Distribution of Pseudocercospora species causing Sigatoka leaf diseases of banana in Uganda and Tanzania.

Sigatoka leaf diseases are a major constraint to banana production. A survey was conducted in Tanzania and Uganda to assess the distribution of Pseudocercospora species and severity of Sigatoka leaf diseases. Pseudocercospora species were identified using species-specific primers. Sigatoka-like leaf diseases were observed in all farms and on all cultivars, but disease severity varied significantly (P < 0.001) between countries, districts/regions within countries, altitudinal ranges and banana cultivars. In all regions except Kilimanjaro, P. fijiensis, the causal agent of black Sigatoka, was the only pathogen associated with Sigatoka disease. Mycosphaerella musae was associated with Sigatoka-like symptoms in Kilimanjaro region. Black Sigatoka disease was more severe in Uganda, with a mean disease severity index (DSI) of 37.5%, than in Tanzania (DSI = 19.9%). In Uganda, black Sigatoka disease was equally severe in Luwero district (mean DSI = 40.4%) and Mbarara district (mean DSI = 37.9%). In Tanzania, black Sigatoka was most severe in Kagera region (mean DSI = 29.2%) and least in Mbeya region (mean DSI = 11.5%). Pseudocercospora fijiensis, the most devastating sigatoka pathogen, was detected at altitudes of up to 1877 m a.s.l. This range expansion of P. fijiensis, previously confined to altitudes lower than 1350 m a.s.l. in East Africa, is of concern, especially for smallholder banana farmers growing the susceptible East African Highland bananas (EAHB). Among the banana varieties sampled, the EAHB, FHIA hybrids and Mchare were the most susceptible. Here, the loss of resistance in Yangambi KM5, a banana variety previously resistant to P. fijiensis, is reported for the first time.

Virulence Diversity of Phakopsora pachyrhizi Isolates From East Africa Compared to a Geographically Diverse Collection.

Soybean rust, caused by the biotrophic pathogen Phakopsora pachyrhizi, is a highly destructive disease causing substantial yield losses in many soybean producing regions throughout the world. Knowledge about P. pachyrhizi virulence is needed to guide development and deployment of soybean germplasm with durable resistance against all pathogen populations. To assess the virulence diversity of P. pachyrhizi, 25 isolates from eight countries, including 17 isolates from Africa, were characterized on 11 soybean genotypes serving as differentials. All the isolates induced tan lesions with abundant sporulation on genotypes without any known resistance genes and on soybean genotypes with resistance genes Rpp4 and Rpp5b. The most durable gene was Rpp2, where 96% of the isolates induced reddish brown lesions with little or no sporulation. Of the African isolates tested, the South African isolate was the most virulent, whereas those from Kenya, Malawi, and some of the isolates from Tanzania had the lowest virulence. An Argentinian isolate was virulent on most host differentials, including two cultivars carrying multiple resistance genes. Ten distinct pathotypes were identified, four of which comprised the African isolates representing considerable P. pachyrhizi virulence. Soybean genotypes carrying Rpp1b, Rpp2, Rpp3, and Rpp5 resistance genes and cultivars Hyuuga and UG5 were observed to be resistant against most of the African isolates and therefore may be useful for soybean-breeding programs in Africa or elsewhere.

Genome-enabled prediction of genetic values using radial basis function neural networks.

The availability of high density panels of molecular markers has prompted the adoption of genomic selection (GS) methods in animal and plant breeding. In GS, parametric, semi-parametric and non-parametric regressions models are used for predicting quantitative traits. This article shows how to use neural networks with radial basis functions (RBFs) for prediction with dense molecular markers. We illustrate the use of the linear Bayesian LASSO regression model and of two non-linear regression models, reproducing kernel Hilbert spaces (RKHS) regression and radial basis function neural networks (RBFNN) on simulated data and real maize lines genotyped with 55,000 markers and evaluated for several trait-environment combinations. The empirical results of this study indicated that the three models showed similar overall prediction accuracy, with a slight and consistent superiority of RKHS and RBFNN over the additive Bayesian LASSO model. Results from the simulated data indicate that RKHS and RBFNN models captured epistatic effects; however, adding non-signal (redundant) predictors (interaction between markers) can adversely affect the predictive accuracy of the non-linear regression models.

First Report of Maize chlorotic mottle virus and Maize Lethal Necrosis in Kenya.

