Significant variables in the calculation of greenhouse gas (GHG) emissions are estimates of birth... more Significant variables in the calculation of greenhouse gas (GHG) emissions are estimates of birth date and slaughter date, as these alter the amount of time on-farm and hence feed used for animals destined for slaughter. Analysis of Beef + Lamb New Zealand Economic Service farm survey data calculated average birth and slaughter dates for both finishing sheep and beef cattle at a regional scale, from 1990-2019. Data were then used to calculate the potential GHG emissions related to lambs and slaughter cattle, and changes over time, and were compared to current national inventory calculations. There was no significant change in sheepmating date over the 30-year period, with a median lambing date of 10 September. Beef cattle mating date became later over the 30-year period. These resulted in calving dates of 20 September for the 1990-2000 period, and 25 September for the 2010-2019 period.The proportions of lambs slaughtered by February (early) or October (late), categories used by the ...
In New Zealand, agriculture accounts for ~49% of greenhouse gas (GHG) emissions at the industry l... more In New Zealand, agriculture accounts for ~49% of greenhouse gas (GHG) emissions at the industry level, but a robust understanding and calculation of emissions at the individual farm level is needed. Based on the methodology used in the national agricultural inventory model (Pickering & Wear 2013), we developed a farm-scale GHG calculator designed to accept both farm and industry level data. The gases considered were CH4 from enteric fermentation and manure management and direct and indirect N2O from animal excreta and fertiliser N applied to soils. We also included the ability to account for the effect of slope on direct emissions from excreta as described in Saggar et al. (2015). The farm-scale GHG calculator was run using the national level data for sheep and beef population and mean live weight from the national inventory model for the 2008/09 and 2009/10 farming years and the results compared with the national inventory model over that time period. For enteric CH4, manure manage...
Abstract Lack of trust is one of the main obstacles standing in the way of taking full advantage ... more Abstract Lack of trust is one of the main obstacles standing in the way of taking full advantage of the benefits artificial intelligence (AI) has to offer. Most research on trust in AI focuses on cognitive ways to boost trust. Here, instead, we focus on boosting trust in AI via affective means. Specifically, we tested and found associations between one's attachment style—an individual difference representing the way people feel, think, and behave in relationships—and trust in AI. In Study 1 we found that attachment anxiety predicted less trust. In Study 2, we found that enhancing attachment anxiety reduced trust, whereas enhancing attachment security increased trust in AI. In Study 3, we found that exposure to attachment security cues (but not positive affect cues) resulted in increased trust as compared with exposure to neutral cues. Overall, our findings demonstrate an association between attachment security and trust in AI, and support the ability to increase trust in AI via attachment security priming.
Significant variables in the calculation of greenhouse gas (GHG) emissions are estimates of birth... more Significant variables in the calculation of greenhouse gas (GHG) emissions are estimates of birth date and slaughter date, as these alter the amount of time on-farm and hence feed used for animals destined for slaughter. Analysis of Beef + Lamb New Zealand Economic Service farm survey data calculated average birth and slaughter dates for both finishing sheep and beef cattle at a regional scale, from 1990-2019. Data were then used to calculate the potential GHG emissions related to lambs and slaughter cattle, and changes over time, and were compared to current national inventory calculations. There was no significant change in sheepmating date over the 30-year period, with a median lambing date of 10 September. Beef cattle mating date became later over the 30-year period. These resulted in calving dates of 20 September for the 1990-2000 period, and 25 September for the 2010-2019 period.The proportions of lambs slaughtered by February (early) or October (late), categories used by the ...
In New Zealand, agriculture accounts for ~49% of greenhouse gas (GHG) emissions at the industry l... more In New Zealand, agriculture accounts for ~49% of greenhouse gas (GHG) emissions at the industry level, but a robust understanding and calculation of emissions at the individual farm level is needed. Based on the methodology used in the national agricultural inventory model (Pickering & Wear 2013), we developed a farm-scale GHG calculator designed to accept both farm and industry level data. The gases considered were CH4 from enteric fermentation and manure management and direct and indirect N2O from animal excreta and fertiliser N applied to soils. We also included the ability to account for the effect of slope on direct emissions from excreta as described in Saggar et al. (2015). The farm-scale GHG calculator was run using the national level data for sheep and beef population and mean live weight from the national inventory model for the 2008/09 and 2009/10 farming years and the results compared with the national inventory model over that time period. For enteric CH4, manure manage...
Abstract Lack of trust is one of the main obstacles standing in the way of taking full advantage ... more Abstract Lack of trust is one of the main obstacles standing in the way of taking full advantage of the benefits artificial intelligence (AI) has to offer. Most research on trust in AI focuses on cognitive ways to boost trust. Here, instead, we focus on boosting trust in AI via affective means. Specifically, we tested and found associations between one's attachment style—an individual difference representing the way people feel, think, and behave in relationships—and trust in AI. In Study 1 we found that attachment anxiety predicted less trust. In Study 2, we found that enhancing attachment anxiety reduced trust, whereas enhancing attachment security increased trust in AI. In Study 3, we found that exposure to attachment security cues (but not positive affect cues) resulted in increased trust as compared with exposure to neutral cues. Overall, our findings demonstrate an association between attachment security and trust in AI, and support the ability to increase trust in AI via attachment security priming.
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