National Academies Press: OpenBook
« Previous: 6 Water-Use Efficiency and Productivity
Suggested Citation:"7 Data." National Academies of Sciences, Engineering, and Medicine. 2018. Science Breakthroughs to Advance Food and Agricultural Research by 2030. Washington, DC: The National Academies Press. doi: 10.17226/25059.
×
Page 94
Suggested Citation:"7 Data." National Academies of Sciences, Engineering, and Medicine. 2018. Science Breakthroughs to Advance Food and Agricultural Research by 2030. Washington, DC: The National Academies Press. doi: 10.17226/25059.
×
Page 95
Suggested Citation:"7 Data." National Academies of Sciences, Engineering, and Medicine. 2018. Science Breakthroughs to Advance Food and Agricultural Research by 2030. Washington, DC: The National Academies Press. doi: 10.17226/25059.
×
Page 96
Suggested Citation:"7 Data." National Academies of Sciences, Engineering, and Medicine. 2018. Science Breakthroughs to Advance Food and Agricultural Research by 2030. Washington, DC: The National Academies Press. doi: 10.17226/25059.
×
Page 97
Suggested Citation:"7 Data." National Academies of Sciences, Engineering, and Medicine. 2018. Science Breakthroughs to Advance Food and Agricultural Research by 2030. Washington, DC: The National Academies Press. doi: 10.17226/25059.
×
Page 98
Suggested Citation:"7 Data." National Academies of Sciences, Engineering, and Medicine. 2018. Science Breakthroughs to Advance Food and Agricultural Research by 2030. Washington, DC: The National Academies Press. doi: 10.17226/25059.
×
Page 99
Suggested Citation:"7 Data." National Academies of Sciences, Engineering, and Medicine. 2018. Science Breakthroughs to Advance Food and Agricultural Research by 2030. Washington, DC: The National Academies Press. doi: 10.17226/25059.
×
Page 100
Suggested Citation:"7 Data." National Academies of Sciences, Engineering, and Medicine. 2018. Science Breakthroughs to Advance Food and Agricultural Research by 2030. Washington, DC: The National Academies Press. doi: 10.17226/25059.
×
Page 101
Suggested Citation:"7 Data." National Academies of Sciences, Engineering, and Medicine. 2018. Science Breakthroughs to Advance Food and Agricultural Research by 2030. Washington, DC: The National Academies Press. doi: 10.17226/25059.
×
Page 102
Suggested Citation:"7 Data." National Academies of Sciences, Engineering, and Medicine. 2018. Science Breakthroughs to Advance Food and Agricultural Research by 2030. Washington, DC: The National Academies Press. doi: 10.17226/25059.
×
Page 103
Suggested Citation:"7 Data." National Academies of Sciences, Engineering, and Medicine. 2018. Science Breakthroughs to Advance Food and Agricultural Research by 2030. Washington, DC: The National Academies Press. doi: 10.17226/25059.
×
Page 104
Suggested Citation:"7 Data." National Academies of Sciences, Engineering, and Medicine. 2018. Science Breakthroughs to Advance Food and Agricultural Research by 2030. Washington, DC: The National Academies Press. doi: 10.17226/25059.
×
Page 105
Suggested Citation:"7 Data." National Academies of Sciences, Engineering, and Medicine. 2018. Science Breakthroughs to Advance Food and Agricultural Research by 2030. Washington, DC: The National Academies Press. doi: 10.17226/25059.
×
Page 106

Below is the uncorrected machine-read text of this chapter, intended to provide our own search engines and external engines with highly rich, chapter-representative searchable text of each book. Because it is UNCORRECTED material, please consider the following text as a useful but insufficient proxy for the authoritative book pages.

7 Data 1. INTRODUCTION The growing availability of data presents an opportunity to improve the resilience and efficiency of food and agricultural production on a scale unimaginable even a decade ago. The convergence of cloud computing, mobile, Internet of Things (IoT), and analytic technologies has resulted in an explosion of data that has been transformational in all sectors of the economy (Cao, 2016). Approximately 90% of the data ever created was generated in the last 2 years (Marr, 2018). By 2025, the amount of data created and copied annually is expected to grow to one trillion gigabytes (Seagate, 2017). Understanding the connec- tions between the biophysical and socioeconomic elements of agricultural systems through research de- pends on generating and analyzing massive amounts of data. Data will be at the center of the next revolu- tion in food and agriculture (United Nations, 2014). The relationship between data, algorithms, and computing is symbiotic. In practice, (raw) data is processed by computers through mathematical models and algorithms into useable information. The value of data comes when it is analyzed to provide information that is used to make decisions based on insights and understandings derived from data. Advances in data storage, communications and processing have led to new research methods and tools that were simply not possible just a decade ago (NIH, 2018). Simi- larly, breakthroughs in agriculture will benefit from and compel state-of-the-art advances in the areas of data, algorithms, and computing. Machine learning and deep learning are two such examples, which will be further discussed in Section 3.1. Data science is the emerging field that sits at the nexus of data, algorithms, and computing. It is an interdisciplinary field of inquiry in which quantitative and analytical approaches, processes, and systems are developed and used to extract knowledge and insights from increasingly large and complex sets of data (NIH, 2018). Data analysis has fostered knowledge creation for hundreds of years; achieving the sci- ence breakthroughs identified in this report will be spurred by data science. In June 2018, the United States announced a new record in the global competition to build the world’s fastest super-computer. The peta-scale machine called “Summit” can perform 200 petaflops (1015 calculations) per second and is more than twice the speed of the incumbent at China’s National Super- computing Center (Top 500, 2018). The next horizon is exascale computing; a machine capable of exe- cuting a billion billion (109 109 = 1018) calculations per second. Exascale computing will enable more complete and accurate representations in earth systems modeling needed to improve our understanding on local to global scales for the food system and enable faster simulations that can, for example, more realis- tically mirror the speeds of biological processes (McDermind et al, 2017). Figure 7-1 shows a data analytics maturity curve that progresses from descriptive to predictive to prescriptive. In the early stages, information provided in the descriptive stage lends hindsight on what happened. The predictive stage is the next stage that provides insight into what will happen. The last stage of analytics maturity is prescriptive and enables foresight in what we can make happen. Much progress has been made in food and agricultural research in establishing descriptive analytics. Scientific break- throughs in food and agricultural disciplines in the next 10-15 years will increasingly address the predic- tive and prescriptive levels of understanding. 94 Prepublication Copy

