testing hypothesis in qualitative research

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Hypothesis Testing – A Complete Guide with Examples

Published by Alvin Nicolas at August 14th, 2021 , Revised On October 26, 2023

In statistics, hypothesis testing is a critical tool. It allows us to make informed decisions about populations based on sample data. Whether you are a researcher trying to prove a scientific point, a marketer analysing A/B test results, or a manufacturer ensuring quality control, hypothesis testing plays a pivotal role. This guide aims to introduce you to the concept and walk you through real-world examples.

What is a Hypothesis and a Hypothesis Testing?

A hypothesis is considered a belief or assumption that has to be accepted, rejected, proved or disproved. In contrast, a research hypothesis is a research question for a researcher that has to be proven correct or incorrect through investigation.

What is Hypothesis Testing?

Hypothesis testing  is a scientific method used for making a decision and drawing conclusions by using a statistical approach. It is used to suggest new ideas by testing theories to know whether or not the sample data supports research. A research hypothesis is a predictive statement that has to be tested using scientific methods that join an independent variable to a dependent variable.  

Example: The academic performance of student A is better than student B

Characteristics of the Hypothesis to be Tested

A hypothesis should be:

• Clear and precise
• Capable of being tested
• Able to relate to a variable
• Stated in simple terms
• Consistent with known facts
• Limited in scope and specific
• Tested in a limited timeframe
• Explain the facts in detail

What is a Null Hypothesis and Alternative Hypothesis?

A  null hypothesis  is a hypothesis when there is no significant relationship between the dependent and the participants’ independent  variables . 

In simple words, it’s a hypothesis that has been put forth but hasn’t been proved as yet. A researcher aims to disprove the theory. The abbreviation “Ho” is used to denote a null hypothesis.

If you want to compare two methods and assume that both methods are equally good, this assumption is considered the null hypothesis.

Example: In an automobile trial, you feel that the new vehicle’s mileage is similar to the previous model of the car, on average. You can write it as: Ho: there is no difference between the mileage of both vehicles. If your findings don’t support your hypothesis and you get opposite results, this outcome will be considered an alternative hypothesis.

If you assume that one method is better than another method, then it’s considered an alternative hypothesis. The alternative hypothesis is the theory that a researcher seeks to prove and is typically denoted by H1 or HA.

If you support a null hypothesis, it means you’re not supporting the alternative hypothesis. Similarly, if you reject a null hypothesis, it means you are recommending the alternative hypothesis.

Example: In an automobile trial, you feel that the new vehicle’s mileage is better than the previous model of the vehicle. You can write it as; Ha: the two vehicles have different mileage. On average/ the fuel consumption of the new vehicle model is better than the previous model.

If a null hypothesis is rejected during the hypothesis test, even if it’s true, then it is considered as a type-I error. On the other hand, if you don’t dismiss a hypothesis, even if it’s false because you could not identify its falseness, it’s considered a type-II error.

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How to Conduct Hypothesis Testing?

Here is a step-by-step guide on how to conduct hypothesis testing.

Step 1: State the Null and Alternative Hypothesis

Once you develop a research hypothesis, it’s important to state it is as a Null hypothesis (Ho) and an Alternative hypothesis (Ha) to test it statistically.

A null hypothesis is a preferred choice as it provides the opportunity to test the theory. In contrast, you can accept the alternative hypothesis when the null hypothesis has been rejected.

Example: You want to identify a relationship between obesity of men and women and the modern living style. You develop a hypothesis that women, on average, gain weight quickly compared to men. Then you write it as: Ho: Women, on average, don’t gain weight quickly compared to men. Ha: Women, on average, gain weight quickly compared to men.

Step 2: Data Collection

Hypothesis testing follows the statistical method, and statistics are all about data. It’s challenging to gather complete information about a specific population you want to study. You need to  gather the data  obtained through a large number of samples from a specific population. 

Example: Suppose you want to test the difference in the rate of obesity between men and women. You should include an equal number of men and women in your sample. Then investigate various aspects such as their lifestyle, eating patterns and profession, and any other variables that may influence average weight. You should also determine your study’s scope, whether it applies to a specific group of population or worldwide population. You can use available information from various places, countries, and regions.

Step 3: Select Appropriate Statistical Test

There are many  types of statistical tests , but we discuss the most two common types below, such as One-sided and two-sided tests.

Note: Your choice of the type of test depends on the purpose of your study 

One-sided Test

In the one-sided test, the values of rejecting a null hypothesis are located in one tail of the probability distribution. The set of values is less or higher than the critical value of the test. It is also called a one-tailed test of significance.

Example: If you want to test that all mangoes in a basket are ripe. You can write it as: Ho: All mangoes in the basket, on average, are ripe. If you find all ripe mangoes in the basket, the null hypothesis you developed will be true.

Two-sided Test

In the two-sided test, the values of rejecting a null hypothesis are located on both tails of the probability distribution. The set of values is less or higher than the first critical value of the test and higher than the second critical value test. It is also called a two-tailed test of significance. 

Example: Nothing can be explicitly said whether all mangoes are ripe in the basket. If you reject the null hypothesis (Ho: All mangoes in the basket, on average, are ripe), then it means all mangoes in the basket are not likely to be ripe. A few mangoes could be raw as well.

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Step 4: Select the Level of Significance

When you reject a null hypothesis, even if it’s true during a statistical hypothesis, it is considered the  significance level . It is the probability of a type one error. The significance should be as minimum as possible to avoid the type-I error, which is considered severe and should be avoided. 

If the significance level is minimum, then it prevents the researchers from false claims. 

The significance level is denoted by  P,  and it has given the value of 0.05 (P=0.05)

If the P-Value is less than 0.05, then the difference will be significant. If the P-value is higher than 0.05, then the difference is non-significant.

Example: Suppose you apply a one-sided test to test whether women gain weight quickly compared to men. You get to know about the average weight between men and women and the factors promoting weight gain.

Step 5: Find out Whether the Null Hypothesis is Rejected or Supported

After conducting a statistical test, you should identify whether your null hypothesis is rejected or accepted based on the test results. It would help if you observed the P-value for this.

Example: If you find the P-value of your test is less than 0.5/5%, then you need to reject your null hypothesis (Ho: Women, on average, don’t gain weight quickly compared to men). On the other hand, if a null hypothesis is rejected, then it means the alternative hypothesis might be true (Ha: Women, on average, gain weight quickly compared to men. If you find your test’s P-value is above 0.5/5%, then it means your null hypothesis is true.

Step 6: Present the Outcomes of your Study

The final step is to present the  outcomes of your study . You need to ensure whether you have met the objectives of your research or not. 

In the discussion section and  conclusion , you can present your findings by using supporting evidence and conclude whether your null hypothesis was rejected or supported.

In the result section, you can summarise your study’s outcomes, including the average difference and P-value of the two groups.

If we talk about the findings, our study your results will be as follows:

Example: In the study of identifying whether women gain weight quickly compared to men, we found the P-value is less than 0.5. Hence, we can reject the null hypothesis (Ho: Women, on average, don’t gain weight quickly than men) and conclude that women may likely gain weight quickly than men.

Did you know in your academic paper you should not mention whether you have accepted or rejected the null hypothesis? 

Always remember that you either conclude to reject Ho in favor of Haor   do not reject Ho . It would help if you never rejected  Ha  or even  accept Ha .

Suppose your null hypothesis is rejected in the hypothesis testing. If you conclude  reject Ho in favor of Haor   do not reject Ho,  then it doesn’t mean that the null hypothesis is true. It only means that there is a lack of evidence against Ho in favour of Ha. If your null hypothesis is not true, then the alternative hypothesis is likely to be true.

Example: We found that the P-value is less than 0.5. Hence, we can conclude reject Ho in favour of Ha (Ho: Women, on average, don’t gain weight quickly than men) reject Ho in favour of Ha. However, rejected in favour of Ha means (Ha: women may likely to gain weight quickly than men)

Frequently Asked Questions

What are the 3 types of hypothesis test.

The 3 types of hypothesis tests are:

• One-Sample Test : Compare sample data to a known population value.
• Two-Sample Test : Compare means between two sample groups.
• ANOVA : Analyze variance among multiple groups to determine significant differences.

What is a hypothesis?

A hypothesis is a proposed explanation or prediction about a phenomenon, often based on observations. It serves as a starting point for research or experimentation, providing a testable statement that can either be supported or refuted through data and analysis. In essence, it’s an educated guess that drives scientific inquiry.

What are null hypothesis?

A null hypothesis (often denoted as H0) suggests that there is no effect or difference in a study or experiment. It represents a default position or status quo. Statistical tests evaluate data to determine if there’s enough evidence to reject this null hypothesis.

What is the probability value?

The probability value, or p-value, is a measure used in statistics to determine the significance of an observed effect. It indicates the probability of obtaining the observed results, or more extreme, if the null hypothesis were true. A small p-value (typically <0.05) suggests evidence against the null hypothesis, warranting its rejection.

What is p value?

The p-value is a fundamental concept in statistical hypothesis testing. It represents the probability of observing a test statistic as extreme, or more so, than the one calculated from sample data, assuming the null hypothesis is true. A low p-value suggests evidence against the null, possibly justifying its rejection.

What is a t test?

A t-test is a statistical test used to compare the means of two groups. It determines if observed differences between the groups are statistically significant or if they likely occurred by chance. Commonly applied in research, there are different t-tests, including independent, paired, and one-sample, tailored to various data scenarios.

When to reject null hypothesis?

Reject the null hypothesis when the test statistic falls into a predefined rejection region or when the p-value is less than the chosen significance level (commonly 0.05). This suggests that the observed data is unlikely under the null hypothesis, indicating evidence for the alternative hypothesis. Always consider the study’s context.

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7.4 Qualitative Research

Learning objectives.

• List several ways in which qualitative research differs from quantitative research in psychology.
• Describe the strengths and weaknesses of qualitative research in psychology compared with quantitative research.
• Give examples of qualitative research in psychology.

What Is Qualitative Research?

This book is primarily about quantitative research . Quantitative researchers typically start with a focused research question or hypothesis, collect a small amount of data from each of a large number of individuals, describe the resulting data using statistical techniques, and draw general conclusions about some large population. Although this is by far the most common approach to conducting empirical research in psychology, there is an important alternative called qualitative research. Qualitative research originated in the disciplines of anthropology and sociology but is now used to study many psychological topics as well. Qualitative researchers generally begin with a less focused research question, collect large amounts of relatively “unfiltered” data from a relatively small number of individuals, and describe their data using nonstatistical techniques. They are usually less concerned with drawing general conclusions about human behavior than with understanding in detail the experience of their research participants.

Consider, for example, a study by researcher Per Lindqvist and his colleagues, who wanted to learn how the families of teenage suicide victims cope with their loss (Lindqvist, Johansson, & Karlsson, 2008). They did not have a specific research question or hypothesis, such as, What percentage of family members join suicide support groups? Instead, they wanted to understand the variety of reactions that families had, with a focus on what it is like from their perspectives. To do this, they interviewed the families of 10 teenage suicide victims in their homes in rural Sweden. The interviews were relatively unstructured, beginning with a general request for the families to talk about the victim and ending with an invitation to talk about anything else that they wanted to tell the interviewer. One of the most important themes that emerged from these interviews was that even as life returned to “normal,” the families continued to struggle with the question of why their loved one committed suicide. This struggle appeared to be especially difficult for families in which the suicide was most unexpected.

The Purpose of Qualitative Research

Again, this book is primarily about quantitative research in psychology. The strength of quantitative research is its ability to provide precise answers to specific research questions and to draw general conclusions about human behavior. This is how we know that people have a strong tendency to obey authority figures, for example, or that female college students are not substantially more talkative than male college students. But while quantitative research is good at providing precise answers to specific research questions, it is not nearly as good at generating novel and interesting research questions. Likewise, while quantitative research is good at drawing general conclusions about human behavior, it is not nearly as good at providing detailed descriptions of the behavior of particular groups in particular situations. And it is not very good at all at communicating what it is actually like to be a member of a particular group in a particular situation.

But the relative weaknesses of quantitative research are the relative strengths of qualitative research. Qualitative research can help researchers to generate new and interesting research questions and hypotheses. The research of Lindqvist and colleagues, for example, suggests that there may be a general relationship between how unexpected a suicide is and how consumed the family is with trying to understand why the teen committed suicide. This relationship can now be explored using quantitative research. But it is unclear whether this question would have arisen at all without the researchers sitting down with the families and listening to what they themselves wanted to say about their experience. Qualitative research can also provide rich and detailed descriptions of human behavior in the real-world contexts in which it occurs. Among qualitative researchers, this is often referred to as “thick description” (Geertz, 1973). Similarly, qualitative research can convey a sense of what it is actually like to be a member of a particular group or in a particular situation—what qualitative researchers often refer to as the “lived experience” of the research participants. Lindqvist and colleagues, for example, describe how all the families spontaneously offered to show the interviewer the victim’s bedroom or the place where the suicide occurred—revealing the importance of these physical locations to the families. It seems unlikely that a quantitative study would have discovered this.

Data Collection and Analysis in Qualitative Research

As with correlational research, data collection approaches in qualitative research are quite varied and can involve naturalistic observation, archival data, artwork, and many other things. But one of the most common approaches, especially for psychological research, is to conduct interviews . Interviews in qualitative research tend to be unstructured—consisting of a small number of general questions or prompts that allow participants to talk about what is of interest to them. The researcher can follow up by asking more detailed questions about the topics that do come up. Such interviews can be lengthy and detailed, but they are usually conducted with a relatively small sample. This was essentially the approach used by Lindqvist and colleagues in their research on the families of suicide survivors. Small groups of people who participate together in interviews focused on a particular topic or issue are often referred to as focus groups . The interaction among participants in a focus group can sometimes bring out more information than can be learned in a one-on-one interview. The use of focus groups has become a standard technique in business and industry among those who want to understand consumer tastes and preferences. The content of all focus group interviews is usually recorded and transcribed to facilitate later analyses.

Another approach to data collection in qualitative research is participant observation. In participant observation , researchers become active participants in the group or situation they are studying. The data they collect can include interviews (usually unstructured), their own notes based on their observations and interactions, documents, photographs, and other artifacts. The basic rationale for participant observation is that there may be important information that is only accessible to, or can be interpreted only by, someone who is an active participant in the group or situation. An example of participant observation comes from a study by sociologist Amy Wilkins (published in Social Psychology Quarterly ) on a college-based religious organization that emphasized how happy its members were (Wilkins, 2008). Wilkins spent 12 months attending and participating in the group’s meetings and social events, and she interviewed several group members. In her study, Wilkins identified several ways in which the group “enforced” happiness—for example, by continually talking about happiness, discouraging the expression of negative emotions, and using happiness as a way to distinguish themselves from other groups.

