Knowledge and Innovation in Agriculture: Contribution to Food Security and Sustainability
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Agricultural innovation refers to the implementation of new or significantly improved products, processes, marketing, or organizational methods in agriculture. Knowledge transmission, integration, diffusion, and utilization are fundamental in supporting innovation in agriculture. Agricultural innovation is sustainable when it takes into account sustainability considerations (environmental, social, economic) and contributes to food system sustainability and food security. The critical role of innovation to make agriculture both more competitive and more sustainable is widely admitted. Innovations and modern techniques can strengthen food system resilience, improve resource efficiency in agriculture, and secure social equity thus contributing to achieving sustainable food security.
Innovation is widely recognized as a critical dimension of sustainable development as well as sustainable consumption and production (European Political Strategy Centre 2016). Innovation, science, and technology have essential roles to play in meeting the interlinked global environmental, social, and economic challenges of environmental sustainability, poverty reduction, social justice, and climate change (STEPS Centre 2010; United Nations 2012). In fact, innovation is seen as a route to economic growth as well as to propose effective solutions to real problems such as poverty (STEPS Centre 2010). The contribution of innovation to sustainability is highlighted in many strategic and policy documents such as the outcomes of the world conferences of Rio, Johannesburg, and Rio+20 and recently in the context of the 2030 Agenda for Sustainable Development and the Sustainable Development Goals (SDGs) (United Nations 2015). Similarly, access to knowledge is considered a necessary condition to boost agricultural productivity (United Nations 2015). According to Leach et al. (2012), delivering SDGs requires a radically new approach to innovation. They add that “What is now needed is nothing short of major transformation—not only in our policies and technologies, but in our modes of innovation themselves—to enable us to navigate turbulence and meet SDGs that respect the safe operating space” (p. 2). Likewise, according to the STEPS Centre (2010), moving toward innovation for sustainability and sustainable development requires a radical shift in the whole innovation process to address the issues of direction of innovation, distribution of innovation costs/benefits, and diversity of innovation pathways and perspectives. Direction, distribution, and diversity issues are particularly relevant in agro-food systems (El Bilali 2019). The aim of SDG2 is to “End hunger, achieve food security and improved nutrition and promote sustainable agriculture” (United Nations 2015). Indeed, agriculture and food system face an unprecedented confluence of pressures (FAO 2014) such as increasing global population; poverty, hunger, and malnutrition; land and water scarcity, degradation, and soil depletion; climate change; food waste; and loss of biodiversity. Knowledge and innovation are fundamental in addressing these challenges.
The chapter explores the contribution of knowledge and innovation in agriculture to food security and sustainability. First, it introduces the concepts of innovation and knowledge in agriculture as well as AKIS (Agricultural Knowledge and Information System/Agricultural Knowledge and Innovation System). Then, it analyses the role of knowledge and innovation in making agriculture more productive (cf. “Food Security”) and sustainable (cf. “Sustainability in Agriculture and Local Food Systems: A Solution to a Global Crisis”). For the former, it takes as an example the “Green Revolution” that contributed significantly to increase food production in many developing countries. For the latter, it considers “precision agriculture,” which allows a more efficient use of inputs (fertilizers, pesticides) and natural resources (soil, water).
Innovation, Knowledge, and Innovation Systems
Since the pioneer work of Schumpeter (1934, 1942), the field of innovation has evolved dramatically so that nowadays there are different understandings and definitions of innovation (OECD and Eurostat 2005; Menrad and Feigl 2007; Sterrenberg et al. 2013). Indeed, innovation is rather an ambivalent term (Shaver 2016) and that may explain the existence of different understandings of what innovation means. The current literature contains many categorizations of innovation. Garcia and Calantone (2002) found 15 different constructs for categorizing innovation from only 21 studies. Variations in the use of the term “innovation” depend on, for example, where the innovation is located in the value chain (e.g., product, process, or organizational innovation), the novelty of the knowledge underlying the innovation, or the extent of the economic/market impact of the innovation (Twomey and Gaziulusoy 2014). According to Stummer et al. (2010), innovations can be categorized according to innovation type (product, service, process, market), dimension (objective or subjective), scope of change (radical, incremental, reapplied), or how innovation was created (closed or open). The OECD and Eurostat (2005) distinguish product, process, marketing, and organizational innovations. Agricultural innovation as well as innovation in the agri-food sector can be classified using the same categories (Avermaete et al. 2004; Avolio et al. 2014). Technical innovations in agriculture can encompass both product innovation and process innovation.
