|
||||||||||||||||||||||||||||
| Future Diets in India | ||||||||||||||||||||||||||||
| Paper Id :
17105 Submission Date :
13/02/2023 Acceptance Date :
22/02/2023 Publication Date :
25/02/2023
This is an open-access research paper/article distributed under the terms of the Creative Commons Attribution 4.0 International, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. For verification of this paper, please visit on
http://www.socialresearchfoundation.com/innovation.php#8
|
||||||||||||||||||||||||||||
| ||||||||||||||||||||||||||||
| Abstract | Projecting future diets can provide helpful information for national and international organizations designing and delivering food and agriculture policies. We, therefore, performed a systematic review of studies that have projected future diets in India; we aimed to identify trends in projected future intake of foods, with consideration of study methodology and reporting quality. | |||||||||||||||||||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Keywords | India, Future Diets, Food Consumption, Food Projections. | |||||||||||||||||||||||||||
| Introduction |
Diets in India are changing and in recent decades there has been a decline in the consumption of some cereals such as millets, while the consumption of salt, oils and animal products have increased (Misra et al., 2011). These dietary changes are likely to have contributed in part to an increased burden of non-communicable diseases (NCDs) in India, a trend seen across many low- and middle-income countries as populations become increasingly urban and incomes rise (World Health Organization, 2003). An estimated 61% of deaths in India were attributable to NCDs in 2017 (World Health Organization, 2017), while the prevalence of stunting (short height for age) amongst children under 5 years old remains extremely high at almost 40% (UNICEF, 2017), indicative of the co-existence of overweight and obesity with under-nutrition (the “dual burden”). Additionally, two-thirds of the Indian population are estimated to be micronutrient deficient, with their diets failing to provide recommended levels of minerals and vitamins such as iron and vitamin A that are typically found in the Indian diet in pulses, coarse cereals, and dark green leafy vegetables (Rao et al., 2018). Addressing this diet-related burden of disease in India remains a pressing need. |
|||||||||||||||||||||||||||
| Aim of study | The aim of our study is Projecting future diets can provide useful information for national and international organisations involved in designing and delivering food and agriculture policy. The study of dietary projections is interdisciplinary and, We therefore performed a systematic review of studies that have projected future diets in India; we aimed to identify trends in projected future intake of foods, with consideration of study methodology and reporting quality. | |||||||||||||||||||||||||||
| Review of Literature |
Changing dietary patterns may also have impacts on environmental parameters (Foley et al., 2005). The agriculture sector accounted for 17.6% of India's greenhouse gas (GHG) emissions in 2007 (Indian Network for Climat, 2010), and due to its large population, India is already the 4th largest emitter of GHGs in the world. Per capita GHG emissions associated with current dietary patterns in India are relatively low compared with that of other countries largely due to low consumption of animal products (Green et al., 2018), but future dietary changes in conjunction with continuing population growth (United Nations. Populatio, 2017) could make it hard for India to meet its targets of reducing GHG emissions intensity by 33–35% below 2005 levels by 2030 (Government of India, 2015). The agriculture sector is also a major user of ground and surface water (FAO AQUASTAT, 2016) and recent changes in dietary patterns in India are linked to increased demand for irrigation water (Harris et al., 2017). This poses an additional issue for environmental sustainability as irrigation water for agricultural use is increasingly being drawn from rapidly depleting groundwater resources (Rodell et al., 2009). Current and future trends in Indian diets therefore have potential implications for health, GHG emissions, ground and surface water availability, and potentially several other environmental factors.
|
|||||||||||||||||||||||||||
| Main Text | Methods 1. Search Strategy This review
follows the Preferred Reporting Items for Systematic Reviews and Meta-Analyses
(PRISMA) guidelines (Liberati et al., 2009). A literature search of
peer-reviewed and grey literature was performed to identify all studies that
modelled future consumption of one or more food items or groups in India to at
least one time point in the future. All studies were included from the
beginning of the databases until the date of search (31st January 2018). For published
literature, six electronic databases were searched systematically, including
the major interdisciplinary databases, and databases related to food and
health: EMBASE, Global Health, MEDLINE, PubMed, Scopus and ISS Web of Science core
collection. We also searched Google Scholar, the CGIAR research platform, and
the repositories of organisations named in identified studies; namely the
United Nations Food and Agriculture Organization (FAO), Indian Council of
Agricultural Research National Institute of Agricultural Economics and Policy
Research (ICAR-NIAP), the International Food Policy Research Institute (IFPRI),
and the International Water Management Institute (IWMI). Citation lists of
papers identified for inclusion and relevant review articles were hand-searched
to identify additional studies. Authors were contacted if the full text was not
found (3 authors contacted; 2 responses; 0 articles provided). The search
strategy was developed initially in PubMed with the same search terms used with
adjustments as needed for other databases. The search terms are summarised in
Table 1 and the full search strategy for each database is detailed in
supplementary material S1. Table 1 Search
terms used in electronic database search.
