Seven sub-sessions
Chair: Stuart Gillespie, International Food Policy Research Institute (IFPRI)
Speakers and presentations
Jessica Heckert, IFPRI
Development and Validation of a Health and Nutrition Empowerment Module for the Women’s Empowerment in Agriculture Index
Slides/Recording
Fiorella Picchioni, University of Reading
Seasonal Patterns of Energy Expenditure, Time-Use, and Food Intake: An Intra-Household Perspective from Rural Ghana
Slides/Recording
Caitlin Herrington, IFPRI/HarvestPlus
Country Prioritization for Biofortified Crop Interventions across Africa, Asia, and Latin America
Slides/Recording
Christopher Turner, London School of Hygiene and Tropical Medicine (LSHTM)
Investigating Food Environments in Low- and Middle-Income Countries Using a Novel Qualitative Geographical Information Systems (Q-GIS) Approach: A Case Study from Telangana, India
Slides / Recording
Sera Young, Northwestern University
Development and Testing of a Household Water Insecurity Measure that is Equivalent across Countries
Slides / Recording
Jacob Mazalale, University of Malawi
Designing a Discrete Choice Experiment to Understand Food Choice Based on Maize Price Variations in Rural Malawi
Slides/Recording
Aurelie Bechoff, Natural Resources Institute
NUTRI-P-LOSS: A Methodology to Estimate Nutritional Postharvest Losses
Slides/Recording
Q&A
Recording
Development and Validation of a Health and Nutrition Empowerment Module for the Women’s Empowerment in Agriculture Index
Jessica Heckert, IFPRI
Introduction
The Women’s Empowerment in Agriculture Index (WEAI) (Alkire et al, 2013) is widely used to measure women’s empowerment in the agricultural sector. The current phase in advancing the WEAI includes adapting it to measure impacts at the project level (pro-WEAI) and developing a module to measure aspects of women’s empowerment related to health and nutrition outcomes. Herein we draw on baseline data from the Gender Assets and Agriculture Project, Phase 2 (GAAP2), a portfolio of agricultural development projects, to validate the pro-WEAI health and nutrition module and develop indicators of women’s empowerment in making decisions related to health and nutrition.
Methods
Baseline surveys from the impact evaluations of five GAAP2 projects (2016-2017) administered the pro-WEAI health and nutrition module to women: TRAIN (n=5,040) and FAARM (n=287) in Bangladesh, SE LEVER (n=1,777) and Grameen Foundation (n=380) in Burkina Faso, and World Vegetable Center (n=713) in Mali. Respondents were asked about decision-making on key topics related to their general health and nutrition (six items) (e.g., consulting a doctor when ill), their health and nutrition while pregnant and/or breastfeeding (11 items) (e.g., eating specific foods when pregnant/breastfeeding), and their child’s health and nutrition (13 items) (e.g., feeding a sick child). For each topic, the woman was first asked which household members generally make the decision, and if jointly, the extent to which she participates in the decision (none, small, medium, or high). Additionally, respondents were asked about the purchasing and ease of acquisition of 12 health and nutrition related products (e.g., specific foods, medications/vitamins/supplements).
We conducted exploratory factor analysis (EFA) for a randomly selected half sample from the two largest samples, one from each region (TRAIN and SE LEVER). Based on the proposed factor structure, we will conduct confirmatory factor analysis (CFA) for the remaining sample and the projects with smaller sample sizes.
Findings and Interpretations
Initial descriptive analyses revealed that nearly all respondents reported participating in these decisions and that doing so to a medium or high extent was correlated with characteristics normally correlated with empowerment (e.g. age). Based on this, we classified a woman as having adequate input into a decision if she was the sole decision maker or reported participating to at least a medium extent in joint decisions. Binary variables, indicating adequate input, were constructed for each decision and used for EFA and CFA.
Preliminary results from the EFA using data from TRAIN Bangladesh suggest a four-factor structure for the health and nutrition module with the following domains: purchasing decisions, product acquisition, general health and nutrition decisions, and feeding children animal-sourced foods. Continued analysis will proceed by 1) further exploring the factor structure in the TRAIN data, 2) using EFA to explore the factor structure of the module in SE LEVER (Burkina Faso) data, and 3) using CFA to confirm the proposed factor structure using the other randomly selected half samples from the TRAIN and SE LEVER baseline survey and data from the other GAAP2 projects (FAARM, Grameen Foundation, and World Vegetable Center).