Data Analysis in Quantitative Research

Although quantitative and qualitative research generally differ along several important dimensions (e.g., the specificity of the research question, the type of data collected), it is the method of data analysis that distinguishes them more clearly than anything else. To illustrate this idea, imagine a team of researchers that conducts a series of unstructured interviews with recovering alcoholics to learn about the role of their religious faith in their recovery. Although this sounds like qualitative research, imagine further that once they collect the data, they code the data in terms of how often each participant mentions God (or a “higher power”), and they then use descriptive and inferential statistics to find out whether those who mention God more often are more successful in abstaining from alcohol. Now it sounds like quantitative research. In other words, the quantitative-qualitative distinction depends more on what researchers do with the data they have collected than with why or how they collected the data.

But what does qualitative data analysis look like? Just as there are many ways to collect data in qualitative research, there are many ways to analyze data. Here we focus on one general approach called grounded theory (Glaser & Strauss, 1967). This approach was developed within the field of sociology in the 1960s and has gradually gained popularity in psychology. Remember that in quantitative research, it is typical for the researcher to start with a theory, derive a hypothesis from that theory, and then collect data to test that specific hypothesis. In qualitative research using grounded theory, researchers start with the data and develop a theory or an interpretation that is “grounded in” those data. They do this in stages. First, they identify ideas that are repeated throughout the data. Then they organize these ideas into a smaller number of broader themes. Finally, they write a theoretical narrative —an interpretation—of the data in terms of the themes that they have identified. This theoretical narrative focuses on the subjective experience of the participants and is usually supported by many direct quotations from the participants themselves.

As an example, consider a study by researchers Laura Abrams and Laura Curran, who used the grounded theory approach to study the experience of postpartum depression symptoms among low-income mothers (Abrams & Curran, 2009). Their data were the result of unstructured interviews with 19 participants. Table 7.1 “Themes and Repeating Ideas in a Study of Postpartum Depression Among Low-Income Mothers” shows the five broad themes the researchers identified and the more specific repeating ideas that made up each of those themes. In their research report, they provide numerous quotations from their participants, such as this one from “Destiny:”

Well, just recently my apartment was broken into and the fact that his Medicaid for some reason was cancelled so a lot of things was happening within the last two weeks all at one time. So that in itself I don’t want to say almost drove me mad but it put me in a funk.…Like I really was depressed. (p. 357)

Their theoretical narrative focused on the participants’ experience of their symptoms not as an abstract “affective disorder” but as closely tied to the daily struggle of raising children alone under often difficult circumstances.

Table 7.1 Themes and Repeating Ideas in a Study of Postpartum Depression Among Low-Income Mothers

The Quantitative-Qualitative “Debate”

Given their differences, it may come as no surprise that quantitative and qualitative research in psychology and related fields do not coexist in complete harmony. Some quantitative researchers criticize qualitative methods on the grounds that they lack objectivity, are difficult to evaluate in terms of reliability and validity, and do not allow generalization to people or situations other than those actually studied. At the same time, some qualitative researchers criticize quantitative methods on the grounds that they overlook the richness of human behavior and experience and instead answer simple questions about easily quantifiable variables.

In general, however, qualitative researchers are well aware of the issues of objectivity, reliability, validity, and generalizability. In fact, they have developed a number of frameworks for addressing these issues (which are beyond the scope of our discussion). And in general, quantitative researchers are well aware of the issue of oversimplification. They do not believe that all human behavior and experience can be adequately described in terms of a small number of variables and the statistical relationships among them. Instead, they use simplification as a strategy for uncovering general principles of human behavior.

Many researchers from both the quantitative and qualitative camps now agree that the two approaches can and should be combined into what has come to be called mixed-methods research (Todd, Nerlich, McKeown, & Clarke, 2004). (In fact, the studies by Lindqvist and colleagues and by Abrams and Curran both combined quantitative and qualitative approaches.) One approach to combining quantitative and qualitative research is to use qualitative research for hypothesis generation and quantitative research for hypothesis testing. Again, while a qualitative study might suggest that families who experience an unexpected suicide have more difficulty resolving the question of why, a well-designed quantitative study could test a hypothesis by measuring these specific variables for a large sample. A second approach to combining quantitative and qualitative research is referred to as triangulation . The idea is to use both quantitative and qualitative methods simultaneously to study the same general questions and to compare the results. If the results of the quantitative and qualitative methods converge on the same general conclusion, they reinforce and enrich each other. If the results diverge, then they suggest an interesting new question: Why do the results diverge and how can they be reconciled?

Key Takeaways

• Qualitative research is an important alternative to quantitative research in psychology. It generally involves asking broader research questions, collecting more detailed data (e.g., interviews), and using nonstatistical analyses.
• Many researchers conceptualize quantitative and qualitative research as complementary and advocate combining them. For example, qualitative research can be used to generate hypotheses and quantitative research to test them.
• Discussion: What are some ways in which a qualitative study of girls who play youth baseball would be likely to differ from a quantitative study on the same topic?

Abrams, L. S., & Curran, L. (2009). “And you’re telling me not to stress?” A grounded theory study of postpartum depression symptoms among low-income mothers. Psychology of Women Quarterly, 33 , 351–362.

Geertz, C. (1973). The interpretation of cultures . New York, NY: Basic Books.

Glaser, B. G., & Strauss, A. L. (1967). The discovery of grounded theory: Strategies for qualitative research . Chicago, IL: Aldine.

Lindqvist, P., Johansson, L., & Karlsson, U. (2008). In the aftermath of teenage suicide: A qualitative study of the psychosocial consequences for the surviving family members. BMC Psychiatry, 8 , 26. Retrieved from http://www.biomedcentral.com/1471-244X/8/26 .

Todd, Z., Nerlich, B., McKeown, S., & Clarke, D. D. (2004) Mixing methods in psychology: The integration of qualitative and quantitative methods in theory and practice . London, UK: Psychology Press.

Wilkins, A. (2008). “Happier than Non-Christians”: Collective emotions and symbolic boundaries among evangelical Christians. Social Psychology Quarterly, 71 , 281–301.

Research Methods in Psychology Copyright © 2016 by University of Minnesota is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License , except where otherwise noted.

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Qualitative vs. Quantitative Research — Here’s What You Need to Know

Will Mellor, Director of Surveys, GLG

Read Time: 0 Minutes

Qualitative vs. Quantitative — you’ve heard the terms before, but what do they mean? Here’s what you need to know on when to use them and how to apply them in your research projects.

Most research projects you undertake will likely require some combination of qualitative and quantitative data. The magnitude of each will depend on what you need to accomplish. They are opposite in their approach, which makes them balanced in their outcomes.

When Are They Applied?

Qualitative  

Qualitative research is used to formulate a hypothesis . If you need deeper information about a topic you know little about, qualitative research can help you uncover themes. For this reason, qualitative research often comes prior to quantitative. It allows you to get a baseline understanding of the topic and start to formulate hypotheses around correlation and causation.

Quantitative

Quantitative research is used to test or confirm a hypothesis . Qualitative research usually informs quantitative. You need to have enough understanding about a topic in order to develop a hypothesis you can test. Since quantitative research is highly structured, you first need to understand what the parameters are and how variable they are in practice. This allows you to create a research outline that is controlled in all the ways that will produce high-quality data.

In practice, the parameters are the factors you want to test against your hypothesis. If your hypothesis is that COVID is going to transform the way companies think about office space, some of your parameters might include the percent of your workforce working from home pre- and post-COVID, total square footage of office space held, and/or real-estate spend expectations by executive leadership. You would also want to know the variability of those parameters. In the COVID example, you will need to know standard ranges of square footage and real-estate expenditures so that you can create answer options that will capture relevant, high-quality, and easily actionable data.

Methods of Research

Often, qualitative research is conducted with a small sample size and includes many open-ended questions . The goal is to understand “Why?” and the thinking behind the decisions. The best way to facilitate this type of research is through one-on-one interviews, focus groups, and sometimes surveys. A major benefit of the interview and focus group formats is the ability to ask follow-up questions and dig deeper on answers that are particularly insightful.

Conversely, quantitative research is designed for larger sample sizes, which can garner perspectives across a wide spectrum of respondents. While not always necessary, sample sizes can sometimes be large enough to be statistically significant . The best way to facilitate this type of research is through surveys or large-scale experiments.

Unsurprisingly, the two different approaches will generate different types of data that will need to be analyzed differently.

For qualitative data, you’ll end up with data that will be highly textual in nature. You’ll be reading through the data and looking for key themes that emerge over and over. This type of research is also great at producing quotes that can be used in presentations or reports. Quotes are a powerful tool for conveying sentiment and making a poignant point.

For quantitative data, you’ll end up with a data set that can be analyzed, often with statistical software such as Excel, R, or SPSS. You can ask many different types of questions that produce this quantitative data, including rating/ranking questions, single-select, multiselect, and matrix table questions. These question types will produce data that can be analyzed to find averages, ranges, growth rates, percentage changes, minimums/maximums, and even time-series data for longer-term trend analysis.

Mixed Methods Approach

You aren’t limited to just one approach. If you need both quantitative and qualitative data, then collect both. You can even collect both quantitative and qualitative data within one type of research instrument. In a survey, you can ask both open-ended questions about “Why?” as well as closed-ended, data-related questions. Even in an unstructured format, like an interview or focus group, you can ask numerical questions to capture analyzable data.

Just be careful. While qualitative themes can be generalized, it can be dangerous to generalize on such a small sample size of quantitative data. For instance, why companies like a certain software platform may fall into three to five key themes. How much they spend on that platform can be highly variable.

The Takeaway

If you are unfamiliar with the topic you are researching, qualitative research is the best first approach. As you get deeper in your research, certain themes will emerge, and you’ll start to form hypotheses. From there, quantitative research can provide larger-scale data sets that can be analyzed to either confirm or deny the hypotheses you formulated earlier in your research. Most importantly, the two approaches are not mutually exclusive. You can have an eye for both themes and data throughout the research process. You’ll just be leaning more heavily to one or the other depending on where you are in your understanding of the topic.

Ready to get started? Get the actionable insights you need with the help of GLG’s qualitative and quantitative research methods.

About Will Mellor

Will Mellor leads a team of accomplished project managers who serve financial service firms across North America. His team manages end-to-end survey delivery from first draft to final deliverable. Will is an expert on GLG’s internal membership and consumer populations, as well as survey design and research. Before coming to GLG, he was the vice president of an economic consulting group, where he was responsible for designing economic impact models for clients in both the public sector and the private sector. Will has bachelor’s degrees in international business and finance and a master’s degree in applied economics.

For more information, read our articles: Three Ways to Apply Qualitative Research ,   Focusing on Focus Groups: Best Practices,   What Type of Survey Do You Need?, or The 6 Pillars of Successful Survey Design

You can also download our eBooks: GLG’s Guide to Effective Qualitative Research or Strategies for Successful Surveys

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How to use and assess qualitative research methods

Loraine busetto.

1 Department of Neurology, Heidelberg University Hospital, Im Neuenheimer Feld 400, 69120 Heidelberg, Germany

Wolfgang Wick

2 Clinical Cooperation Unit Neuro-Oncology, German Cancer Research Center, Heidelberg, Germany

Christoph Gumbinger

Associated data.

Not applicable.

This paper aims to provide an overview of the use and assessment of qualitative research methods in the health sciences. Qualitative research can be defined as the study of the nature of phenomena and is especially appropriate for answering questions of why something is (not) observed, assessing complex multi-component interventions, and focussing on intervention improvement. The most common methods of data collection are document study, (non-) participant observations, semi-structured interviews and focus groups. For data analysis, field-notes and audio-recordings are transcribed into protocols and transcripts, and coded using qualitative data management software. Criteria such as checklists, reflexivity, sampling strategies, piloting, co-coding, member-checking and stakeholder involvement can be used to enhance and assess the quality of the research conducted. Using qualitative in addition to quantitative designs will equip us with better tools to address a greater range of research problems, and to fill in blind spots in current neurological research and practice.

The aim of this paper is to provide an overview of qualitative research methods, including hands-on information on how they can be used, reported and assessed. This article is intended for beginning qualitative researchers in the health sciences as well as experienced quantitative researchers who wish to broaden their understanding of qualitative research.

What is qualitative research?

Qualitative research is defined as “the study of the nature of phenomena”, including “their quality, different manifestations, the context in which they appear or the perspectives from which they can be perceived” , but excluding “their range, frequency and place in an objectively determined chain of cause and effect” [ 1 ]. This formal definition can be complemented with a more pragmatic rule of thumb: qualitative research generally includes data in form of words rather than numbers [ 2 ].

Why conduct qualitative research?

Because some research questions cannot be answered using (only) quantitative methods. For example, one Australian study addressed the issue of why patients from Aboriginal communities often present late or not at all to specialist services offered by tertiary care hospitals. Using qualitative interviews with patients and staff, it found one of the most significant access barriers to be transportation problems, including some towns and communities simply not having a bus service to the hospital [ 3 ]. A quantitative study could have measured the number of patients over time or even looked at possible explanatory factors – but only those previously known or suspected to be of relevance. To discover reasons for observed patterns, especially the invisible or surprising ones, qualitative designs are needed.

While qualitative research is common in other fields, it is still relatively underrepresented in health services research. The latter field is more traditionally rooted in the evidence-based-medicine paradigm, as seen in " research that involves testing the effectiveness of various strategies to achieve changes in clinical practice, preferably applying randomised controlled trial study designs (...) " [ 4 ]. This focus on quantitative research and specifically randomised controlled trials (RCT) is visible in the idea of a hierarchy of research evidence which assumes that some research designs are objectively better than others, and that choosing a "lesser" design is only acceptable when the better ones are not practically or ethically feasible [ 5 , 6 ]. Others, however, argue that an objective hierarchy does not exist, and that, instead, the research design and methods should be chosen to fit the specific research question at hand – "questions before methods" [ 2 , 7 – 9 ]. This means that even when an RCT is possible, some research problems require a different design that is better suited to addressing them. Arguing in JAMA, Berwick uses the example of rapid response teams in hospitals, which he describes as " a complex, multicomponent intervention – essentially a process of social change" susceptible to a range of different context factors including leadership or organisation history. According to him, "[in] such complex terrain, the RCT is an impoverished way to learn. Critics who use it as a truth standard in this context are incorrect" [ 8 ] . Instead of limiting oneself to RCTs, Berwick recommends embracing a wider range of methods , including qualitative ones, which for "these specific applications, (...) are not compromises in learning how to improve; they are superior" [ 8 ].

Research problems that can be approached particularly well using qualitative methods include assessing complex multi-component interventions or systems (of change), addressing questions beyond “what works”, towards “what works for whom when, how and why”, and focussing on intervention improvement rather than accreditation [ 7 , 9 – 12 ]. Using qualitative methods can also help shed light on the “softer” side of medical treatment. For example, while quantitative trials can measure the costs and benefits of neuro-oncological treatment in terms of survival rates or adverse effects, qualitative research can help provide a better understanding of patient or caregiver stress, visibility of illness or out-of-pocket expenses.

