Assessing the users’ need for a spatial decision support system of smallholder farming in Kenya

Accurate data of the natural conditions and agricultural systems with a good spatial resolution are a key factor to tackle food insecurity in developing countries. A broad variety of approaches exists to achieve precise data and information about agriculture. One system, especially developed for smallholder agriculture in East Africa, is the Farm Management Handbook of Kenya. It was first published in 1982/83 and fully revised in 2012, now containing 7 volumes. The handbooks contain detailed information on climate, soils, suitable crops and soil care based on scientific research results of the last 30 years. The density of facts leads to time consuming extraction of all necessary information. In this study we analyse the user needs and necessary components of a system for decision support for smallholder farming in Kenya based on a geographical information system (GIS). Required data sources were identified, as well as essential functions of the system. We analysed the results of our survey conducted in 2012 and early 2013 among agricultural officers. The monitoring of user needs and the problem of non-adaptability of an agricultural information system on the level of extension officers in Kenya are the central objectives. The outcomes of the survey suggest the establishment of a decision support tool based on already available open source GIS components. The system should include functionalities to show general information for a specific location and should provide precise recommendations about suitable crops and management options to support agricultural guidance on farm level

Behind the fog : Forest degradation despite logging bans in an East African cloud forest

Habitat destruction and deterioration are amongst the main drivers of biodiversity loss. Increasing demand for agricultural products, timber and charcoal has caused the rapid destruction of natural forests, especially in the tropics. The Taita Hills in southern Kenya are part of the Eastern Afromontane Biodiversity Hotspot and represent a highly diverse cloud forest ecosystem. However, the cloud forest suffers extremely from wood and timber exploitation and transformation into exotic tree plantations and agricultural fields. Existing conservation regulations and moratoriums aim to prevent further forest destruction. In this study, we analyzed land cover change and shifts in landscape configuration for a fraction of the Taita Hills, based on satellite imageries for the years 2003, 2011 and 2018. We found that the coverage of natural cloud forest further decreased between 2003 and 2018, despite the effort to conserve the remaining cloud forest patches and to reforest degraded areas by various conservation and management initiatives. In parallel, the proportion of exotic tree plantations and bushland strongly increased. Moreover, mean natural forest patch size decreased and the degree of interspersion with other land cover types increased notably. Logging bans for indigenous trees seem to have resulted in local opposition to the planting of indigenous trees and thereby hindered the recovering of the cloud forest. We suggest to enhance local awareness on the ecological value of the natural forest by community-based Conservation Forest Associations and to encourage the planting of indigenous tree species in farmer-owned woodlots. Besides, bottom-up management systems that allow for local participation in decision-making and benefit-sharing related to forest resources would be a way forward to achieve the sustainable use and conservation of the last remaining natural forest patches in the Taita Hills. (C) 2020 The Authors. Published by Elsevier B.V.Peer reviewe

East African coastal forest under pressure

The Arabuko Sokoke dryland coastal forest along the East African coastline provides a unique habitat for many endangered endemic animal and plant species. High demographic pressure with subsequent land-splitting, soil depletion in combination with erratic rainfalls and the collapse of the tourism industry are negatively affecting food security and human livelihood quality in this region. Food crops were originally produced by subsistence farming, but have now to be purchased at local-and super-markets, constituting a major financial burden for the local people. In consequence, overexploitation of natural resources from Arabuko Sokoke forest (illegal logging, charcoal burning, poaching of wild animals) increased during the past years. In this commentary we document ecosystem heterogeneity leading to high species richness. We discuss direct and indirect drivers of habitat degradation of the Arabuko Sokoke forest, and critically reflect current and future solutions. Key drivers of habitat destruction and biodiversity loss are (i) illegal timber logging and removal of woody biomass, (ii) poaching of bush-meat, (iii) exceeding of the carrying capacity by the local elephant population, restricted to Arabuko Sokoke by an electric fence, and (iv) weak governance structures and institutional confusion exacerbating illegal exploitation of natural resources. Potential solutions might be: Provisioning of additional income sources; reforestation of the surrounding areas in the framework of REDD+ activities to create a buffer around the remaining primary forest; improving governance structures that formulates clear guidelines on future usage and protection of natural resources within the Arabuko Sokoke forest; and family planning to counteract human demographic pressure and the exploitation of natural resources

Using indicator species to detect high quality habitats in an East African forest biodiversity hotspot

AbstractSpecies demanding specific habitat requirements suffer, particularly under environmental changes. The smallest owl of Africa, the Sokoke Scops Owl (Otus ireneae), occurs exclusively in East African coastal forests. To understand the movement behaviour and habitat demands of O. ireneae, we combined data from radio-tracking and remote sensing to calculate Species Distribution Models across the Arabuko Sokoke forest in southern Kenya. Based on these data, we estimated the local population size and projected the distribution of current suitable habitats. We found that the species occurs only in Cynometra woodland with large old trees and dense vegetation. Based on home range sizes and the distribution of suitable forest habitats, the local population size was estimated at < 400 pairs. Ongoing selective logging of hard-wood trees and the production of charcoal are reducing habitat quality of which will reduce the low numbers of O. ireneae, and of other specialist forest species, even further. Due to their close connection with intact Cynometra forest, O. ireneae is an excellent indicator of intact forest remnants. In addition, this species is a suitable flagship for the promotion and conservation of the last remaining coastal forests of East Africa

