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

Four experiments conducted over three seasons (2002–05) at the Crops Research Unit, University of Reading, investigated effects of canopy management of autumn sown oilseed rape (Brassica napus L. ssp. oleifera var. biennis (DC.) Metzg.) on competition with grass weeds. Emphasis was placed on the effect of the crop on the weeds. Rape canopy size was manipulated using sowing date, seed rate and the application of autumn fertilizer. Lolium multiflorum Lam., L.rboucheanum Kunth and Alopecurus myosuroides Huds. were sown as indicative grass weeds. The effects of sowing date, seed rate and autumn nitrogen on crop competitive ability were correlated with rape biomass and fractional interception of photosynthetically active radiation (PAR) by the rape floral layer, to the extent that by spring there was good evidence of crop: weed replacement. An increase in seed rate up to the highest plant densities tested increased both rape biomass and competitiveness, e.g. in 2002/3, L. multiflorum head density was reduced from 539 to 245 heads/m2 and spikelet density from 13 170 to 5960 spikelets/m2 when rape plant density was increased from 16 to 81 plants/m2. Spikelets/head of Lolium spp. was little affected by rape seed rate, but the length of heads of A. myosuroides was reduced by 9%when plant density was increased from 29–51 plants/m2. Autumn nitrogen increased rape biomass and reduced L. multiflorum head density (415 and 336 heads/m2 without and with autumn nitrogen, respectively) and spikelet density (9990 and 8220 spikelets/m2 without and with autumn nitrogen, respectively). The number of spikelets/head was not significantly affected by autumn nitrogen. Early sowing could increase biomass and competitiveness, but poor crop establishment sometimes overrode the effect. Where crop and weed establishment was similar for both sowing dates, a 2-week delay (i.e. early September to mid-September) increased L. multiflorum head density from 226 to 633 heads/m2 and spikelet density from 5780 to 15 060 spikelets/m2.

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).

Post-dispersal seed predation of non-target weeds in arable crops

Field experiments were conducted to quantify the natural levels of post-dispersal seed predation of arable weed species in spring barley and to identify the main groups of seed predators. Four arable weed species were investigated that were of high biodiversity value, yet of low to moderate competitive ability with the crop. These were Chenopodium album, Sinapis arvensis, Stellaria media and Polygonum aviculare. Exclusion treatments were used to allow selective access to dishes of seeds by different predator groups. Seed predation was highest early in the season, followed by a gradual decline in predation over the summer for all species. All species were taken by invertebrates. The activity of two phytophagous carabid genera showed significant correlations with seed predation levels. However, in general carabid activity was not related to seed predation and this is discussed in terms of the mainly polyphagous nature of many Carabid species that utilized the seed resource early in the season, but then switched to carnivory as prey populations increased. The potential relevance of post-dispersal seed predation to the development of weed management systems that maximize biological control through conservation and optimize herbicide use, is discussed.

Are the spatial patterns of weeds scale-invariant?

In previous empirical and modelling studies of rare species and weeds, evidence of fractal behaviour has been found. We propose that weeds in modern agricultural systems may be managed close to critical population dynamic thresholds, below which their rates of increase will be negative and where scale-invariance may be expected as a consequence. We collected detailed spatial data on five contrasting species over a period of three years in a primarily arable field. Counts in 20×20 cm contiguous quadrats, 225,000 in 1998 and 84,375 thereafter, could be re-structured into a wide range of larger quadrat sizes. These were analysed using three methods based on correlation sum, incidence and conditional incidence. We found non-trivial scale invariance for species occurring at low mean densities and where they were strongly aggregated. The fact that the scale-invariance was not found for widespread species occurring at higher densities suggests that the scaling in agricultural weed populations may, indeed, be related to critical phenomena.

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).

The competitive ability of pea-barley intercrops against weeds and the interactions with crop productivity and soil N availability

Grain legumes, such as peas (Pisum sativum L.), are known to be weak competitors against weeds when grown as the sole crop. In this study, the weed-suppression effect of pea–barley (Hordeum vulgare L.)intercropping compared to the respective sole crops was examined in organic field experiments across Western Europe (i.e., Denmark, the United Kingdom, France, Germany and Italy). Spring pea (P) and barley(B) were sown either as the sole crop, at the recommended plant density (P100 and B100, respectively), or in replacement (P50B50) or additive (P100B50)intercropping designs for three seasons (2003–2005). The weed biomass was three times higher under the pea sole crops than under both the intercrops and barley sole crops at maturity. The inclusion of joint experiments in several countries and various growing conditions showed that intercrops maintain a highly asymmetric competition over weeds, regardless of the particular weed infestation (species and productivity), the crop biomass or the soil nitrogen availability. The intercropping weed suppression was highly resilient, whereas the weed suppression in pea sole crops was lower and more variable. The pea–barley intercrops exhibited high levels of weed suppression, even with a low percentage of barley in the total biomass. Despite a reduced leaf area in the case of a low soil N availability, the barley sole crops and intercrops displayed high weed suppression, probably because of their strong competitive capability to absorb soil N. Higher soil N availabilities entailed increased leaf areas and competitive ability for light, which contributed to the overall competitive ability against weeds for all of the treatments. The contribution of the weeds in the total dry matter and soil N acquisition was higher in the pea sole crop than in the other treatments, in spite of the higher leaf areas in the pea crops.

