987 resultados para ground cover


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This data set contains information on vegetation cover, i.e. the proportion of soil surface area that is covered by different categories of plants per estimated plot area. Data was collected on the plant community level (sown plant community, weed plant community, dead plant material, and bare ground) and on the level of individual plant species in case of the sown species. Data presented here is from the Main Experiment plots of a large grassland biodiversity experiment (the Jena Experiment; see further details below). In the main experiment, 82 grassland plots of 20 x 20 m were established from a pool of 60 species belonging to four functional groups (grasses, legumes, tall and small herbs). In May 2002, varying numbers of plant species from this species pool were sown into the plots to create a gradient of plant species richness (1, 2, 4, 8, 16 and 60 species) and functional richness (1, 2, 3, 4 functional groups). Plots were maintained by bi-annual weeding and mowing. In 2009, vegetation cover was estimated twice in May and August just prior to mowing (during peak standing biomass) on all experimental plots of the Main Experiment. Cover was visually estimated in a central area of each plot 3 by 3 m in size (approximately 9 m²) using a decimal scale (Londo). Cover estimates for the individual species (and for target species + weeds + bare ground) can add up to more than 100% because the estimated categories represented a structure with potentially overlapping multiple layers. In 2009, in addition to the four community level cover estimates, cover of the moss layer was estimated.

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This data set contains information on vegetation cover, i.e. the proportion of soil surface area that is covered by different categories of plants per estimated plot area. Data was collected on the plant community level (sown plant community, weed plant community, dead plant material, and bare ground) and on the level of individual plant species in case of the sown species. Data presented here is from the Main Experiment plots of a large grassland biodiversity experiment (the Jena Experiment; see further details below). In the main experiment, 82 grassland plots of 20 x 20 m were established from a pool of 60 species belonging to four functional groups (grasses, legumes, tall and small herbs). In May 2002, varying numbers of plant species from this species pool were sown into the plots to create a gradient of plant species richness (1, 2, 4, 8, 16 and 60 species) and functional richness (1, 2, 3, 4 functional groups). Plots were maintained by bi-annual weeding and mowing. In 2010, vegetation cover was estimated twice in May and August just prior to mowing (during peak standing biomass) on all experimental plots of the Main Experiment. Cover was visually estimated in a central area of each plot 3 by 3 m in size (approximately 9 m²) using a decimal scale (Londo). Cover estimates for the individual species (and for target species + weeds + bare ground) can add up to more than 100% because the estimated categories represented a structure with potentially overlapping multiple layers.

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This data set contains information on vegetation cover, i.e. the proportion of soil surface area that is covered by different categories of plants per estimated plot area. Data was collected on the plant community level (sown plant community, weed plant community, dead plant material, and bare ground) and on the level of individual plant species in case of the sown species. Data presented here is from the Main Experiment plots of a large grassland biodiversity experiment (the Jena Experiment; see further details below). In the main experiment, 82 grassland plots of 20 x 20 m were established from a pool of 60 species belonging to four functional groups (grasses, legumes, tall and small herbs). In May 2002, varying numbers of plant species from this species pool were sown into the plots to create a gradient of plant species richness (1, 2, 4, 8, 16 and 60 species) and functional richness (1, 2, 3, 4 functional groups). Plots were maintained by bi-annual weeding and mowing. In 2013, vegetation cover was estimated twice in May and August just prior to mowing (during peak standing biomass) on all experimental plots of the Main Experiment. Cover was visually estimated in a central area of each plot 3 by 3 m in size (approximately 9 m²) using a decimal scale (Londo). Cover estimates for the individual species (and for target species + weeds + bare ground) can add up to more than 100% because the estimated categories represented a structure with potentially overlapping multiple layers.