Over the last decades, a more nuanced and richer picture of innovation has emerged. A significant feature of the development of modern innovation thinking, particularly in relation to sustainability, has been a gradual broadening of the scope of both problem framing and analytical framing (Smith et al. 2010). Firstly, the object of innovation has been extended from the 1980s focus on production technologies toward interest in the entire production and consumption system. Secondly, innovation analytical frames have been enlarged from a focus on the role of the inventor or price signals to include a much broader set of systemic issues that may affect innovation development. Key new ideas include appreciating the importance of actor networks and institutions; the coevolutionary nature of the technologies, institutions, social practices, and business strategies; and a greater understanding of the different types of knowledge and learning processes (Twomey and Gaziulusoy 2014). In the last decades, indeed, there has been a shift from an innovation concept centered on research to innovation as a result of interactions among several actors that establish diverse networks (World Bank 2006) within an innovation system.
In the mid-1980s, the concept of “innovation system” (IS) was introduced. An “innovation system” is the combination of all institutional and economic structures that affect both the direction and the speed of change in society; hence, the concept emphasizes the coevolutionary character of change processes (Hekkert et al. 2007) and takes into account factors beyond just technical change (Lachman 2013). The core idea behind IS approach is that change can be ascribed to both collective and individual actions relating to innovation systems (Freeman 1988). The main focus is to break down a system into its constituents to discover which system elements do not fulfill their intended purpose, thereby hampering the development of the entire system (Jacobsson and Bergek 2011). More recently, attention has turned to the dynamics of innovation and the IS functions (Hekkert et al. 2007; Bergek et al. 2008): entrepreneurial activities, knowledge development, knowledge diffusion and exchange, market formation, resource mobilization, and support from advocacy coalitions.
Innovation concept is strongly linked to that of knowledge, which is fundamental to move toward sustainable practices (Grin et al. 2010). Knowledge (both tacit and explicit) is often claimed to be the most fundamental resource in an innovation system, while learning is the most important process (Lundvall 2007; Wieczorek et al. 2012). Therefore, it is important to pay attention to the different types of knowledge (information, skills, judgment, and wisdom) that are needed in different situations (Loconto 2016).
In the 1990s, variations of the innovation systems approach were devised based on different system boundaries (Freeman 1995), e.g., National Innovation Systems (NIS) and Sectoral Innovation Systems (SIS) such as Agricultural Innovation Systems (AIS). Agriculture innovation system concept emerged in response to shortcomings of linear technology transfer frameworks (Röling 2009). AIS draws attention to individuals and organizations capacity to make knowledge useful in agriculture (Spielman et al. 2009), and multiple sub-systems within agriculture (e.g., education and training, farmers, extension, research, public institutions) are relevant in agricultural innovation (World Bank 2012). In AIS, innovations can emerge from different actors such as farmers and other local actors (Spielman et al. 2011) using different types of knowledge (Biggs 2007).
The Agricultural Knowledge and Information System (AKIS), whose main functions are to foster mutual learning and to encourage knowledge sharing and utilization (FAO 2000), emerged in the same period as AIS. According to Klerkx et al. (2012), AKIS and AIS developed in parallel and share many similarities, the key difference being that AIS methods more explicitly capture the role of infrastructure (knowledge, physical, and financial) and institutions – both formal (policies and regulations) and informal (customs and culture). The concept of AKIS was originally defined as Agricultural Knowledge and Information Systems (Röling and Engel 1991). Recently the AKIS concept has evolved to “Agricultural Knowledge and Innovation System” as it has acquired a second meaning (innovation), and the AKIS was opened up to more public tasks and to innovation support (Klerkx et al. 2009).
