Understanding the impact of farming on aquatic ecosystems
Improvement, development and implementation of cost-effective mitigation measures
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- Effectiveness
- Phosphorus emissions
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Improvement, development and implementation of cost-effective mitigation measuresInformation on the costs of a measure and its effectiveness in terms of emissions reduction is contained in the User Manual on diffuse water pollution (subsequently referred to here as the User Manual).186 The effectiveness information incorporates a considerable amount of expert judgment and is often characterised by large ranges, while the costs are of a generic nature, more comparable to those used for benchmarking farm operations contained in farm management costings publications. The costs and effectiveness values for each measure contained in the User Manual relate specifically to notional 'representative' Model Farm Systems for the main sectors, and are given for two main soil types and a medium level of rainfall. The effectiveness of a measure in reducing pollution will however be sensitive to a wider range of soil types and rainfall, as well as a number of site specific factors. This is highlighted by Johnston and Dawson (2005), who reported that the relative importance of different pathways will vary spatially and with time, both between and within catchments, and that factors affecting phosphorus loss are field-specific. It is also reflected in a substantial research project for English Nature and the Environment Agency. This concluded that different farms and different locations will in general respond most effectively to a different local mix of changes in practice187. A similar view emerged from the Defra Phosphorus Cost Curve project which concluded that both cost and effectiveness may vary considerably with location188. The User Manual acknowledges that its standard Model Farming Systems cannot be extrapolated and applied to the whole of a farming sector across farms of different sizes and in different regions. Also that there may be appreciable differences between a model farm system within a sector and the range of farms found within the sector, with corresponding differences in the applicability and effectiveness of the mitigation methods reviewed. Modelling has been used to extend the assessments in the User Manual to the regional scale according to three climatic (rainfall) categories and two broad soil types (sandy loams and clay loams) as part of Defra’s consultation on diffuse pollution.189 The assessment of the measures which are most cost effective can be complicated by the fact that some pollutants are closely linked and measures often affect more than one pollutant, although sometimes in conflicting ways. Measures which are effective for phosphorus, for example, are also considered to be effective to varying degrees for sediment, organic pollution, BOD, and for those pesticides attaching to soil particles. But applying manures in spring to help reduce nitrate loss for example, may increase ammonia emissions. The effectiveness of a measure will also depend on the extent to which measures are taken up by farmers. This may be influenced by the mechanism used to deliver the measure (whether it is supportive, economic or regulatory) and the receptiveness of farmers. A recent survey of farmer attitudes carried out as part of the ECSFDI found that 81% of farmers do not believe they make a significant contribution to pollution (Craig, pers.comm.). The effectiveness of mechanisms has been addressed by Defra in its consultation on diffuse water pollution from agriculture, which found regulation to be the most cost effective, albeit on the basis of certain specific assumptions (see above). The attributes contributing to effectiveness of a measure in closing the gap required to meet the specified WFD objective are described briefly in the methodology developed for assessing the cost effectiveness of measures for the UK's implementation of the WFD.190 These are listed below, the first four of which relate broadly to the efficacy of a measure, and the remainder to its uptake: Uncertainty of effectiveness; Characteristics of effects: Speed of effect, Durability of effect, Adaptability/reversibility of the measure (how easily it can be adapted to changes in the requirement); Effort (the degree to which a measure is implemented); Practicability; Local acceptability; Motivation for implementation; Presence/absence of responsible organisation; Presence/absence of planning process. The need for future research on measures for mitigating pollution therefore needs to be considered against the background of the disciplined approach enshrined in this new cost effectiveness methodology. The User Manual includes comments for some measures on their speed of effect where the response time is particularly slow, but includes very little specific information. It also includes a brief assessment for each measure on how easy it is to adopt, how it may impact on other farming practices, problems with maximising effectiveness and possible resistance to uptake. Since it is based on standard model farm systems, it does not contain sufficient information to allow assessment of cost effectiveness across the range of situations which exists within farming sectors. It therefore increases the uncertainty of effectiveness and so will score less well in this respect under the cost effectiveness methodology. Defra recognises that variability in soils (clay and sandy) and climate (rainfall) can cause different measures to be the most cost effective in these different situations.191 However, in the light of the research findings cited above, this would not appear to have gone far enough in reflecting the variability inherent in agriculture, Much research has already been carried out to improve the understanding of factors influencing the scale and other characteristics of nitrate loss, and to a considerable extent on phosphorus, although gaps remain. This was acknowledged by Haygarth et al. (2005) who concluded that for P, there are many areas where we lack knowledge192. Much