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TRACKS AND TIRES: OHIO CASE STUDY’S

COMPACTIONSMART Program 2017 Waterloo, Ontario

A.A. Klopfenstein

Food, Agricultural and Biological Engineering

FOOD, AGRICULTURAL AND BIOLOGICAL ENGINEERING

Introduction (Combine Traverse)


Introduction (Applied Downforce)


Introduction


1. Develop an empirical model framework to predict the magnitude of compaction events and the resulting yield penalty;

2. Collect field data to extend the model to estimate yield penalties at higher axles loads, multiple passes, and tracks vs. tires; and

3. Revise model to estimate yield losses for multiple passes, tracks vs. tires, and higher axle loads.

Objectives


Empirical Model Layout

𝑌𝑐𝑐 = 𝑇𝑑𝑐𝑠𝑡𝑐 0.2𝐷𝑡𝑐𝑇𝑡𝑡(5 − 𝑌𝑎𝑡𝑡) 𝑐1𝐿𝑎 + 𝑐2 + 0.1𝐷𝑠𝑐𝑇𝑠𝑡(10 − 𝑌𝑎𝑠𝑡) 𝑐1𝐿𝑎 + 𝑐2

• Ycf – yield compaction reduction factor (0.0 to 1.0)

• La – axle load (tons)

• Stf – soil type factor (0.0 to 1.0)

• c1 – compaction factor 1


• Yatt – years after topsoil trafficking event (0 to 5)

• Yast – years after subsoil trafficking event (0 to 10)

• Dtc – topsoil depth compaction factor

• Dsc – subsoil depth compaction factor

• Ttt – topsoil tillage correction factor

• Tst – subsoil tillage correction factor

• Tdf – traction device factor (0.0 to 1.0)

Topsoil Subsoil


Empirical Model Layout – Soil Profile Factors

Topsoil Subsoil















Duiker, 2004


Topsoil • 0-12 in • Compaction due to

contact pressure Upper Part of Subsoil

• 12-20 in • Contact pressure and

axle load Lower Subsoil

• 20+ in • Axle load


Assumption: moist, arable soil

• 4.4 tons/axle compacts to 12 in

• 6.6 tons/axle compacts to 15.8 in

• 11 tons/axle compacts to 19.7 in

• 16.5 tons/axle compacts to 23.6 in and deeper

Hakaansson, and Reeder, 1994

y = 5.8862x0.5013 R² = 0.9884

0

5

10

15

20

25

30

0 5 10 15 20 25

Dpe

th (I

nche

s)

Axle Load (US Tons)

Axle Load vs Depth of Compaction



• Ds – depth of soil compaction (cm)

• La – axle load (lbs) • If Ds ≤12

Dsc=1 Ddc=0 • If Ds >12

𝐷𝑡𝑐 = 12𝐷𝑠

𝐷𝑠𝑐 = 𝐷𝑠−12𝐷𝑠

• Dtc=topsoil depth compaction factor

• Dsc= subsoil depth compaction factor

Assumptions • Topsoil 0-12 in • Subsoil 12 in and greater • Yield loss linear relationship

𝐷𝑠 = 5.8862𝐿𝑎0.5013



Empirical Model Layout – Time Factor














Topsoil Subsoil


Duiker, 2004

Empirical Model Layout – Time Factor


Empirical Model Layout – Tillage Factor














Topsoil Subsoil


Empirical Model Layout – Tillage Factor

• Td – tillage depth (in)

• Ds – depth of soil compaction (in) • If Td ≤12

𝑇𝑡𝑡 = 𝑇𝑑𝐷𝑠

𝑇𝑠𝑡 = 1

• If Td >12

𝑇𝑡𝑡 = 0 𝑇𝑠𝑡 = 𝑇𝑑−12𝐷𝑠−12



Assumptions • Topsoil 0-12 in • Subsoil 12 in and greater • Tillage correction factor linear

relationship


Empirical Model Layout – Undercarriage Factor














Topsoil Subsoil


Empirical Model Layout – Compaction and Moisture Factors














Topsoil Subsoil


Soil: Hoytville silty clay loam • Poorly drained lake-

bed soil Corn/soybean rotation Plots compacted autumn

• 20 t/axle; 10 t/axle; and control (none)

No-till trials

Materials and Methods


y = 0.0111x - 0.0113

y = 0.0046x - 0.1136

y = 0.0078x - 0.0624

-20%

-10%

0%

10%

20%

30%

40%

50%

0 5 10 15 20 25 30 35 40

Yiel

d Lo

ss (%

)

Axle Load (US Short Ton)

Axle Load vs Yield Loss Corn 2003-2010

WET

DRY

NORMAL

NO YIELD LOSS IN GRAY AREA!


