Throughout the ages, technological change – in agrifood systems and elsewhere – has brought gains in productivity, incomes and human well-being. Today, technological solutions are indispensable to feed a continuously growing population in the face of limited agricultural land, unsustainable natural resource use, and increasing shocks and stresses, including climate change. These solutions are needed to make agriculture more productive and sustainable across all its sectors – crop and livestock production, aquaculture, fisheries and forestry – and boost productivity levels within agrifood systems.
Technological change has reduced the need for manual labour in agriculture. This process of increased agricultural productivity and reallocation of labour away from farming is often referred to as agricultural transformation. It is accompanied by investments in agrifood systems and other physical and market infrastructures. Agricultural automation can be a driver of transformation and create new opportunities. In this respect, motorized mechanization has allowed to automate the performing of agricultural operations, while more recently, digital technologies have been creating new opportunities to automate decisions that precede the performing of physical operations.
Common fears that automation leads to growing unemployment, although understandable, are questionable and generally not supported by historical realities. Overall, automation alleviates labour shortages and can make agricultural production more resilient and productive, improve product quality, increase resource-use efficiency, promote decent employment, and enhance environmental sustainability. Negative socioeconomic impacts of agricultural automation – such as increased unemployment – usually occur when automation is not suited to specific local needs. Risks of negative impacts can be countered by facilitating the transition of farm labourers to other job opportunities, by addressing the barriers that prevent poor, small-scale producers from participating in the benefits, and avoiding policies that subsidize automation in contexts of labour abundance and low rural wages.
Agricultural automation: opportunities abound but not without challenges
Any agriculture-related operation consists of three phases: diagnosis, decision-making and performing. Motorized mechanization automates the performing of agricultural operations such as ploughing, seeding, fertilizing, milking, feeding and irrigating. With digital automation technologies, it becomes possible to automate also diagnosis and decision-making. These technologies increase the precision of agricultural operations and allow more efficient use of resources and inputs, with potential gains in environmental sustainability and improved resilience to shocks and stresses. The technological evolution in agriculture can be summarized as a progressive move from manual tools to animal traction, to motorized mechanization, to digital equipment and finally, to robotics with artificial intelligence (AI).
Against this background, the report 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 and/or improving the timeliness, and potentially the precision, of agricultural operations.
Agricultural automation presents many opportunities: it can raise productivity and allow for more careful crop, livestock, aquaculture and forestry management; it can provide better working conditions and improved incomes, and reduce the workload of farming; and it can generate new rural entrepreneurial opportunities. Technologies beyond the farm can further reduce food loss and waste, enhance food safety, and enable value addition.
In many countries, declining rural labour availability – reflected in rising agricultural wages – is a main driver of agricultural automation. Rising consumer concerns about food quality, safety, taste and freshness, together with environmental concerns, are also driving investment in digital technologies. The same applies to challenges in livestock management and animal welfare that derive from growing herd sizes in livestock production.
On the other hand, agricultural automation can carry the risk of exacerbating social inequalities, as larger and more educated producers have greater capacities (e.g. finance, rural infrastructure, skills) to invest in new technologies or to retrain and learn new skills. Women and youth may face particularly significant obstacles, for example, obtaining quality education and training, as well as having access to land, credit and markets. Furthermore, automation is expected to reduce jobs that involve routine tasks, such as planting and harvesting, but increase skilled jobs requiring, for example, secondary education. In countries with a large rural workforce, this shift in employment can risk deepening inequalities. Overcoming these challenges requires reducing barriers to adoption – faced in particular by small-scale producers, women and youth – to ensure that automated solutions become scale-neutral, that is, accessible to all scales of agricultural producers from small to large. This can be achieved through technological innovations that tailor automation to the conditions of small-scale producers. In addition, innovative institutional arrangements, such as shared assets or machinery hire services, can contribute to scale neutrality by connecting equipment owners to small-scale producers who pay a fee for an automation service instead of bearing the cost of buying the machinery.
Reliance of agricultural automation on heavy machinery may also jeopardize environmental sustainability and contribute to deforestation, farmland monoculture, biodiversity loss, land degradation and soil erosion. However, some new advances in automation, especially in small equipment relying on AI, can actually reverse some of these negative impacts.
