2016 Annual Project Report
alilornia Tomato Research Institute
Published as an industry service by the Members of the California Tomato Research Institute, Inc. 2016 ANNUAL RESEARCH REPORT
INTRODUCTION
Welcome, this report marks the 48th year of continuous crop research sponsored by California processing tomato growers. This report details research funded by the contributing growers of the California Tomato Research Institute, Inc.
It is our goal to provide useful timely information, geared to assisting growers in both daily production decisions and long term crop improvement. The Institute Board of Directors continues to support a broad range of projects, addressing both current problems and long range concerns. Additional resources for growers and allied industry can be found on the pages of www.tomatonet.org and by joining the industry email alert system also found on the front page of www.tomatonet.org .
2016 BOARD OF DIRECTORS
CALIFORNIA TOMATO RESEARCH INSTITUTE, INC.
Rick Blankenship Chairman Huron
Darryl Bettencourt V. Chair Corcoran Dino Del Carlo Sect/Treasurer Stockton Bryan Barrios Zamora Brad Benton Los Banos Daniel Burns Dos Palos Frank Coelho Five Points Brett Ferguson Lemoore Chope Gill Dixon Scott Park Meridian Sal Parra, Jr. Helm Ray Perez Crows Landing Kent Stenderup Arvin Tony Turkovich Winters
California Tomato Research Institute ~ 2016 Annual Report California Tomato Research Institute, Inc. 2016 Research Project Reports
Projects are categorized by project type and listed in order of starting date Use your bookmarks feature to easily navigate to each project.
Agronomic/Water/Nutrient Mgmt. $21,520 Investigation of Sustaining Tomato Plant Health and Gene Miyao $6,000 Yield with Composted Manure & Potassium Evaluating the Nitrogen Budget System in Drip Irrigated Scott Stoddard $7,100 Processing Tomatoes Potential for improved fertigation efficiency via field- Brenna Aegerter $8,420 based sensing devices
Breeding/Genetics/Varieties $71,241 Tomato Genetics Resource Center Roger Chetelat $15,000
Discovering novel genes associated with water stress Dina St. Clair $36,241 tolerance in wild tomato Identifying and testing wild tomato genes that contribute Dina St. Clair $20,000 to water stress tolerance
Insect & Invertibrate Mgmt. $11,429 Consperse Stink Bug Control Evaluations Thomas Turini $11,429
Pathogen and Nematode Mgmt. $329,551 Evaluation of Fungicides, & Others for the Control of Joe Nuñez $4,750 Southern Blight Evaluation of Alternative Nematicides for the Control of Joe Nuñez $14,600 Root-Knot Nematodes of Processing Tomatoes Monitoring Strobilurin Fungicide Resistant Strains of Ioannis Stergiopoulos $30,255 Tomato Powdery Mildew in California Evaluation of Chemical Control of Bacterial Speck Gene Miyao $4,000 California Tomato Research Institute, Inc. 2016 Research Project Reports
Projects are categorized by project type and listed in order of starting date Use your bookmarks feature to easily navigate to each project.
Evaluation of Varieties with Fusarium wilt, Race 3 Gene Miyao $12,500 Resistance Use of Beet Leafhopper Repellents to Manage Curly Top Joe Nuñez $8,300 of Tomatoes Characterization of resistance-breaking root-knot Antoon Ploeg $25,000 nematodes Detection and Management of Tomato Viruses Robert L. Gilbertson $35,000
Seed Transmission and Seed Treatment of Fusarium Mike Davis $15,000 oxysporum f. sp. lycopersici Pathogen characterization of Two Fusarium Species Tom Gordon $45,000
Monitoring beet leafhopper populations in vegetation on Brenna Aegerter $12,000 the floor of the northern San Joaquin Valley Reducing insect virus vectors of Beet Curly Top Virus in Amélie Gaudin $34,846 processing tomatoes through soil health management Management of Fusarium Wilt R3 with Dip Treatments Scott Stoddard $7,800
Developing New Management Techniques for Vector- Clare Casteel $34,500 Borne Diseases of Tomato Bacterial canker of tomato: examining strain relationships Gitta Coaker $46,000 and testing PCR primer specificity
Weed Control and Mgmt. $32,234 Breaking Bindweed: Deciphering Complex Interactions Lynn Sosnoskie $20,301
Automatic Vision Guided Weed Control System for David Slaughter $11,933 Processing Tomatoes
Total $465,975 Project Title: INVESTIGATION OF SUSTAINING TOMATO PLANT HEALTH AND YIELD WITH COMPOSTED MANURE AND POTASSIUM Project Leader: Gene Miyao, UC Cooperative Extension Ben Leacox, Field Assistant, 70 Cottonwood Street, Woodland, CA 95695 UCCE (530) 666-8732 [email protected] Yolo, Solano & Sacramento counties
Summary: The unanticipated outcome of the composted poultry manure and conventional potassium fertilizer trials was no yield gains in the 2016 field tests in soils with low K levels. The expectation was that a strong yield response would be observed in both sites as soil K levels were below 120 ppm K with 1.6% or less K on the soil cation exchange capacity (CEC). We continue to trust that yield responses are likely is soils with potassium levels below 200 ppm using an ammonium acetate extraction method and as a secondary indicator, in combination with potassium levels not exceeding 2% on cation exchange capacity. Composted manure placement was better as a surface application mounded and centered on a pre-formed bed vs. applied in the bottom of an open trench (~5 inches deep) and covered.
Objectives: Evaluate the influence of composted poultry manure and of potassium as a supplemental applied nutrient on yield of canning tomato. Background: Tests using composted poultry manure have demonstrated yield increases as much as 40%, but have also resulted in no responses. The continued testing was an attempt to better define response parameters.
Methods: Rates of K from 50 to 400 lbs. K20/acre were sidedressed, as either K muriate (KCl)
or K sulfate (K2S04) (Table 1). Compost was applied at either 5 or 10 tons/acre; and placed either 1) on the bed top in a center-located pile or 2) applied in the bottom of a ~5-inch trench and covered. Normal spring bed tillage eventually followed months later to shallowly incorporate the compost. Two sites were established in commercial fields with soil K levels of 95 and 119 ppm (and 1.6% K or lower on the CEC) (Tables 2-A and 2-B). Both field sites were irrigated with buried drip on 60-inch centered beds. Previous crops were either sunflower or wheat. Whole leaf plant tissue samples were gathered at early bloom, full bloom and at 1st ripe growth stages to check NPK levels. Yields were measured using grower’s machinery to capture marketable fruit yields weighed in a portable trailer scale. A 5-gallon volumetric sample of nonsorted fruit from the harvester was collected and hand sorted for culls on a percent by weight basis. A subsample of nondefect fruit was submitted to a local PTAB inspection station to analyze for color, pH and °Brix. Results:
Page 1 of 10 California Tomato Research Institute Test site #1 Tissue nutrient levels when compared in generalized group comparisons showed some differences (Table 3). For compost, compared to the control, N was higher at early bloom and NP was higher at 1st ripe stage. Similarly, NP was also increased with K applications by 1st ripe stage. At the 1st ripe stage, NPK was lower than any of the treatments, but K levels tended not to be substantially elevated. Footnotes describe other statistically significant differences (Table 3). Plant stand was similar across the trial area (Table 4). Using a hand-held Greenseeker® to measure ‘greenness’ of the vines, differences at 35 days and 13 days before harvest did not detect substantial differences compared to the control. Overall, vines remained healthy to harvest. A spotty and stunting outbreak of bacterial speck in the late spring was a concern and caused a yield reduction. Yield was not raised from either application of composted manure or potassium (Table 5). Yields were higher in the composted manure treatments, but without statistical significance, in spite of low yield variation. Compost applied on the bed surface performed similarly to placement in a trench. Fruit Brix level was elevated by K sulfate compared to KCl source at a probability level of 7%. Test site #2 Before midseason, Fusarium wilt, race 3 infected a high number of the plants and before harvest caused extreme unevenness in production. In spite of this setback, yields approached 50 tons/A. Plant tissue levels were elevated by compost or K applications in some instances (Table 6). Compost elevated P levels at each of the 3 sampling periods; and %K was raised by full flower and at 1st ripe stages. With the K sidedressed applications, tissue K level was increased in general at full flower and particularly with Kchloride, as a linear response only during the full flowering growth stage. For Ksulfate, linear response to rates were only associated with NP at the 1st ripe stage. Vine healthy varied a bit even with the high level of variation brought mainly from Fusarium disease. Ksulfate appeared to have some partial level of improving plant health (Table 7). Most striking was the greater benefit of the bed top placement of composted manure over a more elaborate trenched application. Fruit yields were remarkably high at near 50 tons/acre and without extreme level of culls in spite of the high incidence of Fusarium wilt race 3 on our susceptible variety (Table 8). Yield advantages were not seen in this trial with any of the treatments. The take-away nugget of information appears to be that with composted manure applications as tested in our trials is that a surface application was as effective as a more laborious trenched application. At this site, the surface application of composted outperformed the trenched application, 54.3 tons vs. 47.8 tons/acre. Discussion: The results from the 2 tests in 2016 did not add to our compiled results charting yield responses to compost or K applications. We continue to expect a yield response associated with soil potassium thresholds below 200 ppm K and, secondarily with less than 2%K on the cation exchange. We recognize that in biological systems there are diverse environments and circumstances that contradict the projections. We continue to encourage
growers and consultants to explore the benefit provided by K applications in tomato production by targeting the low soil K fields. ______We appreciate support of the CTRI and of our cooperating growers: Sam and Steve Meek of J.H. Meek and Sons; Colin and Frank Muller of Muller Ranch. Assistance and material was provided by the UC Davis Long Term Research on Sustainable Agriculture (LTRAS) and the Agriform/Tremont group.
Table 1. Compost and potassium treatments, 2016
treatments 1 non treated 2 compost 5 tons (trench) 3 compost 5 tons (shallow) 4 compost 10 tons (trench) 5 compost 10 tons (shallow) 6 KCl @ 50 lbs K20 sidedress 7 KCl @ 100 lbs K20 sidedress 8 KCl @ 200 lbs K20 sidedress 9 KCl @ 400 lbs K20 sidedress 10 KS04 @ 50 lbs K20 sidedress 11 KS04 @ 100 lbs K20 sidedress 12 KS04 @ 200 lbs K20 sidedress 13 KS04 @ 400 lbs K20 sidedress
GROUP CONTRASTS- for Stats Analysis A control vs. any compost B control vs. any K sidedressed C compost placed: trench vs surface D compost rate: 5 tons vs 10 tons per acre Potassium source: KCl vs. Ksulfate E I Rate K muriate (linear) IIRate K muriate (quadratic) IIIRate K sulfate (linear) IV Rate K sulfate (quadratic)
Table 2-A. Specifications for K and compost test, Muller Ranch, 2016
location: 1/2 mile south of CR 14 x west of CR 102, Knights Landing area Field i.d. Muller #219 38.766614, -121.731073 previous crop Sunflower irrigation buried drip, 1" tape, 2015 fall, new row spacing 60" centers, twin plant lines per bed soil type Sycamore silty clay loam soil sampled 22-Oct 2015 element level rep 1-2 level rep 3-4 P ppm 13 9 K ppm 119 119 K % 1.6% 1.4% CEC 19.2 22.0
apply compost 7-Nov centered on bed, applied on surface or trench 5" deep fert w/ K 1-Mar sidedressed ~10 inches off center x 7 inches deep transplant 12-Apr variety HM 3887 VFFNsw replant missing 12-13 April (reps 1& 2 then 3 & 4) Tissue sample 1st Early bloom @ 4 May (all as whole leaf sample Tissue sample 2nd Full bloom @ 26 May Tissue sample 3rd 1st ripe @ 16 June NDVI 1st 11-Jul NDVI 2nd 31-Jul harvest 14-Aug-16
Table 2-B Specifications for K and compost test, JH Meek and Sons, 2016
location: 3/4 mile south of Cache Creek x 0.25 miles east of CR 102 GPS location 38°43'04.2"N 121°43'27.3"W previous crop Wheat irrigation buried drip since 2013 row spacing 60" centers, twin rows soil type Maria silt loam soil sampled 11-Dec 2015 element level P ppm 13 K ppm 95 K % 1.6% B ppm 8.7 apply compost 26-Feb fert w/ K 29-Feb transplant 16-Apr irrigation ended 3-Aug variety HM 3888 replant missing harvest 17-Aug-16
Table 3. Percent NPK whole leaf nutrient levels, Muller Ranch, Woodland, 2016
early bloom full bloom 1st ripe treatment N1 P1 K1 N2 P2 K2 N3 P3 K3 1 non treated 5.17 0.40 3.03 4.70 0.29 2.34 3.00 0.18 0.72 2 compost 5 tons (trench) 5.26 0.42 3.14 4.67 0.30 2.57 3.48 0.21 0.91 3 compost 5 tons (shallow) 5.27 0.42 3.20 4.80 0.31 2.40 3.59 0.21 0.97 4 compost 10 tons (trench) 5.31 0.43 3.23 4.72 0.30 2.52 3.52 0.22 0.94 5 compost 10 tons (shallow) 5.38 0.42 3.20 4.70 0.31 2.45 3.54 0.21 1.03 6 KCl @ 50 lbs K20 sidedress 5.14 0.39 2.98 4.61 0.27 2.24 3.35 0.20 0.83 7 KCl @ 100 lbs K20 sidedress 5.17 0.39 2.93 4.60 0.28 2.36 3.67 0.22 0.97 8 KCl @ 200 lbs K20 sidedress 5.01 0.38 2.93 4.49 0.28 2.60 3.46 0.21 1.05 9 KCl @ 400 lbs K20 sidedress 5.07 0.38 3.05 4.61 0.28 2.54 3.41 0.20 0.82 10 K2SO4 @ 50 lbs K20 sidedress 5.20 0.40 2.97 4.63 0.28 2.30 3.52 0.20 1.01 11 K2SO4 @ 100 lbs K20 sidedress 5.15 0.40 3.01 4.63 0.29 2.23 3.52 0.21 1.07 12 K2SO4 @ 200 lbs K20 sidedress 5.21 0.40 3.04 4.74 0.29 2.29 3.62 0.22 0.98 13 K2SO4 @ 400 lbs K20 sidedress 5.15 0.39 3.22 4.67 0.28 2.36 3.50 0.21 1.00 LSD 5% 0.164 NS NS NS NS 0.23 NS NS NS F value 2.86 1.92 1.55 1.87 1.64 2.5 1.12 0.86 0.67 % CV 2 6 6 2 7 7 9 11 26 ^ non additivity statistical conflict ^ ^
GROUP CONTRASTS A control vs 5.17 0.40 3.03 4.70 0.29 2.34 3.00 0.18 0.72 any compost 5.30 0.42 3.19 4.72 0.30 2.48 3.53 0.21 0.96 Probability 0.05 0.13 0.11 NS 0.11 0.10 0.00 0.01 0.09
B control vs 5.17 0.40 3.03 4.70 0.29 2.34 3.00 0.18 0.72 any K sidedressed 5.14 0.39 3.02 4.62 0.28 2.37 3.51 0.21 0.99 Probability NS NS NS 0.21 NS NS 0.00 0.01 0.07
C compost placement: trench vs 5.28 0.42 3.19 4.69 0.30 2.54 3.50 0.21 0.95 shallow incorporation 5.32 0.42 3.20 4.75 0.31 2.42 3.56 0.21 0.93 Probability NS NS NS NS 0.20 0.13 NS NS NS
D compost rate 5 tons vs 5.10 0.38 2.97 4.58 0.28 2.43 3.47 0.21 0.92 10 tons 5.18 0.40 3.06 4.67 0.28 2.30 3.54 0.21 1.01 Probability 0.18 NS NS NS NS NS NS NS NS
E Potassium source: KCl 5.10 0.38 2.97 4.58 0.28 2.43 3.47 0.21 0.92 vs. KS04 5.18 0.40 3.06 4.67 0.28 2.30 3.54 0.21 1.01 Probability 0.06 0.13 0.17 0.03 NS 0.02 NS NS 0.28
Probability: F Rate K muriate (linear) 0.11 0.16 NS 0.30 NS 0.01 0.25 0.26 NS G Rate K muriate (quadratic) 0.27 NS 0.27 0.03 NS 0.23 0.03 0.05 0.05 H Rate K sulfate (linear) NS NS 0.06 NS NS NS 0.12 0.07 NS I Rate K sulfate (quadratic) NS NS NS NS NS NS 0.03 0.08 0.19
SUMMARY: Composted poultry manure increased %N at an early growth stage, and N & P at 1st ripe. Sidedressed K increased N and P levels at 1st ripe Chloride form of potassium elevated K tissue levels more than sulfate form during full bloom stage Tissue K levels increased linearly with KCl application rates at full bloom stage AND curvilinear response with N and during 1st ripe with NPK. Tissue N levels rose at 1st ripe stage as a curvilinear response to K sulfate applications. Table 4. Population and vegetative index rating, Muller Ranch, Woodland, 2016
35 days 13 days before before stand harvest harvest treatment count NDVI* NDVI* 1 non treated 98 0.71 0.63 2 compost 5 tons (trench) 100 0.73 0.66 3 compost 5 tons (shallow) 101 0.72 0.64 4 compost 10 tons (trench) 102 0.72 0.63 5 compost 10 tons (shallow) 100 0.73 0.64 6 KCl @ 50 lbs K20 sidedress 104 0.70 0.64 7 KCl @ 100 lbs K20 sidedress 102 0.72 0.64 8 KCl @ 200 lbs K20 sidedress 102 0.71 0.64 9 KCl @ 400 lbs K20 sidedress 103 0.71 0.63 10 K2SO4 @ 50 lbs K20 sidedress 101 0.72 0.65 11 K2SO4 @ 100 lbs K20 sidedress 101 0.71 0.64 12 K2SO4 @ 200 lbs K20 sidedress 100 0.72 0.64 13 K2SO4 @ 400 lbs K20 sidedress 102 0.71 0.64 LSD 5% NS NS NS F value 0.56 1.45 1.04 % CV 4 2 2 non additivity
GROUP CONTRASTS A control vs 98.3 0.71 0.63 any compost 100.8 0.72 0.64 Probability 0.25 0.14 0.05
B control vs 98.3 0.71 0.63 any K sidedressed 101.7 0.71 0.64 Probability 0.10 NS 0.15
C compost placement: trench vs 101.0 0.72 0.64 shallow incorporation 100.6 0.72 0.64 Probability NS NS NS
D compost rate 5 tons vs 100.6 0.72 0.63 10 tons 101.0 0.72 0.64 Probability NS NS 0.20
E Potassium source: KCl 102.6 0.71 0.63 vs. KS04 100.8 0.72 0.64 Probability 0.20 0.17 0.17
probability: F Rate K muriate (linear) NS NS NS G Rate K muriate (quadratic) NS NS 0.27 H Rate K sulfate (linear) NS NS NS I Rate K sulfate (quadratic) NS NS 0.20
Table 5. Yield, sort-out and fruit quality evaluations, Muller Ranch, Woodland, 2016
yield % % % sun % % PTAB Treatment tons/A pink green burn mold BER color Brix pH 1 non treated 81.8 8 1 1 2 0.0 25.8 4.35 4.43 2 compost 5 tons (trench) 83.3 6 2 0 1 0.0 26.3 4.68 4.39 3 compost 5 tons (shallow) 83.2 6 2 2 1 0.0 27.0 4.50 4.37 4 compost 10 tons (trench) 82.8 6 2 1 1 0.1 26.0 4.48 4.41 5 compost 10 tons (shallow) 83.1 7 2 2 2 0.0 26.5 4.40 4.40 6 KCl @ 50 lbs K20 sidedress 79.9 6 3 2 1 0.0 26.5 4.45 4.41 7 KCl @ 100 lbs K20 sidedress 81.2 6 2 3 2 0.2 25.8 4.48 4.45 8 KCl @ 200 lbs K20 sidedress 79.1 7 3 1 2 0.0 27.0 4.35 4.40 9 KCl @ 400 lbs K20 sidedress 81.8 8 1 2 2 0.2 26.3 4.38 4.40 10 K2SO4 @ 50 lbs K20 sidedress 79.1 9 3 1 1 0.0 26.8 4.63 4.41 11 K2SO4 @ 100 lbs K20 sidedress 79.6 8 2 3 2 0.0 26.3 4.43 4.40 12 K2SO4 @ 200 lbs K20 sidedress 81.8 6 3 2 2 0.0 26.5 4.65 4.40 13 K2SO4 @ 400 lbs K20 sidedress 81.3 5 3 1 2 0.0 26.5 4.55 4.40 LSD 5% NS NS NS NS NS NS NS NS NS F value 1.75 0.87 0.69 1.32 0.6 1.27 0.74 0.96 0.85 % CV 3 33 61 79 87 353 4 5 1 ^ non additivity ^ ^ ^
GROUP CONTRASTS A control vs 81.8 7.6 1.4 1.0 1.6 0.0 25.8 4.35 4.43 any compost 83.1 6.2 2.0 1.3 1.2 0.0 26.4 4.51 4.39 Probability 0.32 0.29 NS NS NS NS 0.20 0.21 0.12
B control vs 81.8 7.6 1.4 1.0 1.6 0.0 25.8 4.35 4.43 any K sidedressed 80.5 6.8 2.5 1.8 1.7 0.0 26.4 4.49 4.41 Probability 0.29 NS 0.13 0.21 NS NS 0.18 0.26 NS
C compost placement: trench vs 83.1 5.9 2.1 0.8 0.9 0.1 26.1 4.58 4.40 shallow incorporation 83.2 6.6 1.8 1.9 1.6 0.0 26.8 4.45 4.39 Probability NS NS NS 0.10 NS NS 0.19 0.28 NS
D compost rate 5 tons vs 83.3 6.3 2.2 1.2 0.6 0.0 26.6 4.59 4.38 10 tons 83.0 6.1 1.8 1.5 1.9 0.1 26.3 4.44 4.41 Probability NS NS NS NS 0.00 NS NS 0.20 0.21
E Potassium source: KCl 80.5 6.5 2.4 2.0 1.7 0.1 26.4 4.41 4.42 vs. KS04 80.5 7.1 2.6 1.7 1.7 0.0 26.5 4.56 4.40 Probability NS NS NS NS NS 0.05 NS 0.07 NS
probability: F Rate K muriate (linear) NS NS NS NS NS 0.11 NS NS 0.24 G Rate K muriate (quadratic) 0.11 0.30 0.05 NS NS NS 0.22 NS NS H Rate K sulfate (linear) NS 0.03 0.30 NS NS NS NS NS NS I Rate K sulfate (quadratic) NS NS NS 0.11 NS NS NS 0.26 NS
SUMMARY: Field was high yielding and test was uniform AND without a response to composted manure or K applications Composted manure placement of surface applied vs shallowly trenched (5 inch) resulted in similar yield Fruit quality was not influenced, but with some weak response of K sulfate elevating Brix over K muriate (at p=0.07).
Table 6. NPK whole leaf tissue levels (%), JH Meek and Sons, Woodland, 2016 25 May tissue 9-Jun 30-Jun early bloom full bloom 1st ripe
treatment N1 P1 K1 N2 P2 K2 N3 P3 K3 1 non treated 4.85 0.37 2.65 5.21 0.32 2.51 4.11 0.24 2.26 2 compost 5 tons trench 5.07 0.41 2.73 5.15 0.39 2.96 4.12 0.28 2.43 3 compost 5 tons surface shallow 5.06 0.42 2.76 5.20 0.36 2.57 4.21 0.27 2.31 4 compost 10 tons trench 5.06 0.43 2.70 5.28 0.38 2.82 4.27 0.28 2.60 5 compost 10 tons surface shallow 5.00 0.44 2.85 5.13 0.37 3.01 4.13 0.26 2.60 6 KCl @ 50 lbs K20 sidedress 4.87 0.37 2.54 5.12 0.33 2.65 4.20 0.27 2.21 7 KCl @ 100 lbs K20 sidedress 4.98 0.37 2.62 5.03 0.32 2.75 4.26 0.27 1.90 8 KCl @ 200 lbs K20 sidedress 4.74 0.39 2.75 4.82 0.33 2.96 4.13 0.25 2.39 9 KCl @ 400 lbs K20 sidedress 4.92 0.37 2.71 5.24 0.34 3.07 4.35 0.27 2.26 10 K2SO4 @ 50 lbs K20 sidedress 4.71 0.38 2.68 5.11 0.33 2.70 4.08 0.26 2.27 11 K2SO4 @ 100 lbs K20 sidedress 4.85 0.38 2.70 5.02 0.34 2.94 4.13 0.26 2.31 12 K2SO4 @ 200 lbs K20 sidedress 4.74 0.37 2.77 5.25 0.34 2.90 4.31 0.28 2.32 13 K2SO4 @ 400 lbs K20 sidedress 4.82 0.38 2.74 5.19 0.35 2.91 4.37 0.28 2.40 LSD 5% NS 0.04 NS NS 0.03 NS NS 0.02 0.348 F value 1.97 3.00 1.03 1.05 4.44 1.54 1.15 2.59 2.17 % CV 4 8 6 5 6 10 4 5 10 non additivity
GROUP CONTRASTS A control vs 4.85 0.37 2.65 5.21 0.32 2.51 4.11 0.24 2.26 any compost 5.05 0.43 2.76 5.19 0.38 2.84 4.18 0.27 2.49 Probability 0.06 0.00 0.19 NS 0.00 0.04 NS 0.00 0.10
B control vs 4.85 0.37 2.65 5.21 0.32 2.51 4.11 0.24 2.26 any K sidedressed 4.83 0.38 2.69 5.10 0.34 2.86 4.23 0.27 2.26 Probability NS NS NS NS 0.18 0.02 0.22 0.00 NS
C compost placement: trench vs 5.07 0.42 2.72 5.22 0.39 2.89 4.20 0.28 2.52 shallow incorporation 5.03 0.43 2.81 5.17 0.37 2.79 4.17 0.27 2.46 Probability NS NS 0.24 NS 0.07 NS NS 0.12 NS
D compost rate 5 tons vs 5.07 0.42 2.75 5.18 0.38 2.77 4.17 0.28 2.37 10 tons 5.03 0.44 2.78 5.21 0.38 2.92 4.20 0.27 2.60 Probability NS 0.32 NS NS NS 0.29 NS NS 0.06
E Potassium source: KCl 4.88 0.38 2.66 5.05 0.33 2.86 4.24 0.27 2.19 vs. KS04 4.78 0.38 2.72 5.14 0.34 2.86 4.22 0.27 2.33 Probability 0.15 NS 0.21 0.29 0.16 NS NS 0.27 0.12
probability: F Rate K muriate (linear) NS NS 0.19 NS 0.16 0.00 0.13 0.10 NS G Rate K muriate (quadratic) NS NS NS 0.01 NS 0.32 NS NS NS H Rate K sulfate (linear) NS NS NS NS 0.04 0.08 0.01 0.00 NS I Rate K sulfate (quadratic) NS NS NS NS NS 0.11 NS 0.03 NS
SUMMARY: P tissue levels were increased with composted poultry manure applications of 5 to 10 tons/acre. K tissue levels tended to also be increased with compost. Application of the compost did not appear to be important between surface piled on center of bed or trenched. Tissue levels were more similar between K source of KCl or Ksulfate. Some linear trends for a higher K tissue level during full bloom for KCl especially, and somewhat for Ksulfate as well. With Ksulfate, linear trends with application rates of Ksulfate on tissue N and P level at 1st ripe stage, but not K.
Table 7. Vegetative index and vine health ratings, J.H. Meek and Sons, Woodland, 2016.
3 wks from harvest 11 days from harvest stand green index vine vine plants/plot treatment NDVI*100 necrosis (%) cover (%) (#) 1 non treated 60 45 60 101 2 compost 5 tons trench 60 40 60 102 3 compost 5 tons surface shallow 64 26 74 102 4 compost 10 tons trench 61 41 69 102 5 compost 10 tons surface shallow 63 31 69 101 6 KCl @ 50 lbs K20 sidedress 59 41 60 101 7 KCl @ 100 lbs K20 sidedress 62 36 74 101 8 KCl @ 200 lbs K20 sidedress 59 41 55 102 9 KCl @ 400 lbs K20 sidedress 62 36 64 101 10 K2SO4 @ 50 lbs K20 sidedress 55 59 54 99 11 K2SO4 @ 100 lbs K20 sidedress 62 40 64 101 12 K2SO4 @ 200 lbs K20 sidedress 59 45 60 102 13 K2SO4 @ 400 lbs K20 sidedress 64 29 69 100 LSD 5% 4.4 NS 12.7 NS F value 2.81 1.95 2.16 1.92 % CV 5 30 14 1.1
GROUP CONTRASTS A control vs 60.3 45.0 60.0 101.0 any compost 61.9 34.5 67.9 101.8 Probability NS 0.13 0.12 0.20
B control vs 60.3 45.0 60.0 101.0 any K sidedressed 60.0 40.9 62.4 100.9 Probability NS NS NS NS
C compost placement: trench vs 60.4 40.5 64.4 102.0 shallow incorporation 63.4 28.5 71.3 101.5 Probability 0.06 0.05 0.13 NS
D compost rate 5 tons vs 62.2 33.0 66.9 102.0 10 tons 61.7 36.0 68.8 101.5 Probability NS NS NS 0.27
E Potassium source: KCl 60.2 38.5 63.2 101.3 vs. KS04 59.8 43.3 61.6 100.5 Probability NS 0.30 NS 0.12
probability: F Rate K muriate (linear) NS NS NS NS G Rate K muriate (quadratic) NS NS NS NS H Rate K sulfate (linear) 0.01 0.01 NS NS I Rate K sulfate (quadratic) NS NS NS 0.19
SUMMARY: Green measurement as NDVI indicated compost didn't help much, but surface application better than trenched placement. Vine necrosis reduced with compost application, especially surface applied ahead of shallow incorporation compared to trenched placement Table 8. Fruit yield, culls and quality, J.H. Meek and Sons, Woodland, 2016.
% % % sun % % PTAB treatment tons pink green burn mold BER color Brix pH 1 non treated 50.9 3 3 6 0 0 24.3 4.98 4.58 2 compost 5 tons trench 47.6 3 4 2 1 0 24.5 5.10 4.53 3 compost 5 tons surface shallow 54.8 3 4 4 0 0 23.8 4.95 4.57 4 compost 10 tons trench 47.9 2 6 7 0 0 24.8 5.13 4.56 5 compost 10 tons surface shallow 53.8 2 5 4 1 0 24.8 4.95 4.57 6 KCl @ 50 lbs K20 sidedress 46.1 2 3 7 1 0 23.3 4.88 4.64 7 KCl @ 100 lbs K20 sidedress 50.0 2 4 3 0 0.1 23.5 5.05 4.56 8 KCl @ 200 lbs K20 sidedress 45.1 2 4 8 1 0 23.5 5.20 4.58 9 KCl @ 400 lbs K20 sidedress 50.8 5 4 5 0 0 23.3 4.85 4.61 10 K2SO4 @ 50 lbs K20 sidedress 39.2 2 4 10 2 0 24.0 4.88 4.61 11 K2SO4 @ 100 lbs K20 sidedress 51.5 3 4 3 0 0 23.8 4.93 4.61 12 K2SO4 @ 200 lbs K20 sidedress 46.0 1 4 6 1 0 25.0 5.15 4.57 13 K2SO4 @ 400 lbs K20 sidedress 52.2 3 4 4 0 0 23.8 4.78 4.57 LSD 5% 7.1 NS NS 4.5 NS NS NS NS NS F value 2.93 0.71 0.95 2.29 1.88 1.0 1.65 0.81 0.89 % CV 10 80 37 60 126 721 4 6 1 non additivity ^ ^
GROUP CONTRASTS A control vs 50.9 2.7 3.4 5.7 0.3 0.0 24.3 5.0 4.58 any compost 51.0 2.5 4.7 4.1 0.5 0.0 24.5 5.0 4.56 Probability NS NS 0.13 NS NS NS NS NS NS
B control vs 50.9 2.7 3.4 5.7 0.3 0.0 24.3 5.0 4.58 any K sidedressed 47.6 2.6 4.0 5.8 0.6 0.0 23.8 5.0 4.59 Probability 0.21 NS NS NS NS NS 0.32 NS NS
C compost placement: trench vs 47.8 2.5 4.8 4.4 0.7 0.0 24.7 5.1 4.55 shallow incorporation 54.3 2.6 4.6 3.8 0.4 0.0 24.3 5.0 4.57 Probability 0.01 NS NS NS 0.32 NS NS 0.27 NS
D compost rate 5 tons vs 51.2 2.9 4.1 2.9 0.6 0.0 24.2 5.0 4.55 10 tons 50.9 2.2 5.4 5.3 0.5 0.0 24.8 5.0 4.57 Probability NS NS 0.10 0.12 NS NS 0.19 NS NS
E Potassium source: KCl 48.0 2.9 3.8 5.7 0.6 0.03 23.4 5.0 4.60 vs. KS04 47.2 2.3 4.2 5.9 0.7 0.00 24.2 4.9 4.59 Probability NS NS NS NS NS 0.21 0.03 NS NS
probability: F Rate K muriate (linear) NS 0.06 0.29 NS NS NS 0.31 NS NS G Rate K muriate (quadratic) 0.12 0.15 NS NS 0.30 0.24 NS 0.13 NS H Rate K sulfate (linear) 0.13 NS NS 0.11 0.31 NS NS NS NS I Rate K sulfate (quadratic) 0.22 NS NS NS NS NS 0.26 0.22 NS
SUMMARY: Fusarium wilt was observed prior to full bloom and continued with an incidence approaching50 % of plants Fusaiurm disease level had a major impact on the trial and complicated the results. Yields were not improved with any of the composted manure or potassium applications Composted manure surface-applied over the bed center were superior to trenched (~5 inches deep). KCl had slightly better color compared to K sulfate.
CALIFORNIA TOMATO RESEARCH INSTITUTE, INC. 18650 E. Lone Tree Road Escalon, California 95320-9759
RESEARCH SUMMARY REPORT TO CTRI, NOVEMBER 12, 2016
Project Title: EVALUATING THE NITROGEN BUDGET SYSTEM IN DRIP IRRIGATED PROCESSING TOMATOES: YEAR 2
Project Leader (s): Scott Stoddard UC Cooperative Extension 2145 Wardrobe Ave. Merced, CA 95340 209-385-7403; cell: 209-777-7645 [email protected]
SUMMARY A nitrogen rate trial was conducted in drip irrigated processing tomatoes cv UG19406 to evaluate the use of soil nitrate testing to guide N fertilizer applications. Treatments were 0, 50, 100, 150, and 250 lbs N/A, plus one variable rate treatment based on weekly soil testing. Treatment design was a randomized block with 4 replications; plot size was 1 bed by 100 ft. All nitrogen was injected through surface drip tape on a weekly schedule, and no preplant fertilizers were applied. Soil samples were taken to a depth of 12” every 7 days during the growing season beginning one month after transplanting; leaf and petiole samples were taken at the same time to monitor N in the crop. Nitrogen fertilizer, from UAN32, was applied in the variable rate treatment at 10 – 30 lbs/A if soil NO3-N was less than 16 ppm; the other nitrogen treatments were applied each week at 10 – 30 lbs/A depending on plant growth stage. Over the course of the growing season, soil NO3-N was greater than 16 ppm in only one sampling period, and therefore there was little difference between the timing of N fertilizer applied compared to the other treatments. Total applied N in the variable rate treatment was 200 lbs/A. No significant differences were observed in soil NO3-N between any of the treatments, but there was a significant increase in petiole NO3- N and leaf N as nitrogen fertilizer rates increased. Yields were significantly correlated to increasing N, from 35.6 to 56.3 tons/A as N rates increased from 0 to 250 lbs/A. The very low soil nitrate levels that persisted throughout the growing season may have been due to denitrification as a result of frequent irrigation with surface drip tape in a heavy clay soil. These results suggest that soil NO3-N testing did not help with the timing of fertilizer applications, but they did help to guide the proper rate needed to maximize yield.
INTRODUCTION Considerable research on the N needs of processing tomatoes in California has shown that under typical conditions, N fertilizer rates of 100 – 150 lbs/A maximize yield, and that total N inputs above 220 lbs/A are rarely needed. Work by Don May, Gene Miyao, and Tim Hartz at various
Stoddard CTRI Research Summary 2016 page 1 sites in California1 showed that a yield response in furrow-irrigated tomatoes was unlikely when the pre-sidedress nitrate-N concentration in the top 2 feet of the profile was above 16 ppm. These and other similar rate studies are based on “step-up” trials, where fertilizer rates are increased incrementally to develop a yield response curve on which to base such recommendations. The proposed new N reporting guidelines have a “step down” approach, where total N needs are first determined, then source credits are subtracted to determine a nitrogen fertilizer rate, e.g.:
Total req N – soil nitrate credit – water N credit – organic N credit = fertilizer req.
Therefore, a hypothetical processing tomatoes N budget could look like this: 300 – 65 – 25 – 40 = 170 lbs N/A from fertilizer assuming 16 ppm soil NO3-N at transplanting, 5 ppm NO3-N irrigation water and 30” applied, and 2% O.M. While this provides a number that essentially matches what is currently recommended, this method may be further refined by testing the soil for NO3-N before fertilizer is applied.
The objective of this study was to evaluate the applicability of using in-season soil nitrate and leaf N testing to determine fertilizer application rate and timing in drip irrigated processing tomatoes.
METHODS The test site was located on the Merced College agriculture farm in north Merced. Site was had not been cropped for at least 5 years and had initial soil test nitrogen of 3 ppm NO3-N. Soil type is Marguerite clay loam, which is commonly cropped to tomatoes in Merced County. The entire project area had the beds shaped on May 15, 2016. Standard 5’ beds were used. Planting was delayed due to a last minute change in the field location that delayed listing of beds, drip installation, and transplanting. The drip irrigation system was placed on the surface of the beds, then covered slightly with cultivation. The use of surface drip, rather than buried, was due to the lateness of the season and availability of a drip injection shank. The drip tape was Netafim Streamline 5/8” 6mil 0.22 gph on 12” emitter spacing, attached to 1” black poly tubing so that a randomized, complete block design could be used. This was connected to a PVC manifold via poly tubing. The manifold split the irrigation water into separate sectors so that the independent nitrogen applications could be made to appropriate plots (Figure 1).
1 May, D., Mitchell, J., 2001. Soil testing to optimize nitrogen management for processing tomatoes. FREP Final Report. https://www.cdfa.ca.gov/is/ffldrs/frep/pdfs/completedprojects/97-0365M97-03May.pdf
Stoddard CTRI Research Summary 2016 page 2
Figure 1. Nitrogen injection manifold.
The test design was a randomized block with 4 replications. Plots were one bed (5 feet) by 100 feet in length. Treatments evaluated: 1. No fertilizer (0 lbs N/A) 2. 50 lbs N/A through the drip tape 3. 100 lbs N/A 4. 150 lbs N/A 5. 250 lbs N/A 6. N fertilizer based on soil and tissue tests (“Variable”) Nitrogen source was UN32 and was injected into the appropriate treatments using a battery operated Sure-Flo pump.
The plots were transplanted on June 15, 2016, with cv UG19406 on 60” beds with 12” plant spacing using mechanical finger planters. Plants were from Westside Transplants in Firebaugh. Transplant water was applied at 1500 gallons/A with 5 gal/A equivalent of 5-24-8 + humic acid + Zn & Fe from Helena. No other pre-plant fertilizers were used. Pest management included 2 applications of Admire (imidacloprid) through the drip tape and one Radiant (for worms) application made in August. For weed control, Prowl and Dual Magnum were applied pre plant and incorporated; Matrix was applied 10 days after transplanting. The field was irrigated 3 to 5 days a week based on crop ET x Kc estimates. Irrigation source was Merced Irrigation District water, which was tested during the season and found to contain <1.5 ppm of nitrogen.
Soil, and tissue samples began 12-July for all plots, and were taken every 7 days to monitor the crop for nitrogen in treatment 6. Leaf and petiole samples were taken from the length of the plot, then separated for total leaf N (%N) and petiole nitrate (ppm NO3-N). Soil samples were taken to a depth of 12” near the tape in the wetted zone of the bed. Samples were taken to Denele Labs in Turlock for nitrogen (NO3-N and NH4-N) analyses. N was applied if the soil results were <16 ppm at the same rate as what was applied to the 150 lbs N/A treatments. Nitrogen applications began July 20 and continued through the growing season by injecting every 7 days. Initial rates were 10 lbs N per acre, which increased to 30 lbs per acre per application to match the growth of the crop. Nitrogen application dates and rate are shown in Table 1.
Stoddard CTRI Research Summary 2016 page 3
Plots were harvested 2-Nov-2016 by hand harvesting 10 ft of each plot. Replicated results from lab analyses and yield estimates were analyzed using appropriate AOV models. Means separation was done using Fisher’s Protected LSD at the 95% confidence level.
RESULTS Initial soil and water test results are shown in Tables 2 & 3. Soil nitrate levels in the upper foot were only 3 ppm, very low. Water tests at both the start and end of the season had <1.5 ppm NO3-N, which would provide negligible N based on the amount of irrigation (~ 2.2 acre-ft) during this season.
Current guidelines for soil NO3-N suggest that levels > 16 ppm are sufficient and additional fertilizer N is not needed. Over the growing season, soil NO3-N in treatment 6 (Variable N application) was above 16 ppm only on Sept 15 (Figure 2). Therefore fertilizer N was applied throughout the season, initially to mimic the 150 lbs N rate, but because of persistent low N levels in the soil, continued longer such that 200 lbs N/A was eventually applied through the drip tape.
Leaf and petiole results for the later part of the growing season for treatment 6 are shown in Figure 3. Values are well above sufficiency thresholds because the plots were still being fertilized late into the season. There was a significant increase in petiole NO3-N and leaf N as nitrogen fertilizer rates increased (Figure 4). Thus, while there was little response in soil nitrate nitrogen, the plants were utilizing the increased N from the higher fertilizer rates.
Yield results are shown in Figure 4 and Table 4. There was a significant increase in yield as N fertilizer rates increased. Best yields occurred at 225 lbs N/A. Treatment 6 (applying N only when soil NO3-N indicated) had an average yield of 53.1 tons/acre, which was greater than the 150 N/A treatment with which it was most closely matched. Thus the use of soil testing resulted in an additional 50 lbs N/A being applied, which was justified by the increase in yield observed.
Using the “step down” approach with the results of this trial, the following N fertilizer budget would have been suggested:
300 total N – 52 (soil) – 0 (water) – 40 (organic matter) = 210 lbs N/A assuming that total N requirements (vine, roots, and fruit) were 300 lbs/A.
Stoddard CTRI Research Summary 2016 page 4
Nitrogen use efficiency (NUE) was estimated by calculating the increase in yield of each fertilizer treatment above untreated control, multiplying by 1.53 (USDA: lbs N per 1000 lbs fruit), then dividing by the total N applied. NUE ranged from 0.41 to 0.25 lbs N/applied N. The highest NUE occurred with 100 lbs of fertilizer N.
The results from this trial in 2015 suggested that testing for soil nitrate can help guide the timing of nitrogen applications in drip irrigated tomatoes, but that yield responses can occur even with early season levels above 16 ppm. In 2016, it was not possible to determine if the same threshold could be used because soil NO3-N was rarely above 16 ppm. The soil tests did help in that they indicated additional N was needed in the latter part of the growing season to maximize yield.
Treatment%6%Soil%NO3%
20.0#
18.0#
16.0#
14.0#
12.0#
10.0#
8.0# Soil%NO3)N,%ppm%%
6.0#
4.0#
2.0#
0.0# 7/8/16# 8/5/16# 9/2/16# 9/9/16# 7/15/16# 7/22/16# 7/29/16# 8/12/16# 8/19/16# 8/26/16# 9/16/16# 9/23/16# 9/30/16# 10/7/16# 10/14/16#
Figure 2. NO3-N in soil samples in treatment 6 taken 4-5 days before injecting nitrogen into the drip system was used to guide fertilizer applications to these plots. When levels were below 16 ppm, nitrogen was applied at rates equivalent to the 150 lbs N/A treatment.
Stoddard CTRI Research Summary 2016 page 5
Treatment&6&leaf&and&pe#oles& 6.5# 9000#
6# 8000# 5.5#
5# 7000#
4.5# 6000#
Leaf&N%&& 4#
3.5# 5000# pe#ole&NO3*N,&ppm&
3# 4000# Leaf#%N# 2.5# Pe5ole#NO3# 2# 3000# 7/8/16# 8/5/16# 9/2/16# 9/9/16# 7/15/16# 7/22/16# 7/29/16# 8/12/16# 8/19/16# 8/26/16# 9/16/16# 9/23/16# 9/30/16# 10/7/16# 10/14/16# Figure 3. Leaf %N and petiole NO3-N in treatment 6 in the latter half of the growing season.
Stoddard CTRI Research Summary 2016 page 6
Processing Tomato N Trial 2016
12000 r------, Sept 5 tissue sampling a 10000 E ab Q. ;sooo b ., 0 ·~ 6000 0.. ::::E c c c 4000
2000 ~~~n~~n ~~ a a a
I, ab * 5.5 ....z.. ~ ::::E be 5
55 r 50 • -.<{ • ~ 45 B 40
N Treatment, lbs N/A
Figure 4. Petiole NO3-N (top), leaf N (middle), and fruit yield all showed significant increases as N fertilizer rate increased. The variable N treatment = 200 lbs/A.
Stoddard CTRI Research Summary 2016 page 7
"!a bl e 2 s 01. , sample resu Its , 2016 CEC N03-N Olsen-P X-K X-Na X-Ca X-Mg (est) OM pH E.C. 8 Zn Fe Cu Mn meg/10 mmhos/ Sample DESC QQ!!!. QQ!!l QQ!!!. .QQ!!! QQ!!!. QQ.!!!. Qg_ % em ppm ppm ppm ppm ppm 0-1 2" 12-Jul compos 3.0 8.0 140.0 95.0 2665.0 556.0 2.1 1 .8 6.6 1.4 0.1 1.1 6.9 2.0 2.9 A NR labs I ND = Not determined
Table 3. Irrigation water sample results, 2016.
