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AJCS 10(2):161-168 (2016) ISSN:1835-2707

Influence of row spacing and plant population density on management of "white mould" in
soybean in southern Brazil
David de Souza Jaccoud-Filho1, Felipe Fadel Sartori1,2, Miguel Manosso-Neto1, Cláudio Maurício
Vrisman1,3, Marcelo L. da Cunha Pierre1, Ayrton Berger-Neto1, Hamilton Edemundo Túllio1, Altair
Justino1, Adriel Ferreira da Fonseca1, Sérgio Zanon2

1Universidade Estadual de Ponta Grossa (UEPG), Departamento de Fitotecnia e Fitossanidade, Grupo de
Fitopatologia Aplicada, Brasil
2Universidade Estadual de São Paulo (Esalq/USP), Departamento de Produção Vegetal, Grupo de Fisiologia
Aplicada e Sistemas de Produção, Brasil
3The Ohio State University at Wooster (OSU), Department of Plant Pathology, USA
*Corresponding author: [email protected];[email protected]
Abstract
White mould is a disease caused by the fungus Sclerotinia sclerotiorum (Lib.) de Bary and it has become a major problem for
soybean in Brazil, mainly due to the use of contaminated seeds and machinery, monoculture, and the use of susceptible species as
crop rotation. This study aimed to evaluate the influence of different row spacing and plant population densities on soybean crop in
relation to the levels of incidence and the severity of S. sclerotiorum. Field trials were carried out during 2010-2012 crop seasons.
Row spacings of 0.35, 0.45, 0.60 and 0.75 metres, and plant population densities of 150,000, 200,000, 250,000 and 300,000 plants
ha-1 were used. The incidence and severity of white mould, the yield, and the thousand grain weight were evaluated. Spacing at 0.35
metres increased yield but it caused greater incidence of the disease. A reduced number of plants in the crop rows reduced the
severity of the disease. Farmers with a history of problems with S. sclerotiorum should avoid narrow row spacings and high plant
population densities.
Keywords: Glycine max; Sclerotinia sclerotiorum; incidence; severity; yield.
Abbreviation: TWG_thousand grain weight.
Introduction

White mould (Sclerotinia sclerotiorum (Lib.) de Bary) is one
the wind and can infect plants in a range of 50-100 m from of the most important plant diseases nowadays because it source (Steadman, 1983). Apothecia can germinate well in attacks several crops, including soybean. This disease, which moist soils as well as dry soils, depending on the is difficult to control, is aggravated by the production of temperatures of the soil, according to results found by resistance structures (sclerotia) inside and outside the plants, Matheron and Porchas (2005) in laboratory tests. There are a which return to the soil at the end of the disease cycle. The number of factors related to the time of viability, the potential period of viability of these sclerotia is still uncertain, but for infection, and the spatial distribution of sclerotia in the there have been reports of more than eight years (Adams and soil (Clarkson et al., 2003; Sun and Yang, 2000; Wu and Ayers, 1979). Another important fact that can complicate the Subbarao, 2008). management of the disease is that it has populations that are Plant canopy management been studied because it is a good resistant to fungicides (Gossen et al., 2001), which limits one option for managing diseases like white mould in various of the main ways of managing the pathogen. crops. Soybean varieties with reduced height and lodging, White mould causes an estimated loss of 83.2 to 229 kg.ha- and early cycle showed a reduction of 74% in the appearance 1 for every 10% incidence of the disease in soybean, with of Sclerotinia scletotiorum apothecia and an 88% reduction average losses of 136 kg ha-1 (Danielson et al., 2004). Among in incidence (McDonald et al., 2013). In Canada, crop the environmental factors that can lead to carpogenic management of the disease has been part of the control of germination of sclerotia, the ambient temperature and the white mould for over 25 years; short cycle crops (which depth at which the sclerotia are in the ground stand out (Sun require less heat units) are recommended for areas with a and Yang, 2000; Wu and Subbarao, 2008). Sun & Yang history of the disease (McDonald et al., 2013). (2000) found that low and high light intensities may influence The reduction of moisture in the soil surface interferes with the optimum temperature for the germination of apothecia, the formation of apothecia and ascospores (Saindon et al., and these were produced more rapidly when exposed to high 1995; Schwartz and Steadman, 1978).In crops such as beans, light intensity treatments. The apothecia result from the air circulation between the rows hampers the development of carpogenic germination of the sclerotia and they are the S. sclerotiorum because it prevents the development of largest source of inoculum of the disease because they moisture, reducing the levels of incidence and severity of the produce a lot of ascospores, which are easily transported by disease (Tu, 1988). Plant population density and growth habit (in the case of beans) has a direct effect on the moisture which was favourable to the disease and which was created between the rows. Plants with an upright growth habit allow by the denser canopy in relation to larger row spacings. The greater air circulation