Impact of Poultry Farmers’ Participation in Modern Food Retail Markets on Household Dietary Diversity: Lessons from Southeast Nigeria

On-Farm Crop Species Richness Is Associated with Household Diet Diversity and Quality in Subsistence- and Market-Oriented Farming Households in Malawi

Household food insecurity but not dietary diversity is associated with children's mean micronutrient density adequacy in rural communities across Ghana.

Food security and nutrition continue to pose major challenges in Sub-Saharan Africa, where undernourishment remains widespread. Livelihood diversification has emerged as a promising strategy to improve dietary diversity and other food security and nutritional outcomes. However, little is known about how specific combinations of livelihood activities contribute to nutritional adequacy, and how contextual factors, such as intrahousehold income dynamics, market access, and climate shocks, shape this relationship. This study explores how the specific combinations of livelihood activities, along with contextual factors influence household food security and dietary diversity in Tanzania. The findings aim to inform the design of targeted interventions and policy responses. This study draws on nationally representative household panel data collected in Tanzania through the World Bank’s Living Standards Measurement Study (LSMS), focusing on 2012/2013, 2014/2015, and 2019/2020 survey waves. Using a fractional panel regression (FPR) as well as fixed-effects regression model, the study investigates the determinants and, impact of livelihood diversification on three key indicators of food security and nutrition: household dietary diversity, food consumption score, and total food expenditure. The study found that Tanzanian households exhibited moderate livelihood diversification, typically combining farming with other income sources. While diversification—measured by the Herfindahl-Hirschman Index (HHI)—marginally improved over the study period, food security and nutrition (FSN) indicators (HDDS, FCS, and food expenditure) initially rose between 2012/2013 and 2014/2015 but declined in 2019/2020. Results from the fractional regression model show that diversification was higher among female-headed, younger, and wealthier households, while asset ownership, mobile money use, and shocks were linked to lower diversification. Further, the fixed-effects regression analysis revealed that higher diversification was associated with lower FSN outcomes, suggesting that not all diversification pathways are beneficial—particularly those driven by necessity or centered on low-return, informal activities. The study reveals that higher livelihood diversification is linked to lower food security and nutritional outcomes, possibly due to necessity-driven and low-return diversification strategies. The pattern may also reflect cultural or contextual factors that limit the impact of additional income on food choices and nutrition.

BackgroundIn Northern Uganda, 21 and 52.4% of children under five are underweight and stunted, respectively while 32.9% of pregnant women are anemic. This demographic situation suggests among other issues, a lack of dietary diversity among households. Good nutrition practices that confer dietary quality such as dietary diversity are known to depend on nutrition knowledge and attitude and are shaped by sociodemographic and cultural factors. However, there is a paucity of empirical evidence to support this assertion for the variably malnourished population of Northern Uganda.MethodsA cross-sectional nutrition survey was conducted among 364 household caregivers (182 from two locations in Northern Uganda; Gulu District (the rural) and Gulu City (the urban), selected through a multistage sampling approach. The aim was to determine the status of dietary diversity and its associated factors between rural and urban households of Northern Uganda. The household dietary diversity questionnaire and the food frequency questionnaire on a 7-day reference period were used to collect data on household dietary diversity whereas multiple choice questions and the five points Likert Scale were used to determine knowledge and attitude toward dietary diversity. Consumption of ≤ 5 food groups were regarded as low in dietary diversity, 6–8 food groups as medium and ≥ 9 as high dietary diversity score using the FAO 12 food groups. An Independent two-sample t-test was used to differentiate the status of dietary diversity between the urban and rural areas. The Pearson Chi-square Test was used to determine the status of knowledge and attitude while Poisson regression was used to predict dietary diversity based on caregivers’ nutritional knowledge and attitude and their associated factors.ResultsThe 7-day dietary recall period revealed that dietary diversity was 22% higher in urban (Gulu City) than in the rural area (Gulu District) with rural and urban households achieving medium (score of 8.76 ± 1.37) and high (score of 9.57 ± 1.44) dietary diversity status, respectively. Diets in both locations were dominated by starchy cereals and tubers while animal-source foods and fruits and vegetables were the least consumed. A higher proportion (51.65%) of urban respondents had good nutrition knowledge toward dietary diversity compared to their rural counterparts (23.08%) and a significantly higher proportion (87.91%) of the former exhibited positive attitude towards dietary diversity than the rural counterparts (72.53%). Application of the Poisson regression shows that nutritional knowledge was a positive predictor of dietary diversity in the rural (β = 0.114; ρ = 0.000) than in the urban areas (β = -0.008; ρ = 0.551). Caregivers attitude had no significant effect across locations. In terms of associated factors, marital status is a positive predictor of dietary diversity in the urban (β = 1.700; ρ = 0.001) than the other location (β = -2.541; ρ = 0.008). Whereas education level of household caregiver and household food expenditure show negative effects across the two locations, the educational level of the household head is an outlier as it positively predicted dietary diversity in the rural (β = 0.003; ρ = 0.002) when compared to urban area (β = -0.002; ρ = -0.011).ConclusionRural households in Northern Uganda have medium-level dietary diversity with urban households having high dietary diversity. Diets in both locations are dominated by starchy cereals and roots and tubers. The urban–rural food divide can be harmonized through nutrition education and outreach, specifically focusing on the FAO 12 food groups. Attitude toward consumption of fruits and vegetables which are seasonally abundant would improve dietary diversity and nutritional outcomes in the study area.

