Identifying data needs

We have outlined three example tasks to illustrate how this guide can help users identify data needs. 

The examples focus on analysing cattle systems because they are the highest emitters of GHGs. However, similar analyses can be conducted for other types of livestock systems including poultry, aquaculture, small ruminants, pigs, etc.

Example Task 1: Comparing feed demand with supply for cattle 

Example Task 2: Undertaking Tier 2 livestock calculations for a GHG inventory for cattle 

Example Task 3: Estimating the cost-effectiveness of a livestock GHG mitigation project for cattle 

For each example, the guide will provide an overview of specific data needs – and prompt users to visit the Data Sets page and provide search terms aligned with standardized vocabulary. 

If a user cannot find the secondary data they need in the compiled Data Sets, the guide will prompt you to the section on “Collecting and Managing Data”.

Overview 

Calculating the feed balance entails assessing the available animal feed resources relative to the needs of livestock in a certain geographic area. It involves evaluating feed resources such as grazed forages, crop residues and other resources to determine if they meet the nutritional requirements of livestock in terms of dry matter, protein and energy. 

The performance of livestock (e.g. their milk yields, growth rates, and rates of methane excretion) is dependent on their nutritional status. If the diet provides insufficient nutrients, then the animals will not achieve their full genetic production potential. Studying the feed balance within a region can provide us with insights into nutritional status by illuminating the extent to which there is adequate feed for the livestock population, how this is changing over time and to what extent different policies might change the feed balance. Feed balance also affects commodity prices (for instance through competition between livestock and humans for grains, and amplification of price volatility during droughts or supply shocks) and is therefore important for food security. 

Feed balance information can be used to support decision making. It can help in supporting government planning for feed distribution, herd mobility and destocking strategies in response to crises such as drought in resource limited regions such as sub-Saharan Africa. 

Calculation of feed balance involves several steps: compiling an inventory of feed resources, determining the nutritional value of feed resources, and estimating livestock needs based on livestock numbers and categories. Ideally feed balances should account for seasonal variation in supply and demand. 

• Livestock populations
• Animal level data to estimate feed intake rates (such as body weights, growth rates, physiological states and production levels). 

Feed balance calculations are complex because livestock feeds are highly diverse and often poorly quantified. For example, feeds are often not directly measurable commodities or are widely distributed over extensive grasslands and other rangeland environments which are poorly monitored (FAO 2012). They often depend on assumptions that are not easily verified (e.g. regarding how much feed is available and how evenly, or not, it is distributed). Confidence in feed balances could be improved by combining them with information on animal productivity, as a complementary outcome indicator of feed availability.

1. FAO’s Conducting National Feed Assessments (2012) provides a manual for those undertaking a national feed assessment. It gives guidance on how to undertake an assessment and illustrates the process with case studies. 
2. This paper titled Feed Inventory and Feed Balance of Ethiopia: Salient Findings and Way Forwardfrom Makkar et al. in Feedipedia (2019) quantifies the feed balance on a regional basis in Ethiopia and uses the results to suggest ways in which the feed balance could be improved.
3. Jameel Observatory’s Bridging data and policy: Assessing livestock feed balances for African livestock(2025) explains how work on feed balances in Nigeria has translated into policy impact.
4. USDA’s The Growing Demand for Animal Products and Feed in India: Future Prospects for Production, Trade, and Technology Innovation(2025) estimates future feed balances in India (to 2050), in light of changes in domestic population and food consumption trends.

Overview

Moving from a Tier 1 to a Tier 2 GHG inventory approach requires estimations of livestock emissions based on country-specific parameters rather than the use of default emission factors. Under a Tier 2 approach, feed intake is calculated based on animal production and productivity and nutritional quality of feed. In turn, this determines the emission of methane and nitrous oxide from enteric fermentation and manure excretion.

Using Tier 2 methods for livestock has a range of benefits. For example, they enable the inventory to better reflect a country’s national circumstances and capture reductions of emissions intensity resulting from increased animal productivity and better management practices.

Moving from Tier 1 to Tier 2 may seem daunting, but in most countries, there is plenty of information that, if systematized, provides a good starting point for compiling and maintaining an inventory using the Tier 2 approach (FAO & GRA 2020). In other words, a reasonable approach is to make the best use of existing data resources, then gather further data in a targeted manner, in ways that ensure quality and cost-effectiveness. 

