Document Type
Article
Publication Date
12-2023
Keywords
Soybean; Arkansas; K deficiency
Abstract
The spatial variability of soybean [Glycine max (L.) Merr.] leaf-K (potassium) concentrations must be considered when collecting samples to monitor crop K status. Five commercial soybean fields were sampled at a 0.4-ha grid resolution at two reproductive growth stages to quantify the trifoliolate tissue-K concentration. The objectives of this study were to identify the potential field variability of soybean leaf-K concentrations in typical Mid-south US soybean production fields, evaluate interpolation methods, and develop a sampling protocol for in-season soybean tissue monitoring. No consistent spatial dependencies were found in the leaf-K concentrations across the fields and sample times, indicating that a soybean tissue-K grid sampling protocol cannot be generalized to a specific area size. Inverse distance weighted (IDW) and rasterization interpolation methods were considered to predict leaf-K concentrations between the sampled grid points at grid resolutions ranging from 0.4 to 4 ha. The IDW method consistently predicted leaf-K concentrations between the known values with less error than rasterization. Rather than grid sampling, composite leaf samples should be collected based on management zones to provide a simplified sampling protocol. Within each management zone, a composite sample must consist of uppermost fully expanded trifoliolate leaves collected from at least 18 locations to ensure that the sample measures within the 95% confidence interval of the area average leaf-K concentration. The developed sampling protocol coupled with the dynamic critical tissue-K concentration curve will provide producers with the ability to effectively monitor soybean for potential hidden hunger and verify K deficiency symptoms in season.
Citation
Ortel, C. C., Roberts, T., Hoegenauer, K. A., Poncet, A. M., Slaton, N. A., & Ross, W. J. (2023). Mapping Variability of Soybean Leaf Potassium Concentrations to Develop a Sampling Protocol. Agrosystems, Geosciences & Environment, 6, e20439. https://doi.org/10.1002/agg2.20439
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This work is licensed under a Creative Commons Attribution 4.0 International License.