Purpose Emphasis on the potential roles diverse farm production systems could play in enhancing food consumption variety and nutritional well-being in rural developing countries has increased in recent times. However, there are paucities of empirical works connecting diversity in agricultural production and dietary diversity in Africa, and Nigeria in particular. The purpose of this paper is to, therefore, examine, among others, the causal link between farm production diversity and consumption of varied diets among farm households in Nigeria using a nationally representative panel data. Design/methodology/approach Unlike the simple food count measure, the authors adopt two-dimensional indices to assess food diversity, and estimated both fixed and random effects versions of panel data econometrics models with the two-dimensional indices as regressands. Findings Results show that food production system is less diverse with an average farm household consuming fairly varied foods across seasons. All the econometrics models estimated consistently established positive and statistically significant influence of farm production diversity on household dietary diversity. Higher food prices, especially rice and roots and tubers could substantially reduce dietary diversity with the negative effects likely to be more devastating for low-income farm households. The specificity of household being a net food seller had positive, although weak influence on dietary diversity. Originality/value The findings accentuate, among others, the need for strategies to promote farm production diversity, transform farm households to net-sellers of foods and enable them take advantage of food price signals to boost farm incomes as important pathway for diet quality improvement and reduction of food insecurity, malnutrition and related diseases in rural Nigeria

BackgroundMaize is the most important staple crop for food security and livelihood of smallholder farmers in many parts of sub-Saharan Africa, but it alone cannot ensure food security. Cropping patterns must be diversified to ensure an adequate supply and economic access to greater variety of foods for smallholder farm households. This study measured the effect of crop diversification on household dietary diversity in a selected study locale using a survey of 300 randomly stratified farm households in 10 villages located in the Babati, Kongwa and Kiteto districts of Tanzania.ResultsBased on multiple regression analysis, the study found that simply increasing Simpson’s Index does not influence dietary diversity of farm households due to the presence of interaction effect between Simpson’s Index and crop income. It is much more critical and significant to increase the revenue generated from diversified crops along with other socioeconomic endowment and behavioral characteristics of farm households. This is particularly applicable to poorer smallholder farmers who receive crop income less than US$85 per sales transaction and per season. Particularly, marginal and smallholders might be exposed to the effects of crop diversification and crop income toward increasing in their household dietary diversity score.ConclusionUnder average crop income scenarios, households that diversify their crop production tend to increase their dietary diversity from their existing dietary diversity score at a decreasing rate. However, under below average crop income threshold scenarios, farmers tend to increase their dietary diversity score from their existing score at an increasing rate when they diversify into high-value crops that attract relatively high farm gate values and accrue higher net revenues from the market. Monthly food expenditure also tends to positively influence household dietary diversity, indicating that farm households that spend more on market-purchased food have consistent increases in the their dietary diversity scores at the household level. This study concludes that improving economic access to variety of foods at the smallholder household level by diversifying diets through increased crop diversification should be encouraged within maize-based farming systems of the study locale, through integration of micronutrient-rich foods such as vegetables.

