Using the LateBlight Simulation 

An Investigative Case-Based Learning Resource for Zea's Wild Roots


A Simulation of Potato Farming

LateBlight is a Windows program that is widely available. It is one of the First Review Modules in the BioQUEST Library.

 Problem Posing
 Assessment and Evaluation
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Part 1. Problem Posing

A group of students working with Part 3 of Zea's Wild Roots centered their investigations on the following exchange between Derrick and Maria:

 "You know, what made me decide to study Zea," Derrick said, "was hearing about when my uncle lost his entire crop in 1970 due to southern leaf blight. He just couldn't understand why every field in the county was affected."

Maria nodded her head knowingly and said simply, "Weather..."

The students were unfamiliar with blight, but thought that Maria's cryptic "Weather..." comment deserved to be investigated. Everyone in the group had chosen this unexplained comment as a question they might like to explore.

One of the resources that was available to the group was Late Blight, a computer simulation program involving crop management of potatoes featuring late blight.

The program was developed to model multiple variables affecting potato crop yields. Although the host was potatoes instead of corn, the students wanted to begin by modelling the impact of the weather on a crop plant exposed to blight. It was as good a start as any for no one in the urban group felt they knew very much about agriculture.

Part 2. The Investigation

The program was simple to use, but it was not easy to get started. In fact it required considerable negotiation since many different kinds of models could be investigated. The group finally agreed that the simpler the model was when they started, the easier it would be to interpret the initial results.

The students decided to use one of the default models (in this case, late season potatoes with low resistance to blight) to investigate the effects of weather on the success of the potato crop. There are many other variables they could manipulate as well, including characteristics of the potato and the blight. They chose not to interfere with the progress of the infection by spraying fungicides in this simulation. They intended to look at the progress of an uncontrolled blight infection and then focus on weather variables that might have contributed to the spread of the disease.

The students set weather conditions for the model by choosing coolwet.lwx from the menu.

This first simulation showed rather dramatic effects of a blight infection in late summer. A line graph showing the percent of leaves affected by the blight was produced.

Percent Leaves with Blight for Low Resistance, Late Season Potatoes during Cool, Wet Season


Then the group decided to produce more graphs displaying weather variables to compare with blighted leaves. Two weather variables, temperature and rainfall, were selected to create new plots. The students used the plots to determine if either seemed to be linked with an increase in the percent of leaf blight.

In their first plot below, they observed that temperature see-sawed between 45 and 80 degrees over the summer while blight increased in August and September.

 Temperature and Percent Leaves with Blight for
Low Resistance Late Season Potatoes During Cool, Wet Season


Rainfall, as shown below, was sporadic. It did increase during the late summer when the blight also seemed to increase.

Percent Leaves with Blight and Rainfall for
Low Resistance Late Season Potatoes During Cool, Wet Season

Connections between new infections and rainfall were then further explored.

Number of New Leaf Infections (10,000) and Rainfall for
Low Resistance Late Season Potatoes During Cool, Wet Season



An analysis of the data generated in graphic form resulted in an observation that rain preceded large increases in new infections. There may be other variables at work here, but rainfall seems likely to be contributing to the rise in leaf blight.

By comparing the late summer rainfall shown by the blue area under the curve and the escalation in new infections highlighted in yellow over the rainfall, an argument could be made to link the two. An increase in new infections seems to be triggered by recent rain.


Part 3. Persuasion

The group decided to present their results in a poster on the role of "weather" on crops affected by blight. The students decided to produce another graph by running the simulation under hot, dry conditions since their conclusions on rainfall as a significant variable would be more convincing if they ran the simulation with both seasons, hot and dry versus cool and wet.

One member of the group discovered that an economic report could be generated at the end of the season by simply clicking on the menu. In order to create a dramatic image for the poster, the students decided to also include the economic reports for the two seasons being modeled. The economic report for each simulation was overlaid on each of the plots showing percent leaf blight and rainfall.

Percent Leaves with Blight and Rainfall for
Low Resistance, Late Season Potatoes During Cool, Wet Season


Percent Leaves with Blight and Rainfall for
Low Resistance, Late Season Potatoes During Hot, Dry Season


Farming potatoes during the cool, wet season resulted in a net loss of about $900, while farming during the the hot dry season resulted in a net profit of over $700.  Weather was played a significant role in the spread of the blight.  The group then presented their poster to the class.

Assessment and Evaluation:

During the poster presentation, a student from another group pointed out that the organism causing potato blight was the protist, Phytophthora infestans. Corn blight is caused by a fungus.

In your assesment of this group's work, would they receive a favorable evaluation? Why or why not?


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