Conclusions
Many agricultural development projects aim to both empower women and improve nutrition and health outcomes. Yet currently, there are no standardized metrics to measure women’s empowerment in nutrition and health domains. Development of the WEAI, until now, has not adequately considered the barriers faced by women in making strategic decisions in the areas of health and nutrition (Alkire et al. 2013). Limitations faced by women in these domains are distinct from those experienced in the productive sphere. Increased income and/or production of nutrient-rich foods may not necessarily translate into consumption of these foods by women and children within the household unless women are also empowered to make health- and nutrition-related decisions. The pro-WEAI health and nutrition module has the potential to improve how we measure the full scope of impacts of nutrition-sensitive agricultural development projects and to explain how such programs might lead to improved health and nutrition outcomes.
Seasonal Patterns of Energy Expenditure, Time-Use, and Food Intake: An Intra-Household Perspective from Rural Ghana
Fiorella Picchioni, University of Reading
Introduction
The adverse effects of seasonal hunger on rural livelihoods in low and middle-income countries (LMICs) are widely recognised. Yet, seasonal pressures on labour availability and time use within the household are neglected dimensions in the analysis of food security, nutrition, and health. We fill this gap investigating seasonal intra-household variations of energy expenditure data using accelerometry devices together with individual data on time-use and food intakes.
Methods
Accelerometry devices are widely used in the context of High-Income Countries to examine pathways between non-communicable diseases (NCDs) and physical activity. This paper describes a novel methodology - developed in an IMMANA funded project - that adapts such technologies to improve the empirical measurement of energy expenditure associated with agricultural activities in low income countries contexts.
The study explores the use of accelerometer devices in rural Northern Ghana, across two agricultural systems (irrigated and rain-fed) to account for the role of technology in agricultural production. Data from accelerometry devices, worn for four non-consecutive weeks by two members of 20 households across a full agricultural cycle, are complemented with information on dietary intake and time-use.
Findings and Interpretations
The methodology developed can provide a robust and comprehensive delineation of gender-differentiated energy expenditure across agricultural seasons and production systems. 26,880 hours of detailed data suggest that rural population in Northern Ghana face challenges in terms variations of energy balance ('energy in - energy out') over agricultural seasons, across agricultural systems, age groups, and gender. These preliminary results provide relevant evidence for policies addressing food security, nutrition and health status in rural settings of LMICs.
Conclusions
There is growing recognition of a need to incorporate the human energy expenditure dimension and intra-household labour allocation in evaluating policies addressing malnutrition in rural areas in LMICs, where seasonality plays a key role. The proposed methodological approach can provide an improved and comprehensive picture of all facets of rural household activities, intra-household labour allocation, energy expenditure, and their variations across seasons. Accelerometer devices have been successful in monitoring and understanding of physical activity in relation to ‘lifestyle diseases’ and this study demonstrates that the adapted approach can prove useful to gain an improved picture of the pressures of seasonality on undernutrition in rural areas.
Country Prioritization for Biofortified Crop Interventions across Africa, Asia, and Latin America
Caitlin Herrington, IFPRI/HarvestPlus
Introduction
Globally, two billion people suffer from micronutrient malnutrition. Micronutrient malnutrition impedes proper health and development, also leading to a lifetime of income losses (Alderman et al., 2006). Biofortification, the process of breeding staple food crops to have higher micronutrient content, has proven to be efficacious and cost-effective in addressing micronutrient malnutrition (Bouis and Saltzman, 2017). To determine where and in which crop-micronutrient combinations to invest, this research develops an improved Biofortification Prioritization Index (BPI). This paper improves upon the original methodology, includes an additional eleven crop-micronutrient combinations, and utilizes updated data for 128 countries in Africa, Asia, and Latin America.
