field emissions
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Abstract Purpose To estimate life cycle impacts from introducing the yield-enhancing inoculant containing the nitrogen-fixing bacterium Bradyrhizobium japonicum and the signal molecule lipochitooligosaccharide (LCO) in Argentinian soybean production. The study focuses on soybeans grown in rotation with corn in the Buenos Aires province. We also provide the life cycle impact assessment for the inoculant production. The study represents a novel scope in terms of the studied crop, inoculant type, and location. Methods Consequential LCA is used to assess the cradle-to-gate soybean production systems with and without inoculant use. Stepwise is used for quantification of 16 impacts at mid-point level. Also, the LCA-based guidance of Kløverpris et al. (2020) is followed, and we divide the change in impacts caused by the inoculant’s use into four effects. The field effect accounts for changes in field emissions. The yield effect accounts for additional soybean production in the inoculant system that displaces soybean production elsewhere (system expansion). The upstream effect covers the inoculant production and the downstream effect covers post-harvest changes such as soybean transport and drying. Small plot field-trials data is applied in the biogeochemical model DayCent to estimate field emissions, among others. Results and discussion The use of this inoculant reduces environmental impacts from soybean production in all studied impact categories. The main contributing factor is the yield effect, i.e., reduced impacts via avoided soybean production elsewhere including reduced pressure on land and thereby avoided impacts in the form of indirect land-use-change (iLUC). The field effect is the second-largest contributor to the overall impact reduction. Upstream and downstream effects only had minor influence on results. The yield and field effects are closely tied to the yield change from the inoculant use, which was not fully captured in the DayCent modeling. Thereby, a potential underestimation of the environmental benefits of roughly 10% can be expected, corresponding to the difference of empiric yield data and the modeled yield data in DayCent. Conclusion and recommendations The use of this inoculant shows environmental benefits and no trade-offs for the 16 impacts assessed. Results depend primarily on avoided soybean production (the yield effect) which entails iLUC impacts in Brazil and USA, and to a lesser degree on field emissions modelled with DayCent. Better data and parametrization of DayCent, to better capture the change in yields and estimate field emissions, economic modelling for the system expansion assumptions, and accounting for uncertainty in iLUC modelling could improve the assessment.

Distracted driving jeopardizes the safety of the driver and others. Numerous solutions have been proposed to prevent distracted driving, but the number of related accidents has not decreased. Such a deficiency comes from fragile system designs where drivers are detected exploiting sensory features from strictly controlled vehicle-riding actions and unreliable driving events. We propose a system called ADDICT (Accurate Driver Detection exploiting Invariant Characteristics of smarTphone sensors), which identifies the driver utilizing the inconsistency between gyroscope and magnetometer dynamics and the interplay between electromagnetic field emissions and engine startup vibrations. These features are invariantly observable regardless of smartphone positions and vehicle-riding actions. To evaluate the feasibility of ADDICT, we conducted extensive experiments with four participants and three different vehicles by varying vehicle-riding scenarios. Our evaluation results demonstrated that ADDICT identifies the driver’s smartphone with 89.1% average accuracy for all scenarios and >85% under the extreme scenario, at a marginal cost of battery consumption.