Data FIGURE 7-1 Analytics maturity curve. SOURCE: Centurion, 2015, citing INFORMS Analytics Maturity Model User Guide (www.informs.org). Our understanding of the fundamental scientific underpinnings of the biological, chemical, physical and socioeconomic elements of the food and agricultural system can benefit from better data access, data integration, and data analytics. The previous chapters have indicated specific instances where access to more data and data science can improve the resilience, efficiency, and sustainability of agriculture. This chapter describes the challenges of collecting, integrating, and analyzing a broad range of data types, and ensuring the quality of those data in near real time to afford the research breakthroughs needed. The chap- ter then proposes research needs and potential breakthroughs specific to data science for the food and ag- ricultural enterprise. 2. CHALLENGES 2.1 Challenge 1: Data Heterogeneity and Dimensionality The challenges and opportunities in agricultural and food data can be illustrated by the acronym GEMS, which represents the fact that agriculture yields are modeled as a function of genetic (G), envi- ronment (E), management (M) and socioeconomic (S) factors. To understand yield, there needs to be an understanding of the genetics of the plant or animal being cultivated, the environmental factors affecting growth, the management practices of the farm, and the socioeconomic factors, as well as an understanding of the complex interactions among these factors, represented as G E M S (GEMS). Numerous en- vironmental factors may be of interest (e.g., location, soil type, elevation, inclination, rainfall, precipita- tion, humidity, temperature, dew point) and each may have positive or negative impacts on genotypes. GEMS data is in a wide variety of formats, in different spatial and temporal scales, with different degrees of accuracy and precision which is challenging to harmonize for analysis and discovery (Lu et al, 2016). Data curation and harmonization efforts currently constitute 80 percent of the effort related to data use (Crowdflower, 2016). The curation and analysis of GEMS data leads to the need for data standards and tools to manipulate and analyze the massive geospatial temporal datasets, and pushes the frontier of data, analytics, and com- puting systems (see Box 7-1). The complexities of data, such as the GEMS data, can be explored in terms of the four basic data attributes: volume, variety, velocity, and veracity (see Box 7-2). Prepublication Copy 95

Science Breakthroughs to Advance Food and Agricultural Research by 2030 BOX 7-1 G.E.M.S.™ G.E.M.S.™ (Genetic, Environmental, Management, and Socioeconomic data) is a proprietary plat- form developed by the University of Minnesota that merges domain expertise in the food and agricul- tural sciences with high-performance computing and bioinformatics expertise to drive the development of next-generation agricultural and food informatics (agri-food informatics), data discovery, and analy- sis. G.E.M.S.™ aims to allow researchers to solve problems at multiple functional, temporal, and spa- tial scales related to crop sustainability and food production through provision of interoperable genetic, environmental, management, and socioeconomic data related to agriculture. There are massive datasets involved with each of these components, and a data lake (or a data repository) is required to hold data until they are needed. Currently there are not standards or applica- tion programming interfaces (APIs) to allow those datasets to integrate and merge with one another to be useful. See Section 2.2. SOURCE: Allan et al., 2017. BOX 7-2 Attributes of Data Volume. Data volume is the size of the data and is typically measured in bytes. Genomic experiments generate terabytes (TB) of data. For example, the genome of a single corn plant consists of 2.5 billion DNA bases (USDA-NIFA, 2017a). By 2025, genomics is expected to be on par with or exceed the data needs of the Big Data domains of astronomy, YouTube, and Twitter (Stephens et al., 2015). Variety. Data variety refers to the number of different types of data. Precision agriculture requires many different layers of data to develop a site-specific understanding. For example, variables could include soil type, elevation, slope, temperature, and precipitation. Satellite data can provide critical real-time information, but the heterogeneity (or variety) of data makes it difficult to combine the data in a harmonized way needed for analytical methods to examine specific factors for research or for deci- sions. Amassing spatial-temporal data in a harmonized way would be transformational for real-time decision support (e.g., disease prediction via disease vectors using spatial data). Velocity. Data velocity indicates the speed of data processing. With rapid improvements in the speed of data velocity, real-time continuous monitoring of agriculture is becoming possible. Veracity. Data veracity refers to bias, noise, abnormality, uncertainty, and imprecision. This often- overlooked dimension of data is important in developing appropriate mathematical models and analyt- ics to obtain useful insights. The food and agricultural system is an intricate system-of-systems with multiple dimensions of uncertainty; for instance, at a macro level, agriculture has both demand and supply uncertainty (IOM and NRC, 2015). Example: Improvements in Sensor Technology and Data Velocity Enabling Real-Time Con- tinuous Monitoring in Agriculture. Novel sensors can provide accurate measures of soil, plant, animal, and food characteristics at a high frequency and data density and at any given time and location. The next generation of sensors will revolutionize the ability to deploy prescriptive solutions in near real-time. For example, Figure 7-2 shows how geo-referenced in situ soil sensors could be widely distributed across a farm, measure soil and plant nutrients, and display results in a colored geo-spatial map that allows farmers to determine site-specific actions (e.g., variable application of water and nutrients). Similarly, microelec- tromechanical technology is already being used to develop miniature sensors that can be embedded into plant stems for measuring a plant’s water potential (the hydration relative to its growth and production yield) (Pagay et al., 2014); this could replace traditional labor-intensive, destructive methods that provide 96 Prepublication Copy

Data only point-in-time measurements. Continuous measurements such as these will improve the calibration and performance of water- and nutrient-use models, and may lead to a new understanding of how plants use water and nutrients. Integrating these farm-scale sensor technologies with seasonal or hyper-local weather forecasts, or with measures of water availability in connected ecosystems via the IoT, creates op- portunities to manage farm ecosystems integrated with natural ecosystems (e.g., ecohydrology). For vine- yards, such an understanding could lead to more consistent production of high-quality wine grapes with certain flavor and aroma profiles (Campbell, 2016). Likewise, in planta crop health sensors may quantify biochemical changes in plants caused by an insect pest or a pathogen, alerting and enabling the producer to plan and deploy immediate site-specific control strategies before the infestation occurs and damage is visible and widespread across the field. Increased data velocity will enable dynamic control of agricultur- al equipment in motion in real time—such as precision planters, sprayers, and irrigation—and enable real- time continuous monitoring of individual livestock in a herd using wearable (and other) sensors for preci- sion livestock applications, using advanced technologies such as microfluidics, sound analyzers, image- detection techniques, sweat and salivary sensing, serodiagnosis (diagnosis based on blood sera), and oth- ers. Creating efficient online monitoring systems in real time requires the ability to integrate all the avail- able sensor data and run analysis in real time (Neethirajan, 2017). 2.2 Challenge 2: Data Standards and Interoperability Modern scientific research is becoming a more data-driven, interactive process between multiple stakeholders across the world (EU SCAR, 2015). The term “e-science” has been coined to describe the potential of data-driven and computationally intensive processes to enhance manual laboratory work and fieldwork and reduce dependence on paper-based recording (EU SCAR, 2015). E-science has the poten- tial to enable worldwide collaboration in flexible teams using advanced tools, services, and data reposito- ries. These can include distributed networks or grid computing, high-performance computing, visualiza- tions, simulations, workflows, and provenance documentation (EU SCAR, 2015). FIGURE 7-2 Schematic representation of real-time continuous monitoring of soil, crop, water, and livestock via microelectromechanical and biodegradable sensors. The sensors measure a range of important attributes to generate data. Data can then be analyzed and used for modeling and for predictive and prescriptive agriculture. Prepublication Copy 97

