994 resultados para Farming systems


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This study investigates the function of non-cropped field margins in arable farming systems for enhancing the biodiversity value of beetle communities. Three different sown seed mixtures were used to establish field margins, a Countryside Stewardship mix, a fine grass and forbs mix and a tussock grass and forbs mix. The structure of beetle communities in the first full year of establishment was found to show no difference between the tussock grass and Countryside Stewardship margins. However, both differed from the fine grass margins, which supported lower overall abundance and species richness of beetles. This was attributed to small-scale architectural differences between species of fine and tussock grasses, rather than differences in plant composition. Body size distributions of beetles showed distinct similarities between the Countryside Stewardship and tussock margins. A greater abundance of large beetles was found in fine grass margins, although in all cases these body size distributions were attributed to a small number of species or a taxonomically distinct group. All three margin types included beetle species of conservation value. The importance of these results was discussed in the context of the value of these seed mixtures for invertebrate conversation. (c) 2004 Elsevier B.V. All rights reserved.

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Seed predation by avian and non-avian predators was quantified in the boundaries and cropped areas of cereal fields by presenting known quantities of seed with and without exclusion cages. Predator encounter-rates with the dishes exceeded 99%. Birds removed on average 6.7% seed from the dishes during the seven-day trials compared to 51% by non-avian predators. A comparison was made of the causal factors responsible for predation of Avena fatua, Chenopodium album and Cirsium arvense seeds. A. fatua seeds were preyed most heavily by both avian and non-avian predators. Seed removal by birds was greater in the cropped area than in the field boundary, non-avian predators being generally more active in the field boundary. Seed predation by birds was greater in spring than in any other season, whilst losses to other animals were greater during autumn and winter. Although, birds were not the main seed predators in cereal fields, they may contribute to weed seed depletion, of relevance to reduced-input farming systems where herbicides use is restricted.

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Agri-environment schemes (AESs) have been implemented across EU member states in an attempt to reconcile agricultural production methods with protection of the environment and maintenance of the countryside. To determine the extent to which such policy objectives are being fulfilled, participating countries are obliged to monitor and evaluate the environmental, agricultural and socio-economic impacts of their AESs. However, few evaluations measure precise environmental outcomes and critically, there are no agreed methodologies to evaluate the benefits of particular agri-environmental measures, or to track the environmental consequences of changing agricultural practices. In response to these issues, the Agri-Environmental Footprint project developed a common methodology for assessing the environmental impact of European AES. The Agri-Environmental Footprint Index (AFI) is a farm-level, adaptable methodology that aggregates measurements of agri-environmental indicators based on Multi-Criteria Analysis (MCA) techniques. The method was developed specifically to allow assessment of differences in the environmental performance of farms according to participation in agri-environment schemes. The AFI methodology is constructed so that high values represent good environmental performance. This paper explores the use of the AFI methodology in combination with Farm Business Survey data collected in England for the Farm Accountancy Data Network (FADN), to test whether its use could be extended for the routine surveillance of environmental performance of farming systems using established data sources. Overall, the aim was to measure the environmental impact of three different types of agriculture (arable, lowland livestock and upland livestock) in England and to identify differences in AFI due to participation in agri-environment schemes. However, because farm size, farmer age, level of education and region are also likely to influence the environmental performance of a holding, these factors were also considered. Application of the methodology revealed that only arable holdings participating in agri-environment schemes had a greater environmental performance, although responses differed between regions. Of the other explanatory variables explored, the key factors determining the environmental performance for lowland livestock holdings were farm size, farmer age and level of education. In contrast, the AFI value of upland livestock holdings differed only between regions. The paper demonstrates that the AFI methodology can be used readily with English FADN data and therefore has the potential to be applied more widely to similar data sources routinely collected across the EU-27 in a standardised manner.

