Long-term effectiveness of sowing high and low diversity seed mixtures to enhance plant community development on ex-arable fields

Questions: How is succession on ex-arable land affected by sowing high and low diversity mixtures of grassland species as compared to natural succession? How long do effects persist? Location: Experimental plots installed in the Czech Republic, The Netherlands, Spain, Sweden and the United Kingdom. Methods: The experiment was established on ex-arable land, with five blocks, each containing three 10 m x 10 m experiment tal plots: natural colonization, a low- (four species) and high-diversity (15 species) seed mixture. Species composition and biomass was followed for eight years. Results: The sown plants considerably affected the whole successional pathway and the effects persisted during the whole eight year period. Whilst the proportion of sown species (characterized by their cover) increased during the study period, the number of sown species started to decrease from the third season onwards. Sowing caused suppression of natural colonizing species, and the sown plots had more biomass. These effects were on average larger in the high diversity mixtures. However, the low diversity replicate sown with the mixture that produced the largest biomass or largest suppression of natural colonizers fell within the range recorded at the five replicates of the high diversity plots. The natural colonization plots usually had the highest total species richness and lowest productivity at the end of the observation period. Conclusions: The effect of sowing demonstrated dispersal limitation as a factor controlling the rate of early secondary succession. Diversity was important primarily for its 'insurance effect': the high diversity mixtures were always able to compensate for the failure of some species.

The role of weeds in supporting biological diversity within crop fields

Weeds are major constraints on crop production, yet as part of the primary producers within farming systems, they may be important components of the agroecosystem. Using published literature, the role of weeds in arable systems for other above-ground trophic levels are examined. In the UK, there is evidence that weed flora have changed over the past century, with some species declining in abundance, whereas others have increased. There is also some evidence for a decline in the size of arable weed seedbanks. Some of these changes reflect improved agricultural efficiency, changes to more winter-sown crops in arable rotations and the use of more broad-spectrum herbicide combinations. Interrogation of a database of records of phytophagous insects associated with plant species in the UK reveals that many arable weed species support a high diversity of insect species. Reductions in abundances of host plants may affect associated insects and other taxa. A number of insect groups and farmland birds have shown marked population declines over the past 30 years. Correlational studies indicate that many of these declines are associated with changes in agricultural practices. Certainly reductions in food availability in winter and for nestling birds in spring are implicated in the declines of several bird species, notably the grey partridge, Perdix perdix . Thus weeds have a role within agroecosystems in supporting biodiversity more generally. An understanding of weed competitivity and the importance of weeds for insects and birds may allow the identification of the most important weed species. This may form the first step in balancing the needs for weed control with the requirements for biodiversity and more sustainable production methods.

Vegetation dynamics in abandoned crop fields on a Mediterranean island: Development of succession model and estimation of disturbance thresholds

This case study on the Sifnos island, Greece, assesses the main factors controlling vegetation succession following crop abandonment and describes the vegetation dynamics of maquis and phrygana formations in relation to alternative theories of secondary succession. Field survey data were collected and analysed at community as well as species level. The results show that vegetation succession on abandoned crop fields is determined by the combined effects of grazing intensity, soil and geological characteristics and time. The analysis determines the quantitative grazing thresholds that modify the successional pathway. Light grazing leads to dominance by maquis vegetation while overgrazing leads to phryganic vegetation. The proposed model shows that vegetation succession following crop abandonment is a complex multi-factor process where the final or the stable stage of the process is not predefined but depends on the factors affecting succession. An example of the use of succession models and disturbance thresholds as a policy assessment tool is presented by evaluating the likely vegetation impacts of the recent reform of the Common Agricultural Policy on Sifnos island over a 20-30-year time horizon. (c) 2006 Elsevier B.V. All rights reserved.

Chalcogenide-halides of niobium (V). 1. Gas-phase structures of NbOBr3, NbSBr3, and NbSCl3. 2. Matrix infrared spectra and vibrational force fields of NbOBr3, NbSBr3, NbSCl3, and NbOCl3

