{"status":"OK","data":{"q":"*","total_count":379,"start":0,"spelling_alternatives":{},"items":[{"name":"Probability of Habitat Suitability for Stem Borer Pests and Natural Enemies in Kenya and Tanzania Under Current Climatic Conditions (1970-2000) and Different Climate Change Scenarios (2041-2060, 2081-2100)","type":"dataset","url":"https://doi.org/10.60507/FK2/BBP6GG","global_id":"doi:10.60507/FK2/BBP6GG","description":"The research within the scope of which the presented data was generated was part of the funding initiative ‘Knowledge for Tomorrow-Cooperative Research Project in sub-Saharan Africa on Resource, their Dynamics, and Sustainability’ funded by the Volkswagen Foundation. The overall study aimed at investigating the uncertainties in the effectiveness of biological control of stem borers under different climate change scenarios in Kenya and Tanzania. Using the species distribution modelling approach MaxEnt, the research predicts the current and future distribution of three important lepidopteran stem borer pests of maize in eastern Africa, i.e., Busseola fusca (Fuller, 1901), Chilo partellus (Swinhoe, 1885) and Sesamia calamistis (Hampson, 1910), and two of their parasitoids used for biological control, i.e., Cotesia flavipes (Cameron, 1891) and Cotesia sesamiae (Cameron, 1906). Based on these potential distributions and data collected during household surveys with local farmers in Kenya and Tanzania, future maize yield losses are predicted considering three different Global Circulation Models (GCMs) for four different Shared Socioeconomic Pathway (SSP) scenarios (SSP1-2.6, SSP2-4.5, SSP 3-7.0, SSP5-8.5) and two time periods, i.e., 2041-2060 and 2081-2100. A raster in which probability of habitat suitability is separately specified for each grid cell is the immediate output from species distribution modelling with MaxEnt. Probability of habitat suitability for the respective species hereby is expressed as probability value ranging between 0 (unsuitable habitat) to 1 (perfectly suitable habitat). Probability of habitat suitability was modelled for five species for current climatic conditions, as well as for four SSPs and two time periods. Quality/Lineage: The generated data is based on a collection of presence points from different sources and environmental data from WorldClim. Species Distribution Models (SDMs) have been built using the 'kuenm' package in RStudio. The data on probability of habitat suitability are an immediate output from SDM in R. Habitat suitability is here given as multi-model average of predictions for three Global Circulation Models (GCMs) from CMIP6: CanESM5, CNRM-CM6-1 and MIROC6","published_at":"2023-09-18T10:29:50Z","publisher":"ZEF: Center for Development Research","citationHtml":"Ines Jendritzki, 2023, \"Probability of Habitat Suitability for Stem Borer Pests and Natural Enemies in Kenya and Tanzania Under Current Climatic Conditions (1970-2000) and Different Climate Change Scenarios (2041-2060, 2081-2100)\", https://doi.org/10.60507/FK2/BBP6GG, bonndata, V1","identifier_of_dataverse":"zef","name_of_dataverse":"ZEF: Center for Development Research","citation":"Ines Jendritzki, 2023, \"Probability of Habitat Suitability for Stem Borer Pests and Natural Enemies in Kenya and Tanzania Under Current Climatic Conditions (1970-2000) and Different Climate Change Scenarios (2041-2060, 2081-2100)\", https://doi.org/10.60507/FK2/BBP6GG, bonndata, V1","publicationStatuses":["Published"],"storageIdentifier":"file://10.60507/FK2/BBP6GG","subjects":["Other"],"fileCount":3,"versionId":61,"versionState":"RELEASED","majorVersion":1,"minorVersion":0,"createdAt":"2023-09-18T08:53:27Z","updatedAt":"2023-09-18T10:29:50Z","contacts":[{"name":"Ines Jendritzki","affiliation":"Center for Development Research, Department Ecology and Natural Resources Management (ZEF C), University of Bonn"}],"geographicCoverage":[{"country":"Kenya"},{"other":"Tanzania,"}],"authors":["Ines Jendritzki"]},{"name":"Estimated Reduction in Stem Borer-Associated Maize Yield Losses Through Application of Natural Enemies in Kenya and Tanzania Under Current Climatic Conditions (1970-2000) and Different