CHAPTER 4 97

Innovation in Agriculture and Food Systems in the Digital Age

HAROLD VAN ES and JOSHUA WOODARD, Cornell University

Agriculture and the worldwide food agricultural resources but are technologies in farming, which will system are challenged to feed an currently low-producing (e.g., play a key role in achieving innova- estimated global population of 9.7 West Africa and Southeast tion goals. It is a new direction for

billion people by 2050 with dimin- Europe);3 ‘’, a more estab- in the Digital Age Systems and Food in Agriculture 4: Innovation ishing land and water resources.1 lished concept that is historically • expansion of local and con- Agricultural land areas can no lon- aimed at crop production. Digital trolled environment production ger be expanded because most global agriculture offers new opportunities systems such as urban , arable lands have already been put through the ubiquitous availability greenhouses, and indoor grow- into production. The remaining of highly interconnected and data- ing systems that provide high- lands are increasingly lost to urban- intensive computational technolo- value crops to local and regional ization or need to be preserved for gies as part of the so-called Fourth markets; habitat conservation, biodiversity, Industrial Revolution.6 It can be and climate buffers.2 Moreover, the • improved crop and animal applied to all aspects of agricultural unsustainable overuse of freshwater genetics that facilitate higher production systems, and it reflects a resources from irrigation is making production levels and result shift from generalized management less water available for future crops, in less susceptibility to yield- of resources towards highly and food security is being affected depressing agents such as diseases optimized, individualized, real- by increased risk from climate and insects; and time, hyper-connected and data- change and an uncertain geopoliti- driven management. For example, • greater efficiencies and less waste cal landscape. instead of treating all farm fields in the food supply chain. Concerns with diminishing uniformly, small field zones may resources and expanding populations each receive their own highly opti- are exacerbated by changing diets in mized management prescriptions; Digital agriculture many developing countries (which animals may be monitored and Digital data will be getting collected are now using more animal-based managed individually rather than as at a rate of 40 zettabytes (ZB—the protein and fresh produce). This a whole herd. The desired outcomes equivalent of 40 trillion gigabytes, will ultimately require higher global of digital agriculture are more pro- or GB) per year by 2020.4 Increased production levels of the primary ductive, profitable, and sustainable storage and computational capacity, source of protein, carbohydrates, systems. coupled with high-resolution envi- and nutrients: crops. An effective Digital agriculture can leverage ronmental and remotely sensed data, strategy for gaining enhanced agri- the smart use of data and communi- have created unprecedented oppor- cultural production levels should cation to achieve system optimiza- tunities for data-driven discovery focus on sustainable improvements tion. The tools that enable digital in agriculture and food systems.5