Understanding the past and looking towards the future of agricultural automation
Motorized mechanization has increased significantly across the world, although reliable global data with broad country coverage exist only for tractors and only up to 2009. The use of tractors as farm power was one of the most influential innovations of the twentieth century; it started in the United States of America between 1910 and 1960 and spread to Japan and Europe after 1955. Later, many Asian and Latin American countries saw considerable progress in terms of adoption of motorized machinery, in addition to the emergence of agricultural machinery manufacturing sectors in some countries. With the rise of rental machinery markets, adoption has become more widespread, allowing access for small-scale producers. However, adoption of tractors has stalled in sub-Saharan Africa in past decades, and light hand-held tools remain the main type of equipment used. Efforts during the 1960s and 1970s to promote mechanization, by providing subsidized machinery to farmers and setting up state farms and public hire companies, proved costly and mostly failed due to governance challenges. This is changing with the re-emergence of agriculture on Africa's development agenda, which has led to a renewed interest in automation.
Since the 1970s, digital technologies have found their way to agriculture through various applications. Initially they were mostly simple precision livestock technologies that facilitated management of individual animals based on electronic identification (EID) – also known as electronic tagging – which then paved the way for milking robots in the 1990s. At the same time, digital tools embodied in mechanization, such as machinery with global navigation satellite systems (GNSS), started to appear and enabled autosteer for tractors, fertilizer spreaders and pesticide sprayers. More recently, disembodied devices such as smartphones are being adopted to inform producers through sensors, high-resolution cameras and various apps embedded in them. These technologies can reduce costs and raise productivity; however, adoption seems to be driven also by non-monetary considerations such as increased flexibility in work schedules and better life quality, as in the case of milking robots.
More advanced still are internet of things (IoT) solutions, used, for example, to monitor and sometimes – at least in part – automate decisions about the care of crops, livestock or fish. Digital services also include shared asset services, which connect owners of equipment (e.g. tractors or drones), and sometimes also operators, with farmers in need of such equipment.
Digital technologies hold potential also for non-mechanized precision agriculture. Methodologies for manual, site-specific fertilizer application were developed a long time ago – variable rate technology (VRT) fertilizer for rice is one example, while a hand-held soil scanner is available in several low-income countries in Africa and Asia. Uncrewed aerial vehicle (UAV) services, commonly known as drones, are also being used by non-mechanized farmers in Asia and Africa; GNSS measures field areas (Asia) and maps field boundaries to establish land tenure (Africa).
The current state of digital automation technologies and robotics in agriculture
Digital automation and robotics applications in agriculture are extremely diverse. Smartphones, with a range of sensors and high-resolution cameras built into them, are the most accessible hardware for producers (especially small-scale producers) in low- and middle-income countries. However, low digital literacy in rural areas, lack of available technologies suited to small-scale producers, and the relatively high cost of these technologies remain the biggest barriers to adoption.
More recently, advanced technologies such as autonomous crop robots (e.g. for harvesting, seeding and weeding) have started to be commercialized. Drones are used to gather information and to automate input application, but their use is often strictly regulated.
In the aquaculture sector, automation is on the rise in response to labour scarcity and high wages. In forests, much of the wood harvesting work is already highly mechanized, and mobile robots, combined with new virtual reality and remote sensing techniques, are paving the way for advanced automatic machines. In addition, remote sensing is being used to monitor deforestation. There is also potential for digitalization and automation in controlled environment agriculture (CEA), which includes indoor agriculture and vertical farming. Greenhouses are the most common form of CEA and by their very nature are amenable to environmental monitoring, control and optimization.
Many technological solutions are already available for adoption in high-, middle- and low-income countries. The direction they take and their rate of adoption are greatly influenced by policy choices. Governments need to facilitate access to these technologies by all – in particular, small-scale producers, women, youth and other vulnerable and marginalized groups – and ensure that they are tailored to the specific context and needs of producers. Ideally, governments should create a level playing field for innovative technologies to enable the private sector to meet demand for automation.
One step at a time: simple motorized mechanization still has a role to play
While digital technologies and robotics promise great things, motorized mechanization can still bring many benefits in terms of enhanced incomes, reduced costs, labour savings and less drudgery. It can free up household labour and enable agricultural households to allocate time away from agriculture to pursue off-farm work. There can also be spillover effects on the wider economy. These may occur through increased demand for non-farm goods and services from agricultural households as their labour productivity improves, as well as the expansion of the non-farm economy as labour moves out of agriculture and into sectors with higher labour productivity. Automation can also improve food safety, thanks to preservation and storage technologies, and make agricultural production more resilient, in particular to climate shocks, by allowing farmers to complete farming activities more rapidly and be more flexible in adapting activities to changing weather.
