1. Nutrient Balance Through Directed Soil Sampling By Andy Pike and R. Scott McLean

2. Introduction 1. We started ten years ago at Sun-Ray Farms trying to improve low producing areas. The goal was to identify and attempt to correct soil deficiencies that varied within blocks as well as between blocks. We began with 2 or 3 rectangular zones per block determined by visual breaks in tree health. 2. This process evolved into establishing the soil Electrical Conductivity (EC) to record soil variability vs. tree growth and yield. The EC data was then used to develop polygon management zones which were soil and tissue sampled. The resulting sample values gave a basis to begin to balance the soil through Variable Rate nutrient applications. We discovered that before balancing, the muck and depressional soils had the worst production and the sand with some organic matter had the best production with wide variations in between. This was our progress point at the last Precision Ag Workshop.

3. Introduction 3.The key to this approach is having a model to make recommendations from sample results. The Lord made plants & soil to function as an intricate food/web system which can be correlated across various agronomic crops. The response to nutrient balancing occurs over a two to three year period. Yield mapping is essential to accurately monitor the progress and make further adjustments but the changes in tree appearance will become visually apparent. 4.The investment in this approach to adjust the soil will pay benefits. The poor producing areas can not and will not be equal to the best areas in a block. However, production can be increased from 50 boxes per acre to 250 boxes per acre which raises the overall average. Variable rate application does not save money but it allows you to place your inputs in the correct location. The trees will respond to the changes and make returns on the extra time and expense.

4. FACT: Varied rates of nutrient inputs controlled by soil test prescriptions, can be accurately placed within the root zone at any location across a field. However, soil test prescriptions must first be built upon correct identification of soil variability - AND - correct interpretations of soil test values. If not, increased nutrient variability will result.

5. Measuring Soil Variability Using the Veris ® EC Mapping System (veristech.com)Using the Veris ® EC Mapping System (veristech.com)

6. Considerations: Boundary: an accurate ground-collected boundary specific to scion/rootstock, tree age, spacing, etc… Boundary: an accurate ground-collected boundary specific to scion/rootstock, tree age, spacing, etc… Before EC collection: field should be in uniform state whether tilled, mowed, or irrigated. Before EC collection: field should be in uniform state whether tilled, mowed, or irrigated. Moisture: critical - dry soil inhibits good conductance. The more moisture (especially in depth) the more reliable the data. Moisture: critical - dry soil inhibits good conductance. The more moisture (especially in depth) the more reliable the data. Ground speed: 10 mph should be the maximum if conditions warrant. This equates to a collected data point every 4 meters. Traveling bed-tops on a double-row 50’ bed - results in 66 data points per acre. Ground speed: 10 mph should be the maximum if conditions warrant. This equates to a collected data point every 4 meters. Traveling bed-tops on a double-row 50’ bed - results in 66 data points per acre.

7. GIS - Connecting EC Data and More... Using SSToolbox ® (sstdevgroup.com)Using SSToolbox ® (sstdevgroup.com)

8. Eliminate: all points in location to boundary lines due to compaction (6 meters has been adequate). In addition, select and eliminate all points with negative values - these are mostly where disc blades have temporarily lost contact with soil. NOTE: Large areas of negative values may indicate poor conductance due to lack of moisture. NOTE: Large areas of negative values may indicate poor conductance due to lack of moisture.

9. Create Surface Using Interpolation: Methods: Inverse Distance and KrigingSize: 100’ and 60’

10. Classify Interpolated Surface: Evaluate: classification type and number of classes depends on how specific you want the variability in relation to the size of the field. Goal should be to find groupings and patterns inherent in the data while trying to maintain acceptable size zones (Natural Breaks, 3 to 5 classes).

11. Convert Surface to Polygons Evaluate: If necessary, re-classify surface and convert again. Repeat until achieving desired goals.

12. Edit Map Features Evaluate: using edited EC data points classified by Standard Deviation as a reference, union the features of similar valued polygons and if needed, the features of different valued polygons. The union of two or more features creates one (1) record. If necessary, re-classify surface and convert to new polygons. Repeat until achieving desired goals.

13. Calculate Points Statistics by Polygon Create and Edit: using the edited EC data points, calculate the AVG. EC values within each new polygon feature (record/zone). Edit the data table to create a new attribute field (Zone ID). NOTE:Entering an identifying attribute in relation to EC values is helpful for future quick referencing.

