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Manitoba Agriculture, Food and Rural Initiatives

PROJECT RESULTS

 

Dandelion Biology and Control in Conventional and Zero-Tillage Roundup Ready Canola

 

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Applicant: 

Dr. Rene Van Acker
Department of Plant Science
University of Manitoba
Winnipeg, Manitoba  R3T 2N2  Canada

 

Table of Contents:

 

ARDI Project:

 

#99-304

Total Approved: $30,800
Date Approved: February 8, 2000

Project Status:

Completed April, 2002

 

Background and Objective:

In Western Canada, dandelion (Taraxacum officinale Weber in Wiggers) has long been considered an important weed. Until recently, however, dandelion has not been considered a significant pest within annual cropping systems. Surveys of western Canadian fields conducted in 1986-89 and 1995-97 show an alarming increase in dandelion frequency in Western Canada. In 1986-89, dandelion was present in 6 percent of wheat (Triticum aestivum L.) fields and 13 percent of canola (Brassica napus L.) fields in the Prairie Provinces. In 1995-97, however, dandelion presence increased to 20 percent of wheat fields and 23 percent of canola fields. Based on a relative abundance index, there was an increase in rank of 17 in wheat fields and an increase in rank of 7 in canola fields. In Manitoba alone, dandelion has not increased in rank, whereas in Alberta and Saskatchewan dandelion has increased in rank by 3 and 9, respectively.

The majority of the interest paid to dandelion by researchers in the past has been related to its ability to infest forage crops and urban lawns. Perennial weeds such as Canada Thistle (Cirsium arvense (L.) Scop.), Quackgrass (Elytrigia repens (L.) Nevski.), and Perennial Sowthistle (Sonchus arvensis L.) have garnered considerable research attention in Western Canada in recent years. A considerable amount of information has been gathered regarding their competitive ability, and management techniques have been devised. This research groundwork has not yet been laid for dandelion in field crop scenarios. We do not understand how to properly quantify a dandelion infestation. It is expected that dandelion interference has the ability to reduce crop productivity, yet to what extent? Efficient methods of cultural and chemical control of dandelion populations have not yet been devised. This research aims to develop a foundation from which these concerns might be addressed.

The overall objective of this project was to improve the understanding of dandelion’s role as a crop pest in Manitoba. The project was divided into two components: 1) Distribution and Interference, and 2) Control.

The studies on distribution and interference were designed to answer questions relating to dandelion’s distribution throughout fields, its ability to compete with crops, and its propensity to reduce productivity on a whole-field basis. Our objectives were to determine: 1) the effect of dandelion interference on canola yield, 2) an effective measure of dandelion infestation, 3) an estimate of canola yield loss from dandelion on a whole-field basis, and 4) the patchiness of dandelion in minimum and conventional-tillage fields.

Dandelion control studies were designed to address the questions of how tillage and glyphosate control dandelion populations.

Procedure and Project Activities:

Distribution and Interference Studies

The distribution of dandelion was mapped in six fields across southern Manitoba, which were seeded to canola in 2000. Four of the fields were conventional-tillage fields while two were minimum-tillage fields. Minimum-tillage fields included no more than one harrow pass per year (excluding seeding as a tillage operation) within the past three years. Conventional-tillage fields had at least one year of tillage within the past three years, which included a tillage tool providing more intensive tillage action than a harrow, as well as one spring tillage pass with a tillage tool providing more intensive tillage than a harrow prior to seeding. Within these six fields, fixed quadrats that contained dandelion were marked and within these quadrats all other weeds were controlled by hand weeding. Quadrats were covered with plastic sheeting during in-crop herbicide applications. Quadrat areas were chosen in each field that represented a range of dandelion infestation levels. In each quadrat, the measurements made of canola and dandelion included: 1) dandelion ground cover in-crop, 2) canola ground cover in-crop, 3) dandelion density in-crop, 4) canola density in-crop, 5) total dandelion leaf diameter in-crop, 6) dandelion leaf area at crop harvest, 7) total dandelion dry biomass at crop harvest, 8) dandelion density at crop harvest, 9) total accumulated dandelion root diameter at crop harvest, and 10) canola yield in weed-free and in dandelion infested quadrats. Correlation analysis was conducted between measures of dandelion infestation and canola yield loss. Yield loss models were created for measures of dandelion infestation that were strongly correlated to canola yield loss.

