Parameterization, calibration and validation of the DNDC model for carbon dioxide, nitrous oxide and maize crop performance estimation in East Africa.

Dynamic biogeochemical models are crucial tools for simulating the complex interaction between soils, climate and plants; thus the need for improving understanding of nutrient cycling and reduction of greenhouse gases (GHG) from the environment. This study aimed to calibrate and validate the DeNitrification-DeComposition (DNDC) model for soil moisture, temperature, respiration, nitrous oxide and maize crop growth simulation in drier sub-humid parts of the central highlands of Kenya. We measured soil GHG fluxes from a maize field under four different soil fertility management practices for one year using static chambers and gas chromatography. Using experimental data collected from four management practices during GHG sampling period, we parameterized the DNDC model. The results indicate that the DNDC model simulates daily and annual soil moisture, soil temperature, soil respiration (CO2), nitrous oxide (N2O), N2O yield-scaled emissions (YSE), N2O emission factors (EFs) and maize crop growth with a high degree of fitness. However, the DNDC simulations slightly underestimated soil temperature (2-6%), crop growth (2-45%) and N2O emissions (5-23%). The simulation overestimated soil moisture (9-17%) and CO2 emissions (3-10%). It however, perfectly simulated YSE and EFs. Compared to the observed/measured annual GHG trends, the simulation results were relatively good, with an almost perfect fitting of emission peaks during soil rewetting at the onset of rains, coinciding with soil fertilisation. These findings provide reliable information in selecting best farm management practice, which simultaneously improves agricultural productivity and reduces GHG emissions, thus permitting climate-smart agriculture. The good DNDC simulated YSE and EFs values (Tier III) provide cheaper and reliable ways of filling the huge GHG data gap, reducing uncertainties in national GHG inventories and result to efficient targeting of mitigation measures in sub-Saharan Africa.

Multiple virus lineages sharing recent common ancestry were associated with a Large Rift Valley fever outbreak among livestock in Kenya during 2006-2007.

Rift Valley fever (RVF) virus historically has caused widespread and extensive outbreaks of severe human and livestock disease throughout Africa, Madagascar, and the Arabian Peninsula. Following unusually heavy rainfall during the late autumn of 2006, reports of human and animal illness consistent with RVF virus infection emerged across semiarid regions of the Garissa District of northeastern Kenya and southern Somalia. Following initial RVF virus laboratory confirmation, a high-throughput RVF diagnostic facility was established at the Kenyan Central Veterinary Laboratories in Kabete, Kenya, to support the real-time identification of infected livestock and to facilitate outbreak response and control activities. A total of 3,250 specimens from a variety of animal species, including domesticated livestock (cattle, sheep, goats, and camels) and wildlife collected from a total of 55 of 71 Kenyan administrative districts, were tested by molecular and serologic assays. Evidence of RVF infection was found in 9.2% of animals tested and across 23 districts of Kenya, reflecting the large number of affected livestock and the geographic extent of the outbreak. The complete S, M, and/or L genome segment sequence was obtained from a total of 31 RVF virus specimens spanning the entire known outbreak period (December-May) and geographic areas affected by RVF virus activity. Extensive genomic analyses demonstrated the concurrent circulation of multiple virus lineages, gene segment reassortment, and the common ancestry of the 2006/2007 outbreak viruses with those from the 1997-1998 east African RVF outbreak. Evidence of recent increases in genomic diversity and effective population size 2 to 4 years prior to the 2006-2007 outbreak also was found, indicating ongoing RVF virus activity and evolution during the interepizootic/epidemic period. These findings have implications for further studies of basic RVF virus ecology and the design of future surveillance/diagnostic activities, and they highlight the critical need for safe and effective vaccines and antiviral compounds to combat this significant veterinary and public health threat.

Detection of influenza A virus in live bird markets in Kenya, 2009-2011.

BACKGROUND: Surveillance for influenza viruses within live bird markets (LBMs) has been recognized as an effective tool for detecting circulating avian influenza viruses (AIVs). In Sub-Saharan Africa, limited data exist on AIVs in animal hosts, and in Kenya the presence of influenza virus in animal hosts has not been described. OBJECTIVES: This surveillance project aimed to detect influenza A virus in poultry traded in five LBMs in Kenya. METHODS: We visited each market monthly and collected oropharyngeal and cloacal specimens from poultry and environmental specimens for virological testing for influenza A by real time RT-PCR. On each visit, we collected information on the number and types of birds in each market, health status of the birds, and market practices. RESULTS: During March 24, 2009-February 28, 2011, we collected 5221 cloacal and oropharyngeal swabs. Of the 5199 (99·6%) specimens tested, influenza A virus was detected in 42 (0·8%), including 35/4166 (0·8%) specimens from chickens, 3/381 (0·8%) from turkeys, and 4/335 (1·2%) from geese. None of the 317 duck specimens were positive. Influenza was more commonly detected in oropharyngeal [33 (1·3%)] than in cloacal [9 (0·4%)] specimens. None of the 485 environmental specimens were positive. Virus was detected in all five markets during most (14/22) of the months. Ducks and geese were kept longer at the market (median 30 days) than chickens (median 2days). CONCLUSIONS: Influenza A was detected in a small percentage of poultry traded in LBMs in Kenya. Efforts should be made to promote practices that could limit the maintenance and transmission of AIVs in LBMs.

Enhanced discrimination of African swine fever virus isolates through nucleotide sequencing of the p54, p72, and pB602L (CVR) genes.

Complete sequencing of p54-gene from 67 European, American, and West and East African Swine Fever virus (ASFV) isolates revealed that West African and European ASFV isolates classified within the predominant Genotype I according to partial sequencing of p72 were discriminated into four major sub-types on the basis of their p54 sequences. This highlighted the value of p54 gene sequencing as an additional, intermediate-resolution, molecular epidemiological tool for typing of ASFV viruses. We further evaluated p54-based genotyping, in combination with partial sequences of two other genes, for determining the genetic relationships and origin of viruses responsible for disease outbreaks in Kenya. Animals from Western and central Kenya were confirmed as being infected with ASFV using a p72 gene-based PCR assay, following outbreaks of severe hemorrhagic disease in domestic pigs in 2006 and 2007. Eleven hemadsorbing viruses were isolated in macrophage culture and genotyped using a combination of full-length p54-gene sequencing, partial p72-gene sequencing, and analysis of tetrameric amino acid repeat regions within the variable region of the B602L gene (CVR). The data revealed that these isolates were identical in their p72 and p54 sequence to viruses responsible for ASF outbreaks in Uganda in 2003. There was a minor difference in the number of tetrameric repeats within the B602L sequence of the Kenyan isolates that caused the second Kenyan outbreak in 2007. A practical implication of the genetic similarity of the Kenyan and Ugandan viral isolates is that ASF control requires a regional approach.