Feed quality is cited as one of the parameters most likely to have the biggest impact on calculated emissions (FAO & GRA 2020), and an important factor to consider when defining livestock production systems and sub-categories for inventory purposes. The World Bank’s Enhancing Climate-Smart Outcomes for Livestock Systems: A How-To Guide Following the Project Cycleprovides a list of the parameters required for Tier 2 livestock emission calculations and suggest potential sources of data based on their relative priority (World Bank 2024). For example:

The feed data required for Tier 2 calculation are summarised in the table below. These parameters are used to estimate feed intake and the resulting flows of nutrients and organic matter that derive enteric methane emissions and manure excretion (resulting in methane and nitrous oxide emissions). The parameters required to quantify emissions from feed production are not included. These mainly relate to grass or crop yield, nutrient application rates, energy use, and land use change (these can be found listed in Annex 5 of the aforementioned World Bank document). 

The World Bank recommends that specific feed basket data (description of feed items and their proportion in the total dry matter intake) be gathered via surveys given the strong influence this parameter has on estimated emissions (World Bank 2024). Data on feed nutritional values (e.g., digestible energy and crude protein content) can be obtained from international literature although country specific data should be preferred. The Data Sets contains existing data resources for both composition of the feed ration (useful for where primary data collection is not viable) and nutritional value of feed ingredients.

• Livestock populations disaggregated by production and animal cohort (age & sex) 
• Data to estimate energy requirements and feed intake, i.e.: 
• Live weights 
• Mature weights 
• Live weight gain for growing animals 
• Animal activity levels (walking distance, topography) 
• Milk yield 
• Herd composition, including breeds

For national GHG inventories, emission calculations need to be undertaken periodically (e.g., annually) using the same methods and data sources in all years. This is so that the trend in emissions reflects actual changes in animal production systems and management practices rather than changes in estimation methods (FAO & GRA 2020).

1. The latest IPCC Guidelines for National Greenhouse Gas Inventories(2019).
2. The World Bank’s Enhancing Climate-Smart Outcomes for Livestock Systems: A How-To Guide Following the Project Cycle(2024). 
3. FAO and Global Research Alliance on Agricultural Greenhouse Gases’ joint Livestock Activity Data Guidance (L-ADG): Methods and guidance on compilation of activity data for Tier 2 livestock GHG inventories(2020) provides guidance on compiling the activity data required for using the IPCC Tier 2 approach with livestock.
4. CGIAR Research Program on Climate Change, Agriculture and Food Security’s Tier 2 inventory approaches in the livestock sector: A collection of agricultural greenhouse gas inventory practices(2018) provides examples of livestock GHG inventory practices. 
5. A new approach for improving emission factors for enteric methane emissions of cattle in smallholder systems of East Africa – Results for Nyando, Western Kenya, Goopy et al. (2018), provides an alternative to the Tier 2 method for estimating enteric methane for situations where feed intake is restricted.

Overview 

There are many ways in which agricultural emissions can be reduced, for example the paper Cost-Effectiveness of Greenhouse Gas Mitigation Measures for Agriculture(MacLeod et al. 2015) identified more than 150 individual mitigation measures. The challenge is to identify the “best” set of measures for a particular system and location. One of the key criteria when identifying the best measures is the cost-effectiveness — that is, the cost per unit of GHG mitigated. To quantify cost-effectiveness, information on mitigation measures’ costs and their effects on emissions are required.

Feed production and consumption are key drivers of emissions and are often the focus of livestock mitigation projects. The data required to assess their cost-effectiveness depends on the specific feed measures. A measure designed to improve productivity via changed ration composition will require similar feed data to Task 2 but at a project level. Additionally, the measure will also require information on which nutritional interventions are feasible and how they might change key parameters (e.g. cattle performance, ration digestible energy, rates of induced land use change, etc.). They will also require information on the prices of inputs and outputs, so that the net effect of the measure on farm profit can be determined.

• Livestock population of interest, disaggregated by production and animal cohort (age & sex) 
• Data to estimate energy requirements and feed intake, i.e.: 
• Live weights 
• Mature weights 
• Live weight gain for growing animals 
• Animal activity levels (walking distance, topography) 
• Milk yield 
• Herd composition, including breeds

1. The World Bank’s Enhancing Climate-Smart Outcomes for Livestock Systems: A How-To Guide Following the Project Cycle(Ng’ang’a et al. 2024) provides guidance across the project cycle on how to integrate climate smart approaches into livestock development projects and on how to quantify the contributions of projects to climate smart objectives.
2. A study titled Marginal Abatement Cost Curves for Latin American dairy production: A Costa Rica case study(Duffy et al. 2021) provides an example of estimating the cost-effectiveness of GHG mitigation in Costa Rican dairy production.
3. A report developed for a USAID project, titled Cost-benefit Analysis Of Improved Livestock Management Practices In The Oromia Lowlands Of Ethiopia(2020) conducted a cost-benefit analysis of three feed-related practices (e.g., deferred-rotation grazing, rangeland restoration and fodder cultivation) in Ethiopia.