Remote Sensing as a Tool to Manage Nitrogen for Irrigated Potato Production

Transcription

1 Remote Sensing as a Tool to Manage Nitrogen for Irrigated Potato Production Brian Bohman, Carl Rosen, and David Mulla Department of Soil, Water, and Climate University of Minnesota Potato Remote Sensing Conf. Madison, WI 17 November 2017 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 Machine learning to detect N stress and other disorders 1

2 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 especially on sandy soils N rate is important but timing also plays a critical role Production yield and quality Environmental nitrate leaching Conventional N management Depends on variety and market type Long season varieties like Russet Burbank respond to split applications Planting (1020% of N) Emergence/hilling (5060 % of N) Fertigation (3040% of N) Fertigation timing is often based on petiole nitrate analysis 2

3 1/19/18 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 Objectives 1. To utilize remote sensing to determine the need for inseason variable rate Nfertilizer applications 2. To assess agronomic outcomes from management using adaptiven rates SPAD Meter Cropscan Meter 3

4 1/19/18 Methods Sand Plains Research Farm Becker, MN Hubbard Loamy sand Russet Burbank variety SplitPlot with 4 replicates in RCBD Nitrogen is split plot factor 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. PostEmergence Total lb N ac1 1 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????? 4

5 Remote Sensing + Var. Rate N Nitrogen Sufficiency Index [NSI] NSI = Variable N treatment Well Fertilized Reference If NSI < 95%, then 20 lb N/ac applied as UAN MERIS Terrestrial Chlorophyll Index [MTCI] MTCI = R 751 nm R 713 nm R 713 nm R 676 nm CROPSCAN Multispectral Radiometer (16 Narrow Bands) 751 nm (NearIR), 713 nm (RedEdge), 676 nm (Red) Measurements collected every 12 weeks Results 1. Remote sensing and variable rate nitrogen 2. Agronomic outcomes 5

6 Jun 14 Jul 21 Jul 27 Jul Total lb N ac 1 Control Split 20 UAN 20 UAN 20 UAN 20 UAN CR 240 VR Split 20 UAN 20 UAN 20 UAN 220 ± 5% NSI Jun 10 Jul 20 Jul 27 Jul Total lb N ac 1 Control Split 20 UAN 20 UAN 20 UAN 20 UAN CR 240 VR Split 20 UAN 20 UAN 200 ± 5% NSI 6

7 Results 1. Remote sensing and variable rate nitrogen 2. Agronomic outcomes Marketable Yield A A AB AB B B AB AB AB AB Contrasts Control *** Rate ** Source Var. Rate C D 7

8 Conclusions Variable rate nitrogen application based on remote sensing had a nonsignificant effect on yield compared to conventional practices Nrate reduced by lb N/ac with VRN Potential for improved producer profitability with reduced impacts to the environment Hyperspectral Remote Sensing for N Management in Potato Tyler Nigon, Carl Rosen and David Mulla Department of Soil, Water, and Climate University of Minnesota 8

9 Hyperspectral Remote Sensing Reflectance at specific narrow band discrete wavelengths across a large continuous spectral range Hyperspectral Imagery Collection 9

10 Derivative Spectra The derivative of hyperspectral reflectance data indicates portions of the spectrum where the slope of the reflectance curve changes rapidly LambdaLambda Plots 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 yaxis Look for band combinations with low redundancy 10

11 Hyperspectral Data Cube Use lambdalambda plot to identify best spectral index for N stress Commercial Potato Hyperspectral Imagery (SR8 = (R 860 /(R 550 *R 780 )) vs NDVI (NIRR)/(NIR+R) Russet Burbank Alpine Russet 11

12 Machine Learning for Corn N Deficiency Dimitris Zermas, David Mulla, Vasillios Morellas, Nikos Papanikolopoulos Depts. Computer Science & Engineering, Soil, Water & Climate University of Minnesota Objective Automate corn field surveillance for the early detection and inseason treatment of crop nitrogen stress 12

13 1/19/18 Low Altitude Imaging l High resolution images provide a close up view of the plants and their foliage, allowing a diagnosis of the type and severity of crop deficiency Image of healthy plants Image of N deficient plants Identify Nitrogen Deficiency credit: 13

14 Identify Areas with Nitrogen Deficiency Skeleton of green Skeleton of yellow Edge of green Edge of yellow Identify Nitrogen Deficiency 84.2% 14

15 Diagnostic Applications in Potato Early Blight, Late Blight Black Dot Verticillium Wilt Corky Ring Spot Potato Virus Y Leaf Roll Virus Purple Top PVY Colorado Potato Beetle Potato Aphid EB LRV CPB LB CRS Thank You 15