To determine access to traditional and indigenous foods (TIF) and the association with household food security, dietary diversity and women's BMI in low socio-economic households. Sequential explanatory mixed-methods design, including a random household cross-sectional survey on household food insecurity access (HFIA), household dietary diversity (HDD) and women's BMI, followed by focus group discussions. Two rural and two urban areas of Botswana. Persons responsible for food preparation or an adult in a household (n 400); for BMI, non-pregnant women aged 18-49 years (n 253). Almost two-thirds of households experienced moderate or severe food insecurity (28·8 and 37·3 %, respectively), but more than half of women were overweight or obese (26·9 and 26·9 %, respectively). Median HDD score was 6 (interquartile range 5-7) out of a total of 12. A positive correlation was found between number of TIF accessed and HDD score (r=0·457; P<0·001) and a negative correlation between number of TIF accessed and HFIA score (r=-0·272; P<0·001). There was no correlation between number of TIF accessed and women's BMI (r=-0·066; P=0·297). TIF were perceived as healthy but with declining consumption due to preference for modern foods. TIF may potentially have an important role in household food security and dietary diversity. There is need to explore potential benefits that may be associated with their optimal use on food security and nutrition outcomes.

In light of the continuing rise in food prices during and after the 2008 world food crisis, whether food and cash transfers are equally effective in improving food security and diet quality is debatable. To compare the effects of conditional food and cash transfers of the Ethiopian Productive Safety Net Program (PSNP) on household food security and dietary diversity. Data on household dietary diversity, child anthropometry, food security, and preference of transfer modalities (food, cash, or mixed) were generated from a cross-sectional survey of 195 PSNP beneficiary households (67 receiving food and 128 receiving cash) in Hawella Tulla District, Sidama, southern Ethiopia. Most beneficiaries (96%) reported food shortages, and 47% reported food shortages that exceeded 3 months. Households receiving cash had better household dietary diversity scores (p = .02) and higher consumption of oils and fats (p = .003) and vitamin A-rich foods (p = .002). Compared with households receiving food, households receiving cash were more affected by increases in food prices that forced them to reduce their number of daily meals (p < .001) and spend less on nonstaples (p < .001). While most households receiving food (82%) preferred to continue receiving food, households receiving cash (56%) preferred a mix of food and cash. Households receiving cash had better household dietary diversity than households receiving food, a result suggesting that cash transfers may be more effective. However, the continuing rise infood prices may offset these benefits unless cash transfers are index-linked to food price fluctuations.

