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Diagnostic Nutrient Testing

Diagnostic Nutrient Testing. Rao Mylavarapu Soil & Water Science Department, IFAS University of Florida. Optimum Yield or Quality. ‘Optimum’ should ideally refer to economically and environmentally sustainable returns Existing approaches have been predominantly based on economics. Yield.

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Diagnostic Nutrient Testing

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  1. Diagnostic Nutrient Testing Rao Mylavarapu Soil & Water Science Department, IFAS University of Florida

  2. Optimum Yield or Quality • ‘Optimum’ should ideally refer to economically and environmentally sustainable returns • Existing approaches have been predominantly based on economics Yield Yield/Profit Profit Inputs

  3. Crop Removal vs. Crop Nutrient Requirement • Calibrated Soil Test • Fertilizer amounts adjusted for soil contribution • Based on plant need, not plant uptake • Pollution Potential Low • Fertilize the plant not the soil • No Soil Testing • Always Fertilize • Luxury Consumption • Pollution Potential Hi • Treats soil as a bank

  4. Soil Categories & Extractants for Florida • Acid soils - Mehlich-1 (dilute double acid) -P, K, Ca, Mg, Cu, Zn, Mn • Calcareous soils (pH > 7.4) - AB-DTPA (Ammonium Bicarbonate-DTPA) -P only • Organic soils -Water extraction for phosphorus -Acetic Acid for K, Mg, Ca, Na, Si

  5. Field Calibration • Mehlich-1 for acid soils maybe replaced by Mehlich-3 -Mehlich-3 remains valid over a wider range of soil pH - Mehlich-3 extracts micronutrients efficiently -Mehlich-3 works better for environmental assessments -while nutrient recommendations will remain the same, interpretation will change

  6. Field Calibration • AB-DTPA extraction procedure for calcareous soils needs to be replaced urgently -poor field calibration with the procedure severely limits its applicability -extremely high carbonate levels make calibration difficult -background P concentration higher than the critical limit -both commercial and urban horticultural operations lack a valid soil test

  7. Plant Tissue Testing • Soil and plant diagnostics are complementary and serve different purposes • Tissue test does not completely replace a soil test • Plant analysis is effective in monitoring in-season nutrient needs, especially of perennial crops • The tissue tests confirm suspected nutrient deficiency symptoms, reveal hidden hunger and verify toxicities • Nutrient deficiencies corrected through foliar applications, although not always effective

  8. Complementary Soil & Tissue Testing • Leaf diagnosis shows nutrient status at the time of sampling reflecting soil fertility status • Soil fertility status in turn is also determined by temperature, water, management factors, nutrient balance, etc • Leaf analysis has to be integrated with soil analysis for confirming the deficiency, sufficiency or toxicity

  9. Complementary Soil & Tissue Testing • Two such new tests have been developed and implemented - for Bahia pastures -for commercial Citrus • In both tests, phosphorus requirement and deficiency confirmation is determined by a combination of both soil and a plant tissue test • Potential for a new protocol for Blueberries

  10. Plant Sap Analysis • Analysis of plant sap measures mobile nutrients in the plant system, such as N & K • Sap testing is more sensitive and therefore can detect minor or temporary deficiencies more easily • An accurate diagnosis of plant stress making effective management decisions possible

  11. Plant Sap Analysis • Temperature and time of the day are shown to influence plant sap nitrate content • Sap samples representative of the area are usually taken from petioles of most-recently-matured leaves • In a uniform field, about 20 leaves should be sampled to adequately represent a 5- to 10-acre area

  12. Plant Sap Analysis • Vegetable crops grown in Florida under intensive management can benefit from sap testing and nutrient monitoring • Limitations -The frequency and consistency required for sampling -Validity of the procedure for only mobile nutrients such as N and K

  13. NIR Spectrophotometry Technique provides • routine soil analysis • decision support • soil property classification • soil survey and mapping • precision agriculture • diagnosis of soil problems • contaminated site characterization • input data for models

  14. Spectral Core Laboratory, Soil & Water Science

  15. Why Replace Wet Chemistry? • Near-Infrared Spectroscopy (NIRS) • allows for determination of physical and chemical properties of a sample • Rapid • Instantaneous and non‑destructive • Cheap • One piece of equipment • Portable • In-situ field measurements possible

  16. Soil Test Laboratory Techniques Since automation- • Autoanalyzers- absorption spectrophotometry • ICPs- emission spectrophotometry • Each concentration has a specific level of energy and the instruments capture the wavelength of that specific light energy • analytes will be allowed to react and the resultant compounds will be detected • determinations done indirectly, calibrated and validated and interpreted through indices

  17. Reflectance Method and Processing Reflectance Measurement Signal Processing Statistical Analysis Calibration Validation Interpretation & Recommendation

  18. Previous Work • NIR spectroscopy has been extensively used for many agricultural applications such as water and nutrient stress sensing for agricultural crops Thomas and Oerther, 1972; Stafford et al., 1989a, b; Blackmer et al., 1994; Masoni et al., 1996; Sudduth and Hummel, 1996; Bausch et al., 1998 Wang et al., 2000 (for weed detection)

  19. Study 1 • A total of 270 soil samples were collected from areas representative of three predominant soil orders in Florida: (previously agriculturally unmanaged) Alfisol- approx 1.9 million ha Entisol- approx 3.0 million ha Ultisol- approx 2.8 million ha Lee, W.D., J.F. Sanchez, R.S. Mylavarapu and Choi. 2003. Evaluation of Soil Nutrient Levels Using Spectroscopy. Transactions of ASAE. 46(5):1443-1453.

