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International audience; It has been 35 years since Igbozurike and Raza (1983), and rural communities in Nigeria continue to face many of the challenges identified in the ARMTI seminar. Poverty and rural-urban migration remain widespread in Nigeria. Further issues of security and terrorism have also made their way into the array of problems facing rural communities in Nigeria. Therefore, the aim of this paper is to review the issues affecting the quality of life in 21st century rural Nigeria, in order to ascertain what has changed or remained the same since 1983. In achieving the study aim, the parameters used by Igbozurike and Raza (1983) will be linked with current literature on the quality of life in rural Nigeria. The paper will look at the following parameters: socioeconomic indicators, social services and infrastructure, nutritional status, population structure and mobility, institutional frameworks and the role of Agricultural Development Projects (ADPs).

Grasp planning and most specifically the grasp space exploration is still an open issue in robotics. This article presents a data-driven oriented methodology to model the grasp space of a multi-fingered adaptive gripper for known objects. This method relies on a limited dataset of manually specified expert grasps, and uses variational autoencoder to learn grasp intrinsic features in a compact way from a computational point of view. The learnt model can then be used to generate new non-learnt gripper configurations to explore the grasp space. accepted at SYSID 2021 conference

Introduction The scholarly monograph has been compared to the Hapsburg monarchy in that it seems to have been in decline forever! It was in 2002 that Stephen Greenblatt, in his role as president of the US Modern Language Association, urged his membership to recognise what he called a ‘crisis in scholarly publication’. It is easy to forget now that this crisis, as he then saw it, had nothing to do with the rise of digital technologies, e-publishing, or open access. Indeed, it puts his words in...

The European response to COVID-19 has revealed an inconvenient truth. Despite having integrated public health concerns across all its policies – be it agriculture, consumer protection, or security –, the Union cannot directly act to save people’s lives. Only member states can do so. Yet when they adopted unilateral measures to counter the spread of the virus, those proved not only ineffective but also disruptive on vital supply chains, by ultimately preventing the flow of essential goods and people across the Union. These fragmented efforts in tackling cross-border health threats have almost immediately prompted political calls for the urgent creation of a European Health Union. Yet this call raises more questions than answers. With the aim to offer a rigorous and timely blueprint to decision-makers and the public at large, this Special Issue of the European Journal of Risk Regulation contextualizes such a new political project within the broader constitutional and institutional framework of EU public health law and policy. By introducing the Special, this paper argues that unless the envisaged Health Union will tackle the root causes of what prevented the Union from effectively responding to COVID-19 – the divergent health capacity across the Union –, it might fall short of its declared objective of strengthening the EU’resilience for cross-border health threats.

Background: Tsetse flies transmit trypanosomes that cause the debilitating diseases human African trypanosomosis (HAT) or sleeping sickness in humans and animal African trypanosomosis (AAT) or nagana in livestock. The riverine tsetse species Glossina palpalis gambiensis Vanderplank (Diptera: Glossinidae) inhabits riparian forests along river systems in West Africa. The Government of Senegal has embarked on a project to eliminate a population of this tsetse species from the Niayes area with the objective to manage AAT in the area. The project is implemented following an area-wide integrated pest management approach with an SIT component. The SIT can only be successful when the sterile males that are released in the field are of high biological quality, i.e. have the same dispersal capacity, survival and competitiveness as their wild counterparts. To date, sterile tsetse males have been released by air using biodegradable cardboard cartons that were manually dropped from a fixed-wing aircraft or gyrocopter. The cardboard boxes are however expensive, and the system is rather cumbersome to implement. Methods: A new prototype of an automated chilled adult release system (Bruno Spreader Innovation, (BSI™)) for tsetse flies was tested for its accuracy (in counting numbers of sterile males as loaded into the machine), release rate consistency and impact on quality of the released males. The impact of the release process was evaluated on several performance indicators of the irradiated male flies such as flight propensity, survival, mating competitiveness, premating and mating duration, and insemination rate of mated females. Results: The BSI TM release system counted with a consistent accuracy and released homogenously tsetse flies at the lowest motor speed (0.6 rpm). In addition, the chilling conditions (6 ± 1 o C) and the release process (passing of flies through the machine) had no significant negative impact on the males' flight propensity. No significant differences were observed between the control males (no irradiation and no exposure to the release process), irradiated males (no exposure to the release process) and irradiated males exposed to the release process with respect to mating competitiveness, premating period and mating duration. Only survival of irradiated males that were exposed to the release process was reduced, irrespective of whether the males were held with or without feeding. Conclusion: Although the release process had a negative effect on survival of the flies, the data of the experiments indicate that the BSI machine holds promise for use in operational tsetse SIT programmes. The promising results of this study will now need to be confirmed under operational field conditions in West Africa.