Sample DESC pH E.C.e Nitrate N Potassium Calcium v1agnesiuh Sodium Bicarbonate Boron Chloride Sullfate SAR mmhos ppm ppm ppm ppm ppm ppm ppm ppm ppm
24-Aug during 6.73 0.1 <1.5 0.8 5.9 2.4 2.3 42.7 0.04 2.5 <1.5 0.2
10 -Aug irrigation <1.5
5-Se <1.5 Water samples taken in the middle of an 8 hour irrigation cycle.
Stoddard CTRI Research Summary 2016 page 8
Table 4. Processing tomato soil, leaf, and petiole results on Sept 5 and Oct 5; estimated yield and N Use Efficiency (NUE), Merced 2016. Nitrogen I 5-Sep Soil petiole Leaf 5-0ct Soil petiole Leaf yield NUE plot tTreatment JN03-N , ppm total N, ppm ppm Leaf N. j'o N03-N, ~ppm_ total N ~ ppm ppm Leaf N.% tons/A _green % mold % lons/lb N 1 0 4.8 845.0 2705.0 4.6 4.0 840 1050 4.02 35.621 44.8% 21 ,9% -- 2 50 5.8 895.0 2935.0 4.8 3.0 760 1120 3.86 42.303 28.8% 19.1% 0.4089 3 100 4.8 812.5 3460.0 5.6 3.0 910 186 3.55 48.998 36.8% 19.9% 0.4093 4 150 5.3 1035.0 7625.0 5.7 3.0 920 1290 3.29 49.203 37.9% 11.4% 0.2771 5 250 9.0 1082.5 9125.0 5.3 4.0 750 4230 3.97 56.312 23.0% 17.4% 0.2533 6 Variable 7.3 1007.5 6337.5 5.6 4.0 800 4130 4.06 53.143 33.7% 13.1% 0.2681
Average 6.13 946 5364 5.27 3.5 830.0 2001 .0 3.8 47.597 34.2 17.1 - LSD 0.05 ns ns 1701 0.51 ------5.644 13.6 ns - CV, % 51.1 29.4 21.1 6.4 ------7.9 26.4 53.6 - LSD 0.05 = Least Significant Difference at the 95% confidence level. ns = not significant CV =coefficient of variation -- non replicated data NUE = nitrogen use efficiency, (yield with fertilizer- UTC yield)*1.-53/fertilizer applied
Stoddard CTRI Research Summary 2016 page 9
Potential for improved fertigation efficiency via field-based sensing devices
Project Leader:
Brenna Aegerter, Farm Advisor, UC Cooperative Extension San Joaquin County, 2101 East Earhart Ave. Suite 200, Stockton, CA 95206-3924 (209) 953-6114, [email protected]
Project Collaborators:
Mark Lundy, CE Specialist, Dept. Plant Sciences, University of California, Davis, CA 95616 (530) 752-7724, [email protected]
Martin Burger, Project Scientist, Dept. Land, Air and Water Resources, University of California, Davis, CA 95616 (530) 754-6497, [email protected]
Summary
We collected data from six commercial processing tomato fields in 2014 through 2016 where surface renewal evapotranspiration (ET) stations were installed. In addition to the ET data, we have developed nutrient uptake curves from whole plant sampling, as well as measurements of normalized difference vegetation index (NDVI) from both a handheld meter and from aerial imagery (the latter in 2015 and 2016 only). These tools, although requiring investment in terms of costs (upfront costs for some, recurring costs for others) and requiring increased management time, can provide some insight into decisions regarding scheduling of irrigation and fertilization.
Objective
The goal of this research is to explore the use of two field-based measurement devices to inform more precise management of water and nutrients in subsurface drip-irrigated processing tomatoes.
Procedures
In 2016, the study was conducted in two commercial processing tomato fields under subsurface drip irrigation and located in Yolo and San Joaquin counties. This was in addition to sites followed in 2015 and 2014 (funded by CTRI in 2015). The 2016 Yolo site was located near county roads 102 & 14. It was transplanted on April 17th; bed configuration was a single plant row with 60” between bed centers; spacing within the row was 15”. The 2016 San Joaquin site was located in the Central Delta near Inland Dr. and Stark Rd., near Middle River (San Joaquin River) on Roberts Island. It was transplanted on May 4th. Bed configuration was also a single plant row on 60” centers; spacing within the row was 14”.
At each field, a surface renewal evapotranspiration (ET) station (Tule Technologies, Inc.) was installed after transplanting. Surface-renewal technology is a recently developed, relatively low-cost method that measures the actual crop ET in a 2 to 10 acre area. Surface renewal is based on the analysis of energy residing within the crop canopy during turbulent exchange processes as measured by instruments resembling a simple weather station. The outputs of the surface renewal stations are daily ETa as well as a plant response index (ETa relative to CIMIS reference ET, or ETa/ETo). The station also ties into the grower’s drip tape and records how many hours the system is turned on; based on system specifications the run time is then used to calculate amounts of irrigation water applied. The normalized difference vegetation index (NDVI) is a measurement derived from reflectance from the crop canopy measured at wavelengths of light known to correlate to plant chlorophyll content. NDVI provides a proxy measurement of both biomass accumulation and relative chlorophyll concentration, which can be used as an indicator of nutrient sufficiency/deficiency and plant health/stress. Weekly, NDVI measurements were made with a handheld NDVI meter (Trimble Greenseeker). We walked two rows within a 2-acre area upwind of the Tule station and took multiple readings from both over the center of the bed and over the center of the furrow (values reported are a weighted average of the two types of readings). We also obtained data from aerial images taken by TerrAvion, a commercial provider of aerial photography which delivers images with multiple spectra, including NDVI.
At regular intervals throughout the season, we took above-ground whole plant biomass samples, weighed and oven-dried the samples (four or five plants taken per sampling time point). These tissue samples were analyzed for nitrogen, phosphorous and potassium content by the UC Davis Analytical Lab in 2015. In 2016, we joined forces with the lab of Daniel Geisseler at UC Davis, which has funding from CDFA’s FREP program to develop a decision support tool for processing tomato based on the CropManage software developed by UCCE advisor Mike Cahn. Thus the study sites for this CTRI project were also used for the CropManage project. In 2016, the Geisseler lab processed the nutrient samples as part of their project (and shared the data with this CTRI project), while the CTRI project placed the Tule stations in the fields and shared that data with the CropManage project. By sharing sites and data, we hope to gain a better understand of nutrient and irrigation variables impacting yield.
Results
One aspect of our evaluation of the handheld NDVI meter is whether measurements of NDVI could be useful as a proxy for nutrient uptake measurements. If there were good correlations between NDVI and nutrient uptake, then the handheld meter which provides quick, non-destructive measurements could be an appealing replacement for the more time-consuming and relatively expensive tissue sampling and laboratory analysis. At the 2016 sites, NDVI values increased rapidly as the canopy developed, peaked in the ninth to tenth week, and declined fairly rapidly during fruit sizing and ripening (Figures 1a-d). NDVI peaked earlier in high-yielding sites with higher N uptake than in low-yielding, lower N uptake sites (data not presented here). However, while the meter provides interesting indications of upward or downward trends, based on our observations it cannot replace tissue sampling for nutrient content when evaluating the nutrient status of the crop.
Another question is whether NDVI measurements could be useful in guiding decisions as to when to begin cutting back on irrigation. The point at which the NDVI begins to decline might be useful as an indicator that the vine has begun to senesce and irrigation amounts could safely be reduced. It does appear that peak NDVI is well correlated with the peak in ETa/ETo (Figures 2a-d), suggested that using NDVI could be useful to identify the turning point after which vines begin to senesce and irrigation should be cut back. From our data thus far, it appears that peak or turning point is better predicted by cumulative ET than by days after transplant (data not presented here).
With the addition of aerial imagery in 2015 and 2016, this allowed us to look at the correlation between handheld measurements of NDVI (we made on ground measurements with a Trimble Greenseeker) and aerial NDVI data (from TerrAvion, fixed wing aircraft). Although the values themselves differ, the pattern through the season is very similar for in-field and aerial NDVI (Figures 1a-d).
Some advantages and disadvantages of these tools:
Handheld NDVI meter (Trimble Greenseeker)
- Easy to use (+) - Labor intensive: Measurements are made while walking though field; not feasible to cover entire field (though other units could be tractor-mounted) (-) - Corrects for ambient light, thus time of day of measurement not critical (+) - Management time to look at results - Costs about $500, fairly durable
Aerial Imagery (fixed wing aircraft or drones)
- TerrAvion: User-friendly interface, tech support easily available (+) - Time of day affects NDVI results (-) - Management time to look at results - Relatively cheap to get weekly images of entire fields or ranches
Tule Technologies ET station
- In-field weather station measures ET in your field – not an estimate (+) - Pretty clearly shows when ET rates are slowing (+) - Station data may not represent the entire field, doesn’t represent nearby fields (-) - User friendly interface, tech support easily available (+) - Cost of annual subscriptions for data from stations, recurring annual cost
Table 1. Sites associated with this study and their source of funding. In 2016, sites were shared for both CropManage and this project.
Site (year, location) Nutrient uptake Tule ET NDVI - NDVI – TerrAvion station Greenseeker 2014 – S. Sac Valley Lundy Lundy Lundy --- 2014 – S. Sac Valley Lundy Lundy Lundy --- 2015 – Woodland Aegerter, CTRI CTRI Aegerter, CTRI CTRI 2015 – Delta Aegerter, CTRI CTRI Aegerter, CTRI CTRI 2016 – Woodland Geisseler, FREP CTRI Geisseler, FREP CTRI 2016 – Delta Geisseler, FREP CTRI Geisseler, FREP CTRI 2016 – Tracy Geisseler, FREP --- Geisseler, FREP CTRI 2016 – E. Stockton Geisseler, FREP --- Geisseler, FREP --- 2016 – Huron Geisseler, FREP CTRI Geisseler, FREP ---
A
B
C
D
Figure 1. NDVI (vertical axis) as a function of days after transplanting (horizontal axis). Measured by handheld meter (Greenseeker) or multi-spectral aerial imagery (TerrAvion). Sites are four processing tomato fields located in San Joaquin and Yolo County, 2016. Figure 2. Evapotranspiration (Eta, from Tule) relative to reference ET (ETo, from CIMIS station). Charted along with NDVI and nitrogen uptake rates. A = Roberts Island site 2015 B = Woodland area site 2015 C = Roberts Isl 2016 D = Woodland 2016.
A B
C D
Roger Chetelat, Director & Curator C. M. Rick Scott Peacock, Plant Collections Manager Dept. of Plant Sciences T G R C University of California Davis, CA 95616 Tomato Genetics Resource Center [email protected] http://tgrc.ucdavis.edu
ANNUAL PROGRESS REPORT
2016
Cherry tomatoes and primitive cultivars from Peru and Ecuador. The TGRC received a large number of landrace varieties, cherry tomatoes and wild species accessions collected from the geographic regions where domestication is thought to have taken place. The new accessions exhibit much diversity for fruit size, shape and color. These images show lobed bilocular (top left), elongate bilocular (top right), flat fasciated (center), elongate pink (bottom left), and elongate red (bottom right) types. [photos Scott Peacock]
The TGRC gratefully acknowledges receiving financial support from these institutions or individuals in 2016. UCDAVIS DEPARTMENT OF PLANT SCIENCES College of Agricultural and Environmental Sciences
USDA USDA ~
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M. Allen Stevens
SUMMARY Acquisitions. The TGRC acquired 141 new accessions this year, including a relatively large collection of S. pimpinellifolium, cherry tomatoes and landraces from Peru, Ecuador and Mexico. In addition, we received new mutant stocks of genes alcobaca, suppressor of self- pruning, single flower truss and terminating flower-2. Obsolete or redundant accessions were dropped. The current total of number of accessions maintained by the TGRC is 3,912. Maintenance and Evaluation. A total of 1,207 cultures were grown for various purposes, of which 512 were for seed increase, including 71 wild species accessions. Germination tests were run on 821 seed lots. Progeny tests were performed on 129 stocks of male-steriles, trisomics, and other segregating lines or accessions with unexpected phenotypes. 322 stocks were grown for introgression of the S. sitiens genome. Other stocks were grown for research on interspecific reproductive barriers. All plants grown for seed regeneration were tested twice for PSTVd; no positive plants were found. Newly regenerated seed lots were split, with one sample stored at 5° C to use for filling seed requests, the other stored in sealed pouches at -18° C to better maintain long term seed viability. For backup storage, 104 seed samples were sent to the USDA and 34 samples were sent to the Svalbard Global Seed Vault. Distribution and Utilization. A total of 2,656 seed samples representing 1,423 different accessions were distributed in response to 160 requests from 136 researchers and breeders in 18 countries; at least 22 purely informational requests were also answered. The overall utilization rate (i.e. the number of samples distributed relative to the number of active accessions) was 68%. Information provided by recipients indicates our stocks continue to be used to support a wide variety of research and breeding projects. Our annual literature search uncovered 112 publications mentioning use of our stocks. Documentation. The GIS functions on our website were improved to allow mapping of multiple species, so that users can more easily find all accessions from a particular geographic region. Additional images of mutants and wild species were uploaded, with captions, and are available on our website. Seed request records and passport information on backed up seed lots were provided to the USDA. Research. The TGRC continued research on the mechanisms of interspecific reproductive barriers and on introgression of the S. sitiens genome. We published a paper on natural variation for pollen incompatibility genes in S. habrochaites, and contributed to a paper on reproductive barriers that prevent hybridization between tomato species where they overlap in the wild. We further advanced a set of breeding lines representing the genome of S. sitiens in a cultivated tomato background. The goal of this project is to develop a set of ‘introgression lines’ – prebred stocks containing defined chromosome segments from the donor genome – that will provide the first breeder friendly germplasm resources for this wild species. We contributed to a paper on using reference genome sequence datasets to find indel markers in genomes that have not been sequenced. We also participated in collaborative research projects to develop new mutant resources for gene identification in tomato, or to develop methods to create doubled haploids. ACQUISITIONS
The TGRC acquired 141 new accessions in 2016. Dr. Esther van der Knaap at Univ. of Georgia and Dr. Maria Jose Diez at the Univ. de Valencia, Spain, donated a set of early domesticated forms of S. lycopersicum and accessions of S. pimpinellifolium collected from the regions where domestication steps are thought to have occurred, mainly Ecuador, Peru and
Mexico. The new germplasm significantly increases representation of genetic variation from these geographic regions in our collection. Of particular importance in this regard are the additional cherry tomatoes with domestication traits such as multi-loculed fruit, as well as the Mexican landraces, neither of which were adequately sampled until now. Many of the collections were made by Dr. Fernando Nuez and colleagues. We thank the COMAV genebank at the Univ. de Valencia, Spain, for sharing this germplasm. The National Science Foundation provided funding to carry out maintenance and distribution of this core collection through the TGRC. Dr. Zachary Lippman at Cold Spring Harbor Laboratories donated several mutant stocks affecting flowering responses and inflorescence architecture. The stocks include a new allele of sft (single flower truss2) in the M-82 background. This mutant produces indeterminate inflorescences with single flowers. Heterozygotes show a relaxed expression of determinate habit, which can have the effect of increasing total yield by up to 20%. Mutants at new loci include ssp (suppressor of self-pruning), which restores indeterminate plant growth to otherwise determinate genotypes homozygous for the sp (self-pruning) gene, and tmf-2 (terminal flower-2), which produces inflorescences with single flowers. Dr. Martha Mutschler at Cornell Univ. donated new stocks of alc (alcobaca), including the original landrace by that name from Portugal which was the source of the mutation, a breeding line with the alc gene in a fresh market breeding, disease resistant genotype, and a red-fruited, apparent revertant from alc to wild type. More detailed information on the recently acquired accessions can be found on our website at http://tgrc.ucdavis.edu/acq.aspx. Obsolete or redundant accessions were dropped. The current total of number of accessions maintained by the TGRC is 3,912.
Table 1. Number of accessions of each species maintained by the TGRC. The figures include accessions that are temporarily unavailable for distribution. Solanum spp. Lycopersicon equivalent # Accessions S. lycopersicum L. esculentum, including var. cerasiforme 2,781 S. pimpinellifolium L. pimpinellifolium 334 S. cheesmaniae L. cheesmanii 42 S. galapagense L. cheesmanii f. minor 29 S. chmielewskii L. chmielewskii 16 S. neorickii L. parviflorum 47 S. arcanum L. peruvianum, including f. humifusum 45 S. peruvianum L. peruvianum 70 S. huaylasense L. peruvianum 16 S. corneliomulleri L. peruvianum, including f. glandulosum 53 S. chilense L. chilense 112 S. habrochaites L. hirsutum, including f. glabratum 123 S. pennellii L. pennellii, including var. puberulum 46 S. lycopersicoides n/a 23 S. sitiens n/a 13 S. juglandifolium n/a 5 S. ochranthum n/a 7 Other (interspecific hybrids, RILs) n/a 150 Total 3,912
MAINTENANCE Scott Peacock and his crew of undergraduate student assistants performed a large number of field and greenhouse plantings this year. A total of 1,207 families were grown for various purposes; 512 of these were for seed increase, including 71 of wild species accessions, most of which required greenhouse culture; 322 were for introgression and analysis of the S. sitiens genome. The rest were grown for germination tests, progeny tests, genotyping, research on reproductive barriers, or other purposes. Identifying accessions in need of regeneration begins with seed germination testing. We test all seed lots after 10 years of storage. Seed samples that do not meet our threshold of 80% germination after two weeks are normally regenerated in the same year. Seed lots that meet the threshold are retested again in two years. Other factors, such as available space, age of seed and supply on hand, are also taken into account. Newly acquired accessions are typically regenerated in the first year or so after acquisition because seed supplies are limited and of uncertain viability. This year, 821 seed lots from 2006 or earlier were tested for germination rates. Average germination values continued to be relatively high for most species (Table 2), however we again saw relatively low germination in seed samples of cultivated tomato; seed of S. habrochaites also germinated poorly, as did the more distantly related Solanum spp., all of which have strong seed dormancy. Table 2. Results of seed germination tests. Values are based on samples of 25-50 seeds per accession, and represent the % germination after 14 days at 25°C. Seed lots with a low germination rate are defined as those with less than 80% germination. Date of Avg % # Low # Solanum Species Tested Lots # Tested Germ Germ Grown1 S. lycopersicum 2004-2006 601 69.1 324 450 S. pimpinellifolium 2003-2006 124 82.2 55 37 S. cheesmaniae, S. galapagense 2002-2006 14 83.6 2 2 S. chmielewskii, S. neorickii 1996-2006 18 93.1 1 3 S. chilense 1997-2006 21 84.7 4 5 S. peruvianum, S. arcanum, 1981-2012 14 78.3 5 5 S. corneliomulleri, S. huaylasense S. habrochaites 2004-2006 10 70.4 3 7 S. pennellii 1997-2006 15 94.3 1 3 S. lycopersicoides 2006 1 12 1 0 S. sitiens 2006 1 30 1 0 S. juglandifolium 2006 1 48 1 0 S. ochranthum 2000 1 90 0 0 Total 821 72.8 398 512 1Represents total number of accessions of each species grown for seed increase in 2016. We again planted a relatively large number of cultivated tomato and S. pimpinellifolium genotypes in the field this year, occupying a total of 64 rows. The usual sequential plantings were made to spread the workload. Seedlings were transplanted into the field on four separate dates, starting in late April. Early growth and establishment were satisfactory, and conditions in the field were generally favorable for fruit set, with only a few periods of excessive temperatures, during which manual pollinations were suspended. We used drip irrigation for the first time this year. We found that initial seedling establishment was poor and we lost a significant percentage of plants, particularly in the later plantings. This problem may have been
related to the use of seedling trays with a smaller soil volume than in the past. Despite the establishment problem, later growth was exceptionally good under drip, so good in fact that the indeterminate lines became overgrown and intertwined. The selfing S. pimpinellifolium accessions were planted in a checkerboard layout with extra space on all sides to make it easier to keep plantings separated. Next year we will experiment with curtailing drip irrigation in the rows with indeterminate lines to prevent them from getting so large. We also established a second field trial as a demonstration plot for the Solanaceae Genomics Conference. This plot was planted with a diverse panel of accessions designed to showcase the range of genetic variation in the TGRC’s collection. Accessions included a sample of cultivars, mutants affecting different stages of growth, and representatives of each wild species within the tomato clade. Approx. 60 conference attendees visited our field plot. Most of the wild species, many mutants and certain other genetic stocks require greenhouse culture, either for isolation purposes or because they do not grow or flower well under field conditions. For the mutant stocks, we sow the weakest lines first, and finish with lines of normal vigor. Our schedule of greenhouse plantings of the wild species is based on photoperiod responses: those with the least sensitivity are planted first, in the early spring; those with intermediate reaction are planted in early summer; the most sensitive (i.e. flower Attendees of the Solanaceae Genomics conference at UC best under short days) are planted in mid Davis inspecting the TGRC demonstration field trial. summer for fall blooming. Optimal planting dates and other growing recommendations for each species are listed on our website. Preventing the spread of seed borne pathogens is an important aspect of any seed regeneration program. We inspect all our plantings throughout the growing cycle for disease symptoms. Plants displaying signs of disease are tested with Agdia ImmunoStrips; we currently test for TSWV, ToMV, PepMV and Clavibacter. This year TSWV was a serious problem in our field plots, especially the early plantings. Last year we discovered PSTVd (potato spindle tuber viroid) in some greenhouse plants, and early in 2016 we learned that some of our seed samples might also be contaminated by the viroid. To assess the scale of the problem, we tested 100 accessions in 20 bulks of 5 accessions each. (The tests were performed by Dr. Kai-Shu Ling at the USDA in Charleston, SC, an expert on this pathogen.) Nine of the 20 bulks were rated as positive by the RT-PCR test, i.e. contained at least one positive seed source in each bulk, suggesting a relatively high prevalence of the viroid. When the accessions were tested individually, some were indeed contaminated. We then did grow out tests on seedlings from positive seed lots, and all were negative, suggesting that the rate of seed transmission of PSTVd is quite low. Based on these results, we systematically tested all plants grown for seed increase in 2016, whether in the greenhouse or in the field. We used the nucleic acid hybridization detection method, and tested plants at the seedling stage and again prior to fruit harvest. We tested several hundred plant families by this method, and all were negative, including plants
grown from seed that had previously given a positive RT-PCR test result. As a precaution, we now treat all seed lots used for distribution with bleach (1% hypochlorite for 5 mins); this is in addition to our standard acid treatment (1% HCl for 15 mins). We also improved plant hygiene practices in several areas to reduce the spread of mechanically transmissible pathogens, including sterilization of pollination and pruning tools (10 secs in 20% bleach), frequent changing of gloves when handling plants, and minimization of cultivation, spraying and pruning operations to reducing wounding. These steps have dramatically reduced the incidence of ToMV in our greenhouse and field, and we feel confident they have also been effective at controlling PSTVd. As in the past, we continue to store samples of all newly regenerated seed lots at 5°C – our working collection, used for filling seed requests -- and at -18°C for long term preservation of viability. We continue to use Zeolite beads to dry seed to ultralow moisture levels prior to sealing in foil pouches, then stored at -18°C or 5°C. Our current -18°C seed storage units are at capacity. We obtained an estimate to rebuild our 5°C walk-in seed vault to add a section for -18 storage, and are looking for sources of funding to finance this project. As in the past, large samples of newly regenerated seed lots were sent to the USDA National Laboratory for Genetic Resources Preservation in Ft. Collins, Colorado, and the Svalbard Global Seed Vault in Norway for long-term backup storage. This year 104 accessions were sent to NLGRP and 34 to Svalbard.
EVALUATION All stocks grown for seed increase or other purposes are systematically examined and observations recorded. Older accessions are checked to ensure that they have the correct phenotypes. New accessions are evaluated in greater detail, with the descriptors depending upon type of accession (wild species, cultivar, mutant, chromosomal stocks, etc.). In the case of new wild species accessions, plantings are reviewed at different growth stages to observe foliage, habit, flower morphology, mating system, and fruit morphology. We also record the extent of variation for morphological traits, and in some cases assay genetic variation with markers. Such observations may reveal traits that were not seen at the time of collection, either because plants were not flowering or were in such poor condition that not all traits were evident, or because certain traits were overlooked by the collector. Many genetic stocks, including various sterilities, nutritional, and weak mutants, cannot be maintained in true-breeding condition, hence have to be transmitted from heterozygotes. Progeny tests must therefore be made to verify that individual seed lots segregate for the gene in question. Other accessions may show unexpected segregation or off-types due to outcrossing, and need to be progeny tested to reestablish true breeding lines. We sowed 129 lines for progeny testing of male-steriles or other segregating mutants, as well as various other stocks with incorrect phenotypes. This year’s progeny tests included stocks of the following mutant loci: Never ripe, high pigment, Delta, male-sterile-2, male-sterile-9, male-sterile-10, male-sterile-15, male-sterile-16, male-sterile-31, male-sterile-32, cleistogamous-2, positional sterile, and positional sterile-2. Progeny from putative telotrisomics 2n+7S7S and 2n+9S9S were tested to confirm segregation for the expected trisomic phenotypes. Additional progeny tests were performed on cultivars Marglobe and Kallio’s Alaska Dwarf, and on landraces from various locations in Colombia, Ecuador or Peru that showed unexpected segregation or off-types. Accessions of S. cheesmaniae, S. pimpinellifolium, S. huaylasense and S. habrochaites were grown for observation as well.
DISTRIBUTION AND UTILIZATION A total of 2,656 seed packets of 1,423 unique accessions were sent in response to 160 seed requests from 136 investigators in 18 countries. (These numbers are lower than in previous years because we suspended seed distribution for 6 months this year while we focused on testing the collection for PSTVd.) At least 22 purely informational requests were answered. Relative to the size of the TGRC collection, the number of seed samples distributed was equivalent to a utilization rate of 68%, and 36% of our active accessions were requested at least once during the year. The various steps involved in filling seed requests – selecting accessions, treating and repackaging seeds, entering the information into our database, providing cultural recommendations, obtaining phytosanitary certificates and import permits, etc. – involve a large time commitment. The turn around time on seed requests has undoubtedly been longer than usual due to the need to bleach and repackage seed samples of each accession. The results from hundreds of pathogen tests are now logged in our database. The TGRC crew has worked diligently to fill seed requests while implementing more stringent phytosanitary practices. We continue to use an online payment system to recover the costs of phytosanitary certificates and express mail shipping. We offer four shipment options: standard, FedEx, DHL, or USPS Priority Mail International. Finding the most reliable shipping method for each country takes time. Shipments are sometimes delayed or lost and need to be refilled. Information provided by recipients regarding intended uses of our stocks is summarized in Table 3. As in previous years, there was an emphasis on breeding for resistance to diseases and/or investigations of the molecular biology of host-pathogen interactions. Viral diseases, particularly TYLCV and other begomoviruses, generated the most requests. There continues to be great interest in abiotic stress responses, particularly drought and extreme temperatures. Studies of various reproductive traits, including crossing barriers, anther morphology, and pollen tube growth, were mentioned in a large number of requests. We again received a significant number of requests for instructional uses. As in the past, the largest numbers of requests mentioned only ‘breeding’ or ‘research’. Table 3. Intended uses of TGRC stocks as reported by requestors. Values represent the total number of requests mentioning each keyword or category. Requests addressing multiple topics may be counted more than once. Category # Category # Category # Biotic Stresses Insects 1 Quality 2 Viruses: Safety (Salmonella) 2 Abiotic Stresses GBNV 1 Sugars, solids 2 Drought 3 STMV 1 High temperatures 3 Breeding ToMV 2 Low temperatures 4 Hydroponic production 1 TSWV 1 Salinity 1 Dominant traits 1 TYLCV 2 Unspecified abiotic 5 Introgression 1 Viroids 1 Male sterility 1 Fungi: Fruit Traits Prebreeding 1 Phytophthora spp. 2 Alkaloids 2 Unspecified breeding 20 Powdery mildew 2 Anthocyanins 3
Ralstonia 1 Carotenoids 2 Genetic Studies Verticillium 1 Flavor 1 Complementation tests 1 Nematodes 1 High pigment 3 Genomic stability 1 Unspecified diseases 8 Parthenocarpy 1 Genomics 2
Category # Category # Category # Gene cloning 1 Herbivory 1 Stomatal responses 1 Gene expression 1 Herbicides tolerance 2 Volatiles 2 Mapping 2 Isotope geochemistry 1 Wounding 1 Microbial ecology 1 Physiol./Develop. Miscellaneous Nutrient uptake 1 Anther development 2 Instructional uses 5 Pollen tube growth 2 Branching 1 Unspecified research 25 Reproductive barriers 7 Circadian clock 1 Root bending 1 Ethylene 1 Sesquiterpenes 1 We continue to receive many requests for introgression lines (ILs), nearly isogenic lines (NILs), and other prebreds. A total of 13 requests and 216 seed samples were processed for the S. pennellii ILs, 7 requests and 132 samples for the S. habrochaites ILs, and 11 requests and 213 samples for the S. lycopersicoides ILs. We also sent out 131 samples of S. lycopersicum x S. pimpinellifolium recombinant inbred lines in response to 5 requests. These numbers show that breeders and researchers continue to find many uses for prebred germplasm. Our survey of the 2016 literature and unreviewed papers of previous years uncovered 112 journal articles, reports, abstracts, theses, and patents that mention use of TGRC stocks (see Bibliography, below). Many additional publications were undoubtedly missed, and cases of utilization by the private sector are generally not publicized. These publications, many in high impact journals, show that TGRC germplasm continues to be employed for a wide range of basic and applied research, breeding and educational purposes.
DOCUMENTATION Our database was modified in various ways by Tom Starbuck to improve internal record keeping, fix bugs, and add functionality to our tables, forms, and queries. Tables and forms were added to allow retrieval of pathogen test results. Additional images of mutants and wild species accessions were uploaded and are accessible on our website. Passport data on new accessions was added and records on existing accessions were updated as needed to correct errors or incorporate new information. The geographic coordinates of all S. habrochaites accessions were checked and updated using Google Earth. On our website Tom improved our GIS mapping functionality to display collection sites from multiple species on the same map. For example, a researcher can now see all the species found in a particular region to find areas where two species occur at the same site, or to compare geographic ranges. As in the past, we provided the USDA National Plant Germplasm System with data GIS map displaying multiple wild species. on accessions sent for back up storage as well as our seed distribution records.
RESEARCH The TGRC is pursuing several research projects related to genetic resources and breeding barriers. One project, supported from the National Science Foundation, aims to decipher the genetic mechanisms of pollen/pistil incompatibility in wide crosses of tomato. Dr. Xiaoqiong Qin is investigating the role of two pollen genes, ui1.1 and ui10.1, both from S. pennellii, that are required for pollen to avoid rejection on pistils of certain wild species. We found that ui1.1 encodes an S-Locus F-box (SLF-23) protein that recognizes a specific pistil protein, S9 type S- RNases. Pollen tubes expressing SLF-23 recognize and degrade the S9 S-RNase, while in the absence of SLF-23, the S-RNase inhibits pollen tube growth, leading to an incompatible reaction. We are also studying the role of ui10.1, a pollen factor which acts independently of ui1.1. Dr. Mira Markova studies natural variation for Cullin1, a pollen factor that acts in concert with the SLF proteins. She discovered a number of discrete loss-of-function mutations in Cullin1 in self- compatible species or populations,; certain of these mutations are shared between geographically remote populations of different species, suggesting the mutations are ancient and could predate speciation within the tomato group. Some of her findings were published in the American Journal of Botany this year. Another research project, funded by the USDA-NIFA’s Plant Breeding program, seeks to develop a set of introgression lines representing the genome of S. sitiens. This wild tomato relative is known for its tolerance to drought, salinity, and low temperatures, as well as unique fruit traits and disease resistance. However strong breeding barriers have prevented crosses with tomato until recently. Introgression lines (ILs) are prebred stocks containing defined chromosomal segments from the wild species’ genome in the genetic background of cultivated tomato. The goal is to create a library of 50-100 ILs that capture the S. sitiens genome in a uniform, cultivated tomato genetic background. Stems of a S. sitiens introgression line (R) with Dr. Diana Burkart-Waco and other lab members increased trichome density compared to the background variety, NC 84173 (L). [photo Scott have advanced each line by marker assisted Peacock] selection using DNA markers (CAPs and indels). Most lines are now at the BC4 or BC5 generations, and many have been self-pollinated to obtain stable homozygous lines. The lines will be genotyped to high resolution using next generation sequencing. In collaboration with Dr. Luca Comai and Dr. Allen Van Deynze, we have assisted in the development of a TILLING resource for tomato. With Dr. Comai and Dr. Ann Britt, we are testing the feasibility of producing doubled haploids in tomato by manipulating expression of CENH3. These research projects are funded by industry consortia.
PUBLICATIONS Baek, Y. S., S. M. Royer, A. K. Broz, P. A. Covey, G. López-Casado, R. Nuñez, P. J. Kear, M. Bonierbale, M. Orillo, E. V. D. Knaap, S. M. Stack, B. Mcclure, R. T. Chetelat and P. A. Bedinger (2016) Interspecific reproductive barriers (IRBs) between sympatric populations of wild tomato species (Solanum section Lycopersicon). American Journal of Botany 103: 1964-1978.
Markova, D. N., J. J. Petersen, X. Qin, D. R. Short, M. J. Valle, A. Tovar-Méndez, B. A. Mcclure and R. T. Chetelat (2016) Mutations in two pollen self-incompatibility factors in geographically marginal populations of Solanum habrochaites impact mating system transitions and reproductive isolation. American Journal of Botany 103: 1847-1861. Toal, T. W., D. Burkart-Waco, T. Howell, M. Ron, S. Kuppu, A. Britt, R. Chetelat and S. M. Brady (2016) Indel Group in Genomes (IGG) molecular genetic markers. Plant Physiology 172: 38-61.
SERVICE AND OUTREACH Presentations. Presentations on the TGRC, research projects, and related topics were given to: the Solanaceae Genomics Conference at UC Davis; the International Congress on Sexual Plant Reproduction in Tucson; a workshop on “Discovering the Potential for Genetic Improvement in Tomato Quality in Wild Tomato Germplasm from Peru” in Lima, Peru; the board of directors of the California Tomato Research Institute. Guest lectures were presented in UCD courses on “Conservation Genetics”, “Plants and Society”, and “Plants for the Garden, Orchard and Landscape”. Scott attended the USDA’s Plant Germplasm Operations Committee meeting in Ft. Collins, Colorado. Press Coverage. We provided interviews and/or background information to the College of Agricultural and Environmental Sciences for a video spot on the TGRC, to WIRED magazine for a story on crop wild relatives, and to television channel TV-5 for a piece on the history of tomato. Visitors. The TGRC hosted visitors from UC Davis Cultural Studies, Universidad Nacional de Cuyo (Argentina), Chia Tai Seeds, Turnip Wagon LLC, United Genetics Seeds, Luyin Seed Co, Wenzhou Shenlu Seeds, Mianyang Seeds, Hebrew University of Jerusalem, Michigan State University, Lipman Produce, Sakata Seeds, Heinz Seeds, East West Seeds, HM Clause, Bayer Crop Sciences, Rijk Zwaan Seeds, Universidad Tecnica Particular de Loja (Ecuador), University of Tsukuba (Japan), and King Abdullah University of Science and Technology (Saudi Arabia). PERSONNEL
L to R: Roger, Benny, Tom, Magdalena, Qin, Mira, Adryanna, Richie, Diana, Meilian, and Scott. Scott Peacock continues to run our seed regeneration and testing program, Adryanna Corral is in charge of seed distribution, and Tom Starbuck maintains our database and website.
Undergraduate student assistants included Sarah Yam, Dragomira Zheleva, Magdalena Mendoza, Jacqueline Liu, Richie Ruiz, Lucero Morales and Ryan Hodge. Dr. Xiaoqiong Qin, Dr. Mira Markova, Dr. Diana Burkart-Waco, and Benny Ordonez led research projects in the lab. Diana now works at the UCD Genome Center, and Benny is in Dr. Luca Comai’s lab. Dr. Meilian Tan from Wuhan, China is a visiting scientist in our lab for one year.
TESTIMONIALS Thank you very much for your kind help! We really appreciate your continuous support to our research at AVRDC. – Mohamed I wish to thank you for the seed samples we received a year ago from the Tomato Genetics Resource Center. We successfully used the seeds in our research. – Natallia Nekrashevich As usual, your information is very useful. – Zach Lippman BIBLIOGRAPHY (112 publications mentioning use of TGRC accessions) Abouelsaad, I., D. Weihrauch and S. Renault (2016). "Effects of salt stress on the expression of key genes related to nitrogen assimilation and transport in the roots of the cultivated tomato and its wild salt-tolerant relative." Scientia Horticulturae 211: 70-78. Aflitos, S. A. (2015). "High-throughput comparative genomics for plant breeding and its application in the tomato clade." Ph.D Thesis, Wageningen Agric. Univ. Anderson, L. K., et al. (2014). "Combined fluorescent and electron microscopic imaging unveils the specific properties of two classes of meiotic crossovers." Proc. Natl. Acad. Sci. 111: 13415-13420. Arms, E. M., J. K. Lounsbery, A. J. Bloom and D. A. St Clair (2016). "Complex Relationships among Water Use Efficiency-Related Traits, Yield, and Maturity in Tomato Lines Subjected to Deficit Irrigation in the Field." Crop Science 56(4): 1698-1710. Baek, Y., A. Tovar-Mendez, P. A. Covey, R. T. Chetelat, B. Mcclure and P. A. Bedinger (2016). "Testing SI x SC rule in the tomato clade (Solanum section Lycopersicon) and the role of a low activity S-RNase in interspecific reproductive barriers." Frontiers in Sexual Plant Reproduction IV: PPI14. Baek, Y. S., S. M. Royer, A. K. Broz, P. A. Covey, G. López-Casado, R. Nuñez, P. J. Kear, M. Bonierbale, M. Orillo, E. V. D. Knaap, S. M. Stack, B. Mcclure, R. T. Chetelat and P. A. Bedinger (2016). "Interspecific reproductive barriers (IRBs) between sympatric populations of wild tomato species (Solanum section Lycopersicon)." American Journal of Botany 103: 1964-1978. Ballester, A.-R., Y. Tikunov, J. Molthoff, S. Grandillo, M. Viquez-Zamora, R. De Vos, R. A. De Maagd, S. Van Heusden and A. G. Bovy (2016). "Identification of Loci Affecting Accumulation of Secondary Metabolites in Tomato Fruit of a Solanum lycopersicum x Solanum chmielewskii Introgression Line Population." Frontiers in Plant Science 7: 1428. Beddows, I., T. Kloesges and L. E. Rose (2016). "Wild tomato: population structure and evidence of natural S. chilense x S. peruvianum hybrid populations." Solanaceae Genomics Conference: 25. Bedinger, P. A., Y. Baek, A. K. Broz, A. M. Randle, S. M. Royer, R. T. Chetelat, A. Tovar-Mendez and B. Mcclure (2016). "Elucidating mechanisms and dynamics of reproductive isolation in wild tomato species." Solanaceae Genomics Conference: 25. Bennewitz, S., et al. (2016). "Type IV glandular trichomes of tomato species: from development to biosynthesis of specialized sesquiterpenoids." Solanaceae Genomics Conference: 62. Bergau, N., S. Bennewitz, F. Syrowatka, G. Hause and A. Tissier (2015). "The development of type VI glandular trichomes in the cultivated tomato Solanum lycopersicum and a related wild species S. habrochaites." BMC Plant Biology 15: 289. Bergau, N., A. N. Santos, A. Henning, G. U. Balcke and A. Tissier (2016). "Autofluorescence as a signal to sort developing glandular trichomes by flow cytometry." Frontiers in Plant Science 7(June). Borghesi, E., A. Ferrante, B. Gordillo, F. J. Rodriguez-Pulido, G. Cocetta, A. Trivellini, A. Mensuali-Sodi, F. Malorgio and F. J. Heredia (2016). "Comparative physiology during ripening in tomato rich-anthocyanins fruits." Plant Growth Regulation 80(2): 207-214.
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Feder, A., S. R. Strickler, H. Sun, Q. Ma, Y. Xu, Y. Shi, J. M. Peralta, J. R. Freschi, Z. Feiz, H. J. Klee and J. Giovannoni (2016). "The Solanum lycopersicoides introgression lines: definitions of introgressed regions and identification of fruit quality loci." Solanaceae Genomics Conference: 128. Florez-Rueda, A. M., M. Paris, A. Schmidt, A. Widmer, U. Grossniklaus and T. Staedler (2016). "Genomic imprinting in the endosperm is systematically perturbed in abortive hybrid tomato seeds." Mol. Biol. Evol. Fulop, D., A. Ranjan, O. Itai, M. F. Covington, D. H. Chitwood, D. West, Y. Ichihashi, L. R. Headland, D. Zamir, J. N. Maloof and N. R. Sinha (2016). "A new advanced backcross tomato population enables high resolution leaf QTL mapping and gene identification." G3-Genes Genomes Genetics 6: 3169-3184. Gao, L., W. Zhao, H. Qu, Q. Wang and L. Zhao (2016). "The yellow-fruited tomato 1 (yft1) mutant has altered fruit carotenoid accumulation and reduced ethylene production as a result of a genetic lesion in ETHYLENE INSENSITIVE2." Theoretical and Applied Genetics 129(4): 717-728. Gharbi, E., J.-P. Martinez, H. Benahmed, M.-L. Fauconnier, S. Lutts and M. Quinet (2016). "Salicylic acid differently impacts ethylene and polyamine synthesis in the glycophyte Solanum lycopersicum and the wild-related halophyte Solanum chilense exposed to mild salt stress." Physiologia Plantarum 158(2): 152- 167. Ghiani, A., N. D'agostino, S. Citterio, A. Raiola, R. Asero, A. Barone and M. M. Rigano (2016). "Impact of Wild Loci on the Allergenic Potential of Cultivated Tomato Fruits." PLoS One 11(5): e0155803. Gonzalez-Cendales, Y., A.-M. Catanzariti, B. Baker, D. J. Mcgrath and D. A. Jones (2016). "Identification of I-7 expands the repertoire of genes for resistance to Fusarium wilt in tomato to three resistance gene classes." Molecular Plant Pathology 17(3): 448-463. Gray, S. B., T. W. Toal, K. Kajala and S. B. Brady (2016). "A systems-level study of the effects of elevated atmospheric CO2 on Solanum lycopersicum and Solanum pennellii." Solanaceae Genomics Conference: 40. Guerrero, R. F., A. L. Posto, L. C. Moyle and M. W. Hahn (2016). "Genome-wide patterns of regulatory divergence revealed by introgression lines." Evolution 70(3): 696-706. Hamlin, J., N. A. Sherman and L. C. Moyle (2016). "Epistasis for post mating pre-zygotic isolation." Solanaceae Genomics Conference: 94-95. Han, S. and S. A. Micallee (2014). "Salmonella Newport and Typhimurium colonization of fruit differs from leaves in various tomato cultivars." J. Food Protection 77: 1844-1850. Han, S. and S. A. Micallef (2016). "Environmental metabolomics of the tomato plant surface provides insights on Salmonella enterica colonization." Applied and Environmental Microbiology 82: 3131-3142. Hind, S., et al. (2016). "Tomato receptor flagellin-sensing 3 binds FLGII-28 and activates the plant immune systems." Solanaceae Genomics Conference: 45. Hirich, A., R. Choukr-Allah, G. Fiene, M. Morton and M. Tester (2016). "Screening the responses of tomato rootstocks to drought and salinity stress under irrigation." Solanaceae Genomics Conference: 76. Jiang, J., J. Kiser, H. Tsai, C. Omura, A. Vandeynze, R. Chetelat and L. Comai (2016). "Production and characterization of a tomato TILLING population." Solanaceae Genomics Conference: 73. Kang, J.-H., M. L. Campos, S. Zemelis-Durfee, J. M. Al-Haddad, A. D. Jones, F. W. Telewski, F. Brandizzi and G. A. Howe (2016). "Molecular cloning of the tomato Hairless gene implicates actin dynamics in trichome- mediated defense and mechanical properties of stem tissue." Journal of Experimental Botany 67(18): 5313- 5324. Kevei, Z., et al. (2016). "Genetic analysis of tomato root mutants." Solanaceae Genomics Conference: 83. Kim, B., N. Kim, J. Y. Kim, B. S. Kim, H.-J. Jung, I. Hwang, I.-S. Noua, S.-C. Sim and Y. Park (2016). "Development of a high-resolution melting marker for selecting Fusarium crown and root rot resistance in tomato." Genome 59(3): 173-183. Kraus, C. M., K. R. Munkvold and G. B. Martin (2016). "Natural variation in tomato reveals differences in the recognition of AvrPto and AvrPtoB effectors from Pseudomonas syringae." Molecular Plant 9: 639-649. Last, R. L., et al. (2016). "The tip of the trichome: specialized metabolic diversity in the Solanaceae." Solanaceae Genomics Conference: 39. Leckie, B. M., D. A. D'ambrosio, T. M. Chappell, R. Halitschke, D. M. De Jong, A. Kessler, G. G. Kennedy and M. A. Mutschler (2016). "Differential and Synergistic Functionality of Acylsugars in Suppressing Oviposition by Insect Herbivores." PLoS One 11(4): e0153345. Li, X.-J., X.-J. Chen, X. Guo, L.-L. Yin, G. J. Ahammed, C.-J. Xu, K.-S. Chen, C.-C. Liu, X.-J. Xia, K. Shi, J. Zhou, Y.-H. Zhou and J.-Q. Yu (2016). "DWARF overexpression induces alteration in phytohormone homeostasis, development, architecture and carotenoid accumulation in tomato." Plant Biotechnology Journal 14(3): 1021-1033.
