DETECTING VARIABILITY WITH CROP SENSORS (TOOLS TO IMPROVE SOIL AND FORAGE SAMPLING TECHNIQUES) Stewart Reed Oklahoma State University Biosystems & Agricultural Engineering Dept. 9/18/2018

Outline Sensor Technology Basics The Toolbox Applied Research and Studies Wheat pasture forage biomass sampling Site specific soil sampling for variable rate lime application 9/18/2018

Sensor Basics Three key sensor properties for applied use: Provides scaled data by electronic and/or mechanical means Sensors’ data are always numerical, but are often shown visually (i.e. maps, light bars, electronic display) Data is typically in electronic form, indicating potential value when used by electronic control systems or management software applications (i.e. GIS) 9/18/2018

Five Requirements of Sensor Application Know the sensing conditions position, lighting, power, external components, etc. Know the data collection procedure sensing time, data storage, warm-up, automation/manual settings, etc. Know the data handling techniques storage media, transfer methods to other devices, read-outs, displays Know the reliability and quality calibration, periodic maintenance checks, signs of poor data Know the support accessibility, availability, adequate, multiple levels (dealers, manufacturers, educational) 9/18/2018

What's in the Toolbox Field Tools Support Tools Online Tools Yield Monitors Crop Sensors Soil Sensors Weather Sensors GPS (positioning sensor) Support Tools GIS (data management software) Handheld computer devices (PDA) Personal computer Online Tools Nutrient calculators Weather data Geographical data 9/18/2018

I have these “tools” but don’t quite understand how they are intended to be used. Tools are to be used in combination to address a problem or complete a specific task. Case in point: determine yield variability within a single field Sensor tools needed: yield monitor and GPS Support tools needed: personal computer and GIS software 9/18/2018

Determining Yield Variability Collect data via combine equipped with Yield Monitor and GPS Transfer data using storage media or cable to PC-GIS software Use GIS software statistics to calculate variability Use GIS software to visually observe variability 9/18/2018

Applied Research Study #1: Improving conventional “clipping” method for determining standing forage biomass in a wheat pasture. Study #2: Develop a site specific soil sampling strategy for variable rate lime application on an average wheat field. 9/18/2018

Study #1 Sampling for Wheat Forage Biomass 9/18/2018

Wheat Forage Biomass Defining the Problem: Standing forage biomass is currently measured by a randomized “clipping” method Sample size is 4ft2, and few samples are collected to represent an entire field Example: if 10 samples were collected from an 80 acre field, then 40ft2 is being used to represent 3.48 million square feet. (1acre = 43,560ft2) This method does not accurately represent true forage biomass due to plant growth variability and variability from plant-animal interaction (grazing). 9/18/2018

Wheat Forage Biomass Research Objectives Illustrate plant growth variability using a crop sensor (GreenSeeker) Compare results of conventional clipping method via analysis conducted with crop sensor data Show that clipping method coupled with crop sensor data can be used to enhance the accuracy of measuring forage biomass 9/18/2018

Wheat Forage Biomass Tools Needed Crop Sensor (GreenSeeker) GPS Personal Computer GIS Software Clipping data provided by cooperative research 9/18/2018

OSU Wheat Pasture Research Station Marshall Oklahoma (March 6, 2006) Custom ATV GreenSeeker Mapper NDVI (Normalized Difference Vegetative Index, Red and NIR wavelengths) 0.418 m2 (4.5 ft2) sensor resolution 3.66 m (12 ft) boom width 14.63 m (48 ft) paralleled swaths total sample area = 3.66/14.63 = ¼ of field GPS Crop Sensors 9/18/2018

Wheat Forage Biomass Using GIS Software Raw NDVI Sensor Values With Paddock Layout 9/18/2018 Raw NDVI Sensor Values

Converting Raw Data to a Surface Map Wheat Forage Biomass Converting Raw Data to a Surface Map 9/18/2018

Wheat Forage Biomass Analysis Use average NDVI for each paddock to determine relative forage biomass Calculate CV (from NDVI data) for each paddock to illustrate forage biomass variability Compare average NDVI to clipping value High avg NDVI should correlate with high clipping value Compare using CV Avg NDVI and clipping value comparison should correlate well with low CV values and poorly with high CV values 9/18/2018

NDVI → Wheat Pasture Forage Variability More plant biomass in turn rows because of higher seeding rates. Increased plant biomass in areas where soil moisture is more available due to water holding/runoff characteristics relative to terrain and terrace location. Paddock 3 shows to have more forage biomass than paddocks 2 and 4. NDVI → Wheat Pasture Forage Variability NDVI represents plant vigor. Photosynthetic activity, total live plant biomass, plant water stress, etc. can be represented by NDVI with proper calibration techniques. Small and large scale variability can be attributed to many factors such as stocking density, soil moisture variability, fertilizer inputs, seeding rate, preferential grazing, and water tank/mineral feeder location. 9/18/2018

Mid CV Low CV High CV 9/18/2018

Wheat Forage Biomass Research Results Conclusions: 9/18/2018 Crop sensor NDVI data is capable of showing forage variability both small and large scale. Paddocks with less variability had a significantly stronger relationship between mean NDVI and clipping measurements. Paddocks with a medium or large variability had a poor relationship between mean NDVI and clipping measurements. Clipping procedure can be improved by assessing variability first and then determining proper sample population and location. 9/18/2018

Study #2 Soil Sampling for VR Lime 9/18/2018

Soil Sampling for VR Lime Defining the Problem: 4 to 16 spot samples are collected per field (~80 to 160 acres) and commonly mixed resulting in one single sample sent to the lab Sample collection sites are sometimes arbitrarily chosen in the most convenient locations pH levels in fields often vary on a large area basis 9/18/2018