Empirical Model Layout














Topsoil Subsoil


Results and Discussion

• 36 rows, 22-inch spacing, center fill (3 locations) • 48 rows, 20-inch spacing, row-unit boxes (4 locations) • 36 rows, 20-inch spacing, center fill (5 locations) • Assume: Normal and wet soil moisture, year 0, soil factor of 1, axle load 22,000 lbs

Ahlers, 2012


0

50

100

150

200

250

1 2 3 4 5 6 7 8 9 10 11 12

Cor

n Yi

eld

(bu/

acre

)

Location

Pioneer Yield Loss vs Projected Yield Loss Normal Wet Center Section


67,000 kg~74 t on 3 axles = 22,000 kg~24 t/axle

Food, Agricultural and Biological Engineering


Corn Mass – 69,000 lbs Total Grain Cart Mass – 98,100 lbs

Corn Mass – 61,100 lbs Total Grain Cart Mass– 73,400 lbs


67,000 kg~74 t on 3 axles = 22,000 kg~24 t/axle


2014 Corn Results

Corn Yield (bu/ac)

Machine

Predicted Yield Compaction Model

Average Yield (6 row pass)

Trafficked Yield (bu/ac)

Yield Loss (%)


Yield Loss (%)

Std. Dev.

Wheeled 183.32 11.95 185.5a 10.90 16.3 Killbros 1950 Tracked - - 202.2b 2.88 38.1 Brent 1594

Control Yield (bu/ac) 208.2b - 14.3


2014 Soybean Results

Soybean Yield (bu/ac)

Machine

Predicted Yield Compaction Model Plot Yields and Stats


Yield Loss (%)


Yield Loss (%)

Std. Dev.

Wheeled 33.57 14.36 28.1a 28.32 20.4 Killbros 1950 Tracked - - 36.3b 7.40 17.5 Brent 1594

Control Yield (bu/ac) 39.2b - 9.1


2014 Summary • Corn Results

• Wheeled vs Control • 95% confidence level

• Tracked vs Control • Not able to show statistically significant results

• Wheeled vs Tracked • 80% confidence level

• Soybean Results • Wheeled vs Control

• 95% confidence level

• Tracked vs Control • Not able to show statistically significant results

• Wheeled vs Tracked • 75% confidence level


2015 Compaction Plot

• Beck’s Hybrids PFR Facility • London, Ohio • 41 acres • Spring tillage operation for soil

profile reset • AB lines for passes 45 ft


Traffic Event Treatments

• Grain Cart Configurations • Wheels – 96,000 lbs • Tracks – 103,100 lbs • Equalizer Tracks – 104,800

lbs • Number of Passes (Single,

Double and Triple) • 5 replications

• 3 hybrids • 4 replications

• Randomized block design • 540 total plots


Loaded Grain Cart Weight (1,300 bu)

Wheeled - 96,000 lbs Equalizer - 104,800 lbs, Regular - 103,100 lbs


As-applied Downforce Map


Airscout Imagery

10-11-15 5-19-15 8-14-15


Airscout – 05-20-2015 RGB Image

2015 Compaction Plots


eBee – 06-24-2015 RGB/Thermal Image

eBee – 07-01-2015 RGB/Thermal Image

2015 Compaction Plots


eBee UAV Imagery - NDVI

7-1-15


Tracking Passes

Equalizer Tracks Regular Tracks

Wheels


Soil Measurements • 90 soil zone locations selected

based off RGB image by soil color • Light • Medium • Dark

• Spectrum’s SC 900 penetrometer (to a depth of 18 in.)

• TDR 300 soil moisture sensor (at depths of 0-3 in. and 0-8 in.)


Soil Moisture

• Two soil moisture depths: • 3.0 inches • 8.0 inches

• Zone moisture was taken 2 times

• In the middle (control) • In the track (compaction

zone) • Averaged together for a single

reading in each location • Volumetric water content (VWC)


Soil Moisture (0-8 in. depth)


Soil Moisture (0-3 in. depth)


Soil Cone Penetrometer

• Five readings were taken in the compaction zone and five outside the compaction zone at each location

• A coin was flipped to use either the north or south compacted area from the grain cart

• Data were averaged for each location • Data were summarized for the

following depths: • 0, 5, 10, & 15 in. • 0-6 in. • 7-12 in. • 13-18 in.


Soil Cone Penetrometer Results (0-6 in.)


Soil Cone Penetrometer Results (7-12 in)


Soil Cone Penetrometer Results (13-18 in.)


Harvesting

• Harvested 8 rows to allow for hand measurements of different pass types and soil zones

• Harvested rest of compaction plots in a 2-4-2 harvest pattern to increase yield map resolution and ease of data analysis for compaction zones

• For each pass field weights were collected to support post-harvest yield map correction.