Science Breakthroughs to Advance Food and Agricultural Research by 2030 Providing access to data can accelerate and democratize the scientific process. To do so would re- quire infrastructure improvements for supporting the reuse of scientific data in the food and agricultural realm and enhancements for machines to automatically find and use data. While there are some notable cyberinfrastructure initiatives and data-sharing efforts under way (see Box 7-3), these efforts are only able to address some parts of the vast food system. As a set of universally agreed-upon guidelines referring to interoperability among systems or applications, standards support re-use. One of the major hurdles in data standardization and interoperability is the lack of sufficient knowledge representations (e.g., ontologies, semantic nets, rules) (Jonquet et al., 2018). Knowledge representations provide a representation that a computer system can utilize to solve complex tasks. The FAIR data principles are a set of guiding principles that could facilitate data standardization and interoperability for scientific data management and stewardships. The principles are organized around four concepts: findable, accessible, interoperable, and reuseable (Wilkinson et al., 2016). The FAIR prin- ciples are complementary to open-data philosophy—data can be open without being FAIR, and vice ver- sa. Open data is the idea that some data should be freely available to everyone to use and republish as they wish, without restrictions from copyright, patents or other mechanisms of control (Auer et al., 2007). Government funded awards often require researchers to open their data, but there is little guidance on best practices or dissemination for open data in the United States. The existence of standards is an often-cited success factor for reuse of data and software (Pasquetto et al., 2017). Standards can be administered in a variety of ways: created de facto by private organiza- tions1 or managed by nonprofit organizations.2 The nonprofit consortium of businesses serving the agri- culture sector, AgGateway has identified the need for standards as essential to promoting and enable the industry’s transition to highly data-driven agricultural practices and is working to develop their own (Ag- Gateway, 2017; Smith, 2017). 2.3 Challenge 3: Data Privacy Privacy is a confounding issue for food and agricultural data. In some domains, such as health care, data can be anonymized by removing or encrypting personally identifiable information. The best available privacy protection technology, differential privacy, is inadequate for agricultural data because of its spati- otemporal nature (Shekhar et al., 2017). Anonymizing geospatial data without distorting its granularity is a known problem which does not yet have an adequate solution. Aggregating data is a frequently used approach to anonymize farm- or field-level data. For instance, data from individual fields and farms are aggregated into county-level metrics that are made public. Aggregation limits the granularity of analysis, making intrafield or site-specific analysis impossible. For example, spatial data that is aggregated for anonymization purposes typically lacks the high spatial resolution needed by biophysics based models to accurately capture or predict the system responses. 3. SCIENTIFIC OPPORTUNITIES There are emerging technologies related to data that can transform the food and agricultural system. In particular, there are four promising future areas: artificial intelligence, blockchain, IoT and quantum computing. 1 The computer file format PDF was created by Adobe in 1993 and was a de facto standard for many years before eventually becoming a formal ISO standard in 2005. 2 The nonprofit organization GS1 is best known for the barcode (universal product code) that is scanned by retail- ers more than six billion times every day. 98 Prepublication Copy

Data BOX 7-3 Data Sharing and Cyberinfrastructure Initiatives Related to Food and Agriculture Below are examples of some food and agricultural data sharing and cyberinfrastructure initiatives that are currently under way.  INFEWS (Innovations at the Nexus of Food, Energy, and Water Systems)a is a National Science Foundation (NSF) program that seeks to seeks “to support research that conceptualizes Food- Energy-Water systems broadly and inclusively, including social and behavioral processes (such as decision making and governance), physical processes (such as built infrastructure and new technologies for more efficient resource utilization), natural processes (such as biogeochemical and hydrologic cycles), biological processes (such as agroecosystem structure and productivity), and cyber-components (such as sensing, networking, computation and visualization for decision- making and assessment).” Through funding from NSF and USDA-NIFA the initiative aims to iden- tify and fund the most meritorious and highest-impact projects across the FEW nexus. The initia- tive also promotes international cooperation among scientists and engineers from a range of dis- ciplines and organizations.  CyVerseb is an NSF-funded project that aims “to design, deploy and expand a national cyberinfra- structure for life sciences research and to train scientists in its use.” The project provides life sci- entists with the computational infrastructure to manage large datasets and complex analyses through data storage, bioinformatics tools, image analyses, cloud services, APIs, and other tools. CyVerse is a dynamic virtual organization led by the University of Arizona alongside the Texas Advanced Computing Center, Cold Spring Harbor Laboratory, and the University of North Caroli- na at Wilmington.  USDA VegScapec is a National Agricultural Statistical Service (NASS) web service–based U.S. crop condition monitoring system. It aims to improve objectivity, robustness, quantification, and defensibility of nationwide crop condition monitoring by delivering interactive vegetation indexes that enable users to explore, visualize, query, and disseminate current vegetative cover maps and data. New satellite-based data are loaded on a weekly basis during the growing season. Veg- Scape is built on the CropScaped (a NASS geospatial cropland data service) framework and uses NASA’s MODIS satellite (Yang et al., 2013).  GEOGLAM (International Group on Earth Observations Global Agricultural Monitoring Initiative)e is an initiative developed by the Group on Earth Observations (a partnerships of governments and international organizations) which coordinates satellite monitoring observation systems in different regions of the world in order to enhance crop production projections and weather forecasting data. The initiative provides a framework using Earth observations (EO), including satellite and ground- based observations, designed to build on existing agricultural monitoring programs and initiatives at national, regional, and global levels.  AgMiP (the Agricultural Model Intercomparison and Improvement Project)f is an international pro- ject that utilizes intercomparisons of various methodologies to “improve crop and economic mod- els and ensemble projections and to produce enhanced assessments by the crop and economic modeling communities researching climate change agricultural impacts and adaptation.” AgMiP is using a two-track science approach of model intercomparison and improvement and climate change multimodel assessment. These approaches are facilitated by a series of regional work- shops held in each AgMIP region over a 3-year period as well as by global studies and workshops that focus on particular crops and on global analyses. a https://www.nsf.gov/funding/pgm_summ.jsp?pims_id=505241. b http://www.cyverse.org/about. c https://nassgeodata.gmu.edu/VegScape. d https://nassgeodata.gmu.edu/CropScape. e https://cropmonitor.org. f http://www.agmip.org. Prepublication Copy 99

