Financial Evaluation of Different Vaccination Strategies for Controlling the Bluetongue Virus Serotype 8 Epidemic in the Netherlands in 2008

Background: Bluetongue (BT) is a vector-borne disease of ruminants caused by bluetongue virus that is transmitted by biting midges (Culicoides spp.). In 2006, the introduction of BTV serotype 8 (BTV-8) caused a severe epidemic in Western and Central Europe. The principal effective veterinary measure in response to BT was believed to be vaccination accompanied by other measures such as movement restrictions and surveillance. As the number of vaccine doses available at the start of the vaccination campaign was rather uncertain, the Dutch Ministry of Agriculture, Nature and Food Quality and the Dutch agricultural industry wanted to evaluate several different vaccination strategies. This study aimed to rank eight vaccination strategies based on their efficiency (i.e. net costs in relation to prevented losses or benefits) for controlling the bluetongue virus serotype 8 epidemic in 2008 Methodology/Principal Findings: An economic model was developed that included the Dutch professional cattle, sheep and goat sectors together with the hobby farms. Strategies were evaluated based on the least cost - highest benefit frontier, the benefit-cost ratio and the total net returns. Strategy F, where all adult sheep at professional farms in the Netherlands would be vaccinated was very efficient at lowest costs, whereas strategy D, where additional to all adult sheep at professional farms also all adult cattle in the four Northern provinces would be vaccinated, was also very efficient but at a little higher costs. Strategy C, where all adult sheep and cattle at professional farms in the whole of the Netherlands would be vaccinated was also efficient but again at higher costs. Conclusions/Significance: This study demonstrates that a financial analysis differentiates between vaccination strategies and indicates important decision rules based on efficienc

Survival and dispersal of a defined cohort of Irish cattle

An understanding of livestock movement is critical to effective disease prevention, control and prediction. However, livestock movement in Ireland has not yet been quantified. This study has sought to define the survival and dispersal of a defined cohort of cattle born in Co. Kerry during 2000. The cohort was observed for a maximum of four years, from January 1, 2000 to December 31, 2004. Beef and dairy animals moved an average 1.31 and 0.83 times, respectively. At study end, 18.8% of the beef animals remained alive on Irish farms, including 6.7% at the farm-of-birth, compared with 48.6% and 27.7% for dairy animals respectively. Beef animals werae dispersed to all Irish counties, but mainly to Cork, Limerick, Tipperary and Galway. Dairy animals mainly moved to Cork, Limerick, and Tipperary, with less animals going to Galway, Meath and Kilkenny. The four-year survival probability was 0.07 (male beef animals), 0.25 (male dairy), 0.38 (female beef), and 0.72 (female dairy). Although there was considerable dispersal, the number of moves per animal was less than expected

Cost Analysis of Various Low Pathogenic Avian Influenza Surveillance Systems in the Dutch Egg Layer Sector

Background: As low pathogenic avian influenza viruses can mutate into high pathogenic viruses the Dutch poultry sector implemented a surveillance system for low pathogenic avian influenza (LPAI) based on blood samples. It has been suggested that egg yolk samples could be sampled instead of blood samples to survey egg layer farms. To support future decision making about AI surveillance economic criteria are important. Therefore a cost analysis is performed on systems that use either blood or eggs as sampled material. Methodology/Principal Findings: The effectiveness of surveillance using egg or blood samples was evaluated using scenario tree models. Then an economic model was developed that calculates the total costs for eight surveillance systems that have equal effectiveness. The model considers costs for sampling, sample preparation, sample transport, testing, communication of test results and for the confirmation test on false positive results. The surveillance systems varied in sampled material (eggs or blood), sampling location (farm or packing station) and location of sample preparation (laboratory or packing station). It is shown that a hypothetical system in which eggs are sampled at the packing station and samples prepared in a laboratory had the lowest total costs (i.e. J 273,393) a year. Compared to this a hypothetical system in which eggs are sampled at the farm and samples prepared at a laboratory, and the currently implemented system in which blood is sampled at the farm and samples prepared at a laboratory have 6 % and 39 % higher costs respectively

Enhancing the sustainability performance of Agri-Food Supply Chains by implementing Industry 4.0

