51 resultados para Agriculture--Asia, Central--Maps
Resumo:
Parasites and pathogens are apparent key factors for the detrimental health of managed European honey bee subspecies, Apis mellifera. Apicultural trade is arguably the main factor for the almost global distribution of most honey bee diseases, thereby increasing chances for multiple infestations/infections of regions, apiaries, colonies and even individual bees. This imposes difficulties to evaluate the effects of pathogens in isolation, thereby creating demand to survey remote areas. Here, we conducted the first comprehensive survey for 14 honey bee pathogens in Mongolia (N = 3 regions, N = 9 locations, N = 151 colonies), where honey bee colonies depend on humans to overwinter. In Mongolia, honey bees, Apis spp., are not native and colonies of European A. mellifera subspecies have been introduced ~60 years ago. Despite the high detection power and large sample size across Mongolian regions with beekeeping, the mite Acarapis woodi, the bacteria Melissococcus plutonius and Paenibacillus larvae, the microsporidian Nosema apis, Acute bee paralysis virus, Kashmir bee virus, Israeli acute paralysis virus and Lake Sinai virus strain 2 were not detected, suggesting that they are either very rare or absent. The mite Varroa destructor, Nosema ceranae and four viruses (Sacbrood virus, Black queen cell virus, Deformed wing virus (DWV) and Chronic bee paralysis virus) were found with different prevalence. Despite the positive correlation between the prevalence of V. destructor mites and DWV, some areas had only mites, but not DWV, which is most likely due to the exceptional isolation of apiaries (up to 600 km). Phylogenetic analyses of the detected viruses reveal their clustering and European origin, thereby supporting the role of trade for pathogen spread and the isolation of Mongolia from South-Asian countries. In conclusion, this survey reveals the distinctive honey bee pathosphere of Mongolia, which offers opportunities for exciting future research.
Resumo:
Climate change is expected to have far-reaching negative effects on agricultural production and food security in developing and transition countries. What do we know about these expected impacts, what are the factors that might affect production, and what are the implications for agricultural extension systems?
Resumo:
Tajikistan is judged to be highly vulnerable to risk, including food insecurity risks and climate change risks. By some vulnerability measures it is the most vulnerable among all 28 countries in the World Bank’s Europe and Central Asia Region – ECA (World Bank 2009). The rural population, with its relatively high incidence of poverty, is particularly vulnerable. The Pilot Program for Climate Resilience (PPCR) in Tajikistan (2011) provided an opportunity to conduct a farm-level survey with the objective of assessing various dimensions of rural population’s vulnerability to risk and their perception of constraints to farming operations and livelihoods. The survey should be accordingly referred to as the 2011 PPCR survey. The rural population in Tajikistan is highly agrarian, with about 50% of family income deriving from agriculture (see Figure 4.1; also LSMS 2007 – own calculations). Tajikistan’s agriculture basically consists of two groups of producers: small household plots – the successors of Soviet “private agriculture” – and dehkan (or “peasant”) farms – new family farming structures that began to be created under relevant legislation passed after 1992 (Lerman and Sedik, 2008). The household plots manage 20% of arable land and produce 65% of gross agricultural output (GAO). Dehkan farms manage 65% of arable land and produce close to 30% of GAO. The remaining 15% of arable land is held in agricultural enterprises – the rapidly shrinking sector of corporate farms that succeeded the Soviet kolkhozes and sovkhozes and today produces less than 10% of GAO (TajStat 2011) The survey conducted in May 2011 focused on dehkan farms, as budgetary constraints precluded the inclusion of household plots. A total of 142 dehkan farms were surveyed in face-to-face interviews. They were sampled from 17 districts across all four regions – Sughd, Khatlon, RRP, and GBAO. The districts were selected so as to represent different agro-climatic zones, different vulnerability zones (based on the World Bank (2011) vulnerability assessment), and different food-insecurity zones (based on WFP/IPC assessments). Within each district, 3-4 jamoats were chosen at random and 2-3 farms were selected in each jamoat from lists provided by jamoat administration so as to maximize the variability by farm characteristics. The sample design by region/district is presented in Table A, which also shows the agro-climatic zone and the food security phase for each district. The sample districts are superimposed on a map of food security phases based on IPC April 2011.
Resumo:
Urban agriculture is a phenomenon that can be observed world-wide, particularly in cities of devel- oping countries. It is contributing significantly to food security and food safety and has sustained livelihood of the urban and peri-urban low income dwe llers in developing countries for many years. Population increase due to rural-urban migration and natural - formal as well as informal - urbani- sation are competing with urban farming for available space and scarce water resources. A mul- titemporal and multisensoral urban change analysis over the period of 25 years (1982-2007) was performed in order to measure and visualise the urban expansion along the Kizinga and Mzinga valley in the south of Dar Es Salaam. Airphotos and VHR satellite data were analysed by using a combination of a composition of anisotropic textural measures and spectral information. The study revealed that unplanned built-up area is expanding continuously, and vegetation covers and agricultural lands decline at a fast rate. The validation showed that the overall classification accuracy varied depending on the database. The extracted built-up areas were used for visual in- terpretation mapping purposes and served as information source for another research project. The maps visualise an urban congestion and expansion of nearly 18% of the total analysed area that had taken place in the Kizinga valley between 1982 and 2007. The same development can be ob- served in the less developed and more remote Mzinga valley between 1981 and 2002. Both areas underwent fast changes where land prices still tend to go up and an influx of people both from rural and urban areas continuously increase the density with the consequence of increasing multiple land use interests.