The AKIS approach recognizes that R&D are not the only means of generating or gaining access to knowledge (SCAR-EU 2012). Although the AKIS concept also focuses on research supply, it gives much more attention to the generation and diffusion of knowledge and looks at the multiple factors and interactions that promote innovation in agriculture (World Bank 2006). Consequently, in the AKIS approach, innovation is perceived as a process evolving from the coordination and interaction between a set of agents who contribute to the production, exchange, and utilization of knowledge (Hall et al. 2004; Klerkx et al. 2012). In these collaborative networks, researchers, farmers, agricultural advisors, entrepreneurs, food industries, policy-makers, etc. participate in the creation, diffusion, adaptation, and use of knowledge as well as in providing other resources for innovation (Klerkx et al. 2009).
Innovation and Knowledge in Agriculture: Sustainability, Productivity, and Efficiency
Innovation has become a key issue in the discussion about the relation between agriculture and sustainability (Royal Society 2009; FAO 2012, 2013; Agricultural European Innovation Partnership (EIP-AGRI) 2013; Global Harvest Initiative 2016). It is widely recognized that agricultural sustainability transitions require “sustainable innovation.” Different models of sustainability-oriented innovation have been promoted such as eco-innovation, ecological innovation, environmental innovation, frugal innovation, green innovation, inclusive innovation, and social innovation (Network for Business Sustainability 2012). Sustainable innovation means paying attention to ecological integrity along social values diversity, encouraging plural innovation pathways, promoting fairer and wider distribution of innovation benefits, and fostering inclusive and participatory governance of innovation processes (STEPS Centre 2010).
The critical role of innovation to make agriculture both more competitive and more sustainable is widely admitted. Agricultural innovation is considered vital for meeting the challenges of agriculture development, adapting to climate change, and improving food security (World Bank 2007; Royal Society 2009; IIAC 2014; European Commission 2016; United Nations Conference on Trade and Development 2017). Innovations and modern techniques can strengthen food system resilience, improve resource efficiency in agriculture, and secure social equity thus contributing to achieving sustainable food security (High Level Panel of Experts on Food Security and Nutrition (HLPE) 2017). Moreover, agricultural R&D has been shown to be very profitable (Alston et al. 2000; Rao et al. 2012) and to improve agricultural development, economic growth, and poverty reduction (The International Assessment of Agricultural Knowledge, Science and Technology for Development (IAASTD) 2009). However, in order to respond to future agriculture challenges, innovation will need not only to improve input use efficiency but also to reduce waste and conserve scarce natural resources (OECD 2011; FAO 2017).
The International Assessment of Agricultural Knowledge, Science and Technology for Development (IAASTD) (2009) highlighted that agricultural knowledge, science, and technology (AKST) are crucial to address different sustainable development issues such as food insecurity and poverty. It also highlighted that the scope of agricultural knowledge goes beyond the narrow confines of science and technology (S&T) and encompasses other types of relevant knowledge that are held by farmers, consumers, and end users. According to the High Level Panel of Experts on Food Security and Nutrition (HLPE 2017), there are several promising innovations in agro-food systems that can contribute to food and nutrition security such as precision agriculture, information and communication technologies (ICT), bio-fortification, climate-smart agriculture, technologies to reduce losses and waste along the food chain, and bio- and nanotechnologies. The most prominent, but also maybe challenging, innovations are perhaps found in the digital revolution and the rapidly evolving field of precision breeding and genomics (HLPE 2017). The UN Conference on Trade and Development (2017) analyzed recently the role of science, innovation, and technology in addressing the four dimensions of food security, namely, availability (e.g., improving agricultural productivity through breeding, soil management, irrigation), access, utilization (e.g., nutrition science), and stability.