in five major areas: agriculture are multiple and varied, 2017 Many agricultural improvements and include cross-cutting technolo- • further optimization of resources can be facilitated by these digital gies such as computational decision in currently productive agricul- innovations. and analytics tools, the cloud, sen- tural regions; This chapter defines ‘digital sors, robots, and digital communi- agriculture’ as the deployment of • intensification of production cation tools (Table 1). In addition, computational and information in areas that have good basic field-based activities are enabled THE GLOBAL INNOVATION INDEX INDEX INNOVATION GLOBAL THE 98 THE GLOBAL INNOVATION INDEX 2017 4: Innovation in Agriculture and Food Systems in the Digital Age Table 1:Enablingtechnologies for digitalagriculture and auto-steered vehicles, aerial unmanned spectroscopic sensing, remote and proximal sampling, soil precision monitors, yield systems, information geographical (GPS), such as Global Positioning Systems technologies by geo-locationing Note: GPS = global positioning system; LPWAN = low-power wide-area networks; RTK = Real Time Kinematic high-accuracy positionin high-accuracy Kinematic Time =Real RTK networks; wide-area =low-power LPWAN system; positioning =global GPS Note: Field technologies Cross-cutting rdcinevrnetTp ftcnlg Purpose andbenefits Type oftechnology Production environment On-board computers Collect andprocess Collect field datawithspecialized computer hardware on andsoftware Allowcontinuous adjustmentofapplicationrates to precisely match localized crop technology for equipment(including farm Reducelabour orfatiguewithself-driving On-board computers Variable rate technology Auto-steering andguidance patterns Samplesoilathighspatialresolution (inzones) to andmanagefertility detect rate andmake to continuallymeasure EmploysensorsandGPSon harvesters harvest remote) reflectance sensing(proximalSpectral and Usesmall, vehicles readily aerial to resources deployed monitor farm remote-control systems (UAS,Unmanned aerial ordrones) Precision soilsampling Usecomputerized geographical managementandto mappingto make aidinventory Provide precise resources locationoffarm (fieldequipment,animals, etc.), com- often Yield monitors Geographic information systems (GPS, RTK) Geo-locationing ai rqec DTransmit datawith tagsattachedto production units(mostlyanimals)that identity systems Automated feeding, milking, andmonitoring ID frequency Radio andminimalhumanlabour taskswithefficiency Implement resources ofequipmentandfarm information Gather onthefunctioning to support Provide efficient,inexpensive, and centralized datastorage, computation,andcom- band, LPWAN) Digital communicationtools (mobile, broad- Robots Usedatato develop recommendations for managementandoptimize multitudesof Sensors The cloud Computational decisiontools others. Controlled-environment Controlled-environment others. among systems, feeding and ing (robotic) milk- automated and chips) (RFID identification quency fre- radio include technologies rate technologies. Animal-focused variable and equipment guided tractors, etc., harvesters, to connected sensorsorcontrollers often needs infieldareas withfieldapplicators for crop inputs(chemicals, seed, etc.) input placementandmanagement robots); canalsoprecisely guide equipmentinfieldsto enablehighlyaccurate crop problems oronnutrient/pest animal performance, field equipment–mounted sensors, onsoilpatterns, determinations to crop, make or Measure lightreflectance ofsoilorcrop usingsatellite, airplane, orUAS, imaging, or using imaging UAS in fields yield mapsthatallow for identificationoflocalyieldvariability crop inputprescriptions (, etc.) bined withmeasurements (yield, etc.), orusedto steer equipmentto locations labour needsandfacilitatingindividualized animalmanagement dataonanimals, basicbiometric therebycombined withsensorsthatcollect reducing orfeeding operations automaticallyPerform withrobotic milking systems, often aswell asindividualized management onperformance allow datacollection ofmanagement and computationalresources insupport Allow frequent, real-time resources, farm communicationbetween managers, workers, management decisions management farm munication to support tasks farm g system. data, and analytical capabilities that that capabilities analytical and data, accumulatetially large amounts of robots. and sensors as such technologies bydigital enabled farms, indoor (greenhouses, agriculture etc.) is also increasingly Digital agriculture can poten- can agriculture Digital 99 facilitate the effective employment a nation’s agricultural competiveness working in partnership with of these data are key implementa- and ability to sustain critical natural farm managers. tion factors. The development of resources will be strongly tied to its The above investments each computational tools that address ability to innovate in these aspects of require somewhat different sup- system dynamics and optimization the production system. The question port infrastructures. Large capital are similarly critical; they require a is not whether the global agricul- investments not only require edu- deep understanding of the biologi- tural industry should adopt digital cated farmers to use the equipment cal, physical, chemical, and socio- technologies, but how this adoption effectively, but also need dealership economic processes that together process can occur in an environment networks with competent staff and make agricultural production pos- that encourages it to fully capitalize operational farm credit systems. sible. Therefore digital agriculture on the potential production gains. Digital services such as remote sens- technologies require talent in sci- ing and decision models are highly ence and entrepreneurship. scalable technologies that generally Production efficiencies can be Types of innovation do not involve upfront financial