Dandelion Control in Roundup Ready Canola

Trials were conducted at Carman and Oakville, Manitoba in 1999 and 2000. All sites had established dandelion infestations. Dandelion density was determined prior to applying glyphosate or tillage treatments. Plots were arranged in a randomized split block design with three replicates, with spring pre-seeding tillage (or no tillage) performed across a replicate. This design facilitated the operation of mechanized tillage equipment. Individual plot size was 2 m by 4 m at all sites. Treatments are outlined in Table 2 and included a range of Roundup Transorb rates applied at various crop stages, and in split and single application timings. All treatments were performed both on plots that were either tilled prior to seeding (high disturbance) or direct-seeded (low-disturbance). Shoot biomass of dandelion was measured in the spring of the year following application of the treatments, and was expressed as a percentage of untreated control for each site and tillage treatment prior to statistical analysis.

Results and Discussion:

Dandelion Distribution and Interference

In both minimum- and conventional-tillage fields, dandelion can be a widespread problem with the ability to significantly reduce spring canola (Brassica napus L.) yield. Dandelion distribution was not generally associated with tillage regime. Dandelion could be broadly distributed across either minimum or conventional-tillage fields. Dandelion distribution showed some relationship to past cropping history, especially the recent presence of alfalfa in rotation.

The strength of correlationship between measures of dandelion infestation level and canola yield loss was associated with tillage regime. For conventional-tillage fields, there was no correlation between reduction in canola yield and any measurements of dandelion infestation level. For minimum-tillage fields, the most reliable measures of dandelion interference level were dandelion ground cover, total root diameter, total leaf diameter, and relative ground cover. Dandelion can cause great yield loss in canola. A dandelion infestation providing 50% ground cover caused between 39% and 64% canola yield loss (Figure 1). On a whole-field basis, dandelion has the potential to dramatically reduce canola yield. For example, at one of the field sites, even though dandelion was present in only 18% of quadrats, if dandelion cover averaged only 50% where dandelion was present, yield would be reduced by 7% for the entire field. Producers can scout fields to determine whether dandelion affects small areas of a field or the entire field. With the difficulty in predicting level of competitiveness of dandelion infestations, producers must default to the position that if it is present to a noticeable extent in canola fields, control is warranted.

Dandelion Control in Roundup Ready Canola

Spring tillage prior to seeding had a significant impact on dandelion populations (Table 1). In the 1999 trials, dandelion biomass was reduced by more than 80% with spring tillage alone. The high intensity of these tillage operations using a rotovator was, however, expected to provide significant control. Tillage in spring was quite effective because, at this time, dandelion roots have moved a large portion of their reserves aboveground, leaving the root in a weakened state. It was expected that dandelion control due to tillage would not be as great in the 2000 trials as only one pass was made with the cultivator in high disturbance plots. This hypothesis was correct as the three trials in 2000 exhibited only a 40% reduction in dandelion biomass. Cultivation in 1999 was also to a slightly greater depth than in 2000. In most conventional-tillage systems, farmers use seeding systems that are designed to cut off all aboveground plant biomass. The more intense the tillage action in the seeding system, the greater the dandelion control will be.

Table 1. Influence of tillage (soil disturbance) on dandelion shoot dry matter (standard errors in parentheses) in the untreated control plots plots which did not receive a herbicide treatment) as assessed the following spring (i.e. the year following treatment). 

 

Location

Year

Dandelion shoot dry matter

Low disturbance (g/m2)

High disturbance (g/m2)

LSD (0.10)

Carman

1999

83.6 (25.4)

13.4 (6.3)

55.9

Oakville

1999

52.0 (21.1)

11.6 (5.2)

46.3

Carman (early seeded)

2000

114.2 (19.4)

93.3 (3.9)

42.2

Carman (late seeded)

2000

277.4 (15.7)

129.6 (6.2)

36.0

Oakville

2000

120.3 (22.9)

57.3 (17.3)

61.3

Overall

 

129.5 (22.3)

61.0 (12.7)

43.7

 

If multiple applications of glyphosate fit with a producer’s production system, residual dandelion populations can be reduced to near-negligible levels (Table 2). Two 450 g a.i. ha-1 (1/2 L Roundup Transorb) applications of glyphosate in-crop can provide significant control of dandelion in both low and high disturbance systems. The greatest control of dandelion was found when 900 g a.i. ha-1 (1 L of Roundup Transorb) application was made post-harvest, following an application or applications of glyphosate earlier in the year. Weakening the dandelion plants earlier in the growing season with glyphosate appears to make them more susceptible to post-harvest applications of 900 g a.i. ha-1. If producers can justify several passes across a field, sequential applications of glyphosate might be the preferred method for reducing dandelion populations to negligible levels.

