Linking integrative plant physiology with agronomy to sustain future plant production

Land use has generally been considered a local environmental issue, but it is becoming a force of global importance. Worldwide changes to forests, farmlands, waterways, and air are being driven by the need to provide food, fiber, water, and shelter to more than six billion people. Global croplands, pastures, plantations, and urban areas have expanded in recent decades, accompanied by large increases in energy, water, and fertilizer consumption, along with considerable losses of biodiversity. Such changes in land use have enabled humans to appropriate an increasing share of the planet's resources, but they also potentially undermine the capacity of ecosystems to sustain food production, maintain freshwater and forest resources, regulate climate and air quality, and ameliorate infectious diseases. We face the challenge of managing trade-offs between immediate human needs and maintaining the capacity of the biosphere to provide goods and services in the long term.

Enhanced agricultural production through innovative breeding technology is urgently needed to increase access to nutritious foods worldwide. Recent advances in CRISPR/Cas genome editing enable efficient targeted modification in most crops, thus promising to accelerate crop improvement. Here, we review advances in CRISPR/Cas9 and its variants and examine their applications in plant genome editing and related manipulations. We highlight base-editing tools that enable targeted nucleotide substitutions and describe the various delivery systems, particularly DNA-free methods, that have linked genome editing with crop breeding. We summarize the applications of genome editing for trait improvement, development of techniques for fine-tuning gene regulation, strategies for breeding virus resistance, and the use of high-throughput mutant libraries. We outline future perspectives for genome editing in plant synthetic biology and domestication, advances in delivery systems, editing specificity, homology-directed repair, and gene drives. Finally, we discuss the challenges and opportunities for precision plant breeding and its bright future in agriculture.

Machine learning has emerged with big data technologies and high-performance computing to create new opportunities for data intensive science in the multi-disciplinary agri-technologies domain. In this paper, we present a comprehensive review of research dedicated to applications of machine learning in agricultural production systems. The works analyzed were categorized in (a) crop management, including applications on yield prediction, disease detection, weed detection crop quality, and species recognition; (b) livestock management, including applications on animal welfare and livestock production; (c) water management; and (d) soil management. The filtering and classification of the presented articles demonstrate how agriculture will benefit from machine learning technologies. By applying machine learning to sensor data, farm management systems are evolving into real time artificial intelligence enabled programs that provide rich recommendations and insights for farmer decision support and action.