Digital agriculture


Digital agriculture, sometimes known as smart farming or e-agriculture, are tools that digitally collect, store, analyze, and share electronic data and/or information in agriculture. The Food and Agriculture Organization of the United Nations has described the digitalization process of agriculture as the digital agricultural revolution. Other definitions, such as those from the United Nations Project Breakthrough, Cornell University, and Purdue University, also emphasize the role of digital technology in the optimization of food systems.
Digital agriculture includes precision agriculture. Unlike precision agriculture, digital agriculture impacts the entire agri-food value chain before, during, and after on-farm production. Therefore, on-farm technologies like yield mapping, GPS navigation, and tracking, and variable-rate application, fall under the domain of precision agriculture and digital agriculture. On the other hand, digital technologies involved in e-commerce platforms, e-extension services, warehouse receipt systems, blockchain-enabled food traceability systems, tractor rental apps, etc. fall under the umbrella of digital agriculture but not precision agriculture.

Historical context

Digital technologies are changing traditional agricultural practices. The Food and Agriculture Organization of the United Nations has referred to this change as a revolution: "a 'digital agricultural revolution' will be the newest shift that could help ensure agriculture meets the needs of the global population into the future." Other sources refer to this change as "Agriculture 4.0," indicating its role as the fourth major agricultural revolution. Precise dates of the Fourth Agricultural Revolution are unclear. The World Economic Forum announced that the "Fourth Industrial Revolution" will unfold throughout the 21st century, so the beginning of Agriculture 4.0 is often placed around 2000 or shortly thereafter.
Agricultural revolutions denote periods of technological transformation and increased farm productivity. Agricultural revolutions include the First Agricultural Revolution, the Arab Agricultural Revolution, the British/Second Agricultural Revolution, the Scottish Agricultural Revolution, and the Green Revolution/Third Agricultural Revolution. Despite boosting agricultural productivity, past agricultural revolutions left many problems unsolved. For example, the Green Revolution had unintended consequences, like inequality and environmental damage. First, the Green Revolution exacerbated inter-farm and interregional inequality, typically biased toward large farmers with the capital to invest in new technologies. Second, critics say its policies promoted heavy input use and dependence on agrochemicals, which led to adverse environmental effects like soil degradation and chemical runoff. Digital agriculture technologies have the potential to address negative side effects of the Green Revolution.
In some ways, the Digital Agriculture Revolution follows patterns of previous agricultural revolutions. Scholars forecast a further shift away from labor, a slight shift away from capital, and intensified use of human capital, continuing the trend the British Agricultural Revolution started. Also, many predict that social backlash, possibly around the use of artificial intelligence or robots, will arise with the fourth revolution.
In other ways, the Digital Agriculture Revolution is distinct from its predecessors. First, digital technologies will affect all parts of the agricultural value chain, including off-farm segments. This differs from the first three agricultural revolutions, which primarily impacted production techniques and on-farm technologies. Second, a farmer's role will require more data analytics skills and less physical interaction with livestock/fields. Third, although farming has always relied on empirical evidence, the volume of data and the methods of analysis will undergo drastic changes in the digital revolution. For example, smart farm systems continuously monitor animal behavior, giving insight into their activities at all times. Finally, increased reliance on big data may increase the power differential between farmers and information service providers, or between farmers and large value chain actors.

Technology

Digital agriculture encompasses a wide range of technologies, most of which have multiple applications along the agricultural value chain. These technologies include, but are not limited to:
The FAO estimates the world will need to produce 56% more food to feed over 9 billion in 2050. Furthermore, the world faces intersecting challenges like malnutrition, climate change, food waste, and changing diets. To produce a "sustainable food future," the world must increase food production while cutting greenhouse gas emissions and maintaining the land used in agriculture. Digital agriculture can potentially address these challenges by making the agricultural value chain more efficient, equitable, and environmentally sustainable.

Efficiency

Digital technology changes economic activity by lowering the costs of replicating, transporting, tracking, verifying, and searching for data. Due to these falling costs, digital technology can improve efficiency throughout the agricultural value chain.

On-farm efficiency

On-farm, precision agriculture technologies can minimize inputs required for a given yield. For example, variable-rate application technologies can apply precise amounts of water, fertilizer, pesticide, herbicide, etc. A number of empirical studies find that VRA improves input use efficiency. Using VRA alongside geo-spatial mapping, farmers can apply inputs to hyper-localized regions of their farm, sometimes down to the individual plant level. Reducing input use lowers costs and lessens negative environmental impacts. Furthermore, empirical evidence indicates precision agriculture technologies can increase yields. On U.S. peanut farms, guidance systems are associated with a 9% increase in yield, and soil maps are associated with a 13% increase in yield. One study in Argentina found that a precision agriculture approach based on crop physiological principles could result in 54% higher farm output.
Digital agriculture can improve the allocative efficiency of physical capital within and between farms. Often touted as "Uber for tractors," equipment-sharing platforms like Hello Tractor, WeFarmUp, MachineryLink Solutions, TroTro Tractor, and Tringo facilitate farmer rental of expensive machinery, an on-demand model with parallels to Public transport. These platforms are an example of agricultural Fleet management, often coordinated with Fleet management software. By facilitating a market for equipment sharing, telematics technology ensures fewer tractors sit idle and allows owners to make extra income. Furthermore, farmers without the resources to make big investments can better access equipment to improve their productivity; the Fleet digitalization of agriculture provides benefits such as better Fuel management to prevent Gasoline theft, assistance with Stolen vehicle recovery, and even Driver scoring through data collected by an on-board Telematic control unit.
Digital agriculture improves labor productivity through improved farmer knowledge. E-extension allows for farming knowledge and skills to diffuse at low cost. For example, the company Digital Green works with local farmers to create and disseminate videos about agricultural best practices in more than 50 languages. E-extension services can also improve farm productivity via decision-support services on mobile apps or other digital platforms. Using many sources of information, such as weather data, GIS spatial mapping, soil sensor data, and satellite/drone pictures, e-extension platforms can provide real-time recommendations to farmers. For example, the machine-learning-enabled mobile app Plantix, diagnoses crops' diseases, pests, and nutrient deficiencies based on a smartphone photo. In a randomized control trial, Casaburi et al. found that sugarcane growers who received agricultural advice via SMS messages increased yields by 11.5% relative to the control group.
Finally, digital agriculture improves labor productivity through decreased labor requirements. Automation inherent in precision agriculture, from "milking robots on dairy farms to greenhouses with automated climate control," can make crop and livestock management more efficient by reducing required labor.