Prevalence and determinants of Cryptosporidium spp. infection in smallholder dairy cattle in Iringa and Tanga Regions of Tanzania

The prevalence of Cryptosporidium spp. infection in a cross-sectional study of dairy cattle, from two contrasting dairying regions in Tanzania, were determined by staining smears of faecal samples with the modified Ziehl-Neelsen technique. Of the 1126 faecal samples screened, 19.7% were positive for Cr\yptosporidium spp. The prevalence was lower in Tanga Region than in Iringa Region. The prevalence of affected farms was 20% in Tanga and 21 % in Iringa. In both regions, the probability of detecting Cryptosporidium oocysts in faeces varied with animal class, but these were not consistent in both regions. In Tanga Region, Cryptosporidium oocysts were significantly more likely to be found in the faeces of milking cows. In Iringa Region, the likelihood that cattle had Cryptosporidium-positive faeces declined with age, and milking cattle were significantly less likely to have Cryptosporidium-positive faeces. In this region, 7% of cattle were housed within the family house at night, and this was marginally associated with a higher likelihood that animals had Ctyptosporidium-positive faeces. Our study suggests that even though herd sizes are small, Cryptosporidium spp. are endemic on many Tanzanian smallholder dairy farms. These protozoa may impact on animal health and production, but also on human health, given the close associations between the cattle and their keepers. Further studies are required to assess these risks in more detail, and understand the epidemiology of Cryptosporidium spp. in this management system.

Cross-sectional estimation of Babesia bovis antibody prevalence in cattle in two contrasting dairying areas in Tanzania

The crude prevalence of antibodies to Babesia bovis infection in cattle was estimated by serology using indirect ELISA during the period January to April, 1999. Sera were obtained from 1395 dairy cattle (of all ages, sexes and breeds) on smallholder farms, the majority being kept under a zero grazing regime. The crude prevalence of antibodies to Babesia bovis was 6 % for Tanga and 12 % for Iringa. The forces of infection based on the age sero-prevalence profile, were estimated at six for Iringa and four for Tanga per 100 cattle years-risk, respectively. Using random effect logistic regression as the analytical method, the factors (variables) of age, source of animals and geographic location were hypothesised to be associated with sero-positivity of Babesia bovis in the two regions.

The use of early lactation milk protein content to predict subsequent fertility performance and likelihood of culling, in commercial dairy cows

A dataset of 1,846,990 completed lactation record,; was created Using milk recording data from 8,967 commercial dairy farms in the United Kingdom over a five year period. Herd-specific lactation curves describing levels of milk, Cat and protein by lactation number and month of calving were generated for each farm. The actual yield of milk and protein proportion at the first milk recording of individual cow lactations were compared with the levels taken from the lactation curves. Logistic regression analysis showed that cows production milk with a lower percentage of protein than average had a significantly lower probability of being in-calf at 100 days post calving and it significantly higher probability of being culled at the end of lactation. The culling rates derived from the studied database demonstrate the current high wastage rate of commercial dairy cows. Well of this wastage is due to involuntary culling as a result of reproductive failure.

Bayesian clustering of farm types using the mixtures model

A Bayesian method of classifying observations that are assumed to come from a number of distinct subpopulations is outlined. The method is illustrated with simulated data and applied to the classification of farms according to their level and variability of income. The resultant classification shows a greater diversity of technical charactersitics within farm types than is conventionally the case. The range of mean farm income between groups in the new classification is wider than that of the conventional method and the variability of income within groups is narrower. Results show that the highest income group in 2000 included large specialist dairy farmers and pig and poultry producers, whilst in 2001 it included large and small specialist dairy farms and large mixed dairy and arable farms. In both years the lowest income group is dominated by non-milk producing livestock farms.

Budgetary implications of, and motives for, converting to organic farming: Case study farm business evidence from Great Britain

This paper explores the financial implications of converting to organic farming in Great Britain through a case study of farmers considering conversion in 2002. Most study farmers were motivated to convert for financial, not ideological or life-style reasons; organic meat production was the most common planned enterprise, although those choosing to produce milk, vegetables and cereals were also studied in depth. At the time of study, organic beef and sheep meat production was particularly profitable. It was found that, in these product sectors, a large improvement in Family Farm Income would result if organic production was introduced on the case study farms. With few exceptions, a fall in Family Farm Income during the conversion period would not be an obstacle to farmers changing to organic methods. Fixed cost changes would also not deter conversion but expensive investment in new livestock and appropriate buildings would be required by some of those businesses studied. These findings are, however, dependent upon the price premia assumptions used and, whilst these premia have dropped slightly since the time of study, this would lessen the financial shortfall during the conversion period. There is also the possibility that reversion to conventional agricultural production might occur, perhaps at a faster rate than the original conversion process that was taking place around the turn of the century.