How to conduct qualitative research?

Given that qualitative research is characterised by flexibility, openness and responsivity to context, the steps of data collection and analysis are not as separate and consecutive as they tend to be in quantitative research [ 13 , 14 ]. As Fossey puts it : “sampling, data collection, analysis and interpretation are related to each other in a cyclical (iterative) manner, rather than following one after another in a stepwise approach” [ 15 ]. The researcher can make educated decisions with regard to the choice of method, how they are implemented, and to which and how many units they are applied [ 13 ]. As shown in Fig.  1 , this can involve several back-and-forth steps between data collection and analysis where new insights and experiences can lead to adaption and expansion of the original plan. Some insights may also necessitate a revision of the research question and/or the research design as a whole. The process ends when saturation is achieved, i.e. when no relevant new information can be found (see also below: sampling and saturation). For reasons of transparency, it is essential for all decisions as well as the underlying reasoning to be well-documented.

Iterative research process

While it is not always explicitly addressed, qualitative methods reflect a different underlying research paradigm than quantitative research (e.g. constructivism or interpretivism as opposed to positivism). The choice of methods can be based on the respective underlying substantive theory or theoretical framework used by the researcher [ 2 ].

Data collection

The methods of qualitative data collection most commonly used in health research are document study, observations, semi-structured interviews and focus groups [ 1 , 14 , 16 , 17 ].

Document study

Document study (also called document analysis) refers to the review by the researcher of written materials [ 14 ]. These can include personal and non-personal documents such as archives, annual reports, guidelines, policy documents, diaries or letters.

Observations

Observations are particularly useful to gain insights into a certain setting and actual behaviour – as opposed to reported behaviour or opinions [ 13 ]. Qualitative observations can be either participant or non-participant in nature. In participant observations, the observer is part of the observed setting, for example a nurse working in an intensive care unit [ 18 ]. In non-participant observations, the observer is “on the outside looking in”, i.e. present in but not part of the situation, trying not to influence the setting by their presence. Observations can be planned (e.g. for 3 h during the day or night shift) or ad hoc (e.g. as soon as a stroke patient arrives at the emergency room). During the observation, the observer takes notes on everything or certain pre-determined parts of what is happening around them, for example focusing on physician-patient interactions or communication between different professional groups. Written notes can be taken during or after the observations, depending on feasibility (which is usually lower during participant observations) and acceptability (e.g. when the observer is perceived to be judging the observed). Afterwards, these field notes are transcribed into observation protocols. If more than one observer was involved, field notes are taken independently, but notes can be consolidated into one protocol after discussions. Advantages of conducting observations include minimising the distance between the researcher and the researched, the potential discovery of topics that the researcher did not realise were relevant and gaining deeper insights into the real-world dimensions of the research problem at hand [ 18 ].

Semi-structured interviews

Hijmans & Kuyper describe qualitative interviews as “an exchange with an informal character, a conversation with a goal” [ 19 ]. Interviews are used to gain insights into a person’s subjective experiences, opinions and motivations – as opposed to facts or behaviours [ 13 ]. Interviews can be distinguished by the degree to which they are structured (i.e. a questionnaire), open (e.g. free conversation or autobiographical interviews) or semi-structured [ 2 , 13 ]. Semi-structured interviews are characterized by open-ended questions and the use of an interview guide (or topic guide/list) in which the broad areas of interest, sometimes including sub-questions, are defined [ 19 ]. The pre-defined topics in the interview guide can be derived from the literature, previous research or a preliminary method of data collection, e.g. document study or observations. The topic list is usually adapted and improved at the start of the data collection process as the interviewer learns more about the field [ 20 ]. Across interviews the focus on the different (blocks of) questions may differ and some questions may be skipped altogether (e.g. if the interviewee is not able or willing to answer the questions or for concerns about the total length of the interview) [ 20 ]. Qualitative interviews are usually not conducted in written format as it impedes on the interactive component of the method [ 20 ]. In comparison to written surveys, qualitative interviews have the advantage of being interactive and allowing for unexpected topics to emerge and to be taken up by the researcher. This can also help overcome a provider or researcher-centred bias often found in written surveys, which by nature, can only measure what is already known or expected to be of relevance to the researcher. Interviews can be audio- or video-taped; but sometimes it is only feasible or acceptable for the interviewer to take written notes [ 14 , 16 , 20 ].

Focus groups

Focus groups are group interviews to explore participants’ expertise and experiences, including explorations of how and why people behave in certain ways [ 1 ]. Focus groups usually consist of 6–8 people and are led by an experienced moderator following a topic guide or “script” [ 21 ]. They can involve an observer who takes note of the non-verbal aspects of the situation, possibly using an observation guide [ 21 ]. Depending on researchers’ and participants’ preferences, the discussions can be audio- or video-taped and transcribed afterwards [ 21 ]. Focus groups are useful for bringing together homogeneous (to a lesser extent heterogeneous) groups of participants with relevant expertise and experience on a given topic on which they can share detailed information [ 21 ]. Focus groups are a relatively easy, fast and inexpensive method to gain access to information on interactions in a given group, i.e. “the sharing and comparing” among participants [ 21 ]. Disadvantages include less control over the process and a lesser extent to which each individual may participate. Moreover, focus group moderators need experience, as do those tasked with the analysis of the resulting data. Focus groups can be less appropriate for discussing sensitive topics that participants might be reluctant to disclose in a group setting [ 13 ]. Moreover, attention must be paid to the emergence of “groupthink” as well as possible power dynamics within the group, e.g. when patients are awed or intimidated by health professionals.

Choosing the “right” method

As explained above, the school of thought underlying qualitative research assumes no objective hierarchy of evidence and methods. This means that each choice of single or combined methods has to be based on the research question that needs to be answered and a critical assessment with regard to whether or to what extent the chosen method can accomplish this – i.e. the “fit” between question and method [ 14 ]. It is necessary for these decisions to be documented when they are being made, and to be critically discussed when reporting methods and results.

Let us assume that our research aim is to examine the (clinical) processes around acute endovascular treatment (EVT), from the patient’s arrival at the emergency room to recanalization, with the aim to identify possible causes for delay and/or other causes for sub-optimal treatment outcome. As a first step, we could conduct a document study of the relevant standard operating procedures (SOPs) for this phase of care – are they up-to-date and in line with current guidelines? Do they contain any mistakes, irregularities or uncertainties that could cause delays or other problems? Regardless of the answers to these questions, the results have to be interpreted based on what they are: a written outline of what care processes in this hospital should look like. If we want to know what they actually look like in practice, we can conduct observations of the processes described in the SOPs. These results can (and should) be analysed in themselves, but also in comparison to the results of the document analysis, especially as regards relevant discrepancies. Do the SOPs outline specific tests for which no equipment can be observed or tasks to be performed by specialized nurses who are not present during the observation? It might also be possible that the written SOP is outdated, but the actual care provided is in line with current best practice. In order to find out why these discrepancies exist, it can be useful to conduct interviews. Are the physicians simply not aware of the SOPs (because their existence is limited to the hospital’s intranet) or do they actively disagree with them or does the infrastructure make it impossible to provide the care as described? Another rationale for adding interviews is that some situations (or all of their possible variations for different patient groups or the day, night or weekend shift) cannot practically or ethically be observed. In this case, it is possible to ask those involved to report on their actions – being aware that this is not the same as the actual observation. A senior physician’s or hospital manager’s description of certain situations might differ from a nurse’s or junior physician’s one, maybe because they intentionally misrepresent facts or maybe because different aspects of the process are visible or important to them. In some cases, it can also be relevant to consider to whom the interviewee is disclosing this information – someone they trust, someone they are otherwise not connected to, or someone they suspect or are aware of being in a potentially “dangerous” power relationship to them. Lastly, a focus group could be conducted with representatives of the relevant professional groups to explore how and why exactly they provide care around EVT. The discussion might reveal discrepancies (between SOPs and actual care or between different physicians) and motivations to the researchers as well as to the focus group members that they might not have been aware of themselves. For the focus group to deliver relevant information, attention has to be paid to its composition and conduct, for example, to make sure that all participants feel safe to disclose sensitive or potentially problematic information or that the discussion is not dominated by (senior) physicians only. The resulting combination of data collection methods is shown in Fig.  2 .

Possible combination of data collection methods

Attributions for icons: “Book” by Serhii Smirnov, “Interview” by Adrien Coquet, FR, “Magnifying Glass” by anggun, ID, “Business communication” by Vectors Market; all from the Noun Project

The combination of multiple data source as described for this example can be referred to as “triangulation”, in which multiple measurements are carried out from different angles to achieve a more comprehensive understanding of the phenomenon under study [ 22 , 23 ].

Data analysis

To analyse the data collected through observations, interviews and focus groups these need to be transcribed into protocols and transcripts (see Fig.  3 ). Interviews and focus groups can be transcribed verbatim , with or without annotations for behaviour (e.g. laughing, crying, pausing) and with or without phonetic transcription of dialects and filler words, depending on what is expected or known to be relevant for the analysis. In the next step, the protocols and transcripts are coded , that is, marked (or tagged, labelled) with one or more short descriptors of the content of a sentence or paragraph [ 2 , 15 , 23 ]. Jansen describes coding as “connecting the raw data with “theoretical” terms” [ 20 ]. In a more practical sense, coding makes raw data sortable. This makes it possible to extract and examine all segments describing, say, a tele-neurology consultation from multiple data sources (e.g. SOPs, emergency room observations, staff and patient interview). In a process of synthesis and abstraction, the codes are then grouped, summarised and/or categorised [ 15 , 20 ]. The end product of the coding or analysis process is a descriptive theory of the behavioural pattern under investigation [ 20 ]. The coding process is performed using qualitative data management software, the most common ones being InVivo, MaxQDA and Atlas.ti. It should be noted that these are data management tools which support the analysis performed by the researcher(s) [ 14 ].

From data collection to data analysis

Attributions for icons: see Fig. ​ Fig.2, 2 , also “Speech to text” by Trevor Dsouza, “Field Notes” by Mike O’Brien, US, “Voice Record” by ProSymbols, US, “Inspection” by Made, AU, and “Cloud” by Graphic Tigers; all from the Noun Project

How to report qualitative research?

Protocols of qualitative research can be published separately and in advance of the study results. However, the aim is not the same as in RCT protocols, i.e. to pre-define and set in stone the research questions and primary or secondary endpoints. Rather, it is a way to describe the research methods in detail, which might not be possible in the results paper given journals’ word limits. Qualitative research papers are usually longer than their quantitative counterparts to allow for deep understanding and so-called “thick description”. In the methods section, the focus is on transparency of the methods used, including why, how and by whom they were implemented in the specific study setting, so as to enable a discussion of whether and how this may have influenced data collection, analysis and interpretation. The results section usually starts with a paragraph outlining the main findings, followed by more detailed descriptions of, for example, the commonalities, discrepancies or exceptions per category [ 20 ]. Here it is important to support main findings by relevant quotations, which may add information, context, emphasis or real-life examples [ 20 , 23 ]. It is subject to debate in the field whether it is relevant to state the exact number or percentage of respondents supporting a certain statement (e.g. “Five interviewees expressed negative feelings towards XYZ”) [ 21 ].

How to combine qualitative with quantitative research?

Qualitative methods can be combined with other methods in multi- or mixed methods designs, which “[employ] two or more different methods [ …] within the same study or research program rather than confining the research to one single method” [ 24 ]. Reasons for combining methods can be diverse, including triangulation for corroboration of findings, complementarity for illustration and clarification of results, expansion to extend the breadth and range of the study, explanation of (unexpected) results generated with one method with the help of another, or offsetting the weakness of one method with the strength of another [ 1 , 17 , 24 – 26 ]. The resulting designs can be classified according to when, why and how the different quantitative and/or qualitative data strands are combined. The three most common types of mixed method designs are the convergent parallel design , the explanatory sequential design and the exploratory sequential design. The designs with examples are shown in Fig.  4 .

Three common mixed methods designs

In the convergent parallel design, a qualitative study is conducted in parallel to and independently of a quantitative study, and the results of both studies are compared and combined at the stage of interpretation of results. Using the above example of EVT provision, this could entail setting up a quantitative EVT registry to measure process times and patient outcomes in parallel to conducting the qualitative research outlined above, and then comparing results. Amongst other things, this would make it possible to assess whether interview respondents’ subjective impressions of patients receiving good care match modified Rankin Scores at follow-up, or whether observed delays in care provision are exceptions or the rule when compared to door-to-needle times as documented in the registry. In the explanatory sequential design, a quantitative study is carried out first, followed by a qualitative study to help explain the results from the quantitative study. This would be an appropriate design if the registry alone had revealed relevant delays in door-to-needle times and the qualitative study would be used to understand where and why these occurred, and how they could be improved. In the exploratory design, the qualitative study is carried out first and its results help informing and building the quantitative study in the next step [ 26 ]. If the qualitative study around EVT provision had shown a high level of dissatisfaction among the staff members involved, a quantitative questionnaire investigating staff satisfaction could be set up in the next step, informed by the qualitative study on which topics dissatisfaction had been expressed. Amongst other things, the questionnaire design would make it possible to widen the reach of the research to more respondents from different (types of) hospitals, regions, countries or settings, and to conduct sub-group analyses for different professional groups.

How to assess qualitative research?

A variety of assessment criteria and lists have been developed for qualitative research, ranging in their focus and comprehensiveness [ 14 , 17 , 27 ]. However, none of these has been elevated to the “gold standard” in the field. In the following, we therefore focus on a set of commonly used assessment criteria that, from a practical standpoint, a researcher can look for when assessing a qualitative research report or paper.

Assessors should check the authors’ use of and adherence to the relevant reporting checklists (e.g. Standards for Reporting Qualitative Research (SRQR)) to make sure all items that are relevant for this type of research are addressed [ 23 , 28 ]. Discussions of quantitative measures in addition to or instead of these qualitative measures can be a sign of lower quality of the research (paper). Providing and adhering to a checklist for qualitative research contributes to an important quality criterion for qualitative research, namely transparency [ 15 , 17 , 23 ].

Reflexivity

While methodological transparency and complete reporting is relevant for all types of research, some additional criteria must be taken into account for qualitative research. This includes what is called reflexivity, i.e. sensitivity to the relationship between the researcher and the researched, including how contact was established and maintained, or the background and experience of the researcher(s) involved in data collection and analysis. Depending on the research question and population to be researched this can be limited to professional experience, but it may also include gender, age or ethnicity [ 17 , 27 ]. These details are relevant because in qualitative research, as opposed to quantitative research, the researcher as a person cannot be isolated from the research process [ 23 ]. It may influence the conversation when an interviewed patient speaks to an interviewer who is a physician, or when an interviewee is asked to discuss a gynaecological procedure with a male interviewer, and therefore the reader must be made aware of these details [ 19 ].

