Food Security
https://doi.org/10.1007/s12571-019-00985-0
ORIGINAL PAPER
A model-based exploration of farm-household livelihood
and nutrition indicators to guide nutrition-sensitive agriculture
interventions
Natalia Estrada-Carmona1,2 & Jessica E. Raneri2,3 & Stephanie Alvarez1 & Carl Timler1 & Shantonu Abe Chatterjee1
&
Lenora Ditzler1 & Gina Kennedy2 & Roseline Remans2 & Inge Brouwer4 & Karin Borgonjen-van den Berg4
&
Elise F. Talsma4 & Jeroen C. J. Groot1,5,6
Received: 14 September 2018 /Accepted: 16 October 2019
# The Author(s) 2019
Abstract
Assessing progress towards healthier people, farms and landscapes through nutrition-sensitive agriculture (NSA) requires trans-
disciplinary methods with robust models and metrics. Farm-household models could facilitate disentangling the complex
agriculture-nutrition nexus, by jointly assessing performance indicators on different farm system components such as farm
productivity, farm environmental performance, household nutrition, and livelihoods. We, therefore, applied a farm-household
model, FarmDESIGN, expanded to more comprehensively capture household nutrition and production diversity, diet diversity,
and nutrient adequacy metrics. We estimated the potential contribution of an NSA intervention targeting the diversification of
home gardens, aimed at reducing nutritional gaps and improving livelihoods in rural Vietnam. We addressed three central
questions: (1) Do ‘Selected Crops’ (i.e. crops identified in a participatory process) in the intervention contribute to satisfying
household dietary requirements?; (2) Does the adoption of Selected Crops contribute to improving household livelihoods (i.e.
does it increase leisure time for non-earning activities as well as the dispensable budget)?; and (3) Do the proposed nutrition-
related metrics estimate the contribution of home-garden diversification towards satisfying household dietary requirements?
Results indicate trade-offs between nutrition and dispensable budget, with limited farm-household configurations leading to
jointly improved nutrition and livelihoods. FarmDESIGN facilitated testing the robustness and limitations of commonly used
metrics to monitor progress towards NSA. Results indicate that most of the production diversity metrics performed poorly at
predicting desirable nutritional outcomes in this modelling study. This study demonstrates that farm-household models can
facilitate anticipating the effect (positive or negative) of agricultural interventions on nutrition and the environment, identifying
complementary interventions for significant and positive results and helping to foresee the trade-offs that farm-households could
face. Furthermore, FarmDESIGN could contribute to identifying agreed-upon and robust metrics for measuring nutritional
outcomes at the farm-household level, to allow comparability between contexts and NSA interventions.
Keywords Homegarden .Multi-objective optimisation . Crop diversification . Farm-householdmodel . Humannutritionmetrics
Electronic supplementary material The online version of this article
(https://doi.org/10.1007/s12571-019-00985-0) contains supplementary
material, which is available to authorized users.
* Natalia Estrada-Carmona 4 Division of HumanNutrition andHealth,Wageningen University and
n.e.carmona@cgiar.org Research, 67000AA, Wageningen, The Netherlands
5 Bioversity International, Viale dei Tre Denari, 472/a, 00054
1 Farming Systems Ecology Group, Wageningen University and Maccarese, Fiumicino, Italy
Research, 6700AK, Wageningen, The Netherlands 6 International Maize and Wheat Improvement Center (CIMMYT),
2 Bioversity International, Parc Scientifique Agropolis II, 1990 Carretera México-Veracruz, Km. 45, El Batán,
Boulevard de la Lironde, 34397 Montpellier, France 56237 Texcoco, Mexico
3 Department of Food Safety and Food Quality, Faculty of Bioscience
Engineering, Ghent University, 9000 Ghent, Belgium
Carmona N.E. et al.
1 Introduction In general, the most commonly listed and documented
knowledge gaps that limit the guidance and planning of
Worldwide commitment and interest in supporting nutrition- NSA interventions include: (1) hypothesizing ex-ante NSA
sensitive agriculture (NSA) is growing across multiple sectors intervention impact pathways; (2) anticipating the effect (pos-
(Ruel et al. 2018). Programmes, research and investment pol- itive or negative) of the interventions on nutrition and the
icy can be defined as nutrition-sensitive if they incorporate an environment; (3) identifying complementary interventions
aim to improve the overall nutritional status by addressing the for significant and positive impacts; and (4) anticipating the
underlying causes of nutrition (Herforth and Ballard 2016). trade-offs that a farm-household could face (Ruel et al. 2018;
Addressing the underlying causes include improving access Herforth and Ballard 2016). There is also a lack of emphasis
to safe and nutritious food, reducing health risks through re- on guaranteeing access to and consumption of high-quality
sponsible agricultural practices that protect natural resources diets by all household members (rather than just one target
and human health, improving nutrition knowledge and norms, group, e.g. women or children); a logical and achievable ap-
improving income and empowering Women (Herforth and proach for NSA and important for global development (Ruel
Ballard 2016). The role of agriculture in enhancing nutrition et al. 2018).
is highly recognized although the evidence of its contribution For an integrated analysis, system-basedmodels could play
remains weak and mixed (Herforth and Ballard 2016; Turner a larger role in guiding and planning NSA interventions.
et al. 2014; Webb and Kennedy 2014). For example, increas- Whole farm-household models, capture the diverse household
ing on-farm production diversity is perceived as an effective and production system components and their complex inter-
approach towards improving smallholders’ diet diversity and actions, and so properly reflect the various outcomes linked to
nutrition. Nonetheless, this perception was contested by production, income, environmental impacts, well-being, gen-
Sibhatu and Qaim (2018) after analysing 45 original studies der, health and quality of life (e.g. Jones et al. 2017; Van
that indicated a positive but small average marginal effect of Ittersum et al. 1998). These models can improve the under-
production diversity on dietary diversity. Ruel et al. (2018), on standing, and contribute to the analysis of, the ‘farm-house-
the other hand, found evidence from 44 carefully designed hold’ defined as a family-run enterprise, the household man-
nutrition-sensitive studies where production diversity was aging it and the off-farm income-generating activities by
promoted and subsequently led to improved access to nutri- household members (Ditzler et al . 2018, 2019) .
tious food, which increased the quality of the diet for the most FarmDESIGN is a farm-household model developed to repre-
vulnerable (i.e. women and children) (Ruel et al. 2018). The sent the farm-household and the flow of resources among the
mixed evidence is due to methodological limitations (e.g. farm components (crops, soil, animals, and manure) within
sample sizes, time frame), contextual and seasonal constraints, and outside the farm (e.g. soil nutrient losses) (Fig. 1) (Groot
lack of comparability of the agricultural interventions, non- et al. 2012). We expanded the FarmDESIGN model to calcu-
homogeneity of units of observation (e.g. households, women late various farm-household performance indicators in order
and children) and variability of metrics (Ruel et al. 2018; to capture more accurately farm-household budget, labour and
Verger et al. 2019; Herforth and Ballard 2016; Turner et al. nutrition (farm-household budget and labour modules ex-
2014; Webb and Kennedy 2014). plained in detail in Ditzler et al. 2019; Fig. 1 and Table 1).
Assessing the agriculture-nutrition nexus is challenging The ‘Human nutrition’module integrates various performance
since it is affected by complex, dynamic and scale- indicators that have been proposed to monitor progress to-
dependent interlinkages among farms, markets, wild foods, wards NSA interventions (e.g. Gustafson et al. 2015;
diets, intra-household and gender dynamics (Bellon et al. Herforth et al. 2016; Melesse et al. 2019) and to measure
2016; Remans et al. 2015). For example, contextual factors different aspects related to nutrition such as food consumption
such as competing labour uses (on- farm vs. off- farm), patterns, diet diversity, nutrient supply and nutrient adequacy.
food availability from on- and off-farm sources (e.g. mar- For instance, FarmDESIGN facilitates conducting farm-
kets), environmental constraints (e.g. poor soils), and household scenario analysis through optimization routines
socio-economic status and gender dynamics (e.g. income; (Ditzler et al. 2019; Groot et al. 2012). Consequently, the
and equity) all shape household decisions around on-farm model has the potential to contribute valuable information to
production (Ditzler et al. 2018). These factors also shape the design and guide of NSA interventions by jointly quanti-
the performance of the farm, farm-household resources, fying performance indicators across socio-economic, produc-
crop/varietal preferences, and objectives (Ditzler et al. tive, environmental and nutrition farm-household domains.
2019; Groot et al. 2012). Thus, accounting for contextual Our study tested the expanded FarmDESIGN farm-
factors at the farm-household level could aid in identifying household model to provide guidance and planning on NSA
constraints in the adoption of NSA interventions and sup- interventions, and tested the performance of the incorporated
port the achievement of positive nutritional outcomes metrics in the ‘Human nutrition’ module. We used a case
(Ruel et al. 2018; Herforth and Ballard 2016). study in Vietnam, where diversifying home gardens with
A model-based exploration of farm-household livelihood and nutrition indicators to guide nutrition-sensitive agriculture interventions
Fig. 1 Schematic representation of the FarmDESIGN model showing enterprise (black boxes at the bottom) and the household (white boxes
farm resource flows (i.e. cash, labour, and food) among the farm at the top). The three new modules related to the household calculate
components and the household. Blue arrows represent inflows, while diverse performance indicators at the farm-household level (see
other arrows denote product outflows (green) or losses (red). The black Table 1). OM= organic matter; GHG = greenhouse gases; Product use =
and grey arrows indicate resource flows within the farm–-household sys- allocation of crop and animal products produced on- farm or sourced off
tem. Boxes indicate modules that calculate indicators for the farm farm. Source: From Ditzler et al. (2019)
nutritious crops selected through a participatory approach was model as entities and placing the family enterprise (i.e. the
undertaken as an NSA intervention. Our overall aim was to farm) within the farm-household. This change facilitated cal-
test the impact of a specific nutrition-sensitive intervention, culating diverse farm-household performance indicators for
which, in our study’s case, is crop diversification targeted to the productive, socio-economic, environmental, and nutrition-
home gardens for improving diets and livelihoods. Hence, we al domains. Therefore, the addition of three new farm-
addressed three central questions: (1) Do ‘Selected Crops’ (i.e. household modules ‘Household budget,’ ‘Household labour’
crops identified in a participatory process) in the intervention and ‘Household nutrition’ widens the applicability of
contribute to satisfying household dietary requirements?; (2) FarmDESIGN for modelling farming systems (Ditzler et al.