Consequently, there is still scope for increased use of motorized mechanization in some contexts. In low- and middle-income countries, small-scale producers may benefit more from small machines, such as two-wheel tractors, which represent a less costly option and are more environmentally sustainable than traditional heavy machinery. Recent innovations to tailor motorized machinery to local needs can help countries improve resource-use efficiency and save scarce resources (e.g. water) through innovative synergies between mechanization and other field practices. Agricultural mechanization is therefore high on the policy agenda of many low- and middle-income countries. This is especially the case in sub-Saharan Africa, where agricultural mechanization was neglected for some time, following the earlier failures of state-led mechanization programmes.
Manual technologies and animal traction can also still play a major role in many contexts. Animal traction can be an important source of power for very small, fragmented farm holdings, and advanced manual tools can reduce the need for human power. While less powerful than tractors, both draught animals and advanced manual tools can still help remedy labour shortages and enable higher crop yields and land expansion in many areas. In many cases, they are probably the most viable option to increase power supply.
Thinking ahead: the business case for investing in digital automation
The business case for investing in agricultural technology rests on the potential private gains. The relevant actors – including producers, dealers and service providers – are assumed to make rational decisions that maximize their profits and well-being. Investing in automation technologies entails costs, which tend to increase if technologies are not widely available locally. Suppliers and producers will only make the necessary commitment if the benefits outweigh the costs. For some technologies and in certain conditions, the investment costs may exceed the private benefits; on the other hand, there may be significant benefits for the wider society. In this case, public intervention is needed to align private benefits with the interests of society as a whole.
Given the scarcity of data, 27 case studies, based on interviews with digital automation service providers, were used to shed light on the business case for digital automation in agriculture. The case studies cover all world regions and agricultural production systems (crops, livestock, aquaculture and agroforestry). They represent digital automation solutions at different stages of readiness, with many still in the early stages of development and commercialization. The results reveal only 10 out of the 27 service providers to be profitable and financially sustainable. These ten providers – mostly based in high-income countries – use solutions that are in the mature phase (i.e. widely adopted) and mostly serve large-scale producers. More than one-third of the case studies suggest that farmers are benefiting from these solutions through gains in productivity, efficiency and new market opportunities. Overall, the results indicate that the business case for digital automation technologies is not yet mature, partly because many of these technologies are still in the prototype phase, but also because there are serious barriers to adoption, especially in low- and middle-income countries.
Although the development of many technologies is still in the preliminary stage, several important lessons may be drawn from the case studies. Key factors for adoption are first, awareness of a solution’s ability to perform agricultural operations successfully and second, the ability of farmers to handle the solution. Frequent obstacles to adoption of these technologies are lack of digital literacy, and limited connectivity and availability of other enabling infrastructures, including electricity. These are often compounded by a reluctance to change, generally associated with ageing farming populations. Generational change is indicated as a driver of adoption, with young farmers seen as instrumental in a transformation towards digitalization and advanced automation. Another driver of or barrier to adoption is market conditions – where strong competition among producers drives them to take more risks and adopt new technologies that promise higher productivity and efficiency. Limiting factors can be government regulation of technology imports, absence of policies on data sharing, and insufficient public policies and incentives. On the other hand, if well designed, regulations or public support can be a strong driver of adoption.
Beyond the business case: agricultural automation promises environmental benefits, but more research is needed
In high-income countries, but also in many commercial farms in low- and middle-income countries, agriculture is already highly mechanized, mainly through the use of large machinery. However, this type of mechanization has triggered soil erosion, deforestation and biodiversity loss – all contributing to reduced resilience. Innovations in automation technologies and applied agronomic research can help to explore solutions to address these challenges. For example, motorized mechanization can be tailored to smaller and lighter machinery. Solutions with potential for small-scale producers include small four-wheel and two-wheel tractors. They can minimize biodiversity loss since they do not require substantial field clearing and reshaping. Other small motorized machines, such as power weeders and mobile threshers, may also have benefits in terms of gender equality, because women can operate them easily.
Digital automation technologies that support precision agriculture also present an opportunity for great environmental benefits. They have potential to facilitate the adoption of sustainability practices such as conservation agriculture. There are success stories on the use of computers and IoT to automate greenhouses, leading to savings in water and other inputs. Small swarm robots can lead to environmental benefits by reducing the use of pesticides and herbicides, optimizing the use of other inputs and reducing soil compaction. They are already economically feasible in certain circumstances but more research is needed, especially on their potential for small-scale agriculture, where they should have a comparative advantage over large machinery on farms with irregularly shaped fields.