Climate Change Scenarios (2041-2060, 2081-2100)","type":"dataset","url":"https://doi.org/10.60507/FK2/4PQNBN","global_id":"doi:10.60507/FK2/4PQNBN","description":"The research within the scope of which the presented data was generated was part of the funding initiative ‘Knowledge for Tomorrow-Cooperative Research Project in sub-Saharan Africa on Resource, their Dynamics, and Sustainability’ funded by the Volkswagen Foundation. The study aimed at investigating the uncertainties in the effectiveness of biological control of stem borers under different climate change scenarios in Kenya and Tanzania. Using the species distribution modelling approach MaxEnt, the research predicts the current and future distribution of three important lepidopteran stem borer pests of maize in eastern Africa, i.e., Busseola fusca (Fuller, 1901), Chilo partellus (Swinhoe, 1885) and Sesamia calamistis (Hampson, 1910), and two of their parasitoids used for biological control, i.e., Cotesia flavipes (Cameron, 1891) and Cotesia sesamiae (Cameron, 1906). Based on these potential distributions and data collected during household surveys with local farmers in Kenya and Tanzania, future maize yield losses are predicted considering three different Global Circulation Models (GCMs) for four different Shared Socioeconomic Pathway (SSP) scenarios (SSP1-2.6, SSP2-4.5, SSP 3-7.0, SSP5-8.5) and two time periods, i.e., 2041-2060 and 2081-2100. The reduction potential of stem borer-associated maize yield losses by application of parasitoids (in kg/ha) is extrapolated using previously estimated average maize yield losses in the study area and results from a 2018 household survey conducted by researchers from the International Centre of Insect Physiology and Ecology (icipe) under the cooperative project \"Adaptation for Food Security and Ecosystem Resilience in Africa” (AFERIA) of icipe, the University of Helsinki and the University of York in which local farmers were asked to quantify the reduction of losses in maize yield by stem borer infestation through application of natural enemies. Based on these survey data, 95% confidence intervals (CIs) for maize yield losses were calculated. Potential to reduce yield losses by stem borers for all scenarios are given for mean, lower and upper bound of the CI. Quality/Lineage: Rasters showing reduction potential of stem borer-associated maize yield losses by application of parasitoids were calculated in RStudio using the 'raster' package. Three raster layers were multiplied to quantify the potential of parasitoid application to reduce maize yield losses by stem borer infestation: R(c,e)= B(c,e)*Y(c,p)*Pe where Rc,e represents the potential reduction of yield losses (kg/ha) for each grid cell c by natural enemy e, Bc,e = {1,0} indicates parasitoid presence or absence for each grid cell, Y(c,p) indicates maize yield losses (kg/ha) and Pe represents estimated potential yield losses reduction by the parasitoids (%).","published_at":"2023-09-18T10:31:31Z","publisher":"ZEF: Center for Development Research","citationHtml":"Ines Jendritzki, 2023, \"Estimated Reduction in Stem Borer-Associated Maize Yield Losses Through Application of Natural Enemies in Kenya and Tanzania Under Current Climatic Conditions (1970-2000) and Different Climate Change Scenarios (2041-2060, 2081-2100)\", https://doi.org/10.60507/FK2/4PQNBN, bonndata, V1","identifier_of_dataverse":"zef","name_of_dataverse":"ZEF: Center for Development Research","citation":"Ines Jendritzki, 2023, \"Estimated Reduction in Stem Borer-Associated Maize Yield Losses Through Application of Natural Enemies in Kenya and Tanzania Under Current Climatic Conditions (1970-2000) and Different Climate Change Scenarios (2041-2060, 2081-2100)\", https://doi.org/10.60507/FK2/4PQNBN, bonndata, V1","publicationStatuses":["Published"],"storageIdentifier":"file://10.60507/FK2/4PQNBN","subjects":["Other"],"fileCount":3,"versionId":66,"versionState":"RELEASED","majorVersion":1,"minorVersion":0,"createdAt":"2023-09-18T08:54:16Z","updatedAt":"2023-09-18T10:31:31Z","contacts":[{"name":"Ines Jendritzki","affiliation":"Center for Development Research, Department Ecology