Science Breakthroughs to Advance Food and Agricultural Research by 2030 3.1 Opportunity 1: Artificial Intelligence Artificial intelligence (AI) refers to intelligence displayed by machines, as opposed to natural intel- ligence demonstrated by humans and other animals (Bradford, 2017). The term AI is commonly used when a machine mimics cognitive functions that are typically associated with humans, such as planning, learning, reasoning, problem solving, knowledge representation, perception, motion, manipulation, social intelligence, and creativity. Artificial intelligence combines automation, robotics, and computer vision. Advances in statistics, faster computers, and access to large amounts of data have enabled advances in AI, particularly in the field of machine learning where significant progress has been made in the areas of im- age and pattern recognition, natural language understanding and robotics. The scale and complexity of the food and agricultural system make it well suited for AI, with large amounts of data available to train algorithms. Large amounts of data from ever-more-sophisticated exper- iments and models are being generated, and food and agricultural researchers are challenged with decid- ing what data to collect and how to best process the data generated. A 2016 analysis estimated that a new crop protection product requires the analysis of more than 160,000 compounds—the equivalent to more than 11 years of research and development and overall costs that exceed $280M per commercial product (Brayne et al., 2018). AI will enable new discoveries, not just in analyzing existing data but in augment- ing human creativity around planning new experiments and accelerating discovery and improving effi- ciency. There are promising applications and indicators for AI in agriculture. In selective breeding pro- grams, the development of high-throughput automated phenotyping capabilities can speed the process of breeding to develop resilient yet high-yielding, high-quality crops. AI and machine-learning research on the massive amount of environmental, growth, and genetic data can help scientists, for example, elucidate the connections between system components, predict the yield of different varieties based on early-season plant attributes, and associate specific desirable traits with genetic markers (Baxter et al., 2007). Robots can be designed to harvest crops at a higher volume and faster pace than human laborers, to monitor crop and soil health using computer vision and deep-learning algorithms that analyze data captured by drones, and to more accurately predict crop yields using machine-learning models to better understand environ- mental impacts (Sennar, 2017). Box 7-4 on robotic milking provides an example of integrating AI with sensor technologies in animal agriculture. BOX 7-4 Robotic Milking One agricultural application that simultaneously utilizes and collects large datasets is robotic milk- ing systems (RMSs). Using detection sensors, the robot attaches the cups to the cow teats from un- derneath the udder. These sensors are able to detect how many teats are available and their position- ing to ensure that the cups are attached correctly. Every cow wears an electronic tag making it possible for the RMS to identify each cow and collect production and other data on her. Cows are only allowed access to be milked if a certain period of time has elapsed since their last milking. A single stall can milk 55-65 cows per day, and it is estimated that over 35,000 RMS units are operational on dairy farms around the world. RMS results in a 5-10 percent increase in milk yield as compared to milking two times per day, mainly due to increased milking frequency (de Koning, 2010). A milk analyzer can be included in the RMS to collect data on milk components such as fat, pro- tein, pH, and lactose. These data can provide real-time alerts to notify the farmer of feeding or health problems or signal the need for a management intervention. Furthermore, machine learning would provide an opportunity to tune parameters for assessing milk quality. The profitability of RMS depends on factors such as the amount of milk produced per cow and per robot, labor savings, and length of useful life (Salfer et al., 2017). Although RMS typically have not been profitable in U.S. dairy produc- tion systems to date, advances in robotic design, availability of labor, and higher labor costs in the fu- ture may alter this situation, as has been the case in western and northern Europe, which have the highest rates of RMS adoption (Brouček and Tongeľ, 2015). 100 Prepublication Copy

Data 3.2 Opportunity 2: Blockchain Blockchain is a recent technology advancement with potential for addressing the challenge of creat- ing a more transparent, authentic, and trustworthy digital record of the journey that food and other physi- cal products take across the supply chain (Lougee, 2017). Blockchain technology arose out of the efforts to create the cybercurrency Bitcoin, but its potential goes well beyond its original purpose. As discussed in Chapter 4, at its core, the blockchain is a shared, immutable ledger for recording the history of transac- tions (IEEE, 2018a). It uses a variety of technologies including public–private key cryptography, distrib- uted databases, decentralized processing, hash functions, and consensus algorithms (Ge et al., 2017). This could be valuable in creating transparency in a global food system with multiple disparate actors across the multi-tiered supply chain. More information will be required to address research challenges on food and agricultural supply chains and a platform for studying how complex systems interact. Blockchain presents a potential new source of data and new platform to be leveraged to deliver advances toward transparency to the food sys- tem and transformations in food safety, fraud reduction, market access, waste reduction, and productivity gains (Ahmed and ten Broek, 2017; Ge et al., 2017; Kim and Laskowski, 2017). A systematic mapping study designed to understand the current research topics, challenges and fu- ture directions of blockchain technology from a technical perspective examined 41 primary papers (Yli- Huumo, 2016). It showed that most of the current research on blockchain technologies has been focused on security and privacy issues. Issues in scalability, such as performance and latency, need to be ad- dressed in order to realize pervasive use of blockchain technology in the food system. 3.3 Opportunity 3: IoT IoT is the network of physical devices embedded with electronics, software, sensors, actuators, and connectivity which enables these “things “to connect and exchange data creating opportunities for more direct integration of the physical world into computer-based systems, resulting in efficiency improve- ments, economic benefits, and reduced human exertions (Wasik, 2013; Morgan, 2014). Among its ad- vantages, IoT addresses the high cost of manual data collection which impedes the adoption of beneficial data-driven technologies. IoT technologies enable the seamless data collection from various sensors, cam- eras and drones and are the foundation for intelligent systems, as illustrated in the example above and throughout this report. Farm applications of precision agriculture and precision livestock farming are prime examples of IoT; however, the influence of IoT spans the entire food system (Wolfert et al., 2017). IoT is still in its infancy in the food and agriculture sector (Verdouw et al., 2016). Realizing future subsystems within the food system where IoT devices are capturing and sending data, but in a time- sensitive and synchronized way, “could be stalled by our lack of effective methods to marry computers and networks with timing systems” (NIST, 2015). Challenging requirements for food and agriculture applications need to be addressed in both the technical and non-technical arenas. For example, on-site applications in remote outdoor farming locations often lack internet connectivity needed for communications. Easy access to power and durability and cal- ibration are also essential. The FarmBeats system design explicitly accounts for weather-related power and Internet outages which has enabled six-month long deployments in two U.S. farms (Vasisht et al., 2017). 3.4 Opportunity 4: Quantum Computing In 2007, scientists produced the first evidence that photosynthesis operates though quantum coher- ence giving rise to the new field of quantum biology (Engle, 2007). Quantum phenomena occur in biolog- ical systems, with examples including olfaction (Turin, 2002), DNA mutation (Lowdin, 1965), and Brownian motors in many cellular processes (Krug, 2006). Quantum mechanics may play a vital role in biology (e.g., quantum biology [Ball, 2011]), and the emergence of quantum computing could be used to Prepublication Copy 101

