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Global agronomy, a new field of research. A review

• Review Article
• Published: 25 October 2013
• Volume 34 , pages 293–307, ( 2014 )

Cite this article

• David Makowski 1 , 2 , 7 ,
• Thomas Nesme 3 , 4 ,
• François Papy 5 , 6 &
• Thierry Doré 2 , 1  

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The global impact of agriculture has recently become a major research topic, stressed by the rapid growth of the world population. Agriculture management is indeed influencing the quality of water, air, soil, and biodiversity at the global scale. The main agricultural challenges have already been reviewed, but these reviews did not discuss in detail the adaptations of agricultural techniques to global issues and the research challenges for agronomy. Here, we propose a research planning for global agronomy including the following advices. Agronomists should update their research objects, methods, and tools to address global issues. Yield trends and variations among various regions should be analyzed to understand the sources of these variations. Crop model simulations should be upscaled to estimate potential yields and to assess the effect of climate change and resource scarcity at the global scale. Advanced methods should analyze output uncertainty of complex models used at a global scale. Indeed various global models are actually used, but these models are too complex and the output uncertainty is difficult to analyze. The meta-analysis of published data is a promising approach for addressing global issues, though meta-analysis must be applied carefully with appropriate techniques. Finally, global datasets on the performance and environmental impact of cropping systems should be developed to allow agronomists to identify promising cropping systems.

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Makowski, D., Nesme, T., Papy, F. et al. Global agronomy, a new field of research. A review. Agron. Sustain. Dev. 34 , 293–307 (2014). https://doi.org/10.1007/s13593-013-0179-0

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Published : 25 October 2013

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DOI : https://doi.org/10.1007/s13593-013-0179-0

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AI is giving boost to crop improvement research

by Iowa State University

What is the role of artificial intelligence for crop improvement? Questions about artificial intelligence are becoming more pressing in every discipline. For crop improvement, AI provides a new lens to bridge science and practice, according to Jianming Yu, one of the world's top-ranked scientists in the fields of quantitative genetics and plant breeding.

"People have a lot of questions about how to actively start using AI in crop improvement . However, it is not easy to know how its tools can best be used," said Yu, the Pioneer Distinguished Chair in Maize Breeding and director of the Raymond F. Baker Center for Plant Breeding in Iowa State University's Department of Agronomy. "There are many specific examples of constructive use of these tools, but at a large scale, it really hasn't happened yet."

Helping his peers, students and the public become more knowledgeable about the rapidly evolving field of AI has become a mission for Yu. To this end, he and other co-authors, including Karlene Negus, a genetics doctoral student working with him, have published an overview on the role of artificial intelligence in crop improvement in a scholarly compilation in Advances in Agronomy .

"Many scientists, even those who have relevant backgrounds, don't always know where to begin," Yu said. "We have been receiving feedback that the new paper is very timely and helpful."

Recently, the College of Agriculture and Life Sciences at Iowa State asked Yu and Negus to review highlights of their new publication and reflect on the uses and implications of AI tools in their field.

Yu: One thing we do in this paper is to briefly sketch AI's historical context. It has been developing since the 1940s, and what is considered the third AI summer is underway. Deep learning systems have defined the early years of this era.

For crop improvement, AI has largely been deployed to help process and make sense of very large, high-throughput data sets. Large-scale data has become a new challenge in agronomic research and many other areas of science, and AI tools are already providing diverse solutions.

Negus: The field of AI has been rapidly changing in recent years. It can be difficult to know what methods are relevant for specific uses. To streamline this learning process for areas related to crop improvement, we describe more than 15 types and subtypes of AI and give insights on how they are being used in these fields. These methods are not exhaustive, but I think this provides a good introduction to what's out there today and the building blocks of tools we can expect to be developed in the near future.

While the newsworthy AI of today is most often very sophisticated neural networks, other examples of AI range from comparatively simple robotic process automation, which uses an AI "agent" capable of conducting repetitive processes that have enough variability to prevent the use of standard process automation, to relatively complex expert and fuzzy systems that attempt to replicate the problem-solving capabilities of human experts, to other types of highly advanced machine learning .

