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118
Vol. 53, 2017, No. 2: 118–125 Plant Protect. Sci.
doi: 10.17221/57/2016-PPS
Sugar Beet Yield Loss Predicted by Relative Weed Cover,
Weed Biomass and Weed Density
R GERHARDS*, K BEZHIN and H-J SANTEL
Department of Weed Science, Institute of Phytomedicine,
University of Hohenheim, Stuttgart, Germany
*Corresponding author: roland.gerhards@uni-hohenheim.de
Abstract
Gerhards R., Bezhin K., Santel H.-J. (2017): Sugar beet yield loss predicted by relative weed cover, weed biomass
and weed density. Plant Protect. Sci., 53: 118–125.
Sugar beet yield loss was predicted from early observations of weed density, relative weed cover, and weed biomass
using non-linear regression models. Six field experiments were conducted in Germany and in the Russian Federation
in 2012, 2013 and 2014. Average weed densities varied from 20 to 131 with typical weed species compositions for
sugar beet fields at both locations. Sugar beet yielded higher in Germany and relative yield losses were lower than in
Russia. Data of weed density, relative weed cover, weed biomass and relative yield loss fitted well to the non-linear
regression models. Competitive weed species such as Chenopodium album and Amaranthus retroflexus caused more
than 80% yield loss. Relative weed cover regression models provided more accurate predictions of sugar beet yield
losses than weed biomass and weed density.
Keywords: crop-weed interaction; weed competition; yield loss function
Weed management plays an important role in
sugar beet production. Wide row spacing and slow
development in early growth stages result in late
canopy closure. Up to 100% of the crop yield may be
lost because of weed competition if weed control is
poor or is not performed at all (K & S
1991). Effective weed control is needed mainly during
the critical period of sugar beet development, which
is approximately the period during the first 60 days
after emergence. Then, sugar beet does not tolerate
co-existence with weeds without losing yield (M
& W 2006; J & S 2013). Weeds
need to be removed until the 8-leaf stage of sugar
beet. Emerging weeds after the 8-leaf stage did not
cause any significant sugar beet yield losses (J
et al. 2008). In European sugar beet production,
Chenopodium album L., Amaranthus retroflexus
L., Galium aparine L., Matricaria chamomilla L.,
M.inodora L., Stellaria media (L.) Vill., and Po-
lygonum convolvulus L. are the most abundant weed
species (P 2008).
Multiple (3–4) applications of selective herbicides
are the common practice in European sugar beet weed
control programs. Herbicides are sprayed after every
weed emergence wave to keep the crop weed-free.
Alternatively, post-emergent inter-row hoeing in
combination with herbicide band applications within
the row have successfully been practiced to control
weeds in sugar beet (K et al. 2015).
Precise estimations of sugar beet yield loss due
to weed competition are needed for decisions on
integrated weed management strategies. Usually,
empirical models are used to estimate the crop yield
loss by weed competition from early observations
of weed density (C 1985) and relative weed
cover (K & S 1991; L et al. 1996).
Models fitted better for relative weed cover than for
weed density (A et al. 2013), because relative weed
cover accounts for the size of the crop and weeds and
relative time of emergence (C et al. 1987).
However, tall and upright growing weed species such
as Echninochloa crus-galli L. and C. album were still
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underestimated in their competitive effect on crops
when relative weed cover was measured. Therefore,
M and H (2004) suggested relating
weed biomass to crop yield loss. Still, the problem
remains comparing competitive effects of mixed
weed populations. B and Z (1994) used
a density equivalent to determine the competitive
effects of each weed species.
Estimated crop yield losses may vary consider-
ably between different sugar beet production areas
and years due to different climatic conditions, soil
types, weed populations, productivity, and crop-
ping practices. Results of the relation between sugar
beet yield loss and weed competition have so far
been derived from field studies in Western Europe
(K & S 1991; L et al. 1996) and in
the USA (N et al. 2013). However, sugar
beet yields and summer precipitation have been re-
ported to be lower in the sugar beet production area
in the Russian Federation on Chernozem soils than
in Western Europe and in the USA (V
& I 2013). Therefore, predicted yield losses
may vary between the experimental sites in Germany
and in the Russian Federation.
The objective of this study was to predict sugar
beet yield losses from early observations of weed
density, relative weed cover and weed biomass us-
ing non-linear regression models. The following
hypotheses have been tested:
–Early assessments of weed biomass result in more
accurate predictions of sugar beet yield loss than
those of weed density and relative weed cover.