and Natural Resources Management (ZEF C), University of Bonn"}],"publications":[{}],"geographicCoverage":[{"country":"Kenya"},{"country":"","other":"Tanzania,"}],"authors":["Ines Jendritzki"]},{"name":"Productivity Change and Number of Land Cover Changes in Kenya between 2001 and 2011","type":"dataset","url":"https://doi.org/10.60507/FK2/QM2QKR","global_id":"doi:10.60507/FK2/QM2QKR","description":"The Map shows the overlay of the number of land cover changes with NDVI decrease and increase between 2001 and 2011 referring to NDVI trends. Three classes among the trends are built. Besides a “tolerance class” meaning NDVI trends between -0.005 and +0.005 the dataset was classified into “decreasing” (NDVI Trend <-0.005) and “increasing” (NDVI trend >0.005) vegetation trends. The overlay highlights the southern part of Kenya, especially the counties Narok and Kajiado where a stable land cover and decreasing trends overlap. Within this overlap are also Kitui and Isiolo – both counties that were also highlighted in the OLS-regression output as underpredicting –, parts of Marsabit and some small areas along the coastline. Also again the northwestern area, mainly Turkana Region but also West Pokot and Baringo are expressing increasing trends and seem to be linked to a more stable land cover. Quality/Lineage: Overlay of Number of Land Cover Changes with Productivity trends. It is indicated where positive and where negative productivity trends occurred. Raster data was reclassified and overlayed in ArcGIS. Purpose: Productivity Change and Number of Land Cover Changes in Kenya between 2001 and 2011","published_at":"2023-09-18T10:33:00Z","publisher":"ZEF: Center for Development Research","citationHtml":"Valerie Graw, 2023, \"Productivity Change and Number of Land Cover Changes in Kenya between 2001 and 2011\", https://doi.org/10.60507/FK2/QM2QKR, bonndata, V1","identifier_of_dataverse":"zef","name_of_dataverse":"ZEF: Center for Development Research","citation":"Valerie Graw, 2023, \"Productivity Change and Number of Land Cover Changes in Kenya between 2001 and 2011\", https://doi.org/10.60507/FK2/QM2QKR, bonndata, V1","publicationStatuses":["Published"],"storageIdentifier":"file://10.60507/FK2/QM2QKR","subjects":["Other"],"fileCount":3,"versionId":73,"versionState":"RELEASED","majorVersion":1,"minorVersion":0,"createdAt":"2023-09-18T08:55:10Z","updatedAt":"2023-09-18T10:33:00Z","contacts":[{"name":"Valerie Graw","affiliation":"Center for Development Research, Department Economy and Technological Change (ZEF B), University of Bonn"}],"geographicCoverage":[{"country":"Kenya"}],"authors":["Valerie Graw"]},{"name":"Number of Land Cover Changes in Kenya between 2001 and 2011","type":"dataset","url":"https://doi.org/10.60507/FK2/GDQJ3Q","image_url":"https://bonndata.uni-bonn.de/api/datasets/1649/logo","global_id":"doi:10.60507/FK2/GDQJ3Q","description":"MODIS provides the Land Cover Type Product MCD12Q1 (Friedl et al., 2002) with 500m grid resolution which represents the same pixel size also used for the MODIS NDVI time-series analysis. Annually data provision and a matching pixel size with the MODIS NDVI data used earlier in this study were key elements for choosing this dataset. The Map shows the number of LULC changes as calculated based on the methods described in chapter II.3.3. Stable areas – where land cover changes are zero – can be identified in southern Kenya, Kajiado County in particular, but also in western Kenya north of Lake Victoria, around Lake Turkana, and in the northeastern part of Kenya bordering Ethiopia. Around 33.16% of the total land area experience zero changes from 2001-2011 while 16.11% changed once and 22.92% show two changes. Three (13.98%), four (9.53%) and five (3.42%) changes can still be observed in Map III.11 while areas experiencing more than five changes are occurring in less than 1% of the total land area. The different classes show the number of land cover changes within the observation period. Quality/Lineage: Data was reclassified and analysed in R. Extraction of each land cover change per year and analysis of how many changes occurred in the observation period. Visualization