[EN] In order to enhance the sustainability in the supply chain, its members should define and pursue common objectives in the three dimensions of the sustainability (economic, environmental and social). The Agri-Food Supply Chain (AFSC) is a network of different members such as farmers (producers), processors and distributors (wholesales, retailers.), etc.. In order to achieve the performance objectives of the AFSC, Industry 4.0 technologies can be implemented. The aim of this paper is to present a classification of these technologies according to two criteria: objective to be achieved (environmental or social) specified in the main issues to be covered in each objective and member of the AFSC supply chain where it is implemented. In this work, we focus on technologies that deal with environmental and social sustainability because economic sustainability will depend on the specific characteristics of the business (a supply chain using a specific Industry 4.0 technology may be profitable while others do not).This work has been funded by the Project GV/2017/065 "Development of a decision support tool for the management and improvement of sustainability in supply chains" funded by the Regional Government of Valencia. Authors also acknowledge the Project 691249, RUC-APS: Enhancing and implementing Knowledge based ICT solutions within high Risk and Uncertain Conditions for Agriculture Production Systems.Pérez Perales, D.; Verdecho Sáez, MJ.; Alarcón Valero, F. (2019). Enhancing the sustainability performance of Agri-Food Supply Chains by implementing Industry 4.0. IFIP Advances in Information and Communication Technology. 568:496-503. https://doi.org/10.1007/978-3-030-28464-0_43S496503568Camarinha-Matos, L.M., Fornasiero, R., Afsarmanesh, H.: Collaborative networks as a core enabler of Industry 4.0. In: Camarinha-Matos, L.M., Afsarmanesh, H., Fornasiero, R. (eds.) PRO-VE 2017. IAICT, vol. 506, pp. 3–17. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-65151-4_1Stich, V., Gudergan, G., Zeller, V.: Need and solution to transform the manufacturing industry in the age of Industry 4.0 – a capability maturity index approach. In: Camarinha-Matos, L.M., Afsarmanesh, H., Rezgui, Y. (eds.) PRO-VE 2018. IAICT, vol. 534, pp. 33–42. Springer, Cham (2018). https://doi.org/10.1007/978-3-319-99127-6_3Flores, M., Maklin, D., Golob, M., Al-Ashaab, A., Tucci, C.: Awareness towards Industry 4.0: key enablers and applications for internet of things and big data. In: Camarinha-Matos, L.M., Afsarmanesh, H., Rezgui, Y. (eds.) PRO-VE 2018. IAICT, vol. 534, pp. 377–386. Springer, Cham (2018). https://doi.org/10.1007/978-3-319-99127-6_32Seuring, S., Müller, M.: From a literature review to a conceptual framework for sustainable supply chain management. J. Clean. Prod. 16, 1699–1710 (2008)Prima, W.A., Xing, K., Amer, Y.: Collaboration and sustainable agri-food supply chain: a literature review. In: MATEC (2016). https://doi.org/10.1051/matecconf/20165802004Pérez Perales, D., Alarcón Valero, F., Drummond, C., Ortiz, Á.: Towards a sustainable agri-food supply chain model. The case of LEAF. In: Ortiz, Á., Andrés Romano, C., Poler, R., García-Sabater, J.-P. (eds.) Engineering Digital Transformation. LNMIE, pp. 333–341. Springer, Cham (2019). https://doi.org/10.1007/978-3-319-96005-0_40Savastano, M., Amendola, C., Bellini, F., D’Ascenzo, F.: Contextual impacts on industrial processes brought by the digital transformation of manufacturing: a systematic review. Sustainability 11, 891 (2019)Varela, L., Araújo, A., Ávila, P., Castro, H., Putnik, G.: Evaluation of the relation between lean manufacturing, Industry 4.0, and sustainability. Sustainability 11, 1439 (2019)Bonilla, S.H., Silva, H.R.O., da Silva, M.T., Gonçalves, R.F., Sacomano, J.B.: Industry 4.0 and sustainability implications: a scenario-based analysis of the impacts and challenges. Sustainability 10, 3740 (2018)Bányai, T., Tamás, P., Illés, B., Stankeviciute, Z., Bányai, A.: Optimization of municipal waste collection routing: impact of Industry 4.0 technologies on environmental awareness and sustainability. Int. J. Environ. Res. Public Health. 16, 634 (2019)Lin, K.C., Shyu, J.Z., Ding, K.: A cross-strait comparison of innovation policy under Industry 4.0 and sustainability development transition. Sustainability 9, 786 (2017)Kamble, S.: Sustainable Industry 4.0 framework: a systematic literature review identifying the current trends and future perspectives. In: Process Safety and Environmental Protection Transactions of the Institution of Chemical Engineers, Part B, vol. 117, pp. 408–25. Institution of Chemical Engineers (2018)Franciosi, C., Iung, B., Miranda, S., Riemma, S.: Maintenance for sustainability in the Industry 4.0 context: a scoping literature review. IFAC-Pap. Online 51(11), 903–908 (2018)Bocken, N.M.P., Short, S.W., Rana, P., Evans, S.: A literature and practice review to develop sustainable business model archetypes. J. Clean. Prod. 65, 42–56 (2014)Bourlakis, M., Maglaras, G., Aktas, E., Gallear, D., Fotopoulos, C.: Firm size and sustainable performance in food supply chains: insights from Greek SMEs. Int. J. Prod. Econ. 152, 112–130 (2014)Garbie, I.H.: An analytical technique to model and assess sustainable development index in manufacturing enterprises. Int. J. Prod. Res. 52(16), 4876–4915 (2014)Beier, G., Niehoff, S., Ziems, T., Xue, B.: Sustainability aspects of a digitalized industry - a comparative study from China and Germany. Int. J. Precis. Eng. Manuf. Green Technol. 