GoKawiilStudy sites
Field work took place in ten smallholder farming villages (2,400–3,000 metres above sea level, temperate climate) in Jumla District, Nepal (Fig. 1, Supplementary Fig. 2 and Supplementary Method 2). Jumla is a remote mountainous district, situated in the Karnali Province of western Nepal. Rates of poverty, food insecurity and malnutrition are very high in this region, and 80% of the population directly depends on smallholder agriculture47. The study populations are typical of many smallholder communities around the world (Supplementary Fig. 1) and are characterized by a heavy reliance on small (<2 ha) family-run farms for both their income and nutrition, which makes them heavily dependent on local ecosystem services27. Each study village comprised a cluster of 100–400 closely spaced households interspersed with small vegetable gardens and livestock enclosures. Villages were surrounded by many small (0.01–0.3 ha) arable fields and apple orchards as well as large areas of steep, heavily grazed pasture and native forest (Supplementary Figs. 3 and 4). More than 50 crops are grown in this region, including many pollinator-dependent species such as apples, beans, pumpkins, mustard and buckwheat (Supplementary Tables 2–4).
Study population
Households were considered eligible for inclusion in the study if they were permanent residents in the community (spent at least 10 months of the year in the village) and had at least three out of the four respondent categories in the household (adult man, adult woman, adolescent girl and child under 5 years of age). On the basis of a full census of each village, we identified eligible households, randomly selected 20 from each village and obtained consent from the head of the household. From each household, we aimed to enrol one adult woman, one adult man, one adolescent girl and one child under the age of 5 years as participants in the study. This selection provided a diverse picture of diets in each household, including two particularly vulnerable subgroups: young children (owing to their rapid growth and high nutrient needs) and adolescent girls (who may soon have their first child and whose pre-conception nutrition strongly influences maternal and child health outcomes). Adolescent boys were not included as a separate respondent category as their diets are assumed to be more similar to adult men and they are considered less nutritionally vulnerable than adolescent girls. Participants were considered eligible for inclusion in the study if they were permanent residents of the household and not suffering with an illness or disability that prevented them eating a usual diet or responding to questions (Supplementary Method 3, selection of study participants). Our final study population (Supplementary Table 11) consisted of 776 individuals (215 adult women, 186 adult men, 190 adolescent girls and 185 children under 5 years of age).
Ethics statement
Ethical approval for this study was obtained from the Ethical Review Board of the Nepal Health Research Council (reference 1709) and the Faculties of Life Sciences and Science Research Ethics Committee at the University of Bristol (reference 102982). All procedures involving human participants were conducted in accordance with the relevant institutional and national ethics guidelines. For all participants over 18 years of age, informed consent (signature or thumb print) was obtained; for all participants under 18 years, consent was provided by their parent or guardian and the adolescent girls also provided assent. Participants’ time was remunerated with mobile phone credit vouchers, a widely used and valued resource in the study region.
Dietary-recall surveys and recipe collection
Dietary-recall surveys were conducted for each participant every 2 weeks for an entire year (November 2021 to November 2022). During each recall event (24 in total), we recorded the identity and quantity of every food item consumed by respondents during the previous 24 h. To minimize recall bias, we used a five-stage multipass food-recall method48 (Supplementary Method 3, dietary-recall surveys). Food models were used to estimate portion sizes: cooked rice as a model for rice and other irregular-sized solid foods such as vegetable curries and cooked green leaves; water for portions of dal (soup of beans or pulses), milk or other liquids; playdough for chunks of meat or pieces of fruit; wheat flour for powdered foods like roasted grain flour, salt and chili powder; and dried corn for dried or roasted nuts and grains. Participants were instructed to only report the food that was consumed and not any that was leftover on the plate. Food models were weighed using Salter kitchen scales and then back-converted to estimate the mass of each food item consumed, accounting for differences in food density (Supplementary Method 3, dietary-recall surveys). For foods in packets, or of a standard size, the number of items consumed was recorded. Interviews were conducted by trained data collectors in the Nepali language and information was entered into a customized data-collection form using the cloud-based data-collection platform CommCare (v.2.49; http://www.commcarehq.org/home/) on an Android tablet. All data collection had range checks and internal validity checks built in to help maintain quality control.
To identify the composition of each mixed dish (that is, the quantity of each ingredient in it), we asked the lead cook in replicate local households to prepare the food following their normal recipe (Supplementary Method 4). Each ingredient was weighed as it was added to the cooking dish and then the final weight of the prepared meal was recorded and the proportional composition of each ingredient (by weight) was calculated. For each ingredient in a recipe, we calculated its mean proportional contribution to the recipe (food item) based on the raw weight of edible ingredients across all recipe replicates collected (ten in most cases). For each food item consumed by a participant, we multiplied the grams of food item consumed by the proportional content of each ingredient to determine the grams of each individual ingredient consumed on a given date. We complied a Nepal-specific food composition table from a range of published sources (Supplementary Method 4, food composition data sources), which was used to assign a nutritional composition to each ingredient. Nutrient values for each ingredient were adjusted for cooking losses using the USDA Table of Nutrient Retention Factors (release 6, 2007), applying the appropriate factor for each cooking method (for example, boiled versus fried potato) to ensure that nutrient values reflected the ingredients in the form they were eaten (Supplementary Method 4, nutrient retention factors).
Height and weight measurements
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