N Management in Potato Production. David Mulla, Carl Rosen, Tyler Nigonand Brian Bohman Dept. Soil, Water & Climate University of Minnesota

Transcription

1 N Management in Potato Production David Mulla, Carl Rosen, Tyler Nigonand Brian Bohman Dept. Soil, Water & Climate University of Minnesota

2 Topics Background and conventional nitrogen management Evaluate the use of remote sensing to predict N needs using a nitrogen sufficiency index Examine the ability of hyperspectral imagery to detect N stress in potato Identify the best indices associated with leaf N status

3 Background Potatoes have a high N requirement and shallow root system N is the most limiting nutrient for potato growth Fertilizer N is essential to optimize yield, but management can be challenging in the Midwest with unpredictable rainfall N rate is important but timing also plays a critical role especially on sandy soils

4 Conventional N management Depends on variety and market type Long season varieties like Russet Burbank respond to split applications Planting (10-20% of N) Emergence/hilling (50-60 % of N) Fertigation(30-40% of N) Fertigationtiming is often based on petiole nitrate analysis

5 Conventional N management Petioles collected on a 7 to 10 day schedule from tuber initiation through bulking If petiole nitrate falls below a certain level, additional N is applied Approach is simple, but does not account for spatial variability Remote sensing better suited for precision agriculture and variable rate N applications

6 Objectives 1. To utilize remote sensing to determine the need for in-season variable rate N-fertilizer applications 2. To assess agronomic outcomes from management using adaptive-n rates SPAD Meter Cropscan Meter

7 Methods Sand Plains Research Farm Becker, MN Hubbard Loamy sand Russet Burbank variety Split-Plot with 4 replicates in RCBD Nitrogen is split plot factor

8 Nitrogen Treatments Apr 1 June 23 Jun 14 Jul 21 Jul 27 Jul Apr 30 May 28 Jun 10 Jul 20 Jul 27 Jul Plant. Emerge Post-Emergence Total lbn ac Control 40 DAP Split 40 DAP 60 Urea 15 UAN 15 UAN 15 UAN 15UAN CR 40 DAP 120 ESN Split 40 DAP 120 Urea 20 UAN 20 UAN 20 UAN 20 UAN CR 40 DAP 241 ESN VR Split 40 DAP 120 Urea?????

9 Spectral Indices Canopy reflectance is affected by water, chlorophyll, canopy density and age, soil, etc Leaf water potential and leaf temperature 1981 Crop Water Stress Index (Jackson) Leaf Area Index 1974 NDVI (Rouse, 670 & 800 nm) NDVI = (NIR-R)/(NIR+R)

10 Newer Spectral Indices

11 Remote Sensing of N Stress

12 Remote Sensing + Var. Rate N CROPSCAN Multispectral Radiometer (16 Narrow Bands) Nitrogen Sufficiency Index [NSI] NSI = Variable N treatment Well Fertilized Reference If NSI < 95%, then 20 lbn/ac applied as UAN MERIS Terrestrial Chlorophyll Index [MTCI] MTCI = R 751 nm R 713 nm R 713 nm R 676 nm 751 nm (Near-IR), 713 nm (Red-Edge), 676 nm (Red) Measurements collected every 1-2 weeks

13 Results 1.Remote sensing and variable rate nitrogen 2.Agronomic outcomes

14 Jun 14 Jul 21 Jul 27 Jul Total lbn ac Control Split 20 UAN 20 UAN 20 UAN 20 UAN CR VR Split - 20 UAN 20 UAN 20 UAN 220 ±5% NSI

15 Jun 10 Jul 20 Jul 27 Jul Total lbn ac Control Split 20 UAN 20 UAN 20 UAN 20 UAN CR VR Split - 20 UAN - 20 UAN 200 ±5% NSI

16 Results 1.Remote sensing and variable rate nitrogen 2.Agronomic outcomes

17 Marketable Yield (Note: 70 Mg ha -1 = 625 cwt ac -1 ) Contrasts Control *** Rate ** Source Var. Rate

18 Impact on Quality ESN VRN

19 Hyperspectral Remote Sensing for N Management in Potato Tyler Nigon, Carl Rosen and David Mulla Department of Soil, Water, and Climate University of Minnesota

20 Remote Sensing Platforms

21 Improving Spatial Resolution

22 Types of Remote Sensing Panchromatic reflectance An average over all wavelengths Broad band or multispectral reflectance Reflectance at a few specific discrete wavelengths B, G, R NIR portions of spectrum Hyperspectral reflectance Reflectance at specific narrow band discrete wavelengths across a large continuous spectral range Thermal emission at NIR and MIR wavelengths

23 Panchromatic Image

24 Thermal Infrared Imagery

25 Variable Irrigation via Thermal Imaging

26 Hyperspectral Imagery Collection

27 Hyperspectral Data Cube (RGB)

28 Hyperspectral Remote Sensing Reflectance at specific narrow band discrete wavelengths across a large continuous spectral range

29 Derivative Spectra The derivative of hyperspectral reflectance data indicates portions of the spectrum where the slope of the reflectance curve changes rapidly

30 Calculate the r 2 coeff. for leaf N content at all hyperspectral reflectance bands Graph r 2 coefficient for all possible combinations of band 1 on the x-axis and band 2 on the y-axis Look for band combinations with low redundancy Lambda-Lambda Plots

31 Best Reflectance Wavelengths? The greatest information about plant characteristics with multiple narrow bands includes the longer red wavelengths ( nm), shorter green wavelengths ( nm), red-edge (720 nm), and NIR ( nm and 982 nm) spectral bands The information in these bands is only available in narrow increments of nm, and is easily obscured in broad multispectral bands that are available with older satellites

32 Correlation Between Reflectance and Total N Concentration in Potato Leaf

33 Hyperspectral Data Cube (SR8) SR8 = (R 860 /(R 550 *R 780 )

34 Potato Hyperspectral Imagery (SR8 = (R 860 /(R 550 *R 780 )) vs NDVI (NIR-R)/(NIR+R) SR8 Russet Burbank NDVI Alpine Russet

35 Conclusions Spatial resolution of aerial and satellite remote sensing imagery has improved from 100 s of m to sub-meter accuracy Spectral bandwidth has decreased with the advent of hyperspectral remote sensing Return frequency of satellite remote sensing imagery has improved dramatically A variety of useful spectral indices now exist for various precision agriculture applications in potatoes

36 Conclusions Variable-rate nitrogen application reduced total N- application by lbn ac -1 relative to the recommended rate of 241 lbn ac -1, with a significant improvement in yield and no effect on quality Urea produced the highest total yield, while ESN had the lowest ratio of misshapen tubers Remote sensing of NSI based on MTCI or SR8 spectral indices is an effective strategy to determine variable N- rate without impacting tuber quality

37 Thanks! Funding from: David Mulla (612)