Indian J. Agric. Res.., 48 (4) 258 - 268, 2014
AGRICULTURAL RESEARCH COMMUNICATION CENTRE
www.arccjournals.com
doi:10.5958/0976-058X.2014.00659.3
PATHOGENICITY AND YIELD LOSS ASSESSMENT CAUSED BY MAGNAPORTHE ORYZAE ISOLATES IN CULTIVATED AND WILD RELATIVES OF FINGER MILLET (ELEUSINE CORACANA) * Dagnachew Lule1 , Santie de Villiers2, Masresha Fetene3, Teshome Bogale4, Tesfaye Alemu1, Geleta Geremew4, Getachew Gashaw1 and Kassahun Tesfaye1 Department of Microbial, Cellular & Molecular Biology, Addis Ababa University, Ethiopia
Received: 25-02-2014
Accepted: 31-05-2014 ABSTRACT
Magnaporthe oryzae, a fungal pathogen that attacks more than 50 gramineous species, causes severe yield losses particularly on rice and finger millet. A study was conducted at Bako Agricultural Research Center in Ethiopia during the 2012 cropping season to evaluate the pathogenicity of four different finger millet blast isolates on 12 cultivated finger millet accessions and 4 of its wild relatives (Eleusine coracana subsp. africana). A split plot design, replicated three times, was used. Test accessions showed significant difference (Pd”0.01) but the isolates and genotype by isolate interaction showed no significant differences for grain yield. Pathogenicity tests revealed that isolates Pg. 11 and Pg. 22, isolated from neck and head of finger millet, respectively, were most virulent in infecting all plant tissues. The isolate from neck of the wild type (Pg. 41), infected both the cultivated and its wild types, confirming that wild types or weeds can act as hosts or reservoirs of the pathogen and source of inoculum. The mean grain yield per plant was lower for isolate Pg.22 (2.8g) and Pg.11 (2.9g) but relatively better for Pg.41 (3.6g) and Pg.26 (3.1g), indicating the inverse correlation between infection severity and grain yield. Finger millet accessions such as Acc. 214995 and PW-001-022 recorded lower blast infection consistently during 2011 and 2012 and are thus potential candidates for further evaluation in variety development activities. Among the wild accessions, AAU-ELU-15 showed the least blast infection. Mean yield loss assessment results from 2011 and 2012 field experiments calculated using Relative Area Under Progress Curve (RAUDPC) and multiple point models, respectively, revealed an average of 42% grain yield lost due to blast disease.
Key words: Area Under Disease Development Curve (AUDPC), Eleusine coracana, Isolates, Magnaporthe oryzae, Pathogenicity. INTRODUCTION Blast disease, caused by Magnaporthe oryzae (anamorph Pyricularia oryzae) is a pathogen of many gramineous species (Bheema et al., 2010; Ou, 1985) and the most important constraint for rice (Chauhan et al., 2002; Ou, 1985) and finger millet (Mgonja et al., 2007; Kato et al., 1977; Anon, 1959;). The pathogen also attacks barley (Yaegashi and Nishihara, 1976) wheat (Urashima et al., 1993) and maize (Notteghem, 1990). Sources of the inoculum are known to be seeds, crop residues and several weed species, including wild Eleusine spp. and Setaria
verticillata (Pande et al., 1995, Gashaw et al., 2014). Even though the host range of M. oryzae is extensive, individual isolates of the fungus are specific to only a small number of grass species (Kang et al., 1995). Couch and Kohn (2002) reported that M. oryzae i s new specie that segregated from Magnaporthe grisea and hence grouped blast pathogen isolates from Digitaria spp. as M. grisea and those from a number of other hosts, including rice and finger millet, as M. oryzae. Takan et al. (2012) recently confirmed that M. grisea isolates from
*Corresponding author’s e-mail:
[email protected], 2Department of Chemistry and Biochemistry, Pwani University, Kenya, 3Department of Plant Biology and Biodiversity Management, Addis Ababa University, Ethiopia, 4Bako Agricultural Research Center, P.O.Box 03, Fax: (+ 251)576650267, Bako, Ethiopia
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Digitaria spp. formed a distinct genetic cluster from isolates from cultivated finger millet, its wild types and other weed species based on analyses using amplified fragment length polymorphism (AFLP) and internal transcribed spacer (ITS) sequences. Finger millet (Eleusine coracana subsp. coracana L.) is an important minor cereal crop in large areas of the developing world, especially Africa and India (Ekwamu, 1991) where it represents a critical genetic resource for agriculture and food security of subsistence farmers that inhabit arid, infertile and marginal lands. The crop is widely adapted to adverse agro-ecological conditions and nutritionally rich in methionine, iron and calcium (Barbeau and Hilu, 1993).
