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Digital agriculture refers to tools that digitally collect, store, analyze, and share electronic data and/or information along the agricultural value chain. Other definitions, such as those from the United Nations Project Breakthrough,^[1] Cornell University,^[2]and Purdue University,^[3] also emphasize digital agriculture’s optimization of food systems.

Sometimes known as “smart farming” or “e-agriculture,”^[4] digital agriculture includes (but is not limited to) precision agriculture. Unlike precision agriculture, digital agriculture impacts the entire agri-food value chain — before, during, and after on-farm production.^[5] Therefore, on-farm technologies, like yield mapping, GPS guidance systems, 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

Emerging digital technologies have the potential to change farming beyond recognition.^[6] 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 which could help ensure agriculture meets the needs of the global population into the future.”^[7] Other sources label the change as “Agriculture 4.0,” indicating its role as the fourth major agricultural revolution.^[8] Precise dates of the newest agricultural revolution are unclear. Frankelius considers 2015 as the starting point of the Fourth Agricultural Revolution.^[9] Lombardo et al. date the starting point back to 1997, when the first European conference on precision agriculture took place.^[10] The World Economic Forum announced that the “Fourth Industrial Revolution” (which includes agriculture) will unfold throughout the 21^st century, so perhaps 2000 or shortly thereafter marks the beginning of Agriculture 4.0.^[11] ^[12]

Agricultural revolutions denote periods of technological transformation and increased farm productivity.^[13] 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,^[14] typically biased toward large farmers with the capital to invest in new technologies.^[15] 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.^[16]^[17] 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.^[18]^[19] Also, many predict that social backlash — possibly around the use of artificial intelligence or robots — will arise with the fourth revolution.^[20]^[21]^[22]^[23] Since controversy accompanies every societal transformation, the Digital Agricultural Revolution isn’t new in that respect.

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.^[6]^[24] 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.^[25]^[26]^[27]^[28] 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.^[29]^[30] Finally, increased reliance on big data may increase the power differential between farmers and information service providers,^[6]^[31] or between farmers and large value chain actors (like supermarkets).^[6]

Technology

Digital agriculture employs the Internet of Things, a principle developed by Kevin Ashton that explains how simple mechanical objects can be combined into a network to broaden understanding of that object.^[32] Farmers can adopt artificial intelligence (AI) optimize the use of water and fertilizer. Sensors can measure moisture content and soil fertility in real time. Those readings signal the farmer to optimize the timing of amount of inputs to the crops. Along with AI, soil sensors, and communication networks, digital agriculture uses advanced imaging^[33] to look at temperature gradients, fertility gradients, moisture gradients, and anomalies in a field. Imaging features low financial and ecological cost, and high spatio-temporal resolution. Integration with weather forecasts can prevent overwatering.^[34]

Effects of digital agriculture adoption

The FAO estimates the world will need to produce 56% more food (as compared to 2010, under “business as usual” growth) to feed over 9 billion in 2050.^[35]^[36] Furthermore, the world faces intersecting challenges like malnutrition, climate change, food waste, and changing diets.^[37] To produce a “sustainable food future,” the world must increase food production while cutting GHG emissions and maintaining (or reducing) the land used in agriculture.^[38] Digital agriculture could 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.^[39] Due to these falling costs, digital technology will 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 (VRA) technologies can apply precise amounts of water, fertilizer, pesticide, herbicide, etc. A number of empirical studies find that VRA improves input use efficiency.^[40]^[41]^[42] 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.^[43] 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.^[44]^[45] One study in Argentina found that a precision agriculture approach based on crop physiological principles could result in 54% higher farm output.^[46]

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,^[47]^[48] WeFarmUp,^[49]^[50] MachineryLink Solutions,^[51] TroTro Tractor, and Tringo^[52] facilitate farmer rental of expensive machinery. By facilitating a market for equipment sharing, digital 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.

Digital agriculture improves labor productivity through improved farmer knowledge. E-extension (electronic provision of traditional agricultural extensionservices) 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.^[53]^[54] E-extension services can also improve farm productivity via decision-support services on mobile apps or other digital platforms. Using many sources of information — weather data, GIS spatial mapping, soil sensor data, satellite/drone pictures, etc. — 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.^[55] In a randomized control trial, Casaburi et al. (2014) found that sugarcane growers who received agricultural advice via SMS messages increased yields by 11.5% relative to the control group.^[56]

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”^[57] — can make crop and livestock management more efficient by reducing required labor.^[58]^[59]

Off-farm/market efficiency

Besides streamlining farm production, digital agriculture technologies can make agricultural markets more efficient. Mobile phones, online ICTs, e-commerce platforms, digital payment systems, and other digital agriculture technologies can mitigate market failures and reduce transaction costs throughout the value chain.

Reducing information asymmetry: Price information affects competitive markets’ efficiency because it impacts price dispersion, arbitrage, and farmer and consumer welfare. Since the marginal cost of digitally delivering information approaches zero, digital agriculture has the potential to spread price information. Aker and Fafchamps find that the introduction of mobile phone coverage in Niger reduced spatial price dispersion for agri-food products, especially for remote markets and perishable goods.^[60] Similarly, price information provided by Internet kiosks (“e-choupals”) in India led to an increase in farmers’ net profits as traders lost monopsony power.^[61] Other examples of digital platforms for price information include MFarm^[62] and Esoko.^[63]
Matching buyers and sellers: E-commerce lowers the search costs of matching buyers and sellers, potentially shortening the value chain.^[55] Rather than go through dozens of intermediaries, farmers can sell directly to consumers.^[64]^[65] Market access services can also solve the matching problem without necessarily hosting online transactions. For example, Esoko sends market information (prices for specific commodities, market locations, etc.) to agents and farmers, connecting them to commodity buyers.^[66]^[63] All of these matching platforms help smallholders coordinate with buyers and enter both regional and global value chains.^[67] Finally, it’s important to note that digital technologies can also facilitate matching in financial and input markets, not just producer-to-consumer output sales.
Lowering transaction costs in commercial markets: Digital payments — whether integrated in e-commerce platforms or in mobile money accounts, e-wallets, etc. — reduce transactions costs within agricultural markets. The need for safe, rapid monetary transactions is particularly apparent in rural areas. Plus, digital payments can provide a gateway to bank accounts, insurance, and credit.^[68] Using distributed ledger technologies or smart contracts is another way to reduce trust-related transaction costs in commercial markets.^[69]^[67] Many retail and food companies have partnered with IBM to develop blockchain pilots related to food safety and traceability, and Alibaba is testing blockchain to reduce fraud in agri-food e-commerce between China and Australia/New Zealand.^[67]
Lowering transaction costs in government services: Digital payments can also streamline government delivery of agricultural subsidies. In 2011, the Nigerian Federal Ministry of Agriculture and Rural Development started delivering fertilizer subsidy vouchers to e-wallets on mobile phones; by 2013, they had reached 4.3 million smallholders nationwide.^[70] Compared to the previous program, the e-vouchers cut costs — from 2011 to 2013, the cost per smallholder farmer receiving fertilizer went from US$225-300 to US$22. The e-vouchers also reached more smallholders, increasing from between 600,000-800,000 in 2011 to 4.3 million in 2013.^[70] In the second phase of the program, the Nigerian government developed the Nigerian Agricultural Payment Initiative (NAPI), which distributed PIN-enabled ID cards that hold subsidy information and provide access to loans and grants.^[71] Other e-wallet/e-voucher systems for agricultural subsidies exist or have been piloted in Colombia,^[72]^[73] Rwanda,^[70] Zambia,^[74] Mali, Guinea, and Niger.^[75] Besides reducing subsidy costs, governments can harness digital technology to save time. When Estonia implemented their e-ID and X-Road system, time spent applying for agricultural subsidies decreased from 300 minutes to 45 minutes per person.^[76]

Rarely does one single digital agriculture technology solve one discrete market failure. Rather, systems of digital agriculture technologies work together to solve multifaceted problems. For example, e-commerce solves two efficiency issues: difficulty matching buyers and sellers, especially in rural areas, and the high transaction costs associated with in-person, cash-based trade.

Equity

Digital agriculture shows promise for creating a more equitable agri-food value chain. Because digital technologies reduce transaction costs and information asymmetries, they can improve smallholder farmers’ market access in a number of ways:

Financial inclusion

Digital agriculture technologies can expand farmers’ access to credit, insurance, and bank accounts for a number of reasons. First, digital technology helps alleviate the information asymmetry that exists between farmers and financial institutions. When lenders decide a farmer’s credit ceiling or insurance premium, they’re usually uncertain about what risks the farmer presents. Digital technology reduces the costs of verifying farmers’ expected riskiness. The Kenyan company M-Shwari uses customers’ phone and mobile money records to assess creditworthiness.^[77] Organizations like FarmDrive and Apollo Agriculture incorporate satellite imagery, weather forecasts, and remote sensor data when calculating farmers’ loan eligibility.^[78]^[79] Drone imagery can confirm a farmer’s physical assets or land use^[80] and RFID technology allows stakeholders to monitor livestock,^[81] making it easier for insurers to understand farmers’ riskiness. In all instances, low-cost digital verification reduces lenders’ uncertainty: the questions “will this farmer repay the loan?” and “what risks does this farmer face?” become clearer.

