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USDA grant supports ag production technology

USDA grant supports ag production technology

Planting

A University of Nebraska-Lincoln project to support next-generation framework for variable-rate technology, which allows for optimal application of fertilizer and other crop inputs based on soil and crop variations in production agriculture, has received support from the U.S. Department of Agriculture.

The University of Nebraska-Lincoln is working on next-generation framework for variable-rate technology, which allows for optimal application of fertilizer and other crop inputs based on soil and crop variations.

The project has received support from the U.S. Department of Agriculture in the form of a $935,560 grant from the National Institute of Food and Agriculture’s Agriculture and Food Research Initiative.

Variable-rate technology is key to addressing variations in soil makeup, temperature and other variables. Using variable-rate technology can help maximize yield and profit, while minimizing the agricultural inputs or footprints on the environment in agricultural production.

Variable rate fertilizer applications began in the late 1980s, and university research continued during that time with machinery development and field testing. The variable rate application improved the efficiency of farm inputs, maintained or improved crop yield and quality, and protected water quality.

Historically, variable rate guidance was based off of triangulation from radio beacons, and GPS technology has improved significantly since then. Today, advanced technology also provides time management, digital farm records and traceability of crop production.

Yeyin Shi, assistant professor and agricultural information systems engineering in UNL’s department of biological systems engineering, is leading the cyber physical systems, networking and physical processes enabling the project.

Shi specializes in advanced technology and methodologies to generate, extract, manage and utilize data to increase efficiency, quality and sustainability in agricultural production. Shi and her team will integrate stress sensing, networking and data-driven modeling with classic plant and soil biophysical principles and well-recognized management practices, to provide a scalable framework for the real-time in-season variable-rate water and nitrogen applications.

The team plans to use the massive amount of data generated in daily agricultural production into a training process for model self-improving, while keeping the farmers’ privacy and computational efficiency in mind.

The new framework will be put to the test at the Eastern Nebraska Research, Extension, and Education Center (ENREEC) near Mead, and a commercial field at Paulman’s Farm near North Platte.

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