participation in daily activities as reported by mothers (‘primary caretakers’)

Variables

Low (n=29)

Medium (n=31)

High (n=31)

Time spent on childcare activities (mean±SD)

243.1±129.0

254.8±106.1

237.5±100.2

Time spent on all other income-generating activities

293.1±156.0

281.9±143.7

281.3±154.2

Time spent on all dairy activities, (n=41)

117.3±106.9

83.3±75.0

Details on household involvement in dairy activities, % (n)

Mother is involved in dairy activities, (n=62)

83.9% (26)

48.4% (15)

Labour hired to help with dairy activities

19.4% (6)

22.6% (7)

Labour hired and mother reported no time spent on dairy activities

12.9% (4)

22.6% (7)

How Are IYCF Practices Associated with Dairy Intensification?

Mothers of all children below 12 months old reported that they were still breastfeeding their infants (Table 11). Fifty-eight per cent (n=7) of infants below 6 months of age were currently exclusively breastfed, with little difference across level of intensification. The first foods, or weaning foods, introduced to young children included water, milk, uji, and other mashed or semi-solid foods generally introduced in that order starting around the child’s third or fourth month. A woman farmer from the low-intensity production group explained:

From one month to six months, we prefer for them to either breastfeed or drink maize flour porridge.

Table 11

Summary of select infant and young child feeding practices by level of dairy intensification

Variables

Low

Medium

High

Currently breastfeeding, % (n)

 Children below 12 months (n=24)

100% (11)

100% (7)

100% (6)

 Children 12–23 months (n=25)

20% (5)

32% (8)

20% (5)

Exclusively breastfed for first 6 months, % (n)

 Children below 6 months (n=12)

60% (3)

66.7% (2)

50% (2)

 Children 6–11 months (n=11)

40% (2)

 Children 12–23 months (n=24)

28.6% (2)

10% (1)

20% (2)

Median age of introduction of select complementary foods, in months

 Water (n=77)

4 (1, 6)

3 (1, 7)

2 (1, 6)

 Cow’s milk (n=80)

6 (2, 6)

4 (3, 6)

3.5 (2, 6)

 Porridge (n=81)

6 (2, 6)

3.5 (2, 6)

4 (3, 6)

 Mashed or semi-solid foods (n=76)

7 (6, 9)

6 (6, 8)

6 (5, 8)

Results from the survey indicate that mothers from the high-intensity households tended to introduce substances other than breast milk earlier than mothers from the medium-intensity and low-intensity households (Table 11). The ages at which children were introduced to different weaning foods – water, cow’s milk, uji, and mashed or semi-solid foods – was earlier with level of intensification. The data from the FGDs did not provide information to clearly explain these observed differences between the intensification categories, but several women mentioned breast milk insufficiency owing to poor diet or hard work as reasons why they did not exclusively breastfeed. One woman from the high-intensity production group said:

If the work is more, we are saying breast milk is not enough for the kid because of a lot of work.

How Is Dairy Intensification Associated with General Childcare, Childcare Strategies and Sick Care?

Women in the FGDs, regardless of level of dairy intensification, reported remaining with their young child for a large portion of the day. Women in the FGDS explained that when they went to work on the farm or to pick tea in the morning, the youngest child would most often be carried on the mother’s back. In the afternoon, when dairy activities (except for the first milking) would occur, young children were either with the mother or left with older siblings just returning from school. The household survey data revealed no differences across levels of intensification in the average time (around 3.5 hours) mothers reported spending away from their youngest child. Thirty-one per cent of women from medium-intensity households (n=8) reported leaving the youngest child with a sibling under 13 years old compared to 20 per cent (n=3) of women from high-intensity households (data not shown). Regardless of level of intensification, mothers reported providing care for their youngest child during times of illness. Seventy per cent of primary caretakers from the low-intensity households reported that they cared for the index child during his/her last bout of illness, compared to 83 per cent of primary caretakers in the medium-intensity households and 81 per cent of primary caretakers from high-intensity households (data not shown). Dairy intensification did not appear to have any negative effects on the childcare strategies we examined.

