vi
developers’ immediate control. Policy bias against agriculture, disrespect for property rights, and inadequate farmer access to information, finance, insurance, and
markets can also inhibit adoption.
Ultimately, users adopt innovations that make life easier for them, raise the profitability of what they do without adding too much risk, and help them realize their
dreams and aspirations. Market research to understand how a proposed innovation
helps users “get the job done” can lead to relevance-enhancing insight and design.
Thus, market-led approaches to agricultural innovation are superior to purely
science- based work from an adoptability point of view.
Science is a means to an end that can be assembled in a variety of ways, but the
focus should be on what users want. The private sector knows this. The public sector
appears often not to be aware of it. The need to innovate is not trivial and the list of
failed innovation attempts in different sectors and industries is long. The common
factor in publicly and privately driven innovation successes over the past 50 years in
agriculture involved products and solutions considered relevant by users and disseminated systematically through national programs and input markets.
Therefore, the authors persuasively contend that innovation requires business
models that create and deliver value. Interacting with the market and clarifying user
needs is the place to start, followed by identifying potential technologies and R&D
strategies to address them —not the other way around. Ecosystems of skills, methods, partnerships, and resources need to be assembled to drive the task, assess progress from time to time, learn from failure, change course if required, test prototypes,
and finally bring products to market. Claims of success should be held back until
proof of sustained adoption is demonstrated.
Business models are also needed to improve the enabling environment for uptake
of innovations by farmers and across agrifood value chains. The transaction costs of
accessing markets, information, and farm services are often high from farmers’ perspectives and need to be reduced. This book discusses partnership-based ventures
where public and private actors work together to achieve better value. It explores the
role of digital technologies and big data in agricultural innovation and assesses
“market-making” efforts to help farmers respond to new opportunities such as nutritious, high-value, profitable crops.
Agricultural and food markets are undergoing rapid change for two main reasons: (1) rising consumer demand for quality and convenience and (2) changing
economies of scale and scope in procurement, processing, wholesale, and retail.
This changes the technology frontier, calls for appropriate decision support systems
for farmers and others in value chains, and opens prospects for new input and output
traits for crops. Agricultural and food innovation systems are invited to respond.
Studies have shown that investment in agricultural R&D pays high dividends in
terms of productivity growth and other rewards. There should be more of it, particularly in parts of the world where malnutrition abounds, and where climate change is
particularly damaging to food production. The effectiveness of agricultural R&D
efforts varies, however, depending on the approach researchers and developers
take—in other words, the business model they follow. Scientific exploration alone
is unlikely to lead to adoptable technologies and traits.
Foreword
developers’ immediate control. Policy bias against agriculture, disrespect for property rights, and inadequate farmer access to information, finance, insurance, and
markets can also inhibit adoption.
Ultimately, users adopt innovations that make life easier for them, raise the profitability of what they do without adding too much risk, and help them realize their
dreams and aspirations. Market research to understand how a proposed innovation
helps users “get the job done” can lead to relevance-enhancing insight and design.
Thus, market-led approaches to agricultural innovation are superior to purely
science- based work from an adoptability point of view.
Science is a means to an end that can be assembled in a variety of ways, but the
focus should be on what users want. The private sector knows this. The public sector
appears often not to be aware of it. The need to innovate is not trivial and the list of
failed innovation attempts in different sectors and industries is long. The common
factor in publicly and privately driven innovation successes over the past 50 years in
agriculture involved products and solutions considered relevant by users and disseminated systematically through national programs and input markets.
Therefore, the authors persuasively contend that innovation requires business
models that create and deliver value. Interacting with the market and clarifying user
needs is the place to start, followed by identifying potential technologies and R&D
strategies to address them —not the other way around. Ecosystems of skills, methods, partnerships, and resources need to be assembled to drive the task, assess progress from time to time, learn from failure, change course if required, test prototypes,
and finally bring products to market. Claims of success should be held back until
proof of sustained adoption is demonstrated.
Business models are also needed to improve the enabling environment for uptake
of innovations by farmers and across agrifood value chains. The transaction costs of
accessing markets, information, and farm services are often high from farmers’ perspectives and need to be reduced. This book discusses partnership-based ventures
where public and private actors work together to achieve better value. It explores the
role of digital technologies and big data in agricultural innovation and assesses
“market-making” efforts to help farmers respond to new opportunities such as nutritious, high-value, profitable crops.
Agricultural and food markets are undergoing rapid change for two main reasons: (1) rising consumer demand for quality and convenience and (2) changing
economies of scale and scope in procurement, processing, wholesale, and retail.
This changes the technology frontier, calls for appropriate decision support systems
for farmers and others in value chains, and opens prospects for new input and output
traits for crops. Agricultural and food innovation systems are invited to respond.
Studies have shown that investment in agricultural R&D pays high dividends in
terms of productivity growth and other rewards. There should be more of it, particularly in parts of the world where malnutrition abounds, and where climate change is
particularly damaging to food production. The effectiveness of agricultural R&D
efforts varies, however, depending on the approach researchers and developers
take—in other words, the business model they follow. Scientific exploration alone
is unlikely to lead to adoptable technologies and traits.
Foreword