Modeling land suitability for Coffea arabica L. in Central America

Climate data availability plays a key role in development processes of policies, services, and planning in the agricultural sector. However, data at the spatial or temporal resolution required is often lacking, or certain values are missing. In this work, we propose to use a Bayesian network approach to generate data for missing variables. As a case study, we use relative humidity, which is an important indicator of land suitability for coffee production. For the model, we first extracted climate data for the variables precipitation, maximum and minimum air temperature, wind speed, solar radiation and relative humidity from the surface reanalysis dataset Climate Forecast System Reanalysis. We then used machine learning algorithms to define the model structure and parameters from the relationships of the variables found in the dataset. Precipitation, maximum and minimum air temperature, wind speed, and solar radiation are then used as proxy variables to infer missing values for monthly relative humidity and relative humidity for the driest month. For this, we used both complete and incomplete initial data. In both scenarios of data availability, the comparison of estimated and measured values of relative humidity shows a high level of agreement. We conclude that using Bayesian Networks is a practical solution to estimate relative humidity for coffee agricultural planning

Machine Intelligence in Africa: a survey

In the last 5 years, the availability of large audio datasets in African countries has opened unlimited opportunities to build machine intelligence (MI) technologies that are closer to the people and speak, learn, understand, and do businesses in local languages, including for those who cannot read and write. Unfortunately, these audio datasets are not fully exploited by current MI tools, leaving several Africans out of MI business opportunities. Additionally, many state-of-the-art MI models are not culture-aware, and the ethics of their adoption indexes are questionable. The lack thereof is a major drawback in many applications in Africa. This paper summarizes recent developments in machine intelligence in Africa from a multi-layer multiscale and culture-aware ethics perspective, showcasing MI use cases in 54 African countries through 400 articles on MI research, industry, government actions, as well as uses in art, music, the informal economy, and small businesses in Africa. The survey also opens discussions on the reliability of MI rankings and indexes in the African continent as well as algorithmic definitions of unclear terms used in MI.Comment: Accepted and to be presented at DSAI 202

Modelling spatial variability of coffee (Coffea Arabica L.) crop condition with multispectral remote sensing data.

Doctor of Philosophy in Environmental Science. University of KwaZulu-Natal, Pietermaritzburg, 2017.Abstract available in PDF file

Simple identification tools in FishBase

Simple identification tools for fish species were included in the FishBase information system from its inception. Early tools made use of the relational model and characters like fin ray meristics. Soon pictures and drawings were added as a further help, similar to a field guide. Later came the computerization of existing dichotomous keys, again in combination with pictures and other information, and the ability to restrict possible species by country, area, or taxonomic group. Today, www.FishBase.org offers four different ways to identify species. This paper describes these tools with their advantages and disadvantages, and suggests various options for further development. It explores the possibility of a holistic and integrated computeraided strategy

Machine Learning in Image Analysis and Pattern Recognition

This book is to chart the progress in applying machine learning, including deep learning, to a broad range of image analysis and pattern recognition problems and applications. In this book, we have assembled original research articles making unique contributions to the theory, methodology and applications of machine learning in image analysis and pattern recognition