Conclusions

This article presents findings of FGDs and a household survey conducted in three sites in Rift Valley Province in Kenya, looking at the pathways between dairy intensification and child nutrition. Three categories of households were considered: low intensity households (those with no milking cow at time of survey); medium-intensity households (those whose best cow produces up to 6 litres per day) and high-intensity households (those whose best cow produces more than 6 litres per day). Three conceptual pathways were explored: food production; agricultural income; and women’s empowerment linked to childcare practices.

Regarding the first pathway, household and individual milk consumption was higher in the medium- and high-intensity households than the low-intensity households. Children under 5 years old in high-intensity households received a greater amount of milk than children in medium-intensity or low-intensity households. In particular, children aged 12–23 months in the high-intensity households were receiving twice as much cow’s milk as children in low-intensity households. Other studies in Kenya have found that increased dairy production had positive impacts on household welfare, particularly increasing consumption of dairy products (Mullins et al, 1996; Nicholson et al, 2004). In the Nicholson et al (2004) study in coastal Kenya, households that owned dairy cows consumed 1.0 litre per week more dairy products than households that did not own dairy cows. The qualitative results from our study suggest that milk was an important part of household diets and that increased production did not lead to compromises in household milk consumption. Milk is only one part of an overall nutritious diet, and therefore findings on dietary diversity can better illustrate the role of dairy intensification in improving diet quality. We did not see any clear differences in the household FCS in our study across the three levels of dairy intensification households. For the IDDSs, children in this sample had on average relatively diverse diets, with a higher percentage meeting the minimum dietary diversity requirements for their age than what has been reported in the latest Kenya Demographic and Health Survey (in which only 55.9 per cent of children 6–23 months met the minimum requirements; Kenya National Bureau of Statistics & ICF Macro, 2010). The exception was that in the medium-intensity production households, only 68 per cent of children met the minimum requirements. We could not explain this difference based on the other data that were collected. A larger quantitative study, controlling for confounding factors such as wealth, could more clearly analyse the relationship between dairy intensification and dietary diversity.

Regarding the agricultural income pathway, while women seem to be gaining control over evening milk sales decisions, men seem to be increasingly controlling total dairy income with intensification. However, this trend is somewhat countered by the increase in households reporting joint decisions in milk income management with intensification. While joint decision making on milk sales is desirable, some of this increase in joint managing of total milk income may be attributed to men increasingly taking over evening milk sales decisions, especially in the high-intensity households where more evening milk was being sold. This would imply a loss of women’s sole decision making on evening milk and income from evening milk.

Looking at the quality of childcare, women from medium-intensity production households reported spending 30 more minutes a day on dairy activities than women from high-intensity production households, even though the time allocated to childcare activities and income‐generating activities was relatively similar across all levels of intensification. Households in the high-intensity category, however, compensated for the increased labour requirements by hiring labour. The increased workload, especially for the medium-intensity households and women’s reports in the FGDs, suggest that dairy intensification could have an impact on women’s time use, further impacting on their care giving activities and child nutrition. The relationship between increased workloads and child nutrition need to be further investigated. Increased maternal workload may increase the demand for alternative caregivers, who may provide lower-quality childcare. In our study, about 30 per cent of mothers in medium-intensity households reported leaving their youngest child in the care of a preteen caretaker when carrying out dairying activities.

Lastly, the additional time demands generated by dairy intensification may make it harder for women to breastfeed, and thereby lead to earlier weaning and introduction of complementary food. This is consistent with the finding that a lower percentage of women from medium-intensity households and high-intensity households are breastfeeding children aged 12–24 months, and introducing supplementary foods at an increasingly earlier age than women from low-intensity production households. It is therefore important to investigate this pathway further, taking into account these mitigating factors – seen largely in the medium-intensity households – to ensure that agricultural interventions do not harm the nutrition and well-being of women and children.