Machine learning (ML) is a type of AI that uses large data sets to improve through experience, or learn, and then uses the outcomes to solve problems or make predictions. ML is being put into practice widely in the crop improvement field. ML methods using genomic, enviromic, phenomic and other multi-omic approaches are helping researchers capture environmental and genetic variations to better understand their influences on crop breeding and management.

Yu: Together, these applications are quickly revolutionizing agricultural practices in the laboratory, the greenhouse and the field.

For researchers in crop improvement to adopt AI methods, it is desirable to know the potential advantages of AI methods over traditional methods. For breeders, the improved capacity to monitor and forecast crop growth and health under different genetic, environmental and management combinations has the potential to greatly facilitate decisions about crop selection. For producers, it will be desirable to leverage AI to improve sustainability and resiliency through enhanced on-farm production management.

Keeping up is a challenge that those involved in crop improvement are familiar with. For the last century, that challenge has been framed around keeping up with the demand of a growing world population, and this continues to be the major concern. Now, changing climates further complicates the task. AI has great potential to help with these challenges, but we have a lot of work to do to fully capitalize on this potential, and we need to rapidly increase training and skills in these areas.

Even so, if the prior success achieved from leveraging innovative technologies for crop improvement is any indication, the future of AI-assisted crop improvement is bright.

Provided by Iowa State University

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Despite High Potential, 75 Vulnerable Economies Face ‘Historic Reversal’

In Half of IDA Countries, Income Gap with Wealthiest Economies is Widening

WASHINGTON, April 15, 2024 — Despite their high potential to advance global prosperity, one-half of the world’s 75 most vulnerable countries are facing a widening income gap with the wealthiest economies for the first time in this century, a new World Bank report has found . Taking full advantage of their younger populations, their rich natural resources, and their abundant solar-energy potential can help them overcome the setback.

The report, The Great Reversal: Prospects, Risks, and Policies in International Development Association Countries , offers the first comprehensive look at the opportunities and risks confronting the 75 countries eligible for grants and zero to low-interest loans from the World Bank’s International Development Association (IDA). These countries are home to a quarter of humanity—1.9 billion people. At a time when populations are aging nearly everywhere else, IDA countries will enjoy a growing share of young workers through 2070—a huge potential “demographic dividend.” These countries are also rich in natural resources, enjoy high potential for solar-energy generation, and boast a large reservoir of mineral deposits that could be crucial for the world’s transition to clean energy.

Yet a historic reversal is underway for them. Over 2020-24, average per capita incomes in half of IDA countries—the largest share since the start of this century—have been growing more slowly than those of wealthy economies. This is widening the income gap between these two groups of countries. One out of three IDA countries is poorer, on average, than it was on the eve of the COVID-19 pandemic . The extreme-poverty rate is more than eight times the average in the rest of the world: one in four people in IDA countries struggles on less than $2.15 a day. These countries now account for 90% of all people facing hunger or malnutrition. Half of these countries are either in debt distress or at high risk of it. Still, except for the World Bank Group and other multilateral development donors, foreign lenders—private as well as government creditors—have been backing away from them.

“The world cannot afford to turn its back on IDA countries,” said Indermit Gill, the World Bank Group’s Chief Economist and Senior Vice President . “The welfare of these countries has always been crucial to the long-term outlook for global prosperity. Three of the world’s economic powerhouses today—China, India, and South Korea—were all once IDA borrowers. All three prospered in ways that whittled down extreme poverty and raised living standards. With help from abroad, today’s batch of IDA countries has the potential to do the same.”

More than half of all IDA countries—39 in all—are in Sub-Saharan Africa. Fourteen of them—mainly small island states—are in East Asia, and eight are in Latin America and the Caribbean. In South Asia, all countries except for India are IDA countries. Thirty-one IDA countries have per capita incomes of less than $1,315 a year. Thirty-three are fragile and conflict-affected states.

