Quantifying the impact of a smart farming system application on local-scale air quality of smallhold farms in Greece

Abstract

Smart farming (SF) has emerged as a scientific approach exploiting technology advances for the management of agricultural practices, focusing on the control of resources and chemicals used. There is still limited evidence in the scientific literature in regard to the efficiency of SF, particularly for targeted environmental issues, such as air pollutant emissions from agricultural activities. The present paper expoits quantitative data collected from questionnaires to farmers of 6 pilot areas in Greece, participating in the LIFE GAIA Sense project. Emissions and pollutant levels were calculated for two consecutive years in these pilot areas, namely 2019 (baseline year) and 2020, which is the first SF application year. The methodology for calculating realistic emissions data, following a combined tier 1/tier2 approach is presented. To this purpose, detailed activity data of the specific SF application areas related to agricultural activities were acquired, based on the responses of participating farmers to targeted questionnaires. Calculated emissions were used as input data for air quality modeling simulations to examine the efficiency of SF in reducing local pollutant concentrations. The results show significant emissions and concentrations reductions in five out of the six pilot areas, for all pollutants and greenhouse gases studied, due to the decrease in fuel consumption and N fertilizer applied, as a result of the farmers following the SF advice. Particularly for NH3, which is an agricultural air pollutant of concern due to its health and environmental impacts, emission reductions of around 30% (and by up to almost 60%) were calculated.