Open in a separate window 2. Selection criteria & data extraction Titles were screened by a single reviewer (CC) for
relevance. Abstracts were screened in duplicate (CC, FB) and consensus on any
discrepancies reached through discussion with a third reviewer (FH). The population
outcome (P0) framework (James et al., 2016) was followed to develop the
inclusion criteria for studies to be selected for the review: Population: 1. The studies projected direct (human only) consumption
in India Outcome: 2. Projections were of one or more food items or food
groups 3. The projections presented were the original work of
the author(s) 4. Consumption was projected to at least one time point
in the future beyond the year in which this review was conducted (2018) 5. Published in English Relevant data were extracted by a single reviewer (CC)
from the identified studies into a database, and all data were checked against
the original studies by a second reviewer (SC). Extracted data were as follows:
study authors and publication year; food consumption data source; projection
model method; assumptions and variables; GDP growth rate scenarios; food groups
or items; baseline year & values; and projection years & values. 3. Study reporting quality There are no existing criteria for evaluating quality of dietary projection modelling studies. Reporting quality assessment parameters were developed by two reviewers (CC, FB) using the Authority, Accuracy, Coverage, Objectivity, Date, Significance (AACODS) grey literature checklist (Tyndall, 2010) and a set of quality criteria used previously for critiquing dietary simulation models (Grieger et al., 2017). Six parameters relating to the clear description of methodology and reporting of results were used to assess study reporting quality, one point was given for each criterion and quality scores ranged from 0 to a maximum of 6 (supplementary material S2). |
|||||||||||||||||||||||||||
| Analysis | The methods and data sources used in included projection studies were
highly heterogeneous and quantitative synthesis was not possible. A narrative
synthesis approach was used and extracted data from each study was tabulated.
Baseline and projected consumption estimates were presented graphically for
food groups reported in two or more studies. For studies that reported
projections under different scenarios (typically several estimates of economic
growth over the projection period) a single “most conservative” estimate was
presented graphically to avoid over representation of individual studies.
Dietary consumption values reported as kg/year were converted to
kg/capita/month using the baseline and projected population estimates reported
by study authors. Missing baseline population estimates were obtained from
United Nations estimates for the appropriate year (United
Nations. Populatio, 2017). Dietary consumption values reported in
one paper as kcal/capita (Alexandratos
and Bruinsma, 2012) could not be converted to kg/capita/month due to
the aggregation by the study authors of food items into food groups. |
|||||||||||||||||||||||||||
| Result and Discussion | The initial
database search identified 5,111 studies. After removal of duplicates and
screening of titles and abstracts, 26 studies remained. Hand-searching of
review papers and reference lists identified a further 18 studies for full text
screening. Of the 44 potentially relevant studies, 33 were excluded during full
text screening (full texts of 4 potentially relevant studies could not be
obtained despite multiple attempts). A total of 11 papers reporting projections
of direct food consumption (i.e. food consumed by humans only) were included in
the review (Fig. 1). Seven excluded studies predicted trends in total food
consumption (i.e. food consumed by humans in addition to food used as
agricultural seed, animal feed, food for industrial use and waste), and one
study did not state whether projections related to direct or total food
consumption.
