54-5 Evaluation of Tissue Nutrient Concentration Using Portable X-Ray Flourescence.
Poster Number 710
See more from this Division: ASA Section: Agronomic Production Systems
See more from this Session: General Sensor-Based Nutrient Management: II
Monday, November 4, 2013
Tampa Convention Center, East Exhibit Hall
Abstract:
Optimum fertilizer rates are very important for agricultural production systems throughout the world. Currently, the best means of determining these optimum fertilizer rates are through soil testing. However, it is not economically feasible to have soil samples analyzed every year for large scale production systems. The lack of soil sampling, combined with increasing fertilizer costs can result in either high unnecessary inputs costs due to over application of fertilizers or unexpected nutrient deficiencies through under-application. While tissue sampling can alleviate some of these issues, there is typically a lag between the initial collection of leaves to the correct diagnosis of a nutrient deficiency. The lag between laboratory analysis and when the plants receive a corrective foliar application can sometimes extend beyond the optimum window for a foliar applied fertilizer. To reduce the amount of time for tissue analysis, field portable x-ray fluorescence (P-XRF) is being used to evaluate the un-dried, whole leaf samples from corn and soybeans at multiple locations in Louisiana, U.S.A. Corn and soybean samples were collected at various growth stages to determine the relationships between P-XRF and the traditional laboratory techniques. Results indicate the best relationship between laboratory methods and P-XRF readings was for Zn with an r2 = 0.66. Both K and S show promising results between laboratory methods and P-XRF; however, both were weaker than that found for Zn. These relationships are expected to improve with increased data points over a wider range of nutrient concentrations.
See more from this Division: ASA Section: Agronomic Production Systems
See more from this Session: General Sensor-Based Nutrient Management: II