Farm level tree planting in Pakistan: The role of farmers' perceptions and attitudes

The low proportion of forested land and continuing degradation of existing forest cover are serious threats to the sustainability of forestry in Pakistan. Farm forestry has been identified as a feasible solution, particularly in the plain areas. Applying the Theory of Planned Behaviour in a survey of 124 farmers in Dera Ismail Khan district of Pakistan's North West Frontier Province showed that farmers' willingness to grow trees on their farms is a function of their attitudes towards the advantages and disadvantages of growing trees, their perception of the opinions of salient referents and factors that encourage and discourage farm level tree planting. Farmers viewed farm forestry as economically beneficial and environmentally friendly. Tree planting was perceived as increasing income, providing wood for fuel and furniture, controlling erosion and pollution and providing shade for humans and animals. Farmers saw hindrance in agricultural operations and the harbouring of insects, pests and diseases as negative impacts of tree planting; however, these were outweighed by their perceptions of positive impacts. Tree growing decisions of farmers were influenced by the opinions of family members, owners/tenants, fellow farmers and village elders. The factors that significantly predicted farm level tree planting were availability of barren land, lack of markets, lack of nurseries and damage caused by animals and humans. Farm forestry programmes are more likely to be successful if they acknowledge and address the factors which underlie farmers' reasons for planting or not planting trees.

Dynamics of QoI sensitivity in Mycosphaerella fijiensis in Costa Rica during 2000 to 2003

From 1997 onward, the strobilurin fungicide azoxystrobin was widely used in the main banana-production zone in Costa Rica against Mycosphaerella fijiensis var. difformis causing black Sigatoka of banana. By 2000, isolates of M. fijiensis with resistance to the quinolene oxidase inhibitor fungicides were common on some farms in the area. The cause was a single point mutation from glycine to alanine in the fungal target protein, cytochrome b gene. An amplification refractory mutation system Scorpion quantitative polymerase chain reaction assay was developed and used to determine the frequency of G 143A allele in samples of M. fijiensis. Two hierarchical surveys of spatial variability, in 2001 and 2002,found no significant variation in frequency on spatial scales <10 in. This allowed the frequency of G143A alleles on a farm to be estimated efficiently by averaging single samples taken at two fixed locations. The frequency of G 143A allele in bulk samples from I I farms throughout Costa Rica was determined at 2-month intervals. There was no direct relationship between the number of spray applications and the frequency of G143A on individual farms. Instead, the frequency converged toward regional averages, presumably due to the large-scale mixing of ascospores dispersed by wind. Using trap plants in an area remote from the main producing area, immigration of resistant ascospores was detected as far as 6 km away both with and against the prevailing wind.

Susceptibility to the anticoagulants bromadiolone and coumatetralyl in wild Norway rats (Rattus norvegicus) from the UK and Germany

A new blood clotting response test was used to determine the susceptibility, to coumatetralyl and bromadiolone, of laboratory strains of Norway rat from Germany and the UK (Hampshire), and wild rats trapped on farms in Wales (UK) and Westphalia (Germany). Resistance factors were calculated in relation to the CD strain of Norway rat. An outbred strain of wild rats, raised from rats trapped in Germany, was found to be more susceptible to coumatetralyl by a factor of 0.5-0.6 compared to the CD strain. Homozygous and heterozygous animals of a strain of resistant rats from Westphalia were cross-resistant to coumatetralyl and bromadiolone, with a higher resistance factor for bromadiolone than that found in both UK strains. Our results show that the degree of altered susceptibility and resistance varies between strains of wild rat and between resistance foci. Some wild rat strains may be more susceptible than laboratory rat strains. Even in a well-established resistance area, it may be difficult to find infestations with resistance high enough to suspect control problems with bromadiolone, even after decades of use of this compound.

Control of cocoa swollen shoot disease by eradicating infected trees in Ghana: A survey of treated and replanted areas

Cocoa farms that had been treated and replanted in Ghana during the most recent phase of the cocoa swollen shoot virus (CSSV) eradication campaign were surveyed. Farms that were replanted close to adjoining old cocoa farms or which contained old trees were common in most (38) of the 41 cocoa farms surveyed. CSSV infections were apparent in 20 (53%) out of these 38 farms and they pose a serious risk of causing early infections of the re-planted farms. Control strategies that isolate the newly planted farms by a boundary of immune crops as barriers to reduce CSSV re-infection are discussed. (c) 2005 Elsevier Ltd. All rights reserved.

