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Remote Sensing as a Tool to Manage Nitrogen for Irrigated

Potato Production

Potato Remote Sensing Conf.Madison, WI

17 November 2017

Brian Bohman, Carl Rosen, and David MullaDepartment of Soil, Water, and Climate

University of Minnesota

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

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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 (10-20% of N)§ Emergence/hilling (50-60 % of N)§ Fertigation (30-40% of N)

§ Fertigation timing is often based on petiole nitrate analysis

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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

Objectives1. To utilize remote sensing to determine the need for

in-season variable rate N-fertilizer applications2. To assess agronomic outcomes from management

using adaptive-N rates

SPAD Meter Cropscan Meter

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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

Nitrogen Treatments

2016 22 Apr 1 June 23 Jun 14 Jul 21 Jul 27 Jul2017 29 Apr 30 May 28 Jun 10 Jul 20 Jul 27 Jul

Plant. Emerge. --------- Post-Emergence -------- Total----------------------------- lb N ac-1 -------------------------------

1 Control 40 DAP - - - - - 402 160 Split 40 DAP 60 Urea 15 UAN 15 UAN 15 UAN 15UAN 1603 160 CR 40 DAP 120 ESN - - - - 1604 240 Split 40 DAP 120 Urea 20 UAN 20 UAN 20 UAN 20 UAN 2405 240 CR 40 DAP 241 ESN - - - - 2406 VR Split 40 DAP 120 Urea ? ? ? ? ?

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Remote Sensing + Var. Rate NNitrogen Sufficiency Index [NSI]

NSI = Variable N treatmentWell Fertilized Reference

CROPSCAN Multispectral Radiometer

(16 Narrow Bands)

MTCI =R 751 nm– R 713 nm R 713 nm–R 676 nm

MERIS Terrestrial Chlorophyll Index [MTCI]

751 nm (Near-IR), 713 nm (Red-Edge), 676 nm (Red)

If NSI < 95%, then 20 lb N/ac applied as UAN

Measurements collected every 1-2 weeks

Results

1. Remote sensing and variable rate nitrogen

2. Agronomic outcomes

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23 Jun 14 Jul 21 Jul 27 Jul Total------------------- lb N ac-1 -------------------

Control - - - - 40240 Split 20 UAN 20 UAN 20 UAN 20 UAN 240240 CR - - - - 240VR Split - 20 UAN 20 UAN 20 UAN 220

2016

± 5% NSI

28 Jun 10 Jul 20 Jul 27 Jul Total------------------- lb N ac-1 -------------------

Control - - - - 40240 Split 20 UAN 20 UAN 20 UAN 20 UAN 240240 CR - - - - 240VR Split - 20 UAN - 20 UAN 200

2017

± 5% NSI

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Results

1. Remote sensing and variable rate nitrogen

2. Agronomic outcomes

C

D

BAB

B

ABABA AB

ABABA

Marketable Yield ContrastsControl ***Rate **Source –Var. Rate –

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ConclusionsVariable rate nitrogen application based on

remote sensing had a non-significant effect on yield compared to conventional practices

N-rate reduced by 20 – 40 lb N/ac with VRN

Potential for improved producer profitability with reduced impacts to the environment

Hyperspectral Remote Sensing for N Management

in PotatoTyler Nigon, Carl Rosen and David MullaDepartment of Soil, Water, and Climate

University of Minnesota

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Hyperspectral Remote Sensing• Reflectance at specific narrow band discrete

wavelengths across a large continuous spectral range

Hyperspectral Imagery Collection

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Derivative Spectra• The derivative of hyperspectral reflectance data

indicates portions of the spectrum where the slope of the reflectance curve changes rapidly

Lambda-Lambda Plots• Calculate the r2

coeff. for leaf N content at all hyperspectral reflectance bands• Graph r2

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

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Hyperspectral Data Cube• Use lambda-lambda plot to identify best spectral

index for N stress

Commercial Potato Hyperspectral Imagery (SR8 = (R860/(R550*R780)) vs NDVI (NIR-R)/(NIR+R)

Russet Burbank

Alpine Russet

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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 in-season treatment of crop nitrogen stress

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Low Altitude Imagingl 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: www.pioneer.com

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Identify Areas with Nitrogen Deficiency

Skeleton of green

Skeleton of yellow

Edge of green

Edge of yellow

Identify Nitrogen Deficiency84.2%

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Diagnostic Applications in Potato

• Early Blight, Late Blight• Black Dot• Verticillium Wilt• Corky Ring Spot• Potato Virus Y• Leaf Roll Virus• Purple Top• Colorado Potato Beetle• Potato Aphid

CRSPVY LRV

EB

LB

CPB

Thank You