Methods
The BPI is a composite, crop-specific index which accounts for the intensity and level of supply and demand of a specific crop, in a country, and the micronutrient deficiency rates for the micronutrient(s) that can be bred into the specific crop(s) (Asare-Marfo et al, 2013). Three necessary conditions must be met for a country to be considered for a biofortified crop intervention: (1) the country must be a producer of the crop, (2) the country’s population must consume a large portion of the crop, and (3) the country’s population suffers from micronutrient deficiencies. The production sub-index is comprised of three variables while the consumption sub-index is comprised of two, with both indices utilizing three year-averaged data to smooth seasonality or shocks. Each of the micronutrient deficiency sub-indices (vitamin A, iron, and zinc) are comprised of two variables. A geometric mean is used for the BPI analysis so that the sub-indices complement one another. A country’s BPI is calculated by using secondary, country-level data primarily compiled from the Food and Agriculture Organization, the World Health Organization, UNICEF, the World Bank, and Wessels et al. (2012). Where needed, data imputations were calculated based on available data to address data constraints.
Findings and Interpretations
The country-crop-micronutrient specific BPIs rank countries both globally and within regions (Africa, Asia, and Latin America) according to their suitability for biofortification intervention investments. Preliminary results show that Africa is the priority region for the introduction of vitamin A enriched crops, with first country crop rankings as follows: vitamin A maize (Malawi), vitamin A cassava (Angola), vitamin A sweet potato (Equatorial Guinea), vitamin A banana (Burundi), and vitamin A plantain (Gabon). An African country ranks number one in five of the six iron-crop combinations: iron bean (Burundi), iron pearl millet (Niger), iron cowpeas (Niger), iron sorghum (Burkina Faso), and iron potatoes (Malawi). While Africa is not the priority region for the introduction of zinc biofortified crops, two African countries rank number one for the introduction of zinc sorghum and zinc potatoes; Burkina Faso and Malawi, respectively.
Further analysis will calculate area-weighted and population-weighted BPIs for each crop-micronutrient combination which decision-makers may prioritize given their agenda. As is evidenced, Africa remains the prioritized region of the world which can most readily benefit from the introduction of biofortified staple food crop interventions. While BPI results can be used to inform biofortification investment decision-making, they should not be used as the sole tool.
Conclusions
As biofortification continues to prove its efficacy and effectiveness in alleviating micronutrient malnutrition, analyses are needed to help identify the most fruitful areas of investment and implementation. This research develops an improved Biofortification Prioritization Index (BPI) which ranks sixteen country-crop-micronutrient combinations for their biofortification potential across 128 countries in Africa, Asia, and Latin America. Africa ranks as the priority region for the introduction of the five vitamin A biofortified crops and five of the six iron crops. While Asia ranks as the priority region for zinc biofortified crops, two African countries, Burkina Faso and Malawi, are ranked as the priority country for zinc sorghum and zinc potatoes. Due to Africa’s great potential benefit from biofortified crops, continued efforts in developing biofortified crop varieties is essential while also conducting nutrition and economics research to maximize impact and build the evidence base.
The BPI can guide biofortification investment decisions but should not be the sole tool used for decision-making. While the level of analysis in this research is at the national level, subnational BPIs are also being developed to identify proper areas for biofortification interventions within heterogeneous countries such as Ethiopia (Funes et al, 2015) and Nigeria (Herrington et al, 2018).
Investigating Food Environments in Low- and Middle-Income Countries Using a Novel Qualitative Geographical Information Systems (Q-GIS) Approach: A Case Study from Telangana, India
Christopher Turner, LSHTM
Introduction
Food environments in low and middle-income countries (LMICs) pose significant challenges to existing methods and metrics developed in high income-countries. Qualitative geographical information systems (Q-GIS) may provide new insights into food environments and food acquisition practices by integrating participatory visual methods with in-depth interviews (IDIs). This paper presents a novel methodological approach, featuring geo-tagged mobile phone photography and graphic-elicitation interview techniques. A case study from two urbanising villages in Telangana, India, is presented to illustrate the research design and implementation. Findings address the performance of the mobile technology, participatory photography, graphic-elicitation techniques, and participant’s experiences throughout the research process.
Methods
Two rapidly urbanising Indian villages, Patelguda and Thummaloor (from the Andhra Pradesh Children and Parents Study - APCAPS), were purposively selected to provide a sampling frame of participants exposed to a range of built and food environments. Households from the APCAPS Household Survey (2013) were eligible for inclusion if an adult male and female aged 18-65 was living at the residence. Households (n=8) were randomly sampled, and an adult male and female were recruited from each.
Participants (n=16) received a brief training session and were subsequently tasked with photographing their food environment and food acquisition practices over a three-day period using a GPS-enabled Samsung J2 mobile phone.