These environmental benefits are currently location-specific; what is more, many solutions are still in the early stages of development and commercialization. Therefore, more research, including testing, is needed. If both policymakers and producers are fully aware of the benefits of these technologies, investment in their development should expand. Transitioning to renewable energy is also important and can offer fresh opportunities to power automation, especially in remote rural areas, but – once again – research is needed to explore which off-grid renewable energy solutions can most efficiently power each type of machinery.
Agricultural automation has complex impacts on labourers and can also benefit consumers
Measuring the overall employment impacts of agricultural automation is very difficult because it requires large amounts of data tracking all the transformations and the associated reallocation of workers, not only in farm activities, but also upstream and downstream. As agricultural transformation unfolds, people exit agriculture to seek higher-paying jobs, and the share of people employed in agriculture continues to decline. The process reshapes labour supply and demand within entire agrifood systems. When all nodes in agrifood systems are changing simultaneously, it is almost impossible to ascribe labour market and socioeconomic impacts to specific occurrences of agricultural automation.
The possible effects of agricultural automation on farm employment are likely to be diverse. Demand for low-skill labour is likely to decrease as many tasks become automated. Meanwhile, automation boosts the demand for relatively skilled workers. Looking at agrifood systems in their entirety, automation could decrease low-paying seasonal employment on farms but increase higher-paying and less seasonal employment upstream and downstream.
Implications of automation may also differ for different types of farms. For small-scale and subsistence farmers, automation can free up family labour for non-farm employment, but may also allow production to expand. On family commercial farms, it can both free up family labour and reduce demand for hired labour, but if commercial agricultural activities expand as a result of automation, there may be more need for hired workers. Corporate commercial farms are the most automated with a corresponding drop in labour requirements on farms. Nevertheless, even in this case, if automation adoption is spurred by rising wages and scarce labour, it will tend to increase labour productivity and wages without causing unemployment.
If automation occurs where there is an abundance of labour, and is incentivized by subsidies that make automation artificially cheap, there is a serious risk of displacing labour and generating unemployment, with major socioeconomic implications, especially for the poorest and least skilled, who may not easily find employment elsewhere.
Agricultural automation has significant socioeconomic impacts on consumers, because it results in reduced costs of food production. Developments in digital automation may also create new entrepreneurial opportunities beneficial to consumers – for example, by allowing the revival of nutrient-dense heirloom crops that were difficult to automate – and substantially reduce production costs for organic foods, which are currently very labour-intensive.
The agricultural automation process must be inclusive and not leave anybody behind
Agricultural automation must involve those who experience vulnerability, exclusion and marginalization, in particular small-scale producers, pastoralists, small-scale fisherfolk, small-scale foresters and forest communities, agricultural wage-workers, informal microenterprises and workers, landless people, and migrant labourers. Involving women, youth and persons with disabilities is particularly important.
The gender implications of on-farm automation are complex. However, women lag behind men in agricultural technology adoption due to barriers in access to capital, inputs and services (e.g. information, extension, credit, fertilizer), and in some contexts also as a result of cultural norms. Policymakers and local implementation partners need to promote gender-sensitive technology development, dissemination and service provision.
Young farmers appear to be the first to eagerly embrace the process. Agricultural automation promises new types of jobs that require a strong skill set. A solid human capital development and capacity-building agenda, with a focus on youth, must be a priority.
As labour-saving automation expands on farms, not only does the farm workforce become smaller, it becomes more skilled. An important challenge is to facilitate a transition of the agricultural workforce from low-skill manual activities to working with more complex technologies. However, fears that automation will displace millions of farm workers without other job prospects are misplaced. The automation of agricultural jobs, with the consequent evolution of the farm workforce, is a gradual process that differs across localities, crops and farm tasks. The incentives to adopt labour-saving automation are greatest for specific labour-intensive farm tasks that are easily automated at low cost. As some tasks become automated, others will remain labour-intensive.
If the available automation technologies are not scale-neutral, there is a risk that small-scale producers and processors may be pushed out of business because they lack the economies of scale necessary to remain competitive. However, this is not an inevitable outcome of automation in agriculture; the key is for scale-neutral, low-cost automation to become ubiquitous.