IAASTD (2009) highlights that “There is ample evidence available from the literature that AKST investments have contributed significantly to organizational and institutional innovations in the form of methods, tools development, capacity strengthening, and understanding how institutes interact with each other in achieving developmental goals” (p. 516). According to Hinrichs (2014), social and organizational innovations are as central to sustainability transitions in food systems as any particular innovative technology. Indeed, transition to sustainable agro-food systems requires complex and holistic change processes in which social innovation plays as big a role as technological innovation (IPES-Food 2015). It is widely admitted nowadays that to meet sustainability challenges, more attention should be paid to social innovations and grassroots innovation actors and processes (Leach et al. 2012). Similarly, IAASTD (2009) suggests that future agricultural innovation needs to address not only simple technological and technical issues but also social ones to contribute more effectively in addressing pressing challenges such as climate change and food security. This broader understanding of innovation in agriculture is nowadays widespread predominantly in developed countries such as those of the European Union (SCAR-EU 2012).
Green Revolution and precision farming, which are analyzed hereafter, represent two prominent examples of the use of innovation and knowledge in achieving food security and making agriculture more sustainable through improvement of agriculture productivity and production efficiency.
The Green Revolution represents a clear example of how innovation and new technologies can boost agricultural production and productivity, especially in developing countries, thus contributing to food security. The term “Green Revolution” (GR) was first used in 1968 by William S. Gaud, former director of the US Agency for International Development (USAID). It refers to a series of research and technology transfer initiatives that took place first in Mexico between 1944 and 1970, which increased agriculture production in many developing countries (Evenson 2003; Swaminathan 2006; Hazell 2009; Ameen and Raza 2018). Indeed, the GR began in 1944 when the Mexican government, with financial support of the Rockefeller Foundation, founded the International Maize and Wheat Improvement Centre (CIMMYT) to improve the agricultural output of Mexican farms (Hazell 2009). The results in terms of wheat production were astounding, so that Mexico became not only self-sufficient by 1956 but also an exporting country by 1964 (Kindall and Pimentel 1994). The GR involved the use of high-yielding cereal grains (wheat, maize, rice) and hybridized seeds, expansion of irrigation infrastructure, mechanization, and distribution of pesticides (insecticides, fungicides, herbicides) and synthetic mineral fertilizers (Khush 2001; Evenson 2003; Swaminathan 2006; Ameen and Raza 2018). The GR was mutually reinforced by a complex policy that included, among others, extension services, input subsidy, agricultural products marketing, and investments in agricultural science, research, and technology (Khush 2001; Evenson 2003).
It is widely believed that there would have been greater famine, food insecurity, and malnutrition without the GR (Khush 1999; Evenson 2003; Godfray et al. 2010). Indeed, world grain production increased by over 250% between 1950 and 1984 mainly thanks to the GR that transformed agriculture around the globe (Thurner 2013). However, the GR had mixed socioeconomic and environmental outcomes (Evenson 2003; Swaminathan 2006; Chhetri and Chaudhary 2011). Many small farmers went into debt and the increased level of mechanization on farms removed a large source of employment from the rural economy (Chaudhry 1982; Swaminathan 2006; Thompson et al. 2007; Hazell 2009; Ameen and Raza 2018). Furthermore, reliance on the use of pesticides and fertilizers to increase crop productivity determined in some areas land degradation as well as water pollution and depletion (Shiva 1993; Evenson 2003; Swaminathan 2006). The spread of the GR also affected biodiversity and agro-biodiversity (Shiva 1993; Matson 1997; Swaminathan 2006; IAASTD 2009). Moreover, despite different attempts to introduce the concepts and approaches of the GR to Africa, benefits were mainly seen in Southeast Asia and Latin America (UN Millennium Project 2005; Denning et al. 2009).
