Visualizing Data with Bar Charts and Pie Charts

Raw agricultural data – crop yields by district, farm budget breakdowns, market share by commodity – means very little until it’s organized into a form that people can quickly understand. That’s where charts come in. Among all the tools in a data analyst’s toolkit, bar charts and pie charts are the two most widely used for displaying categorical data. They convert rows of numbers into clear visuals, making it easier to spot patterns, compare categories, and communicate findings to stakeholders. Knowing how each chart works – and when to use one over the other – is a foundational skill in agribusiness data analysis.

What is a bar chart? 🔗

A bar chart is a graphical display that represents categorical data using rectangular bars. The length or height of each bar corresponds directly to the value it represents. One axis lists the categories being compared (such as crop types or regions), while the other axis shows the measured values (such as yield in tonnes per hectare). This structure makes it straightforward to compare multiple categories side by side at a glance.

Bar charts are particularly effective in agribusiness because so much agricultural data is inherently comparative. Comparing crop yield across regions, fertilizer use across farming systems, or livestock population trends over time all lend themselves naturally to the bar chart format.

Types of bar charts 🔗

Not all bar charts are identical. There are four main variations, each suited to a different analytical purpose:

Vertical bar chart (column chart): The most common form. Categories are placed along the horizontal axis and values on the vertical axis. It works well for comparing a moderate number of distinct categories, such as annual production volumes for different commodities.

Horizontal bar chart: The axes are flipped – categories run along the vertical axis and values extend horizontally. This format is especially useful when category names are long, as it avoids the need to rotate labels. For example, if you’re comparing named farm cooperatives with lengthy titles, a horizontal bar chart keeps things readable.

Stacked bar chart: Each bar is divided into segments that together make up the bar’s total value. This is ideal for showing composition – for instance, how total water usage on a farm breaks down across irrigation, livestock, and processing. One standard goal of a stacked bar chart is to support relative judgments about how sub-categories contribute to the whole.

Grouped (clustered) bar chart: Multiple bars are placed side by side within each category grouping. A grouped bar chart comparing crop yields for wheat, corn, and soybeans across several regions is a classic agribusiness example. It allows direct comparison of sub-categories both within and across groups simultaneously.

How to construct a bar chart 🔗

Building a bar chart follows a clear sequence. First, collect the categorical data you want to visualize – for example, the yield (in kg/ha) of five different crop types in a given season. Next, choose the most appropriate type of bar chart based on whether you need simple comparison, compositional breakdown, or multi-variable analysis. Label both axes clearly with category names and measurement units. Plot each bar so its height or length accurately reflects the data value, and always start the value axis at zero – a non-zero baseline distorts comparisons by making small differences appear far more significant than they are. Finally, add a title and, for grouped or stacked charts, a legend to explain color coding.

What is a pie chart? 🔗

A pie chart represents data as slices of a circle. The full circle equals 100% of the data, and each slice represents one category’s proportional share of that total. It is best suited for displaying data distributions and proportions – particularly when the message you want to convey is how a whole is divided among its parts.

In agriculture, pie charts are commonly used to show how a farm’s budget is allocated across inputs like seeds, fertilizer, labor, and equipment. They are equally useful for displaying the share of total agricultural land devoted to different crops, or the proportion of export revenue contributed by various commodities.

How to construct a pie chart 🔗

Start by gathering the categorical data and calculating each category’s proportion relative to the whole – this is done by dividing each category’s value by the total and multiplying by 100 to get a percentage. Crucially, you cannot use a pie chart for averages or ratios, because those values won’t sum to 100% – pie charts are strictly for shares. Once proportions are calculated, draw a circle and divide it into slices using angles proportional to each category’s share (a category with 25% share occupies 90° of the 360° circle). Label each slice clearly with the category name and its percentage value. Where there are many small categories, group them into a single “Other” slice to keep the chart readable. For best legibility, sort slices from largest to smallest starting at the 12 o’clock position and moving clockwise.

Limitations of pie charts 🔗

Pie charts have real practical limitations worth knowing. Humans are not accurate at comparing angles or arc lengths, which is precisely what reading a pie chart requires. When two slices are close in size, it becomes genuinely difficult to tell which is larger without numerical labels. This is why pie charts work best with a small number of clearly differentiated categories – ideally no more than five or six. Beyond that, the chart becomes visually cluttered and the slices too narrow to interpret accurately.