Yield Data Statistics

Statistical Summary for Yield

Equalizer Tracks Regular Tracks Wheels

Compacted Control Compacted Control Compacted Control Min 55.1 86.7 19.9 58.1 27.5 56.7 Max 184.0 205.1 192.9 217.2 194.0 213.3 SD 34.7 27.0 39.6 36.1 36.0 31.1

Mean 127.0ab 157.0a 123.0c 153.0c 114.0bd 149.0d

• Data were summarized for the following:

• 1, 2, & 3 passes • Hybrid • Undercarriage

• Statistics on following configurations:

• Equalizer vs regular • Equalizer vs wheels • Regular vs wheels • Each undercarriage vs hybrid • Each undercarriage and

number of passes vs hybrid

Hybrid Passes Test Description Equalizer Tracks Regular Tracks Wheels

Compacted Control Compacted Control Compacted Control All All Compacted zone vs control 127 157 123 153 114 149 All 1 Compacted zone vs control 129 165 130 158 117 152 All 2 Compacted zone vs control 128 152 126 152 116 150 All 3 Compacted zone vs control 125 156 108 145 111 146


Yield Results (Number of Passes)


Yield Results (Tires vs. Tracks)


-15%

-10%

-5%

0%

5%

10%

15%

20%

25%

30%

35%

40%

0 5 10 15 20 25 30 35 40 45 50

Yie

ld L

oss (

%)

Axles Load (US Short Ton)

WET

NORMAL

DRY

NO YIELD LOSS IN GRAY AREA!

Table of New Compaction Correction Factors

Soil Moisture Factor 1 Factor 2

Old New Old New Wet 0.0110725 0.0067504 -0.0112554 -0.0047539

Normal 0.0078363 0.0056458 -0.0624250 -0.0312272 Dry 0.0046000 0.0045411 -0.1135946 0.0067504

Model Correction Factors


Model Predictions

Modeled Compaction Yield Reduction Without Track Correction Factor (%)

Soil Moisture Equalizer Tracks Regular Tracks Wheels

Wet 28.1% 27.7% 21.2%

Normal 21.3% 21.0% 15.0%

Dry 19.7% 19.4% 13.5%

Yield Reduction for Field Results

Equalizer Tracks Regular Tracks Wheels 19.1% 19.6% 23.5%


2016 Compaction Plot

• Beck’s Hybrids PFR Facility • London, Ohio • 40 acres • Spring tillage operation for soil

profile reset • AB lines for passes 45 ft


Traffic Event Treatments

• Grain Cart Configurations • Wheels 1 – 96,000 lbs

• Flotation 1250/45R32 • Wheels 2 – 96,000 lbs

• IF 1250/50R32 • Equalizer Tracks – 104,800 lbs

• 42 inch belt • Full and Half Load • Number of Passes (Single and

Triple) • 2 hybrids • Randomized block design • 96 total plots


2016 Soybean Compaction Plots – Manned Flight (0.5 m)

Visible 7-12-16 ADVI 7-12-16


2016 Soybean Compaction Plots – UAV eBee Flight with Sequoia (4 in)

Visible 7-21-16 NDVI 7-21-16 Visible 9-7-16 NDVI 9-7-16


2016 Soybean Compaction Plots (Initial Results)


Conclusions

Develop an empirical model framework to predict the magnitude of compaction events and the resulting yield penalty. 1. A relatively simple and robust empirical model was developed for predicting

yield reduction for corn (Note: Original model did a good job of predicting yield losses for grain carts up to 20 T within 2.0% of 2014 study.).

Collect field data to extend the model to estimate yield penalties at higher axles loads, multiple passes, and tracks vs. tires. 2. Data were collected for grain cart loads of 48 T, tracks vs. wheels, and multiple

passes. Grain carts caused up to a 23.5% yield reduction across all hybrids and configurations evaluated.

Revise model to estimate yield losses for multiple passes, tracks vs. tires, and higher axle loads. 3. Number of passes vs. mean yield reduction were not significant for the grain

cart configurations evaluated. 4. Yield reduction for Equalizer Tracks averaged 19.1 % across all treatments

compared to 19.6% for the regular tracks. 5. Yield reductions for Equalizer Tracks vs. Wheels were significant so a

correction factor of 0.8065 was added to the model.


1. Modify compaction prediction model to include multiple crops (i.e., soybeans, wheat, and canola);

2. Conduct field research and verification for soil type correction factors;

3. Conduct field research to determine and verify tillage correction factor, pass multiplier, multiple axles, and soil moisture content to yield loss model;

4. Conduct field research for the creation of tire construction correction factors (i.e., bias ply, radial and IF); and

5. Merge remote sensed imagery, machine data and model results for refinement of yield maps resolution.

Future Work

tracks tires: ohio case study s title slide goes here · 3/3/2017 · title slide goes here...

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