Soil Sampling for VR Lime Defining the Problem (continued): Conventional soil sampling techniques are not capable of detecting soil pH variability (or any soil nutrient variability) Flat rate lime applications do not address soil pH variability and can result in economic loss and/or a reduction in crop performance 9/18/2018

Soil Sampling for VR Lime Research Objectives Show that yield and crop sensor data can be used to identify potential soil pH variability Compare intense soil sampling results with improved site sampling method to verify strategy is sufficient for VR lime 9/18/2018

Soil Sampling for VR Lime Tools Needed Crop Sensor (GreenSeeker) Yield Monitor GPS (one or two units) Personal Computer GIS Software Other Tools Soil sample kit (probe and bags) Hand held device with GPS Navigation software (installed on hand held device) 9/18/2018

160 Acre Oklahoma Wheat Field Terraces Drainage Ditch Ridge Terraces Terraces Drainage Ditch Grass Waterway Ridge Terraces 2003 NAIP Aerial Photo 9/18/2018

Soil Sampling for VR Lime Collect yield and NDVI data Load sample site locations into hand held device Load data into GIS software Collect soil samples from field and send to lab Visually and statistically interpret variability Load lab results into GIS software Develop soil sampling regions from analysis Analyze lab results and generate VR lime map 9/18/2018

Yield and NDVI Data Collected with Sensor Tools Shown in GIS Software Grain Yield, 2005 Average: 49 bu/ac CV: 0.21 GreenSeeker NDVI, Dec 2004 Average: 0.65 CV: 0.12 9/18/2018

Analytical Check List Look for large scale variability Identify common trends shown in both yield and NDVI maps Identify small scale areas with low variability Identify small scale areas with high variability CAUTION: it is the most difficult to collect reliable soil samples from small areas with high variability 9/18/2018

Analyzing Variability and Determining Sampling Sites Mean=54.65 bu/acre CV=0.17 Mean=44.12 bu/acre CV=0.20 Mean=0.69 NDVI CV=0.10 Mean=0.31 NDVI CV=0.13 9/18/2018

Define regions based off visual interpretation. Analyze regions’ variability using CV. Total Regions: 22 Mean CV: 0.110 Max CV: 0.167 Min CV: 0.078 CV>0.11: 7 Use yield data to further characterize potential variability. CV = 0.083 0.119 0.084 0.105 0.108 0.108 0.112 0.119 0.120 0.120 0.103 0.094 0.093 0.102 0.110 0.073 0.086 0.092 0.137 0.094 0.078 0.167 9/18/2018

Calculate CVs from yield data for each region. Use CVs to reaffirm high or low variability. Divide regions where necessary based on yield data. Mean CV: 0.15 Max CV: 0.20 Min CV: 0.10 CV>0.17: 6 CV = 0.16 0.15 0.12 0.20 0.18 0.10 0.15 0.19 0.16 0.19 0.12 0.16 0.17 0.18 0.15 0.14 0.11 0.14 0.19 0.11 0.12 0.19 9/18/2018

Two options for regions with potentially high variability: Increase number of samples (i.e. instead of 16 samples per region, use 32) Divide regions into smaller areas with more homogeneity 9/18/2018

New Region Layout 9/18/2018

Final Layout Total Regions: 30 Small Regions: 5 30 Samples @ $10 each=$300 9/18/2018

~115 acres needs 1 ton/acre of lime pH Results Lime Application Map 5.7 6.4 5.3 6.7 5.6 5.6 5.5 5.8 1 ton/acre (50% ECCE) 5.9 5.7 6.7 7.8 6.2 6.8 6.9 6.5 7.7 5.3 5.8 5.8 7.0 6.0 7.1 5.8 6.6 5.3 6.6 7.1 ~115 acres needs 1 ton/acre of lime 9/18/2018

Verify Using Intensive Soil Sampling Divide into small sized regions using multiple years of yield, NDVI, and Veris (EC) data. Recalculate VR lime application map using intensive soil sample results. Compare to previous method that used only 30 regions (30 soil samples). 9/18/2018

2005 49 bu/ac 2006 16 bu/ac 9/18/2018

April 2006 Dec 2004 9/18/2018

NDVI 2006 Yield 2006 Veris EC 2006 9/18/2018

Approximately ¾ acre per sample site (similar to grid sampling). Soil Samples: 217 Approximately ¾ acre per sample site (similar to grid sampling). ~$2170 9/18/2018

Soil Sample Results (pH Surface Map) <6.5 >7.0 >6.5 and <7.0 9/18/2018

VR Lime Application Map Approximately 114 acres need 1 ton of line (50% ECCE). (41.5 acres needs 0) @$15/ton, ~$620 savings Few small areas need 1.5-2 tons of lime. 9/18/2018

Large Region Sampling vs. Intensive Sampling 1 ton 1 ton 0 ton 0 ton 9/18/2018

(@2 bu/ac loss, @$9/bu, ~$175 loss Over applied: 12.7 acres (@$15/ton, ~$190 loss) Under applied: 9.7 acres (@2 bu/ac loss, @$9/bu, ~$175 loss Total loss: ~$365 9/18/2018

Conventional Method Total Samples: 12 (1 lab sample) Avg pH: 5.9 Max pH: 7.5 Min pH: 5.0 BI: ~6.7 Recommendation Rate: 1ton/acre (50% ECCE) Lime Cost: $2332 9/18/2018

Conclusion Sensor Data (i.e. yield and NDVI) can be used to assess variability in a field Site sampling strategies can be significantly improved using sensor data, especially for pH management and forage analysis. Improved site sampling has similar results to intensive sampling, with significantly reduced costs. 9/18/2018