Climate change is a reality, and irrigated agriculture is a significant human activity that contributes to greenhouse gas (GHG) emissions and, therefore, to global warming. Traditionally, the carbon footprint associated with GHG emissions has been computed by fixed electrical energy transformation rates (kgCO2e kWh−1). However, this transformation rate is not constant because it depends on a country's power generation mix, which combines of the energy sources supplying a country (coal, nuclear, wind, photovoltaic, hydraulic, natural gas, etc.). Fixed transformation rates have been traditionally employed to estimate the carbon footprint that derives from irrigated agricultural activity. Nevertheless, these fixed rates can be inaccurate for not considering variations over time. Thus, in this work, a new decision support system called Carbon_in_WaterDSS was developed in Python and applied to a real-world scenario to determine a dynamic and realistic energy transformation rate and to compute an accurate carbon footprint. Different time periods were considered according to irrigation stakeholders' water management criteria, crop water requirements, energy costs and pricing electricity tariff periods. The results show that the energy transformation rate and, therefore, the carbon footprint value vastly vary during the irrigation season and over for a day (from 0.066 kgCO2e kWh−1 to 0.490 kgCO2e kWh−1), unlike the values found in other works. This work also highlights how farmers choose the most economical energy periods to use conventional electricity. However, this hourly choice criterion is not the option that generates the lowest carbon footprint value in most analysed years.
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