Forage Harvest Logistics Considerations

Transcription

1 Forage Harvest Logistics Considerations Brian D. Luck, Ph.D. Assistant Professor and Extension Specialist InfoAg Conference St. Louis, Missouri July 17-19, /24/18 University of Wisconsin Madison 1

2 Background Bachelor s Degree: University of Kentucky (2005) Biosystems and Ag. Engineering Machinery Systems Master s Degree: University of Kentucky (2009) Biosystems and Ag. Engineering Machinery Systems/Precision Ag. Ph.D.: Mississippi State University (2013) Agricultural and Biological Engineering Animal Facility Systems (Spatial management of Broiler Chickens) 7/24/18 University of Wisconsin Madison 2

3 Problem Statement Forage harvest is a time sensitive process Crop quality declines sharply if optimum window is missed This equates to dollars lost for the dairy farmer in lower quality feed Inefficient harvest costs custom harvester dollars as well How efficiently is forage being harvested? 7/24/18 University of Wisconsin Madison 3

4 Methods Operation A Owner Operator Dairy 4,000 cow dairy 2 SPFH, 12 trucks including 2 different size straight truck and 2 semitractor trailer Identification Specifications (volume [m 3 ]) Small Truck Hoist [a] (16.5) Medium Truck Conveyor [b] (41.3) Semi-Truck Conveyor [b] (67.1) SPFH 7.5 m corn header, 3.8 m pickup header [a] hoist operated dump bed [b] conveyer floor dump bed 7/24/18 University of Wisconsin Madison 4

5 Methods Operation B Custom Harvest Operation 2 SPFH, 7 transport vehicles including multiple straight truck sizes and tractor towed carts. Identification Specifications (volume [m 3 ]) Med Truck Hoist [a] (35.2) Tractor Conveyor [b] (38.3) SPFH 7.5 m corn header, 3.8 m pickup header [a] hoist operated dump bed [b] conveyer floor dump bed 7/24/18 University of Wisconsin Madison 5

6 SPFH Instrumentation Controller Area Network data collected on SPFH Vector loggers used GPS collected via Vector loggers GPS data collected at 1 Hz and CAN data collected as generated with message priority 7/24/18 University of Wisconsin Madison 6

7 Transport Vehicle Instrumentation Arduino GPS loggers on the transport vehicles GPS data collected at 1 Hz 7/24/18 University of Wisconsin Madison 7

8 Methods Total Data Collected over 2015 and 2016 harvest season 948 Load Cycles in ,332 Load Cycles in 2016 Matlab software Parse data and identify working states per pre-defined rules 7/24/18 University of Wisconsin Madison 8

9 Working State Rules SPFH work states and pertinent data State Identification Harvesting Feedroll speed > 0, Cutterhead speed > 0, Vehicle speed > 0 Delayed Metal detected > 0, Metal size > 0, Metal position > 0 Travelling Feedroll speed = 0, Vehicle speed > 0 Idle Vehicle speed = 0 Non-productive GPS location was within geo-fence of maintenance shop Down No CAN data collected during GPS time Transport vehicle work states and pertinent data State Identification Harvesting SPFH = harvesting, Transport within SPFH harvest geo-fence Unloading Transport GPS location within geo-fence of unloading site Travelling Vehicle speed > 0 Idle Vehicle speed = 0 Transport to Bunker Time occurring between harvest and unload Transport to Field Time occurring between unload and harvest 7/24/18 University of Wisconsin Madison 9

10 Working State Decisions 7/24/18 University of Wisconsin Madison 10

11 Working State Decisions 7/24/18 University of Wisconsin Madison 11

12 Operation B Single Day (one transport and one harvester) 7/24/18 University of Wisconsin Madison 12

13 Calculated Variables Harvester Utilization Transport Utilization Idle Utilization (Harvester and Transport) Transport Productivity Mg * km * h -1 Statistical Analysis SAS 9.4 Proc MIXED at 95% Confidence with means separated by Fisher s LSD 7/24/18 University of Wisconsin Madison 13

14 Results Mean SPFH Utilization 2015 (Operation A) 61% 2016 (Operation A) 61% 2016 (Operation B) 65% Mean SPFH Utilization Overall by Crop Crop Estimate (%) Standard Error (%) Letter Group [a] Corn A Alfalfa AB Alfalfa B Alfalfa B [a] means with the same letter were not significantly different 7/24/18 University of Wisconsin Madison 14

15 Results Transport Utilization for the study period Operation Year U Ht [a] U Tt [b] U It [c] Average(%) S.D. (%) Average(%) S.D. (%) Average(%) S.D. (%) A A B [a] utilization of transports for harvest [b] utilization of transports for travel [c] utilization of transports for idle 7/24/18 University of Wisconsin Madison 15

16 Cycle Analysis 2016 Cycle Duration Operation A 2016 Cycle Duration Operation B 7/24/18 University of Wisconsin Madison 16