Food Security Monitoring for Developing Countries in the Age of Big Data

Approximately 817 million people are currently estimated to be undernourished and 85 million people across 46 countries are estimated to be in need of food emergency assistance over the course of 2019. Conflict, migration and climate-related disasters are expected to further exacerbate already existing risks to food security. Important pillars that contribute to anticipating crises and informing a potential emergency response are early warning and monitoring systems. The emergence of big data as well as increasing Internet and mobile phone adoption rates across developing countries have enabled the inclusion of different, timelier, more frequent and spatially disaggregated data, as well as the at-risk population itself into monitoring systems. This dissertation is placed at the intersection of food security monitoring, early warning and big data. The first part of this thesis focuses on exploring the information content of current early warning systems (EWSs) for food security risks. We evaluate the information content of the four largest international monitoring system for food security against a conceptual benchmark. We find that EWSs have partially moved towards the inclusion of more diverse indicators for risk monitoring. However, our results further show that timely information is missing, information is published irregularly and the geographical scope of monitoring systems is smaller than stated. Subsequently, this thesis explores ways to improve monitoring systems for food security by using two strings of new data, i.e. Internet metadata and direct assessments from the at-risk population gathered via mobile phones. We test whether Internet metadata in the form of Google search queries (GSQ) can improve now-casts of crop prices in Ethiopia, Kenya, Mozambique, Malawi, Rwanda, Tanzania, Uganda, Zambia and Zimbabwe. In an pseudo-out-of-sample, one-step-ahead forecasting environment, we find models containing the Google search-string textit{maize} to beat the benchmark model in 8 of the 9 countries. By including the GSQ data, we reduce the now-casting error of maize prices between 3% and 23% and achieve the largest improvements for Malawi, Kenya, Zambia and Tanzania with improvements larger than 14%. Furthermore, using a citizen-science approach this thesis analyzes whether the at-risk population can validly assess the food security status of their community, by collecting near real-time food security assessments over an 8 month period from the local population in Kenya. We test the validity of the gathered information against standard food security indicators, i.e. the food consumption score (FCS) and reduced coping strategy index (rCSI), using Pooled Poisson, Negative Binomial and Poisson Fixed Effects models. We find robust results that the assessments from the at-risk population conform to the FCS and rCSI observed during the study period.Ernährungssicherheits-Monitoring für Entwicklungsländer im Zeitalter von Big Data Schätzungen zufolge sind derzeit rund 817 Millionen Menschen unterernährt und 85 Millionen Menschen in 46 verschiedenen Ländern werden im Laufe des Jahres 2019 auf Nahrungsmittelhilfe angewiesen sein. Voraussichtlich werden Konflikte, Migration und klimabedingte Katastrophen die bereits bestehenden Risiken für die Ernährungssicherheit in Zukunft weiter verschärfen. Frühwarn- und Überwachungssysteme für die Ernährungssicherheit sind in diesem Kontext wichtige Säulen, die zur Antizipation von Krisen beitragen und eine potenzielle Notfallintervention auslösen und gestalten. Das Aufkommen von Big Data sowie steigende Internet- und Handy-Nutzung in Entwicklungsländern haben die Einbeziehung verschiedener, häufiger und räumlich detaillierter Daten sowie die Integration der gefährdeten Bevölkerung selbst in Überwachungssysteme ermöglicht. Diese Dissertation befindet sich an der Schnittstelle von Frühwarnsystemen für die Ernährungssicherheit und Big Data. Zunächst untersucht diese Dissertation den Informationsgehalt aktueller Frühwarnsysteme (EWSs) für Ernährungssicherheitsrisiken. Dabei wird der Informationsgehalt von vier großen, internationalen Überwachungssystemen für die Ernährungssicherheit anhand eines konzeptionellen Benchmarks für Frühwarnsystem analysiert. Wir stellen fest, dass EWSs eine breite Bandweite an Indikatoren abdecken und der anfängliche Fokus auf Verfügbarkeit ebenfalls um die Zugangskomponente zu Nahrung erweitert wurde. Unsere Ergebnisse zeigen jedoch weiterhin, dass zeitnahe Information fehlt, Information unregelmäßig veröffentlicht wird und die geografische Reichweite der Überwachungssysteme geringer ist als angegeben. Anschließend untersucht diese Arbeit Möglichkeiten, Überwachungssysteme für die Ernährungssicherheit zu verbessern, indem sie das Potenzial zwei neuer Datenströme für Frühwarnsysteme untersucht, i.e. Internet-Metadaten und die direkten Einschätzungen der Risikopopulation selbst. Wir prüfen, ob Modelle, basierend auf Internet-Metadaten in Form von Google-Suchanfragen (GSQ) die textit{now-casts} von Maispreisen in Äthiopien, Kenia, Mosambik, Malawi, Ruanda, Tansania, Uganda, Sambia und Simbabwe verbessern können. In einer textit{Now-Casting, Pseudo-Out-of-Sample-}Umgebung, finden wir, dass Modelle, die den Google-Suchstring textit{maize} enthalten, das Benchmark-Modell in 8 der 9 Länder schlagen. Durch die Einbeziehung der GSQ-Daten reduzieren wir den Forecasting-Fehler von Maispreisen zwischen 3% und 23% und erzielen die größten Verbesserungen in Malawi, Kenia, Sambia und Tansania mit mehr als 14%. Desweitern analysiert diese Dissertation anhand eines Citzen-Science Ansatzes, ob lokale Teilnehmer den Ernährungssicherheitsstatus der lokalen Bevölkerung einschätzen können. Anhand von Mobiltelefonen und in nahezu Echtzeit wurden dazu über einen Zeitraum von acht Monaten Bewertungen der Ernährungssicherheit von der lokalen Bevölkerung in Kenia gesammelt. In Pooled-Poisson-, Negative-Binomial- und Poisson-Fixed-Effects-Modellen analysieren wir die Validität der gesammelten Informationen im Vergleich zu Indikatoren für die Ernährungssicherheit, i.e. Lebensmittelkonsum (FCS) und Bewältigungsstrategien (rCSI). Wir finden robuste Ergebnisse, dass die Einschätzungen der Risikopopulation mit den Werten des FCS und rCSI übereinstimmen, die während des Untersuchungszeitraums beobachtet wurden