Second, digital technology facilitates trust between farmers and financial institutions. A range of tools create trust, including real-time digital communication platforms and blockchain/distributed ledger technology/smart contracts. In Senegal, a digitalized, supply-chain-tracking system allows farmers to collateralize their rice to obtain the credit necessary for planting. Lenders accept rice as collateral because real-time, digital tracking assures them the product wasn’t lost or damaged in the post-harvest process.^[82]

Market inclusion

Middlemen often extract exorbitant rents from farmers when purchasing their harvest or livestock. Why? First, smallholders in remote areas may be unaware of fair market prices. As a result, middlemen (who typically have better information about market conditions and prices) accrue significant market power and profits.^[83] A study conducted in the central highlands of Peru found that farmers who received market price information via mobile phone SMS increased their sales prices by 13-14% relative to farmers without access to the information.^[84] Second, smallholders produce tiny harvests compared to large producers, so they lack bargaining power with middlemen. If smallholders can aggregate or form a cooperative to sell their products together, they have more leverage. Online platforms and mobile phones can facilitate aggregation, such as Digital Green’s Loop app.^[85] Third, connecting producers with final consumers can eliminate intermediaries’ monopsony power, thereby raising producer profits.^[61] As mentioned above in the efficiency section, e-commerce or other market linkage platforms can connect a small farmer directly to consumers around the world.

Potential inequities resulting from digital agriculture

Though digital technologies can facilitate market access and information flow, there’s no guarantee they won’t exacerbate existing inequalities. Should constraints prevent a range of farmers from adopting digital agriculture, it’s possible that the benefits will only accrue to the powerful.

Large farms: When a digital agriculture technology requires lots of upfront investment, only large farms with sufficient assets and credit access will adopt it.^[55] For example, large farms are most likely to adopt precision agriculture technologies because of high costs.^[86]
Digital divide: The uneven access to information and communication technologies (ICTs) may lead to uneven adoption of — and thereby uneven gains from — digital agriculture. When digital technologies require specific skills, benefits may accrue to digitally literate farmers positioned to take advantage of such opportunities.^[87]^[88]^[89]
Gender: Given gender-based disparities in ICT access^[90]^[53] and the gender gap in agribusiness value chains,^[91] men seem more likely to adopt digital agriculture.^[55] Therefore, digital technologies could perpetuate gender inequalities in the agricultural sector.^[92]
Unskilled labor: Advances in on-farm productivity, particularly through digitized automation and precision agriculture, may threaten low-skilled jobs.^[14] According to the OECD, agriculture will be one of the sectors most affected by automation^[93] and McKinsey Global Institute projects that automation will displace 15% of agricultural workers in Mexico and 30% in Germany.^[94]
Agribusinesses and service providers: Increased reliance on big data may increase the power differential between agribusinesses/information service providers and farmers.^[6]^[31] If smallholders lack access to and/or control of their data, they may lose bargaining power vis-à-vis large value chain actors (like supermarkets) and data collectors.^[95]

Environment

Boosting natural resource efficiency is the “single most important need for a sustainable food future,” according to the World Resource Institute.^[38] As mentioned in the on-farm efficiency section, precision farming — including variable rate nutrient application, variable rate irrigation, machine guidance, and variable rate planting/seeding — could minimize use of agricultural inputs for a given yield.^[96]^[97] This could mitigate resource waste and negative environmental externalities,^[98] like greenhouse gas (GHG) emissions,^[99] soil erosion,^[100] and fertilizer runoff.^[43] For example, Katalin et al. 2014 estimate that switching to precision weed management could save up to 30,000 tons of pesticide in the EU-25 countries.^[101] González-Dugo et al. 2013 found that precision irrigation of a citrus orchard could reduce water use by 25 percent while maintaining a constant yield.^[102] Basso et al. 2012 demonstrated that variable-rate application of fertilizer can reduce nitrogen application and leaching without affecting yield and net return.^[103]

However, precision agriculture could also accelerate farms’ depletion of natural resources because of a rebound effect; increasing input efficiency does not necessarily lead to resource conservation.^[104] Also, by changing economic incentives, precision agriculture may hinder environmental policies’ effectiveness: “Precision agriculture can lead to higher marginal abatement costs in the form of forgone profits, decreasing producers' responsiveness to those policies."^[104] In other words, holding pollution constant, precision agriculture allows a farmer to produce more output — thus, abatement becomes more expensive.

Off-farm, digital agriculture has the potential to improve environmental monitoring and food system traceability. The monitoring costs of certifying compliance with environmental, health, or waste standards are falling because of digital technology.^[105] For example, satellite and drone imagery can track land use and/or forest cover; distributed ledger technologies can enable trusted transactions and exchange of data; food sensors can monitor temperatures to minimize contamination during storage and transport.^[55] Together, technologies like these can form digital agriculture traceability systems, which allow stakeholders to track agri-food products in near-real-time. Digital traceability yields a number of benefits, environmental and otherwise:

Reduced food waste: Of all the food calories produced in a year, 25% are wasted between on-farm production and consumers.^[38] Traceability systems facilitate better identification of supply-side weaknesses — where is food lost downstream of the farm, and how much is wasted?^[106] Emerging digital innovations, such as milk cartons that track milk from “farm to fridge,”^[107] can address demand-side waste by providing consumers with more accurate expiration dates.
Consumer trust: Ensuring food safety, quality, and authenticity has become an important regulatory requirement in high-income countries. Use of RFID tags and blockchain technologies to certify agri-food products’ characteristics could provide near-real-time quality signals to consumers.^[67]
Improved producer welfare: Producers who can leverage environmental certification could sell their products at a premium,^[43]^[108] because blockchain technologies could enable greater trust in labels like “sustainable,” “organic” or “fair trade.”^[67]

Barriers

The four main barriers to digital agriculture include development costs, security, case studies and employment.

Development costs include gathering long-term data to support the cost effectiveness of digital technology. A 2017 trial in the UK reported that for an 80-acre farm, costs were approximately $15 per acre per year for soil scanning and UAS monitoring.^[109] Farmers pay for this service; they require a significant output increase to motivate them.

For digital agriculture, the biggest security fear is that hackers could launch agricultural warfare. This could take many forms: whether it be systems shutting off, giving incorrect readings, or contamination of fertilizer or water.^[34] If farming comes to rely on hackable systems, agricultural warfare could be launched on a massive scale.

Case studies, such as the 2017 UK trial, are a way to test developing ideas in a real-life setting.^[110]

As of 2013, some one billion people were employed in agriculture. Introducing technology would potentially leave some workers out.^[111]

Sustainable Development Goals

According to Project Breakthrough, digital agriculture can help advance the United Nations Sustainable Development Goals by providing farmers with more real-time information about their farms, allowing them to beter decisions. Technology allows for improved crop production by understanding soil health. It allows farmers to use fewer pesticides on their crops. Soil and weather monitoring, reduces water waste. Digital agriculture ideally leads to economic growth by allowing farmers to get the most production out of their land. The loss of agricultural jobs can be offset by new job opportunities in manufacturing and maintaining the necessary technology for the work. Digital agriculture also enables individual farmers to work in concert, collecting and sharing data using technology.^[34]

