DOI: 10.5061/dryad.tdz08kq07
title: Multiple cropping alone does not improve year-round food security among smallholders in rural India

description: 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&lt;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.
description: 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.
 <strong>Sample R code:</strong>
 data &lt;- 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&lt;-function(x){<br>    
 xmean&lt;-mean(x,na.rm=TRUE)<br>    
 xsd&lt;-sd(x,na.rm=TRUE)<br>    
 xtrans&lt;-(x-xmean)/(xsd)<br>     }
 z(data$Age)-&gt;Age_z<br>
 z(data$HHM)-&gt;HHM_z<br>
 z(data$Asset_index)-&gt;Asset_z<br>
 z(data$HHSS_R)-&gt;HHSS_R_z<br>
 z(data$MON)-&gt;MON_z<br>
 z(data$Crop_no)-&gt;Crop_no_z<br>
 z(data$MFRQ)-&gt;MFRQ_z<br>
 z(data$FPS_FI)-&gt;FPS_FI_z<br>
 z(data$NFAS_FI)-&gt;NFAS_FI_z<br>   //write a
 new CSV file with standardized variables, along with the rest of the
 variables. Use this modified file for the regressions.//
 f_m &lt;- glm(HFIAS ~ GEN + Age_z + HHM_z + EDUHH + Asset_z +
 OCU + HHSS_R_z + MON_z + Crop_no_z + PLLS + LSSL + <br>     IRLS +
 HGAG + COKS + FPDC + FCDC + MFRQ_z + FPS_FI_z + NFAS_FI_z + factor(Season)
 + factor(District) + factor(Village), data=data)
 summary(f_m)
description: More detailed information can be found in the ReadMe file and the
 linked article.