996 resultados para Agricultural landscapes


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There are over 1400 catalogued human pathogens, with approximately 62% classified as zoonotic (Taylor et al., 2001). Most evidence of direct transmission of pathogens to humans involves domestic and companion animals, whereas the reservoir for most zoonoses is wildlife; yet there are relatively few well-documented cases for the direct involvement of transmission from wildlife to humans (Kruse et al., 2004). In part, this absence of evidence reflects the mobility of wildlife, the difficulty accessing relevant samples, and the smaller number of studies focused on characterizing wildlife pathogens relative to the human and veterinary literature (McDiarmid, 1969; Davis et al., 1971; Hubalek, 2004).

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There is a lively debate on whether biodiversity conservation and agricultural production could be better reconciled by land sparing (strictly separating production fields and conservation areas) or by land sharing (combining both, agricultural production and biodiversity conservation on the same land). The debate originates from tropical countries, where agricultural land use continues to increase at the expense of natural ecosystems. But is it also relevant for Europe, where agriculture is withdrawing from marginal regions whilst farming of fertile lands continues to be intensified? Based on recent research on farmland biodiversity we conclude that the land sharing – land sparing dichotomy is too simplistic for Europe. Instead we differentiate between productive and marginal farmland. On productive farmland, semi-natural habitats are required to yield ecosystem services relevant for agriculture, to promote endangered farmland species which society wants to conserve even in intensively farmed regions, and to allow migration of non-farmland species through the agricultural matrix. On marginal farmland, high-nature value farming is a traditional way of land sharing, yielding high quality agricultural products and conserving specialized species. To conserve highly disturbance-sensitive species, there is a need for nature reserves. In conclusion, land sparing is not a viable olution for Europe in both productive and marginal farmland but because of different reasons in each type of farmland.

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Very high resolution remotely sensed images are an important tool for monitoring fragmented agricultural landscapes, which allows farmers and policy makers to make better decisions regarding management practices. An object-based methodology is proposed for automatic generation of thematic maps of the available classes in the scene, which combines edge-based and superpixel processing for small agricultural parcels. The methodology employs superpixels instead of pixels as minimal processing units, and provides a link between them and meaningful objects (obtained by the edge-based method) in order to facilitate the analysis of parcels. Performance analysis on a scene dominated by agricultural small parcels indicates that the combination of both superpixel and edge-based methods achieves a classification accuracy slightly better than when those methods are performed separately and comparable to the accuracy of traditional object-based analysis, with automatic approach.

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Habitat restoration, including revegetation of linear strips and enlargement of remnant patches, may benefit native fauna in highly fragmented landscapes. Such restoration has occurred around the world, even though the relative importance of strips and patches of vegetation remains controversial. Using reptile communities from south-eastern Australia, we assessed the conservation value of revegetation in strips and alongside remnant patches compared with remnant vegetation and cleared roadsides. We also examined the distance that reptiles occurred from remnant patches into linear vegetation. We found that reptile species richness and counts did not substantially differ between revegetated, remnant and cleared habitats, or between linear strip and patch treatments. This may indicate that species sensitive to land clearing have already been lost from the landscape. These results imply that if specialist species have already been lost, we may be unable to measure the effects of agriculture on biodiversity. Furthermore, revegetation with the expectation that fauna will recolonize may be unrealistic and translocations may be necessary. Unexpectedly, we recorded higher species richness and counts of rare reptile species in remnant linear strips as distance from remnant patches increased. Ground-layer attributes were important for increasing reptile species richness and counts and in structuring reptile communities, explaining approximately three times as much variation as remnant shape or vegetation type (remnant, revegetated, cleared). Management agencies should protect and effectively manage remnant linear strips if rarer reptiles are to be retained, paying particular attention to ground-layer attributes. The decision to include ground layers in future revegetation activities will be more important than the shape of restored areas.

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Private property accounts for much of the planet's arable land, and most of this has been cleared for agricultural production. Agricultural areas retain only fragments of their original vegetation and this has been detrimental to many native plant and animal species. Habitat restoration and revegetation may be able to reconnect and enlarge existing remnant areas in agricultural landscapes and, thereby, enhance native plant and animal communities. However, conservation initiatives will be successful only if landowners actively participate in restoration actions. This study used four hundred postal questionnaires to assess the degree to which landowners in two regions of south-eastern Australia adopt restoration activities, their opinions regarding remnant and revegetated land and their management actions in these areas. One hundred and seventy nine completed questionnaires were received. Three quarters of respondents had undertaken restoration on their property or were planning to revegetate in the future. Landcare members were most likely to have previously revegetated and future revegetation intentions were best predicted by previous restoration activities and a primary income source that was off-farm. Landowners were more likely to manage restored and remnant areas if they perceived threats such as weeds, pest animals and fire risk would be detrimental to their property, than to enhance environmental outcomes. These results indicate that landowners are interested in restoring natural areas, but without greater assistance to restore ground layers and manage perceived threats posed by fire and invasive plants and animals, restoration actions will not have their desired biodiversity benefits.