Species concepts in Cercospora: spotting the weeds among the roses

The genus Cercospora contains numerous important plant pathogenic fungi from a diverse range of hosts. Most species of Cercospora are known only from their morphological characters in vivo. Although the genus contains more than 5 000 names, very few cultures and associated DNA sequence data are available. In this study, 360 Cercospora isolates, obtained from 161 host species, 49 host families and 39 countries, were used to compile a molecular phylogeny. Partial sequences were derived from the internal transcribed spacer regions and intervening 5.8S nrRNA, actin, calmodulin, histone H3 and translation elongation factor 1-alpha genes. The resulting phylogenetic clades were evaluated for application of existing species names and five novel species are introduced. Eleven species are epi-, lecto- or neotypified in this study. Although existing species names were available for several clades, it was not always possible to apply North American or European names to African or Asian strains and vice versa. Some species were found to be limited to a specific host genus, whereas others were isolated from a wide host range. No single locus was found to be the ideal DNA barcode gene for the genus, and species identification needs to be based on a combination of gene loci and morphological characters. Additional primers were developed to supplement those previously published for amplification of the loci used in this study. TAXONOMIC NOVELTIES: New species - Cercospora coniogrammes Crous & R.G. Shivas, Cercospora delaireae C. Nakash., Crous, U. Braun & H.D. Shin, Cercospora euphorbiae-sieboldianae C. Nakash., Crous, U. Braun & H.D. Shin, Cercospora pileicola C. Nakash., Crous, U. Braun & H.D. Shin, Cercospora vignigena C. Nakash., Crous, U. Braun & H.D. Shin. Typifications: epitypifications - Cercospora alchemillicola U. Braun & C.F. Hill, Cercospora althaeina Sacc., Cercospora armoraciae Sacc., Cercospora corchori Sawada, Cercospora mercurialis Pass., Cercospora olivascens Sacc., Cercospora violae Sacc.; neotypifications - Cercospora fagopyri N. Nakata & S. Takim., Cercospora sojina Hara.

New insights into the structure of exotic nuclei using the RISING active stopper

This conference paper outlines the operation and some of the preliminary physics results using the GSI RISING active stopper. Data are presented from an experiment using combined isomer and beta‐delayed gamma‐ray spectroscopy to study low‐lying spectral and decay properties of heavy‐neutron‐rich nuclei around A∼190 produced following the relativistic projectile fragmentation of 208Pb primary beam. The response of the RISING active stopper detector is demonstrated for both the implantation of heavy secondary fragments and in‐situ decay of beta‐particles. Beta‐delayed gamma‐ray spectroscopy following decays of the neutron‐rich nucleus 194Re is presented to demonstrate the experimental performance of the set‐up. The resulting information inferred from excited states in the W and Os daughter nuclei is compared with results from Skyrme Hartree‐Fock predictions of the evolution of nuclear shape.

Designing a sampling scheme to reveal correlations between weeds and soil properties at multiple spatial scales

Weeds tend to aggregate in patches within fields and there is evidence that this is partly owing to variation in soil properties. Because the processes driving soil heterogeneity operate at different scales, the strength of the relationships between soil properties and weed density would also be expected to be scale-dependent. Quantifying these effects of scale on weed patch dynamics is essential to guide the design of discrete sampling protocols for mapping weed distribution. We have developed a general method that uses novel within-field nested sampling and residual maximum likelihood (REML) estimation to explore scale-dependent relationships between weeds and soil properties. We have validated the method using a case study of Alopecurus myosuroides in winter wheat. Using REML, we partitioned the variance and covariance into scale-specific components and estimated the correlations between the weed counts and soil properties at each scale. We used variograms to quantify the spatial structure in the data and to map variables by kriging. Our methodology successfully captured the effect of scale on a number of edaphic drivers of weed patchiness. The overall Pearson correlations between A. myosuroides and soil organic matter and clay content were weak and masked the stronger correlations at >50 m. Knowing how the variance was partitioned across the spatial scales we optimized the sampling design to focus sampling effort at those scales that contributed most to the total variance. The methods have the potential to guide patch spraying of weeds by identifying areas of the field that are vulnerable to weed establishment.

NU-PROCESS IN EXOTIC MODELS

The exact physical conditions generating the abundances of r-elements in environments such as supernovae explosions are still under debate. We evaluated the characteristics expected for the neutrino wind in the proposed model of type-II supernova driven by conversion of nuclear matter to strange matter. Neutrinos will change the final abundance of elements after freeze out of r-process nucleosynthesis, specially those close to mass peaks.