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Many weeds occur in patches but farmers frequently spray whole fields to control the weeds in these patches. Given a geo-referenced weed map, technology exists to confine spraying to these patches. Adoption of patch spraying by arable farmers has, however, been negligible partly due to the difficulty of constructing weed maps. Building on previous DEFRA and HGCA projects, this proposal aims to develop and evaluate a machine vision system to automate the weed mapping process. The project thereby addresses the principal technical stumbling block to widespread adoption of site specific weed management (SSWM). The accuracy of weed identification by machine vision based on a single field survey may be inadequate to create herbicide application maps. We therefore propose to test the hypothesis that sufficiently accurate weed maps can be constructed by integrating information from geo-referenced images captured automatically at different times of the year during normal field activities. Accuracy of identification will also be increased by utilising a priori knowledge of weeds present in fields. To prove this concept, images will be captured from arable fields on two farms and processed offline to identify and map the weeds, focussing especially on black-grass, wild oats, barren brome, couch grass and cleavers. As advocated by Lutman et al. (2002), the approach uncouples the weed mapping and treatment processes and builds on the observation that patches of these weeds are quite stable in arable fields. There are three main aspects to the project. 1) Machine vision hardware. Hardware component parts of the system are one or more cameras connected to a single board computer (Concurrent Solutions LLC) and interfaced with an accurate Global Positioning System (GPS) supplied by Patchwork Technology. The camera(s) will take separate measurements for each of the three primary colours of visible light (red, green and blue) in each pixel. The basic proof of concept can be achieved in principle using a single camera system, but in practice systems with more than one camera may need to be installed so that larger fractions of each field can be photographed. Hardware will be reviewed regularly during the project in response to feedback from other work packages and updated as required. 2) Image capture and weed identification software. The machine vision system will be attached to toolbars of farm machinery so that images can be collected during different field operations. Images will be captured at different ground speeds, in different directions and at different crop growth stages as well as in different crop backgrounds. Having captured geo-referenced images in the field, image analysis software will be developed to identify weed species by Murray State and Reading Universities with advice from The Arable Group. A wide range of pattern recognition and in particular Bayesian Networks will be used to advance the state of the art in machine vision-based weed identification and mapping. Weed identification algorithms used by others are inadequate for this project as we intend to collect and correlate images collected at different growth stages. Plants grown for this purpose by Herbiseed will be used in the first instance. In addition, our image capture and analysis system will include plant characteristics such as leaf shape, size, vein structure, colour and textural pattern, some of which are not detectable by other machine vision systems or are omitted by their algorithms. Using such a list of features observable using our machine vision system, we will determine those that can be used to distinguish weed species of interest. 3) Weed mapping. Geo-referenced maps of weeds in arable fields (Reading University and Syngenta) will be produced with advice from The Arable Group and Patchwork Technology. Natural infestations will be mapped in the fields but we will also introduce specimen plants in pots to facilitate more rigorous system evaluation and testing. Manual weed maps of the same fields will be generated by Reading University, Syngenta and Peter Lutman so that the accuracy of automated mapping can be assessed. The principal hypothesis and concept to be tested is that by combining maps from several surveys, a weed map with acceptable accuracy for endusers can be produced. If the concept is proved and can be commercialised, systems could be retrofitted at low cost onto existing farm machinery. The outputs of the weed mapping software would then link with the precision farming options already built into many commercial sprayers, allowing their use for targeted, site-specific herbicide applications. Immediate economic benefits would, therefore, arise directly from reducing herbicide costs. SSWM will also reduce the overall pesticide load on the crop and so may reduce pesticide residues in food and drinking water, and reduce adverse impacts of pesticides on non-target species and beneficials. Farmers may even choose to leave unsprayed some non-injurious, environmentally-beneficial, low density weed infestations. These benefits fit very well with the anticipated legislation emerging in the new EU Thematic Strategy for Pesticides which will encourage more targeted use of pesticides and greater uptake of Integrated Crop (Pest) Management approaches, and also with the requirements of the Water Framework Directive to reduce levels of pesticides in water bodies. The greater precision of weed management offered by SSWM is therefore a key element in preparing arable farming systems for the future, where policy makers and consumers want to minimise pesticide use and the carbon footprint of farming while maintaining food production and security. The mapping technology could also be used on organic farms to identify areas of fields needing mechanical weed control thereby reducing both carbon footprints and also damage to crops by, for example, spring tines. Objective i. To develop a prototype machine vision system for automated image capture during agricultural field operations; ii. To prove the concept that images captured by the machine vision system over a series of field operations can be processed to identify and geo-reference specific weeds in the field; iii. To generate weed maps from the geo-referenced, weed plants/patches identified in objective (ii).

