Atmospheric ammonia (NH3) released from agriculture is contributing significantly to acidification and atmospheric NH3 may have on human health is much less readily available. The potential direct impact of NH3 on the health of the general public is under-represented in scientific literature, though there have been several studies which indicate that NH3 has a direct effect on the respiratory health of those who handle livestock. These health impacts can include a reduced lung function, irritation to the throat and eyes, and increased coughing and phlegm expulsion. More recent studies have indicated that agricultural NH3 may directly influence the early on-set of asthma in young children. In addition to the potential direct impact of ammonia, it is also a substantial contributor to the fine particulate matter (PM2.5) fraction (namely the US and Europe); where it accounts for the formation of 30% and 50% of all PM2.5 respectively. PM2.5 has the ability to penetrate deep into the lungs and cause long term illnesses such as Chronic Obstructive Pulmonary Disease (COPD) and lung cancer. Hence, PM2.5 causes economic losses which equate to billions of dollars (US) to the global economy annually. Both premature deaths associated with the health impacts from PM2.5 and economic losses could be mitigated with a reduction in NH3 emissions resulting from agriculture. As agriculture contributes to more than 81% of all global NH3 emissions, it is imperative that food production does not come at a cost to the world's ability to breathe; where reductions in NH3 emissions can be easier to achieve than other associated pollutants.

Machinery traffic imposes a negative effect on soil structure, leading to soil compaction. Studies to date have primarily focused on the influence of applied wheel loads on soil structure. Few studies have assessed the impact of commercial farm operations on soil structure and crop performance, particularly on field headlands in a temperate maritime climate such as Ireland. A survey was conducted on 41 conventionally managed field sites to investigate the effect of field position (field edge, turning, transition and in-field zones) in relation to machinery operations on soil structure. Soil texture classes ranged from sandy loam to clay loam. All sites used plough-based crop establishment. Soil structural condition was assessed visually using the visual evaluation of soil structure method (VESS) for the topsoil (0−250 mm), and Double Spade below plough depth (250−400 mm). Quantitative soil measurements such as shear strength, bulk density and porosity using soil cores post-harvest, and soil cone penetration resistance were taken at two time points in the crop growth cycle. For most measurements of soil structure, the in-field zone of least machinery traffic produced the best scores (Sq 2.81 & DS 2.48), and the turning zone returned the poorest scores in the 0−250 mm soil layer (Sq 3.31 & DS 2.91). The strongest quantitative scores for the in-field and turning zones, respectively, were for trowel penetration resistance in the upper (2.49 & 3.20) and lower (3.41 & 4.05) soil depth layers and for shear vane (38.17 & 53.59 kPa) for the same zones. The visual assessments and some of the quantitative measurements (0−250 mm soil layer) followed the zone order trend of: turning, field edge, transition and in-field, for increasing machinery traffic. The results show that the visual soil indicators used in this study are more sensitive than quantitative soil measurements such as soil bulk density (ρb) or porosity (TP and MP) at detecting soil structural differences between zones, particularly below plough depth (>250 mm soil depth).

The GenESIS research is a new four year project funded by DAFM and features researchers from Teagasc, UCD, Trinity College Dublin, NUI Galway, National Botanic Gardens assisted by Coillte and hopes to document the genetics of Sitka spruce forests in Ireland and develop genotyping tools to assist tree-breeding efforts. The new research involves the development of a new genotyping platform that will be utilised for assessing differences in the genetic makeup of trees, used to assess diversity in Sitka spruce populations, for DNA fingerprinting, and also for genomic selection which has the potential to accelerating tree breeding efforts.

Insect pollinators are suffering global declines, necessitating the evaluation and development of methods for long-term monitoring and applied field research. Accordingly, this study evaluated the use of trap nests (“bee hotels”) as tools for investigating the ecology of cavity nesting Hymenoptera within Irish agricultural landscapes. Three trap nests consisting of 110 mm diameter plastic pipe containing 100 cardboard nest tubes of varying diameter were placed at eight apple orchards and eight oilseed rape sites and left in the field for five months. Sealed nest tubes occurred at 15 of the 16 sites, and in 77% of the 48 nests. However, only 7% of the 4800 individual nest tubes were sealed, and only 4% produced cavity-nesting Hymenoptera. Three cavity nesting bee species (Hylaeus communis, Osmia bicornis, Megachile versicolor) and two solitary wasp species (Ancistrocerus trifasciatus, A. parietinus) emerged from nest tubes. There were significant differences among species in terms of emergence date and the diameter of nest tubes from which they emerged, the latter allowing the calculation of niche width and niche overlap, and informing choice of tube size in future studies/conservation efforts. Trap nests, therefore, offer a valuable tool for fundamental ecological research and a model system for investigating interactions between stem-nesting species within their wider ecological networks. The ability of trap nests to actually increase farmland pollinator abundance and diversity as part of agri-environment schemes requires additional investigation. However, used in sufficient numbers, these trap nests provide valuable biogeographical data for cavity nesting Hymenoptera and offer a viable means for long term monitoring of these species in Irish farmland.