gained both from the integration At the farm enterprise level, differ- in the Digital Age Systems and Food in Agriculture 4: Innovation or knowledge investments on the of data associated with multiple ent types of technology investments part of farm owners or managers, technologies and from the real- may be distinguished: but are generally pay-as-you-go time transfer of data between field 1. Capital investments that pro- arrangements. However, in order to equipment, barn, office, and the mote efficiencies (computer effectively incorporate digital tech- cloud. The recent surge in digital hardware/software, robotic sys- nologies, a farm-specific knowledge agriculture technologies has led to tems, variable-rate technolo- base that involves a more sustained the accumulation of large amounts gy, sensors, high-precision GPS, commitment to technology invest- of data. High-resolution soil data, etc.). These are invariably offered ments and analytics is still required, site-specific weather maps, aerial by established equipment com- and it demands both educated imagery, nutrient applications, and panies that have made significant farmers and local consultants who milking and animal health records technology investments and typ- are trained in digital agriculture are being continuously generated ically compete in global markets. technologies. by farms. Much of that information can be sent via broadband or mobile 2. Service investments that pro- connections to cloud-based services, vide actionable information (re- Where does innovation in digital but inadequate telematics (the long- mote sensing, cloud-based deci- agriculture occur? distance transfer of digital informa- sion models, etc.). These services Digital agriculture innovation is tion) often constrains the potential are offered by companies ranging both knowledge- and skills-inten- benefits from these technologies. from global corporations to small sive because agricultural production In addition, farmers and research- tech companies. systems are complex and multifac- ers are finding it difficult to man- 3. Farm knowledge and human eted and solutions require knowl- age, interpret, or make use of their capital investments that involve edge ranging from broad to specific. data as a result of their volume and the development of highly lo- For example, tools that optimize complexity.7 Growth in hybrid fields calized actionable knowledge for nitrogen dynamics (see below) such as computational agriculture, a specific farm, herd, or crop- need to consider soil, weather, and computational sustainability, and growing environment (opti- crop-related processes that all have data science that aim to use farm data mized seeding, nutrient and pest interacting physical, biological, and are partial responses to these needs.8 management, animal feeding, chemical components. These in

In the end, agriculture will fol- etc.). These investments involve turn need to be considered in the