–Relative yield losses of sugar beet are higher in the
Russian experiments than at the German site due
to weed competition and the competitive ability
of weed species.
MATERIAL AND METHODS
Experimental sites. Six field studies were con-
ducted in typical sugar beet growing areas of Germany
and the Russian Federation. Three field experiments
were conducted at the experimental station of the
University of Hohenheim – Ihinger Hof (IHO), Baden
Württemberg, Germany (48°74'03''N, 8°91'56''E) in
2012, 2013 and 2014. The soil at IHO is classified
as Haplic Luvisol, the soil type is a silty clay loam
with high fertility and good water retention capacity.
Three experiments were carried out in the Rus-
sian Federation, two in 2013 and one in 2014 at an
experimental station located at Doktorovo (DOK)
in the Lipetsk region (52°78'47''N, 39°02'72''E). The
soil at the Russian locations is a typical Voronic
Chernozem with high content of organic matter and
high biological activity.
Environmental conditions and cropping prac-
tices. The climate at IHO is temperate cool with
average yearly temperatures of 9.2°C in 2012, 8.7°C
in 2013, and 10.4°C in 2014. The cumulative annual
precipitation was favourable for sugar beet growth
with 727mm in 2012, 923 mm in 2013, and 763 mm
in 2014 except for two short periods of drought in
spring in 2012 and 2014. The sites in the Russian
Federation at DOC are characterised by a temperate
continental climate with average yearly temperatures
of 7.0°C in 2013 and 6.6°C in 2014 and annual pre-
cipitation totals of 462 mm in 2013 and 340 mm in
2014. All three summer periods were hot and dry.
Experimental design. The trials were arranged
as completely randomized block design with four
replications. All experimental plots were 8 m long
and 3 m wide with a row distance of 0.5 m. Sugar
beets were sown at a density of 110 000 seeds/ha af-
ter strip tillage in April (IHO) and early May (DOK).
The previous crop was winter wheat at all locations.
The experimental design includes four treatments.
Treatment 1 is an untreated control. Treatment 2
was kept weed-free by continuous hand-weeding. In
treatment 3 and 4, weed infestation was manipulated
to achieve a wide range of infestation levels over the
experiment. This facilitates modelling the relation-
ship between weed competition and yield loss. At
IHO, a relatively low weed pressure of approximately
20–40weeds/m2 was expected in the untreated control
plots. Therefore, 400 and 800seeds/m2 of C. album
were sown in treatments 3 and 4 to increase weed
density by approximately 50 and 100%. At DOK, a
higher natural weed infestation of 100–150 weeds/m2
was expected. Therefore, 35 and 70% of the emerged
plants of C. album and A. retroflexus were removed
by hand in treatments 3 and 4 to establish targeted
weed densities.
Data collection and analysis. The number of
emerged sugar beets (n/ha) was counted at the
BBCH12 (H et al. 1997) development stage of the
crop and averaged over all plots in all experiments.
All weed infestation measurements were carried
out at the BBCH 18 growth stage of the crop. Weed
density per species was counted within a 1 m2 frame
in the centre of each plot. Relative weed cover was
calculated by digital image analysis. RGB images of
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1 m2 were taken in the sampling areas where weeds
were counted before. The images were processed
with the computer program ImageJ Version 1.47a.
Green colour of weeds and sugar beet was separated
from soil using the Colour Threshold procedure. To
distinguish between crop and weed, an interactive
graphic editing program was utilised to eliminate
the leaf area of weeds and only to display the crop
leaf area. Crop and weed biomass was also meas-
ured in the same sampling area where weeds were
counted before. Entire crop and weed plants were
dug out, washed and underground and aboveground
plant parts were collected separately and dried in a
hot-air oven at 80°C for 72 h until the weight was
constant. Dry weight was recorded. In autumn, sugar
beets were harvested manually in an area of 2.5 m2
per plot. Fresh mass of sugar beets was recorded.
For the analysis of extractable sugar content, beets
were washed and processed to measure their sugar
content. At DOK, a portable refractometer was used
to determine the %Brix value of the sugar juice and
at IHO, the laboratory polarimetric method was
applied. Both methods correspond to the ICUMSA
standards (ICUMSA 2013) and provided equal results.