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This data set contains information on vegetation cover, i.e. the proportion of soil surface area that is covered by different categories of plants per estimated plot area. Data was collected on the plant community level (sown plant community, weed plant community, dead plant material, and bare ground) and on the level of individual plant species in case of the sown species. Data presented here is from the Main Experiment plots of a large grassland biodiversity experiment (the Jena Experiment; see further details below). In the main experiment, 82 grassland plots of 20 x 20 m were established from a pool of 60 species belonging to four functional groups (grasses, legumes, tall and small herbs). In May 2002, varying numbers of plant species from this species pool were sown into the plots to create a gradient of plant species richness (1, 2, 4, 8, 16 and 60 species) and functional richness (1, 2, 3, 4 functional groups). Plots were maintained by bi-annual weeding and mowing. In 2008, vegetation cover was estimated twice in May and August just prior to mowing (during peak standing biomass) on all experimental plots of the Main Experiment. Cover was visually estimated in a central area of each plot 3 by 3 m in size (approximately 9 m²) using a decimal scale (Londo). Cover estimates for the individual species (and for target species + weeds + bare ground) can add up to more than 100% because the estimated categories represented a structure with potentially overlapping multiple layers.

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This data set contains information on vegetation cover, i.e. the proportion of soil surface area that is covered by different categories of plants per estimated plot area. Data was collected on the plant community level (sown plant community, weed plant community, dead plant material, and bare ground) and on the level of individual plant species in case of the sown species. Data presented here is from the Main Experiment plots of a large grassland biodiversity experiment (the Jena Experiment; see further details below). In the main experiment, 82 grassland plots of 20 x 20 m were established from a pool of 60 species belonging to four functional groups (grasses, legumes, tall and small herbs). In May 2002, varying numbers of plant species from this species pool were sown into the plots to create a gradient of plant species richness (1, 2, 4, 8, 16 and 60 species) and functional richness (1, 2, 3, 4 functional groups). Plots were maintained by bi-annual weeding and mowing. In 2002, vegetation cover was estimated only once in Septemper just prior to mowing (during peak standing biomass) on all experimental plots of the Main Experiment. Cover was visually estimated in a central area of each plot 3 by 3 m in size (approximately 9 m²) using a decimal scale (Londo). Cover estimates for the individual species (and for target species + weeds + bare ground) can add up to more than 100% because the estimated categories represented a structure with potentially overlapping multiple layers. In 2002, cover on the community level was only estimated for the sown plant community, weed plant community and bare soil. In contrast to later years, cover of dead plant material was not estimated.

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This data set contains information on vegetation cover, i.e. the proportion of soil surface area that is covered by different categories of plants per estimated plot area. Data was collected on the plant community level (sown plant community, weed plant community, dead plant material, and bare ground) and on the level of individual plant species in case of the sown species. Data presented here is from the Main Experiment plots of a large grassland biodiversity experiment (the Jena Experiment; see further details below). In the main experiment, 82 grassland plots of 20 x 20 m were established from a pool of 60 species belonging to four functional groups (grasses, legumes, tall and small herbs). In May 2002, varying numbers of plant species from this species pool were sown into the plots to create a gradient of plant species richness (1, 2, 4, 8, 16 and 60 species) and functional richness (1, 2, 3, 4 functional groups). Plots were maintained by bi-annual weeding and mowing. In 2003, vegetation cover was estimated twice in May and August just prior to mowing (during peak standing biomass) on all experimental plots of the Main Experiment. Cover was visually estimated in a central area of each plot 3 by 3 m in size (approximately 9 m²) using a decimal scale (Londo). Cover estimates for the individual species (and for target species + weeds + bare ground) can add up to more than 100% because the estimated categories represented a structure with potentially overlapping multiple layers. In 2003, cover on the community level was only estimated for the sown plant community, weed plant community and bare soil. In contrast to later years, cover of dead plant material was not estimated.