References

  1. Agarwal, B. (1997) Bargaining and gender relations: Within and beyond the household. Feminist Economics 3(1): 1–51.CrossRefGoogle Scholar
  2. Akre, J. (1989) Physiological development of the infant and its implications for complementary feeding. Bulletin of the World Health Organisation 67(6): 55–67.Google Scholar
  3. Allen, L.H. (1993) The nutrition CRSP. What is marginal malnutrition, and how does it affect human function? Nutrition Review 51(9): 255–267.CrossRefGoogle Scholar
  4. Allen, L.H. (2013) Comparing the value of protein sources for maternal and child nutrition. Food and Nutrition Bulletin 34(2): 263–266.CrossRefGoogle Scholar
  5. Arimond, M. and Ruel, M.T. (2004) Dietary diversity is associated with child nutritional status: Evidence from 11 demographic and health surveys. The Journal of Nutrition 134(10): 2579–2585.Google Scholar
  6. Begum, J.M. (1994) The impact of dairy development on protein and calorie intake of pre-school children. Indian Journal of Medical Sciences 48(3): 61–64.Google Scholar
  7. Berti, P.R., Krasevec, J. and FitzGerald, S. (2004) A review of the effectiveness of agriculture interventions in improving nutrition outcomes. Public Health Nutrition 7(5): 599–609.CrossRefGoogle Scholar
  8. Bhutta, Z.A. et al (2008) What works? Interventions for maternal and child undernutrition and survival. The Lancet 371(9610): 417–440.CrossRefGoogle Scholar
  9. Black, R.E. et al (2008) Maternal and child undernutrition: Global and regional exposures and health consequences. The Lancet 371(9608): 243–260.CrossRefGoogle Scholar
  10. Borgerhoff Mulder, M.M. (1989) Marital status and reproductive performance in Kipsigis women: Re‐evaluating the polygyny‐fertility hypothesis. Population Studies 43(2): 285–304.CrossRefGoogle Scholar
  11. Curry, J. (1996) Gender and livestock in African production systems: An introduction. Human Ecology 24(2): 149–160.CrossRefGoogle Scholar
  12. Dewey, K. (2003) Guiding Principles for Complementary Feeding of the Breastfed Child. Washington DC: PAHO and WHO.Google Scholar
  13. Dror, D.K. and Allen, L.H. (2011) The importance of milk and other animal-source foods for children in low-income countries. Food and Nutrition Bulletin 32(3): 227–243.CrossRefGoogle Scholar
  14. Engle, P.L. (1991) Maternal work and child‐care strategies in peri‐urban Guatemala: Nutritional effects. Child Development 62(5): 954–965.CrossRefGoogle Scholar
  15. Engle, P.L., Bently, M. and Pelto, G. (2000) The role of care in nutrition programmes: Current research and a research agenda. Proceedings of the Nutrition Society 59(1): 25–35.CrossRefGoogle Scholar
  16. Engle, P.L., Menon, P. and Haddad, L. (1997) Care and Nutrition: Concepts and Measurements. Washington DC: IFPRI.Google Scholar
  17. Food and Agricultural Organisation of the United Nations (2008) Guidelines for Measuring Household and Individual Dietary Diversity, Version 4. Rome: FAO.Google Scholar
  18. Food and Agricultural Organisation of the United Nations (2011) The State of Food and Agriculture – Women in Agriculture: Closing the Gender Gap for Development. Rome: FAO.Google Scholar
  19. Gewa, C.A., Oguttu, M. and Savaglio, L. (2011) Determinants of early child-feeding practices among HIV infected and noninfected mothers in rural Kenya. Journal of Human Lactation 27(3): 239–249.CrossRefGoogle Scholar
  20. Gillespie, S., Harris, J. and Kadiyala, S. (2012) The Agriculture-nutrition Disconnect in India, What Do We Know? Washington DC: IFPRI. IFPRI Discussion Paper 01187.Google Scholar