and greater penetration of sunlight, authors also mentioned that, according to Adams (1975), when compared to semi-erect or prostrate plants, which sclerotia on the soil surface deteriorate with alternating results in a rapid drop in humidity. Upright cultivars have moisture and drought in the soil, a situation that occurs with straighter lines of plants and, depending on the row spacing, larger row spacings because smaller row spacings retain more plants in adjacent lines do not touch each other (Napoleão et moisture in the soil. Severity significantly lower than that al., 2006). A denser canopy provides ideal moisture and found in row spacings of 0.45 metres was observed in the temperature conditions for the development of S. row spacings of 0.35 metres (Table 1). A different result was sclerotiorum (Blad et al., 1978; Boland and Hall, 1988). observed by Macena et al. (2011) in beans, where increased The aim of the present study was to evaluate how different row spacing led to a reduction in the severity of white mould. row spacings and plant population densities of soybean may Plant population density only influenced the severity of white influence the incidence and severity of white mould. mould during the 2011-2012 crop season. 150,000 plants.ha-1 showed significantly lower levels of severity than the other Results and Discussion
plant population densities (Table 1). In experiments with beans, Vieira et al. (2005) and Paula Junior et al. (2009) In the 2010-2011 crop season, disease was only observed in observed reductions in the severity of the disease in low plant the experiment in the two last assessments, at the R5.4 and population densities. Vieira et al. (2010) observed that a R5.5 phenological stages (Fehr et al., 1971). In the 2011- decrease in plant population density from 240,000 plants.ha-1 2012 crop season, the disease was observed from the R5.1 to 80,000 plants.ha-1 was effective in reducing white mould in phenological stage (Fehr et al., 1971) onwards during the beans, also increasing crop yield in areas with high pressure experiment. Results from R5.5 phenological stage (Fehr et of the disease. According to Heiffig et al. (2006) and Herbert al., 1971) are showed. and Litchfield (1982), low plant population densities have lower rates of leaf area in the same space, which may lead to Incidence and severity levels of white mould
increased air circulation and light penetration between the plants and, hence, a lower severity of the disease due to The two crops seasons showed large differences between moisture reduction and increased temperature. Vieira et al. each other regarding the values for the incidence and severity (2012) observed similar results in beans, where broad row of the disease. Unfavourable climate conditions for the spacing and low plant population density showed to a development of the disease (Tmax.=26 ºC; Tave.=21 ºC; promisor spatial arrangement for manage white mould when Tmin.=17 ºC) during the period of susceptibility of the crop fungicide is not used. (R1 and R2) (Fehr et al., 1971) resulted in low values for incidence and severity in the 2010-2011 crop season, which Yield and Thousand grain weight (TGW)
led to large variation in the data and distortion in the surface plot (see Fig 1 and 3). For variables (incidence and severity), There was no interaction between row spacings and plant smaller row spacings and plant population densities presented population densities for yield in any of the two field trials on the highest levels, although they were lower than 1% (Fig 1 both crop seasons. All treatments showed average yields higher than 4,000 kg.ha-1 (Fig 5 and 6). Differences were There was no interaction between row spacing and plant only observed between row spacings for both crop seasons. population density for incidence and severity in the 2011- Row spacing of 0.35 metres resulted in improved yield in 2012 crop season (table 1). Unlike the 2010-2011crop season, both crop seasons, differing significantly from row spacings all treatments showed incidence of the disease (Fig 2) and of 0.60 and 0.75 metres in the first crop season and from all averages of severity above 40% (Fig 4). Differences in others in the second crop season (Table 1). A reduction in incidence were only observed between the different row row spacing resulted in increased yield in both crop seasons spacings, where 0.45 metres and 0.75 metres differed (Table 1). However, there was no influence of plant significantly between each other; the 0.45 metres row spacing population density on the yield in either of the years (Table had the highest values and the 0.75 metres row spacing had 1). According to Knebel et al. (2006) and Rambo et al. the lowest values. Differences in severity were observed (2003), the factor that has the greatest impact on yield is row between row spacings and plant population densities. For the spacing. Nakagawa et al. (1986) did not observe an increase former, only row spacings of 0.35 metres to 0.45 metres in yield due to increased plant population density. The higher differed significantly, where the first had lower level of yield where reduced row spacings were used may have been severity than the second (Table 1). Regarding plant due to greater interception of solar radiation during the population