The influence of winter oilseed rape (Brassica napus ssp oleifera var. biennis) cultivar and grass genotype on the competitive balance between crop and grass weeds

Three experiments conducted over two years (2002-04) at the Crops Research Unit, University of Reading, investigated competition between autumn sown oilseed rape cultivars (Brassica napus L. ssp. oleifera var. biennis (DC.) Metzg.) and Lolium multiflorum Lam., L. x boucheanum Kunth and Alopecurus myosuroides Huds., sown as indicative grass weeds. Rape cultivar (cv.) had a substantial effect on grass weed seed return. Over the six cultivars tested, L. multiflorum spikelet production ranged from just under 400 spikelets/m(2) in the presence of cv. Winner to nearly 5800 in competition with cv. Lutin. Cultivar competitiveness was associated with high biomass, large dense floral layers and early stem extension. There was some evidence of differential competitive tolerance between rape cultivars. The results suggested that rape cultivars could be screened for competitiveness by measuring floral layer interception of photosynthetic active radiation. L. x boucheanum cultivars varied in ability to compete with rape. In the absence of inter-specific competition, spikelet density was similar for Aberecho and Polly (circa 31000 spikelets/m(2)) but when grown with rape Polly outyielded Aberecho (i.e. 12 090 and 7990 spikelets/m(2) respectively).

Comparison of heat-treated rapeseed expeller and solvent-extracted soya-bean meal as protein supplements for dairy cows given grass silage-based diets

Sixteen early to mid lactation Finnish Ayrshire dairy cows were used in a cyclic change-over experiment with four 21-day experimental periods and a 4 5 2 factorial arrangement of treatments to evaluate the effects of heat-treated rapeseed expeller and solvent-extracted soya-bean meal protein supplements on animal performance. Dietary treatments consisted of grass silage offered ad libitum supplemented with a fixed amount of a cereal based concentrate (10 kg/day on a fresh weight basis) containing 120, 150, 180 or 210 g crude protein (CP) per kg dry matter (DM). Concentrate CP content was manipulated by replacement of basal ingredients (g/kg) with either rapeseed expeller (R; 120, 240 and 360) or soya-bean meal (S; 80, 160 and 240). Increases in concentrate CP stimulated linear increases (P < 0.05) in silage intake (mean 22.5 and 23.8 g DM per g/kg increase in dietary CP content, for R and S, respectively) and milk production. Concentrate inclusion of rapeseed expeller elicited higher (P < 0.01) milk yield and milk protein output responses (mean 108 and 3.71 g/day per g/kg DM increase in dietary CP content) than soya-bean meal (corresponding values 62 and 2.57). Improvements in the apparent utilization of dietary nitrogen for milk protein synthesis (mean 0.282 and 0.274, for R and S, respectively) were associated with higher (P < 0.05) plasma concentrations of histidine, branched-chain, essential and total amino acids (35, 482, 902 and 2240 and 26, 410, 800 and 2119 mu mol/l, respectively) and lower (P < 0.01) concentrations of urea (corresponding values 4.11 and 4.52 mmol/l). Heat-treated rapeseed expeller proved to be a more effective protein supplement than solvent-extracted soya-bean meal for cows offered grass silage-based diets.

Effect of a cellulase treatment on extraction of antioxidant phenols from black currant (Ribes nigrum L.) pomace

The effect of a commercial cellulase preparation on phenol liberation and extraction from black currant pomace was studied. The enzyme used, which was from Trichoderma spp., was an effective "cellulase-hemicellulase" blend with low P-glucosidase activity and various side activities. Enzyme treatment significantly increased plant cell wall polysaccharide degradation as well as increasing the availability of phenols for subsequent methanolic extraction. The release of anthocyanins and other phenols was dependent on reaction parameters, including enzyme dosage, temperature, and time. At 50 degrees C, anthocyanin yields following extraction increased by 44% after 3 h and by 60% after 1.5 h for the lower and higher enzyme/substrate ratio (E/S), respectively. Phenolic acids were more easily released in the hydrolytic mixture (supernatant) and, although a short hydrolysis time was adequate to release hydroxybenzoic acids (HBA), hydroxycinnamic acids (HCA) required longer times. The highest E/S value of 0.16 gave a significant increase of flavonol yields in all samples. The antioxidant capacity of extracts, assessed by scavenging of 2,2'-azinobis(3-ethylbenzothiazoline-6-sulfonic acid) radical cation, the oxygen radical absorbance capacity, and the ferric reducing antioxidant potential depended on the concentration and composition of the phenols present.