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Effects of agricultural intensification (AI) on biodiversity are often assessed on the plot scale, although processes determining diversity also operate on larger spatial scales. Here, we analyzed the diversity of vascular plants, carabid beetles, and birds in agricultural landscapes in cereal crop fields at the field (n = 1350), farm (n = 270), and European-region (n = 9) scale. We partitioned diversity into its additive components alpha, beta, and gamma, and assessed the relative contribution of beta diversity to total species richness at each spatial scale. AI was determined using pesticide and fertilizer inputs, as well as tillage operations and categorized into low, medium, and high levels. As AI was not significantly related to landscape complexity, we could disentangle potential AI effects on local vs. landscape community homogenization. AI negatively affected the species richness of plants and birds, but not carabid beetles, at all spatial scales. Hence, local AI was closely correlated to beta diversity on larger scales up to the farm and region level, and thereby was an indicator of farm-and region-wide biodiversity losses. At the scale of farms (12.83-20.52%) and regions (68.34-80.18%), beta diversity accounted for the major part of the total species richness for all three taxa, indicating great dissimilarity in environmental conditions on larger spatial scales. For plants, relative importance of alpha diversity decreased with AI, while relative importance of beta diversity on the farm scale increased with AI for carabids and birds. Hence, and in contrast to our expectations, AI does not necessarily homogenize local communities, presumably due to the heterogeneity of farming practices. In conclusion, a more detailed understanding of AI effects on diversity patterns of various taxa and at multiple spatial scales would contribute to more efficient agri-environmental schemes in agroecosystems.

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Agricultural systems models worldwide are increasingly being used to explore options and solutions for the food security, climate change adaptation and mitigation and carbon trading problem domains. APSIM (Agricultural Production Systems sIMulator) is one such model that continues to be applied and adapted to this challenging research agenda. From its inception twenty years ago, APSIM has evolved into a framework containing many of the key models required to explore changes in agricultural landscapes with capability ranging from simulation of gene expression through to multi-field farms and beyond. Keating et al. (2003) described many of the fundamental attributes of APSIM in detail. Much has changed in the last decade, and the APSIM community has been exploring novel scientific domains and utilising software developments in social media, web and mobile applications to provide simulation tools adapted to new demands. This paper updates the earlier work by Keating et al. (2003) and chronicles the changing external challenges and opportunities being placed on APSIM during the last decade. It also explores and discusses how APSIM has been evolving to a “next generation” framework with improved features and capabilities that allow its use in many diverse topics.

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Feral pigs (Sus scrofa) consume and damage crops and impact the environment through predation, competition and habitat disturbance, although supporting dietary data are lacking in agricultural landscapes. This study was undertaken to determine the relative importance of food items in the diet of feral pigs in a fragmented agricultural landscape, particularly to assist in predicting the breadth of likely impacts. Diet composition was assessed from the stomach contents of 196 feral pigs from agricultural properties in southern Queensland. Feral pigs were herbivorous, with plant matter comprising >99% of biomass consumed. Crops were consumed more frequently than non-crop species, and comprised >60% of dietary biomass, indicating a clear potential for direct economic losses. Consumption of pasture and forage species also suggests potential competition for pasture with domestic stock. There is little evidence of direct predation on native fauna, but feral pig feeding activities may impact environmental values. Seasonal differences in consumption of crop, pasture or animal food groups probably reflect the changing availability of food items. We recommend that future dietary studies examine food availability to determine any dietary preferences to assist in determining the foods most susceptible to damage. The outcomes of this study are important for developing techniques for monitoring the impacts of feral pigs, essential for developing management options to reduce feral pig damage on agricultural lands.