density, 150,000 plants.ha-1 differed significantly growing season (Board and Harville, 1992; Taylor, 1980). from all others, presenting lower severity level (Table 1). Regarding TGW, all treatments showed weights higher Row spacing of 0.45 metres are most commonly used in than 160 grams in both crop seasons (Fig 7 and 8). In the soybean in Brazil. However, this size resulted in the highest 2010-2011 crop season there was no interaction between row incidence and severity levels of white mould during the 2011- spacings and plant population densities. Differences were 2012 crop seasons. Significantly lower incidence than 0.45 only observed between the different row spacings, where metres treatments was only found for row spacing of 0.75 0.75 metres presented higher weight and were significantly metros (Table 1). This may have been related to the fact that different from the others row spacings (Table 1). For the larger row spacings provide increased air circulation, light 2011-2012 crop season there was interaction between the penetration, drier soil, fewer apothecia and shorter leaf factors; the lowest TGW was obtained for the row spacing of wetness duration (McDonald et al., 2013), thereby resulting 0.45 metres associated with a plant population density of in a reduction in the incidence of the disease. In soybean field 250,000 plants ha-1 (Table 1). In a similar way to yield, the trials carried out in the USA by Grau and Radke (1984), different row spacings also influenced the TGW values. Row smaller row spacings led to high levels of white mould. The spacing of 0.75 metres resulted in the largest values during authors explained that this fact was due to the microclimate,

Table 1. Tukey's multiple-comparison analysis at 5% of: yield 2010/2011 (Yld 10/11), thousand grain weight 2010-2011 (TGW
10/11), incidence 2011-2012 (Inc 11/12), severity 2011-2012 (Sev 11/12), sclerotia.plants-1 2011-2012 (Scl.plt-1), yield 2011-2012
(Yld 11/12) and thousand grain weight 2011-2012 (TGW 11/12). Values of incidence and severity represent the last assessment of
the disease. Values marked with "*"represent the coefficient of variation for each analysis.
1 Values with different letters significantly different by Tukey s multicomparison analyses at a confidence level of 95%. 2 Lower case letters refer to row spacings; Upper case letters refer to plant population densities. Fig 1. Response surface plot relating interactions between incidence of white mould of 2010-2011 crop season at R5.5 phenological
stage. Different colours in the legend mean significantly different values in the chart.
Table 2. Combinations of different row spacings and plant population densities, resulting in each of the sixteen treatments.
150,000 plants.ha-1 200,000 plants.ha-1 250,000 plants.ha-1 300,000 plants.ha-1 150,000 plants.ha-1 200,000 plants.ha-1 250,000 plants.ha-1 300,000 plants.ha-1 150,000 plants.ha-1 200,000 plants.ha-1 250,000 plants.ha-1 300,000 plants.ha-1 150,000 plants.ha-1 200,000 plants.ha-1 250,000 plants.ha-1 300,000 plants.ha-1 Fig 2. Boxplot relating incidence in 2011-2012 crop season at R5.5 phenological stage. Averages of treatments are represented by
"*".
the 2010-2011 crop season and they also resulted in one of
Materials and Methods
the highest values during the 2011-2012 crop season (Table 1). Different plant population densities did not alter the TGW values during the 2010-2011 crop season (Table 1). However, there was interaction between this factor and row spacing in The experiments were carried out in the city of Arapoti, state the second crop season. These results disagree with those of Parana, Brazil (Alfisol CEC0-10 cm = 8.98 cmol.dm-3; found by Vazquez et al. (2008), where the variations in the organic matter 0-10 cm = 30.89 g.dm-3; altitude = 966 metres) in spatial arrangement did not affect the thousand grain weight. an area naturally infested by the disease. Prior to the installation of the experiments, the soil was sampled at4 Sclerotia per plant production
points of 0.25 m² and 0.05 m depth (Jaccoud-Filho et al., 2010) in the area in order to determine the number of There was no measurement of this variable in the 2010-2011 sclerotia.m-2: 55 sclerotia.m-² was found in the 2010-2011 crop season due to low incidence and severity of the disease. crop season and 31 sclerotia.m-² in 2011-2012 crop season. All treatments presented production of sclerotia per plant higher than 10 (Fig 9). There was no interaction between the Sowing and plant material
factors or significant differences between the row spacings and plant population densities (Table 1). Neither row In the first year of the experiment, sowing was performed on spacings nor plant population densities showed an influence December 10th, 2010 and in the second year, on October 11st, on the variable of number of sclerotia per plant (Table 1), 2011. The cultivar used in both years of the experiment was contrary to the findings of Macena et al. (2011), where Apollo RR® (susceptible to white mould, of indeterminate production of sclerotia decreased as the row spacings growth, and maturation stage of 5.5) and crop management was performed according to the standards of the farm where the experiments were performed, except for the application of products whose target was the white mould.