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^ ^a ^b ^c ^d ^e Jouanjean, Marie-Agnes (2019-02-15). "Digital Opportunities for Trade in the Agriculture and Food Sectors". OECD Food, Agriculture, and Fisheries Papers, No. 122. doi:10.1787/91c40e07-en.
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 {{short description|Category of technologies in agriculture}}{{pov|date=January 2019}}{{Orphan|date=December 2018}}
+'''Digital agriculture''' refers to tools that digitally collect, store, analyze, and share electronic data and/or information along the agricultural value chain. Other definitions, such as those from the United Nations Project Breakthrough,<ref>{{Cite web|url=http://breakthrough.unglobalcompact.org/disruptive-technologies/digital-agriculture/|title=Digital Agriculture: feeding the future|website=Project Breakthrough|language=en|access-date=2019-07-25}}</ref> Cornell University,<ref>{{Cite web|url=https://cuaes.cals.cornell.edu/digital-agriculture/|title=Digital Agriculture {{!}} Cornell University Agricultural Experiment Station|website=cuaes.cals.cornell.edu|access-date=2019-07-25}}</ref>and Purdue University,<ref>{{Cite web|url=https://ag.purdue.edu/digitalag|title=Home|website=Purdue University Digital Agriculture|language=en-US|access-date=2019-07-25}}</ref> also emphasize digital agriculture’s optimization of food systems.
-'''Digital agriculture''' or '''precision agriculture''' combines technology with detailed data to improve harvests while using fewer resources. Digital agriculture has the potential to transform how farmers produce a variety of crops.
+Sometimes known as “smart farming” or “e-agriculture,”<ref>{{Cite web|url=http://www.oecd.org/agriculture/topics/technology-and-digital-agriculture/|title=Technology and digital in agriculture - OECD|website=www.oecd.org|access-date=2019-07-25}}</ref> digital agriculture includes (but is not limited to) [[precision agriculture]]. Unlike precision agriculture, digital agriculture impacts the entire agri-food value chain — before, during, and after on-farm production.<ref>{{Cite journal|last=Shepherd, Turner, Small, and Wheeler|first=|date=2018|title=Priorities for science to overcome hurdles thwarting the full promise of the ‘digital agriculture’ revolution|url=https://doi.org/10.1002/jsfa.9346|journal=Journal of the Science of Food and Agriculture|volume=|pages=|via=}}</ref> Therefore, on-farm technologies, like yield mapping, GPS guidance systems, 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 ==
+Emerging digital technologies have the potential to change farming beyond recognition.<ref name=":1">{{Cite journal|last=Wolfert|first=Sjaak|last2=Ge|first2=Lan|last3=Verdouw|first3=Cor|last4=Bogaardt|first4=Marc-Jeroen|date=2017-05-01|title=Big Data in Smart Farming – A review|url=http://www.sciencedirect.com/science/article/pii/S0308521X16303754|journal=Agricultural Systems|volume=153|pages=69–80|doi=10.1016/j.agsy.2017.01.023|issn=0308-521X}}</ref> 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 which could help ensure agriculture meets the needs of the global population into the future.”<ref>FAO 2019. “Digital technology in agriculture and rural areas: Briefing paper.” ''Food and Agriculture Organization of the United Nations:'' Rome. <nowiki>http://www.fao.org/3/ca4887en/ca4887en.pdf</nowiki>.</ref> Other sources label the change as “Agriculture 4.0,” indicating its role as the fourth major agricultural revolution.<ref>{{Cite journal|last=Rose and Chilvers|first=|date=2018|title=Agriculture 4.0: Responsible Innovation in an Era of Smart Farming|url=https://doi.org/10.3389/fsufs.2018.00087|journal=Frontiers in Sustainable Food Systems|volume=|pages=|via=}}</ref> Precise dates of the newest agricultural revolution are unclear. Frankelius considers 2015 as the starting point of the Fourth Agricultural Revolution.<ref>Lejon and Frankelius 2015 — as cited in Frankelius, Norman, and Johansen 2017. “Agricultural innovation and the role of institutions: Lessons from the game of drones.” ''Journal of Agricultural and Environmental Ethics'', pp. 1-27. <nowiki>https://link.springer.com/article/10.1007/s10806-017-9703-6</nowiki>.</ref> Lombardo et al. date the starting point back to 1997, when the first European conference on precision agriculture took place.<ref>Lombardo, Sarri, Corvo, and Vieri 2017. "Approaching the Fourth Agricultural Revolution: Analysis of Needs for the Profitable Introduction of Smart Farming in Rural Areas." ''Proceedings of the 8<sup>th</sup>International Conference on Information and Communication Technologies in Agriculture, Food, and Environment (HAICTA 2017).''Chiana, Greece, 21-24 September 2017. <nowiki>https://flore.unifi.it/retrieve/handle/2158/1112565/296930/360.pdf</nowiki>.</ref> The World Economic Forum announced that the “Fourth Industrial Revolution” (which includes agriculture) will unfold throughout the 21<sup>st</sup> century, so perhaps 2000 or shortly thereafter marks the beginning of Agriculture 4.0.<ref>{{Cite book|title=The Fourth Industrial Revolution|last=Schwab|first=Karl|publisher=Crown Publishing Group|year=2018|isbn=|location=|pages=}}</ref> <ref>Schwab 2018. ''The Fourth Industrial Revolution''. Encyclopedia Britannica. <nowiki>https://www.britannica.com/topic/The-Fourth-Industrial-Revolution-2119734</nowiki>.</ref>
+Agricultural revolutions denote periods of technological transformation and increased farm productivity.<ref>Allen 2003. “Tracking the agricultural revolution in England.” ''Economic History Review,''Volume 52, Issue 2, pp. 209-235. <nowiki>https://onlinelibrary.wiley.com/doi/abs/10.1111/1468-0289.00123</nowiki>.</ref> Agricultural revolutions include the [[Neolithic Revolution|First Agricultural Revolution]], the [[Arab Agricultural Revolution]], the [[British Agricultural Revolution|British/Second Agricultural Revolution]], the [[Scottish Agricultural Revolution]], and the [[Green Revolution|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,<ref name=":2">{{Cite journal|last=Freebairn|first=|date=1995|title=Did the Green Revolution Concentrate Incomes? A Quantitative Study of Research Reports|url=https://www.utsc.utoronto.ca/~kmacd/IDSC10/Readings/Readings/impact%20assessment/g-r.pdf.|journal=World Development|volume=23(2)|pages=265-279|via=}}</ref> typically biased toward large farmers with the capital to invest in new technologies.<ref>{{Cite journal|last=Junankar|first=P. N.|date=1975|title=Green Revolution and Inequality|url=https://www.jstor.org/stable/4536986|journal=Economic and Political Weekly|volume=10|issue=13|pages=A15–A18|issn=0012-9976}}</ref> 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.<ref>Pingali 2012. “Green Revolution: Impacts, limits, and the path ahead.” ''Proceedings of the National Academy of Sciences of the United States of America''. 2012 Jul 31; 109(31): 12302–12308. Published online 2012 Jul 31. <nowiki>https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3411969/9</nowiki>.</ref><ref>{{Cite news|url=http://www.fao.org/english/newsroom/focus/2003/gmo2.htm|title=Crop breeding: the Green Revolution and the preceding millennia|last=Food and Agriculture Organization of the United Nations|first=|date=|work=FAO Newsroom|access-date=}}</ref> 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.<ref>{{Cite journal|last=Struik and Kuyper|first=|date=2017|title=Sustainable intensification in agriculture: the richer shade of green. A review|url=https://link.springer.com/article/10.1007/s13593-017-0445-7.|journal=Agronomy for Sustainable Development|volume=|pages=37-39|via=}}</ref><ref>Bronson 2018. “Smart Farming: Including Rights Holders for Responsible Agricultural Innovation.” ''Technology Innovation Management Review'', Vol. 8, Issue 2. <nowiki>https://link.springer.com/article/10.1007/s13593-017-0445-7</nowiki>.</ref> Also, many predict that social backlash — possibly around the use of artificial intelligence or robots — will arise with the fourth revolution.<ref>Rose and Chilvers 2018. “Agriculture 4.0: Broadening Responsible Innovation in an Era of Smart Farming.” ''Frontiers in Sustainable Food Systems''. <nowiki>https://doi.org/10.3389/fsufs.2018.00087</nowiki>.</ref><ref>Macnaghten, P. (2015). “A Responsible Innovation Governance Framework for GM Crops,” in Governing Agricultural Sustainability,” eds P. Macnaghten, and S. W. Carro-Ripalda (London: Earthscan from Routledge), 225–239. doi: 10.4324/9781315709468-19. </ref><ref>Macnaghten, P., and Chilvers, J. (2014). The future of science governance: publics, policies, practices. Environ. Plann. C 32, 530–548. doi: 10.1068/c1245j. </ref><ref>Hartley, S., Gillund, F., van Hove, L., and Wickson, F. (2016). Essential features of responsible governance of agricultural biotechnology. PLoS Biol. 14:e1002453. doi: 10.1371/journal.pbio.1002453.</ref> Since controversy accompanies every societal transformation, the Digital Agricultural Revolution isn’t new in that respect.
