Measuring and modelling mixture toxicity of imidacloprid and thiacloprid on Caenorhabditis elegans and Eisenia fetida

While the standard models of concentration addition and independent action predict overall toxicity of multicomponent mixtures reasonably, interactions may limit the predictive capability when a few compounds dominate a mixture. This study was conducted to test if statistically significant systematic deviations from concentration addition (i.e. synergism/antagonism, dose ratio- or dose level-dependency) occur when two taxonomically unrelated species, the earthworm Eisenia fetida and the nematode Caenorhabditis elegans were exposed to a full range of mixtures of the similar acting neonicotinoid pesticides imidacloprid and thiacloprid. The effect of the mixtures on C. elegans was described significantly better (p<0.01) by a dose level-dependent deviation from the concentration addition model than by the reference model alone, while the reference model description of the effects on E. fetida could not be significantly improved. These results highlight that deviations from concentration addition are possible even with similar acting compounds, but that the nature of such deviations are species dependent. For improving ecological risk assessment of simple mixtures, this implies that the concentration addition model may need to be used in a probabilistic context, rather than in its traditional deterministic manner. Crown Copyright (C) 2008 Published by Elsevier Inc. All rights reserved.

The analysis of ranked observations of soil structure using indicator geostatistics

Structure is an important physical feature of the soil that is associated with water movement, the soil atmosphere, microorganism activity and nutrient uptake. A soil without any obvious organisation of its components is known as apedal and this state can have marked effects on several soil processes. Accurate maps of topsoil and subsoil structure are desirable for a wide range of models that aim to predict erosion, solute transport, or flow of water through the soil. Also such maps would be useful to precision farmers when deciding how to apply nutrients and pesticides in a site-specific way, and to target subsoiling and soil structure stabilization procedures. Typically, soil structure is inferred from bulk density or penetrometer resistance measurements and more recently from soil resistivity and conductivity surveys. To measure the former is both time-consuming and costly, whereas observations made by the latter methods can be made automatically and swiftly using a vehicle-mounted penetrometer or resistivity and conductivity sensors. The results of each of these methods, however, are affected by other soil properties, in particular moisture content at the time of sampling, texture, and the presence of stones. Traditional methods of observing soil structure identify the type of ped and its degree of development. Methods of ranking such observations from good to poor for different soil textures have been developed. Indicator variograms can be computed for each category or rank of structure and these can be summed to give the sum of indicator variograms (SIV). Observations of the topsoil and subsoil structure were made at four field sites where the soil had developed on different parent materials. The observations were ranked by four methods and indicator and the sum of indicator variograms were computed and modelled for each method of ranking. The individual indicators were then kriged with the parameters of the appropriate indicator variogram model to map the probability of encountering soil with the structure represented by that indicator. The model parameters of the SIVs for each ranking system were used with the data to krige the soil structure classes, and the results are compared with those for the individual indicators. The relations between maps of soil structure and selected wavebands from aerial photographs are examined as basis for planning surveys of soil structure. (C) 2007 Elsevier B.V. All rights reserved.

MEASUREMENT AND MODELING OF THE TOXICITY OF BINARY MIXTURES IN THE NEMATODE CAENORHABDITIS ELEGANS-A TEST OF INDEPENDENT ACTION

Ecological risk assessments must increasingly consider the effects of chemical mixtures on the environment as anthropogenic pollution continues to grow in complexity. Yet testing every possible mixture combination is impractical and unfeasible; thus, there is an urgent need for models that can accurately predict mixture toxicity from single-compound data. Currently, two models are frequently used to predict mixture toxicity from single-compound data: Concentration addition and independent action (IA). The accuracy of the predictions generated by these models is currently debated and needs to be resolved before their use in risk assessments can be fully justified. The present study addresses this issue by determining whether the IA model adequately described the toxicity of binary mixtures of five pesticides and other environmental contaminants (cadmium, chlorpyrifos, diuron, nickel, and prochloraz) each with dissimilar modes of action on the reproduction of the nematode Caenorhabditis elegans. In three out of 10 cases, the IA model failed to describe mixture toxicity adequately with significant or antagonism being observed. In a further three cases, there was an indication of synergy, antagonism, and effect-level-dependent deviations, respectively, but these were not statistically significant. The extent of the significant deviations that were found varied, but all were such that the predicted percentage effect seen on reproductive output would have been wrong by 18 to 35% (i.e., the effect concentration expected to cause a 50% effect led to an 85% effect). The presence of such a high number and variety of deviations has important implications for the use of existing mixture toxicity models for risk assessments, especially where all or part of the deviation is synergistic.