low other industrial sectors in that 2017 the collection of data—often context of a wide diversity of prac- the benefits from digital technolo- from investments discussed un- tices, production environments, and gies will materialize and become der (1) and (2)—that are analysed socioeconomic conditions on farms. a source of increased production to generate farm-specific rec- Solutions are often more complex efficiencies once ubiquitously avail- ommendations. These knowl- and less scalable than optimization able data are effectively employed. edge investments are made at processes in manufacturing indus- In a global economic environment, the local level, with consultants tries or communications. This is THE GLOBAL INNOVATION INDEX INDEX INNOVATION GLOBAL THE 100 THE GLOBAL INNOVATION INDEX 2017 4: Innovation in Agriculture and Food Systems in the Digital Age unrelated, agricultural universities Not forinnovation. critical is plines disci- other with collaboration when at a time isolationism intellectual cultivate universities agricultural separate is, medicine—that and ing, engineer- sciences, ciplines—basic dis- relevant other from agriculture of separation institutional common many developing in innovation countriesuniversity-based is the on Europe). Aconstraint Western and America Northern in (mostly countries developed in institutions agricultural prominent nationally typically associated with the inter- solutions. technology innovative offer also countries, same the in based cally typi- companies, Yetgies. smaller technolo- to innovate digital with ability bytheir marketplace the in themselves differentiate increasingly and Europe Western and America marily headquartered in Northern pri- are leaders corporate These market. global competitive a highly in ofcompanies number by asmall controlled are purchases farm major most where point the to consolidated These industries have in recent years services. and etc.) goods chemicals, (seed, consumable and equipment) (farm durable offer that companies global-scale afew with associated mostly is technologies agriculture digital in innovation Corporate located? they are Where universities. companies, and top agricultural (ag-tech) technology agricultural innovative smaller companies, Ag’ by‘Big led are agriculture in tions agriculture. into inroads few made have nies global digital technology compa- leading the and slow relatively been has agriculture in innovation digital why reason primary the arguably University innovations are are innovations University innova- digital most Currently robotic milking and feeding systems systems feeding and milking robotic to attracted be may farms size medium- Similarly, technologies. damage if done without precision crop risks that cultivation weed onmechanical rely they because systems guidance equipment and planting precision from greatly efit ben- can growers vegetable organic Forexample, environment. duction pro- foraspecific compelling highly orare scale-dependent less are they because farms small and medium to attractive are technologies ture agricul- digital factor.10 Some tion adop- important an also is farmers of competence technology but the efficiencies, ofscale aresult as ment oninvest- returns earlier provide investments capital because readily more agriculture digital in engage described below.described America of are factors ofthose Some (USA). States United State, York New in offarmers survey a recent in factors important as report that in cited all were resources educational relevant and information, technical development, and access), research data mobile (e.g., reliable structure infra- to related Factors agriculture.9 into technologies ofadvanced tion nities associated with the penetra- opportu- and ofconcerns number and literature analyses identified a onsurveys based report A recent Issues with digital adoption agriculture nomic barriers. eco- and institutional, structural, of because part in ofcountries set alimited in occur agriculture digital in innovations primary the all, In lower remuneration. offers and gious presti- less considered is profession the because professors and students talented most the notattract do ally gener- also countries developing in Farm size: Farm Large farms tend to tend farms Large Data Consortium have emerged to Health the CyVerse, and Socrata, Public-private partnerships such as but, so far, with very little effect.15 putforward,14 been have agencies public-sector among sharing data forgreater calling proposals islative leg- practices.13best context, this In accepted ofgenerally short fall tices prac- management data widespread researchersagricultural suggest that of survey ofarecent Results areas. research data and associated priority agricultural towards attitudes agency realms. economics and agricultural in absent generally are infrastructure sharing data forcommunity ventures and data, private-sector valuable to access universal nothave do nities commu- scientific and Public-sector recommendations. management ofnext-generation development the and analytics foraggregated ability avail- their about arise concerns entities, corporate bylarge lated accumu- increasingly are data As availability. around revolves issue issues.12 privacy and ownership data on certification third-party offer Transparency) Data Ag (e.g., tiatives ini- butnonprofit statutes, current in not protected generally are data for interests.11data corporate Farm the repurpose may that companies large with than cooperatives local and trusted partners such as universities with data sharing comfortable more generally are Farmers time. this agricultural data are unresolved at around concerns legal because issues ownership and privacy data with concerned are They services. increasingly through cloud-based accumulate large amounts of data, they technologies agriculture tal shortages. labour of farm because greenhouses or automated A third issue is government government is issue A third data related, and Asecond, Data: As farmers adopt digi- adopt farmers As 101 coordinate and increase data shar- equipment guidance technology Cloud-based nitrogen advisors ing and access, which are important that requires reliable relay stations Agriculture includes some ‘wicked steps for data gathered under public and mobile connections; and low- problems’, including the use of nitro- auspices. power wide-area networks that offer gen fertilizer that is needed to grow Analytics and management opportunities for the widespread use many of the world’s crops at high gap: Production environments (soil, of sensor technology and equipment production levels. The widespread climate, crops, animals, etc.) vary communications. Advanced con- adoption of nitrogen fertilizer use greatly in agriculture. The effective nectivity investments in rural areas after World War II and especially employment of digital technologies are generally expensive because of during the has therefore requires locally appro- low customer density and are often greatly enhanced food production priate analytics and management not regarded as economically justi- and reduced malnutrition. But it responses. In general, the engineer- fied by communications companies. has also led to serious environ- ing innovations by means of sensors, Business development and mental concerns, including high robotics, and software are rapidly employment: Many farmers and energy use, greenhouse gas emis-