14. Create Soil Test Points (Sampling Scheme) Patience: using the original edited EC data points classified by Standard Deviation as a reference for location and variability within each zone, place a composite of points within each zone. Keep in mind someone has to collect the soil samples. NOTE:a distance relationship of the points between each zone should exist for final interpolation of soil test values. Edit Table: after creating a new attribute field for the data table, select the points within each zone and assign the matching identifying attribute for that zone, double checking all points. This is a requirement for joining soil test data results.

15. Create Reference Lines Sampling without ERROR: sampling labor can be facilitated by placing lines on each bed path where points exist. When the driver exits one bed, he/she drives quickly to the next line without having to judge where the next required path will be, thereby eliminating potential missed cores. Create Map Layout: a layout for reference is also helpful when sampling. Armed with soil test zones (polygons), a sampling scheme (points and reference lines), a grower can now begin sampling the soil EC variability within each field… This is Directed Soil Sampling

16. EC - Fertility Connection Look Closer: there is much affecting EC - moisture, topography, temperature, soil texture, compaction, organic matter, and soluble salts (minerals). Higher EC values don’t necessarily mean better fertility. A high yielding (production), proper nutrient- balanced, well drained soil may have lower EC values. Where pH has been addressed without regard to location, lower EC values in heavy soils (muck) can correlate with higher yielding areas, versus higher EC areas - representing more organic matter and a lower pH. Higher EC values in lighter soils (sand) can correlate to larger trees, but not necessarily higher yields. It could be said that higher EC values in all soils have the most potential, given proper nutrient balance, moisture, and drainage.

17. EC = Soil Type Redefined

18. Interpretation of Soil Test Values Test your Soil-Tester: using soil test results, can the agronomist or soil lab explain what is a good soil? A poor soil? Does this represent what’s in the field? Are the recommendations generated by the soil test - for crop requirements, or soil requirements? Nutrient Index: analysis with interpretation. True interpretation requires desired values that are based on crop production. Desired values based on production enables the determination of an excess and/or a deficit. pH = Measurement + Character: properly constructed, a soil’s pH is the foundation of fertility and nutrient balance. The total amount of cations (Ca + Mg + K + Na) found in a soil in relation to pH, determines a soil’s Total Exchange Capacity (TEC). TEC helps reveal how much of each cation is enough, and/or how much of a each cation is too much.

19. Base Saturation to the Rescue The Low-Down on Nutrient Balance: by calculating base saturation percent using TEC, you can better understand how the pH is constructed - AND - assign desired values.

20. Cation Balance 101 The Right Environment: regarding pCa, pMg, pK, and pNa in addition to pH. The table below represents average soil test data from 157 soil samples across 1,474 acres over a three (3) year period. All samples were collected through the direction of EC data. The sampling was done every year during the month of December. VRT applications for calcium and magnesium began in January, 2001 and have continued every year (as soil tests required) in January 2002, 2003 and are scheduled for the same this month. VRT applications for potassium began in February, 2002 - May, 2003 - and scheduled again for February, 2004.

21. Nutrient Deficits = Potential Prescriptions Satisfy Deficits Properly: the table below represents average amounts (pounds per ton) of calcium and magnesium for sources of liming materials in the central and southwest Florida area. How Much of What?: using the deficit of 641# for Ca and 224# for Mg, there are multiple possible solutions… which is less expensive? What about deficits of 157# for Ca and 95# for Mg? Now throw in variable Ca/Mg deficit ratios across one (1) field...

22. Calculate Crop Input Recommendations GIS at Work: the three (3) recommendations below solve calcium and magnesium deficit requirements using Dolomitic Lime, Hi-Cal Lime, and MgSO 4... Total estimated cost: $41.13/treated acre.

23. The Same For Less Another Look: the three (3) recommendations below solve the same deficit requirements using Dolomite, Hi-Cal Lime, and MgSO 4... Total estimated cost: $38.30/treated acre.

24. Improper Placement = Misapplication Instruct and Supervise: recommendations for banding underneath tree canopy (50% coverage), broadcasting bedtops (75% coverage), or broadcasting every middle (100% coverage), all require different amounts.

25. Summary Nutrient Balance is Nutrient Management: GIS joins activities that are vital to optimize the understanding and management of soil fertility. The first activity starts with correct identification of soil variability. Relying on EC data eliminates error in the determination of soil core sites - giving the grower confidence in a directed soil sampling approach. Next, joining soil test data with a nutrient index that equates deficits - frees the grower to make better management decisions and avoid nutrient imbalance. Coupled with other critical activities, GIS is capable of giving further insight and lead the grower through the “unlearning” process by redefining soil fertility.