In any case, the assumption that limiting automation can preserve agricultural employment and incomes is ill-founded. Indeed, policies to restrict automation will only make farms less competitive and unable to expand their production. To improve wages and working conditions for their workers, farms must become more productive through new technologies. Without labour productivity-enhancing technologies, the prospects of moving poor farm workers out of poverty and food insecurity are dim.
Introducing a roadmap for efficient, sustainable and inclusive agricultural automation: policies, investments and institutions
Agricultural automation has strong potential for contributing to sustainable and inclusive rural development based on intensive, but sustainable, agriculture. However, achieving this potential is not automatic and depends on the socioeconomic context, as well as the policy and institutional environment in which the process of agricultural automation plays out. Whether countries gain or lose from the process depends on how they manage the transition. Countries that build the necessary physical, economic, legal and social infrastructures for digital automation stand to benefit. Countries that ignore the challenge may lose.
Like any technological change, agricultural automation inevitably entails some disruption, bringing benefits but also giving rise to trade-offs. The report proposes a range of possible options regarding policies, institutions, legislation and investments. Together they form a roadmap to ensure that agricultural automation contributes to efficient, productive, sustainable, resilient and inclusive agrifood systems. Some options focus on creating a conducive environment for business in agriculture, in particular regarding investments in automation technologies, and these need to be complemented by regulations and other actions to guarantee they lead to environmental sustainability and climate resilience. Lastly, policies and programmes must be in place to ensure the process works for all, especially marginalized groups, such as women, small-scale producers and youth.
Governments will also need to balance trade-offs between different, and sometimes conflicting, economic, environmental and social objectives. The proposed policies, investments and other public actions – discussed in the next section as part of a roadmap for agricultural automation – do not carry the same weight in all contexts. Governments must prioritize actions based on the challenges faced and their national capacities. One important cross-cutting area for government intervention is that of general services support (GSS), which represents government actions that, without distorting incentives or favouring certain actors over others (or certain sectors within agriculture), create an enabling environment for doing business in agriculture and agrifood systems.
Agriculture-targeted policies and interventions also affect automation uptake
A number of agriculture-specific policies can support automation more directly and help overcome barriers to adoption, especially for small-scale producers. Governments can influence the adoption process through credit policies that directly target agricultural automation. Investment loans are the most common solution for financing automation and they come in various forms, such as contract-based securities, loan guarantee schemes, joint liability groups, leasing, and matching grants. In addition, “smart” targeted subsidies that do not distort markets can play a role. Improved land tenure security is essential, as insecure land tenure restricts producers’ access to credit because they cannot use land titles as collateral. Reducing import duties for machinery, digital equipment and spare parts, and improving customs procedures can also help to lower the transaction costs of automation technologies and spur uptake.
Human capital development is needed to overcome digital illiteracy, for example, through vocational training centres. Knowledge and skills of manufacturers, owners, operators, technicians and farmers must all be strengthened, with youth as a strategic target as they are often the key drivers of automation. Improving agricultural extension and rural advisory services can facilitate adoption. Public extension services have always played an important role in ensuring inclusive agricultural automation. However, the shortage of well-trained extension personnel is a major constraint in most low- and middle-income countries.
While human capital is key for users (i.e. farmers and service providers), it is equally important for those involved in innovations (e.g. researchers and scientists). Governments can fund or conduct applied research and development on automation technologies, in particular aiming at solutions adapted to local needs and those of small-scale producers. An important area of research is impact assessment of precision agriculture solutions in terms of profitability, environmental sustainability and inclusiveness. There needs to be a focus on both small machinery and low-tech digital solutions, such as interactive voice response (IVR), unstructured supplementary service data (USSD) and short message service (SMS). Small machinery may be more suited to local conditions and small farms, while low-tech solutions may more easily reach all farmers at a low cost.
Finally, governments need to develop quality assurance and safety standards, which may be managed by public, market and third-sector organizations. Automation safety laws and regulations need to be based on inclusive consultation with all stakeholders, and must be transparent and supported by measures to ensure compliance by users.
Policies, institutions and investments beyond agrifood systems affect agricultural automation uptake
General policies and investments not specifically aimed at agrifood systems can shape the enabling environment, including infrastructure. Road infrastructure is particularly poor in low-income countries and in most of sub-Saharan Africa. Improving this infrastructure can reduce the transaction costs of access to machinery, spare parts, repairs and fuel, and facilitate the emergence of service markets. Investing in energy infrastructure, for example, through development of off-grid electricity from renewable resources, is equally important as no automation technology works without energy. The availability of renewable energy based on local investments can buffer both shocks in the energy sector and fluctuations in fuel prices.