advancing, but the ability to make ag-professionals agree that digi- sions (through nitrous oxide), and in the Digital Age Systems and Food in Agriculture 4: Innovation the technology smart and applicable tal agriculture has a bright future, water quality degradation. Notably to local production environments offers good business and employ- many of the world’s estuaries (Gulf lags behind. ment opportunities, and will of Mexico, Baltic Sea, etc.) experi- Education and research gaps: result in environmental benefits ence low oxygen levels (hypoxia) The engagement of digital agricul- and efficiencies.16 But it may also from nitrogen inflows, which in ture requires knowledgeable and profoundly impact businesses and turn result in the high mortality of skilled farm managers and labourers, employment in rural areas around critical fish species. as well as a cadre of well-educated the globe. In high-wage countries, These concerns are in large part consultants and service providers. farmers are eager to employ auto- related to excessive nitrogen use, Most educational institutions are mation and digital technologies to where more fertilizer is applied than inadequate in offering such instruc- reduce challenges with their farm is needed for the crop. This appears tion, and professional talent tends labour force—which often depends wasteful, but where farmers are to favour urban over rural living. on migrant workers and therefore uncertain about the ‘right’ amount In addition, few institutions have poses legal and management chal- of fertilizer needed they actually the capacity or resources to answer lenges. Digital technologies will respond in an economically rational the research questions that advanced also facilitate those management manner to the realities of their pro- farmers ask. farm enterprises that are larger than duction environments, avoiding the Connectivity and digital would otherwise be possible, and high risk of under-nourishing their divide: Agriculture by its very may intensify the global trend of crops and incurring yield losses. nature is mostly conducted in rural farm consolidation. In developing Most of the uncertainties are asso- areas that are poorly connected, countries where wages are lower and ciated with (1) variable production even in the most developed coun- farms generally smaller, digital tech- environments (soil, crop, manage- tries. The industry is therefore nologies will help advance improved ment), and (2) weather variability. highly impacted by the so-called management practices and better Recent technological develop- digital divide. This current state access to markets (e.g., through ments have proven that data and of inadequate connectivity limits mobile technologies), but will also model computations can address the full deployment of digital agri- impact employment opportunities these uncertainties and offer more culture technologies in most rural in rural areas. reliable nitrogen management areas, including broadband access advice to farmers through cloud-

for information communication; based services. This technology 2017 mobile (cellular) coverage and data Examples of digital agriculture offers real-time nitrogen fertilizer transmission speeds for uploading technology implementation advice, based on weather condi- and downloading data from field Implementing digital agriculture tions, that is specific to field zones equipment or remote farm build- technology can take different forms. and thereby allows farmers to more ings; universal access to precision Three of these are considered below. precisely match nutrient additions THE GLOBAL INNOVATION INDEX INDEX INNOVATION GLOBAL THE 102 THE GLOBAL INNOVATION INDEX 2017 4: Innovation in Agriculture and Food Systems in the Digital Age are: services cloud-based such employing others. among management, pest and irrigation for employed be can technologies impacts.17 Similar environmental negative reducing while profits farmers’ itincreases opportunity: awin-win offer proven to has ogy technol- this evaluations, field farm on- In (Figure 1). needs crop with Adapt-N.com. Source: Figure 1:Real-timenitrogenfieldadvicethroughacloudservice • cloud-based and mobile commu- mobile and cloud-based • employment• at scale allows for services such scalability high the • of the status of farm resources. offarm status of the monitoring real-time and access forcontinuous allow nications and costs, adoption down drive can and expense (hectare) dramatic reductions in per-unit environments, growing many in employed rapidly be to technology the allows provide Some of the main advantages of two hectares).18 But through lease lease through But hectares).18 two than offewer comprised are ings absentee landowners (82% of hold- bymultiple owned often are fields large is, ofheirs—that number great a with plots smaller into offields sion a in subdivi- resulted privatization land associated the and farms, tive collec- former ofthe many dated liqui- Bulgaria reforms, the After facilities). livestock and (fields units production consolidated highly and farms cooperative large included by Eastern European standards, and efficient relatively was agriculture of1989, country’s the reforms nomic eco- and political Bulgaria’s to Prior inBulgaria Precision farmingservices technologies. ensemble on based advice management ing offer- technologies sensor field low-cost with tools data-intensive computational, ofhighly gration inte- the be likely will deployment The next phase of technology oftechnology phase next The modern precision technologies in technologies precision modern implement to farmers with works that acompany is NIK example, For services. and products associated offering are providers service nical tech- regional and equipment, field advanced purchasing are farmers Many methods. farming of precision adoption forthe opportunities tional excep- created also and Bulgaria, in farming large-scale viable in resulted supplies. food stable good management employment, rural farms, support to practices,through direct payments intended in Bulgaria’s and agriculture, much of it billion US$4 around invested Policy Agricultural Common EU 2007, the in accession Union European its since Furthermore, crops. primary as maize and sunflower, wheat, with agriculture, large-scale through land ofthe majority vast the cultivate still can farmers private landowners, agreements with many individual These developments have have developments These 103

Bulgaria.19 These technologies are producers face risk all the same. Notes offered through (1) strategic partner- Several programmes have emerged 1 UN DESA, 2015. ships with Northern American and recently to address these issues using 2 Montgomery, 2007.