The global food price crises of 2007–2008 and 2010–2011 drew attention to the need for addressing the underlying determinants of malnutrition in low- and middle-income countries (LMICs; Brinkman, de Pee, Sanogo, Subran, & Bloem, 2010; Webb, 2010). Specifically, as the primary source of food and income in LMICs, agriculture received renewed focus. Making agriculture work for nutrition—nutrition-sensitive agriculture—has climbed the international development agenda (Ruel, Alderman, & Maternal and Child Nutrition Study Group, 2013). More recently, given the sharp increase in diet-related chronic diseases underpinned by overweight and obesity in LMICs and the threats of climate change to diets, attention has expanded to leverage food systems to optimize nutrition, health and environmental outcomes (Johnston, Fanzo, & Cogill, 2014). Donors, researchers and implementers mobilized research agendas to invest in understanding how to strengthen agriculture and food systems to realize nutrition outcomes sustainably. Development of conceptual frameworks to aid the investigation of agriculture-food system and nutrition linkages, highlighting multiple direct and indirect complex pathways (Global Panel, 2015; Hawkes, Turner, & Waage, 2012; Johnston et al., 2014; Kadiyala, Harris, Headey, Yosef, & Gillespie, 2014; Lock et al., 2010; Masters et al., 2018). Empirical examination of the linkages between agriculture-food systems and nutrition and the key pathways mediating or modifying these relationships and systematic reviews (Arimond & Ruel, 2004; Girard, Self, McAuliffe, & Olude, 2012; Ruel, Quisumbing, & Balagamwala, 2018). Experimental studies and novel methodological approaches, with improved rigour in testing conventional (e.g., homestead food production) and novel intervention models (e.g., market-based interventions for nutrition; use of participatory videos). These efforts led to widespread recognition of inadequate tools, methods and metrics to study the direct, indirect and dynamic relationships between in agriculture-food systems and nutrition outcomes. There have been several calls to accelerate the development of innovative tools, methods and metrics to underpin the development of a robust scientific evidence base needed to guide policy investments in agriculture-food systems for improved nutrition and health. In response to this demand, several projects and programmes were launched specifically to develop new research methods, including the DFID-funded Innovative Metrics and Methods for Agriculture and Nutrition Actions (IMMANA) programme (Innovative Methods and Metrics for Agriculture and Nutrition Actions [IMMANA], 2018). Research undertaken under IMMANA and others (CGIAR, 2018; Global Dietary Database [GDD], 2014; International Dietary Data Expansion Project [INDDEX], 2018; Sustainable and Healthy Food Systems [SHEFs], 2018) have built on existing theoretical underpinnings and have helped to refine hypothesized pathways, illuminating additional aspects and dynamics between agriculture or food systems and nutrition outcomes, such as food environments, environmental factors and food safety. The Food and Agriculture Organization (FAO) of the United Nations (UN) adopted the High-Level Panel of Experts (HPLE) definition of a food system: "all the elements (environment, people, inputs, processes, infrastructures, institutions, etc.) and activities that relate to the production, processing, distribution, preparation and consumption of food, and the output of these activities, including socio-economic and environmental outcomes" (HLPE, 2017). Agriculture and health are part of the broader milieu of a food system. As such, there has been a proliferation of innovations in programme design and implementation, as well as in metrics and methods and their application. While the body of evidence on effectiveness of food systems to improve nutrition has been recently summarized (Ruel et al., 2018), the portfolio of new methods and metrics has not. It is now necessary to take stock of these developments and plan for the future to support the production of effective and relevant research. Recognition of the multiple pathways through which nutrition impact is achieved brought about new conceptual frameworks, and along with them, new thinking in how to measure the complexity and dynamism within these systems. The innovations that emerged range from new technology to new indices to the application of methods from other fields. New metrics and methods have been developed throughout the pathways (household production, decision-making, income, etc.) linking agriculture and nutrition. In a standard effectiveness map, the row headings are interventions, and the column headings outcomes. In this map, those thematic pathways or domains will be considered the "interventions" through which nutrition is improved. We consider our "outcome" to be tools, metrics or methodological innovations, which are the columns of this map. Some innovations have been widely adopted across settings, and others are still in development. Therefore, each example innovation will be mapped using the studies that pertain to the innovation. Innovations have taken place at every level of measurement (individual, household, national, etc.) and correspond to certain cross-cutting themes. These additional aspects will be coded internally on the map. As an example of technology application at an individual and household level, researchers have utilized accelerometers to measure calorie expenditure in new ways to address intra-household food allocation and gender roles (Zanello, Srinivasan, & Nkegbe, 2017). At a community level, researchers have employed wearable cameras and GIS technology to map changing food environments in urban areas (Schrempft, van Jaarsveld, & Fisher, 2017). A methodological innovation at a sub-national level has been to use Bayesian theory and decision-analysis for making policy that affects nutrition (Yet et al., 2016). More thorough data collection and new indices to capture prices of nutritious foods in markets at the regional level have led to better estimates of cost of nutritious diets in Ghana (Masters et al., 2018). New innovation in this field also includes tools to conceptualize and operationalize food systems, including how to frame and measure cost-effectiveness of complex interventions, which have a range of outcomes (Masters et al., 2018). In this EGM, the columns will be types of innovation or novel application. The aim of the gap map is to articulate and summarize the innovation in tools, metrics and methods that have been created and applied to understand food systems and agriculture-nutrition linkages in the last ten years. We have chosen the ten-year period based on the focus on and funding for agriculture-nutrition linkages that emerged following the global food price crisis in 2007–2008, as well as wanting to focus on new innovations, which, by definition, would no longer be novel if developed more than a decade ago. We also aim to highlight gaps and opportunities for future development. Although the intervention-outcome framework is most common for maps on effectiveness studies, this framework will be organized differently. We will take an approach that considers tools, metrics and methods (types of innovation/application in the columns) for agriculture-food systems-nutrition research (thematic domain in the rows). The map will be organized around a combination of conceptual frameworks that include the definition of food systems offered by the HPLE report on nutrition (HLPE, 2017), predefined pathways to improved nutrition (Global Panel, 2015; Hawkes et al., 2012; Herforth, Nicolò, Veillerette, & Dufour, 2016; Kadiyala et al., 2014), as well as additional themes that have been identified as more research is being undertaken on this topic (Grace et al., 2018; Masters, 2016; SHEFs, 2018) (Appendix APPENDIX A). These conceptual frameworks (illustrated in Appendix A) overlap a great deal. For instance, each highlights the role of on-farm production as a means for direct consumption as well as a potential income source, both which influence food availability and diet quality, and thus contribute to nutrition outcomes. Frameworks 1 (Kadiyala et al., 2014), 2 (Hawkes et al., 2012) and 3 (Herforth et al., 2016) are very similar—in fact the most substantive difference is the visual organization of components. These frameworks include aspects of women's time, income