Sustainable Agriculture Systems and Technologies. Группа авторов

      where Y _{t + n} is the prevalence of the next year, Y _t is the prevalence of the given year, and b% is the annual rate of reduction (UNICEF 2015).

The calculation of Average Annual Rate of Return revealed that the AARR of stunting in India is 2.2% which means till 2030 the prevalence will be reduced up to 33% if the situation remains same. This is a positive pace but slower in speed because the target of SDG is to reduce the stunting prevalence by 50% by 2030 from the level of 2012. The AARR of wasting (Table 1.2) shows that there are states like Arunachal Pradesh, Tripura, Himachal Pradesh, Punjab, Mizoram, Chhattisgarh, West Bengal, and Nagaland which have an AARR of more than 3%. These states will reach around 50% reduction by 2030. But at the same time, states like, Bihar, Tamil Nadu, Madhya Pradesh, Rajasthan, and Jharkhand have AARR lower than 1.5% which brings down the national average and makes reaching the SDG target tough.

      In conclusion, we can say that the most vulnerable states in terms of hunger security are Madhya Pradesh, Uttar Pradesh, Jharkhand, and Bihar. The states which have performed well in reducing the prevalence are Tripura and Himachal Pradesh. Both the states have least prevalence of hunger. Giving us a lesson, although centrally sponsored schemes are there, still regional disparity makes the difference. States with higher dependence on agriculture and tribal population have higher level of hunger, indicating unequal income distribution and lack of improvement in influencing factors.

1.3.2 Association with SocioEconomic Indicators

Poverty affects nutrition (Nelson 2000) leading to undernutrition in the poor population. Stunting was the most dominant phenomenon among children under five years. So the percentage of stunting was associated with socioeconomic factors like percentage of population below poverty line, per capita GDP, and growth rate of the state. Undernutrition is a result of poor dietary intake, poor maternal health combined with lack of safe water and sanitation along with poor health services (UNICEF 1998). Undernutrition is responsible for poor mental health (Martins et al. 2011), higher vulnerability to ill health, and a reduced physical work capacity (Non et al. 2016), which is making the workforce inefficient and thus posing a problem in the economic development of nations. Further, under nutrition which is caused by poverty also leads back to undernutrition which again causes poverty and the cycle continues. The marginal propensity to consume is higher for poor people because they spend higher proportion of their income in consumption. Thus, decline in income will hamper the consumption expenditure. Bivariate analysis was done to work out relationship between prevalence of stunting and each economic factor under consideration. The association between the percentage of stunted children and the percentage of the population below poverty line is strong and positive (Figure 1.4a). This is anticipated because poverty leads to insufficient food intake, less prenatal care, child malnutrition, and unhealthy diet. A few states however deviate from the predicted line. Meghalaya, Rajasthan, and Gujarat are clear negative outliers with a much higher percentage of stunted children as compared to their poverty level. Goa, Kerala, Manipur, and Arunachal Pradesh, on the other hand, are positive deviants, i.e. they have lower percentage of stunting in comparison to the level of poverty. Improved drinking water sources, improved sanitation facility, use of iodized salt, literacy among women, antenatal care, and anemia among children and women also impact the level of undernutrition in India (Ghosh 2020).

Table 1.2 Average Annual Rate of Return of stunting and wasting from 2005–2006 to 2015–2016.

      Source: Calculated by author from data obtained from NFHS 4.

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