Sampling and saturation

The aim of qualitative sampling is for all variants of the objects of observation that are deemed relevant for the study to be present in the sample “ to see the issue and its meanings from as many angles as possible” [ 1 , 16 , 19 , 20 , 27 ] , and to ensure “information-richness [ 15 ]. An iterative sampling approach is advised, in which data collection (e.g. five interviews) is followed by data analysis, followed by more data collection to find variants that are lacking in the current sample. This process continues until no new (relevant) information can be found and further sampling becomes redundant – which is called saturation [ 1 , 15 ] . In other words: qualitative data collection finds its end point not a priori , but when the research team determines that saturation has been reached [ 29 , 30 ].

This is also the reason why most qualitative studies use deliberate instead of random sampling strategies. This is generally referred to as “ purposive sampling” , in which researchers pre-define which types of participants or cases they need to include so as to cover all variations that are expected to be of relevance, based on the literature, previous experience or theory (i.e. theoretical sampling) [ 14 , 20 ]. Other types of purposive sampling include (but are not limited to) maximum variation sampling, critical case sampling or extreme or deviant case sampling [ 2 ]. In the above EVT example, a purposive sample could include all relevant professional groups and/or all relevant stakeholders (patients, relatives) and/or all relevant times of observation (day, night and weekend shift).

Assessors of qualitative research should check whether the considerations underlying the sampling strategy were sound and whether or how researchers tried to adapt and improve their strategies in stepwise or cyclical approaches between data collection and analysis to achieve saturation [ 14 ].

Good qualitative research is iterative in nature, i.e. it goes back and forth between data collection and analysis, revising and improving the approach where necessary. One example of this are pilot interviews, where different aspects of the interview (especially the interview guide, but also, for example, the site of the interview or whether the interview can be audio-recorded) are tested with a small number of respondents, evaluated and revised [ 19 ]. In doing so, the interviewer learns which wording or types of questions work best, or which is the best length of an interview with patients who have trouble concentrating for an extended time. Of course, the same reasoning applies to observations or focus groups which can also be piloted.

Ideally, coding should be performed by at least two researchers, especially at the beginning of the coding process when a common approach must be defined, including the establishment of a useful coding list (or tree), and when a common meaning of individual codes must be established [ 23 ]. An initial sub-set or all transcripts can be coded independently by the coders and then compared and consolidated after regular discussions in the research team. This is to make sure that codes are applied consistently to the research data.

Member checking

Member checking, also called respondent validation , refers to the practice of checking back with study respondents to see if the research is in line with their views [ 14 , 27 ]. This can happen after data collection or analysis or when first results are available [ 23 ]. For example, interviewees can be provided with (summaries of) their transcripts and asked whether they believe this to be a complete representation of their views or whether they would like to clarify or elaborate on their responses [ 17 ]. Respondents’ feedback on these issues then becomes part of the data collection and analysis [ 27 ].

Stakeholder involvement

In those niches where qualitative approaches have been able to evolve and grow, a new trend has seen the inclusion of patients and their representatives not only as study participants (i.e. “members”, see above) but as consultants to and active participants in the broader research process [ 31 – 33 ]. The underlying assumption is that patients and other stakeholders hold unique perspectives and experiences that add value beyond their own single story, making the research more relevant and beneficial to researchers, study participants and (future) patients alike [ 34 , 35 ]. Using the example of patients on or nearing dialysis, a recent scoping review found that 80% of clinical research did not address the top 10 research priorities identified by patients and caregivers [ 32 , 36 ]. In this sense, the involvement of the relevant stakeholders, especially patients and relatives, is increasingly being seen as a quality indicator in and of itself.

How not to assess qualitative research

The above overview does not include certain items that are routine in assessments of quantitative research. What follows is a non-exhaustive, non-representative, experience-based list of the quantitative criteria often applied to the assessment of qualitative research, as well as an explanation of the limited usefulness of these endeavours.

Protocol adherence

Given the openness and flexibility of qualitative research, it should not be assessed by how well it adheres to pre-determined and fixed strategies – in other words: its rigidity. Instead, the assessor should look for signs of adaptation and refinement based on lessons learned from earlier steps in the research process.

Sample size

For the reasons explained above, qualitative research does not require specific sample sizes, nor does it require that the sample size be determined a priori [ 1 , 14 , 27 , 37 – 39 ]. Sample size can only be a useful quality indicator when related to the research purpose, the chosen methodology and the composition of the sample, i.e. who was included and why.

Randomisation

While some authors argue that randomisation can be used in qualitative research, this is not commonly the case, as neither its feasibility nor its necessity or usefulness has been convincingly established for qualitative research [ 13 , 27 ]. Relevant disadvantages include the negative impact of a too large sample size as well as the possibility (or probability) of selecting “ quiet, uncooperative or inarticulate individuals ” [ 17 ]. Qualitative studies do not use control groups, either.

Interrater reliability, variability and other “objectivity checks”

The concept of “interrater reliability” is sometimes used in qualitative research to assess to which extent the coding approach overlaps between the two co-coders. However, it is not clear what this measure tells us about the quality of the analysis [ 23 ]. This means that these scores can be included in qualitative research reports, preferably with some additional information on what the score means for the analysis, but it is not a requirement. Relatedly, it is not relevant for the quality or “objectivity” of qualitative research to separate those who recruited the study participants and collected and analysed the data. Experiences even show that it might be better to have the same person or team perform all of these tasks [ 20 ]. First, when researchers introduce themselves during recruitment this can enhance trust when the interview takes place days or weeks later with the same researcher. Second, when the audio-recording is transcribed for analysis, the researcher conducting the interviews will usually remember the interviewee and the specific interview situation during data analysis. This might be helpful in providing additional context information for interpretation of data, e.g. on whether something might have been meant as a joke [ 18 ].

Not being quantitative research

Being qualitative research instead of quantitative research should not be used as an assessment criterion if it is used irrespectively of the research problem at hand. Similarly, qualitative research should not be required to be combined with quantitative research per se – unless mixed methods research is judged as inherently better than single-method research. In this case, the same criterion should be applied for quantitative studies without a qualitative component.

The main take-away points of this paper are summarised in Table ​ Table1. 1 . We aimed to show that, if conducted well, qualitative research can answer specific research questions that cannot to be adequately answered using (only) quantitative designs. Seeing qualitative and quantitative methods as equal will help us become more aware and critical of the “fit” between the research problem and our chosen methods: I can conduct an RCT to determine the reasons for transportation delays of acute stroke patients – but should I? It also provides us with a greater range of tools to tackle a greater range of research problems more appropriately and successfully, filling in the blind spots on one half of the methodological spectrum to better address the whole complexity of neurological research and practice.

Take-away-points

Acknowledgements

Abbreviations.

Authors’ contributions

LB drafted the manuscript; WW and CG revised the manuscript; all authors approved the final versions.

no external funding.

Availability of data and materials

Ethics approval and consent to participate, consent for publication, competing interests.

The authors declare no competing interests.

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

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Methodology

• Qualitative vs. Quantitative Research | Differences, Examples & Methods

Qualitative vs. Quantitative Research | Differences, Examples & Methods

Published on April 12, 2019 by Raimo Streefkerk . Revised on June 22, 2023.

When collecting and analyzing data, quantitative research deals with numbers and statistics, while qualitative research deals with words and meanings. Both are important for gaining different kinds of knowledge.

Common quantitative methods include experiments, observations recorded as numbers, and surveys with closed-ended questions.

Quantitative research is at risk for research biases including information bias , omitted variable bias , sampling bias , or selection bias . Qualitative research Qualitative research is expressed in words . It is used to understand concepts, thoughts or experiences. This type of research enables you to gather in-depth insights on topics that are not well understood.

Common qualitative methods include interviews with open-ended questions, observations described in words, and literature reviews that explore concepts and theories.

Table of contents

The differences between quantitative and qualitative research, data collection methods, when to use qualitative vs. quantitative research, how to analyze qualitative and quantitative data, other interesting articles, frequently asked questions about qualitative and quantitative research.

Quantitative and qualitative research use different research methods to collect and analyze data, and they allow you to answer different kinds of research questions.

Quantitative and qualitative data can be collected using various methods. It is important to use a data collection method that will help answer your research question(s).

Many data collection methods can be either qualitative or quantitative. For example, in surveys, observational studies or case studies , your data can be represented as numbers (e.g., using rating scales or counting frequencies) or as words (e.g., with open-ended questions or descriptions of what you observe).

However, some methods are more commonly used in one type or the other.

Quantitative data collection methods

• Surveys :  List of closed or multiple choice questions that is distributed to a sample (online, in person, or over the phone).
• Experiments : Situation in which different types of variables are controlled and manipulated to establish cause-and-effect relationships.
• Observations : Observing subjects in a natural environment where variables can’t be controlled.

Qualitative data collection methods

• Interviews : Asking open-ended questions verbally to respondents.
• Focus groups : Discussion among a group of people about a topic to gather opinions that can be used for further research.
• Ethnography : Participating in a community or organization for an extended period of time to closely observe culture and behavior.
• Literature review : Survey of published works by other authors.

A rule of thumb for deciding whether to use qualitative or quantitative data is:

• Use quantitative research if you want to confirm or test something (a theory or hypothesis )
• Use qualitative research if you want to understand something (concepts, thoughts, experiences)

For most research topics you can choose a qualitative, quantitative or mixed methods approach . Which type you choose depends on, among other things, whether you’re taking an inductive vs. deductive research approach ; your research question(s) ; whether you’re doing experimental , correlational , or descriptive research ; and practical considerations such as time, money, availability of data, and access to respondents.

Quantitative research approach

You survey 300 students at your university and ask them questions such as: “on a scale from 1-5, how satisfied are your with your professors?”

You can perform statistical analysis on the data and draw conclusions such as: “on average students rated their professors 4.4”.

Qualitative research approach

You conduct in-depth interviews with 15 students and ask them open-ended questions such as: “How satisfied are you with your studies?”, “What is the most positive aspect of your study program?” and “What can be done to improve the study program?”

Based on the answers you get you can ask follow-up questions to clarify things. You transcribe all interviews using transcription software and try to find commonalities and patterns.

Mixed methods approach

You conduct interviews to find out how satisfied students are with their studies. Through open-ended questions you learn things you never thought about before and gain new insights. Later, you use a survey to test these insights on a larger scale.

It’s also possible to start with a survey to find out the overall trends, followed by interviews to better understand the reasons behind the trends.

Qualitative or quantitative data by itself can’t prove or demonstrate anything, but has to be analyzed to show its meaning in relation to the research questions. The method of analysis differs for each type of data.

Analyzing quantitative data

Quantitative data is based on numbers. Simple math or more advanced statistical analysis is used to discover commonalities or patterns in the data. The results are often reported in graphs and tables.

Applications such as Excel, SPSS, or R can be used to calculate things like:

• Average scores ( means )
• The number of times a particular answer was given
• The correlation or causation between two or more variables
• The reliability and validity of the results

Analyzing qualitative data

Qualitative data is more difficult to analyze than quantitative data. It consists of text, images or videos instead of numbers.

Some common approaches to analyzing qualitative data include:

• Qualitative content analysis : Tracking the occurrence, position and meaning of words or phrases
• Thematic analysis : Closely examining the data to identify the main themes and patterns
• Discourse analysis : Studying how communication works in social contexts

If you want to know more about statistics , methodology , or research bias , make sure to check out some of our other articles with explanations and examples.

• Chi square goodness of fit test
• Degrees of freedom
• Null hypothesis
• Discourse analysis
• Control groups
• Mixed methods research
• Non-probability sampling
• Quantitative research
• Inclusion and exclusion criteria

Research bias

• Rosenthal effect
• Implicit bias
• Cognitive bias
• Selection bias
• Negativity bias
• Status quo bias

Quantitative research deals with numbers and statistics, while qualitative research deals with words and meanings.

Quantitative methods allow you to systematically measure variables and test hypotheses . Qualitative methods allow you to explore concepts and experiences in more detail.

In mixed methods research , you use both qualitative and quantitative data collection and analysis methods to answer your research question .

The research methods you use depend on the type of data you need to answer your research question .

• If you want to measure something or test a hypothesis , use quantitative methods . If you want to explore ideas, thoughts and meanings, use qualitative methods .
• If you want to analyze a large amount of readily-available data, use secondary data. If you want data specific to your purposes with control over how it is generated, collect primary data.
• If you want to establish cause-and-effect relationships between variables , use experimental methods. If you want to understand the characteristics of a research subject, use descriptive methods.

Data collection is the systematic process by which observations or measurements are gathered in research. It is used in many different contexts by academics, governments, businesses, and other organizations.

There are various approaches to qualitative data analysis , but they all share five steps in common:

• Prepare and organize your data.
• Review and explore your data.
• Develop a data coding system.
• Assign codes to the data.
• Identify recurring themes.

The specifics of each step depend on the focus of the analysis. Some common approaches include textual analysis , thematic analysis , and discourse analysis .

A research project is an academic, scientific, or professional undertaking to answer a research question . Research projects can take many forms, such as qualitative or quantitative , descriptive , longitudinal , experimental , or correlational . What kind of research approach you choose will depend on your topic.

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Comprehensive criteria for reporting qualitative research (ccqr): reporting guideline for global health qualitative research methods.

1. Introduction

2.1. search strategy, 2.2. eligibility criteria, 2.3. study selection process, 2.4. quality assessment of articles, 4. discussion, 5. conclusions, supplementary materials, author contributions, conflicts of interest.

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Sinha, P.; Paudel, B.; Mosimann, T.; Ahmed, H.; Kovane, G.P.; Moagi, M.; Phuti, A. Comprehensive Criteria for Reporting Qualitative Research (CCQR): Reporting Guideline for Global Health Qualitative Research Methods. Int. J. Environ. Res. Public Health 2024 , 21 , 1005. https://doi.org/10.3390/ijerph21081005

Sinha P, Paudel B, Mosimann T, Ahmed H, Kovane GP, Moagi M, Phuti A. Comprehensive Criteria for Reporting Qualitative Research (CCQR): Reporting Guideline for Global Health Qualitative Research Methods. International Journal of Environmental Research and Public Health . 2024; 21(8):1005. https://doi.org/10.3390/ijerph21081005

Sinha, Priyanka, Binita Paudel, Tamara Mosimann, Hanan Ahmed, Gaotswake Patience Kovane, Miriam Moagi, and Angel Phuti. 2024. "Comprehensive Criteria for Reporting Qualitative Research (CCQR): Reporting Guideline for Global Health Qualitative Research Methods" International Journal of Environmental Research and Public Health 21, no. 8: 1005. https://doi.org/10.3390/ijerph21081005

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Recent advances in the population biology and management of maize foliar fungal pathogens exserohilum turcicum , cercospora zeina and bipolaris maydis in africa.