Resumo:
BACKGROUND: Diversity patterns of livestock species are informative to the history of agriculture and indicate uniqueness of breeds as relevant for conservation. So far, most studies on cattle have focused on mitochondrial and autosomal DNA variation. Previous studies of Y-chromosomal variation, with limited breed panels, identified two Bos taurus (taurine) haplogroups (Y1 and Y2; both composed of several haplotypes) and one Bos indicus (indicine/zebu) haplogroup (Y3), as well as a strong phylogeographic structuring of paternal lineages. METHODOLOGY AND PRINCIPAL FINDINGS: Haplogroup data were collected for 2087 animals from 138 breeds. For 111 breeds, these were resolved further by genotyping microsatellites INRA189 (10 alleles) and BM861 (2 alleles). European cattle carry exclusively taurine haplotypes, with the zebu Y-chromosomes having appreciable frequencies in Southwest Asian populations. Y1 is predominant in northern and north-western Europe, but is also observed in several Iberian breeds, as well as in Southwest Asia. A single Y1 haplotype is predominant in north-central Europe and a single Y2 haplotype in central Europe. In contrast, we found both Y1 and Y2 haplotypes in Britain, the Nordic region and Russia, with the highest Y-chromosomal diversity seen in the Iberian Peninsula. CONCLUSIONS: We propose that the homogeneous Y1 and Y2 regions reflect founder effects associated with the development and expansion of two groups of dairy cattle, the pied or red breeds from the North Sea and Baltic coasts and the spotted, yellow or brown breeds from Switzerland, respectively. The present Y1-Y2 contrast in central Europe coincides with historic, linguistic, religious and cultural boundaries.
Resumo:
A comprehensive inventory of local and introduced soil and water conservation (SWC) measures presented in standardized fact sheets and completed with a special focus on the underlying reasons (problems) of acceptance / rejection. Different approaches are analysed and measures identified which are adapted to the specific local context. Second part of the study: soil assessment resulting in a consistent local classification of soil types and soil fertility, comparison with scientific classifications. Different topical maps show the spatial distribution of SWC measures, their condition, degradation hotspots, soil types, soil fertility and interrelations between these parameters. Based on the conclusions and the outcome of a stakeholder workshop recommendations are given for further activities in research and implementation of SWC in the Central Highlands of Eritrea.
Resumo:
Bovine dilated cardiomyopathy (BDCMP) is a severe and terminal disease of the heart muscle observed in Holstein-Friesian cattle over the last 30 years. There is strong evidence for an autosomal recessive mode of inheritance for BDCMP. The objective of this study was to genetically map BDCMP, with the ultimate goal of identifying the causative mutation. A whole-genome scan using 199 microsatellite markers and one SNP revealed an assignment of BDCMP to BTA18. Fine-mapping on BTA18 refined the candidate region to the MSBDCMP06-BMS2785 interval. The interval containing the BDCMP locus was confirmed by multipoint linkage analysis using the software loki. The interval is about 6.7 Mb on the bovine genome sequence (Btau 3.1). The corresponding region of HSA19 is very gene-rich and contains roughly 200 genes. Although telomeric of the marker interval, TNNI3 is a possible positional and a functional candidate for BDCMP given its involvement in a human form of dilated cardiomyopathy. Sequence analysis of TNNI3 in cattle revealed no mutation in the coding sequence, but there was a G-to-A transition in intron 6 (AJ842179:c.378+315G>A). The analysis of this SNP using the study's BDCMP pedigree did not conclusively exclude TNNI3 as a candidate gene for BDCMP. Considering the high density of genes on the homologous region of HSA19, further refinement of the interval on BTA18 containing the BDCMP locus is needed.
Resumo:
The highly pathogenic avian influenza (HPAI) H5N1 virus that emerged in southern China in the mid-1990s has in recent years evolved into the first HPAI panzootic. In many countries where the virus was detected, the virus was successfully controlled, whereas other countries face periodic reoccurrence despite significant control efforts. A central question is to understand the factors favoring the continuing reoccurrence of the virus. The abundance of domestic ducks, in particular free-grazing ducks feeding in intensive rice cropping areas, has been identified as one such risk factor based on separate studies carried out in Thailand and Vietnam. In addition, recent extensive progress was made in the spatial prediction of rice cropping intensity obtained through satellite imagery processing. This article analyses the statistical association between the recorded HPAI H5N1 virus presence and a set of five key environmental variables comprising elevation, human population, chicken numbers, duck numbers, and rice cropping intensity for three synchronous epidemic waves in Thailand and Vietnam. A consistent pattern emerges suggesting risk to be associated with duck abundance, human population, and rice cropping intensity in contrast to a relatively low association with chicken numbers. A statistical risk model based on the second epidemic wave data in Thailand is found to maintain its predictive power when extrapolated to Vietnam, which supports its application to other countries with similar agro-ecological conditions such as Laos or Cambodia. The model’s potential application to mapping HPAI H5N1 disease risk in Indonesia is discussed.