Abstract
Agriculture contributes 18% of India's greenhouse gas (GHG) emissions. Yet, little is known about the energy requirements of individual crops, making it difficult to link nutrition-enhancing dietary changes to energy consumption and climate change. We estimate the energy and CO 2 intensity of food grains (rice, wheat, sorghum, maize, pearl millet and finger millet) taking into account their irrigation requirements, water source, dependence on groundwater, yields, fertilizer and machinery inputs. Rice is the most energy-intensive cereal, while millets are the least. Total energy use contributes 16% of GHG emissions for rice, due to its high methane emissions, and 56% for wheat. Fertilizer production and use dominates GHG emissions from all crops, contributing 52% of GHGs from cereals. Energy intensities vary by up to a factor of four across the country, due to varying water requirements, irrigation sources and groundwater table depths. The results suggest that replacing rice with other cereals has the potential to reduce energy consumption and GHGs, though the spatial variation of production shifts would influence the extent of this reduction and the possible trade-offs with total production.
Original language | English |
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Pages (from-to) | 841-849 |
Journal | Science of the Total Environment |
Volume | 654 |
DOIs | |
Publication status | Published - 1 Mar 2019 |
Externally published | Yes |
Funding
NDR and MPC were supported by the European Research Council Starting Grant No. 637462 , DecentLivingEnergy. KFD was supported by The Nature Conservancy's NatureNet Science Fellows programs and Columbia University 's Data Science Institute and Earth Institute. NDR and MPC were supported by the European Research Council Starting Grant No. 637462, DecentLivingEnergy. KFD was supported by The Nature Conservancy's NatureNet Science Fellows programs and Columbia University's Data Science Institute and Earth Institute.
Funders | Funder number |
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Data Science Institute and Earth Institute | |
Nature Conservancy's NatureNet | |
Columbia University | |
Horizon 2020 Framework Programme | 637462 |
European Research Council |