• 1 Department of Plant and Soil Sciences, Forestry and Agricultural Biotechnology Institute (FABI), University of Pretoria, Pretoria, South Africa
• 2 Department of Biochemistry, Genetics and Microbiology, Forestry and Agricultural Biotechnology Institute (FABI), University of Pretoria, Pretoria, South Africa

Maize is the most widely cultivated and major security crop in sub-Saharan Africa. Three foliar diseases threaten maize production on the continent, namely northern leaf blight, gray leaf spot, and southern corn leaf blight. These are caused by the fungi Exserohilum turcicum , Cercospora zeina , and Bipolaris maydis , respectively. Yield losses of more than 10% can occur if these pathogens are diagnosed inaccurately or managed ineffectively. Here, we review recent advances in understanding the population biology and management of the three pathogens, which are present in Africa and thrive under similar environmental conditions during a single growing season. To effectively manage these pathogens, there is an increasing adoption of breeding for resistance at the small-scale level combined with cultural practices. Fungicide usage in African cropping systems is limited due to high costs and avoidance of chemical control. Currently, there is limited knowledge available on the population biology and genetics of these pathogens in Africa. The evolutionary potential of these pathogens to overcome host resistance has not been fully established. There is a need to conduct large-scale sampling of isolates to study their diversity and trace their migration patterns across the continent.

1 Introduction

Food demand driven by exponential human population growth over the past fifty years has shifted cropping systems from farms with high genotypic diversity to genetically uniform crops (termed monocultures) ( Zhan et al., 2014 ). More recently, there has been an increased adoption of conservation agriculture ( Rodenburg et al., 2020 ; Jat et al., 2021 ; Reicosky, 2021 ). These two factors have led to favorable conditions for crop pathogens that persist in the soil, including some foliar pathogens, to cause severe global disease outbreaks ( Dill-Macky and Jones, 2000 ; Bateman et al., 2007 ; Simón et al., 2011 ; Bebber, 2015 ).

Maize production in the 2021/2022 production season was calculated at one billion tons ( FAOSTAT, 2024 ). Yield has increased at a rate of 3.2% per year between 1972 and 2021 ( Knoema, 2023 ). This is more than the 2.4% yield increase required per year to meet the expected global production demand by 2050 ( Ray et al., 2013 ). Despite the cultural and food security importance of maize in many countries in sub-Saharan Africa, only 7.5% of the global maize crop is grown on the continent ( FAOSTAT, 2024 ).

A worldwide survey indicated that biotic factors were responsible for 23% of maize yield losses annually ( Savary et al., 2019 ). The three foliar fungal diseases - northern (corn) leaf blight (NLB), gray leaf spot (GLS), and southern corn leaf blight (SCLB), contributed to more than 4% of these estimated yield losses ( Savary et al., 2019 ). Sub-Saharan Africa yield losses for NLB were estimated at more than 1% ( Figure 1 ). Unfortunately, survey data was not gathered for the other two diseases. However, GLS is widespread on the African continent ( Nsibo et al., 2021 ), and SCLB has been reported from Egypt, Gambia, Ghana, Kenya, Malawi, Nigeria, Sierra Leone, South Africa, Sudan, and Eswatini ( Rong and Baxter, 2006 ; Aregbesola et al., 2020 ) ( Figure 1 ). Here, we review recent advances in the population biology and management of the causal pathogens of NLB, GLS, and SCLB, with a focus on Africa.

Figure 1 Distribution of three Maize leaf diseases in Africa. Northern leaf blight (NLB) is indicated in green, gray leaf spot (GLS) in red and southern corn leaf blight (SCLB) in blue. GLS is the most widely distributed disease in Africa, followed by NLB. Citations of the reports of the diseases in African countries are listed in Table 1 and from the USDA database https://fungi.ars.usda.gov/ .

2 Global distribution and causal agents of NLB, GLS, and SCLB

2.1 northern leaf blight.

Northern leaf blight also known as Northern corn leaf blight (NCLB) or Turcicum leaf blight (TLB), has persisted for decades as a major foliar disease in maize producing regions of the World ( Drechsler, 1923 ; Savary et al., 2019 ). Following its first discovery in Parma Italy, NLB was only well documented in the United States of America (USA) in 1878 and only emerged as an outbreak in 1889 ( Drechsler, 1923 ). The disease has since appeared in the Americas, and Asia ( Shi et al., 2017 ; Bashir et al., 2018 ; Mueller et al., 2020 ; Navarro et al., 2021 ). In Africa, NLB was first reported in Uganda in 1924 and has since been reported in several sub-Saharan African countries ( Figure 1 ; Table 1 ) ( Emechebe, 1975 ; Adipala et al., 1995 ; Abebe and Singburaudom, 2006 ; Haasbroek et al., 2014 ; Nieuwoudt et al., 2018 ; Berger et al., 2020 ).

Table 1 The epidemiological characteristics of NLB, GLS and SCLB causal pathogens.

The causal pathogen of NLB is Exserohilum turcicum (Pass.) K.J. Leonard & Suggs, which is the asexual form of this hemibiotrophic Dothideomycete ( Leonard and Suggs, 1974 ). Researchers have defined physiological races of E. turcicum based on six maize qualitative resistance genes namely; Ht1, Ht2, Ht3, Htn1, Htn2 , and Htm1 , that E. turcicum is able to overcome ( Jordan et al., 1983 ; Jindal et al., 2019 ; Navarro et al., 2021 ; Muñoz-Zavala et al., 2023 ). The race groups are determined based on the screening of a differential set of maize lines, each with a different resistance gene ( Leonard et al., 1989 ). Routine screening of E. turcicum races is carried out using maize differential genotypes in some maize producing countries ( Weems and Bradley, 2018 ; Jindal et al., 2019 ; Turgay et al., 2020 ; Navarro et al., 2021 ; Muñoz-Zavala et al., 2023 ). However, maize resistance responses in these germplasm sets are highly variable and dependent on growth room/glasshouse conditions, and genetic background effects ( Weems and Bradley, 2018 ; Jindal et al., 2019 ). To date, there are 29 described race groups based on the combinations of maize resistance genes that can be overcome, with race 0 defined as being able to overcome all known resistance genes ( Table 2 – see references within). Race 0 has been described from countries in all the continents except South America ( Table 2 ). Twelve of the race combinations have been reported from African countries ( Table 2 ), although screening has only been done on E. turcicum isolates from South Africa, Kenya, Uganda, and Zambia ( Craven and Fourie, 2011 ).

Table 2 The physiological races of Exserohilum turcicum and Bipolaris maydis and their global distribution.

Research on maize resistance genes to E. turcicum has revealed that the Htn1 gene encodes ZmWAK-RLK1, a wall-associated receptor-like kinase ( Hurni et al., 2015 ). Further research provided evidence that maize Ht2 and Ht3 genes encoded the same ZmWAK-RLK which corresponded to a different allele of ZmWAK-RLK1 ( Yang et al., 2021 ). This is consistent with previous work that Htn1 , Ht2 , and Ht3 map to chromosome 8 ( Yang et al., 2021 ). This brings into question the validity of using Ht genes only to allocate E. turcicum race classes when screening with differential maize panels. The authors propose that responses may be confounded by “modifier genes” as a result of (i) different genetic backgrounds, and (ii) variation in the size of the introgression surrounding an Ht gene in each differential maize line ( Yang et al., 2021 ). This may explain why some isolates of E. turcicum gave different responses and were thus classified as race 2 or race 3 on the “differential” maize lines carrying Ht 2 and Ht 3 genes. In addition, it is well known amongst maize pathologists that responses to the pathogen are very sensitive to environmental conditions, which confounds reproducibility in E. turcicum race screening using differential panels ( Weems and Bradley, 2018 ).

2.2 Gray leaf spot

Globally, GLS is the second most economically important foliar disease of maize after NLB, and is the most important foliar disease in the USA and Canada ( Mueller et al., 2016 , Mueller et al., 2020 ). Gray leaf spot disease was first reported in 1925 ( Tehon and Daniels, 1925 ), and only became economically important in the late 1970s in the USA ( Latterell and Rossi, 1983 ) and has since been reported in the Americas ( Zhu et al., 2002 ; Juliatti et al., 2009 ; Neves et al., 2015 ; Mueller et al., 2020 ) and Asia ( Manandhar et al., 2011 ; Liu and Xu, 2013 ). In Africa, GLS was first reported in 1988 in South Africa ( Ward et al., 1997 ) and has since been reported in sub-Saharan Africa ( Figure 1 ; Table 1 ).

Cercospora zeae-maydis Tehon & E.Y Daniels ( Tehon and Daniels, 1925 ), and Cercospora zeina Crous & U. Braun ( Crous and Braun, 2003 ) cause GLS. Cercospora zeae-maydis is predominant in the Americas and Asia, whereas C. zeina is found in Africa, Brazil, some parts of Asia, and the Eastern corn belt of the USA ( Wang et al., 1998 ; Goodwin et al., 2001 ; Okori et al., 2003 ; Meisel et al., 2009 ; Neves et al., 2015 ; Duan et al., 2022 ). Although other Cercospora spp. have been associated with GLS, namely Cercospora spp. CPC 12062, a single isolate from South Africa ( Crous et al., 2006 ) and Cercospora sorghi var. maydis Ellis & Everh. reported in Kenya ( Kinyua et al., 2010 ) and Brazil ( Neves et al., 2015 ), their role in pathogenicity has not been determined. The rest of this review will, therefore, focus on C. zeina , which is the predominant pathogen in Africa.

2.3 Southern corn leaf blight

Southern corn leaf blight, also known as Maydis leaf blight, was first reported in the USA in 1923 ( Drechsler, 1925 ) and became a serious concern in the 1970s ( Smith et al., 1970 ). Since then, SCLB reports have emerged from Western Europe, Asia and Africa ( Munjal and Kapoor, 1960 ; Ullstrup, 1972 ; Fisher et al., 1976 ; Gregory et al., 1979 ; Carson et al., 2004 ; Singh and Srivastava, 2012 ; Manzar et al., 2022 ). The first report of SCLB in Africa was an outbreak in 1974 in South Africa, which resulted in the withdrawal of Texas male-sterile cytoplasm (T-cms) maize germplasm from the country’s breeding programs ( Levings, 1990 ; Rong and Baxter, 2006 ) ( Table 1 ). Since then, no reports on SCLB have emerged from the country and on the rest of the continent until two decades later in Kenya ( Mwangi, 1998 ) and recently on seeds in Nigeria ( Biemond et al., 2013 ), thus indicating that SCLB can be a seedborne disease. These reports indicate that SCLB is present in Africa ( Figure 1 ), currently at levels where its severity and occurrence are still insignificant. However there is the potential of SCLB becoming a severe and serious phytosanitary threat to maize production in Africa.

Bipolaris maydis (Y. Nisik. & C. Miyake) Shoemaker is the causative pathogen of SCLB ( Smith et al., 1970 ). Previously known as Cochliobolus heterostrophus , B. maydis has been adopted as the most widely accepted species name ( Rossman et al., 2013 ). Four physiological races of B. maydis (races O, T, C, and S) are known globally ( Smith et al., 1970 ; Wei et al., 1988 ; Levings, 1993 ; Manamgoda et al., 2014 ), while races C and S only exist in China ( Wei et al., 1988 ; Wang et al., 2010 ; Zhao et al., 2012 ; Bruns, 2017 ) ( Table 2 ).

Overall, the USA was the first to report NLB, GLS, and SCLB in the early 20th-century ( Drechsler, 1925 ; Tehon and Daniels, 1925 ). This could have been a result of the USA’ system of Land Grant Universities with farmer extension services, vigilant crop disease diagnosis activities, and adoption of hybrid maize breeding ( Duvick, 2001 ). At that time, maize production expanded and the planting of monocultures increased, creating a high risk of disease if susceptible genotypes were planted ( Dodd, 2000 ; Duvick, 2001 ).

Gray leaf spot differs from NLB and SCLB, with no known physiological races among its populations. Physiological races of E. turcicum and B. maydis follow the “gene for gene” hypothesis. Races are classified based on a pathogen’s ability to overcome resistance genes in maize inbred lines, often by loss of an avirulence gene recognized by a specific maize resistance gene ( Leonard et al., 1989 ). However, the GLS-maize pathosystem has no known virulence genes or major resistance genes, so there are no known physiological races.

3 Disease epidemiology and economic impact of NLB, GLS and SCLB on maize

Disease epidemiology entails an understanding of the dynamics of disease development and proliferation in space and time ( Milgroom and Peever, 2003 ). Several biotic, abiotic, and edaphic factors contribute to plant diseases ( Milgroom and Peever, 2003 ). The knowledge on the above mentioned predisposing factors and epidemiological parameters, such as infection efficiency, latent period, and spore production in disease development, is therefore crucial in deciding the nature of control strategies to adopt ( de Vallavieille-Pope et al., 2000 ; Milgroom and Peever, 2003 ). This section reviews the epidemiology of NLB, GLS, and SCLB, the factors that favor their development, and their economic impact.

3.1 Northern leaf blight

Upon infection of maize leaves, E. turcicum causes greyish lesions that start as chlorotic flecks and later mature into elliptical or cigar-shaped lesions from 2.5 to 30 cm in length ( White, 1999 ) ( Figures 2 , 3 ). Disease establishment occurs within 6–18 h post-infection, and mature lesions develop within two weeks of host-pathogen interaction under favorable environmental conditions ( Levy and Cohen, 1983 ; Bentolila et al., 1991 ; White, 1999 ; Kotze et al., 2019 ) ( Table 1 ; Figure 2 ). The pathogen invades the host through the epidermis and blocks the vascular tissues ( Kotze et al., 2019 ). This causes plant lodging and a reduction in photosynthetic leaf area, leading to 30%–91% yield losses in cases of severe infections during silking and grain filling ( Tilahun et al., 2012 ; Nwanosike et al., 2015 ; Jindal et al., 2019 ; Berger et al., 2020 ). The NLB disease is a splash- and wind-borne polycyclic disease that spreads via conidia from infected debris left in the fields ( Figure 2 ), and from secondary infections over long distances across fields ( Schwartz and David, 2005 ).

Figure 2 Asexual life cycles of Exserohilum turcicum , Cercospora zeina , and Bipolaris maydis . (A) Primary inoculum overwinters on maize debris as conidiophores until the next growing season, when they are dispersed in the form of conidia. (B) Under favorable conditions, conidia are dispersed and land on young maize plants. (C) Conidia germinate, penetrate plant cells, and later develop into small chlorotic spots. (D, E) Mature lesions develop from the lower leaves to younger leaves. These later give rise to conidia (secondary inoculum), which disperse to the younger plants, and the cycle repeats. Et, E. turcicum ( Leonard, 1977b ; Levy and Cohen, 1983 ; Bentolila et al., 1991 ; White, 1999 ; Kotze et al., 2019 ); Cz, C. zeina ( Beckman and Payne, 1982 ; Latterell and Rossi, 1983 ; Ward et al., 1999 ; Wisser et al., 2011 ) and Bm, B. maydis ( Jeffers, 2004 ; Wisser et al., 2011 ; Singh and Srivastava, 2012 ). Citations refer to sources for details of each pathogen’s disease cycle. (C) leaf cross sections were adapted from Supplementary Figure S1 of Wisser et al. (2011) . The unit for free water is hours. RH = relative humidity.