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1972) and (3) the model that relates the area under the disease-progress curve to yield loss, which can be described as the midway between the criticaland multiple-point models (Van der Plank, 1963). The duration of the epidemic relative to the life cycle of the crop is a primary consideration in applying the appropriate model (James, 1974).
Identification of the degree of pathogenicity of different races/isolates is crucial to develop integrated and effective disease management strategies. Besides, it is essential to screen genotypes resistant/tolerant to multiple isolates in order to develop stable high yielding improved variety(ies) or that are to be used as recurrent lines in hybridization programs. Genes for race-specific Severe yield losses have regularly been resistance are rapidly overcome by the pathogen recorded due to the blast fungus that infects the (Bonman et al., 1991) and so cannot support different above ground tissues of finger millet. As sustainable crop production. Therefore, the objective such, 80-90% yield loss in Mysore, India of the present study was: (1) to evaluate the (Venkataryan, 1947), up to 46% in Japan in 1973 pathogenicity of four different finger millet blast (M. (Kato et al., 1977) and up to 90% in Eastern Africa oryzae) isolates collected from different hosts and (Mgonja et al., 2007). In Ethiopia, although the crop regions of Ethiopia and (2) to screen finger millet has a potential yield up to 3.0 tons per hectare accessions and its wild relatives for resistance to these (Tadesse et al., 1995), production and productivity multiple blast isolates for further utilization in variety is adversely affected by various biotic and abiotic improvement. constraints among which blast is the most severe MATERIALS AND METHODS and the national average yield never exceeds 1.5 Experimental site: Field experiments were tons ha-1 (CSA, 2012). All aerial parts of the plant conducted at Bako Agricultural Research Center can be affected in wet and humid environments. Leaf (1650 m.a.s.l, 7°17'12.5'’N; 38°25'47.5'’E), 259 km surfaces become speckled with oval lesions and West of Addis Ababa. Average annual rainfall for plants become prone to lodging when stems are the two seasons, 2011 and 2012, was 1425.30 mm infected. However, most severe yield loses occur from and 886.50 mm, relative humidity 60.50%, 57.8% panicle infection (Mgonja et al., 2007; Ekwamu, and mean temperature 21.20°C and 20.4°C, 1991). respectively. The dominant soil type of the area was Various yield loss assessment models and Nitosol. approaches have been reported based on disease epidemics, crop types and other host-pathogenenvironment interaction effects (Mousanejad et al., 2010; Teng and Gaunt, 1980; James et al., 1968). However, the three most common assessment methods are: (1) critical-point models that provide estimates of loss for any given level of disease at a specific time (Mousanejad et al., 2010; James et al., 1968) or any specified time when a particular severity of disease is reached (Large, 1952), (2) multiple-point models estimate that loss over time as a disease-progress curve consisting of multiple disease assessment time intervals (James et al.,
Experimental materials Plant materials: Twelve cultivated finger millet accessions and four wild relatives were included in this study. The cultivated accessions were originally collected from major finger millet producing regions of Ethiopia supplemented by four accessions that were introduced from Zambia and Zimbabwe (Table 1). The wild accessions were collected from different parts of Ethiopia, particularly West Shoa, East Wollega, East Shoa and Sidama administrative zones (Table 1). The cultivated accessions were selected based on the results of an advanced screening trial in 2011 of 225 finger millet accessions using a single
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INDIAN JOURNAL OF AGRICULTURAL RESEARCH TABLE 1: List of test accessions with their passport information
Accession 214988 PW-001-022 214995 203362 BKFM0034 BKFM0043 BKFM0011 BKFM0028 BKFM0047 BKFM0051 100032 203356 AAU-Elu-5 AAU-Elu-59 AAU-Elu-65 AAU-Elu-15
