Washington Tree Fruit Research Commission

Research Reports

Decay risk prediction models and novel decay control methodology     (2010)

FINAL PROJECT REPORT
WTFRC Project #
YEAR 0/0
Organization Project #
Title:Decay risk prediction models and novel decay control methodology    
PI:Robert A.Spotts
Organization:OSU Mid-Columbia Ag Research and Extension Center Telephone/email: 541-386-2030 ext. 15/ robert.spotts@oregonstate.edu
 PDF version of report

Co-PIs

Organization:  OSU Mid-Columbia Ag Research and Extension Center

Chang-Lin Xiao

David Sugar

Cooperators

Ag Canada (Peter Sholberg, Dan O’Gorman)

Significant findings

·         Two years of validation of the gray mold risk prediction model showed that decay risk was accurately predicted for field run fruit, but decay levels were too low in fruit that had been run over the packing line and treated with disinfectants and fungicides for accurate risk prediction.

·         The factors that are important for gray mold prediction did not affect blue mold similarly.  Blue mold is more closely related to contamination of packinghouse surfaces and water.

·         Resistance of pear fruit to decay changes yearly and can be quantified.

·         The relationship between decay and spore load is important for establishing action thresholds for packinghouse water systems.  Results emphasize the importance of good sanitation.

·         Topsin, Pristine, and Ziram reduced gray mold, while Topsin reduced blue mold.

Results and discussion

1.      Validate gray mold decay risk prediction model 

The first complete year of validation was in 2007-2008 and included pear fruit from 34 orchards inOR and WA (Table 1).  The second year of validation was in 2008-2009 and used fruit from 37 orchards (Table 2).  

Pear fruit from 9 and 6 orchards in 2007-8 and 2008-9, respectively, were stored field-run. Gray mold in this fruit ranged from 0.4 to 8.9% in 2007-8 and 4.5 to 21.3% in 2008-9 (Table 3).  The model predictions matched well with the levels of gray mold in both years.   

Fruit from commercial storages had 0.07 to 0.32% gray mold in 2007-8 and 0.03 to 2.68% in 2008-9 (Table 4). Percent gray mold was reduced from 90 to 99% when run over the packing line and placed in commercial storage when compared with field run fruit from the same orchards without any postharvest treatments.  Postharvest treatments and cold storage conditions varied considerably among packinghouses, and the model predictions were not useful with the low levels of decay that are typical of commercial conditions.   

It is important to note that orchard rating was the most significant predictor of gray mold risk. Problem orchards often had old trees with dead limbs and poor weed control. Fruit on lower limbs often were intermingled with various weeds and grasses.  Preharvest fungicide application was the second most important predictor of gray mold risk.     

 Table 1. Gray mold risk model validation orchards 2007-8
  2007 Preharvest fungicide Orchard RatingTime stored (mo)Predicted RiskTotal %Bot
PackerOrchardHarvest DateDNARain
A19/15LYesYes1NDLND
A29/12LYesYes2NDMND
A39/24LYesNo1NDLND
A49/6LYesYes2NDMND
A59/4LNoYes26H2.85
A69/7LNoYes26H3.29
A79/18LYesYes1NDLND
B19/20LZiramYes27M0.33
B29/24LZiramNo26L0.51
B39/8LZiramYes28M0.29
B49/8LZiramYes28M1.39
B59/8LZiramYes27.75M0.22
B69/19LZiramYes26M0.88
B79/20LZiramYes27.75M0.37
C19/8LYesYes24.5M0.22
C29/10LYesYes35.25H0.39
C39/8LYesYes24.5M0.08
C49/15LYesYes17.5L0.04
C5NDLYesYes2NDMND
C6NDLYesYes34.5H0
C79/11LYesYes14.5L0
C89/13LYesYes34.5H0.28
D19/17LTopsinYes2NDMND
D29/17LTopsinYes26.25M0.01
D39/21LTopsinYes2NDMND
D49/10LTopsinYes26.5M0.24
D59/14LTopsinYes36H3.48
D69/19LYesYes26.5M0.21
D79/14LNoYes36E9.9
D89/14LNoYes36E7.8
E19/6LNoNo24M0.54
E29/6LZiramNo24L0.18
E39/6LNoNo24M0.18
F18/30LYesYes26M0.15
F29/17LNoNo26M0.07
F39/17LYesNo26L1.3
=fruit not in commercial storage but field run in MCAREC or SOREC room.  ND=Not determined.
     
Table 2. Gray mold risk model validation orchards 2008-9
  2008 Preharvest fungicide Orchard RatingTime stored (mo)Predicted RiskTotal %Bot
PackerOrchardHarvest DateDNARain
A19/27LTopsinYes28(4.5)M0.23
A29/17LYesNo28L0.11
A39/28HTopsinYes28.7H1.02
A49/22LTopsinYes2.58M+0.51
A59/27LTopsinYes18.75L1.07
A69/20LTopsinYes28(4.5)M0.07
A79/19HTopsinYes25H-0.48
A89/29LTopsinYes1.54.5L+0.17
A99/28LTopsinYes28.75M0.16
B19/5LYesNo26L1.75
B29/15LNoNo26M0.25
B39/12LNoNo26M0.67
B49/16LNoNo26M2.4
B59/15LNoNo26M2.68
B69/12LYesNo26L0.12
C19/29LTopsinYes25.5M1.79
C29/16LTopsinNo17L1.19
C310/7LTopsinYes25M2.36
C49/15LTopsinNo26L0.03
C517-SepLTopsinNo27L0.12
D17-OctLYes?Yes26.5M0.56
D210/9LYesYes25.5M0.1
D310/8LziramYes25M0.24
D410/8LYesYes25M0.1
D59/23LTopsinYes36.75H0.14
E19/23LNo+No25.25M-1.06
E29/15LNo+No25M-0.5
E39/15LNo+No25M-0.38
E49/15LTopsinNo25L1.2
E59/23LZiramNo25.25L1.02
E69/15HNoNo25H0.42
F19/11LNoNo28M5.1
F29/17LNoNo28M4.6
F39/19LOrganic-NoNo38H13.2
F49/23LTopsinYes28M4
F59/19HOrganic-NoNo38E21.3
F610/2LYesYes28M4.2
=fruit not in commercial storage but field run in MCAREC room. 

