From the Neolithic to the present day, agriculture has helped shape human society. Agricultural practices have evolved with technological advances and the resources being produced. It is now well known that contemporary agriculture will face major challenges including growing demographics, climate change and antibiotic resistance in the near future. At the same time, new analytical capabilities have made it possible to process big data at an unprecedented rate. To respond effectively to these challenges, agriculture will have to rely on cutting-edge technologies and new big data analytical approaches. Analyzing this data has already led to better understanding of complex systems, such as the microbiome-host interaction, integration of multi-omics datasets and the development of rigorous analytical approaches and predictive models. The use of integrative systems biology and big data in agriculture is getting more and more attention and this will contribute in fueling a digital shift that will make it possible to meet these great challenges directly.
This research topic aims to cover different uses of massive or complex systems biology data in agriculture, whether relating to animals, plants, soils, or even agricultural practices. Manuscripts connecting multiple (systems) facets of agriculture are particularly welcome, as are those presenting new computational resources (tools, databases, algorithms, etc.).
We welcome contributions in the form of original research, review, mini review, case report, hypothesis and theory, and perspective, both experimental and computational studies making use of systems level approaches that cover, but are not limited to, following themes:
- Integrative systems biology approaches in agriculture.
- Integrative Data and Models in agriculture
- The use of artificial intelligence in agriculture.
- The impacts of climate change on agricultural production.
- The quality and efficiency of the value chain (from farm to fork).
- Adaptation, improvement and understanding of livestock and plants.
- The evolution of pathogens in an agricultural context (including antibiotic resistance).
- Plant-microbiome interactions
From the Neolithic to the present day, agriculture has helped shape human society. Agricultural practices have evolved with technological advances and the resources being produced. It is now well known that contemporary agriculture will face major challenges including growing demographics, climate change and antibiotic resistance in the near future. At the same time, new analytical capabilities have made it possible to process big data at an unprecedented rate. To respond effectively to these challenges, agriculture will have to rely on cutting-edge technologies and new big data analytical approaches. Analyzing this data has already led to better understanding of complex systems, such as the microbiome-host interaction, integration of multi-omics datasets and the development of rigorous analytical approaches and predictive models. The use of integrative systems biology and big data in agriculture is getting more and more attention and this will contribute in fueling a digital shift that will make it possible to meet these great challenges directly.
This research topic aims to cover different uses of massive or complex systems biology data in agriculture, whether relating to animals, plants, soils, or even agricultural practices. Manuscripts connecting multiple (systems) facets of agriculture are particularly welcome, as are those presenting new computational resources (tools, databases, algorithms, etc.).
We welcome contributions in the form of original research, review, mini review, case report, hypothesis and theory, and perspective, both experimental and computational studies making use of systems level approaches that cover, but are not limited to, following themes:
- Integrative systems biology approaches in agriculture.
- Integrative Data and Models in agriculture
- The use of artificial intelligence in agriculture.
- The impacts of climate change on agricultural production.
- The quality and efficiency of the value chain (from farm to fork).
- Adaptation, improvement and understanding of livestock and plants.
- The evolution of pathogens in an agricultural context (including antibiotic resistance).
- Plant-microbiome interactions