  20. Average absorbance of the samples acquired from three different Soil Orders Water Water

  21. Prediction of Ca with the samples from three representative soil orders (Alfisol, Entisol, and Ultisol) by PLS regression

  22. Prediction of pH with the samples from three representative soil orders (Alfisol, Entisol, and Ultisol) by PLS regression

  23. Study 2 • A set of 1933 samples, representative of major soil orders in Florida, was assembled from samples submitted to the IFAS Extension Soil Testing Laboratory for routine testing during 2004-05 • High-resolution diffuse reflectance spectra of each soil in the visible and near infrared regions were used to predict observations • Made using standard laboratory analytical procedures for soil pH, Mehlich-1 extractable phosphorus, potassium, calcium, magnesium, copper, manganese and zinc, percent organic matter (OM), and saturated hydraulic conductivity (Ksat) Cohen, M., Mylavarapu, R.S., Lee. W.S. and Clark, M. 2007. Reflectance Spectroscopy for Routine Agronomic Soil Analyses. Soil Science. 172(6):569-485

  24. Results • We observed model efficiency to be positively associated with mean analyte concentration • Categorical modeling (threshold-based classes) was successful for pH, M-1 P, Mg, and Ksat • In addition, categorical condition for analytes where continuous prediction was insufficient (M-1 Cu, Mn) was successfully diagnosed • Predictions of M-1 K and Zn categories were statistically significant but of insufficient accuracy to be of diagnostic utility

  25. Predicted & Validated Soil Properties

  26. Study 3 (on-going) • A sample set consisting of 1,000 soil and corresponding 1,000 tissue samples is being collected from through out the state • Commodities targeted- forages, vegetables, peanuts, citrus (Ridge, Flatwoods and Indian River) • Soil and leaf tissue reflectances will be correlated • A subset of soil samples was used to determine the influence of multiple moisture levels • Soil texture will also be determined for a subset of soil samples Mylavarapu, R.S., Schumann, A.W., Obreza, T.A., Cornejo, C. 2007. Rapid Soil & Tissue Analysis Techniques Using Near Infrared Reflectance Spectroscopy. FDACS Project 61580, Annual Progress Report.

  27. Dry • Results • PLSR for P in DRY and MOIST soils samples respectively • Moist refers to samples as they arrive from the field • Dry samples are oven dry Moist

  28. NIRS technology • for soil and tissue analysis has been found promising for application in agriculture and in soil research • Cheaper and faster than conventional soil testing- a single spectrum can provide, simultaneously, useful information for many physical, chemical properties • Decision making easier at the field as well as watershed or regional scales

  29. NIRS technology • Calibrations being verified very successfully, paving way for creating models for real-life applications • Sensors able to predict with accuracy and precision for on-the-go applications • Statistical robustness and flexibility becoming available

  30. Diagnostic Testing and Environmental Assessment • Most critical current need • Need to generate new knowledge to bridge the gap between diagnostic testing for agronomic production and environmental impact assessment • Typically multiple factors influence the fate of the nutrients and nutrient cycling in the natural and agricultural systems • In Florida, such tools have been developed and are being fine-tuned Example, the Florida Phosphorus Index (PI) • Although originally intended for organic sources of nutrients, PI can be applied to any situation where nutrients are applied • Similar, tools for nitrogen and heavy metals can be developed

  31. T H E F L O R I D A P I N D E X

  32. Environmental Diagnostics • a “Capacity factor” can be obtained from the UF/IFAS Extension Soil Testing Lab or calculated from the following equation: • Capacity factor = [(0.15- Soil Test P/31)/(Soil Test Fe/56 )+Soil Test Al/27)]*[(Soil Test Fe/56)+(Soil Test Al/27)*31 The Soil Test refers to concentrations of the elements in Mehlich 1, expressed in ppm (mg kg-1)

  33. Priorities • Develop, calibrate and establish a valid soil extraction procedure for nutrient recommendations for calcareous soils, a major soil category for commercial and urban landscape horticulture in the state • Validate the Florida P-Index, a critical tool for phosphorus management, particularly when organic sources are used and for various sensitive ecosystems in the state • Develop a tool for nitrogen management to assess the plant uptake efficiency and environmental loss

  34. Priorities • Determine the feasibility of complementary soil and tissue analyses for phosphorus management for perennial crops and landscape plants and grasses • Integrate recommendations for irrigation management with nutrient recommendations for commercial horticultural crops • Further develop VNIR techniques and continue field calibration for wider diagnostic applications • Develop newer diagnostic techniques for environmental impact assessment

  35. Thank You! raom@ufl.edu

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