The promise of big data in agriculture is very alluring. It is a biological manufacturing system, wrought with all the complexities one might expect from a system that incorporates and relies on the interactions between humans, machines, natural systems, chemistry, biology, weather, and climate to achieve maximum productivity. Present-day agriculture is moving very fast from the green revolution to the evergreen revolution. Basic priorities in present-day agriculture are to keep pace with a rapidly increasing population and global demand for food, secure its own sustainability in an era of chemicalization and industrialization, cope with the fast-emerging technologies, and withstand unprecedented and abrupt changes to the global climate. Deeper insights into the basic biological mechanisms that determine the organism’s physiology as well as that of its progeny could create a better understanding of how biological processes are connected within the networks of genes, proteins, and metabolites that regulate the phonology of the organisms. Such efforts can improve agricultural productivity, address the impacts of climate change on crops, enable adaptations in organisms to counter the effects of stresses (biotic and abiotic), drive evolutionary mechanisms in microbes and other organisms, define pathogen interactions with host plants and animals, govern environmental processes including energy generation, next-generation commercial crops and biofuels, management of global carbon, and remediation of contaminated environmental resources.
Innovations in computational analysis and data integration will be instrumental in overcoming challenges in agriculture. For example, analyses of single-cell omics data need to address the problems associated with handling very large, sparse data sets. Novel challenges also arise in data representation, management, and analysis as well as in attempting to gain insights from multi-assay studies. These challenges exist even when well-established data models and analysis workflows exist for each assay individually. This Research Topic will highlight advances in bioinformatics and data science in agriculture in all its diversity, covering both method developments and their applications in agriculture.
We welcome contributions on themes such as:
• Big data driven agriculture;
• Big data analytics in plant breeding and genomics;
• The use of remote sensing technologies to advance crop productivity;
• Interdisciplinary efforts in high-throughput field phenotyping;
• The potential for digital agriculture to enhance the capacity of plant breeders and agronomists;
• Explorations of how large and comprehensive datasets in plant breeding, genomics, remote sensing, and analytics will benefit agriculture;
• Protocols for the collection and analysis of agricultural big data.
The promise of big data in agriculture is very alluring. It is a biological manufacturing system, wrought with all the complexities one might expect from a system that incorporates and relies on the interactions between humans, machines, natural systems, chemistry, biology, weather, and climate to achieve maximum productivity. Present-day agriculture is moving very fast from the green revolution to the evergreen revolution. Basic priorities in present-day agriculture are to keep pace with a rapidly increasing population and global demand for food, secure its own sustainability in an era of chemicalization and industrialization, cope with the fast-emerging technologies, and withstand unprecedented and abrupt changes to the global climate. Deeper insights into the basic biological mechanisms that determine the organism’s physiology as well as that of its progeny could create a better understanding of how biological processes are connected within the networks of genes, proteins, and metabolites that regulate the phonology of the organisms. Such efforts can improve agricultural productivity, address the impacts of climate change on crops, enable adaptations in organisms to counter the effects of stresses (biotic and abiotic), drive evolutionary mechanisms in microbes and other organisms, define pathogen interactions with host plants and animals, govern environmental processes including energy generation, next-generation commercial crops and biofuels, management of global carbon, and remediation of contaminated environmental resources.
Innovations in computational analysis and data integration will be instrumental in overcoming challenges in agriculture. For example, analyses of single-cell omics data need to address the problems associated with handling very large, sparse data sets. Novel challenges also arise in data representation, management, and analysis as well as in attempting to gain insights from multi-assay studies. These challenges exist even when well-established data models and analysis workflows exist for each assay individually. This Research Topic will highlight advances in bioinformatics and data science in agriculture in all its diversity, covering both method developments and their applications in agriculture.
We welcome contributions on themes such as:
• Big data driven agriculture;
• Big data analytics in plant breeding and genomics;
• The use of remote sensing technologies to advance crop productivity;
• Interdisciplinary efforts in high-throughput field phenotyping;
• The potential for digital agriculture to enhance the capacity of plant breeders and agronomists;
• Explorations of how large and comprehensive datasets in plant breeding, genomics, remote sensing, and analytics will benefit agriculture;
• Protocols for the collection and analysis of agricultural big data.