Species
Country/Regions of collection
Admin. zone
Altitude m a s l
Zambia Pipeline cultivar Zambia Zimbabwe Eth/Oromia Eth/Oromia Eth/B.Gumuz Eth/Oromia Eth/Oromia Eth/Oromia Eth/Amhara Zimbabwe Eth/Oromia Eth/SNNP Eth/Oromia Eth/Oromia
NI NI NI East Wollega East Wollega Assosa Zone West Wollega East Wollega Illuababora East Gojam NI West Shoa Sidama Zone East Shoa West Wollega
1300 1130 1420 1454 1601 1428 1608 1334 2227 1980 1420 1632 1805 1677 1938
E. coracana subsp. coracana E. coracana subsp. coracana E. coracana subsp. coracana E. coracana subsp. coracana E. coracana subsp. coracana E. coracana subsp. coracana E. coracana subsp. coracana E. coracana subsp. coracana E. coracana subsp. coracana E. coracana subsp. coracana E. coracana subsp. coracana E. coracana subsp. coracana E. coracana subsp. africana E. coracana subsp. africana E. coracana subsp. africana E. coracana subsp. africana
Key: Alt = altitude, Eth = Ethiopia, NI = not identified, m.a.s.l = meters above sea level, SNNP = Southern National Nationalities and Peoples Region
isolate collected from naturally highly infected leaf, neck and seed (head) of finger millets (Lule et al., 2013). Thus, Acc. 214988, PW-001-022 and Acc.214995 were selected among moderately resistant groups with percentages head blast severity index and head blast RAUDPC value ranged from 21-40%; Acc. 203356, BKFM0034, BKFM0028 and BKFM0047 were among moderately susceptible with 41-60%; BKFM043, BKFM0011, Acc. 203362 and BKFM0051 were among the susceptible groups with 61-80%; but Acc. 100032 was from highly susceptible groups with greater than 80% head blast severity index, as suggested by Mgonja et al.(2007).
Experimental D esign and Treatment Arrangement: A split plot design was used, with the fungal isolates assigned to the main plot and the test accessions to the sub plots. The trial was replicated three times and the plot size was single rows of 2 m long with 40 cm spacing between rows. The fertilizer rates of 100kg DAP and 50kg Urea per hectare was applied. Inoculum production and inoculation of the isolates: For artificial inoculation, pure cultures of each isolate of the pathogen were grown on potato dextrose agar (PDA). Inoculated plates were incubated for seven days at 27± 10C under 12 hours Magnaporthe oryzae isolates: Four isolates of M. light/dark in an incubator (Tredway et al., 2003). oryzae, collected from East Wollega and West Wollega Pure cultures were identified as per characteristic zones of Oromia regional state, Metekel zone of mycelia and spores after seven days of incubation, Benishangul Gumuz regional state and Awi zone of by microscopy. Spore concentrations were adjusted Amhara regional state of Ethiopia, were used. Three to 2.5 x 10 6 spores/ml in water with a isolates such as Pg. 11, Pg. 22 and Pg. 26 were haemocytometer. A 20-liter capacity portable collected from cultivated finger millet plants and one sprayer was adjusted to full coverage for inoculation. isolate (Pg. 41) from E. coracana subsp. africana Inoculation was done during humid and cloudy that were severely infected with leaf, neck and seed conditions (Han et al., 2003). Five drops of surfactant (80% Cita Wat) was added to 20 -liter (head) blast under natural conditions (Table 2). TABLE 2: List of M.oryzae isolates with their passport information Isolate code Pg.11 Pg.22 Pg.26 Pg.41
Region/Zone of collection
Specific site name
Alt (m.a.s.l
longitude
latitude
Oromia/W. Wollega B.Gumuz/Metekel Amhara/Awi zone Oromia/W. Wollega
Leta bobina Medihin Bizira Keni Dandi Gudi
1622 1033 1679 1650
035039"29.8' 036021"27.5' 0360 27"02.9' 035018"41.8'
09016"19.3' 11013"15.4' 11001"26.1' 090 39"43.8'
Key: NI= not identified, Alt= altitude, m.a.s.l= meter above sea level
Source Finger millet neck Finger millet head Finger millet leaf Wild type neck
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spore suspensions to enhance adhesion of spores to the host tissue. Plants were inoculated twice; first at 63 days after planting (DAP) to initiate leaf blast, followed at 94 DAP, mainly to intensify head blast infection.
