Open data and interoperability standards : opportunities for animal welfare in extensive livestock systems

Extensive livestock farming constitutes a sizeable portion of agriculture, not only in relation to land use, but in contribution to feeding a growing human population. In addition to meat, it contributes other economically valuable commodities such as wool, hides and other products. The livestock industries are adopting technologies under the banner of Precision Livestock Farming (PLF) to help meet higher production and efficiency targets as well as help to manage the multiple challenges impacting the industries, such as climate change, environmental concerns, globalisation of markets, increasing rules of governance and societal scrutiny especially in relation to animal welfare. PLF is particularly dependent on the acquisition and management of data and metadata and on the interoperability standards that allow data discovery and federation. A review of interoperability standards and PLF adoption in extensive livestock farming systems identified a lack of domain specific standards and raised questions related to the amount and quality of public data which has potential to inform livestock farming. A systematic review of public datasets, which included an assessment based on the principles that data must be findable, accessible, interoperable and reusable (FAIR) was developed. Custom software scripts were used to conduct a dataset search to determine the quantity and quality of domain specific datasets yielded 419 unique Australian datasets directly related to extensive livestock farming. A FAIR assessment of these datasets using a set of non-domain specific, general metrics showed a moderate level of compliance. The results suggest that domain specific FAIR metrics may need to be developed to provide a more accurate data quality assessment, but also that the level of interoperability and reusability is not particularly high which has implications if public data is to be included in decision support tools. To test the usefulness of available public datasets in informing decision support in relation to livestock welfare, a case study was designed and farm animal welfare elements were extracted from Australian welfare standards to guide a dataset search. It was found that with few exceptions, these elements could be supported with public data, although there were gaps in temporal and spatial coverage. The development of a geospatial animal welfare portal including these datasets further explored and confirmed the potential for using public data to enhance livestock welfare.Doctor of Philosoph

An investigation of change in drone practices in broadacre farming environments

The application of drones in broadacre farming is influenced by novel and emergent factors. Drone technology is subject to legal, financial, social, and technical constraints that affect the Agri-tech sector. This research showed that emerging improvements to drone technology influence the analysis of precision data resulting in disparate and asymmetrically flawed Ag-tech outputs. The novelty of this thesis is that it examines the changes in drone technology through the lens of entropic decay. It considers the planning and controlling of an organisation’s resources to minimise harmful effects through systems change. The rapid advances in drone technology have outpaced the systematic approaches that precision agriculture insists is the backbone of reliable ongoing decision-making. Different models and brands take data from different heights, at different times of the day, and with flight of differing velocities. Drone data is in a state of decay, no longer equally comparable to past years’ harvest and crop data and are now mixed into a blended environment of brand-specific variations in height, image resolution, air speed, and optics. This thesis investigates the problem of the rapid emergence of image-capture technology in drones and the corresponding shift away from the established measurements and comparisons used in precision agriculture. New capabilities are applied in an ad hoc manner as different features are rushed to market. At the same time existing practices are subtly changed to suit individual technology capability. The result is a loose collection of technically superior drone imagery, with a corresponding mismatch of year-to-year agricultural data. The challenge is to understand and identify the difference between uniformly accepted technological advance, and market-driven changes that demonstrate entropic decay. The goal of this research is to identify best practice approaches for UAV deployment for broadacre farming. This study investigated the benefits of a range of characteristics to optimise data collection technologies. It identified widespread discrepancies demonstrating broadening decay on precision agriculture and productivity. The pace of drone development is so rapidly different from mainstream agricultural practices that the once reliable reliance upon yearly crop data no longer shares statistically comparable metrics. Whilst farmers have relied upon decades of satellite data that has used the same optics, time of day and flight paths for many years, the innovations that drive increasingly smarter drone technologies are also highly problematic since they render each successive past year’s crop metrics as outdated in terms of sophistication, detail, and accuracy. In five years, the standardised height for recording crop data has changed four times. New innovations, coupled with new rules and regulations have altered the once reliable practice of recording crop data. In addition, the cost of entry in adopting new drone technology is sufficiently varied that agriculturalists are acquiring multiple versions of different drone UAVs with variable camera and sensor settings, and vastly different approaches in terms of flight records, data management, and recorded indices. Without addressing this problem, the true benefits of optimization through machine learning are prevented from improving harvest outcomes for broadacre farming. The key findings of this research reveal a complex, constantly morphing environment that is seeking to build digital trust and reliability in an evolving global market in the face of rapidly changing technology, regulations, standards, networks, and knowledge. The once reliable discipline of precision agriculture is now a fractured melting pot of “first to market” innovations and highly competitive sellers. The future of drone technology is destined for further uncertainty as it struggles to establish a level of maturity that can return broadacre farming to consistent global outcomes