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CALIFORNIA TOMATO RESEARCH INSTITUTE, INC. 2016 ANNUAL SUMMARY REPORT
Project Title: Discovering novel genes associated with water stress tolerance in wild tomato
Project Leader: Dr. Dina St. Clair, University of California-Davis, Department of Plant Sciences, Davis, CA 95616 Tel. (530) 752-1740; [email protected] Co-investigator: Dr. Oded Cohen, Postdoctoral Researcher, University of California- Davis, Department of Plant Sciences Objective: Discovery of novel genes in a targeted chromosome 9 region of S. habrochaites controlling traits associated with slow-onset water stress Introduction: Wild tomato (S. habrochaites) is highly tolerant to water stress. Previously, we studied the genetic control of water stress tolerance in wild tomato using a set of 18 breeding lines, each containing a unique smaller segment of chromosome 9 from S. habrochaites in an otherwise cultivated tomato background. We tested the lines in replicated experiments in the field and greenhouse to assess their water stress tolerance. In field experiments, the breeding lines were subjected to slow-onset water stress caused by severely restricted drip irrigation. We collected data on traits related to water use efficiency (delta 13C, specific leaf area, shoot biomass), maturity and yield; all these traits mapped to the chromosome 9 region of S. habrochaites represented in our breeding lines (Lounsbery et al. 2016). Our greenhouse experiments exposed the same set of breeding lines to rapid-onset water stress induced by root chilling. The ability to maintain shoot turgor under root chilling also mapped to the same chromosome 9 region as the field traits (Arms et al. 2015; Lounsbery et al. 2016). Our results show that a defined region on chromosome 9 from S. habrochaites contains genes and/or gene regulatory elements that govern the water stress tolerance of wild tomato. We also conducted a gene expression experiment (mRNA-Sequencing) to help identify genes involved in tolerance to rapid-onset water stress. Two breeding lines, Line 175 (water stress-tolerant, containing a chromosome 9 region from S. habrochaites) and Line 163 (water stress-susceptible, does not contain S. habrochaites), were exposed to rapid-onset water stress induced by root chilling, and their roots analyzed via mRNA-Sequencing to quantify gene expression. The majority of genes expressed in roots of both lines were located on chromosome 9. Furthermore, some genes within Line 175 may be unique to S. habrochaites. The possibility of novel genes within S. habrochaites is also supported by the fact that the S. habrochaites genome is 50% larger than the cultivated tomato genome. Summary: Our goal was to obtain DNA sequence of the S. habrochaites chromosome 9 target region for discovery of novel genes and regulatory elements. Our approach was to use our existing wild tomato S. habrochaites BAC genomic library to identify and sequence BACs from the target region. We developed new marker “probes” for chromosome 9, used them to 2 probe the library, and identified 22 BACs. Physical mapping via DNA fingerprinting of the 22 BACs (plus 30 BACs previously identified) indicated that the group of 52 BACs do not represent the entire target region. There are significant gaps between sub-groups of BACs. Our results suggest that necessary parts of the target region may not be physically present in the BAC library, limiting its usefulness and thus requiring an alternative approach. Recent technological developments have made whole-genome sequencing a cost-effective alternative (that does not use BACs) to obtain complete sequence of the entire target region. Results/Findings: Postdoctoral researcher Oded Cohen used the cultivated tomato reference genome sequence (SLv2.5, available on the SGN website) to design 15 new DNA marker “probes” spaced approximately 50 kb from each other in the chromosome 9 target region. He designed DNA primers for PCR amplification for each new marker “probe” and tested the primers to verify that they amplified sequences from the target region in wild tomato. Each of the amplified sequences was then cloned into a bacterial plasmid vector, and the resulting amplicons were used as radiolabeled 32P “probes”. These probes were individually hybridized to a set of library filters that contain DNA from all the BAC clones in our S. habrochaites BAC genomic library. The 22 BAC clones that showed positive hybridization results were isolated from our library. Each of the 22 BACs was grown in liquid culture and the plasmids were extracted from the bacteria. These plasmids contain S. habrochaites genomic fragments that are expected to be from the chromosome 9 target region. The next step was to orient the BAC clones to each other to determine their physical relationship to each other (i.e., physical DNA mapping). Cohen and new graduate student Amanjot Kaur conducted “DNA fingerprinting” analysis of the BACs to create a physical map. The 22 new BACs, plus 30 BACs identified previously from this same S. habrochaites library by former graduate student Erin Arms, were included in the analysis. Fingerprinting of the 52 BACs was performed by digesting the plasmid DNA from each BAC with restriction enzymes, sizing the DNA fragments on a capillary DNA analyzer, and using software to obtain the physical order of the BAC fragments to each other. Fingerprinting analysis of the BACs was performed in collaboration with Dr. Ming Cheng Luo, UCD Plant Sciences Department. Dr. Luo has extensive experience in physical mapping and plant genome sequencing. The BAC fingerprinting data indicates that some of the BAC sub-groups do not overlap with each other. That is, there are “gaps” in coverage of the wild genome sequence in this region, which means that the 52 BACs cannot be arranged (“tiled”) into a contiguous representation of this region. Consequently, the 52 BACs we identified from our S. habrochaites library (22 new BACs plus the 30 previously identified BACs) do not represent the entire target region of chromosome 9 despite our use of multiple “probes” to identify BACs for this region. Our results suggest that some genomic regions may not be physically present in the S. habrochaites BAC library. We conclude that it is not feasible to use the BAC library to obtain the entire target region of chromosome 9 of S. habrochaites for DNA sequencing and discovery of novel genes. Our results indicate that an alternative to BACs for sequencing chromosome 9 of S. habrochaites is needed. Recent technological advancements (long-read DNA sequencing and computational power) have now made it cost-effective and efficient to sequence and assemble the whole genome of S. habrochaites to obtain the target region sequence.
2016 CTRI Annual Summary Report: St.Clair CALIFORNIA TOMATO RESEARCH INSTITUTE, INC. 2016 ANNUAL SUMMARY REPORT
Project Title: Identifying and testing wild tomato genes that contribute water stress tolerance
Project Leader: Dr. Dina St. Clair, University of California-Davis, Department of Plant Sciences, Davis, CA 95616 Tel. (530) 752-1740; [email protected] Co-investigator: Dr. Oded Cohen, Postdoctoral Researcher, University of California- Davis, Department of Plant Sciences Objective: Identifying and testing genes from wild S. habrochaites controlling root-to-shoot signaling that regulates stomata in response to water stress
Introduction: Wild tomato (S. habrochaites) is highly tolerant to water stress. Our prior studies on the genetic control of water stress tolerance in wild tomato used a set of 18 breeding lines derived from S. habrochaites in replicated experiments in the field and greenhouse to assess their water stress tolerance. The breeding lines were subjected to slow-onset water stress caused by severely restricted drip irrigation in the field. We collected data on traits related to water use efficiency (delta 13C, specific leaf area, shoot biomass), maturity and yield; all these traits mapped to the chromosome 9 region of S. habrochaites (Lounsbery et al. 2016). We also tested the same set of breeding lines to rapid-onset water stress induced by root chilling in refrigerated hydroponic tanks in the greenhouse. The ability to maintain shoot turgor under root chilling also mapped (QTL stm9) to the same chromosome 9 region as the field traits (Arms et al. 2015; Lounsbery et al. 2016). The ability to regulate stomata in response to water stress and maintain shoot turgor in wild tomato involves root-to-shoot signaling. Altogether, our results indicate that this chromosome 9 region from S. habrochaites contains genes and regulatory elements that control multiple traits conferring tolerance to both rapid-onset and slow-onset water stress. With prior CTRI support, former graduate student Erin Arms also conducted a gene expression experiment (mRNA-Sequencing) to help identify genes involved in tolerance to rapid-onset water stress. Two breeding lines differing in tolerance, Line 175 (tolerant, with a chromosome 9 region from S. habrochaites) and Line 163 (susceptible, without S. habrochaites), were exposed to rapid-onset water stress induced by root chilling. Their roots were analyzed via mRNA-Sequencing to identify and quantify gene expression. Some of the genes expressed in roots differed between the two lines, and the majority of expressed genes were located on chromosome 9, some within QTL stm9. Summary: Our goal was to identify and test genes on chromosome 9 that control root-to-shoot signaling for stomatal regulation under rapid-onset water stress. Postdoctoral researcher Oded Cohen was responsible for this research project. He identified four candidate genes, and made 2 gene “knock out” constructs with CRISPR. Two of four constructs were partially tested. Preliminary results indicated that gene knock outs for two of the genes prevented root growth in plant shoots transformed with Agrobacterium rhizogenes, suggesting negative developmental effects. Cohen did not complete the experiments for this project.
Results/Findings: Postdoctoral researcher Oded Cohen conducted this research. To identify candidate genes that may control the root-to-shoot signal under rapid onset water stress, he used several lines of evidence: our existing mRNA-Sequencing gene expression data (mentioned previously), location alignment of QTL stm9 (using linked markers) to the cultivated tomato reference genome SLv2.4, and bioinformatics analysis. He selected four genes as candidates (designated 430, 440, 470 and 480) for testing via CRISPR to “turn off” (silence) gene expression. The sequence of each gene was already known from the tomato reference genome and our mRNA-Seq data set. For each candidate gene, several target sequence sites were chosen for introduction of mutations that would most likely cause the gene to turn off. Subsequently, he built four CRISPR constructs (“turn off” instructions for each candidate gene) in a CRISPR backbone plasmid. These constructs are stored in our -80C freezer. Cohen introduced the four CRISPR constructs into the bacteria Agrobacterium rhizogenes (hairy root system) that is used to characterize the genotype of CRISPR-induced mutations. Tomato seedlings of Line 175 (with the chromosome 9 S. habrochaites introgression) and Line 163 (without the introgression, as a control) were grown on sterile media to the first true leaf stage. Seedlings were cut above their roots and the stems dipped in a solution of A. rhizogenes carrying a CRISPR construct or an empty vector control, then placed on rooting media. Preliminary results indicated that knocking out genes 470 or 480 caused a severe negative effect on development, preventing roots from growing. Therefore there were no roots available for further analysis of the effects of knocking out the genes. He did not complete testing of CRISPR constructs for genes 430 and 440 in A. rhizogenes. He also introduced the plasmid constructs into the bacteria Agrobacterium tumefaciens for use in plant transformation. However, the bacterial colonies either failed to grow or grew very poorly, for unknown reasons. The few colonies that did grow were not verified or sequenced by him. Therefore, they could not be used to do plant transformation and subsequent testing of the transformed plants to determine whether the CRISPR “knock out” constructs for each gene affected a plant’s response to rapid-onset water stress. In the middle of this project, Oded Cohen chose to take a 2.5 month leave of absence, from mid-February through the end of April 2016, to visit his home country, which stalled progress. Funding ran out for his postdoctoral position in mid-August, 2016. Therefore, the experiments were not completed by Cohen. Going forward, my lab group will incorporate the useful, partial information gathered in this project to inform our future research on wild tomato genes that control tolerance to water stress. The CRISPR constructs for four candidate genes are stored in our freezer and available for future experiments.
2016 CTRI Annual Summary Report: St.Clair Research Project Report California Tomato Research Institute 18650 Lone Tree Rd., Escalon, CA 95320
Project Title: Evaluation of tactics for improvement of stink bug control
Project Leader: Thomas A. Turini University of California Agriculture and Natural Resources Vegetable Crops Farm Advisor in Fresno County 550 E. Shaw Avenue, Suite 210 Fresno, CA 93710 Tel: (559) 375-3147 Email: [email protected]
Co-Primary Investigators: Frank Zalom Dept. of Entomology and Nematology Univ. of California Davis, CA 95616 Tel: (530) 752-3687 Fax: (530) 752-1537 Email: [email protected]
Peter B. Goodell, PhD Cooperative Extension Advisor, Integrated Pest Management UC Statewide IPM Program 9240 So Riverbend Ave Parlier CA 93654 559/646-6515 office 559/646-6593 - fax [email protected]
SUMMARY Populations of Consperse stink bug, Euschistus conspersus, were low to moderate in much of Fresno County in 2016, but caused substantial damage in some fields areas. Efforts were made to find overwintering stink bugs, but none were detected in the areas previously identified during 2016. The pheromone baited traps failed to capture stink bugs prior to detection on plant material in 2016, but had been consistently effective under the conditions of this study in 2014 and 2015. This highlights the need for careful monitoring of crops and weeds in high risk areas even with the use of these traps which have been useful during 2 of 3 years. The 2016 insecticide efficacy comparisons showed combinations of pyrethroids and neonicotinoids reduced Consperse stink bug damage. These findings were consistent with those of 2014 in which Warrior II (lambda cyhalothrin) and Venom provided a level of control as compared to the untreated, but were in contrast to the finding in 2015 where only Warrior II provided any control. Under the conditions of these studies, Warrior II consistently reduced damage. Consperse stink bug is a potentially devastating pest and control is difficult, but pheromone baited traps, while not completely effective can help with early detection, and Warrior II as well as possibly pyrethroid and neonicotinoid mixtures can help reduce risk of damage.
OBJECTIVES • Document sources of stink bug and seasonal population dynamics in Central CA processing tomatoes • Assess efficacy of insecticides against Consperse stink bug
PROGRESS Seasonal Population Monitoring: In late 2015 and 2016, stink bug population densities had been assessed in the Huron area, which historically had reported the greatest and most persistent population densities of Consperse stink bugs. Methods: During the 2015 and 2016 seasons in the Fresno County production area, population densities were evaluated where the most damaging levels of the pest were documented in 2014, which was in the Huron area. At 5 sites pheromone baited traps were deployed in areas in which there was history of issues with the pest (Fig.1). Any crops at the sites were inspected and traps were checked at 14 to 21 day intervals from the date that they were deployed on 17 Feb. The sites of focus were as follows: sb 1 South of Gale Ave and west of S. Butte Ave: Directly north of citrus grove where Consperse stink bugs were detected in late 2014. Strips of broccoli-raab / wheat, grain crop, and cotton were planted in strips for purpose of serving as a trap crop. Tomatoes were planted to the east of the monitored site. sb 2 Adjacent to Gale Ave., approximately 1 mile west of Lassen Ave. Adjacent to offices with minimal landscaping. Tomatoes were directly south. Strips of broccoli-raab / wheat, grain crop, and cotton were planted in strips for purpose of serving as a trap crop. sb 3 Adjacent of Gale Ave, approximately ¼ mile east of Lassen Ave. Across Gale and slightly west of a house with landscaping. Tomatoes were adjacent to the south. sb 4 Adjacent of Gale Ave, approximately 1 mile east of Lassen Ave. Tomatoes were adjacent to the east. Strips of broccoli-raab / wheat and grain crop were planted in strips for purpose of serving as a trap crop. sb 5 At Sisquyou Ave and Gale Ave. A tomato field was north east.
Results: Overwintering sites were not located during the 2015-2016 winter. However, near a site found to harbor Consperse stink bug in winter of 2014-2015 (sb1), high population densities of Consperse stink bug were detected on 17 Mar 2016 on black mustard. By mid-June a Consperse stink bug was captured in pheromone baited traps. Evidence of stink bug feeding was detected in processing tomatoes by mid-July.
Insecticide Comparisons: Insecticide efficacy and insecticide program comparisons for control of stink bug were compared at the University of California West Side Research and Extension Center in 2016 for the third consecutive season.
Methods: Processing tomato transplants, cv. H5608, were planted on 24 May and were irrigated with buried drip through the season. In the 2016 insecticide trials, materials detailed in Table 1 were evaluated. As compared to the treatment list in 2014 and 2015, the 2016 list was similar to the 2015 trial with a large number of tank mixes of older materials some pre- mixed materials. Also, several insecticides with novel modes of action that were evaluated in 2015 were re-evaluated in the 2016 study.
Treatments were arranged in a four- replicate randomized complete block design and each plot was 65 feet of a single 60 in bed. Treated beds were separated by an untreated bed between treated areas. Also, 5 feet of untreated row separated each treated area within the row, which is between replications. All materials were applied with a backpack CO2-powered sprayer at the equivalent of 50 gal tank mix per acre at 32 psi. The spray boom was equipped three Teejet 8004 EVS double flat fan nozzles spaced 19-in. apart and was used for all applications in the efficacy trial. All materials were applied with 0.25% Activator v/v.
The effectiveness of the insecticide treatments were determined by in-season evaluations as well as by a evaluation at harvest. On 2 and 15 Sep; canopy was shaken, pulled back and the ground was inspected for 5 minutes for presence of Consperse stink bug and the fruit damage was rated on a scale of 0 to 10, with 0 being unaffected and completely damaged fruit receiving a rating of 10. A pre-treatment evaluation was made on 24 Aug. Yields per acre were calculated based on a hand harvest of 20 row feet harvested from 23-28 Sep. Twenty- five to 35 pounds of randomly selected fruit within the harvest area were hand sorted into red, green, sunburn, rot and stink bug injury categories. Each set of fruit were weighed and percentage of the sample represented by each group by weight is presented. Fruit chemistry was determined at the Processing Tomato Advisory Board Laboratory located at the Los Gatos processing plant in Huron, Fresno County California. Analysis of Variance was performed and Least Significant Difference at P=0.05 is presented.
Results: Stink bug damage at this trial site was sufficient to see treatment differences. The stink bug counts within the canopy as well as in-season ratings of fruit damage were variable and largely inconclusive (Table 2). However, the differences observed in terms of percentage of stink bug-damaged fruit were different among treatments (Table 3). Based on the percentage of fruit showing stink bug feeding damage, the best performing treatments included Warrior II 1.92 oz + Brigadier 9.85 fl oz + Beleaf 50SG 4.28 fl oz, Warrior II 1.92 fl oz, Dimethoate 4EL 1.0 pts + Brigadier 9.85 fl oz, Sequoia 4.5 fl oz + Warrior II 1.92 fl oz, Warrior II 1.92 oz + Rimon 0.83EC 12.0 fl oz and Venom 70 SG 4 oz. The Endigo also reduced the damage as compared to the untreated control.
Discussion The concluding year of the three year study did not support the findings in 2014 and 2015 in terms of detection of Consperse stink bug in traps prior to finding the pest in the crop or in weeds. Previously, where a trap was in place, the pest was found prior to detection on plant material, but in 2016, both in commercial sites in the Huron area and at the insecticide efficacy site at the West Side Research Extension Center, the pest was rarely captured in the traps. While there may have been issues with the lures in 2016, these findings bring attention to the need for the use of both scouting the field as well as using these pheromone baited traps that enabled us to detect the pest earlier during two of three years.
At the site of the insecticide efficacy trial, the first detection of the insect pest was much later in 2016. In 2014, Consperse stink bug were captured by mid-July and in 2015, the first capture was in late June. In mid-Aug, the damage was detected in 2016. On 24 Aug, 2016, both nymphs and adults were found in the canopy, but there were no captures in the traps.
Differences among treatments were present in 2016 and were generally more similar to the 2014 study than the 2015 work. In 2016, several pyrethroid materials as well as Venom had lower levels of damage as compared to the untreated control. This was the case in 2014. In contrast, in 2015, differences among treatments in terms of the percentages of stink bug- damaged fruit were most pronounced in treatments including lambda cyhalothrin, as in Warrior II, but no other materials, including Venom, were different than the untreated control. A consistency among all three years was the performance of lambda cyhalothrin, but during two of three years there was activity of other pyrethroids and neonicotinoids.
Figure 1. Sites monitored for stink bugs in 2015-2016.
Table 1. Insecticides and combinations of insecticides tested at the University of California West Side Research and Extension Center. Trade name of material tested (common name-IRAC mode of action number) Dimethoate 4EL 1.0 pts (dimethoate – 1B) + Brigadier 9.85 fl oz (bifenthrin + imidicloprid – 3A + 4A) Warrior II 1.92 fl oz (Lambda cyhalothrin – 3A) Endigo ZC 4.5 fl oz (lambda-cyhalothrin + thiamethoxam – 3A + 4A) Leverage 2.7 3.75 oz (cyfluthrin + imidicloprid – 3A + 4A) Warrior II 1.92 oz (lambda-cyhalothrin – 3A) + Beleaf 50SG 4.28 fl oz (flonicamid – 9C) Danitol 10.67 oz (fenpropathrin -3A) + Belay 4 oz (clothanidin – 4A) Warrior II 1.92 oz (lambda-cyhalothrin – 3A) + Rimon 0.83EC 12.0 fl oz (novaluron - 15) Belay 4 oz (clothanidin – 4A) + Beleaf 50SG 4.28 fl oz (flonicamid – 9C) Venom 70 SG 4 oz (dinotefuran – 4A) Sequoia 4.5 fl oz (sulfoxaflor – 4C) + Warrior II 1.92 fl oz (Lambda cyhalothrin – 3A) Silvanto 14 fl oz (flupyradifurone – 4D) Warrior II 1.92 oz (lambda-cyhalothrin – 3A) + Brigadier 9.85 fl oz (bifenthrin + imidicloprid – 3A + 4A) + Beleaf 50SG 4.28 fl oz (flonicamid – 9C) Torac 21.0 fl oz (tolfenpyrad – 21A) Exirel 20.5 fl oz (cyantranilaprole – 28) Untreated
Table 2. Impact of insecticides on yield and quality of processing tomatoes at West Side Research and Extension Center, 2015. Stink bug counts (per 4 ft)z Stink bug damage (0-10)y Treatmentx 24 Aug 2 Sep 15 Sep 24 Aug 2 Sep 15 Sep Dimethoate 4EL 1.0 pts + Brigadier 2.25 4.75 1.50 1.25 1.50 0.88 9.85 fl oz Warrior II 1.92 oz + Beleaf 50SG 2.25 1.75 1.50 1.50 0.88 0.88 4.28 fl oz Danitol 10.67 oz + Belay 4 oz 2.25 1.75 1.50 1.00 1.13 1.25 Venom 70 SG 4 oz 0.50 1.00 3.00 0.75 0.63 1.25 Warrior II 1.92 oz + Brigadier 9.85 fl 2.50 0.75 0.75 1.50 0.75 1.38 oz + Beleaf 50SG 4.28 fl oz Warrior II 1.92 oz + Rimon 0.83EC 1.50 1.50 2.25 0.75 1.13 1.50 12.0 fl oz Untreated 1.75 3.00 2.25 0.75 0.88 1.50 Leverage 2.7 3.75 oz 1.50 0.25 2.50 1.00 0.63 1.50 Exirel 20.5 fl oz 0.50 2.75 2.50 0.50 1.38 1.88 Torac 21.0 fl oz 0.50 1.00 4.00 0.50 0.75 1.88 Warrior II 1.92 fl oz 1.25 0.75 4.25 0.50 1.00 1.88 Silvanto 14 fl oz 2.75 1.50 6.00 1.25 0.63 2.13 Endigo ZC 4.5 fl oz 1.25 0.75 5.75 1.00 0.63 2.25 Belay 4 oz + Beleaf 50SG 4.28 fl oz 3.50 2.50 3.50 1.50 1.25 2.38 Sequoia 4.5 fl oz + Warrior II 1.92 fl 1.25 1.00 6.00 0.25 0.75 2.50 oz LSD (P=0.05)t 0.93s NS 4.25 NS 0.69 3.38 CV (%) 70.08 126.62 95.62 119.74 52.33 70.54 w The 24 Aug counts were prior to the first treatment, which was made on 25 Aug. v Materials separated by a “+” were applied together. t Means within a column that have differences greater than the Least Significant Difference at probability of 5% (LSD0.05), which appears below are considered significantly different. ts All means appearing above ‘NS’ have no significant differences among them as determined by LSD0.05.
Table 3. Impact of insecticides on yield and quality of processing tomatoes at West Side Research and Extension Center, 2015. Fruit quality (%)z PTABy yield Sun solids x o Treatment (tons/acre) reds greens burn rot stink bug color ( brix) pH Warrior II 1.92 oz + Brigadier 67.74 63.20 15.32 0.74 8.87 11.87 24.25 4.075 4.468 9.85 fl oz + Beleaf 50SG 4.28 fl oz Warrior II 1.92 fl oz 65.48 54.46 14.75 1.67 12.14 16.98 23.75 4.175 4.433 Dimethoate 4EL 1.0 pts + 66.08 55.80 15.19 0.00 11.24 17.76 24.25 4.025 4.500 Brigadier 9.85 fl oz Sequoia 4.5 fl oz + Warrior II 62.28 54.85 12.23 1.16 13.19 18.57 23.25 4.150 4.498 1.92 fl oz Warrior II 1.92 oz + Rimon 70.77 53.38 11.16 1.54 13.60 20.32 23.50 4.150 4.468 0.83EC 12.0 fl oz Venom 70 SG 4 oz 60.53 54.16 14.23 0.75 10.32 20.54 23.75 4.075 4.503 Endigo ZC 4.5 fl oz 64.98 51.39 15.76 1.49 9.88 21.48 23.50 4.150 4.488 Torac 21.0 fl oz 68.61 43.89 17.91 0.05 12.25 25.90 24.25 4.125 4.485 Danitol 10.67 oz + Belay 4 oz 68.83 43.79 14.36 0.87 14.66 26.31 23.50 4.150 4.453 Warrior II 1.92 oz + Beleaf 50SG 58.15 45.29 15.54 0.43 11.75 26.99 23.50 4.075 4.468 4.28 fl oz Belay 4 oz + Beleaf 50SG 4.28 fl 67.80 43.59 11.57 0.83 13.82 30.18 23.25 3.925 4.463 oz Silvanto 14 fl oz 66.64 39.07 17.83 0.35 12.20 30.55 24.00 4.025 4.470 Exirel 20.5 fl oz 66.61 44.26 14.40 0.58 10.05 30.71 23.25 4.000 4.455 Leverage 2.7 3.75 oz 68.65 40.16 11.90 0.78 16.15 31.01 23.50 4.225 4.468 Untreated 63.37 42.84 16.24 1.28 8.09 31.55 23.25 3.975 4.508 LSD (P=0.05)u 6.63 12.964 NSt NS NS 9.458 NS NS NS CV (%) 7.07 18.89 39.00 127.3 41.23 27.57 3.60 4.030 0.053 z Twenty-five to 35 pounds of fruit per plot were hand sorted into red, green, sunburn, rot and stink bug injury categories. Each set of fruit were weighed and percentage of the sample represented by each group by weight is presented. y Fruit chemistry was determined at the Processing Tomato Advisory Board Laboratory located at the Los Gatos processing plant in Huron, Fresno County California. x The experimental area was transplanted on 24 May with cv. H5608 processing tomato plants at UC West Side Research and Extension Center. Foliar applications were made with a backpack sprayer at 50 gpa. All applications were made on 25 Aug and 8 Sep. w Yields per acre were calculated based on a hand harvest of 20 row feet harvested from 23-28 Sep. v Materials separated by a “+” were applied together. u Means within a column that have differences greater than the Least Significant Difference at probability of 5%, which appears below are considered significantly different. t All means appearing above ‘NS’ have no significant differences among them as determined by LSD (P=0.05)
Figure 4. Regression analysis of beet leafhopper counts verses fruit yield. Regression of BLH Counts Vs Yield 100 y = -1.4293x + 154.5 90 R² = 0.6933 Prob=0.088 80 70 60 50
Total BLH 40 30 20 10 0 50 55 60 65 70 75 80 85 Yield in Lbs
The silver reflective mulch had a highly significant effect in repelling the amount of BLH away from the tomato plants. But the Surround as a spray on mulch and the green dye turf paint were also able to significantly repel BLH away from the tomato plants. Comparing total BLH counts to the amount of tomatoes harvested at the end of the season reveals that the higher BLH counts resulted in less harvest yield. This is presumably due to increased amount of curly top brought in by the BLH.
It must be noted that these plots were single row treatments by 40 feet in length and that all of these plots were side by side. The effect of the silver reflective mulch, Surround as a spray on mulch and the green dye as a spray on mulch is surprising. The effects would likely be greater if the plot size was larger and consisted of several beds in width. Acre size plots may have a stronger repelling effect than these single row plots and have a greater impact in reducing the incidence of curly top.
Southern Blight of Tomatoes 2016
Joe Nunez, UC Cooperative Extension Advisor, 1031 South Mount Vernon Ave., Bakersfield CA 93307. Phone: 661-868-6222, Email: [email protected]
For 2016 we began a southern blight nursery at the UCCE-Kern Research Farm at Shafter by adding sclerotia of Sclerotium rolfsii to the soil and adding sclerotia to the tomato transplant trays just before planting. Once the transplanting was completed a surface drip irrigation system was installed and all nutrients were applied by chemigation.
Halley 3155 transplants were planted on 4/7/16 and the first application of fungicides for control of southern blight was on 5/23/16. Treatments were applied with the canopy lifter-sprayer that we had fabricated two years earlier. The fungicides under evaluation have been reported as to having efficacy on S. rolfsii from previous studies. The treatments for 2016 were as follows:
1. Control 2. Omega 500F @ 16 fl oz/A 3. Convoy @ 32 fl oz/A 4. Quadris @ 6.2 fl oz/A 5. Fontelis @ 24 fl oz/A 6. TebuStar 45W @ 0.45 lb/A 7. Luna Sensation @ 11 fl oz/A 8. Priaxor @ 8 fl oz/A 9. Blocker 4F @ 5 pints/A
A total of 3 fungicide applications were made on 5/23/16, 6/14/16 and 7/12/16. The plots were evaluated for percent canopy reduction, fruit yield and root discoloration at the end of the season.
Table 6 lists the average percent canopy reduction for each of the treatments. Although there was no significant differences among treatment means it points out that the control had the least amount of canopy reduction. This is not due to less southern blight infection but most likely due to the fact that the canopy lifter-sprayer was not used in the control plots. This indicates that the canopy lifter-sprayer itself had injured the plants as it moved through the plots. Figure 5 graphically shows the percent reduction over a 4 week period. It shows a distinct grouping of 3 levels of canopy reduction. The highest percent canopy reduction occurred with 3. Convoy, 4. Quadris and 7. Luna Sensation with an average of 65.7 percent canopy reduction. The next group was 2. Omega 500, 5. Fontelis, 6. Tebustar, 8. Priaxor and 9. Blocker with an average percent canopy reduction of 56.1. Performing contrast comparisons shows that the control has significantly less canopy reduction than all other treatments combined (table 6). Conducting contrast comparisons also shows that the treatments 2. Omega 500, 5. Fontelis, 6. Tebustar, 8. Priaxor and 9. Blocker had significantly less canopy reduction than treatments 3. Convoy, 4. Quadris and 7. Luna Sensation (table 6). The fact that the canopy lifter-sprayer was not used on the control plots confounds the interpretation of this data but it appears Omega 500, Fontelis, Tebustar, Priaxor and Blocker had a significant effect in reducing the amount of canopy loss due to southern blight. Table 6. ANOVA and contrast comparison for percent canopy reduction. Treatment Percent Canopy Reduction 1. Control 40 2. Omega 500F 60 3. Convoy 64 4. Quadris 67.5 5. Fontelis 54 6. TebuStar 45W 58 7. Luna Sensation 66 8. Priaxor 52.5 9. Blocker 4F 60 Prob.= 0.1893 %CV= 26.01 LSD P=0.05 not significant
Contrast Comparisons Control verses all treatments Sum of Squares = 1562.562 Probability = 0.014
Convoy, Quadris and Luna Sensation verses Omega 500, Fontelis, Tebusta, Priaxor and Blocker Sum of Squares = 947.534 Probability = 0.050
Figure 5. Percent canopy reduction over a 4 week period. Percent Canopy Reduc on Over Time 80 1 70 2 60 3 50 40 4 30 5 20 6
Percent Canopy Reduc on 10 7
0 8 8/2/2016 8/9/2016 8/16/2016 8/23/2016 9/1/2016
The differences in yield were also similar to the reduction in canopy results (table 7). The control had the greatest yield, again most likely due to the fact that the canopy lifter-sprayer was not used in the control plots. There was also two groupings in the yields in regards to the treatments. The grouping of 3. Convoy, 4. Quadris and 7. Luna Sensation had the smallest yield at 26.3 lbs on average while 2. Omega 500, 5. Fontelis, 6. Tebustar, 8. Priaxor and 9. Blocker had a significant higher yield at 36.1 lbs. Contrast comparison analysis (table 7) shows that differences among these 2 groupings is significant.
Table 7. ANOVA and contrast comparison for tomato fruit yield (lbs). Treatment Yield (lbs) 1. Control 47.0 2. Omega 500F 38.9 3. Convoy 24.9 4. Quadris 24.9 5. Fontelis 42.4 6. TebuStar 45W 31.7 7. Luna Sensation 29.1 8. Priaxor 40.2 9. Blocker 4F 27.4 Prob.= 0.4258 %CV= 52.64 LSD P=0.05 not significant
Contrast Comparisons Control verses all treatments Sum of Squares = 941.971 Probability = 0.098
Convoy, Quadris and Luna Sensation verses Omega 500, Fontelis, Tebustar ,Praxior and Blocker Sum of Squares = 896.718 Probability = 0.106
Lastly root discoloration was evaluated and analyzed for statistical differences. As was with case in percent canopy reduction and yield there were the same 2 grouping of fungicides. The first group, Convoy, Quadris and Luna Sensation did not reduce root discoloration while the second group did consisting of Omega, Fontelis, Tebustar, Priaxor and Blocker(table 8). Priaxor had the least amount of root discoloration, even compared to the control. Contrast comparison analysis was able show significant differences among the 2 fungicide groupings and that Priaxor alone had significantly less root discoloration compared to all other treatments.
Table 8. ANOVA and contrast comparison for percent of roots discolored. Treatment Percent Roots Discolored 1. Control 51.9 2. Omega 500F 80.4 3. Convoy 90.0 4. Quadris 89.4 5. Fontelis 66.2 6. TebuStar 45W 63.5 7. Luna Sensation 80.3 8. Priaxor 40.0 9. Blocker 4F 64.2 Prob.= 0..1195 %CV= 40.59 LSD P=0.05 not significant
Contrast Comparisons Control verses all treatments Sum of Squares = 1751.067 Probability = 0.150
Convoy, Quadris and Luna Sensation verses Omega 500, Fontelis, Tebustar ,Priaxor and Blocker Sum of Squares = 5268.752 Probability = 0.016
Priaxor verses all other treatments (including control) Sum of Squares = 4904.370 Probability = 0.020
Conducting field trials for southern blight has been very difficult due to inherit variability in any soil born pest problem but also due to the interaction of curly top and the amount of southern blight that actually develops in a field trial. Some of those issues have been addressed by inoculating a portion of the UCCE-Kern Research Farm with Sclerotium rolfsii. The 2016 trials finally produced some measurable differences in treatments but the 2016 field trials also revealed another unanticipated issue. That one is the fact that the canopy lifter-sprayer device alone injures the plant. All of the treatments were applied with this device but was not used on the control plots. The control plots looked better at the end of the season just due to the fact that they were not jostled, knocked and shoved by the canopy lifter. Therefore a true control was not present in this year’s field trials.
However, by closely examining the data we were able to show some differences among the fungicides we tested. The results were similar whether looking at percent canopy reduction, yield or root discoloration. The fungicides Omega, Fontelis, Tebustar, Priaxor and Blocker all seemed to reduce the impact of southern blight.
The trials will be repeated but with obvious changes to protocol. The control will be treated with the canopy lifter like all of the other treatments. The canopy lifter will also be modified so that less damage to the canopy occurs. Lastly the trial will be confined to the inoculated site at the UCCE-Kern which is providing more uniform disease pressure across the entire trial location.
Nematicide Screening Trials 2016
Joe Nunez, UC Cooperative Extension Advisor, 1031 South Mount Vernon Ave., Bakersfield CA 93307. Phone: 661-868-6222, Email: [email protected]
Conventional Nematicides-In 2016 a nematicide trial was conducted with the use of new nematicide products applied through a buried drip system. This drip application trial was somewhat unconventional in the fact that PVC pipe with small holes drilled into it was used as the “buried drip tape.” The reason for this is that in previous years we encounter a lot of buried drip tape that has been chewed on by gophers. To circumvent that issue we made a drip tubing out of PVC pipe.
The tubing was buried 8 inches into the center of 60 inch shaped beds in a nematode nursery maintained at the UCCE-Kern Research Farm. A manifold was constructed so that the nematicides to be tested could be injected into separate ports by use of a CO2 pressurized tank. Each of the products were applied pre-plant on 4/20/16 and a post-plant application on 5/30/16 for Velum and DP-1.
Tomatoes of variety Halley 3155 were hand planted on 4/21/16 onto each plot on a 18 inch row spacing. The plots were 25 feet in length by one bed. Each treatment was replicated 5 times in a randomized complete block design. The treatments were as follows:
1. Control 2. Velum 6.5 fl oz/A pre and 21 DAP post 3. Nimitiz 5 pints/A as a 24 band 4. Nimitz 5 pts/A as a full 60inch bed 5. DP pre at 30.7 fl oz/A & 1 post at 15.4 fl oz/A On 7/21/16 approximately 5 roots per plot were harvested and evaluated for root knot nematode injury. The roots were graded on a 1 to 10 scale with 1 being no galling present and 10 being the entire root system galled. The results of the trial are listed on table 1.
Table 1. . Average Root Knot Nematode Injury Rating for Tomato Conventional Nematicide Trial Treatment Nematode Root Rating 1. Control 4.8 A 2. Velum 6.5 fl oz/A pre and 21 DAP post 1.8 B 3. Nimitiz 5 pints/A as a 24 band 2.2 B 4. Nimitz 5 pts/A as a full 60inch bed 1.7 B 5. DP pre at 30.7 fl oz/A & 1 post at 15.4 fl oz/A 3.0 AB Probability 0.0238 % Coefficient of Variation 52.16% LSD P=0.05 1.910
There were significant differences among the treatment means. The non-treated control had significantly more root knot nematode injury than the Velum and Nimitz treatments. The experimental DP treatment was not different from the control nor from the other treatments. Nimitz was applied at 5 pints per acre but either as 5 pints calculated as a 24 inch in the transplant row or 5 pints calculated over the entire 60 inch bed.
Biological Nematicides- A biological nematicide trial was conducted at the UCCE-Kern Research Farm located near Shafter California in the spring and summer of 2016 to investigate the efficacy of several biological nematicides on tomatoes. The UCCE-Kern Research Farm maintains an area with a known high population of Meloidogyne incognita nematodes. The trials were located in this nematode nursery to insure adequate nematode pressure was present.
Sixty inch beds were formed and bed shaped and planted with nematode susceptible Halley 3155 tomatoes (Orsetti). Each plot was one bed by 30 feet in length and replicated 5 times in a randomized complete block design. The trial was planted on 4/20/16 and treated on 4/21/16, 5/19/16 and 6/15/16. Treatments were applied by adding the biologicals into a garden watering can with approximately 1 gallon of water and sprinkling the mixture onto the top of 1 individual plot. A solid set sprinkler system was then used to apply water to incorporate the material into the soil.
The treatments for both trials were as follows; 1. Control 2. Nematode Control 1 gal/A 3. Majestene @ 1.5 gal/A 4. EMUNE @ 2 gal/A 5. EMUNE Plus @ 2 gal/A 6. OXVA @ 0.5 gal/A 7. OXVA @ 1 gal/A
On 8/1/16 five tomato roots per plot where harvested and then evaluated for root knot nematode injury. Roots where rated on a 1 to 10 scale with a 1 being no visible galling present and a 10 being the entire root system galled. The results are listed in Table 2.
Table 2. Average Root Knot Nematode Injury Rating for Tomato Biological Nematicide Trial Treatment Average Nematode Rating * 1. Control 8.1 2. Nematode Control 1 gal/A 6.8 3. Majestene @ 1.5 gal/A 8.3 4. EMUNE @ 2 gal/A 8.4 5. EMUNE Plus @ 2 gal/A 6.0 6. OXVA @ 0.5 gal/A 6.3 7. OXVA @ 1 gal/A 5.7 Probability= 0.3068 %CV= 28.27 LSD P=0.05 Not Significant Planted on 4/20/16 with Halley BOS 3155 1st application on 4/21/16, 2nd on 5/19/16 and 3rd on 6/15/16 Harvested on 8/1/16 *Nematode rating scale: 1=no nematode galling, 10=100% of roots galled.
The means were not significantly different from each other although there was some numerical differences that may justify re-evaluating some of these materials in 2017. In this trial the control had one of the highest average galling index while EMUNE Plus and both rates of OXVA seemed to suppress nematode damage in comparison.
The trials did not reveal any biological product as being able to significantly control root knot nematode. However, there is some likelihood that further testing of at least two of these materials may show some efficacy in reducing nematode damage in organic systems.
Monitoring of the species dynamics causing tomato powdery mildew in California and of their resistance to QoI (strobilurin) fungicides.
Ioannis Stergiopoulos1, Brenna Aegerter2, and Eugene Miyao3
1 Assistant Professor, Department of Plant Pathology, University of California Davis, 578 Hutchison Hall, One Shields Avenue, Davis, CA 95616-8751 Phone: 530-400-9802, fax: 530-752-1199, email: [email protected] 2 Cooperative Extension Farm Advisor, UCCE, San Joaquin County, 2101 E. Earhart Ave. Ste. 200, Stockton, CA 95206, Phone: 209-953-6100, fax: 209-953-6128, email: [email protected] 3 Cooperative Extension Farm Advisor, Yolo-Solano-Sacramento counties, 70 Cottonwood Ave., Woodland, CA 95695, Phone: 530-666-8732, email: [email protected]
Abstract:
Powdery mildews are obligate biotrophic fungi that cause extensive diseases in crops. In tomato, powdery mildew infections are caused by the species Leveillula taurica and Oidium neolycopersici, which are associated with the disease in field and greenhouse grown tomatoes, respectively. Control of the disease mainly relies on the use of sulfur-dusts and chemical control agents such as QoI (strobilurin) and azole fungicides. However, the continuous use of fungicides increases the danger for fungicide resistance development and in 2014 and 2015 we have reported that strains with the notorious G143A substitution that confers high levels of resistance to QoI fungicides are already present in California. In addition, we have established that the presence of the G143A mutation in field strains of L. taurica is associated with mitochondrial heteroplasmy for the cytb gene, suggesting that resistance in the fields is not yet complete but rather correlates to the relative abundance of the A143 (resistant) allele as compared to the G143 (sensitive) one. Based on these findings, the objective of our study in 2016 was to continue monitoring the frequency and spread in California of powdery mildew strains with potential resistance to QoI fungicides, and to conduct fungicide field trials in order to evaluate the degree to which currently used QoI products select for more resistant strains. Our studies have shown that 48.6% of the samples that were collected from tomato fields in 2016 are expected to be composed of strains that are sensitive to QoIs, 50.5% of the samples to have strains that are semi-sensitive to QoIs, and 0.9% of the samples to contain strains that are resistant to QoIs. Thus, the situation in 2016 is different to the one observed in 2015, where samples having resistant strains accounted for 18.5% of the samples. Thus, resistance does not seem to accumulate over time and possibly correlates to the level at which QoI fungicides are used each year in the fields. Indeed, analysis of the samples from the fungicides trails has shown that the abundance of the A143 resistant allele is significantly higher in samples from field plots that were treated with CabrioTM (pyraclostrobin) or QuadrisTM (azoxystrobin) and that a positive correlation seems to exist between the frequency of application and the abundance of the A143 allele. Finally, although infections by powdery mildew in California are mainly associated with Leveillula taurica, we have identified and molecularly confirmed that that Oidium lycopersici, a species that so far was only reported in Australia, has been introduced into the state and it is co-infecting tomato plants together with L. taurica. This rather unexpected finding complicates the situation in the fields, as the dynamics of the disease can change dramatically during the growing season, depending on which of the two species is the dominant one.
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1. Introduction Powdery mildew epidemics occur almost yearly in vegetable and tomato-growing areas of California and can impact fruit production and quality[1]. Current control methods of the disease rely mostly on the use of sulfur-dusts and commercial fungicides [2], such as azoles and QoIs (also known as strobilurins). Rally® (myclobutanil), CabrioTM (pyraclostrobin), QuadrisTM (azoxystrobin), and Quadris TopTM (azoxystrobin and difenoconazole) are the most widely used azole and QoI fungicides against powdery mildew [2], but the continuous use of these compounds in the fields across California increases the danger for fungicide resistance development [2]. In fungi, resistance to QoIs mostly correlates with point mutations in the mitochondrial cytochrome b (cytb) gene, which encodes for their enzymatic target in the cell [3, 4]. Specifically three amino acid substitutions have been detected in fungi as the ones predominately governing resistance to QoIs [5]. These are the mutation causing a change from phenylalanine to leucine at position 129 (F129L), the mutation causing a change from glycine to arginine at position 137 (G137R), and the mutation causing a change from glycine to alanine at position 143 (G143A). Resistance factors associated with the three mutations are different, with the G143A conferring the highest levels of resistance to QoIs, which in addition is often complete. In contrast, the G137R and F129L mutations confer only low levels and partial resistance [5]. We have previously reported the cloning of the cytb gene from Leveillula taurica and demonstrated that strains with the notorious G143A mutation are already established in California. In contrast, the F129L and G137R mutations were absent in field populations of the fungus. Our analyses in 2014 and 2015, also indicated that the presence of the G143A mutation in field strains of L. taurica is associated with mitochondrial heteroplasmy for the cytb gene, suggesting that resistance in the fields is not yet complete but rather correlates with the portion of mitochondria in each strain carrying the mutated A143 cytb allele. Heteroplasmy is commonly described as the coexistence of more than one types of mitochondrial genomes (mitotypes) in a single cell and although it regularly occurs in plants, it has only rarely been described in fungi. In L. taurica mitochondrial heteroplasmy for cytb implies that some mitotypes carry the wild-type (sensitive) G143 allele, while others the mutated (resistant) A143 one. As fungal cells contain multiple mitochondria in a single cell, the quantity of mitotypes representing the A143 allele will define the level of resistance against QoIs [6]. The observed mitochondrial hetroplasmy for cytb also implies that differences in QoI sensitivity among field isolates of L. taurica exist that most likely correlate with the portion of the mitochondria harboring the A143 allele. Thus, it is important to continuously monitor the situation and determine whether the frequent use of QoIs would lead to an increase of the A143 allele in populations of the fungus and eventually the fixation of resistance in the fields. Along the same lines, it is also important to examine how stable the observed heteroplasmy is in the absence of QoI fungicides and whether heteroplasmic isolates for the G143A mutation could revert back to wild-type. This might be possible because the presence of both the G143 and A143 alleles in a single strain could imply a fitness penalty associated with the G143A mutation. If such is the case, then implementing a QoI-free period for a number of fungal generations, could potentially reverse fungicide resistance and reset the situation in the fields. The objective of our study in 2016 was to continue monitoring the frequency and spread in California of powdery mildew strains with potential resistance to QoI fungicides, and to conduct fungicide field trials in order to evaluate the degree to which currently used QoI products select for more resistant strains. Although infections by powdery mildew in California are mainly associated with Leveillula taurica, we have identified and molecularly confirmed that a new species, Oidium lycopersici, has been introduced into the state. This rather unexpected finding complicates the situation in the fields, as the dynamics of the disease can change dramatically during the growing season, depending on which of the two species is the dominant one. Thus, a third objective of our study was to monitor the distribution dynamics of the two species that cause powdery mildew disease in tomato in California.