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estimates were normalized by dividing with the total area of the graph (number of days from first appearance of disease until the end of the observation period) (Shtienberg et al., 1990; Fry, 1968). In this study, the number of days from first Data collection and analysis: Ten plants per row appearance/recording of the disease to end of the were randomly selected and tagged prior to heading observation period was 60 for leaf blast and 30 for for plant-based disease assessment. Leaf blast head and neck blast. The normalized AUDPC was severity was recorded on a scale of 1 to 9 (Chen et referred to as the relative area under disease progress al., 2013; Thakur et al., 2009; Mgonja et al., 2007). curve (RAUDPC) and this value was employed in Head blast and neck blast scores were calculated as different analysis packages used in the study. the number of diseased sample plants divided by The ANOVA for all characters were the total number of plants sampled in a plot and calculated by using SAS (SAS, 2008) software. The multiplied by 100 (IBPGR, 1985). Grain yield and relationship between disease severity and yield was other agronomic traits were recorded according to determined by regression and correlation analysis the finger millet descriptors (IBPGR, 1985). Disease and the treatment means were separated by assessment was made every ten days interval Duncan’s multiple range tests using SAS software. commencing from 63 rd days after planting to Monographs and other graphics used to visualise physiological maturity. Accordingly, disease severity disease trends was computed using EXCEL. Data and incidence for leaf blast was recorded seven from the 2011 season for the cultivated finger millet times, but head blast and neck blast was recorded accessions used in this study was included for reliable four times depending on the appearance of the estimates of yield loss and identifying resistant disease on the tissue of the plant. The severity score genotypes across years. This data showed that blast recorded from ten-selected plants were converted to infection was progressive throughout vegetative percentage severity index or disease index as: growth and hence infection of the leaf or neck area of the plant contributed significantly (P0.01) to the n [ S i ] x 100 development of head infection (Lule et al., 2013). i 1 Similarly, Mgonja et al. (2007) and Ekwamu (1991) Dj n x 9 reported that severe yield losses recorded when the th Where Dj was disease index at j round score, Si panicle was affected by blast disease. Therefore, in was the severity recorded from ith sample plant, n this study, head blast infection was used as the was the total number of plants considered per plot principal parameter to estimate yield loss for both and 9 was the maximum value of the scoring scale the 2011 and 2012 seasons. and was constant. The values obtained for the Infection rates in 2012 were variable during different assessment periods were used to calculate the different assessment periods. Therefore, multiple the area under disease progress curve (AUDPC) over point models were employed to relate the yield loss the recording period, which was used to quantify (YL) assessments to the percentage disease severity and summarize the severity of disease over time scores of several time points during the growth according to Shaner and Finnay (1977) as: season using a multiple regression equation as suggested by James (1974): ni 1 ( Y Y xi ) AUDPC { xi 1 (t t i )} i 1 %YL= b1x1+ b2x2+ b3x3+ … + bnxn i 1 2 Where, AUDPC was the area under disease Where b1… bn were the partial regression progress curve, Yxi was the disease severity index coefficients from the first up to nth round assessment recorded for x-accession at ith assessment period; ti periods, and x 1 … x n were the corresponding was the time interval of disease recording for the percentage disease severity scores (severity index) subsequent assessment periods. The sum total of for the 1st up to the nth round assessment period. disease progress during the different assessment The blast infection rate in 2011 progressed periods was used as the final AUDPC. AUDPC consistently throughout crop development