Fig. 1 PRISMA chart
showing the numbers of studies at each stage of the search. 1. Data sources
and projection methods The eleven
included studies varied substantially in data sources, timescales, level of
aggregation and projection models adopted (Table 2). Studies used different
baseline years and projected for between 10 to 50 years, to future time points
between 2020 and 2050; most studies projected consumption to between 2020 and
2026, two studies projected to 2050. In seven of the eleven included studies,
consumer expenditure data from the National Sample Survey Office (NSSO)
large-scale nationally representative Indian household surveys were used to
determine food demand at baseline (Amarasinghe et al., 2007; Bhalla et al.,
1999; Chand, 2007; Dastagiri, 2004; Dyson and Hanchate, 2000; Ganesh-Kumar et
al., 2012; Kumar et al., 2009). Other studies obtained baseline data from:
household consumer expenditure from the World Bank combined with United Nations
Food and Agriculture Organisation (FAO) food balance sheets (n = 1)
(Alexandratos and Bruinsma, 2012); United States Department of Agriculture
(USDA) Production, Supply and Distribution data (PSD) (n = 1) (Carriquiry et
al., 2010); a combination of Organisation for Economic Co-operation and
Development (OECD) and FAO databases (n = 1) (OECD-FAO, 2017); and FAO food balance
sheets (n = 1) (Rosegrant et al., 1999). Five studies disaggregated their
results into rural and urban populations (Amarasinghe et al., 2007; Chand,
2007; Dastagiri, 2004; Dyson and Hanchate, 2000; Kumar et al., 2009), and one
study at regional and state level (Dyson and Hanchate, 2000). Table 2 Overview of
characteristics of studies included in the systematic review.
Open in a
separate window GDP, gross
domestic product; FBS, food balance sheet; HHCE, household consumption
expenditure; NSSO, National Sample Survey Office; USDA PSD, United States
Department of Agriculture production, supply and distribution; FAO, Food and
Agriculture Organisation of the United Nations; FAPRI, Food and Agricultural
Policy Research Institute; QUAIDS, quadratic almost ideal demand system; OECD,
Organisation for Economic Co-operation and Development; IMPACT, international
model for policy analysis of agricultural commodities and trade. The projection
methods used by the included studies were heterogeneous. One study used modified
trend analysis of prior NSSO collection rounds spanning 22 years (Dyson and
Hanchate, 2000). Six studies used projection methods based on a variety of
socio-economic demand characteristics such as population growth and
urbanisation, using baseline data collected by the NSSO (Amarasinghe et al.,
2007; Bhalla et al., 1999; Chand, 2007; Dastagiri, 2004; Ganesh-Kumar et al.,
2012; Kumar et al., 2009). Of these, one study used the food characteristic
demand system (FCDS) to derive demand elasticities taking into account regional
effects and income distribution (Kumar et al., 2009), one study used the
quadratic almost ideal demand system (QUAIDS) method (Ganesh-Kumar et al.,
2012), and one used the generalised least square (GLS) procedure (Dastagiri,
2004) to calculate demand or expenditure elasticities. These were then
incorporated into models with other variables of demand, most frequently
population growth, income growth and urbanisation. Three studies
employed econometric partial equilibrium projection models; either the
international model for policy analysis of agricultural commodities and trade
(IMPACT) model developed by IFPRI (Rosegrant et al., 1999) or commodity outlook
models (Carriquiry et al., 2010;OECD-FAO, 2017). Partial equilibrium models incorporate
demand (consumption), a simplified representation of supply (production), and
trade within the agricultural sector, considered in isolation from other
economic sectors (Parappurathu et al., 2014). The partial equilibirium models
included in this review are global and are used to generate multi-regional
projections. The IMPACT model incorporates variables such as urbanisation,
population growth, commodity prices and growth in agricultural productivity, as
well as global trade policy. It assumes that income growth will cause
significant shifts from main staples to meat and livestock products, mostly in
low and middle income countries (Rosegrant et al., 1995). The Food and
Agricultural Policy Research Institute (FAPRI) Agricultural Outlook model uses
a previously developed macroeconomic forecast and assumes existing farm policy
and current trade agreements and custom unions (Carriquiry et al., 2010). The
OECD-FAO Agricultural Outlook model uses macroeconomic forecasts of the OECD
Economic Outlook and International Monetary Fund (IMF) World Economic Outlook,
and also assumes current agricultural policies remain unchanged (OECD-FAO,
2017). The partial equilibrium models use national level food availability data
as opposed to NSSO data. One study derives consumption projections of food
groups from proportional share of future per capita dietary energy intake based
on consumer expenditure projections (Alexandratos and Bruinsma, 2012). 2. Study Reporting
Quality We evaluated
each study for six features of reporting quality (Table 3). Most studies
reported projection timeline and baseline data source. Few studies reported
limitations of the projection methods used, and validation of the models. One
study met all six quality criteria (OECD-FAO, 2017), and one study met five
criteria (Ganesh-Kumar et al., 2012) (supplementary material S2). No relevant
study was excluded from the review based on reporting quality. Table 3
Number of
studies meeting reporting quality criteria (n = 11).