was done in ArcGIS. Purpose: Number of Land Cover Changes in Kenya between 2001 and 2011","published_at":"2023-09-18T10:33:08Z","publisher":"ZEF: Center for Development Research","citationHtml":"Valerie Graw, 2023, \"Number of Land Cover Changes in Kenya between 2001 and 2011\", https://doi.org/10.60507/FK2/GDQJ3Q, bonndata, V1","identifier_of_dataverse":"zef","name_of_dataverse":"ZEF: Center for Development Research","citation":"Valerie Graw, 2023, \"Number of Land Cover Changes in Kenya between 2001 and 2011\", https://doi.org/10.60507/FK2/GDQJ3Q, bonndata, V1","publicationStatuses":["Published"],"storageIdentifier":"file://10.60507/FK2/GDQJ3Q","subjects":["Other"],"fileCount":3,"versionId":74,"versionState":"RELEASED","majorVersion":1,"minorVersion":0,"createdAt":"2023-09-18T08:55:14Z","updatedAt":"2023-09-18T10:33:08Z","contacts":[{"name":"Valerie Graw","affiliation":"Center for Development Research, Department Economy and Technological Change (ZEF B), University of Bonn"}],"geographicCoverage":[{"country":"Kenya"}],"authors":["Valerie Graw"]},{"name":"The Economics of Adaptation to Climate Change in Bangladesh, Follow-up Survey 2012","type":"dataset","url":"https://doi.org/10.60507/FK2/UBLHJO","global_id":"doi:10.60507/FK2/UBLHJO","description":"A survey of agricultural households was conducted in early 2011 in order to provide background information on landownership, size of operation, rice production, input use, and farm practices in rural communities, as well as to identify and assess existing climate change adaptation strategies. A resurvey was conducted in late 2012 to build on the initial round of the survey, known as the Bangladesh Climate Change Adaptation Survey, with a greater focus on gender and asset dynamics. We tried to track all the households including the split with an attrition rate of 2.66 percent.e this template for data such as statistics, surveys, etc. Quality/Lineage: The data was cleaned after collecting from the field using STATA and was processed thereafter in STATA.","published_at":"2023-09-18T10:45:37Z","publisher":"ZEF: Center for Development Research","citationHtml":"Muntaha Rakib, 2023, \"The Economics of Adaptation to Climate Change in Bangladesh, Follow-up Survey 2012\", https://doi.org/10.60507/FK2/UBLHJO, bonndata, V1","identifier_of_dataverse":"zef","name_of_dataverse":"ZEF: Center for Development Research","citation":"Muntaha Rakib, 2023, \"The Economics of Adaptation to Climate Change in Bangladesh, Follow-up Survey 2012\", https://doi.org/10.60507/FK2/UBLHJO, bonndata, V1","publicationStatuses":["Published"],"storageIdentifier":"file://10.60507/FK2/UBLHJO","subjects":["Other"],"fileCount":3,"versionId":80,"versionState":"RELEASED","majorVersion":1,"minorVersion":0,"createdAt":"2023-09-18T08:56:10Z","updatedAt":"2023-09-18T10:45:37Z","contacts":[{"name":"Muntaha Rakib","affiliation":"Center for Development Research, Department Political and Cultural Change (ZEF A), University of Bonn"}],"authors":["Muntaha Rakib"]},{"name":"Survey on Spatial Planning, Strategic Environmental Assessment and Ecosystem Services in Chile, 2015","type":"dataset","url":"https://doi.org/10.60507/FK2/V2FKQV","image_url":"https://bonndata.uni-bonn.de/api/datasets/1703/logo","global_id":"doi:10.60507/FK2/V2FKQV","description":"This research addresses the understanding of three concepts: spatial planning, strategic environmental assessment and ecosystem services, in a multi-actor setting in Chile. For that a survey based on a questionnaire application was implemented involving government institutions, research institutions and consultants. The questionnaire consisted in 20 questions including a set of Likert, closed and open ended questions. The focus of the questionnaire was on issues such as network relations among actors and on conceptual understanding, perceptions and challenges for integrating ES in SEA and spatial planning, knowledge on methodological approaches, and the connections and gaps in science-policy. The main findings suggest that a common understanding of SEA and especially of ES is still in an initial stage in Chile when we consider the context of