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(Lule et al., 2013). Therefore, normalized area under the disease progress curve (AUDPC) was applied. This approach followed a similar logic to the multiple point models except that it involved integration of the entire disease progress curve (Zadoks, 1985) and assumed that yield loss was proportional to the amount and duration of the disease and that the infection rate progressed consistently (James, 1974; Mousanejad et al., 2010). RESULTS AND DISCUSSION Analysis of variance: Variation among the test accessions were significant for grain yield, leaf blast (Pd” 0.01) and neck blast RAUDPC (Pd” 0.05) but not significant for head blast RAUDPC (Table 3). Likewise, the different fungal isolates showed significant variation for neck blast (Pd”0.01) and head blast (Pd”0.05) but not for leaf blast and grain yield (Table 3). Similarly, Lule et al. (2013), Muyonga et al. (2000) and Tsehaye and Kebebew (2002) reported significant variations among finger millet genotypes for blast disease, grain yield and other agronomic traits. Mean comparison using Duncan’s multiple range test (Alpha = 0.05) depicted significant variation between isolates of M. oryzae for neck blast RAUDPC and head blast RAUDPC but no significant difference for grain yield and leaf blast RAUDPC (Table 4). Pathogenicity assessment of M. oryzae isolates Pathogenicity tests revealed that all M. oryzae isolates were able to infect all accessions of finger millet and its wild relatives evaluated in this study. The aggressiveness and virulence characters of these isolates were different for the different test accessions. M. oryzae isolate Pg.11 caused the most severe
FIG 1: Finger millet leaf blast severity indexrecorded per fungal isolate during the growth season.
FIG 2: Finger millet neck blast severity recorded per fungal isolate during the growth season
FIG 3: Finger millet head blast severity recorded per fungal isolate during the growth season
infection on all plant tissues (leaf, neck and head) followed by isolate Pg. 22, but severity score was least for isolate Pg.26 (Figs 1 to 3, Table 4). Gashaw et al. (2014) also reported that isolate Pg.11 showed the highest disease score on all the three finger millet varieties used in their study. Isolate Pg. 41, isolated from the neck of E. coracana subsp. africana also infected all tissues of both cultivated and wild types, underlining the potential of finger millet wild relatives and weeds to serve as inoculum sources. Takan et al. (2012) reported that at least eight haplotypes were common to cultivated and wild millets, further emphasizing the importance of wild hosts in pathogen epidemiology. Although the different blast isolates showed no significant variations for grain yield, lower mean grain yield per plant was recorded for Pg. 22 (2.8g) and Pg. 11 (2.9g) and relatively better for Pg. 41 (3.6g) and Pg. 26 (3.1g) (Table 4), indicating the inverse correlation between infectivity and grain yield. Muyonga et al. (2000) also reported that the
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TABLE 3: Mean squares for grain yield and leaf, neck and head blast disease relative area under disease progression curve (RAUDPC) Source of variation Block Genotype Isolates Genotype × Isolates Error CV (%) Mean
df
LBRAUDPC
NBRAUDPC
HBRAUDPC
GYPPL (g)
2 15 3 45 126
46.59 3415.06* * 171.12 126.24 170.25 26.02 50.14
1564.92* * 115.41* 419.42* * 38.15 62.21 12.3 6.35
5931* * 180.86 471.71* 69.57 129.65 11.38 13.45
1.794 8.06* * 5.75 4.14 3.28 28.59 3.09
Key: df = degrees of freedom, * = significant, ** = highly significant, prefix LB, NB and HB refers to leaf blast, neck blast and head blast respectively, RAUDPC = relative area under disease progress curve, GYPPL= grain yield per plant
most severe blast disease infection on finger millet cultivars lead to the lowest grain yields. Takan et al. (2003) also reported that finger millet blast isolates that caused different types of blast on this crop (leaf, neck and head/panicle blast) did not show genetic variation and belonged to the same molecular groups; hence the same isolates were capable of causing different types of blast under suitable agroecological conditions.
from weed hosts and 225 isolates from cultivated finger millet using RFLP markers and found that blast isolates from weed hosts, except Digitaria, were not genetically distinct and in most cases belonged to the same genetic groups as isolates from finger millet cultivars.