Precision and Digital Agriculture
ICT is one of the instruments contemplated to make agriculture smarter and more sustainable (Bello and Aderbigbe 2014; Svenfelt and Zapico 2016; Giraldo and Rosset 2018; El Bilali and Allahyari 2018; FAO and ITU 2019). Thanks to their disruptive potential, ICTs hold the potential to contribute to transition toward sustainability in agriculture (World Bank 2011; Poppe et al. 2013; FAO et al. 2017). Precision agriculture (PA) is a widely cited example of ICT application in agriculture (Allahyari et al. 2016; Balafoutis et al. 2017; Ess and Morgan 2003; McBratney et al. 2005) that is leading to what can be called a “Third Green Revolution” (following the plant breeding and genetics revolutions) (Smart AKIS 2016).
Ess and Morgan (2003) defined precision farming as “Managing each crop production input (fertilizer, limestone, herbicide, seed, insecticide, etc.) on a site-specific basis to reduce waste, increase profits, and maintain the quality of the environment.” It is a modern farming model that uses information technology to add exactness to the quality, quantity, location, and timing in agricultural inputs application (Mintert et al. 2016). It consists in the utilization of sensors to optimize the use of pesticides, fertilizers, and water. PA came into use in the 1980s as global positioning system (GPS) became accessible by some farmers, especially in developed countries. The methods of precision farming rely mainly upon a combination of satellite navigation and positioning technology, new sensor technologies, and the Internet of Things (Schrijver et al. 2016). There was a strong uptake of precision agriculture technologies (PATs) during the 1990s in North America (Daberkow and McBride 2003), but PA growth rate flattened during 2000s. However, PATs are currently taking up again so that PA global market amounted to 2.3 billion in 2014. Expected annual growth rate till 2020 is 12%, and US and European markets are considered the most promising ones (Roland 2016 in Balafoutis et al. 2017).
There were many attempts to provide a typology of PATs. McBratney et al. (2005) classified PATs into three main categories: hardware and sensors, data analysis and decision support systems, and commodity and whole-farm focus. Meanwhile, Zarco-Tejada et al. (2014) identified three categories of PATs: remote sensing, guidance systems, and variable rate applications. Similarly, Schwarz et al. (2010) classified PATs into guidance systems (e.g., automatic guidance for tractors), recording technologies (e.g., sensors, mapping devices), and reacting technologies (e.g., variable rate technologies). PATs include variable rate technologies (e.g., variable rate nutrient application, variable rate irrigation, variable rate pesticide application, variable rate planting/seeding) as well as precision physical weeding technology, machine guidance (driver assistance or auto-guidance), and controlled traffic farming (Balafoutis et al. 2017). Technologies of PA are nowadays present not only in all crop production stages (soil preparation, seeding, crop management, harvesting), but also increasingly used in livestock production. In fact, ICT-controlled systems are also used to improve both cost-effectiveness and overall sustainability of operations (Banhazi et al. 2012; European Union 2017).
Svenfelt and Zapico (2016) argue that ICT solutions can improve the sustainability of agriculture by increasing efficiency, networking agro-food chain actors, and enhancing traceability and transparency. Likewise, El Bilali and Allahyari (2018) put that “ICTs can contribute to agro-food sustainability transition by increasing resource productivity, reducing inefficiencies, decreasing management costs, and improving food chain coordination” (p. 456). According to FAO and the International Telecommunication Union (FAO and ITU 2016), benefits of e-agriculture include increasing markets efficiency, improving vertical and horizontal linkages, facilitating information sharing and networking, developing value-added services, and increasing food and nutrition security and safety. Moreover, ICT-based smart irrigation systems can reduce water consumption (Evans and King 2010; Mutchek and Williams 2010), GHG emissions, and carbon footprint (Mutchek and Williams 2010), thanks to reduced energy use thus mitigating climate change (European Union 2014). Apart from addressing climate change issue, ICTs have also been put forward to achieve food security and to enhance agri-food systems sustainability (Svenfelt and Zapico 2016).