The molecular structures of NbOBr3, NbSCl3, and NbSBr3 have been determined by gas-phase electron diffraction (GED) at nozzle-tip temperatures of 250 degreesC, taking into account the possible presence of NbOCl3 as a contaminant in the NbSCl3 sample and NbOBr3 in the NbSBr3 sample. The experimental data are consistent with trigonal-pyramidal molecules having C-3v symmetry. Infrared spectra of molecules trapped in argon or nitrogen matrices were recorded and exhibit the characteristic fundamental stretching modes for C-3v species. Well resolved isotopic fine structure (Cl-35 and Cl-37) was observed for NbSCl3, and for NbOCl3 which occurred as an impurity in the NbSCl3 spectra. Quantum mechanical calculations of the structures and vibrational frequencies of the four YNbX3 molecules (Y = O, S; X = Cl, Br) were carried out at several levels of theory, most importantly B3LYP DFT with either the Stuttgart RSC ECP or Hay-Wadt (n + 1) ECP VDZ basis set for Nb and the 6-311 G* basis set for the nonmetal atoms. Theoretical values for the bond lengths are 0.01-0.04 Angstrom longer than the experimental ones of type r(a), in accord with general experience, but the bond angles with theoretical minus experimental differences of only 1.0-1.5degrees are notably accurate. Symmetrized force fields were also calculated. The experimental bond lengths (r(g)/Angstrom) and angles (angle(alpha)/deg) with estimated 2sigma uncertainties from GED are as follows. NbOBr3: r(Nb=O) = 1.694(7), r(Nb-Br) = 2.429(2), angle(O=Nb-Br) = 107.3(5), angle(Br-Nb-Br) = 111.5(5). NbSBr3: r(Nb=S) = 2.134(10), r(Nb-Br) = 2.408(4), angle(S=Nb-Br) = 106.6(7), angle(Br-Nb-Br) = 112.2(6). NbSCl3: Nb=S) = 2.120(10), r(Nb-Cl) = 2.271(6), angle(S=Nb-Cl) = 107.8(12), angle(Cl-Nb-Cl) = 111.1(11).

Investigation into the performance of an algorithm for deriving concise descriptions of optic flow fields for real-time control

This paper describes a region-based algorithm for deriving a concise description of a first order optical flow field. The algorithm described achieves performance improvements over existing algorithms without compromising the accuracy of the flow field values calculated. These improvements are brought about by not computing the entire flow field between two consecutive images, but by considering only the flow vectors of a selected subset of the images. The algorithm is presented in the context of a project to balance a bipedal robot using visual information.

A framework towards multi-modal fingerprinting scheme for multimedia assets

Fingerprinting is a well known approach for identifying multimedia data without having the original data present but what amounts to its essence or ”DNA”. Current approaches show insufficient deployment of three types of knowledge that could be brought to bear in providing a finger printing framework that remains effective, efficient and can accommodate both the whole as well as elemental protection at appropriate levels of abstraction to suit various Foci of Interest (FoI) in an image or cross media artefact. Thus our proposed framework aims to deliver selective composite fingerprinting that remains responsive to the requirements for protection of whole or parts of an image which may be of particularly interest and be especially vulnerable to attempts at rights violation. This is powerfully aided by leveraging both multi-modal information as well as a rich spectrum of collateral context knowledge including both image-level collaterals as well as the inevitably needed market intelligence knowledge such as customers’ social networks interests profiling which we can deploy as a crucial component of our Fingerprinting Collateral Knowledge. This is used in selecting the special FoIs within an image or other media content that have to be selectively and collaterally protected.

Enhancing change detection in low-quality surveillance footage using markov random fields

Urban surveillance footage can be of poor quality, partly due to the low quality of the camera and partly due to harsh lighting and heavily reflective scenes. For some computer surveillance tasks very simple change detection is adequate, but sometimes a more detailed change detection mask is desirable, eg, for accurately tracking identity when faced with multiple interacting individuals and in pose-based behaviour recognition. We present a novel technique for enhancing a low-quality change detection into a better segmentation using an image combing estimator in an MRF based model.

A framework towards a multi-modal fingerprinting scheme for multimedia assets

Fingerprinting is a well known approach for identifying multimedia data without having the original data present but instead what amounts to its essence or 'DNA'. Current approaches show insufficient deployment of various types of knowledge that could be brought to bear in providing a fingerprinting framework that remains effective, efficient and can accommodate both the whole as well as elemental protection at appropriate levels of abstraction to suit various Zones of Interest (ZoI) in an image or cross media artefact. The proposed framework aims to deliver selective composite fingerprinting that is powerfully aided by leveraging both multi-modal information as well as a rich spectrum of collateral context knowledge including both image-level collaterals and also the inevitably needed market intelligence knowledge such as customers' social networks interests profiling which we can deploy as a crucial component of our fingerprinting collateral knowledge.

Classifying monetary economics: fields and methods from past to future

We propose a simple, yet sufficiently encompassing, classification scheme of monetary economics. It comprises three fundamental fields and six recent areas that expand within and across these fields. The elements of our scheme are not found together and in their mutual relationships in earlier studies of the relevant literature; neither does this attempt aim to produce a relatively complete systematization. Our intention in taking stock is not finality or exhaustiveness. We rather suggest a viewpoint and a possible ordering of the accumulating knowledge. Our purpose is to promote discussion on the evolving nature and internal consistency of monetary economics at large.

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).