Participants’ photographs were downloaded, mapped in ArcGIS software, and printed onto chart paper. Charts visualising maps and photographs were used in conjunction with graphic-elicitation techniques in follow-up in-depth one-to-one interviews with participants about a) daily food acquisition practices, and; b) experiences with the participatory photography.
Analysis featured triangulation and cross-examination of maps, photographs and interview transcripts. Coding of data sources provided contextualized geo-narratives of food environment interactions, including situated knowledge, understanding and perceptions of food acquisition practices as part of daily life. Thematic analysis was used to identify convergent and/or salient themes.
Findings and Interpretations
Preliminary findings revealed that participants took on average 42 photographs over the three-day data collection period (minimum 7, maximum 86). Participants selected a mean of 15 photographs for inclusion in the follow-up interview. Eighty-seven percent of all photographs were successfully geo-coded with GPS.
Participants successfully photographed their food environment using the mobile phone, reporting no hindrance to their daily activities. Regular support visits during data collection were highly valued by the participants.
Participants photographed a range of food sources, including formal and informal markets, own production, and transfers/gifts. Participants documented their food environment throughout the diurnal cycle, reflecting the intricate spatio-temporal realities of food acquisition. Key food environment dimensions captured in photographs included food availability, vendor and product properties, and marketing.
Follow-up interviews featuring graphic-elicitation techniques probed additional dimensions, revealing knowledge and critical perspectives on prices, accessibility, affordability, convenience and desirability. Transcripts revealed in-depth situated knowledge and understanding about how people interact with their food environment and make food choices as part of their daily lives.
Conclusions
This paper presents a novel qualitative geographical information systems (Q-GIS) approach that may be used to investigate food environments and food acquisition practices in LMICs.
The integration of participatory photography with graphic-elicitation techniques and IDIs provides a unique opportunity to study food environments from the emic perspective, providing insights into the who, what, when, where, why and how of food acquisition and consumption.
This case study reveals how the novel Q-GIS research design presented can elicit the kinds of tacit, situated and embedded knowledge of intra-household gendered food acquisition practices within LMIC settings. Such knowledge is vital in order to inform the design of targeted interventions and policies that are able to facilitate healthier food environments, improve food security and tackle malnutrition in all its forms.
Development and Testing of a Household Water Insecurity Measure that is Equivalent across Countries
Sera Young, Northwestern University
Introduction
Water insecurity - the inability to access and benefit from affordable, adequate, reliable, and safe water for wellbeing and a healthy life - will worsen with projected climate change, increased water use, and inequalities in distribution, with far-reaching social, political, health, and economic consequences. Global epidemiologic data on the prevalence, severity, and changes in household water insecurity are unavailable, however, because no cross-culturally validated tool exists to measure such phenomena. Therefore, we sought to develop the first scale to assess household-level water insecurity across a variety of ecological and cultural settings.
Methods
We developed, validated, and implemented a 20-item household water insecurity scale in Kenya (clinicaltrials.gov - NCT02979418; Boateng et al, under review; clinicaltrials.gov - NCT02815579). This questionnaire was expanded through literature review, expert consultation, and qualitative work to include 32 items that captured experiences of water scarcity and excess elsewhere e.g. Texas, Bolivia, and Ethiopia.
To refine and test the 32-items, a researchers' network spanning multiple sites in low- and middle-income countries was established: the Household Water InSecurity Experiences (HWISE) Consortium (http://sites.northwestern.edu/hwise/). For each site, a questionnaire with water insecurity and other items for validation was translated to local languages. Cognitive interviewing was conducted with the target population. The questionnaire was then revised and administered to approximately 250 households per site.
Resultant data were analyzed using both Classic Test Theory and Item Response Theory. In each site, exploratory factor analysis was used to examine the structure of responses, compare the number of factors with expectations based on content knowledge, and compare the factor structure. Using Rasch modelling, a subset of items that appeared promising for measuring a primary dimension of water insecurity were analyzed by site to examine misfit. Subsequently, scores will be compared for measure and scalar equivalence, and further validated.
