A minimum set of nutrition indicators for agriculture surveys: attempting to bridge the agriculture-nutrition data gap
Katie Ricketts is the Program/Research Manager for Tata-Cornell Agriculture and Nutrition Initiative (TCi), a long-term research initiative based at Cornell University.
For decades, agricultural surveys have focused on tracking household income and employment patterns, food supply and food prices, farm management and agronomy practices. Often, this has given economists and others a good sense of what and why certain types of foods are available or affordable to rural communities. However, understanding how human nutrition status has changed in response to agricultural changes or interventions has largely remained a mystery. Rarely is nutritional data collected alongside mainstream, large-scale and long-term agriculture datasets like, for example, those undertaken by the World Bank in Africa or the International Center for Research in the Semi Arid Tropics (ICRISAT) in India. A better understanding of the links between agriculture and nutrition will require new thinking around how agriculture and nutrition surveys can collect the same, critical data needed for meaningful comparison and analysis of nutritional trends.
Filling the agriculture-nutrition data void: enter the MNDA project by TCi
The Minimum Nutrition Dataset for Agriculture (MNDA) is a Tata-Cornell Agriculture and Nutrition Initiative project looking to develop and agree upon the most essential metrics for determining nutritional status. Along with interns and other TCi staff, I am working to take the existing metrics and develop a short 1-2 page, generalizable survey that can easily be included in longitudinal agriculture surveys. Such metrics include proxies and indicators that can identify:
- Dietary diversity,
- Anthropometric and clinical indicators,
- Biochemical markers,
- Metrics around intra-household allocation, and
- Metrics around early childhood care (e.g., breastfeeding).
Beginning with Dietary Diversity
In December 2013 we started the process of building the MNDA by gathering together a global group of experts from nutrition, economics, sociology, and natural resource management together at Cornell University. During this workshop, we outlined the basic components and indicator categories (mentioned above) necessary for measuring human nutrition.
In early 2014 the dietary diversity module was selected as the first module to develop and pilot test. Dietary diversity information can be difficult to gather (recall periods and cutoff amounts are problematic for determining what and ‘how much counts’), and expensive, and time consuming to undertake. However, assessment of household and individuals’ access to a diverse diet is a key metric that is useful for identifying how food insecurity can contribute to malnutrition. Lack of dietary diversity is a particularly severe problem among poor populations in the developing world as starchy staples and grains—with little or no animal products and few fresh fruits and vegetables—dominate diets (see a review on diets in the developing world here and here). The MNDA dietary diversity module was designed to rapidly assess dietary diversity, yield similar results to an intensive approach, and require less than 30 minutes of a respondent’s time.
Pilot testing the first MNDA module: preliminary results
After a winter/springtime filled with designing the survey instrument, I took off in early summer with five TCi interns to India to pilot and validate the instrument in real field conditions. Prabhu Pingali, our TCi Director, later joined us. Two villages in Telangana and Maharashtra were surveyed and over 140 households participated (see reflections from our interns who lived in our sample villages here and here). Our participants, women between 18-45 years old, were randomly drawn from a larger group that had previously participated in ICRISAT’s intensive survey (which had included a dietary diversity component). Our MNDA module asked women respondents to recall what they ate over the past three days.
To obtain a woman’s dietary diversity score, the individual foods she ate were collapsed into 9 possible food groups, yielding a score between 1-9. All foods eaten by the household were collapsed into a total of 12 possible food groups, yielding a score between 1-12. We followed the individual and household food groups and scoring method outlined in Food and Agriculture (FAO)/FANTA Dietary Diversity Guidelines. The same FAO/FANTA food groups were used to derive the dietary diversity scores yielded from the the ICRISAT intensive nutrition survey. If the MNDA dietary diversity module captured the same dietary information as that captured by the ICRISAT intensive survey—in less time and with fewer questions—amongst the same group of respondents, we could then conclude that the MNDA dietary diversity module offered operational advantages and should be included in the larger MNDA metrics set.
Findings from the 2014 pilot showed that the overall distribution of MNDA dietary diversity scores, including the sample mean of the women who participated, were not significantly different from the intensive ICRISAT survey (figure below). Broken down by body-mass index groups (BMI) that were categorized into low-BMI (underweight), average BMI (normal) and high-BMI (overweight) sub-groups, no significant differences emerged amongst the groups regarding distribution of scores collected. In sum, the MNDA was able to collect similar dietary diversity score information as the intensive nutrition survey in less time (in an average of 27 minutes) and with fewer questions.
In addition to capturing similar dietary diversity scores effectively and efficiently, the MNDA captures additional information useful for contextualizing diets and better understanding how access might be shaped by the overall food system. This additional information includes atypical eating patterns (days where women fasted or went to a special event), eating that occurred outside the home and identification of where food items were procured. In fact, we found that in our pilot testing, atypical eating patterns (in our case, fasting) were confounding our results. Once I controlled for fasting we started to see a strong connection between BMI and dietary diversity scores. Variables such as atypical eating behaviors, eating outside the home, and food procurement have been listed by the FAO dietary diversity collection guidelines as important factors to consider when evaluating diets through dietary diversity scores.
Moving ahead: building and piloting other MNDA modules
In collaboration with scientists and researchers at ICRISAT, our interns did a great job on the data collection front for our pilot. Right now I am continuing to analyze and improve the survey results, given the positive preliminary findings we have thus far. Keep a lookout for an upcoming paper Prabhu Pingali and I have with the New York Academy of Sciences on the purpose of the MNDA as well as a more detailed analysis of the work undertaken this past summer in practitioner and academic publications.