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Benchmarking Yield Potential for Soybean in North Central States

By Peyton Ginakes
 
 
The University of Minnesota is partnering with nine other North Central states to close the soybean yield gap by combining big data from producers with location-specific modelling.
 
Models are capable of predicting maximum yield potentials based on soil type, weather data, and management practices. However, yield gaps exist where producers’ soybean yields fall short of maximum yield potentials. That’s why ten North Central states are conducting a survey for more detailed information from soybean producers. Survey results from thousands of producers across the region allows researchers to use a ‘big data’ approach in determining which management practices can close the yield gap in localized regions. More information on what researchers have concluded thus far can be found in the Corn & Soybean Digest article, Data pegs soybean yield gap.
 

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Designing a Robotic Berry Picker

Video: Designing a Robotic Berry Picker


Since blackberries must be harvested by hand, the process is time-consuming and labor-intensive. To support a growing blackberry industry in Arkansas, food science associate professor Renee Threlfall is collaborating with mechanical engineering assistant professor Anthony Gunderman to develop a mechanical harvesting system. Most recently, the team designed a device to measure the force needed to pick a blackberry without damaging it. The data from this device will help inform the next stage of development and move the team closer to the goal of a fully autonomous robotic berry picker. The device was developed by Gunderman, with Yue Chen, a former U of A professor now at Georgia Tech, and Jeremy Collins, then a U of A undergraduate engineering student. To determine the force needed to pick blackberries without damage, the engineers worked with Threlfall and Andrea Myers, then a graduate student.