Findings and Interpretations
To date, the 32-item questionnaire has been administered in 15 sites: 9 in sub-Saharan Africa, 4 in Latin America, and 2 in Asia. Another 10 sites are expected by the end of the study. A total of 4,651 individuals have been surveyed. Based on cognitive interviewing and field experiences during survey implementation, 10 items were eliminated for being idiosyncratic. In some sites, the data were consistent with a one-dimensional model, whereas in the majority of sites, the data were consistent with two dimensions. Four items commonly associated with a second dimension were set aside, and the remaining 18-items were analyzed for misfit and equivalence. Infit estimates were consistently good in each site. Some of the items showed consistent severity ordering across sites, while others did not. For example, the items "worrying about not having enough water for all household needs", "feeling upset about one's water situation", "water supply from main source being interrupted" and "sleeping thirsty" were ordered consistently across sites, while items such as "not being able to wash hands" were not.
Conclusions
Based on the analyses to date, the likelihood of achieving a scale that is scalar equivalent across sites is high. Analyses to further reduce the number of items to an equivalent subset and to assess validity will be concluded by August 2018. The next step will be to implement the scale in available large-scale surveys in order to assess population prevalence and severity. Cross-culturally appropriate cut-off points will then be determined.
The final scale is expected to illuminate the relationships between food and water insecurity, as well as determine the influence that water insecurity has on economic, nutrition, and physical and psychosocial health outcomes. These data will help to inform policy regarding water insecurity, and to measure the impact of interventions that are designed to improve water security. Ultimately, this scale will allow for a more complete understanding of the causes and consequences of water insecurity across multiple disciplines.
Designing a Discrete Choice Experiment to Understand Food Choice Based on Maize Price Variations in Rural Malawi
Jacob Mazalale, University of Malawi
Introduction
Discrete choice experiments (DCEs) are attribute-driven experimental techniques used to elicit consumers’ preferences, with potential to identify underlying preferences more clearly than survey data. DCEs describe behaviours, e.g. food choices, using particular characteristics or ‘attributes’, and must critically be informed by the particular study context. Greater rigour has been called for in implementing and reporting the process of designing DCEs, including attributes and attribute levels (i.a. Coast, 2007). This paper describes the process of and key issues encountered with developing a DCE for investigating food choice responses to a change in price of a staple (maize) in rural Malawi.
Methods
To design the DCE, attributes and attribute-levels were derived from household and market surveys conducted in May 2017 in Phalombe and Lilongwe Districts of Malawi. The household survey asked respondents to list foods, and their respective quantities, purchased from local markets by households in the previous seven-day period. Food quantities were elicited using standardised measures such as kilograms, or where non-standardised measures were used (e.g., a ‘bucket’ of maize), the study used two standardised cups. The ‘large cup’ held one litre of water: the ‘small cup’ 500 milliliters of water. A market survey collected market price data of the food products and quantities identified previously. The study estimated that on average, a household consumed food products valued at MK1000.00 (US$1.40) each two to three days. Based on this, a DCE was designed consisting of ten scenarios. Each scenario contained three baskets. Each basket contained maize (the staple), and a maximum of rice (maize substitute), cabbage (rare vegetable), dried fish (a healthy protein food), and the soda ‘Frozy’ (similar to Fanta; an unhealthy product) and costing between MK900 and MK1100. The DCE was piloted three times, with data collectors, in a rural area near Zomba city, and in a poorer region of Zomba.
Findings and Interpretations
Using a modified Federov algorithm in NGENE software, a d-error-minimising efficient design generated two sets of five DCE scenarios each (10 scenarios in total). One set of five scenarios had maize at a high price (MK400/kg); the other had maize at a low price (MK150)/kg). The scenarios were presented to 200 respondents in each district; and in different orders to avoid ordering bias. We will conduct analysis in STATA15 software, using multinomial, mixed and latent class logit models (analysis of pilot data already complete).
Four broad issues were faced: establishing an appropriate model of food choice when it was possible to have only five goods as attributes; constructing baskets that provided sufficient attribute variability; constructing a viable instrument, with appropriate, easily interpreted photographs; and establishing a choice scenario around prices and basket values was clear to respondents. Our piloting exercises provided an opportunity to understand the importance of assumption setting and implementation context.
We identified important issues regarding attribute inclusion and context setting that may be of interest to others seeking to use a DCE approach for understanding food choice. Our results show that the DCE approach can be a useful addition to the tools used by researchers.