+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.<ref name=":1" /><ref>{{Cite journal|last=Eastwood|first=C.|last2=Klerkx|first2=L.|last3=Ayre|first3=M.|last4=Dela Rue|first4=B.|date=2017-12-26|title=Managing Socio-Ethical Challenges in the Development of Smart Farming: From a Fragmented to a Comprehensive Approach for Responsible Research and Innovation|url=http://link.springer.com/10.1007/s10806-017-9704-5|journal=Journal of Agricultural and Environmental Ethics|language=en|doi=10.1007/s10806-017-9704-5|issn=1187-7863}}</ref> 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.<ref>{{Cite journal|last=Carolan|first=Michael|date=2017|title=Publicising Food: Big Data, Precision Agriculture, and Co-Experimental Techniques of Addition: Publicising Food|url=http://doi.wiley.com/10.1111/soru.12120|journal=Sociologia Ruralis|language=en|volume=57|issue=2|pages=135–154|doi=10.1111/soru.12120|via=}}</ref><ref>{{Cite journal|last=Driessen|first=Clemens|last2=Heutinck|first2=Leonie F. M.|date=2015|title=Cows desiring to be milked? Milking robots and the co-evolution of ethics and technology on Dutch dairy farms|url=http://link.springer.com/10.1007/s10460-014-9515-5|journal=Agriculture and Human Values|language=en|volume=32|issue=1|pages=3–20|doi=10.1007/s10460-014-9515-5|issn=0889-048X|via=}}</ref><ref>{{Cite journal|last=Eastwood|first=C.|last2=Klerkx|first2=L.|last3=Ayre|first3=M.|last4=Dela Rue|first4=B.|date=2017-12-26|title=Managing Socio-Ethical Challenges in the Development of Smart Farming: From a Fragmented to a Comprehensive Approach for Responsible Research and Innovation|url=http://link.springer.com/10.1007/s10806-017-9704-5|journal=Journal of Agricultural and Environmental Ethics|language=en|doi=10.1007/s10806-017-9704-5|issn=1187-7863}}</ref><ref>{{Cite journal|last=Holloway|first=Lewis|last2=Bear|first2=Christopher|date=2017|title=Bovine and human becomings in histories of dairy technologies: robotic milking systems and remaking animal and human subjectivity|url=https://www.cambridge.org/core/product/identifier/S2058850X17000029/type/journal_article|journal=BJHS Themes|language=en|volume=2|pages=215–234|doi=10.1017/bjt.2017.2|issn=2058-850X}}</ref> 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.<ref>{{Cite journal|last=Bronson|first=|date=2018|title=Smart Farming: Including Rights Holders for Responsible Agricultural Innovation|url=https://link.springer.com/article/10.1007/s13593-017-0445-7.|journal=Technology Innovation Management Review|volume=8(2)|pages=|via=}}</ref><ref>{{Cite journal|last=Wolf, S.A. and Wood, S.D.|first=|date=1997|title=Precision farming: environmental legitimation, commodification of information, and industrial coordination|url=|journal=Rural Sociology|volume=62|pages=180-206|doi=10.1111/j.1549-0831.1997.tb00650|via=Elsevier Science Direct}}</ref> Finally, increased reliance on big data may increase the power differential between farmers and information service providers,<ref name=":1" /><ref name=":3">{{Cite journal|last=Carbonell|first=|date=2016|title=The ethics of big data in agriculture|url=https://policyreview.info/node/405/pdf.|journal=Internet Policy Review: Journal on Internet Regulation|volume=5(1)|pages=|via=}}</ref> or between farmers and large value chain actors (like supermarkets).<ref name=":1" />
 == Technology ==
-Digital agriculture employs the [[Internet of Things]], a principle developed by [[Kevin Ashton]] that explains how simple mechanical objects can be combined into a network to broaden understanding of that object.<ref>{{Cite web|url=https://www.smithsonianmag.com/innovation/kevin-ashton-describes-the-internet-of-things-180953749/|title=Kevin For example, Ashton Describes "The Internet of Things"|last=Gabbai|first=Arik|website=Smithsonian|language=en|access-date=2018-12-09}}</ref> Farmers can adopt [[artificial intelligence]] (AI) optimize the use of water and fertilizer. Sensors can measure moisture content and [[Soil fertility|soil fertility]] in real time. Those readings signal the farmer to optimize the timing of amount of inputs to the crops. Along with AI, [https://cropwatch.unl.edu/ssm/sensing soil sensors], and [[Communication networks|communication networks]], digital agriculture uses advanced imaging<ref name="uav-zhang-kovacs">{{cite journal|last1=Zhang|first1=Chunhua|last2=Kovacs|first2=John M.|title=The application of small unmanned aerial systems for precision agriculture: a review|url=https://link.springer.com/article/10.1007/s11119-012-9274-5|journal=Precision Agriculture|volume=13|issue=6|pages=693–712|date=2012-07-31|access-date=2019-02-28}}</ref> to look at temperature gradients, fertility gradients, moisture gradients, and anomalies in a field. Imaging features low financial and ecological cost, and high spatio-temporal resolution. Integration with weather forecasts can prevent overwatering.<ref name=":0">{{Cite web|url=http://breakthrough.unglobalcompact.org/disruptive-technologies/digital-agriculture/|title=Digital Agriculture: feeding the future|website=Project Breakthrough|language=en|access-date=2018-12-10}}</ref>
+Digital agriculture employs the [[Internet of Things]], a principle developed by [[Kevin Ashton]] that explains how simple mechanical objects can be combined into a network to broaden understanding of that object.<ref>{{Cite web|url=https://www.smithsonianmag.com/innovation/kevin-ashton-describes-the-internet-of-things-180953749/|title=Kevin For example, Ashton Describes "The Internet of Things"|last=Gabbai|first=Arik|website=Smithsonian|language=en|access-date=2018-12-09}}</ref> Farmers can adopt [[artificial intelligence]] (AI) optimize the use of water and fertilizer. Sensors can measure moisture content and [[soil fertility]] in real time. Those readings signal the farmer to optimize the timing of amount of inputs to the crops. Along with AI, [https://cropwatch.unl.edu/ssm/sensing soil sensors], and [[communication networks]], digital agriculture uses advanced imaging<ref name="uav-zhang-kovacs">{{cite journal|last1=Zhang|first1=Chunhua|last2=Kovacs|first2=John M.|title=The application of small unmanned aerial systems for precision agriculture: a review|url=https://link.springer.com/article/10.1007/s11119-012-9274-5|journal=Precision Agriculture|volume=13|issue=6|pages=693–712|date=2012-07-31|access-date=2019-02-28}}</ref> to look at temperature gradients, fertility gradients, moisture gradients, and anomalies in a field. Imaging features low financial and ecological cost, and high spatio-temporal resolution. Integration with weather forecasts can prevent overwatering.<ref name=":0">{{Cite web|url=http://breakthrough.unglobalcompact.org/disruptive-technologies/digital-agriculture/|title=Digital Agriculture: feeding the future|website=Project Breakthrough|language=en|access-date=2018-12-10}}</ref>
+== Effects of digital agriculture adoption ==
+The FAO estimates the world will need to produce 56% more food (as compared to 2010, under “business as usual” growth) to feed over 9 billion in 2050.<ref>FAO 2017. ''The Future of Food and Agriculture: Trends and Challenges''. Rome. Accessed July 11, 2019. <nowiki>http://www.fao.org/3/a-i6583e.pdf</nowiki>.</ref><ref>{{Cite web|url=https://www.wri.org/blog/2018/12/how-sustainably-feed-10-billion-people-2050-21-charts.|title=Insights: WRI's Blog|website=World Resources Institute|language=en|access-date=2019-07-26}}</ref> Furthermore, the world faces intersecting challenges like malnutrition, climate change, food waste, and changing diets.<ref>{{Cite journal|last=Godfray, Beddington, Crute, Haddad, Lawrence, Muir, Pretty, Robinson, Thomas, and Toulmin|first=|date=2010|title=Food Security: The Challenge of Feeding 9 Billion People|url=https://science.sciencemag.org/content/327/5967/812|journal=Science|volume=|pages=|via=}}</ref> To produce a “sustainable food future,” the world must increase food production while cutting GHG emissions and maintaining (or reducing) the land used in agriculture.<ref name=":4">{{Cite web|url=https://www.wri.org/publication/creating-sustainable-food-future-final-report|title=Creating a Sustainable Food Future|date=2019-07-19|website=World Resources Institute|language=en|access-date=2019-07-26}}</ref> Digital agriculture could 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.<ref>{{Cite journal|last=Goldfarb and Tucker|first=|date=2017|title=Digital Economics|url=http://www.nber.org/papers/w23684|journal=National Bureau of Economic Research|volume=Working Paper No. 23684|pages=|via=}}</ref> Due to these falling costs, digital technology will 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|variable-rate application]] (VRA) technologies can apply precise amounts of water, fertilizer, pesticide, herbicide, etc. A number of empirical studies find that VRA improves input use efficiency.<ref>Stamatiadis (EU Project Manager) 2013. “HydroSense – Innovative precision technologies for optimized irrigation and integrated crop management in a water-limited agrosystem.” <nowiki>http://ec.europa.eu/environment/life/project/Projects/index.cfm?fuseaction=search.dspPage&n_proj_id=3466&docType=pdf</nowiki>.