Table 2. Influence of glyphosate dosage and time of application on dandelion shoot dry matter (standard errors in parentheses) as assessed in the spring following the year of treatment).  A glyphosate resistant canola crop was grown in all plots in the year of treatment.

Treatment no.

Glyphosate dosage (g/ha)

Application timing (stageb)

Dandelion shoot dry mattera (% of controls)

Canola yield

Low disturbance (g/m2)

High disturbance (g/m2)

1 - - 100 (7.6) 52 (16.0) 139 (17.7)
2 900 pre-plant 40 (6.0) 158 (12.6) 178 (16.4)
3 1350 pre-plant 36 (7.4) 159 (14.0) 165 (14.2)
4 1800 pre-plant 38 (7.5) 157 (13.9) 151 (12.0)
5 450 0-3 leaves 68 (9.5) 154 (21.5) 161 (12.5)
6 900 0-3 leaves 61 (8.6) 142 (15.8) 161 (20.1)
7 450+ 0-3 leaves      
  450 4-6 leaves 43 (5.2) 124 (17.4) 174 (15.6)
8 900 maturity 47 (8.4) N/Ad (N/A)  
9 1800 maturity 25 (5.0) N/A (N/A)  
10 900 post-harvest 4 (1.4) N/A (N/A)  
11 1800 post-harvest 6 (3.3) N/A (N/A)  
12 2700 post-harvest 2 (1.1) N/A (N/A)  
13 900+ pre-plant      
  900 post-harvest 1 (0.4) 172 (15.0) 142 (12.8)
14 450+ 0-3 leaves      
14 450+ 4-6 leaves      
  900 post-harvest 1 (0.3) 123 (19.4) 161 (13.8)
LSD (0.05     14.7 28.6 (28.0)  

a based on results of analysis of variance, dandelion shoot dry matter (expressed as % of untreated control) was pooled over sites and tillage intensities and canola yield was pooled only over sites.

b canola crop stage.  This time of application is commercially referred to as 'pre-harvest'.

N/A - Not applicable.  Those treatments that were applied at canola crop maturity or post-harvest were not expected to influence canola yield.

 

In both low and high disturbance plots, post-harvest applications of glyphosate (900 to 2700 g a.i. ha-1) provided significant control, yet control was not improved by increasing glyphosate rate. The fact that control was excellent and high rates were not necessary provides good evidence that post-harvest applications of glyphosate provided the greatest efficacy. If a simple herbicide system is desired to control dandelion post-harvest application of glyphosate at a rate of 900 g a.i. ha-1 in either direct seeded or conventional-tillage systems would suit. It is possible that 900 g a.i. ha-1 is a more than adequate rate and a lesser rate could be used, but this was not tested. The value of even lower rates is limited, however, because glyphosate is relatively inexpensive and the cost savings from reduced rates might not balance the increased risk of weed escapes. Producers may also want to intensify their tillage operations to improve effectiveness in cutting off all aboveground dandelion biomass, possibly at greater depths in the soil profile.

When residual dandelion populations were compared among treatments receiving single applications of 900 g a.i. ha-1 of glyphosate, it became clear that post-harvest applications provide the greatest control.

Conclusion:

Dandelion is becoming a problem weed in western Canadian field cropping systems. Its distribution in fields is not necessarily related to the level of tillage practiced within that field, but more likely related to the cropping history in that field and the presence of perennial crops in rotation in which dandelion remained uncontrolled. It is difficult to predict the impact of dandelion infestation on canola yield. Measures of dandelion relating to canola yield loss were more reliable in fields receiving less tillage. Dandelion can, however, cause high levels of yield loss, and even a moderate and patchy infestation of dandelion in a canola field could warrant control. Control with glyphosate is best achieved at rates of 900 g a.i. ha-1 (I L Roundup Transorb/acre) applied post-harvest in either direct-seeded or conventional-tillage systems.

Acknowledgements:

The authors acknowledge the financial support of Canada-Manitoba Agri-Food Research and Development Initiative (ARDI), the Natural Sciences and Engineering Research Council, and generous financial support and staff help from Monsanto Canada.

 

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