Does the adoption of Selected Crops contribute to improving 2019). Ditzler et al. (2019) detailed the changes in the
household livelihoods (i.e. does it increase leisure time for ‘Household budget,’ and ‘Household labour’ modules in par-
non-earning activities as well as the dispensable budget)?; ticular (Fig. 1).
and (3) Do the proposed nutrition-related metrics estimate Overall, the model represents the farm-household on an
the contribution of home-garden diversification towards satis- annual basis and integrates a Pareto-based multi-objective op-
fying household dietary requirements? timization algorithm with a bio-economical model to generate
alternative farm configurations (Groot et al. 2016; 2012). The
alternative farm configurations are feasible farm-household
2 Materials and methods configurations that deviate from the initially represented
farm-household. The alternative farm configurations allocate
2.1 The expanded FarmDESIGN model available resources depending on the objectives optimized for,
the decision variables and the constraints (see Table 1 for a
The previous version of the FarmDESIGN model was unable detailed description of the selected objectives, decision
to capture off-farm data for the household’s employment, lei- variables, and constraints). The objectives in the optimization
sure activities and food use (Ditzler et al. 2019). The model can be any indicator assessing farm-household performance
therefore could not capture the different livelihood strategies across domains (Groot et al. 2012; Ditzler et al. 2019).
or the food availability of the farm-household. We overcame Decision variables determine the manoeuvring space and in-
this limitation by adding the household and its members to the dicate which aspects of the farm-household configuration,
Carmona N.E. et al.
Table 1 FarmDESIGNmodel domains and performance indicators in each domain classified as: objectives, decision variables, model constraints and indicators in this modelling study. HH=Household;
SC=Selected Crops. Other nutrition-related metrics used as indicators, but not as objectives, are listed in Fig. 2
Domain Performance indicators Description Units Status Range of variation
Environmental Organic matter - OM crop residues Crop residues and livestock manures applied as a kg ha−1 year−1 Indicator
soil amendment.
Social Crop labour Labour requirement related to crop cultivation hours year−1 Indicator
HH leisure time Time for non-earning albeit important activities hours year−1 Objective >0
(home maintenance, household, and family chores, (maximize)
community/family events participation, holidays and Constraint
childcare)
Nutritional HH food consumption On and off farm food set aside for consumption kg year−1 HH−1 or Constraint ± 3 SD reported household
g person−1 year−1 consumption among farms
HH consumed on-farm production Quantity set aside for consumption from kg year−1 HH−1 or Indicator
each crop and food item (28 food items) g person −1 year−1
HH Consumed SC production Quantity set aside for consumption from kg year−1 HH−1 or Indicator
SCs (12 food items) g person −1 year−1
SC consumption Share of HH food consumption from SCs % Indicator
On-farm consumption Consumed on-farm production over HH food consumption % Indicator
Food cost Cost of food over farm income % Indicator
Nutrient deviations NDCa, NDFe, NDVitA, NDZn % of requirement Objective
(maximize)
Food group consumption On- and off-farm consumption at the kg year−1 HH−1 or Constraint Plant-based food groups: ± 3*regional
food group level g person −1 year−1 consumption averages; Cereals and
animal-based food groups: ± 0.5*regional
consumption averages (NIN 2012)
Economic Crop gross margin Revenue from crop products based on USD year−1 Indicator
production and sale price, cultivation
cost and crop subsidies
HH dispensable budget On- and off-farm income minus food USD year-1 Objective
cost and other expenditures (maximize)
Productivity Farm Total area ha Constraint Farm area 1.2-2.0 ha
Components Area for each farm component ha Decision variable Home garden 0-0.5 ha; Grassland, rice,
and maize area remain constant;
Each fruit crop could occupy
up to the max fruit area – 0.55 ha
Crop area Area for each crop ha Decision variable Water Spinach area in the fish pond is
0-0.006 ha whereas other IC could occupy up
to the max home garden area – 0.5 ha (option for
diversifying the field with French beans)
HG area The home-garden area as a percentage % Indicator
of the farm area
SC area SCs area as a percentage of the % Indicator
home-garden area
a One working day ~ 8 h
A model-based exploration of farm-household livelihood and nutrition indicators to guide nutrition-sensitive agriculture interventions
resource allocation and input levels can vary in the optimiza- abundance-based metrics (Fig. 2). Count-based metrics are
tion (Groot et al. 2012,); whereas constraints limit the explo- estimated using the presence of unique food items or species.
ration space so that the model yields realistic and desirable For example, the species richness of a diet is measured as the
farm configurations (Groot et al. 2012). Therefore, alternative number of species consumed in the diet from on- and off- farm
farm configurations are useful for exploring trade-offs and sources (SRD; e.g. Lachat et al. 2017), whereas the species
synergies when optimizing farm-household objectives across richness in the production system is measured as the number
selected domains. of harvested crops for consumption (SRP; e.g. Herrero et al.
2017) (Fig. 2, Appendix 1). The household dietary diversity
score (HDDS) measures the number of food groups in the diet
2.2 The ‘human nutrition’ module in FarmDESIGN by any household member in the past 24 h (includes 12 food
groups in the score) and is a proxy indicator mainly for house-
The new ‘Human nutrition’ module assesses the potential con- hold food security access (Kennedy et al. 2011; Verger et al.
tribution of a household’s on-farm annual production (i.e. plants, 2019). The nutritional functional diversity metric considers
livestock and fish) or off-farm food acquisition (e.g. purchased both the species diversity and the specific characteristics of
from the market) to meet the dietary requirements of the house- each species, in this case the nutrient profiles (Petchey and
hold. The new module calculates several metrics that capture Gaston 2007). The nutrient profiles for each species or food
production diversity, diet diversity and nutrient adequacy (see item (i.e. from food composition tables) were standardized by
Fig. 2, Appendix 1 for detailed metrics description, equations, a reference adult, in this case, the dietary reference intakes for
assumptions, input data and limitations). an adult male in the age group of 19-50 years. Then, the
The ‘Human nutrition’ module calculates diet diversity nutrient contribution of each food item was used to create a
metrics from household diet assessments, whereas production tree diagram (functional dendrogram) where each branch rep-
diversity is calculated using farming systems characterization resents one species or food item. Therefore, the nutritional
(Diet – D; Production - P). Nutrient adequacy metrics assess functional diversity metric is the Euclidian distance among
the sufficiency of nutrient intake from a given diet and con- food items in the tree diagram, where lower values indicate
sumption pattern. The nutrient adequacy metric in food items that are closer together and have similar nutrient
FarmDESIGN considers dietary requirements of all house- profiles, whereas larger values suggest dissimilar food items
hold members by using dietary reference intakes, either as contributing to a wider range of nutrients. The tree diagram
recommended dietary allowances or as estimated average re- was created using ‘vegan’ and ‘hclust’ packages in R
quirements depending on data availability; whereas food com- (Oksanen et al. 2016; R Core Team 2016) and as recommend-
position tables are used to estimate the nutrient intakes (see ed by Petchey and Gaston (2007). FarmDESIGN uses the tree
Table 2). The newmodule also accounts for food consumption diagram to calculate the nutritional functional diversity of the
patterns, or food patterns at the food group level, and nutrient diet (NFDD) and the harvested crops for consumption (NFDP)
loss due to processing and cooking procedures through nutri- (see Fig. 2, Appendix 1).
ent retention factors. FarmDESIGN also incorporates other commonly used
Diet diversity, production diversity and nutrient adequacy metrics for assessing food items’ abundance in diets and
metrics are divided into two groups of count-based and
Fig. 2 Metrics included in this modelling study calculated with the new and off-farm food set aside for household consumption. The nutrient
‘Human Nutrition’ module within the FarmDESIGN model (see adequacy metric is calculated by considering the combined nutritional
Appendix 1 for a detailed description of each metric). Themetrics capture demand based on the age and gender of each household member. The
the different components of species diversity (i.e. species richness and nutritional functional diversity considers 13 different nutrients (i) whereas
abundance) and their nutritional contribution. Green and blue areas indi- the nutrient deviation and nutrient yield metrics focus on the household
cate count- and abundance-based metrics, respectively. The metrics are nutrient requirements and food contributions for likely deficient nutrients
calculated for on-farm production set aside for household consumption such as Vitamin A, Zn, Ca, Fe
(superscript P) and for the diet (superscript D) by accounting for the on-
Carmona N.E. et al.
Table 2 Description of the dietary reference intakes
Dietary reference intakes (DRI)
A set of nutrient-based reference values that indicate the average daily nutrient intake that is recommended to ensure the absence of signs of the nutrients’
deficiency, as well as a reduction in the risk of chronic degenerative disease
Estimated average requirements (EAR) Recommended dietary allowance (RDA):
EAR is the average daily nutrient intake level estimated to meet the RDA is the average daily nutrient intake level that is sufficient to meet the
requirement of half the healthy individuals in a particular life stage and nutrient requirement of nearly all (97 to 98%) healthy individuals in a
gender group particular stage of life and gender group.
