Measuring the Environmental Performance of Agriculture

This dashboard explores seven key aspects of agri-environmental performance:

1. Key characteristics of the agricultural sector
   The agriculture characteristics section presents a short summary of the agricultural context, focusing on agricultural land and cropland as well as livestock and irrigated area indicators.
2. Agricultural land use change
   The agricultural land use change section focuses on total agricultural land, cropland, and pasture.
3. Production and input use
   The production and input-use panel shows trends of agricultural production and input use indicators. Production is measured using the agricultural production index of the FAO. Inputs are expressed on a per-hectare basis to control for the quantity of agricultural land.
4. Nutrients
   The nutrient balances section shows long-term trends in nitrogen and phosphorus balances and efficiency of use; nutrient balance indicators can act as a signal for the potential environmental impact of agriculture on air, soil and water.
5. Greenhouse gas emissions
   The section on greenhouse gas emissions presents long-term trends of total greenhouse gas emissions and emissions intensity per USD of Gross Production Value (in constant 2014-2016 dollars). Agricultural activities contribute to global warming by emitting nitrous oxide and methane, two powerful greenhouse gases.
6. Ammonia emissions
   The ammonia emissions section presents trends of agricultural ammonia emissions, which are associated with two major types of environmental problems: acidification of soil and water, and eutrophication of water bodies.
7. Farmland birds biodiversity
   The farmland birds biodiversity section shows the trends of the farmland birds index, which is used as a proxy for farmland biodiversity, although this is not suitable in all contexts (e.g. where birdlife includes invasive species).

Methodologies to measure indicators are not well-established in all cases and may differ across countries. Caution must be exercised in comparing the evolution of indicators across countries, as a given change in a particular indicator may have differing effects depending on the specific context. In some cases, indicators may differ from official statistics due to methodological differences. For some countries, some sections and figures are omitted due to lack of data.

To account for differences in the size of OECD countries, the scales of the figures are different across countries and the performance of a given country is compared to that of the OECD median. Nevertheless, national-level data can conceal significant subnational differences and variations in indicators may not necessarily reflect the impact of policies. To minimise the effect of weather or market shocks on indicator levels, the dashboard presents multi-year trends.

This indicator covers four types of land use: total agricultural land, arable crops, permanent crops and pasture (OECD, 2023 _[1]). For some countries, due to methodological differences, the sum of cropland (arable crops plus permanent crops) and pasture is not equal to total agricultural land.

Livestock unit (LU) is a reference unit which facilitates the aggregation of different categories of livestock. It is usually derived in terms of relative feed requirements. Conversion ratios are generally based on metabolisable energy requirements, with one unit being considered as the needs for maintenance and production of a typical dairy cow and calf (FAO, 2011 _[2]).

Land area equipped for irrigation that is actually irrigated in the reference year. This indicator covers both fully controlled and partially controlled irrigation. It refers to the physical area of land irrigated (OECD, 2023 _[1]).

The indicator captures the direct use of energy (solid fuels, petroleum products, gas, electricity, renewables, heat) by primary agriculture, which includes energy consumption for irrigation, drying, horticulture, machinery and livestock housing (OECD, 2023 _[1]). Direct on-farm energy consumption acts is a driver of climate change through greenhouse gas emissions, although emissions of CO₂ from fossil fuel energy use are a minor contributor to agricultural greenhouse gas emissions compared to methane and nitrous oxide. There are also secondary environmental concerns with regard to energy consumption in agriculture, related to air pollution from burning fossil fuels, such as particulate matter and ozone depletion (OECD, 2013 _[3]). 

Inorganic fertiliser use is proxied by fertiliser sales, which are part of nutrient balance indicators (see the description of nutrient balance indicators below). Both N and P fertilisers are crucial inputs used in the production of crops. However, excess fertiliser application results in nutrient runoff and leaching, potentially polluting water bodies. N fertiliser use also contributes to pollution emissions from ammonia and nitrous oxide.

