MRV guidance on livestock, agroforestry and rice systems
The practice of MRV of livestock mitigation actions is still in an early stage. With the exception of policies, whose effects are reflected in national GHG inventories, there are few operational practical examples of MRV systems. However, a number of countries have begun the process of designing MRV systems.
The recommendations presented here draw on interviews with people involved in MRV system development in eight countries (Brazil, China, Costa Rica, Colombia, Ecuador, Ethiopia, Kenya, and Uganda) supplemented by reference to other experiences in the available literature.
Measuring emission reductions
Countries seeking to measure emission reductions through the national GHG inventory should consider adopting a dynamic Tier 2 approach by involving stakeholders, exploring alternative data sources and increasing transparency.
Involving key stakeholders in developing inventory improvement plans
Where countries seek to measure and report progress in livestock sector mitigation through the national GHG inventory, enabling the GHG inventory to capture changes in production and productivity is a key area for inventory improvement in most countries. Political and financial support for making inventory improvements are likely to be stronger where key stakeholders are aware of the contribution of proposed improvements to policy goals in climate, livestock, environment, or other sectors. Where suitable platforms do not exist, agencies responsible for livestock inventory compilation (whether researchers or officials) may need to consider other ways to engage relevant stakeholders in defining policy goals, identifying and justifying improvements, and related investments.
Exploring alternative data sources
There is a range of data collection options for dynamic Tier 2 approaches. Assessing available data and gaps in availability are a key starting point. In some countries, it may be relevant to link discussions on inventory improvement with livestock statistics data improvements processes, such as country improvement plans in relation to the Global Strategy for Improvement of Agricultural Statistics (GSARS) or the System of Economic-Environmental Accounting (SEEA). Agricultural censuses and sample surveys between censuses provide an opportunity to collect relevant data. Where regular data collection is not possible, modeling, expert judgment, and other data procedures are options to consider. Features constituting acceptable inventory compilation procedures within the MRV framework set out in the IPCC guidelines should be discussed among relevant stakeholders.
Increasing transparency of national GHG inventory documentation
Initially, few countries have all the data needed for a dynamic Tier 2 approach available. Increasing the transparency of data sources, methodologies and improvement plans can moderate the effects of pragmatic inventory compilation decisions on inventory accuracy.
Strengthen synergies between improvements in statistical systems or other livestock data systems and improvements in MRV by exploring links with statistic improvement strategies and supporting production and performance data improvements.
Exploring links with livestock statistics improvement strategies
Close collaboration between agencies involved in livestock inventory compilation and statistical agencies has been identified as an enabler of inventory improvement and a barrier where collaboration is weak. Globally, large gaps in the availability and quality of livestock statistics are common. Above all, this impedes effective decision-making for investment in the livestock sector. Therefore, there are potential synergies between improvements in livestock MRV and stakeholders’ needs for improved data in the sector. At the international level, the GSARS and SEEA are major initiatives of direct relevance to livestock statistics. Both are translated into action plans at the country level. The potential for linking these processes with MRV processes should be further explored.
Supporting improvements in livestock production and performance data
Some countries wishing to adopt a Tier 2 approach have begun by investing in direct measurement of emissions, but progress is hindered by subsequent recognition that longer data time series and further experiments are required. The IPCC Tier 2 models can also be populated with data on livestock production practices and performance while using default values for parameters that are rarely measured. Yet significant gaps remain in many countries in data availability on livestock populations, herd structure, feeding and other parameters. Improved availability and quality of data on these parameters can not only benefit inventory improvement but also benefit decision-making by stakeholders in the livestock sector. Scientific research on appropriate data collection methods for these key parameters is much more limited than research on emissions.
In addition to reviews and guidance on ‘gold standard’ or ‘best practice’ data collection methods, guidance on alternative methods suited to different purposes and contexts should be developed. Comparisons between methods and assessments of relative costs would enhance stakeholders’ understanding of practical trade-offs between methods. This should also include methods for modeling livestock population changes and production parameters to enable the in-filling of data gaps between national surveys. In particular, livestock sector mitigation actions will be implemented directly through or in association with the private sector. Embedding MRV of mitigation actions in existing data systems has been identified as a potentially cost-effective option in several countries. At present, very little is understood about the private sector’s needs for improved data that could be served through MRV systems, or their current data collection practices and how these might be improved and linked to MRV systems.
