リビングラジカル固相重合法によるバイオハイブリッド用ポリマーの創製

指導教員: 石原, 一

Impacts of greening measures and flat rate regional payments of the Common Agricultural Policy on Scottish beef and sheep farms

SUMMARYThe latest Common Agricultural Policy (CAP) reforms could bring substantial changes to Scottish farming communities. Two major components of this reform package, an introduction of environmental measures into the Pillar 1 payments and a move away from historical farm payments towards regionalized area payments, would have a significant effect on altering existing support structures for Scottish farmers, as it would for similar farm types elsewhere in Europe where historic payments are used. An optimizing farm-level model was developed to explore how Scottish beef and sheep farms might be affected by the greening and flat rate payments under the current CAP reforms. Nine different types of beef and sheep farms were identified and detailed biophysical and financial farm-level data for these farm types were used to parameterize the model. Results showed that the greening measures of the CAP did not have much impact on net margins of most of the beef and sheep farm businesses, except for ‘Beef Finisher’ farm types where the net margins decreased by 3%. However, all farm types were better off adopting the greening measures than not qualifying for the greening payments through non-compliance with the measures. The move to regionalized farm payments increased the negative financial impact of greening on most of the farms but it was still substantially lower than the financial sacrifice of not adopting greening measures. Results of maximizing farm net margin, under a hypothetical assumption of excluding farm payments, showed that in most of the mixed (sheep and cattle) and beef suckler cattle farms the optimum stock numbers predicted by the model were lower than actual figures on farm. When the regionalized support payments were allocated to each farm, the proportion of the mixed farms that would increase their stock numbers increased whereas this proportion decreased for beef suckler farms and no impact was predicted in sheep farms. Also under the regionalized support payments, improvements in profitability were found in mixed farms and sheep farms. Some of the specialized beef suckler farms also returned a profit when CAP support was added.</jats:p

Trade-offs<i> </i>between indicators of performance and sustainability in breeding suckler beef herds

SUMMARYManagement of beef suckler cattle herds requires a difficult but vitally important balance between farm profits, animal health and welfare and sustainable food production. A dynamic programming (DP) model was implemented to investigate the consequences of replacement and management decisions on the interactions and possible trade-offs between animal welfare, fertility and profitability in breeding beef suckler cattle herds. The model maximized profit from the current cow and all successors by identifying the best keep/replace decision. The 150 states incorporated in the DP model were all combinations of: ten cow-parity, five calving periods including one barren state (five in total) as fertility indicators and three body condition scores at weaning as an animal welfare indicator reflecting feeding and nutritional conditions of animals. Statistical models were fitted to data from a breeding suckler cattle herd, consisting of performance records of 200 cattle over 5 years, to parameterize the DP model. Estimated parameters used in the DP model were: (i) probabilities of transitions between states and (ii) probability of involuntary culling. These estimates were used in the form of conditional probabilities of successful or failed (as a result of involuntary culling) transitions to the next state. In addition, statistical models were used to estimate probability of calving difficulty. There was strong evidence (P&lt;0·001) that parity affected calving difficulty and weak evidence (P= 0·067) that parity affected the incidence of involuntary culling. The DP model outcomes indicated that cows calving very early, i.e. those who conceived in the first 21 days after artificial insemination, showed reduced frequencies of calving difficulty as well as voluntary culling, and so gave better financial returns than late-calving cows and barren cows. As a result, fewer replacements were needed that reduced the frequency of calving difficulty, further implying a win–win scenario for both profit and welfare. In contrast, in late-calving animals, the frequency of calving difficulty increased and they were less profitable and more prone to be culled. Results of sensitivity analysis showed that the optimum voluntary culling rate was sensitive to commodity market prices. These findings suggest well-informed nutrition and reproduction management could deliver a win–win outcome for profit and animal welfare.</jats:p

An approach to holistically assess (dairy) farm eco-efficiency by combining Life Cycle Analysis with Data Envelopment Analysis models and methodologies

Eco-efficiency is a useful guide to dairy farm sustainability analysis aimed at increasing output (physical or value added) and minimizing environmental impacts (EIs). Widely used partial eco-efficiency ratios (EIs per some functional unit, e.g. kg milk) can be problematic because (i) substitution possibilities between EIs are ignored, (ii) multiple ratios can complicate decision making and (iii) EIs are not usually associated with just the functional unit in the ratio's denominator. The objective of this study was to demonstrate a 'global' eco-efficiency modelling framework dealing with issues (i) to (iii) by combining Life Cycle Analysis (LCA) data and the multiple-input, multiple-output production efficiency method Data Envelopment Analysis (DEA). With DEA each dairy farm's outputs and LCA-derived EIs are aggregated into a single, relative, bounded, dimensionless eco-efficiency score, thus overcoming issues (i) to (iii). A novelty of this study is that a model providing a number of additional desirable properties was employed, known as the Range Adjusted Measure (RAM) of inefficiency. These properties altogether make RAM advantageous over other DEA models and are as follows. First, RAM is able to simultaneously minimize EIs and maximize outputs. Second, it indicates which EIs and/or outputs contribute the most to a farm's eco-inefficiency. Third it can be used to rank farms in terms of eco-efficiency scores. Thus, non-parametric rank tests can be employed to test for significant differences in terms of eco-efficiency score ranks between different farm groups. An additional DEA methodology was employed to 'correct' the farms' eco-efficiency scores for inefficiencies attributed to managerial factors. By removing managerial inefficiencies it was possible to detect differences in eco-efficiency between farms solely attributed to uncontrollable factors such as region. Such analysis is lacking in previous dairy studies combining LCA with DEA. RAM and the 'corrective' methodology were demonstrated with LCA data from French specialized dairy farms grouped by region (West France, Continental France) and feeding strategy (regardless of region). Mean eco-efficiency score ranks were significantly higher for farms with 30% maize in the total forage area before correcting for managerial inefficiencies. Mean eco-efficiency score ranks were higher for West than Continental farms, but significantly higher only after correcting for managerial inefficiencies. These results helped identify the eco-efficiency potential of each region and feeding strategy and could therefore aid advisors and policy makers at farm or region/sector level. The proposed framework helped better measure and understand (dairy) farm eco-efficiency, both within and between different farm groups