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2. Methods, Results & Discussion 2.1 Collection of infected material from tomato fields and genomic DNA isolations. A total of 509 samples (each sample represents a 1 cm in diameter leaf disk from an infected leaf) were collected in 2016 from 13 fields in Yolo (6 fields), Sacramento (3 fields), San Joaquin (3 fields), and Merced (1 field) counties (Table 1). The number of samples collected in 2016 was considerably smaller as compared to 2015 (1,673 samples from 29 fields), mainly due to the considerably lower disease pressure in 2016. The first samples were collected in early August and then sampling continued till the end of September on a 7-10 days basis and depending on the amount of disease pressure (Table 1). As in previous years, sampling in the field was performed by collecting multiple compound leaves from a number of plants that were randomly distributed within each field. In the lab, 1 cm in diameter discs of infected leaf tissue were excised from individual leaflets and placed individually in a 2.0 mL microcentifuge tube. The remaining leaflets and/or compound leaves were placed in an envelope and stored at -80°C, with all the information regarding the collection of this samples recorded on the outside of the envelop. Genomic DNA from each sample was extracted using the DNAeasy Plant Kit (96-wells format) and the DNA was subsequently evaluated for quality and purity. Our goal was to analyze 15-20 samples per field in terms of the powdery mildew species that is found on it and the presence of A143 allele in the sample. However, in many cases this proved difficult as obtaining good quality DNA for use in subsequent PCRs is challenging when using infected plant tissue that is often necrotic or highly wilted. This considerably increased our workload as on average DNA had to be extracted from 3-5 samples and PCRs had to be repeated with different reaction conditions in order to finally obtain reliable results for one sample. Despite such difficulties, at the time of preparation of this report (November 1st 2016), DNA was isolated from over 800 samples, including samples from the fungicide trails, and over 2,000 PCR reactions were performed. Complete set of data with regard to species identification as well as the presence of the G143A mutation in cytb are available for 109 field samples as well as for 364 samples from the fungicides trials.
2.2 Molecular identification of Oidium lycopersici as a new species of powdery mildew infecting tomatoes in open fields of California. Tomato powdery mildew infections in Northern America and California in specific are known to be caused by the phylogenetically related species Leveillula taurica [7] and Oidium neolycopersici [8], which are primarily associated with the disease in field and greenhouse grown tomatoes, respectively [9]. Macroscopically the two species can be empirically distinguished by the fact that L. taurica grows mainly on the lower side of the leaves penetrating deeply into the mesophyll of the leaf tissue, while O. neolycopersici grows mainly epiphytically on the upper side of the leaves and on the stems. Reproductive spores, known as conidia, produced by the two species are also markedly different with L. taurica producing large, single-celled, lanceolate primary conidia of 50-70 µm in length that are borne singly on conidiophores and secondary cylindrical conidia of 50-70 µm in length that can be in chains. In contrast, O. neolycopersici produces conidia singly or most frequently in pseudochains of 2-6 ellipsoid-shaped conidia with a mean length slightly over 30 µm. A third species, Oidium lycopersici (synonyms Oidium lycopersicum, Euoidium lycopersici,) is also known to infect tomato but it is reported only in Australia. The conidia of this fungus are cylindrical in shape with a mean average length smaller than 30 µm that are formed in chains of 3-7
3 conidia. In the past O. neolycopercisi and O. lycoperscisi were mistaken for a single species (i.e. O. lycopersici) but molecular data has clearly shown that they are distinct species, with the former one being the species causing the powdery mildew outbreaks in Europe, Asia, and USA. Understanding which species are associated with the disease is essential because tomato cultivars are known to be differential resistant against each of the three species. Infected material that was collected from the fields in 2015 and 2016 showed clear symptoms of infections by L. taurica, most typically seen as chlorotic lesions in the lower side of the leaves that turned into necrotic in heavily infected tissue or in samples that were collected later in the season. Abundant sporulation of the fungus was also observed mainly on the lower side of the leaves and occasionally on the upper side as well. Examination of the fungal conidia under an optical or a scanning electron microscope (SEM) showed that these were typical of L. taurica, thus confirming that this pathogen is the major causal agent of powdery mildew epidemics in the fields. Despite that the majority of the samples showed typical symptoms of infections by L. taurica, however, closer inspection showed that a few of them also exhibited signs of infections by Oidium spp. Specifically, in a few samples the presence of fungal spores and mycelium could be seen on stems and petioles as well as growing epiphytically on the upper leaf surface. These symptoms are typically associated with Odium spp. and not L. taurica as the latter is known to infect only leaves and to grow endophytically in the leaf tissue rather than epiphytically. Examination of these samples under a desktop scanning electron microscope at 5 kV and 500-3000x magnification indicated the presence of ovate to cylindrical single-celled conidia with slightly wrinkled surfaces, measuring approximately 15-25 x 12-15 µm in size. Conidia were formed in chains of 3-5 spores on top of straight and unbranched conidiophores that measured 100-150 µm in length. Based on these morphological characteristics the fungus was identified as O. lycopersici. To further confirm the presence of O. lycopersici, genomic DNA that was isolated from 109 field samples was used as a template for the PCR amplification of a 404 bp fragment that spanned part of the internal transcribed spacer 1 (ITS1), the 5.8S ribosomal DNA gene, and the internal transcribed spacer 2 (ITS2) of O. lycopersici. Amplification was performed using oligonucleotide primers Oidlyc-ITS1-F2 5´-TGCACCGACCGGCTTC-3´ and Oidlyc-ITS2- R1 5´-TACCTGATTCGAGGTCAAC-3´ that were designed on the rDNA sequence of O. lycopersici that is deposited in GenBank (AF229021.1). Using these primers, amplicons were obtained from 30 of the field samples (Table 1), whereas subsequent sequencing and BLASTn analysis of a few of these amplicons confirmed that the sequences matched 100% to the partial rDNA sequence of O. lycopersici isolate Ol-1 clone 1 (HQ286673.1), thus confirming the presence of this species on tomato plants in California. These novel findings have been reported as a “Disease Note” in the scientific journal “Plant Disease”[10] and a representative sequence from one of the samples is deposited in GenBank under the Accession No. KU182432. Next to the 30 samples for which the presence of O. lycoperssici could be confirmed based on ITS data, an additional 48 samples from the 109 field samples that were analyzed in 2016 are suspected of having low levels of infections by O. lycopersici as well, but further analysis is needed in order to confirm the presence of this species on those samples. Part of the uncertainty is generated by the fact that although in some cases the primers used for amplification of the ITS sequences from O. lycopersici generate a clear
4 single amplicon of the expected size, in several others they generate a complex pattern of bands that is difficult to interpret. In order to resolve whether O. lycopersici is present in such samples, additional PCR analysis is currently carried out using new primers sets that were designed on the rDNA sequences of this species. PCR analyses using species-specific ITS primers for L. taurica (i.e. Levtau_ITS2-F1: 5’- CAGCGTGAAGACCTCGG-3’ and Levtau_ITS2-R1: 5’- CCA GAA GAA GTA CAA AAG TCG CC -3’) showed the presence of this species in all of the samples. This indicates that although O. lycopersici can be found in some fields co-infecting tomato plants with L. taurica, the later remains the primary causative agent of powdery mildew epidemics in California. Till, now the presence of O. lycopersici had only been reported in Australia and to our knowledge this is the first of report of O. lycopersici in California. Thus, O. lycopersici represents an invasive fungal species for this region that could have a serious impact to the local produce. This is particularly true because our data clearly show that it co-infects tomato plants with L. taurica, thereby increasing the disease pressure. Currently there are no known varieties with resistance to O. lycopersici and the sensitivity of the pathogen to fungicides used against powdery mildew in tomato is also unknown. Our data also show that the pathogen is already well stablished and has spread across several counties in California. Thus, it needs to be closely monitored and its impact on the local produce to be carefully calculated.
2.3 Identification of mutations in cytb associated with resistance to QoI fungicides. To monitor for shifts in the frequency of fungicide resistant strains, we screened field populations of the pathogen for mutations in the cytb gene that are known confer resistance to QoI fungicides [5]. Since we have already partially cloned this gene from L. taurica and developed primers for PCR amplifications, this monitoring approach is rather straightforward. Specifically, using primers RSCBF1 (5’- TAT TAT GAG AGA TGT AAA TAA TGG -3’) and RSCBR2 (5’- AAC AAT ATC TTG TCC AAT TCA TGG -3’) a single cytb fragment of ~300 bp in size is amplified from samples infected with powdery mildew. The translated 100 amino acid product of this fragment spans the crucial region between amino acid positions 127 to 147 that contains residues F129, G137, and G143 that confer resistance to QoIs when mutated [5]. Sequencing of the 300 bp PCR product can then indicate whether one of these mutations is present in the sample. However, as heteroplasmy for the notorious G143A mutation is frequently observed in L. taurica (see our 2015 report), tolerance to QoI fungicides is assessed both qualitatively, by the presence or absence of the G143A mutation, and quantitatively, by estimating the relative abundance of the A143 (resistant) and G143 (sensitive) alleles in each sample. This is done by quantifying in the Sanger sequence trace data the height rations of the two heterozygous peaks that correspond to the nucleotides that code for the A143 and G143 alleles of cytb. Specifically, at the nucleotide position responsible for the critical G143A substitution two overlapping peaks can be found, namely one for guanine (G) and one for cytosine (C), with G leading to the wild-type amino acid (G143) and C to the mutation (A143) that confers resistance to QoIs. The relative quantification of the peak heights of these two nucleotides is done using a combination of the ab1PeakReporter software (Life Technologies) and custom Pearl scripts. Heteroplasmy for cytb is thought to confer quantitative resistance to QoIs that positive correlates to the relative abundance of the two alleles. Thus, in a single sample, the higher the abundance of the A143 allele as compared to the G143 one, the higher its tolerance to QoIs is expected to be [6]. As we
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currently do not know what levels of resistance are conferred by different rations of the two alleles in a single sample, we arbitrary classified as sensitive (S), strains in which the relative abundance of the A143 allele as compared to the G143 one was between 0-10% (i.e. 100-90% of the mitochondria will have the G143 cytb allele and only 0-10% of the mitochondria will have the A143 cytb allele), as semi-sensitive (S- S), strains in which the relative abundance of the A143 allele was between 11-50%, and as resistant (R), strains in which the relative abundance of the A143 allele was between 51-100%. We do not report the F129L and G137R mutations, as these are absent from our samples. As of November 1st 2016, the partial 300 bp cytb fragment was amplified and sequenced from 109 field samples that represented all 13 fields from which samples were collected in 2016 (Table 1). The presence of the notorious G143A substitution that leads to high levels of resistance towards QoI fungicides was identified in 57 of the samples indicating that strains with some level of resistance to QoIs are present in California. However, most of these samples are predicted to have intermediate to low levels of resistance towards QoI fungicides as (i) the A143 allele was always found in combination with the G143 one and (ii) the percentage of the A143 allele (average of 22%) in all heteroplasmic samples was relative low as compared to the G143 one. Specifically, based on the relative abundance of the A143 allele as compared to the G143 one, 48.6% (n=54) of the samples are expected to be composed of strains that are sensitive to QoIs, 50.5% (n=56) of the samples to have strains that are semi-sensitive to QoIs, and 0.9% (n=1) of the samples to contain strains that are resistant to QoIs. Thus, the situation in 2016 is different to the one observed in 2015, where samples composed of sensitive strains accounted for 54.8% (n=80) of the total samples analyzed that year, samples consisting of semi-sensitive strains accounted for 26.7% (n=39) of the samples, and samples having resistant strains accounted for 18.5% (n=27) of the samples (Figure 1).
Figure 1. 100% Stack bars graphs showing the percentage of strains (Y-axis) that can be classified as sensitive (left bar), semi-sensitive (middle bar), and resistant (right bar) in 2015 and 2016. Numbers in each segment correspond to the actual number of strains assigned to the different sensitivity classes.
It is currently unknown whether the lower levels of the A143 allele in samples collected in 2016 correlates to the lower disease pressure this year and possibly to the reduced use of QoI fungicides in the fields. What is encouraging is the fact that resistance does not seem to accumulate over time and possibly correlates to the level at which QoI fungicides are used in the fields. If the case, this will support the notion that a fitness penalty is associated with the G143A mutation in tomato powdery mildew. It is thus possible that under selection pressure (i.e. heavy use of QoIs in the field) the frequency of the A143 allele increases in a population but when the selection pressure is lifted then this allele is largely purged. Thus, implementing QoI-free periods (e.g. by rotating with azole fungicides) would likely preserve the efficacy of these compounds by restoring the sensitivity levels in populations of the fungus and averting the fixation of QoI-resistant strains (i.e. strains having only the A143 allele) in the fields.
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2.4 Evaluation of the degree to which currently applied fungicide spaying programs and products select for QoI-tolerant strains. Current control of powdery mildew in the fields involves the use of mainly the azole fungicide RallyTM (myclobutanil), the QoI fungicides CabrioTM (pyraclostrobin) and QuadrisTM (azoxystrobin) as well as Quadris TopTM (azoxystrobin and difenoconazole), a mixture of an azole with a QoI fungicide. As in 2015, a fungicide field trial was set-up in 2016 in order to evaluate the degree to which the currently used QoI fungicides select for QoI-tolerant strains. If indeed the case, then depending on the strength of selection, QoI products would have to be altered with fungicides of a different chemistry (e.g. azole fungicides) in order to slow down the progression of QoI resistance development in the fields. The fungicide trial was established by co-PI Brenna Aegerter in a commercial processing tomato field (variety H 5508) located on Union Island, north of Tracy. Four products were tested, which as aforementioned represent the most commonly used fungicides against powdery mildew in tomato, namely Rally 4 oz, Cabrio 16 oz, Quadris 6 oz, and Quadris Top 8 oz (Table 2). The experimental set-up was similar to that used in 2015. Briefly, the field was transplanted on June 10th and drip‐irrigated. Each experimental plot consisted of a single bed with a double plant row and 80-inch spacing between bed centers; plots measured 30 feet long. The experimental design was a randomized complete block design with three replications. The trial area was managed by the grower similarly to the rest of the field except that no sulfur or mildew fungicides were applied to the test area. Experimental fungicide applications were initiated prior to disease appearance; the first application was on July 25th with subsequent applications following on a 10- or 20-day interval as specified by the treatment (see table for treatment dates). A non-treatment control was also included in the experimental design (Table 2). All fungicides were applied in a water volume equivalent to 50 gallons per acre and included a non-ionic surfactant adjuvant at 0.25% v/v. Applications were made with a CO2 backpack sprayer (operating at 34 psi at the boom) and a handheld boom with four nozzles (hollow cone TXVS‐18), two of which were on drops. Plots were sampled for mildew at 4-5 days after each fungicide application. Leaves with symptoms or sporulation were selected from each plot and send overnight to UCD for analysis. Plots were rated for the percentage of the foliage that was mildew- symptomatic (sporulation or mildew-induced necrosis). A total of 135 samples (5 samples from each field plot; Table 2) were collected from the experimental plots and genomic DNA was isolated for further analysis. PCR amplification of the cytb gene from the samples was performed using primers RSCBF1and RSCBR2 and the obtained amplicon was purified and sequenced. As with the field samples, tolerance to QoI fungicides was assessed by estimating using the Sanger sequence trace data the relative abundance of the A143 (resistant) and G143 (sensitive) alleles of the cytb gene in each sample. We classified again as sensitive (S), strains in which the relative abundance of the A143 allele as compared to the G143 one was between 0-10% (i.e. 100-90% of the mitochondria will have the G143 cytb allele and only 0-10% of the mitochondria will have the A143 cytb allele), as semi- sensitive (S-S), strains in which the relative abundance of the A143 allele was between 11-50%, and as resistant (R), strains in which the relative abundance of the A143 allele was between 51-100%. Although analysis of the samples is still undergoing, our preliminary results show that the abundance of the A143 resistant allele is significantly higher ( 2 test P=0.019) in samples from field plots that were treated with Cabrio and Quadris, as compared to samples that were obtained from the other experimental plots. Notably, the active ingredient in both Cabrio and Quadris belongs solely to the QoI class, and thus it is perhaps not surprising that these two compounds select for strains that are potentially more tolerant to this class of fungicides. Specifically, based on the relative abundance of the A143 allele as compared to the G143 one, 62.7% (n=32 samples) and 62.8% (n=27 samples) of the samples collected from plots that were treated every 10 days with Cabrio and Quadris respectively, are expected to be composed of strains that are semi-sensitive or resistant to QoIs. In contrast, only 33.3% (n=14 samples), 40,7% (n=22 samples) and 42.8% (n=19 samples) of the samples that were recovered from the non-treated plots, plots treated
7 with Rally (a triazole fungicide), and plots treated with Quadris Top (a mixture of a QoI with an azole fungicide), respectively are expected to contain semi-tolerant or resistant to QoIs strains (Figure 1, Table 3). In a similar way, in the 20 days interval treatments, 54.1% (n=13 samples) and 52.4% (n=11 samples) of the samples recovered from plots that were treated with Cabrio and Quadris respectively, are expected to contain strains that are semi-sensitive or resistant to QoIs, as compared to 33.3% (n=14 strains), 25.0% (n=6 samples) and 61.9% (n=13 samples), of the samples collected from the non-treated plots, and plots treated with Rally or Quadris Top, respectively (Figure 2, Table 3). In addition, a positive correlation seems to be present between the frequency of application of either Cabrio or Quadris and the abundance of the resistant A143 cytb allele in the samples, as seen by comparing the 10-day and 20-day interval treatments. Although the analysis is not yet completed, our preliminary results suggest that applications with QoIs increase the abundance of the A143 allele in heteroplasmic strains, thereby possibly increasing their tolerance to QoIs as well. Given the fact that the A143 allele is observed less frequently in the absence of QoI fungicides and that this allele almost always co-occurs with the wild-type G143 allele, it is possible that the G143A substitution has a fitness penalty for the fungus and thus it is purged once selection pressure is removed. This is important because if true, it will imply that increasing the QoI-free period could possibly decrease tolerance to these fungicides in a large portion of the fungal population.
Figure 2. 100% Stack bars graphs showing the percentage of strains (Y-axis) that can be classified as sensitive (green segments), semi-sensitive (bleu segments), and resistant (red segments) for each fungicide treatment (X-axis). Numbers in each segment correspond to the actual number of strains assigned to the different sensitivity classes.
3. References
1. Jones WB, Thomson SV. Source of inoculum, yield, and quality of tomato as affected by Leveillula taurica. Plant Dis. 1987;71(3):266-8. 2. Guzman-Plazola RA. Development of a spray forecast model for tomato powdery mildew (Leveillula taurica (Lev) Arn.). Davis, California: University of California, Davis; 1997. 3. Bartlett DW, Clough JM, Godwin JR, Hall AA, Hamer M, Parr‐Dobrzanski B. The strobilurin fungicides. Pest Manag Sci. 2002;58(7):649-62.
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4. Lupetti A, Danesi R, Campa M, Tacca MD, Kelly S. Molecular basis of resistance to azole antifungals. Trends Mol Med. 2002;8(2):76-81. 5. Gisi U, Sierotzki H, Cook A, McCaffery A. Mechanisms influencing the evolution of resistance to Qo inhibitor fungicides. Pest Manag Sci. 2002;58(9):859-67. 6. Lesemann SS, Schimpke S, Dunemann F, Deising HB. Mitochondrial heteroplasmy for the cytochrome b gene controls the level of strobilurin resistance in the apple powdery mildew fungus Podosphaera leucotricha (Ell. & Ev.) ES Salmon. J Plant Dis Protect. 2006;113(6):259-66. 7. Correll JC, Gordon TR, J. EV. Host range, specificity, and biometrical measurements of Leveillula taurica in California. Plant Dis. 1987;71(3):248-51. 8. Jones H, Whipps JM, Gurr SJ. The tomato powdery mildew fungus Oidium neolycopersici. Molecular Plant Pathology. 2001;2(6):303-9. 9. Corell JC, Gordon TR, Elliott VJ. The epidemiology of powdery mildew on tomato. Caliornia Agriculture. 1988:8- 10. 10. Salvucci A, Aegerter BJ, Miyao EM, Stergiopoulos I. First report of powdery mildew caused by Oidium lycopersici in field-grown tomatoes in California. Plant Dis. 2016;100(7):1497.
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Table. 1. Field samples analyzed in this study. See footnotes for further details.
Number of Number of Number of Number of samples with samples that are Number of samples samples Tomato Sampling Oidium sensitive, semi- Field Location Treatment samples analyzed as with variety date lycopersici sensitive or obtained 1 of Leveillula (confirmed / resistant to QoIs 11/01/2016 2 taurica 3 unresolved) 4 (S / S-S / R) 5 Clifton Ct Rd., Union South Delta, San Quadris Top H 5508 8/8/2016 35 7 7 5 / 2 6 / 1 / 0 Island Joaquin Jack Tone Rd. & Collegeville, San Fontelis HM 1794 8/8/2016 79 5 5 1 / 4 5 / 1 / 0 Mariposa Rd. Joaquin Minturn Rd. & Le Grand, Merced Unknown Unknown 8/11/2016 48 4 4 0 / 4 4 / 1 / 0 Buchanan Hollow Rd.
Knight’s landing Unknown Unknown 8/20/2016 44 9 9 2 / 7 2 / 7 / 0
Fresh Market Davis, Yolo None Various 8/20/2016 70 5 5 0 / 5 5 / 0 / 0
Teal Bend Golf Course Elkhorn area, Yolo None BP 2 8/20/2016 40 6 6 0 / 6 6 / 0 / 0
Walnut Grove, Sulfur dust, Quadris Walnut Grove East HM 4909 9/16/2016 46 9 9 0 / 9 3 / 6 / 0 Sacramento Top and Cabrio Tremont x Mace South Davis none Various 9/16/2016 22 13 13 2 / 5 3 / 9 / 1 Blvd, Yolo Unknown Unknown Pepper Field 4A NW Sacramento 9/23/2016 30 9 9 0 7 / 2 / 0
Unknown Unknown Pepper Field 7 NW Sacramento 9/23/2016 30 10 10 1 5 / 5 / 0
Armstrong UCD UC Davis, Yolo None Various 9/9/2016 24 13 13 6 / 4 3 / 10 / 0
Harlan CR 104 NE Woodland Sulfur dust Various 9/9/2016 19 11 11 6 / 2 1 / 10 / 0
Quadris Top, Quintec, Clifton Ct. Rd., Union South Delta, San Sonata, sulfur dust, H 5508 9/29/2016 22 8 8 7 4 / 4 / 0 Island Joaquin and Quadris Top
TOTAL 509 109 109 30 / 48 54 / 56 / 1
1 Each sample represents a 1 cm in diameter leaf disk from an infected leaf.
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2 Analysis is still ongoing (11/1/2016). Our goal was to analyze 15-20 samples per field in terms of the powdery mildew species that is found on them as well as for the presence of strains with the G143A mutation. 3 The presence of L. taurica was determined by PCR with using the ITS-specific primers Levtau_ITS2-F1: 5’-CAGCGTGAAGACCTCGG-3’ and Levtau_ITS2-R1: 5’- CCA GAA GAA GTA CAA AAG TCG CC -3’). 4 The presence of O. lycopersici was determined by PCR with using the ITS-specific primers Oidlyc-ITS1-F2 5´-TGCACCGACCGGCTTC-3´ and Oidlyc-ITS2-R1 5´- TAC CTG ATT CGA GGT CAAC -3´’). In several cases these primers could not resolve whether the species was present or not in the analyzed samples, and thus these samples are undergoing further analysis using additional sets of species-specific ITS primers. 5 Tolerance to QoI fungicides was assessed by estimating using sequencing electropherograms the relative abundance of the A143 (resistant) and G143 (sensitive) alleles of cytb in each sample. As we currently do not know what levels of resistance are conferred by different rations of the two alleles in a single sample, we arbitrary classified as sensitive (S), strains in which the relative abundance of the A143 allele as compared to the G143 one was between 0-10% (i.e. 100-90% of the mitochondria will have the G143 cytb allele and only 0-10% of the mitochondria will have the A143 cytb allele), as semi-sensitive (S-S), strains in which the relative abundance of the A143 allele was between 11-50%, and as resistant (R), strains in which the relative abundance of the A143 allele was between 51-100%.
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Table 2. Spraying and sampling scheme followed for the fungicide field trials. The number of samples analyzed so far (November 1st 2016) from each experimental block is shown as well. Our goal to analyze a minimum of 5 samples per plot.
25 Jul spray 6 Aug spray 15 Aug spray 24 Aug spray 2 Sep spray 12 Sep spray 21 Sep spray Blk 1 Blk 2 Blk 3 29 Jul sample* 11 Aug sample 19 Aug sample 30 Aug sample 8 Sep sample 16 Sep sample 29 Sep sample 1 Non-treated 101 205 306 n/a1 n/a n/a 2 3 4 0 4 4 4 5 3 4 3 5 5 5 5 0 5 0 10 -day interval 6 Rally 4 oz 106 209 302 n/a n/a n/a 0 n/a n/a 0 5 3 5 5 4 4 5 5 4 5 3 0 4 2
7 Cabrio 16 oz 105 207 301 n/a n/a n/a 4 3 2 0 5 4 3 4 0 5 4 5 3 4 5 0 5 4
8 Quadris 6 oz 108 202 309 n/a n/a n/a 0 n/a n/a 0 0 4 5 0 5 4 2 4 4 5 5 0 5 0
9 Quadris Top 8 oz 107 201 307 n/a n/a n/a n/a 2 n/a 0 0 4 0 5 1 4 5 5 4 5 5 0 4 0 20 -day interval 2 Rally 4 oz 103 204 308 n/a n/a n/a n/a n/a n/a 0 3 2 n/a n/a n/a 5 5 5 n/a n/a n/a 0 4 0
3 Cabrio 16 oz 104 206 305 n/a n/a n/a n/a n/a n/a 0 1 3 n/a n/a n/a 5 6 5 n/a n/a n/a 0 4 0
4 Quadris 6 oz 102 208 304 n/a n/a n/a n/a n/a n/a 0 3 3 n/a n/a n/a 4 4 4 n/a n/a n/a 0 3 0
5 Quadris Top 8 oz 109 203 303 n/a n/a n/a n/a n/a n/a 0 4 3 n/a n/a n/a 4 5 5 n/a n/a n/a 0 4 0 1 n/a: not applicable as no infections by powdery mildew were detected for this samples
Table 3. The number of sensitive (in green), semi-sensitive (in blue), and resistant strains (in red) identified for each treatment at each sampling date.
25Jul spray 6 Aug spray 15 Aug spray 24 Aug spray 2 Sep spray 12 Sep spray 21 Sep spray
29 Jul sample* 11 Aug sample 19 Aug sample 30 Aug sample 8 Sep sample 16 Sep sample 29 Sep sample 1 Non-treated n/a n/a n/a 7 2 0 5 3 0 10 2 0 11 1 0 9 6 0 5 0 0
10 6 Rally 4 oz n/a n/a n/a 5 3 0 11 3 0 10 4 0 2 10 0 4 2 0 Spraying at - day interval 7 Cabrio 16 oz n/a n/a n/a 8 1 0 1 8 0 4 2 1 10 4 0 4 8 0 0 8 1
8 Quadris 6 oz n/a n/a n/a 0 4 0 8 2 0 8 2 0 0 13 1 0 5 0
9 Quadris Top 8 oz n/a n/a n/a 1 3 0 6 0 0 8 6 0 5 8 1 3 1 0
20 2 Rally 4 oz n/a n/a n/a n/a n/a n/a 3 2 0 n/a n/a n/a 13 2 0 n/a n/a n/a 2 2 0 Spraying at - day interval 3 Cabrio 16 oz n/a n/a n/a n/a n/a n/a 1 3 0 n/a n/a n/a 9 7 0 n/a n/a n/a 1 3 0
4 Quadris 6 oz n/a n/a n/a n/a n/a n/a 2 4 0 n/a n/a n/a 7 5 0 n/a n/a n/a 0 2 1
5 Quadris Top 8 oz n/a n/a n/a n/a n/a n/a 3 4 0 n/a n/a n/a 5 5 0 n/a n/a n/a 0 4 0
12
Project Title: EVALUATION OF CHEMICAL CONTROL OF BACTERIAL SPECK Project Leader: Gene Miyao, UC Cooperative Extension 70 Cottonwood Street, Woodland, CA 95695 (530) 666-8732 [email protected] Summary: Our test was not able to demonstrate treatment combinations that improved bacterial speck control substantially over the copper treatment, Kocide. Disease level was too low to confidently assess chemical control effectiveness in spite of multiple inoculation efforts and supplemental micro sprinkler irrigations to wet foliage to encourage disease.
Objectives: Evaluate foliar applications of chemicals for bacterial speck control. Background: Bacterial speck is often a problem when rainy, cool weather conditions persist in the spring. Control is challenging especially when multiple rain events trigger the onset and development of the disease. Plant resistance that once was effective has been overrun. Most spray control programs include copper, which has provided modest control at best. Procedures: A field test was established at the Department of Plant Pathology’s Armstrong field research site on the UC Davis campus. Materials included Kocide, Dithane, Actigard, Oxidate, Regalia, Agriphage, Serenade Optimum and Agri-Mycin 17. All treatments included copper as a tank mix except the phage treatment. Multiple applications were made during the season. Sprays were applied with hand-held equipment. A non-ionic surfactant was added. The Davis site was transplanted on April 6th with variety BP2 on single plant lines on beds centered on 5 feet. Sprinklers were used initially to establish the stand. Thereafter, irrigation via a buried drip line. Treatment applications were initiated on April 21 and were repeated last on May 9th for a total of 3 applications. A timer-controlled, microsprinkler irrigation system kept foliage intermittently wet using short durations, multiple times during the evening to encourage disease development. The pathogen was post treatment sprayed initially to inoculate plants and reapplied. Bacterial speck infection occurred in last week of May during the early flowering growth stage and progressed slightly.
Results: Infection level averaged 7% in the nontreated controls. Treatments with similar disease levels as the controls were Agriphage and Agri-Mycin 17, at 5 and 4%, respectively. Although all other treatments performed better than the nontreated control, the caution is that performance was in a low disease event where severity was low and duration of progression was short. Disease did not progress likely due to warm and dry weather thereafter.
Yield of marketable fruit as well as total fruit biomass was not statistically significantly different among the treatments. Yield variation was high. PTAB color was statistically
significant between the control vs the group of any treatment, 25.3 vs 24.0. All other comparisons were not significant for: Brix, pH, or any of the fruit cull categories of pink, green, sun damaged, mold or blossom end rot (BER).
Level of immature green and pink colored fruit was high at 22.5%. Fruit ripening was slow at this site. Further delaying the harvest would have increased the level of rots with this 149-day old planting.
Acknowledgements: Experiment station personnel at Armstrong, Mike Eldridge and Bryan Pellissier, assisted. Inoculum provided by the Gitta Coaker lab. Ag Seed Unlimited donated tomato transplants. Product from various chemical manufacturers. CTRI funded support.
Table 1. Schedule of activity, bacterial speck chemical control evaluation, Plant Pathology Dept, Armstrong, UC Davis campus, 2015
location Experiment station, Armstrong, UC Davis. field rows rotation tomato 2015, fallow 2014 irrigation sprinklers to establish stand thereafter surface drip drip Toro AquaTrax 5/8" diameter, 6 mil tape w/ emitters @ 12" with 0.20 gph at 8 psi (0.34 gpm/100') 13 rows x 120 ft = 1560 linear ft drip = 5.3 gpm output
Planting 6-Apr-16 cultivar BP 2 plot length 37.5 ft per plot on 5' centered beds inoculate May 7 @ 10 8 initial level x dilution of 1:3 of isolates A9 and 407 reinoculate with 75% relative humidity, 6-12 mph south, 57F @ 10 8 initial level x dilution of 1:4 mister 10-Apr set up 4 times per night. 8 pm, 11pm, 2 am, 7 am @ 6 to 10 minutes per @ 10 gph with 2 sprinklers per plot centered on guard row infection 3-Jun detect harvest 2-Sep @ 149 days
Table 2 Spray schedule temp relative mph growth spray # date time °F humidity wind direction stage band comments 1 21-Apr 4:00 PM 73 49% 11 SW 4-5 true leaf 50% windy, no disease 2 2-May 9:00 AM 67 52% 4 to 6 SW pre 1st open flower 50% start AgriMek as #9 trt inoculate 7-May 9:00 AM 60 ? 100% 4 SW 1st open flower 20% misting 3 9-May 9:00 AM 70 60% 2 SW 1st open flower 50% sunny, warm inoculate 20-May 5:00 PM 100% 8 SW 20% canopy 25% windy, cold, drizzle
Table 3. Rainfall from CIMIS station at Davis, CA
date inches 8-Apr 0.04 9-Apr 0.17 10-Apr 0.07 5-May 0.02 6-May 0.02 7-May 0.09 20-May 0.58 21-May 0.02 25-May 0.02 27-May 0.12
Table 4. Disease level, fruit yield, quality and culls, Plant Pathology Armstrong facility, UC Davis, 2016
total 3-Jun market fruit % leaves yield biomass PTAB % % % sun % % Treatment (product/ac) diseased tons/A tons/A color Brix pH pink green burn mold BER 1 non treated control 7.0 36.8 55.3 25.3 4.05 4.51 3.4 22.6 2.1 5.4 0.0 2 copper (Kocide 3000) 1.75 lbs 0.5 41.9 58.2 24.5 4.00 4.48 4.5 18.1 1.5 4.4 0.0 3 copper plus Dithane 1.75 & 2 lbs 2.3 38.9 56.1 23.8 4.23 4.46 4.1 22.1 1.3 3.2 0.0 4 Actigard(0.75 oz) with copper 0.0 45.8 64.7 23.8 4.15 4.48 3.5 21.5 1.2 3.9 0.1 5 Oxidate with copper 1:100 @25gpa 0.5 41.8 56.3 23.5 4.05 4.45 3.1 18.0 1.6 3.5 0.0 6 Regalia(3 qrt) with copper 2.1 37.2 52.5 24.0 4.13 4.46 3.4 17.1 2.3 6.3 0.0 7 Agriphage 2 pints w/out copper 4.6 43.2 59.7 24.3 4.18 4.47 3.4 17.9 2.5 4.9 0.1 8 Serenade Opti (20 oz) with copper 1.4 41.3 58.5 24.0 4.05 4.51 3.7 17.6 3.0 5.9 0.0 9 Agri-Mycin 17 @ 0.25 lbs 3.8 46.5 62.2 24.0 4.18 4.46 3.7 14.8 1.5 5.0 0.0 LSD 5% 4.1 NS NS NS NS NS NS NS NS NS NS F value 2.7 0.6 0.7 1.3 0.6 0.9 0.6 0.8 1.4 1.0 0.7 % CV 113 21 15 4 5 1 30 31 56 45 309 ^ significant non-additivity problem ^
Group comparison nontreated control vs. 7.0 36.8 55.3 25.3 4.05 4.51 3.4 22.6 2.1 5.4 0.0 any chemical program 1.9 42.1 58.5 24.0 4.12 4.47 3.7 18.4 1.9 4.6 0.0 Probability 0.00 0.26 0.49 0.01 NS 0.14 NS 0.18 NS NS NS
Summary: Disease level was very low in spite of inoculating with pathogen and microsprinkler irrigating in the spring Some chemical treatments had statistical separation, but all under light disease pressure. High percentage of green fruit even with delayed harvest (at 149 days) Fruit quality (color, Brix and pH) without statistical separation Level of culls was not statistically significantly different
Project Title: EVALUATION OF VARIETIES WITH FUSARIUM WILT, RACE 3 RESISTANCE Project Leader: Gene Miyao, UC Cooperative Extension 70 Cottonwood Street, Woodland, CA 95695 (530) 666-8732 [email protected] Co-Investigators: Brenna Aegerter, UCCE, San Joaquin County Scott Stoddard, UCCE, Merced & Madera counties Tom Turini, UCCE, Fresno County Amber Vinchesi, UCCE, Colusa & Sutter/Yuba counties Summary: In response to increased incidence of Fusarium wilt, race 3, a team of 5 UC Farm Advisors conducted field tests in commercial fields with a history of the pathogen race 3 of Fusarium oxysporum f. sp. lycopersici. A uniform set of 15 varieties included a susceptible (H 8504) and two tolerant (HM 3887 and DRI 319) cultivars evaluated. Incidence of Fusarium with H 8504 averaged 24%, 20% and 29% in the 3 northern sites (Knights Landing, Woodland and Stockton) and with low (2%) or no Fusarium detected in the southern sites (Dos Palos and Huron). In the combined analysis of the 3 sites with moderately high Fusarium incidence, the top yielding varieties were HM 3887 and N 6428. H 8504 was in the lowest yielding group. Brix performance was location dependent and performance varied across locations. Relative yield performance of the varieties in the Dos Palos site varied considerably from the northern sites although N 6428 was also in the highest yielding group. The Fresno site did not develop symptoms of Fusarium infection. Fruit ripening was spread from an average of 21% green fruit alongside 13% rots, an unusual combination. HM 3887 was in the lowest yielding group, while the test had uncharacteristically high variation for yield.
Objective: Compare performance of Fusarium wilt, race 3 resistant varieties in replicated tests from Colusa to Fresno in grower fields with a history of Fusarium wilt, race 3. Procedures: Fifteen (15) varieties were compared which included a susceptible and 2 purported tolerant varieties among the Fusarium wilt, race 3 resistant lines (12). Varieties are listed in Table 1. Consultation on variety list, seed collection and greenhouse support provided by Ag Seeds and Timothy, Stewart and Lekos. Tests were initiated in Knights Landing (Colusa/Sutter), Woodland (Yolo), Stockton (San Joaquin Co.), Dos Palos (Merced) and Huron (Fresno) by the local farm advisor. Field trial design was a randomized complete block with 4 replications. The specifics of the trials are listed in Table 2. Bed configuration varied from single lines on 60-inch centered beds (2) to double lines on 80-inch centered beds (2). One test was configured on 72-inch centered beds with double drip lines. All tests were irrigated with buried drip tape with the exception of the Sutter location which was sprinkler irrigated season long. All trials were mechanically transplanted with commercial planters and mechanically harvested except for the Fresno site where a subsection of each plot was hand harvested. Fruit yield was measured using a portable cart with weigh sensors to collect fruit off the mechanical harvester. All trials except for the Dos Palos site included a subsample of
fruit to sort to measure culls by weight. Small, bagged samples from all sites were delivered to a local Processing Tomato Advisory Board (PTAB) inspection station to determine fruit color, Brix and pH. Plant stands were counted in each plot to later calculate % infected plants from multiple visits to visually tally the total number of symptomatically diseased Fusarium wilt plants. Lab confirmation at UC Davis was made from these tests. Within about a month before harvest, because of onset of unrelated vine necrosis, plants with late Fusarium infections could not be easily determined. The late growth stage assessment shifted to visually rate vine necrosis. Results: Incidence of Fusarium with susceptible H 8504 averaged 24%, 20% and 29% in the 3 northern sites (Knights Landing, Woodland and Stockton) and with low (2%) or no Fusarium detected in the southern sites (Dos Palos and Huron).
In the combined analysis of the 3 sites with moderately high Fusarium incidence, the top yielding varieties were HM 3887 and N 6428 (Table 3). H 8504 was in the lowest yielding group. Brix performance was location dependent and performance varied across locations. The robustness of the combined 3 site test should be the focus over the 3, individual location results for Sutter, Yolo, San Joaquin.
Plant growth season long was exceptional in the Sutter field. Culls level including sunburn were modest (Table 4) and vine necrosis was moderate, in spite of an Ethrel application. Interestingly, overall yields were high with an average of 57 tons/acre, while blossom end rot was prevalent, but especially high with BQ 406 and H 1310, both around 6%. The high yielding group was led by HM 3887 with 64.8 tons/acre and included SVS 2493, HM 58801 and N 6428. DRI 319 had the highest Brix with 6.28, but was also among the lowest yielding, although at 50 tons/acre.
In the Yolo test, HM 3887 and N 6428 were in the highest yielding variety group (Table 5). Average yield spanned almost 20 tons between highest and lowest yields. Sunburn level was over 20% in both BQ 142 and SVS 2793. Vine growth was good.
In the San Joaquin location, variety yield separation was modest, partly because yields between highest and lowest averaged 10 tons/acre (Table 6). Highest yielding varieties, led by N 6428, appeared to have a higher percent of immature fruit. H 1310 had over 8% blossom end rot.
Only individual test results are shown for Dos Palos (Table 7) and Fresno (Table 8).
Relative yield performance of the varieties in the Dos Palos site varied considerably from the northern sites although N 6428 was also in the highest yielding group. Along with N 6428, SVS 2493 and N 6429 were the top yielders, all with over 65 tons/acre. DRI 319 and BQ 406 were in the °Brix leaders with 5.8 and 5.6, respectively, but yields were among the lowest. Fusarium incidence was low with the susceptible H 8504 with 2.3%.
The Fresno site did not develop symptoms of Fusarium infection. Fruit ripening was
spread from an average of 21% green fruit alongside 13% rots, an unusual combination. HM 3887 was in the lowest yielding group, while the test had uncharacteristically high variation for yield.
Yields across the 5 individual locations are listed in Table 9 to display the relative ranking variation.
Discussion: The trial results from the northern sites suggest that some specific varieties with only tolerance to Fusarium wilt race 3 perform well, in the case of HM 3887. The race 3 resistant variety N 6428 also was in the highest yielding group. However, variety performance tends to be greatly influenced by environmental factors, thus suggesting that no single variety will likely be the top performer under a wide range of conditions. Genetically, for disease resistance, these race 3 resistant varieties performed well under disease pressure, especially compared the susceptible control, H 8504. The risk of continuing to rely on a tolerant variety when disease pressure reaches some high level is not recommended. As pioneering plant breeder Jack Hanna once told me: ‘it takes many trials over many years under a wide range of conditions to understand the weakness and strength of any particular tomato variety.’ Of course, the catch is: time is short to make variety selections. We hope our trials provide some guidance.
Acknowledgements: We especially thank all our grower cooperators. In some cases, harvest was by the Morningstar Company. Ag Seeds and T, S & L provided variety consultation, collection of seed and greenhouse support. The Processing Tomato Advisory Board analyzed our fruit quality. Advisor Brenna Aegerter compiled the data and ran the combined and many individual statistical analysis of variance tests. Finally, we are thankful for the funding support from the California Tomato Research Institute and its contributing growers.
Table 1. Variety evaluation, Fusarium wilt, race 3 resistance, 2016
Disease variety resistance Fol, race 3 Company 1 H 8504 VFFNP Susceptible Heinz 2 HM 3887 VFFNsw Tolerant HM Clause 3 DRI 319 VFFNPsw Tolerant Monsanto 4 BQ 141 VFFF3NPsw resistant Woodbridge 5 BQ 142 VFFF3NPsw resistant Woodbridge
6 BP 16 VFFF3NPsw resistant BHN
7 BP 2 VFFF3NPsw resistant BHN
8 SVS 8232 VFFF3NPsw resistant Monsanto
9 SVS 2493 VFFF3NPsw resistant Monsanto
10 H 1310 VFFF3NPsw resistant Heinz
11 BQ 406 VFFF3NPsw resistant Woodbridge 12 HM 58801 VFFF3Nsw resistant HM Clause 13 H 1539 VFFF3Nsw resistant Heinz 14 N 6428 VFFF3Nsw resistant Nunhems 15 N 6429 VFFF3NswLv resistant Nunhems
Code: Disease Resistance V = Verticillium wilt F = race 1 Fusarium wilt FF = race 1 & 2 Fusarium wilt FFF3 = race 1, 2 & 3 Fusarium wilt N = Root Knot Nematode (some species) P = Bacterial speck sw = Tomato spotted wilt virus Lv = Tomato powdery mildew (Leveillula taurica )
Table 2. Specifications
SUTTER YOLO SAN JOAQUIN Mark and Dave Richter with Morning Star harvester & Don Beeman Farms with Rick Marchucci; harvested by Cooperators Richter crew Salvador Duenas Morning Star 38.630861, -121.785556; 37.917250, -121.462828 W. of South Woodland, CR 99 Stockton, Jones Tract, Bacon adjacent, west side, ~400 ft N Island Rd near Highway 4 North of Knights Landing, near of Goodner Lane between CR Location Ensley and Armour Rd. 25A & CR 26 Single plant row, 60" centered Double plant rows, 80" Single plant row, 60" centered beds centered beds beds Sprinkler irrigated with hand Irrigation lines Drip exclusively Drip exclusively Transplanted 19-May-16 7-Apr-16 28-Apr-16 Harvested 23-Sep-16 10-Aug-16 3-Sep-16 Wheat 2015; Tomatoes 2010, Rotation 2005 tomatoes 2015, 2013 tomatoes 2015, 2014
Comments Ethrel applied Sept 7 Field variety: BQ 141 field variety HM 9905; peat soil Disease More Fusarium infestation on FOL R3 California ("Yolo") comments: reps 1 and 2 vs 4. strain confirmed presence of F. solani - light also Verticillium wilt grew well during season symptoms showed later in extensive vole damage to drip growth stage than anticipated tape, thus leaking, and some and incidence increased late season decline due to somewhat slowly Phytophthora
MERCED FRESNO Cooperators Todd Diedrich, Diedrich Farms Westside grower 37.058607, -120.565442; north of Dos Palos, NW corner of Hwy 152 and Willis Rd, Location Double plant rows, 72" Double plant rows, 80" centered beds centered beds Irrigation Drip, double lines Drip Transplanted 7-May-16 11-May-16 Harvested 16-Sep-16 21-Sep-16 Rotation tomatoes 2015 ??? 3-row trial: "easiest harvest Hand harvest of 15ft; sort out Comments ever" on 25 to 35 lbs of fruit Disease comments: FOL R3 both strains isolated No Fusarium wilt
Table 3. Yield and fruit quality from high Fusarium wilt incidence trials, combined Sutter, Yolo and San Joaquin counties with Brix from all 5 locations, 2016.