H ost reaction to different M. oryzae isolates: Accessions of E. coracana subsp. africana were less affected by leaf blast than cultivated finger millet and Moreover, it appeared that an isolate variation among these accessions was not significant obtained from different tissues of finger millet and (Table 4). Higher neck blast and head blast severity its wild type infected both cultivated and wild types was recorded for accessions AAU-ELU-59 and to a variable degree among accessions as well as AAU-ELU-65 (Table 4). Finger millet Acc. 203362 different tissues of individual accessions. Takan et was most susceptible to leaf, neck and head blast al. (2012) subsequently characterized 55 isolates (Table 4). Mean comparison using Duncan’s multiple TABLE 4: Duncan’s multiple range tests for the test accessions and M.oryzae isolates. Means with the same letter are not significantly different. Treatment no
Accession
LBRAUDPC
NBRAUDPC
HBRAUDPC
GYPPL (g)
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 Fungal isolate Pg.11 Pg.22 Pg.41 Pg.26
214988 PW-001-022 214995 203362 BKFM0034 BKFM0043 BKFM0011 BKFM0028 BKFM0047 BKFM0051 100032 203356 AAU-Elu-5 AAU-ELU-59 AAU-ELU-65 AAU-ELU-15
60.42a 56.77a 54.42a 64.37a 56.63a 63.26a 54.53a 62.44a 61.47a 58.55a 60.80a 57.39a 23.64b 24.66b 15.76b 24.26b
8.19a 1.94b 5.55ab 7.78ab 2.78b 4.58ab 5.69ab 7.22ab 4.86ab 5.00ab 4.58ab 6.94ab 2.92b 11.36a 11.94a 10.83a
17.50ab 7.80b 10.83ab 17.78ab 10.28b 10.97ab 10.97ab 11.11ab 12.50ab 11.03ab 13.05ab 12.86ab 10.56ab 18.64ab 21.67a 16.39ab
3.30abc 3.16 abc 3.05 abc 1.82c 3.24 abc 3.15 abc 3.61ab 3.99a 4.13a 3.67ab 3.60ab 4.06a 2.79 abc 1.96bc 1.69c 2.08bc
-
51.68a 50.12a 48.04a 50.80a
9.583a 7.673ab 5.035bc 3.064c
16.67a 15.57a 12.71ab 9.34b
2.86a 2.69a 3.55a 3.12a
Key: Trt = Treatment, Prefix LB, NB and HB refers to leaf blast, neck blast and head blast, RAUDPC = relative area under disease progress curve, GYPPL= grain yield per plant, g= gram
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range test (Alpha = 0.05) indicated no significant differences between the test accessions for head blast RAUDPC except for the wild type AAU-ELU-65 (21.67 %) and two cultivated accessions, PW-001022 (7.80 %) and BKFM0034 (10.28 %)(Table 4). Blast severity and incidence were different between the 2011 and 2012 growing seasons probably due to the variation in host-pathogen-environment interactions in general as well as the rainfall shortage later in the 2012 season in particular, which probably reduced the severity of the infection.
and none of them were resistant. Mgonja et al., (2007) reported nine out of 95 finger millet germplasms were resistant to blast disease. Thakur et al. (2009) evaluated 432 different pearl millet lines for blast resistance and found 25 lines consistently resistant to the disease.