Improving communication infrastructure and internet connectivity is critical for the proper functioning of agricultural automation. Poor connectivity is widespread even in some rural areas in high-income countries. Policies can grant tax concessions or provide low interest loans for rural internet providers. Legislation can play an important role – promoting public–private–community partnerships to improve connectivity and related infrastructure in rural areas and provide data services and support. Investments should also target associated enabling infrastructures, such as public datasets on weather forecasts and calendars for crop and livestock production.
While physical infrastructure is a primary concern, institutions, macroeconomic conditions and broader institutional capacity are also key to agricultural automation uptake. Improving general credit markets is important; indeed, small-scale producers’ access to credit at affordable interest rates is usually limited, making it impossible to finance automation technologies. It is vital to strengthen institutional and political capacity to guide the development of automation technologies; if, on the other hand, powerful private technology companies get there first, the consequences are potentially negative with spillover effects on wider society. What is more, if transparent national data policies are put in place – including data protection, data sharing and privacy regulations – they themselves can facilitate digital automation. Other enablers are the development of national data infrastructures and the promotion of interoperability, that is, accurate and reliable communication among machines. Finally, exchange rate policies and trade policies can affect automation patterns through the import costs for machinery, digital equipment and spare parts.
If done right, agricultural automation will contribute to inclusive and sustainable agrifood systems
Even assuming countries are able to create a level playing field for the provision by the private sector of innovative technologies, challenges linked to automation will remain. Agricultural automation faces three specific challenges: to not leave marginalized groups behind; to avoid increased unemployment and job displacement; and to prevent environmental damage. Policies can play a role in addressing these challenges and ensuring that automation contributes to an inclusive and sustainable agricultural transformation. Therefore, action by policymakers will most likely be required.
First, governments need to ensure that women, youth and other disadvantaged groups benefit from automation. Policies addressing disadvantages faced by women (e.g. improving women’s land rights or facilitating their access to credit and extension) also help increase women’s access to automation. Public research and development can focus on gender-friendly mechanization technologies tailored to the needs of women. Furthermore, a specific agenda on agricultural automation is needed, targeting rural youth and other disadvantaged groups, ensuring that they acquire the necessary skills to perform the new high-skill jobs associated with automation.
Second, governments need to safeguard against negative effects on employment. Where automation emerges as a response to market forces (e.g. rising rural wages) and replaces unpaid family labour, it is unlikely to generate unemployment. On the other hand, if artificially pushed by public efforts (e.g. through subsidized imports of machinery), it could lead to unemployment, job displacement and lower rural wages. Policymakers should therefore not promote automation before it is actually needed. At the same time, they should not inhibit its adoption based on the claim that it will displace labour and create unemployment. Policy support that provides public or collective goods through GSS is the most likely to allow for a smooth transition towards greater automation without creating unemployment. This includes supporting agricultural research and development and knowledge transfer services.
Third, policies need to ensure that agricultural automation contributes to sustainable and resilient agrifood systems. While motorized mechanization has generated many benefits, it has also produced negative environmental impacts, including biodiversity loss, soil compaction and erosion, and degraded water quality. More advanced digital automation technologies, such as precision agriculture, can minimize or avoid these impacts. Applied technical and agronomic research should explore automation solutions that best fit local agroecological conditions, and governments should facilitate adoption of environmentally friendly automation technologies. Farmers can best choose which automated solutions fit their local agroecological conditions, but governments must create an enabling environment, including information on available technologies.
In conclusion, if care is taken to address the above challenges, agricultural automation can function as a catalyst to support the attainment of the Sustainable Development Goals (SDGs), particularly SDGs 1, 2, 3, 9 and 10. The right mix of technologies – as well as appropriate policies, interventions and investments – will depend on the level of economic development, the institutions in place, local agronomic characteristics, and policymakers’ objectives. It is important that policymakers recognize the context specificity of adoption and assess the particular problems facing an area (e.g. connectivity, inequality, poverty, food insecurity, malnutrition) before combining policy instruments for action. It is up to agricultural producers to choose which technologies to adopt. It is up to governments to provide an enabling environment where innovation can thrive, as well as the necessary incentives to make the adoption process as inclusive as possible.