Suitability of thermal UAV data to detect stones and artificial objects in agriculture

Stones on agricultural land can cause serious damage to agricultural machinery, when they are getting inside the machinery. This phenoma is especially pronounced in regions with high frequency of stones occurring on agricultural lands, e.g. in glacial morainic landscapes, as they occur in northern Germany. Therefore, stones must be removed from farmland several times a year. A worfklow for drone-based detection of stones is currently under development at the Geoecology department of MLU Halle to assist solving this problem. With our workflow, we demonstrate the particular suitability of UAS-based thermal data to differentiate between stones and soil surface on agricultural lands. Thermal inertia effects can be used to make significant temperature differences between stone and soil detectable. Which enables precise stone detection through UAS based thermal imaging. We have conducted extensive laboratory testing to investigate the suitability of thermal imaging to detect stones and to find the optimal pre-requisite for thermal UAV flights

Mark-release-recapture meets Species Distribution Models: Identifying micro-habitats of grassland butterflies in agricultural landscapes.

Habitat demands and species mobility strongly determine the occurrence of species. Sedentary species with specific habitat requirements are assumed to occur more patchy than mobile habitat generalist species, and thus suffer stronger under habitat fragmentation and habitat deterioration. In this study we measured dispersal and habitat preference of three selected butterfly species using mark-release-recapture technique. We used data on species abundance to calculate Species Distribution Models based on high-resolution aerial photographs taken using RGB / NIR cameras mounted on a UAV. We found that microhabitats for species with specific habitat requirements occur spatially restricted. In contrast, suitable habitats are more interconnected and widespread for mobile habitat generalists. Our models indicate that even managed grassland sites have comparatively little habitat quality, while road verges provide high quality micro-habitats. In addition, dispersal was more restricted for specialist butterfly species, and higher for the two other butterfly species with less ecological specialisation. This study shows synergies arising when combining ecological data with high precision aerial pictures and Species Distribution Models, to identify micro-habitats for butterflies. This approach might be suitable to identify and conserve high quality habitats, and to improve nature conservation at the ground

Digital In Situ Data Collection in Earth Observation, Monitoring and Agriculture—Progress towards Digital Agriculture

Digital solutions in agricultural management promote food security and support the sustainable use of resources. As a result, remote sensing (RS) can be seen as an innovation for the fast generation of reliable information for agricultural management. Near real-time processed RS data can be used as a tool for decision making on multiple scales, from subplot to the global level. This high potential is not yet fully applied, due to often limited access to ground truth information, which is crucial for the development of transferable applications and acceptance. In this study we present a digital workflow for the acquisition, processing and dissemination of agroecological information based on proprietary and open-source software tools with state-of-the-art web-mapping technologies. Data is processed in near real-time and thus can be used as ground truth information to enhance quality and performance of RS-based products. Data is disseminated by easy-to-understand visualizations and download functionalities for specific application levels to serve specific user needs. It thus can increase expert knowledge and can be used for decision support at the same time. The fully digital workflow underpins the great potential to facilitate quality enhancement of future RS products in the context of precision agriculture by safeguarding data quality. The generated FAIR (findable, accessible, interoperable, reusable) datasets can be used to strengthen the relationship between scientists, initiatives and stakeholders

Digital In Situ Data Collection in Earth Observation, Monitoring and Agriculture&mdash;Progress towards Digital Agriculture

Digital solutions in agricultural management promote food security and support the sustainable use of resources. As a result, remote sensing (RS) can be seen as an innovation for the fast generation of reliable information for agricultural management. Near real-time processed RS data can be used as a tool for decision making on multiple scales, from subplot to the global level. This high potential is not yet fully applied, due to often limited access to ground truth information, which is crucial for the development of transferable applications and acceptance. In this study we present a digital workflow for the acquisition, processing and dissemination of agroecological information based on proprietary and open-source software tools with state-of-the-art web-mapping technologies. Data is processed in near real-time and thus can be used as ground truth information to enhance quality and performance of RS-based products. Data is disseminated by easy-to-understand visualizations and download functionalities for specific application levels to serve specific user needs. It thus can increase expert knowledge and can be used for decision support at the same time. The fully digital workflow underpins the great potential to facilitate quality enhancement of future RS products in the context of precision agriculture by safeguarding data quality. The generated FAIR (findable, accessible, interoperable, reusable) datasets can be used to strengthen the relationship between scientists, initiatives and stakeholders