Infection of bryozoans by Tetracapsuloides bryosalmonae at sites endemic for salmonid proliferative kidney disease

Laboratory-reared colonies of the bryozoans Fredericella sultana and Plumatella fungosa were placed upstream of 2 fish farms endemic for salmonid proliferative kidney disease (PKD) to assess rates of infection of bryozoans by Tetra caps uloides bryosalmonae, the causative agent of PKD. Colonies were deployed in the field for 8 trial periods of 2 wk each throughout the summer of 2001. Following each trial, bryozoan colonies were maintained in laboratory culture for 28 d and were regularly monitored for infection by searching for sac stages of T bryosalmonae. Infections were never identified by observations of sac stages, however positive PCR results and sequencing of cultured material confirmed that cryptic infections were present in colonies of both species deployed at one site. The possibility that PCR results reflected contamination of surfaces of bryozoans can be excluded, given the short period of spore viability of T bryosalmonae. Highest rates of infection occurred when 4 of 23 colonies of F sultana and 1 of 12 colonies of P. fungosa were infected during the period 10 to 24 July. No infections were detected from mid-August to late October at this site. None of the colonies at the other site became infected throughout the period of study. Our data provide the first estimates of infection rates of bryozoans by T bryosalmonae. Additionally, they provide evidence that a cryptic stage can be maintained within bryozoan hosts for a period of 4 to 6 wk.

Vibrio parahaemolyticus associated with mass mortality of postlarval abalone, Haliotis diversicolor supertexta (L.), in Sanya, China

Outbreaks of mass mortality in postlarval abalone, Haliotis diversicolor supertexta (L.), have swept across south China since 2002 and in turn have resulted in many abalone farms closing. Twenty-five representative bacterial isolates were isolated from a sample of five diseased postlarval abalone, taken 15 d postfertilization during an outbreak of postlarval disease in Sanya, Hainan Province, China in October 2004. A dominant isolate, referred to as Strain 6, was found to be highly virulent to postlarvae in an experimental challenge test, with a 50% lethal dose (LD50) value of 3.2 x 10(4) colony forming units (CFU)/mL, while six of the other isolates were weakly virulent with LD50 values ranging from 1 x 10(6) to 1 x 10(7) CFU/mL, and the remaining 18 isolates were classified as avirulent with LD50 values greater than 1 x 10(8) CFU/mL. Using both an API 20E kit and 16S recombinant DNA sequence analysis, Strain 6 was shown to be Vibrio parahaemolyticus. It was sensitive to 4 and intermediately sensitive to 5 of the 16 antibiotics used when screening the antibiotic sensitivities of the bacterium. Extracellular products (ECPs) prepared from the bacterium were lethal to postlarvae when used in a toxicity test at a concentration of 3.77 mg protein/mL, and complete liquefaction of postlarvae tissues occurred within 24 h postexposure. Results from this study implicate V. parahaemolyticus as the pathogen involved in the disease outbreaks in postlarval abalone in Sanya and show that the ECPs may be involved in the pathogenesis of the disease.

Assessing the environmental performance of English arable and livestock holdings using data from the Farm Accountancy Data Network (FADN)

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.

Local versus organic: a turn in consumer preferences and willingness-to-pay

Demand for local food in the United States has significantly increased over the last decade. In an attempt to understand the drivers of this demand and how they have changed over time, we investigate the literature on organic and local foods over the last few decades. We focus our review on studies that allow comparison of characteristics now associated with both local and organic food. We summarize the major findings of these studies and their implications for understanding drivers of local food demand. Prior to the late 1990s, most studies failed to consider factors now associated with local food, and the few that included these factors found very little support for them. In many cases, the lines between local and organic were blurred. Coincident with the development of federal organic food standards, studies began to find comparatively more support for local food as distinct and separate from organic food. Our review uncovers a distinct turn in the demand for local and organic food. Before the federal organic standards, organic food was linked to small farms, animal welfare, deep sustainability, community support, and many other factors that are not associated with most organic foods today. Based on our review, we argue that demand for local food arose largely in response to corporate cooptation of the organic food market and the arrival of “organic lite.” This important shift in consumer preferences away from organic and toward local food has broad implications for the environment and society. If these patterns of consumer preferences prove to be sustainable, producers, activists, and others should be aware of the implications that these trends have for the food system at large.

Final report on research project PS2010, proof of concept of automated mapping of weeds in arable fields

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).

The impact of large scale atmospheric circulation patterns on wind power generation and its potential predictability: a case study over the UK

Over recent years there has been an increasing deployment of renewable energy generation technologies, particularly large-scale wind farms. As wind farm deployment increases, it is vital to gain a good understanding of how the energy produced is affected by climate variations, over a wide range of time-scales, from short (hours to weeks) to long (months to decades) periods. By relating wind speed at specific sites in the UK to a large-scale climate pattern (the North Atlantic Oscillation or "NAO"), the power generated by a modelled wind turbine under three different NAO states is calculated. It was found that the wind conditions under these NAO states may yield a difference in the mean wind power output of up to 10%. A simple model is used to demonstrate that forecasts of future NAO states can potentially be used to improve month-ahead statistical forecasts of monthly-mean wind power generation. The results confirm that the NAO has a significant impact on the hourly-, daily- and monthly-mean power output distributions from the turbine with important implications for (a) the use of meteorological data (e.g. their relationship to large scale climate patterns) in wind farm site assessment and, (b) the utilisation of seasonal-to-decadal climate forecasts to estimate future wind farm power output. This suggests that further research into the links between large-scale climate variability and wind power generation is both necessary and valuable.