The UNFCCC’s Koronivia Joint Work on Agriculture (KJWA) creates an opening for agroforestry to take on an important role in response to climate change, especially in Africa where many African nations have plans to use agroforestry to meet climate goals. However, technical and institutional barriers often prevent agroforestry from being represented in UNFCCC MRV processes. Therefore, in order for agroforestry contributions to be recognized and rewarded countries need reliable systems for MRV of agroforestry.
Major recommendations for MRV of agroforestry
At the regional level, there is a need for more sharing of experience and capacity-building on agroforestry and other forms of trees outside forests in both national forest and GHG inventories. Regional and international organizations supporting MRV capacity-building should recognize the significance attached to agroforestry in African countries’ climate change strategies, and convene experience sharing and capacity-building on the topic (see Box 1).
In particular, regional and international organizations should:
- Continue to strengthen technical capacities to provide a consistent representation of the land, including trees outside forests, in national inventories;
- Increase access to specific data for carbon quantification of agroforestry to support MRV of mitigation co-benefits;
- Assist in scaling project- and program-level MRV of agroforestry to national MRV systems;
- Continue capacity-building on creating sustainable GHG inventories, and coordination between the different MRV systems in the UNFCCC; and
- Give a prominent profile to agroforestry in the implementation of the Koronovia Joint Work Plan.
|Box 1. Tree crops and forests in Ghana
|Ghana’s REDD+ Strategy identified expansion of cocoa and other tree crops as a key driver of forest degradation and deforestation. The country has proposed large-scale sub-national programs focusing on the main cocoa and shea-producing regions. The Cocoa Forest REDD+ Program (GCFRP) and the Shea Savanna Woodland Programme are to be supported from different sources of climate finance. Each program has proposed a distinct MRV system that links with UNFCCC-related national MRV systems while also meeting the MRV requirements of each funding source. The design of the GCFRP is more advanced than for the shea program and illustrates a general approach that could be applied to multiple sub-national agroforestry programs. For the GCFRP, the FCPF Carbon Fund will pay for emission reductions verified in accordance with the methodological framework of the fund. A forest reference level for the program area, consistent with the national REDD+ FRL, has been defined following the national forest definition, which excludes tree crops such as cocoa but includes timber plantation species. The program MRV system proposes to use high-resolution (Landsat 8) imagery to detect and report forest cover change every two years, with specific monitoring methods proposed for tracking the key drivers (e.g., illegal logging and timber harvest, fuel-wood collection and fire). Within the cocoa landscape, increasing shade trees is one climate-smart option. Individual projects embedded in the GCFRP are investigating the potential for using carbon-market methodologies to value the carbon increment in the cocoa landscape.
Although there have been advancements in the development of mitigation options for rice cultivation there has not been sufficient implementation of those options by rice producers or incorporation of those options into administrative policies. In addition, methodologies for implementing mitigation programs in rice cultivation have not been well documented (Minamikawa et al., 2018).
This section provides basic information about the implementation of MRV in the agricultural sector, specifically for soil GHG emissions under paddy rice management.
Major recommendations for project measuring
- Data essential for CH4 emission calculation are (1) the emission factor (EF) and SFw (scaling factor for water management), (2) the area of the project, and (3) the duration of the crediting period.
- The responsible party shall approximately calculate the length and breadth of a project necessary to make it economically profitable.
- The EF and SFw of CH4 for project water management should be obtained for the project area to maximize the additionality.
- The responsible party should monitor and report long-term soil organic carbon (SOC) changes during the crediting period to confirm the existence or absence of the effect of these changes on the total GHG emission.
- Uncertainties associated with the calculated results that stem from both GHG monitoring and agricultural activity should be quantified to meet the principle of conservativeness.
- Model simulation is a sophisticated approach for GHG calculations in a wide area, but it requires many input parameters.
Major recommendations for project monitoring and reporting
Monitoring and reporting are essential to demonstrate the appropriate implementation of project water management:
- Items that should be monitored and reported include basic information on agricultural activities, including water management, and rice productivity during the crediting period.
- Criteria for appropriately implementing project water management shall be determined based on the definition of project water management, the required level of assurance, the spatial scale of the project area, and the limitations imposed by MRV costs.
Major recommendations for project validation
This section brings practical tips and near-future expectations for the validation and verification process implemented by a third party. Although the validation and verification process is independent of the monitoring and reporting processes implemented by the responsible party, the methodology for validation and verification should be developed by common consent among all concerned parties in accord with the MRV principles.
- Verifiers and validators should understand the ability and limitations of the currently available techniques to quantify soil GHG emissions with high spatiotemporal variability.
- In the near future, more comprehensive and innovative methods need to be developed and adopted to reduce MRV costs in the validation and verification process.