Call for applications: 2019 Syngenta Crop Challenge in Analytics prize

Corn is one of the world’s most important crops. Each year, breeders create several new corn products, known as experimental hybrids. Corn breeders work to create corn hybrids that can maintain high yield across a wide range of environments.
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Corn is one of the world’s most important crops. Each year, breeders create several new corn products, known as experimental hybrids. Corn breeders work to create corn hybrids that can maintain high yield across a wide range of environments. Historically, identifying the best hybrids has been by trial and error, with breeders testing their experimental hybrids in a diverse set of locations and measuring their performance to select the highest yielding hybrids. This process can take many years. Corn breeders would benefit from accurate models that can predict performance across a range of environmental scenarios. 
One way of modeling corn yield is that any particular hybrid (experimental cross of corn varieties) has a maximum yield potential, which then decreases depending on the environment in which it is grown. Every environment will have certain characteristics, or limiting factors, that are suboptimal for any hybrid, causing the actual yield to be less than the yield potential.
Some potential environmental stresses that can have a negative effect on yield are poor weather (heat, drought, cold, etc.), soil lacking nutrients, insect damage or pathogens. The degree of each stress and how resistant a particular hybrid is to the stresses encountered will determine how much the yield is impacted. In addition, certain stresses, when faced at the same time, can have a stronger impact than the combined individual stresses.
A strong understanding of how a hybrid reacts when facing certain stresses (and combined stresses) could be a powerful tool for developing hybrids for regions that are less hospitable for corn, allowing farmers the potential to productively grow corn where currently it is challenging. Furthermore, individual farmers benefit from having access to this type of information because they can better manage risk across their acres.


Using feature engineering on environmental data (daily weather, soil, plant/harvest dates, any other available data), develop metrics representing the amount of stress that corn would face in any particular environment across a growing season. The objective is to individually model heat stress, drought stress, and stress due to the combination of heat and drought. Each stress will obviously depend on the weather at each location, but the impact can also vary depending on soil type and when the stress occurs throughout the growing season. These stresses are not the only factors affecting yield but, typically, the higher the stress, the lower the typical yield would be.

A sub-analysis that can be done at this step is measuring the impact of the interaction of heat stress and drought stress. Can the yield loss due to these stresses be explained by the individual contributions of heat and drought stress, or does the interaction of the two stresses significantly contribute to yield loss? 


Using the stress metrics developed in Objective #1, classify hybrids as either tolerant or susceptible to each type of stress using the hybrid’s yield across different environments. One possible way of doing this is by conducting a linear regression of yield against each stress, and classify hybrids based on the slope of that regression line. You are encouraged to use more complex or non-linear models in order to build a better classifier. 
Dateline for submission: Before midnight Eastern Time, January 18, 2019
Source: Syngenta

Illustration Photo: Corn field (credits: Ali Eminov / Flickr Creative Commons Attribution-NonCommercial 2.0 Generic (CC BY-NC 2.0))

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