Reductions in ammonia (NH3) and nitrous oxide (N2O) emissions from agricultural systems are critical for achievement of sustainability targets that underpin international efforts on climate and biodiversity. Urease inhibitors (UI) such as N-(n-butyl) thiophosphoric triamide (NBPT) and nitrification inhibitors (NI) such as dicyandiamide (DCD) slow down microbial and chemical N transformation rates in soil, resulting in decreased environmental N losses. To date there has been minimal assessment of the long-term non-target impacts of UI and NI on soil microbial communities and biological function in grasslands. Utilising a temperate grassland field experiment where fertilisers (with or without inhibitors) were repeatedly applied over a five year period, we assessed the impact of individual or combined inhibitor use on microbial community composition, abundance and function via a combination of functional assays, quantitative polymerase chain reaction (qPCR) assays and amplicon sequence analysis. We also investigated the effect of N inhibitor use on the N functional community, and whether the form of applied N fertiliser (i.e. calcium ammonium nitrate (CAN) or Urea) affected microbial community composition and function. Treatments included a Control (no N); CAN; Urea; Urea + NBPT (UI); Urea + DCD (NI); and Urea + DCD + NBPT (NI & UI). There was no impact of either UI or NI use on non-target microbial community composition or abundance. Function and the abundance of N cycling communities were mainly unaffected by fertilisation or the use of inhibitors. The observed effect of NI was primarily on the nitrification process. There was a significant reduction in nitrification potential associated with the use of NI, and in the case of the Urea + DCD treatment a reduction in COMAMMOX nitrifier abundance, and an increase in potential N mineralisation and N2O emissions. Finally, there was a significant impact of fertilisation and fertiliser type (i.e. CAN or Urea) on the fungal community structure but no impact on bacterial community structure. These results provide a knowledge base that will inform policy regarding the utilisation of N inhibitors as a mitigation measure for reducing gaseous N losses in grasslands.

Plant productivity, decomposition and nutrient cycling are controlled by plant-soil-biota interactions. However, it remains poorly understood how plant species diversity and diversity interactions impact belowground communities that modulate these processes in intensively-managed grassland systems. In managed grassland communities, comprising species selected for agronomic performance, we investigated how plant species diversity affected the soil nematode community and associated ecological indices with a focus on assessing abovegroundbelowground interactions. A total of 27 nematode taxa were identified from 61 experimental field plots in which plant species diversity was systematically manipulated from a pool of six plant species within three functional groups (FGs; grasses, legumes, herbs). In general, there were strong effects of plant species identity on the nematode community; interspecific interaction effects did not consistently occur, but where they did were best related to plant community evenness. The equi-proportional six-species plant community had a significantly higher nematode diversity, maturity index (MI), structure index (SI) and proportion of sensitive taxa (omnivore and predators) but a lower enrichment index (EI) than the individual monocultures. The two legumes (Trifolium pratense and Trifolium repens) had the highest EI but lowest abundance of fungivores and channel index, indicating a bacterial- dominated decomposition pathway. Moreover, the community structure of nematodes in the equiproportional six-species community was significantly different from that in the monocultures. This change in community structure was associated with factors highly correlated with plant diversity, including higher aboveground biomass yield and total nitrogen in harvested biomass as well as lower biomass of weed species. Overall, our results show that multi-species forage sward mixtures that include grasses, legumes, and herbs can have a positive effect on the soil nematode community and nematode-based soil quality indices. This is of practical relevance for farmers and for EU agricultural policy targeted at sustainability, soil health and farming for biodiversity benefits.

This paper reviews existing on-farm GHG accounting models for dairy cattle systems and their ability to capture the effect of dietary strategies in GHG abatement. The focus is on methane (CH4) emissions from enteric and manure (animal excreta) sources and nitrous oxide (N2O) emissions from animal excreta. We identified three generic modelling approaches, based on the degree to which models capture diet-related characteristics: from ‘none’ (Type 1) to ‘some’ by combining key diet parameters with emission factors (EF) (Type 2) to ‘many’ by using process-based modelling (Type 3). Most of the selected on-farm GHG models have adopted a Type 2 approach, but a few hybrid Type 2 / Type 3 approaches have been developed recently that combine empirical modelling (through the use of CH4 and/or N2O emission factors; EF) and process-based modelling (mostly through rumen and whole tract fermentation and digestion). Empirical models comprising key dietary inputs (i.e., dry matter intake and organic matter digestibility) can predict CH4 and N2O emissions with reasonable accuracy. However, the impact of GHG mitigation strategies often needs to be assessed in a more integrated way, and Type 1 and Type 2 models frequently lack the biological foundation to do this. Only Type 3 models represent underlying mechanisms such as ruminal and total-tract digestive processes and excreta composition that can capture dietary effects on GHG emissions in a more biological manner. Overall, the better a model can simulate rumen function, the greater the opportunity to include diet characteristics in addition to commonly used variables, and thus the greater the opportunity to capture dietary mitigation strategies. The value of capturing the effect of additional animal feed characteristics on the prediction of on-farm GHG emissions needs to be carefully balanced against gains in accuracy, the need for additional input and activity data, and the variability encountered on-farm.