Open in a
separate window 3. Future
dietary trends at national level in India Baseline and
projected consumption data were available for 9 foods or food groups reported
on two or more occasions. Per capita consumption of rice (n = 7 studies), wheat
(n = 7 studies) and pulses (n = 5 studies) is projected to remain broadly
unchanged in the future at around 6kg, 5kg and 1kg/capita/month, respectively
(Fig. 2; supplementary material S3 for raw data). Total cereal consumption
(inclusive of rice, wheat, maize, sorghum and millet; n = 5 studies) is
projected to decline in three studies using NSSO consumption data with negative
price elasticities for cereals (Chand, 2007; Dyson and Hanchate, 2000; Kumar et
al., 2009) and increase in one study using NSSO consumption data with positive
price elasticies for cereals (James et al., 2016). One study that used the
IMPACT model projected a slight increase in future total cereal consumption
(Rosegrant et al., 1999). Per capita consumption of all other foods or food
groups, namely sugar, dairy, meat, vegetables and fruit, is projected to
increase in the future in all studies irrespective of data source or projection
method, with the magnitude of future increase greatest for dairy and
vegetables. In one study reporting values in kcal/capita that could not be presented
graphically, findings are consistent with those of other studies: namely a
decrease in future cereal consumption and an increase in dairy, sugar and meat
consumption by 2050 (Alexandratos and Bruinsma, 2012).   Fig. 3 Comparison of
diets in India in 2012 and projected diets in 2025–26.*2012 values are mean
consumption for adults aged 16–59 years calculated from results of 68th NSSO
round (25). Error bars represent 95% confidence interval. **2025–26 consumption
represents the mean of selected of studies included in this review that project
to this time point. Error bars represent upper and lower range of the data
where more than one value available for each food group.
|
|||||||||||||||||||||||||||
| Conclusion | Beyond the benefit of predicting supply-demand gaps in the future, consumption projections provide a means to determine the impacts of changing consumption patterns on environmental outcomes such as land and water use, and health. This will be useful for policymakers across health, nutrition, agriculture and environmental sectors to respond to the changing reality of their country, thus determining the future health of their population. | |||||||||||||||||||||||||||
| Limitation of the Study | This review has several strengths including our thorough and systematic approach to identify all relevant studies in published and grey literature, to provide an overview of the available projections of food consumption in India. Previous research on dietary projections has focused on staple cereals but despite heterogeneity in methods, data and reporting quality of the included studies we were able to report projections of future consumption for a diverse range of foods and food groups. There are a number of limitations to our review. Firstly, we searched for studies reported in the English language only, and may have missed studies published in other languages, particularly other official languages of India. Second, the variation in the projection methods and the gaps in reporting of the studies included in the review limited our ability to conduct quantitative analyses. Third, our review was able to report projections for nine major food groups, but sufficient data were not available to report projections of other foods such as salt, oils and nuts/seeds that have been linked to both population health and the environment. Additionally, given the nature of the data available, our review was not able to report and adjust for projected trends in total dietary energy intake. We are therefore not able to estimate directly the effects of projected changes to food consumption on health. Some gaps therefore remain in our understanding of the likely future composition and health effects of diets in India. There are also limitations to consider arising from the sources of data used in projection studies. The majority of the studies used household consumer expenditure data collected by