Extraction of polyphenols from processed black currant (Ribes nigrum L.) residues

The total phenol and anthocyanin contents of black currant pomace and black currant press residue (BPR) extracts, extracted with formic acid in methanol or with methanol/water/acetic acid, were studied. Anthocyanins and other phenols were identified by means of reversed phase HPLC, and differences between the two plant materials were monitored. In all BPR extracts, phenol levels, determined by the Folin-Ciocalteu method, were 8-9 times higher than in the pomace extracts. Acid hydrolysis liberated a much higher concentration of phenols from the pomace than from the black currant press residue. HPLC analysis revealed that delphinidin-3-O-glucoside, delphinidin-3-O-rutinoside, cyanidin-3-O-glucoside, and cyanidin-3-O-rutinoside were the major anthocyanins and constituted the main phenol class (approximate to 90%) in both types of black currant tissues tested. However, anthocyanins were present in considerably lower amounts in the pomace than in the BPR. In accordance with the total phenol content, the antioxidant activity determined by scavenging of 2,2'-azinobis(3-ethylbenzothiazoline-6- sulfonic acid) radical cation, the ABTS(center dot+) assay, showed that BPR extracts prepared by solvent extraction exhibited significantly higher (7-10 times) radical scavenging activity than the pomace extracts, and BPR anthocyanins contributed significantly (74 and 77%) to the observed high radical scavenging capacity of the corresponding extracts.

Corynebacterium ciconiae sp. nov., isolated from the trachea of black storks (Ciconia nigra)

SEight unidentified Gram-positive, rod-shaped organisms were recovered from the tracheas of apparently healthy black storks (Ciconia nigra) and subjected to a polyphasic taxonomic analysis. Based on cellular morphology and biochemical criteria the isolates were tentatively assigned to the genus Corynebacterium, although three of the organisms did not appear to correspond to any recognized species. Comparative 16S rRNA gene sequencing studies demonstrated that all of the isolates were phylogenetically members of the genus Corynebacterium. Five strains were genotypically identified as representing Corynebacterium falsenii, whereas the remaining three strains represented a hitherto unknown subline, associated with a small subcluster of species that includes Corynebacterium mastitidis and its close relatives. On the basis of phenotypic and phylogenetic evidence, it is proposed that the unknown isolates from black storks represent a novel species within the genus Corynebacterium, for which the Corynebacterium ciconiae sp. nov. is proposed. The type strain is CECT 5779(T) (= BS13(T) =CCILIG 47525(T)).

Proof of concept of automated mapping of weeds in arable fields

The project investigated whether it would be possible to remove the main technical hindrance to precision application of herbicides to arable crops in the UK, namely creating geo-referenced weed maps for each field. The ultimate goal is an information system so that agronomists and farmers can plan precision weed control and create spraying maps. The project focussed on black-grass in wheat, but research was also carried out on barley and beans and on wild-oats, barren brome, rye-grass, cleavers and thistles which form stable patches in arable fields. Farmers may also make special efforts to control them. Using cameras mounted on farm machinery, the project explored the feasibility of automating the process of mapping black-grass in fields. Geo-referenced images were captured from June to December 2009, using sprayers, a tractor, combine harvesters and on foot. Cameras were mounted on the sprayer boom, on windows or on top of tractor and combine cabs and images were captured with a range of vibration levels and at speeds up to 20 km h-1. For acceptability to farmers, it was important that every image containing black-grass was classified as containing black-grass; false negatives are highly undesirable. The software algorithms recorded no false negatives in sample images analysed to date, although some black-grass heads were unclassified and there were also false positives. The density of black-grass heads per unit area estimated by machine vision increased as a linear function of the actual density with a mean detection rate of 47% of black-grass heads in sample images at T3 within a density range of 13 to 1230 heads m-2. A final part of the project was to create geo-referenced weed maps using software written in previous HGCA-funded projects and two examples show that geo-location by machine vision compares well with manually-mapped weed patches. The consortium therefore demonstrated for the first time the feasibility of using a GPS-linked computer-controlled camera system mounted on farm machinery (tractor, sprayer or combine) to geo-reference black-grass in winter wheat between black-grass head emergence and seed shedding.

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).