EIP-AGRI Focus Group : Precision farming

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New Technology Tools and Life Cycle Analysis (LCA) Applied to a Sustainable Livestock Production

Agriculture 4.0, a combination of mechanical innovation and information and communication technologies (ICT) using precision farming, omics technologies and advanced waste treatment techniques, can be used to enhance the biological potential of animal and crop productions and reduce livestock gaseous emissions. In addition to animal proteins being excellent nutritional ingredients for the human diet, there is a growing concern regarding the amount of energy spent converting vegetable crops into animal protein and the relevant environmental impacts. Using the value chain analysis derived from the neoclassic production theory extended to industrial processing and the market, the hypothesis to be tested concerns the sustainability and convenience of different protein sources. The methodology implies the use of life cycle analysis (LCA) to evaluate the efficiency of different livestock diet ingredients. The use of feeding products depend upon various factors, including cost reduction, consumer acceptance, incumbent industry response, civil society support, policy consensus, lower depletion of natural resources, improved sustainable agri-food supply chain and LCA. EU policy makers should be aware of these changes in livestock and market chains and act proactively to encourage the use of alternative animal proteins

Development of the production module of an IoT cloud platform for precision livestock farming

Project Work report presented as partial requirement for obtaining the Master’s degree in Information Management, with a specialization in Knowledge Management and Business IntelligenceThe agricultural livestock production sub-sector is characterized by its many geographically dispersed production points. Records are usually collected in handwritten forms that are then transposed to separate Excel sheets by technical assistants. These documents are used to evaluate "one-shot" results at the end of the animal production batches and are filed together with the other documents in the batch. Most of the collected data is usually not organized in a way that allows easy historical analysis and qualitative assessment of economic and technical results. Farmcontrol, the provider of an IoT solution for livestock farm monitoring wanted to somehow respond to current difficulties and add meaningful context to the enormous stream of sensor data that are generated. Its goal was to present relevant real-time insights to livestock producers while helping organize everyday farm tasks and benchmark results. To tackle this challenge Farmcontrol developed a new module for its cloud software that promoted the current work project

Precision livestock farming towards broiler welfare

Due to intensification of the livestock system the ratio between number of broilers and number of farmers have been increasing, making impossible the individualized attention to animals without the use of appropriate tools. Increasingly societal concern on broiler welfare requires farmers to find means to improve animal welfare level. Precision livestock farming (PLF) emerges as a possible solution as it enables the monitoring of animals and its environment 24/7. The present study aims to provide information on how PLF technologies can address broiler welfare and to evaluate reasons for their adoption (or non-adoption) by farmers. The results discussions and analysis are based in the three main pillars that guide the present research: animal welfare, PLF technologies and innovation adoption. Methodologically, the study consists of two different steps. Initially, a systematic review of the literature was carried out to identify which are the PLF technologies related to broiler welfare and to assess how they address birds ́ welfare. Results indicate that most PLF technologies are related to image analysis and mainly focused on broiler health improvements. In the second stage, an empirical research was carried out with broiler farmers in the Southern Brazil. From this survey, information on broiler farmers ́ opinions towards broiler welfare and PLF potentialities were assessed as well as on the determinants and limiting factors for technologies adoption. In general, Brazilian broiler farmers attribute great importance to broiler welfare and perceive the current level of welfare as high; however higher scores for importance than for perception indicate that there is room for welfare improvements. In broiler farmers ́ opinions, providing animals food/water and good housing and health conditions are more important than provide means for the animals to express their natural behaviors. Broiler farmers believe that technologies can help them on welfare improvements and are willing to adopt them even when no extra income come from this. Broiler farmers with less experience, producing chicken grillers, having other farm activity besides broiler production and presenting high beliefs on PLF potentialities regarding animal welfare improvements are more likely to adopt PLF technologies. Major limiting factors for PLF technologies adoption are regarding technology high prices, maintenance requirements and to possible financial consequences with technical problems. It is expected the present thesis to be useful to clarify about PLF technologies opportunities in the broiler farmers point of view and that the results obtained to be valuable to increase PLF adoption, which can potentially improve animal and farmers welfare alike.A intensificação do sistema produtivo aumentou a relação entre o número de frangos de corte e o número de trabalhadores rurais, impossibilitando a atenção individualizada aos animais sem o uso de ferramentas adequadas. Em paralelo, a sociedade pressiona os produtores a encontrarem meios para aumentar o nível bem-estar animal (BEA). Tecnologias da zootecnia de precisão (ZP)surgem como possívelsolução, pois possibilitam o monitoramento dos animais e de seu ambiente de forma contínua. O presente estudo objetiva fornecer informações sobre como as tecnologias da ZP abordam o bem-estar de frangos de corte e avaliar os fatores que influenciam a sua adoção pelos produtores. A discussão e a análise dos resultados baseiam-se em três pilares, a saber: BEA, tecnologias da ZP e adoção de inovações. Metodologicamente, o estudo é composto por duas etapas distintas. Inicialmente, uma revisão sistemática da literatura foi realizada para identificar quais são as tecnologias da ZP relacionadas ao bem-estar de frangos de corte e para avaliar como elas abordam o bem-estar das aves. Os resultados indicam que a maioria das tecnologias está relacionada à análise de imagens e principalmente focada na melhoria da saúde dos frangos. Na segunda etapa, foi realizada uma pesquisa empírica com produtores de frangos de corte no Sul do Brasil. A partir desta pesquisa, foram avaliadas informações sobre as opiniões dos criadores de frangos de corte em relação ao BEA e às potencialidades das tecnologias, bem como sobre os fatores determinantes e limitantes para adoção de tecnologias. Em geral, os avicultores brasileiros atribuem grande importância ao bem-estar dos frangos e consideram alto o nível atual de BEA; no entanto, maiores escores para importância do que para percepção indicam que há espaço para melhorias. Na opinião dos produtores, fornecer aos animais comida/água e boas condições de alojamento e saúde é mais importante do que fornecer meios para que os animais expressem seus comportamentos naturais. Os produtores acreditam que as tecnologias podem ajudá-los a aumentar o BEA e estão dispostos a adotá-las mesmo que isso não resulte em maior renda. Produtores com menos experiência, que produzem grillers, que possuem mais de uma atividade agropecuária e que acreditam nas potencialidades das tecnologias em melhorar o BEA são mais propensos a adotar tecnologias. Os principais fatores limitantes para a adoção de tecnologias são os preços elevados, as exigências de manutenção e as possíveis consequências financeiras com problemas técnicos. Espera-se que a presente tese seja útil para esclarecer sobre as oportunidades da ZP do ponto de vista dos produtores e que os resultados obtidos sejam valiosos para aumentar a adoção de tecnologias, as quais podem melhorar o BEA e o bem-estar dos produtores