Source: IOM (2003) and Devaney and Barr (2002)
production for consumption. The Shannon-Weaver (H) or values for deficiencies of Fe = 29% and Vit A = 14%; NIN
Simpson (D) diversity indexes jointly assess species richness 2012).
and distribution evenness. Therefore, both indexes indicate if The eco-region, characterized by the Tropical and
diets (HD, DD) or the food production for consumption (HP, Subtropical Moist Broadleaf Forests biome type (Olson et al.
DP) are dominated by one food item or crop species respec- 2001), experiences a monsoonal climate where 92% of the
tively (see Appendix 1 for a detailed description of these yearly rainfall is concentrated between April and October
metrics). The Shannon-Weaver (H) index is also often calcu- (long-term average: 1309 mm season−1; Hijmans et al.
lated to assess the overall diversity of the production areas and 2005), with limited precipitation from November to March
not only those set aside for household consumption, for in- (long-term average: 109 mm season−1; Hijmans et al. 2005).
stance we also calculated this index to calculate all the crops The monsoonal rainy period is characterized by a warm cli-
planted in the home garden (HHG). The farm nutritional yield mate (long-term average: 22-26 °C; Hijmans et al. 2005),
for nutrient i (Yi) is a novel metric proposed by DeFries et al. whereas temperatures around 15 °C are typical in the dry
(2015). This metric estimates for each nutrient the number of period, mainly from December to January.
reference adults whose dietary reference intakes are entirely The Doan Ket village, in Muong Bon commune (Mai Son
covered per year given an area and production. We used the district), is located in a mountainous region, 500 m above sea
same reference adult as in the nutritional functional diversity level, dominated by lowlands. The Doan Ket village is mainly
metric, although, any other age group, life stage, and sex could composed of individuals from the Khinh ethnic group and was
equally be used. The Yi values, calculated for the whole farm established in the 1960s as part of the resettlement from the
production, are divided by the farm area to facilitate compa- Hoa Binh dam. The small village is relatively close to Hat Lot
rability across contexts (i.e. number of reference adults ha−1). and Son La city, itis well connected with paved roads, and it
Finally, the nutrient deviation metric (NDi) assesses nutrient reports extremely low population densities (1 person per
adequacy by comparing the availability of nutrient i from con- 100 m in 2015; WorldPop 2013).
sumed food from on- and off-farm sources against the house-
hold dietary requirement calculated from the dietary reference
intakes given a household’s demography and size (Ditzler 2.4 Participatory selection of potential crop
et al. 2019). Negative deviations indicate a nutritional contri- diversification strategies
bution that is lower than the household dietary requirements,
whereas positive deviations indicate a surplus (see Appendix The research took place in the context of the CGIARResearch
1 for a detailed description of each metric). Program – (CRP) ‘Integrated Systems for the Humid Tropics’,
a global programme aimed at supporting the intensification of
2.3 Study site integrated agricultural systems to improve the livelihoods of
poor farming families, while guaranteeing ecosystems integ-
The Son La province in the Northwest of Vietnam has rity in Asia, Africa, and the Americas. The Program was
emerged as an important agricultural region due to the inten- grounded in research for development through collaborative
sified production of maize, rice and cassava (ILRI 2014). and participatory approaches (Hiwasaki et al. 2016). One of
Despite poverty reductions and productivity increases, malnu- the interventions of the Humidtropics program in Vietnam
trition continues to be a problem in the region. The percentage focused on diversifying home gardens for income and nutri-
of children under five experiencing iron (Fe), vitamin A (Vit tion security (Hiwasaki et al. 2016). In particular, home-
A), calcium (Ca), zinc (Zn) and vitamin C (VitC) deficiencies garden diversification promoted nutritious crops with a poten-
continues to be relatively high (NIN 2012), with iron and tial for reducing nutritional gaps, while increasing the con-
vitamin A deficiencies above national averages (national sumption of underrepresented foods in the local diet,
A model-based exploration of farm-household livelihood and nutrition indicators to guide nutrition-sensitive agriculture interventions
including vitamin A-rich fruit and vegetables, dark green leafy for farm size, crop species cultivation costs, household ex-
vegetables, pulses, nuts and seeds (Van Hoi et al. 2015). penses and crop yields (Tables 3 and 4). The food consump-
The selection of nutritious crops was conducted through a tion pattern for the household in the Baseline farm was gen-
participatory process including stakeholders from different erated using the most commonly consumed food items among
sectors (Van Hoi et al. 2015). The intervention focused on farmers from on- and off-farm sources. We then calculated the
15 out of the 30 nutritious crops selected (hereafter: mean and standard errors of the self-reported quantities for the
‘Selected Crops’) based on local consumer preferences and most commonly consumed food items along with their selling
knowledge of agronomic potential (Van Hoi et al. 2015). and purchasing prices (Table 3; see Appendix 2 for a visual
Our modelling efforts considered ten Selected Crops with suf- representation of the farm-household).
ficient data including pulses (soybean [Glycine max (L.)
Merr.]), vitamin A-rich fruits and vegetables (carrot [Daucus 2.5.1 Nutrition data
carota L.], papaya [Carica papaya L.], orange-fleshed sweet
potato [Ipomoea batatas, Lam], pumpkin [Cucurbita pepo]), We calculated the dietary reference intakes through the revised
nuts and seeds (peanuts [Arachis hypogaea L.]), dark green recommended dietary allowances for the Vietnamese popula-
leafy vegetables (mustard greens [Brassica juncea (L.) tion (Khan and Hoan 2008). We chose the revised recom-
Czern.], water spinach [Ipomoea aquatica Forsk.]), in addi- mended dietary allowances, since they were estimated for
tion to other vegetables (French beans [Phaseolus vulgaris the Vietnamese population, whereas the available estimated
L.], and cowpeas [Vigna minima (Roxb.) Ohwi and Ohashi]) average requirements were available for humans in general.
(See Table 3 for the plant parts consumed). The household nutrient requirement is the sum of each house-
hold member’s requirement given their age group, gender and
2.5 Characterisation of the Doan Ket households reproductive status (i.e., pregnant, lactating). The nutrients
and farming systems included in the modelling study were the dietary energy for
moderate work category, vitamins (A retinol activity equiva-
We surveyed eight farms (including 34 people above the age lent, C, Thiamine, Riboflavin, Niacin, B6, Folate, B12) and
of six, and three babies under 12 months) in Doan Ket be- minerals (calcium, magnesium, iron [5% bioavailability] and
tween November 2014 and September 2015. The farming zinc [poor zinc absorption]). We used the 13 nutrients to cal-
systems and the dietary patterns in the village were culate the functional diversity metrics (NFDP and NDFD), and
characterised through a comprehensive survey. We used the Vit A, Ca, Zn and Fe to calculate nutrient deviations (NDi) and
IMPACTLite survey, a standardized tool used worldwide that farm nutritional yield (Yi) metrics. The nutrient profiles for
facilitates collecting household information on key farm- every food item commonly consumed among farmers
household performance and livelihoods indicators (Rufino (Table 3) mainly originate from the Vietnamese food compo-
et al. 2013). The comprehensive and data-intensive survey sition table which provides nutrient contents per 100 g edible
collects information about the household structure, fields, portion of raw ingredients (SMILING D.5-a 2013). We also
cropping activities (yield, inputs and labour), livestock activ- used the USDA and theWest African food composition tables
ities (production and labour), household expenditure and in a few cases (USDA 2007; Stadlmayr et al. 2012). We con-
household-level food consumption (Rufino et al. 2013). As sidered potential nutrient losses due to cooking methods by
part of the survey, we only used one dietary recall to assess using the USDA average retention factors values per food
food consumption, therefore excluding seasonality of the group (USDA 2016). We excluded condiments from the
foods consumed. The dietary recall recorded the foods con- modelling effort. Foods in the Baseline farm and model-
sumed from on- and off-farm sources during the week prior to generated alternative farm configurations with quantities be-
the interview (7-day dietary recall) with the approximate low 5 g person−1 day−1 were excluded from the count- and
quantity (weight-kg or volume-lt) based on the memory of abundance-based metrics. The selected cut off value is rough-
the interviewee. One focus group discussion with eight partic- ly double than the one used in the region to identify food-
ipants held in September 2015 complemented survey infor- b a s ed r e commenda t i on s f o r Ch i l d r en ( 2 . 22 g
mation on four topics: land-use mapping, crops cultivation children−1 day−1) (Ngoc Chau 2016).
(rotation and crop yields), dietary patterns and knowledge on
nutritious crops. 2.5.2 Baseline farm objectives, decision variables
We built a ‘Baseline farm’ in FarmDESIGN based on the and constraints
eight farms surveyed in Doan Ket (Table 4). The Baseline
farm includes the most common (i.e. representing ≥4 farms) We used the multi-objective evolutionary algorithm in
household size and demographics, farm components (fish FarmDESIGN to explore options to improve the performance
pond, home garden, grassland areas, perennial fruit plants), of the Baseline farm for six objectives (Tables 1, 3 and 4). The
cultivated crop species and the average self-reported values algorithm generates a set of alternative farm configurations
Carmona N.E. et al.