Statistical analysis. e relation of weed density and
weed biomass to the relative yield loss of sugar beet
was estimated by fitting both parameters into the non-
linear regression model proposed by C (1985):
YL = q × d (1)
1 + q × d/a
where: YL – relative yield loss; d – weed density or weed bio-
mass; q – yield loss per unit of weed parameter; a – maxi-
mum yield loss
The effect of weed cover on the relative yield loss
of sugar beet was estimated by fitting the same two
parameters into a non-linear regression model pro-
posed by K and S (1991):
YL = q × LW (2)
1 +
[(
q
)
– 1
]
× LW
a
where: YL – relative yield loss; LW – relative weed cover;
q – yield loss per unit of weed parameter; a – maximum
incurred yield loss
The relative yield loss was calculated using the
following function:
YL = Ywf – Yw (3)
Ywf
where: YL – relative yield loss; Ywf – weed-free yield;
Yw–yield in weedy plots
The relative weed cover was calculated according
to the equation:
LW = Lweed (4)
Lweed + Lcrop
where: LW – relative weed cover; Lweed – weed cover;
Lcrop–crop cover
The fit to the model was tested by plotting normal
QQ plots and residuals distribution plots. For model-
ling weed-crop interaction, the statistical programR,
Version 2.15.0 (2015) was used.
RESULTS
Crop and weed densities, crop yields. In all ex-
periments, the sugar beet emerged, established and
normally developed further (Table 1).
Weed densities were higher at DOK with a to-
tal density of 58–131 weeds/m2 than at IHO with
20–86weeds/m2 (Table 2). The composition of weed
infestations was also different in both regions. Warm-
season weeds A. retroflexus and E. crus-galli occurred
only in the Russian Federation. S. media, C. album,
and M. inodora dominated at IHO (Table 2). Weed
compositions were representative of sugar beet pro-
duction areas at both locations.
On average, yields were roughly 45% lower at the
Russian site than at IHO in all treatments, likely
caused by low precipitation and shorter growing
season there (Table 3).
Modelling weed-crop interactions. All datasets
show a positive correlation between weed density
and sugar beet yield loss. Even low weed densities
already caused significant yield reductions. At DOK
2013, 2014 and IHO 2013, 50% of the maximum weed
density caused about 80% yield reduction. Weed
competition and maximum yield losses on average
Table 1. e number of emerged sugar beets (number/ha) in all experiments
DOK 1 2013 DOK 2 2013 DOK 2014 IHO 2012 IHO 2013 IHO 2014
Crop density 102 800 104 400 97 200 90 000 100 000 107 200
IHO – Ihinger Hof, Germany; DOK – Lipetsk region, Russia
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doi: 10.17221/57/2016-PPS
were higher at DOK than at IHO. The weed density
model shows a satisfactory fit to the data with regres-
sion coefficients ranging from 0.95 to 0.98 (Figure 1).
Relative weed cover data were distributed less ho-
mogeneously than weed densities (Figure 2). Hence,
at DOK and IHO most values of relative weed cover
ranged between 0.5 and 1 and similar as for weed
density, only very few data were in the range of the
economic weed threshold. At DOK 2 2013, the most
abundant weed species was A. retroflexus L. regard-
less of the high variation in weed density (from 25to
110plants/m2), relative weed cover ranged from
0.75 to 1. A similar situation was observed at DOK1
2013, where Chenopodium album L. was the most
abundant species. Relative weed cover regression
graphs look less steep compared to the lines for weed
density. Therefore, the weed cover model predicted
a lower yield reduction at lower relative weed cover,
which is not in line with the weed density analysis.
As it was expected, the relative yield loss of sugar
beet was correlated positively with weed biomass.
However, the estimated yield loss was less accurate
than for relative weed cover. The site with the highest
density of A. retroflexus showed a 95% sugar beet yield
reduction caused by only 10 g/m2 of weed biomass
at BBCH 18, which was 1.8% of the maximum weed
biomass (Figure 3). The graphs of weed biomass
and weed cover models look very similar. Like the
relative weed cover ‒ sugar beet yield regression
model, the weed biomass model predicted the lowest
relative yield loss for IHO 2014. This complies with
the output of the weed cover model.
