Data, and sample sources thereof, on water quality life cycle impact assessments pertaining to catchment scale acidification and eutrophication potentials and the benefits of on-farm mitigation strategies

Based on recent spatially aggregated June Agriculture Survey data and site-specific environmental data, information from common farm types in the East of England was sourced and collated. These data were subsequently used as key inputs to a mechanistic environmental modelling tool, the Catchment Systems Model, which predicts environmental damage arising from various farm types and their management strategies. The Catchment Systems Model, which utilises real-world agricultural productivity data (samples and appropriate consent provided within the Mendeley Data repository) is designed to assess not only losses to nature such as nitrate, phosphate, sediment and ammonia, but also to predict how on-farm intervention strategies may affect environmental performance. The data reported within this article provides readers with a detailed inventory of inputs such as fertiliser, outputs including nutrient losses, and impacts to nature for 1782 different scenarios which cover both arable and livestock farming systems. These 1782 scenarios include baseline (i.e., no interventions), business-as-usual (i.e., interventions already implemented in the study area) and optimised (i.e., best-case scenarios) data. Further, using the life cycle assessment (LCA) methodology, the dataset reports acidification and eutrophication potentials for each scenario under two (eutrophication) and three (acidification) impact assessments to offer an insight into the importance of impact assessment choice. Finally, the dataset also provides its readers with percentage changes from baseline to best-case scenario for each farm type

Greenhouse gas and ammonia emission mitigation priorities for UK policy targets

Acknowledgements Many thanks to the Association of Applied Biologist’s for organising and hosting the ‘Agricultural greenhouse gases and ammonia mitigation: Solutions, challenges, and opportunities’ workshop. This work was supported with funding from the Scottish Government’s Strategic Research Programme (2022-2027, C2-1 SRUC) and BBSRC (BBS/E/C/000I0320 and BBS/E/C/000I0330). We also acknowledge support from UKRI694 BBSRC (United Kingdom Research and Innovation-Biotechnology and Biological Sciences 695 Research Council; United Kingdom) via grants BBS/E/C/000I0320 and BBS/E/C/000I0330. and Rothamsted Research's Science Initiative Catalyst Award (SICA) supported by BBSRC.Peer reviewedPublisher PD

Farm-level emission intensities of smallholder cattle (Bos indicus; B. indicus–B. taurus crosses) production systems in highlands and semi-arid regions

Ruminants are central to the economic and nutritional life of much of sub-Saharan Africa, but cattle are now blamed for having a disproportionately large negative environmental impact through emissions of greenhouse gas (GHG). However, the mechanism underlying excessive emissions occurring only on some farms is imperfectly understood. Reliable estimates of emissions themselves are frequently lacking due to a paucity of reliable data. Employing individual animal records obtained at regular farm visits, this study quantified farm-level emission intensities (EIs) of greenhouse gases of smallholder farms in three counties in Western Kenya. CP was chosen as the functional unit to capture the outputs of both milk and meat. The results showed that milk is responsible for 80–85% of total CP output. Farm EI ranged widely from 20 to >1000 kg CO2-eq/kg CP. Median EIs were 60 (Nandi), 71 (Bomet), and 90 (Nyando) kg CO2-eq/kg. Although median EIs referenced to milk alone (2.3 kg CO2-eq/kg milk) were almost twice that reported for Europe, up to 50% of farms had EIs comparable to the mean Pan-European EIs. Enteric methane (CH4) contributed >95% of emissions and manure ∼4%, with negligible emissions attributed to inputs to the production system. Collecting data from individual animals on smallholder farms enabled the demonstration of extremely heterogeneous EI status among similar geographical spaces and provides clear indicators on how low EI status may be achieved in these environments. Contrary to common belief, our data show that industrial-style intensification is not required to achieve low EI. Enteric CH4 production overwhelmingly drives farm emissions in these systems and as this is strongly collinear with nutrition and intake, an effort will be required to achieve an “efficient frontier” between feed intake, productivity, and GHG emissions

An experimental study of the influence of periphytic algae on invertebrate abundance in a Hong Kong stream

Algal biomass, invertebrate morphospecies richness and total abundance decline with greater shading intensity. The responses of individual invertebrate taxa varied: some (especially Trichoptera) were unaffected by shading, whereas grazers (Baetidae, Psephenidae and Elmidae) declined as shading increased. Significant regressions of the densities of individual taxa upon algal and detrital standing stocks in cages had positive slopes, but algal biomass increased during the study while detrital standing stocks declined. Abundance of invertebrates declined or remained rather stable over time. Density increases resulting from a positive association with algae were apparently offset by declines in abundance correlated with reductions in detritus. Declines in algal biomass were associated with greater shading to which animals may respond directly. The plastic covers on two groups of cages (deeply shaded and unshaded) which had been placed in the stream for 28 days were reversed so that cages which had been shaded became unshaded and vice versa. The cages were recovered on day 33. Only Coleoptera demonstrated a positive association with algae inside cages. -from Authorslink_to_subscribed_fulltex