  21. Haddad, L.J. (2000) A conceptual framework for assessing agriculture-nutrition linkages. Food and Nutrition Bulletin 21(4): 367–373.CrossRefGoogle Scholar
  22. Herforth, A. and Harris, J. (forthcoming) Improving Nutrition through Agriculture: Understanding and Applying Key Pathways and Principles. Washington DC: USAID-SPRING Technical Brief #1, in press.Google Scholar
  23. Huss-Ashmore, R. (1996) Livestock, nutrition, and intrahousehold resource control in Uasin Gishu District, Kenya. Human Ecology 24(2): 191–213.CrossRefGoogle Scholar
  24. Kenya National Bureau of Statistics & ICF Macro (2010) Kenya Demographic and Health Survey, 2008 2009. Calverton, MD: Kenya National Bureau of Statistics and ICF Macro.Google Scholar
  25. Kimani-Murage, E.W. et al (2011) Patterns and determinants of breastfeeding and complementary feeding practices in urban informal settlements, Nairobi Kenya. BMC Public Health 11(1): 396–406.CrossRefGoogle Scholar
  26. Kristjanson, P. et al (2010) Livestock and Women’s Livelihoods: A Review of the Recent Evidence. Nairobi: ILRI. ILRI Discussion Paper No. 20.Google Scholar
  27. Leroy, J.L. and Frongillo, E.A. (2007) Can interventions to promote animal production ameliorate undernutrition? The Journal of Nutrition 137(10): 2311–2316.Google Scholar
  28. Long, J.K., Murphy, S.P., Weiss, R.E., Nyerere, S., Bwibo, N.O. and Neumann, C.G. (2011) Meat and milk intakes and toddler growth: A comparison feeding intervention of animal-source foods in rural Kenya. Public Health Nutrition 15(6): 1100–1107.CrossRefGoogle Scholar
  29. Meinzen‐Dick, R. et al (2010) Engendering Agricultural Research. Washington DC: IFPRI. IFPRI Discussion Paper 00973.Google Scholar
  30. Montagne, J.F., Engle, P.L. and Zeitlin, M.F. (1998) Maternal employment, childcare and nutritional status of 12–18 month‐old children in Managua, Nicaragua. Social Science and Medicine 46(3): 403–414.CrossRefGoogle Scholar
  31. Mullins, G., Wahome, L., Tsangari, P. and Maarse, L. (1996) Impacts of intensive dairy production on smallholder farm women in coastal Kenya. Human Ecology 24(2): 231–253.CrossRefGoogle Scholar
  32. Mutinda, G. and Baltenweck, I. (2013) Tackling gender blindness in EADD. New Agriculturist Magazine Online at http://www.newag.info/en/focus/focusItem.php?a=2927&utm_source=feedburner&utm_medium=feed&um_campaign=Feed%3A+ilrimedia+%28ILRI+in+the+media%29.
  33. Ngigi, M. (2005) The Case of Smallholder Dairying in Eastern Africa. Washington DC: IFPRI. IFPRI EPT Discussion Paper 131.Google Scholar
  34. Nicholson, C.F., Mwangi, L., Staal, S.J. and Thornton, P.K. (2003) Dairy cow ownership and child nutritional status. Paper presented at the Agricultural and Applied Economics Association conference. Montreal, Quebec.Google Scholar
  35. Nicholson, C.F., Thornton, P.K. and Muinga, R.W. (2004) Household-level impacts of dairy cow ownership in coastal Kenya. Journal of Agricultural Economics 55(2): 175–195.CrossRefGoogle Scholar
  36. Nor, B., Ahlberg, B.M., Doherty, T., Zembe, Y., Jackson, D. and Ekstrom, E.C. (2011) Mother’s perceptions and experiences of infant feeding within a community-based peer counselling intervention in South Africa. Maternal and Child Nutrition 8(4): 448–458.CrossRefGoogle Scholar
  37. Randolph, T.F. et al (2007) Invited review: Role of livestock in human nutrition and health for poverty reduction in developing countries. Journal of Animal Science 85(11): 2788–2800.CrossRefGoogle Scholar