Science Breakthroughs to Advance Food and Agricultural Research by 2030 explore natural phenomena in the physical world—phenomena which traditional computing paradigms are ill-suited to represent at scale. Quantum mechanics is a subdiscipline of physics that explains the physical world and attempts to explore how it functions (Feynman, 2002). Nature follows the laws of quantum mechanics, in which par- ticles behave in “strange” ways. For example, caffeine molecules in a cup of coffee are difficult to model, and the detailed structure and properties are difficult to understand because the particles can take on more than one state and can even interact with other particles that are far away. The computers currently in use have not been able to take into account such complex properties because classical computers code infor- mation in bits that represent 0 or 1 values. On the other hand, quantum computers operate on the basis of qubits and incorporate two key principles of quantum physics: superposition and entanglement. Superpo- sition allows a qubit to simultaneously take on the value of both 0 and 1, and entanglement allows qubits in a superposition to be correlated with one another (the state of one can be dependent on the other). Clas- sical computers use a binary format as on-off switches, while quantum computers use qubits that can act as sophisticated on-off switches and can be used to solve more complex problems in quantum scales (in- cluding nature) (IEEE, 2018b). Quantum computing technology is in its infancy. Many fundamental challenges exist including de- veloping long-living qubits for computation, scaling the number of qubit processors, effective quantum error correction. However, major efforts are under way across the globe in academia and industry and promising early results are unfolding (Alibaba, 2018; Google, 2018; IBM Q, 2018; Intel, 2018; Knight, 2017; Microsoft, 2018). In 2016, the first publically available quantum computing service on the cloud was made available. Quantum computers may lead to revolutionary breakthroughs in discovery of new materials for agriculture, agrochemical discovery, and artificial intelligence algorithms that could impact food and agriculture. Molecular modeling has been identified as an area for exploration with quantum computing (Emerging Technology from the arXiv, 2017). Other areas in which quantum computing could help with is analyzing genomics data for plants, animals, or the soil microbiome. Quantum computing could make it possible to perform real-time analytics on various types of data (e.g., weather data at high spatial resolution) (Accenture Labs, 2017; Popkin, 2017). This unique juncture in time provides an oppor- tunity for food and agricultural research to help shape the next era of computing. 4. BARRIERS TO SUCCESS Data science is a rapidly evolving field and data science skills for working with Big Data are in high-demand in all sectors, including food and agricultural research and agricultural economics (Wood- ward, 2016). Data science tools need to become more useable by non-computer science domain experts. Traditional agricultural and food science programs are evolving to better integrate the variety of disci- plines needed; for instance, the digital agriculture initiative at Cornell University is aimed at generating innovating research at the intersection of agriculture, computing, and engineering (Cornell University, 2018). New partnerships, prizes, and conferences are needed to spark convergence between those who are new to food and agriculture and current food and agricultural researchers. Initiatives such as the Syngenta Crop Challenge in Analytics awarded by the Analytics Society of INFORMS show promise in fostering much-needed cross-industry collaboration (INFORMS, 2018). Reward systems for generating and sharing data in research environments may need to be considered, for example, creating incentive structures or changing the approach to tenure and promotion evaluations (USDA-NIFA, 2017b). It will be important to train a workforce that can manage, analyze, and manipulate large datasets and enable the workforce’s convergence with food and agricultural scientists. Furthermore, it may be necessary to develop a culture that supports and rewards sharing of data (including combinations of private and public data) by sets of communities of researchers, standardizes protocols, harmonizes experimental designs, and addresses ownership, privacy and security concerns unique to the food and agricultural enterprise. Funding sources, public or private, need to be available for developing the standards and protocols needed harmonize agri- cultural data sets and make them interoperable. 102 Prepublication Copy

Data The collection of data from numerous stakeholders is required to create the pool of Big Data needed to well-represent the multi-dimensional food system. To incentivize data sharing by individual stakehold- ers or researches, reward systems may require new technologies that can identify and appropriately attrib- ute value derived from the pool of Big Data to the individual contributions. These value attribution tech- nologies may be conceptually similar in spirit to marketing attribution, the process of identifying a set of user actions (events, touchpoint) that contribute in some manner to a desired outcome, and then assigning a value to each of these (Priest, 2018), but will need to account for the unique complexities in the food and agricultural domain. Ownership of food and agricultural data can be a confounding issue, especially when it comes to IoT data. Ownership is a legal concept related to property, and U.S. law recognizes various categories of property (for example, real property, personal property, and intellectual property). However, data generat- ed in the food and agricultural sectors may not belong in any of those categories of property. For example, farming data is a compilation of data generated from field operations (real property) using sensors on equipment (personal property) and represents intellectual property on maximizing yield. Questions emerge around legal ownership of data (which confers control) and how it can be used. There is currently little legislative or judicial rulings for guidance. The American Farm Bureau Federation developed the Privacy and Security Principles for Farm Data (AFBF, 2014) which lays out 13 data principles, but these are only guidelines. Users of data will need to consider the implications of data ownership, including pro- tecting ownership of data, abiding by legal usage, and ensuring appropriate sharing with others. Ambigui- ty and uncertainty around ownership can cause inefficiencies and limit sharing and value creation. 5. RECOMMENDATIONS Advances in data sciences can transform how data can be better collected, analyzed, and used for food and agricultural research. While there are many opportunities, the following actions merit high prior- ity:  Accelerate innovation by building a robust digital infrastructure that houses and provides FAIR (findable, accessible, interoperable, and reuseable) and open access to agri-food datasets.  Develop a strategy for data science in food and agricultural research, and nurture the emerging area of agri-food informatics by adopting and influencing new developments in data science and information technology in food and agricultural research  Address privacy concerns and incentivize sharing of public, private, and syndicated data across the food and agricultural enterprise by investing anonymization, value attribution and related technologies. REFERENCES Accenture Labs. 2017. Innovating with Quantum Computing: Enterprise Experimentation Provides View into Fu- ture of Computing. Available at https://www.accenture.com/t00010101T0000000w/br-pt/acnmedia/PDF-45/ Accenture-Innovating-Quantum-Computing-Novo.pdf (accessed April 26, 2018). AFBF (American Farm Bureau Federation). 2014. Privacy and Security Principles for Farm Data. Available at https://www.fb.org/issues/technology/ data-privacy/privacy-and-security-principles-for-farm-data (accessed April 26, 2018). AgGateway. 2017. AgGateway Releases Annual Report and 5-Year Plan: Enable Companies to Increase Efficiency, Agility and Profitability. Available at http://www.aggateway.org/Newsroom/2017PressReleases/AgGateway ReleasesAnnualReportand5-YearPlan.aspx (accessed April 26, 2018). Ahmed, S., and N. ten Broek. 2017. Food supply: Blockchain could boost food security. Nature 550 (7674):43. Alibaba, 2018. Alibaba Cloud and CAS Launch One of the World’s Most Powerful Public Public Quantum Compu- ting Services. Available at https://www.alibabacloud.com/press-room/alibaba-cloud-and-cas-launch-one-of- the-worlds-most (accessed July 9, 2018). Prepublication Copy 103