A bar chart, by contrast, uses length along a common baseline – something the human eye judges with far greater precision. This is why a bar chart can be used for a broader range of data types, not just for breaking down a whole into components, and is generally the safer default choice when in doubt.

Bar charts vs. pie charts: choosing the right one 🔗

The choice between the two charts comes down to your analytical purpose. A pie chart can only be used if the sum of individual parts adds up to a meaningful whole – it is purpose-built for part-to-whole comparisons. A bar chart is appropriate across a wider range of situations, including comparisons that do not form a complete whole, trend tracking, and datasets with many categories.

Use a pie chart when you have a small number of categories (three to six), when all categories together form 100% of a total, and when the primary message is about proportional share rather than precise magnitude. For example, showing that a single crop variety accounts for more than half of a country’s total grain production is a message where the pie chart’s part-to-whole structure adds clarity.

Use a bar chart when you need to compare values across multiple categories, when precision matters, when there are more than six categories, or when values do not sum to a meaningful total. Bar charts are also more effective for handling larger datasets and complex data with multiple values – something common in regional agribusiness reporting.

Practical applications in agribusiness 🔗

Both chart types have clear, concrete uses in agricultural data work.

Bar chart applications 🔗

Bar charts are the go-to tool whenever comparison across categories is the primary goal. They are well-suited for comparing crop yields across districts or growing seasons, tracking fertilizer application rates across different farming systems, analyzing livestock population changes over multiple years, and displaying market price variations for agricultural commodities across different markets. A grouped bar chart is particularly effective for comparing the performance metrics of different livestock breeds, helping farmers make evidence-based decisions on breed selection.

Pie chart applications 🔗

Pie charts are most effective when the question being answered is “what share of the total does each category represent?” Farmers can use pie charts to visualize how agricultural land is allocated – cropland versus pasture versus orchard – at a glance. They are also useful for illustrating how a farm budget is distributed across seeds, labor, machinery, and fertilizers, helping prioritize spending decisions. In market analysis, a pie chart showing which crop commodities dominate export revenue quickly communicates competitive position in a way that resonates with non-technical audiences.

Best practices for both chart types 🔗

Regardless of which chart you choose, a few universal principles apply. Always label axes, slices, or segments clearly – never assume the audience will interpret colors or positions without guidance. Use consistent, distinguishable colors, especially in stacked or grouped formats, and avoid color palettes that are difficult for color-blind readers to distinguish. Keep charts uncluttered: avoid unnecessary gridlines, outline boxes, and excessive tick marks that add visual weight without adding information. For bar charts, always start the value axis at zero. For pie charts, always include percentage labels directly on or beside the slices, since the chart’s visual precision alone is insufficient for exact readings.

It is also important to match chart type to audience. A pie chart communicates proportional story quickly in a boardroom presentation. A grouped bar chart serves better in a technical report where analysts need to make precise comparisons across multiple variables. Pie charts tap into our instinctive ability to assess proportions, making them valuable for non-specialist audiences, while bar charts provide the precision and flexibility that analytical work demands.

What do you think? When you look at agricultural data in reports or presentations, do you find it easier to interpret information from a bar chart or a pie chart – and does your preference change depending on what question is being answered? If a farm manager asked you to present both crop yield comparisons and budget allocation in a single report, how would you decide which chart type to use for each?

References

1. https://en.wikipedia.org/wiki/Bar_chart
2. https://medium.com/analytics-mastery/how-to-use-bar-charts-for-data-analysis-in-agricultural-science-a-step-by-step-guide-with-r-451457554468
3. https://www.domo.com/learn/charts/vertical-bar-charts
4. https://www.atlassian.com/data/charts/stacked-bar-chart-complete-guide
5. https://nilimesh.substack.com/p/leveraging-bar-charts-in-agricultural
6. https://www.atlassian.com/data/charts/bar-chart-complete-guide
7. https://www.syncfusion.com/blogs/post/bar-chart-vs-pie-chart
8. https://nastengraph.medium.com/pie-charts-best-practices-2f8ac3b73c80
9. https://dreveal.com/why-pie-charts-are-hard-to-read-and-why-bar-charts-are-better/
10. https://www.atlassian.com/data/charts/how-to-choose-pie-chart-vs-bar-chart
11. https://www.syncfusion.com/blogs/post/bar-vs-pie-blazor-charts
12. https://www.scribd.com/document/856762973/spreadsheets-cala
13. https://inforiver.com/insights/7-types-of-bar-charts-abcs-and-advantages/
14. https://www.displayr.com/why-pie-charts-are-better-than-bar-charts/