Current and Future Technologies for Wine Grape Crop Estimation

Transcription

1 Current and Future Technologies for Wine Grape Crop Estimation Luis Sanchez E&J Gallo Winery 16th Annual Enology & Viticulture Conference & Tradeshow Penticton, BC / July 20, 2015

2 Vineyard Yield Forecasting Business need for improved methods: Effectively manage grape supply Know the quantity Anticipate quality Estimate the cost Harvest logistics and winery capacity efficiency Metric based cultural practices Pruning Irrigation Shoot and cluster thinning

3 Business goal: Vineyard Yield Forecasting Wineries would like +/- 5% accuracy In Australia, an reduction in error from 33% to 20% was valued as having an $85MM annual value for the industry (Updated: $ MM) The accuracy of our current crop estimation methods typically ranges between 15% and 35% average

4 Challenges for yield forecasting Vineyard spatial variability Annual variability in cluster & berry weight Hang time/berry desiccation in red cultivars Variable machine harvesting efficiency Time and cost of measure-based methods

5 Yield (tons/acre) Cluster weight (g) Spatial variability Vine # Vine #

6 0.6 Cluster weight at commercial harvest (lbs) DLM B6 DLM B10 Lodi 1 Lodi 3 Laguna N Laguna Ripp 5B Ripp 4A Schmierer Leventini R14 6A1 6A2 Block Seasonal bunch weight estimates may vary up to 50% from historical averages 1A 4D D23 T12 3B 5A 3B 9B D03 D14

7 Cluster weight (% of maximum) Hang time (Extended maturation) 100 Lodi Merlot % Max 25 Brix 26 Brix 27 brix 28 Brix Maximum weight at 21.1 to 23.6 Brix

8 Yield components For count/measurebased methods Yield per acre Y ie ld p e r vin e V in e s per acre C lu ste rs p e r vin e In-season measurement C lu ste r w e ight Inflorescence s p e r sh o o t Shoot s p e r vin e B e rrie s per cluster B e rry w e ight Cluster prim ordia p e r n o d e N o d e s p e r vin e P e rce n t b u d b re a k F lo w e rs p e r inflorescenc e P e rce n t fruit set B e rry abscisio n Pre-season measurement Modified after Tim Creagh, EIT, NZ

9 Yield components Cluster weight Yield/vine = Clusters/vine x berries/cluster x berry weight 60% 30% 10% Contribution to variation in yield

10 MAPE = Mean Absolute Percentage Error Source: GMS, blocks > 3 acres, all varieties Bunch Count Forecast GVI SJV COASTAL Bunch Count Forecast Non-GVI SJV COASTAL

11 Mean vs. absolute difference Block Estimated Tons/acre Delivered Mean difference (%) Absolute difference (%) A B C D E F G Average

12 9 All GVI Mean weighed yield (tons per acre) Forecast Actual 10% 27% 20% 17% 18% 11% 15% 22% Mean absolute percent differences by block Figure 1. Estimated vs. actual mean weighed GVI yield per acre and mean absolute percent differences

13 Research on measure-based forecasting Pre-season Weather modeling Bud dissection In-season Trellis tension monitors Grape Forecaster Pronofrut CMU sensor External Spain, France, Australia, Germany

14 Research on measure-based forecasting Pre-season Weather modeling Bud dissection In-season Trellis tension monitors Gallo-Commercialized Grape Forecaster Pronofrut CMU sensor External Spain, France, Australia, Germany

15 100 spurs 50 canes Bud dissection size = 3 nodes long size = nodes long

16 Bud dissection Potential fruitfulness = cluster primordia per node Dissect under stereomicroscope Count inflorescence primordia per node Report inflorescence primordia per spur or cane

17 Potential fruitfulness vs. actual yield

18 yearly mean yields for SJV Barbera (tons/acre) yearly mean yields for SJV Barbera (tons/acre) Bud dissection block selection Individual vs. average regional yields for selected SJV Barbera blocks Jean Hardy Barbera Ray Pool Barbera y = x R² = y = x R² = yearly yields for block (tons/acre) yearly yields for block (tons/acre)

19 Barbera Cab. Sauv. Chard Chenin Blanc Fiesta F. Colombard Grenache Merlot Muscat of A. Petite Sirah Pinot gris Pinot noir Rubired Riesling Sauv. blanc Syrah Symphony Teroldego Thompson White Zin Zinfandel Current bud dissection blocks S. Joaquin Valley North Coast Central Coast Washington Total 135 blocks (from 24 in 2004 / 66 in 2011 / 87 in 2012) SJV (91) / N. Coast (22) / C. Coast (12) / WA (13) More emphasis on varieties by region