3.2 Gray leaf spot

Cercospora zeina invades the host leaf tissues intracellularly resulting in irregular chlorotic lesions that after 14 days post infection, mature into grey to tan linear rectangular lesions that run parallel with leaf veins ( Latterell and Rossi, 1983 ; Ward et al., 1999 ) ( Figures 2 , 3 ). Extensive disease development results in the coalescence of the lesions, blighting, necrosis of the leaf tissue, reduced photosynthetic area and plant lodging ( Paul and Munkvold, 2005 ; Lennon et al., 2016 ). The calculations made based on spore size (40 - 165 µm × 4 - 9 µm), wind speed (varies per location) and the height of vertical mixing of the atmosphere above the crop, estimate flight distances of spores to range between 0.1 - 40 km as wind speed increases from 1 to 10 m/s ( Ward et al., 1999 ). The spores have also been reported to spread to a distance of 80 - 160 km annually, making it a fast-spreading disease ( Manandhar et al., 2011 ). Yield losses due to GLS have been estimated to be 20–80% ( Latterell and Rossi, 1983 ; Ward et al., 1999 ; Manandhar et al., 2011 ).

3.3 Southern corn leaf blight

Irrespective of race, B. maydis infections in maize generally take between 12 to 18 h for fungal penetration, and 2 to 3 days to form mature lesions ( Singh and Srivastava, 2012 ) ( Figure 2 ; Table 2 ). The race O causes small diamond-shaped lesions that elongate into rectangular lesions limited within veins to a length of 20-30 mm that later coalesce, resulting in the entire leaf blighting ( Jeffers, 2004 ; Singh and Srivastava, 2012 ). The race T causes oval-shaped yellow to brown lesions that are larger than race O ( Jeffers, 2004 ; Singh and Srivastava, 2012 ), and produces a T-cms-specific polyketide toxin (T toxin) that is specific to T-cms maize genotypes ( Condon et al., 2018 ). This race, whose origin is still a mystery, was implicated in a serious epidemic in the USA in 1970 ( Bruns, 2017 ). The SCLB disease thrives in hot and humid agroecosystems ( Warren, 1975 ) ( Table 1 ). Yield losses of 10–40% due to SCLB infections have been reported, depending on the physiological race, environment and the maize hybrid grown ( Fisher et al., 2012 ; Bruns, 2017 ).

All three fungal pathogens infect the same plant parts (leaves). They have similar growth requirements of moderate temperatures between 20°C and 30°C with relative humidity above 90% favoring disease establishment. Yield losses from individual pathogens can be 10-80%. Therefore, there is a need to determine the impact of combined infections by measuring the percentage of co-occurrence of these three diseases on a plant, field, and larger spatial scale to model their combined potential yield losses. This will facilitate the development of management strategies that target both single and co-infections.

4 Diagnosis of NLB, GLS, SCLB and identification of their causal pathogens

Crop disease diagnosis and the identification of the causal organism up to species level are becoming more critical. This is because more disease epidemics are emerging globally as a result of increased anthropogenic activities, such as global trade and expansion of pathogen ranges due to climate change ( Hulme, 2009 ; Elad and Pertot, 2014 ; Prakash et al., 2014 ; Chaloner et al., 2021 ). Failure to accurately diagnose diseases and correctly detect the causal pathogens leads to inadequate or delayed implementation of control measures, thus causing a reduction in crop yield and quality ( Miller et al., 2009 ). Similar to other plant diseases, NLB, GLS, and SCLB and their corresponding causal pathogens, have been diagnosed based on symptoms, morphological characteristics, and molecular phylogenetics.

4.1 Field diagnosis of NLB, GLS, and SCLB

Traditionally, plant disease diagnosis is performed through conventional visual field inspection of infected plant tissues (symptoms) using experienced technical human resources ( Bock et al., 2010 ). Two standard scales (1–5 and 1–9, where 1 = resistant and 5 or 9 = susceptible) are being used to rate the severity of NLB ( Abebe et al., 2008 ; Asea et al., 2009 ; Vivek et al., 2010 ; Kumar et al., 2011 ), GLS ( Bubeck et al., 1993 ; Munkvold et al., 2001 ; Danson et al., 2008 ; Chung et al., 2011 ; Berger et al., 2014 ; Benson et al., 2015 ), and SCLB ( Zwonitzer et al., 2009 ; Chung et al., 2010 , Chung et al., 2011 ; Singh and Srivastava, 2012 ) ( Table 2 ). Some plant pathologists have preferred using the 1 to 9 scale in the field and later converted it to a scale of 1 to 5 using the following formula: 0.5 * (disease score (1 to 9 scale) + 1) = disease score (1 to 5 scale) ( Vivek et al., 2010 ). These scales are used to assess disease severity over time which can be expressed as area under disease progress curve (AUDPC) or disease index ( Ma et al., 2022 ). While this traditional method has been refined over time, it is plagued by the inherent subjectiveness of disease estimates and is time-consuming ( Nutter et al., 1993 ; Bock et al., 2008 ; Poland and Nelson, 2011 ). Sometimes, morphological traits are misleading because of the similarities between disease symptoms. For instance, race O lesions of SCLB are sometimes mistaken for GLS, whereas the initial symptoms of NLB, GLS, and SCLB (chlorotic spots) can potentially lead to misidentification ( Figure 3 ).

Figure 3 Symptomatic differences in NLB, GLS, and SCLB in maize. Each pathogen causes distinct disease symptoms during the intermediate and late stages of the infection cycle. Symptoms are prone to misidentification at early stages. All three diseases exhibit chlorotic spots in their early stages, making them difficult to diagnose. In the intermediate to late stages, each disease assumes its distinct lesion shape (i.e., cigar-shaped lesions for NLB, fine rectangular lesions for GLS, and rectangular lesions with irregular margins for SCLB symptoms, especially at the late stage). SCLB and GLS are not as clearly distinct as NLB. Scale bars = 2 cm.

Digital imaging techniques based on standard RGB images or hyperspectral images captured manually with cameras, mobile phones, or captured automatically with drones or by satellites hold great promise for crop disease diagnostics ( Mohanty et al., 2016 ; DeChant et al., 2017 ). These methods involve training computer models with datasets of disease images which have been pre-classified by plant pathologists. The models are then tested on new sets of disease images to evaluate the accuracy of diagnosis. Examples of machine learning methods that have been used for plant disease diagnosis are spectral angle mapper (SAM), partial least squares regression (PLSR), support vector machines (SVMs), and convolutional neural networks (CNNs) ( Xie et al., 2012 ; Stewart and McDonald, 2014 ; Mutka and Bart, 2015 ; Pauli et al., 2016 ; MuLaosmanovic et al., 2020 ).

This is a very active area of research that is also being applied to maize foliar diseases such as NLB and GLS with accuracies of greater than 90%, but it is still in its infancy since most models are being trained on images with only one disease symptom type ( Zhang, 2013 ; Xu et al., 2015 ; Mohanty et al., 2016 ; Qi et al., 2016 ; Singh et al., 2016 ; DeChant et al., 2017 ; Zhang et al., 2018 ; Craze et al., 2022 ; Pan et al., 2022 ). Attempts are underway to diagnose individual foliar diseases with more field-realistic images with multiple disease symptom types; for example a neural network model was developed to identify GLS symptoms on maize leaves which had mixed symptoms of NLB, common rust, and white spot disease ( Craze et al., 2022 ).

These high-throughput diagnostic methods are a foundation for understanding pathogen ecology, epidemiology, and biology. Their integration into management programs for several plant diseases has the potential to foster a more targeted approach for the prevention of epidemics.

4.2 Morphological and physiological diagnosis and detection

For years, pathogen identification has relied on conventional techniques such as culturing, re-inoculation, microscopy, and biochemical assays ( Sharma and Sharma, 2016 ). Morphological methods, which depend on visible signs of post-fungal infections, such as symptoms and fungal propagules, can be used to distinguish between E. turcicum and B. maydis , based on a hilum. Bipolaris maydis has a subtle hilum ( Alcorn, 1988 ), while E. turcicum has a truncated, prominent hilum with a bubble ( Leonard, 1974 ) ( Figure 4 ). Cercosporoid fungi, however, are mainly distinguished based on conidia, hila, and pigmentation of their asexual structures ( Crous and Braun, 2003 ; Crous et al., 2006 ; Nsibo et al., 2021 ). Cercospora zeina conidia are characterized by their septate, hyaline, thin walls, smooth apex, and thick darkened and refractive hila ( Figure 4 ). These characteristics are similar to those of C. zeae-maydis . However, they differ in conidia shape, conidiophore length, and growth rate ( Crous et al., 2006 ). Furthermore, C. zeae-maydis produces a photoactive phytotoxin, cercosporin, in vitro , whereas C. zeina does not ( Goodwin et al., 2001 ; Crous et al., 2006 ; Swart et al., 2017 ).

Figure 4 Asexual structures of Exserohilum turcicum , Cercospora zeina , and Bipolaris maydis in maize. (A–C) illustrates the conidiophores for (A) E. turcicum ; (B) C. zeina ; (C) B. maydis . (D–F) illustrate conidia of (D) E. turcicum ; (E) C. zeina ; (F) B. maydis . (G–I) Conidiophores of (G) E. turcicum , (H) C. zeina and (I) B. maydis on the surfaces of maize leaves Scale bars: (A–F) = 10 µm, (G–I) = 100 µm. (C, F, I) photos provided by Ms. Anu Elizabeth Ajayi, International Institute of Tropical Agriculture (IITA), Nigeria.

Disease diagnosis of NLB, GLS, and SCLB based on morphological differences of the fungal morphology is possible. However, this often requires isolation and culturing of the fungal pathogen, which is time-consuming. This makes these approaches inadequate for accurate and timely species-level identification ( McCartney et al., 2003 ; Pryce et al., 2003 ).

4.3 Molecular identification

More advanced methods of identification, such as PCR-based amplification of nucleic acids and sequencing, are increasingly being employed for E. turcicum , C. zeina , and B. maydis . These methods can be more sensitive, are highly specific, faster, and require limited prior knowledge of the pathogen or expertise in plant pathology ( McCartney et al., 2003 ; Ward et al., 2004 ). PCR amplification followed by sequencing of a fragment of the nuclear ribosomal DNAs (rDNAs), particularly the internal transcribed spacer (ITS), nested between conserved sequences of the 18S, 5.8S, and 28S rRNA gene regions, has been extensively employed for fungal species identification. The ITS marker is used as a universal barcode and is applicable for E. turcicum ( Goh et al., 1998 ; Weikert-Oliveira et al., 2002 ; Ramathani et al., 2011 ; Haasbroek et al., 2014 ; Hernández-Restrepo et al., 2018 ), C. zeina ( Dunkle and Levy, 2000 ; Crous et al., 2006 ; Meisel et al., 2009 ; Korsman et al., 2012 ; Liu and Xu, 2013 ; Bakhshi et al., 2015 ; Neves et al., 2015 ) and B. maydis ( Goh et al., 1998 ; Emami and Hack, 2002 ; Manamgoda et al., 2012 ; Gogoi et al., 2014 ) ( Table 3 ). The ITS region has multiple (identical) copies in the genome and it’s PCR products are small (less than 1 Kb) which allow for easy PCR amplification, even in dilute or partially degraded DNA ( White et al., 1973 ; Gardes et al., 1991 ; Lee and Taylor, 1992 ; Schoch et al., 2012 ).

Table 3 The universal molecular bar codes used in the identification of the causal pathogens of NLB, GLS and SCLB.

Other available DNA targets for pathogen identification include regions of the translation elongation factor 1-α, calmodulin, β-tubulin, glyceraldehyde-3-phosphate dehydrogenase and mating type genes ( Carbone and Kohn, 1999 ; James et al., 2006 ; Walker et al., 2012 ). Most of these gene regions have been employed for the identification of E. turcicum ( Ramathani et al., 2011 ; Haasbroek et al., 2014 ; Hernández-Restrepo et al., 2018 ), C. zeina ( Meisel et al., 2009 ; Bakhshi et al., 2015 ; Muller et al., 2016 ; Nsibo et al., 2019 , Nsibo et al., 2021 ), and B. maydis ( Leonard, 1974 ; Turgeon et al., 1995 ; Manamgoda et al., 2012 ; Tan et al., 2016 ) in Africa and around the world ( Table 3 ).

Various species-specific PCR diagnostic tools that do not require sequencing have been developed ( Table 4 ). For E. turcicum , mating-type genes are currently the only available species-specific diagnostic method. The amplification of PCR products of 608 bp and 393 bp using either a MAT1-1 F or MAT1-2 F primer together with MAT_CommonR primer indicates the presence of MAT1-1 or MAT1-2 , respectively ( Henegariu et al., 1997 ; Haasbroek et al., 2014 ) ( Table 4 ).

Table 4 The species-specific molecular bar codes used in the identification of the causal pathogens of NLB, GLS and SCLB.

A species-specific PCR diagnostic that can differentiate three maize Cercospora species ( C. zeina , C. zeae-maydis , and Cercospora sp.) was based on the histone H3 gene region ( Crous et al. (2006) . A multiplex PCR was used where universal primers CylH3F and CylH3R amplify a 389-bp fragment common to all three species. This universal primer pair is multiplexed with species-specific primers CzeaeHIST, CzeinaHIST, or CmaizeHIST in three separate PCR reactions for each unknown sample. Each reaction produces the common 389-bp fragment, and one of the three reactions will give a species-diagnostic 284-bp fragment ( Crous et al., 2006 ) ( Table 4 ). Limitations of this approach is the need to do three PCR reactions per sample, and the requirement for highly optimized PCR conditions to ensure only the correct species-specific primer binds to the target.

A cytochrome P450 reductase ( cpr1 ) has been used to distinguish C. zeina and C. zeae-maydis from other maize pathogens. The CPR1_F and CPR1-R primers amplify a 164bp product from C. zeina and C. zeae-maydis but not from other maize pathogens ( Korsman et al., 2012 ). Furthermore, the assay can also be used to differentiate C. zeina and C. zeae-maydis based on melting temperature differences between the products that can be measured after a real-time PCR reaction ( Korsman et al., 2012 ).

Rapid identification of C. zeina or C. zeae-maydis is routinely carried out using primers in the cercosporin biosynthesis CTB 7 gene region, since different sizes are produced for C. zeina compared to C. zeae-maydis ( Swart et al., 2017 ; Nsibo et al., 2019 , Nsibo et al., 2021 ). Cercospora zeina mating type markers amplify fragments that differentiate C. zeina MAT1-1 from MAT1-2 strains, with no amplification from species like C. zeae-maydis ( Muller et al., 2016 ; Nsibo et al., 2019 , Nsibo et al., 2021 ) ( Table 4 ). For B. maydis , a multiplex mating-type PCR assay was optimized using primers MAT113, MAT123, and MATcon5 to amplify 702-bp ( MAT1-1 ) and 547-bp ( MAT1-2 ) fragments unique to B. maydis ( Gafur et al., 1997 ) ( Table 4 ).