</ref><ref>{{Cite journal|last=Tekin|first=|date=2010|title=Variable rate fertilizer application in Turkish wheat agriculture: Economic assessment|url=|journal=African Journal of Agricultural Research|volume=5(8)|pages=647-652|doi=|via=}}</ref><ref>Biggar et al. 2013. “Greenhouse Gas Mitigation Options and Costs for Agricultural Land and Animal Production within the United States.” ''ICF International – Report for USDA.''<nowiki>https://www.usda.gov/oce/climate_change/mitigation_technologies/GHG_Mitigation_Options.pdf</nowiki>.</ref> 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.<ref name=":5">{{Cite journal|date=2017|editor-last=Pedersen|editor-first=Søren Marcus|editor2-last=Lind|editor2-first=Kim Martin|title=Precision Agriculture: Technology and Economic Perspectives|url=https://doi.org/10.1007/978-3-319-68715-5|journal=Progress in Precision Agriculture|language=en-gb|doi=10.1007/978-3-319-68715-5|issn=2511-2260}}</ref> 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.<ref>{{Cite journal|last=Saavoss|first=Monica|date=2018|title=Productivity and profitability of precision agriculture technologies on peanut farms|url=|journal=USDA Economic Research Service|volume=|pages=|via=}}</ref><ref>{{Cite journal|last=Ortiz|first=B. V.|last2=Balkcom|first2=K. B.|last3=Duzy|first3=L.|last4=van Santen|first4=E.|last5=Hartzog|first5=D. L.|date=2013-08-01|title=Evaluation of agronomic and economic benefits of using RTK-GPS-based auto-steer guidance systems for peanut digging operations|url=https://doi.org/10.1007/s11119-012-9297-y|journal=Precision Agriculture|language=en|volume=14|issue=4|pages=357–375|doi=10.1007/s11119-012-9297-y|issn=1573-1618}}</ref> One study in Argentina found that a precision agriculture approach based on crop physiological principles could result in 54% higher farm output.<ref>{{Cite journal|last=Monzon|first=J. P.|last2=Calviño|first2=P. A.|last3=Sadras|first3=V. O.|last4=Zubiaurre|first4=J. B.|last5=Andrade|first5=F. H.|date=2018-09-01|title=Precision agriculture based on crop physiological principles improves whole-farm yield and profit: A case study|url=http://www.sciencedirect.com/science/article/pii/S1161030118302533|journal=European Journal of Agronomy|volume=99|pages=62–71|doi=10.1016/j.eja.2018.06.011|issn=1161-0301}}</ref>
+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,<ref>{{Cite web|url=https://www.npr.org/sections/goatsandsoda/2016/03/29/472129577/meet-a-tractor-that-can-plow-fields-and-talk-to-the-cloud|title=Meet A Tractor That Can Plow Fields And Talk To The Cloud|website=NPR.org|language=en|access-date=2019-07-26}}</ref><ref>{{Cite web|url=https://www.hellotractor.com/home|title=Hello Tractor Site|website=Hello Tractor|language=en-US|access-date=2019-07-26}}</ref> WeFarmUp,<ref>{{Cite web|url=http://parisinnovationreview.com/articles-en/agriculture-and-food-the-rise-of-digital-platforms|title=Agriculture and food: the rise of digital platforms - Paris Innovation Review|website=parisinnovationreview.com|access-date=2019-07-26}}</ref><ref>{{Cite web|url=https://www.wefarmup.com/fr/|title=Location et Prestation de matériels agricoles - WeFarmUp|website=www.wefarmup.com|language=fr|access-date=2019-07-26}}</ref> MachineryLink Solutions,<ref>{{Cite web|url=https://www.nvdaily.com/news/local-news/machinery-link-where-uber-meets-agriculture/article_ee46cbb7-0ddc-512c-a128-e80d661ee08a.html|title=Machinery Link: Where Uber meets agriculture|last=Zuckerman|first=Jake|website=The Northern Virginia Daily|language=en|access-date=2019-07-26}}</ref> TroTro Tractor, and Tringo<ref>{{Cite web|url=https://www.ictworks.org/uber-for-tractors-is-really-a-thing-in-developing-countries/|title=Uber for Tractors is Really a Thing in Developing Countries|last=Vota|first=Wayan|date=2017-05-31|website=ICTworks|archive-url=|archive-date=|dead-url=|access-date=2019-07-26}}</ref> facilitate farmer rental of expensive machinery. By facilitating a market for equipment sharing, digital 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.
+Digital agriculture improves labor productivity through improved farmer knowledge. E-extension (electronic provision of traditional [[agricultural extension]]<nowiki/>services) 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.<ref name=":6">{{Cite journal|last=World Bank|date=2017-06-27|title=ICT in Agriculture (Updated Edition)|url=https://openknowledge.worldbank.org/handle/10986/27526|language=en-US}}</ref><ref>{{Cite web|url=https://www.digitalgreen.org/videos/|title=Videos|website=Digital Green|language=en-US|access-date=2019-07-26}}</ref> E-extension services can also improve farm productivity via decision-support services on mobile apps or other digital platforms. Using many sources of information — weather data, GIS spatial mapping, soil sensor data, satellite/drone pictures, etc. — 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.<ref name=":7">{{Cite journal|last=World Bank|first=|date=2019|title=The Future of Food: Harnessing Digital Technologies to Improve Food System Outcomes|url=https://openknowledge.worldbank.org/handle/10986/31565|journal=|location=Washington, DC|volume=|pages=|via=}}</ref> In a randomized control trial, Casaburi et al. (2014) found that sugarcane growers who received agricultural advice via SMS messages increased yields by 11.5% relative to the control group.<ref>Casaburi et al. 2014. “[https://pdfs.semanticscholar.org/1040/e169e5070d08cf9df864fd26c899a9ba8365.pdf?_ga=2.245185733.1950360298.1563463761-1781288121.1563463761 Harnessing ICT to Increase Agricultural Production: Evidence from Kenya.]” </ref>
+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”<ref>{{Cite web|url=https://cuaes.cals.cornell.edu/digital-agriculture/|title=Digital Agriculture {{!}} Cornell University Agricultural Experiment Station|website=cuaes.cals.cornell.edu|access-date=2019-07-26}}</ref> — can make crop and livestock management more efficient by reducing required labor.<ref>{{Cite journal|last=Morgan-Davies|first=Claire|last2=Lambe|first2=Nicola|last3=Wishart|first3=Harriet|last4=Waterhouse|first4=Tony|last5=Kenyon|first5=Fiona|last6=McBean|first6=Dave|last7=McCracken|first7=Davy|date=2018-02-01|title=Impacts of using a precision livestock system targeted approach in mountain sheep flocks|url=http://www.sciencedirect.com/science/article/pii/S1871141317303670|journal=Livestock Science|volume=208|pages=67–76|doi=10.1016/j.livsci.2017.12.002|issn=1871-1413}}</ref><ref>{{Cite news|url=https://www.newyorker.com/magazine/2019/04/15/the-age-of-robot-farmers|title=The Age of Robot Farmers|last=Seabrook|first=John|date=2019-04-08|access-date=2019-07-26|language=en|issn=0028-792X}}</ref>
+==== Off-farm/market efficiency ====
+Besides streamlining farm production, digital agriculture technologies can make agricultural markets more efficient. Mobile phones, online ICTs, e-commerce platforms, digital payment systems, and other digital agriculture technologies can mitigate market failures and reduce transaction costs throughout the value chain.
+* '''Reducing information asymmetry:''' Price information affects competitive markets’ efficiency because it impacts price dispersion, arbitrage, and farmer and consumer welfare. Since the marginal cost of digitally delivering information approaches zero, digital agriculture has the potential to spread price information. Aker and Fafchamps find that the introduction of mobile phone coverage in Niger reduced spatial price dispersion for agri-food products, especially for remote markets and perishable goods.<ref>{{Cite journal|last=Fafchamps|first=Marcel|last2=Aker|first2=Jenny C.|date=2015-01-01|title=Mobile Phone Coverage and Producer Markets: Evidence from West Africa|url=https://academic.oup.com/wber/article/29/2/262/1661486|journal=The World Bank Economic Review|language=en|volume=29|issue=2|pages=262–292|doi=10.1093/wber/lhu006|issn=0258-6770}}</ref> Similarly, price information provided by Internet kiosks (“e-choupals”) in India led to an increase in farmers’ net profits as traders lost monopsony power.<ref name=":8">{{Cite journal|last=Goyal|first=Aparajita|date=2010|title=Information, Direct Access to Farmers, and Rural Market Performance in Central India|url=https://www.jstor.org/stable/25760218|journal=American Economic Journal: Applied Economics|volume=2|issue=3|pages=22–45|issn=1945-7782}}</ref> Other examples of digital platforms for price information include MFarm<ref>{{Cite web|url=https://www.agrilinks.org/blog/ict-innovations-mfarm-agribusiness-meets-app-economy-kenya|title=ICT Innovations: with Mfarm, agribusiness meets the app economy in Kenya|last=Andres|first=Dustin|date=20 July 2012|website=USAID Feed the Future: AgriLinks|archive-url=|archive-date=|dead-url=|access-date=}}</ref> and Esoko.<ref name=":9">{{Cite web|url=https://esoko.com|title=Esoko website|last=|first=|date=|website=|archive-url=|archive-date=|dead-url=|access-date=}}</ref>