the NSSO in India, either from a single data collection round or multiple rounds over time. Household consumer expenditure surveys have many advantages over other methods of collecting consumption information (Fiedler et al., 2012), and the NSSO database is considered a high-quality nationally representative data source (Natrajan and Jacob, 2018). However as collection relies on self-reporting of quantities and monthly expenditure on different food items by participants, it is subject to recall bias and misreporting, particularly of culturally sensitive foods (Natrajan and Jacob, 2018). Additionally, as expenditure is used as a proxy for consumption, household food waste, sharing of food between households, and meals eaten outside the home are not consistently included in reports using NSSO data, making comparisons between rounds difficult (Fiedler et al., 2012). Several studies analysed data from FAO food balance sheets and used data on national-level food availability as a proxy for household consumption. Findings of this review should therefore be interpreted as projections of relative, rather than absolute, future changes in consumption. In most cases, the models used to estimate future food consumption patterns were parameterised to account for likely future changes in demand-side factors such as population growth, urbanisation, expenditure elasticities and income growth. However, it is possible that food supply may become a limiting factor according to predicted impacts of environmental change on cereal and vegetable yields (Knox et al., 2012, 2016; Scheelbeek et al., 2018), and future consumption projections would need to take this into consideration. The IMPACT model has recently been updated to incorporate food security and climate change scenarios (Robinson et al., 2015). Lastly, India is a very culturally diverse setting, with food choices influenced by household purchasing power and cultural factors such as caste and religion (Natrajan and Jacob, 2018). Future consumption projections may seek to capture cultural differences by disaggregating projections into states or including population sub-groups into models to reflect the contextual relationships that drive dietary choices. Future possibilities and policy relevance- Our review identified a relatively small number of studies projecting future diets in India using a diversity of approaches. There would be a benefit to developing a set of best practices for dietary projection work to help researchers and policy makers. Methods for the validation of projection approaches (including defined sensitivity analysis) should also be considered. Similarly, while interdisciplinary systematic reviews are increasingly performed (Aleksandrowicz et al., 2016; Scheelbeek et al., 2018), current review guidelines are tailored to biomedical sciences. There is a need to develop robust interdisciplinary systematic review methods and quality assessment criteria, to improve scientific reporting in academic literature. The projected changes in food consumption in India identified in this review may have important impacts on population health, potentially for both the burdens of undernutrition and non-communicable disease, but the changes will require a food system that is able to deliver these new diets in an equitable and environmentally-sustainable manner. Income growth and urbanisation are currently major drivers of the demand for food, and there are major implications for the environment from food production in India (Green et al., 2018; Harris et al., 2017; Vetter et al., 2017). Comprehensive improvement of food systems, on nutritional, environmental, and social fronts, will require a range of policies that focus on food security, sustainable nutrition and healthy dietary choices. |
|||||||||||||||||||||||||||
| Acknowledgement | This study forms part of the Sustainable & Healthy Food Systems (SHEFS) programme supported by the Wellcome Trust’s Our Planet, Our Health programme [grant number 205200/Z/16/Z]. The Wellcome Trust had no role in the design, analysis or writing of this article. | |||||||||||||||||||||||||||
