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INFLUENCE OF STARTER NITROGEN APPLICATION ON YIELDS OF TWO SOYA BEAN VARIETIES GROWN ON FERRIC LUVISOL IN OYO STATE, NIGERIA
Published 2016
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MOLECULAR ANALYSIS OF MECHANISMS AND IDENTIFICATION OF FACTORS OF PYRETHROID RESISTANCE IN ANOPHELES GAMBIAE SENSU LATO IN SOUTHWESTERN NIGERIA AND SOUTHERN BENIN REPUBLIC
Published 2011
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MOLECULAR ANALYSIS OF MECHANISMS AND IDENTIFICATION OF FACTORS OF PYRETHROID RESISTANCE IN ANOPHELES GAMBIAE SENSU LATO IN SOUTHWESTERN NIGERIA AND SOUTHERN BENIN REPUBLIC
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FRELIP Subject
Anopheles
2 results
2
Pyrethroid resistance
2 results
2
The development of resistance to insecticides by Anopheles mosquitoes continues to threaten the success of malaria control programmes in West Africa. Local data on mechanisms and factors causing resistance in the region are scanty. This study was designed to investigate the environmental factors and mechanisms implicated in resistance to pyrethroids by Anopheles gambiae in southwestern Nigeria and southern Benin Republic. Larvae of Anopheles mosquito were collected in 2007 from 19 localities in the six states of southwestern Nigeria and 18 localities in the seven divisions of southern Benin and reared to adults. These were identified morphologically and with Polymerase Chain Reactions (PCR). They were also bioassayed for susceptibility to pyrethroids. Molecular characterisation of pyrethroid resistant phenotypes was carried out using PCR and microarray analyses of the expressed genes. Dissolved Oxygen (DO) and pH were determined using a digital multipurpose meter while physical appearances of breeding sites were assessed visually. Xenobiotic factors such as Spilled Engine Oil (SEO) and agricultural pesticides that might contribute to the emergence of resistance in Anopheles populations were examined through bioassay. Associations between pyrethroid resistance with environmental factors and molecular profiles of Anopheles were evaluated using Chi square. A. gambiae complex genotyped in Nigeria comprised of 73.6 % A. arabiensis and 26.3 % A. gambiae sensu stricto; while those genotyped in Benin were 92.9 % A. gambiae s.s. and 7.0 % A. melas. Pyrethroid resistance in Nigeria and Benin were recorded in 68.4 % and 94.4 % of the localities examined respectively. Breeding sites contaminated with SEO (B-SEO) or Pesticide Residues (B-PR) had low DO (B-SEO = 13.4 ± 1.5 mg/l, B-PR=12.2 ± 1.7 mg/l), the Non-contaminated Breeding sites (B-NC) had higher levels of DO (B-NC=33.1 ± 2.3) and mainly produced pyrethroid-susceptible Anopheles (p<0.05). Significant variations in pH were not recorded. Differences in habitation by resistant-Anopheles in breeding sites contaminated with SEO or pesticide residues were observed. A. gambiae found around the two agricultural sites (Houeyiho, Benin and Ajibode, Nigeria) exposed to synthetic pesticides showed significant levels of pyrethroid resistance with mortality rates of 70.0 % and 89.7% respectively. A. gambiae larvae survived at SEO concentrations below 11.8x10-3 μL/cm2. Ninety six percent of larval mortality resulted from direct cuticle contact with SEO whereas only four percentage mortality was from larval suffocation. A cross resistance phenomenon was recorded with SEO and pyrethroids. A. gambiae showed the presence of elevated frequencies of knock down resistance West (kdr-W) mutations in Benin samples (kdr-W ranged from 0.6 to 0.9) and absence of kdr-W in Nigeria samples. Two detoxification genes (CYP6P3 and CYP6M2) were up-regulated in resistant-Anopheles. Additional detoxification genes specific to agricultural and SEO sites were also over-expressed in the resistant populations. There was an association between residual synthetic pesticides, spilled engine oil and emergence of pyrethroid-resistance in A. gambiae in Nigeria and Benin Republic. The diversified profile of identified metabolic genes reflected the influence of a range of xenobiotics on selection of resistance in mosquitoes.
2 results
2
Xenobiotics
2 results
2
CROPGRO-soya bean model
1 results
1
Nitrogen is a major soil nutrient for the growth and development of crops. Nitrogen deficiency in-between pre-nodulation and nodulation in soya bean is critical to enhancing its growth and yield. Decision Support Systems for Agro-technology Transfer (DSSAT) model like CROPGRO-soya bean could be a useful tool for predicting starter nitrogen requirement in soya bean. However, information on starter nitrogen application to overcome N-deficiency-induced-stress during early seedling growth and prediction using CROPGRO-soya bean is limited. This study was undertaken to investigate the effect of starter nitrogen on soya bean yield grown on ferric luvisol in Oyo State, Nigeria.Field experiment involving two soya bean varieties (TGx1485-1D and TGx1448-2E) and five starter nitrogen rates (0, 5, 15, 25 and 35 kg/ha) was carried out for two seasons at Ipapo and Gbonran in Itesiwaju Local Government Area of Oyo State. The experiment was laid out as split-plot in a randomised complete block design with three replications. Nitrogen rate and soya bean variety were main and sub-plot factors, respectively. Shoot Dry Weight (SDW, t/ha), Grain Yield (GY, t/ha), Harvest Index (HI) and Shoot Nitrogen Content (SNC, g/kg) were measured. In screenhouse, soil from each location was filled into pots (5 kg) and each variety was planted under optimum management practices in a completely randomised design with three replications. Data on days to seedling emergence, days to first pod appearance and days to physiological maturity were obtained, fitted and used to calibrate CROPGRO-soya bean model. Data on weather parameters, soil description and characterisation (pH, field capacity, permanent wilting point, bulk density and hydraulic conductivity) were used for predicting phenology and yield. Data were analysed using ANOVA at α0.05, and model outputs were evaluated with percentage error (PE).Nitrogen rate had no significant effect on SDW, while variety TGx1448-2E had significantly higher SDW (2.9±0.3) and GY (1.5±0.1) than TGx1485-1D (2.3±0.3 and 1.3±0.1, respectively) at both locations. Nitrogen rate had no significant effects on GY and HI at Gbonran. However, 15 kgN/ha produced a significantly highest GY (1.4±0.1) than the control (1.2±0.1), while HI at 5 kgN/ha (0.60±0.02) was highest at Ipapo. Variety TGx1485-1D had significantly higher HI (0.58±0.05) than TGx1448-2E (0.52±0.05) at both locations. Nitrogen rate had no significant effect on SNC at both locations. Variety TGx1485-1D had significantly higher SNC (33.0±1.5) than TGx1448-2E (26.0±1.5) at Ipapo. CROPGRO-Soya bean model predicted soya bean phenology across nitrogen rates within 0 – 1 day of the observed values at both locations. Predicted yield of TGx1485-1D was 1.5 t/ha across N rates with PE < 15%, while that of TGx1448-2E was 2.6 t/ha with PE > 30%. Application of starter nitrogen had no effect on yields of soya bean at Gbonran but 15 kgN/ha enhanced grain yield of soya bean at Ipapo. Variety TGx1448-2E had better biomass yield, while TGx1485-1D had better nitrogen content on ferric luvisol. Prediction of CROPGRO-soya bean model was reliable for phenology and yield of TGx1485-1D but not reliable for yield of TGx1448-2E
1 results
1
Soya bean grain yield
1 results
1
Soya bean phenology
1 results
1
Starter nitrogen rate
1 results
1
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