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The paper by Vetter et al. (2017) presented greenhouse gas (GHG) emissions associated with the production of major food commodities in India. Emissions of GHGs were calculated using the Cool Farm Tool and were based on farm management data for major crops (including cereals like wheat and rice, pulses, potatoes, fruits and vegetables) and livestock-based products (milk, eggs, chicken and mutton meat). Methods correction We corrected the calculated emissions by fixing errors in the model script and refined the methodological approach to accommodate the fixes. The correction included (i) fixing an error in application of the Stehfest and Bouwman (2006) model for calculating the N2O emissions, (ii) fixing a double counting of methane emissions for rice per kg production, (iii) excluding data points for plots with zero recorded yield (to avoid division by zero in carbon footprints), (iv) choosing appropriate crop classes for cotton and sugarcane, and (v) fix the percentage of rice under flooding conditions as previously it was not representative of the area under this management. Livestock emissions were revised after additional discussion with livestock experts. The new feed emissions were based on the amended GHG emissions of the crops, and the GHG emissions from enteric fermentation and manure were adjusted to match the Indian dataset in Herrero et al. 2013. For each crop and livestock species, weighted average emissions were calculated considering state-level total area under crop obtained from the Directorate of Economics and Statistics of the Government of India (http://eands.dacnet.nic.in) and total number of animals obtained from the 19th livestock census of the Government of India (GOI, 2012). Results update GHG emissions of food items up to farm-gate The updated results are given in Table 1 and Fig. 2. For paddy rice, >10,000 plots were available for analysis with a wide range of management practices, which is reflected in the GHG emission results (Fig. 2B). The main reason for the wide range in GHG emissions seen in rice was water management, the main determinant of CH4 emissions. In particular, continuous flooding generated the highest CH4 emissions, while longer and more frequent periods of drying reduced emissions. For example, changing the water regime from continuously flooded to multiple drying of the field, reduced CH4 emissions 9-fold (data not shown). The GHG emissions weighted by state level area gave a value of 2408 kg ha-1. Emissions of GHGs per kg product i.e. GHG emission intensity (Fig. 2C) varied markedly between livestock types. Emissions were highest for mutton meat (as the example for ruminant meat), followed by other livestock products such as dairy (milk) and poultry products (eggs, poultry meat). Emissions intensities were greater for livestock products than for crops. Mean GHG emissions were <0.8 kg CO2eq kg-1 product for all crops. The intensity of GHG emissions among crops was greatest in rice (0.73 kg CO2eq kg-1), followed by the group of other pulses. Greater GHG emissions intensity in pulses was mainly because of lower yield compared to other crop items. Intensities of GHG emissions from other crop groups were lower in the order of pulses, nuts and oils, cereals, fruits, roots and vegetables. GHG emissions from food consumption in India Fig. 3 A shows relative reported consumption by weight of commodities in the Indian Migration Study (IMS), while 3B shows their relative contribution to emissions. Livestock products other than ruminant meat (principally milk, poultry and eggs) contributed the most to total dietary GHG emissions. Although ruminant meat had the greatest GHG emissions per unit product, it contributed less to overall GHG emissions (21.7%) as consumption was low, accounting for only 0.4% of the mass of total food intake. Rice was the third largest contributor to total emissions (9.4%). Cereals other than rice and fruit products accounted for 12.9% and 22.5% of reported consumption by weight, respectively, yet as their emissions per unit of product were low, they made a relatively small contribution to total dietary GHG emissions, representing only 5.3% and 2% of total emissions, respectively. The groups “other” (including various crops from the subgroups nuts and oils, spices, and vegetables), potato and pulses also contributed little to total dietary GHG emissions compared to livestock. Amended discussion GHG emissions from livestock products Emissions of GHGs associated with livestock products depended largely on feed inputs, and in other studies had been shown to range between 0.8–2.4 kg CO2eq kg-1 milk, 1.7–6.6 kg CO2eq kg-1 eggs, 2.5–6.9 kg CO2eq kg-1 poultry meat and 10–20 kg CO2eq kg-1 mutton and lamb (Bellarby et al., 2013). Most of these values came from model-based studies which focused on Europe. Our results for milk in India were within the range of these studies. The calculated emissions of poultry products were lower than in the above studies, but gave similar results to those reported by Pathak et al. (2010). The GHG emissions for mutton were greater, resulting from embedded emissions in feed, which were 50–75% of the total GHG emissions per-animal-per-year. GHG emissions associated with Indian diets Overall, national GHG emissions associated with diets were greatest for livestock products such as milk and eggs (Fig. 3), because these were widely consumed products with high GHG emissions per unit of product. Although there was limited consumption of ruminant meat in India, its high GHG intensity means that it was the second greatest contributor to GHG emissions.
Vetter, S. H., Sapkota, T. B., Hillier, J., Stirling, C. M., Macdiarmid, J. I., Aleksandrowicz, L., … Smith, P. (2019, February 15). Corrigendum to “Greenhouse gas emissions from agricultural food production to supply Indian diets: Implications for climate change mitigation” [Agric. Ecosyst. Environ. 237 (2017) 234–241](S0167880916306065)(10.1016/j.agee.2016.12.024). Agriculture, Ecosystems and Environment. Elsevier B.V. https://doi.org/10.1016/j.agee.2018.11.012