| References | 1. Aleksandrowicz L., Green R., Joy E.J.M., Smith P., Haines A. The impacts of dietary change on greenhouse gas emissions, land use, water use, and health: a systematic review. PLoS One. 2016;11(11) doi: 10.1371/journal.pone. e0165797. [PMC free article] [PubMed] [CrossRef] [Google Scholar]
2. Aleksandrowicz L., Tak M., Green R., Kinra S., Haines A. Comparison of food consumption in Indian adults between national and sub-national dietary data sources. Br. J. Nutr. 2017;117(7):1013–1019. doi: 10.1017/S0007114517000563. [PubMed] [CrossRef] [Google Scholar]
3. Alexandratos N., Bruinsma J. FAO; Rome: 2012. World Agriculture towards 2030/2050: the 2012 Revision. ESA Working Paper No. 12-03. [Google Scholar]
4. Amarasinghe U.A., Shah T., Singh O.P. International Water Management Institute; Columbo, Sri Lanka: 2007. Changing Consumption Patterns: Implications on Food and Water Demand in India. [Google Scholar]
5. Bhalla G.S., Hazell P., Kerr J. International Food Policy Research Institute; Washington D.C., USA: 1999. Prospects for India's Cereal Supply and Demand to 2020. [Google Scholar]
6. Carriquiry M., Dong F., Du X., Elobeid A.E., Fabiosa J.F., Hart C. IOWA; USA: 2010. FAPRI 2010 U.S. And World Agricultural Outlook. FAPRI Staff Reports No 4. FAPRI. [Google Scholar]
7. Chand R. Demand for foodgrains. Econ. Pol. Wkly. 2007;42(52):10–13. [Google Scholar]
8. Dastagiri M.B. National Centre for Agricultural Economics and Policy Research; New Delhi, India: 2004. Demand and Supply Projections for Livestock Products in India. [Google Scholar]
9. Drewnowski A., Popkin B.M. The nutrition transition: new trends in the global diet. Nutr. Rev. 1997;55(2):31–43. doi: 10.1111/j.753-4887.1997.tb01593.x. [PubMed] [CrossRef] [Google Scholar]
10. Du S., Mroz T.A., Zhai F., Popkin B.M. Rapid income growth adversely affects diet quality in China - particularly for the poor! Soc. Sci. Med. 2004;59(7):1505–1515. doi: 10.1016/j.socscimed.2004.01.021. [PubMed] [CrossRef] [Google Scholar]
11. Dyson T., Hanchate A. India's demographic and food prospects: state-level analysis. Econ. Pol. Wkly. 2000;35(46):4021–4036. [Google Scholar]
12. Fiedler J.L., Lividini K., Bermudez O.I., Smitz M.F. Household Consumption and Expenditures Surveys (HCES): a primer for food and nutrition analysts in low-and middle-income countries. Food Nutr. Bull. 2012;33(3_Suppl. 2):S170–S184. doi: 10.1177/15648265120333S205. [PubMed] [CrossRef] [Google Scholar]
13. Foley J.A., DeFries R., Asner G.P., Barford C., Bonan G., Carpenter S.R. Global consequences of land use. Science. 2005;309(5734):570–574. doi: 10.1126/science.1111772. [PubMed] [CrossRef] [Google Scholar]
14. Ganesh-Kumar A., Mehta R., Pullabhotla H., Prasad S.K., Ganguly K., Gulati A. International Food Policy Research Institute; New Delhi, India: 2012. Demand and Supply of Cereals in India. [Google Scholar]
15. Government of India . 2015. India's Intended Nationally Determined Contribution: Working towards Climate Justice.http://www4.unfccc.int/ndcregistry/PublishedDocuments/India% 20First/INDIA%20INDC%20TO%20UNFCCC.pdf [Google Scholar]
16. Green R.F., Joy E.J.M., Harris F., Agrawal S., Aleksandrowicz L., Hillier J. Greenhouse gas emissions and water footprints of typical dietary patterns in India. Sci. Total Environ. 2018;643:1411–1418. doi: 10.1016/j.scitotenv.2018.06.258. [PMC free article] [PubMed] [CrossRef] [Google Scholar]
17. Grieger J.A., Johnson B.J., Wycherley T.P., Golley R.K. Evaluation of simulation models that estimate the effect of dietary strategies on nutritional intake: a systematic review. J. Nutr. 2017;147(5):908–931. doi: 10.3945/jn.116.245027. [PubMed] [CrossRef] [Google Scholar]
18. Harris F., Green R.F., Joy E.J.M., Kayatz B., Haines A., Dangour A.D. The water use of Indian diets and socio-demographic factors related to dietary blue water footprint. Sci. Total Environ. 2017;587:128–136. doi: 10.1016/j.scitotenv.2017.02.085. [PMC free article] [PubMed] [CrossRef] [Google Scholar]
19. Indian Network for Climate Change Assessment. India: Greenhouse Gas Emissions 2007. Ministry of Environment and Forests, Government of India; India: 2010. [Google Scholar]
20. James K.L., Randall N.P., Haddaway N.R. A methodology for systematic mapping in environmental sciences. Environ. Evid. 2016;5(1):7. doi: 10.1186/s13750-016-0059-6. [CrossRef] [Google Scholar]
