Abstract: This study aimed to determine short term farm responses of Irish dairy farms under climate change. The Irish National Farm Survey data and Irish weather data were the main datasets used in this study. A set of simulation models were used to determine grass yields and field time under a baseline scenario and a future climate scenario. An optimising farm level model which maximises farm net income under limiting farm resources was then run under these scenarios. Changes in farm net incomes under the climate change scenario compared to the baseline scenario were taken as a measure to determine the effect of climate change on farms. Any changes in farm activities under the climate run compared to the baseline run were considered as farm’s responses to maximise farm profits. The results showed that there was a substantial increase in yields of grass (49% to 56%) in all regions. The impact of climate change on farms was different based on the regions. Dairy farms in the Border, Midlands and South East regions suffered whereas dairy farms in other regions generally fared better under the climate change scenario. For a majority of farms, a substitution of concentrate feed with grass based feeds and increasing stocking rate were identified as the most common farm responses. However, farms replaced concentrate feed at varying degree. Dairy farms in the Mid East showed a move towards beef production system where medium dairy farms in the South East regions shifted entire tillage land to grass land. Farms in the South East region also kept animals on grass longer under the climate change scenario compared to the baseline scenario.

Abstract: Greenhouse gas (GHG) mitigation is one of the main challenges facing agriculture, exacerbated by the increasing demand for food, in particular for livestock products. Production expansion needs to be accompanied by reductions in the GHG emission intensity of agricultural products, if significant increases in emissions are to be avoided. Suggested farm management changes often have systemic effects on farm, therefore their investigation requires a whole farm approach. At the same time, changes in GHG emissions arising offfarm in food supply chains (pre- or post-farm) can also occur as a consequence of these management changes. A modelling framework that quantifies the whole-farm, life-cycle effects of GHG mitigation measures on emissions and farm finances has been developed. It is demonstrated via a case study of sexed semen on Scottish dairy farms. The results show that using sexed semen on dairy farms might be a costeffective way to reduce emissions from cattle production by increasing the amount of lower emission intensity ‘dairy beef’ produced. It is concluded that a modelling framework combining a GHG life cycle analysis model and an economic model is a useful tool to help designing targeted agri-environmental policies at regional and national levels. It has the flexibility to model a wide variety of farm types, locations and management changes, and the LCA-approach adopted helps to ensure that GHG emission leakage does not occur in the supply chain.

Abstract: Agro-meteorological quantities are often in the form of time series, and knowledge about their temporal scaling properties is fundamental for transferring locally measured fluctuations to larger scales and vice versa. However, the scaling analysis of these quantities is complicated due to the presence of localized trends and nonstationarities. The objective of this study was to characterise scaling properties (i.e. statistical self-similarity) of the chosen agro-meteorological quantities through multifractal detrended fluctuation analysis (MFDFA). For this purpose, MFDFA was performedwith 11 322 measured time series (31 yr) of daily air temperature, wind velocity, relative air humidity, global radiation and precipitation from stations located in Finland, Germany, Poland and Spain. The empirical singularity spectra indicated their multifractal structure. The richness of the studied multifractals was evaluated by the width of their spectrum, indicating considerable differences in dynamics and development. In log-log plots of the cumulative distributions of all meteorological parameters the linear functions prevailed for high values of the response, indicating that these distributions were consistent with power-law asymptotic behaviour. Additionally, we investigated the type of multifractality that underlies the q-dependence of the generalized Hurst exponent by analysing the corresponding shuffled and surrogate time series. For most of the studied meteorological parameters, the multifractality is due to different long-range correlations for small and large fluctuations. Only for precipitation does the multifractality result mainly from broad probability function. This feature may be especially valuable for assessing the effect of change in climate dynamics.