AbstractIt is widely agreed that competition regulates plant populations and shapes communities. Many studies have suggested that crop and grassland competition can be used for cost-effective sustainable weed control. However, effective weed management requires a precise knowledge of the effects of agronomic practices and there is a lack of quantitative indicators to compare and predict the success of weed biocontrol by competition.We studied weed abundance dynamics over a 12-year period in crop-grassland rotations (rotation treatments consisted of maize, wheat and barley crops, alternating with temporary grassland maintained for three or six years in the rotation and fertilised with two different levels of nitrogen). In addition to classical statistical analysis of the different aforementioned rotation treatments, we also modelled weed abundance as a function of the crop and grassland competition, expressed here by biomasses harvested in the preceding years.We show that weed abundance decreases over the years in grassland and subsequent crops only if the grassland receives sufficient nitrogen fertiliser. Our model had a much greater explanatory power than the rotation treatments. This model estimates a critical biomass level above which weeds are suppressed in subsequent years, and below which they tend to thrive. This critical biomass level was 24.3 and 4.7 tonnes ha−1 of dry matter for crops and grassland, respectively, highlighting the greater competitiveness of grasslands than of crops. Several clear differences between weed functional groups emerged.Synthesis and applications - This new modelling approach directly links the interannual dynamics of weed populations to current and previous biomass production levels. This approach facilitates the development of environment-friendly weed management strategies and paves the way for comparisons of the competitiveness against weeds of crops and grassland under various pedoclimatic conditions and agronomic practices.

In this paper, we report on the outputs and adoption of the Agrisemantics Working Group of the Research Data Alliance (RDA), consisting of a set of recommendations to facilitate the adoption of semantic technologies and methods for the purpose of data interoperability in the field of agriculture and nutrition. From 2016 to 2019, the group gathered researchers and practitioners at the crossing point between information technology and agricultural science, to study all aspects in the life cycle of semantic resources: Conceptualization, edition, sharing, standardization, services, alignment, long term support. First, the working group realized a landscape study, a study of the uses of semantics in agrifood, then collected use cases for the exploitation of semantics resources a generic term to encompass vocabularies, terminologies, thesauri, ontologies. The resulting requirements were synthesized into 39 hints for users and developers of semantic resources, and providers of semantic resource services. We believe adopting these recommendations will engage agrifood sciences in a necessary transition to leverage data production, sharing and reuse and the adoption of the FAIR data principles. The paper includes examples of adoption of those requirements, and a discussion of their contribution to the field of data science. © 2020 The Author(s). Brandon Whitehead acknowledges with thanks the support of the CABI Development Fund. CABI is an international intergovernmental organization and we gratefully acknowledge the core financial support from our member countries (and lead agencies) including the United Kingdom (Department for International Development), China (Chinese Ministry of Agriculture), Australia (Australian Center for International Agricultural Research), Canada (Agriculture and Agri-Food Canada), Netherlands (Directorate-General for International Cooperation), and Switzerland (Swiss Agency for Development and Cooperation). See https:// www.cabi.org/about-cabi/who-we-work-with/key-donors/ for details. Sophie Aubin, Clement Jonquet, Emna Amdouni, Romain David and Catherine Roussey were supported, in part, by the French National Research Agency (ANR) Data to Knowledge in Agronomy and Biodiversity (D2KAB – www.d2kab.org – ANR-18-CE23-0017). Romain David was partly supported by the EPPN2020 project (H2020 grant N°731013), the EOSC-Life european program (grant agreement N°824087), the ‘Infrastructure Biologie Sante’ PHENOME-EMPHASIS project funded by the French National Research Agency (ANR-11-INBS-0012) and the ‘Programme d’Investissements d’Avenir’.

International audience

Biophysical parameters and more specifically the leaf area index provide an absolute quantification of the biomass of vegetation allowing an overview of the development status of a plant. However, the estimation of the leaf area index requires sophisticated and complex algorithms. This paper proposes a new procedure to estimate the leaf area index using Sentinel-2 data. The proposed procedure relies on the 2-D convolutional network known as the UNet algorithm for regression. The architecture of the UNet algorithm is adapted to account for the processing of large chunks of Sentinel-2 data. Moreover, the adopted procedure makes use of the dropout as a Bayesian approximation at the inference step in order to allow estimating the algorithm confidence interval, which is a very important quality indicator for the production of biophysical parameters. The proposed procedure is validated on multiple Sentinel-2 tiles and years and compared to the multilayer perceptron algorithm and the Sentinel Application Platform of the European Space Agency, also known as SNAP. The UNet and multilayer perceptron algorithms provide coherent results when compared to the results obtained using the SNAP software with an average correlation of 0.99 for both algorithms. However, the UNet algorithmprovides better results in terms of average Euclidean distance, mean squared error and R2 score. One main advantage of the UNet algorithm is the vast reduction of inference time when compared to the SNAP software and the multilayer perceptron regressor. The estimation of the leaf area index of a Sentinel-2 tile at 20 m requires 18 seconds, 13.5 minutes and 15 minutes using the UNet, multilayer perceptron and SNAP, respectively. This advantage allows a massive production of temporal sequences of leaf area index based on Sentinel-2 images that will be ready to use for land cover/use applications. Furthermore, experiments conducted on multiple crop types prove that the proposed approach can serve as a generic procedure to estimate the leaf area index regardless of the crop type.