Modeling of farm-specific marginal abatement costs of non-CO2 greenhouse gas mitigation measures in Austria

Abstract

Agriculture is a major source of non-CO2 greenhouse gas (GHG) emissions, namely methane (CH4) and nitrous oxide (N2O), and reactive trace gases, such as ammonia (NH3). CH4 emissions originate primarily from enteric fermentation of ruminants and during manure storage. N2O emissions are produced in microbial processes of soils and manure. Emissions of NH3 arise from livestock housing systems, manure storage and application to the soil as well as during grazing. Mitigating GHG emissions has emerged as a key priority for policy makers, researchers and stakeholders, evident in the ambitious emission reduction targets set at both the EU and national levels. However, mitigation measures at the farm level incur different marginal abatement costs (MACs) due to farm and regional specific characteristics. Farm specific calculations of MACs are still limited. Therefore, we aim at (i) modeling non-CO2 GHG emissions, (ii) computing MACs of mitigation measures and (iii) identifying cost-efficient mitigation measures for the Austrian farms using the Farm Optimization Model FAMOS. FAMOS is a mixed-integer linear farm optimization model implemented in GAMS (General Algebraic Modeling Systems; https://www.gams.com/). It is extended with a non-CO2 GHG emission accounting module that follows the guidelines for national GHG inventories provided by the Intergovernmental Panel on Climate Change. Country and farm-specific emission factors are used in the non-CO2 GHG emission accounting. This module enhances the accuracy of emission calculations at the farm level. FAMOS maximizes farm net returns, defined as the sum of market revenues and policy payments minus the costs of production and investment, subject to the farm’s resource endowments such as available land, livestock housing capacity and farm family labor. Agronomic production relationships (e.g., fertilizer and feed balances), farm management practices (e.g., crop rotations, fertilization, irrigation, tillage, feeding and grazing strategies), and legal compliances (e.g., CAP measures and payments, fertilizer intensities as part of the Austrian agri-environmental OEPUL programme) are taken into account. The model uses farm level data from various data sources (e.g., Farm Structure Survey, Integrated Administration and Control System, Standard Gross Margin Catalogue) and is individually solved for each farm in Austria. The model results show that the MACs of mitigation measures differ between farm types and agricultural production regions. For instance, MACs are higher for specialized farms with few and labor-intensive management options. The MACs are lower for managerial measures (e.g., changes in fertilizer management), compared to technological (e.g., changes in livestock housing) and agronomic measures (e.g., cover cropping). Our analysis complements the existing research by calculating MACs of selected mitigation measures at farm level. These results may inform farmers, farm consultants and policy makers in fostering the implementation of cost-efficient mitigation strategies at farm level.