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This study investigated the effect of using a trailing-shoe system to apply cattle slurry, under different conditions of grass height (low [ LG]: freshly cut sward [4-5 cm height] vs. high [ HG]: application delayed by 7-19 d and applied to taller grass sward [4-11 cm] height) and month of application (June vs. April), on the nitrogen fertilizer replacement value ( NFRV) and apparent N recovery ( ANRS) of cattle slurry applied to grassland. NFRV was calculated using two methods: (i) NFRVN based on the apparent recovery of slurry-N relative to that of mineral-N fertilizer; and (ii) NFRVDM based on DM yield. The effect of applying slurry into HG swards, relative to LG swards, decreased the DM yield by 0·47 t ha−1 ( P ≤ 0·001), N uptake by 5 kg ha−1 ( P = 0·05), ANRS by 0·05 kg kg−1 ( P = 0·036), NFRVN by 0·05 kg kg−1 ( P = 0·090) and NFRVDM by 0·11 kg kg−1 ( P < 0·001). It was concluded that the main factor causing these decreases with HG, compared with LG applications, was wheel damage affecting subsequent N uptake and growth of the taller grass sward. [ABSTRACT FROM AUTHOR]

Grass growth in temperate regions is highly seasonal and difficult to predict. A model that can predict grass growth from week to week would offer a valuable management and budgeting tool for grassland farmers. Many grass growth models have been developed, varying from simple empirical to complex mechanistic models. Three published grass growth models developed for perennial ryegrass swards in temperate climates were selected for evaluation: Johnson & Thornley (1983) (J&T model), Jouven et al. (2006) (J model) and Brereton et al. (1996) (B model). The models were evaluated using meteorological data and grass growth data from Teagasc Moorepark as a framework for further refinement for Irish conditions. The accuracy of prediction by the models was assessed using root mean square error (RMSE) and mean square prediction error (MSPE). The J&T model over-predicted grass growth in all 5 years examined and predicted a high primary grass growth peak, while the J and B models predicted grass growth closer to that measured. Overall, the J model had the smallest RMSE in 3 of the 5 years and the B model in 2 of the 5 years. In spring (February–April), the B model had the lowest RMSE and MSPE. In mid-season (April–August), the B model had the closest prediction to measured data (lowest RMSE), while in autumn (August–October) the J model had the closest prediction. The models with the greatest potential for grass growth prediction in Ireland, albeit with some modifications, are the J model and the B model. [ABSTRACT FROM PUBLISHER]

Temporal prediction of soil moisture and evapotranspiration has a crucial role in agricultural and environmental management. A lack of Irish models for predicting evapotranspiration and soil moisture conditions for arable soils still represents a knowledge gap in this particular area of Irish agro-climatic modelling. The soil moisture deficit (SMD) crop model presented in this paper is based on the SMD hybrid model for Irish grassland (Schulte et al., 2005). Crop and site specific components (free-draining soil) have been integrated in the new model, which was calibrated and tested using soil tension measurements from two experimental sites located on a well-drained soil under spring barley cultivation in south-eastern Ireland. Calibration of the model gave an R² of 0.71 for the relationship between predicted SMD and measured soil tension, while model testing yielded R² values of 0.67 and 0.65 (two sites). The crop model presented here is designed to predict soil moisture conditions and effective drainage (i.e., leaching events). The model provided reasonable predictions of soil moisture conditions and effective drainage within its boundaries, i.e., free-draining land used for spring cereal production under Irish conditions. In general, the model is simple and practical due to the small number of required input parameters, and due to model outputs that have good practical applicability, such as for computing the cumulative amount of water-soluble nutrients leached from arable land under spring cereals in free-draining soils. [ABSTRACT FROM AUTHOR]

The article presents a study on the impact of agriculture on the environment in Ireland. Agriculture in Ireland involves about 270,000 people, 6.191 million cattle, 1.678 million pigs, 4.257 million sheep and 10.7 million poultry. Emerging technologies in Ireland for waste-water treatment include continuously moving biofilm reactors, activated sludge systems, rotating biological contractors, and fluidised-bed biofilm reactors. Legislation in the country reportedly focused on preventing nutrient losses from agricultural sources.

Increased global demand for dairy produce and the abolition of EU milk quotas have resulted in expansion in dairy production across Europe and particularly in Ireland. Simultaneously, there is increasing pressure to reduce the impact of nitrogen (N) losses to air and groundwater on the environment. In order to develop grassland management strategies for grazing systems that meet environmental targets and are economically sustainable, it is imperative that individual mitigation measures for N efficiency are assessed at farm system level. To this end, we developed an excel-based N flow model simulating an Irish grass-based dairy farm, to evaluate the effect of farm management on N efficiency, N losses, production and economic performance. The model was applied to assess the effect of different strategies to achieve the increased production goals on N utilization, N loss pathways and economic performance at farm level. The three strategies investigated included increased milk production through increased grass production, through increased concentrate feeding and by applying a high profit grass-based system. Additionally, three mitigation measures; low ammonia emission slurry application, the use of urease and nitrification inhibitors and the combination of both were applied to the three strategies. Absolute N emissions were higher for all intensification scenarios (up to 124 kg N ha -1 ) compared to the baseline (80 kg N ha -1 ) due to increased animal numbers and higher feed and/or fertiliser inputs. However, some intensification strategies showed the potential to reduce the emissions per ton milk produced for some of the N-loss pathways. The model showed that the assessed mitigation measures can play an important role in ameliorating the increased emissions associated with intensification, but may not be adequate to entirely offset absolute increases. Further improvements in farm N use efficiency and alternatives to mineral fertilisers will be required to decouple production from reactive N emissions., (Copyright © 2019 Elsevier B.V. All rights reserved.)

The Carbon Navigator has been developed to support the objective of reducing the carbon intensity of the dairy and beef sectors of Irish agriculture. The system is designed as a knowledge transfer (KT) tool aimed at supporting the realisation at farm level of the mitigation potential. The objective of this paper is to outline the potential role of KT in reducing greenhouse gas emissions in the context of a growing body of science, which identifies potential mitigation. The EU policy framework for agriculture and the environment is examined in terms of its effectiveness in supporting the reduction in emission intensity of agriculture. The important role for KT in reducing agricultural emissions is highlighted. The Carbon Navigator is introduced as a potential aid to achieving improved adoption of emission-reducing technologies and practices at farm level. The paper outlines the criteria guiding the selection of mitigation technologies in Irish ruminant agriculture, describes the technologies and practices included in the system and outlines the basis for their inclusion. The approach of developing the Carbon Navigator to integrate into existing infrastructure and data systems as well as into the existing KT systems is outlined.