Datasets can lead to risk-reducing solutions in agriculture

October 8, 2019 — Farming is like the stock market: past crop yields do not necessarily predict future crop yields. And, farming is hard work. There are capital investments in equipment, the costs of seed and soil management, and countless hours of human labor. Unlike other jobs, the outcome is not always predictable.

Scientists from around the world are gathering in November at the Embracing the Digital Environment ASA, CSSA, SSSA International Annual Meeting in San Antonio, Texas. A day-long symposium covering topics in “Predictive Agriculture” will start to address some of the unknowns of growing, and how to give farmers an edge. The two-part symposium will run on Monday, November 11th, 2019.

The meeting is sponsored by the American Society of Agronomy, Crop Science Society of America, and the Soil Science Society of America.

Predictive Agriculture: Forecasting and Crop Adaption

Farmers need to decide both the crop type (genotype, (G)) and management (M) techniques in advance of the growing season. The environment (E) under which they grow their crops is unpredictable. Scientists will present various models that allow for designing systems that take these “G x M x E” factors into consideration. Factors like plant transpiration, soil management and a variety of crop types will be discussed.

“We are fortunate to have advances in information and sensing technologies that made large datasets,” says Carlos Messina, a crop scientist with Corteva Agriscience. “Making prediction algorithms available to practitioners has resulted in the development of useful solutions. It is opportune to ask how this encapsulated knowledge will contribute to the advancement of agricultural science and technology.”

Presenters in this section include Graeme Hammer, University of Queensland; Walid Sadok, University of Minnesota; Andrew McDonald, Cornell University; Jørgen Olesen, Aarhus University; Patrick Ewing, Michigan State University; and Kwang Soo Kim, Seoul National University.

Predictive Agriculture: Advances in Predictive Breeding

The future of crop breeding can also rely on predictive agriculture techniques. Quite simply, breeding for one trait, for example kernel color in corn, can be straightforward. However, environmental qualities and management techniques can confound even the simplest breeding project. When you add in the desire to breed for multiple traits – say kernel color, yield, and disease resistance – the models get complicated.

Speakers include Mark Cooper, University of Queensland; Fred van Eeuwijk, Wageningen University; Elhan Ersoz, Umbrella Genetics; Jacob Washburn, Cornell University; and Carlos Messina, Corteva Agriscience.

For more information about the Embracing the Digital Environment 2019 meetingvisit are invited to attend the conference. Pre-registration by Oct. 25, 2019 is required. Visit for registration information.

For information about “Predictive Agriculture: Forecasting and Crop Adaption,” visit For further information about “Predictive Agriculture: Advances in Predictive Breeding,” visit

To speak with one of the scientists, contact Susan V. Fisk, 608-273-8091, to arrange an interview.

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