Research Highlight - Southeast United States

The development of a more profitable and environmentally-sound production system is essential to maintain a competitive rice industry in the Mid-South region of the United States. Nitrogen fertilizer is one of the major agricultural inputs in rice production. Research on Precise Mid-Season Nitrogen Rate Determination for Use Efficiency and Yield Optimization of Rice has been in the field since 2008 in both Mississippi and Louisiana to update a working algorithm of a sensor-based N decision tool for estimating the mid-season N requirement of rice.

The components of the working algorithm include a yield potential predictive equation and an in-season estimate of responsiveness of rice to N fertilization. Sensor readings were collected from seven (variety x N) trials established in Crowley and Rayville, Louisiana, and in Stoneville, Mississippi, once a week for five consecutive weeks starting at panicle initiation. Prior to regression analysis, all data were grouped in two ways: 1) according to the number of days from seeding to sensing (DAS), and 2) according to cumulative growing degree days (GDD).

The highest association (r²= 0.59) between actual grain yield and the sensor-based yield estimate was obtained from the 1,701 to 1,900 GDD group. A rice grain yield potential predictive equation was developed using these data. A mid-season estimate of rice response to N was predicted using a second equation. Generally, the sensor-based N decision tool made mid-season N rate recommendations that resulted in total N inputs close to optimal N rates for each site. In most cases, variably applying mid-season N to rice based on sensor readings resulted in higher N use efficiency and net economic return compared with flat N rate application.

Refinement of the working algorithm will focus on: 1) adjusting the mid-season estimate of rice response to N by accounting for the difference between pre-flood- and mid-season-applied N with respect to boosting rice grain yield, 2) determining the optimal sensing scheme orientation to minimize any water reflectance effect especially in low-biomass producing areas/plots, and 3) adding more data points to update the predictive models for yield potential and in-season estimates of rice response to N.

MS-16 Project Leader: Timothy Walker, Mississippi State University, Delta Research and Extension Center, PO Box 197, Stoneville, MS 38776
LA-23 Project Leader: Dustin Harrell, Louisiana State University, Rice Research Station, 1373 Caffey Road, Rayne, LA 70578

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Full list of Southeastern US research currently supported by IPNI

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IPNI's strategic goal of facilitating research on environmentally responsible use of plant nutrients needed for agriculture to meet future global demand for food, feed, fiber, and fuel is accomplished through partnerships with colleges, universities, government agencies, and other institutions and organizations around the world where IPNI Programs are established.