Recently, high throughput diagnostics are being employed for the early detection of crop diseases. For example, nano-material-enabled sensors including carbon-based, metal- and metal oxide-based nanomaterials, are currently being used in the early detection of plant diseases based on the changes in the physiology of plants ( Li et al., 2021 ). In addition, the RNA programmable nuclease of CRISPR/Cas is a nucleic acid detection tool that is currently being employed for crop disease diagnosis ( Zhang et al., 2020 ; Wheatley et al., 2021 ; Liang et al., 2024 ). These methods are yet to be optimized for the detection of E. turcicum , B. maydis and C. zeina .

5 Genomic information for Exserohilum turcicum , Cercospora zeina and Bipolaris maydis

The development of molecular diagnostic tools and population genomics studies (see later) for these fungi will be increasingly supported in the future by genomics data, especially genome sequences. The first genome sequences that were available were developed using short-read Illumina sequencing, namely for USA strains of E. turcicum and B. maydis ( Condon et al., 2013 ) and an African strain of C. zeina (CMW25467) from Zambia ( Wingfield et al., 2017 ) ( Table 5 ). Subsequently, these genome sequences were improved, for example, by including RNAseq data for better annotation, and using long read sequencing such as PacBio.

Table 5 Reference genome sequences.

Details of the reference genome sequences for E. turcicum , C. zeina , B. maydis are presented in Table 5 . It should be noted that some genome sequences are available from GenBank ( https://www.ncbi.nlm.nih.gov/ ), whereas others are on the Mycocosm site at the Joint Genome Institute (JGI) ( https://genome.jgi.doe.gov/portal/ ). Currently, the reference sequence for the NLB pathogen S. turcica ( E. turcicum ) Et28A race 23N and another USA strain NY001 race 1 are based on Illumina data ( Table 5 ) ( Condon et al., 2013 ). These assemblies resulted in genome sizes of 43 Mb and 38.4 Mb, respectively. However, due to short read sequencing it is likely the repetitive parts of these genomes are not fully assembled. The only genomics data set for an African isolate of E. turcicum is an in planta RNAseq time course for strain 2 (race 23N) and strain 103 (race 1) from South Africa ( Human et al., 2020 ).

The reference genome for C. zeina is strain CMW25467 from Zambia in Africa ( Wingfield et al., 2022 ). This high-quality genome sequence was determined using PacBio, resulting in 22 scaffolds ( Table 5 ). In addition, Illumina genome sequences for 30 isolates of C. zeina from five countries in Africa were used for a population genomics study ( Welgemoed et al., 2023 ). Recently, PacBio genomes for C. heterostrophus ( B. maydis ) race T strain C4 and race O strain C5 from the USA were reported ( Haridas et al., 2023 ). These assemblies were 37.8 and 36.5 Mb in size in 70 and 53 scaffolds, respectively. Interestingly, the T-toxin biosynthetic cluster in race T was situated as dispersed genes within large stretches of repetitive DNA ( Haridas et al., 2023 ). Overall, the number of predicted protein coding genes in these maize foliar pathogens were in a similar range of 11702 – 12547 ( Table 5 ).

Genomic sequence information for these maize fungal pathogens opens several new avenues for disease control, as well as a deeper understanding of maize-pathogen interactions. Protein-coding gene catalogues of E. turcicum , C. zeina , and B. maydis can be searched for effector genes, known to be important for pathogenicity. Machine learning tools such as Effector P are used for this ( Sperschneider and Dodds, 2022 ), as was done for Bipolaris spp. and E. turcicum ( Condon et al., 2013 ; Human et al., 2020 ). Fungal effector genes interact with host proteins either directly or indirectly, and effector discovery is the first step in identifying host targets, eventually leading to effector-based breeding ( Vleeshouwers and Oliver, 2014 ).

Gene catalogues for these maize pathogens can also prove useful for developing novel approaches for disease control, such as RNAi-based fungicides. In a recent study in C. zeina , a phylogenomic approach was used to first determine that this pathogen had the machinery for RNAi ( Marais et al., 2024 ). This entailed comparing the protein-coding gene catalogue of 99 Dothideomycetes fungal genomes, and then drawing phylogenetic trees for orthogroups of Dicer-like, RNA-dependent RNA Polymerase and Argonaute. RNAi targets were identified in the C. zeina gene catalogue, allowing design of gene-specific dsRNAs. In a proof of concept, the dsRNA treatment disrupted the metabolic activity of the fungus in vitro , and reduced GLS disease when applied to inoculated maize leaves ( Marais et al., 2024 ).

6 Management of NLB, GLS, and SCLB

Effective disease management strategies should aim to interfere with the most vulnerable stages of the pathogen life cycle to reduce the rate of disease development ( Ward and Nowell, 1998 ; Shah and Dillard, 2010 ; Reddy et al., 2013 ). Cultural practices, chemical usage, and host genetic resistance are extensively employed in managing NLB, GLS, and SCLB.

6.1 Cultural practices for the control of NLB, GLS, and SCLB

Similar management strategies, including the use of tillage practices, rotation with non-host crops, and manipulation of environmental factors, are being used against NLB, GLS, and SCLB to reduce the amount of initial inoculum of the causal pathogens in the field ( Ward and Nowell, 1998 ; Hooda et al., 2017 ). Deep tillage ensures the burial and destruction of pathogen inoculum in the soil ( Payne and Waldron, 1983 ; Huff et al., 1988 ). Rotations for at least two years with non-host crops reduce fungal inoculum, especially in seasons with low disease incidence ( Sharma and Payak, 1990 ; Ward and Nowell, 1998 ). In addition, manipulation of favorable environmental conditions (temperature, relative humidity, and leaf wetness) for pathogen development, especially early in the growing season, is crucial for hindering early season disease development ( Ward and Nowell, 1998 ). Although these cultural practices are useful in managing these diseases and may be effective in low-risk areas, they are less effective when the disease is well-established ( Ward et al., 1997 ; Lipps et al., 1998 ; Ward and Nowell, 1998 ).

6.2 Chemical control of NLB, GLS and SCLB

Broad-spectrum fungicides, specifically demethylation inhibitor (DMI), quinone outside inhibitor (QoI), and succinate dehydrogenase inhibitor (SDHI), are effective against NLB, GLS, and SCLB, especially in susceptible hybrids ( Reddy et al., 2013 ; Weems and Bradley, 2017 ; Dai et al., 2018 ; Neves and Bradley, 2019 ; Sun et al., 2023 ). Furthermore, Iturin A2, a Bacillus subtilis compound, was developed into a fungicide effective against B. maydis and other fungi ( Gong et al., 2006 ; Kim et al., 2010 ; Ye et al., 2012 ). Iturin A2 could potentially treat other maize pathogens like E. turcicum and C. zeina and should be tested. Despite fungicide effectiveness, resistance has developed in other cereal pathogens like Zymoseptoria tritici , Pyrenophora teres f. teres , and Magnaporthe oryzae ( Bohnert et al., 2018 ; Ellwood et al., 2019 ; Garnault et al., 2019 ). A few fungicide sensitivity studies have been conducted on E. turcicum and B. maydis to DMI, QoI, and SDHI fungicides. To date, all have revealed high sensitivities to fungicides, with no resistance buildup yet ( Chapara et al., 2012 ; Weems and Bradley, 2017 ; Yuli et al., 2017 ; Hou et al., 2018 ). However, Africa lacks baseline sensitivity studies on E. turcicum , C. zeina , and B. maydis , despite increasing fungicide demands and use in large-scale field plantings. These studies are needed before fungicide resistance monitoring in maize foliar pathogens is initiated in Africa. Chemical control is also too expensive for many smallholder farmers. Therefore, affordable and long-lasting strategies such as host resistance through breeding need to be integrated and utilized.

6.3 Breeding for resistance against NLB, GLS and SCLB

Host plant resistance is the most economical, eco-friendly, and adjustable approach for maize disease management. Nelson et al. (2018) pointed out that effective resistance depends on the effect and strength of resistance genes in the host. Major genes provide complete or near-complete resistance, while quantitative resistance involves multiple minor genes with small additive effects ( St. Clair, 2010 ).

6.4 Qualitative breeding for resistance

Resistance to E. turcicum in maize is both qualitative and quantitative and can be used either separately or in combination with qualitative resistance following a gene-to-gene model ( Welz and Geiger, 2000 ; Ogliari et al., 2005 ) ( Table 2 ). Qualitative resistance is mediated by Helminthosporium turcicum ( Ht ) resistance genes ( Welz and Geiger, 2000 ). The four well-known Ht genes include Ht1, Ht2, Ht3 , and Htn1 , where the functions of Ht1, Ht2 and Ht3 have yet to be characterized ( Van Staden et al., 2001 ; Yin et al., 2003 ; Ogliari et al., 2005 ). The Htn1 gene is highly conserved in E. turcicum hosts, particularly maize, sorghum, rice, and foxtail millet ( Setaria italica ), and encodes a wall-associated receptor-like kinase that confers resistance against race 12 ( Hurni et al. (2015) . Other resistance genes include HtP against races 123x and 23rx ( Ogliari et al., 2005 ) and the recessive genes ht4 and rt that confer resistance to a wide range of E. turcicum races ( Ogliari et al., 2005 ).

For GLS, a qualitative resistance locus is yet to be characterized. To date, GLS resistance is qualitatively inherited. Few major resistance quantitative trait loci including Qgls 8 derived from teosinte ( Zhang et al., 2017 ), and gRgls 1 and qRgls 2 from maize ( Zhang et al., 2012 ) have been precisely defined.

Qualitative genes can confer resistance to B. maydis races. For instance, rhm gene mainly protects maize against race O and, to a lesser extent, race T strains ( Zaitlin et al., 1993 ; Chang and Peterson, 1995 ). Chang and Peterson (1995) proposed a two-gene model in which two homozygous recessive genes, rhm1 and rhm2 which, in combination, increased host resistance to B. maydis . This was confirmed by the reduced lesion size as compared to the control experiment ( Chang and Peterson, 1995 ) or the effect of an individual gene ( Simmons et al., 2001 ). Qualitative resistance is effective against race O, while the best defense against race T is to avoid T-cms maize germplasm in breeding programs ( Leonard, 1977a ). Resistance to C and S races is so far unknown ( Rong and Baxter, 2006 ).

6.5 Quantitative breeding for resistance

Quantitative disease resistance is known to reduce disease severity and incidence, rather than completely eliminate the disease ( Young, 1996 ; Poland et al., 2009 ). In recent years, QTL mapping studies have characterized several traits of crops, including resistance to several plant pathogens ( Bernardo, 2008 ; Xu and Crouch, 2008 ).

Quantitative trait loci for resistance against NLB span the entire maize genome and have been identified in several mapping populations ( Welz and Geiger, 2000 ; Wisser et al., 2006 ; Chen et al., 2016 ; Wang et al., 2018 ; Wende et al., 2018 ; Rashid et al., 2020 ). Using techniques such as genome-wide nested association mapping, QTLs with several potential candidate genes have been characterized and confirmed to confer resistance against NLB ( Poland et al., 2011 ; Rashid et al., 2020 ). Although many QTLs are known to confer resistance to a broad spectrum of E. turcicum races, some QTLs are known to confer race-specific resistance to NLB ( Chung et al., 2010 , Chung et al., 2011 ).

Hot spots of QTLs conferring resistance to GLS span discrete regions of chromosomes 1, 2, 3, 4, 5, and 7 ( Lehmensiek et al., 2001 ; Berger et al., 2014 ). Most notably, a candidate gene encoding a maize caffeoyl-CoA O-methyltransferase that confers quantitative resistance to GLS and SCLB has been cloned, implicating lignin and the phenylpropanoid pathway in maize defense against foliar diseases ( Yang et al., 2017 ).

Many of these QTLs are derived from bi-parental crosses between susceptible and resistant genotypes tested under different disease pressures, germplasm backgrounds, and environmental conditions ( Clements et al., 2000 ; Lehmensiek et al., 2001 ; Balint-Kurti et al., 2008 ; Berger et al., 2014 ). A majority of the QTLs are environment-specific; however, many QTLs expressed in several environments have also been characterized ( Berger et al., 2014 ). These can be introgressed into maize genotypes grown in different environments. Molecular breeding to develop GLS resistant maize for small-holder farmers in Africa has been reported ( Kibe et al., 2020 ). Recently, advanced populations of maize developed by CIMMYT were used in a combination with linkage and association mapping with genome wide SNP markers to identify QTLs for GLS and NLB resistance in East Africa ( Omondi et al., 2023 ).

Using recombinant inbred lines (RILs), Carson et al. (2004) identified 11 QTLs spanning chromosomes 1, 2, 3, 4, 7, and 10, which are associated with SCLB resistance. Additional SCLB resistance QTLs have been characterized from different maize genotypes at different maturity stages ( Balint-Kurti and Carson, 2006 ; Zwonitzer et al., 2009 ; Negeri et al., 2011 ).

Thus, qualitative, and quantitative resistance breeding are crucial for managing NLB, GLS, and SCLB. Maize geneticists have made great progress in identifying the genomic loci associated with resistance to one or more of these three diseases. To identify these loci tools such as the nested association mapping (NAM) panel, and the availability of genome wide SNP markers ( Benson et al., 2015 ) have been employed. Importantly, some loci appear to confer multiple disease resistance, and recent advances have validated some QTL in independent maize populations ( Lopez-Zuniga et al., 2019 ). A limitation is that most of these studies have been carried out in the USA and Europe often with inbred lines adapted to these temperate climates, with disease scoring carried out against local populations of each pathogen ( Technow et al., 2013 ). Molecular marker-assisted breeding tools can now be employed to introgress these resistance alleles into germplasm adapted to maize-producing regions in Africa to determine whether crop protection is conferred against pathogen populations on the continent.

7 Recent advances in population genetics of the causal pathogens of NLB, GLS, and SCLB

Pathogen survival is based on its ability to adapt to constant environmental changes through evolution ( McDonald, 1997 ). Therefore, management strategies to counteract these fast-changing lifestyles must be guided by understanding the genetics of populations and their evolution in response to changing environments rather than a focus on individual “model” pathogen strains ( McDonald, 1997 ).