Marketable disease yield PTAB °Brix °Brix vari i.d. Variety resistance tons/A color pH °Brix Sutter Yolo SJ Merced Fresno 2 1 HM 3887 VFF Nsw 57.6 a 24.9 4.42 5.09 5.20 4.93 5.15 5.2 4.63 14 2 N 6428 VFFF3Nsw 55.0 ab 23.3 4.41 4.96 5.40 4.73 4.75 5.0 4.78 7 3 BP 2 VFFF3NPsw 52.8 bc 22.5 4.53 4.98 5.43 4.73 4.80 5.2 4.70 12 4 HM 58801 VFFF3Nsw 52.5 bc 23.9 4.42 5.45 5.73 5.38 5.25 5.2 4.38 4 5 BQ 141 VFFF3NPsw 52.5 bc 21.8 4.43 4.80 5.05 4.58 4.78 5.2 4.88 15 6 N 6429 VFFF3NswLv 51.4 bcd 23.4 4.47 4.99 5.55 4.58 4.85 5.2 4.58 8 7 SVS 8232 VFFF3NPsw 51.0 bcd 21.5 4.39 5.28 5.50 5.25 5.08 5.4 4.53 9 8 SVS 2493 VFFF3NPsW 50.8 bcd 22.4 4.47 4.89 5.10 4.78 4.80 5.4 4.65 10 9 H 1310 VFFF3NPsw 50.3 cde 22.7 4.43 4.93 5.38 4.58 4.83 5.0 4.95 3 10 DRI 319 VFF NPsw 48.7 cde 23.1 4.39 5.58 6.28 5.35 5.13 5.8 4.40 13 11 H 1539 VFFF3Nsw 47.8 de 21.0 4.45 4.87 5.03 4.78 4.80 5.3 4.48 6 12 BP 16 VFFF3NPsw 47.6 de 23.3 4.42 5.17 5.68 4.83 5.00 5.2 4.83 11 13 BQ 406 VFFF3NPsw 47.4 de 21.9 4.47 5.23 5.50 5.00 5.18 5.6 4.73 1 14 H 8504 VFF NP 46.0 ef 23.4 4.36 4.70 5.03 4.53 4.55 5.0 4.50 5 15 BQ 142 VFFF3NPsw 42.7 f 22.3 4.46 5.09 5.68 4.88 4.73 5.3 4.45 LCD 5% 4.39 0.77 0.04 0.45 0.22 0.33 0.34 0.30 % CV 11 4 1 5 6 3 5 4.5 5 *Location x variety interaction *
Table 4. Fusarium wilt, race 3 resistant processing tomato variety trial results, Richter Bros., Knights Landing, Sutter County, 2016.
% % vine Disease Yield Fol plants necrosis PTAB % % % sun % % Variety ID Variety Resistance Tons/A plants 7-Sep 16-Sep Brix Color pH pink green burn mold BER 2 1 HM 3887 VFFNsw 64.8 A 16.3 32 43 5.20 23.5 4.42 0 1 4 2 1.1 9 2 SVS 2493 VFFF3NPsw 62.2 AB 0.9 25 72 5.10 21.5 4.44 0 0 8 2 1.3 12 3 HM 58801 VFFF3Nsw 61.8 ABC 0.3 16 35 5.73 23.8 4.37 0 0 3 2 1.7 14 4 N 6428 VFFF3Nsw 60.3 ABCD 0.3 22 54 5.40 22.0 4.34 1 0 2 1 3.1 4 5 BQ 141 VFFF3NPsw 59.2 BCD 0.3 14 57 5.05 21.3 4.38 0 0 4 1 1.1 15 6 N 6429 VFFF3NswLv 58.8 BCD 0.0 18 65 5.55 22.5 4.39 0 0 2 2 2.9 10 8 H 1310 VFFF3NPsw 57.9 BCDE 0.3 8 35 5.38 21.3 4.40 1 1 3 1 5.8 7 7 BP 2 VFFF3NPsw 57.3 BCDE 0.0 16 57 5.43 21.0 4.47 0 0 3 2 1.0 1 9 H 8504 VFFNP 56.9 CDE 23.7 50 61 5.03 22.0 4.28 1 0 4 1 1.5 13 11 H 1539 VFFF3Nsw 55.9 DEF 0.3 19 43 5.03 20.5 4.41 0 0 1 0 1.0 8 10 SVS 8232 VFFF3NPsw 55.5 DEF 0.0 21 61 5.50 20.5 4.33 0 0 5 4 1.8 11 12 BQ 406 VFFF3NPsw 52.9 EFG 0.0 18 54 5.50 20.8 4.44 0 0 3 1 6.3 6 13 BP 16 VFFF3NPsw 51.3 FG 3.2 25 65 5.68 22.8 4.33 1 0 2 2 1.6 3 14 DRI 319 VFFNPsw 50.4 G 23.7 35 72 6.28 22.8 4.35 1 0 8 3 0.7 5 15 BQ 142 VFFF3NPsw 49.1 G 0.0 22 50 5.68 21.3 4.40 0 0 5 2 2.2 LSD 5% 5.05 6.3 10.9 13.6 0.45 0.88 0.07 NS 0.4 3.0 1.3 1.9 % CV 6 95.5 33.8 17.4 5.8 2.8 1.2 110.7 96.2 56.0 57.8 60.6 Average 57.0 4.6 22.6 54.9 5.4 21.8 4.4 0.5 0.3 3.7 1.6 2.2 ^significant non-additivity issue ^ ^
Table 5. Evaluation of yield, fruit quality and culls, processing tomato variety trial, Don Beeman Farms, Woodland, 2016.
Marketable infected % variety disease Yield Fol Plants vine PTAB % % % sun % % i.d. variety resistance tons/A (%) necrosis Brix color pH pink green burn mold BER 2 1 HM 3887 VFFNsw 64.2 A 13.0 46 4.93 26.5 4.46 4 1 9 1 0.0 14 2 N 6428 VFFF3Nsw 59.8 AB 0.0 35 4.73 24.3 4.47 4 2 6 0 0.3 4 3 BQ 141 VFFF3NPsw 58.7 BC 0.0 35 4.58 22.3 4.52 1 1 12 0 0.0 3 4 DRI 319 VFFNPsw 56.8 BCD 7.3 35 5.35 23.3 4.44 1 1 11 0 0.0 7 5 BP 2 VFFF3NPsw 56.6 BCD 0.0 54 4.73 22.8 4.63 1 1 10 1 0.0 12 6 HM 58801 VFFF3Nsw 54.8 CD 0.0 21 5.38 24.3 4.49 2 1 7 0 0.1 15 7 N 6429 VFFF3NswLv 54.3 CD 0.0 76 4.58 24.3 4.58 1 1 15 1 0.2 8 8 SVS 8232 VFFF3NPsw 54.2 CD 0.0 61 5.25 22.0 4.48 2 1 12 1 0.1 6 9 BP 16 VFFF3NPsw 52.8 DE 0.0 43 4.83 24.0 4.51 3 3 7 2 0.0 9 10 SVS 2493 VFFF3NPsW 52.8 DE 0.0 72 4.78 22.8 4.54 1 1 23 0 0.0 11 11 BQ 406 VFFF3NPsw 52.7 DE 0.3 43 5.00 22.5 4.55 2 1 11 0 0.3 10 12 H 1310 VFFF3NPsw 51.9 DE 0.0 65 4.58 24.0 4.50 5 1 8 1 1.6 13 13 H 1539 VFFF3Nsw 48.6 EF 0.2 75 4.78 21.8 4.55 2 2 11 0 0.2 1 14 H 8504 VFFNP 46.5 F 19.7 87 4.53 24.8 4.42 6 1 15 1 0.3 5 15 BQ 142 VFFF3NPsw 44.8 F 1.3 61 4.88 22.3 4.55 3 2 27 1 0.4 LSD 5% 5.0 0.22 1.3 0.07 2.0 1.8 5.3 NS 0.5 % CV 6 3 4 1 56 97 30 112 139 Average 54.0 4.86 23.4 4.51 3 1 12 1 0.2 ^ significant non-additivity issue ^ ^ ^
Table 6. Fusarium wilt, race 3 resistant processing tomato variety trial results, Rick Marchucci and Morning Star, Upper Jones Tract, Stockton, 2016.
Estimated Sun Fusarium vine F3 Variety Total yield w marketable yield x °Brix pH Color Pink Green burn Mold BER wilt y necrosis zNDVI z ID Variety category (t/ac) (t/ac) % % % % % % % # 1 N 6428 resistant 44.7 a 43.4 a 4.75 4.42 23.8 8 2 10 1 1.6 0.0 51 0.41 7 2 BP 2 resistant 44.5 a 39.4 abc 4.80 4.49 23.8 5 10 5 1 0.3 0.0 29 0.48 2 3 HM 3887 tolerant 43.9 a 40.6 ab 5.15 4.37 24.8 10 4 10 4 0.6 17.4 24 0.45 8 4 SVS 8232 resistant 43.2 ab 39.7 abc 5.08 4.38 22.0 3 4 11 5 1.5 0.0 65 0.40 # 5 H 1310 resistant 41.2 abc 39.3 abc 4.83 4.40 22.8 5 2 13 2 8.4 0.3 65 0.38 # 6 N 6429 resistant 41.1 abc 39.9 abc 4.85 4.44 23.5 1 1 8 2 1.0 0.6 68 0.39 # 7 HM 58801 resistant 41.0 abc 37.1 bcde 5.25 4.41 23.8 3 7 5 2 0.6 0.0 13 0.56 4 8 BQ 141 resistant 39.7 bcd 37.4 bcd 4.78 4.40 22.0 3 2 10 3 0.5 0.0 68 0.37 # 9 H 1539 resistant 39.0 cd 37.7 bcd 4.80 4.40 20.8 2 1 7 2 0.4 0.3 60 0.40 6 # BP 16 resistant 38.9 cd 35.6 cde 5.00 4.42 23.3 5 6 7 2 0.3 0.0 41 0.42 3 # DRI 319 tolerant 38.7 cd 36.8 bcde 5.13 4.40 23.3 2 2 10 3 0.9 21.7 76 0.37 9 # SVS 2493 resistant 37.4 cde 34.4 def 4.80 4.44 23.0 1 1 9 7 0.2 0.0 78 0.33 # # BQ 406 resistant 36.7 de 34.3 def 5.18 4.42 22.5 2 4 8 3 2.2 1.0 48 0.45 1 # H 8504 susceptible 34.6 e 32.8 ef 4.55 4.37 23.5 2 1 22 4 1.1 28.6 84 0.27 5 # BQ 142 resistant 34.1 e 30.9 f 4.73 4.43 23.3 3 4 9 5 2.3 0.3 59 0.41 LSD@5% 4.1 4.6 0.33 --- 1.7 4.4 2.6 --- 3.2 2.7 4.8 20.4 0.06 %CV 7 9 5 1 5 81 54 65 72 130 72 26 11 Average 39.9 37.3 4.91 4.41 23.1 3.8 3.4 9.6 3.1 1.5 4.68 55.1 0.40
Values represent the mean of 4 observations; means in the same column followed by the same letter are not significantly different according to Fisher’s Least Significant Difference Test. W Total yield is from weigh trailer weights from machine harvest with limited sorting X Estimated marketable yields are the total yields listed above minus green and mold percentages calculated from hand sort out of an ~25-lb fruit sample. Y Percentage of plants exhibiting symptoms of Fusarium wilt by August 5th (~1 month before harvest) Z Vine necrosis and NDVI (‘greenness’) measured on August 22nd
Table 7. Fusarium wilt, race 3 resistant processing tomato variety trial results, Todd Diedrich, Diedrich Farms, Dos Palos, 2016.
Yield PTAB 11-Jul
Var # Variety Tons/A Color Brix pH Fol % 14 1 N 6428 68.9 a 23.8 5.0 4.45 - 9 2 SVS 2493 67.4 a 22.3 5.4 4.42 - 15 3 N 6429 65.9 ab 23.5 5.2 4.51 - 12 4 HM 58801 63.2 bc 26.0 5.2 4.44 - 10 5 H 1310 63.1 bc 22.5 5.0 4.44 - 13 6 H 1539 61.4 cd 21.0 5.3 4.45 - 4 7 BQ 141 60.2 cde 22.5 5.2 4.45 - 7 8 BP 2 60.0 cde 22.8 5.2 4.54 - 5 9 BQ 142 59.4 cde 22.5 5.3 4.50 - 6 10 BP 16 58.8 de 23.3 5.2 4.46 - 2 11 HM 3887 58.2 de 24.0 5.2 4.40 0.9 8 12 SVS 8232 57.9 de 21.3 5.4 4.44 - 3 13 DRI 319 57.2 e 23.5 5.8 4.41 0.2
1 14 H 8504 57.1 e 22.5 5.0 4.29 2.3 11 15 BQ 406 57.0 e 22.3 5.6 4.50 - LSD 0.05 3.99 0.2 0.34 0.07 CV % 4.6 3.7 4.5 1.2 Average 61.0 22.9 5.25 4.44 0.2
Table 8. Fusarium wilt, race 3 resistant processing tomato variety trial results, Westside, Fresno County, 2016.
Marketable Fruit Yield biomass PTAB % % % lbs/ plants/ Var # Variety Tons/A Tons/A color Brix pH green sun rots 50 fruit acre 11 1 BQ 406 46.0 65.4 28.0 4.73 4.37 18 2 11 7.8 7780 12 2 HM 58801 44.6 64.6 27.5 4.38 4.45 21 0 9 7.2 7290 15 3 N 6429 40.7 66.6 29.8 4.58 4.40 28 1 9 7.2 7112 10 4 H 1310 39.3 53.9 25.5 4.95 4.43 17 2 9 8.9 6072 13 5 H 1539 38.5 56.6 26.3 4.48 4.34 23 2 8 7.8 7616 6 6 BP 16 36.0 54.3 28.8 4.83 4.34 16 2 16 8.2 7532 7 7 BP 2 34.4 47.9 27.0 4.70 4.41 19 0 9 7.6 7434 3 8 DRI 319 30.8 49.3 28.0 4.40 4.35 25 1 12 6.6 7728 5 9 BQ 142 30.0 49.1 27.5 4.45 4.40 28 1 10 7.8 7252 14 10 N 6428 26.7 47.8 29.3 4.78 4.39 18 4 24 8.0 6333 8 11 SVS 8232 25.4 42.6 23.8 4.53 4.46 23 0 17 6.4 6697 1 12 H 8504 25.3 39.6 31.8 4.50 4.41 26 2 8 7.0 7467 9 13 SVS 2493 24.3 34.4 26.8 4.65 4.45 14 0 15 6.7 6688 4 14 BQ 141 24.2 37.0 26.3 4.88 4.50 19 2 14 7.2 7411 2 15 HM 3887 23.5 42.4 27.8 4.63 4.42 21 2 22 6.7 7775 LSD 0.05 7.98 7.82 3.44 0.30 0.07 8.5 NS 10.1 1.0 NS CV % 17 11 9 5 1 28 143 55 10 12 Average 32.6 50.1 27.6 4.6 4.4 21 1 13 7.4 7213 ^significant non-additivity ^ ^
Table 9. Yields from Fusarium wilt, race 3 resistant processing tomato variety trials from 5 Central Valley locations, 2016.
Sutter Yolo SJoaquin Merced Fresno Disease Yield Yield Yield Yield Yield variety resistance Tons/A % tons/A % Tons/A % Tons/A % Tons/A % 1 H 8504 VFFNP 56.9 (0.1) 46.5 (13.8) 32.8 (12.1) 57.1 (6.4) 25.3 (22.4) 2 HM 3887 VFFNsw 64.8 13.8 64.2 19.0 40.6 8.8 58.2 (4.7) 23.5 (28.1) 3 DRI 319 VFFNPsw 50.4 #### 56.8 5.3 36.8 (1.3) 57.2 (6.3) 30.8 (5.5) 4 BQ 141 VFFF3NPsw 59.2 3.9 58.7 8.7 37.4 0.3 60.2 (1.4) 24.2 (25.9) 5 BQ 142 VFFF3NPsw 49.1 #### 44.8 (17.0) 30.9 (17.2) 59.4 (2.7) 30.0 (8.1) 6 BP 16 VFFF3NPsw 51.3 #### 52.8 (2.1) 35.6 (4.6) 58.8 (3.6) 36.0 10.3 7 BP 2 VFFF3NPsw 57.3 0.6 56.6 4.9 39.4 5.8 60.0 (1.7) 34.4 5.2 8 SVS 8232 VFFF3NPsw 55.5 (2.5) 54.2 0.4 39.7 6.5 57.9 (5.1) 25.4 (22.3) 9 SVS 2493 VFFF3NPsw 62.2 9.3 52.8 (2.2) 34.4 (7.7) 67.4 10.5 24.3 (25.7) 10 H 1310 VFFF3NPsw 57.9 1.7 51.9 (3.9) 39.3 5.4 63.1 3.3 39.3 20.4 11 BQ 406 VFFF3NPsw 52.9 (7.1) 52.7 (2.4) 34.3 (7.9) 57.0 (6.7) 46.0 40.9 12 HM 58801 VFFF3Nsw 61.8 8.4 54.8 1.5 37.1 (0.6) 63.2 3.5 44.6 36.7 13 H 1539 VFFF3Nsw 55.9 (1.8) 48.6 (10.0) 37.7 1.2 61.4 0.6 38.5 17.9 14 N 6428 VFFF3Nsw 60.3 5.9 59.8 10.9 43.4 16.5 68.9 12.9 26.7 (18.3) 15 N 6429 VFFF3NswLv 58.8 3.3 54.3 0.7 39.9 6.9 65.9 8.0 40.7 24.7 LSD 5% 5.05 5.0 4.6 3.99 7.98 % CV 6 6 9 4.6 17 Average 57.0 54.0 37.3 61.0 32.6
change in % from average within the same site
Management of Curly Top with Reflective Mulches 2016 Joe Nunez, UC Cooperative Extension Advisor, 1031 South Mount Vernon Ave., Bakersfield CA 93307. Phone: 661-868-6222, Email: [email protected]
Several different materials and techniques were tested as a method to repel the beet leafhopper (BLH) from landing on to tomato plants thus preventing the transmission of curly top virus (CTV). The materials tested where a silver reflective plastic mulch which was laid onto the top of 60 inch tomato beds. Use of silver reflective mulch is a proven method of repelling many insect pests away from crops. Surround (kaolin clay) was used as a spray on reflective mulch onto the top of the tomato beds. A separate treatment was applying Surround on to the canopy to determine if it would repel BLH from landing on tomato plants. Another spray on mulch was painting the soil surface green with green turf paint. Beet leafhoppers prefer landing on plants with bare soil around them and not plants with dense canopies. The thought with the green spray on mulch was that the green soil surface would appear as a dense plant canopy thus repelling the BLH. Lastly a newer systemic material was tried to determine if it would help reduce the incidence of curly top.
The treatments were as follows: 1. Control 4. Surround Soil 2. Silver Reflective Mulch 5. Green Dye soil 3. Surround Canopy 6. Verimark
The individual plots were 1 bed by 40 feet in length. Each treatment was replicated 5 times in a randomized complete block design. The silver reflective mulch was applied by rolling the 48 inch roll on the top of the bed and sealing the entire edge with soil to prevent it from blowing away. The Surround was applied by adding Surround to a garden watering can containing approximately 2 gallons of water and agitating it well while applying the solution onto the soil surface. It was allowed to dry before a second application was made to ensure the soil surface was white in color. The green turf paint was applied by the same method. Surround was applied onto the canopy with the use of a garden sprayer. Verimark was applied by drenching a transplant tray at a rate of 13.5 fluid ounces per planted acre. The Verimark was only applied once. The Green dye and Surround soil was re-applied as needed following a rain event. The Surround canopy was applied every week to cover the new plant growth. The control plots were normal bare soil. Halley 3155 was planted by hand at a 18 inch spacing on 4/716. The field was irrigated with a surface drip irrigation system. Yellow sticky cards where placed just above the canopy in each plot and changed on a weekly basis to monitor BLH and total leafhoppers. Monitoring with yellow sticky cards continued until the canopy covered the top of the beds. There were significant differences among treatment means for total leafhopper counts and total beet leafhopper counts. Figure one shows the total number of beet leafhoppers caught each week by each treatment. It shows that the yellow sticky traps in the control plots were consistently catching higher numbers of BLH than the other treatments. Toward the last two weeks it can be seen that the differences between treatment means were not as great. This was expected as the canopy grew to such a point that it covered the top of the beds.
Figure 1. BLH counts per week by treatments. Beet Lea opper Counts per Week 20 Control 15 Silver mulch 10 Surround Canopy
5 Surround Soil
0 Green Dye Verimark
The total BLH counts for the season was significant as shown in table 3. The control had the most BLH present in its canopy while the silver reflective mulch had significantly less as did the Surround soil and the green dye. The Surround canopy and Verimark was the same as the control. Figure 2 shows this data graphically.
Table 3. Total number of BLH captured over season on yellow sticky traps. Treatment Average Total BLH Count for Season 1. Control 94.8 A 2. Silver Reflective Mulch 35.0 C 3. Surround Canopy 73.0 AB 4. Surround Soil 52.0 BC 5. Green Dye 62.2 B 6. Verimark 76.2 AB Probability= 0.0130 %CV= 28.60 LSD P=0.05 24.73
Fig 2. Bar graph of the total number of BLH caught during season. 100 Total Number of BLH 90
80
70
60
50
40
30
20
10
0 Control Silver mulch Surround Surround Soil Green Dye Verimark Canopy
The amount of curly top damage in the trial was evaluated by estimating the percent of canopy loss for each plot. At the end of the season the control had the greatest percent canopy reduction at just over 50 percent canopy loss. However there were no significant differences among the treatment means. Table 4 and figure 3 show the amount of canopy reduction due to curly top numerically and graphically.
Table 4. Percent Canopy Reduction at end of season due to curly top. Treatment Percent canopy reduction at end of season 1. Control 52 2. Silver Reflective Mulch 44 3. Surround Canopy 48 4. Surround Soil 40 5. Green Dye 38 6. Verimark 38 Probability= 0.5993 %CV= 34.38 LSD P=0.05 Not Significant
Figure 3. Percent Canopy Reduction at end of season due to curly top. Percent Canopy Reduc on at End of Season
60
50
40
30
20 Percent Reduc on
10
0 control Silver Mulch Surround Surround Soil Green Mulch VeriMark Canopy
Although the yields were not significant there is a strong trend among the treatments. The control had the least about of fruit harvested while the silver reflective mulch and green dye had the greatest yields (table 5.) Conducting a regression analysis revealed that the greater number of BLH trapped during the season resulted in less fruit yield at harvest (figure 4).
Table 5. Average weight of tomatoes harvested per treatment. Treatment Lbs of tomato fruit harvested 1. Control 51.4 2. Silver Reflective Mulch 80.9 3. Surround Canopy 54.8 4. Surround Soil 59.9 5. Green Dye 73.3 6. Verimark 53.2 Probability= 0.4509 %CV= 43.77 LSD P=0.05 Not Significant
CTRI report 2016, Ploeg, Page 1
CTRI Research Report, 2016
Antoon Ploeg, Dept. nematology, UC Riverside. [email protected]. (951) 827-3192
Title: Characterization of resistance-breaking root-knot nematodes
INTRODUCTION Root knot nematodes can cause extensive damage to many crops. The problems are most pronounced in warmer climates as these nematodes require soil temperatures in the root zone exceeding 65˚F before they become active and able to invade the crop’s roots. It has been estimated that in the US nematode pests are responsible for at least $11 billion crop losses (Becker, 2014); more than half of those are caused by root-knot nematodes (Meloidogyne spp.). Annual crop losses in processing tomato production in CA have been estimated between 10-20% (Koenning et al, 1999), although current wide-spread use of rkn- resistant tomato cultivars and/or nematicides might have mitigated some of the problems. However, the increasing occurrence of resistance-breaking root-knot nematode populations in CA processing tomato production fields (Roberts, 1995, Kaloshian et al., 1996, Williamson and Kumar, 2006) is of major concern. All RKN resistant tomato varieties have the Mi gene, which renders the plants resistant to three species of warm-climate RKN: M. incognita, M javanica, and M. arenaria. Another RKN species (M. hapla), that prefers cooler climates and therefor in California is mostly restricted to northern parts is able to infest these Mi-resistant plants, but is easily distinguished from the other species by the symptoms on the roots and is not as damaging. Another species that is able to overcome resistance is M. enterolobii. This potentially very damaging species has been reported in the southeastern US, but has so far not been found in California. Resistance is less effective under high (>82F) soil temperatures. Although frequent use of RKN resistant tomato varieties probably is an important factor in the appearance of resistance breaking populations, there are also reports of resistant tomatoes becoming infested with RKN in fields that had no history of tomato cropping (Kaloshian et al., 1996). So far, all resistance- breaking populations in California appear to belong to M. incognita, although studies from elsewhere also have reported resistance-breaking populations of M. javanica and M. arenaria (Semblat et al., 2001; Cortada et al., 2011). On top of the variability in virulence between RKN populations, there also appears to be variability in the level of resistance of tomato varieties carrying the Mi-gene (Pérez et al., 2006; Roberts, 1995). In this study we will document the occurrence, identify (morphologically and molecularly) resistance- breaking RKN populations from California processing tomato fields, determine level of variability among these populations, and among common Mi-resistant tomato varieties, and determine the ability of these populations to reproduce on other nematode resistant crop varieties. The ultimate goal is to be able to reliably predict the resistance-breaking nature of RKN populations by using molecular markers, but much basic knowledge (as outlined above) is still lacking, and up to now such efforts by others (e.g. Cortada et al., 2010) have not been successful.
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MATERIAL AND METHODS During 2014 and 2015, 16 RKN populations were collected from processing tomato fields where typical root-knot symptoms were observed, in spite of the fact that resistant varieties were grown. Nematodes in samples taken from these fields were extracted from soil and or root samples, examined under the microscope to confirm presence of RKN and added to a susceptible ‘Daniela’ and a resistant ‘Celebrity’ tomato plant. Plants were in pots and grown at the Dept. of Nematology, UCR greenhouse. After at least 6 weeks, the roots of plants were washed free of soil, roots examined for presence of galling, and nematodes were extracted from the roots. Out of the initial 16 populations, 2 populations did not show any symptoms on the resistant Celebrity, and failed to reproduce. These populations were not further used in our study.
Of the remaining 14 populations, five were used in our initial set of experiments: Population #3: Woodland #4: Bakersfield #10: San Joaquin #11: Woodland #12: Woodland
For location see also Figure 1.
Single RKN egg masses were handpicked from infested ‘Celebrity’ roots and individually transferred to a new resistant tomato ‘Celebrity’ seedling (one egg mass per plant). Plants were grown for at least 8 weeks to allow for nematode multiplication, and nematodes from these plants were then further multiplied on a series of ‘Celebrity’ seedlings.
Reproduction and symptom formation of these populations were tested on a range of 7 tomato varieties: ‘Daniela’ (susceptible control), ‘Celebrity’ (resistant control) and the resistant processing tomatoes ‘H5608’, ‘DR1319’, ‘N6366’, “HM3887’, and ‘H8504’. The avirulent population of RKN Meloidogyne incognita 77 was included as a control.
Two replicated experiments were done in 1L plastic pots filled with steam-sterilized sandy soil in a greenhouse. The experiments were set up according to a completely randomized design with five replicates (total 6 RKN populations x 7 tomato varieties x 5 replicates = 210 pots per experiment, see Figure 2). A three-week-old tomato seedling was planted in each pot, and inoculated with 1,000 RKN second-stage juveniles (J2) 10 days after planting. Plants were grown for 6 weeks, fertilized with liquid fertilizer weekly through and automated drip system. At harvest, roots were washed free of soil and rated for severity of root galling (0-10 scale: 0=no galling, 10=100% of root system galled). Roots were then placed in a mist chamber for 7 days to extract RKN J2. The suspensions coming of the roots were collected, and the number of J2 from each root system was counted under a dissecting microscope under 40x magnification. The first replicate of the experiment was during May-July, 2016, the second during July-September, 2016. Differences between populations, tomato varieties, and interactive effects were analyzed statistically using SAS software at 95% confidence level. Prior to analysis, J2 counts were log(x+1)-transformed.
CTRI report 2016, Ploeg, Page 3
All populations collected were able to cause galling and reproduce on all of the resistant tomato varieties tested. As expected, the control M. incognita population only produced symptoms and reproduced on the susceptible tomato ‘Daniela’. The interactive effect ‘variety x population’ was significant for both root-galling and nematode reproduction (J2 per root system). This could be expected, as the M. incognita control population was expected to behave differently on the different tomato varieties (only symptoms and reproduction on susceptible tomato Daniela) from the resistance-breaking populations (symptoms and reproduction on all tomato varieties).
Effects on root –galling: In the first experiment, the control Mi population only produced significant galling on the susceptible ‘Daniela’. There were only minor differences between the processing tomato varieties when inoculated with the resistance-breaking populations. All processing tomatoes reacted with moderate (>3) to high root (>5) galling levels, with the exception of varieties ‘H5608 ‘and ‘DR1319’ when inoculated with population nr. 12. Averaged over all populations, there were no significant differences in galling among the processing tomatoes (range 3.9 – 4.7) (see Table 1A, Figure 3).
Table 1A. Average (n=5) galling on different tomato cultivars inoculated with 6 root-knot nematode (Meloidogyne) populations. Greenhouse pot trial. Replicate 1. Differences between tomato varieties. Cultivar Nematode population1 Mi-control 3 4 10 11 12 avg Daniela (S) 4.2 a 5.0 a 2.8 a 3.6 b 5.2 ab 2.4 b 3.9 b Celebrity (R) 0.2 b 4.2 a 4.6 a 6.2 a 3.8 b 0.2 c 3.2 c H5608 (R) 0.0 b 6.4 a 4.6 a 6.2 a 6.2 a 2.6 b 4.3 ab DR1319 (R) 0.0 b 5.6 a 5.0 a 6.6 a 6.4 a 2.4 b 4.3 ab N6366 (R) 0.2 b 5.6 a 5.2 a 5.2 ab 6.2 a 3.2 b 4.3 ab HM3887 (R) 0.0 b 5.6 a 5.4 a 5.2 ab 7.4 a 4.8 a 4.7 a H8504 (R) 0.0 b 4.0 a 4.4 a 5.8 a 6.2 a 3.0 b 3.9 b different letters within the same column represent significant differences at the 95% confidence level.
When examing differences between the RKN populations, it is clear that overall the control Mi population was least virulent. Averaged over all tomato varieties the galling (0.7) was significantly lower than galling caused by the other populations. Among the resistance-breaking populations, population nr.11 caused the highest galling (5.9). Looking at the individual processing tomato varieties, there were minor differences between the populations: of the five resistance-breaking populations, population nr 12 caused the lowest galling on all of the processing tomatoes, and averaged over all varieties caused significantly lower galling (2.7) than the other resistance-breaking populations (Table 1B, Figure 3).
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Table 1B. Average (n=5) galling on different tomato cultivars inoculated with 6 root-knot nematode (Meloidogyne) populations. Greenhouse pot trial. Replicate 1. Differences between RKN populations. Nematode Tomato cultivar population Daniela Celebrity H5608 DR1319 N6366 HM3887 H8504 Avg (S) (R) (R) (R) (R) (R) (R) Mi Control 4.2 ab 0.2 c 0.0 c 0.0 d 0.2 c 0.0 c 0.0 d 0.7 e 3 5.0 a 4.2 b 6.4 a 5.6 ab 5.6 a 5.6 b 4.0 c 5.2 b 4 2.8 bc 4.6 b 4.6 ab 5.0 b 5.2 a 5.4 b 4.4 bc 4.6 c 10 3.6 abc 6.2 a 6.2 a 6.6 a 5.2 a 5.2 b 5.8 ab 5.5 ab 11 5.2 a 3.8 b 6.2 a 6.4 a 6.2 a 7.4 a 6.2 a 5.9 a 12 2.4 c 0.2 c 2.6 b 2.4 c 3.2 b 4.8 b 3.0 c 2.7 d different letters within the same column represent significant differences at the 95% confidence level.
In the second replicate, galling differences between the processing tomato varieties were again minor. Galling was high (>5) on all processing varieties when inoculated with any of the resistance-breaking nematode populations. Averaged over all populations, galling on H8504 was slightly but significantly lower (5.3) than on the other processing tomatoes (Table 2A, Figure 4).
Table 2A. Average (n=5) galling on different tomato cultivars inoculated with 6 root-knot nematode (Meloidogyne) populations. Greenhouse pot trial. Replicate 2. Differences between tomato varieties. Cultivar Nematode population1 Mi-control 3 4 10 11 12 avg Daniela (S) 8.2 a 7.8 a 5.2 c 7.4 ab 7.0 a 6.2 a 7.0 a Celebrity (R) 1.2 bc 6.8 a 5.4 bc 6.2 b 6.0 a 6.8 a 5.4 c H5608 (R) 1.4 b 7.6 a 7.4 a 8.0 a 6.6 a 7.0 a 6.3 b DR1319 (R) 0.6 c 8.0 a 7.4 a 6.6 ab 7.4 a 7.6 a 6.3 b N6366 (R) 0.8 bc 8.0 a 7.4 a 7.8 a 6.8 a 5.4 a 6.0 b HM3887 (R) 0.8 bc 7.6 a 7.0 ab 7.6 ab 7.4 a 6.0 a 6.1 b H8504 (R) 0.8 bc 7.2 a 5.4 bc 6.2 b 6.6 a 5.6 a 5.3 c different letters within the same column represent significant differences at the 95% confidence level.
Like in the first replicate, there were some slight differences between the nematode populations. Averaged over all tomato varieties, population nr. 3 caused significantly more severe galling (7.6) than the other populations. However, each resistant-breaking population was able to cause high levels (>5) of galling on all processing tomatoes. Of the resistance-breaking populations, population nr.12 in general resulted in less severe galling than the other resistance breaking populations (average 6.4) (Table 2B, Figure 4). This is consistent with the results from the first replicate.
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Table 2B. Average (n=5) galling on different tomato cultivars inoculated with 6 root-knot nematode (Meloidogyne) populations. Greenhouse pot trial. Replicate 2. Differences between RKN populations. Tomato cultivar Nematode Daniela Celebrity H5608 DR1319 N6366 HM3887 H8504 Avg population (S) (R) (R) (R) (R) (R) (R) Mi Control 8.2 a 1.2 c 1.4 b 0.6 c 0.8 d 0.8 c 0.8 b 2.0 d 3 7.8 a 6.8 a 7.6 a 8.0 a 8.0 a 7.6 a 7.2 a 7.6 a 4 5.2 c 5.4 b 7.4 a 7.4 ab 7.4 ab 7.0 ab 5.4 a 6.5 c 10 7.4 ab 6.2 ab 8.0 a 6.6 b 7.8 a 7.6 a 6.2 a 7.1 b 11 7.0 ab 6.0 ab 6.6 a 7.4 ab 6.8 b 7.4 a 6.6 a 6.8 bc 12 6.2 bc 6.8 a 7.0 b 7.6 ab 5.4 c 6.0 b 5.6 a 6.4 c different letters within the same column represent significant differences at the 95% confidence level.
Effects on nematode reproduction: In the first replicate, RKN J2 root populations were higher on ‘H5608’ than on ‘HM3887’ and ‘H8504’ (P <0.05). The susceptible variety ‘Daniela’ was among the best hosts for each nematode population. There was hardly any nematode reproduction (<100 J2 per root system) by the Mi-control population on the resistant cultivars. Cultivar ‘H8505’ was a slightly poorer host for population nr.3 than ‘H5608’ but all processing tomatoes were equally good hosts for each of the other populations. The average reproduction factor on the processing tomatoes was 7.5 (Table 3A, Figure 5)
Table 3A. Average (n=5) root nematode infestation levels (J2 per root system x1,000) on different tomato cultivars inoculated with 6 root-knot nematode (Meloidogyne) populations. Greenhouse pot trial. Replicate 1. Differences between tomato varieties. Cultivar Nematode population Mi- 3 4 10 11 12 Avg control Daniela (S) 9.2 a 7.2 abc 5.3 a 4.5 a 11.1 a 1.8 a 6.4 a Celebrity (R) 0.0 b 5.2 bc 2.2 a 10.7 a 7.3 a 0.5 b 4.3 d H5608 (R) 0.1 b 19.5 a 17.0 a 8.8 a 24.6 a 4.8 a 12.6 ab DR1319 (R) 0.0 b 14.6 ab 13.6 a 15.5 a 12.8 a 2.0 a 9.8 abc N6366 (R) 0.0 b 7.4 abc 2.7 a 9.0 a 6.9 a 4.8 a 5.3 bcd HM3887 (R) 0.0 b 7.0 abc 3.8 a 4.2 a 9.8 a 6.5 a 5.2 cd H8504 (R) 0.0 b 4.8 c 1.2 a 6.8 a 7.2 a 5.5 a 4.4 cd different letters within the same column represent significant differences at the 95% confidence level. Statistical analysis on log(x+1)-transformed data, non-transformed data shown.
When examining differences between the RKN populations, The Mi control population had the lowest reproduction averaged over all tomato cultivars, as could be expected. Of the other populations, population nr.12 had the lowest reproduction overall, which corresponds with this population also resulting in the lowest galling overall. All populations reproduced about equally well on all processing tomatoes, with only population nr 4. having a slightly lower production than the other resistance breaking populations on ‘H8504’ (Table. 3B, Figure 5). CTRI report 2016, Ploeg, Page 6
Table 3B. Average (n=5) root nematode infestation levels (J2 per root system x1,000) on different tomato cultivars inoculated with 6 root-knot nematode (Meloidogyne) populations. Greenhouse pot trial. Replicate 1. Differences between RKN populations. Nematode Tomato cultivar population Daniela Celebrity H5608 DR1319 N6366 HM3887 H8504 Avg (S) (R) (R) (R) (R) (R) (R) Mi Control 9.2 a 0.0 c 0.1 b 0.0 b 0.0 b 0.0 b 0.0 c 1.2 d 3 7.2 a 5.2 a 19.5 a 14.6 a 7.4 a 7.0 a 4.8 a 9.4 a 4 5.3 a 2.2 a 17.0 a 13.6 a 2.7 a 3.8 a 1.2 b 6.8 bc 10 4.5 a 10.7 a 8.8 a 15.5 a 9.0 a 4.2 a 6.8 a 8.3 abc 11 11.1 a 7.3 ab 24.6 a 12.8 a 6.9 a 9.8 a 7.2 a 11.5 ab 12 1.8 a 0.5 bc 4.8 a 2.0 a 4.8 a 6.5 a 5.5 a 3.7 c different letters within the same column represent significant differences at the 95% confidence level. Statistical analysis on log(x+1)-transformed data, non-transformed data shown.
In the second replicate, ‘HM3887’ and ‘H8504’ were again the most resistant varieties, allowing only low levels of nematode reproduction. However, when looking at the individual nematode populations, there were no differences in host status between the processing tomatoes. Whereas the average galling in the second replicate was higher than in the first replicate, data on nematode reproduction showed the reverse: in the second replicate, the nematode reproduction factor on the processing tomatoes was on average 3.1 (7.5 in first replicate) (Table 4A, Figure 6).
Table 4A. Average (n=5) root nematode infestation levels (J2 per root system x1,000) on different tomato cultivars inoculated with 6 root-knot nematode (Meloidogyne) populations. Greenhouse pot trial. Greenhouse pot trial. Replicate 2. Differences between tomato varieties. Cultivar Nematode population Mi-control 3 4 10 11 12 Avg Daniela (S) 8.3 a 1.0 b 0.9 a 4.2 a 2.1 a 3.3 a 3.3 a Celebrity (R) 0.2 c 2.2 b 1.0 a 2.9 a 2.0 a 6.4 a 2.4 bc H5608 (R) 0.9 ab 4.1 b 2.2 a 1.6 a 9.4 a 3.5 a 3.6 ab DR1319 (R) 0.3 bc 11.1 a 2.3 a 7.3 a 9.4 a 4.4 a 5.8 ab N6366 (R) 0.3 bc 2.5 b 1.7 a 8.3 a 2.6 a 3.6 a 3.2 abc HM3887 (R) 0.3 bc 4.2 b 1.0 a 0.8 a 2.4 a 2.9 a 1.9 c H8504 (R) 0.3 bc 1.0 b 0.8 a 1.6 a 0.9 a 2.1 a 1.1 c different letters within the same column represent significant differences at the 95% confidence level. Statistical analysis on log(x+1)-transformed data, non-transformed data shown.
As in the first replicate, the major difference between populations was between the Mi control and the resistance breaking populations. Although averaged over all tomato cultivars, population nr.4 reproduced not as well as population nr.12, there were no significant differences between the resistance breaking populations when examining the resistant tomatoes separately. (Table 4B, Figure 6).
CTRI report 2016, Ploeg, Page 7
Table 4B. Average (n=5) root nematode infestation levels (J2 per root system x1,000) on different tomato cultivars inoculated with 6 root-knot nematode (Meloidogyne) populations (1,000 J2/pot). Greenhouse pot trial. Replicate 2. Differences between RKN populations. Tomato cultivar Nematode Daniela Celebrity H5608 DR1319 N6366 HM3887 H8504 Avg population (S) (R) (R) (R) (R) (R) (R) Mi Control 8.3 a 0.2 b 0.9 a 0.3 b 0.3 b 0.3 a 0.3 b 1.5 c 3 1.0 c 2.2 a 4.1 a 11.1 a 2.5 a 4.2 a 1.0 a 3.7 ab 4 0.9 c 1.0 a 2.2 a 2.3 a 1.7 a 1.0 a 0.8 a 1.4 b 10 4.2 ab 2.9 a 1.6 a 7.3 a 8.3 a 0.8 a 1.6 a 3.8 ab 11 2.1 b 2.0 a 9.4 a 9.4 a 2.6 a 2.4 a 0.9 a 4.1 ab 12 3.3 b 6.4 a 3.5 a 4.4 a 3.6 a 2.9 a 2.1 a 3.7 a different letters within the same column represent significant differences at the 95% confidence level. Statistical analysis on log(x+1)-transformed data, non-transformed data shown.
Nematode identification: Of the total of 14 resistance-breaking populations, three were tested so far by CDFA, Sacramento, using PCR-based methods. All three populations (nrs. 1, 2, 3): were identified as Meloidogyne incognita. Recently our laboratory has acquired equipment and necessary chemicals for PCR-based identification of four major root-knot nematode species, and we will identify the remainder of the populations collected so far in the very near future.
CONCLUSION 2016 Sixteen populations were collected from processing tomato fields where resistance-breaking occurred. When nematodes extracted from root or soil from these fields were inoculated onto the nematode resistant cultivar 'Celebrity', 14 populations were able to break the resistance. Five populations were selected for greenhouse experiments with 5 nematode resistant processing tomato varieties. Although there were some minor differences between the five tomato varieties, they generally responded very similarly with respect to the degree of galling and the level of nematode reproduction that occurred on the root systems. At the same time, although the five resistance-breaking populations were very different from the control avirulent RKN population, all four resistance-breaking populations generally caused similar symptoms and reproduced equally well on the resistant tomato varieties. So far, there are no indications of major differences between resistant processing tomatoes, or resistance-breaking RKN populations. In future work, an additional set of populations will be tested, and these populations will also be tested on other nematode resistant vegetable crops to determine if they are able to reproduce on these as well.
CTRI report 2016, Ploeg, Page 8
Figure 1: Location of original populations used in greenhouse experiments.
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Figure 2: Overview of greenhouse pot trial
CTRI report 2016, Ploeg, Page 10
Figure 3:
Root Galling on tomato vars. inoculated with different RKN populaGon (rep1) 10
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Figure 4: Root Galling on tomato vars. inoculated with different RKN populaHon (rep2) 10
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CTRI report 2016, Ploeg, Page 11
Figure 5: RKN J2 on roots of different tomato vars inoculated (1,000 J2 )with different RKN populaFons (rep1)
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Figure 6: RKN J2 on roots of different tomato vars inoculated (1,000 J2 )with different RKN populaGons (rep2)
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2016 Annual Progress Report
Project Title: Detection and Management of Tomato Viruses
Project Leader: Robert L. Gilbertson, Professor of Plant Pathology Department of Plant Pathology University of California-Davis Davis, CA 95618 Phone: 530-752-3163 E-mail: [email protected]
Cooperating personnel: Ozgur Batuman, Maria Rojas, Mônica Macedo and Tom Turini
Introduction:
In 2016, we continued our work on viruses impacting processing tomato production in California, with an emphasis on providing support to the industry and Farm Advisors for virus detection and identification. An emphasis was to apply this information to support the implementation of integrated pest management (IPM) of tomato spotted wilt caused by Tomato spotted wilt virus (TSWV) and curly top caused by Beet curly top virus (BCTV). Both of these objectives involved the application of molecular detection tools that we have developed over the years for detecting the range of viruses that infect processing tomatoes in California and the continued assessment and development of IPM programs for tomato spotted wilt and curly top.