Science Breakthroughs to Advance Food and Agricultural Research by 2030 Allan, G., J. Erdmann, A. Gustafson, A. Joglekar, M. Milligan, G. Onsongo, K. Pamulaparthy, P. Pardey, T. Prather, S. Senay, K. Silverstein, J. Wilgenbusch, Y. Zhang, and P. Zhou. 2017. G.E.M.S: An innovative agroinformat- ics data discovery and analysis platform. Available at https://rdmi.uchicago.edu/papers/08162017165531 paperwilgenbusch081617.pdf (accessed July 9, 2018). Auer, S. R., C. Bizer, G. Kobilarov, J. Lehmann, R. Cyganiak, and Z. Ives, Z. 2007. DBpedia: A Nucleus for a Web of Open Data. The Semantic Web. Lecture Notes in Computer Science. 4825. p. 722. doi:10.1007/978-3-540- 76298-0_52. ISBN 978-3-540-76297-3. Ball, P. 2011. Physics of life: The dawn of quantum biology. Nature 474:272-274. Baxter, I., M. Ouzzani, S. Orcun, B. Kennedy, S. S. Jandhyala, and D. E. Salt. 2007. Purdue Ionomics Information Management System. An integrated functional genomics platform. Plant Physiology 143(2):600-611. Bradford, A. 2017. Empirical evidence: A Definition. LiveScience. Available at https://www.livescience.com/21 456-empirical-evidence-a-definition.html (accessed July 9, 2018). Brayne, S., S. McKellar, and K. Tzafestas. Artificial Intelligence in the Life Sciences & Patent Analytics: Market Developments and Intellectual Property Landscape. London: IP Pragmatics Ltd. Available at https://www.ip- pragmatics.com/media/1049/ip-pragmatics-artificial-intelligence-white-paper.pdf (accessed July 9, 2018). Brouček, J., and P. Tongeľ. 2015. Adaptability of dairy cows to robotic milking: A review. Slovak Journal of Ani- mal Science 48(2):86-95. Campbell, G.2016. The tensiometer: Micro-sized. Environmental Biophysics. Available at http://www.environ mentalbiophysics.org/tensiometers-micro-sized/ (accessed July 9, 2018). Cao, L. 2016. Data science and analytics: A new era. International Journal of Data Science and Analytics 1(1):1-2. Centurion, C. 2015. Moving along the analytics maturity curve. River Logic Blog. Available at https://blog.river logic.com/moving-along-the-analytics-maturity-curve (accessed July 9, 2018). Cornell University. 2018. Digital Agriculture: Cornell University Agricultural Experiment Station. Available at https://cuaes.cals.cornell.edu/digital-agriculture (accessed April 26, 2018). Crowdflower. 2016. Data Science Report. Available at http://visit.crowdflower.com/rs/416-ZBE-142/images/Crowd Flower_DataScienceReport_2016.pdf (accessed July 9, 2018). de Koning, K. 2010. Automatic milking—Common practice on dairy farms. Pp. V59-V63 in Proceedings of the First North American Conference on Robotic Milking Elora, ON, Canada: Precision Dairy Operators. Emerging Technology from the arXiv. 2017. Google Reveals Blueprint for Quantum Supremacy. MIT Technology Review. Available at https://www.technologyreview.com/s/609035/google-reveals-blueprint-for-quantum-sup remacy (accessed July 9, 2018). Engel, G. S., T. R. Calhoun, E. L. Read, T. K. Ahn, T. Mancal, Y. C. Cheng, R. E. Blakenship and G. R. Fleming. 2007. Evidence for wavelike energy transfer through quantum coherence in photosynthetic systems. Nature 466:782-786. EU SCAR (European Union Standing Committee on Agricultural Research). 2015. Agriculture Knowledge and In- novations Systems Towards the Future: A Foresight Paper. Strategic Working Group AKIS-3 Report. Availa- ble at https://ec.europa.eu/research/scar/pdf/akis-3_end_report.pdf (accessed March 15, 2018). Feynman, R. 2002. Richard Feynman on quantum physics and computer simulation. Los Alamos Science No. 27. Los Alamos National Laboratory. Available at http://permalink.lanl.gov/object/tr?what=info:lanl-repo/larepo rt/LA-UR-02-4969-02 (accessed July 9, 2018). Ge, L., C. Brewster, J. Spek, A. Smeenk, and J. Top. 2017. Blockchain for Agriculture and Food: Findings from the Pilot Study. Wageningen Economic Research. Available at https://www.wur.nl/upload_mm/b/3/d/df37c4ff-14 f3-43b9-a34d-6ed8599d8aba_2017-112%20Ge_def.pdf (accessed July 9, 2018). Google, 2018. A preview of bristlecone: Google’s new quantum processor. Available at https://ai.googleblog.com/ 2018/03/a-preview-of-bristlecone-googles-new.html (accessed July 9, 2018). IBM Q. 2018. What is Quantum Computing? Available online at https://www.research.ibm.com/ibm-q/learn/what- is-quantum-computing/ (accessed March 15, 2018). IEEE. 2018a. Blockchain Overview. Available at https://blockchain.ieee.org/about (accessed March 15, 2018). IEEE. 2018b. Quantum computers strive to break out of the lab. Available at https://spectrum.ieee.org/computing/ hardware/quantum-computers-strive-to-break-out-of-the-lab (accessed March 15, 2018). INFORMS (Institute for Operations Research and the Management Sciences). 2018. Syngenta Crop Challenge. Available at http://connect.informs.org/analytics/news-events/news-events-articles/news-events-news-events- articles-syngenta (accessed April 26, 2018). Intel, 2018. Intel starts testing smallest ‘sping qubit’ chip for quantum computing. Available at https://www.intc. com/investor-relations/investor-education-and-news/investor-news/press-release-details/2018/Intel-Starts-Test ing-Smallest-Spin-Qubit-Chip-for-Quantum-Computing/default.aspx (accessed March 15, 2018). 104 Prepublication Copy