IDA countries share similar opportunities. The “demographic dividend”—a deep and growing reserve of young workers—is one of them. Abundant natural resources is another. These countries account for about 20% of global production of tin, copper, and gold. In addition, some IDA countries possess critical mineral deposits essential for the global energy transition. Because of their abundant sunshine, most IDA countries are well situated to take advantage of solar energy. On average, their long-term daily solar-electricity generation potential is among the highest in the world.

This potential, however, comes with risks that must be managed. To reap the demographic dividend, IDA governments will need to undertake policies to improve education and health outcomes and make sure that jobs are available for the rising number of young people who will enter the workforce in the coming decades. To seize the full potential of their natural-resource wealth, IDA countries will need to improve policy frameworks and build stronger institutions capable of better economic management. All of this will require ambitious domestic policy reforms—and significant financial support from the international community.

“IDA countries have incredible potential to deliver strong, sustainable, and inclusive growth. Realizing this potential will require them to implement an ambitious set of policies centered on boosting investment,” said Ayhan Kose, the World Bank’s Deputy Chief Economist and Director of the Prospects Group . “ This means improving fiscal, monetary, and financial policy frameworks and advancing an array of structural reforms to strengthen institutions and enhance human capital."

IDA countries today have large investment needs. In the poorest of them, closing existing development and infrastructure gaps and building resilience to climate change will require investment that amounts to nearly 10% of GDP. The costs of climate disasters have doubled in IDA countries over the past decade: Economic losses from natural disasters average 1.3% of GDP a year—four times the average of other emerging market and developing economies. Such needs will require IDA countries to generate sustained investment booms—the type that boosts productivity and incomes and reduces poverty. Historically, such investment booms have often been sparked by a comprehensive package of policy measures—to bolster fiscal and monetary frameworks, ramp up cross-border trade and financial flows, and improve the quality of institutions. Such reforms are never easy, the report notes. They need careful sequencing and implementation. But previous IDA countries have shown they are possible.

IDA countries will need significant international financial support to make progress and lower the risk of “protracted stagnation,” the report notes . Stronger cooperation on global policy issues—including fighting climate change, facilitating more timely and effective debt restructurings, and supporting cross-border trade and investment—will also be crucial to help IDA countries avert a lost decade in development .

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This Research Topic invites contributions that explore innovative strategies for protecting the environment through sustainable agronomic practices. The objective of this Research Topic is to identify and promote innovative solutions and best practices for protecting the environment through sustainable agronomic practices. We believe that sustainable agriculture is essential for protecting the environment and ensuring food security for future generations. By adopting interdisciplinary collaboration, we hope to bring together experts from diverse backgrounds to discuss the latest research and best practices in environmental protection and sustainable agronomy. Specifically, we are interested in research that addresses the following topics: - The role of technology in agricultural sustainability - Economic and social impacts of agronomic practices - Cross-disciplinary approaches in agriculture - Legal and policy frameworks for sustainable agriculture - The effects of climate change on agricultural systems and society We welcome contributions from all disciplines, including agronomy, ecology, environmental science, economics, law, and policy. We also encourage contributions from practitioners, policymakers, and other stakeholders. We believe that this Research Topic will make a significant contribution to the field of sustainable agriculture and will help to advance the global agenda for sustainable development.

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New guidelines reflect growing use of AI in health care research

by NDORMS, University of Oxford

The widespread use of artificial intelligence (AI) in medical decision-making tools has led to an update of the TRIPOD guidelines for reporting clinical prediction models. The new TRIPOD+AI guidelines are launched in the BMJ today.

The TRIPOD guidelines (which stands for Transparent Reporting of a Multivariable Prediction Model for Individual Prognosis Or Diagnosis) were developed in 2015 to improve tools to aid diagnosis and prognosis that are used by doctors. Widely used, their uptake by medical practitioners to estimate the probability that a specific condition is present or may occur in the future, has helped improve transparency and accuracy of decision-making and significantly improve patient care.