multiple actors. Additionally, the lack of institutional guidelines and methodological support is considered the main challenge for integration. We conclude that preconditions exist in Chile for integrating ES in SEA and the spatial planning practice, but they strongly depend on an appropriate governance scheme which encourages a close interaction science-policy as well as collaborative work and learning. This research was based on three sequential stages: 1) identification of key actors; 2) collection of information about an actor’s understanding and perceptions through a questionnaire, and 3) data processing, based on the questionnaire and data processing. We adopted a mixed method including qualitative and quantitative analyses, so that representativeness, but also individual perceptions of our actors could be addressed. Purpose: The aim of this work was to address the folowing research questions: 1) Who are the key actors to be included to enable the implementation of ES in spatial planning through the SEA process, and which are the current network relations based on their associated conceptual understanding? 2) How is the integration of ES in SEA and spatial planning perceived by the different actors, and which challenges are recognized? 3) Which methodological approaches are identified for SEA, and which are considered as shared between SEA and ES? 4) Which are the critical connections and gaps in the relation science-policy and which channels of communication/information are used by the actors for their knowledge and understanding of ES and SEA.","published_at":"2023-09-18T10:36:37Z","publisher":"ZEF: Center for Development Research","citationHtml":"Daniel Rozas, 2023, \"Survey on Spatial Planning, Strategic Environmental Assessment and Ecosystem Services in Chile, 2015\", https://doi.org/10.60507/FK2/V2FKQV, bonndata, V1","identifier_of_dataverse":"zef","name_of_dataverse":"ZEF: Center for Development Research","citation":"Daniel Rozas, 2023, \"Survey on Spatial Planning, Strategic Environmental Assessment and Ecosystem Services in Chile, 2015\", https://doi.org/10.60507/FK2/V2FKQV, bonndata, V1","publicationStatuses":["Published"],"storageIdentifier":"file://10.60507/FK2/V2FKQV","subjects":["Other"],"fileCount":3,"versionId":83,"versionState":"RELEASED","majorVersion":1,"minorVersion":0,"createdAt":"2023-09-18T08:56:29Z","updatedAt":"2023-09-18T10:36:37Z","contacts":[{"name":"Daniel Rozas","affiliation":"Center for Development Research, Department Ecology and Natural Resources Management (ZEF C), University of Bonn"}],"geographicCoverage":[{"country":"Chile"}],"authors":["Daniel Rozas"]},{"name":"Deforestation Hotspots in Brazil, 2005-2012","type":"dataset","url":"https://doi.org/10.60507/FK2/FR6LSN","global_id":"doi:10.60507/FK2/FR6LSN","description":"Natural land resources in Brazil have been subject to strong pressure from agricultural expansion over the past two decades. This map identifies and classifies deforestation hotspots in the Southern American country. Moreover, it hints to land use change dynamics such as leakage effects in tropical areas. The map represents the period between 2005-2012, and classifies deforestation hotspots in three categories: a) reduced, b) increased, and c) new. Quality/Lineage: Land cover information from Global Forest Watch (https://data.globalforestwatch.org/) was used to identify deforested pixels per year. ArcGIS 10 was used to create spatial statistics of yearly information. R and RStudio were used to classify each grid cell as a hotspot and its type, and to convert the resulting cover information into a shapefile.","published_at":"2023-09-18T10:48:44Z","publisher":"ZEF: Center for Development Research","citationHtml":"Javier Miranda, 2023, \"Deforestation Hotspots in Brazil, 2005-2012\", https://doi.org/10.60507/FK2/FR6LSN, bonndata, V1","identifier_of_dataverse":"zef","name_of_dataverse":"ZEF: Center for Development Research","citation":"Javier Miranda, 2023, \"Deforestation Hotspots in Brazil, 