disease reaction, implying that diverse finger millet populations are planted within a country or region (Tables 1, 4). Acc. 214988 grouped among the moderately resistant accessions in 2011 but it was relatively highly infected in 2012, emphasizing the importance of multi-season trials to identify consistently resistant/tolerant genotypes for further utilization. Gashaw et al., (2014) evaluated two improved finger millet varieties (Tadesse and Boneya) and one local landraces for blast reaction
The possible reason for the variation in yield loss between the 2011 and 2012 growing seasons might be due to the change in weather condition particularly rainfall shortage at later growing stage in 2012, relative humidity and generally variation in host-pathogen-environment interactions probably reduced the severity of the infection in 2012. Stetina et al. (2006) reported similar observations. The pooled mean for both the 2011 and 2012 cropping seasons indicated an average of 42% grain yield
Regression and Correlation Coefficient Analysis: Although not significant, both regression (r) and correlation (r2) analyses portrayed expected negative associations of neck blast RAUDPC (r = -0.322, r2 = -0.071) and head blast RAUDPC (r = -0.064, r2 In 2012, PW-001-022, Acc.214995, = -0.009) to grain yield (Table 5), similar to the BKFM0034 and the wild type AAU-ELU-15 findings of Lule et al (2013), on head blast AUDPC. exhibited better resistance to leaf, neck and head Days to 50% heading and days to 50% maturity blast (Table 4). The first two were found moderately showed significant positive association to grain yield resistant in 2011 and thus showed consistently lower (Table 5). These observed positive correlations due infection by blast isolates from a single host plant, to gene effects can be the result of strong linkage multiple isolates sampled from various regions and between the genes or may be due to pleiotropic from multiple host plants and are therefore, potential genes that control the traits in the same direction candidates for further breeding activities. (Kearsey and Pooni, 1996). In contrast, Lule et al. Acc. 203362 was characteri zed as (2012) and Wolie and Dessalegn (2011) reported susceptible in 2011 (Lule et al., 2013). In 2012, it that days to heading and days to maturity correlated also showed higher infection by leaf, neck and head negatively with grain yield for finger millet. blast followed by the lowest grain yield per plant Yield Loss Assessment: For the 2011 season, (1.82g, Table 4). Acc. BKFM0047 (4.13g) and Acc. relative yield loss assessments based on RAUDPC 203356 (4.06g) produced more grain and were revealed severe yield losses due to blast disease, among the moderately susceptible group according ranging from 40.22% for Acc. 214988 to 99.06% to the 2011 records and moderate infection in 2012 for Acc. 100032 with an average of 73.32% (Table (Table 4). Muyonga et al. (2000) reported that among 6). For the 2012 cropping season, the average yield the five different varieties evaluated for blast loss estimated using multiple point models was much resistance, the highest grain yield was recorded from less at 9.8% (Table 6). The different test accessions the most resistant variety and vice versa for the most showed variations with higher yield loss (16.9 %) susceptible variety. Conversely, Anon (1959) recorded for Acc. 203362 among the cultivated and reported that the low yielding variety MO 359 was 15.74% for AAU-ELU-65 among the wild types immune to infection and therefore a good candidate (Table 6). Lower percentage losses (6.1% and parent for breeding for disease resistance. 7.55%) were recorded for PW-001-022 and Acc Finger millet accessions collected from the 214995 among cultivated finger millet, respectively, same region/country showed different levels of and AAU-ELU-5 (7.32%) from the wild types.