20 Laser ablation tomography

21 In-season measurements

22 Lag-time method for estimating yield Tons per acre (% of maximum) Merlot Sonoma Degree-day bio-fix for 50% crop weight too inconsistent to provide accurate estimates Growing Degree Days Chiotti CH2 Barrelli Creek A04 Barrelli Creek A16

23 Grape Forecaster Measure-based software system designed for vineyard sampling and yield forecasting Result of a 10-year research effort by the Australian wine industry Stratified random sampling spots in a vineyard block clumping stratified random vs. random

24 Grape Forecaster Segments of row rather than whole vines Segment = slice of known length across a vine row Minimizes time, difficulty and expense of sampling Ideal length function of: vine age - training pruning 1 meter for GVI Coastal / 60 cm for GVI-SJV

25 Grape Forecaster Validation Research : Tested GF on 38 blocks System statistically sound Software was made more Gallo-friendly Accuracy 10-20% 2008: Shortcut studies (row and cluster sampling) Harvest efficiency determinations Coastal GVI for cluster counting only ~ 190 blocks 2009: Coastal GVI : 300+ blocks 2010: Coastal+SJV GVI 450+ blocks

26 Impact of Grape Forecaster Implementation Performance in Napa Eastern Section Percent of blocks at each MAPE class MAPE (%) % 25% 46% 10 32% 47% 65% 20 52% 77% 87%

27 Grape Forecaster Summary Can achieve MAPE under 20% Requires dedication and sufficient labor Data QA is essential data automation?

28 Research on measure-based forecasting Pre-season Weather modeling Bud dissection In-season Trellis tension monitors Research level Grape Forecaster Pronofrut CMU sensor External Spain, France, Australia, Germany

29 Weather-based forecasting Baldwin, Australia, 1960 s

30 Adjusted R 2 (%) Factors that Improved Model Fit LODI CABERNET SAUVIGNON GDD + Precipitation + Frost + Prior Year Yield + Like Pattern + Year + Interactions

31 MAPE (%) Opportunities Current model regression trend-line over time with data increase Current model average MAPE 10% MAPE possible by 2017 with additional years of data if trend continues. Model improves with additional years of data

32 Pronofrut, DEYANU-Chile Uses current available spatial data Pre-sampling for assessment of variability Systematic sampling sequence Not random Precise spots: every n rows, n vines, n clusters Precise vine count not needed Time consuming

33 Results for wine grapes in Chile Company Variety Duration (h:m) Area (ha) Error (%) Sta. Emiliana Carmenere 1: Undurraga Carmenere 2: Undurraga Cabernet 9: Undurraga Cabernet 6: Juvei Camps Chardonnay Juvei Camps Pinot Noir

34 Trellis tension monitors

35 CMU sensor Automation of berry counting

36 CMU sensor

37 Berry imaging system

38 Accuracy Berry Imaging System can identify and count 99.9% of the berries present in an image Due to canopy occlusion, system measures between 15% and 30% of the actual berries per vine Need block calibration to adjust berry count to actual yield Currently characterizing and modeling canopy occlusion for GVI vineyards

39 Yield Monitor September 2013 Image Estimate June 2013 Automation of berry counting Estimated yield = 7.91 t/a Actual yield = 7.10 t/a

40 Progress since 2010 Collaboration and support of CMU s berry imaging and counting system: Year Coverage/ shift Results turnaround 2010 ½ row 1 month rows 2 weeks acres 24 hours

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42 External research Wall-Ye V.I.N. robot France VineRobot Europe PHENObot Germany USW, Australia

43 vitisflower Spain Flowers per cluster counting app for smartphones (free) University of La Rioja (Vinetics Research Project)

44 Knowledge/data General strategy for the future - GVI Forecast Final July May January Yield weather model Bud dissection Historical data Inputs Maturity (IBMP), HiRes NDVI, Weather Berry counts (Grape Forecaster, Pronofrut, CMU Sensor), HiRes NDVI Bunch counts Grape Forecaster, Pronofrut Accuracy

45 General strategy for the future - GVI Leveraging of GIS data Yield maps HiRes NDVI Canopy volume (LiDAR/ PhoDAR)

46 Conclusion Spatial and temporal vine variability are the main challenges for correct sampling and accurate yield prediction in vineyards Cost of measured-based forecasting is still a fraction of the potential benefit of accurate estimation Bud dissection and improved count-based forecasting are the main contributions of our research effort Through research partnerships worldwide we will continue developing and testing new platforms for more efficient sampling and accurate yield estimation