2005-2012\", https://doi.org/10.60507/FK2/FR6LSN, bonndata, V1","publicationStatuses":["Published"],"storageIdentifier":"file://10.60507/FK2/FR6LSN","subjects":["Other"],"fileCount":3,"versionId":99,"versionState":"RELEASED","majorVersion":1,"minorVersion":0,"createdAt":"2023-09-18T08:59:04Z","updatedAt":"2023-09-18T10:48:44Z","contacts":[{"name":"Javier Miranda","affiliation":"Center for Development Research, Department Economy and Technological Change (ZEF B), University of Bonn"}],"publications":[{}],"geographicCoverage":[{"country":"Brazil"}],"authors":["Javier Miranda"]},{"name":"Secondary Data on Social Indicators and Public Expenditure on District and Regional Level in Tanzania (1996-2010)","type":"dataset","url":"https://doi.org/10.60507/FK2/4HPJDK","global_id":"doi:10.60507/FK2/4HPJDK","description":"Secondary data on social indicators and public expenditure on district and regional level in Tanzania (1996-2010), as for example: THINV: Logarithm of deflated public per capita spending on health in the short- and long term (total spending of the current and the last five budget years) SANI: Latrines per 100 pupils INFRA: Percentage of women and men age 15-49 who reported serious problems in accessing health care due to the distance to the next health facility URB: Percentage of people living in urban areas TAINV: Logarithm of deflated public per capita spending on agriculture (current and previous budget year)* BREASTF: Percentage who started breastfeeding within 1 hour of birth, among the last children born in the five years preceding the survey IODINE: Percentage of households with adequate iodine content of salt (15+ ppm) MEDU: Percentage of women age 15-49 who completed grade 6 at the secondary level VACC: Percentage of children age 12-23 months with a vaccination card TWINV: Logarithm of deflated public per capita spending on water in the short- and long term (total spending of the current and the last five budget years)* TEINV: Logarithm of deflated public per capita spending on education in the short- and long term (total spending of the current and the last five budget years)* LABOUR: Percentage of women and men employed in the 12 months preceding the survey LAND: Per capita farmland in ha (including the area under temporary mono/mixed crops, permanent mono/mixed crops and the area under pasture) RAIN: Yearly rainfall in mm etc. Purpose: The uploaded data were the basis for the following PhD-thesis: The optimal allocation of scarce resources for health improvement is a crucial factor to lower the burden of disease and to strengthen the productive capacities of people living in developing countries. This research project aims to devise tools in narrowing the gap between the actual allocation and a more efficient allocation of resources for health in the case of Tanzania. Firstly, the returns from alternative government spending across sectors such as agriculture, water etc. are analysed. Maximisation of the amount of Disability Adjusted Life Years (DALYs) averted per dollar invested is used as criteria. A Simultaneous Equation Model (SEM) is developed to estimate the required elasticities. The results of the quantitative analysis show that the highest returns on DALYs are obtained by investments in improved nutrition and access to safe water sources, followed by spending on sanitation. Secondly, focusing on the health sector itself, scarce resources for health improvement create the incentive to prioritise certain health interventions. Using the example of malaria, the objective of the second stage is to evaluate whether interventions are prioritized in such a way that the marginal dollar goes to where it has the highest effect on averting DALYs. PopMod, a longitudinal population model, is used to estimate the cost-effectiveness of six isolated and combined malaria intervention approaches. The results of the longitudinal population model show that preventive interventions such as insecticide–treated bed nets (ITNs) and intermittent presumptive treatment with Sulphadoxine-Pyrimethamine (SP) during pregnancy had the highest health returns (both US$ 41 per DALY averted). The third part of this dissertation focuses on the political economy aspect of the allocation of scarce resources for health improvement. The objective