+* '''Matching buyers and sellers:''' E-commerce lowers the search costs of matching buyers and sellers, potentially shortening the value chain.<ref name=":7" /> Rather than go through dozens of intermediaries, farmers can sell directly to consumers.<ref>{{Cite journal|last=Zeng|first=Yiwu|last2=Jia|first2=Fu|last3=Wan|first3=Li|last4=Guo|first4=Hongdong|date=2017-07-24|title=E-commerce in agri-food sector: a systematic literature review|url=https://www.wageningenacademic.com/doi/10.22434/IFAMR2016.0156|journal=International Food and Agribusiness Management Review|language=en|volume=20|issue=4|pages=439–460|doi=10.22434/IFAMR2016.0156|issn=1559-2448}}</ref><ref>Hobbs et al. 2011. “[https://law.usask.ca/documents/research/estey-journal/Hobbs-Boyd-Kerr-e-commerce-estey-2001.pdf International e-commerce: a solution to penetrating niche markets for food?]” ''Estey Centre for Law and Economics in International Trade''. </ref> Market access services can also solve the matching problem without necessarily hosting online transactions. For example, Esoko sends market information (prices for specific commodities, market locations, etc.) to agents and farmers, connecting them to commodity buyers.<ref>Brugger 2011. "[https://www.gsma.com/mobilefordevelopment/wp-content/uploads/2011/12/Syngenta_Report_on_mAgriculture_abridged_web_version.pdf Mobile applications in agriculture]." ''Syngenta Foundation''.</ref><ref name=":9" /> All of these matching platforms help smallholders coordinate with buyers and enter both regional and global value chains.<ref name=":10">{{Cite journal|last=Jouanjean|first=Marie-Agnes|date=2019-02-15|title=Digital Opportunities for Trade in the Agriculture and Food Sectors|url=https://www.oecd-ilibrary.org/agriculture-and-food/digital-opportunities-for-trade-in-the-agriculture-and-food-sectors_91c40e07-en|journal=OECD Food, Agriculture, and Fisheries Papers, No. 122|language=en|volume=|pages=|doi=10.1787/91c40e07-en|via=}}</ref> Finally, it’s important to note that digital technologies can also facilitate matching in financial and input markets, not just producer-to-consumer output sales.
+* '''Lowering transaction costs in commercial markets:''' Digital payments — whether integrated in e-commerce platforms or in mobile money accounts, e-wallets, etc. — reduce transactions costs within agricultural markets. The need for safe, rapid monetary transactions is particularly apparent in rural areas. Plus, digital payments can provide a gateway to bank accounts, insurance, and credit.<ref>{{Cite journal|last=Lonie|first=|date=2010|title=Innovations in Rural and Agricultural Finance: M-PESA: Finding New Ways to Serve the Unbanked in Kenya|url=http://www.ifpri.org/publication/innovations-rural-and-agricultural-finance-m-pesa|journal=IFPRI: 2020 Vision for Food, Agriculture and the Environment|volume=|pages=|via=}}</ref> Using distributed ledger technologies or smart contracts is another way to reduce trust-related transaction costs in commercial markets.<ref>{{Cite journal|last=Hakobyan|first=Artavazd|last2=Buyvolova|first2=Anna|last3=Meng|first3=Yuan Ting|last4=Nielson|first4=David J.|date=2018-01-01|title=Unleashing the Power of Digital on Farms in Russia - and Seeking Opportunities for Small Farms|url=http://documents.worldbank.org/curated/en/174171539109767548/Unleashing-the-Power-of-Digital-on-Farms-in-Russia-and-Seeking-Opportunities-for-Small-Farms|journal=The World Bank Group|language=en|volume=|pages=1–50|via=}}</ref><ref name=":10" /> Many retail and food companies have partnered with IBM to develop blockchain pilots related to food safety and traceability, and Alibaba is testing blockchain to reduce fraud in agri-food e-commerce between China and Australia/New Zealand.<ref name=":10" />
+* '''Lowering transaction costs in government services:''' Digital payments can also streamline government delivery of agricultural subsidies. In 2011, the Nigerian Federal Ministry of Agriculture and Rural Development started delivering fertilizer subsidy vouchers to e-wallets on mobile phones; by 2013, they had reached 4.3 million smallholders nationwide.<ref name=":11">{{Cite journal|last=Tarazi|first=Michael|last2=Grossman|first2=Jeremiah|date=2014-06-01|title=Serving smallholder farmers : recent developments in digital finance|url=http://documents.worldbank.org/curated/en/119581468148758424/Serving-smallholder-farmers-recent-developments-in-digital-finance|language=en|pages=1–16}}</ref> Compared to the previous program, the e-vouchers cut costs — from 2011 to 2013, the cost per smallholder farmer receiving fertilizer went from US$225-300 to US$22. The e-vouchers also reached more smallholders, increasing from between 600,000-800,000 in 2011 to 4.3 million in 2013.<ref name=":11" /> In the second phase of the program, the Nigerian government developed the Nigerian Agricultural Payment Initiative (NAPI), which distributed PIN-enabled ID cards that hold subsidy information and provide access to loans and grants.<ref>{{Cite journal|last=Martin, Harihareswara, Diebold, Kodali, and Averch|first=|date=2016|title=Guide to the Use of Digital Financial Services in Agriculture|url=https://www.usaid.gov/sites/default/files/documents/15396/Digital_Financial_Services_for_Ag_Guide.pdf|journal=USAID|volume=|pages=|via=}}</ref> Other e-wallet/e-voucher systems for agricultural subsidies exist or have been piloted in Colombia,<ref>{{Cite journal|last=IFAD|first=|date=2016|title=Lessons learned: Digital financial services for smallholder households|url=https://www.ifad.org/documents/38714170/40185433/lessons+learned+-+Digital+financial+services+for+smallholder+households.pdf/f3867c13-d74d-4c71-aceb-6bde51cd965c.|journal=International Fund for Agricultural Development|volume=|pages=|via=}}</ref><ref>{{Cite journal|last=Marulanda and the Bankable Frontier Associates|first=|date=2015|title=Colombia's Coffee Growers' Smart ID card: Successfully Reaching Rural Communities with Digital Payments|url=https://btca-prod.s3.amazonaws.com/documents/31/english_attachments/Colombia-Cafeteros-Case-Study-Long-ENG-Jan-2015.pdf?1438939088|journal=Better than Cash Alliance|volume=|pages=|via=}}</ref> Rwanda,<ref name=":11" /> Zambia,<ref>{{Cite journal|last=Sitko|first=Nicholas J.|last2=Bwalya|first2=Richard|last3=Kamwanga|first3=Jolly|last4=Wamulume|first4=Mukata|date=2012|title=Assessing the Feasibility of Implementing the Farmer Input Support Programme (FISP) Through an Electronic Voucher System in Zambia|url=https://ideas.repec.org/p/ags/midcpb/123210.html|journal=Food Security Collaborative Policy Briefs 123210, Michigan State University, Department of Agricultural, Food, and Resource Economics.|language=en|volume=|pages=|via=}}</ref> Mali, Guinea, and Niger.<ref>{{Cite journal|last=World Bank Group|first=|date=2019|title=AFCW3 Economic Update, Spring 2019: Digitizing Agriculture - Evidence from E-Voucher Programs in Mali, Chad, Niger, and Guinea|url=https://openknowledge.worldbank.org/handle/10986/31576|journal=|language=English|volume=|pages=|via=}}</ref> Besides reducing subsidy costs, governments can harness digital technology to save time. When Estonia implemented their e-ID and X-Road system, time spent applying for agricultural subsidies decreased from 300 minutes to 45 minutes per person.<ref>{{Cite journal|last=Kärner|first=Ene|date=2017-09-21|title=The Future of Agriculture is Digital: Showcasting e-Estonia|url=http://journal.frontiersin.org/article/10.3389/fvets.2017.00151/full|journal=Frontiers in Veterinary Science|volume=4|doi=10.3389/fvets.2017.00151|issn=2297-1769}}</ref>
+Rarely does one single digital agriculture technology solve one discrete market failure. Rather, systems of digital agriculture technologies work together to solve multifaceted problems. For example, e-commerce solves two efficiency issues: difficulty matching buyers and sellers, especially in rural areas, and the high transaction costs associated with in-person, cash-based trade.
+=== Equity ===
+Digital agriculture shows promise for creating a more equitable agri-food value chain. Because digital technologies reduce transaction costs and information asymmetries, they can improve smallholder farmers’ market access in a number of ways:
+==== Financial inclusion ====
+Digital agriculture technologies can expand farmers’ access to credit, insurance, and bank accounts for a number of reasons. First, digital technology helps alleviate the information asymmetry that exists between farmers and financial institutions. When lenders decide a farmer’s credit ceiling or insurance premium, they’re usually uncertain about what risks the farmer presents. Digital technology reduces the costs of verifying farmers’ expected riskiness. The Kenyan company M-Shwari uses customers’ phone and mobile money records to assess creditworthiness.<ref>Cook and McKay. "Top 10 Things to Know About M-Shwari." ''Consultative Group to Assist the Poor - Blog.'' 2 April 2015.</ref> Organizations like FarmDrive and Apollo Agriculture incorporate satellite imagery, weather forecasts, and remote sensor data when calculating farmers’ loan eligibility.<ref>{{Cite web|url=https://www.mckinsey.com/industries/agriculture/our-insights/winning-in-africas-agricultural-market|title=Winning in African agriculture {{!}} McKinsey|website=www.mckinsey.com|language=en|access-date=2019-07-26}}</ref><ref>{{Cite web|url=https://farmdrive.co.ke/|title=FarmDrive|website=farmdrive.co.ke|access-date=2019-07-26}}</ref> Drone imagery can confirm a farmer’s physical assets or land use<ref>{{Cite journal|last=Sylvester|first=Gerard|date=2018|title=E-agriculture in action: drones for agriculture|url=http://www.fao.org/3/i8494en/i8494en.pdf|journal=FAO and ITU|volume=|pages=|via=}}</ref> and RFID technology allows stakeholders to monitor livestock,<ref>{{Cite book|url=http://elibrary.worldbank.org/doi/book/10.1596/978-1-4648-1002-2|title=ICT in Agriculture (Updated Edition): Connecting Smallholders to Knowledge, Networks, and Institutions|last=World Bank|date=2017-06-27|publisher=The World Bank|isbn=9781464810022|language=en|doi=10.1596/978-1-4648-1002-2}}</ref> making it easier for insurers to understand farmers’ riskiness. In all instances, low-cost digital verification reduces lenders’ uncertainty: the questions “will this farmer repay the loan?” and “what risks does this farmer face?” become clearer.