4, 227–234 (2017)Pérez, D., Verdecho, M.J., Alarcón, F: Industry 4.0 for the development of more sustainable decision support tools for agri-food supply chain management. In: 13rd International Conference on Industrial Engineering and Industrial Management, XXIII, Gijón, Spain (2019)Xiaolin, L., Linnan, Y., Lin, P., Wengfeng, L., Limin, Z.: Procedia engineering county soil fertility information management system based on embedded GIS. Procedia Eng. 29, 2388–2392 (2012)Satyanarayana, G.V.: Wireless sensor based remote monitoring system for agriculture using ZigBee and GPS. In: 2013 (CAC2S), pp. 110–114 (2013)Phillips, A.J., Newlands, N.K., Liang, S.H.L., Ellert, B.H.: Integrated sensing of soil moisture at the field-scale: measuring, modeling and sharing for improved agricultural decision support. Comput. Electron. Agric. 107, 73–88 (2014)Liopa-tsakalidi, A., Tsolis, D., Barouchas, P.: Application of mobile technologies through an integrated management system for agricultural production. Procedia Technol. 8, 165–170 (2013). (Haicta)Yerpude, S., Singhal, T.K.: Impact of Internet of Things (IoT) data on demand forecasting. Indian J. Sci. Technol. 10, 5 (2017)Wolfert, S., Ge, L., Verdouw, C., Bogaardt, M.: Big data in smart farming – a review. Agric. Syst. 153, 69–80 (2017)Castka, P., Balzarova, M.A.: ISO 26000 and supply chains-on the diffusion of the social responsibility standard. Int. J. Prod. Econ. 111(2), 274–286 (2008)Stock, T., Obenaus, M., Kunz, S., Kohl, H.: Industry 4.0 as enabler for a sustainable development: A qualitative assessment of its ecological and social potential. Process. Saf. Environ. 118, 254–267 (2018)Verdecho, M.J., Pérez, D., Alarcón F.: Proposal of a customer-oriented sustainable balanced scorecard for agri-food supply chains. In: 12th International Conference on Industrial Engineering and Industrial Management, Girona, Spain, 12–13 July (2018)Valcour, P.M., Hunter, L.W.: Technology, organizations, and work-life integration. In: Kossek, E.E. Lambert, S.J. (eds.), Work and Life Integration: Organizational, Cultural, and Individual Perspectives, pp. 61–84. Lawrence Erlbaum Associates, Mahwah (2005)Arntz, M., Gregory, T., Zierahn, U.: The risk of automation for jobs in OECD countries: a comparative analysis. In: OECD Social, Employment and Migration Working Papers, no. 189. OECD Publishing, Paris (2016)Grubert, J., Langlotz, T., Zollmann, S., Regenbrecht, H.: Towards pervasive augmented reality: context-awareness in augmented reality. IEEE Trans. Vis. Comput. Graph. 23, 1 (2016)Velthuis, A.G.J.: New Approaches to Food-Safety Economics. Kluwer Academic Publishers, Dordrecht (2003)Sándor, Z.P., Csiszár, C.: Development stages of intelligent parking information systems for trucks. Acta Polytechnica Hungarica 10(4), 161–174 (2013)Scognamiglio, V., Arduini, F., Palleschi, G., Rea, G.: Biosensing technology for sustainable food safety. Trends Analyt. Chem. 62, 1–10 (2014)Brynjolfsson, E., McAfee, A.: The Second Machine Age. Work, Progress, and Prosperity in a Time of Brilliant Technologies. W.W. Norton & Company, London (2014)Smith, A., Caiazza, T.: Automation in everyday life (2017). http://assets.pewresearch.org/wpcontent/uploads/sites/14/2017/10/03151500/PI_2017.10.04_Automation_FINAL.pdfHefferon, K.L.: Nutritionally enhanced food crops; progress and perspectives. Int. J. Mol. Sci. 16, 3895–3914 (2015)Glass, S., Fanzo, J.: Genetic modification technology for nutrition and improving diets: an ethical perspective. Curr. Opin. Biotech. 44, 46–51 (2017)Moe, T.: Perspectives on traceability in food manufacture’. Trends Food Sci. Technol. 9(5), 211–214 (1998)Latino, M., Corallo, A., Menegoli, M.: From Industry 4.0 to Agriculture 4.0: how manage product data in agri-food supply chain for voluntary traceability, a framework proposed. In: 20th International Conference on Food and Environment (ICFE), Rome (2018)Linus, U.O.: Traceability in agriculture and food supply chain: a review of basic concepts, technological implications, and future prospects. J. Food Agric. Environ. 1(1), 101–106 (2003)Maumbe, B.M., Okello, J.: Uses of information and communication technology (ICT) in agriculture and rural development in Sub-Saharan Africa: experiences from South Africa and Kenya. IJICTRDA 1(1), 1–22 (2010)Dlodlo, N., Kalezhi, J.: The internet of things in agriculture for sustainable rural development. In: International Conference on Emerging Trends in Networks and Computer Communications (ETNCC) (2015

