9-April-2013 Linden, AB Improved In-Crop Variable Rate N Application

Transcription

1 9-April-2013 Linden, AB Improved In-Crop Variable Rate N Application Tom Jensen tjensen@ipni.net

2 A not-for-profit, scientific organization dedicated to responsible management of plant nutrients for the benefit of the human family, Member Companies Affiliate Organizations 2

3 IPNI Northern Great Plains Region 3

4 Variability in a Field

5 How We Previously Dealt with Field Variability Managed the field as the most common occurring area Ignored unusual areas Developed best average rate of application Benefits Best option we had On average it was reasonably good Problems Too little applied in some areas Too much applied in other areas

6 Conventional Uniform field No reason to treat portions of the field differently Average values for the field, from 10 to 20 cores (15).

7 Grid Sampling and Geo-Grouping (Kriging) In this example 72 separate samples on a quarter section (160 acres) Expensive but effective, soil ph, sodicity

8 Technologies that Changed the Way We can Manage Field Variability Global Positioning Systems (GPS) Graphic Information Systems Guidance Systems Yield Monitors Variable Rate Application

9 Global Posi*oning Systems (GPS)

10 Graphic Informa*on Systems

11 Guidance Systems

12 Yield Monitors

13 How Informa*on is Gathered Yield Monitors Remote sensing Satellite Aircra5 Low Al8tude Drones On The Go Crop Sensors

14 Variable Rate Application Pre-Plant In-Crop At Planting

15 Apply the Right Form at the Right Rate, Right Time, and Right Place

16 In- Crop Variable Rate N Top- Dressing

17 Normalized Difference Vegeta*ve Index - NDVI Calculated from the red and near- infrared bands Equivalent to a plant physical examina8on Correlated with: Plant biomass Crop yield Plant nitrogen Plant chlorophyll Water stress Plant diseases Insect damage

18 0.5 Visible Near Infrared Reflectance (%) 0.25 Measure of living plant cell s ability to reflect infrared light 0.00 Photosynthetic Plant Potential Reflectance Wavelength (nm)

19 Comparison of Commercially Available Crop Sensors Comments: all three correlate to biomass and chlorophyll Crop Circle and GreenSeeker, more influenced by biomass or crop height CropSpec, more influenced by chlorophyll content

20 Improving Remote Sensing of Crop N Status in the Semi- Arid Great Plains Dr. Jan Eitel University of Idaho, Dr. Dan Long, ARS- USDA Oregon, et al Problem of exis8ng in- crop remote sensing: Not accurate under moisture stress, low biomass condi8ons Late stage of accuracy e.g. early heading, flowering Chance to improve protein content but not much yield

21 There is a possibility to improve how we calculate crop N status Use the information from existing sensors but do some different calculations. GreenSeeker (Trimble Navigation Limited, Sunnyvale, CA, USA) that measures Normalized Difference Vegetation Index (NDVI); and Crop CircleTM (Holland Scientific, Inc., Lincoln, NE, USA) that measures NDVI and Normalized Difference Red Edge (NDRE), and can be used to calculate some other indexes. A calculated crop sensed index that was found to improve the correlation to crop N status under water limited growing conditions, compared to using NDVI or NDRE, was the ratio of Modified Chlorophyll Absorption Ratio Index (MCARI) and Second Modified Triangular Vegetation Index (MTVI2) or (MCARI/MTVI2) (Eitel et al., 2007; Eitel et al., 2008)

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23 What about another approach? Green scanning lazer Capable to use at a much earlier growth stage, e.g. late 8llering, or stem elonga8on Taken at an angle

24 Other poten*al benefits Reduce or remove the effect of background effects of soil, residue, and leaf edges compared to leaf surfaces The r2 values measured using this green laser scanning system ranged between 0.53 and 0.58 for regression models rela8ng foliar N concentra8on to raw laser return intensity values, when used at Zadoks stage 32 (i.e., late 8llering to early stem elonga8on).

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