less appears to be known about other pollutants. In the light of the above, the following recommendations are made: Effectiveness: To create a database of quantitative data on the effectiveness of measures for the mitigation of various agricultural pollutants. The data needs to reflect the range of variables which have a significant effect on the efficiency of measures, such as soil type and condition, climate, topography and farm type. It should systematically cover relevant attributes of effectiveness required for the WFD methodology, for example scale of effect, timing and duration of effect. It also needs to cover the effects on other water pollutants, which may be positive or negative. This would likely be a major undertaking, and prioritisation would be needed. There are relatively few data sets quantifying concentrations and loads of multiple pollutants for many of the mitigation methods, as is recognised by IGER (IGER, pers. comm.). While the Defra project on cracking clay soils (WQ0118) is looking at multiple pollutants from manures, it will be plot based and so will not address scale effects. Also, it will be limited in the number of farming systems included and will not address a number of the attributes of effectiveness detailed in the WFD methodology. Scale: To investigate whether the most cost effective measures for different pollutants differ at a field-by-field scale, and whether it would be feasible to predict the most cost effective measures at this scale. If the measures applied are not the most cost effective for a particular situation, this will increase the costs unnecessarily and lead to misallocation of resources. It may also result in the objective for that water body not being achieved. This recommendation would provide part of the information needed for ensuring costs are not higher than necessary. Defra estimates the costs of implementing measures to tackle diffuse water pollution from agriculture to meet WFD objectives at £140-200m p.a. (8-11% of farm income for England in 2006). With costs of this order under consideration, the potential savings from developing approaches and database to fine-tuning measures to achieve the most cost-effective solution are likely to be substantial. ADAS supports recommendations 25 and 26. It believes it would be feasible to predict the most cost effective measures at a field scale, and that much of the information required to do this, for example on slope, is available (Chambers, pers comm.). Phosphorus emissions: As a priority, there is a need to identify and fill gaps in the existing data on the effectiveness of agricultural measures for phosphorus in achieving ecological improvement, as distinct from achieving reductions in total phosphorus as a chemical standard. Environmental standards for the achievement of Good Ecological Status have been proposed on the basis of soluble reactive phosphorus in rivers and total phosphorus in lakes. The achievement of the standards will require consideration of all sources of phosphorus in the catchments of affected water bodies. It is widely believed that there are important differences in the ecological impact of phosphorus from agriculture compared to other sources involving factors such as the chemical form of phosphorus, particle sizes and behaviour, the timing of emissions and the extent to which these are effectively removed from the water body before uptake by biota can occur. Agricultural phosphorus sources generally have less soluble phosphate and emissions to water bodies are strongly biased towards winter when temperatures are low and flows are high, which may reduce the impact relative to other sources and so limit the scope for achieving improvements in status through agricultural measures alone. There may also be differences between phosphorus derived from the respective agricultural sources and transported through different pathways which could influence the ranking of measures for cost-effectiveness. However, in some catchments it is likely that the achievement of good ecological status will require measures to reduce emissions of phosphorus from both point and diffuse (agricultural) sources. Costs: To create a database of costs of environmental mitigation measures for agriculture capturing significant differences, such as those between soil and enterprise types, regions, size and structure of businesses and farming system employed. Benchmark-type generic costs at national level as currently provided in the User Manual are unlikely to be an adequate basis for deriving estimates of cost effectiveness. While the Manual goes some way to differentiating costs by using broad geoclimatic categories (2 soil types and 3 levels of rainfall) and certain enterprise types, it takes no account of other factors such as the varying size of enterprises and it does not cover all sectors (e.g. sheep) nor all types of enterprise (e.g. layers). The costs data in the User Manual also differ widely from other published sources in some respects, and so are open to question. As with effectiveness, breaking down the ranges associated with the figures according to the underlying factors would seem necessary. Costs for environmental mitigation measures regionally differentiated according to production system used and scale of operation would seem more appropriate than the current approach. A pilot project to assess the scope for finer resolution in cost data is recommended. Catchment studies: Approaches to reducing impacts from diffuse pollutants from agriculture should be tested and developed at a catchment-scale in a small number of pilot catchments. Information should be gathered on the dynamics and trade-offs in selecting measures for a wide range of agricultural pollutants. Catchment studies are required to assess and validate the performance of combinations of measures in reducing ecological impacts, and to compare this with predicted effectiveness. Such studies could help ascertain the extent to which improvements in cost effectiveness can be achieved by field-level targeting, and the resources needed to achieve this. Download 432.61 Kb. Share with your friends:
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