This indicator refers to irrigation and other agricultural abstractions (such as for livestock) from rivers, lakes, reservoirs and groundwater (shallow wells and deep aquifers), but excludes precipitation directly onto agricultural land (OECD, 2023 _[1]). “Water abstraction” is different from “water consumption”, which relates to water depleted and not available for re-use.

Nutrient balance indicators can act as a signal for the potential environmental impact of agriculture on water and air. The OECD agricultural nutrient balance indicators are gross balances. They are calculated at the national level, and measure the difference between the total quantity of nutrient inputs entering an agricultural system (mainly fertilisers and livestock manure), and the quantity of nutrient outputs leaving the system (mainly the uptake of nutrients by crops and grassland). In the case of nitrogen, the gross nutrient balance includes all emissions of environmentally harmful nitrogen compounds from agriculture into the soil, water and the air, while the net balance excludes air emissions. In the case of phosphorus, there are no air emissions, so the gross balance is the same as the net balance. Gross balances are expressed in kilogrammes of nutrient surplus per hectare of agricultural land per annum. It is important to bear in mind that these indicators are proxies for environmental pressures at the national level, and do not take sub-national differences into consideration. There are several limitations that could limit cross-country comparisons of nutrient balance levels, such as the precision and accuracy of the underlying nutrient conversion factors and the uncertainties involved in estimating nutrient uptake by pasture areas and some fodder crops (OECD, 2013 _[3] and EUROSTAT/OECD, 2013 _[4]). 

Nitrogen and phosphorus use efficiency are calculated as nutrient outputs divided by the nutrient inputs. These ratios give an indication of the relative utilisation of nutrients applied to an agricultural production system. In principle, by decreasing the nutrient surplus over time, the nutrient use efficiency increases. Nutrient use efficiency depends on the production system and its management. 

Agriculture is not only responsible for GHG emissions due to the direct management and operation of farms but also indirectly due to the conversion of natural habitats such as forested lands and peatlands to agricultural fields (OECD, 2019 _[5]). This indicator was obtained from the United Nations Framework Convention on Climate Change (UNFCCC) database on national inventory reports (NIR) (UNFCCC, 2022 _[6]) for OECD countries included in Annex I of the UNFCCC. For OECD countries not included in Annex I, data were compiled directly by the OECD via direct questionnaire. While the UNFCCC requires countries to use common reporting format (CRF) tables to ensure robust and standardised reporting, estimates made by individual member countries may vary, depending on factors and methods used in their own calculations. In addition, assumptions made in agricultural GHG emission calculations simplify complex agricultural systems, introducing uncertainty into the estimate of GHG emissions. Although the OECD questionnaire for OECD countries not included in Annex I follows the CRF tables to facilitate the treatment of the responses, the same caveats as for UNFCCC inventories apply. 

This indicator measures agricultural emissions of greenhouse gases per agricultural gross production value. It helps to assess whether growth in agricultural production is decoupled from greenhouse gas emissions of the sector. Value of gross production has been compiled by multiplying gross production in physical terms by output prices at the farm gate, measured in constant 2014-16 USD million. Thus, value of production measures production in monetary terms at farm gate-level. Agricultural gross production value measures production in monetary terms at farm-gate level and is calculated by multiplying gross production quantities by output prices at farm gate (FAOSTAT, 2023 _[7]). Since intermediate uses within the agricultural sector (seed and feed) have not been subtracted from production data, this value of production aggregate refers to the notion of "gross production" (FAOSTAT, 2023 _[7]). Forthcoming in the OECD Agri-environmental database.