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Methods for assessing the sustainability of agricultural systems do often not fully (i) take into account the multifunctionality of agriculture, (ii) include multidimensionality, (iii) utilize and implement the assessment knowledge and (iv) identify conflicting goals and trade-offs. This chapter reviews seven recently developed multidisciplinary indicator-based assessment methods with respect to their contribution to these shortcomings. All approaches include (1) normative aspects such as goal setting, (2) systemic aspects such as a specification of scale of analysis and (3) a reproducible structure of the approach. The approaches can be categorized into three typologies: first, top-down farm assessments, which focus on field or farm assessment; second, top-down regional assessments, which assess the on-farm and the regional effects; and third, bottom-up, integrated participatory or transdisciplinary approaches, which focus on a regional scale. Our analysis shows that the bottom-up, integrated participatory or transdisciplinary approaches seem to better overcome the four shortcomings mentioned above.

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Grass buffer strips have been widely sown to mitigate against intensive agricultural management practices that have negatively impacted on invertebrate and plant biodiversity in arable farming systems. Typically, such strips are floristically species poor and are dominated by grasses. In the present study, we developed management practices to enhance the floristic and structural diversity of these existing strips for the benefit of spiders, a key provider of natural pest control in crops. Across three UK arable farms, we investigated the benefits of: (i) scarification to create germination niches into which wildflower seeds were sown and (ii) the effect of graminicide applications to suppress grass dominance. Spiders were sampled twice per year (July and September) during 2008 and 2009. The combination of scarification with wildflower seeds, as well as graminicide, resulted in the greatest wildflower cover and lowest grass cover, with a general trend of increased abundance of adult and juvenile spiders. The abundance of Pachygnatha degeeri, Bathyphantes gracilis and juvenile wolf spiders of the genus Pardosa was positively correlated with wildflower cover, probably reflecting increased prey availability. Sward structure was negatively correlated with Erigone atra, Oedothorax fuscus and juvenile Pardosa abundance. Management that utilizes existing commonly adopted agri-environment options, such as grass buffer strips, represents a potentially important conservation tool for increasing the quantity and quality of invertebrate habitats. This can maximize opportunities for the provision of multiple ecosystem services, including pest regulation by predators such as spiders. These management practices have the potential to be incorporated into existing U.K. and European agri-environment schemes.

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Sustainable Intensification (SI) of agriculture has recently received widespread political attention, in both the UK and internationally. The concept recognises the need to simultaneously raise yields, increase input use efficiency and reduce the negative environmental impacts of farming systems to secure future food production and to sustainably use the limited resources for agriculture. The objective of this paper is to outline a policy-making tool to assess SI at a farm level. Based on the method introduced by Kuosmanen and Kortelainen (2005), we use an adapted Data Envelopment Analysis (DEA) to consider the substitution possibilities between economic value and environmental pressures generated by farming systems in an aggregated index of Eco-Efficiency. Farm level data, specifically General Cropping Farms (GCFs) from the East Anglian River Basin Catchment (EARBC), UK were used as the basis for this analysis. The assignment of weights to environmental pressures through linear programming techniques, when optimising the relative Eco-Efficiency score, allows the identification of appropriate production technologies and practices (integrating pest management, conservation farming, precision agriculture, etc.) for each farm and therefore indicates specific improvements that can be undertaken towards SI. Results are used to suggest strategies for the integration of farming practices and environmental policies in the framework of SI of agriculture. Paths for improving the index of Eco-Efficiency and therefore reducing environmental pressures are also outlined.

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Interest in sustainable farming methods that rely on alternatives to conventional synthetic fertilizers and pesticides is increasing. Sustainable farming methods often utilize natural populations of predatory and parasitic species to control populations of herbivores, which may be potential pest species. We investigated the effects of several types of fertilizer, including those typical of sustainable and conventional farming systems, on the interaction between a herbivore and parasitoid. The effects of fertilizer type on percentage parasitism, parasitoid performance, parasitoid attack behaviour and responses to plant volatiles were examined using a model Brassica system, consisting of Brassica oleracea var capitata, Plutella xylostella (Lepidoptera) larvae and Cotesia vestalis (parasitoid). Percentage parasitism was greatest for P. xylostella larvae feeding on plants that had received either a synthetic ammonium nitrate fertilizer or were unfertilized, in comparison to those receiving a composite fertilizer containing hoof and horn. Parasitism was intermediate on plants fertilized with an organically produced animal manure. Male parasitoid tibia length showed the same pattern as percentage parasitism, an indication that offspring performance was maximized on the treatments preferred by female parasitoids for oviposition. Percentage parasitism and parasitoid size were not correlated with foliar nitrogen concentration. The parasitoids did not discriminate between hosts feeding on plants in the four fertilizer treatments in parasitoid behaviour assays, but showed a preference for unfertilized plants in olfactometer experiments. The percentage parasitism and tibia length results provide support for the preference–performance hypothesis