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Many common bird species have declined as a result of agricultural intensification and this could be mitigated by organic farming. We paired sites for habitat and geographical location on organic and nonorganic farms in Ontario, Canada to test a priori predictions of effects on birds overall, 9 guilds and 22 species in relation to candidate models for farming practices (13 variables), local habitat features (12 variables), or habitat features that influence susceptibility to predation. We found that: (1) Overall bird abundance, but not richness, was significantly (p < 0.05) higher on organic sites (mean 43.1 individuals per site) than nonorganic sites (35.8 individuals per site). Significantly more species of birds were observed for five guilds, including primary grassland birds, on organic vs. nonorganic sites. No guild had higher richness or abundance on nonorganic farms; (2) Farming practice models were the best (ΔAIC < 4) for abundance of birds overall, primary grassland bird richness, sallier aerial insectivore richness and abundance, and abundance of ground nesters; (3) Habitat models were the best for overall richness, Neotropical migrant abundance, richness and abundance of Ontario-USA-Mexico (short-distance) migrants and resident richness; (4) Predation models were the best for richness of secondary grassland birds and ground feeders; (5) A combination of variables from the model types were best for richness or abundance overall, 13 of 18 guilds (richness and abundance) and 16 of 22 species analyzed. Five of 10 farming practice variables (including herbicide use, organic farm type) and 9 of 13 habitat variables (including hedgerow length, proportion of hay) were significant in best models. Risk modeling indicated that herbicide use could decrease primary grassland birds by one species (35% decline from 3.4 to 2.3 species) per site. Organic farming could benefit species of conservation concern by 49% (an increase from 7.6 to 11.4 grassland birds). An addition of 63 m of hedgerow could increase abundance and richness of short distance migrants by 50% (3.0 to 4.8 and 1.3 to 2.0, respectively). Increasing the proportion of hay on nonorganic farms to 50% could increase abundance of primary grassland bird by 40% (6.7 to 9.4). Our results provide support for alternative farmland designs and agricultural management systems that could enhance select bird species in farmland.

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Quadratic programming techniques were applied to household food consumption data in England and Wales to estimate likely changes in diet under healthy eating guidelines, and the consequences this would have on agriculture and land use in England and Wales. The first step entailed imposing nutrient restrictions on food consumption following dietary recommendations suggested by the UK Department of Health. The resulting diet was used, in a second step as a proxy for demand in agricultural commodities, to test the impact of such a scenario on food production and land use in England and Wales and the impacts of this on agricultural landscapes. Results of the diet optimisation indicated a large drop in consumption of foods rich in saturated fats and sugar, essentially cheese and sugar-based products, along with lesser cuts of fat and meat products. Conversely, consumption of fruit and vegetables, cereals, and flour would increase to meet dietary fibre recommendations. Such a shift in demand would dramatically affect production patterns: the financial net margin of England and Wales agriculture would rise, due to increased production of high market value and high economic margin crops. Some regions would, however, be negatively affected, mostly those dependent on beef cattle and sheep production that could not benefit from an increased demand for cereals and horticultural crops. The effects of these changes would also be felt in upstream industries, such as animal feed suppliers. While arable dominated landscapes would be little affected, pastoral landscapes would suffer through loss of grazing management and, possibly, land abandonment, especially in upland areas.

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The assessment of the potential landscape impacts of the latest Common Agricultural Policy reforms constitutes a challenge for policy makers and it requires the development of models that can reliably project the likely spatial distribution of land uses. The aim of this study is to investigate the impact of 2003 CAP reforms to land uses and rural landscapes across England. For this purpose we modified an existing economic model of agriculture, the Land-Use Allocation Model (LUAM) to provide outputs at a scale appropriate for informing a semi-quantitative landscape assessment at the level of ‘Joint Character Areas’ (JCAs). Overall a decline in the cereal and oilseed production area is projected but intensive arable production will persist in specific locations (East of England, East Midlands and South East), having ongoing negative effects on the character of many JCAs. The impacts of de-coupling will be far more profound on the livestock sector; extensification of production will occur in traditional mixed farming regions (e.g. the South West), a partial displacement of cattle by sheep in the upland regions and an increase in the sheep numbers is expected in the lowlands (South East, Eastern and East Midlands). This extensification process will affect positively those JCAs of mixed farming conditions, but it will have negative impacts on the JCAs of historically low intensity farming (e.g. the uplands of north-west) because they will suffer from under-management and land idling. Our analysis shows that the territorialisation between intensively and extensively agricultural landscapes will continue.

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Developing models to predict the effects of social and economic change on agricultural landscapes is an important challenge. Model development often involves making decisions about which aspects of the system require detailed description and which are reasonably insensitive to the assumptions. However, important components of the system are often left out because parameter estimates are unavailable. In particular, measurements of the relative influence of different objectives, such as risk, environmental management, on farmer decision making, have proven difficult to quantify. We describe a model that can make predictions of land use on the basis of profit alone or with the inclusion of explicit additional objectives. Importantly, our model is specifically designed to use parameter estimates for additional objectives obtained via farmer interviews. By statistically comparing the outputs of this model with a large farm-level land-use data set, we show that cropping patterns in the United Kingdom contain a significant contribution from farmer’s preference for objectives other than profit. In particular, we found that risk aversion had an effect on the accuracy of model predictions, whereas preference for a particular number of crops grown was less important. While nonprofit objectives have frequently been identified as factors in farmers’ decision making, our results take this analysis further by demonstrating the relationship between these preferences and actual cropping patterns.