1. Detection and diagnosis
In 2016, we received a total of 285 plant samples from growers and/or collected in field surveys for viral disease diagnosis. Depending on the symptoms and circumstances, these samples were subjected to selected tests for infection with a range of viruses (note that not all the samples were subjected to the same tests). The tests that the samples were subjected to included immunostrips, RT-PCR and PCR and DNA sequencing. The majority of samples came from growers in Fresno County, and were tomatoes submitted for testing for infection by TSWV and BCTV. Other viruses that were tested for in some samples included Alfalfa mosaic virus (AMV), Tomato necrotic spot virus (ToNSV), Tomato necrotic dwarf virus (ToNDV), Pelargonium zonate spot virus (PZSV), tobamoviruses, ilarviruses, torradoviruses (Tomato necrotic dwarf virus, ToNDV) and criniviruses as well as phytoplasma (big bud).
As summarized in Table 1, 69% of the 167 samples tested for TSWV were positive for infection with the virus. This reflects the continuing presence of TSWV in processing tomatoes, and the range of symptoms induced by the virus depending on the age of the tomato plant. Indeed, many of these samples came from late-season infections, where plant show a shoot die- back or flagging that is not typical of tomato spotted wilt. This also reflects the higher pressure of the virus late in the season. Finally, although the rapid immunostrip test is available to detect TSWV in the field, the diversity of symptoms associated with TSWV infection make it challenging to identify infections in some plants, particularly late in the season.
Table 1. Summary of results of virus tests on samples received from growers and/or collected in field surveys in 2016.
RT-PCR AND/OR PCR NUMBER OF POSITIV NEGATIV INCIDENC PLANTS TESTS SAMPLES E E E TSWV 167 Tomato 116 51 69% Other TSWV 14 2 12 14% Crops BCTV 120 Tomato 67 53 56% Mixed OTHER PATHOGENS 93 21 72 23% crops SW-5 GENE 240 Tomato 229 11 95% TOTAL 634
The other virus most commonly detected in the submitted samples was BCTV. Here, 56% of the 120 samples tested for BCTV were positive. Many of the positive samples came from a relatively small number of fields, some of which were organic fields, which had high incidences of curly top. However, 44% of the samples were negative (and also were not infected with other viruses), demonstrating that other abiotic factors are causing virus-like symptoms. This is also consistent with the fact that the overall incidence of BCTV in 2016 was low to moderate. While the symptoms of curly top are usually fairly distinctive, early season symptoms can be confused with tomato spotted wilt or other factors and, with the lack of an immunostrip for BCTV, growers often want a definitive result. This information also can be used by growers to make decisions regarding application of insecticides to try to control beet leafhoppers, the vector of the curly top virus.
Although most samples had symptoms of TSWV or BCTV infection, 23% of the samples with other types of symptoms were infected with other viruses or the big bud phytoplasma. Most of these samples were infected with the big bud phytoplasma, which seems to reflect an increase in this diseases in processing tomatoes in California. A smaller number of samples were infected with AMV or ToNSV. Notably, the whitefly-transmitted torradovirus ToNDV, which was detected in a late-planted experimental plot in Kern County in 2015, was not detected in processing tomatoes in 2016, although it was detected in weed samples from the Imperial Valley.
In terms of non-tomato samples, one nightshade and one Datura plant tested positive for TSWV, and these were collected from fresh market tomato fields that had severe TSWV outbreaks in Fresno County (see below). One onion plant from Fresno was tested positive for Iris yellow spot virus, a close relative of TSWV.
Samples of tomatoes and other plants that tested negative in all these tests for virus infection either had nutritional deficiency symptoms or apparent chemical damage. This was consistent with the negative test results.
Overall, these results reveal a continuing need in the industry for accurate and rapid detection of viruses and virus-like agents, as well as for other diseases and insects. Rapid and accurate diagnosis is critical for implementation of management strategies as well as for rapid identification of new and emerging pathogens (see below).
2. Identification of a resistance-breaking strain of TSWV infecting fresh market tomato varieties in Fresno County
In April 2016, severe symptoms of TSWV (stunting, leaf, stem and petiole necrosis and concentric rings on fruits) were observed on plants in fields of fresh market tomatoes plants with cultivars that have the Sw-5 gene that confers resistance to TSWV. Samples of these plants were sent to us and we confirmed infection with TSWV with immunostrips and RT-PCR and DNA sequencing. Tests for other viruses were negative. This raised the possibility that a resistance- breaking (RB) strain of TSWV had emerged in Central California.
On 17 June, Ozgur Batuman, Bob Gilbertson and Tom Turini visited the original fresh market tomato field in Cantua Creek from which the samples of the Sw-5 tomato plants showing typical symptoms of spotted wilt were sent to us. We also visited another fresh market field in Firebaugh with Sw-5 varieties showing typical spotted wilt symptoms and two processing tomato fields with plants showing spotted wilt symptoms. As in our previous experiments, we used PCR and immunostrip tests to confirm that the Sw-5 fresh market tomato samples from these fields with spotted wilt symptoms were infected with TSWV. Furthermore, using PCR and sequencing, we established that they were likely infected with a RB strain, based on the detection of the ‘YPT’ motif in the NSm gene (non-RB strains have a CPT motif).
During this June trip, we collected samples with spotted wilt symptoms from Sw-5 fresh market plants at Cantua Creek and Firebaugh as well as samples from processing tomato fields with spotted wilt (it was not clear whether these had Sw-5 or not). PCR tests for the Sw-5 gene confirmed that the fresh market varieties (Q27, Q99 and 1795) had the Sw-5 gene, whereas the processing tomato varieties did not have Sw-5 (one field) or seemed to have a mixture of Sw-5 and non-Sw-5 plants (the other field). Immunostrip and PCR tests confirmed infection with TSWV in all samples with spotted wilt symptoms, and the isolates from the Sw-5 fresh market varieties were again confirmed to be a RB strain based on detection of the YPT motif in the NSm gene (Table 2). The plants from the processing tomato fields were mostly infected with non-RB TSWV strains.
Next, selected samples with RB isolates were then used in greenhouse sap-transmission (rub- inoculation) experiments with tomato seedlings. Two RB TSWV isolates (SW19 from Firebaugh and SW21 from Cantua Creek) and two non-RB TSWV isolates (SW24 and T36) were used to inoculate three SW-5 fresh market tomato varieties (HM1794, HM1795 and Q27) as well as one non-SW-5 tomato processing tomato variety (H 8504) in our greenhouse at UC Davis Tables 2 and 3). A total of 10 plants of each variety were rub-inoculated with each TSWV isolate (Table 3). Mock-inoculated plants served as controls. Plants were maintained for 21 days in a temperature controlled greenhouse (80 F day and 70 F night temperatures and 16 h day and 8 h night), and symptoms were recorded.
As shown in Table 3, the putative RB TSWV isolates (SW19 and SW21) infected some plants of each of the of the three Sw-5 fresh market tomatoes, although the rate of infection was relatively low (10-30%). Interestingly, the RB strains did not infect (based upon appearance of symptoms) the non-Sw-5 variety (H 8504), and the non-RB TSWV isolates did not infect the Sw-5 varieties or the non-Sw-5 variety (Table 3).
All the Sw-5 tomatoes with spotted wilt symptoms were confirmed to have the Sw-5 gene based on PCR and two primer pairs (Table 3), and all plants were confirmed to be infected with TSWV by PCR and immunostrips (data not shown). Furthermore, all six selected plants with spotted wilt symptoms that were tested were found to be infected with the RB isolate based on the presence of the YPT motif in the NSm gene as determined by PCR and sequencing (Table 4). Taken together, the results of all our experiments strongly indicate that the outbreaks of spotted wilt in the fresh market varieties in Cantua Creek and Firebaugh were due to the emergence or introduction of a RB isolate of TSWV. Significantly, these RB isolates were able to infect Sw-5 varieties under field and greenhouse conditions.
Table 2. TSWV isolates that were used in inoculation experiments in the greenhouse Location collected NSm TSWV Isolate from/tested with TSWV Symptoms Motif and TMV immunostrip
SW21 (Resistant Typical TSWV Cantua Creek, 13-7A, Breaking TSWV symptoms, concentric YPT Tomato, TSWV +, TMV - Isolate) rings, Typical TSWV SW24 (Non RB Organic Proc Tomato, TSWV symptoms, concentric CPT TSWV) +, TMV - rings,
SW19 (RB TSWV Firebaugh, Q-99 tomato, Typical TSWV YPT Isolate) TSWV +, TMV - symptoms
Typical TSWV-like leaf T36 (Non RB KY Yolo tomato field, symptoms, green CPT TSWV) TSWV +, TMV - shoots, BCTV -
Table 3. Summary of the results of mechanical transmission experiments with isolates of TSWV associated with spotted wilt symptoms in Sw-5 fresh market tomato varieties and Sw-5 fresh market varieties Infected/Inoculated plants with TSWV Isolates Tomato Variety SW5 SW19 SW19 SW24 T36 Non Total* Gene RB RB Non RB* Isolates* Isolates* RB* HM 1794 Yes 3/10 2/10 0/10 0/10 5/40 HM 1795 Yes 2/10 3/10 0/10 0/10 5/40 Q 27 Yes 1/10 2/10 0/10 0/10 3/40 H 8504 No 0/10 0/10 0/10 0/10 0/40 * (infected/inoculated)
Table 4. Results of tests for the Sw-5 gene in tomato cultivars used in mechanical transmission experiment and for the YPT motif in the TSWV NSm gene that indicates a resistance breaking (RB) strain SW-5 PCR Tests
Symptomatic Plants 2F2R Primers f2r2 primers NSm motifa HM 1794-9 + + YPT* HM 1794-10 + + ND HM 1794-23 + + YPT* HM 1794-24 + + ND HM 1794-25 + + ND HM 1795-4 + + YPT* HM 1795-7 + + ND HM 1795-8 + + ND HM 1795-21 + + ND HM 1795-24 + + YPT* Q 27-4 + + YPT* Q 27-7 + + YPT* Q 27-29 + + ND aYPT is an amino acid motif in the TSWV NSm gene (shown by an asterisk) that is associated with RB strains (this motif is CPT in non-RB TSWV isolates). ND= not determined
To further confirm that this RB TSWV can infect Sw-5 processing tomato varieties, we inoculated eight SW-5 and three non-sw-5 processing tomato varieties in a greenhouse at UC Davis. These included the main processing tomato varieties grown in California. In these experiments, the RB-TSWV strains from Fresno infected the SW-5 varieties much faster (10-14 days after inoculation) and more severely compared with non-sw-5 tomato varieties. Thus, this new RB-TSWV strain that emerged in the Central Valley tomato fields can infect commercial TSWV resistant and susceptible processing varieties.
Table 5. Response of processing tomato varieties to mechanical inoculation of the RB TSWV strains. Processing tomato experiment
Symptom evaluation SEQUENCE Variety Resist isolate 1 2 3 Infected/total SW5 Nsm YPT CPT BQ273-1 yes SW19 RB TSWV 5 0 1 6/10 6/6 6/6 1/1 0/1 BP2-1 yes SW19 RB TSWV 10 0 0 10/10 10/10 10/10 2/2 0/2 DRI319-1 yes SW19 RB TSWV 3 0 2 5/10 5/5 5/5 2/2 0/2 HM3887-1 yes SW19 RB TSWV 1 0 0 1/10 1/1 1/1 1/1 0/1 H5608-1 yes SW19 RB TSWV 7 0 0 7/10 7/7 7/7 2/2 0/2 H8504-1 no SW19 RB TSWV 0 3 0 3/10 0/3 3/3 2/2 0/2 N6366-1 no SW19 RB TSWV 0 1 0 1/10 0/1 1/1 1/1 0/1 BQ273-1 yes SW21 RB TSWV 1 0 0 1/10 1/1 1/1 0/0 0/0 BP2-1 yes SW21 RB TSWV 3 0 0 3/10 3/3 3/3 2/2 0/2 DRI319-1 yes SW21 RB TSWV 4 0 0 4/10 4/4 4/4 2/2 0/2 HM3887-1 yes SW21 RB TSWV 1 0 0 1/10 1/1 1/1 1/1 0/1 H5608-1 yes SW21 RB TSWV 0 0 0 0/10 0/0 0/0 0/0 0/0 H8504-1 no SW21 RB TSWV 1 2 0 3/10 0/3 3/3 1/3 2/3 N6366-1 no SW21 RB TSWV 0 3 0 3/10 0/3 3/3 0/3 3/3 BQ273-1 yes T36 TSWV 0 0 0 0/10 xxx xxx xxx xxx BP2-1 yes T36 TSWV 0 0 0 0/10 xxx xxx xxx xxx DRI319-1 yes T36 TSWV 0 0 0 0/10 xxx xxx xxx xxx HM3887-1 yes T36 TSWV 0 0 0 0/10 xxx xxx xxx xxx H5608-1 yes T36 TSWV 0 0 0 0/10 xxx xxx xxx xxx H8504-1 no T36 TSWV 0 0 0 0/10 xxx xxx xxx xxx N6366-1 no T36 TSWV 0 0 0 0/10 xxx xxx xxx xxx BQ273-1 yes Mock 0 0 0 0/3 xxx xxx xxx xxx BP2-1 yes Mock 0 0 0 0/3 xxx xxx xxx xxx DRI319-1 yes Mock 0 0 0 0/3 xxx xxx xxx xxx HM3887-1 yes Mock 0 0 0 0/3 xxx xxx xxx xxx H5608-1 yes Mock 0 0 0 0/3 xxx xxx xxx xxx H8504-1 no Mock 0 0 0 0/3 xxx xxx xxx xxx N6366-1 no Mock 0 0 0 0/3 xxx xxx xxx xxx Symptom evaluation determined weekly over three weeks and total number of infected plants presented
Taken together, these results indicate that the outbreaks of TSW in Sw-5 fresh market varieties in the Central Valley of California were due to the emergence of RB strains of TSWV. Furthermore, these RB strains were able to infect Sw-5 fresh market and processing tomato varieties in mechanical inoculation experiments. Subsequently, RB strains of TSWV were also detected in one processing tomato field planted with a Sw-5 variety. This is the first report of RB strains of TSWV in California, and this situation should be carefully monitored given that ~50% of the processing tomato varieties grown in California possess the Sw-5 gene.
CALIFORNIA TOMATO RESEARCH INSTITUTE, INC. 18650 E. Lone Tree Road Escalon, California 95320-9759
Project Title: Seed Transmission and Seed Treatment of Fusarium oxysporum f. sp. lycopersici
Project Leader(s) Hung Doan, Graduate Student Researcher, Department of Plant Pathology, One Shields Ave, University of California, Davis, CA 95616, phone 530-752-3831, email [email protected],
Gene Miyao, Cooperative Extension Farm Advisor, Yolo-Solano-Sacramento counties, 70 Cottonwood St., Woodland, CA 95695, phone 530-666-8732, email [email protected],
Thomas Gordon, Professor, Department of Plant Pathology, One Shields Ave, University of California, Davis, CA 95616, phone 530-754-9893, email [email protected].
Mike Davis, Cooperative Extension Specialist (emeritus), Department of Plant Pathology, One Shields Ave, University of California, Davis, CA 95616, email [email protected].
Summary: Fusarium oxysporum f. sp. lycopersici race 3 was recovery from seeds harvested from infected plants in commercial fields. In addition, Fusarium colonies were recovered from two commercial seed lots. Molecular characterization and pathogenicity tests need to be conducted to confirm pathogenicity of these isolates. The incidence of FOL race 3 from tomato seed batches was reduced by ~98% without affecting seed germination or vigor when FOL race 3-infected tomato seed was immersed in a hot water bath for 10 min at 55°C. FOL race 3 was eradicated from seed without reducing seed germination or vigor when seeds were immersed in a fungicide (Azoxystrobin or Fludioxonil) hot-water bath for 10 min at 55°C. In addition, FOL race 3 was also eradicated when seeds were pretreated in water at 23°C for 1 hour followed by immersion for 10 min in the water at 55°C. Grow-out assays need to be conducted to confirm the efficacy of these seed treatments on the eradication of FOL from infected tomato seed. Data from this preliminary experiment indicate that FOL race 3, if not eliminated by seed treatments, can be a source of unintended infestations into commercial fields.
Future direction: This study will be repeated with seeds harvested from 2016. In addition, molecular characterization and pathogenicity tests need to be conducted to confirm pathogenicity of Fusarium colonies recovered from commercial seed lots. Likewise, grow-out assays need to be conducted to confirm the efficacy of seed treatment on the eradication of FOL from infected tomato seed. In order to understand the modes of seed infection, we constructed a fluorescent- labeled FOL race 3. Using this fluorescent-labeled FOL race 3 we expect to show that FOL race 3 can access seeds systemically through the xylem.
Objectives: 1. Evaluate the importance of the seed-borne potential of FOL in the long-distance dissemination of FOL genotypes. 2. Develop a seed treatment procedure for the elimination of Fusarium spp. from infected tomato seeds. Methods: Objective 1: Evaluate the importance of the seed-borne potential of FOL in the long- distance dissemination of FOL genotypes. To evaluate the importance of the seed-borne potential of FOL in the long-distance dissemination of FOL genotypes, seeds were harvested from naturally infected tomato plants (from three separate fields), fermented, and/or treated in 1% hydrochloric acid (HCL) for 15-20 minutes, and rinsed with water. Seed were dried at 35 to 38°C for 36 hours and stored at room temperature (22 to 23°C). In addition, seven commercial seedlot (10,000 seeds each) were also used. Seeds were plated on Komada's medium (12 seeds per plate) and monitored at 7 and 14 days for growth of Fusarium species. All potential Fusarium colonies were transferred onto APDA and incubated under constant fluorescent light at room temperature (22 ± 1°C) to verify the identity of the fungus. After 5 days, DNA were extracted from Fusarium-like colonies and fragments of the translation elongation factor 1α gene were amplified and sequenced using standard kits. Pathogenicity tests were conducted on a subsample of the Fusarium colonies isolated using tomato cultivars Early Pak 7 (susceptible), VFN-8 (resistant to race 1), Walter (resistant to race 1 and 2), CXD 282 (resistant to race 1, 2, and 3). Varieties were inoculated with a spore suspension of each Fusarium species isolated and transplanted into replicated blocks consisting of one plant per pot, with a minimum of ten reps per variety. The pots were arranged in a randomized complete block design. The varieties were evaluated for resistance base on disease severity, expressed as foliar severity and vascular discoloration. These varieties were tested in two repeated trials.
Objective 2: Develop a seed treatment procedure for the elimination of Fusarium spp. from infected tomato seeds. In order to develop seed treatments for the disinfection of tomato seeds, we required seed heavily infected with FOL race 3 since natural infection is low to undetectable. Immature and mature fruit were injected with a syringe (0.5 mm x 25.4 mm) containing approximately 5 to 10 µL of 103 conidia/mL of FOL race 3. Water was used to mock inoculate tomato fruit for seed that served as a negative control. Matured fruit were hand harvested, fermented, and/or treated in 1% hydrochloric acid, and rinsed with water. Seed were dried at 35 to 38°C for 36 hours and stored at room temperature (22 to 23°C). A series of seed treatments using hot water bath at 55 or 60°C for 10 to 20 min were tested on seed to determine the optimum combination to eliminate the pathogen from seed without reducing seed germination or vigor. Seed germination and vigor assays were conducted following AOSA guidelines (Association of Official Seed Analysts, 1999). In addition to the heat treatment, seeds were pretreated at 23°C and agitated on a shaker bed at 100 rpm for 1 hour in sterile deionized water or 30% potato dextrose broth followed by immersion for 10 minutes at 55 or 60°C in either water, 0.12 g a.i. of azoxystrobin (Mertek® 340-F) or 0.12 g a.i. of fludioxonil (Cannonball® WP). After each pretreatment-treatment combination, all seed were immediately immersed for 5 min in 4 L of 22°C distilled water, dried for 3 days, plated, monitored, and isolates of FOL race 3 were detected as previously described. Pathogenicity assays will be conducted as previously described.
Results: Fusarium oxysporum f. sp. lycopersici race 3 was recovery from seeds harvested from infected plants in three commercial fields (Figure 1). In these commercial fields, the average incidence of FOL was moderate (17.73%) from seed that was nontreated (dried) and high (72.4%) in seed that was fermented, dried, and plated. In acid treated seed, the incidence of FOL was extremely low (0.25%). Fusarium oxysporum f. sp. lycopersici race 3 was also recovered from commercial seed lots (Table 1). Seven Fusarium colonies were isolated from one out of six commercial seed lots tested (10,000 seeds/lot). In addition, one Fusarium colony was isolated from a commercial seed lot from 5,000 seeds. Pathogenicity tests need to be conducted to confirm pathogenicity of these isolates.
Seed germination and vigor were not affected by hot water treatments for 20 min at 23°C (control) and 10 min at 55°C (Figure 2). Treatments for 20 min at 55°C; 10 min at 60°C; and 20 min at 60°C significantly reduced both germination and vigor (Figure 2). The incidence of FOL was 52% in nontreated seed, 51% in seed treated at 23°C water bath for 10 minutes, 50% in seed pretreated in 23°C water bath for 1 hr, and 52% in seed pretreated in 23°C potato dextrose broth bath for 1 hour.
FOL race 3 was eradicated from seed collected in 2015 when seeds were immersed in azoxystrobin or fludioxonil at 55°C or 60°C for 10 minutes. FOL race 3 was also eradicated when seeds were pretreated in water at 23°C for 1 hour followed by immersion for 10 min in the water at 55°C or 60°C. In addition, FOL was eliminated from seed immersed for 10 min in a 60°C water bath, however (Table 2). FOL was not eliminated from seed that was not pretreated in potato dextrose broth at 23 °C for 1 hour followed by 10 min immersion in hot water at 50°C or 60°C.
Figure 1. Recovery of FOL race 3 from seeds harvested in commercial fields.
100% 90% 80% 70% 60% 50% 40%
Seed infec on (%) 30% 20% 10% 0% Non-treated Fermented HCL Treated The average incidence of FOL race 3 isolated from 5000 tomato seeds harvested from three commercial fields. The percentage of FOL race 3 in seeds harvested, dried (non-treated) and plated directly onto Komada’s medium was moderate (17.73%) while high (72.4%) from seeds harvested, fermented, and dried, and extremely low (0.12%) from seeds harvested, fermented, hydrochloric acid treated, and dried. HCL treatment significantly reduced FOL race 3 but did not eliminate Fusarium from seed in fruit harvested from infected plants.
Table 1. Recovery of FOL from commercial seed lots of various cultivars. Fusarium per Path Seed lot 10,000 seeds FOL? test 1 0 0 0 2 0 0 0 3 0 0 0 4 0 0 0 5 7 Pending Pending 6 0 0 0 7Z 1 1 Pending
Recovery of FOL race 3 from commercial seed lots. 10,000 seeds were plated directly onto Komada’s medium and monitored at 7 and 14 days for growth of Fusarium species. All potential Fusarium colonies were transferred onto APDA and incubated under constant fluorescent light at room temperature (22 ± 1°C) to verify the identity of the fungus. ZThere was only 5,000 seeds in commercial seedlot 7.
Figure 2. The effect of hot water immersion on seed quality Germina on and Vigor Assays 100 A A A 90 B B 80 D D D Germina on 70 Vigor 60 E
50 F 40
30 20 C
Seed Germina on and Vigor (%) 10 G 0 Untreated 23°C/20min 55°C/10min 55°C/20min 60°C/10min 60°C/20min Temperature (°C)
Effect of hot water treatments on germination and vigor of tomato seeds. Values represent mean percentages of germination and vigor of tomato seed (Moneymaker, Bonny Best) of three replications in each of two independent trials. Germination and vigor assays were conducted following AOSA guidelines. Two hundred seed of each cultivar was immersed for 10 or 20 min in a hot water bath heated to 55 or 60°C. Germination and vigor are independent variables. Different letters for germination or vigor indicate significant differences according to Tukey’s HSD test at P = 0.05.
Table 2: Effect of immersing tomato seed in water or potato dextrose broth followed by immersion in water or fungicides on viability of Fusarium oxysporum f. sp. lycopersici race 3 in seed collected in 2015.
Pretreatment Treatment Rate % InfectedW (23°C, 100rpm, 1hr) (10min) (g a.i./L water) Nontreated None 0 52.14 aX Nontreated Water (23°C) 0 51.04 a Nontreated Water (55°C) 0 1.06 b Nontreated Water (60°C) 0 0 c Nontreated Azoxystrobin (55°C) 0.12 0 c Nontreated Azoxystrobin (60°C) 0.12 0 c Nontreated Fludioxonil (55°C) 0.12 0 c Nontreated Fludioxonil (60°C) 0.12 0 c Water None 0 50.26 a Water Water (55°C) 0 0 c Water Water (60°C) 0 0 c Water Azoxystrobin (55°C) 0.12 0 c Water Azoxystrobin (60°C) 0.12 0 c Water Fludioxonil (55°C) 0.12 0 c Water Fludioxonil (60°C) 0.12 0 c Potato dextrose broth Z None 0 51.65 a Potato dextrose broth Water (55°C) 0 14.9 d Potato dextrose broth Water (60°C) 0 0.13 b Potato dextrose broth Azoxystrobin (55°C) 0.12 0 c Potato dextrose broth Azoxystrobin (60°C) 0.12 0 c Potato dextrose broth Fludioxonil (55°C) 0.12 0 c Potato dextrose broth Fludioxonil (60°C) 0.12 0 c
WInfection (%) is the percentage of recovery of Fusarium oxysporum f. sp. lycopersici race 3 in 500 seed collected from field site 1 and site 2 in 2015 plated on Komada's medium. XMeans of infections (%) followed by a common letter in the same column are not significantly different according to Tukey's HSD test at P = 0.05. Values are means of 2 replications. Z30% potato dextrose broth (PDB) composed of 1.2 grams potato starch and 6 grams of dextrose per liter of water.
CALIFORNIA TOMATO RESEARCH INSTITUTE, INC. 18650 E. Lone Tree Road Escalon, California 95320-9759
Project Title: Pathogen characterization of Fusarium wilt (Fusarium oxysporum f. sp. lycopersici) and Fusarium crown and root rot (F. oxysporum f. sp. radicis-lycopersici) in California Project Leader(s) Hung Doan, Graduate Student Researcher, Department of Plant Pathology, One Shields Ave, University of California, Davis, CA 95616, phone 530-752-3831, email [email protected]
Gene Miyao, Cooperative Extension Farm Advisor, Yolo-Solano-Sacramento counties, 70 Cottonwood St., Woodland, CA 95695, phone 530-666-8732, email [email protected]
Thomas Gordon, Professor, Department of Plant Pathology, One Shields Ave, University of California, Davis, CA 95616, phone 530-754-9893, email [email protected]
Mike Davis, Cooperative Extension Specialist (emeritus), Department of Plant Pathology, One Shields Ave, University of California, Davis, CA 95616, email [email protected]
Summary: Based on molecular markers and pathogenicity tests, two genotypes exist of each FOL race 3 (California and Florida), FOL race 2, and FORL in California. These Fusarium can be distinguished from each other using two published primer sets. FOL race 3 California genotype can be distinguished from FOL race 3 Florida genotype as well as other FOL races and FORL by using six3-G121A-F2/six3-R2 and six3-G137C-F1/six3-R2 primers and primers sp13, sp23, sprl, and unif. Host range experiments revealed that FOL race 3 California and Florida genotypes are able to sustain itself on the roots of many plant species but its capability of invading xylem tissue is limited to tomato.
Objectives: 1. Develop reliable and efficient PCR primers for the rapid detection of FOL race 3. Rapid identification will be a useful, practical pest management tool. 2. Characterize isolates of FOL and FORL in California using both molecular markers and pathogenicity tests. In recent years, field symptoms between the two diseases have become blurred; thus, more information is needed for variety-selection decisions specific to field sites.
Objective 1. Develop reliable and efficient PCR primers for the rapid detection of FOL race 3. To design FOL race 3 specific PCR primers using single base extension methods based on single nucleotide polymorphisms (SNP), a large collection of sequences of single copy genes of Fusarium spp. is needed. There is a large number of SNPs in the intergenic spacer region (IGS), ammonia ligase 2 gene, and translation elongation factor 1α that will provide a rich source of genetic diversity to develop FOL race 3 specific PCR detection methods. Using this method, primers had been developed for Fusarium oxysporum f. sp. vasinfectum race 3 of cotton and Fusarium oxysporum f. sp. chrysanthemi of chrysanthemum (Egamberdiev et al. 2014; Li et al. 2010). These sequences will be collected from Fusarium spp. from California and Florida and from deposited sequences in the NCBI database and aligned with the Megalign module of DNAstar 6.0 where race-specific SNPs will be recognized. A primer with race-specific nucleotide on 3’ end will be designed to anneal strictly on the SNP site using Primer3 program (Rozenand and Skaletsky, 2000). These race-specific SNPs primers will be tested on various Fusarium spp. isolated from California and Florida.
Objective 2: Characterize isolates of FORL and FOL in California using both molecular markers and pathogenicity tests.
In order to characterize FOL and FORL with molecular markers, an array of isolates was needed. FOL and FORL was isolated from symptomatic tomato plants from various growing regions in California and were stored on dried filter paper at 4°C. To isolate FOL and FORL, stem or crown tissue from symptomatic plants was washed with anti-bacterial soap, immersed in 0.6% sodium hypochlorite (10% bleach) for 1 min, and placed on acidified potato dextrose agar plates. FOL and FORL genotypes from Florida (obtained from Dr. Gary Vallad) were used to compare with California genotypes. Total genomic DNA was extracted from FORL cultures and fragments of three independent loci, including β-tubulin, translation elongation factor 1α, and phosphate permease, and will be amplified and sequenced using standard kits. The sequences of these genes will be compared between different FORL isolates in California and Florida to create a phylogenetic tree. FOL or FORL isolates with different sequences are considered different genotypes. These genes have been successfully used to characterize other Fusarium wilt pathogens.
Pathogenicity tests will be conducted on processing and fresh market tomato varieties. Some of the tomato cultivars used will included cultivars with the Frl gene which confers resistance to crown and root rot FORL and I-1, I-2, I-3, I-4, I-5, I-6, and I-7 genes which confer resistance to Fusarium wilt. Varieties will be inoculated with a spore suspension of each genotype and transplanted into replicated blocks consisting of one plant per pot, with a minimum of ten reps per variety. The pots will be arranged in a randomized complete block design. The varieties will be evaluated for resistance based on disease severity, expressed as foliar severity, vascular discoloration, and total dry weight. These varieties will be tested in two repeated trials. The pathogenicity test will support the molecular marker assay to characterize different genotypes of FOL and FORL. Host range evaluation will be conducted on bean, broccoli, cotton, corn, lettuce, melon, onion, pepper, potato, sunflower, wheat, and some common summer (hairy nightshade, black nightshade) and winter (chickweed, sowthistle, prickly lettuce) weeds.
Results: The whole genome for Fusarium oxysporum f. sp. lycopersici race 3 Florida genotype was recently uploaded (October 2016). Two genotypes of FOL race 3 (from California and Florida) are being submitted for whole genome sequencing. Once sequenced, the genomes will be returned and assembled to reference sequences. We will look for unique polymorphism from which specific primers will be developed. We have optimized published sets of primers from Hirano and Arie, 2006 (primers: sp13, sp23, sprl, unif) and Lievens et al., 2009 (six3-G121A-F2, six3-G134A-F2, six3-G137C-F1, six3-R2) for the rapid detection of FOL race 3. FOL race 3 California genotype can be distinguished from FOL race 3 Florida genotype as well as other FOL races and FORL by using six3-G121A-F2/six3-R2 and six3-G137C-F1/six3-R2 primers (Table 1) and primers sp13, sp23, sprl, and unif (Table 2). These primers will only work on Fusarium oxysporum f. sp. lycopersici and F. oxysporum f. sp radicis-lycopersici. Further research is needed to optimize one reaction to detect and differentiate FOL race 3.
The FOL and FORL isolates collected from 2013-2015 were characterized (Figure 1). We still need to characterize isolates from 2016. Two genotypes of FORL, FOL race 3, and FOL race 2 exist in California (Figure 1). These genotypes were confirmed by molecular markers and by differential host range with tomato cultivars Early Pak 7 (susceptible), VFN-8 (resistant to race 1), Walter (resistant to race 1 and 2), CXD 282(resistant to race 1, 2, and 3), and IBR 301 (resistant to FORL). Two greenhouse trials were conducted to study the host range of FOL race 3 California and Florida genotypes. In both trials, none of the isolates of FOL race successfully colonized the stem tissue of any crop other than tomato. However, the fungus was successfully recovered from the outer root surface of many crops (except rye grass), including weeds (Table 3). Hence, it appears that the fungus is able to sustain itself on the roots of many plants but its capability of invading the xylem tissue is limited to tomato. Although FOL race 3 California genotype is genetically different from the FOL race 3 Florida genotype, the two share the near identical host range tested, i.e., each is limited to tomato and root surfaces of many crops and weeds.
Table 1: Detection of FOL race 3 genotypes with six3 primer sets
Primers six3- six3- six3-G121A- G134A- g137C- F2/six3-R2 FOL F2/six3-R2 F1/six3-R2 FOL race 1 - - - FOL race 2 - - - FOL race 3, CA - - + FOL race 3, FL + - - FORL - - - + amplicon detected; – no amplicon detected.
Table 1: Detection of FOL genotypes with sp13, sp23, spr1 primer sets
Primers FOL sp13 sp23 sprl unif FOL race 1 - + + + + + FOL race 2 - + + + + + FOL race 3, + - + + + CA FOL race 3, + + + - + FL FORL + + + + + - + number of amplicon detected (i.e ++= two bands); – no amplicon detected
Figure 1. Fusarium spp. isolated from infected plants in Yolo, San Joaquin, Fresno, Merced, Colusa, and Kern counties from 2013- 2015. Phylogeny tree based on IGS and EF sequences.
Table 3. Host Range of Fusarium oxysporum f. sp. lycopersici (FOL) race 3 California and Florida Genotypes FOL FOL Race 3 Race 3 Host Genotype California Genotype Florida Tomato +/+/+ +/+/+ Onion 0/0/+ 0/0/+ Pumpkin 0/+/+ 0/+/+ Lettuce 0/0/+ 0/0/+ Sunflower 0/0/+ 0/0/+ Watermelon 0/+/+ 0/+/+ Bean 0/0/+ 0/+/+ Pepper 0/0/+ 0/0/+ Sweet corn 0/0/+ 0/0/+ Broccoli 0/0/+ 0/0/+ Common purslane 0/+/+ 0/+/+ Lamb’s quarters 0/+/+ 0/+/+ Redstem filaree 0/+/+ 0/+/+ Velvet leaf 0/+/+ 0/+/+ Field bindweed 0/+/+ 0/+/+ Nightshade 0/+/+ 0/+/+ Rye grass 0/0/+ 0/0/+
z 0 or + (failure or success) of recovering of the fungus from stem/crown/outer surface of roots.
California Tomato Research Institute, Inc. Annual Report
Project Title: Monitoring beet leafhopper populations in vegetation on the floor of the northern San Joaquin Valley
Project Leader: Brenna Aegerter, Farm Advisor, University of California Cooperative Extension, San Joaquin County, 2101 E. Earhart Ave. Ste. 200, Stockton, CA 95206, phone 209-953-6114, FAX 209-953- 6128, email: [email protected]
Co-Principal Investigator:
Jhalendra Rijal, IPM Advisor, University of California Cooperative Extension, Stanislaus County, 3800 Cornucopia Way Ste. A, Modesto, CA 95358, email: [email protected]
Objective:
To monitor beet leafhopper populations associated with valley floor vegetation and to assess their risk to processing tomatoes in the northern San Joaquin Valley.
Summary:
Populations of beet leafhopper were monitored in areas of processing tomato production in western parts of San Joaquin and Stanislaus counties. In general, we found the levels to be low of both virus incidences in the local tomato crop, as well as the population levels of the beet leafhopper vector.
Approach:
Our study was conducted in San Joaquin and Stanislaus counties, with our efforts focused on areas of the two counties which have had recurring problems with curly top disease during the past three seasons. The areas we monitored included upper Roberts Island (e.g. S. Inland Dr.), Union Island (Howard Rd., Tracy Blvd., Clifton Court Rd.), Jones Tract (Bacon Island Rd.), Tracy/Banta area (Banta Rd., Bird Rd., Linne Rd.) and western Stanislaus county (Highway 33, Howard Rd., Rogers Rd., Ward Ave., Fink Rd., River Rd.). At each of 25 sites, we placed a
yellow sticky trap (provided by CDFA’s Curly Top Control Program) which we have been servicing bi-weekly. Traps were placed at knee height, outside of tomato fields but in most cases in close proximity to tomato fields. In addition, we use sweep netting to look for BLH on weeds present along the roadside or field edges, including some areas not directly adjacent to the sticky traps. For trap locations adjacent to processing tomato fields, we made visual estimates of curly top incidence bi-weekly until late season.
Results: Overall, beet leafhopper counts and curly top incidence were low in the northern San Joaquin Valley during 2016. At half of the fields (ten out of twenty fields), the incidence of curly top was below 1%. Another five fields (one-quarter) had incidences near the 1% level. And one quarter had disease levels between 1 and 5%. The five fields which had higher disease incidences (2 to 5%) were not correlated with higher trap counts or even higher weed pressure. Weeds were generally abundant, not within fields so much, but on adjacent properties (roadsides, small fallow areas, rail rights-of-way, etc.). Sweep net surveys of weeds generally yielded zero to one BLH per 10 sweeps, although very occasionally higher counts were obtained (2 to 3 BLH). Sweep net counts never reached the state treatment threshold of 10 BLH per 10 sweeps. Most commonly encountered weeds harboring BLH were Russian thistle (Salsola), salt bush (Atriplex) and fleabane (Conyza). Highest trap counts were observed beginning in mid- October. We suspect some of these individuals may have been migrating, although most were male (which do not migrate). Our identification of these late-season hoppers is being confirmed with the very generous cooperation of the Gilbertson lab. We had hoped that this study would provide some information that could inform the development of reasonable treatment threshold numbers (i.e. treat tomatoes when BLH counts in adjacent vegetation exceed X). Treatment thresholds for adjacent weedy habitat would help growers decide whether to treat tomatoes to control the vector or suppress transmission. However, because of the low vector and disease pressure, we could not meet this objective.
Table 1. Survey counts of beet leafhopper (BLH) on sticky traps in the vicinity of processing tomato field in Stanislaus and San Joaquin counties. BCTV incidence reflects the percentage if plants with Curly Top disease within selected monitored tomato fields adjacent to our sticky traps. Orange shaded sites are tomato fields, green-shaded sites are weedy areas in the vicinity of tomato production.
April to mid- mid-Sep to mid-Oct to mid-May to mid-Jun mid-Jun to mid-Jul mid-Jul to mid-Aug mid-Aug to mid-Sep May mid-Oct early Nov
transplant and BCTV trap BCTV trap BCTV trap BCTV trap WEST STANISLAUS CO harvest dates Weeds, other notes trap counts incidence counts incidence counts incidence counts incidence counts trap counts trap counts TOMATO - STAN1 W: May-Oct, E: June-Sept (fresh market) 0 0 none 0 both <1% 0 <1% 0 0 0 BEAN - STAN2 trap adjacent to bean field 0 --- 0 --- 0 --- 0 --- 0 0 0 TOMATO - STAN3 late April-late August alfalfa, Russian thistle 0 < 1% 0 <1% 0 1% 0 2% 0 2 1 TOMATO - STAN3b mid May-mid Sept 0 0 1% 3 1 to 2% 1 3% 0 2 30 TOMATO - STAN4 late March-early August 0 < 1% 0 < 1% 0 1 harvested 0 1 1 TOMATO - STAN5 mid May-mid Sept Crop affected by fusarium wilt r3 0 NONE 0 1% 0 3% 3 3% 0 1 26 TOMATO - STAN6 late May-late Sept Mustard, alfalfa, nettle 0 0 1% 0 5% 0 3% 1 1 10 TOMATO -STAN6B early June-late Oct 0 0 <1% 0 <1% 0 <1% 0 1 3 TOMATO -STAN7 mid-May-mid-Sept bean fld to west 0 < 1% 0 3% 0 5% --- 5% 0 0 20 TOMATO -STAN8 late May-late Sept 0 none 0 <1% 0 <1% 2 1% 0 2 1 TOMATO -STAN9 late April-late August 0 none 0 none 0 <1% 1 harvested 0 0 0 TRACY/BANTA AREA WEEDS - BANTA RD1 0 --- 0 --- 0 --- 0 0 0 17 TOMATO -TRACY2 mid May-mid Sept 0 none 0 none 0 <1% 0 <1% 0 0 2 WEEDS - BANTA RD3 0 --- 0 --- 0 --- 1 --- 0 4 20 WEEDS - BANTA RD4 0 --- 0 --- 0 --- 0 --- 0 2 7 WEEDS - AHERN Russian thistle, Salt bush, fleabane 0 --- 0 --- 0 --- 1 --- 0 2 2 TOMATO -TRACY6 mid-May-mid Sept 0 none 0 0 1% 0 1% 0 0 3 TOMATO -TRACY7 mid-May-mid Sept pigweed 0 <1% 0 <1% 0 1% 0 1% 0 0 5 TOMATO -TRACY8 mid-May-mid Sept 0 none 0 0 1% 0 1% 0 0 3 TOMATO -TRACY9 mid-May-mid Sept Conyza/fleabane 0 0 0 1% 1 1% 0 0 42 CENTRAL & SOUTH DELTA TOMATO -DELTA1 early May-early Sept 0 none 0 < 1% 0 <1% 1 <1% 0 0 0 TOMATO -DELTA2 early May-early Sept 0 0 0 <1% 0 <1% 0 0 6 TOMATO -DELTA3 late May-late sept 0 0 <1% 0 <1% 4 <1% 0 0 44 TOMATO -DELTA4 early May-early Sept saltbush 0 0 0 <1% 0 <1% 0 0 2 TOMATO -DELTA5 Russian thistle 0 0 0 none 0 none 0 0 6
Report for CTRI, 2016
Reducing insect virus vectors of Beet Curly Top Virus in processing tomatoes through soil health management
Project leader Amélie Gaudin, Assistant Professor of Agroecology, Department of Plant Sciences, University of California Davis. Email: [email protected], Phone: 530-752-1212. Website: http://gaudin.ucdavis.edu
Co-investigators: Rachel Vannette, Assistant Professor of chemical and microbial ecology, Department of Entomology and Nematology, University of California Davis. Email: [email protected], Phone: (616) 901-2782
Christian Nansen, Assistant Professor of insect ecology and remote sensing, Department of Entomology and Nematology, University of California Davis. Email: [email protected], Phone: 530-752-2728. Website: http://chrnansen.wix.com/nansen2
Kate M Scow, Professor of Soil Science and Microbial Ecology, Department of Land Air and Water Resources, Director of the Russell Ranch Sustainable Agricultural Facility, University of California Davis. Email: [email protected], Phone: 530-752-4632
Collaborators Robert Gilbertson (UCD-Plant Pathology), John Breen (Actagro), Emma Torbert (ASI), Israel Herrera (Russell Ranch)
Rationale and objectives
Despite tomatoes not being a preferred host for the beet leafhopper (C. tenellus), the primary vector of Beet Curly Top Virus (BCTV), this virus remains a major production constraint each year for processing tomatoes (UC ANR Publication 3470). Leafhopper feeding in the agricultural landscape is also non-random, but little information is available on why one tomato field is more attractive than another to beet leafhoppers in a landscape dominated by processing tomatoes. Past research has shown that crop plant ability to resist or tolerate insect pests and viral infection is tied to physical, chemical and biological soil properties (Altieri & Nicholls 2003, Vannette & Hunter 2009). We hypothesized that 1) soil heath building management strategies such as cover crops, compost and humic substances can significantly decrease crop susceptibility to insect pests and the viruses they vector and that 2) shifts in plant health and nutrient content can be detected using canopy reflectance profiling with UAV, providing an emerging tool to forecast pest outbreaks and an unprecedented opportunity to use remote sensing technologies to improve management of processing tomatoes.
The specific objectives of this proposal were to:
1) Assess impact of soil health management practices (cover crops, compost and humic substances) on tomato susceptibility to insect virus vectors of beet curly top virus (BCTV) 2) Identify key root-soil interactions that improve plant resistance to insects and the plant viruses they vector. 3) Develop remote sensing tools for early detection of tomato susceptibility to beet leafhoppers.
Procedure
We conducted proof of concept greenhouse experiments to test how management practices influenced leafhoppers preference and the role of soil microbes in this process. We collected soils from one of Scott Park’s field and UC Davis Russell Ranch Sustainable Agricultural Facility where soil management practices in a tomato/corn rotation have been maintained for 23 years as part of a large-scale experiment. At Russell ranch, we collected soils from the following regimes: (1) synthetic fertilizer use +/- humic substances, (2) integrated management using winter cover crops and synthetic fertilizer and (3) winter cover crops and chicken manure compost. Processing tomato plants (Heinz 8504) at the 4 leaf stage were transplanted into 5 gallon pots filled with a soil from each of the treatments. Half of the soil was sterilized and leafhoppers carrying the BCTV were given the choice to feed on tomato plants growing in the different soils with or without soil microbes. Two replicates of the experiment were conducted and harvested on 08/03 and 10/20 respectively. Soil chemical properties (Total C and N, OM and POxC, fertility, pH, CEC), and soil microbial composition were measured in all four soils. Leafhopper preference was measured by feeding behavior (stipples, sheaths) and oviposition (# eggs) along with characterization of virus abundance, soil microbial population, plant growth and nutrient level and canopy reflectance profile. Tomato growth responses and other parameters listed above were compared among soil management regimes in the presence and absence of soil microbes and BCTV.