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This data set contains information on vegetation cover, i.e. the proportion of soil surface area that is covered by different categories of plants per estimated plot area. Data was collected on the plant community level (sown plant community, weed plant community, dead plant material, and bare ground) and on the level of individual plant species in case of the sown species. Data presented here is from the Main Experiment plots of a large grassland biodiversity experiment (the Jena Experiment; see further details below). In the main experiment, 82 grassland plots of 20 x 20 m were established from a pool of 60 species belonging to four functional groups (grasses, legumes, tall and small herbs). In May 2002, varying numbers of plant species from this species pool were sown into the plots to create a gradient of plant species richness (1, 2, 4, 8, 16 and 60 species) and functional richness (1, 2, 3, 4 functional groups). Plots were maintained by bi-annual weeding and mowing. In 2005, vegetation cover was estimated twice in May and August just prior to mowing (during peak standing biomass) on all experimental plots of the Main Experiment. Cover was visually estimated in a central area of each plot 3 by 3 m in size (approximately 9 m²) using a decimal scale (Londo). Cover estimates for the individual species (and for target species + weeds + bare ground) can add up to more than 100% because the estimated categories represented a structure with potentially overlapping multiple layers. In 2005, dead plant material was found only in a few plots. Therefore, cover of dead plant material is zero for most of the 82 plots.

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This data set contains information on vegetation cover, i.e. the proportion of soil surface area that is covered by different categories of plants per estimated plot area. Data was collected on the plant community level (sown plant community, weed plant community, dead plant material, and bare ground) and on the level of individual plant species in case of the sown species. Data presented here is from the Main Experiment plots of a large grassland biodiversity experiment (the Jena Experiment; see further details below). In the main experiment, 82 grassland plots of 20 x 20 m were established from a pool of 60 species belonging to four functional groups (grasses, legumes, tall and small herbs). In May 2002, varying numbers of plant species from this species pool were sown into the plots to create a gradient of plant species richness (1, 2, 4, 8, 16 and 60 species) and functional richness (1, 2, 3, 4 functional groups). Plots were maintained by bi-annual weeding and mowing. In 2006, vegetation cover was estimated twice in June and August just prior to mowing (during peak standing biomass) on all experimental plots of the Main Experiment. Cover was visually estimated in a central area of each plot 3 by 3 m in size (approximately 9 m²) using a decimal scale (Londo). Cover estimates for the individual species (and for target species + weeds + bare ground) can add up to more than 100% because the estimated categories represented a structure with potentially overlapping multiple layers. In 2006, dead plant material was found only in a few plots. Therefore, cover of dead plant material is zero for most of the 82 plots.

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This data set contains information on vegetation cover, i.e. the proportion of soil surface area that is covered by different categories of plants per estimated plot area. Data was collected on the plant community level (sown plant community, weed plant community, dead plant material, and bare ground) and on the level of individual plant species in case of the sown species. Data presented here is from the Main Experiment plots of a large grassland biodiversity experiment (the Jena Experiment; see further details below). In the main experiment, 82 grassland plots of 20 x 20 m were established from a pool of 60 species belonging to four functional groups (grasses, legumes, tall and small herbs). In May 2002, varying numbers of plant species from this species pool were sown into the plots to create a gradient of plant species richness (1, 2, 4, 8, 16 and 60 species) and functional richness (1, 2, 3, 4 functional groups). Plots were maintained by bi-annual weeding and mowing. In 2007, vegetation cover was estimated twice in June and August just prior to mowing (during peak standing biomass) on all experimental plots of the Main Experiment. Cover was visually estimated in a central area of each plot 3 by 3 m in size (approximately 9 m²) using a decimal scale (Londo). Cover estimates for the individual species (and for target species + weeds + bare ground) can add up to more than 100% because the estimated categories represented a structure with potentially overlapping multiple layers. In 2007, dead plant material was found only in a few plots. Therefore, cover of dead plant material is zero for most of the 82 plots.

