Jiang announced as winner of CARD PhD Dissertation Award

John Crespi, interim director of CARD, has announced Dr. Ginger Zehui Jiang is the recipient of the fifth annual CARD Award for Best PhD Dissertation in Agricultural, Environmental, and Energy Economics Policy.
Jiang’s dissertation examines the use of Long Short Term Memory, a type of recurrent neural network previously only used in natural language processing, to predict county-level corn yields in the Corn Belt. Jiang’s work is significant for two reasons—it shows the viability of using LSTM for crop yield predictions and it provides accurate county-level corn yield predictions without the use of farm management and corn seed data. The full text of Jiang’s dissertation abstract is included below.
Jiang was awarded a $500 prize, and will have her name added to the Dissertation Award winners plaque in the CARD offices.
Jiang’s program of study committee consists of Dermot Hayes, Chad Hart, Alejandro Plastina, Baskar Ganapathysubramanian, and Soumik Sarkar.
To be considered for the award, graduate students had to submit a copy of their dissertation and a brief summary of how the topic of research related to one of CARD's research areas and complete their final oral examination in 2018.

Predicting County Level Corn Yields Using Deep Long Short Term Memory Models in the Corn Belt

Zehui Jiang

Corn yield prediction is beneficial as it provides valuable information about production and prices prior to harvest. Publicly available high-quality corn yield prediction can help address emergent information asymmetry problems and, in doing so, improve price efficiency in futures markets. This paper is the first to predict corn yield using Long Short-Term Memory (LSTM), a special Recurrent Neural Network method. Our prediction is only 0.83 bushel/acre lower than actual corn yields in the Corn Belt, whose difference is lower than the prediction from USDA.
Eighty percent of our LSTM county-level corn yield predictions fall in the +/-20 region. Results show our LSTM model can provide good early prediction and accurate Corn Belt county-level corn yield prediction without farm management and corn seed data. Our LSTM models for county level corn yield prediction in the Corn Belt are an improvement to the USDA prediction. And more importantly, our models provide a publicly available source that will contribute to eliminating the information asymmetry problem that arises from private company crop yield prediction.

(Released April 2019)