Conclusions
Based on information derived from a household and a market survey in two districts of rural Malawi, the research team has designed a DCE to be used in assessing the impact of maize price changes on food choice and dietary diversity. The overall study will make some key contributions to the literature on food choice. First, we found that using a cup to standardise food quantities improved the internal validity of the study findings. Secondly, this is among a small number of studies to have used a DCE approach to study food choices, and the only study of which we are aware to have done this in a low- or middle-income country setting, and to document the process and issues faced with the DCE development. Data for the experiment were collected in January and February 2018, and we are currently analysing the study results. To date, very few studies have used DCE study approaches to elicit food choice preferences, and the growing number of studies expected in this area would likely benefit from awareness of the process, issues, and novel solutions that we have encountered and developed.
NUTRI-P-LOSS: A Methodology to Estimate Nutritional Postharvest Losses
Aurelie Bechoff, Natural Resources Institute
Introduction
A third of food produced is lost between harvest and the consumer. This is a particular concern in low and middle- income countries (LMICs) where postharvest losses can translate into nutritional deficits for families who already suffer from food security challenges. Global population growth necessitates innovative solutions to limit food waste and loss, which causes nutritional losses that could impact people’s nutritional intake. The NUTRI-P-LOSS project is developing a methodology to estimate nutritional postharvest losses (NPHLs) in the food system, from harvest to market.
Methods
NUTRI-P-LOSS uses a three-fold approach that comprises a 1) literature review, 2) laboratory trials to measure nutritional loss (under controlled conditions) and 3) field experiments to validate a predictive model of NHPLs. We focus on three crops: maize (including biofortified orange maize), sweet potato (including biofortified orange fleshed sweet potato) and cowpea, in Uganda and Zimbabwe, and on key-nutrient loss: macronutrients (protein, lipid, carbohydrate) and micronutrients considered the most important in terms of deficiencies (vitamin A, zinc, and iron). The NUTRI-P-LOSS tool will enable prediction of NPHLs related to: (1) physical weight losses; (2) other changes not associated with weight loss. Initially we undertook an online survey to consult experts and stakeholders working in nutrition, agriculture and the food security sectors about the usefulness of the NPHL estimate. The survey sought to understand the needs of the potential users of the platform with regards to NPHL estimate. Laboratory and field trials compared nutritional changes in stored commodities with and without insect infestations. Our final outcome is an estimation model to predict NPHLs. The model will be developed as a Nutri-P-LOSS algorithm that will freely accessible through the African Postharvest Losses Information System (APHLIS) online platform.
Findings and Interpretation
The expert consultation indicated the key-nutrients selected by the NUTRI-P-LOSS project were the most important and relevant for the application. In addition, experts had an interest in antinutrients (e.g. cyanogens, phytates).
Laboratory trials showed that insect infestation significantly increases humidity (water activity) in the commodity, which will impact quality. Storage time significantly influenced on some nutrients in dried orange-fleshed sweet potato: carotenoids followed a rapid loss (90% loss in trans-B-carotene after 16 weeks) and glucose, fructose, sucrose increased with time (around 20% after 16 weeks). With maize and cowpea, the relationship between percentage damage and percentage weight loss and nutritional change could be visualised and demonstrated that insect infestation has more complex impacts than simple physical loss.
Household surveys with 600 farmers across two countries (Zimbabwe and Uganda) provided insights into storage techniques and attitudes which can be used to inform improvements.
The model must include factors associated with fractional loss (through insect damage) as well as weight changes through shelf life (with and without insect damage.) This is most critical for maize as insect-mediated fractional loss of germ may selectively impact lipid content and stability of provitamin A carotenoids in grain through enhancement of lipid oxidation.
Conclusions
A combination of literature review, laboratory simulation, and field work helps build a robust estimate of nutritional losses throughout the value chain. Challenges, however, are to reconcile those different approaches to develop a coherent and realistic model for estimating nutritional losses. The NUTRI-P-LOSS system presents opportunities to predict impacts of different storage scenarios in order to aid farmers, extension specialists and other actors in making informed decisions about when to keep or consume a stored commodity.
The survey’s results confirmed the need for a nutritional postharvest loss estimate in the agriculture and nutrition community and suggested that there will be scope for the NUTRI-P-LOSS’s concept and methodology to be applied to other nutrients and crops. Based on case studies use of the NUTRI-P-LOSS tool, policy recommendations can be generated regarding nutritional implications of nutritional loss at different steps of the food system.