7.1 Population genetics of Exserohilum turcicum

Microsatellite markers have replaced earlier techniques such as Random Amplified Polymorphic DNA (RAPD) and Amplified Fragment Length Polymorphism (AFLP) markers to study the global population structure of E. turcicum . Reports from Asia, Europe, the Americas, and Africa show that E. turcicum is genetically and genotypically diverse, with higher diversity in Asia and Africa ( Borchardt et al., 1998b ; Ferguson and Carson, 2004 ; Dong et al., 2008 ; Haasbroek et al., 2014 ; Tang et al., 2015 ; Nieuwoudt et al., 2018 ). European E. turcicum populations are characterized by low genetic diversity ( Borchardt et al., 1998a ; Turgay et al., 2021 ) and are partially differentiated due to the Alps ( Borchardt et al., 1998a ). African geographic boundaries, like mountains and the large lakes of the Rift Valley, may affect E. turcicum population structure, although this has not yet been investigated. Sexual recombination is a major evolutionary factor in E. turcicum’s global population structure, even in Europe, where sexual occurrences are rare, based on the frequency distribution of mating types and a lack of observed sexual structures in nature or in the laboratory ( Fan et al., 2007 ; Turgay et al., 2021 ). Mating-type genes were found to be equally distributed and frequent in several countries where mating-type studies have been conducted, except in Europe, indicating sexual recombination ( Haasbroek et al., 2014 ; Human et al., 2016 ; Weems and Bradley, 2018 ). Even though S. turcica , the sexual stage of E. turcicum is very rare in nature, with the only existing report being from Thailand ( Bunkoed et al., 2014 ), it has been induced under laboratory conditions using Sach’s medium with barley culm ( Moghaddam and Pataky, 1994 ; Fan et al., 2007 ).

Population genetic analysis allows for the study of physiological race distribution, potential race re-emergence, and the identification of alternative hosts. However, limited knowledge exists on the population genetic diversity and race diversity of E. turcicum in Africa, with the exception of populations in Kenya and South Africa. Therefore, understanding the population structure of E. turcicum in maize-growing African countries is needed.

7.2 Population genetics of Cercospora zeina

Prior to the classification of the GLS causal pathogens into two distinct species ( Crous et al., 2006 ), all studies conducted on GLS referred to the disease as being caused by C. zeae-maydis ( Latterell and Rossi, 1983 ; Lipps, 1998 ; Ward et al., 1999 ; Okori et al., 2003 ). As more studies based on taxonomy, molecular and phylogenetic tools emerged, it became evident that there were two sibling species, C. zeina (formerly known as C. zeae-maydis type II) and C. zeae-maydis . Initial molecular studies of Cercospora isolated from maize in Africa indicated the presence C. zeae-maydis type II ( C. zeina ) using AFLP and RFLP markers with genetic relatedness to Type II, and not Type 1 isolates in the Americas ( Wang et al., 1998 ; Dunkle and Levy, 2000 ; Okori et al., 2003 ; Liu and Xu, 2013 ; Muller et al., 2016 ). Subsequent surveys in sub-Saharan Africa confirmed that C. zeina was the only causal pathogen in Africa since to date, no isolates of C. zeae-maydis have been found in a collection of more than 1000 isolates from East and Southern Africa ( Nsibo et al., 2021 ).

Populations studies have shown that C. zeina is a highly diverse pathogen in Africa with a partially defined population structure within and among countries ( Okori et al., 2003 , Okori et al., 2015 ; Muller et al., 2016 ; Nsibo et al., 2019 , Nsibo et al., 2021 ). Given that maize is non-native to Africa, the dominance of C. zeina on the continent is attributed to more than one introduction, followed by several sexual recombination events, intra-continent gene flow, migration, and local adaptations ( Nsibo et al., 2021 ). This work was further refined by genome sequencing of 30 isolates of C. zeina from two countries in East Africa (Kenya, Uganda) and three countries in Southern Africa (Zambia, Zimbabwe and South Africa) ( Welgemoed et al., 2023 ). This showed population differentiation but no major differences in diversity indices between regions, indicating two possible introductions over the approximately 500-year time period since maize entered the continent ( Welgemoed et al., 2023 ). The study of C. zeina populations from other continents and the search for alternate hosts is underway to explore alternative hypotheses, including the hypothesis that C. zeina in Africa was derived from a host jump from an unidentified grass species onto maize ( Dunkle and Levy, 2000 ; Crous et al., 2006 ).

Although sexual structures of C. zeina have not been observed under field and laboratory conditions, cryptic sexual recombination has been suggested based on the presence of mating-type genes with equal distribution and frequency, in addition to the low levels of linkage disequilibrium among some populations ( Groenewald et al., 2006 ; Muller et al., 2016 ; Nsibo et al., 2021 ). Possible explanations for the failure of laboratory experiments to induce or discover the sexual stage of C. zeina may include the absence of environmental parameters that the pathogen encounters in nature to trigger sexual recombination. It could also be a failure to systematically monitor the development of an ascocarp in this presumably asexual pathogen ( Dyer et al., 1992 ) or fertility decline in the pathogen ( Dyer and Paoletti, 2005 ).

There is clear evidence that C. zeina is a well-established pathogen in Africa with the potential to threaten food production on the continent if not monitored to determine its diversity and migration patterns and deploy more effective management strategies.

7.3 Population genetics of Bipolaris maydis

There is limited information regarding the genetic diversity of B. maydis . RAPD markers have been used to understand the genetic structure of B. maydis populations, especially in India, where most reports have emerged. In India, B. maydis has been reported to be highly diverse, with little to no population differentiation ( Karimi, 2003 ; Jahani et al., 2011 ; Gogoi et al., 2014 ), suggesting that gene flow plays a major evolutionary role in the population structure of the pathogen. Furthermore, the physiological race O is the most predominant race in India ( Gogoi et al., 2014 ; Pal et al., 2015 ), with high genetic variability among isolates of the same race ( Gafur et al., 2002 ; Pal et al., 2015 ). Sexual recombination is another major evolutionary factor driving observed genetic diversity ( Gafur et al., 1997 , Gafur et al., 2002 ). The availability of the B. maydis genome ( Condon et al., 2013 ) offers a unique opportunity to develop more robust molecular markers, such as microsatellite and single-nucleotide polymorphism (SNP) markers, that can be exploited to enable comprehensive studies of the pathogen from all the countries where the disease exists.

Bipolaris maydis is a potential threat to maize production in Africa although it has only been reported in Kenya ( Mwangi, 1998 ) and South Africa ( Rong and Baxter, 2006 ). The risk of its spreading to other countries is heightened by the fact that B. maydis is both an air- and seed-borne pathogen ( Aylor and Lukens, 1974 ; Manoj and Agarwal, 1998 ; Biemond et al., 2013 ). Due to increasing anthropogenic activities and global trade, unreported incidences of the pathogen in the rest of Africa are possible. Therefore, countries where SCLB has not yet been reported must be vigilant through the establishment of phytosanitary regulations and bodies that test and ensure the movement of healthy seeds across geographical boundaries. Methods such as roguing, seed dressing, and proper storage to minimize contamination have been suggested as alternative ways to ensure seed health ( Biemond et al., 2013 ).

8 Breeding for multiple disease resistance against NLB, GLS and SCLB

Qualitative and quantitative disease resistance strategies, either individually or in combination ( McDonald and Linde, 2002 ), are important for the management of NLB, GLS, and SCLB. These strategies are based on the development of advanced maize genetic populations and screening for disease resistance across multiple environments (see examples in next section). Multi-environment field testing aims to expose the maize populations with the “diversity” of pathogen genotypes (i.e. races). This, however, can now be done more systematically by supplementing the pathogen diversity by artificial inoculation if pathogen population genetics and race typing data are available.

The co-occurrence of maize foliar diseases such as NLB, GLS and SCLB in some maize production regions of Africa presents an additional challenge for maize breeders. As described above, many resistance QTL are available for each disease, however each locus may have a small effect and thus breeders need to introgress several QTL for durable resistance ( Nelson et al., 2018 ). Researchers have, therefore, searched for multiple disease resistance loci in maize and other plants ( Wiesner-Hanks and Nelson, 2016 ).

A multiple disease resistance QTL associated with resistance to NLB, GLS and SCLB was identified by association mapping with a panel of 253 genetically diverse maize genotypes that had been scored for each disease in the field ( Wisser et al., 2011 ). Moderate resistance to all three diseases was found to be associated with alleles of a maize glutathione S-transferase (GST) gene ( Dean et al., 2005 ; Wisser et al., 2011 ). This maize GST may play a general defense role against all three diseases through detoxification of fungal secondary metabolites.

In another study, quantitative resistance to both GLS and SCLB was associated with alleles of the ZmCCoAOMT2 gene , which encodes a caffeoyl-CoA O -methyltransferase ( Yang et al., 2017 ). This enzyme is involved in the phenylpropanoid pathway and lignin production, thus potentially contributing to defense barriers against the invading fungal pathogens. In another study, the search for robust QTL for resistance to NLB and SCLB was carried out using the maize Nested Associated Mapping (NAM) panel, high density markers, and field testing in the USA and China. Some of the identified QTLs conferred resistance to both NLB and SCLB, and one of the candidate genes was ZmCCoAOMT2 , providing validation of the previous finding ( Li et al., 2018 ).

Backcross populations developed between four multiple disease resistant and two susceptible maize lines were used to identify several QTLs associated with resistance to NLB, GLS, and/or SCLB ( Lopez-Zuniga et al., 2019 ). Several of these QTL conferred resistance to two of the diseases, and six to all three (NLB, GLS and SCLB) ( Lopez-Zuniga et al., 2019 ). Further work validated these QTL by developing populations in more uniform genetic backgrounds, and two QTL were confirmed to be associated with resistance to all three diseases ( Martins et al., 2019 ).

A take home message from these studies is that quantitative resistance to each of these three foliar diseases is generally conferred by many QTL in a single maize genotype, each with minor but additive effects ( Li et al., 2018 ). In addition, QTL conferring resistance to more than one disease are rare and would be limited to common responses to the different pathogens ( Martins et al., 2019 ).

Pyramiding qualitative resistance genes has been successful in other cereal pathosystems, such as wheat against the Ug99 races of stem rust ( Zhang et al., 2019 ). However, combining QTLs for Ug99 resistance has been proposed as the most durable strategy for resistance ( Singh et al., 2006 ). Pyramiding QTL would be the most durable strategy in maize against NLB, GLS, and SCLB when integrated with other management strategies.

Recent findings about both qualitative resistance genes like the Ht genes and multiple disease resistance QTLs described above have potential to benefit maize disease resistance breeders in Africa. However, several factors need to be fulfilled namely (i) access to germplasm and research capacity, (ii) efficacy of resistance loci in local environments, and (iv) ongoing research into pathogen population dynamics across the continent ( Nsibo et al., 2021 ). Understandably, tightened phytosanitary regulations limits the ease with which maize germplasm can be shipped around the world. Fortunately, many African countries have historical access to maize germplasm through national breeding programmes, seed companies or NGOs such as the CGIAR institutes ( Berger et al., 2020 ; Kibe et al., 2020 ). There are ongoing successes in releasing stress-tolerant maize varieties to farmers in Africa, which provides a good foundation to build on ( Worku et al., 2020 ).

This highlights the importance of regional and international networks to address the threat of these three maize foliar diseases in Africa. Regional collaboration is important since fungal pathogens do not respect country borders. A successful example is the ongoing collaboration between universities in South Africa and Kenya which started with surveillance of the GLS pathogen C. zeina ( Nsibo et al., 2021 ). Subsequently, CIMMYT came on board to expand the project to maize disease resistance breeding ( Omondi et al., 2023 ). The collaboration had a strong component of capacity building with maize foliar disease workshops and postgraduate exchanges and training. The network is now supporting new maize disease reports in other countries of southern Africa. International linkages are key, for example collaboration with a German University has brought in whole genome-based population genomics expertise to the C. zeina project ( Welgemoed et al., 2023 ), and funding from the British Society for Plant Pathology has facilitated expansion of the project to E. turcicum .

9 Conclusion

This review summarizes recent advances in NLB, GLS, and SCLB disease resistance breeding, as well as the ecology and population genetics of their causal pathogens in Africa. All three diseases exist on the continent and threaten its maize production and food security. These diseases are polycyclic in nature and can infect maize under overlapping environmental conditions within a single growing season. With the increasing adoption of conservation agriculture and monocropping, foliar diseases are likely to escalate to all maize-producing countries owing to the accumulation of inoculum and shared dispersal mechanisms. Several management strategies at the commercial level, particularly cultural practices, fungicide usage, and breeding for resistance, are being increasingly adopted and used in Africa. However, since most farming is on a small scale, fungicide usage is not widespread due to its cost implications and its aftereffects on soil and human health. As such, there is an increasing adoption of breeding for resistance at a small-scale level, used in combination with cultural practices. Notably, limited knowledge is available on the population biology and genetics of E. turcicum , C. zeina , and B. maydis in Africa; thus, the evolutionary potential of these pathogens to overcome resistance has not been fully established. Therefore, there is a need to conduct large-scale sampling of isolates across the continent to study their diversity and trace their migration patterns across the continent.

Author contributions

DN: Data curation, Formal analysis, Investigation, Methodology, Software, Validation, Visualization, Writing – original draft, Writing – review & editing. IB: Conceptualization, Data curation, Formal analysis, Resources, Software, Supervision, Validation, Writing – review & editing. DB: Funding acquisition, Investigation, Methodology, Project administration, Resources, Supervision, Validation, Visualization, Writing – review & editing.

The author(s) declare financial support was received for the research, authorship, and/or publication of this article. This research was supported by (i) a PhD fellowship to DN from the Intra-ACP Mobility Project for Crop Scientists for Africa Agriculture of the European Union; (ii) grant #98617 to DB from the Research and Technology Fund of the Department of Agriculture Forestry and Fisheries, administered by NRFSA; (iii) grant #120389 to DB from NRFSA; and (iv) support to DB from the Small Grant fund of the British Society for Plant Pathology.

Acknowledgments

The authors acknowledge Glenda Brits from the Department of Education Innovation at the University of Pretoria for illustrating Figures 1 and 2 .

Conflict of interest

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

The author(s) declared that they were an editorial board member of Frontiers, at the time of submission. This had no impact on the peer review process and the final decision.

Publisher’s note

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Keywords: Africa, maize, Setosphaeria turcica , Cochliobolus heterostrophus , population biology, northern corn leaf blight, grey leaf spot, turcicum leaf blight

Citation: Nsibo DL, Barnes I and Berger DK (2024) Recent advances in the population biology and management of maize foliar fungal pathogens Exserohilum turcicum , Cercospora zeina and Bipolaris maydis in Africa. Front. Plant Sci. 15:1404483. doi: 10.3389/fpls.2024.1404483

Received: 21 March 2024; Accepted: 01 July 2024; Published: 01 August 2024.

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Copyright © 2024 Nsibo, Barnes and Berger. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY) . The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

*Correspondence: Dave K. Berger, [email protected]

† ORCID : David L. Nsibo, orcid.org/0000-0002-2035-9868 Irene Barnes, orcid.org/0000-0002-4349-3402 Dave K. Berger, orcid.org/0000-0003-0634-1407

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