But research methods have moved on since 2015, and we are witnessing an acceleration of studies that are developing prediction models using AI, specifically machine learning methods. Transparency is one of the six core principles underpinning the WHO guidance on ethics and governance of artificial intelligence for health. TRIPOD+AI has therefore been developed to provide a framework and set of reporting standards to boost reporting of studies developing and evaluating AI prediction models regardless of the modeling approach.

The TRIPOD+AI guidelines were developed by a consortium of international investigators, led by researchers from the University of Oxford alongside researchers from other leading institutions across the world, health care professionals , industry, regulators, and journal editors. The development of the new guidance was informed by research highlighting poor and incomplete reporting of AI studies, a Delphi survey, and an online consensus meeting.

Gary Collins, Professor of Medical Statistics at the Nuffield Department of Orthopaedics, Rheumatology and Musculoskeletal Sciences (NDORMS), University of Oxford, and lead researcher in TRIPOD, says, "There is enormous potential for artificial intelligence to improve health care from earlier diagnosis of patients with lung cancer to identifying people at increased risk of heart attacks. We're only just starting to see how this technology can be used to improve patient outcomes.

"Deciding whether to adopt these tools is predicated on transparent reporting. Transparency enables errors to be identified, facilitates appraisal of methods and ensures effective oversight and regulation. Transparency can also create more trust and influence patient and public acceptability of the use of prediction models in health care."

The TRIPOD+AI statement consists of a 27-item checklist that supersedes TRIPOD 2015. The checklist details reporting recommendations for each item and is designed to help researchers, peer reviewers, editors, policymakers and patients understand and evaluate the quality of the study methods and findings of AI-driven research.

A key change in TRIPOD+AI has been an increased emphasis on trustworthiness and fairness. Prof. Carl Moons, UMC Utrecht said, "While these are not new concepts in prediction modeling, AI has drawn more attention to these as reporting issues. A reason for this is that many AI algorithms are developed on very specific data sets that are sometimes not even from studies or could simply be drawn from the internet.

"We also don't know which groups or subgroups were included. So to ensure that studies do not discriminate against any particular group or create inequalities in health care provision, and to ensure decision-makers can trust the source of the data, these factors become more important."

Dr. Xiaoxuan Liu and Prof Alastair Denniston, Directors of the NIHR Incubator for Regulatory Science in AI & Digital Health care are co-authors of TRIPOD+AI explained, "Many of the most important applications of AI in medicine are based on prediction models. We were delighted to support the development of TRIPOD+AI which is designed to improve the quality of evidence in this important area of AI research."

TRIPOD 2015 helped change the landscape of clinical research reporting bringing minimum reporting standards to prediction models. The original guidelines have been cited over 7500 times, featured in multiple journal instructions to authors, and been included in WHO and NICE briefing documents.

"I hope the TRIPOD+AI will lead to a marked improvement in reporting, reduce waste from incompletely reported research and enable stakeholders to arrive at an informed judgment based on full details on the potential of the AI technology to improve patient care and outcomes that cut through the hype in AI-driven health care innovations," concluded Gary.

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Changing partisan coalitions in a politically divided nation, party identification among registered voters, 1994-2023.

Pew Research Center conducted this analysis to explore partisan identification among U.S. registered voters across major demographic groups and how voters’ partisan affiliation has shifted over time. It also explores the changing composition of voters overall and the partisan coalitions.

For this analysis, we used annual totals of data from Pew Research Center telephone surveys (1994-2018) and online surveys (2019-2023) among registered voters. All telephone survey data was adjusted to account for differences in how people respond to surveys on the telephone compared with online surveys (refer to Appendix A for details).

All online survey data is from the Center’s nationally representative American Trends Panel . The surveys were conducted in both English and Spanish. Each survey is weighted to be representative of the U.S. adult population by gender, age, education, race and ethnicity and other categories. Read more about the ATP’s methodology , as well as how Pew Research Center measures many of the demographic categories used in this report .

The contours of the 2024 political landscape are the result of long-standing patterns of partisanship, combined with the profound demographic changes that have reshaped the United States over the past three decades.

Many of the factors long associated with voters’ partisanship remain firmly in place. For decades, gender, race and ethnicity, and religious affiliation have been important dividing lines in politics. This continues to be the case today.