Table 3 List of consumed and produced food items described by their contributions to daily household consumption at the food group level used in the Baseline farm (see Table 4). Selected
values for calcium (Ca), iron (Fe), zinc (Zn) and vitamin A (Vit A), yearly food consumption Crops for home -garden diversification are in bold. HH=Household; gs = growing season
pattern, crop yield, food items sale and purchasing price and maximum allowed quantity for
Food group Food item Contributions to daily values* No. farmers Modelled average values Constraint
consumed from max
quantity
per
food group
(kg year−1
HH−1)
Ca Fe Zn Vit A On- Off-farm Plant fresh Approx. Selling Approx. Purchasing Combined
farm (market) yield (kg ha−1 prod for price* purchased price (USD consumption
prod gs−1); animal consumption (USD for consumption kg−1) (kg year−1
prod (kg year−1 kg−1) (kg year-1 HH-1) HH−1)
(kg day−1) HH−1)
Dark green leafy mustard 5-15% 5-15% 5-15% ≥15% 3 0 8125.0 116.1 0.3 0.0 – 116.1 (±172.4) 219
vegetables greens
pak choy 5-15% <5% <5% ≥15% 5 0 3333.0 89.7 0.4 0.0 – 89.7 (±208.6)
pumpkin 5-15% 5-15% <5% ≥15% 0 0 461.0 0.0 0.0 0.0 – 0.0 (±80.3)
(leaves)
sweet potato <5% 5-15% <5% ≥15% 0 0 152.3 0.0 0.0 0.0 – 0.0 (±80.3)
(leaves)
water 5-15% 5-15% <5% ≥15% 1 0 6400.0 0.0 0.1 0.0 – 0.0 (±255.5)
spinach
Eggs eggs 5-15% 5-15% 5-15% ≥15% 3 5 0.0 4.4 1.6 9.0 3.1 13.8 (±91.25) 47.5
Fish and sea food fish (tilapia) <5% <5% 5-15% 0% 8 0 0.0 123.0 1.3 0.0 – 123 (±2.0) 124.1
Grains maize <5% 5-15% ≥15% <5% 6 0 2185.0 25.6 0.2 0.0 – 25.6 (±71.6) 1365.1
rice <5% <5% 5-15% 0% 2 7 7493.1 316.8 0.3 852.3 1.1 1169.1 (±100.0)
Meat and poultry beef <5% <5% 5-15% ≥15% 0 7 0.1 0.0 11.8 14.4 12.5 14.4 (±10.0) 182.5
chicken <5% 5-15% 5-15% ≥15% 6 3 0.0 73.8 2.8 18.8 5.5 92.6 (±126.2)
goat <5% <5% 5-15% ≥15% 0 5 – 0.0 – 5.1 7.5 5.1 (±5.7)
pig <5% <5% 5-15% ≥15% 2 7 0.2 0.0 0.0 88.0 4.4 88.0 (±88.0)
Nuts and seeds peanut 5-15% 5-15% 5-15% <5% 2 4 1019.0 0.0 1.3 2.7 2.6 2.7 (±40.2) 45.6
Other fruits banana <5% <5% <5% <5% 5 3 13,111.0 109.4 0.1 0.0 – 109.4 (±109.6)
guava <5% <5% <5% 5-15% 5 1 3434.0 35.3 0.6 0.0 – 35.3 (±64.8)
longan <5% <5% <5% 0% 6 0 3906.3 43.1 0.4 0.0 – 43.1 (±6.9)
orange <5% <5% 5-15% ≥15% 2 4 – 0.0 – 8.9 1.6 8.9 (±110.2)
pomelo <5% <5% <5% 0% 3 1 2275.0 20.2 0.3 0.0 – 20.2 (±436.0)
watermelon <5% <5% 5-15% ≥15% 2 5 – 0.0 – 22.2 0.7 22.2 (±239.1)
Other vegetables bamboo shoot <5% <5% 5-15% ≥15% 3 6 – 0.0 – 73.6 0.9 73.6 (±11.1) 219
cabbage <5% <5% 5-15% <5% 6 1 11,500.0 44.3 0.3 0.0 – 44.3 (±275.0)
cowpea <5% 5-15% <5% <5% 0 0 1097.0 0.0 0.8 0.0 – 0.0 (±62.1)
(fresh pod)
French bean <5% <5% <5% <5% 3 0 3583.0 5.2 0.3 0.0 – 5.2 (±98.9)
(fresh pod)
spring onion 5-15% <5% <5% 5-15% 6 2 7755.0 23.9 0.3 0.0 – 23.9 (±3.5)
tomato <5% 5-15% 5-15% 5-15% 4 4 12,945.0 58.0 0.1 21.7 0.3 79.7 (±195.5)
black bean 5-15% ≥15% ≥15% <5% 2 4 650.6 1.2 1.1 10.6 2.3 11.7 (±60.9) 219
mung bean 5-15% ≥15% 5-15% <5% 2 5 600.0 0.0 0.8 9.0 1.6 9.0 (±59.6)
A model-based exploration of farm-household livelihood and nutrition indicators to guide nutrition-sensitive agriculture interventions
Table 3 (continued)
Food group Food item Contributions to daily values* No. farmers Modelled average values Constraint
consumed from max
quantity
per
food group
(kg year−1
HH−1)
Ca Fe Zn Vit A On- Off-farm Plant fresh Approx. Selling Approx. Purchasing Combined
farm (market) yield (kg ha−1 prod for price* purchased price (USD consumption
prod gs−1); animal consumption (USD for consumption kg−1) (kg year−1
prod (kg year−1 kg−1) (kg year-1 HH-1) HH−1)
(kg day−1) HH−1)
Pulses soybean ≥15% ≥15% ≥15% <5% 1 0 6910.6 0.0 0.4 0.0 – 0.0 (±12.1)
(beans, peas, and tofu <5% <5% 5-15% ≥15% 0 8 – 0.0 – 152.8 0.6 152.8 (±29.8)
lentils)
Vitamin A-rich mango <5% <5% <5% 5-15% 6 1 806.3 80.0 0.5 0.0 – 80.0 (±120.0) 191.6
fruits papaya (ripe) <5% 5-15% <5% 5-15% 3 3 19,381.0 15.2 0.4 0.0 – 15.2 (±138.5)
Vitamin A-rich carrot <5% <5% 5-15% ≥15% 0 4 11,454.0 0.0 0.6 2.6 0.6 2.6 (±109.5) 54.7
vegetables, roots orange flesh <5% <5% <5% ≥15% 0 0 7614.0 0.0 0.4 0.0 – 0.0 (±9.7)
and tubers sweet
potato
pumpkin <5% <5% <5% ≥15% 4 4 16,938.0 0.0 0.1 23.8 0.1 23.8 (±323.4)
White roots and Irish potato <5% <5% 5-15% ≥15% 0 5 – 0.0 – 7.2 0.8 7.2 (±15.7) 36.5
tubers and pale sweet <5% <5% <5% <5% 3 2 7614.0 12.5 0.4 0.0 – 12.5 (±9.7)
plantains potato
radish <5% 5-15% <5% <5% 4 0 7342.5 0.0 0.0 0.0 – 0.0 (±17.7)
taro/arrow <5% 5-15% <5% <5% 3 4 9810.0 8.7 0.5 12.6 1.0 21.3 (±71.6)
root
*Daily values calculated using the recommended dietary allowances for an adult Vietnamese male in the age group of 19-50 years. Nutrient profiles for each crop sourced mainly for the Vietnamese food
composition table (SMILING D.5-a 2013). See section 2.4.1. for detailed information
Carmona N.E. et al.
Table 4 Doan Ket farming systems characterization used to build the Baseline farm using the most common (mode) household size and demographics, farm components and cultivated crop species
(number of farms reported cultivating the crop) and the average (mean) farm size, household expenses, purchased foods and off-farm labour
Baseline farm Excluded from the Baseline farm
No. Mode or mean (SE) Unit Most cultivated species (No. farms) Selected Crops (No. farms Least cultivated species (No. farms)
farms cultivating
the crop already)
Farm-household
Size 8 4.63 (0.32) person
Demographics 8 Three women Age group
14-18; 19-30;
31-50
Two men
19-30; 31-50
Labour capacity 8 6784 (687) hours year−1
Other expenses 5 1594 (400) USD year−1
Purchased food quantity 8 1625 (21) kg year−1
Purchased food 8 62 %
proportion
Off-farm labour 6 1278 (324) hours year−1
Farm size 8 1.97 (0.28) ha
On-farm production
Home garden (ha)a 6 0.01 (0.00) ha tomato (5), Pak choy (5), cabbage (6), pumpkin (4), mustard greens (3), coriander (3), chili (3), eggplant (3),
radish (4), taro (4), mung bean peanuts (2), garlic (3), cucumber (1), lettuce (1), pepper
(4), pale sweet potato (3) soybean (1), cowpeas (0), orange (1)
flesh sweet potato (0), carrot (0)
Grasslands (ha) 4 0.04 (0.01) ha cattle (6), chicken (6), pig, (4) duck (3), Buffalo (1), pigeon (1)
Fishponds (ha) 8 0.09 (0.04) ha tilapia (8) water spinach (0)
Fruit area (ha) 8 0.55 (0.28) ha mango (8), longan (7), banana papaya (3) watermelon (2), orange (2), jackfruit (2)
(5), guava (5), pomelo (5) persimmon (1), pineapple (1)
Fields (ha) 8 1.29 (0.38) ha maize (8), spring onion (6), rice (4) French bean (6), black bean (2) cassava (3), coffee (3)
a Crop rotations evaluated on the Baseline farm: Pak choy-orange sweet potato-mustard greens-mung bean; tomato-pumpkin-soybean; peanut-cabbage-cowpeas; taro-radish; spring onion six seasons;
French bean three seasons; carrot three seasons
A model-based exploration of farm-household livelihood and nutrition indicators to guide nutrition-sensitive agriculture interventions
(solutions) that are iteratively improved using Pareto-based Finally, we compared production diversity and diet diver-
ranking (Groot et al. 2007, 2010, 2012; Groot and Rossing sity metrics across groups of alternative farm configurations
2011). The objectives were to maximize the food supply (on- using the Kruskal-Wallis test and the post-hoc Dunn’s analy-
and off-farm) necessary to satisfy the household dietary re- sis. We identified the count- and abundance-based metrics
quirement, focusing on the four potentially deficient nutrients measuring diversity in the diet or on the farm with significant-
(Vit A, Ca, Fe and Zn; the nutrient deviation NDi for each ly higher or lower values in the N + L+ and N + L– groups of
nutrient is an objective), while simultaneously improving farms; farms theoretically satisfying household dietary re-
household dispensable budget and household leisure time. quirements and leading to desirable nutritional outcomes.
