Resource information
Leaf area index (LAI; the single-sided leaf area per unit area of ground) is a measure of plant standing crop in terrestrial ecosystems and is a key parameter in meteorological, climate and hydrological models. While the LAI of individual species can be measured by destructive harvesting and LAI of whole landscapes can be estimated by remote sensing, estimates of species-specific LAI at the landscape-scale are lacking. Working on a 125ha farm in Somerset, UK during the summers of 2007 and 2008, we developed a method to identify sources of variation in total leaf area over time, between habitats and between species. We measured LAI for all vegetation types on the farm, namely: crops, unmanaged non-woody vegetation, hedgerows and trees. We used established methods to estimate LAI, mostly by gap fraction analysis with a LI-COR LAI-2000 Plant Canopy Analyzer and we divided LAI into its constituent species based on the data from field surveys. We found that the total LAI of the farm was 4.1, although this varied substantially between habitats from 3.3 for cereal fields to 8.0 for woods. Each month we measured LAI and found that, depending on the month, 61-81% of the leaf area was comprised of crop species (including grass and clover in pastures and short-term leys), 7-10% was woody vegetation and the remainder was non-woody semi-natural vegetation. We developed predictive models of LAI, based on our measurements of LAI in each habitat, and found that, for non-woody vegetation, variation in LAI was a function of vegetation type and vegetation height, while for hedgerows LAI was a function of hedge height and width. From our data and our predictive models we predicted changes in LAI due to five likely farm management scenarios. Changes that would have a huge impact on the visual landscape (loss of woods, reduction in hedge size) were predicted to have a smaller effect on farm-scale LAI than changes that would be visually more subtle (changes in vegetation length of grass fields). This type of modelling could be combined with regional land use data to predict the effect of regional agricultural changes (changes in land use or the intensity of land management) on LAI, and hence on ecosystems, biogeochemical cycling, carbon sequestration and hydrology.