Fig 3. Response surface plot relating interactions between severity of white mould of 2010-2011 crop season at R5.5 phenological
stage. Different colours in the legend mean significantly different values in the chart.
Fig 4. Boxplot relating severity in 2011-2012 crop season. Averages of treatments are represented by "*".
Fig 5. Boxplot relating yield in 2010-2011 crop season at R5.5 phenological stage. Averages of treatments are represented by "*".
Fig 6. Boxplot of data relating to yield in 2011-2012 crop. Averages of treatments are represented by "*".
Fig 7. Boxplot of data relating to thousand grain weight in 2010-2011 crop. Averages of treatments are represented by "*".
Treatments and traits measured
was performed. To evaluate the yield, four lines of four metres in length per plot (the same that was assessed for The treatments consisted of four row spacings (0.35, 0.45, incidence and severity of white mould) were harvested in 0.60 and 0.75 metres) and four plant population densities both crop seasons. Assessments of yield and thousand grain (150,000, 200,000, 250,000 and 300,000 plants.ha-1) (Table weight (TGW) were made in the laboratory after correction 2). The incidence and severity assessments of the disease of grain water content to 130g.kg-1. started at R1 phenological stage (Fehr et al., 1971) in four rows of four metres length in each plot and were made weekly until the crop reached the R5.5 phenological stage Experimental design and statistical analyses:
(Fehr et al., 1971) in both crop seasons. The measurement of the incidence values was made using the percentage of The experimental design was a randomized complete block in infected plants per metre, and in terms of severity, a 4x4 factorial scheme (row spacings X plant population percentages of 1 to 100 were awarded, in accordance with densities), totalling sixteen treatments (table 2) with four Juliatti et al. (2013). Thus, the incidence values presented in replications. Each plot had twelve lines, ten metres long, this article refer to the percentage of diseased plants per varying the width of the plot depending on the spacing used. metre. The severity values shown are averages of infected Variance analyses, Shapiro-Wilk test, Bartlett test and plants. During the evaluations in 2011-2012, five plants per Tukey s multicomparison test was performed on all variables plot that showed the presence of the disease were randomly using R® software (R Core Team, 2013). The Shapiro-Wilk marked with coloured tape to quantify the sclerotia.plant-1, and Barltlett tests indicated the necessity for data opening up the five plants which were marked with tape to transformation for incidence and severity of both crop count the resistance structures in the same day that harvest seasons. The 2010-2011 incidence and severity could not be Fig 8. Boxplot relating TGW in 2011-2012 crop season. Averages of treatments are represented by "*".
Fig 9. Boxplot relating sclerotia.plant-1 in 2011-2012 crop season. Averages of treatments are represented by "*".
transformed by any method. 2011-2012 incidence was Conclusions
transformed by Box-Cox transformation (Box and Cox, 1964) and 2011-2012 severity was transformed by the The use of row spacing of 0.75 metres was effective in equation x=1/cos(x). As 2010-2011 incidence and severity reducing the incidence of disease. However, such row could not be transformed, it was decided to perform response spacing led to a large reduction in the soybean yield. Farmers surface analyses, which consisted of variance analyses of the with a history of disease in their area should not adopt regression of row spacings and plant population densities to reduced row spacing to increase productivity, due to high risk each variable. The response surface analysis showed a of incidences of this disease. A reduction in plant population uniform covariance matrix, a necessary condition according density is a good strategy to reduce the severity of Sclerotinia to Huynh-Feldt (H-F), to carry out univariate statistical sclerotiorum. analysis for an assay in randomized blocks. Functions of the type Y = b0 + b1X1 + b2X2 + b11X12 + b22X22 + b12X1X2 were adopted, where Y was the dependent variable, b0 to b12 were the regression coefficients, X1 corresponded to the spacing The authors would like to thank the National Research and X2 corresponded to the populations of plants. The Council (CNPq) and the Ministry of Agriculture, Livestock estimates were made with p≤.05 using STATISTICA 10® and Supply (MAPA) for financing this project. We would software (Stat-Soft, 2010). also like to thank the Fazenda Mutuca for all their support during the development of the experiments and Sérgio Zanon, viability of sclerotia of Sclerotinia minor and S. crop scientist and MSc candidate at Esalq/USP, for his help sclerotiorum. Plant Dis. 89:50- 54. regarding the statistical analysis. McDonald MR, Gossen BD, Kora C, Parker M, Boland G (2013) Using crop canopy modification to manage plant References
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