17 Transport Productivity Type Estimate (Mg km h -1 ) Standard Error (Mg km h -1 ) Letter Group [a] Semi-truck A Med. Str. Truck B Sm. Str. Truck C a] means with the same letter were not significantly different Type Estimate (Mg km h -1 ) Standard Error (Mg km h -1 ) Letter Group [a] Semi-trucks A Med. Trucks [b] B Med. Trucks [c] BC Tractor BC Sm. Trucks C [a] means with the same letter were not significantly different [b] medium-sized transport trucks of Operation A [c] medium-sized transport trucks of Operation B 7/24/18 University of Wisconsin Madison 17

18 Other Implications 7/24/18 University of Wisconsin Madison 18

19 Conclusions Opportunities exist for optimization gains in forage harvest process 61% and 65% Overall SPFH Harvest Utilization for two differing operations respectively Cycle analysis showed load and unload time were close to that of theoretical zero distance load Cycle duration was linearly related to travel distance Semi-trucks were the most productive transports Cost prohibitive for entire fleet Wet conditions 7/24/18 University of Wisconsin Madison 19

20 Acknowledgments Funding provided by the University of Wisconsin- Madison College of Agriculture and Life Sciences Additional funding provided by CNH Industrial 7/24/18 University of Wisconsin Madison 20

21 Harvest Cycle Model Online Asset Allocation Tool Developed a simple model to simulate the forage harvest process Utilizes user inputs for all equipment Cycles through once per minute checking machine states Closely simulates the actual forage harvest process Changeable to test different machinery configurations 7/24/18 University of Wisconsin Madison 21

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24 Model Verification Three operations were selected for comparison with the model Operation Harvester Capacity (Mg h -1 ) Transport Vehicles Transport Vehicle Capacity (Mg) Field Size (ha) Total Travel Distance (km) Road Speed (km h-1) Field Speed (km h-1) Unload Time (s) Total Yield (Mg) [a] semitrucks 23.1, 24.6, straight trucks 13.1, 11.6, 13.6, straight trucks 14.5, 14.4, [a] The yield was assumed to be at 32% dry matter 7/24/18 University of Wisconsin Madison 24

25 Model Results Operation Observed Time (min) Model (min) Error (%) Predicted Cycle Analysis (min) Error (%) /24/18 University of Wisconsin Madison 25

26 Equipment Allocation Scenarios Dissimilar Transport Vehicle Capacity What happens when one transport is larger than the others? Each scenario had the same total transport capacity. Two harvester capacities One matched to the capacity of the transports and one greater than the capacity of the transports. 7/24/18 University of Wisconsin Madison 26

27 Equipment Allocation Examples Equally sized vehicles resulted in the shortest harvest times. Unevenly sized transports? Load the smallest transport first 7/24/18 University of Wisconsin Madison 27

28 Equipment Allocation Examples One Larger Transport Vehicle (increasing capacity) Loading largest transport last shortened or maintained harvest time reduction One Slower Transport Vehicle Loading the slow vehicle last minimized slow vehicle effect. Total Harvest Time (min) Total Harvest Time (min) Yield (Mg) Yield (Mg) Vehicle Order Vehicle Order Identical Larger First Larger Last Identical First Slow Last Slow /24/18 University of Wisconsin Madison 28

29 Switching Gears Particle Size Assessment 7/24/18 University of Wisconsin Madison 29

30 Corn Silage Particle Size Assessment Why is it important? Smaller particle size means increased surface area Increased surface area increases enzymatic hydrolysis potential Increasing digestion of the starch which increases milk production 7/24/18 University of Wisconsin Madison 30

31 Corn Silage Particle Size Assessment 7/24/18 University of Wisconsin Madison 31

32 Image Analysis Methods Calibration Images Objects of known size in the image Verified with Mitutoyo Calipers Accuracy of ± mm (± in) Calibration 1.5 in used to determine pixel size within image Various camera angles tested for effect of particle size measurement. Camera closer = better results Camera Height (m) Estimate (mm) Standard Error Letter Group A B C 7/24/18 University of Wisconsin Madison 32

33 Image Analysis Methods 7/24/18 University of Wisconsin Madison 33

34 Image Analysis Results Percent under 4.75 mm Processor Gap Sample Image analysis Sieve wet dry sieved dry /24/18 University of Wisconsin Madison 34

35 Corn Silage Particle Size Assessment Corn Silage Image Processing App SilageSnap! Release early 2018 (hopefully) 7/24/18 University of Wisconsin Madison 35

36 SilageSnap! Collect a sample (built on 600 ml samples) Water separate the sample as best you can Spread the kernels out on a dark background Any foreign matter will be considered a kernel, so the cleaner the better Place the coin in the center of the image Ensure that no kernels are touching (as best you can) Take the picture! 7/24/18 University of Wisconsin Madison 36

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40 KPS Recommendations Check often! Train all people involved in the harvest process to look for large kernel pieces in the silage. Maintenance, Maintenance, Maintenance! Bearings hot, worn rolls, etc Adjust often Replace worn rolls sooner rather than later to maintain adequate KPS! 7/24/18 University of Wisconsin Madison 40

41 Questions? Brian D. Luck Biological Systems wimachineryextension.bse.wisc.edu (608) /24/18 University of Wisconsin Madison 41

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