here is to positively assess how political party competition and the access to mass media directly affect the distribution of district resources for health improvement. Estimates of cross-sectional and panel data regression analysis imply that a one-percentage point smaller difference (the higher the competition is) between the winning party and the second-place party leads to a 0.151 percentage point increase in public health spending, which is significant at the five percent level. In conclusion, we can say that cross-sectoral effects, the cost-effectiveness of health interventions and the political environment are important factors at play in the country’s resource allocation decisions. In absolute terms, current financial resources to lower the burden of disease in Tanzania are substantial. However, there is a huge potential in optimizing the allocation of these resources for a better health return.","published_at":"2023-09-18T10:41:19Z","publisher":"ZEF: Center for Development Research","citationHtml":"Michael Simon, 2023, \"Secondary Data on Social Indicators and Public Expenditure on District and Regional Level in Tanzania (1996-2010)\", https://doi.org/10.60507/FK2/4HPJDK, bonndata, V1","identifier_of_dataverse":"zef","name_of_dataverse":"ZEF: Center for Development Research","citation":"Michael Simon, 2023, \"Secondary Data on Social Indicators and Public Expenditure on District and Regional Level in Tanzania (1996-2010)\", https://doi.org/10.60507/FK2/4HPJDK, bonndata, V1","publicationStatuses":["Published"],"storageIdentifier":"file://10.60507/FK2/4HPJDK","subjects":["Other"],"fileCount":3,"versionId":110,"versionState":"RELEASED","majorVersion":1,"minorVersion":0,"createdAt":"2023-09-18T09:00:28Z","updatedAt":"2023-09-18T10:41:19Z","contacts":[{"name":"Michael Simon","affiliation":"Center for Development Research, Department Economy and Technological Change (ZEF B), University of Bonn"}],"geographicCoverage":[{"other":"Tanzania,"}],"authors":["Michael Simon"]},{"name":"Distribution of Stem Borer Pests and Natural Enemy Species in Kenya and Tanzania under Current Climatic Conditions (1970-2000) and Different Climate Change Scenarios (2041-2060, 2081-2100)","type":"dataset","url":"https://doi.org/10.60507/FK2/OBGEAR","global_id":"doi:10.60507/FK2/OBGEAR","description":"The research within the scope of which the presented data was generated was part of the funding initiative ‘Knowledge for Tomorrow-Cooperative Research Project in sub-Saharan Africa on Resource, their Dynamics, and Sustainability’ funded by the Volkswagen Foundation. The overall study aimed at investigating the uncertainties in the effectiveness of biological control of stem borers under different climate change scenarios in Kenya and Tanzania. Using the species distribution modelling approach MaxEnt, the research predicts the current and future distribution of three important lepidopteran stem borer pests of maize in eastern Africa, i.e., Busseola fusca (Fuller, 1901), Chilo partellus (Swinhoe, 1885) and Sesamia calamistis (Hampson, 1910), and two of their parasitoids used for biological control, i.e., Cotesia flavipes (Cameron, 1891) and Cotesia sesamiae (Cameron, 1906). Based on these potential distributions and data collected during household surveys with local farmers in Kenya and Tanzania, future maize yield losses are predicted considering three different Global Circulation Models (GCMs) for four different Shared Socioeconomic Pathway (SSP) scenarios (SSP1-2.6, SSP2-4.5, SSP 3-7.0, SSP5-8.5) and two time periods, i.e., 2041-2060 and 2081-2100. The rasters show the species predicted current and future distribution for four different SSPs and time periods 2041-2060 and 2081-2100. The distribution rasters are based on a raster displaying probability of habitat suitability which was converted into binary range maps by application of different threshold levels, i.e., 1) Balance training omission, predicted area and threshold values Cloglog threshold, 2) Maximum training sensitivity plus specificity Cloglog threshold, 3) Equal training sensitivity and specificity Cloglog threshold and 4) 10th percentile training