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Vine-growing in the Less-Favoured Areas of Greece is facing multiple challenges that might lead to its abandonment. In an attempt to maintain rural populations, Rural Development Schemes have been created that offer the opportunity to rural households to maintain or expand their farming businesses including vine-growing. This paper stems from a study that used data from a cross-sectional survey of 204 farmers to investigate how farming systems and farmers’ perception of corruption, amongst other socio-economic factors, affected their decisions to continue vine-growing through participation in Rural Development Schemes, in three remote Less-Favoured Areas of Greece. The Theory of Planned Behaviour was used to frame the research problem with the assumption being that an individual’s intention to participate in a Scheme is based on their prior beliefs about it. Data from the survey were reduced and simplified by the use of non-linear principal component analysis. The ensuing variables were used in selectivity corrected ordered probit models to reveal farmers’ attitudes towards viticulture and rural development. It was found that economic factors, perceived corruption and farmers’ attitudes were significant determinants on whether to participate in the Schemes. The research findings highlight the important role of perceived corruption and the need for policies that facilitate farmers’ access to decision making centres.

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This study investigates the quality of retail milk labelled as Jersey & Guernsey (JG) when compared with milk without breed specifications (NS) and repeatability of differences over seasons and years. 16 different brands of milk (4 Jersey & Guernsey, 12 non specified breed) were sampled over 2 years on 4 occasions. JG milk was associated with both favourable traits for human health, such as the higher total protein, total casein, α-casein, β-casein, κ-casein and α-tocopherol contents, and unfavourable traits, such as the higher concentrations of saturated fat, C12:0, C14:0 and lower concentrations of monounsaturated fatty acids. In summer, JG milk had a higher omega-3:omega-6 ratio than had NS milk. Also, the relative increase in omega-3 fatty acids and α-tocopherol, from winter to summer, was greater in JG milk. The latter characteristic could be of use in breeding schemes and farming systems producing niche dairy products. Seasonality had a more marked impact on the fatty acid composition of JG milk than had NS milk, while the opposite was found for protein composition. Potential implication for the findings in human health, producers, industry and consumers are considered.

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The eutrophication (cultural or anthropogenic) is induced by man and can have different origins, such as domestic sewage, industrial and agricultural activities, including the effluent still breeding systems of aquatic organisms. The expansion of aquaculture, with production of biomass and increase of nutrients in water may cause acceleration of productivity of algae, changing the ecology of aquatic systems. In addition, these waste water may present a risk to health through the transfer of pathogens from manure, plant residues, composted material, among others that are major sources of organic waste in some farming systems. Depending on the trophic level of fish ponds, which are dynamic environments, different planktonic species with short reproductive cycle and adapted to the changes contained in these systems can appear in high abundance. Water quality in the systems for raising fish is related to several factors, such as water source, management (liming, fertilizing, cleaning), cultivated species and quantity and composition of exogenous food. In order to minimize environmental impacts, there are techniques to improve the quality of water in fish farming systems and thus satisfactory answers can be obtained through the application of management practices. This paper aims to review the subject that deals with changes in water quality resulting from the activity of freshwater fish culture in Brazil. Search also recommend techniques of good management practices to minimize the impact generated by the activity.

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Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)

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Atualmente, no cenário nacional, a cultura da soja em plantio direto é muito utilizada na integração agricultura-pecuária. No ano agrícola de 2006/2007, no município de Selvíria (MS), entre as latitudes de 20 ° 18 ' 05S e 20 ° 18 ' 28S e as longitudes de 52 ° 39 ' 02W e 52 ° 40 ' 28W, foi analisada a produtividade da soja, em plantio direto, em função de alguns atributos físicos de um Latossolo Vermelho distroférrico. O objetivo foi selecionar, entre os atributos do solo pesquisados, aquele que melhor se apresentasse para explicar a variabilidade espacial da produtividade agrícola. Para isso, foi instalada a malha geoestatística para a coleta dos dados do solo e da planta, contendo 120 pontos amostrais, numa área de 4.068 m² e declive homogêneo de 0,025 m m-1. do ponto de vista linear e espacial, a elevada produtividade de grãos de soja pôde ser explicada em função da densidade do solo e da umidade volumétrica. A baixa variabilidade da maioria dos atributos do solo indicou que o plantio direto é um sistema que proporciona a homogeneização do ambiente físico do solo.