Ammonia emissions are associated with acidification of soil and water and eutrophication of water bodies (OECD, 2013 _[3]), which contribute to biodiversity loss and loss of ecosystems functions. Ammonia emissions for OECD countries were obtained from data officially submitted by the Parties to the Convention on Long Range Transboundary Air Pollution (CLRTAP) to the European Monitoring and Evaluation Programme (EMEP) programme via the United Nations Economic Commission for Europe (UNECE). Emissions reported under the CLRTAP tend to follow a bottom-up approach: they are calculated by applying emissions factors to geo-localised farm activities (Morán et al., 2016 _[8]). While reporting under the CLRTAP ensures standardised formats and facilitates consistency, there could be differences in terms of emissions factors and methodologies used across countries. Moreover, emissions are known to vary through the year and a national figure can mask spatial heterogeneity within countries (OECD, 2013 _[3]). 

While there are several biodiversity indicators for farmland that could potentially be tracked (OECD, 2013 _[3]), very few of them are consistently collected for multiple countries. One indicator that is available for multiple countries is the farmland bird index, which tracks the population of a selected group of breeding bird species that is dependent on agricultural land for nesting or breeding. In general, indicators based on bird populations tend to be good biodiversity indicators since, given their position in the food chain, they reflect the general health and transformation of ecosystems (OECD, 2013 _[3]). However, bird populations may not be appropriate measures of farmland biodiversity in all contexts, particularly in areas where they are mainly considered as invasive species. A decrease in the index means that the population abundance of bird species is declining, representing biodiversity loss. If it is constant, there is no overall change. An increase implies an increase in the farmland bird population. Note that a trend in the composite index of farmland birds can hide significant changes for individual species. An increase in the index could reflect an increase in abundance of some bird species at the expense of others. The index can also be quite volatile over time, which could affect the assessment of its trends.

The farmland bird indicator used here mainly draws on national bird monitoring programmes and since 2019 it is collected by the OECD. These national indices vary significantly in the number and type of species they include (ranging from 8 to 39 bird species, to reflect varying national situations), and the variety of methods used to derive the indices.

Data for the AEI database is compiled through a combination of national statistical sources, international organisations, and OECD-specific surveys. The collection process is coordinated with Eurostat, FAOSTAT, the UNFCCC, and other relevant bodies to ensure consistency and comparability across countries.

• For EU countries, Norway, and Switzerland: Eurostat leads data collection efforts, with the OECD integrating the updates into the AEI database.
• For non-EU OECD countries: The OECD Secretariat distributes tailored questionnaires to collect missing or country-specific data.
• For non-OECD partner countries: Data is obtained from international databases, complemented by national reporting where available.

The methodologies used align with international standards, including the Eurostat/OECD Handbook on Nutrient Budgets and IPCC Guidelines for National Greenhouse Gas Inventories.

The main data source is the OECD Agri-environmental indicators database. The FAO Food and agriculture data database is the data source for livestock units, gross value of production and agricultural production index. The UNFCCC Greenhouse gas inventory database is the data source for greenhouse gas emissions.

OECD (2019), Trends and drivers of agri-environmental performance in OECD countries, OECD Publishing, Paris, https://doi.org/10.1787/b59b1142-en.

OECD (2013), OECD Compendium of Agri-environmental Indicators, OECD Publishing, Paris, https://doi.org/10.1787/9789264186217-en.

1. OECD (2023), Agri-environmental Indicators Database.
2. FAO (2011), Guidelines for the preparation of livestock sector reviews, Food and Agriculture Organization of the United Nations.
3. OECD (2013), OECD Compendium of Agri-environmental Indicators, OECD Publishing, Paris, https://doi.org/10.1787/9789264186217-en.
4. EUROSTAT/OECD (2013), Methodology and Handbook Eurostat/OECD, Nutrient Budgets, EU-27, Norway, Switzerland.
5. OECD (2019), Trends and Drivers of Agri-environmental Performance in OECD Countries, OECD Publishing, Paris, https://doi.org/10.1787/b59b1142-en.
6. UNFCCC (2022), National Inventory Submissions 2022.
7. FAO (2023), FAOSTAT Value of Agricultural Production.
8. Morán, M. et al. (2016), “Ammonia agriculture emissions: From EMEP to a high resolution inventory”, Atmospheric Pollution Research, https://doi.org/10.1016/j.apr.2016.04.001.