Table 1 lists the objectives, decision variables, and constraints
set in this modelling study. We configured FarmDESIGN op-
timization to yield 500 solutions after 1000 iterations to ensure 3 Results
stable outcomes.
3.1 Characteristics of the baseline farm
2.5.3 FarmDESIGN outputs - alternative farm configurations
(solutions) Twelve food groups and 22 species from on- and off-farm
sources were consumed in quantities above 5 g person
We analysed the FarmDESIGN outputs at three levels. Firstly, −1 day−1 by the household in the Baseline farm (Table 2).
we assessed the general trends of the 500-alternative farm- The Baseline farm set aside 12% of the whole on-farm pro-
household configurations to identify trade-offs and synergies duction for household consumption, representing 47% of the
between the objectives. At this level, we looked at the food total (on- and off-farm sources) foods consumed. The total
consumption patterns from on- and off-farm sources (diet) and household food cost was USD 2597 year−1, which is 63% of
from the Selected Crops only (intervention). the total income.
Secondly, we identified the indicators (see the list in Large crop margins (USD 4864 year−1) contributed to a
Table 1) associated to alternative farm configurations with positive household dispensable budget in the Baseline farm.
desirable (or undesirable) livelihood or nutritional outcomes. French beans, maize and rice sales contributed to 84% of the
Alternative farm configurations with desirable livelihoods crop margin (51%, 24%, and 9%, respectively) with other
were those with a household dispensable budget and leisure contributions from vegetables (spring onion, tomato, Pak choi
time equal to or larger than in the Baseline farm (hereafter L+). and cabbage) and fruits (longan, mango, banana, pomelo, pa-
For instance, L+ farm configurations would potentially lead to paya and guava). The on-farm production allowed only
a larger dispensable budget and leisure time for non-earning 77 days free from agriculture-related activities to each one of
activities. Alternative farm configurations with desirable nu- the four households’members working on the farm across the
tritional outcomes were those with positive nutrient deviations whole year (Figs. 3 and 4).
for Ca, Fe, Vit A and Zn (hereafter N+). Thus, N+ farm con-
figurations potentially set aside enough and more nutritious 3.2 Trade-offs and synergies among multiple
on- and off-farm food to satisfy household dietary require- objectives
ments. Undesirable livelihood (L–) and nutrition (N–) values
indicate farm allocations yielding suboptimal configurations We found a trade-off between dispensable budget and house-
and resource allocations. We tested median statistical differ- hold diet, and between dispensable budget and leisure time.
ences (at p value <0.05) among groups of alternative farm Hence, increasing household dispensable budget would be
configurations (i.e. N + L+, N–L+, N + L–, N–L–) through associated with a decline in nutrient adequacy for satisfying
the Kruskal-Wallis test and the post-hoc Dunn’s analysis household dietary requirements (lower NDi for all four nutri-
(dunn.test package; Dinno 2017) in R (R Core Team 2016). ents; Fig. 3b, c, d, and e) as well as leisure time (Fig. 3a). In
Both tests are appropriate for unbalanced sample sizes. particular, nutrient deviations were drastically reduced where
Thirdly, we selected two contrasting farms from among the household dispensable budgets exceeded USD 6000 year−1,
500 alternative farm configurations to compare farm configu- i.e. three times more than in the Baseline farm (Fig. 3b, c, d,
rations and production for consumption in extreme situations. and e). Household leisure time was uncorrelated with the op-
The ‘Surplus farm’ had the maximum positive nutrient devi- timized nutrient deviations (Fig. 3f, g, h, and i). We found a
ations for the Ca, Fe, Vit A and Zn, and had a larger household synergetic increase in nutrient deviations with positive and
dispensable budget and leisure time than the Baseline farm. significant correlations among all four nutrients.
On the contrary, the ‘Deficit farm’ had the lowest nutrient Nonetheless, the degree of increase in NDi varied among the
deviations across the four optimized nutrients and a lower nutrients, with increases in Vit A and Zn more easily attained
household dispensable budget or leisure time than the than in Ca and Fe (Fig. 3j, k, l, m, n, and o). This is likely
Baseline farm. linked to the fact that food items in the local diet (including
Carmona N.E. et al.
Fig. 3 Relationships (Pearson’s
correlation coefficient and p
value) between the livelihood
outcomes and nutritional
outcomes. Livelihood outcomes
are measured as the dispensable
budget (available budget after
expenditures) and, leisure time for
non-earning activities. Nutritional
outcomes are measured as the
nutrient deviations of four likely
deficient nutrients (Iron-Fe, Zinc-
Zn, Vitamin A-Vit A and
Calcium-Ca) where positive de-
viations indicate the household
yearly nutrient requirements were
theoretically satisfied. The black
square indicates the values for the
Baseline farm, whereas other dots
represent the alternative farm
configuration colour-coded based
on the nutritional and livelihood
outcomes
Selected Crops) more commonly contribute to the daily nutri- quantity of the Selected Crops produced are set aside for home
tional requirements for Vit A (ten food items) and Zn (six food consumption (e.g. Surplus farm) allowed the household to attain
items) than Fe (three food items) and Ca (one food item) its nutritional needs, whereas farmers planting larger areas with
(Table 3). the Selected Crops (e.g. Deficit farm) and not setting aside pro-
Only a few alternative farm configurations (8% of config- duce for consumption (selling it instead) failed to attain their
urations, 46 farms) achieved simultaneous improvements in nutritional needs (Fig. 4a and d). We found that the deviations
both nutrition and livelihoods (N + L+), farm configurations for NDVitA and NDZn drastically increased from slightly larger
potentially representing win-win situations. An additional 7% than zero in the Baseline farm to a surplus of 100% to 200% in
of the configurations (31 farms) yielded desirable nutritional the alternative farms. This suggests a twofold or larger increase in
improvement although they reduced household dispensable the supply of those nutrients than what the household requires.
budget or leisure time (N + L–). The remaining 85% of the Fe, however, would remain insufficient (20% below the house-
configurations (423 farms), yielded undesirable nutritional hold requirements) regardless of the food consumed from on-
outcomes where Ca and Fe requirements were unmet, and off-farm sources (Fig. 3j, k, and l) or the quantity of the
resulting in negative NDCa and NDFe (Fig. 3l). Selected Crops’ production set aside for consumption (Fig. 4d);
and despite the current consumption of fish and seafood, and
3.3 Contribution of selected crops to livelihoods meat and poultry (Table 3, Appendix 3). If the farm-household
and nutrition set aside >700 g person −1 day−1 of the production of Selected
Crops for consumption it could theoretically satisfy their calcium
The quantity of Selected Crops produced and set aside for con- needs (positive deviations), given the modelling assumptions
sumption is a better indicator for measuring nutritional contribu- (Fig. 4b; Table 3). Although the Selected Crops represent five
tion than the area allocated to Selected Crops. Our modelling different food groups, including dark green leafy vegetables, nuts
study suggests that the farm-household configurations where a and seeds, other vegetables, pulses, vitamin A fruits, and vitamin
A model-based exploration of farm-household livelihood and nutrition indicators to guide nutrition-sensitive agriculture interventions
Fig. 4 Values for the seven
maximized objectives across
alternative farm configurations
given the area allocated to
planting the Selected Crops in the
home garden and the quantity of
Selected Crops production set
aside for household consumption.
Nutrient deviations for vitamin A
(Vit A), zinc (Zn), calcium (Ca)
and iron (Fe) (a, b), household
(HH) dispensable budget (c, d)
and household leisure time (e, f).
Each point represents an alterna-
tive farm configuration
Avegetables (Table 3), it remains uncertain if it would be feasible in significantly larger contributions from on-farm (including larg-
to consume >700 g person −1 day−1 of the Selected Crops. er proportion of Selected Crops production set aside for house-
Besides, a large quantity of Selected Crops’ production for con- hold consumption) and significantly smaller crop gross margins
sumption (>700 g person −1 day−1) would lead to household from selling on-farm production in N + L+ and N +L– farm
dispensable budget reductions from ~USD9,000 year−1 to configurations (Figs. 5b-d). In the latter farms, the household
USD3,500 year−1 (Fig. 4e). Overall, the trade-off between nutri- food cost represented around 67%of the total income, suggesting
ent deviation and household dispensable budget was less pro- that satisfying household dietary requirements demands incur-
nounced for alternative farmswith household dispensable budget ring substantial costs (Fig. 5e).
values like or slightly larger than the Baseline farm (Fig. 4e). The However, the total food costs in N +L+ and N+L– alterna-
trade-off between Selected Crops production for household con- tive farmswas only 1.1 times larger than in the Baseline farm due
sumption or income generation suggests high profitability for the to the increased crop gross margin, whereas the total food con-
Selected Crops if they are not consumed by the household and sumption from on- and off-farm sources was 1.7 times more in
are instead sold at the market. N +L+ and N+L– alternative farms compared to the Baseline
Home-garden areas on the alternative farms occupied farm (Fig. 5a and e). This suggests that the income generated
around 4% (Standard Error - SE = 0.03) of the whole farm from selling some of the production of the Selected Crops with
area, whereas the Selected Crop areas only occupied between high market value could potentially contribute to covering the
10 and 40% of the home-garden area, despite their potential cost of achieving desirable nutritional outcomes (Fig. 5b).
economic and nutritional contribution (Fig. 4b, and e). Although the optimization excluded environmental objec-
Alternative farms with Selected Crop areas occupying more tives, our results suggest that organic matter from crop residues
than 30% of the home-garden area tended to have a larger was significantly larger in N +L+ and N+L– alternative farms,
household dispensable budget and more leisure time, because yet lower than the Baseline farm (Fig. 5i), indicating a likely
some of the Selected Crops are less labour-intensive and gen- negative effect on soil quality after home garden diversification
erate larger income than others (Fig. 4b and c). with the crops selected using a participatory approach.