BHiveSense: An integrated information system architecture for sustainable remote monitoring and management of apiaries based on IoT and microservices

Precision Beekeeping, a field of Precision Agriculture, is an apiary management strategy based on monitoring honeybee colonies to promote more sustainable resource usage and maximise productivity. The approach related to Precision Beekeeping is based on methodologies to mitigate the stress associated with human intervention in the colonies and the waste of resources. These goals are achieved by supporting the intervention and managing the beekeeper’s timely and appropriate action at the colony’s level. In recent years, the growth of IoT (Internetof-Things) in Precision Agriculture has spurred several proposals to contribute to the paradigm of Precision Beekeeping, built on different technical concepts and with different production costs. This work proposes and describes an information systems architecture concept named BHiveSense, based on IoT and microservices, and different artefacts to demonstrate its concept: (1) a low-cost COTS (Commercial Off-The-Shelf) hive sensing prototype, (2) a REST backend API, (3) a Web application, and (4) a Mobile application. This project delivers a solution for a more integrated and sustainable beekeeping activity. Our approach stresses that by adopting microservices and a REST architecture, it is possible to deal with long-standing problems concerning interoperability, scalability, agility, and maintenance issues, delivering an efficient beehive monitoring system.info:eu-repo/semantics/publishedVersio

Precision technologies to address dairy cattle welfare: focus on lameness, mastitis and body condition

Specific animal-based indicators that can be used to predict animal welfare have been the core of protocols for assessing the welfare of farm animals, such as those produced by the Welfare Quality project. At the same time, the contribution of technological tools for the accurate and realtime assessment of farm animal welfare is also evident. The solutions based on technological tools fit into the precision livestock farming (PLF) concept, which has improved productivity, economic sustainability, and animal welfare in dairy farms. PLF has been adopted recently; nevertheless, the need for technological support on farms is getting more and more attention and has translated into significant scientific contributions in various fields of the dairy industry, but with an emphasis on the health and welfare of the cows. This review aims to present the recent advances of PLF in dairy cow welfare, particularly in the assessment of lameness, mastitis, and body condition, which are among the most relevant animal-based indications for the welfare of cows. Finally, a discussion is presented on the possibility of integrating the information obtained by PLF into a welfare assessment framework.FE1B-06B2-126F | Jos? Pedro Pinto de Ara?joN/