 
Table 3. Anjou pears stored field-run at MCAREC and SOREC for gray mold risk model validation 2007-8 and 2008-9 
 2007-82008-9
 Predicted risk level Avg. gray mold (%)z  No. orchards Avg. gray mold (%)z  No. orchards
Low1.3a1--
Moderate0.3a34.5a4
High3.2b313.2b1
Extreme8.9c221.3c1
zFruit stored six months; different letters indicate statistical differences at P = 0.05.
 
Table 4. Pears run over commercial packing lines and stored in commercial cold rooms for gray mold risk model validation 2007-8  and 2008-9
 2007-82008-9
Predicted risk levelAvg. gray mold (%)z No. orchardsAvg. gray mold (%)z No. orchards
Low0.46a40.68a10
Moderate0.35a120.83a17
High0.44a40.51a4
zFruit stored 4 to 8.75 months; different letters indicate statistical differences at P = 0.05.

 2.Develop blue mold decay risk prediction model 

Blue mold decay levels were low in fruit from orchards used for the gray mold model. The factors that are important for gray mold prediction did not affect blue mold similarly.  It appears that blue mold is more closely related to contamination of packinghouse surfaces and water systems (drenchers, dump tanks, flumes) than to orchard factors. 

Resistance of pear fruit to decay changes yearly.  We developed a test to measure this at the beginning of each packing season.  Fruit resistance eventually needs to be incorporated into gray mold and blue mold risk prediction models. 

3.   Implement real time DNA techniques for rapid detection of decay spores in packinghouse water systems 

Spore concentration, DNA extraction, and real time PCR protocols that have been successful forBotrytis are inadequate for Penicillium.  Considerable effort has been focused on developing a protocol for detection of Penicillium spore numbers considered in the threshold range (100 to 300 per ml or less) for blue mold problems in packinghouses.  Because of the lack of efficient and specific primers for P. expansum, the protocol remains under development. 

The relationship between decay and spore load is important for establishing action thresholds for packinghouse water systems.  For blue mold and Mucor rot, the steep curve between 0 and 500 spores per ml indicates that reduction of spore numbers in this part of the curve will result in significant reductions in decay.  These results emphasize the importance of good sanitation. 

4.   Evaluate new fungicides and biological control agents in preharvest and postharvest integrated systems 

In 2007-8, all tested preharvest fungicides reduced gray mold. In both years, Topsin was the most effective preharvest fungicide for control of blue mold (Table 5). 

Table 5. Preharvest fungicides for control of postharvest decay of d’Anjou pear fruit
 2007-82008-9
Fungicide and rate/AGray mold (%)Blue mold (%)Blue mold (%)
Topsin 70WP 1/0 lb2.2a6.2a15.5a
Pristine 38WG 14.5 oz3.1a21.3b25.2ab
Ziram 76DF 8.0 lb2.9a19.4b---
Yucca Ag Aide 2%------24.6ab
Silmatrix 2%------46.9c
Unsprayed7.5b26.0b33.7bc
In 2007, all fungicides contained Nutraphos 24.  In 2008, Pristine used at 18.5 oz with Silgard 4.0 oz. Fungicides applied 2 wks before harvest and evaluated after 3, 6, and 8 months at 30ºF. Numbers followed by the same letter within columns are not significantly different at P = 0.05 according to protected LDS.

 5.   Develop pre- and post-storage integrated programs for decay control (Xiao: coordinator and d’Anjou pears in WA; Spotts: d’Anjou pears in Hood River; Sugar: Bosc pears in Medford) 

Dr. Xiao will report the results for this objective.

Executive Summary

Gray mold is one of the most serious decay problems of pear fruit in the Pacific Northwest and is estimated to cost the pear industry about $6 million per year. The main thrust of this project was to develop a model to predict, at harvest, the risk of gray mold for pear fruit in long-term cold storage. The model is driven by four factors that include: i) preharvest fungicide application, ii) preharvest rainfall, iii) an orchard management rating, and iv) amount of DNA of Botrytis on the fruit surface.  A simplified version without the DNA factor also was developed. The model classifies gray mold risk as low, moderate, high, or extreme. It is important to note that orchard rating (orchard condition) was the most significant predictor of gray mold risk. Problem orchards often had old trees with dead limbs and poor weed control. Fruit on lower limbs often were intermingled with various weeds and grasses.  Preharvest fungicide application was the second most important predictor of gray mold risk.  This project has identified effective preharvest fungicides for gray mold. The model works best for field run fruit rather than for fruit run over the packing line that has been subjected to various postharvest treatments. Gray mold risk prediction at harvest is a valuable tool for packinghouse managers to determine which fruit is most suitable for long-term storage. The prediction also is useful to growers to help understand the factors that cause fruit to be at risk of decay and to make the necessary changes in horticultural and pest management practices to lower the risk of gray mold.

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