Achieving and maintaining food and nutrition security is an important Sustainable Development Goal (SDG), especially in countries with largely vulnerable population with high occurrence of hunger and malnutrition. By studying a small-scale agricultural system in India, we aim to understand the current state of dietary diversity and food insecurity among the farmer communities. The study landscape has witnessed a steady rise in multiple cropping along with irrigation over the last two decades. Whether this multiple cropping can be expected to improve year-round food security is not well understood. We specifically examine if planting multiple food crops within a year is associated with dietary diversity and food security. We collected information on demographic and economic variables, farming activities and livelihood choices, from 200 unique households for three seasons (monsoon/wet, winter, summer) during 2016-2018 (n=600). Based on both a 24-hour and a 30-day recall, we calculated several indicators, including the Household Dietary Diversity Score (HDDS), the Minimum Dietary Diversity for Women (MDD-W), and Household Food Insecurity Access Scale (HFIAS). At least 43% of the sample population experiences moderate to severe food insecurity in all seasons. Cereals (mainly rice) remain the most important food item irrespective of the season, with negligible consumption of other nutrient-rich food such as tubers, fish, eggs, and meats. Around 81% of women in all seasons do not consume a minimally diverse diet. Multiple cropping is associated with higher food security only during monsoon, while selling monsoon crops is associated with winter food security. Households practicing multiple cropping consume more pulses (a plant-based protein source) compared to single-cropping or non-farming households (p<0.05). We find that multiple cropping cannot be used as a cure-all strategy. Rather a combination of income and nutrition strategies, including more diverse home garden, diverse income portfolio, and access to clean cooking fuel, is required to achieve year-round dietary diversity or food security.

We focused on 40 villages in five districts in the state of Madhya Pradesh – Balaghat, Chhindwara, Dindori, Mandla and Seoni – to understand the agriculture-diet-nutrition dynamics across agricultural households from various socio-economic settings. First, we created a stratum based on agricultural practices (predominantly single cropping vs. double cropping) in the five study districts. Then we randomly selected eight villages from each of the five districts to represent each of the agricultural practice categories. Within each village five households were randomly selected (household n = 200) by the interviewer based on location within the village and availability of the respondents. The same individual in each household (male or female adult, based on availability) was interviewed for each of the three seasons (summer, monsoon/wet season, winter/dry season), resulting in 600 surveys. 

We utilized the Food and Nutrition Technical Assistance (FANTA) project recommendations for the questionnaire surveys for food security and diversity indicators, namely the household food insecurity access scale (HFIAS) and household dietary diversity score (HDDS). We collected food availability data through a 30-day recall and food consumption data through a 24-hour recall. The interviewer avoided visiting a village right after the weekly market days, whenever possible, to avoid misrepresentation of dietary diversity. The interviewer administered the questionnaires three times a year for monsoon (June-October), winter (November-February), and summer (March-May) seasons. Using the data collected on 16 individual foods or food groups consumed over the last 24 hours, we calculated HDDS by aggregating food items into 12 groups: cereals, white tubers/roots, vegetables, fruits, meat, eggs, fish/seafood, pulses/nuts/seeds, milk/milk products, oils/fats, sweets, spices/condiments/beverages. The respondents were first asked about the dietary consumption at the household level over the last 24 hours (breakfast/snack, morning meal, lunch, dinner). Based on the qualitative responses, the interviewer assigned a value of 'yes' (1) or 'no' (0) to each of the food groups.

We calculated the Minimum Dietary Diversity for Women (MDD-W) for each eligible respondent. It is a dichotomous indicator for women consuming at least five out of ten defined food groups (grains/tubers, pulses, nuts/seeds, dairy, meat/fish, egg, dark green leafy vegetable, other vitamin-A enriched vegetables, other vegetables, and other fruits) over the last 24 hours.

We used food availability surveys to construct HFIAS that represents varying levels of food insecurity over the past 30 days based on responses to nine occurrence and nine frequency-of-occurrence questions. This indicator reflects three domains perceived as central to the experience of food insecurity: (1) anxiety about household food supply (2) insufficient quality, including variety, preferences and social acceptability and (3) insufficient food supply and intake and their physical consequences.

Using HDDS and HFIAS as response variables, we ran regression models (see sample R code below) to identify the most important associations of different factors including demographic, economic, farming activities and livelihood choices. All continuous predictor variables were centered on the mean values before running the regression models.

Sample R code:

data <- read.csv(file = 'Data.csv', header=TRUE)

//standardize these variables (by centering the values on the mean value) before running the models: Age, HHM, Asset_index, HHSS_R, MON, Crop_no, FPS_FI, NFAS_FI//

z<-function(x){
    xmean<-mean(x,na.rm=TRUE)
    xsd<-sd(x,na.rm=TRUE)
    xtrans<-(x-xmean)/(xsd)
    }

z(data$Age)->Age_z
z(data$HHM)->HHM_z
z(data$Asset_index)->Asset_z
z(data$HHSS_R)->HHSS_R_z
z(data$MON)->MON_z
z(data$Crop_no)->Crop_no_z
z(data$MFRQ)->MFRQ_z
z(data$FPS_FI)->FPS_FI_z
z(data$NFAS_FI)->NFAS_FI_z
 

//write a new CSV file with standardized variables, along with the rest of the variables. Use this modified file for the regressions.//

f_m <- glm(HFIAS ~ GEN + Age_z + HHM_z + EDUHH + Asset_z + OCU + HHSS_R_z + MON_z + Crop_no_z + PLLS + LSSL + 
    IRLS + HGAG + COKS + FPDC + FCDC + MFRQ_z + FPS_FI_z + NFAS_FI_z + factor(Season) + factor(District) + factor(Village), data=data)

summary(f_m)

More detailed information can be found in the ReadMe file and the linked article.