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This data set contains information on vegetation cover, i.e. the proportion of soil surface area that is covered by different categories of plants per estimated plot area. Data was collected on the plant community level (sown plant community, weed plant community, dead plant material, and bare ground) and on the level of individual plant species in case of the sown species. Data presented here is from the Main Experiment plots of a large grassland biodiversity experiment (the Jena Experiment; see further details below). In the main experiment, 82 grassland plots of 20 x 20 m were established from a pool of 60 species belonging to four functional groups (grasses, legumes, tall and small herbs). In May 2002, varying numbers of plant species from this species pool were sown into the plots to create a gradient of plant species richness (1, 2, 4, 8, 16 and 60 species) and functional richness (1, 2, 3, 4 functional groups). Plots were maintained by bi-annual weeding and mowing. In 2004, vegetation cover was estimated twice in May and August just prior to mowing (during peak standing biomass) on all experimental plots of the Main Experiment. Cover was visually estimated in a central area of each plot 3 by 3 m in size (approximately 9 m²) using a decimal scale (Londo). Cover estimates for the individual species (and for target species + weeds + bare ground) can add up to more than 100% because the estimated categories represented a structure with potentially overlapping multiple layers. In 2004, cover on the community level was only estimated for the sown plant community, weed plant community and bare soil. In contrast to later years, cover of dead plant material was not estimated.

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Forest biomass has been having an increasing importance in the world economy and in the evaluation of the forests development and monitoring. It was identified as a global strategic reserve, due to its applications in bioenergy, bioproduct development and issues related to reducing greenhouse gas emissions. The estimation of above ground biomass is frequently done with allometric functions per species with plot inventory data. An adequate sampling design and intensity for an error threshold is required. The estimation per unit area is done using an extrapolation method. This procedure is labour demanding and costly. The mail goal of this study is the development of allometric functions for the estimation of above ground biomass with ground cover as independent variable, for forest areas of holm aok (Quercus rotundifolia), cork oak (Quercus suber) and umbrella pine (Pinus pinea) in multiple use systems. Ground cover per species was derived from crown horizontal projection obtained by processing high resolution satellite images, orthorectified, geometrically and atmospheric corrected, with multi-resolution segmentation method and object oriented classification. Forest inventory data were used to estimate plot above ground biomass with published allometric functions at tree level. The developed functions were fitted for monospecies stands and for multispecies stands of Quercus rotundifolia and Quercus suber, and Quercus suber and Pinus pinea. The stand composition was considered adding dummy variables to distinguish monospecies from multispecies stands. The models showed a good performance. Noteworthy is that the dummy variables, reflecting the differences between species, originated improvements in the models. Significant differences were found for above ground biomass estimation with the functions with and without the dummy variables. An error threshold of 10% corresponds to stand areas of about 40 ha. This method enables the overall area evaluation, not requiring extrapolation procedures, for the three species, which occur frequently in multispecies stands.

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Sibelco Australia Limited (SAL), a mineral sand mining operation on North Stradbroke Island, undertakes progressive rehabilitation of mined areas. Initial investigations have found that some areas at SAL’s Yarraman Mine have failed to redevelop towards approved criteria. This study, undertaken in 2010, examined ground cover rehabilitation of different aged plots at the Yarraman Mine to determine if there was a relationship between key soil and vegetation attributes. Vegetation and soil data were collected from five plots rehabilitated in 2003, 2006, 2008, 2009 and 2010, and one unmined plot. Cluster (PATN) analysis revealed that vegetation species composition, species richness and ground cover differed between plots. Principal component analysis (PCA) extracted ten soil attributes that were then correlated with vegetation data. The attributes extracted by PCA, in order of most common variance, were: water content, pH, terrolas depth, elevation, slope angle, leaf litter depth, total organic carbon, and counts of macrofauna, fungi and bacteria. All extracted attributes differed between plots, and all except bacteria correlated with at least one vegetation attribute. Water content and pH correlated most strongly with vegetation cover suggesting an increase in soil moisture and a reduction in pH are required in order to improve vegetation rehabilitation at Yarraman Mine. Further study is recommended to confirm these results using controlled experiments and to test potential solutions, such as organic amendments.