and employment. The same three highlight interacting aspects of care, education or knowledge, as well as overall health as drivers of nutrition outcomes. Each framework also has differences, both in how it is visualized and the content. Each represents "indirect" determinants of nutrition outcomes, such as the role of climate, the environment, policy, governance and culture, inequity, and so forth, but some are shown as an outside layer of influence, whereas some of these are considered within the central framework. For instance, Framework 4 shows the interactions between environment or sustainability aspects and food production, highlighting that human health should always be balanced against planetary health, since they are symbiotic in the long run (Tuomisto et al., 2017). Several highlight important domains that are not equally represented on the other. Frameworks 5 (Masters, 2016) and 6 (Global Panel, 2015) propose the most current thinking about markets and the economic role of nutrition. These support the idea that production will lead to consumption only where, when and for whom markets are missing. The Global Panel Metrics and Methods Framework (Framework 6) puts the food environment as the central milieu into which other dynamics feed, and diet diversity, adequacy and safety as general by-products of that food environment. In contrast, Framework 2 (Hawkes et al., 2012) specifically articulates the subsequent layers of the food environment that progressively lead to nutritional status. We will use all frameworks generally to ensure that the EGM is comprehensive and that the domains within conceptual pathways in agriculture to nutrition literature are represented and categorized logically, while maintaining iterative methods of refining the domains based on search results. Governments, non-government donors, implementing agencies and academia have all made significant investments, both intellectually and financially, in improving agriculture or food systems for nutrition outcomes. This investment has gone beyond scholarship and documentation and taken the form of application and innovation of tools, metrics and methods at every level. Stakeholders have called for a synthesis project on this topic in order to visualize the current portfolio of these developments, strategically plan the next wave of investment and shape the next generation of agriculture-nutrition research. There are no current gap maps on the topic of metrics and methods on the topic of agriculture and food systems for nutrition (or to improve nutrition outcomes). Some mapping exercises have been undertaken on pathways between agriculture and nutrition, namely the 2012 LCIRAH "Current and planned research on agriculture for improved nutrition: a mapping and a gap analysis", which led the way to the IMMANA programme (IMMANA, 2018). The FAO Compendium of nutrition-sensitive indicators also summarizes the most well-established indicators on the subject (Herforth et al., 2016), but does not fully capture innovative tools and methodologies, as well as metrics that are in development currently. Furthermore, to our knowledge, none of these synthesis projects have been systematic or published as a formal EGM, and overall there have been no EGM of tools, metrics, or methodologies; rather most existing gap maps focus on effectiveness studies. The main objective of this EGM is to guide funders and researchers in the most promising areas of innovation within the study of food systems or agriculture to nutrition pathways, and demonstrate their phase of development and other thematic trends. We also will be able to demonstrate where there are gaps in existing innovative tools, metrics and methods that correspond to key domains identified in these conceptual frameworks. Empty cells in the map will indicate where no new methods, metrics or tools either exist or have been developed in the last decade within those domains. Furthermore, we intend that this EGM will then be used to shape future investments in this field, both by pursuing opportunities to take the most promising developments to the next level, and focusing attention on where there are gaps in available tools, metrics and methods. A secondary objective of this EGM is to identify trends in investigation and application that would be suitable for further synthesis. All previous EGMs published to date have been compilations of effectiveness studies, therefore this EGM will be novel in many aspects. We are not aware of any protocols or published EGMs on which to model this project. There are several synthesis reports on this topic (Hawkes et al., 2012; Herforth et al., 2016), but as mentioned previously, none of them are current, systematic or are formal EGMs. We will use published, well-established conceptual frameworks to define thematic domains of agriculture-to-nutrition in order to categorize the identified tools, metrics and methods. Therefore, our "intervention" will be each broad domain on the food systems or agriculture to nutrition pathway. The columns of our map, (outcome in effectiveness maps) will be each item of innovation (tool, metric or method) that has been developed or applied to capture or measure these domains. Limit the search to work published after 2008. Identify completely new tools, metrics or methods that were introduced after 2008 with no previous iterations. Identify tools, metrics or methods that existed prior to 2008 but have been significantly revised or modified since. As a "significant" change or modification is difficult to determine, we will rely on the group or authors' own assertions and explanations, and make an expert judgement as a group when unclear. Identify new, novel or innovative applications of existing tools and methods. This will mostly entail applying these cross-disciplinarily. This will be the most difficult aspect of "newness" to define, and we therefore will also rely on the authors' description and justification, and secondarily make a collective expert decision. Because some of the tools, metrics and methods are in their infancy, while others are globally adopted and have become standard practice, we will code each study based on the current (e.g. in 2019) stage of development of the innovation. We will further code and categorize innovations by several thematic filters (e.g., gender, equity, economics, technology, private-sector engagement, conflict or political fragility), geographical application, and level of measurement. The "intervention" (rows), will be defined using agriculture-to-nutrition conceptual frameworks mentioned previously, divided into "domains" of influence on agriculture and food systems or nutrition, such as household or on-farm production, food policy and governance, or food environments and markets. The columns, or categories of innovations/applications, will be grouped by different classifications of tools (technology application and instruments to capture data on a range of agriculture-nutrition topics), metrics (new indices and measures to quantify agriculture-nutrition linkages) and methods (research design and analytical approaches applied to agriculture and nutrition research). We will code each study related to an item and group the items iteratively once all items have been mapped. We have chosen to do this since some well-adopted innovations will have many papers that use the tool, metric or method, while others will have only a few, and some will apply the tool, metric or method in different ways. Users will be able to see each individual item as well as grouping by tool, metric or method. Traditional EGMs include a quality assessment of each item, such as a risk of bias rating, which are not designed to evaluate tools, methods or metrics. In place of a quality assessment, this EGM will summarize the stage of development or application (explained below). We will add filter codes for certain cross-cutting themes such as gender or private-sector engagement. It will also categorize the measurement level (individual, household, district, national, etc.), and setting or geographical application (Asia, Africa, global, etc.). We also may add other filters as the search progresses. The framework structure is shown in Appendix APPENDIX B. This map will only include tools, metrics and methods that have been applied to agriculture-nutrition pathways in any country at any level: individual, household, community or district, sub-national, national and