The promised benefits of precision technologies (PTs) include improved efficiency, quality, animal health and welfare and reduced environmental impacts. To date, PTs (including sensors, algorithms, ...big data, decision-support tools, etc.) have had a relatively modest impact in pasture-based dairying systems in comparison with other agricultural sectors such as arable production. The areas animals roam and graze in pasture-based systems and the associated connectivity challenges may, in part at least, explain the comparatively reduced use of PTs in those systems. Thus, there are very few technologies designed specifically to increase pasture utilisation with the exception of global positioning systems (GPS) and Bluetooth-enabled Plate Meters. Terrestrial and satellite-based spectral analysis of pasture biomass and quality is still in the development phase. Therefore, one of the key drivers of efficiency in pasture-based systems has only been marginally impacted by PTs. In contrast, technological development in the area of fertility and heat detection has been important and offers significant potential value to dairy farmers. In general PTs can be described as good at measurement, data collection and storage but fall down around interpretation and providing useful outputs to end users. As a result, it is unclear if farm management is being sufficiently improved to justify widespread adoption of PTs. A needs-driven development of PTs and decision-support tools are required for the succesful integration within agriculture. Further cost/benefit analysis is also required to determine the efficiency of investing in PTs and what, if any, factors affect the variation in the returns.
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•The Most GG model is dynamic and mechanistic and represents N flux at grazing.•The inputs have been kept simple to ensure its future utilisation.•Its main outputs are grass growth, N leaching and ...grass N content.•The MoST GG model is sensible to soil type, management and weather condition.•It can recreate the response to N fertilisation and defoliation management.
In humid-temperate regions grazed grass is the most economical means of feeding ruminant livestock. Grass growth is often highly variable and therefore difficult to predict. It is influenced by many factors including climatic conditions, soil type and soil nutrients. The Moorepark St. Gilles Grass Growth model (MoSt GG model) is a dynamic model developed in C++ describing daily grass growth at the paddock level. It was developed by adapting an existing grass growth model to include a nitrogen (N) component and a soil water component. The model is effective in grazing and cutting scenarios. Inputs include weather data, grazing management decisions and N fertiliser application. Outputs include daily grass growth, soil mineral N content, grass N uptake, grass N content and NO3− leaching. The MoSt GG model was evaluated against measured data using 2 years data from an experimental farm; the predicted and measured biomass for each paddock was compared. The mean root mean square prediction error (RMSPE) at the measurement level was 505 kg DM/ha. When averaged by week of year, the RMSPE was reduced to 321 kg DM/ha. The MoSt GG model was also evaluated against a range of management scenarios including N fertiliser application rate (from 0 to 650 kg N/ha per year) and defoliation management, and weather conditions. The grass growth response to N fertiliser application was, on average, 9.6 kg DM/kg N applied with a minimum response of 0.8 kg DM/kg N applied and a maximum response of 16.2 kg DM/kg N applied, which is in the range of previously published studies. The MoSt GG model responds to daily weather conditions, patterns and methods of sward defoliation, and describes daily variations in soil mineral and organic N content.
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•An agent based dairy farm model for pasture base system.•Tight interaction between the animal and the grass characteristic.•Take into account the impact of the grass height on the intake of the ...animals.•Evaluated on a French and Irish farm in term of milk yield and grass utilisation.•Possibility to simulate a wide range of pasture based dairy system.
Modelling pasture-based systems is a challenge for modellers worldwide. However, models can play a vital role as grazing management tools and help the decision making process at farm level. The objective of this paper is to describe and evaluate the pasture-based herd dynamic milk (PBHDM) model. The PBHDM model comprises the herd dynamic milk (HDM) model and integrates it with a grazing management and a paddock sub-model. Animal intake at grazing is dependent on the animal characteristics but also on grass availability and quality. It also depends on the interactions between the animal and the grass during the defoliation process. Management of grass on farm can be regulated through different rules during the grazing season including the decision to cut some paddocks in the case of a grass surplus and to allocate supplementation in the case of a grass deficit. The PBHDM was evaluated by comparing model outputs with two grazing systems one in France and one in Ireland. For both farms the grazing season is longer than 7 months. Model outputs that were compared to the actual experimental data included milk production, pre- and post-grazing height and feed supplementation levels. These outputs were all compared on a weekly basis while paddock residence time and total grass harvested as conserved grass silage was evaluated over the grazing season as a whole. The model was capable of reproducing the two grazing systems with acceptable accuracy. It simulated the pre- and post-grazing height with a maximal difference between the actual and the simulated average height through the year of 0.4cm. The model has a tendency to slightly over-estimate the milk production especially in autumn. However in general the model is relatively accurate with a root mean square error less than 20% for the simulated farms.
