Call for Applications: 2020 Syngenta Crop Challenge in Analytics

The competition brings together experts in mathematics, computer science and analytics, emphasizing the importance of cross-industry collaboration necessary to feed a growing population with limited natural resources.
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Eligible Countries | Regions | Cities  Worldwide
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Commercial corn is processed into multiple food and industrial products. It is widely known as one of the world’s most important crops. Each year, plant breeders create new corn products, known as experimental hybrids, by crossing two “parents” together. The parents are known as inbreds and the development of the inbreds takes up the bulk of a corn breeding program. Most of that effort is spent evaluating the inbreds by crossing to another inbred, called a “tester.” 

It is a plant breeder’s job to identify the best parent combinations by creating experimental hybrids and assessing the hybrids’ performance by “testing” it in multiple environments to identify the hybrids that perform best. 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, then selecting the highest yielding hybrids. The process of selecting the correct parent combinations and testing the experimental hybrids can take many years and is inefficient, simply due to the number of potential parent combinations to create and test.

Given historical hybrid (inbred by tester) performance data across years and locations, how can we create a model to predict/impute the performance of the crossing of any two inbred and tester parents? 
For example, given 5,000 inbreds (parents), the number of potential crosses is 12,497,500 —far more than can be created or tested. Due to limited testing resources, breeders are only able to select a small subset of all the possible inbred combinations, which can lead to lost opportunities. 

This issue is the basis for the 2020 Syngenta Crop Challenge in Analytics. Can an accurate model be constructed to predict the performance of crossing any two inbreds? Such a model would allow breeders to focus on the best possible combinations. 

In simpler terms, can we use hybrid data collected from crossing inbreds and testers together to predict the result of cross combinations that have not yet been created and tested? Namely, are we able to construct a recommender system to propose new parent combinations based on the hybrid performance from other parent combinations and attributes they have in common? 

The following Table 1 is an illustration of the challenge. Each “X” is the set of observed performance data points of hybrids from their corresponding inbred by tester combinations. With the information from the table, how can a model be built to predict/impute the mean yield of each missing combinations (“?”)?

Table 1. Research question illustration. 

  Tester 1 Tester 2 Tester 3
Inbred 1 X X X
Inbred 2 X ? ?
Inbred 3 ? X X
Inbred 4 ? ? X
Inbred 5 X X ?


The objective is to estimate yield performance of the cross between inbred and tester combinations in a given holdout set. Specifically, we are asking for the mean yield performance of each inbred by tester combination in the holdout set. 


  • Each response in the holdout must be completed
  • Many approaches can be used such as statistical approaches, machine learning and collaborative filtering

Application Deadline: 21 January 2020

Source: Syngenta

Corn seedlings (Image by bobitexshop from Pixabay)

Illustration Photo: Corn seedlings (Image by bobitexshop from Pixabay)

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