Yet there also have been profound changes – in some cases as a result of demographic change, in others because of dramatic shifts in the partisan allegiances of key groups.

The combined effects of change and continuity have left the country’s two major parties at virtual parity: About half of registered voters (49%) identify as Democrats or lean toward the Democratic Party, while 48% identify as Republicans or lean Republican.

In recent decades, neither party has had a sizable advantage, but the Democratic Party has lost the edge it maintained from 2017 to 2021. (Explore this further in Chapter 1 . )

Pew Research Center’s comprehensive analysis of party identification among registered voters – based on hundreds of thousands of interviews conducted over the past three decades – tracks the changes in the country and the parties since 1994. Among the major findings:

The partisan coalitions are increasingly different. Both parties are more racially and ethnically diverse than in the past. However, this has had a far greater impact on the composition of the Democratic Party than the Republican Party.

The share of voters who are Hispanic has roughly tripled since the mid-1990s; the share who are Asian has increased sixfold over the same period. Today, 44% of Democratic and Democratic-leaning voters are Hispanic, Black, Asian, another race or multiracial, compared with 20% of Republicans and Republican leaners. However, the Democratic Party’s advantages among Black and Hispanic voters, in particular, have narrowed somewhat in recent years. (Explore this further in Chapter 8 .)

Education and partisanship: The share of voters with a four-year bachelor’s degree keeps increasing, reaching 40% in 2023. And the gap in partisanship between voters with and without a college degree continues to grow, especially among White voters. More than six-in-ten White voters who do not have a four-year degree (63%) associate with the Republican Party, which is up substantially over the past 15 years. White college graduates are closely divided; this was not the case in the 1990s and early 2000s, when they mostly aligned with the GOP. (Explore this further in Chapter 2 .)

Beyond the gender gap: By a modest margin, women voters continue to align with the Democratic Party (by 51% to 44%), while nearly the reverse is true among men (52% align with the Republican Party, 46% with the Democratic Party). The gender gap is about as wide among married men and women. The gap is wider among men and women who have never married; while both groups are majority Democratic, 37% of never-married men identify as Republicans or lean toward the GOP, compared with 24% of never-married women. (Explore this further in Chapter 3 .)

A divide between old and young: Today, each younger age cohort is somewhat more Democratic-oriented than the one before it. The youngest voters (those ages 18 to 24) align with the Democrats by nearly two-to-one (66% to 34% Republican or lean GOP); majorities of older voters (those in their mid-60s and older) identify as Republicans or lean Republican. While there have been wide age divides in American politics over the last two decades, this wasn’t always the case; in the 1990s there were only very modest age differences in partisanship. (Explore this further in Chapter 4 .)

Education and family income: Voters without a college degree differ substantially by income in their party affiliation. Those with middle, upper-middle and upper family incomes tend to align with the GOP. A majority with lower and lower-middle incomes identify as Democrats or lean Democratic. There are no meaningful differences in partisanship among voters with at least a four-year bachelor’s degree; across income categories, majorities of college graduate voters align with the Democratic Party. (Explore this further in Chapter 6 .)

Rural voters move toward the GOP, while the suburbs remain divided: In 2008, when Barack Obama sought his first term as president, voters in rural counties were evenly split in their partisan loyalties. Today, Republicans hold a 25 percentage point advantage among rural residents (60% to 35%). There has been less change among voters in urban counties, who are mostly Democratic by a nearly identical margin (60% to 37%). The suburbs – perennially a political battleground – remain about evenly divided. (Explore this further in Chapter 7 . )

Growing differences among religious groups: Mirroring movement in the population overall, the share of voters who are religiously unaffiliated has grown dramatically over the past 15 years. These voters, who have long aligned with the Democratic Party, have become even more Democratic over time: Today 70% identify as Democrats or lean Democratic. In contrast, Republicans have made gains among several groups of religiously affiliated voters, particularly White Catholics and White evangelical Protestants. White evangelical Protestants now align with the Republican Party by about a 70-point margin (85% to 14%). (Explore this further in Chapter 5 .)

