J.Mycopathol.Res. 62(2) : 417-422, 2024, ISSN : 0971-3719 (Print), 2583-6315 (Online)© Indian Mycological Society, Department of Botany,
University of Calcutta, Kolkata 700 019, India

10.57023/JMycR.62.2.2024.417

Influence of meteorological variables on severity of anthracnose/pod
blight of soybean in Medziphema, Nagaland
SENTINA LONGKUMER1, N. TIAMEREN AO1, SUSANTA BANIK1, PANKAJ NEOG2, DAMETRE LYTAN2,PEZANGULIE CHAKRUNO3*
1
Dept. of Plant Pathology, 2Dept. of Entomology, and3AICRP on Soybean, SAS, Nagaland University,
Medziphema Campus, Nagaland- 797106
Received : 08.02.2024

Accepted : 29.04.2024

Published : 26.06.2024

The most detrimental production limitations of soybean are foliar diseases. These diseases are mainly
brought about by various pathogens such as fungi, bacteria and viruses which pose a big threat to theagricultural production. One such problem is brought about by a fungal pathogen, Colletotrichum truncatum
which causes soybean anthracnose. The current investigation was conducted to study on the epidemiologyof anthracnose and its association with the weather variables during the Kharif season of 2022 at the AICRP
Soybean farm, Medziphema, Nagaland. The findings indicated that September and October was the ideal
period for anthracnose development in soybean. The correlation studies of disease severity with weather
variables revealed that maximum temperature showed a negative correlation with the disease severity overthe investigation period, whereas the maximum relative humidity showed a positive correlation.
Keywords: Anthracnose, Colletotrichum truncatum, correlation, soybean.

INTRODUCTION
Soybean [Glycine max (L.) Merrill] is one of themost important legume crop in the world,
accounting for up to 25% of the world’s edible oil,belonging to the family Leguminaceae and subfamily Papilionaceae. Soybean has the highest
protein content (40 to 45%) among pulse crops
and also contains a good amount of salts,
minerals and vitamins (such as Riboflavin andThiamine). Pathogens such as fungus, bacteria,
viruses, and nematodes, many of which are seedborne and seed-transmitted, harm soybean invarious ways. The crop’s decreased productivity
may be attributed to the prevalence of a numberof bacterial and fungal diseases, including seed
and seedling rots, bacterial pustules, bacterial leaf
blight, aerial blight, anthracnose, charcoal rot andsoybean mosaic (Mathpal and Singh, 2017).
There are 36 fungi, 3 bacteria, and 4 viruses thatare known to affect soybean in India.

*Correspondence:pezangulie@gmail.com

Any disease’s intensity and transmission are
significantly influenced by environmental factors.
The development of disease is influenced byfavourable host, pathogen, and environmental
conditions. Throughout time, a number ofenvironmental factors, such as temperature,
relative humidity, prolonged periods of direct
sunlight, and rainfall, have a significant impact on
how rapidly plant diseases begin to spread anddevelop (Jeena et al. 2022). For a disease todevelop, a combination of ideal environmental
condition and a plant’s vulnerable developmental
stage are required (Joshi et al. 2018). It may be
helpful to investigate the interactions of weather
parameters that contribute to the development of
the disease in order to assess and predict crop
losses and build management strategies for the
disease. This will make it possible for researchers
to stay informed when epidemics of plantdiseases emerge. In order to identify specificweather patterns that cause the disease to
manifest itself severely in epiphytic environments,
the aim of this research was to investigate the418