  38. Ruel, M.T. (2003) Operationalizing dietary diversity: A review of measurement issues and research priorities. The Journal of Nutrition 133(11): 3911S–3926S.Google Scholar
  39. Shreenath, S. et al (2011) Exploratory Assessment of the Relationship between Dairy Intensification, Gender and Child Nutrition among Smallholder Farmers in Buret and Kipkelion Districts, Kenya. Nairobi: ILRI.Google Scholar
  40. Smith, L.C., Ramakrishnan, U., Ndiya, A., Haddad, L. and Martorell, R. (2003) The Importance of Women’s Status for Child Nutrition in Developing Countries. Washington DC: International Food Policy Research Institute.Google Scholar
  41. Staal, S.J., Pratt, A.N. and Jabbar, M. (2008) Dairy Development for the Resource Poor, Part 1: A Comparison of Dairy Policies and Development in South Asia and East Africa. Rome: FAO.Google Scholar
  42. Swindale, A. and Bilinsky, P. (2006) Household Dietary Diversity Score for Measurement of Household Food Access: Indicator Guide (v. 2). Washington DC: Food and Nutrition Technical Assistance Project and Academy for Educational Development.Google Scholar
  43. TechnoServe Kenya (2008) The dairy value chain in Kenya. Nairobi: TechnoServe Kenya and East Africa Dairy Development Project. (Available from (http://www.eadairy.org/inside.php?articleid=8).
  44. United Nations Children’s Fund, Statistics and Monitoring Section/Policy and Practice (2012) Country profile – Kenya maternal, newborn, and child survival. New York: UNICEF. Accessed from http://www.childinfo.org/files/maternal/DI%20Profile%20-%20Kenya.pdf.
  45. Von Bulow, D. (1992) Bigger than men? Gender relations and their changing meaning in Kipsigis society, Kenya. Journal of the International African Institute 62(4): 523–546.CrossRefGoogle Scholar
  46. Walton, C., Taylor, J., VanLeeuwen, J., Yeudall, F. and Mbugua, S. (2014) Associations of diet quality with dairy group membership, membership duration and non-membership for Kenyan farm women and children: A comparative study. Public Health Nutrition 17(2): 307–316.CrossRefGoogle Scholar
  47. Webb Girard, A., Cherobon, A., Mbugua, S., Kamau-Mbuthia, E., Amin, A. and Sellen, D.W. (2012) Food insecurity is associated with attitudes towards exclusive breastfeeding among women in urban Kenya. Maternal and Child Nutrition 8(2): 199–214.CrossRefGoogle Scholar
  48. Whaley, S.E. et al (2003) The impact of dietary intervention on the cognitive development of Kenyan school children. The Journal of Nutrition 133(11): 3965S–3971S.Google Scholar
  49. World Food Programme, Vulnerability Analysis and Mapping Branch (2008) Food Consumption Analysis – Calculation and Use of the Food Consumption score in Food Security Analysis. Rome: WFP.Google Scholar
  50. World Health Organisation (2000) Complementary Feeding – Family Foods for Breastfed Children. Geneva: WHO.Google Scholar
  51. World Health Organisation (2010) Indicators for Assessing Infant and Young Child Feeding Practices Part 2: Measurement. Geneva: WHO.Google Scholar

Copyright information

© European Association of Development Research and Training Institutes (EADI) 2016

Authors and Affiliations

  • Jemimah Micere Njuki
    • 1
  • Amanda Wyatt
    • 2
  • Isabelle Baltenweck
    • 3
  • Kathryn Yount
    • 4
  • Clair Null
    • 4
  • Usha Ramakrishnan
    • 4
  • Aimee Webb Girard
    • 4
  • Shreyas Sreenath
    • 4
  1. 1.IDRCNairobiKenya
  2. 2.International Food Policy Research InstituteWashington DCUSA
  3. 3.International Livestock Research InstituteNairobiKenya
  4. 4.Emory UniversityAtlanta, GAUSA

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