Can genetically-modified cotton contribute to sustainable development in Africa?

Genetically modified (GM) crops and sustainable development remain the foci of much media attention, especially given current concerns about a global food crisis. However, whilst the latter is embraced with enthusiasm by almost all groups, GM crops generate very mixed views. Some countries have welcomed GM, but others, notably those in Europe, adopt a cautious stance. This article aims to review the contribution that GM crops can make to agricultural sustainability in the developing world. Following brief reviews of both issues and their linkages, notably the pros and cons of GM cotton as a contributory factor in sustainability, a number of case studies from resourcepoor cotton farmers in Makhathini Flats, South Africa, is presented for a six-year period. Data on expenditure, productivity and income indicate that Bacillus thuringiensis (Bt) cotton is advantageous because it reduces costs, for example, of pesticides, and increases income, and the indications are that those benefits continued over at least the six years covered by the studies. There are repercussions of the additional income in the households; debts are reduced and money is invested in children's education and in the farms. However, in the general GM debate, the results show that GM crops are not miracle products which alleviate poverty at a stroke, but nor is there evidence that they will cause the scale of environmental damage associated with indiscriminate pesticide use. Indeed, for some GM antagonists, perhaps even the majority, such debates are irrelevant – the transfer of genes between species is unnatural and unethical. For them, GM crops will never be acceptable despite the evidence and pressure to increase world food production.

Impacts of a pesticide on pollinator species richness at different spatial scales

Pesticides are an important potential cause of biodiversity and pollinator decline. Little is known about the impacts of pesticides on wild pollinators in the field. Insect pollinators were sampled in an agricultural system in Italy with the aim of detecting the impacts of pesticide use. The insecticide fenitrothion was over 150 times greater in toxicity than other pesticides used in the area, so sampling was set up around its application. Species richness of wild bees, bumblebees and butterflies were sampled at three spatial scales to assess responses to pesticide application: (i) the ‘field’ scale along pesticide drift gradients; (ii) the ‘landscape’ scale sampling in different crops within the area and (iii) the ‘regional’ scale comparing two river basins with contrasting agricultural intensity. At the field scale, the interaction between the application regime of the insecticide and the point in the season was important for species richness. Wild bee species richness appeared to be unaffected by one insecticide application, but declined after two and three applications. At the landscape scale, the species richness of wild bees declined in vine fields where the insecticide was applied, but did not decline in maize or uncultivated fields. At the regional scale, lower bumblebee and butterfly species richness was found in the more intensively farmed basin with higher pesticide loads. Our results suggest that wild bees are an insect pollinator group at particular risk from pesticide use. Further investigation is needed on how the type, quantity and timing of pesticide application impacts pollinators.

Ecological and life-history traits predict bee species responses to environmental disturbances

The ability to predict the responses of ecological communities and individual species to human-induced environmental change remains a key issue for ecologists and conservation managers alike. Responses are often variable among species within groups making general predictions difficult. One option is to include ecological trait information that might help to disentangle patterns of response and also provide greater understanding of how particular traits link whole clades to their environment. Although this ‘‘trait-guild” approach has been used for single disturbances, the importance of particular traits on general responses to multiple disturbances has not been explored. We used a mixed model analysis of 19 data sets from throughout the world to test the effect of ecological and life-history traits on the responses of bee species to different types of anthropogenic environmental change. These changes included habitat loss, fragmentation, agricultural intensification, pesticides and fire. Individual traits significantly affected bee species responses to different disturbances and several traits were broadly predictive among multiple disturbances. The location of nests – above vs. below ground – significantly affected response to habitat loss, agricultural intensification, tillage regime (within agriculture) and fire. Species that nested above ground were on average more negatively affected by isolation from natural habitat and intensive agricultural land use than were species nesting below ground. In contrast below-ground-nesting species were more negatively affected by tilling than were above-ground nesters. The response of different nesting guilds to fire depended on the time since the burn. Social bee species were more strongly affected by isolation from natural habitat and pesticides than were solitary bee species. Surprisingly, body size did not consistently affect species responses, despite its importance in determining many aspects of individuals’ interaction with their environment. Although synergistic interactions among traits remain to be explored, individual traits can be useful in predicting and understanding responses of related species to global change.