Assessment of vector/host contact: comparison of animal-baited traps and UV-light/suction trap for collecting Culicoides biting midges (Diptera: Ceratopogonidae), vectors of Orbiviruses

<p>Abstract</p> <p>Background</p> <p>The emergence and massive spread of bluetongue in Western Europe during 2006-2008 had disastrous consequences for sheep and cattle production and confirmed the ability of Palaearctic <it>Culicoides </it>(Diptera: Ceratopogonidae) to transmit the virus. Some aspects of <it>Culicoides </it>ecology, especially host-seeking and feeding behaviors, remain insufficiently described due to the difficulty of collecting them directly on a bait animal, the most reliable method to evaluate biting rates.</p> <p>Our aim was to compare typical animal-baited traps (drop trap and direct aspiration) to both a new sticky cover trap and a UV-light/suction trap (the most commonly used method to collect <it>Culicoides</it>).</p> <p>Methods/results</p> <p>Collections were made from 1.45 hours before sunset to 1.45 hours after sunset in June/July 2009 at an experimental sheep farm (INRA, Nouzilly, Western France), with 3 replicates of a 4 sites × 4 traps randomized Latin square using one sheep per site. Collected <it>Culicoides </it>individuals were sorted morphologically to species, sex and physiological stages for females. Sibling species were identified using a molecular assay. A total of 534 <it>Culicoides </it>belonging to 17 species was collected. Abundance was maximal in the drop trap (232 females and 4 males from 10 species) whereas the diversity was the highest in the UV-light/suction trap (136 females and 5 males from 15 species). Significant between-trap differences abundance and parity rates were observed.</p> <p>Conclusions</p> <p>Only the direct aspiration collected exclusively host-seeking females, despite a concern that human manipulation may influence estimation of the biting rate. The sticky cover trap assessed accurately the biting rate of abundant species even if it might act as an interception trap. The drop trap collected the highest abundance of <it>Culicoides </it>and may have caught individuals not attracted by sheep but by its structure. Finally, abundances obtained using the UV-light/suction trap did not estimate accurately <it>Culicoides </it>biting rate.</p