What this report tells us – and what it doesn’t

In most cases, the partisan allegiances of voters do not change a great deal from year to year. Yet as this study shows, the long-term shifts in party identification are substantial and say a great deal about how the country – and its political parties – have changed since the 1990s.

The steadily growing alignment between demographics and partisanship reveals an important aspect of steadily growing partisan polarization. Republicans and Democrats do not just hold different beliefs and opinions about major issues , they are much more different racially, ethnically, geographically and in educational attainment than they used to be.

Yet over this period, there have been only modest shifts in overall partisan identification. Voters remain evenly divided, even as the two parties have grown further apart. The continuing close division in partisan identification among voters is consistent with the relatively narrow margins in the popular votes in most national elections over the past three decades.

Partisan identification provides a broad portrait of voters’ affinities and loyalties. But while it is indicative of voters’ preferences, it does not perfectly predict how people intend to vote in elections, or whether they will vote. In the coming months, Pew Research Center will release reports analyzing voters’ preferences in the presidential election, their engagement with the election and the factors behind candidate support.

Next year, we will release a detailed study of the 2024 election, based on validated voters from the Center’s American Trends Panel. It will examine the demographic composition and vote choices of the 2024 electorate and will provide comparisons to the 2020 and 2016 validated voter studies.

The partisan identification study is based on annual totals from surveys conducted on the Center’s American Trends Panel from 2019 to 2023 and telephone surveys conducted from 1994 to 2018. The survey data was adjusted to account for differences in how the surveys were conducted. For more information, refer to Appendix A .

Previous Pew Research Center analyses of voters’ party identification relied on telephone survey data. This report, for the first time, combines data collected in telephone surveys with data from online surveys conducted on the Center’s nationally representative American Trends Panel.

Directly comparing answers from online and telephone surveys is complex because there are differences in how questions are asked of respondents and in how respondents answer those questions. Together these differences are known as “mode effects.”

As a result of mode effects, it was necessary to adjust telephone trends for leaned party identification in order to allow for direct comparisons over time.

In this report, telephone survey data from 1994 to 2018 is adjusted to align it with online survey responses. In 2014, Pew Research Center randomly assigned respondents to answer a survey by telephone or online. The party identification data from this survey was used to calculate an adjustment for differences between survey mode, which is applied to all telephone survey data in this report.

Please refer to Appendix A for more details.

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Report Materials

Table of contents, behind biden’s 2020 victory, a voter data resource: detailed demographic tables about verified voters in 2016, 2018, what the 2020 electorate looks like by party, race and ethnicity, age, education and religion, interactive map: the changing racial and ethnic makeup of the u.s. electorate, in changing u.s. electorate, race and education remain stark dividing lines, most popular.

About Pew Research Center Pew Research Center is a nonpartisan fact tank that informs the public about the issues, attitudes and trends shaping the world. It conducts public opinion polling, demographic research, media content analysis and other empirical social science research. Pew Research Center does not take policy positions. It is a subsidiary of The Pew Charitable Trusts .

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The Research-Backed Benefits of Daily Rituals

• Michael I. Norton

A survey of more than 130 HBR readers asked how they use rituals to start their days, psych themselves up for stressful challenges, and transition when the workday is done.

While some may cringe at forced corporate rituals, research shows that personal and team rituals can actually benefit the way we work. The authors’ expertise on the topic over the past decade, plus a survey of nearly 140 HBR readers, explores the ways rituals can set us up for success before work, get us psyched up for important presentations, foster a strong team culture, and help us wind down at the end of the day.

“Give me a W ! Give me an A ! Give me an L ! Give me a squiggly! Give me an M ! Give me an A ! Give me an R ! Give me a T !”

• Michael I. Norton is the Harold M. Brierley Professor of Business Administration at the Harvard Business School. He is the author of The Ritual Effect and co-author of Happy Money: The Science of Happier Spending . His research focuses on happiness, well-being, rituals, and inequality. See his faculty page here .

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