presence Cloglog threshold. Grid cells carrying a probability value above the respective threshold show species presence (cell assigned a value of 1), while grid cells with a probability value below the threshold show species absence (cell assigned a value of 0). Accordingly, 4 presence-absence rasters were obtained for a species current distribution and calculated in their sum, while 12 presence-absence rasters were calculated for each climate change scenario, subsequently also calculated in their sum. The raster therefore specifies grid cells where species distribution is predicted to be more (grid cell carrying a high sum value), or less, likely (grid cell carrying a low cell value).","published_at":"2023-09-18T10:42:48Z","publisher":"ZEF: Center for Development Research","citationHtml":"Ines Jendritzki, 2023, \"Distribution of Stem Borer Pests and Natural Enemy Species in Kenya and Tanzania under Current Climatic Conditions (1970-2000) and Different Climate Change Scenarios (2041-2060, 2081-2100)\", https://doi.org/10.60507/FK2/OBGEAR, bonndata, V1","identifier_of_dataverse":"zef","name_of_dataverse":"ZEF: Center for Development Research","citation":"Ines Jendritzki, 2023, \"Distribution of Stem Borer Pests and Natural Enemy Species in Kenya and Tanzania under Current Climatic Conditions (1970-2000) and Different Climate Change Scenarios (2041-2060, 2081-2100)\", https://doi.org/10.60507/FK2/OBGEAR, bonndata, V1","publicationStatuses":["Published"],"storageIdentifier":"file://10.60507/FK2/OBGEAR","subjects":["Other"],"fileCount":3,"versionId":118,"versionState":"RELEASED","majorVersion":1,"minorVersion":0,"createdAt":"2023-09-18T09:01:27Z","updatedAt":"2023-09-18T10:42:48Z","contacts":[{"name":"Ines Jendritzki","affiliation":"Center for Development Research, Department Ecology and Natural Resources Management (ZEF C), University of Bonn"}],"publications":[{}],"geographicCoverage":[{"country":"","other":"Tanzania,"},{"country":"Kenya"}],"authors":["Ines Jendritzki"]},{"name":"A Holistic Sustainability Assessment of Smallholder Farms in Kenya, 2016 - Dataset 2","type":"dataset","url":"https://doi.org/10.60507/FK2/SUGF41","global_id":"doi:10.60507/FK2/SUGF41","description":"In our research we assess the sustainability performance of 400 smallholder farms practicing organic (i.e. certified or non-certified) and non-organic agriculture (i.e. conventional or other) using the Sustainability Monitoring and Assessment RouTine (SMART)-Farm Tool and examine differences between these farm categories using multivariate analyses. We also identify general gaps in sustainability performance for all farms. Quality/Lineage: Multivariate data analyses. Purpose: The results are important to provide a basis for informed decision-making in design of future policy measures and other interventions aiming at sustainable smallholder farming which is significant for sustainable development in Kenya and other countries of sub-Saharan Africa.","published_at":"2023-09-18T10:43:13Z","publisher":"ZEF: Center for Development Research","citationHtml":"Juliet Wanjiku Kamau, 2023, \"A Holistic Sustainability Assessment of Smallholder Farms in Kenya, 2016 - Dataset 2\", https://doi.org/10.60507/FK2/SUGF41, bonndata, V1","identifier_of_dataverse":"zef","name_of_dataverse":"ZEF: Center for Development Research","citation":"Juliet Wanjiku Kamau, 2023, \"A Holistic Sustainability Assessment of Smallholder Farms in Kenya, 2016 - Dataset 2\", https://doi.org/10.60507/FK2/SUGF41, bonndata, V1","publicationStatuses":["Published"],"storageIdentifier":"file://10.60507/FK2/SUGF41","subjects":["Other"],"fileCount":3,"versionId":121,"versionState":"RELEASED","majorVersion":1,"minorVersion":0,"createdAt":"2023-09-18T09:01:41Z","updatedAt":"2023-09-18T10:43:13Z","contacts":[{"name":"Juliet Wanjiku Kamau","affiliation":"Center for Development Research, Department Ecology and Natural Resources Management (ZEF C), University of Bonn"}],"publications":[{}],"geographicCoverage":[{"country":"Kenya"},{"country":"","other":"Kajiado County,"},{"country":"","other":"Murang'a County,"}],"authors":["Juliet Wanjiku Kamau"]}],"count_in_response":10}}