Browsing Theses - REDP by Title
Now showing items 2-4 of 4
Improving Public Access to the Irish Countryside for Walking – Investigation of Supply and Demand Side FactorsIncreased interest and demand for land based recreational amenities has seen the rise of conflict between landowners and recreationalists (particularly walkers) in the Republic of Ireland. A right of access to the countryside for recreation prevalent across other developed nations does not apply. Stakeholders have tabled various proposals to address this situation ranging from a right to roam across the countryside to a compensation payment to landowners for recreational access. Whilst policy makers are aware of the economic opportunities associated with open-air outdoor recreation activities, rational public decision making requires that economic benefits and costs should be clearly identified and valued to justify any policy intervention. To-date no such evaluation has been undertaken. This thesis explores supply and demand side factors that influence public access provision to the Irish countryside for recreational walking. Firstly, contingent valuation was used to measure the willingness to pay of consumers for improved public access and trail improvements on commonage farmland based on two case study sites in the Connemara region. Secondly, a national representative survey was used to explore the attitudes of landowners across the Republic of Ireland to the wider provision of public access for recreational walking on farmland, including the potential opportunity costs to agriculture as well as the level of compensation demanded by landowners. This thesis argues that based on derived welfare estimates there is significant scope for policy interventions to improve public access to the countryside in the Republic of Ireland.
Investigation into the bio-physical constraints on farmer turn-out-date decisions using remote sensing and meteorological data.Grass is the most common landcover in Ireland and covers a bigger percentage (52%) of the country than any other in Europe. Grass as fodder is Ireland’s most important crop and is the foundation of its most important indigenous industry, agriculture. Yet knowledge of its distribution, performance and yield is scant. How grass is nationally, on a farm by farm, year by year basis managed is not known. In this thesis the gaps in knowledge about grassland performance across Ireland are presented along with arguments on why these knowledge gaps should be closed. As an example the need for high spatial resolution animal stocking rate data in European temperate grassland systems is shown. The effect of high stocking density on grass management is most apparent early in the growing season, and a 250m scale characterization of early spring vegetation growth from 2003-2012, based on MODIS NDVI time series products, is constructed. The average rate of growth is determined as a simple linear model for each pixel, using only the highest quality data for the period. These decadal spring growth model coefficients, start of season cover and growth rate, are regressed against log of stocking rate (r2 19 = 0.75, p<0.001). This model stocking rate is used to create a map of grassland use intensity in Ireland, which, when tested against an independent set of stocking data, is shown to be successful with an RMSE of 0.13 Livestock Unit/ha for a range of stocking densities from 0.1 to 3.3 Livestock Unit/ha. This model provides the first validated high resolution approach to mapping stocking rates in intensively managed European grassland systems. There is a demonstrated a need for a system to estimate current growing conditions. Using the spring growth model constructed for estimating stocking density a new style of grass growth progress anomaly map in the time-domain was developed. Using the developed satellite dataset 1 and 12 years of ground climate station data in Ireland, NDVI was modelled against time as a proxy for grass growth This model is the reference for estimating current seasonal progress of grass growth against a ten year average. The model is developed to estimate Seasonal Progress Anomalies in the Time domain (SPAT), giving a result in terms of “days behind” and “days ahead” of the norm. SPAT estimates for 2012 and 2013 are compared to ground based estimates from 30 climate stations and have a correlation coefficient of 0.897 and RMSE of 15days. The method can successfully map current grass growth trends compared to the average and present this information to the farmer in simple everyday language. This is understood by the author to be the first validated growth anomaly service, and the first for intensive European grasslands. The decisions on when to turn out cattle (the turn out date (TOD)) from winter housing to spring grazing is an important one on Irish dairy farms which has significant impacts on operating costs on the farm. To examine the relationship of TOD to conditions, the National Farm Survey (NFS) of Ireland database was geocoded and the data on turn out dates from 199 farms across Ireland over five years was used. A fixed effects linear panel data model was employed to explore the association between TOD and conditions, as it allows for unobserved variation between farmers to be ignored in favour of modelling the variance year on year. The environmental variables used in the analysis account for 38% of the variance in the turn out dates on farms nationwide. National seasonal conditions dominate over local variation, and for every week earlier grass grows in spring, farmers gain 3.7 days in grazing season but ignore 3.3 days of growth that could have been used. Every 100mm extra rain in spring means TOD is a day later and every dry day leads to turn out being half a day earlier. A well-drained soil makes TOD 2.5 days earlier compared to a poorly drained soil and TOD gets a day later for every 16km north form the south coast. This work demonstrates that precision agriculture 1 driven by optical and radar satellite data is closer to being a reality in Europe driven by enormous amounts of free imagery from NASA and the ESA Sentinel programs coupled with open source meteorological data and models and new developments in data analytics.
Reducing the carbon footprint of red meatThe contribution of ruminant agriculture towards climate change is significant and responsible for approximately 14.5% of anthropogenic global greenhouse gas emissions. The reduction of sectorial emissions is dependent on farmer decision-making at a multitude of scales, which comprise of the field scale, the farm, farmer typologies (farm scale with focus on farmers), and the community-scale. This conceptual framework provides the basis for the research carried out in this PhD. The first research chapter builds upon previous work carried out by Bangor University where farmers deemed the most practical mitigation measure they could adopt on their farming enterprises was the planting of leguminous crops. The research in this thesis demonstrated that grass-clover systems offered the same yield as grass swards receiving conventional amounts of nitrogen fertiliser. However, nitrous oxide emissions from the grass-clover sward were significantly lower. My second research chapter moves onto the farm scale and investigates the carbon footprint (CF) from 15 farming enterprises over two timescales. Considerable reductions in the CF of beef and lamb were demonstrated if efficiencies were increased to match those of the least-emitting producers. On-farm decisions are motivated by personal interests and goals. Hence, the third research chapter identifies distinct types of farmers based on perceptions of climate change. Four farmer types were identified which can aid the dissemination of climate change information and consequently increase the adoption of climate change measures. The final chapter evaluates social capital and collaboration amongst farmers at the community scale; such interactions can serve to facilitate mitigation and adaptation. Although overall collaboration was low, there was considerable latent social capital which can be used to further encourage collective action. The work carried out in this thesis can help reduce the livestock sector’s greenhouse gas emissions across numerous scales; thereby helping the industry meet its emission targets.