3.4 Synergies between nutrition and livelihoods 3.5 Comparison of nutrition-related metrics
Farm configurations with desirable nutritional outcomes (i.e. Alternative farm configurations significantly increased the on-
N +L+ and N+L–) originated from increasing food consump- farm production diversity for consumption in relation to the
tion mainly from on-farm production rather than from off-farm Baseline farm (Fig. 6). Farms with desirable nutritional out-
sources such as the market (Fig. 5a, d and g). This was reflected comes (N + L+ and N + L– farms), meaning the farms that
Carmona N.E. et al.
Fig. 5 Range of variability across indicators and grouped alternative farm Dunn’s test significance at p value <0.05). SC=Selected Crops, OM=
configurations with desirable (+) or undesirable (−) Nutritional (N) or organic matter, HG = home garden (including Water Spinach area in the
Livelihood (L) outcomes. The different symbols along the right-hand Fishpond), H Shannon index. Dark blue areas indicate mean values along
vertical axis indicate significant differences among groups (Post-hoc the distribution of the alternative farms (points)
could satisfy the household dietary requirements for at least dietary reference intakes for YCa, YFe and YVitA (Fig. 6j, k, l,
three nutrients (Vit A, Ca and Zn), scored significantly larger and m). The large contributions on Zinc were linked to maize,
median values for the metrics NFDD, NFDP, HD
, D
D (Fig. 6b, which contributes >15% of the daily values for this nutrient. Yet
g, h and i). The abundance metrics, HP and DP
, were not maize production is mainly used to feed the livestock and is not
significantly different among farm groups when measuring consumed by household members as part of their daily diets
on-farm production diversity for household consumption, al- (Table 1). Likewise, the nutrient yield results indicate that the
though most of the N + L+ farms tended to have larger HP whole farm production could satisfy the Fe dietary reference
values (Fig. 6c and d). This suggested that in this context, intakes for ~25 reference adults (YFe) on average in N + L+
the functional diversity metrics (a more recently proposed and N + L– farms although the nutrient deviation indicated
metric) tend to capture the contribution to household dietary (NDFe) household Fe requirements remained unmet (NDFe)
requirements better than richness and abundance metrics (Figs. 6l and 3d). The large YFe values were linked to unfeasible
when measuring on-farm production diversity. consumption levels for Fe (e.g. 1057 g ofmaize in a day to satisfy
Measuring the nutrient yield for the whole production could Fe daily requirements). For instance, we found that using Yi on
lead to misguiding results. The Yi metric, which captures the only one nutrient can also lead to misguiding conclusions, since
contribution of the whole farm production (e.g. food produced farms with desirable nutritional outcomes (N +L+ and N+L–)
for animal feed), indicated that increasing the number of refer- included farm configurationswhere the trade-off among nutrients
ence adults with covered yearly dietary reference intakes for YZn was minimized with significantly smaller median values for Ca
reduced the number of reference adults with covered yearly
A model-based exploration of farm-household livelihood and nutrition indicators to guide nutrition-sensitive agriculture interventions
Fig. 6 Range of variability across human nutrition metrics values and farm sources of food for household consumption, whereas production (P)
grouped alternative farm configurations given desirable (+) or metrics only consider on-farm production for household consumption.
undesirable (−) Nutritional (N) or Livelihoods (L) outcomes. Different Refer to Fig. 2 for metrics’ description (see also Appendix 1). Dark blue
symbols indicate significant differences among groups (Post-hoc Dunn’s areas indicate mean values along the distribution of the alternative farms
test significance at p value <0.05). Diet (D) metrics consider on- and off- (points)
and Fe, and significantly largermedian values for Zn in particular farm exemplified a farm configuration leading to a win-win
(Fig. 6j, k, and l). situation where both nutrition and livelihoods would be im-
proved (hence an N + L+ farm). There was an improvement in
3.6 The surplus and deficit alternative farms: Sell it both dimensions despite the large contribution of on-farm
or eat it? production for household consumption and the large food cost
(Fig. 5a, d, g, and e). Home garden area and crop area even-
The selected extreme farm configurations of Surplus and ness (HHG) were similar in both Surplus and Deficit farms.
Deficit showed potentially contrasting pathways for home (Fig. 5f). Nonetheless, the larger crop labour required in the
garden diversification in the Doan Ket context. The Surplus
Carmona N.E. et al.
Deficit farm is likely linked to the larger areas planted with functional diversity (NFDP) and larger evenness crop produc-
carrots and French beans (Figs. 5h and 7). tion for consumption (HP
, D
P) than the Deficit farm (Fig. 6a, b,
The Deficit farm is the extreme example of a farm config- c, and d). This modelling study shows how measuring diver-
uration maximizing income over nutritional contribution and sity only on the production side (SRP, NFDP, HP, DP) is there-
even choosing a cheaper diet than in the Baseline farm (hence fore limited and not suitable for measuring the nutritional
an N–L– farm) (Fig. 5a, d, and g). For example, the food cost contribution of NSA interventions. The production diversity
in the Deficit farm represents 59% of the total farm-household metrics are particularly limited in the cases where farm-
expenditures, whereas the Baseline Farm and Surplus farm households opt for selling their diversified production rather
had larger values (62% and 68% respectively) (Fig. 5e). than consuming it.
Despite the lowest food cost in the Deficit farm, it set aside The larger crop gross margins in the Deficit farm originated
a larger quantity of food for consumption from on- and off- from the sale of mostly the Selected Crops such as water
farm sources of 1683 g person −1 day−1 compared to the spinach (dark green leafy vegetables), soybeans (pulses), cow-
Baseline farm (1393 g person −1 day−1), yet less than the peas and spring onions (other vegetables) (Fig. 7). The areas
Surplus farm (2454 g person −1 day−1). The Surplus farm under cultivation for those crops were similar in both farms.
consumed an extra food group (HDDS) and one additional Nonetheless, the quantity of Selected Crops’ production set
species (SRD) with more even distributions among food items aside for household consumption was at least four times
(HD
, D
D) and larger functional diversity (NFDD) than in the smaller in the Deficit farm than in the Surplus farm (Fig. 7).
Deficit farm (Fig. 6e, f, g, h, and i). The differences in pro- Other sold crops had larger planted areas in the Deficit farm
duction diversity were smaller, where the Surplus farm had the than in the Baseline and the Surplus farms, yet consumption
same number of species for consumption (SRP), slightly larger remained similar or lower (i.e. Selected Crops: carrot, French
Fig. 7 Changes between the
Baseline farm (zero X-axes) and
the Surplus and Deficit farms. *
indicates Selected Crops. Papaya
areas in the Surplus farm =
3521 m2 and the Deficit farm =
5425 m2
A model-based exploration of farm-household livelihood and nutrition indicators to guide nutrition-sensitive agriculture interventions
and guava). The Deficit farm reduced meat and poultry con- while exploring potential intervention (i.e. home garden diver-
sumption from the on-farm (chicken) and off-farm (pork) sification) pathways. Lastly, we discuss how FarmDESIGN
sources (Fig. 7). The Deficit farm also reduced the consump- facilitates testing the robustness of the different metrics
tion of other fruits (banana and guava) from on-farm produc- linking on-farm production and household dietary require-
tion, while it increased the consumption of other vegetables ments in agriculture-nutrition projects.
(mainly tomato) from off-farm sources. The reduced tomato
area planted and the increment in the quantity of tomato pur- 4.1 NSA interventions and potential development
chased reflected that buying tomato was cheaper than produc- pathways
ing it in the Doan Ket and modelling context (Fig. 7).
The Surplus farm increased its production for household Interventions such as home-garden diversification are com-
consumption ofmost of the Selected Crops, except for peanuts mon in Vietnam. Starting in 1986, the Vietnamese govern-
and pumpkin, which remained aligned with the number of ment actively promoted these interventions under the Doi
Selected Crops set aside in the Baseline farm (Fig. 7). The Moi policy; the Garden-Pond-Livestock system (VAC:
Surplus farm increased its consumption of other fruits (guava Vuon-Ao-Chuong in Vietnamese) (Luu 2001). The VAC sys-
and banana) and other vegetables (mostly cowpea and spring tem contributes between 30 and 60% of the total household
onion), while it reduced consumption of tomato from on-farm income (Trinh et al. 2003). In North Vietnam, home-garden
production. Contrary to the Deficit farm, the Surplus farm production contributes on average 13% of the household total
increased its consumption of pork (Fig. 7). income (Trinh et al. 2003), which is in line with the 16%
The area planted to Papaya (vitamin A-rich fruits) expand- estimated for the Baseline farm in this modelling study.
ed greatly in both Deficit and Surplus farms, occupying al- Nonetheless, food production for household consumption
most the whole fruit area in the Deficit farm (0.54 ha) albeit from the diversified farming system is insufficiently contrib-
with quantities set aside for household consumption that were uting to the nutrient deficiencies identified in the Baseline
nonetheless three times smaller than in the Surplus farm (Fig. farm and reported in the general nutrition survey 2009-2010
7). Ripe papaya has the highest values for the optimized nu- (NIN 2012). Likewise, maximizing income generation over
trients (Ca, Fe and Vit A) among the farm perennial fruit nutrition is a trend reported in the study region where home
plants, hence the preference to plant such large papaya areas gardens are transitioning from subsistence-oriented towards
across all alternative farm configurations. Papaya, banana and more profitable and commercial oriented home gardens, re-
guava cultivation areas expanded at the expense of mango, ducing species diversity and limiting the contribution to
longan and pomelo areas. In both farm configurations household nutrition (Mohri et al. 2013). Implementing an
(Deficit and Surplus) compared to the Baseline farm, tofu (a NSA intervention in this region thus demands careful plan-
refined soybean product sourced off-farm) consumption re- ning to maximize the likelihood of adoption, and to avoid the
duced whereas soybean consumption from on-farm produc- likely negative consequences of such interventions (e.g. re-
tion increased due to soybeans larger content of the optimized ductions in organic matter from crop residues), as indicated
nutrients and lower cost. by this modelling study.