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TABLE 5: Correlation and regression analysis among the major blast parameters and agronomic traits (independent variable) and grain yield per plant (dependent variable) Blast disease and Other agronomic traits NBRAUDPC HBRAUDPC Days to heading Days to maturity GYPPL
Pearson correlation coefficien NBRAUDPC 1.00
HBRAUDPC 0.793* * 1.00
Regression coefficient DH -0.172* -0.241* * 1.00
DM -0.124 -0.144* 0.465* * 1.00
GYPPL
Intercept
Grain yield
-0.071 -0.009 0.225* * 0.278* * 1.00
7.35 13.26 77.83 145.42 -
-0.322 -0.064 0.375* * 0.289* * -
Key: the prefix NB and HB refers to neck blast and head blast respectively, RAUDPC= relative area under disease progress curve, DH= days to heading, DM= days to maturity, GYPPL= grain yield per plant
TABLE 6: Percentage yield loss (YL) assessment suffered by the various test accessions (top secction) and caused by the various fungal isolates (bottom section) during the 2011 and 2012 main cropping season Accession
% YL in 2011
% YL in 2012
Average YL (%)
214988 PW-001-022 214995 203362 BKFM0034 BKFM0043 BKFM0011 BKFM0028 BKFM0047 BKFM0051 100032 203356 AAU-ELU-5 AAU-ELU-59 AAU-ELU-65 AAU-ELU-15 Mean Isolates Pg.11 Pg.22 Pg.41 Pg.26 Mean
40.22 51.65 53.98 82.55 75.14 96.73 86.25 74.08 76.2 90.38 99.06 53.55 Not evaluated Not evaluated Not evaluated Not evaluated 73.32
12.84 6.1 7.55 16.9 8.26 9.92 8.92 8.08 8.8 8.13 9.08 8.86 7.32 11.6 15.74 8.63 9.80
26.53 28.88 30.77 49.73 41.70 53.33 47.59 41.08 42.50 49.26 54.07 31.21 7.32 11.60 15.74 8.63 42.00
Not Not Not Not
12.08 11.07 8.84 6.74 9.67
12.08 11.07 8.84 6.74 9.67
evaluated evaluated evaluated evaluated
loss due to finger millet blast (Table 6). Similarly, an average finger millet yield loss of 41.8% was reported in Ethiopia (Gashaw et al., 2014); up to 90% in Eastern Africa (Mgonja et al., 2007) and up to 46% in Japan (Kato et al., 1977).
Pg. 11 (56.2%) followed by Pg. 41 (49.3%) and the lowest for Pg. 26 (30.6%).
CONCLUSION Highly significant (Pd” 0.01) variation was recorded among the test accessions for grain yield, Isolate Pg. 11 (12.08%) followed by Pg. 22 although the different M. oryzae isolates showed no (11.07%) caused relatively highest yield losses (Table significant variation in impact on grain yield. 6). Similarly, Gashaw et al. (2014) evaluated six Genotype x isolate interaction was also not finger millet blast isolates (Pg. 11, Pg. 20, Pg. 22, significant. This suggested that when resistance is Pg. 26, Pg. 40 and Pg. 41) under green house observed in a variety, this will be durable across conditions in 2011, which included the four tested different isolates, even though pathogenicity varied isolates in this study on three finger millet cultivars slightly among the isolates and infection level varied (Boneya, Tadesse and a local check) and found that among the test accessions. Isolates Pg. 11 and Pg. the highest percentage yield loss was recorded for 22, isolated from neck and head of finger millet,
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respectively, were most virulent in infecting all plant tissues. The isolate from neck of the wild type (Pg. 41), infected both the cultivated its wild types, confirming that wild types or weeds can act as hosts or reservoirs of the pathogen and source of inoculum. The finger millet accessions Acc.214995 and PW-001-022 showed consistently better resistance/ tolerance in both 2011 and 2012 and hence are promising candidates for further evaluation in variety development activities. An average percentage yield loss of 42% was recorded due to blast infection during 2011 and 2012 combined. Generally, finger millet has received limited research attention compared to other cereal crops, also with regard to blast disease and the development of resistant
cultivars, yield loss assessment and evaluation of fungal isolate pathogenicity, particularly in Ethiopia. Therefore, the present study provides preliminary information regarding blast isolate pathogenicity, relative yield losses caused by blast, as well as the interaction and performance of some cultivated finger millet accessions and its wild types to the disease. ACKNOWLEDGEMENTS This study was supported by the SIDA BioInnovate collaborative research project 01/2010. Staff members of the Cereal crop technology generation team of Bako Agricultural Research Center are acknowledged for field support and data collection.
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