How to select Best Available Techniques to reduce NH3 emissions from the agricultural sector? Results from integrating the pillars of sustainability into a Bayesian Belief Network model

Concerns about ammonia (NH₃) emission from agriculturre have increased in recent years due to its contribution to atmospheric fine particulate matter formation (PM2.5 and PM10). To reduce NH₃ losses a wide range of techniques has been reported, from the management of livestock and storage of excreted animal manure to the types and methods of application of organic and mineral fertilizers. The successful implementation of such techniques requires following a collaborative bottom-up approach that engages farmers and practitioners to the full technology valuation. Bayesian Belief Networks (BBNs) are probabilistic models that represent expert knowledge in any particular situation –e.g. in the agroecosystem– and evaluate the potential effects of different management scenarios. By linking multiple variables in a cause-and-effect relationship, BBNs can provide both diagnosis and prognosis aiding the decision-making process. In this work, a BBN was built to integrate quantitative experimental data and quali-quantitative stakeholder assessment. The aim was to provide recommendations to policy makers and practitioners about the most promising best available techniques (BAT) that combine environmental effectiveness in reducing NH₃ emissions with economic and sociocultural acceptability by farmers at the regional scale. The variability of the livestock sector (swine, cattle and dairy cows) and agricultural systems (climate, soils, crops, etc.) across the Veneto region, NE Italy, was included in the BBN model. For the livestock sector management practices such as those related to feeding (e.g., precision feeding), overcrowding (e.g., breeding density), healthcare (e.g., infirmary spaces), etc. were considered. Estimates of NH₃ losses from N fertilizer application techniques (e.g., closed slot injection Vs. surface distribution, N fertilizer in conservation vs conventional tillage) came from the integration of experimental, literature, and modelling data by using the modified version of DNDC v.CAN biogeochemical model. Stakeholders were engaged in evaluating the NH₃ mitigation effectiveness of the proposed techniques. Perceptions about economic attractiveness and sociocultural acceptability were enquired, and results were introduced in the BBN as utility nodes to determine BATs. Results showed that the BBN model was able to embed into a single network quantitative outcomes from technical solutions with quali-quantitative assessment by stakeholders. By combining social and economic valuation with the technical potential of NH₃ reduction, the BBN model acted as an effective tool to recommend the most promising BAT that should be supported by policy makers. The greatest room for improvement was found in the livestock supply chain, from the stable management to the manure distribution in the field. In contrast, stakeholders were unfamiliar with the most innovative techniques (e.g. precision farming, closed slot injection of mineral fertilizers), whose uncertainty in the costs and difficulties in the implementation would hinder their application despite their environmental benefits.