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•A grass growth model uses weather forecasts instead of weather observations.•Weather forecasts give reasonable predictions in a grass growth model.•ECMWF forecasts are most useful weather input in ...practice.
Grass growth models have retrospectively predicted grass growth in Ireland using weather observations. However, to predict future grass growth to aid farm management, weather forecasts are necessary inputs. The Moorepark St. Gilles grass growth model (MoSt GGM) is mechanistic and was developed to predict perennial ryegrass growth on any Irish farm. To date, it has used local farm information, (retrospective) weather data and management factors to predict daily paddock-level grass growth. Here, we include weather forecasts in the MoSt GGM and assess its performance through two studies: daily grass growth predictions at four nitrogen fertiliser application levels using weather forecasts up to ten days in advance were compared with those using weather observations; and the GGM predictions for an Irish dairy farm using observed and forecast weather were compared with on-farm grass growth observations from 2013 to 2016. In the first study, all weather inputs captured the rise in grass growth predictions with higher fertiliser application. Based on the Root Mean Squared Error (RMSE), European Centre for Medium-Range Weather Forecasts (ECMWF) forecasts outperformed a forecast based on climatological averages as GGM inputs up to six days in advance, and up to ten days in advance after bias correction. In the second study, ECMWF forecasts were the best weather forecast to predict grass growth since they captured weather variability well and did not require the local weather observations necessary for bias corrections. Weather forecasts are useful inputs to the MoSt GGM, and yield accurate weekly predictions that could aid management decisions.
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Understanding care seeking behaviour is vital to enabling access to care. In the context of low back pain (LBP), chiropractors offer services to patients of all ages. Currently, geriatric ...sub-populations tend to be under-investigated, despite the disproportionate effects of LBP on older adults. In the Netherlands, the chiropractic profession is relatively unknown and therefore, generally speaking, is not considered as the first choice for conservative musculoskeletal primary health care. The aim of this paper was to explore the experiences of older adults with LBP, seeking chiropracic care for the first time, in order to identify perceived barriers and facilitators in this process.
Stage 1: Participants 56 years of age and older with chronic LBP who either sought or did not seek chiropractic care were interviewed to provide detailed information on the factors that promoted or impeded care-seeking behaviour. A purposive sampling strategy was used to recruit participants through a network of researchers, chiropractors and other healthcare professionals offering musculoskeletal health care services. Individuals with underlying pathology, previous surgery for LBP, or insufficient mastery of the Dutch language were excluded. Data were collected until saturation was reached and thematically analysed. Stage 2: To further explore the themes, a focus group interview was conducted with a provider stakeholder group consisting of:two physiotherapists, a nurse practitioner, a geriatrician, and a chiropractor. All interviews were conducted online, voice recorded, and transcribed verbatim.
We interviewed 11 older adults with low back pain. During this process four themes emerged that captured their perception and experiences in either seeking or dismissing chiropractic care for their LBP; these being 'generic', 'financial', 'expectation', and 'the image of the chiropractor'. The focus group members largely confirmed the identified themes, highlighting a lack of awarenes and accessibility as key barriers to care. On the other hand, whe chiropractior as an alternative care provider, with a focus on manual interventions, was seen as a facilitator.
The lack of knowledge about chiropractic care was found to be the most important barrier to seeking care. The most important facilitator was insufficient resolution of their symptoms following previous care, making patients look further for a solution for their problem. These barriers and facilitators seem not to differ greatly from barriers and facilitators found among younger patients with neck pain. Age and health condition may therefore be weak determinants of care. This new information may help us optimize accessibility for older adults to the chiropractor.
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DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, SIK, UILJ, UKNU, UL, UM, UPUK
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