global. The problem we are considering is any domain that exists on the conceptual pathway between agriculture and/or food systems and nutrition outcomes. These domains have been grouped (through using frameworks and extensive rounds of expert consultation) by broad themes around food production, food safety, value-chains, markets and food environments, food policy and governance, environment and climate, among others. We have organized the domains around broad themes in order to group items with minimal double- or triple-coding, but we do envision that some items will appear in more than one domain. Whenever possible we will select a "primary" domain and use the filters and codes to indicate other aspects of the tools that are cross-cutting, such as gender, technology or economics. The first column lists 12 broad domains, and the second column are examples of what types of work would fit in to these domains. All included tools, metrics or methods must explicitly relate to either agriculture/food systems, or to nutrition. Any tools, metrics and methods that are not related to either agriculture/food systems or nutrition will be excluded. Most of the domains could be measured at various levels (individual, national, global, etc.). We will not differentiate in the domains, but rather in the internal coding of each tool, metric or method innovation. As the initial results are identified, we may refine these domains and add sub-domains using an iterative methodology. Specifically, we propose the following overarching categories based on the conceptual frameworks discussed previously (Table 1). The domains of food safety, food environments and economic evaluations each have systematic reviews on methods and metrics. We will include the items that are included in those reviews and also use these items to test the framework. The primary "outcome", of the gap map (i.e., the columns in the map) is the innovative item (tool, metric or method) created and applied to studying and describing the broad agriculture to nutrition pathways. We will define "tool" as is a vehicle or an aid to collect information and data (e.g., a survey module to collect data required to compute an index or a piece of technology). "Metrics" will be defined as the parameters (measures) or indices used for measurement, comparison or tracking performance (e.g., disability adjusted life years; household dietary diversity score and Women's Empowerment in Agriculture Index [WEAI]). We define "method" as the process and approach involved in a systematic inquiry of relationships between agriculture, nutrition and health and generally refer to study design or application of an analytical method to this topic (e.g., impact evaluations using various types of counterfactuals, pathway analyses, decision analyses). Several types of "innovation" are described above. We will identify each tool metric or method and group them together once identified. Some methods or tools will have slight variation in their application or analysis, but we will group these logically together as an item if the construction of the item is similar. We will include all of the studies on the map. Each item might have multiple innovative components that fit distinctly within the categories of tools, metrics or methods. We propose that these will not be exclusive, rather the appropriate component of a paper or project will be categorized accordingly, and the total number of items summarized separately. In order to determine what is substantial enough for inclusion as a separate item (rather than a much smaller exploratory innovation and we will use the published or study considering only primary and secondary We will group tools, metrics and methods into the following with some examples in the column (Table a tool, metric or method developed or applied definition in or since 2008. In published and/or as well as projects from that tools, metrics and methods in development. In any for the of and/or used to quantify or a potential between agriculture or food systems and nutrition. This will be defined by the tool, metric or method being related to either the broader agriculture field, food systems as defined in the Panel of Experts (HPLE) or the broader nutrition field, since several methods and metrics used to study this are only explicitly to one of the pathway. Nutrition or nutritional on the of the pathway will be including all of malnutrition diet-related chronic of nutrition measurement diets, or diet-related diseases or diet-related will be interventions to improve nutrition and their pathways to the influence of and food on nutrition; governance and policy through which agriculture and nutrition are and between climate, and/or and nutrition at a and so that have been to agriculture, food systems and nutrition pathways, as long as explicitly in to these pathways and the as new, novel or For and and or and metrics or methods not applied to the domains that agriculture, food systems and nutrition, as explicitly defined by the HPLE or the conceptual frameworks included in the metrics or methods developed or applied prior to 2008. with no in studies. the are for production, or they will be the are as a for or models for general (i.e., if the to or they will be excluded. studies not explicitly related to production, or other related themes. the for studies, if the are specifically mentioned in the of agriculture or if studies, they will be the are a model of general or not mentioned in to they will be excluded. nutrition. nutrition. the paper is related to nutrition and nutritional food for diseases (e.g., or as etc.) will all be excluded. The gap map will include primary research of any as certain study may in fact be an innovative method of application to study the agriculture-nutrition will be excluded. Study types that may demonstrate new innovations or novel applications could include a new study standard study using new or innovative tools, metrics or methods, or studies specifically or a new tool, metric or method. These would therefore as a primary study describing how the approach or design is a a or guide for a new or a developed impact or an impact using well-established study but using new tools or metrics. We will use the described to determine what is considered or (Table We will include research in the EGM as long as it within the agriculture/food systems to nutrition framework and the inclusion and could be undertaken as part of a or be Traditional methods such as focus group or individual will have to be innovative or applied in new ways to be considered for this documentation of the food environment through participatory using such as to or a new within a survey are all examples of innovation in methods that will be included in the Any innovation or application of tools, metrics and methods place will be could be from a country or or they could be applied with to LMICs approaches, etc.). We will include studies and identified through expert and We will a comprehensive and project search that includes with search in Appendix APPENDIX with in Appendix APPENDIX We will also search various project and research and key and in the of key both of research and International Research Research for Development FAO IMMANA The impact and since The evaluations Development The of the Nutrition and The of the The of the The The key we will use for tracking et Global Panel Hawkes et Herforth et Kadiyala et and et et Some included studies and reports We will use a method of researchers to search and then on and the first of with a or a decision in the of For the search we will use with by a We do not plan to use any or The in with the IMMANA will and an to the initial and to the inclusion and and further We will then the code the projects and the coding for stage of innovation or application, which will a quality assessment in this of innovation or application in development and or internal and effectiveness and and widespread application Global or geographical application global, etc.) filters will be used to identify cross-cutting which will not have (e.g., cost of dynamics and be about or about gender in and health gender women's (e.g., The filters categories will be applied if the of the item specifically the of the item to that or the research item is to a The same research in of the search and will the coding of these filters and which will have been through with subject and a one but may also be modified once

