Search

There is an urgent need for transformational change in global and UK agriculture. Current practices undermine the long-term future of farming and impoverish ecosystems in the UK and elsewhere. However, change is not happening at the scale and pace which is needed. Work by David Abson, drawing on ideas by Donella Meadows, explored this failure of progress and proposed a research agenda focused on transformational leverage points which influence sustainability. These points are centred on three realms of leverage: reconnecting people to nature, restructuring institutions and rethinking how knowledge is created and used in pursuit of sustainability. In this paper, these ideas are explored through a combined researcher/stakeholder workshop focused on transformational change in UK livestock systems. Workshop participants were asked to discuss and identify potential levers of change under the three realms identified by Abson. The multiplicity of levers identified and the interactions across realms emphasise the need for new kinds of knowledge creation which are highly transdisciplinary, as well as emphasising the complexity of levers which are likely to play a role in the transformation of livestock food systems in the UK and elsewhere. [ABSTRACT FROM AUTHOR]

This paper presents an alternative approach for high spatial resolution vegetation productivity mapping at a regional scale, using a combination of Normalised Difference Vegetation Index (NDVI) imagery and widely distributed ground-based Above-ground Net Primary Production (ANPP) estimates. Our method searches through all available single-date NDVI imagery to identify the images which give the best NDVI-ANPP relationship. The derived relationships are then used to predict ANPP values outside of field survey plots. This approach enables the use of the high spatial resolution (30 m) Landsat 8 sensor, despite its low revisit frequency that is further reduced by cloud cover. This is one of few studies to investigate the NDVI-ANPP relationship across a wide range of temperate habitats and strong relationships were observed (R² = 0.706), which increased when only grasslands were considered (R² = 0.833). The strongest NDVI-ANPP relationships occurred during the spring "green-up" period. A reserved subset of 20% of ground-based ANPP estimates was used for validation and results showed that our method was able to estimate ANPP with a RMSE of 15-21%. This work is important because we demonstrate a general methodological framework for mapping of ANPP from local to regional scales, with the potential to be applied to any temperate ecosystems with a pronounced green up period. Our approach allows spatial extrapolation outside of field survey plots to produce a continuous surface product, useful for capturing spatial patterns and representing small-scale heterogeneity, and well-suited for modelling applications. The data requirements for implementing this approach are also discussed. [ABSTRACT FROM AUTHOR]

The Countryside Survey (CS) of Great Britain (GB) provides a unique and statistically robust series of datasets, consisting of an extensive set of repeated ecological measurements at a national scale, covering a time span of 29 years. CS was first undertaken in 1978 to provide a baseline for ecological and land use change monitoring in the rural environment of GB, following a stratified random design, based on 1 km squares. Originally, eight random 1 km squares were drawn from each of 32 environmental classes, thus comprising 256 sample squares in the 1978 survey. The number of these sites increased to 382 in 1984, 506 in 1990, 569 in 1998 and 591 in 2007. Detailed information regarding vegetation types and land use was mapped in all five surveys, allowing reporting by defined standard habitat classifications. Additionally, point and linear landscape features (such as trees and hedgerows) are available from all surveys after 1978. From these stratified, randomly located sample squares, information can be converted into national estimates, with associated error terms. Other data, relating to soils, freshwater and vegetation, were also sampled on analogous dates. However, the present paper describes only the surveys of landscape features and habitats. The resulting datasets provide a unique, comprehensive, quantitative ecological coverage of extent and change in these features in GB. Basic results are presented and their implications discussed. However, much opportunity for further analyses remains. Data from each of the survey years are available via the following DOIs: Landscape area data 1978: http://doi.org/10.5285/86c017ba-dc62-46f0-ad13-c862bf31740e, 1984: http://doi.org/10.5285/b656bb43-448d-4b2c-aade-7993aa243ea3, 1990: http://doi.org/10.5285/94f664e5-10f2-4655-bfe6-44d745f5dca7, 1998: http://doi.org/10.5285/1e050028-5c55-42f4-a0ea-c895d827b824, and 2007: http://doi.org/10.5285/bf189c57-61eb-4339-a7b3-d2e81fdde28d; Landscape linear feature data 1984: http://doi.org/10.5285/a3f5665c-94b2-4c46-909e-a98be97857e5, 1990: http://doi.org/10.5285/311daad4-bc8c-485a-bc8a-e0d054889219, 1998: http://doi.org/10.5285/8aaf6f8c-c245-46bb-8a2a-f0db012b2643, and 2007: http://doi.org/10.5285/e1d31245-4c0a-4dee-b36c-b23f1a697f88; Landscape point feature data 1984: http://doi.org/10.5285/124b872e-036e-4dd3-8316-476b5f42c16e, 1990: http://doi.org/10.5285/1481bc63-80d7-4d18-bcba-8804aa0a9e1b, 1998: http://doi.org/10.5285/ed10944f-40c8-4913-b3f5-13c8e844e153, and 2007: http://doi.org/10.5285/55dc5fd7-d3f7-4440-b8a7-7187f8b0550b. [ABSTRACT FROM AUTHOR]