Different factors such as the genotype, environmental conditions, temperature stress, solar radiation and others can influence the phytochemical status of plants. The concentration of phenolic acids and alkylresorciols (ARs) as well as their chemical composition and biological activity have been determined in twelve winter wheat cultivars grown at eight European locations. This was the first winter wheat multi-location field trial of the European Consortium for Open Field Experimentation (ECOFE). Extracts from grain were analyzed using a UPLC-PDA-ESI-MS system (phenolic acids), UPLC-PDA-MS/MS (alkylresorcinols) and TLC-DPPH• test with ImageJ program (antiradical activity). The phenolic acid profile consisted of five hydroxybenzoic acid and four hydroxycinnamic acid derivatives, among which ferulic and sinapic acids were predominated. The ARs profile consisted of nine AR derivatives, among which 5-n-heneicosylresorcinol (C21:0) and 5-n-nonadecanylresorcinol (C19:0) were predominated. Our study showed significant differences in phenolic acids and AR content between wheat cultivars, as well as between locations. We observed a positive correlation between the biological activity of extracts and the total amount of phenolic acids and ARs. Two cultivars, Chambo and Julius (average of all sites) and samples from the Spanish site (average of all cultivars) showed the highest content and composition of nutritional substances.

Precision and sustainable agriculture requires information about soil pH and organic matter (OM) content at higher spatial and temporal scales than current agronomic sampling and analytical methods allow. This study examined the accuracy of spectral models using high throughput screening (HTS) in diffuse reflectance mode in mid Infra-red (MIR)/DRIFT combined with machine learning algorithms to predict soil pH(CaCl2) and %OM in shallow and deeper topsoils compared to laboratory methods. Models were developed from an archive of samples taken on a 4 km2 grid from the northern half of Ireland (Terra Soil project), which includes 18,859 samples (9,396 shallow + 9,463 deeper). The application of Cubist models showed that for different depths there are minor different spectral group associations with pH and %OM values. These differences resulted in a loss of accuracy in the extrapolation of the topsoil model to predict values from deeper topsoils or vice versa. Therefore we recommend the use of samples from both depths to build a calibration model.The proposed methodology was able to determine %OM and pH using a unique multivariate regression model for both depths, with RMSEP values of 1.12 and 0.89 %; RPIQ values of 42.34 and 38.48; R2val of 0.9989 and 0.9993 for %OM determinations in shallow and deeper topsoils, respectively. For pH determinations the RMSEP values obtained were 0.25 and 0.34; RPIQ values of 6.04 and 4.94; R2val 0.9385 and 0.8954. Both regression models are classified as excellent predictions models, yielding RPIQ values >4.05 for shallow and deeper topsoils. The results demonstrated the high potential of HTS-DRIFT combined with machine learning algorithms as a rapid, accurate, and cost-effective method to build large soil spectral libraries, displaying predicted results similar to two separate soil laboratory methods (pH and LOI).

Cultivation of Agaricus bisporus is a large horticultural industry for many countries worldwide, where a single variety is almost grown exclusively. Mushroom virus X (MVX), a complex of multiple positive-sense single stranded RNA (ss(+)RNA) viruses, is a major pathogen of typical A. bisporus crops. MVX can manifest a variety of symptoms in crops and is highly infective and difficult to eradicate once established in host mycelium. Currently our knowledge regarding the molecular response of A. bisporus fruit bodies to MVX infection is limited. In order to study the response of different A. bisporus strains with different susceptibilities to MVX, we designed a model system to evaluate the in-vitro transmission of viruses in A. bisporus hyphae over a time-course, at two crucial phases in the crop cycle. The symptom expression of MVX in these varieties and the transcriptomic and proteomic response of fruit bodies to MVX-infection were examined. Transmission studies revealed the high potential of MVX to spread to uninfected mycelium yet not into the fruit bodies of certain strains in a crop. MVX affected colour and quality of multiple fruit bodies. Gene expression is significantly altered in all strains and between times of inoculation in the crop. Genes related to stress responses displayed differential expression. Proteomic responses revealed restriction of cellular signalling and vesicle transport in infected fruit bodies. This in-depth analysis examining many factors relevant to MVX infection in different A. bisporus strains, will provide key insights into host responses for this commercially important food crop.