To assess household food insecurity and dietary diversity as correlates of maternal and child anthropometric status and anemia in rural Cambodia. Trained interviewers administered a survey to 900 households in four rural districts of Prey Veng, Cambodia. The Household Food Insecurity Access Scale (HFIAS) and Household Dietary Diversity Score (HDDS) were used to assess household food insecurity and dietary diversity. The height, weight and hemoglobin concentration of the mother and youngest child under 5 years in each household were measured. Multivariate logistic regression models were constructed to assess the association between household food insecurity and dietary diversity, and child stunting and wasting, maternal thinness, maternal and child anemia. The mean (s.d.) HFIAS and HDDS scores were 5.3 (3.9) and 4.7 (1.6), respectively. The respective prevalences of mild, moderate and severe food insecurity were 33, 37 and 12%. Maternal thinness, child stunting and child wasting were present in 14.6, 25.4 and 8.1% of respondents, respectively. The risk of maternal thinness, but not child stunting or wasting, increased as the severity of household food insecurity increased. Household food insecurity was also positively associated with maternal, but not child, anemia. Household dietary diversity status was not significantly associated with any of the outcomes we assessed. Efforts to improve household food security are important as a means of promoting maternal nutritional status; however, additional research is needed to better understand the role of other factors that are driving the burden of child undernutrition in Cambodia.