Innovation and New Technologies in Agriculture: Potential Drawbacks and Perspectives Diversity
Innovations imply different directions of development, not all of which are sustainable, and which should be subject to democratic debate (STRN 2010). The three perspectives on sustainable food security and food system sustainability analyzed by Garnett (2014) – namely, efficiency increase, demand restraint, and food system transformation – also reflect different values and ideologies on the role of technology and innovation in the agro-food arena. For the efficiency perspective, the boundaries of environmental limits can be expanded or overcome by using technology to accommodate humanity. The vision underlying the efficiency perspective is that technology can be used to deliver development goals (e.g., food security, well-being) with less environmental impact. So, it can be assumed that advocates of this perspective have a positive attitude toward new technologies and innovation. Meanwhile, for the demand restraint perspective, technology is sometimes problematic and can be used by humans to further damage the environment and nature. Furthermore, critically inclined research on agricultural and food systems change has generally viewed capital-intensive technologies as contributing to the vast restructuring of food and agriculture and, sometimes, “sustaining the unsustainable” (Buttel 2006), especially referring to genetically modified (GM) crops. Because some technologies have abetted industrialization, consolidation, and global neo-liberalization of food and agriculture, technology may be categorically dismissed by some scholars and food system actors as a potentially productive analytical entry point for work on sustainability transitions in food and agriculture (Hinrichs 2014). In fact, innovation and technology in agriculture may also negatively affect the environment and rural livelihoods and that may explain increasing mistrust in certain institutionalized forms and fields of science (Millstone and van Zwanenberg 2000) such as genetics. The High Level Panel of Experts on Food Security and Nutrition (HLPE 2017) identified in a recent note knowledge and technology as one of the critical and emerging issues for food security and nutrition. As there are diverging views on the suitability of different innovations and technologies to improve food security in a sustainable way in different contexts and for different kinds of users (cf. “Small-Scale Food Producers: Challenges and Implications for SDG2”), HLPE (2017) recommended to assess all innovations and technologies against their long-term environmental, economic, and social impacts. Such an assessment should take into consideration not only technical sustainability and economic profitability but also environmental friendliness and social justice in each use context (e.g., Dunmade 2002; Kriesemer and Virchow 2012).
The main objective of this chapter was to explore the contribution of knowledge and innovation in agriculture to food security and sustainability. As shown above, knowledge and innovation in agriculture not only have a vital contribution to food security and sustainability but also are central in achieving the different aims of SDG 2, i.e., ending hunger, achieving food security and improved nutrition, and promoting sustainable agriculture. Through the examples of the “Green Revolution” and “precision agriculture,” the chapter highlighted the role of knowledge and innovation in making agriculture more productive (cf. “Food Security”) and more efficient. Moreover, deployment of knowledge and innovation is crucial in addressing pressing challenges such as climate change. However, impacts will depend on the types of knowledge and innovation deployed. Therefore, policies in agriculture should support the development of Agricultural Knowledge and Innovation Systems (AKIS) that promote all types of knowledge (traditional, scientific) as well as all kinds of sustainability-oriented innovation. In fact, besides research-based innovations, also farmer innovations, based on their traditional knowledge and know-how, should be promoted. Moreover, while technical innovation (e.g., new technologies) is crucial in increasing agricultural production and productivity, evidence shows that also social innovation has an important role to play in agriculture sustainability. Indeed, the scope of innovation in the agro-food sector needs to be broadened with a particular focus on innovation impacts in terms of sustainability that’s to say the contribution of innovation to agro-food sustainability transitions. For that, there is a need for more emphasis on the concept of “sustainable” innovation. Future agro-food innovation needs, indeed, to address not only simple technological and technical issues but also social ones. It is also crucial to involve end users (farmers, herders, fisher folk) not only to make sure that innovations are appropriate, relevant, and accessible but also to ensure the inclusiveness of the innovation cycle, especially toward women and smallholders. Further research is needed to elucidate functional and operational linkages between knowledge and innovation to ensure that knowledge is used to develop relevant innovations and vice versa.
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