Since last fall, when the University of Arkansas Division of Agriculture initiated the N-STaR Nitrogen Soil Test for Rice, farmers have submitted about 1,500 silt loam soil samples for analysis.
N-STaR offers field-specific recommendations for nitrogen applications that, in many cases, have the potential to reduce application rates by half or more. Using N-STaR ensures that producers are applying the correct N rate to maximize rice yields on silt loam soils.
The Division of Agriculture has posted short video tutorials to the Web that demonstrate how to construct the tools and follow the procedures for collecting and submitting samples for N-STaR.
More than 1,200 of the soil samples have come from Arkansas growers, mostly in Arkansas, Prairie and Lonoke counties. Some 200 to 300 samples have come from Louisiana farmers.
Trent Roberts, research assistant professor of crop, soil and environmental sciences, said samples must be submitted to the Division by mid-April in order to be certain the results will be returned before planting season. During the busiest periods -- usually following dry weather when farmers can get into their fields to collect samples -- the lab requires 7 to 10 days to process and return samples.
The video tutorials are intended to help farmers and agricultural consultants assemble the tools needed and to make sure the samples are properly collected, packaged and submitted to the N-STaR lab, Roberts said.
The first video demonstrates how to assemble the tools needed to collect the samples. It can be found here.
A downloadable document describing the same procedure can be downloaded here.
A video demonstrating how to collect soil samples from silt loam fields can be found here.
A demonstration of how to submit the samples is here.
A downloadable document describing the procedures for collecting and submitting the samples can be found here.
The information form for submissions is here.
A document with an overview of the N-STaR system can also be downloaded here.
Roberts said a video demonstrating how to interpret the information returned after the analysis is expected to be available online in the near future.