Determining the influence of rainfall patterns and carbendazim on the surface activity of the earthworm Lumbricus terrestris

Carbendazim is highly toxic to earthworms and is used as a standard control substance when running field-based trials of pesticides, but results using carbendazim are highly variable. In the present study, impacts of timing of rainfall events following carbendazim application on earthworms were investigated. Lumbricus terrestris were maintained in soil columns to which carbendazim and then deionized water (a rainfall substitute) were applied. Carbendazim was applied at 4 kg/ha, the rate recommended in pesticide field trials. Three rainfall regimes were investigated: initial and delayed heavy rainfall 24 h and 6 d after carbendazim application, and frequent rainfall every 48 h. Earthworm mortality and movement of carbendazim through the soil was assessed 14 d after carbendazim application. No detectable movement of carbendazim occurred through the soil in any of the treatments or controls. Mortality in the initial heavy and frequent rainfall was significantly higher (approximately 55%) than in the delayed rainfall treatment (approximately 25%). This was due to reduced bioavailability of carbendazim in the latter treatment due to a prolonged period of sorption of carbendazim to soil particles before rainfall events. The impact of carbendazim application on earthworm surface activity was assessed using video cameras. Carbendazim applications significantly reduced surface activity due to avoidance behavior of the earthworms. Surface activity reductions were least in the delayed rainfall treatment due to the reduced bioavailability of the carbendazim. The nature of rainfall events' impacts on the response of earthworms to carbendazim applications, and details of rainfall events preceding and following applications during field trials should be made at a higher level of resolution than is currently practiced according to standard International Organization for Standardization protocols.

Model selection for the mixed logit with Bayesian estimation

In this paper, the mixed logit (ML) using Bayesian methods was employed to examine willingness-to-pay (WTP) to consume bread produced with reduced levels of pesticides so as to ameliorate environmental quality, from data generated by a choice experiment. Model comparison used the marginal likelihood, which is preferable for Bayesian model comparison and testing. Models containing constant and random parameters for a number of distributions were considered, along with models in ‘preference space’ and ‘WTP space’ as well as those allowing for misreporting. We found: strong support for the ML estimated in WTP space; little support for fixing the price coefficient a common practice advocated and adopted in the environmental economics literature; and, weak evidence for misreporting.

Folsomia candida (Collembola): A "standard" soil arthropod

Folsomia candida Willem 1902, a member of the order Collembola (colloquially called springtails), is a common and widespread arthropod that occurs in soils throughout the world. The species is parthenogenetic and is easy to maintain in the laboratory on a diet of granulated dry yeast. F. candida has been used as a "standard" test organism for more than 40 years for estimating the effects of pesticides and environmental pollutants on nontarget soil arthropods. However. it has also been employed as a model for the investigation of numerous other phenomena such as cold tolerance, quality as a prey item, and effects of microarthropod grazing on pathogenic fungi and mycorrhizae of plant roots. In this comprehensive review. aspects of the life history, ecology, and ecotoxicology of F candida are covered. We focus on the recent literature, especially studies that have examined the effects of soil pollutants on reproduction in F candida using the protocol published by the International Standards Organization in 1999.

Pesticide productivity and transgenic cotton technology: The South African smallholder case

This paper empirically investigates how the productivity of pesticide differs in Bt versus non-Bt technology for South African cotton smallholders, and what the implications for pesticide use levels are in the two technologies. This is accomplished by applying a damage control framework to farm-level data from Makhathini flats, KwaZulu-Natal. Contrary to findings elsewhere, notably China, that farmers over-use pesticides and that transgenic technology benefits farmers by enabling large reductions in pesticide use, the econometric evidence here indicates that non-Bt smallholders in South Africa under-use pesticide. Thus, the main potential contribution of the new technology is to enable them to realise lost productivity resulting from under-use. By providing a natural substitute for pesticide, the Bt technology enables the smallholders to circumvent credit and labour constraints associated with pesticide application. Thus, the same technology that greatly reduces pesticide applications but only mildly affects yields, when used by large-scale farmers in China and elsewhere, benefits South-African smallholder farmers primarily via a yield-enhancing effect.