Farm production diversity is associated with greater household dietary diversity in Malawi: Findings from nationally representative data

To describe: household dietary diversity across four zones in Ethiopia; the relationship between household dietary diversity and consumption of vitamin A-rich foods; and the relationship between household dietary diversity and food security status. This was a cross-sectional survey. Data were collected using structured questionnaires in the local language. Household dietary diversity scores measured types of foods households consumed, and households were classified by food security status using a modified version of the Household Food Insecurity Access Scale. An ordinal logistics regression model was created to assess the relationship between three tiers of dietary diversity (low, medium and high) and food security while controlling for agricultural zone, educational variables and household characteristics. Rural households in Tigray, Ethiopia. Three hundred households in Tigray, Ethiopia, were interviewed. Of the households, 23, 47 and 30 % had low, medium and high dietary diversity, respectively. Among households with high dietary diversity, eggs and fruit were the most common foods added to the diet. In the fully adjusted model, participants who reported being food secure had 1·8 increased odds of greater dietary diversity (95 % CI 1·0, 3·2) compared with participants who were food insecure. Food security was positively associated with dietary diversity. In order to enhance health, interventions that improve dietary diversity and vitamin A consumption should remain important areas of focus for health leaders in the region.

There are few studies on the influence of agroforestry intervention in the farming and food system. We thus conducted this study to assess farm production diversity and household dietary diversity in the coffee-based agroforestry in Deusa village, Solukhumbu district, Nepal. This study collected data through questionnaire survey, food diary checklist for 24 hours diet recall, transect walk, focus group discussions, and key informant interviews. We compared farm production diversity and household dietary diversity scores between two agroforestry types - traditional and coffee-based. We used Pearson’s Chi-Square and Fisher’s Exact tests to assess the association between agroforestry type and 16 food groups wise consumption. Results showed that the farm production diversity is positively associated with the household dietary diversity. Among 16 food groups, households under coffee-based agroforestry system were more likely to consume dark green leafy vegetables (Chi square- 5.385; df=1; p&lt;0.05), and descriptive statistics showed relatively higher consumption for most of the other food groups. It indicates that agroforestry intervention can be beneficial to improve farm production diversity and household dietary diversity in the longer run. Thus, agroforestry promotion is not only important in enhancing biodiversity and farm income but also equally vital in improving food and nutrition security for smallholders.

This study was conducted to find out agricultural production diversity and household diet diversity in farming households and examine their statistical relationship. Data on production, consumption, and socioeconomic factors were collected from the cross-sectional survey using the semi-structured questionnaire in 2018, in which 120 respondents (60 from Kailali district and 60 from Syangja district) were interviewed. Agricultural production diversity was defined from species count for each household, and household diet diversity was obtained from dietary diversity score (DDS) using 12 food groups by the FAO in preceding 24 h recall period. Data analysis in the SPSS showed that the average species count of each household was 11.79 with average crop and livestock count of 7.95 and 3.88, respectively. Average DDS was 7.7 with minimum value 4 and maximum value 10. Agricultural production diversity and household diet diversity were positively correlated (0.249**, at 0.01 level). Household diet diversity was positively correlated with size of landholding and size of kitchen garden. The consumption behavior shows that 100% of household have consumed cereals, 75% have consumed milk products, 52% have consumed fruits, and only 21% of the respondents have consumed meat and egg in the last 24 h recall period. Percentage of household consuming milk and milk products were higher in Syangja, whereas households consuming meat, egg, and fish were higher in Kailali. Wheat items were major alternative staple food in Kailali, whereas maize, millet, and wheat items were common alternative staple foods in Syangja. This study suggests that diversified agricultural production system is a promising strategy to provide diversified diet and ultimately improve food and nutrition security of farming households.

Agricultural insurance through the lens of rural household dietary diversity