21. Knox J., Hess T., Daccache A., Wheeler T. Climate change impacts on crop productivity in Africa and South Asia. Environ. Res. Lett. 2012;7(3) 034032. [Google Scholar]
22. Knox J., Daccache A., Hess T., Haro D. Meta-analysis of climate impacts and uncertainty on crop yields in Europe. Environ. Res. Lett. 2016;11(11):113004. [Google Scholar]
23. Kumar P., Joshi P.K., Birthal P.S. Demand projections for food grains in India. Agric. Econ. Res. Rev. 2009;22(2):237–243. [Google Scholar]
24. Liberati A., Altman D.G., Tetzlaff J., Mulrow C., Gøtzsche P.C., Ioannidis J.P.A. The PRISMA statement for reporting systematic reviews and meta-analyses of studies that evaluate health care interventions: explanation and elaboration. PLoS Med. 2009;6(7) doi: 10.1371/journal.pmed. e1000100. [PMC free article] [PubMed] [CrossRef] [Google Scholar]
25. Minocha S., Thomas T., Kurpad A.V. Are ‘fruits and vegetables’ intake really what they seem in India? Eur. J. Clin. Nutr. 2018;72(4):603. [PubMed] [Google Scholar]
26. Misra A., Singhal N., Sivakumar B., Bhagat N., Jaiswal A., Khurana L. Nutrition transition in India: secular trends in dietary intake and their relationship to diet‐related non‐communicable diseases. J. Diabetes. 2011;3(4):278–292. doi: 10.1111/j.753-0407.2011.00139.x. [PubMed] [CrossRef] [Google Scholar]
27. National Institute of Nutrition . Indian Council of Medical Research Hyderabad; India: 2011. Dietary Guidelines for Indians: a Manual. [Google Scholar]
28. Natrajan B., Jacob S. ‘Provincialising’Vegetarianism. Economic & Political Weekly. 2018;53(9):55. [Google Scholar]
29. OECD/FAO . OECD Publishing; Paris, France: 2017. OECD-FAO Agricultural Outlook 2017-2026. [Google Scholar]
30. Parappurathu S., Kumar A., Kumar S., Jain R. National Centre for Agricultural Economics and Policy Research; New Delhi, India: 2014. Commodity Outlook on Major Cereals in India. [Google Scholar]
31. Petersen K.S., Flock M.R., Richter C.K., Mukherjea R., Slavin J.L., Kris-Etherton P.M. Healthy dietary patterns for preventing cardiometabolic disease: the role of plant-based foods and animal products. Current developments in nutrition. 2017;1(12):117. cdn. 001289. [PMC free article] [PubMed] [Google Scholar]
32. Rao N.D., Min J., DeFries R., Ghosh-Jerath S., Valin H., Fanzo J. Healthy, affordable and climate-friendly diets in India. Glob. Environ. Chang. 2018;49:154–165. [Google Scholar]
33. Robinson S., Mason-D'Croz D., Sulser T., Islam S., Robertson R., Zhu T. IFPRI; Washington D.C: 2015. The International Model for Policy Analysis of Agricultural Commodities and Trade (IMPACT): Model Description for Version 3. IFPRI Discussion Paper 1483. [Google Scholar]
34. Rodell M., Velicogna I., Famiglietti J.S. Satellite-based estimates of groundwater depletion in India. Nature. 2009;460(7258):999. [PubMed] [Google Scholar]
35. Rosegrant M.W., Agcaoili-Sombilla M., Perez N.D. International Food Policy Research Institute; Washington D.C., USA: 1995. Global Food Projections to 2020: Implications for Investment. [Google Scholar]
36. Rosegrant M.W., Leach N., Gerpacio R.V. Alternative futures for world cereal and meat consumption. Proc. Nutr. Soc. 1999;58(2):219–234. doi: 10.1017/S0029665199000312. [PubMed] [CrossRef] [Google Scholar]
37. Scheelbeek P.F.D., Bird F.A., Tuomisto H.L., Green R., Harris F.B., Joy E.J.M. Effect of environmental changes on vegetable and legume yields and nutritional quality. Proc. Natl. Acad. Sci. Unit. States Am. 2018:201800442. doi: 10.1073/pnas.1800442115. [PMC free article] [PubMed] [CrossRef] [Google Scholar]
38. Segovia-Siapco G., Sabaté J. Health and sustainability outcomes of vegetarian dietary patterns: a revisit of the EPIC-Oxford and the Adventist Health Study-2 cohorts. Eur. J. Clin. Nutr. 2018:1. [PubMed] [Google Scholar]
39. Springmann M., Godfray H.C.J., Rayner M., Scarborough P. Analysis and valuation of the health and climate change cobenefits of dietary change. Proc. Natl. Acad. Sci. Unit. States Am. 2016;113(15):4146–4151. [PMC free article] [PubMed] [Google Scholar] |
|||||||||||||||||||||||||||
| Endnote | Appendix ASupplementary data to this article can be found online at https://doi.org/10.1016/j.gfs.2019.05.006. |
|||||||||||||||||||||||||||