Table 2. Density (number/m2) of the most abundant weed species in all experiments measured at the BBCH 18 growth
stage of the crop (H et al. 1997) in untreated control plots using a 1 m2 frame in the centre of each plot
Weed species DOK 1 2013 DOK 2 2013 DOK 2014 IHO 2012 IHO 2013 IHO 2014
Amaranthus retroflexus 21.2 43.7 4.0 –––
Chenopodium album 39.1 2.8 28.9 5.8 27.6 7.5
Cirsium arvense – – 0.4 –––
Echinochloa crus-galli 1.4 1.3 1.3 –––
Fumaria officinalis 4.2 2.0 ––––
Galium aparine – – 0.2 –––
Galeopsis tetrahit 8.8 1.5 0.4 –––
Lamium purpureum 7.8 2.7 18.6 – 1.5 –
Matricaria inodora 1.0 – – 1 43.1 13.7
Poa annua – – – – 3 –
Polygonum aviculare – 1.5 2.2 –––
Polygonum convolvulus 17.1 2.3 46.2 8.2 3 –
Polygonum lapathifolium 3.3 – 9.2 –––
Setaria glauca 1.0 –––––
Sonchus arvensis –––3.0 2.5 –
Stellaria media –––– 5.2 –
laspi arvense 1.9 – 1.2 –––
Veronica persica –––0.7 – 4.9
Viola arvensis – – 18.5 1.0 – –
Total weed density 106.8 57.8 131.1 19.7 85.9 26.1
IHO – Ihinger Hof, Germany; DOK – Lipetsk region, Russia
Table 3. Average sugar beet yield (t/ha) and white sugar yield (t/ha) of the weed-free control at all experimental locations
DOK 1 2013 DOK 2 2013 DOK 2014 IHO 2012 IHO 2013 IHO 2014
Sugar beet yield 45.2 54.6 37.9 83.3 82.9 95.0
White sugar yield 7.2 10.2 8.6 15.0 12.8 16.8
IHO – Ihinger Hof, Germany; DOK – Lipetsk region, Russia
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Figure 1. Relation of the relative yield loss of sugar beet to weed density in all experimental locations and years
IHO – Ihinger Hof, Germany; DOK – Lipetsk region, Russia
Table 4. Regression parameters calculated for the two-parameter model of relative (A) sugar beet yield loss–weed
density interaction modified according to C (1985), (B) sugar beet yield loss-relative weed cover interaction
according to K and S (1991), and (C) sugar beet yield loss-weed biomass interaction modified according
to C (1985)
Environment A B C
q ± SE a ± SE q ± SE a ± SE q ± SE a ± SE
DOK 1 2013 0.06 ± 0.06 1.07 ± 0.17 1.78 ± 1.38 0.97 ± 0.05 0.08 ± 0.12 0.96 ± 0.09
DOK 2 2013 0.44 ± 0.48 0.86 ± 0.04 11.01 ± 16.89 0.83 ± 0.02 1.41 ± 1.77 0.83 ± 0.01
DOK 2014 0.06 ± 0.03 0.99 ± 0.07 10.76 ± 7.11 0.90 ± 0.02 0.09 ± 0.06 0.95 ± 0.05
IHO 2012 0.22 ± 0.17 0.80 ± 0.15 1.77 ± 1.44 0.72 ± 0.08 0.01 ± 0.01 0.77 ± 0.18
IHO 2013 0.05 ± 0.02 1.02 ± 0.12 1.01 ± 0.34 0.93 ± 0.05 0.01 ± 0.001 1.15 ± 0.21
IHO 2014 0.01 ± 0.003 1.37 ± 0.95 0.13 ± 0.03 0.43 ± 0.03 0.01 ± 0.001 0.58 ± 0.07
q – competitive ability of the weeds ± standard error (SE); a – maximum yield loss ± SE; IHO – Ihinger Hof, Germany;
DOK–Lipetsk region, Russia
Relative weed cover provided the best estimator
of sugar beet yield loss, followed by weed biomass
and weed density (Table 4). The weed density regres-
sion model showed no strong correlation between
relative damage coefficient and relative yield loss
and the standard errors for q and a in the data set
were higher than for relative weed cover and weed
biomass.
Relative yield loss of sugar beet
1.0
0.8
0.6
0.4
0.2
0
0 50 100 150 200 250
Weed density (number/m2)
1.0
0.8
0.6
0.4
0.2
0
1.0
0.8
0.6
0.4
0.2
0
Relative yield loss of sugar beet
1.0
0.8
0.6
0.4
0.2
0
1.0
0.8
0.6
0.4
0.2
0
1.0
0.8
0.6
0.4
0.2
0
0 50 100 150 200 250
Weed density (number/m2)
0 50 100 150 200 250
0 50 100 150 200 250
0 50 100 150 200 250
0 50 100 150 200 250
DOK 1 2013
DOK 2 2013
DOK 2014
IHO 2012
IHO 2013
IHO 2014
y = 0.44x/[1 + (0.44x/0.86)] y = 0.05x/[1 + (0.09x/1.02)]
y = 0.01x/[1 + (0.01x/1.37)]
y = 0.22x/[1 + (0.22x/0.80)]
y = 0.06x/[1 + (0.06x/1.06)]
y = 0.06x/[1 + (0.06x/0.99)]
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DISCUSSION
Over all seasons, A. retroflexus and E. crus-galli
were among the most abundant weed species at the
DOK trial site, located in the central Chernozem
region of the Russian Federation. Continental cli-
mate at DOK characterised by hot summers and
water shortage gives a competitive advantage to C-4
plants over C-3 plants (Z & R 2007). Cool-
season weed species such as S. media and M.inodora
were abundant only at IHO, located in southwest-
ern Germany in an area with the more maritime
type of climate. Therefore, the results of the study
confirm the hypothesis that warm and dry growing
conditions in the Russian Federation favour more
warm-season weed species than in the temperate
region of Germany.