Crop diversification in the home gardens in tandem with
other activities such as promoting other naturally occurring
4 Discussion vegetables and educational and promotional interventions
could increase the successful adoption of the Selected Crops
The global commitment to end malnutrition through nutrition- for desirable nutritional outcomes and better livelihoods. The
sensitive agriculture (NSA) requires the use of robust participatory selection and promotion of nutritious crops is
methods, models, and metrics that disentangle the complex novel in the area and responds to the expectation and interest
relationship between agriculture- and nutrition (Herforth and of the community. Although, promoting other naturally occur-
Ballard 2016). The use of whole farm-household models en- ring vegetables in the region with larger nutritional contribu-
ables ex-ante assessments of the potential trade-offs and chal- tions (Ogle et al. 2001) could help to fill the nutritional gaps of
lenges that NSA interventions could pose to a farm-house- the Selected Crops (e.g. low iron or calcium contributions).
hold. Farm-household models also enabled us to estimate Empirical evidence in Bangladesh indicates the feasibility of
the potential contribution of agricultural interventions towards tripling home garden production and vegetable consumption
satisfying household dietary requirements and improving (Ferdous et al. 2016). In this modelling study we estimated
household livelihoods. In this study we showed the applica- that improving household nutrient adequacy could be reached
bility of the new ‘Household Nutrition’ module included in by almost doubling on-farm contributions to household con-
the expanded FarmDESIGN model for estimating several sumption, although the large consumption of the Selected
metrics linked to diet and nutrition. We discuss how the ex- Crops for nutritional outcomes (i.e. calcium - Ca) remains to
ante analysis could facilitate designing NSA interventions be tested. For instance, increasing the consumption will
Carmona N.E. et al.
require changes in both crop production and consumption configurations and food allocations for household consump-
behaviour and preferences to make the most of the nutrient tion from on- and off-farm sources. We compared the metrics
potential of the new or underutilized Selected Crops (e.g. values against the nutrient deviation; a theoretical assessment
pumpkin and sweet potato leaves, orange flesh sweet potato). of the contribution to household dietary requirements. We
Hence there appears to be a key role to be played by educa- found that the usefulness of SRD was limited in the context
tional and promotional interventions (Berti et al. 2004; Ruel of Doan Ket even though it was proposed as an appropriate
and Levin 2000). metric for measuring food diversity in individual diets and
Working with the communities on food preferences and nutritional adequacy of diets (Lachat et al. 2017). The SRD
nutrition awareness could help farmers to soften the trade-off and the HDDS performed poorly given the non-significant
between nutrition and income. For example, this modelling differences between farms that could and could not satisfy
study confirms that the market opportunity of the Selected the household dietary requirements for vitamin A, calcium
Crops (e.g. large crop gross margin) could help to cover the and zinc. It is important to note that household-level metrics
larger food costs linked to desirable nutritional outcomes. This of diet are often associated with household food access, rather
is in line with Greiner (2017) who found that a food-based than dietary quality, and hence nutrition of individuals –
approach1 is a cost-efficient strategy to improve nutritional which might explain the poor performance of SRD which
status, particularly in areas where multiple nutrients are defi- was validated as an individual, rather than a household-level
cient. Nonetheless, our results also suggest that home-garden indicator. We found that other metrics such as functional di-
diversification could also lead to maximize income generation versity and Shannon’s and Simpson’s diversity indexes for the
rather than nutritional outcomes, as is already the case in the household diet (on- and off-farm sources) (NFDD, HD, DD)
area. Maximizing income does not necessarily result in more performed better at predicting desirable nutritional outcomes
and nutritious food available from the market. For example, in Doan Ket. All those metrics scored significantly higher
despite the diversity of food items outsourced from the market median values among farm configurations with desirable nu-
(18 food items) in the Baseline farm, the alternative farm tritional outcomes (N+).
configurations seldom reduced the on-farm production for On the contrary, only one metric measuring on-farm pro-
household consumption to replace these with food from the duction diversity for consumption, the nutritional functional
market. On the contrary, few food items outsourced from the diversity (NFDP), was significantly larger in farms with desir-
market were replaced by on-farm production due to larger able nutritional outcomes. Functional diversity metrics have
nutrient content and lower cost in the modelling study. This been proposed recently to measure production diversity (e.g.
suggests that only improving market access for subsistence Remans et al. 2011; DeClerck et al. 2014), whereas richness
farms as a promising livelihood and development strategy as and abundance metrics to measure production diversity are
suggested by Sibhatu and Qaim (2018) could be limited and used more commonly (See Appendix 1). Hence, the NFDP
context-dependent. seems a promising metric that needs to be tested in a wider
range of farm-household contexts. Our results also support
4.2 Robustness of metrics for comparability that H tends to be more sensitive to rare species than D (Peet
across NSA interventions 1974). For instance, H could be more appropriate in cases
where diets and on-farm production are dominated by “rare”
A wide range of metrics is commonly used to assess dietary species and food items.
diversity and on-farm production diversity (Herforth and The farm nutritional yield (Yi) metric is a novel metric able
Ballard 2016; Sibhatu and Qaim 2018). The linkages or asso- to capture the balanced production of nutrients. It does, how-
ciations between dietary and on-farm production diversity are ever, require careful interpretation if the destination of the
analysed using different metrics and at different scales (e.g. produced food is unknown or if analysed only for one nutrient.
Berti 2015; Sibhatu et al. 2015). Therefore, the need for sys- The Yi metric considers the whole farm production, which
tematic assessment of the robustness and applicability of the could bias the real contribution to human and household nu-
different metrics under different contexts, scales and socio- trition. Similarly, greater Yi values for a certain nutrient could
ecological settings is increasingly recognized (Powell et al. be misleading, since we found that farm configurations with
2015; Herforth and Ballard 2016). Identifying agreed-upon desirable nutritional outcomes had greater YZn values and
and robust metrics to measure nutritional outcomes at the lower YCa, YFe, and YVitA values than farm configurations
farm-household level will allow comparability across NSA with sub-optimal nutritional outcomes.
interventions and contexts.
In this modelling exercise, we compared diverse and com- 4.3 Study limitations
monly used metrics in NSA interventions across farm
This modelling study is supported by an intense data collec-
1 http://www.fao.org/ag/agn/nutrition/household_food_en.stm tion aiming to capture farming systems and food consumption
A model-based exploration of farm-household livelihood and nutrition indicators to guide nutrition-sensitive agriculture interventions
across eight households in Doan Ket. Data collection was part towards attaining household nutritional needs. Capturing per-
of the pilot phase of the project in order to test the effective- formance indicators (and their interactions) across the diverse
ness of the intervention and guide it rather than to inform farm-household domains helps to foresee trade-offs, synergies
policy recommendations. Therefore, the Baseline farm prop- and unintended consequences of an intervention. For exam-
erly represents a considerable portion of farm-households in ple, the potential adverse environmental effect from the reduc-
the village, yet the results from this modelling study ignore tion of organic matter and the trade-off between household
other farm-household types that are less common in the area nutrition and dispensable budget due to the high market value
with production systems dominated by the excluded crops of the Selected Crops in Doan Ket. This information can con-
(e.g. coffee or cassava; Table 1). A larger sampling effort is tribute to designing and identifying complementary interven-
therefore suggested for a more comprehensive characteriza- tions that will improve the positive effect of the NSA inter-
tion of the impact of the intervention across the diverse farm- vention. Moreover, the easy and simultaneous calculation of
household types that characterise the region. Similarly, the several metrics estimating production diversity, diet diversity,
available food consumption data for Doan Ket was particular- nutrient supply, and nutrition adequacy will facilitate identify-
ly limited since it is based on one weekly dietary recall, which ing the most robust metrics to infer an intervention’s contribu-
is more subjective to recall error (Kennedy et al. 2011). tion to household dietary requirements across contexts.