Since 1978, a series of national surveys (Countryside Surveys) have been carried out by the Centre for Ecology and Hydrology (formerly the Institute of Terrestrial Ecology) to gather data on the natural environment in Great Britain. As the sampling framework for these surveys is not optimised to yield data on rarer or more specialised habitats, a survey was commissioned by the then Department of the Environment (DOE, now the Department for Environment Food & Rural Affairs, DEFRA), in the 1990s to carry out additional survey work in English landscapes which contained semi-natural habitats that were perceived to be under threat, or which represented areas of concern to the Ministry. The landscapes were: lowland heath, chalk and limestone grasslands, coasts and uplands. These landscapes were chosen from a list identified as 'key habitats' in the Countryside Stewardship Scheme, an agri-environment scheme initiated in 1991. The survey design was a series of gridded, stratified, randomly selected 1 km squares taken as representative of classes derived from environmental classifications (or spatial masks) for each of the four landscape types in England determined from a statistical land classification. This resulted in a total of 213 of these squares being surveyed in the summers of 1992 and 1993, with information being collected regarding vegetation species, land cover, landscape features and land use. Data from the survey were collected using standardised, repeatable methods, with the database now providing a valuable baseline against which future ecological changes, resulting from a range of different drivers, may be compared. Following the surveys, the data were analysed and described in a series of contract reports showing that valuable habitats were restricted in all landscapes and that the majority were within designated land. The data set provides major potential for analyses, beyond those published in the reports published in 1996, for example in relation to climate change, agri-environment policies and land management. Precise locations of the plots are restricted, largely for reasons of landowner confidentiality. However, the representative nature of the data set makes it highly valuable for evaluating the status of the associated landscapes and vegetation covered. Both land cover data and vegetation plot data were collected during the surveys in 1992 and 1993, and are available via the following DOI: https://doi.org/10.5285/7aefe6aa-0760-4b6d-9473-fad8b960abd4. The spatial masks are also available from: https://doi.org/10.5285/dc583be3-3649-4df6-b67e-b0f40b4ec895. [ABSTRACT FROM AUTHOR]

The importance of habitat for biodiversity is well established, but the two most commonly used methods to measure habitat (field survey and remote sensing) have seldom been explicitly compared., We compare high-resolution sample-based field survey (Countryside Survey) with medium-resolution remotely sensed habitat data (the highest resolution of Land Cover Map available) for Great Britain. Variation in abundance of 60 bird species from 335 1 km squares was modelled using habitat predictors from the two methods. Model comparisons assessed the explanatory power of (i) field survey vs. remotely sensed data and (ii) coarse information on habitat areas (Broad Habitats) vs. fine-grained information on Landscape Features., Field survey data (combining Broad Habitat and Landscape Feature predictors) explained more variation in bird abundance than remotely sensed data (comprising Broad Habitat predictors only) for 57 species and had significantly higher mean explanatory power, averaged across 60 species models. The relative explanatory power of remote sensing, as a proportion of that provided by field data, was measured at 73%, averaged across 60 species models. Predictions from field survey Broad Habitat data were more accurate than those from either remotely sensed Broad Habitat data or field survey Landscape Feature data, averaged across 60 species models., High-resolution data generate more reliable models of predicted local population responses to land use change than lower resolution remotely sensed data. Collection of field data is typically costly in time, labour and resources, making use of remote sensing more feasible for assessment at larger spatial extents if data of equivalent value are produced, but the cost-benefit threshold between the two is likely to be context specific. However, integration of field survey with remotely sensed data provides accurate predictions of bird distributions, which suggests that both forms of data should be considered for future biodiversity surveys. [ABSTRACT FROM AUTHOR]