Gujarat Climate Dashboard: Data Insights & Gaps in the AFOLU Sector
Introduction
The Agriculture, Forestry, and Other Land Use (AFOLU) sector, defined by the Intergovernmental Panel on Climate Change (IPCC), is a critical component of global greenhouse gas (GHG) emissions and carbon sequestration. AFOLU sector includes three key categories:
Livestock - encompassing enteric fermentation & manure management.
Land - covering forest land, cropland, grassland, wetlands, settlements & Other land.
Aggregate Sources and Non CO2 Emissions Sources on Land - which account for emissions from agricultural soils, rice cultivation, burning of crop residues, and other non-CO2 gases like methane (CH4) and nitrous oxide (N2O).
This article examines key insights, trends, identified data gaps, and recommendations for Gujarat’s AFOLU Sector, utilizing graphical representations to illustrate the data spanning from 2012 to 2022. In cases where data for specific years was unavailable, interpolation has been employed to derive estimates, ensuring a comprehensive analysis of the sector's contribution over the past decade.
Data Landscape
Accurately estimating GHG emissions from Gujarat’s AFOLU sector relies on comprehensive activity data across various sub-sectors. Key requirements include livestock population trends, manure management systems, land area distribution, and land-use transitions. Data on fertilizer application, biomass burning, and rice cultivation further refine emissions estimates. However, the absence of information on harvested wood products remains a critical gap in the assessment.
Data Requirements & Availability
For livestock emissions (3A), activity data is primarily sourced from the government livestock census, available for 2012 and 2017. Since census data is not available for other years, estimates have been derived through interpolation. Manure management systems, a key determinant of methane and nitrous oxide emissions, are not directly tracked in available datasets, so default IPCC values have been applied to estimate emissions.
In the land sector (3B), data is drawn from platforms such as BHUVAN, ISRO’s geo-platform and the Gujarat Forest Department, covering the period from 2012 to 2021, though with gaps in some years. While total land area by category (e.g., forest land, cropland, grassland, wetlands, and settlements) is available, specific land-use change data is lacking. This means that while we can track overall changes in land type areas over time, information on transitions between categories—such as land converted to cropland or forest land converted to settlements—is unavailable.
For aggregate sources and non-CO₂ emissions (3C), data availability is highly variable. There are no official records or satellite-derived estimates of biomass burning, making it difficult to assess emissions from wildfires or agricultural burning. Fertilizer application data, which is critical for estimating N₂O emissions from agricultural soils, is incomplete, requiring assumptions for certain years or regions. Rice cultivation data is available, but Gujarat has relatively low rice production, limiting its overall impact on emissions estimates.
In contrast, harvested wood products (3D) present a major data gap, with no available records on wood product production, trade, decay rates, or long-term carbon storage. The lack of this data means that emissions or removals associated with harvested wood must either be excluded or estimated using broad national/regional assumptions.
Sectoral insightshttps://lh7-rt.googleusercontent.com/docsz/AD_4nXeIs5PmpHDjnyW6lT4hg-bcPGP9KpHIfC97NtGmdrHwF4ksvm7HKyELXruPSnccIcVxpG0wMLDdRWB1Rbnv21EAPir3BYXXpvmfGH4TUSjDcOUoY4WMZ3OZH0nEgqxxXKNj8cosiA?key=VO1y6TDKh86r3NTBwxB4MEmt
In the AFOLU sector, livestock is the dominant source of emissions, accounting for 65.4% of total sectoral emissions, primarily from enteric fermentation and manure management. Aggregate sources, including fertilizer use and biomass burning, contribute 19.2% and the land sector contributes 15.4%. The land sector is a mix of emissions from land use change patterns as well as removals due to carbon sinks. However, the lack of detailed land-use transition data limits a full understanding of sequestration drivers and emissions trends.
Livestock
Livestock emissions in Gujarat are primarily driven by enteric fermentation (54.9%) and manure management (45.1%), highlighting the need for targeted mitigation strategies in both areas. The emissions trend remains stable over the years, reflecting consistent livestock populations and management practices. However, the sudden drop in emissions post-2017 is due to unavailability of census report data for the subsequent years. As previously mentioned, since census data is available for every five years, the estimates between 2012 and 2017 have been derived through interpolation.
Land
https://lh7-rt.googleusercontent.com/docsz/AD_4nXcuYKuH4zdgQ8Haymlq9G88SClP-Q90tfdmXx81qoe7HgTmMW-5-E1VqwD6GbupFvhzPZncnhj-TkuLlzkJ6HFNstzNqRu19DVYdJCKFR_YDnKYM5ebU4aZex0GgYt7Ds5NOQPhbQ?key=VO1y6TDKh86r3NTBwxB4MEmthttps://lh7-rt.googleusercontent.com/docsz/AD_4nXdjKwITT9xWzwh9B-F_OHu9dE2sTNlIQQ3mcU58420VT7TrtK7bY-v9QFVFLQzxQmIRHxVV32KkgFMwchunzDIA1O-zi0OfANQ1_i1DxthTWC2C86Gm8wReanO2ZByPlY5JTu-ROw?key=VO1y6TDKh86r3NTBwxB4MEmt
The land sector in Gujarat exhibits significant variability in emissions and carbon sequestration trends across different land categories from 2012 to 2022, as captured using data from the Principal Chief Conservator of Forests, Gujarat, and Bhuvan (ISRO).