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Three simulations of evapotranspiration were done with two values of time step,viz 10 min and one day. Inputs to the model were weather data, including directly measured upward and downward radiation, and soil characteristics. Three soils were used for each simulation. Analysis of the results shows that the time step has a direct influence on the prediction of potential evapotranspiration, but a complex interaction of this effect with the soil moisture characteristic, rate of increase of ground cover and bare soil evaporation determines the actual transpiration predicted. The results indicate that as small a time step as possible should be used in the simulation.

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Australia’s rangelands are the extensive arid and semi-arid grazing lands that cover approximately 70% of the Australian continent. They are characterised by low and generally variable rainfall, low productivity and a sparse population. They support a number of industries including mining and tourism, but pastoralism is the primary land use. In some areas, the rangelands have a history of biological decline (Noble 1997), with erosion, loss of perennial native grasses and incursion of woody vegetation commonly reported in the scientific and lay literature. Despite our historic awareness of these trends, the establishment of systems to measure and monitor degradation, has presented numerous problems. The size and accessibility of Australia’s rangeland often mitigates development of extensive monitoring programs. So, too, securing on-going commitment from Government agencies to fund rangeland monitoring activities have led to either abandonment or a scaled-down approach in some instances (Graetz et al. 1986; Holm 1993). While a multiplicity of monitoring schemes have been developed for landholders at the property scale, and some have received promising initial uptake, relatively few have been maintained for more than a few years on any property without at least some agency support (Pickup et al. 1998). But, ironically, such property level monitoring tools can contribute significantly to local decisions about stock, infrastructure and sustainability. Research in recent decades has shown the value of satellites for monitoring change in rangelands (Wallace et al. 2004), especially in terms of tree and ground cover. While steadily improving, use of satellite data as a monitoring tool has been limited by the cost of the imagery, and the equipment and expertise needed to extract useful information from it. A project now under way in the northern rangelands of Australia is attempting to circumvent many of the problems through a monitoring system that allows property managers to use long-term satellite image sequences to quickly and inexpensively track changes in land cover on their properties

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Synthetic backcrossed-derived bread wheats (SBWs) from CIMMYT were grown in the north-west of Mexico (CIANO) and sites across Australia during 3 seasons. A different set of lines was evaluated each season, as new materials became available from the CIMMYT crop enhancement program. Previously, we have evaluated both the performance of genotypes across environments and the genotype x environment interaction (G x E). The objective of this study was to interpret the G x E for yield in terms of crop attributes measured at individual sites and to identify the potential environmental drivers of this interaction. Groups of SBWs with consistent yield performance were identified, often comprising closely related lines. However, contrasting performance was also relatively common among sister lines or between a recurrent parent and its SBWs. Early flowering was a common feature among lines with broad adaptation and/or high yield in the northern Australian wheatbelt, while yields in the southern region did not show any association with the maturity type. Lines with high yields in the southern and northern regions had cooler canopies during flowering and early grain filling. Among the SBWs with Australian genetic backgrounds, lines best adapted to CIANO were tall (>100 cm), with a slightly higher ground cover. These lines also displayed a higher concentration of water-soluble carbohydrates in the stem at flowering, which was negatively correlated with stem number per unit area when evaluated in southern Australia (Horsham). Possible reasons for these patterns are discussed. Selection for yield at CIANO did not specifically identify the lines best adapted to northern Australia, although they were not the most poorly adapted either. In addition, groups of lines with specific adaptation to the south would not have been selected by choosing the highest yielding lines at CIANO. These findings suggest that selection at CIMMYT for Australian environments may be improved by either trait based selection or yield data combined with trait information. Flowering date, canopy temperature around flowering, tiller density, and water-soluble carbohydrate concentration in the stem at flowering seem likely candidates.