Summary of progress and results
Objective 1: Assess impact of soil health management practices on tomato susceptibility to insect virus vectors of beet curly top virus (BCTV)
We first spent great efforts establishing a new virulent beet leafhopper colony from few insects donated by Prf Gilbertson. Leafhoppers are very challenging insects to established in lab settings, especially when virulent, but the colony is now up and running and available for further research. In the first replicate, we observed a slight trend towards more Figure 1: Leafhopper Feeding Preference insect feeding in soils deprived of their biological life 100 80 (sterilized) highlighting the potential role of soil microbes in 60 40 * * reducing plant attractiveness to the leafhoppers. This was % Feeding 20 * significant for all soils but the one collected in the mixed 0 Conventional - RR Mixed-RR Organic 1 - RR Organic 2 - Park system (cover crop + fertilizer) (Figure 1). Soil microbes Sterile Non-sterile
Figure 2 : Dry Biomass reduced feeding preference by 77% in conventional systems 7 and 60% on average in organic systems. However, plants of 6 5 the first replicate presented signs of heat stress due to cooler 4 * * 3 breakdown in the greenhouse over the summer. We therefore 2
Dry biomass (g/plant) 1 0 decided to repeat the experiment to ensure accuracy and Conventional-RR Conv + HS-RR Mixed-RR Organic 1-RR Organic 2-Park
Sterile Non-sterile robustness of our results. We also included in the second
Figure 3: Leafhopper Survival, 5 days after exposure replicate a 5-way choice bioassay in which leafhoppers 120 carrying the BCTV are given the choice to feed on plants 100 growing in the different soils rather than in sterile/non sterile 80
60 to elucidate why one tomato field is more attractive than % survival 40 another to beet leafhoppers in a landscape dominated by 20 processing tomatoes. 0 Conventional-RR Conv+HS-RR Mixed-RR Organic 1-RR Organic 2-Park
Sterile Non-sterile Biomass accumulation in the second replicate was higher and more representative of field-grown plants. Interestingly, sterilizing soil increased plant biomass in Scott Parks’ organic soils (Figure 2), maybe due to the flush of nutrient provided by higher soil microbial biomass present in organic soils. Similar results were observed when comparing aphid feeding preference in organic soils for another project (data not shown). Survival of leafhoppers carrying the BCTV was lower when plants were gown on Scott Park’s soils compared to conventional although microbial population did not affect survival rates (Figure 3). We are currently analyzing virus abundance, and doing microscopic measurements of feeding reference and oviposition. Once analysis is complete, we will be able to confirm whether soil organisms promote or negatively affect tomato plant health, beet leafhopper population performance and BCTV abundance, and how this effect varies across a soil management gradient. Objective 2: Identify key root-soil interactions that improve plant resistance to insects and the plant viruses they vector.
Preliminary results indicate that use of cover crops and/or compost affect host selection by beet leafhoppers and BCTV expression and that soil microbes play a role in this process. Root and rhizosphere soil harvest was completed and DNA is being extracted, with plans to submit for sequencing by the end of November. Virus samples are being processed and data should be available by the new year with complete analyses done in January. Soils are also being processed for measurements of nutrient content, aggregation and total Carbon.
Objective 3: Develop remote sensing tools for early detection of tomato susceptibility to beet leafhoppers.
Airborne remote sensing data were collected on potted plants at 30 m altitude before and after exposure to leafhoppers for both replications (08/03 and 10/20). Reflectance data were acquired in 111 spectral bands ranging from 565-966 nm. The acquisition of airborne remote sensing data includes acquisition of continuous white calibration data, which are used to correct for subtle variations in solar intensity. Here, we present reflectance data from tomato plants before exposure to leafhoppers (first replication only).
Figure 4 Figure 4a shows in false color potted tomato plants from five of the 10 combinations of soil treatment and with/without soil sterilization. A representative image of a tomato plant (Figure 4b) shows that there is considerable variation in “greenness” among pixels from a single tomato plant. Thus, radiometric filtering was deployed to select pixels with spectral reflectance profiles within specified ranges. Based on analysis of over 9000 hyperspectral spectra, we examined the relative effect of soil treatment and soil sterilization in each of the 11 spectral bands (Figure 4c). It is seen that very high F-values were obtained, and they represent the highly significant plant responses to both treatment factors. From this analysis, it is seen that the impact of different soils on tomato reflectance profile was most pronounced in spectral bands near 565 nm and around 700 nm. The impacts of soil sterilization were most pronounced around 700 nm but especially in spectral bands near 850 nm. From this preliminary analysis, we demonstrate that hyperspectral airborne remote sensing technologies can be used to detect and quantify plant responses to soil management and we have now identified the spectral ranges in which tomato plants appear to show the strongest responses to the examined treatment factors for further analysis and field study. Correlation of spectral profiles with plant nutrient content and insect preference still remains to be analyzed.
Conclusion and future work
Taken together these results suggest soil health building management practices may increase plant health and ability to resist plant pests and pathogens. However, more information is needed to better understand the impact of soil health components on plant health, defense mechanisms and insect pressure. We will continue our analysis of results and propose to conduct on-farm field trials at Russell Ranch and in various organic and conventional grower’s field representing a wider management gradient of organic inputs and soil characteristics to verify patterns found in the first year greenhouse experiment. Soils will be fully characterized and the functional significance of changes in soil physiochemical properties for nutrient release and dynamics of crop uptake will be elucidated. Sequencing of the microbial population will reveal impact of soil building management practices on both beneficial and pathogenic fungi such as Fusarium oxysporum, which is of great concern to the processing tomato industry. Our remote sensing approach will be scaled up to the field-scale to monitor plant nutrition and susceptibility to beet leafhoppers and potentially provide a novel and state of the art tool for integrated fertility and pest management.
CALIFORNIA TOMATO RESEARCH INSTITUTE, INC. 18650 E. Lone Tree Road Escalon, California 95320-9759
RESEARCH SUMMARY REPORT TO CTRI, NOVEMBER 2016
Project Title: MANAGEMENT OF FUSARIUM OXISPORUM RACE 3 WITH CHEMICAL AND BIOFUNGICIDE TRANSPLANT DIPS
Project Leader (s): Scott Stoddard Tom Turini UC Cooperative Extension UC Cooperative Extension 2145 Wardrobe Ave. 550 E. Shaw Ave. Suite 210-B Merced, CA 95340 Fresno, CA 93710 209-385-7403; cell: 209-777-7645 [email protected] [email protected]
SUMMARY Small plot trials were conducted in Merced and Fresno Counties to evaluate the efficacy of fungicides and biofungicides applied as transplant drenches on controlling the development of Fusarium wilt in processing tomatoes. The Merced trial was located in a commercial field that was severely infected with Fusarium oxysporum f. sp. lycopersici, hereafter referred to as Fusarium race 3, or F3. Regalia, Serenade Soil, Accomplish, Maxim, Quadris Top, CAN17, and Vellum One were applied at label recommended rates, typically 1 to 4 quarts per 100 gallons. Varieties used in the Merced location were H8504, HM3887, HM58801, and BP141. H8504 was used at the Fresno County location. At the Merced location, variety had a significant affect on the number of Fusarium infected plants, severity of infection, plant vigor, and yield. Susceptible variety H8504 developed significantly more F3 and had reduced yields compared to the other cultivars. With H8504, Maxim fungicide significantly reduced the number of Fusarium infected plants on the 13-July evaluation, however, by the end of the season this affect had diminished and no significant difference in the F3 ratings or yield were observed. Interesting, in the F3 resistant BQ141, an increase in yield was noted when fungicides were used as plant drenches. No F3 was observed in the Fresno location, and no differences in crop response were noted between the Maxim and untreated treatments. The results of these trials indicate that the use of variety resistance is more effective for controlling F3 in fields where this disease occurs, but that fungicides applied at drenching may offer additional benefits and improved yield.
INTRODUCTION Fusarium wilt race 3 of tomato (Solanum lycopersicum) caused by the soil fungal pathogen Fusarium oxysporum f. sp. lycopersici Race 3 Florida or Yolo (F3), has increased in both incidence and severity in the Merced and Fresno production areas over the past few years. Plant losses ranged from insignificant to > 50% in some fields in 2015. Current UCIPM guidelines recommend variety resistance and rotation to manage this disease, however, many chemical and biological fungicides (biofungicides and “plant growth stimulators”) are registered and claim control of Fusarium. A study by Amini and Sideovich1 showed reductions in Fusarium wilt of 22 – 100% from 6 different fungicides applied as soil drenches, for example.
The objective of this project was to evaluate various fungicide and biofungicide products alone and in combination on their ability to delay or suppress the onset of Fusarium wilt disease in processing tomatoes.
METHODS Two trials were conducted, in Fresno and Merced Counties. The Merced trial was located in a commercial field southwest of Merced with a history of Fusarium wilt problems in processing tomatoes. Initial drench applications were made immediately before transplanting on May 11, 2016, by adding a small amount of product into a gallon of water, then applying 3/8 of this solution to 150 plants while still in the tray (Figure 1). Regalia (extract of Raynoutria sachalinensis), Serenade Soil (Bacillus subtilis), Accomplish (Bacillus spp), Maxim (fludioxonil), Quadris Top (azoxystrobin + difenconazole), CAN17 (calcium ammonium nitrate solution 17%), and Velum One (fluopyram) were applied at label recommended rates, typically 1 to 4 quarts per 100 gallons. The Maxim (fludioxonil) rate was based on the rate established for Cannonball fungicide (same a.i.) used for melons of 0.25 lbs a.i per acre. The field variety was BP141, which is F3 resistant. Additional Maxim fungicide application were also applied to susceptible and tolerant varieties H8504, HM3887, and HM58801. Additional applications of the biofungicides Regalia, Accomplish, and Serenade Soil were made by drenching the plants again on May 20 and June 2 (2 and 4 weeks after transplanting). In total, 13 treatments were applied (Table 1).
The Fresno County location followed the same basic method of applying the treatments, except that only the susceptible variety H8504 was used. Both trials were located in commercial fields with drip irrigation. Treatment design was a randomized block with 4 replications. Plots were one bed wide by 25 – 50 feet long, using 25 – 50 plants. Fusarium type and race determination from infected plots were conducted by the Mike Davis lab. They isolated, extracted DNA, and identified the pathogen using PCR and gel electrophoresis on purified sequences.
Plant stand counts were take at 2 weeks after transplanting to evaluate potential phytotoxicity of the treatments. One treatment, Quadris Top at 12 ml per gallon of water (approximately 8 oz/A applied to 8700 plants) caused nearly 100% plant loss. These plots were replanted with new plants treated at 50% rate and evaluated again after 2 weeks. Crop phytotoxicity was still severe,
1 Amini J. and D.F. Sideovich. 2010. The effects of fungicides on Fusarium oxysporum f. sp. lycopersici associated with Fusarium wilt of tomato. J. Plant Prot. Res. 50, 172-178. with about 50% plant death. None of the other treatments caused any significant plant losses as compared to the untreated control.
Plots were visually evaluated for F3 in mid July by counting the number of chlorotic and diseased looking plants. Samples submitted to the Mike Davis Lab on July 13 confirmed the presence of Fol race 3, both Yolo and Florida isolates. The number of symptomatic plants in the plots with Maxim fungicide was about half that of the untreated. The resistant field variety BP141 has shown no Fusarium symptoms, nor any growth differences from any of the treatments except for Quadris Top, which resulted in nearly complete stand loss from phytotoxicity.
Plots were hand harvested on September 8 and 22 for the Merced and Fresno County locations respectively. A 10’ section from the middle of each plot was weighed and separated into red, green, and sunburned fruit. Because of time constraints, all plots were not harvested at either location, and therefore a complete statistical analysis could not be performed. Additional plant samples from F3 symptomatic plants were submitted to the Mike Davis lab for Fusarium diagnosis.
RESULTS Results for the Merced trial location with F3 resistant variety BQ141 are shown in Table 1. Plant stand was significantly reduced from the Quadris Top drench application as compared to the untreated control, but none of the other treatments caused plant phytotoxicity. Very few plants in any of the treatments showed any sign of F3 by the July 13 evaluation date, indicating that the variety resistance was holding up well at this location. Except for the Quadris Top plots, plant vigor and yield were excellent at the end of the season, with an average yield of 69 tons/A.
Fludioxymil (Maxim) was also applied to certain plots using H8504 (susceptible), HM3887 (tolerant), and HM58801 (resistant) at the equivalent rate of 16 oz per acre. With H8504 and HM3887, Fusarium infected plants were significantly reduced at the July 13 evaluation (Table 2). This suggests that this fungicide delayed the onset of F3 in susceptible plants. However, at the September 7 evaluation there was no difference in the amount or severity of F3 in these same varieties. More than 90% of the H8504 plants were infected, and about 50% of HM3887. Samples from these plots confirmed that the pathogen was F3 most of the time. Yields were significantly less for H8504 at this location as compared to the other varieties (Figure 2), but the use of a fungicide at transplanting (Maxim 1x, Maxim 2x, or Velum One) did not significantly increase yields when averaged across all varieties.
While transplant fungicides did not seem to help improve yields with susceptible varieties, there was a slight improvement in yield with F3 resistant BQ141 (Figures 2 and 3).
Results from the Fresno County location are shown in Table 3. Unlike in Merced, there was no F3 found in any of the treatments, and no differences were seen in sunburn or yield with Maxim as compared to the untreated control.
The results of these trials indicate that the use of variety resistance is more effective for controlling F3 in fields where this disease occurs, but that fungicides applied at drenching may offer additional benefits and improved yield. An additional year of funding is requested to evaluate this with susceptible and resistant varieties.
ACKNOWLEDGEMENTS Many thanks to our cooperators, George Seascholtz with Seascholtz Farms, and Wolf Farms.
Table 1. Results for all fungicide drench treatments, 2016. ifrial l All with variety BQ141, 20 gallons/33 t rays (20 gallons per A equivalent) 25-May 13-Jul 7-5ep yield Treatment product concentration timing plant stand % #infected F3 rating Vigor tons/A 1 water transplants 85.6 0.0 0.0 3.6 59.859 2 Regalia 4 qts/100 gallons transplants 89.6 0.0 0.0 4.6 66.211 3 Regalia 2 qts/100 gallons planting+ 2 weeks + 4 weeks 75.2 0.2 0.0 4.8 75.141 4 Serenade Soil 4 qts/1 00 ga lions transplants 90.4 0.0 0.0 4.4 77.537 5 Serenade Soil 2 qts/1 oo ga lions planting+ 2 weeks + 4 weeks 95.2 0.0 0.0 4.6 77.246 6 Accomplish LM 4 qts/100 gallons transplants 96.8 0.0 0.0 4.6 74.778 7 Maxim 12.5 ml/gal transplants 93.6 0,2 0.0 4.2 72.890 8 Maxim 25 ml/gal transplants '88.8 0.0 0.0 4.6 77.392 9 Quadris Top 6 ml/gal transplants 32.8 0.4 1.0 1.8 39.785 10 CAN17 1 qt/100 gallons planting+ 4 weeks 95.0 0.0 0.0 5.0 68.680 11 Accomplish+ Maxim transplants 87.2 0.0 0.0 4.8 72.237 12. Accomplish+ Acadian planti ng+ 2 weeks+ 4 weeks 85.0 0.0 0.0 43 72.600 13 Vellum One 12.5 ml/gal planting 86.4 0.4 0.0 4.0 67.736
Average 84.7 0.1 0.1 4.2 69.392 LSD 0.05 17.9 0.78 CV% 14.9 13.0 plant stand ~ plant stand 2 week> afteltransplanting #Infected ~ average number of plants In each treatment with visible Fusarium symptoms. F3 rnting ~ $Ubjecdve rating on 0 · 5 scale, where 0 = no di$ease and 5 = complete death of plant. vfgor ; overall plot vigor, on 0 • 5 scale: 0 = dead, 1 = verv poor, 2 =poor, 3 = moderate, 4 ; good, 5; excellt!Jlt LSD 0.05 ~ Least Significant Difference at the 95% confidence level. -- = not enough data to statisdcallv analyze. CV% ; coetficlt!Jlt of varrattoo,
Table 2. Results for fungicide drench treatments with susceptible, tolerant, and resistant cultivars, 2016. Trial 2 25-May 13-Jul 7-Sep 7-Sep 7-Sep yield tteatment Variety Fungicide plant stand % II infected F3 ratfng Vigor F3lab + tons/(4 lA H8504 UTC 97 7.3 4.5 0.3 3 54.595 18 Maxim plant drench. 25 ml in tgallon water (o.:n% al), 16 oz/A 99 2.5 4.0 0.8 3 53.724 2A HM3887 UTC 90 2.3 2.5 3.0 3 67.082 28 Maxim plant drench. 25 ml in 1 gallon water (0.21% al), 16 oz/A 97 1.0 2.0 3.3 4 64.590 3A HM58801 UTC 81 0.8 0.0 3.5 60.355 3B Maxim plant drench. 25 ml in 1 gallon water (0.21% ai), 16 oz/A 95 0.3 0.0 3.0 2 56.241 4A 8Q141 UTC 84 0.0 0.0 3.5 58.770 4B Maxim plant drench. 12.5 ml in 1 gallon water (0.21% al), 8 oz/A 100 0.0 0.0 4.3 72.890 4C Maxim plant drench. 25 ml in 1 gallon water (0.21% ai), 16 oz/A 80 0,0 0.0 4.3 0 77.392 5 Vellum One, 12.5 ml/gallon 84 0.5 0.0 3.8 68.897
Average 90.7 LS 1.4 3.0 2.1 63.477 Variety LSD 0.05 ns 1.11 0.42 0.92 7.61 Fungicide LSD 0.05 ns 0.79 ns ns ns Variety x fungicide F·test ns 0.002 ns ns 0.02 CV% 11.9 70.9 30.1 31.1 10.0 plant stand =plant stand 2 weeks after transplanting 11 infected ~ av~rage numbet of pl~nts in each treatment with visible Fusarium svmptomr;, F3 rating =s ubjec~vo r•~ng on 0 • 5 5Cale. where 0; no dl s e• ~• and 5: complete death or pl;rnt. vtgor =over-all plot vigor, on 0 . S .scale: 0 =dead_. 1 : very poor, 2 = POOt 3 ~ modecat~, 4 =good, 5 =- exceltent F31ab+ = number of positive lab sample\ confirmlrtg Fusarium raw 3 {out of 4 pos\ible). Variety LSO ~ Least Sign1fi Figure 1. Applications of fungicides/biofungicides were made directly to the plants before transplanting and as a drench treatment 2 and 4 weeks after planting. Processing Tomato Fungicide Drench 2016 main effect of fungicide 70 a 65 ~ v 60 -."' "'c .8 55 50 45 main effect of variety 70 65 ~ v 60 -."' "'c .8 55 50 45 H8504 HM3887 HM58801 BQ141 variety x fungicide interaction 75 70 65 ~ v 60 -."' "'c .8 55 50 45 40 Figure 2. Main effect of fungicide treatment, variety, and the variety x fungicide interaction. While the use of fungicide drenches (fludioxymil 1x, 2x, or fluopyram) did not improve yields overall, with BQ141 there was a significant increase. PT F3 Drench Trial 2016 Graph 120 100 r *- c: 80 ":g "' -c: 60 Q."' 40 20 ~ 9 1 2 13 10 11 12 7 6 3 5 8 4 Treatment PT F3 Drench Tri al 2016 Graph 90 80 70 -.<1: "'c: 60 B so 40 30 m 9 1 2 13 10 11 12 7 6 3 5 8 4 Treatment Figure 3. Plant stand and yield was significantly reduced only for the Quadris Top (treatment 9) as compared to the untreated control (treatment 1), but all the other treatments had slightly improved yield in cultivar BQ141. November 10, 2016 Research Project Proposal – Annual Report The California Tomato Research Institute Project Title: Developing New Management Techniques for Vector-Borne Diseases of Tomato Project Year: 2014-2015 Project Leader: Clare Casteel, Department of Plant Pathology, One Shields Ave, University of California, Davis, CA 95616 phone 951-961-5365, email [email protected] The objectives of this proposal are to: 1) Investigate the role of ethylene in tomato-vector-pathogen interactions 2) Evaluate the use of ethylene inhibitors for management of vector borne pathogens of tomato 3) Examine potential non-target impacts of ethylene application on tomato production Overview of Outputs: Activities: To address objective 1, laboratory and greenhouse experiments were conducted to determine if ethylene induction increases insect attraction to tomatoes and if ethylene inhibitors (ReTain) can be used to prevent increased attraction. Results were promising. To address Objective 2 and 3 we established a trial site on Armstrong Research Farm at UC Davis. In this field trial we treated processing tomatoes with two different ethylene inhibitors prior to transplant (ReTain and/or Smart Fresh). We observed minimal spread of PVY for all treatments. No impact on yield or development was detected in field trials. Dissemination: The results will be released to tomato growers, processors, and plant breeders at the California Tomato Conference (November 14, 2016) organized by Gene Miyao (UC Farm Advisor) and in this report. Results: Objective 1: Investigate the role of ethylene in tomato-vector-pathogen interactions. Ethylene is a natural plant volatile that is released during instances of plant stress, such as transplant or infection. Previous studies have demonstrated that ethylene induction increases plant susceptibility to insects and, because of this, ethylene may be used as a host-finding cue by insects. The goal here was to determine if insects prefer to settle on infected tomato and if ethylene is the cue used to locate these plants. We have completed this objective 1 for PVY. We demonstrated that aphids prefer virus-infected tomatoes and that inhibiting ethylene production blocks the attraction of aphids to infected plants (Fig. 1A). In fact aphids were repelled from infected plants when ethylene was temporarily inhibited. This suggests that spread could be minimized in the field by blocking stress-induced ethylene. We would like to continue this objective using leafhoppers and curly top virus next year Objective 2: Evaluate the use of ethylene inhibitors for management of vector borne pathogens of tomato. PVY infection increases vector attraction and reproduction through increased ethylene production (Objective 1). As the majority of tomatoes are transplanted and stressed early in the growing season, stress induced ethylene may be attracting insect vectors early in the season. The goal here was to evaluate using ethylene inhibitors for management of transplant susceptibility to disease. To address Objective 2 we established a trial site on Armstrong Research Farm at UC Davis. In this field trial we treated processing tomatoes with two different ethylene inhibitors prior to transplant (ReTain and/or Smart Fresh). We infected plants with PVY in all treatments, released insects, and monitored PVY spread. We observed low transmission rates for all treatments including controls (Data not shown). Next year we would like to repeat the work in the field but we will increase the number of infected insects used to better simulate outbreak. We would also like to better develop application methods by treat plants prior to transplant and during the growing season. Objective 3: Examine potential non-target impacts of ethylene application on tomato production. Using the field site we establish in Object 2, we measured non-target impacts of ethylene inhibitor treatment prior to transplant in the field for Objective 3. We surveyed insect populations and observed insect numbers peak in early august. No significant differences in pest populations were observed however early in the season a trend for reduced pest populations was observed, which is promising (Fig. 1B). Next year we will repeat the work in the field but we would like to treat plants prior to transplant and during the growing season. We also examined fruit quality, yield, and plant growth. No differences in plant development, fruit maturity, yield, disease, or plant size was observed among treatments (Fig 1C). Figure 1. (A) Aphid attraction to PVY infected tomato can be prevented by inhibiting ethylene (Retain). (B) A trend for fewer insects were found in sweep surveys of tomatoes treated with Retain and MCP (SmartFresh). (C) No differences in the percentage of good fruit, immature fruit (green), or diseased fruit were observed among treatments at harvest 2016 Annual Progress report Project Title: Bacterial canker of tomato: examining strain relationships and testing PCR primer specificity. Project Leader: Gitta Coaker Associate Professor Department of Plant Pathology University of California Davis Phone: 530-752-6541 E-mail: [email protected] Co-Project Leader: Robert Gilbertson Professor Department of Plant Pathology University of California, Davis Phone: 530-752-3163 E-mail: [email protected] Introduction: Bacterial canker of tomato, caused by Clavibacter michiganensis subsp. michiganensis (Cmm), can cause significant losses in greenhouse and field tomato production under favorable environmental conditions (Eichenlaub and Gartemann 2011). There are no existing chemical or genetic disease control methods. Although seed disinfestation with HCl is effective, bacterial canker can still develop if seed treatment is not complete or if other sources of inoculum are present. Detection of the disease in the field is now commonly performed with immunostrips, but these are not very sensitive and are prone to false positives due to the presence of non-pathogenic bacteria that are closely related to Cmm. PCR with Cmm primers is a more sensitive method, but the currently available primers may only detect a subset of Cmm strains present in the field. Therefore, detection of Cmm through immunostrips and PCR can result in false positives and false negatives, respectively. Previously, we sequenced 34 Cmm and five nonpathogenic Clavibacter genomes to identify more robust PCR detection primers. This is the second year of a proposed two year grant. In the funded research, we seek to validate the specificity of particular PCR primers after inoculation on tomato, investigate the relationship of different Cmm strains, and monitor new Cmm strains if an outbreak occurs. The objectives of the proposed research are to: Objective 1: Test the detection specificity of promising PCR primers for Cmm detection. We have generated a multiplex PCR assay that is capable of identifying all current strains of pathogenic Cmm and differentiating between pathogenic and non-pathogenic Clavibacter. In the next year, we will test for detection sensitivity in tomato. 1 Objective 2: Use existing genome sequence data to investigate strain phylogeny and virulence on tomato. Genome sequences were used to generate detailed phylogenetic analyses of pathogenic Cmm and non-pathogenic Clavibacter. Comparative genomics was used identify conserved genes correlated with virulence in California strains. This information can now be used for identifying new strain relationships in the event of a bacterial canker outbreak. Objective 3: Monitoring potential bacterial canker outbreaks in California. We have worked with farm advisors to isolate bacteria from symptomatic tomato plants during a canker outbreak in 2016 and performed pathogenicity testing. Isolated Cmm strains will be examined for strain aggressiveness. In the event of more severe disease pressure, genome sequencing will be performed. Progress for Objective 1: Test the detection specificity of promising PCR primers for Cmm detection. Despite the need to develop sets of PCR primers that specifically detect Cmm, the existing diagnostic primers are frequently non-specific or detect only a subset of pathogenic strains. The goal of Objective 1 was to develop a robust PCR detection strategy for pathogenic Cmm. Previously, we tested the specificity of published primers designed based on genes present both on the Cmm chromosome and plasmids (Kleitman, Barash et al. 2008; Cho, Lee et al. 2012). These diagnostic primers were frequently non-specific and gave positive results for non- pathogenic Clavibacter or subspecies pathogenic on potato (Cho, Lee et al. 2012). In order to develop a robust Cmm detection platform, we used genome sequence information to design and test different primer pairs targeting both the chromosome and plasmids. This year we have generated a multiplex PCR assay that amplifies three targets. Two targets are specific for pathogenic Cmm and amplify regions of a chromosomal gene (Sab) as well as a gene found on the pCM1 plasmid (celA). Sab is a sugar transporter and is likely used by Cmm to obtain food in the form of sugars from tomato. celA is required to induce robust wilt and canker symptoms and can target tomato cell walls for degradation. A previous celA primer used by the industry was not specific to Cmm, but our redesigned celA primers only amplify Cmm DNA by PCR. The final target amplifies all Clavibacter species at the genus level and is based on a region of 16S rRNA. It is advantageous to be able to amplify all Clavibacter species because this serves as a positive control that the PCR assay is working and also identifies the presence of non-pathogenic Clavibacter. This multiplex PCR assay results in three bands of different size that can be easily separated on a DNA gel. The multiplex PCR assay enabled specific detection of Cmm when tested against a panel of 116 bacterial strains including Cmm, saprophytic Clavibacter, and other tomato pathogens (Table 1, Figure 1). Table 1. Specificity of multiplex PCR targets. + = detected in all samples, - = not detected in any samples. C. michiganensis subsp. sepedonicus causes bacterial ring rot of potato. Bacteria # strains Sab celA 16S Cmm 82 + + + Nonpathogenic Clavibacter 6 - - + C. michiganensis subsp. sepedonicus 10 - - + Other tomato bacterial pathogens 20 - - - 2 Figure 1. Agarose gel demonstrating multiplex PCR specificity and banding patterns. NP = non-pathogenic Clavibacter isolated from tomato. Cms = Clavibacter michiganensis subsp. sepedonicus (causal agent of potato ring rot and non-pathogenic on tomato), Pst = Pseudomonas syringae pv. tomato (causal agent of bacterial speck disease on tomato). Arrows indicate amplified PCR targets. In the next year, we will validate the sensitivity of the multiplex PCR diagnostic after inoculation of tomato. If there is another suspected outbreak of bacterial canker in 2017, we will attempt to isolate Clavibacter from symptomatic plants and detect the presence of Cmm using the multiplex PCR detection platform. We have tested multiple Clavibacter isolates from tomatoes exhibiting canker symptoms in 2016 and confirmed that they are Cmm with the multiplex PCR detection platform. Progress for Objective 2: Use existing genome sequence data to investigate strain phylogeny and virulence on tomato. We have completed this objective. Previously, we sequenced 34 Cmm genomes and five non-pathogenic Clavibacter genomes. Table 2 summarizes the genome sequencing for all non- pathogenic strains as well as 11 Cmm genomes from California. We have analyzed all sequenced genomes to investigate differences at the gene level. These analyses have enabled us to investigate strain relationships, identify components in Cmm required for infection of tomato, and identify conserved genes that can be used for specific PCR-based detection of Cmm. 3 Table 1: Clavibacter strains whose genomes were sequenced in this study. Pathogenicity was verified on tomato and all pathogenic strains are Clavibacter michiganensis subsp. michiganensis. Strains Isolated Collected Pathogenicity Sequencing Platform Contigs Genome Plasmids on tomato Size (bp) CASJ001 1999 California + MiSeq, PacBio 4 3423339 pCM1 CASJ002 1999 California + MiSeq, PacBio 3 3423641 pCM1/pCM2 CASJ003 1999 California + MiSeq 315 3294503 pCM1/pCM2 CASJ004 1999 California + MiSeq 477 3351833 pCM1/pCM2 CASJ005 2001 California + MiSeq 321 3284480 pCM1/pCM2 CASJ006 2002 California + MiSeq, PacBio 4 3499162 pCM1/pCM2 CASJ007 2011 California + MiSeq, PacBio 11 3400049 pCM1 CASJ008 2002 California + HiSeq 39 3392817 pCM1/pCM2 CAYO001 2001 California + MiSeq, PacBio 3 3481307 pCM1/pCM2 CA00001 2000 California + HiSeq 120 3625805 pCM1/pCM2 CA00002 2000 California + HiSeq, PacBio 4 3371744 pCM1/pCM2 CASJ009 2011 California - MiSeq, PacBio 2 3422081 pSap1 CFBP7494 1999 Europe - HiSeq 15 3425817 - CFBP7576 1997 Europe - HiSeq 372 3399000 pCM1/pCM2 CFBP8017 2006 Europe - HiSeq 65 3284576 - CFBP8019 - Europe - HiSeq 18 3076288 - We have identified Clavibacter genes that are useful for predicting genetic relationships between strains. We have performed high-resolution genetic analyses using 1,000 single nucleotide polymorphisms. We have also conducted lower resolution analyses using the DNA sequence of 6 housekeeping genes, a technique known as multilocus sequence analyses (MLSA) (Figure 2). Both approaches verified that Cmm strains isolated over multiple years in California can be distinguished from one another (Figure 2). These findings have implications in strain tracking and should enable us to determine if plants in different fields are infected by different Cmm strains. In the next year, we propose to analyze 31 strains isolated from the canker outbreak in 2016 to determine if this outbreak was caused by the same Cmm strain or was caused by different strains. The phylogenetic analyses revealed that non-pathogenic strains of Clavibacter isolated from tomato can be very closely related to Clavibacter subspecies that can infect other crops, such as pepper and alfalfa (Figure 2). These findings have implications for crop rotations and indicate that non-pathogenic Clavibacter strains on tomato could be pathogens on other plants and represent a reservoir for disease. 4 Figure 2. MLSA phylogeny. Phylogeny of different C. michiganensis strains based on the sequence of six housekeeping genes. A maximum likelihood approach was used to obtain the phylogenetic tree with 1000 bootstrap replicates. Sequenced strains are not colored and labeled with (N) = non-pathogenic strain or (P) = pathogenic Cmm strain. C. michiganensis subsp. michiganensis (Cmm, blue color, infects tomato), C. michiganensis subsp. tessellarius (Cmt, brown color, infects wheat), C. michiganensis subsp. insidiosus (Cmi, violet color, infects alfalfa), C. michiganensis subsp. nebraskensis (Cmn, light green color, infects corn), C. michiganensis subsp. capcici (Cmc, dark green color, infects pepper), and C. michiganensis subsp. sepedonicus (Cms, yellow color, infects potato). Tomato seed-associated C. michiganensis subsp. californiensis (Cmc, purple color, non-pathogenic) strains clustered as a distinct group. Cmm strains cluster together but the California strains are not always the most closely related to one another. Non-pathogenic Clavibacter strains are more diverse and can cluster with subspecies that cause disease on other plants. Progress for objective 3: Monitoring potential bacterial canker outbreaks in California. We have worked with the farm advisor Gene Miyao to isolate bacteria from symptomatic tomato plants during a canker outbreak in 2016. The Coaker and Gilbertson labs have isolated Clavibacter from 31 symptomatic plants collected between May and July of 2016. Plant samples 5 were provided by Gene Miyao. We have extracted DNA from most of the Clavibacter isolates (at least 3 isolates were obtained from each sample) and confirmed that they are Cmm with our multiplex PCR detection platform described above. We have inoculated half of the strains isolated in 2016 on tomato plants in the greenhouse and confirmed that they are pathogenic and can cause bacterial canker. In the next year, we will test the remaining samples isolated in 2016 for pathogenicity on tomato. We will also perform detailed infection assays to determine how aggressive the strains isolated in 2016 are compared to strains isolated from California in previous years. As described above, we will investigate the genetic relationships of the strains isolated in 2016 as well as any strains that are predicted to be Cmm in 2017. Summary: Bacterial canker is a disease that has the potential to cause substantial losses in greenhouse and field production systems. In the late 1990s-early 2000s, 2011 and 2016 the disease caused losses to processing tomato production in California. Recently, it has re-emerged as a problem in greenhouse production, particularly with the increased use of grafting. In addition, Cmm is carried on or in seed and it can colonize and infect transplants before disease symptoms appear. Once the disease has become established in the field, there are no effective chemical control options available. Furthermore, there are no commercially available resistant varieties. A combination of good horticultural practices, sensitive and specific pathogen detection, knowledge of strain relationships, and robust chemical control strategies are necessary for effective disease control. We have developed a multiplex PCR-based diagnostic that is able to detect Cmm and distinguish pathogenic and non-pathogenic Clavibacter on tomato. This diagnostic can be used by the industry and seed testing laboratories. Furthermore, we have investigated strain relationships and developed tools to efficiently track new strains of Cmm. A more detailed understanding of strain relationships can be used to determine how infection is occurring, which can be used for disease control. This information can be used to select representative strains to identify resistant germplasm and ultimately identify targets for chemical disease control. References: Cho, M. S., J. H. Lee, et al. (2012). "A quantitative and direct PCR assay for the subspecies- specific detection of Clavibacter michiganensis subsp. michiganensis based on a ferredoxin reductase gene." J Microbiol 50(3): 496-501. Eichenlaub, R. and K. H. Gartemann (2011). "The Clavibacter michiganensis subspecies: molecular investigation of gram-positive bacterial plant pathogens." Annu Rev Phytopathol 49: 445-464. Kleitman, F., I. Barash, et al. (2008). "Characterization of a Clavibacter michiganensis subsp. michiganensis population in Israel." European J Plant Pathol 121(4): 463-475. 6 TITLE: Breaking bindweed: Deciphering the complex interactions among weed, water, herbicide and crop to improve Convolvulus arvensis control in processing tomato. PROJECT LEADER: Lynn M. Sosnoskie, Assistant Project Scientist 259 D Robbins Hall, University of California - Davis Department of Plant Sciences, MS-4 One Shields Avenue Davis, CA 95616 (229) 326-2676 [email protected] CO-PI: Bradley D. Hanson, Extension Weed Specialist 276 Robbins Hall, University of California - Davis Department of Plant Sciences, MS-4 One Shields Avenue Davis, CA 95616 (530) 752-8115 [email protected] SUMMARY: Field bindweed (Convolvulus arvensis) is a deep-rooted perennial that is difficult to control once it has become established. Since 2013, we have been engaged in research to evaluate the management of field bindweed in processing tomatoes. Results from our trials suggest: 1. Soil-applied trifluralin (PPI), rimsulfuron (PRE), and sulfentrazone (PPI) can suppress field bindweed vines; S-metolachlor has not been shown to be effective against perennial plants. 2. Sub-surface, layered applications of trifluralin can be effective at suppressing field bindweed, although crop injury may occur depending on transplant root ball placement. 3. With respect to POST herbicide applications, glyphosate can control field bindweed (i.e. reduce vine number, vine cover (length, biomass)) for up to six weeks; rimsulfuron and carfentrazone provide almost no suppression of perennial vines. 4. Glyphosate drift can severely injure processing tomatoes. PRE and POST applications of foliar micronutrients (Mn, Zn, Mn+Zn) did not appear to chelate glyphosate and prevent crop damage in our studies. 5. Plant growth regulators provided inconsistent results with respect to transplant establishment; it is uncertain if treatments with these products will give processing tomatoes a competitive edge over emerging weeds. INTRODUCTION: Processing tomato production in California has changed, dramatically, over the last half-century. Improved cultivars, conversion from seeded to transplanted production, commercialization of the mechanical harvester, and the steady adoption of drip irrigation have helped to expand the size and economic value of the industry (Mitchell et al. 2012). In 2013, California led the nation in the production of processing tomatoes in terms of hectares planted and harvested (105,000 ha), total yield (10 million metric tons), and total value of production ($918 million). Processing tomatoes are a significant component of the agricultural value chain in the Central Valley, where the majority of production occurs; in 2013, Fresno, Kings, Merced, San Joaquin, Stanislaus, and Yolo Counties each produced tomato crops valued at more than $100 million. Field bindweed (Convolvulus arvensis L.), a deep-rooted and drought tolerant perennial, (DeGennaro and Weller 1984; Frazier 1943a, 1943b, 1943c; Sharma and Singh 2007; Shrestha et al. 2007; Swan and Chancellor 1976; Weaver and Riley 1982; Wiese and Lavake 1986; Yerkes and Weller 1996) is a significant concern of the processing tomato industry in California. If allowed to compete with tomatoes during canopy establishment, field bindweed can significantly reduce both fruit number and quality (Lanini and Miyao 1989). Furthermore, field bindweed vines can become physically entwined with tomato plants, which, in turn, can reduce harvest efficiency (Mitich 1991). Although bindweed seedlings are relatively easy to manage using physical and chemical control strategies, established plants with extensive root systems are relatively tolerant to most management practices. For example, perennial bindweed control with tillage and cultivation is made more difficult by the weed’s significant below-ground nutrient reserves and regenerative capacity (Derscheid et al. 1970; Frazier 1943a, 1943b, 1943c; Swan 1980; Swan and Chancellor 1976; Weaver and Riley 1982). Infrequent mechanical cultivation may also facilitate plant spread by dispersing root fragments as opposed to exhausting stored energy. Suppression of established bindweed using chemical tools may be equally challenging, especially in crops like processing tomato, where effective herbicide options are limited. PRE-PLANT INCORPORATED/PRE-EMERGENCE HERBICIDE EFFICACY: Between 2013 and 2015, multiple field studies were conducted to evaluate the efficacy of pre-plant incorporated (PPI) and pre-emergence (PRE) herbicides on the suppression of field bindweed. All trials were conducted at the University of California, Davis, research farm (38 32’N, 121 47’W), where the soil is a fine, silty loam (Yolo series, 37% sand, 41% silt, 22% clay; 1.5-3% OM; pH 6.7-7.2) (Haar et al. 2002). The fields used in this study were known to be heavily infested with field bindweed (Lanini and Sosnoskie, personal observations). Tomatoes were transplanted between April and June and were sprinkler-irrigated with 1 inch of water immediately after planting to facilitate crop establishment. Furrow irrigation was used, thereafter, to maximize weed pressure in the study (Sutton et al. 2006). All herbicides were applied using a CO2- pressurized backpack sprayer equipped with three 8002VS flat-fan nozzles (TeeJet Technologies, Wheaton, IL) spaced 16-20 in apart and calibrated to deliver 20-30 GPA. All herbicides were applied to the soil surface 1 day before transplanting; trifluralin, S- metolachlor, and sulfentrazone were mechanically incorporated immediately thereafter. Field bindweed cover (defined as the percent (%) of the plot area that was occupied by field bindweed) was assessed until tomato canopy closure began to occur. Field bindweed cover at 4 weeks after transplanting (WAT), as observed in multiple herbicide trials, is displayed in Figure 1. Results suggest that trifluralin (as Treflan at 32 oz/A) applied PPI, rimsulfuron (as Matrix at 4 oz/A) applied PRE, and sulfentrazone (as Zeus at 3.2, 4.5 or 6 oz/A) applied PPI were effective at suppressing field bindweed at 4 WAT. Field bindweed cover ranged from 11-76% in the untreated checks (mean = 51% cover). Cover in the S-metolachlor (as Dual Magnum at 27 oz/A) treatments (mean = 51% cover) did not differ, substantially, from the controls. Mean field bindweed vine cover in the trifluralin, rimsulfuron, and sulfentrazone treatments ranged from 4-35% cover. Mean cover, averaged over trials was 16% for trifluralin, 10% for rimsulfuron, and between 4 and 17% for sulfentrazone, depending on rate. Figure 1. Bindweed cover (% of plot area covered by bindweed vines) in response to pre-emergence incorporated (PPI) and pre-emergence (PRE) herbicides at 4 WAT. Trials were conducted between 2013 and 2015. Each bar within a treatment class (i.e. UTC, Treflan), etc…) represents the mean observation from an individual trial. SUB-SURFACE LAYERED (SSL) APPLICATIONS OF TRIFLURALIN FOR THE SUPPRESSION OF FIELD BINDWEED: The successful control of deep-rooted perennials, such as field bindweed, is dependent upon herbicides reaching latent root and shoot buds. The majority of root/rhizome biomass for field bindweed is located within the top 2 feet of the soil profile, although some vertical roots can reach depths of more than 10 feet. Treflan and other residual herbicides registered for use in processing tomatoes are usually incorporated into the top 2-3 inches of the soil profile; because of their shallow placement, these herbicides may not suppress bindweed vines that are emerging from deeply buried rhizomes. In response to concerns that (traditional) incorporation strategies were diluting the herbicides and distributing them unevenly throughout the bed, Carlson et al. (1980) suggested applying the herbicidal products in a thin, concentrated, horizontal layer below the typically treated zone, instead. Sub-surface applications of alachlor in both dry and lima beans were able to suppress yellow nutsedge (Cyperus esculentus) for up to 6 weeks, thereby ‘reducing the weed’s early competitive potential’. In 2015, we undertook a similar study in processing tomatoes. This trial was conducted at the University of California, Davis, research farm, which was described previously. Specifically, our research was focused on describing how sub-surface layered (SSL) (to a depth of 4-6 inches) applications of trifluralin (as Treflan at 2 pt/A) interacted with surface applied herbicides ((trifluralin, S-metolachlor (as Dual Magnum at 27 oz/A) and sulfentrazone (as Zeus at 3 oz/A)) with respect to field bindweed control. SSL applications of trifluralin were made using horizontal spray blades (each with a spray width of 6 inches) one day prior to transplanting. Trifluralin was applied as either a banded or a broadcast application; the application rig was calibrated to deliver 40 GPA. A banded blade application received trifluralin only to the outer-most 6 inches of each bed (i.e. the bed shoulders); a broadcast application received trifluralin 4-6 inches deep across the entire width of the bed. Each type of SSL trifluralin application was repeated three times. For the purpose of comparison, three beds were left ‘untreated’ or ‘blank’ in that they did not receive any SSL trifluralin. Surface applied, and then mechanically incorporated, residual herbicides, plus a non-treated check, were overlaid on top of the SSL treatments. All surface herbicides were applied using a CO2-pressurized backpack sprayer equipped with three 8002VS flat- fan nozzles (TeeJet Technologies, Wheaton, IL) spaced 16-20 inches apart and calibrated to deliver 20-30 GPA. Field bindweed cover (defined as the percent (%) of the plot area that was occupied by field bindweed) and percent crop injury were assessed until tomato canopy closure began to occur. Field bindweed cover was greatest in the plots that did not receive a SSL trifluralin application (none), followed by the banded treatment (band), across all of the surface applied, PPI herbicides (Table 1). Field bindweed cover was lowest when SSL trifluralin was applied as a broadcast application across the entire width of the bed at a depth of 4-6 inches. When averaged over all surface herbicide treatments, bindweed cover in the broadcast-treated plots was 7, 18, 36, and 12% at 1 (19 June), 2 (28 June), 3 (3 July), and 4 (10 July) WAT, respectively. Conversely, mean bindweed cover ranged from 10-11, 33-24, 49-50, and 26-31% at 1, 2, 3, and 4 WAT, respectively, in the banded and non-treated plots. When averaged over all SSL treatments, bindweed cover was the greatest in the untreated check (UTC) (16-66%) and the S-metolachlor treatment (10- 47%). As was observed, previously, trifluralin and sulfentrazone were more effective at suppressing field bindweed cover (5-34%), relative to the control plots. Percent (%) Weed Cover SSL PPI/PRE 19-Jun 28-Jun 3-Jul 10-Jul UTC 20 63 77 51 Treflan 7 22 35 12 Band Dual Mag 11 33 53 29 Zeus 4 12 30 13 UTC 11 32 48 25 Treflan 6 17 33 7 Broadcast Dual Mag 6 16 37 13 Zeus 4 9 27 5 UTC 17 55 73 55 Treflan 4 21 35 11 None Dual Mag 12 47 51 38 Zeus 6 15 43 21 Band 11 33 49 26 Main effects - Broadcast 7 18 36 12 SSL None 10 34 50 31 UTC 16 50 66 44 Main effects - Treflan 6 20 34 10 PPI/PRE Dual Mag 10 32 47 27 Zeus 5 12 33 13 Table 1. Bindweed cover in response to sub-surface layered (SSL) Treflan and surface applied (PPI) herbicides. SSL Treflan was applied using a set of 6-inch-wide spray blades positioned at a depth of 4-6 inches below the bed-top; a banded blade application received trifluralin only to the outer-most 6 inches of each bed, a broadcast application received trifluralin to the entire width of the bed, and a blank application received no sub-surface treatment of trifluralin. All herbicide applications were made 1 day before tomato transplanting. Herbicides: Treflan = trifluralin, Dual Mag = Dual Magnum = S-metolachlor, Zeus = sulfentrazone. Percent (%) Crop Injury SSL PPI/PRE 19-Jun 28-Jun 3-Jul 10-Jul UTC 0 2 8 17 Treflan 13 20 17 17 Band Dual Mag 17 33 25 28 Zeus 17 35 22 27 UTC 2 13 13 22 Treflan 20 32 27 33 Broadcast Dual Mag 17 23 22 28 Zeus 25 47 30 35 UTC 0 0 0 0 Treflan 0 7 15 17 None Dual Mag 0 7 13 15 Zeus 5 28 18 17 Band 12 23 18 22 Main effects - Broadcast 16 29 23 30 SSL None 1 10 12 12 UTC 1 5 7 13 Main effects - Treflan 11 19 19 22 PPI/PRE Dual Mag 11 21 20 24 Zeus 16 37 23 26 Table 2. Tomato injury in response to sub-surface layered (SSL) Treflan and surface applied (PPI) herbicides. SSL Treflan was applied using a set of 6-inch- wide spray blades positioned at a depth of 4-6 inches below the bed-top; a banded blade application received trifluralin only to the outer-most 6 inches of each bed, a broadcast application received trifluralin to the entire width of the bed, and a blank application received no sub-surface treatment of trifluralin. All herbicide applications were made 1 day before tomato transplanting. Herbicides: Treflan = trifluralin, Dual Mag = Dual Magnum = S-metolachlor, Zeus = sulfentrazone. Crop injury was greatest when SSL trifluralin was applied as a broadcast application across the entire width of the bed at a depth of 4-6 inches (Table 2). When averaged over all surface herbicide treatments, crop injury in the broadcast-treated plots was 16, 29, 23, and 30% at 1 (19 June), 2 (28 June), 3 (3 July), and 4 (10 July) WAT, respectively. Conversely, crop injury for the banded SSL application and the non-treated control at 1, 2, 3, and 4 WAT was 12 and 1%, 23 and 10%, 18 and 12%, 22 and 12%, respectively. When averaged over all SSL treatments, tomato injury was the lowest in the untreated check (UTC) (1-13%); crop injury in the trifluralin, S-metolachlor, and sulfentrazone treatments ranged from 11-37%. The extensive injury observed in this trial is likely due to the fact that the transplants were relatively short in height and root-balls could not be positioned below the treated zone. The aforementioned trial was expanded in 2016. SSL applications (at a depth of 4-6 inches) of trifluralin (as Treflan at 32 oz/A) were made at approximately 21 days before transplanting (DBT) (application on 28 April, 2016) or else at 2 DBT (application on 17 May, 2016) using horizontal spray blades that were 6 inches wide. The timing component was included in this study to see if advance SSL applications of trifluralin could suppress bindweed without causing crop injury. SSL trifluralin was applied to either the entire width of the bed (broadcast) or else banded along the shoulders. The rig was calibrated to deliver the spray solution at a rate of 40 GPA. To determine if the cutting action of the blades affected bindweed development, independent of the trifluralin spray, the broadcast and banded treatments were repeated without a concomitant herbicide application. A non-treated (no SSL trifluralin, no soil disturbance due to blade movement through the soil) was also included in the trial. Surface applied residual herbicides (trifluralin as Treflan at 32 oz/A and trifluralin plus S-metolachlor as Dual Magnum at 27 oz/A), plus a non-treated check, were overlaid on top of the SSL treatments. All surface herbicides were applied using a CO2-pressurized backpack sprayer equipped with three 8002VS flat-fan nozzles (TeeJet Technologies, Wheaton, IL) spaced 16-20 in apart and calibrated to deliver 20-30 GPA. Herbicides were mechanically incorporated (PPI) after application. Field bindweed cover (defined as the percent (%) of the plot area that was occupied by field bindweed), bindweed density, and percent crop injury were assessed until canopy closure began to occur. Results from preliminary analyses suggest that bindweed cover was affected by the interaction between the SSL trifluralin and surface applied, PPI herbicide applications (Figure 2). The plots that did not receive SSL trifluralin applied nor any surface applied, PPI herbicides (No SSL, UTC) had the highest weed cover (15-66%) on 8, 14. and 22 June, 2016, compared to all other treatments. Bindweed cover in plots that did receive trifluralin applied SSL but did not receive surface applied, PPI herbicides (SSL, UTC) ranged from 4-42%. Bindweed cover in the plots treated with surface applied, PPI trifluralin and trifluralin plus S-metolachlor did not exceed 13% on any observation date, regardless of the SSL treatment. With respect to vine density, field bindweed was affected by the application of SSL trifluralin and the use of surface-applied and incorporated herbicides (Table 3), but not the interaction between them. Perennial vine density was reduced in the SSL trifluralin treatment (2, 4, 4 vines/m2) as compared to the no SSL treatment (4, 7, 5 vines/m2). Perennial vine density was also lower in the trifluralin and trifluralin plus S- metolachlor treated plots (2-3 vines/m2) as compared to the untreated check (6-12 vines/m2). Broadleaf weed density was affected by the use of surface-applied and incorporated herbicides (Table 3). Broadleaf weed density was lower in the trifluralin and trifluralin plus S-metolachlor treated plots (1-3 vines/m2) as compared to the untreated check (6-12 vines/m2). Results from preliminary statistical analyses indicate that field bindweed cover and density and broadleaf weed density were not affected by the timing of the SSL applications (2 or 21 DBT) nor by the arrangement of the spray blades/placement of the SSL trifluralin application (banded or broadcast). Figure 2. Bindweed cover (% of plot area covered by bindweed vines) in response to sub-surface layered (SSL) Treflan and pre-plant (PPI) herbicides. All PPI herbicide applications were made 1 day before tomato transplanting. Data are averaged over SSL application dates (2 and 21 DBT) and blade arrangements (banded and broadcast). Herbicides: Treflan = trifluralin, Dual Mag = Dual Magnum = S- metolachlor, Zeus = sulfentrazone, UTC = untreated. 6/8/2016 6/14/2016 6/22/2016 Vines Broadleaf weeds Vines Broadleaf weeds Vines Broadleaf weeds numbers per m2 SSL No SSL Treflan 4.0 na 6.7 na 5.0 na SSL Treflan 1.6 na 4.4 na 3.9 na Surface applied, PPI UTC 5.8 11.5 11.7 10.9 8.8 7.9 Treflan 1.6 0.5 2.9 2.0 2.3 1.6 Treflan+Dual Magnum 1.8 1.7 2.8 2.4 2.6 1.6 Table 3. Bindweed density (number of bindweed vines/m2) in response to sub-surface layered (SSL) Treflan and pre-plant incorporated (PPI) herbicides. Broadleaf weed density (number of broadleaf weeds/m2) in response to PPI herbicides. All PPI herbicide applications were made 1 day before tomato transplanting. Data are averaged over SSL application dates (2 and 21 DBT) and blade arrangements (banded and broadcast). Herbicides: Treflan = trifluralin, Dual Mag = Dual Magnum = S-metolachlor, Zeus = sulfentrazone, UTC = untreated. Percent (%) Crop Injury 6/8/2016 6/14/2016 6/22/2016 Days before transplanting 2 3.1 5.4 4.9 21 1.7 3.5 6.7 Surface applied, PPI UTC 0.6 1.1 5.3 Treflan 3.3 5.9 5.7 Treflan + Dual Magnum 3.4 6.5 6.4 Table 4. Processing tomato injury in response to the timing of sub-surface layered (SSL) Treflan applications and the type of surface applied, pre-plant incorporated (PPI) herbicides. All surface herbicide applications were made 1 day before tomato transplanting. Data are averaged over arrangement of the spray blades/placement of the SSL trifluralin application. Herbicides: Treflan = trifluralin, Dual Mag = Dual Magnum = S-metolachlor, UTC = untreated. Percent (%) Crop Injury 6/8/2016 6/14/2016 6/22/2016 Band no SSL 2.0 4.1 5.9 Band plus SSL 2.3 4.0 5.5 Broadcast no SSL 1.8 3.5 4.3 Broadcast plus SSL 4.1 7.3 7.4 Untreated (no blades, no SSL) 2.1 3.5 6.0 Table 5. Processing tomato injury in response to the arrangement of the spray blades/placement of the sub-surface layered (SSL) Treflan application. Data are averaged over SSL application dates and surface applied, pre-plant incorporated (PPI) herbicides. Herbicides: Treflan = trifluralin. Processing tomato injury was influenced by the timing of the SSL trifluralin applications and the type of PPI herbicides applied (Table 4). With the exception of the 22 June observation date, crop injury was greatest where SSL trifluralin was applied closer to planting (i.e. 2 DBT vs 21 DBT). With respect to the PPI herbicides, injury to processing tomatoes was greater in the trifluralin and trifluralin plus S-metolachlor treatments on 8 and 14 June as compared to the untreated check (UTC). Processing tomato injury was also influenced by the arrangement of the spray blades/placement of the SSL trifluralin application (banded or broadcast) (Figure 5). The greatest amount of injury (4-7%) was observed when trifluralin was applied broadcast, at a depth of 4-6 inches across the width of the entire bed. Crop injury in the untreated check (no blade, no SSL), the banded SSL application of trifluralin, and the treatments that included only the movement of the spray blades through the soil (with no application of trifluralin) ranged from 2-6%. All plots were hand-weeded in July, and the tomatoes allowed to grow without competitive interference for the remainder of the season. Mature fruits were harvested on 18 August, 2016. The mean yield across all plots equaled 28 lbs/10 ft2. No differences were observed with respect to the type and timing of SSL applications nor the use of PPI herbicides. POST-EMERGENCE HERBICIDE EFFICACY: Post-emergence herbicides (applied as a pre-plant burn down, for post-harvest field cleanup, or used in-crop) can be important tools for managing field bindweed infestations. In 2013 and 2014, we conducted two trials at the University of California, Davis, research farm to evaluate the efficacy of glyphosate (as Roundup Powermax), rimsulfuron (as Matrix), and carfentrazone (as Shark) for the control of vigorously growing field bindweed vines. All herbicides were applied post-emergence (POST) using a CO2-pressurized backpack sprayer equipped with three 8002VS flat-fan nozzles (TeeJet Technologies, Wheaton, IL) spaced 16-20 in apart and calibrated to deliver 30 GPA. Adjuvants were used according to label recommendations. Control of field bindweed was rated for 3-5 weeks after application. Figure 3. Bindweed control (%) in response to post-emergence applied herbicides (in field, 2013) for up to 35 days (or 5 weeks) after herbicide application (DAA). Herbicides: Roundup Powermax = glyphosate, Shark = carfentrazone, Matrix = rimsulfuron. Figure 4. Bindweed control (%) in response to post-emergence applied herbicides (in field, 2014) at application and at 1, 2, and 3 weeks after treatment (WAT). Herbicides: Roundup Powermax = glyphosate, Shark = carfentrazone, Matrix = rimsulfuron. Results presented in Figures 3 and 4 show that POST herbicide applications of rimsulfuron and carfentrazone were largely ineffective at controlling field bindweed. In the 2013 trial, Field bindweed control with rimsulfuron did not exceed 50%; in 2014, rimsulfuron was unable to provide more than 20% control at any observation date. Carfentrazone will burn down aboveground bindweed vines, giving the appearance of effective management, although regrowth can rapidly occur. In our research trials, carfentrazone controlled field bindweed 60 and 95% at 1 WAT; however, within 3-5 WAT, control fell to 5 and 40%. Glyphosate is slower to demonstrate activity, although its suppressive ability may persist for a longer period of time. A similar study was conducted in 2016 (Table 6), except that estimates of bindweed cover, plant vigor (on a scale of 1-5, where 1 = poor and 5 = excellent) and the percentage (%) of vines that were producing flowers were determined instead of control. The untreated check plots produced more cover (40-73%) at 1, 3, and 5 WAT as compared to the glyphosate (23-50%) and rimsulfuron (32-43%) treated plots; the higher rate of glyphosate was more effective at suppressing bindweed (25-25% cover) than the lower rate (23-50%). Bindweed vigor in the untreated plots ranged between 3 and 3.5 at every observation date. The vigor ratings for the glyphosate and rimsulfuron treatments ranged from 3-4 at 1 WAT to 1-2.2 at 5 WAT. Reductions in vigor were associated with chlorosis and necrosis of leaf and stem tissue. Field bindweed flowering was also affected by POST herbicide treatments. Few vines exhibited any flowers, regardless of treatment on 18 May. On 1 June and 14 June, 42 and 50% of the vines untreated check plots were flowering; conversely, less than 7% of the vines in the glyphosate or rimsulfuron treatments were flowering at the same observation periods. Percent (%) Cover Herbicide 18-May 1-Jun 14-Jun UTC 40.0 61.7 73.3 Roundup Powermax 1 qt/A 38.3 50.0 23.3 Roundup Powermax 2 qt/A 25.0 31.7 26.7 Matrix 2 oz/A 31.7 40.0 43.3 Plant Vigor (1=poor, 5=Excellent) Herbicide 18-May 1-Jun 14-Jun UTC 3.5 3.5 3.3 Roundup Powermax 1 qt/A 4.0 1.3 1.0 Roundup Powermax 2 qt/A 2.7 1.3 1.7 Matrix 2 oz/A 2.8 1.7 2.2 Percent (%) Flowering Herbicide 18-May 1-Jun 14-Jun UTC 2.0 41.7 50.0 Roundup Powermax 1 qt/A 2.0 5.0 0.0 Roundup Powermax 2 qt/A 0.0 6.0 1.3 Matrix 2 oz/A 3.7 4.3 6.7 Table 6. Field bindweed cover (percent (%) of plot area covered with vines), plant vigor, and percent (%) of vines with flowers in response to post-emergence (POST) herbicide applications at approximately 1 (18 May, 3 (1 June), and 5 (14 June) weeks after treatment (WAT). Roundup Powermax = glyphosate, Matrix = rimsulfuron, UTC = untreated. A comparable trial was also conducted in the greenhouse (Tables 7, 8, 9). Results show that bindweed injury, growth, and biomass accumulation were more affected by glyphosate than by rimsulfuron and carfentrazone. The injury observed in the rimsulfuron and carfentrazone treatments was more severe than what had been witnessed, previously in field trials. This is likely due to the fact that the field bindweed plants used in the greenhouse had been grown from exhumed rhizomes and did not possess the ample storage reserves that large, field-grown patches are expected to have. As has been described, previously, the highest rate of glyphosate was the most effective treatment for injuring field bindweed and suppressing plant growth. 11/6/2015 (0 Days after treatment) 12/3/2015 (28 Days after treatment) Herbicide Rate Number Vines > 4" Length (cm) Longest Vine Number Vines > 4" Length (cm) Longest Vine UTC na 5.5 a 42.5 a 19.0 a 51.6 a Roundup Pmax 1 qt/A 7.0 a 39.1 a 6.3 ab 24.9 ab Roundup Pmax 2 qt/A 6.3 a 38.9 a 1.3 c 13.6 b Matrix 2 oz/A 8.0 a 35.9 a 16.5 a 35.1 ab Shark 2 oz/A 8.3 a 40.5 a 1.5 bc 23.4 ab Significance NS NS P < 0.05 P < 0.05 Field bindweed plants were grown from 10 cm long, fall-exhumed rhizomes planted in 5 in pots. All plants were grown in the same heated greenhouse. Herbicide were applied at 30 GPA using a cabinet sprayer. Each treatment was replicated 4X. Table 7. Number of field bindweed vines greater than 4 inches in length and length in cm of the longest vines at the time of application and at 28 days after treatment (DAT) in response to post-emergence (POST) herbicide applications. Roundup Powermax = glyphosate, Shark = carfentrazone, Matrix = rimsulfuron, UTC = untreated. Percent (%) Injury Herbicide Rate 3 DAT 7 DAT 14 DAT 21 DAT 28 DAT UTC na 0.0 a 0.0 a 0.0 a 0.0 a 0.0 a Roundup Pmax 1 qt/A 2.5 a 6.3 bc 23.8 c 60.0 c 68.8 cd Roundup Pmax 2 qt/A 2.5 a 12.5 d 71.3 d 92.5 d 95.0 d Matrix 2 oz/A 1.3 a 4.0 cd 12.5 b 17.5 b 12.5 b Shark 2 oz/A 91.3 b 95.0 e 91.3 e 85.0 d 80.0 cd Significance P < 0.05 P < 0.05 P < 0.05 P < 0.05 P < 0.05 Field bindweed plants were grown from 10 cm long, fall-exhumed rhizomes planted in 5 in pots. All plants were grown in the same heated greenhouse. Herbicide were applied at 30 GPA using a cabinet sprayer. Each treatment was replicated 4X. Table 8. Field bindweed injury at 3, 7, 14, 21 and 28 days after treatment (DAT) in response to post-emergence (POST) herbicide applications. Roundup Powermax = glyphosate, Shark = carfentrazone, Matrix = rimsulfuron, UTC = untreated. 12/3/2015 (28 Days after treatment) Herbicide Rate Aboveground Biomass (g) Belowground Biomass (g) UTC na 17.7 a 21.4 a Roundup Pmax 1 qt/A 6.5 bc 6.5 abc Roundup Pmax 2 qt/A 1.5 d 3.0 c Matrix 2 oz/A 13.0 b 12.1 abc Shark 2 oz/A 2.1 bc 5.1 bc Significance P < 0.05 P < 0.05 Field bindweed plants were grown from 10 cm long, fall-exhumed rhizomes planted in 5 in pots. All plants were grown in the same heated greenhouse. Herbicide were applied at 30 GPA using a cabinet sprayer. Each treatment was replicated 4X. Table 9. Above and below ground field bindweed biomass at 28 days after treatment (DAT) in response to post-emergence (POST) herbicide applications. Roundup Powermax = glyphosate, Shark = carfentrazone, Matrix = rimsulfuron, UTC = untreated. It is important to recognize that the timing of herbicide applications can significantly affect weed control performance. For example, numerous growers, commercial applicators, and university personnel have reported that the performance of several herbicides (i.e. glyphosate, glufosinate, paraquat) may fluctuate with respect to application time of day (diurnally). Waltz et al. (2004), Mohr et al. (2007) and Sellers et al. (2003) reported that glyphosate and glufosinate are more injurious to weeds when applied early to mid-day, as opposed to in the evening. Conversely, the activity of some photosystem inhibitors, such as paraquat, may be improved at night because the herbicide can be translocated prior to light activation (Slade and Bell 1966). Possible factors influencing herbicide performance include: circadian changes in leaf angle that affect herbicide interception; differences in humidity and temperature that may affect herbicide absorption and translocation; the presence of dew, which can reduce herbicide retention; and physiological processes that may be affected by lack of sunlight. In 2016, we conducted trial to evaluate if the herbicidal efficacy glyphosate (as Roundup Powermax at 1 and 2 qt/A), rimsulfuron (as Matrix at 2 oz/A), carfentrazone (as Shark at 2 oz/A), and paraquat (as Gramoxone Inteon at 3 pt/A) varied with the time of day the herbicides were applied. Herbicides were applied to vigorously growing bindweed vines on 29 June, 2016, at five different times during the day: sunrise, 2 hours after sunrise, mid-day, 2 hours before sunset, and at sunset. All herbicides were applied using a CO2-pressurized backpack sprayer equipped with three 8002VS flat-fan nozzles (TeeJet Technologies, Wheaton, IL) spaced 16-20 in apart and calibrated to deliver 30 GPA. Field bindweed cover (percent of the plot area covered with field bindweed vines) was rated for 5 weeks after the treatments were applied (WAT). Percent (%) Field Bindweed Cover Herbicide and Rate 6/29/2016 7/7/2016 7/14/2016 7/21/2016 7/28/2016 8/4/2016 UTC 69 66 72 62 56 37 Matrix 2 oz/A 71 65 66 55 53 36 Roundup 1 qt/A 73 67 19 8 5 5 Roundup 2 qt/A 71 62 7 3 3 2 Shark 2 oz/A 70 4 9 21 37 45 Gramoxone Inteon 3 pt/A 66 4 10 21 42 42 P- Values Herbicide NS <0.0001 <0.0001 <0.0001 <0.0001 <0.0001 Timing NS NS NS NS NS NS Herbicide * Timing NS NS NS NS NS NS Herbicides applied at 30 GPA on 6/30/2016 Timings included: sunrise, 2 hrs after sunrise, midday, 2 hrs before sunset, sunset Bindweed cover decreased over time in the UTC, Matrix treatments due to powdery mildew Regrowth in the plots treated with contact herbicides was vigorous, largely mildew free Table 9. Field bindweed cover for up to 5 weeks after treatment (WAT), which occurred on 29 June, 2016, in response to post-emergence (POST) herbicide applications made at sunrise, 2 hours after sunrise, midday, 2 hours before sunset, and sunset. Herbicides: Roundup Powermax = glyphosate, Shark = carfentrazone, Matrix = rimsulfuron, Gramoxone Inteon = paraquat, UTC = untreated. Results from the diurnal timing trial (Table 9) indicated that herbicide, alone, had a significant effect on bindweed cover; the timing of herbicide applications and the interaction between herbicide and the time of day when the herbicides were applied were not significant. Although the carfentrazone and the paraquat treatments worked rapidly (field bindweed cover was reduced by more than 90% at 1 WAT), the vines regrew vigorously; cover at 5 WAT was 45 and 42%, which was similar to what was observed for the untreated check (37%). Glyphosate, while slower acting due to its systemic nature, was significantly better at reducing field bindweed cover at 5 WAT (2 and 5%) relative to the untreated check. In this trial, rimsulfuron did not reduce field bindweed cover, relative to the control, at any point in time. Bindweed cover in the untreated check and the rimsulfuron treatment decreased over time due to the development of powdery mildew, which resulted in leaf loss. Interestingly, the bindweed regrowth in the carfentrazone and paraquat treatments was robust and did not show signs of infection during the course of this study. USING FOLIAR MICRONUTRIENTS TO PREVENT OR MITIGATE INJURY IN PROCESSING TOMATOES RESULTING FROM GLYPHOSATE DRIFT: California is an agriculturally diverse state; in 2012, more than 400 commodities were produced on 80,500 farms and ranches. The estimated value of California agriculture totaled $42.6 billion, and accounted for 11.3% of the national total in farm cash receipts. With respect to specialty crops, the state produces nearly half of all US- grown fruits, nuts, and vegetables, which contribute a total $24 billion to the California agricultural economy. The diversity of crops within the state suggests that different commodities are likely to be grown in close proximity to each other, which can result in significant economic losses in the event of a spray misapplication and off-target herbicide drift. Glyphosate, which is the predominant herbicide in perennial crops, can cause significant injury to trees, vines, and annual crops and reduce yields under extreme circumstances. Our lab has worked, recently, to describe the interactions between glyphosate and divalent metals; more specifically, we have been evaluating using foliar micronutrient fertilization to prevent or correct glyphosate injury. Preliminary greenhouse projects were undertaken in 2014 to evaluate the use of a commercially available foliar fertilizer (Smart Trio® = 4% N, 3% S, 0.3% B, 3% Mn, 3% Zn) following glyphosate applications to mitigate herbicide injury in tomato transplants. Greenhouse-grown tomato (2-5 lf) were sprayed with 0.001x, 0.01x, 0.1x solutions of glyphosate (where 1x = 1 lb ae/A). Foliar nutrient treatments (no nutrient treatment, Smart Trio® at 1 qt/A, Smart Trio® at 2 qt/A) were applied at the first signs of injury on new leaf tissue (e.g. chlorosis) to simulate the observation and response a grower might make under field conditions. Plant biomass was harvested and weighed at 14 DAT. Results showed that tomato fresh weight did not differ among glyphosate rate treatments, with the exception of the highest rate (0.1x) and that the POST applications of foliar micronutrients were unable to mitigate herbicide injury (Table 10). Glyphosate Rate No Foliar Smart Trio 1 qt/A Smart Trio 2 qt/A Fresh Weight (g) at 14 DAT 0.001x 18.3 19.3 17.9 0.01x 15.0 18.3 15.9 0.1x 5.6 4.2 5.1 Fresh weight of untreated tomatoes (no micro, no glyph) = 17.9 g Table 10. Tomato fresh weight (g) at 14 days after treatment (DAT) with foliar micronutrients (Smart Trio®) following applications of 0.001x, 0.01x, 0.1x solutions of glyphosate (where 1x = 1 lb ae/A). Herbicide = Roundup Powermax = glyphosate. An additional set of greenhouse studies were conducted in 2015 to evaluate the use of foliar micronutrients (Zn and Mn), both preceding and following glyphosate applications, to mitigate herbicide injury in tomato transplants. Greenhouse-grown tomato (2-5 lf) seedlings were treated with 0.03X, 0.06X, 0.09X solutions of glyphosate (where 1x = 1 lb ae/A). Foliar micronutrient treatments (no nutrient treatment, 0.1% Mn in MnSO4*H2O form, 0.1% Zn in ZnSO4*7H2O, and 0.1% Mn plus 0.1% Zn) were applied either 1 day before (PRE) or 1 day after (POST) glyphosate applications. Plants were evaluated for 14 days after treatment (DAT) when all above-ground biomass was harvested and weighed. Untreated checks (no glyphosate and no foliar nutrients) were also included. Glyphosate Rate No Foliar Micros Mn PRE Zn PRE Mn+Zn PRE Mn POST Zn POST Mn+Zn POST Mean Fresh weight in grams (g) at 14 days after treatment (DAT) 0x 29 28 27 29 29 30 37 29 0.03x 17 21 25 22 17 20 20 20 0.06x 14 18 20 20 15 17 16 17 0.09x 12 13 16 16 12 12 9 13 Mean 18 20 22 22 18 20 19 Table 11. Tomato fresh weight (g) at 14 days after treatment (DAT) with foliar micronutrients (Mn, Zn, Mn+Zn) either preceding or following applications of 0.03x, 0.06x, 0.09x solutions of glyphosate (where 1x = 1 lb ae/A). Herbicide = Roundup Powermax = glyphosate. Tomato seedlings that were not treated with glyphosate nor micronutrients (0x glyphosate, no foliar micros) had an average mass of 29 g per plant; the application of foliar micronutrients in the absence of glyphosate did not affect plant biomass (Table 11). Results showed that tomato injury at 14 days after treatment increased with increasing glyphosate rate; symptoms included chlorosis, necrosis, leaf deformations, ‘witches brooming’, and shortened internodes. Although PRE applications of Zn and Mn+Zn increased tomato biomass across all herbicide treatments, the differences were not statistically significant. A field trial was also conducted to evaluate the potential of foliar micronutrient applications for preventing or mitigating glyphosate injury. It was decided that greenhouse based trials should also be replicated under field conditions so that crop injury could be observed for a greater period of time and that the effects of micronutrients and glyphosate on yield could be assessed. Field grown processing tomatoes were treated with 0.03X, 0.06X, 0.09X solutions of glyphosate (where 1x = 1 lb ae/A) at the onset of flowering. Foliar micronutrient treatments (no nutrient treatment, Smart Mn® (6% Mn) at 2 qt/A, Smart Zn® (6% Zn) at 2 qt/A, Smart Mn® at 1 qt/A + Smart Zn® at 1 qt/A) were applied either 1 day before (PRE) or 1 day after (POST) glyphosate applications. Plant injury was recorded at 7, 14 and 28 days after treatment (DAT); tomato fruits was harvested 90 days after transplanting. Results from statistical analyses indicated that glyphosate rate, alone, significantly affected crop injury and crop yield; micronutrient applications (both PRE and POST) and the interactions between glyphosate and micronutrient applications were non-significant (Table 12). Injury increased with increasing rates of glyphosate. Injury in the 0.03x (3%) treatment ranged from 3% at 7 DAT to 30% at 28 DAT. Injury in the 0.06x (6%) treatment ranged from 11-58% and injury in the 0.09x (9%) treatment ranged from 20- 64%. As glyphosate rate and crop injury increased, tomato fruit yield (both total and marketable) decreased. Plants that did not receive any glyphosate yielded 43 lbs of fruit/10 ft2, 32 lbs of which were red and marketable. In the 0.03x (3%) glyphosate treatment, total yield equaled 25 lbs of fruit/10 ft2, of which 6 lbs were red and marketable. The 0.06x (6%) and 0.09x (9%) treatments yielded 12 and 4 lbs of fruit/10 ft2, respectively, although only 2 and 1 lbs were red. Percent (%) injury Pounds (lbs) of fruit Glyphosate rate 7 DAT 14 DAT 28 DAT Red Green Total 0% 0 0 0 32 11 43 3% 3 26 30 6 19 25 6% 11 45 58 2 10 12 9% 20 54 64 1 3 4 Table 11. Tomato fresh weight (g) at 7, 14, and 28 days after treatment (DAT) and crop yields (lbs per 10 ft2) at 90 days after transplanting following applications of 0.03x (3%), 0.06x (6%), 0.09x (9%) solutions of glyphosate (where 1x = 1 lb ae/A). Herbicide = Roundup Powermax = glyphosate. PLANT GROWTH REGULATORS TO ENHANCE EARLY SEASON CROP ESTABLISHMENT AND IMPROVE CROP COMPETITIVENESS: One strategy for integrated weed control is to assure that crops remain competitive under stressful production conditions where weeds may be more likely to grow and develop more quickly than the crop. Therefore, optimizing the health and vigor of the crop should be an important component of any weed control program. The chemical 1-methylcyclopropene (1-MCP), which is traditionally sold under the SmartFreshSM Quality System, has been used, commercially, to enhance the transportability/storability of harvested fruits and vegetables. 1-methylcyclopropene is similar in structure to ethylene and binds to ethylene receptors in plants, thereby preventing ethylene perception. Ethylene, which is stress signal, has been associated with reduced plant growth under certain conditions (Kim et al. 2007; Kim et al. 2008). Preventing ethylene perception could prevent plants from detecting stress and reduce the impacts of undesirable environmental conditions on plant development. Results from field trials conducted at the UCD research farm in 2015 varied with respect to planting date. There were no differences in the growth and development of untreated tomatoes and tomatoes that were drenched with a 50 ppm solution of 1-MCP 24 hours prior to transplanting for the April and June planting dates. For the May planting date, 1-MCP was shown to have a negative effect on tomato vigor, stand evenness, and crop injury. Plants that were treated with 1-MCP exhibited 3, 34, and 31% injury at 1, 3, and 5 weeks after transplanting (WAT), respectively; injury for plants that were not treated with 1-MCP was 3, 25, and 23%. Plant vigor was rated on a scale that ranged from 1 (= poor) to 5 (= excellent); results showed that 1-MCP treated plants were less vigorous (3, 2.9, 3.4) than non-treated plants (2.7, 3.4, 3.6) at 1-5 WAT. Similar observations were recorded with respect to tomato stand evenness (1 = unevenly sized plants, 3 = plants all the same size); stands of 1-MCP treated tomatoes were less even (1.8, 2.3, 2.7) than the non-treated stands (1.9, 2.5, 2.9) at 1-5 WAT. Despite the observed dissimilarities with respect to injury, vigor, evenness between the 1-MCP and untreated tomato plants, there were no significant differences in yield at the end of the season. Trials were also conducted in 2016 to evaluate the effects of pre-transplant treatments of exogenous abscisic acid (ABA) (which has also been shown to affect drought stress responses in vegetable seedlings (Aeghara and Leskovar 2012; Berkowitz and Rabin 1988; Leskovar and Cantliffe 1992; Sharma et al. 2005)), on tomato growth, canopy development, and weed suppressive ability. Not unlike the 2015 trials, no differences were observed among any treatments, given the conditions of our study. CONCLUSIONS: California leads the nation in the production of processing tomatoes with respect to acres planted and harvested, yield per acre, total production, and total value of production. In 2012, California growers harvested approximately 300,000 acres of processing tomatoes, worth an estimated $1.4 billion, with Fresno, Kings, Merced, San Joaquin, Stanislaus and Yolo Counties each producing crops valued at more than $100 million. Historically, processing tomato production has been heavily dependent on pre- plant, inter-crop tillage operations and in-crop cultivation for weed control (Mitchell et al. 2012; Shrestha et al. 2007; Sutton et al. 2006). While effective at managing many weed species, especially during the seedling stage, frequent mechanical disturbances can negatively impact human and environmental health by increasing dust production and greenhouse gas emissions, and by reducing soil quality (Reicosky et al. 1997; Mitchell et al. 2012). Physical weed suppression programs are also accompanied by significant financial costs with respect to labor, fuel, and equipment purchase and maintenance (Mitchell et al. 2012). As a result, California tomato growers have been steadily transitioning towards minimum-tillage production systems. The adoption of reduced-tillage programs in processing tomatoes has been facilitated by growers’ decisions to switch from furrow- to sub-surface drip-irrigation (Mitchell et al. 2012). Because drip-irrigation limits soil surface wetting, the density and biomass of many small-seeded, annual, broadleaf weed species, such as common lambsquarters (Chenopodium album L.), pigweeds (Amaranthus spp.), and black nightshade (Solanum nigrum L.), can be significantly reduced relative to furrow irrigation systems (Shrestha et al. 2007; Sutton et al. 2006). Consequently, weed management practices for drip-systems may be less dependent on physical control measures, such as cultivation and hand-weeding (Mitchell et al. 2012; Shrestha et al. 2007; Sutton et al. 2006). The use of drip-irrigation and minimum-tillage can, however, create an environment where field bindweed (Convolvulus arvensis L.), a deep-rooted and drought tolerant perennial, can survive, grow, and compete with crops (Sharma and Singh 2007; Shrestha et al. 2007; Wiese and Lavake 1986). With respect to processing tomatoes, interspecific interference for up to eight weeks after transplanting (WAT) can significantly reduce fruit number and quality. Furthermore, field bindweed vines can become physically entwined in the crop canopies, which, in turn, can reduce crop harvest efficiency (Mitich 1991). The purpose of our research over the last four years has been to evaluate currently recommended and novel weed control practices for the suppression of field bindweed. Our results have shown: Trifluralin, rimsulfuron (PRE), and sulfentrazone have some activity (for up to 4 to seeks weeks) against perennial bindweed vines, while S-metolachlor does not. Trifluralin, rimsulfuron, and S-metolachlor are labeled for use in tomatoes for the residual control of emerging weeds (http://ipm.ucanr.edu/PMG/r783700311.html). Sulfentrazone has/has had a supplemental label (as Zeus) allowing for its use in transplanted tomatoes for the suppression of yellow nutsedge. Always check current manufacturers labels to ensure that an effective and legal application is being made before applying any herbicides. In addition to perennial bindweed suppression, rimsulfuron is effective against most nightshade species, pigweeds, and lambsquarters; trifluralin will control many annual grasses as well as some broadleaf weed species. S-metolachlor, while not effective against bindweed, can suppress nutsedge and nightshades. Most soil-applied herbicides require 0.5 to 1 inch of precipitation or irrigation for activation. Furthermore, water needs to be distributed evenly to ensure adequate coverage and maximum control. Although drip-irrigation can reduce labor costs, prevent some disease development, improve water use efficiency, and reduce surface wetting, which reduces weed seed germination, it may not be effective at activating many residual herbicides. Growers with significant field bindweed problems should be mindful of how their irrigation protocols may affect herbicide performance. Results from trials not discussed in this report have shown that the activities of rimsulfuron and sulfentrazone against field bindweed were significantly improved when sprinkler irrigation was used for herbicide activation. The application of trifluralin, sub-surface, using a spray blade is currently labeled for bindweed control in grapes in California. According to the Treflan HFP label: “Treflan HFP can be applied using a specially equipped spray blade for the control of field bindweed in grapes and in plantings of almond, apricot, grapefruit, lemon, nectarine, orange, peach, pecan, tangelo, tangerine, and walnut trees. Destroy existing weeds with soil tillage before applying Treflan HFP to prevent interference with operation of the spray blade. Application requires a spray blade capable of operation at 4 to 6 inches below the soil surface. Equip the blade with nozzles located under the blade and directed so as to allow spray to be trapped in a thin layer as the blade is pulled through the soil. Use a nozzle spacing sufficient to insure application of a uniform horizontal layer. Apply Treflan HFP in 40 to 80 gallons of water per acre. Operate blade at a depth of 4 to 6 inches. Some soils may develop cracks as they dry after rainfall or irrigation. Field bindweed may emerge if the cracks extend through the layer of Treflan HFP. Prevent or eliminate cracks by shallow discing or other tillage. Avoid deep tillage which disturbs the subsurface layer. Cultivation or tillage also aids the control of germinating seeds.” Although SSL applications of trifluralin appear to be effective at suppression bindweed in processing tomatoes, this strategy is not labeled for use in this crop. Crop injury is a significant concern when transplant root-balls are not positioned below the treated zones (as was observed in 2015). Carryover effects on rotation crops were not investigated in these trials and is a significant concern. Rimsulfuron is labeled for POST use in processing tomatoes (http://ipm.ucanr.edu/PMG/r783700311.html) although its efficacy as a POST herbicide, with respect to field bindweed control, is poor. As was discussed, previously, rimsulfuron applied PRE may suppress the emergence of field bindweed vines. Paraquat and carfentrazone are both labeled for the control of emerged weeds prior to transplanting, although perennial weed control will not be long lasting as both products are contact herbicides and will do little more than burn off any above ground foliage. Glyphosate, was the most effective POST product for suppressing field bindweed growth across all trials; it is labeled for use as a pre-plant burn down. One of the tenets of integrated pest management is to start off clean and remain clean to prevent weed interference in the current and future crops. Growers with significant bindweed problems should strive to ensure effective burn down of existing vines prior to crop planting and following harvest; the management of bindweed in rotation crops and following rotation crop harvest is also encouraged. Glyphosate has a high affinity for and can form complexes with divalent cations minerals including Mn and Zn. These complexes can, in turn, affect the herbicidal efficacy of glyphosate; as a consequence, they could also provide a mechanism for reducing crop injury due to glyphosate. Results from both greenhouse and field studies indicate that the applications of Mn and Zn salt solutions to tomato foliage before or after glyphosate exposure were insufficient to protect processing tomatoes from damage. Our studies, for the most part, looked at relatively high rates of glyphosate (1% to 10% of 1 lb ae/A rate); it is possible that foliar micronutrient applications could reduce symptom development from lower glyphosate doses. Ethylene, which is stress signal, has been associated with reduced plant growth under certain conditions, such as drought. Preventing ethylene perception prevents the plant from detecting stress and could reduce the impacts of undesirable environmental conditions on plant development.1-methylcyclopropene is similar in structure to ethylene and binds to ethylene receptors in plants, thereby preventing ethylene perception. While 1-MCP has been primarily used, post-harvest, to inhibit fruit ripening, there are some efforts to evaluate its use, at crop transplanting, to reduce transplant shock. Exogenous abscisic acid (ABA) has also been shown to affect drought stress responses and has been studied as a tool to reduce early season plant stress. Results from our trials did not detect an advantage of using plant growth regulators at or near the time of transplanting to improve tomato vigor and stand development (which could increase crop competitiveness with weeds). More work will be needed to investigate these tools under more growing conditions and using a wider range of treatment doses to more fully explore these opportunities. ACKNOWLEDGEMENTS: We would like to thank Tom Lanini for his early guidance with these projects. We would also like to thank the Jim Jackson and Garry Pearson and all of the University field staff who helped with the establishment and maintenance of these trials. We extend our gratitude to the members of Brad Hanson’s laboratory group for all of their assistance. LITERATURE CITED: Agehara, S. and DI Leskovar. 2012. Journal of the American Society for Horticultural Science 137:400-410. Berkowitz, GA and J Rabin. 1988. Plant Physiology 86:329-331. Carlson, HL et al. 1980. California Agriculture. De Gennaro, FP and SC Weller. 1984. Weed Science 32:525-528. Dall'Armellina, AA and RL Zimdahl. 1989. Weed Sci 37: 314-318. Derscheid, LA et al.1970. Weed Science 18:590-596 Frazier, JC. 1943a. Plant Physiology 18:167-184. Frazier, JC. 1943b. Plant Physiology 18:315-323. Frazier, JC. 1943c. Botanical Gazette 104:417-425. Kim, HJ et al. 2007. Postharvest Biology and Technology 45:66-72. Kim, HJ et al. 2008. Plant, Cell and Environment 31:1744-1755. Lanini, WT, and EM Miyao. 1989. Proceedings of the Western Society of Weed Science. Leskovar, DI and DJ Cantliffe. 1992. Journal of the American Society for Horticultural Science 117:389-393. Mitchell JP, et al. 2012. Hort Technology 22:617-626 Mitich, LW.1991.Weed Technology 5:913–915 Mohr, K et al. 2007. Weed Technology 21:7-13. Reicosky, DC et al. 1997. Soil and Tillage Research 41:105-118. Sellers, BA et al. 2003. Weed Technology 17:302-306. Sharma, N et al. 2005. Journal of Vegetable Science 11:41-56. Sharma, SD and M Singh. 2007. Hort Science 42:1221-1226. Shrestha, A et al. 2007. Journal of Sustainable Agriculture 31:91-112. Slade, P and EG Bell. 1966. Weed Research 6:267-274. Sutton, et al. 2006. Weed Technology 20:831-838. Swan, DG.1980. Washington State University, College of Agriculture Research Center Bulletin 0888. Swan, DG and RJChancellor. 1976. Weed Science 24:306–308. Waltz, AL et al. 2004. Weed Technology 18:931-939 Weaver, SE and WR Riley. 1982. Canadian Journal of Plant Science 62:461–472. Wiese, AF and DE Lavake. 1986. Weed Science 34:77-80. Yerkes, D and SC Weller. 1996. Weed Technology 10:565–569. Project Title: Automatic Vision Guided Weed Control System for Processing Tomatoes Principle Investigator: David C. Slaughter, Professor Biological & Agricultural Engineering Department University of California Davis, CA 95616 (530) 752-5553 [email protected] Cooperators: Steven Fennimore, Ken Giles, Thuy Nguyen, Burt Vannucci, Leland Neilson, Ryan Billing, and Vivian Vuong, University of California, Davis Abstract/Summary: The overall goal of this project is to develop a novel computer-vision guided technique for automating weed control in processing tomato. The specific objective of this project for 2016 was to integrate the computer vision portion of the automated machine developed in 2015 with the robotic within-row mechanical weed hoes. The complete system for Automated vision guided weed control shown in figure 1. Figure 1. Postdoctoral scholar, Thuy Nguyen monitoring the automated vision guided within- row weed control system in operation on the UC Davis campus farm in 2016. Our team at UC Davis has developed a novel technology for creating a unique crop marking system that allows a computer vision system to readily distinguish tomato plants from weeds, including nightshade. This new technology does not involve use of biotechnology or transgenes. Experimental trials conducted on the UC Davis campus farm show that the new method for high- speed machine vision recognition of transplanted processing tomato plants when grown outdoors in the field, has a success rate approaching 100%. Background: Previous methods of using computer vision for automated weed control in row crops are typically based upon simplistic approaches, such as defining the crop as the largest green object in the view or by defining weeds to be the plants that are not growing in a grid-like plant spacing. While these methods can sometimes be successful under ideal conditions, they are not reliable when used on a real farm. The novel approach developed at UC Davis uses a much more robust approach at automatic computer recognition of the crop through the implementation of a technique called crop signaling. In crop signaling, a unique, visual crop identifier is applied to the crop at planting, since at this time there is no ambiguity in identifying the crop plants. The signal persists until the time when hand weeding is required. In this case, the computer vision system is then designed to search for the plants possessing the unique crop signal and identifies them as tomato plants. Results: The plant label implementation of the crop signaling technology was evaluated in a weed control trial conducted on the UC Davis campus farm, Fig. 2. The photo in Fig. 2 shows the tomato Figure 2. Crop signaling trial on the UC Davis campus farm in 2016 using a biodegradable plant label on each tomato plant. plants and the green biodegradable plant labels just after transplanting. A photograph of the same field trial, 4-weeks later (but in a section of the field with pink biodegradable plant labels) when weeds were present in the row, is shown in Fig. 3. Figure 3. Crop signaling trial showing the biodegradable plant label on each tomato plant and weeds growing in the row at 4-weeks after transplanting. The mean weed load between tomato plants in central 8-inch uncultivated region along the row centerline the field trial was 5.3 weeds per square foot, on average, with a standard deviation of 1.1 weeds per square foot. The robotic computer vision system for detecting the crop signaling compound was 100% effective at detecting the tomato plants (all tomato plants were correctly detected) in the field trial. The automatic robotic miniature hoes were also very effective at killing weeds between tomato plants in the row, with 83.3% of weeds automatically removed on average. The average time for a person to remove weeds manually using a hoe was 15.9 minutes per 200 feet of row. When the robotic weed control system was used in the trial before manual weeding, the average time for a person to remove weeds manually when following the robot was 3.6 minutes per 200 feet of row. This represents a 77% reduction, on average, in the labor required for manual hoeing to remove weeds in the row center between tomato plants. Conclusions: The results show that the plant label implementation of the crop signaling technology was an effective method of allowing a computer vision guided robot to automatically detect processing tomato plants 4 weeks after transplanting. A prototype robotic weed control machine was developed using this technology and successfully tested in 2016. The robot successfully removed 83% of weeds in the trial automatically, reducing the manual labor requirements for weed control by 77%. Acknowledgements: The authors would like to express their gratitude to CTRI for the research funding provided.