+Second, digital technology facilitates trust between farmers and financial institutions. A range of tools create trust, including real-time digital communication platforms and blockchain/distributed ledger technology/smart contracts. In Senegal, a digitalized, supply-chain-tracking system allows farmers to collateralize their rice to obtain the credit necessary for planting. Lenders accept rice as collateral because real-time, digital tracking assures them the product wasn’t lost or damaged in the post-harvest process.<ref>{{Cite web|url=https://www.agrilinks.org/post/lets-get-digital-un-blocking-finance-farmers-senegal|title=Let's Get Digital: Un-Blocking Finance for Farmers in Senegal|last=Poublanc|first=Christophe|date=26 October 2018|website=USAID Feed the Future: Agrilinks Blog|archive-url=|archive-date=|dead-url=|access-date=}}</ref>
+==== Market inclusion ====
+Middlemen often extract exorbitant rents from farmers when purchasing their harvest or livestock. Why? First, smallholders in remote areas may be unaware of fair market prices. As a result, middlemen (who typically have better information about market conditions and prices) accrue significant market power and profits.<ref>{{Cite journal|last=Mitchell|first=Tara|date=2014|title=Is Knowledge Power? Competition and Information in Agricultural Markets|url=https://ideas.repec.org/p/iis/dispap/iiisdp456.html|journal=The Institute for International Integration Studies Discussion Paper Series|language=en|volume=|pages=|via=}}</ref> A study conducted in the central highlands of Peru found that farmers who received market price information via mobile phone SMS increased their sales prices by 13-14% relative to farmers without access to the information.<ref>{{Cite book|url=https://ageconsearch.umn.edu/record/150418/?ln=en|title=The Role of Price Information in Agricultural Markets: Experimental Evidence from Rural Peru|last=|first=|date=2013|publisher=IFPRI|year=|isbn=|editor-last=Nakasone|editor-first=Eduardo|location=|pages=|language=en-US}}</ref> Second, smallholders produce tiny harvests compared to large producers, so they lack bargaining power with middlemen. If smallholders can aggregate or form a cooperative to sell their products together, they have more leverage. Online platforms and mobile phones can facilitate aggregation, such as [[Digital Green]]’s Loop app.<ref>{{Cite web|url=https://www.digitalgreen.org/blogs/loop-mobile-app-makes-farm-to-market-linkages-easy/|title=LOOP Mobile App Makes Farm to Market Linkages Easy|last=Thomas|first=Susan|date=|website=Digital Green|language=en-US|archive-url=|archive-date=|dead-url=|access-date=2019-07-26}}</ref> Third, connecting producers with final consumers can eliminate intermediaries’ monopsony power, thereby raising producer profits.<ref name=":8" /> As mentioned above in the efficiency section, e-commerce or other market linkage platforms can connect a small farmer directly to consumers around the world.
+==== Potential inequities resulting from digital agriculture ====
+Though digital technologies can facilitate market access and information flow, there’s no guarantee they won’t exacerbate existing inequalities. Should constraints prevent a range of farmers from adopting digital agriculture, it’s possible that the benefits will only accrue to the powerful.
+* '''Large farms''': When a digital agriculture technology requires lots of upfront investment, only large farms with sufficient assets and credit access will adopt it.<ref name=":7" /> For example, large farms are most likely to adopt precision agriculture technologies because of high costs.<ref>{{Cite journal|last=Schimmelpfennig|first=|date=2016|title=Farm Profits and Adoption of Precision Agriculture|url=https://www.ers.usda.gov/webdocs/publications/80326/err-217.pdf?v=0|journal=USDA Economic Research Service|volume=Report no. 217|pages=|via=}}</ref>
+* [[Digital divide|'''Digital divide''']]: The uneven access to information and communication technologies (ICTs) may lead to uneven adoption of — and thereby uneven gains from — digital agriculture. When digital technologies require specific skills, benefits may accrue to digitally literate farmers positioned to take advantage of such opportunities.<ref>{{Cite journal|last=Acemoglu|first=D|date=1998|title=Why Do New Technologies Complement Skills? Directed Technical Change and Wage Inequality|url=|journal=The Quarterly Journal of Economics|volume=113(4)|pages=1055-1089|via=}}</ref><ref>{{Cite book|title=The Race Between Education and Technology|last=Goldin and Katz|first=|publisher=Belknap Press|year=2008|isbn=|location=Cambridge, MA|pages=}}</ref><ref>{{Cite journal|last=Cole and Fernando|first=|date=2012|title=Mobile'izing Agricultural Advice: Technology Adoption, Diffusion and Sustainability|url=|journal=Harvard Business School Finance Unit|volume=Research Paper No. 13-047|pages=|via=}}</ref>
+* '''Gender''': Given gender-based disparities in ICT access<ref>{{Cite book|url=http://elibrary.worldbank.org/doi/book/10.1596/978-1-4648-1259-0|title=The Global Findex Database 2017: Measuring Financial Inclusion and the Fintech Revolution|last=Demirguc-Kunt|first=Asli|last2=Klapper|first2=Leora|last3=Singer|first3=Dorothe|last4=Ansar|first4=Saniya|last5=Hess|first5=Jake|date=2018-04-19|publisher=The World Bank|isbn=9781464812590|language=en|doi=10.1596/978-1-4648-1259-0}}</ref><ref name=":6" /> and the gender gap in agribusiness value chains,<ref>{{Cite journal|last=Roscoe|first=Alexa|last2=Hoffmann|first2=Nathalie Ilona|date=2016-10-01|title=Investing in women along agribusiness value chains|url=http://documents.worldbank.org/curated/en/355831511503089525/Investing-in-women-along-agribusiness-value-chains|language=en|pages=1–65}}</ref> men seem more likely to adopt digital agriculture.<ref name=":7" /> Therefore, digital technologies could perpetuate gender inequalities in the agricultural sector.<ref>{{Cite journal|last=Mendonca, Crespo, and Simoes|first=|date=2015|title=Inequality in the Network Society: An Integrated Approach to ICT Access, Basic Skills, and Complex Capabilities|url=https://www.researchgate.net/publication/273300147_Inequality_in_the_network_society_An_integrated_approach_to_ICT_access_basic_skills_and_complex_capabilities|journal=Telecommunications Policy|volume=39|pages=192-207|via=}}</ref>
+* '''Unskilled labor''': Advances in on-farm productivity, particularly through digitized automation and precision agriculture, may threaten low-skilled jobs.<ref name=":2" /> According to the OECD, agriculture will be one of the sectors most affected by automation<ref>{{Cite journal|last=Quintini|first=Glenda|last2=Nedelkoska|first2=Ljubica|date=2018-03-08|title=Automation, skills use and training|url=https://www.oecd-ilibrary.org/employment/automation-skills-use-and-training_2e2f4eea-en;jsessionid=yvhLUxB6vC19Fv-yu9Zi5G4t.ip-10-240-5-107|journal=OECD Directorate for Employment, Labour, and Social Affairs - Employment, Labour, and Social Affairs Committee|language=en|volume=|pages=|doi=10.1787/2e2f4eea-en|via=}}</ref> and McKinsey Global Institute projects that automation will displace 15% of agricultural workers in Mexico and 30% in Germany.<ref>{{Cite journal|last=McKinsey & Company|first=|date=2017|title=Jobs lost, jobs gained: workforce transitions in a time of automation|url=https://www.mckinsey.com/~/media/BAB489A30B724BECB5DEDC41E9BB9FAC.ashx|journal=McKinsey Global Institute|volume=|pages=|via=}}</ref>
+* '''Agribusinesses and service providers''': Increased reliance on big data may increase the power differential between agribusinesses/information service providers and farmers.<ref name=":1" /><ref name=":3" /> If smallholders lack access to and/or control of their data, they may lose bargaining power vis-à-vis large value chain actors (like supermarkets) and data collectors.<ref>Maru, Berne, De Beer, Ballantyne, Pesce, Kalyesubula, Fourie, Addison, Collett, and Chaves 2018. “Digital and Data-Driven Agriculture: Harnessing the Power of Data for Smallholders.” ''Global Forum on Agricultural Research and Innovation (GFAR); Global Open Data for Agriculture and Nutrition (GODAN); Technical Centre for Agricultural and Rural Cooperation (CTA).''<nowiki>https://cgspace.cgiar.org/bitstream/handle/10568/92477/GFAR-GODAN-CTA-white-paper-final.pdf?sequence=3&isAllowed=y</nowiki>.</ref>