Folsomia candida (Collembola): A "standard" soil arthropod

Folsomia candida Willem 1902, a member of the order Collembola (colloquially called springtails), is a common and widespread arthropod that occurs in soils throughout the world. The species is parthenogenetic and is easy to maintain in the laboratory on a diet of granulated dry yeast. F. candida has been used as a "standard" test organism for more than 40 years for estimating the effects of pesticides and environmental pollutants on nontarget soil arthropods. However. it has also been employed as a model for the investigation of numerous other phenomena such as cold tolerance, quality as a prey item, and effects of microarthropod grazing on pathogenic fungi and mycorrhizae of plant roots. In this comprehensive review. aspects of the life history, ecology, and ecotoxicology of F candida are covered. We focus on the recent literature, especially studies that have examined the effects of soil pollutants on reproduction in F candida using the protocol published by the International Standards Organization in 1999.

GM crops: a potential for pest mismanagement

In the absence of equivalent research on genetically modified (GM) pest-resistant crops, their impact in pest management can be predicted from experience with traditionally bred varieties which share with GM crops the characteristic that the resistance is based on high expression of a single toxin. Such varieties lead to the rapid selection of tolerant pest strains, damage biological control and induce tolerance to synthetic pesticides. By contrast, polygenic and more broadly based resistant varieties will maintain their resistance for longer, and often synergise beneficially with biological control. The pests also become more susceptible to insecticides, giving the opportunity for applications which are selective in favour of natural enemies. However, although GM crops compare badly with traditional pest-resistant varieties, they compare favourably with insecticides, the technology they are most likely to replace.

Gene transcription in Daphnia magna: effects of acute exposure to a carbamate insecticide and an acetanilide herbicide.

Daphnia magna is a key invertebrate in the freshwater environment and is used widely as a model in ecotoxicological measurements and risk assessment. Understanding the genomic responses of D. magna to chemical challenges will be of value to regulatory authorities worldwide. Here we exposed D. magna to the insecticide methomyl and the herbicide propanil to compare phenotypic effects with changes in mRNA expression levels. Both pesticides are found in drainage ditches and surface water bodies standing adjacent to crops. Methomyl, a carbamate insecticide widely used in agriculture, inhibits acetylcholinesterase, a key enzyme in nerve transmission. Propanil, an acetanilide herbicide, is used to control grass and broad-leaf weeds. The phenotypic effects of single doses of each chemical were evaluated using a standard immobilisation assay. Immobilisation was linked to global mRNA expression levels using the previously estimated 48h-EC(1)s, followed by hybridization to a cDNA microarray with more than 13,000 redundant cDNA clones representing >5000 unique genes. Following exposure to methomyl and propanil, differential expression was found for 624 and 551 cDNAs, respectively (one-way ANOVA with Bonferroni correction, Ppesticides promoted transcriptional changes in energy metabolism (e.g., mitochondrial proteins, ATP synthesis-related proteins), moulting (e.g., chitin-binding proteins, cuticular proteins) and protein biosynthesis (e.g., ribosomal proteins, transcription factors). Methomyl induced the transcription of genes involved in specific processes such as ion homeostasis and xenobiotic metabolism. Propanil highly promoted haemoglobin synthesis and up-regulated genes specifically related to defence mechanisms (e.g., innate immunity response systems) and neuronal pathways. Pesticide-specific toxic responses were found but there is little evidence for transcriptional responses purely restricted to genes associated with the pesticide target site or mechanism of toxicity.

Prospects and challenges for sustainable management of the mango stone weevil, Sternochetus mangiferae (F.) (Coleoptera: Curculionidae) in West Africa: a review

Commercial mango production in Ghana is a relatively young industry faced with several pest problems including the mango stone weevil, Sternochetus mangiferae (F.). There is an urgent need to control this and other pests to facilitate access to the international export market for fresh mango fruits. A literature survey identifies stone weevil control tactics in the areas of host plant resistance, administrative and legislative controls, use of pesticides, biological control, cultural control and quarantine and phytosanitary measures that have been developed in other mango-producing areas. We assess these pest management approaches for their relevance to Ghana and West Africa, with emphasis on the research required for their appropriate, effective and sustainable use in the systems of mango production of the West African sub-region. The importance of processing and value addition technologies, as a means of circumventing the quarantine hurdles of S. mangiferae, is highlighted.

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).