In September 2011, a high incidence of a new maize (Zea mays L.) disease was reported at lower elevations (1,900 m asl) in the Longisa division of Bomet County, Southern Rift Valley, Kenya. The disease later spread to the Narok South and North and Naivasha Districts. By March 2012, the disease was reported at up to 2,100 m asl. Diseased plants had symptoms characteristic of virus diseases: a chlorotic mottle on leaves, developing from the base of young whorl leaves upward to the leaf tips; mild to severe leaf mottling; and necrosis developing from leaf margins to the mid-rib. Necrosis of young leaves led to a "dead heart" symptom, and plant death. Severely affected plants had small cobs with little or no grain set. Plants frequently died before tasseling. All maize varieties grown in the affected areas had similar symptoms. In these regions, maize is grown continuously throughout the year, with the main planting season starting in November. Maize streak virus was present, but incidence was low (data not shown). Infected plants were distributed throughout affected fields, with heavier infection along field edges. High thrips (Frankliniella williamsi Hood) populations were present in sampled fields, but populations of other potential disease vectors, such as aphids and leafhoppers, were low. Because of the high thrips populations and foliar symptoms, symptomatic plants were tested for the presence of Maize chlorotic mottle virus (MCMV) (3) using tissue blot immunoassay (TBIA) (1). Of 17 symptomatic leaf samples from each Bomet and Naivasha, nine from Bomet and all 17 from Naivasha were positive for MCMV. However, the observed symptoms were more severe than commonly associated with MCMV, suggesting the presence of maize lethal necrosis (MLN), a disease that results from maize infection with both MCMV and a potyvirus (4). Therefore, samples were tested for the presence of Sugarcane mosaic virus (SCMV), which is present in Kenya (2). Twenty-seven samples were positive for SCMV by TBIA, and 23 of 34 samples were infected with both viruses. Virus identities were verified with reverse-transcription (RT)-PCR (Access RT-PCR, Promega) and MCMV or SCMV-specific primers. MCMV primers (2681F: 5'-ATGAGAGCAGTTGGGGAATGCG and 3226R: 5'-CGAATCTACACACACACACTCCAGC) amplified the expected 550-bp product from three leaf samples. Amplicon sequences were identical, and had 95 to 98% identity with MCMV sequences in GenBank. SCMV primers (8679F: 5'-GCAATGTCGAAGAAAATGCG) and 9595R: 5'-GTCTCTCACCAAGAGACTCGCAGC) amplified the expected 900-bp product from four leaf samples. Amplicon sequences had 96 to 98% identity, and were 88 to 96% identical with SCMV sequences in GenBank. To our knowledge, this is the first report of MCMV and of maize coinfection with MCMV and SCMV associated with MLN in Kenya and Africa. MLN is a serious threat to farmers in the affected areas, who are experiencing extensive to complete crop loss. References: (1) P. G. S. Chang et al. J. Virol. Meth. 171:345, 2011. (2) Delgadillo Sanchez et al. Rev. Mex. Fitopat. 5:21, 1987. (3) Jiang et al., Crop Prot. 11:248, 1992. (4) R. Louie, Plant Dis. 64:944, 1980.

A competitive polymerase chain reaction to quantify DNA of Leptosphaeria maculans during blackleg development in oilseed rape.

An assay based on the competitive polymerase chain reaction technique was developed to quantify Leptosphaeria maculans during blackleg disease development in oilseed rape leaves. By means of primers specific to the highly virulent type of L. maculans, a heterologous internal control template was prepared by amplifying and cloning DNA from Leptosphaeria korrae under low-stringency annealing conditions. Coamplification of L. maculans with the internal control DNA provided accurate quantification of 1 to 10(9) copies of target DNA. The assay was applied to a comparative study of L. maculans colonization of resistant and susceptible rape cultivars. The assay revealed that lesion size was associated with the quantity of L. maculans DNA during the first 12 days after inoculation of the susceptible cultivar Westar and the moderately resistant cultivar Legend. In these cultivars, the quantity of DNA per lesion increased during the first 12 days after inoculation and then declined. This decline in detectable fungal DNA coincided with abundant sporulation, rapid necrosis, and the onset of leaf senescence. Trace amounts of L. maculans DNA were detected in the resistant cultivar Glacier, in which lesion size was similar to that in the wounded, uninoculated check. The assay is rapid, accurate, and very sensitive and can be incorporated into conventional disease screening programs.