section_path: Production/Agricultural automation
section_name: Agricultural automation
paragraph_text: Agricultural automation
  
  Different definitions exist for agricultural automation and for the variety of tools and technologies that are used to automate production. One view is that agricultural automation refers to autonomous navigation by robots without human intervention. Alternatively it is defined as the accomplishment of production tasks through mobile, autonomous, decision-making, mechatronic devices. However, FAO finds that these definitions do not capture all the aspects and forms of automation, such as robotic milking machines that are static, most motorized machinery that automates the performing of agricultural operations, and digital tools (e.g., sensors) that automate only diagnosis. FAO defines agricultural automation as the use of machinery and equipment in agricultural operations to improve their diagnosis, decision-making or performing, reducing the drudgery of agricultural work or improving the timeliness, and potentially the precision, of agricultural operations.
  
  The technological evolution in agriculture has involved a progressive move from manual tools to animal traction, to motorized mechanization, to digital equipment and finally, to robotics with artificial intelligence (AI). Motorized mechanization using engine power automates the performance of agricultural operations such as ploughing and milking. With digital automation technologies, it also becomes possible to automate diagnosis and decision-making of agricultural operations. For example, autonomous crop robots can harvest and seed crops, while drones can gather information to help automate input application. Precision agriculture often employs such automation technologies. Motorized machines are increasingly complemented, or even superseded, by new digital equipment that automates diagnosis and decision-making. A conventional tractor, for example, can be converted into an automated vehicle allowing it to sow a field autonomously.
  
  Motorized mechanization has increased significantly across the world in recent years, although reliable global data with broad country coverage exist only for tractors and only up to 2009. Sub-Saharan Africa is the only region where the adoption of motorized mechanization has stalled over the past decades.
  
  Automation technologies are increasingly used for managing livestock, though evidence on adoption is lacking. Global automatic milking system sales have increased over recent years, but adoption is likely mostly in Northern Europe, and likely almost absent in low- and middle-income countries. Automated feeding machines for both cows and poultry also exist, but data and evidence regarding their adoption trends and drivers is likewise scarce.
  
  Measuring the overall employment impacts of agricultural automation is difficult because it requires large amounts of data tracking all the transformations and the associated reallocation of workers both upstream and downstream. While automation technologies reduce labor needs for the newly automated tasks, they also generate new labor demand for other tasks, such as equipment maintenance and operation. Agricultural automation can also stimulate employment by allowing producers to expand production and by creating other agrifood systems jobs. This is especially true when it happens in context of rising scarcity of rural labor, as is the case in high-income countries and many middle-income countries. On the other hand, if forcedly promoted, for example through government subsidies in contexts of abundant rural labor, it can lead to labor displacement and falling or stagnant wages, particularly affecting poor and low-skilled workers.