Farmer and Greenhouse games



The online Farmer game and Greenhouse game allow players to grow and sell various crops, while also determining the proper amount of water, fertilizer and pesticides that should be used for each crop. Players can choose any options they like, but they increase their chances of winning if they use the interactive data visualizations and statistical models. Both games follow growth models for Iowa farmland. Each plot represents approximately one tenth of an acre and the yields for each crop are measured in bushels.


Throughout these games, multiple data visualizations can be used to understand the optimum amount of water and fertilizer needed for each crop. In addition, droughts, pest infestations, and changing market prices can make it even more difficult to win.


The following link allows you to play these games: Stat2Games.

You may be asked to install Unity Web Player; this may take a few minutes. Many browsers will require you to allow popups before they will run these Stat2Games. These games may not run on the most recent versions of Chrome or Firefox. We are currently working on fixing the issue.


In the Farmer game, players have to deal with realistic conditions that cannot always be easily controlled, such as the amount of rain, arrival of pests, or the quality/nutrient level of the soil. Players win by completing multiple quests throughout the game. This typically takes students 30 - 60 minutes.


The Greenhouse game has very similar underlying models as Farmer for yield, but players are able to control more factors, such as the exact amount of water added to the soil. Data can be collected within just a few minutes.


The Introdcuctory GreenHouse Lab is available and connects to the following Nature article.


Greenhouse_Reg1_Developing a Statistical Model: Student Handout 
The goal of this lab is for students to learn how to use linear regression models in a meaningful context. This lab ask students to collect and analyze data related to water, yield, and profits. Students compare regression models and make predictions using these models. An app is used to create graphs and statistical models, so students only need a very basic knowledge of regression.



  • Provide a context where students are intrinsically motivated to use models to make meaningful decisions with data.
    • Understand and interpret slope, y-intercept, R-squared values, regression models, and p-values in a way that will help them win a game.
  • Explore and analyze data using appropriate graphs and numerical tools.
    • Outliers and influential points in regression models
    • Provide clear examples of sampling variability (while textbook data is fixed, all samples from a population will vary)
  • Provide a structured approach to gathering data. This involves understanding variability and isolating key factors of interest in experimental design, such as:
    • Selecting appropriate response variables, factors and levels
    • Addressing unwanted variability (restricting the population of interest, blocking, and adding additional variables or interaction terms into a model)
    • Comparing linear, quadratic, and more complex models.
    • Derive appropriate, actionable conclusions from data.
  • How have we addressed violations in the statistical assumptions? Are there data points that should be removed?
  • Is the sample size large enough?
  • Does the sample data properly reflect the population? Is it a true simple random sample of the population? Are there any reasons to believe the data may be biased based upon how the data was collected?
  • When can we make conclusions about associations between variables? When can we use sample data to show evidence of causality?


Data visualizations that allow you to view all Farmer data: Farmer Data Visualizations

Data visualizations that allow you to view all Greenhouse data: Greenhouse Data Visualizations




Thanks to Grinnell students Grace Tsui, Henry Firestone, Linh Tang, Huizhuoma Jiang, and Zhaoqing Wu for creating, editing and maintaining these on-line games.