Meteorological variables affecting anthracnose of soybean

association between meteorological variables andthe severity of anthracnose.
MATERIALS AND METHODS
During Kharif of 2022 soybean seeds of ten
cultivars namely JS 95-60, JS 335, Shivalik, JS
93-05, Punjab 1, PK 472, NRC 7, PK 262, Monetta
and Macs 1460 were sown in the AICRP Soybean
farm which was at located at 25.7566°N and
93.8681°E longitudes at 310 m above sea levelon 14th July 2022. The seeds were planted in plotsmeasuring 3 m × 1.35 m, with a row-to-row
distance of 45 cm and a plant-to-plant distanceof 5 cm, using a Randomised Block Design(RBD) with three replications. To find out more
about how diseases spread naturally in theenvironment and its relationship with the weather
parameters, the experiment was conducted in an
epiphytotic setting.
The anthracnose disease was identified in the fieldsetting with reference to literatures of various
researchers, the soybean plants which weresuspected to have been infected by C. truncatum
were then brought to the laboratory of Department
of Plant Pathology, SAS, Medziphema. And further
identification was done with the aid of a compound
microscope (10X & 40X) and the infected tissue
was isolated in potato dextrose agar medium. The
PDA was prepared according to the procedurelaid out by Ricker and Ricker (1936) which was
incubated for 48 – 72 hrs at 25±2°C. For obtaining
pure culture of the pathogen, hyphal tip isolation
approach was used. This was accomplished bytaking a disc of the fungal colony and placing it
on PDA plates, where it was cultured for one totwo days. The pathogen was identified using acompound microscope (10X & 40X) objective
where the mycelium was mounted on a glass
slide with lactophenol blue and observed underthe microscope. The culture was taken out withcork borer and put on PDA slants where it was
kept as a pure culture. Later, morphologicalstudies of the pathogen, C. truncatum was donewith the help of a compound microscope and thedimensions of the spores and setae were
calculated with the help of micrometry.
After the disease incidence, the data wasrecorded on a weekly basis until harvest. To score[J.Mycopathol.Res :the severity of the disease and determine the PDI
occurrence, ten randomly chosen plants fromeach plot were labelled. The chosen plants wererated on a scale of 0 to 9 (Table 1) for disease
severity (Mayee and Datar, 1986). Using thefollowing formula, the percent disease index (PDI)of anthracnose in various cultivars were
calculated.
Percent Disease Index (PDI) :PDI=

Sum of individual rating x 100

x 100

Number of leaves examinated x
maximum disease rating

Data compilation and statistical analysis were
performed using MS Excel, the data were angulartransformed and mean dif ferences werecorrected using the Least Significance Difference(LSD) test at the 5% level of significance (Gomez
and Gomez, 1984) while Karl Pearson’s
correlation coefficient was used to calculate thecorrelation.
RESULTS AND DISCUSSION
The symptoms of anthracnose werecharacterized by asymmetrical brown spots onstems, pods, and leaf petioles. Symptoms suchas rolled leaves and crimson veins were also
seen. Diseased stems and pods exhibited
patches of a dark brown or reddish brown tint on
their surface. Sunken lesions on stems, petioles
and pods and black fruiting bodies (acervuli) of
the fungus covered the afflicted areas when theinfected tissues reached an advanced level.
Plants infected with the anthracnose disease also
exhibited veinal necrosis and premature
defoliation on their leaves. The findings were inagreement with those of other researchers,
including Gupta and Chauhan (2005), Mukhtar
and Ahmed (2006), Borah (2019).
The isolated fungus grew into a colony which hada smooth, spherical shape and grew at a medium
rate (4-5 days). The colony colour ranged fromblack, grey, and white-grey which had a fluffy,dense texture. The growth rate of the isolate
ranged from 65 – 70 mm. On PDA, conidia
generated by C. truncatum isolates wereabundant and morphologically consistent.

Sentina Longkumer and others

62(2) June, 2024]

419

Table 1: Disease Rating scale

Scale

Description0

No lesions/spots.

1

1% leaf area covered with lesions/spots.

3

1.1–10% leaf area covered with lesions/spots, no spots on stem.

5

10.1–25% of the leaf area covered, no defoliation; little damage.

7

25.1–50% leaf area covered; some leaves drop; death of a few plants; damage
conspicuous.

9

More than 50% area covered, lesions/spot very common on all plants, defoliationcommon; death of plants common; damage more than 50%.

Table 2 :Meteorological data during the period of investigation

Dates ofRelative Humidity (%)

Temperature(C)observation

Rainfall(mm)

No. of rainy
days

Tmax (C)

Tmin(C)

RHmax (%)

RHmin (%)

05-09-2022

33.4

24.4

89

67

21.7

2

12-09-2022

31.9

23.5

91

72

42.8

3

19-09-2022

31.8

23.6

91

71

80.1

2

26-09-2022

32.8

23.2

91

70

81.2

2

03-10-2022

31.9

23.5

95

74

31

3

10-10-2022

31.8

22.7

91

71

2.9

1

17-10-2022

30.9

20.6

94

65

19.7

3

Source: Weather data at ICAR RC – NEH Region, NagalandCentre, Jharnapani, Medziphema – 797106

Single-celled, uninucleate, hyaline, falcate/curved
with oil globule in cytoplasm, and with dimensions
from the isolate of PDA ranging from 20.7 to 23.7
µm × 3.6 to 4.0 µm, C. truncatum conidia weredetected. The setae that were seen were black,emerging from a mass of conidia that was
mucilaginous and longer than the conidiophores.
They were wider at the base and tapered towards
the apex, measuring approximately 65.7 to 142.7µm × 4.3 to 5.3 µm. Such findings were supported
by literatures of various researchers, Jagtap andSontakke (2009) on the morphological charactersof conidia, the features of the colony growth andcolour corroborated with the findings of Masoodi
et al. (2013), while the dimensions andmeasurement of the conidia and setae mirrored
the findings of Nagaraj (2013) and Boufleur et al.
(2021).

