1.6 Climate mitigation

Purpose of indicator

Globally, the agriculture and land use sector contributes approximately 18% of total greenhouse gas emissions, rising to nearly 60% of GHG emissions in parts of the developing world where agriculture dominates the economy. With the world set to exceed the emissions threshold for keeping warming below 1.5°C in less than 10 years (Liu et al., 2023), mitigation from all sectors is a top global priority.

This indicator is relevant for global climate mitigation goals (Paris Agreement) and nationally determined contributions (NDCs). It is also relevant for the post-2020 Global Biodiversity Framework Target 10 on ‘Proportion of agricultural area under productive and sustainable agriculture’, where changes in soil organic carbon stocks is a complementary indicator (CBD/COP/15/L.26).

Key Metadata

Metadata Item	Description
Indicator Name	Net greenhouse gas emissions
Theme	Climate mitigation
SDGs Targeted	Paris Agreement, SDG 13
Data Source	Farmer survey, literature review, and/or field measurements
Measurement	Qualitative score of net greenhouse gas emissions (alternative: carbon storage in soil and above-ground biomass)
Measurement Units	5-point Likert scale (alternative: t C ha⁻¹ yr⁻¹)

Guidance on Measurement

We discuss two options for measurement of net greenhouse gas emissions: i) qualitative assessment of climate mitigation potential of on-farm practices, ii) quantitative assessment of carbon storage and sequestration only, based on soil organic carbon (SOC) and estimates of carbon stored in live woody biomass.

Qualitative assessment

Given the importance of climate change mitigation and the relative difficulty of accurately quantifying net GHG emissions from smallholder agriculture across diverse country contexts, we propose a qualitative approach for integration of mitigation into all performance assessments.

Measurement involves:

Classifying farming practices according to their net emissions: 1,2,3,4,5, where 1 represents high emitting and low sequestrating practices, and 5 represents low emitting and high sequestrating practices.
Collecting household survey data on the share of cropland under each practice.
Calculating a weighted average score for all practices found on the farm, using the share of cropland as weights.

Note that the final score could instead be weighted by productivity for an emissions intensity estimate, if practice data are collected for each main crop/livestock/fish.

Locally relevant farming practices need to be identified and classified into a net emissions category. The classification can be done through literature review and local experts.

Some qualitative assessments/reviews of mitigation potential of agricultural practices already exist. We use one such review by Bell et al. (2018) to provide an example scoring of practices by emission category. The overall score is an average of the component score. In the overall score, 1 represents high emitting and low sequestrating practices, and 5 represents low emitting and high sequestrating practices.

Practices	Carbon sequestration in soils	Carbon sequestration in biomass	Avoided GHG emissions	Overall score	References
Cropping systems
Agroforestry	5	5	4	4.66	Bell et al. (2018)
Biochar	3	3	2	2.66	Bell et al. (2018)
Crop rotation	3	3	3	3	Bell et al. (2018)
Drip irrigation	3	3	2	2.66	Bell et al. (2018)
Embedded natural (hedgerows)	4	5	4	4.33	Bell et al. (2018)
Green manure	4	3	2	3	Bell et al. (2018)
Intercropping	5	3	5	4.33	Bell et al. (2018)
Microdosing	3	3	2	2.66	Bell et al. (2018)
Mulching	4	3	3	3.33	Bell et al. (2018)
Reduced tillage	4	3	4	3.66	Bell et al. (2018)
Lowland rice systems
Biochar	4	4	2	3.33	Bell et al. (2018)
Green manure	4	4	4	4	Bell et al. (2018)
Microdosing	4	4	2	3.33	Bell et al. (2018)
Rice-fish integration	3	3	4	3.33	Bell et al. (2018)
Alternate wetting and drying	3	3	4	3.33	Bell et al. (2018)
Livestock systems
Improved feed quality	4	4	4	4	Bell et al. (2018)
Planting N-fixing legumes	4	3	3	3.33	Bell et al. (2018)
Rotational grazing	4	3	3	3.33	Bell et al. (2018)
Fish systems
Improved feed quality	3	3	3	3	Bell et al. (2018)

Quantitative assessment

Measurement involves collecting data on tree diameter at breast height (dbh), tree height, number of trees, and collecting data on soil organic carbon through laboratory analysis of soil samples.

Carbon storage and sequestration in woody biomass:

Get on-farm tree count for trees with dbh > 50cm or >10m in height
Convert above and below-ground biomass to t/ha using IPCC conversion factors.

Soil carbon storage:

Use the median SOC in the 0-20cm zone from across the three plots per farm (see Soil Health indicator).

Additional GHG measurement options (beyond the scope of the standard HOLPA tool, see Complementary Indicators)

The most precise, quantitative measurements of GHG emissions are obtained through field measurements of GHG fluxes, either through collection of gas samples and later analysis in a gas chromatography lab (e.g. for ruminant methane emission), or through real-time measurement of gas fluxes using an infrared gas analyzer (e.g. leaf photosynthesis and respiration, soil respiration, or landscape eddy covariance). These methods require costly equipment and/or laboratory analysis, as well as expert technicians to collect and analyze the samples. Further details on the procedures are provided in the section on Complementary Indicators.

In lieu of direct measurement, all other approaches to quantifying GHG emissions from agriculture rely on collecting activity data and using emissions factors to convert activities into emissions levels (e.g. from IPCC). Activity data is a description of the farm practices and areas (or number of livestock) over which they are practiced. Activity data can be collected via household surveys, potentially supplemented with measurement of farm sizes. Higher levels of detail in the activity data (amounts of inputs used, crops and crop varieties, soil types, feed formulations, tree species in agroforestry systems, etc.) generally allow for more precise estimation of emissions. However, conversion of activity data into quantitative emissions relies on the use of emissions factors. Most widely available emissions factors (e.g. Tier 1 Approach) are based on data from temperate countries (Ogle et al. 2013; Albanito et al. 2017) and may not be appropriate for estimating GHG emissions from agricultural activities in tropical countries.

Models of GHG emissions also rely on collection of activity data and emissions factors, but many can account for local conditions, such as soil types, livestock breeds, climate, etc to produce more appropriate emissions estimates for tropical or developing countries not well represented in the Tier 1 approach. Several models are available for calculating emissions, including the Ex-Ante Carbon Balance Tool (EXACT) and the Cool Farm Tool, however use of these models requires a degree of technical expertise.

Guidacne on Data Entry and Reporting

no information is available

Calculation Method


  ### join the practices listed in the survey data with the reference scores
  tmp <- ecological_practices %>%
    left_join(
      ref_cli_mitigation %>%
        select(ag_practice_id, score),
      by = c("practice_number" = "ag_practice_id")
    ) %>% #change to number when possible

    ## group by farm; calculate total area of each practice, the share of total area per practice and the weighted score
    ## (practice scores weighted by area of land that practice is implemented on)
    group_by(farm_id, owner_id, submission_id) %>%
    mutate(
      total_area = sum(practice_area_ha),
      practice_share = practice_area_ha / total_area,
      weighted_cc_score = score * practice_share
    ) %>%

    ## overall KPI is the mean of all weighted scores

    summarise(kpi8_climate_mitigation = mean(weighted_cc_score, na.rm = TRUE))

  ### include the new variable in the performance_indicators data frame
  performance_indicators <- performance_indicators %>%
    left_join(
      tmp %>% select(farm_id, owner_id, submission_id, kpi8_climate_mitigation)
    )