Nonetheless, our comparison with the regional averages indi- Among the metrics tested here, the most robust metrics includ-
cates similar consumption levels at the food group level (See ed the count-based nutritional functional diversity (production
Appendix 3). The ‘Human Nutrition’ module is potentially for consumption and diet) as well as the abundance-based
overestimating consumption or nutrient intake since food Shannon-Weaver and Simpson’s diversity indexes (diet).
waste is currently not being captured by FarmDESIGN. Similarly, we found the farm system yield to be a novel metric
Another inherent limitation of the model is the yearly analysis, that requires careful interpretation.
which underestimates issues related to seasonal food availabil- The global commitment to ending malnutrition through
ity, food price seasonality and intra-household food NSA requires the use of multiple transdisciplinary, holistic
distributions. and system-oriented approaches. Models such as the farm-
Despite the considerable efforts in collecting accurate data, household model presented here facilitate and foster commu-
the modelling effort is potentially underestimating the species nication among the multiple disciplines involved in NSA by
richness in on-farm production and household diets. For ex- presenting a set of clearly articulated and tested metrics that
ample, ethnobotanical studies identified 38.6 species on aver- can be used to measure both production and nutritional out-
age per home harden in Northern Vietnam (Vlkova et al. comes, two sides of the food system that for so long have been
2011). Identifying home garden species through food or crop operating in parallel without harnessing their joint potential.
recalls may ignore other important crop and varietal species
used for household consumption, as well as species used for Acknowledgments We thank the CGIAR Research Programs:
other purposes such as medicine, firewood, fodder, materials Humidtropics; Agriculture for Nutrition and Health (A4NH); Roots,
Tubers and Bananas (RTB); Water, Land and Ecosystems (WLE) and
for construction or crafts (Sêdami et al. 2017; Vlkova et al. all the donors who supported this research through their contributions to
2011). Despite these limitations, here we show how the ex- the CGIAR Fund. For a list of Fund donors, please see http://www.cgiar.
panded FarmDESIGN model facilitates measuring the impact org/our-funders/. BesidesWageningen University & Research also
of interventions by easily looking at the whole system (e.g. provided strategic funds, under the program ‘Global One Health.’ We
are grateful to the Vietnamese Fruit and Vegetable Research Institute
diets), a subset of the system (e.g. Selected Crops) and at (FAVRI), Dr. Pham Hoi, Son Nuygen, and Lan Huong from CARES-
alternative farm-household configurations with contrasting Vietnam who facilitated, translated and provided logistical help during
strategies for predicting likely farm-household trajectories. data collection. We are most grateful to the Vietnamese farmers for their
Besides the wide range of performance indicators across time and patience in supporting this research. We thank Olga Spellman
(Bioversity International Science Writing Service) for English and tech-
farm-household domains, which facilitates measuring unex- nical editing of this paper.
pected impacts (e.g. reduction on leisure time) and the calcu-
lation of different metrics commonly used in NSA.
Compliance with ethical standards
5 Conclusion Conflict of interest The authors confirm that they have no conflict of
interest.
We applied a farm-household model to evaluate the effects of Open Access This article is distributed under the terms of the Creative
a nutrition-sensitive agriculture (NSA) intervention (i.e. Commons At t r ibut ion 4 .0 In te rna t ional License (h t tp : / /
creativecommons.org/licenses/by/4.0/), which permits unrestricted use,
home-garden diversification) on representative farming distribution, and reproduction in any medium, provided you give
households from Doan Ket, Vietnam. FarmDESIGN facili- appropriate credit to the original author(s) and the source, provide a link
tates estimating the potential contribution of an intervention to the Creative Commons license, and indicate if changes were made.
Carmona N.E. et al.
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Carmona N.E. et al.
Natalia Estrada-Carmona is a Carl Timler holds a Masters in
Postdoctoral Fellow at the Organ ic Agr icu l tu re f rom
Farming Systems Ecology Wageningen University. He is
G r o u p , Wa g e n i n g e n a n d currently a Ph.D. candidate in
Research; and at theProductive the Farming Systems Ecology
and resilient farms, forest and G r o u p a t Wa g e n i n g e n
landscapes initiative, Bioversity University. His primary research
international. She focuses on esti- interests include smallholder
mating, through modeling and farming systems analysis, whole-
pa r t i c ipa to ry app roaches , farm bio-economic modeling,
agrobiodiversity’s contribution to sustainable and ecological intensi-
multifunctional farms and land- fication, and farm(er) diversity.
scapes. Natalia has contributed to His experiences lie mostly in
projects in Latin America and the Eastern and Southern Africa.
Caribbean (Costa Rica, Cuba, and
Colombia), Africa (Zambia and Burkina Faso) and Asia (Viet Nam). She
has a Ph.D. in natural resources management from the University of Shantonu Abe Chatterjee is a
Idaho, US and CATIE and holds a MSc on environmental socio- Ph.D. Candidate and Research
economics from CATIE, Costa Rica. Assistant at the Institute of
Geography, Unive r s i ty o f
Cologne. His research interests
Jessica Raneri is a Research cover agroecological production
Support Officer at the Nutrition systems in Japan and India, sys-
& M a r k e t i n g D i v e r s i t y tems analysis in agriculture, and
P r o g r a mm e , B i o v e r s i t y regional food supply chains, as
International. She is involved in well as the ethnobotany of ‘for-
designing and implementing gotten’ vegetables. He has a
‘Agricul tural Biodiversi ty, Master ’s Degree in Organic
Nutrition and Dietary’ assess- Agriculture from Wageningen
ments using participatory, qualita- University.
tive and quantitative methods.
She is currently is leading a pro-
ject in Vietnam designed to im- Lenora Ditzler is a Graduate
prove dietary diversity through Research Assistant with the
an integrated systems perspective. Farm Systems Ecology Group at
J e s s i c a a l so suppo r t s t he Wageningen Univers i ty &
Sustainable Diets activities and believes that it is crucial to understand Research. She works with a range
how biodiversity can be utilized to improve the sustainability of food of farming systems analysis and
systems and quality of diets. design tools, including whole-
Jessica started her career as a Clinical Nutritionist in Australia and farm models and participatory
moved into international research-for-development in 2010. At methods, and has contributed to
Bioversity International, she began working on the Conservation & projects in India, Bangladesh,
Availability research areawithin the scope of ex situ conservation, follow- Vi e t n am , E t h i o p i a , a n d
ed by working with the DDG office developing and validating a ‘Rapid Zimbabwe. In her current re-
Biodiversity Assessment’ method in Ghana. search, she is examining the ef-
fects of spatial, temporal, and ge-
netic crop diversity on the deliv-
Stephanie Alvarez has a post- ery of agroecosystem services in European arable farming systems.
doctorate position at Wageningen Lenora holds a MSc in Agroecology from the Norwegian University of
University, as part of the Farming Life Sciences.
Systems Ecology team. She is
currently involved in the CGIAR
research programme “Integrated
Systems for the Humid Tropics”
and the FAO project “Supporting
smallholder farmers in southern
A f r i c a t o b e t t e r m a n a g e
climaterelated risks to crop pro-
duction and post-harvest han-
dling” . She has previously
worked for the (French institute)
CIRAD on the modelling and
analysis of crop-livestock systems in Brazil and Madagascar.
A model-based exploration of farm-household livelihood and nutrition indicators to guide nutrition-sensitive agriculture interventions
Gina Kennedy is a Scientist with Karin Borgonjen-van den Berg
the Nutrition and Marketing is a Research Diet ic ian at
D i v e r s i t y P r o g r amme o f theDivision of Human Nutrition,
Bioversity International. For the Wageningen University. Her ex-
past ten years, she has worked pertise covers Human Nutrition
on nutrition assessment in devel- and Health, Dietary assessment,
oping countr ies , including Food-based dietary guidelines,
assessing the contribution of agri- Dietetics.
cultural biodiversity on nutrient
i n t a k e . P r i o r t o j o i n i n g
Bioversity, she worked for the
Nutrition Division of FAO on
food-based indicators for use in
food and nutrition security pro-
grams, nutrition assessment and
nutrient requirements. She has a Ph.D. in public health nutrition from
Wageningen University and her Master of Public Health from the Elise F. Talsma (Ph.D.) is
University of Alabama, Birmingham. Ass is tant Professor a t the
Division of Human Nutrition.
Her research focuses on improv-
Roseline Remans is an associate ing food and nutrition security
r e s e a r c h s c i e n t i s t a t t h e and dietary quality of women,
Agriculture and Food Security children and adolescents, apply-
Center, the Earth Institute, ing a food system approach in
Columbia University. Her re- low and middleincome countries.
search focuses on synergies and
tradeoffs between agriculture, the
environment, and human nutri-
tion. Roseline has a masters and
Ph.D. in biosystems engineering
from the University of Leuven,
in collaboration with CIAT,
Colombia, the National Soils Jeroen C. J. Groot specialized in
Institute, Cuba and El Centro fa rming sys tems analys i s ,
Internacional de Fijacion de modelbased landscape planning
Nitrogeno (CIFN), Mexico. She worked for two years at CIFN, Mexico and design, and participatory
as project coordinator and scientist. After her Ph.D., she joined the Earth modeling and gaming. He holds
Institute supported by a European Marie Curie scholarship. a Ph.D. in Agronomy and MScs
in grassland science, animal phys-
iology and tropical animal hus-
Inge Brouwer is Associate bandry. He performed post-doc
Professor at the Division of research in national and interna-
Human Nutrition, Wageningen tional projects concerning nutrient
University. Her research focuses cycling, modeling of sustainabili-
on improvement of the dietary qual- ty indicators and design of mixed
ity of (young) women and children, farms in multifunctional land-
in low andmiddle-income countries scapes dominated by dairy farm-
through agricultural or food systems ing systems. In the EULACIAS project he coordinated model develop-
approaches, with special emphasis ment in a cooperative team effort with universities in Mexico, Uruguay
on micronutrient deficiencies. She and Argentina. Currently, Dr Groot has a coordinating role in integrated
has acquired many research funds, farming systems analysis addressing multi-scale issues of productivity,
from national funding programmes, natural resource management, human nutrition and gender equity for
from EU FP6 and FP7 Framework CGIAR Research Programs. Project sites are located in Asia (e.g.
Programmes as well as from food Nepal, Bangladesh), Africa (e.g., Ghana, Tanzania, Ethiopia, Malawi)
industries. She coordinated a large and Latin America (e.g., Ecuador, Uruguay and Mexico).
FP7 research programme INSTAPA focusing at the improvement of nutrition
through enhancing staple crops (sorghum, millet, maize, and cassava) in
Africa through biofortification, fortification and post-harvest processing in-
volving over 40 scientists from European and African countries. She super-
vised research programmes of more than 20 international Ph.D. fellows in
Africa andAsia. At themoment she is the leader of the A4NHFlagship Food
Systems for Healthier Diets research programme in collaboration with the
CGIAR research institutes.