The disease assessment came from the field in
their natural setting. It has been found thatnumerous environmental factors influence theonset of disease. During the investigation period,
the first week of September saw the onset of thedisease symptoms. The disease incidence andseverity in the crop during the Kharif season of2022 were observed when the following climaticconditions prevailed: Temperature: 20.6 to
33.4°C, Relative Humidity: 65 to 95%, Rainfall:
2.9 – 81.2 mm, Number of Rainy Days: 1 – 3 inMedziphema, Nagaland (Table 2).
These weather patterns were shown to befavorable for both the occurrence and severity ofthe disease, which shows that a warm, humid
environment favors the onset of anthracnose/pod
blight in the field. This is supported by the findings

420

Meteorological variables affecting anthracnose of soybean[J.Mycopathol.Res :Table 3: Percent Disease index in different cultivars of soybean infected with anthracnose
Percent disease index (%)
Cultivars

5 Sept2022

12 Sept
2022

19 Sept
2022

26 Sept
2022

3 Oct
2022

10 Oct2022

th

17 Oct
2022

th

Mean

11.11
(19.43)
17.11(24.36)
13.33(21.14)20.22(26.54)26.67(31.00)15.56(23.22)
15.56(23.22)
13.33(21.14)28.00(31.92)
7.33(15.37)
2.00

24.45(29.58)20.00(26.57)
13.33(21.14)22.22(28.02)31.11(33.89)20.00(26.36)20.00(26.14)17.78(24.63)31.33(34.01)
12.48(20.59)2.19

26.22(30.76)
27.78(31.68)22.22(28.03)25.11(29.93)31.11(33.86)23.33(28.78)22.22(28.03)22.22(28.07)36.67(37.25)14.44(21.92)
2.22

27.78(31.64)28.89(32.45)23.97(29.25)31.49(34.11)
31.55(34.07)23.33(28.85)33.33(35.25)23.33(28.85)39.78(39.07)
21.11(27.34)1.80

28.19(32.06)37.78(37.86)28.89(32.41)34.44(35.90)36.67(37.22)24.45(29.58)35.56(36.52)23.33(28.78)46.67(43.08)22.00(27.78)2.07

32.00(34.43)48.89(44.36)33.33(35.22)53.33(46.92)37.78(37.92)27.78(31.77)40.00(39.23)27.33(31.46)48.89(44.36)26.67(31.00)
1.58

22.82

SEm±

10.00
(18.43)16.67(23.86)10.00(18.43)15.56(23.02)
16.67(23.86)10.00(18.43)
12.22(20.32)
10.48(18.88)27.78(31.79)3.81(6.59)
2.38

CD (P=0.05)

7.07

5.93

6.50

6.59

5.34

6.16

4.68

JS 95-60
JS 335
Shivalik
JS 93-05
Punjab 1
PK 472NRC 7
PK 262
Monetta
MACS 1460

th

th

th

th

rd

27.68
21.20
28.91
30.22
20.63
25.55
19.68
37.01
15.40

Figures in the table are mean values and those in parenthesis are angular transformed values

Table 4: Correlation coefficient (r) of percent disease incidenceof different cultivars of soybean caused by C.truncatum withweather variables during September to October 2022

Temperature (°C)

Relative humidity (%)

Cultivars

Rainfall(mm)

No. of rainy
days

Maximum

Minimum

Maximum

Minimum

JS 95-60-0.756*-0.3590.659-0.109-0.115-0.120JS 335-0.487-0.7020.683-0.198-0.220-0.281

Shivalik-0.598-0.7020.707-0.045-0.103-0.094

JS 93-05-0.461-0.672

0.797*-0.076-0.277-0.246

Punjab 1-0.482-0.5410.677-0.016-0.053-0.059PK 472-0.655-0.556

0.700

0.025

0.017

0.016

NRC 7

-0.664-0.5120.833*-0.006-0.317-0.162PK 262-0.731-0.548

0.726

0.014

-0.036-0.009

Monetta-0.587-0.6090.691-0.169-0.242-0.240

MACS 1460-0.689-0.5130.823*

0.000-0.245-0.125y = Disease severity; x = W eather parameters62(2) June, 2024]

Sentina Longkumer and others

421

Table 5: Simple regression between disease severitycaused by Colletotrichum truncatum and weather variables during September
to October 2022
Simple regression
Maximum temperature (°C)
Minimum temperature (°C)
Maximum relative humidity(%)
Minimum relative humidity(%)
Rainfall (mm)Number of rainy days