Data IOM and NRC (Institute of Medicine and National Research Council). 2015. A Framework for Assessing Effects of the Food System. Washington, DC: The National Academies Press. Jonquet, C., A. Toulet, E. Amund, S. Aubin, E. D. Yeumo, V. Emonet, J. Graybeal, M. Laporte, M. A. Musen, V. Pesce, and P. Larmande. 2018. AgroPortal: A vocabulary and ontology repository for agronomy. Computers and Electronics in Agriculture 144:126-143. Kim, H. M., and M. Laskowski. 2017. Blockchain: Promise of Economy, Sustainability, and Transparency for Global Food Production. Knight, W. 2017. IBM raises the bar with a 50-qubit quantum computer. MIT Technology Review. Available at https://www.technologyreview.com/s/609451/ibm-raises-the-bar-with-a-50-qubit-quantum-computer/ (accessed March 15, 2018). Krug, H., H. Brune, G. Schmid, U. Simon, V. Vogel, D. Wyrwa, H. Ernst, A. Grunwald, W. Grunwald, H. Hofmann. 2006. Nanotechnology: Assessment and Perspectives. Springer-Verlag Berlin and Heidelberg GmbH & Co. K. pp. 197-240. Lougee, R. 2017. Are “bytes” and “blocks” the secret ingredients to transforming food safety? SwissRe Institute. Available at http://institute.swissre.com/research/library/Food_Safety_Robin_Lougee.html (accessed March 15, 2018). Lowdin, P. 1965. Quantum genetics and the aperiodic solid. Some aspects on the Biological problems of heredity, mutations, aging and tumours in view of the quantum theory of the DNA molecule. Advances in Quantum Chemistry. Volume 2. pp. 213-360. Academic Press. Lu, S., L Shao, M. Freitag, L. Klein, J. Renwick, F. Marianno, C. Albrecht, H. Hamann. 2016. IBM PAIRS curated big data service for accelerated geospatial data analytics and discover, 2016 IEEE International Conference on Big Data. Available at https://ieeexplore.ieee.org/document/7840910 (accessed March 15, 2018). Marr, B. 2018. How much data do we create every day? The mind-blowing stats everyone should read. Available at https://www.forbes.com/sites/bernardmarr/2018/05/21/how-much-data-do-we-create-every-day-the-mind-blow ing-stats-everyone-should-read/#1d51008860ba (accessed July 9, 2018). McDermid, S., L. Mearns, and A. Ruane. 2017. Representing agriculture in Earth Systems Models: Approaches and priorities for development. Journal of Advances 9(5):2230-2265. Microsoft. 2018. Microsoft Quantum: Research. Available at https://www.microsoft.com/en-us/research/lab/quan tum/ (accessed March 15, 2018). Morgan, J. 2014. A Simple Explanation of “The Internet of Things.’ Available at https://www.forbes.com/sites/ jacobmorgan/2014/05/13/simple-explanation-internet-things-that-anyone-can-understand/#958725c1d091 (ac- cessed July 9, 2018). Neethirajan, S. 2017. Recent advances in wearable sensors for animal health management. Sensing and Bio-Sensing Research 12:15-29. NIH (National Institutes of Health). 2018. NIH strategic plan for data science. Available at https://commonfund. nih.gov/sites/default/files/NIH_Strategic_Plan_for_Data_Science_Final_508.pdf (accessed March 15, 2018). NIST (National Institute of Standards and Technology). 2015. Lack of effective timing signals could hamper “inter- net of things” development. Available at https://www.nist.gov/news-events/news/2015/03/lack-effective-tim ing-signals-could-hamper-internet-things-development (accessed March 15, 2018). Pagay, V., M. Santiago, D. A. Sessoms, E. J. Huber, O. Vincent, A. Pharkya, T. N. Corso, A. N. Lakso, and A. D. Stroock. 2014. A microtensiometer capable of measuring water potentials below −10 MPa. Lab on a Chip 14(15):2806-2817. Pasquetto, I., B. Randles, and C. Borgman. 2017. On the reuse of scientific data. Data Science Journal 16:8. Popkin, G. 2017. Quantum computer simulates largest molecule yet, sparking hope of future drug discoveries. Sci- ence. Available at http://www.sciencemag.org/news/2017/09/quantum-computer-simulates-largest-molecule- yet-sparking-hope-future-drug-discoveries (accessed March 15, 2018). Priest, C. 2018. How machine learning can help marketers measure multi-touch attribution. Available at https://www.ama.org/partners/content/Pages/how-machine-learning-can-help-marketers-measure-multi-touch- attribution.aspx (accessed July 9, 2018). Salfer, J., M. Endres, W. Lazarus, K. Minegishi, and B. Berning. 2017. Dairy robotic milking systems—What are the economics? eXtension. Available at http://articles.extension.org/pages/73995/dairy-robotic-milking-sys tems-what-are-the-economics (accessed April 26, 2018). Seagate 2017. Data Age 2025. Available at https://www.seagate.com/www-content/our-story/trends/files/Seagate- WP-DataAge2025-March-2017.pdf (accessed March 15, 2018). Sennar, K. 2017. AI in agriculture—present applications and impact. Techemergence. Available at https://www.tech emergence.com/ai-agriculture-present-applications-impact/ (accessed March 15, 2018). Prepublication Copy 105