European technology leaders that index-based insurance schemes.20 3 Foley, 2011. allow for capital and service invest- Initially, pilot programmes in the 4 Tien, 2013; Song et al., 2016. ments (farm management software, developing country context relied mapping and navigation hardware heavily on station-level weather 5 Woodard, 2016a. and software, precision application data. However, these data are often 6 Schwab, 2016. equipment, auto-steering and guid- sparse and are themselves difficult to 7 van Es et al., 2016. ance systems, weather and satellite verify. In recent years there has been 8 Woodard, 2016a, 2016b. monitoring, irrigation equipment, a movement towards a different solu- 9 van Es et al., 2016. etc.), and (2) skilled field profes- tion: using remotely sensed data to 10 Castle et al., 2015. sionals who implement technologies determine losses. The Index Based 11 Castle et al., 2015. on farms and help develop local Livestock Insurance programme 12 Further information about Ag Data

knowledge. In summary, the rapid (IBLI) in Kenya and Ethiopia was in the Digital Age Systems and Food in Agriculture 4: Innovation Transparency is available at http://www. adoption of digital farming technol- one of the earlier adopters of this fb.org/ag-data. ogy in Bulgaria can be attributed to approach.21 As newer remote sens- 13 Fernandez et al., 2016. a combination of: ing platforms come online, as well 14 Murray, 2015. as lower-cost custom options (e.g., • large-scale production units that 15 Woodard, 2016a. nano-satellites, unmanned aerial are a result of land reforms under systems, etc.), there will likely be a 16 van Es et al., 2016. socialist governments prior to large movement towards designing 17 Sela et al., 2016; Sela et al. 2017. 1989, the risk management programmes of 18 European Commission, 2015. • a workable land lease system that the future around these sensing tech- 19 More information about NIK is available at allows private farmers to manage nologies to indicate both when losses http://www.nik.bg/en. large land tracks with multitudes occur and the extent of those losses. 20 Woodard et al., 2016. of small land owners, 21 Woodard et al., 2016. • farm payments from the Euro- Conclusions pean Union, and The penetration of advanced digital References • strategic partnerships with lead- technologies into the agricultural Castle, M., B. D. Lubben, and J. Luck. 2015. ing technology providers. industry is progressing rapidly in ‘Precision Agriculture Usage and Big advanced economies, and is increas- Agriculture Data’. Cornhusker Economics, University of Nebraska-Lincoln Extension. ingly impacting developing coun- Available at http://agecon.unl.edu/ Remote sensing and financial risk tries. Because of several unique documents/2369805/20977275/5-27-15.pdf/ b80d3d0a-684e-4bdd-993c-96246691bc95. management to alleviate poverty characteristics of agriculture (involv- European Commission. 2015. ‘Bulgaria: Common The USA has long had major govern- ing its highly localized and variable Agricultural Policy’. DG Agriculture and Rural ment programmes in place to facili- resources, poor connectedness in Development, Unit for Agricultural Policy tate risk management for farmers in rural areas, education and research Analysis and Perspective. 15 March. Available at http://ec.europa.eu/agriculture/sites/ various forms. Today the bulk of that gaps, support businesses, and global agriculture/files/cap-in-your-country/pdf/ funding is allocated to risk manage- players), digital agriculture requires bg_en.pdf. ment and insurance programmes special consideration from govern- Fernandez, P., C. Eaker, S. Swauger, and M. L. E. Steiner Davis. 2016. ‘Public Progress, Data with great success. However, uptake ments and industry leaders. This will Management and the Land Grant Mission: A

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