Forest land consistently acts as a carbon sink, with the highest absorption in 2012, followed by a decline until 2014, a stable trend from 2015 to 2021, and a sharp reduction in sequestration in 2022.
https://lh7-rt.googleusercontent.com/docsz/AD_4nXcy4avAxgK99pY--3vcXy-aEMkL5m4nd9mO42X3BFTwj665oZ8O3J0ocdpHZM42kKsV9rf4fmrF2qEGvhuG9kCdeK6XxrwQuW42s7cI1TOWkxeS6Hpyv0RzIveFTVRT7jwl07700g?key=VO1y6TDKh86r3NTBwxB4MEmthttps://lh7-rt.googleusercontent.com/docsz/AD_4nXega1tA2OPerQvvYeLp9_YUrx58_MNmjxMKhl215fjn4fQ6m7fH7WN1raVekDCwBwzU5VvSbHHHUrn9FJkd3-c1J5qSNJEcIMehrsX1KjFsgYXKkamKF6krotp-OQoaq0L5Hpa-?key=VO1y6TDKh86r3NTBwxB4MEmt
Cropland emissions show a highly fluctuating pattern, peaking in 2018 before dropping sharply in 2019, turning negative in 2021, and rebounding to high emissions in 2022. Wetland emissions remain stable until 2017, peak in 2018, and gradually decline thereafter, while settlements exhibit near-zero emissions until 2016, a spike in 2017, and another sharp rise in 2022.
https://lh7-rt.googleusercontent.com/docsz/AD_4nXd4Vgd88A3TMifRC_N-D47puD7vFRk-LZEdLRH88-EOlip0Gnw9pycqkLXQt8L77saZgj3FNAF663UPL4RqWqRv3Wf0s-lR71FmiIDib-vq8OH0qtadhyJLJVO-E2j7pfHfGLZW1g?key=VO1y6TDKh86r3NTBwxB4MEmthttps://lh7-rt.googleusercontent.com/docsz/AD_4nXcjqwTQdvaCYlcuRWi-O_nOX4fFgXlmqGCzUYs3yB0AkA-jRYT4d8kPmQjUvduevyl70ZqxthxSyVzYuJEN5ZGEBJhO7pdDi8E2rtb4S9zSeftOlBtEsZf3jhF-MS_vNujKry2fRg?key=VO1y6TDKh86r3NTBwxB4MEmt
Other land categories largely act as carbon sinks, with the highest absorption in 2018, but show a declining sequestration trend from 2019 onwards. The spike in emissions observed in 2018 for cropland, wetlands, and other land categories remains unexplained, with no clear attribution in official reports. Potential factors could include changes in cropping patterns, climatic variations, or land management practices, but further investigation is needed to confirm the underlying drivers.
Aggregate Sources and Non-CO2 Emission Sources on Land
For the non-CO2 sources the trend shows relatively stable emissions from 2012 to 2017, with direct N₂O emissions—primarily from synthetic fertilizers, manure application, and biological nitrogen fixation—being the dominant source. Indirect N₂O emissions, which result from nitrogen losses through volatilization, leaching, and runoff, contribute a smaller but consistent share. Given that this sector is closely linked to livestock emissions, its data availability aligns with the livestock sector, relying primarily on livestock census data and IPCC default values for manure management.
Bridging Data Gaps
While emissions data for the land sector is improving, several gaps remain that make it difficult to fully capture carbon fluxes. Forest land estimates lack information on fuelwood extraction, disturbances, and drained organic soils, which impact emissions and sequestration.
Tracking land-use change is also challenging, as transitions between land categories are not well-documented. In cropland and grassland, data on biomass growth, soil carbon changes, and the role of climate and soil factors is incomplete. Similarly, for wetlands, settlements, and other land types, there is limited information on land-use conversions and organic soil management. In aggregate sources, such as biomass burning and soil amendments, gaps in activity data affect emissions estimates.
Key recommendations
To improve the accuracy and comprehensiveness of land sector emissions estimates, enhanced data collection, monitoring, and reporting mechanisms are essential.
1. More Frequent and Granular Data Collection
The livestock census, currently conducted every five years, should transition to a two-year cycle to capture more frequent changes in livestock populations and manure management practices. Similarly, land-use data should be updated regularly, incorporating annual or biennial assessments to improve emission estimates from croplands, forests, and other land categories.
2. Leveraging Remote Sensing and Geospatial Technologies
Remote sensing and satellite imagery can be integrated into land monitoring efforts to track land-use changes and vegetation cover with higher accuracy. Platforms like ISRO’s Bhuvan can provide real-time insights into land conversion and degradation patterns in a manner aligning with the IPCC requirements.
3. Addressing Data Gaps in Aggregate and Non-CO₂ Emissions
Biomass burning, soil amendments, and nitrogen inputs remain underreported. Developing a standardized tracking mechanism for emissions from urea fertilization, lime application, and managed organic soils will ensure a more complete representation of land-related emissions.
4. Enhancing Data Integration and Coordination
To improve the accuracy and usability of the emissions inventory, data from multiple government departments like the Gujarat Forest Department, research institutions like Agricultural universities, and satellite monitoring agencies can be systematically integrated. Strengthening inter-agency coordination—particularly between the Forest Department, Agriculture Ministry, and environmental agencies—will help ensure a more harmonized and comprehensive dataset.
5. Capacity Building and Institutional Strengthening
Regular training programs and knowledge-sharing workshops should be conducted for government officials, researchers, and local communities to enhance their ability to collect, interpret, and utilize emissions data effectively.
Conclusion
A robust and data-driven emissions inventory is essential for Gujarat’s state-level net-zero strategy. While significant progress has been made in estimating emissions across key sectors, data gaps and methodological constraints must be addressed to enhance accuracy and policy relevance. Strengthening data collection cycles, leveraging remote sensing technologies, and improving inter-agency coordination will enable more informed decision-making. By prioritizing these improvements, Gujarat can develop a more comprehensive emissions profile, ensuring that its climate policies are grounded in reliable evidence and aligned with long-term sustainability goals.