To curb the impacts arising from the agricultural sector, the actions undertaken by policymakers, and ultimately by the farmers, are of paramount importance. However, finding the best strategy to reduce impacts, and especially assessing the effects of the interactions and mutual influence among farmers, is very difficult. To this aim, this paper shows an application of an agent-based model (ABM) coupled with life cycle assessment (LCA), which also includes multi-objective optimization of farming activities (including both crop cultivation and livestock breeding) from an economic and environmental perspective. The environmental impacts are assessed using the impact assessment scores calculated with the Environmental Footprint 3.0 life cycle impact assessment method and the study is conducted “from cradle to farm gate”. The model is applied to all the farms in Luxembourg, whose network is built utilizing neighborhood interactions, through which a parameter known as farmer’s green consciousness is updated at each time step. The optimization module is instantiated at the end of each time step, and decision variables (the number of livestock units and land allocation) are assigned based on profitability and specified environmental impact categories. If only profit optimization is considered (i.e., when farmers’ green consciousness is de-activated), the results show a 9% reduction in the aggregated environmental impacts (obtained as the Environmental Footprint single score) and a 5.5% increase in overall profitability. At the farm level, simulations display a clear trade-off between environmental sustainability and financial stability, with a 25% reduction in overall emissions possible if farming activities are carried out using the single score impact in the objective function, though this results in an 8% reduction in profitability over 10 years.

This research aims to develop a predictive model to discriminate milk produced from a cattle diet either based on grass or not using milk mid-infrared spectrometry and the month of testing (an indirect indicator of the feeding ration). The dataset contained 3,377,715 spectra collected between 2011 and 2021 from 2449 farms and 3 grazing traits defined following the month of testing. Records from 30% of the randomly selected farms were kept in the calibration set, and the remaining records were used to validate the models. Around 90% of the records were correctly discriminated. This accuracy is very good, as some records could be erroneously assigned. The probability of belonging to the GRASS modality allowed confirmation of the model’s ability to detect the transition period even if the model was not trained on this data. Indeed, the probability increased from the spring to the summer and then decreased. The discrimination was mainly explained by the changes in the milk fat, mineral, and protein compositions. A hierarchical clustering from the averaged probability per farm and year highlighted 12 groups illustrating different management practices. The probability of belonging to the GRASS class could be used in a tool counting the number of grazing days.

Biowaste and manure are resources readily available as feedstock for biogas production. Possible scenarios with increased use of biowaste and manure for biogas production in the Grand Duchy of Luxembourg are investigated in this study using an Agent-Based Model (ABM) coupled with Life Cycle Assessment (LCA). ABMs are particularly suitable to simulate human-natural systems, since they allow modelers to consider behavioral aspects of individuals. On the other hand, when it comes to the assessment of a system’s environmental sustainability, LCA is largely recognized as a sound methodology and widely used in research, industry, and policy making. The paper simulates three different scenarios that reproduce 10 years and can help policymakers building emission mitigation strategies. The aim is to increase the number of biogas plants or change the feedstock composition for anaerobic digestion in Luxembourg whilst observing the expected environmental impacts generated by these changes. The first scenario (Scenario A) is the baseline scenario, which simulates the current situation, with 24 operating biogas plants. The results of Scenario A show that, on average, 63.02 GWh of electricity production per year is possible from biogas. The second scenario (Scenario B) foresees an increase in the manure share (which is initially 63%) in the biogas feedstock composition along with an increase in the number of biogas production plants. The third scenario (Scenario C) only concerns increasing the amount of manure in the feedstock composition without the introduction of new plants. The results of Scenario C show that an 11% increase in electricity production is possible if more farms contribute to the production by bringing their excess manure to the biogas plant. This value is even higher (14%) in Scenario D where more biowaste is made available. The aggregated life-cycle impact assessment (LCIA) single scores, calculated with the ReCiPe method, show that Scenario C has the lowest impacts (although by only around 7% compared to the worst performing scenario, i.e., Scenario D), while Scenario D allows the highest electricity production (71.87 GWh in the last year of the simulation). As a result, the inclusion of more livestock farms into already established biogas cooperatives (as in Scenario C) can pave the way for an increase in electricity production from renewables and can bring a reduction in environmental impacts (more than 35% for the Terrestrial Ecotoxicity impact category and more than 27% in categories such as Agricultural Land Occupation, Marine Eutrophication and Water Depletion), thanks to the exploitation of manure for biogas production.

To curb the impacts arising from the agricultural sector the actions undertaken by policymakers, and ultimately by the farmers, are of paramount importance. However, finding the best strategy to reduce impacts, and especially assessing the effects of the interactions and mutual influence among farmers, is very difficult. To this aim, this paper shows an application of an agent-based model (ABM) coupled with life cycle assessment (LCA), which also includes multi objective optimization of farming activities (including both crop cultivation and livestock breeding) from an economic and environmental perspective. The environmental impacts are assessed using the impact assessment scores calculated with the Environmental Footprint (EF) 3.0 life cycle impact assessment (LCIA) method and the study is conducted “from cradle to farm gate”. The model is applied to all the farms in Luxembourg, whose network is constructed utilizing neighborhood interactions, through which a parameter known as farmer's green consciousness (GC) is updated at each time step. The optimization module is instantiated at the end of each time step, and decision variables (the number of livestock units and land allocation) are assigned based on profitability and specified environmental impact categories. If only profit optimization is considered (i.e. when farmers’ GC is de-activated), the results show a 9% reduction in the aggregated environmental impacts (obtained as EF “single score”) and a 5.5% increase in overall profitability. At the farm level, simulations display a clear trade-off between environmental sustainability and financial stability, with a 25% reduction in overall emissions possible if farming activities are carried using the EF single score impact in the objective function, though this results in an 8% reduction in profitability over ten years.