One reason for the higher weed infestation in
the experiments at the Russian site than at IHO in
Germany are large soil weed seed banks, which are
yearly replenished by the seeds dropping from un-
controlled weeds (K 2012). A second reason
for higher weed infestations at DOK in the Russian
Federation is the high soil organic matter content,
which strongly reduces the availability of soil active
herbicides in the soil water (M & W 2006)
due to adsorption and enhanced breakdown. A third
reason could be the lower competitive ability of the
crop due to water deficiency.
The hypothesis that the relative yield loss due to
weed competition and the competitive ability of weed
species are higher under Russian growing conditions
than in Germany is confirmed. The regression esti-
mates of weed cover and weed biomass gave higher
maximum yield losses and relative damage coefficients
for the Russian site than for the German experiments.
At DOK, the relative weed cover model estimate of
qparameter ranged between 1.8 and 11.0. At IHO,
q-values ranged only from 0.1 to 1.8. e highest value
of q was calculated for DOK 2 in 2013 and 2014, where
A. retroflexus, C. album, and P. convolvulus were the
most abundant weed species. is corresponds with
Figure 2. Relation of the relative yield loss of sugar beet to relative weed cover in all experimental locations and years
IHO – Ihinger Hof, Germany; DOK – Lipetsk region, Russia
Relative yield loss of sugar beet
1.0
0.8
0.6
0.4
0.2
0
0 0.2 0.4 0.6 0.8 1.0
Relative weed cover
1.0
0.8
0.6
0.4
0.2
0
1.0
0.8
0.6
0.4
0.2
0
0 0.2 0.4 0.6 0.8 1.0
Relative weed cover
0 0.2 0.4 0.6 0.8 1.0
0 0.2 0.4 0.6 0.8 1.0
0 0.2 0.4 0.6 0.8 1.0
0 0.2 0.4 0.6 0.8 1.0
Relative yield loss of sugar beet
1.0
0.8
0.6
0.4
0.2
0
1.0
0.8
0.6
0.4
0.2
0
1.0
0.8
0.6
0.4
0.2
0
y = 0.13x/[1 + ((0.13/0.43) – 1)x]
y = 1.01x/[1 + ((1.01/0.93) – 1)x]
y = 1.76x/[1 + ((1.76/0.72) – 1)x]) – 1)x]
y = 11.01x/[1 + ((11.01/0.83) – 1)x]
y = 1.78x/[1 + ((7.78/0.97) – 1)x]
y = 10.76x/[1 + ((10.76/0.9) – 1)x]
DOK 1 2013
DOK 2 2013
DOK 2014
IHO 2012
IHO 2013
IHO 2014
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the statement that these species are among the most
serious competitors of sugar beets (P 2008;
J & S 2013). Serious yield reductions were
recorded when these species escaped control (P
2008; G et al. 2012; J & S 2013).
e steep regression line at low infestation levels at
DOK could also be a result of severe drought. At DOK
2014, annual precipitation was about 240 mm, which
is less than 50% of the long-term average precipitation
in this region (Hydrometcentre of Russia 2015). L
et al. (1996) also found a strong influence of climatic
conditions on the crop-weed interaction.
e use of weed biomass in forecast tools of crop
yield loss is very complicated. It requires destructive
sampling and further processing of collected material,
which needs time and equipment. In comparison, scout-
ing for weed density is more feasible. e measurement
of weed cover gave the most accurate prediction of the
sugar beet yield loss in our study. Weed cover seems to
be the most suitable parameter for decision algorithms
for weed management in sugar beets.
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Received: 2016–04–08
Accepted after corrections: 2016–12–04
Publishe online: 2017–01–25
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