Science Breakthroughs to Advance Food and Agricultural Research by 2030 Shekhar, S., J. Colletti, F. Munoz-Arriola, L. Ramaswamy, C. Krintz, L. Varshney, and D. Richardson. 2017. Intel- ligent Infrastructure for Smart Agriculture: An Integrated Food, Energy and Water System. Computing Com- munity Consortium white paper. Available at https://arxiv.org/ftp/arxiv/papers/1705/1705.01993.pdf (accessed March 15, 2018). Smith, W. 2017. Uncertainty and Opportunity: Recent News Clips of Interest. Presentation, AgGateway 2017 Annu- al Conference: Efficiency-Opportunity-Profitability, November 6-9, 2017, San Diego, California. Stephens, Z.D., S.Y. Lee, F. Faghri, R.H. Campbell, C. Zhai, M.J. Efron, R. Iyer, M.C. Schatz, S. Sinha, and G.E. Robinson. 2015. Big data: astronomical or genomical? PLoS Biol 13(7):e1002195. Top 500. 2018. Available at https://www.top500.org/lists/2018/06/ (accessed March 15, 2018). Turin L 2002. A method for the calculation of odor character from molecular structure. Journal of Theoretical Biol- ogy 216(3):367-385. United Nations. 2014. A world that counts. Available at http://www.undatarevolution.org/report/ (accessed March 15, 2018). USDA-NIFA (U.S. Department of Agriculture National Institute of Food and Agriculture). 2017a. Big data yields big opportunities in agriculture. Blog. Available at https://www.usda.gov/media/blog/2017/07/17/big-data- yields-big-opportunities-agriculture (accessed March 15, 2018). USDA-NIFA. 2017b. Executive Summary: Changing the Face, Place, and Space of Agriculture. Available at https://nifa.usda.gov/sites/default/files/resource/Data%20Summit%20Summary.pdf (accessed March 15, 2018). Vasisht, D, Z. Kapetanoic, J. Won, X. Jin, R. Chandra, A. Kapoor, S. Sinha, M. Sudarshan, S. Stratman. 2017. FarmBeats: An IoT Platform for Data-Driven Agriculture. 14th USENIX Sympostium on Networked Systems Design and Implementation, March 27-29, 2017. Boston, MA, USA. Available at https://www.usenix.org/ system/files/conference/nsdi17/nsdi17-vasisht.pdf (accessed March 15, 2018). Verdow, C., S. Wolfert, B. Tekinerdogan. 2016. Internet of Things in agriculture. Available at https://www. researchgate.net/publication/312164156_Internet_of_Things_in_agriculture (accessed March 15, 2018). Wasik, B. 2013. In the programmable world, all our objects will act as one. Available online at https://www.wired. com/2013/05/internet-of-things-2/ (accessed July 9, 2018). Wilkinson, M. D., M. Dumontier, I. J. Aalbersberg, G. Appleton, M. Axton, A. Baak, N. Blomberg, J.-W. Boiten, L. Bonino da Silva Santos, P. E. Bourne, J. Bouwman, A. J. Brookes, T. Clark, M. Crosas, I. Dillo, O. Dumon, S. Edmunds, C. T. Evelo, R. Finkers, A. Gonzalez-Beltran, A. J. G. Gray, P. Groth, C. Goble, J. S. Grethe, J. He- ringa, P. A. C ’t Hoen, R. Hooft, T. Kuhn, R. Kok, J. Kok, S. J. Lusher, M. E. Martone, A. Mons, A. L. Pack- er, B. Persson, P. Rocca-Serra, M. Roos, R. van Schaik, S.-A. Sansone, E. Schultes, T. Sengstag, T. Slater, G. Strawn, M. A. Swertz, M. Thompson, J. van der Lei, E. van Mulligen, J. Velterop, A. Waagmeester, P. Wit- tenburg, K. Wolstencroft, J. Zhao, and B. Mons. 2016. The FAIR Guiding Principles for scientific data man- agement and stewardship. Scientific Data 3:160018. Wolfert, S., L. Ge, C. Verdouw, M. Bogaardt. 2017. Big Data in Smart Framing – A review. Agricultural Systems 153:69-80. Woodward, J. 2016. Big data and ag-analytics: An open source, open data platform for agricultural and environmen- tal finance, insurance, and risk. Agricultural Finance Review 76(1):15-26. Yang, Z., G. Yu, L. Di, B. Zhang, W. Han, and R. Mueller. 2013. Web service-based vegetation condition monitor- ing system—VegScape. Pp. 3638-3641 in 2013 IEEE International Geoscience and Remote Sensing Symposi- um (IGARSS). Institute of Electrical and Electronics Engineers. Yli-Huumo, J., D. Jo, and S. Choi. 2016. Where is current research on blockchain technology? A systematic review. PLOS One. Available at https://doi.org/10.1371/journal.pone.0163477 (accessed March 15, 2018). 106 Prepublication Copy

Next: 8 A Systems Approach »
Science Breakthroughs to Advance Food and Agricultural Research by 2030 Get This Book
×
Buy Prepub | $69.00 Buy Paperback | $60.00
MyNAP members save 10% online.
Login or Register to save!
Download Free PDF

For nearly a century, scientific advances have fueled progress in U.S. agriculture to enable American producers to deliver safe and abundant food domestically and provide a trade surplus in bulk and high-value agricultural commodities and foods. Today, the U.S. food and agricultural enterprise faces formidable challenges that will test its long-term sustainability, competitiveness, and resilience. On its current path, future productivity in the U.S. agricultural system is likely to come with trade-offs. The success of agriculture is tied to natural systems, and these systems are showing signs of stress, even more so with the change in climate.

More than a third of the food produced is unconsumed, an unacceptable loss of food and nutrients at a time of heightened global food demand. Increased food animal production to meet greater demand will generate more greenhouse gas emissions and excess animal waste. The U.S. food supply is generally secure, but is not immune to the costly and deadly shocks of continuing outbreaks of food-borne illness or to the constant threat of pests and pathogens to crops, livestock, and poultry. U.S. farmers and producers are at the front lines and will need more tools to manage the pressures they face.

Science Breakthroughs to Advance Food and Agricultural Research by 2030 identifies innovative, emerging scientific advances for making the U.S. food and agricultural system more efficient, resilient, and sustainable. This report explores the availability of relatively new scientific developments across all disciplines that could accelerate progress toward these goals. It identifies the most promising scientific breakthroughs that could have the greatest positive impact on food and agriculture, and that are possible to achieve in the next decade (by 2030).

  1. ×

    Welcome to OpenBook!

    You're looking at OpenBook, NAP.edu's online reading room since 1999. Based on feedback from you, our users, we've made some improvements that make it easier than ever to read thousands of publications on our website.

    Do you want to take a quick tour of the OpenBook's features?

    No Thanks Take a Tour »
  2. ×

    Show this book's table of contents, where you can jump to any chapter by name.

    « Back Next »
  3. ×

    ...or use these buttons to go back to the previous chapter or skip to the next one.

    « Back Next »
  4. ×

    Jump up to the previous page or down to the next one. Also, you can type in a page number and press Enter to go directly to that page in the book.

    « Back Next »
  5. ×

    To search the entire text of this book, type in your search term here and press Enter.

    « Back Next »
  6. ×

    Share a link to this book page on your preferred social network or via email.

    « Back Next »
  7. ×

    View our suggested citation for this chapter.

    « Back Next »
  8. ×

    Ready to take your reading offline? Click here to buy this book in print or download it as a free PDF, if available.

    « Back Next »
Stay Connected!