Regression
equationy = 0.116x + 34.96
y = -0.063x +
24.87

B

SE(b)

t value

Pr> |tR2-3.321

1.867-1.779

0.135

0.388

-5.7533.382-1.701

0.150

0.367
0.586

y = 0.281x + 83.83

2.079

0.781

2.661*

0.045(<0.05)y = -0.077x + 72.5

-0.0640.405-0.159

0.880

0.005

-0.1120.262-0.428

0.686

0.035

-2.5267.027-0.359

0.734

0.025

y = -0.315x +16.25
y = -0.01x + 0.677

Fig.1: Disease spread as evidenced and following rating scalefrom 0 to 9 (Mayee and Datar (1986). Ratings are assigned basedon standardized criteria for symptom manifestation and severity
assessment.

of Gurjar (2021) where in his study reported thatmaximum temperature (28.5- 33.3°C), minimum
temperature (17.1-21.3°C), maximum RH (7693%), minimum RH (39-79%) and rainfall (7.2165 mm) remained most favorable f oranthracnose disease development.
The disease severity was noted on ten different
soybean cultivars over the course of sevenweeks for the correlation assessment whichranged from 3.81 (MACS 1460) to 48.89 (Monetta
and JS 335) and their PDI ranged from 15.40 to
37.01 for MACS 1460 and Monetta respectively(Table 3).
The analysis of the relationships between theweather parameters and the severity of thedisease revealed that the maximum temperature
had a negatively significant correlation with ‘r’values (JS 95-60 = -0.756) while maximum
relative humidity showed a positive significant
correlation (JS 93-05 = 0.797, NRC 7 = 0.833,
MACS 1460 = 0.823) with the disease severity
during the critical period from the first week of
September to the third week of October 2022
(Table 4).

However, there was no correlation between theminimum temperature, minimum relative
humidity, the total amount of rain, the number ofrainy days, and the anthracnose severity. Such
differences in the severity of anthracnose, its
widespread incidence, and its correlation withmeteorological variables, were also recorded byearlier researchers (Rana and Khushal, 2005;
Kulkarin, 2009; Mali, 2012). Negative correlation
was observed in minimum temperature anddisease severity for cultivar JS 95-60 (-0.756) andrelative humidity and disease severity were foundto be positively correlated for cultivars JS 93-05(0.797), NRC 7 (0,833) and MACS 1460 (0.823).
The findings also corroborated with the study of
Dnyanoba (2014) who carried out a research on
the prevalence of anthracnose in the soybean
cultivars JS-335 and MAUS-71 and reported that
there was a significant but negative correlation
with temperature (max and min).While the results
were in direct opposition to Gurjar (2021) findings
from studies he conducted to examine therelationship between weather variables and theseverity of disease.
Between disease severity (PDI) of anthracnoseand meteorological variables, a linear regression
was fitted. This was carried out in order todetermine the degree of association between the
independent variable (x) which is the weather
parameters and the dependent variable (y) which
is PDI. The R2 values of independent variables
were as follows: temperature 0.388 (max),0.367(min); relative humidity 0.586 (max), 0.005 (min);rainfall 0.035 and number of rainy days 0.025(Table5).
A more detailed understanding of the impact ofweather variables on disease severity can be422

Meteorological variables affecting anthracnose of soybean

obtained from the regression table. Table 5
illustrates the relative contributions of the following
factors to the onset and severity of the disease:maximum temperature 38.8%; minimum
temperature 36.7%; maximum relative humidity
58.6%; minimum relative humidity 0.5%; rainfall
about 3.5%; and number of rainy days only 2.5%.
Through the present investigation it is plausible
to conclude that weather variables does play rolein the onset and progression of the disease
anthracnose incited by C. truncatum. Plant
diseases are greatly influenced by weather
conditions, and understanding how various
weather factors interact to cause severe
infections in the field may assist with creating
models that forecast for the progression of thedisease, so that vital strategies for management
can be applied at a favorable time. Early
implementation of management practices will help
to lessen the need for chemical spraying, stop
major infection outbreaks, and reduce production
losses. The results of the current study,
depending on the analysis, can aid in disease
prediction.
ACKNOWLEDGEMENT
The authors are extremely grateful towards the
Department of Plant Pathology, School of
Agricultural Sciences (SAS), Nagaland University
and AICRP Soybean, SAS, Medziphema Campus
both provided invaluable resources and thefacilities that were necessary for the research.
The authors also wish to express gratitude to theteaching and non teaching staff of SAS, Nagaland
University f or their assistance, valuable
contributions and helpful suggestions.
DECLARATIONSConflict of Interest: Authors declare no conflict ofinterest.
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