+=== Environment ===
+Boosting natural resource efficiency is the “single most important need for a sustainable food future,” according to the World Resource Institute.<ref name=":4" /> As mentioned in the on-farm efficiency section, precision farming — including variable rate nutrient application, variable rate irrigation, machine guidance, and variable rate planting/seeding — could minimize use of agricultural inputs for a given yield.<ref>{{Cite journal|last=Bongiovanni|first=R.|last2=Lowenberg-Deboer|first2=J.|date=2004-08-01|title=Precision Agriculture and Sustainability|url=https://doi.org/10.1023/B:PRAG.0000040806.39604.aa|journal=Precision Agriculture|language=en|volume=5|issue=4|pages=359–387|doi=10.1023/B:PRAG.0000040806.39604.aa|issn=1573-1618}}</ref><ref>{{Cite journal|last=Eory|first=Vera|last2=Barnes|first2=Andrew|last3=Gómez-Barbero|first3=Manuel|last4=Soto|first4=Iria|last5=Wal|first5=Tamme Van der|last6=Vangeyte|first6=Jurgen|last7=Fountas|first7=Spyros|last8=Beck|first8=Bert|last9=Balafoutis|first9=Athanasios|date=2017|title=Precision Agriculture Technologies Positively Contributing to GHG Emissions Mitigation, Farm Productivity and Economics|url=https://www.mdpi.com/2071-1050/9/8/1339|journal=Sustainability|language=en|volume=9|issue=8|pages=1339|doi=10.3390/su9081339|via=}}</ref> This could mitigate resource waste and negative environmental externalities,<ref>{{Cite journal|last=European Parliament|first=|date=2014|title=Precision Agriculture: An Opportunity for EU Farmers - Potential Support with the CAP 2014-2020|url=http://www.europarl.europa.eu/RegData/etudes/note/join/2014/529049/IPOL-AGRI_NT%282014%29529049_EN.pdf|journal=EU Parliament Directorate-General for Internal Policies, Policy Dept. B, Structural and Cohesion Policies: Agriculture and Rural Development|volume=|pages=|via=}}</ref> like greenhouse gas (GHG) emissions,<ref>{{Cite journal|last=Eory|first=Vera|last2=Barnes|first2=Andrew|last3=Gómez-Barbero|first3=Manuel|last4=Soto|first4=Iria|last5=Wal|first5=Tamme Van der|last6=Vangeyte|first6=Jurgen|last7=Fountas|first7=Spyros|last8=Beck|first8=Bert|last9=Balafoutis|first9=Athanasios|date=2017|title=Precision Agriculture Technologies Positively Contributing to GHG Emissions Mitigation, Farm Productivity and Economics|url=https://www.mdpi.com/2071-1050/9/8/1339|journal=Sustainability|language=en|volume=9|issue=8|pages=1339|doi=10.3390/su9081339|via=}}</ref> soil erosion,<ref>{{Cite journal|last=Berry, Delgado, Khosla, and Pierce|first=|date=2003|title=Precision conservation for environmental sustainability|url=https://doi.org/10.3390/su9081339|journal=Journal of Soil and Water Conservation|volume=|pages=332-339|via=}}</ref> and fertilizer runoff.<ref name=":5" /> For example, Katalin et al. 2014 estimate that switching to precision weed management could save up to 30,000 tons of pesticide in the EU-25 countries.<ref>{{Cite journal|last=Katalin|first=Takács-György|last2=Rahoveanu|first2=Turek|last3=Magdalena|first3=Maria|last4=István|first4=Takács|date=2014-01-01|title=Sustainable New Agricultural Technology – Economic Aspects of Precision Crop Protection|url=http://www.sciencedirect.com/science/article/pii/S2212567114001518|journal=Procedia Economics and Finance|series=1st International Conference 'Economic Scientific Research - Theoretical, Empirical and Practical Approaches', ESPERA 2013|volume=8|pages=729–736|doi=10.1016/S2212-5671(14)00151-8|issn=2212-5671}}</ref> González-Dugo et al. 2013 found that precision irrigation of a citrus orchard could reduce water use by 25 percent while maintaining a constant yield.<ref>{{Cite journal|last=Gonzalez-Dugo|first=V.|last2=Zarco-Tejada|first2=P.|last3=Nicolás|first3=E.|last4=Nortes|first4=P. A.|last5=Alarcón|first5=J. J.|last6=Intrigliolo|first6=D. S.|last7=Fereres|first7=E.|date=2013-12-01|title=Using high resolution UAV thermal imagery to assess the variability in the water status of five fruit tree species within a commercial orchard|url=https://doi.org/10.1007/s11119-013-9322-9|journal=Precision Agriculture|language=en|volume=14|issue=6|pages=660–678|doi=10.1007/s11119-013-9322-9|issn=1573-1618}}</ref> Basso et al. 2012 demonstrated that variable-rate application of fertilizer can reduce nitrogen application and leaching without affecting yield and net return.<ref>{{Cite journal|last=Basso, Sartori, Cammarano, and Florentino|first=|date=2012|title=Environmental and economic evaluation of N fertilizer rates in a maize crop in Italy: A spatial and temporal analysis using crop models|url=https://link.springer.com/article/10.1007%2Fs11119-013-9322-9|journal=Biosystems Engineering|volume=113(2)|pages=103-111|via=}}</ref>
+However, precision agriculture could also accelerate farms’ depletion of natural resources because of a [[Rebound effect (conservation)|rebound effect]]; increasing input efficiency does not necessarily lead to resource conservation.<ref name=":12">{{Cite journal|last=Schieffer|first=J.|last2=Dillon|first2=C.|date=2015-02-01|title=The economic and environmental impacts of precision agriculture and interactions with agro-environmental policy|url=https://doi.org/10.1007/s11119-014-9382-5|journal=Precision Agriculture|language=en|volume=16|issue=1|pages=46–61|doi=10.1007/s11119-014-9382-5|issn=1573-1618}}</ref> Also, by changing economic incentives, precision agriculture may hinder environmental policies’ effectiveness: “Precision agriculture can lead to higher marginal abatement costs in the form of forgone profits, decreasing producers' responsiveness to those policies."<ref name=":12" /> In other words, holding pollution constant, precision agriculture allows a farmer to produce more output — thus, abatement becomes more expensive.
+Off-farm, digital agriculture has the potential to improve environmental monitoring and food system traceability. The monitoring costs of certifying compliance with environmental, health, or waste standards are falling because of digital technology.<ref>{{Cite web|url=https://www.gsma.com/mobilefordevelopment/blog-2/the-role-of-digital-in-improving-traceability-and-certification-in-the-agricultural-last-mile/|title=The role of digital in improving traceability and certification in the agricultural last mile|last=|first=|date=2018-11-26|website=GSMA mAgri: Mobile for Development|language=en-GB|archive-url=|archive-date=|dead-url=|access-date=2019-07-26}}</ref> For example, satellite and drone imagery can track land use and/or forest cover; distributed ledger technologies can enable trusted transactions and exchange of data; food sensors can monitor temperatures to minimize contamination during storage and transport.<ref name=":7" /> Together, technologies like these can form digital agriculture traceability systems, which allow stakeholders to track agri-food products in near-real-time. Digital traceability yields a number of benefits, environmental and otherwise:
+* '''Reduced food waste''': Of all the food calories produced in a year, 25% are wasted between on-farm production and consumers.<ref name=":4" /> Traceability systems facilitate better identification of supply-side weaknesses — where is food lost downstream of the farm, and how much is wasted?<ref>{{Cite journal|last=World Economic Forum and McKinsey & Company|first=|date=2019|title=Innovation with a Purpose: Improving Traceability in Food Value Chains through Technology Innovation|url=http://www3.weforum.org/docs/WEF_Traceability_in_food_value_chains_Digital.pdf|journal=World Economic Forum: System Initiative on Shaping the Future of Food|volume=|pages=|via=}}</ref> Emerging digital innovations, such as milk cartons that track milk from “farm to fridge,”<ref>{{Cite web|url=https://cals.cornell.edu/news/future-cartons-will-track-milk-farm-fridge/|title=Future cartons will track milk from farm to fridge {{!}} CALS|last=Friedlander|first=Blaine|date=|website=cals.cornell.edu|archive-url=|archive-date=|dead-url=|access-date=2019-07-26}}</ref> can address demand-side waste by providing consumers with more accurate expiration dates.
+* '''Consumer trust''': Ensuring food safety, quality, and authenticity has become an important regulatory requirement in high-income countries. Use of RFID tags and blockchain technologies to certify agri-food products’ characteristics could provide near-real-time quality signals to consumers.<ref name=":10" />
+* '''Improved producer welfare''': Producers who can leverage environmental certification could sell their products at a premium,<ref name=":5" /><ref>{{Cite web|url=https://blogs.worldbank.org/voices/dead-branding-society-blockchain-death-food-branding-we-know-it|title=Dead Branding Society: Is blockchain the death of food branding as we know it?|last=Kent, Lampietti and Hasiner|first=|date=2019|website=World Bank Blogs|language=en|archive-url=|archive-date=|dead-url=|access-date=2019-07-26}}</ref> because blockchain technologies could enable greater trust in labels like “sustainable,” “organic” or “fair trade.”<ref name=":10" />
 == Barriers ==