The role of ecosystem services in the spatial assessment of land degradation: a transdisciplinary study in the Ethiopian Great Rift Valley

Land degradation is a widespread problem that affects about 1.5 billion people globally. It can be defined as the decline in the productive capacity of the land, and the loss of functionality of ecosystems. Overall, land degradation leads to ecosystem services degradation, because it affects and causes the depletion of several soil functions (e.g. sediment retention, nutrient cycling, carbon stocks, and water retention). Therefore, it is also a constraint in securing food production and it could cause food insecurity. Hence, land degradation represents a considerable problem especially in developing countries, where people strongly rely on the ecosystems and natural resources for their livelihoods. The principal aim of this study was to assess land degradation by integrating different sources of knowledges and data, to derive a synthesis relevant to inform decision-making processes, and to target priority areas for conservation and restoration interventions. In this study, three ecosystem services (ESS) were modelled to infer land degradation in a small area, in the Halaba special woreda, located in the Ethiopian Great Rift Valley. In particular, sediment erosion and retention, nutrient retention and export, and carbon storage and sequestration were modelled. Data from a local soil survey, from global coverage datasets, and from a supervised land use cover classification were used for the ESS modelling. Remote Sensing data were used during the parametrisation phase of the ESS modelling. Local knowledges and perspectives were gathered using an extensive participatory approach that targeted the communities of three kebeles in the study area, and the experts of the Halaba woreda Agricultural Office. 33 focus group discussions and 32 semi-structured interviews were conducted in the summer 2016. The information acquired through the ESS modelling and during the participatory approach was then integrated in a Bayesian Belief Network (BBN), a probabilistic graphical model, to derive a spatial explicit land degradation risk assessment. The results showed that assessing land degradation through the lens of key ecosystem services represents a valid approach. The ESS modelling results showed that the study area is characterised by high soil erosion rates, low carbon storage and sequestration, and low nitrogen retention. Moreover, the ESS modelling also showed that using data from global coverage datasets could affect the reliability of the ESS assessment. Furthermore, the qualitative study, derived from the participatory approach, highlighted the presence of complex linkages between environmental and socio-economic factors, which exacerbate land degradation. The integration of ESS modelling results, participatory approach and literature data in the BBN proved to be an efficient approach to derive a synthesis of the several knowledges acquired during the several steps of this PhD project. Overall, this study demonstrated that a transdisciplinary and interdisciplinary approach is an effective means to address land degradation risks, taking into consideration people needs and priorities. In order to reverse land degradation trends, there is the need to adopt intense restoration and sustainable land management programs. However, there is also the need to couple conservation interventions with development strategies, such as market access and development, land tenure system improvements, off-farm job opportunities generation, and livelihoods diversification. This could foster land conservation and restoration, and could support sustainable economic growth and inclusive development

INTEGRATED MODELING OF LAND USE AND CLIMATE CHANGE IMPACTS ON MULTISCALE ECOSYSTEMS OF CENTRAL AFRICAN WATERSHEDS

Assessment and management of ecosystem services demands diverse knowledge of the system components. Land use change occurring mainly through deforestation, expansion of agriculture and unregulated extraction of natural resources are the greatest challenges of the Congo basin and yet is central to supporting over 100 million people. This study undertook to implement an integrated modeling of multiscale ecosystems of central African watersheds and model the impact of anthropogenic factors on elephant population in Greater Virunga landscape. The study was conducted at varied scales, regional, landscape, and community. Regional study included watershed analysis and hydrological assessment using remotely sensed data implemented in Geographical information System. Species distribution modeling using generalized linear models using presence only and pseudo absence data was also done at regional scale. The major findings were that ecosystem services for the Congo basin are spatially varied and there are significant differences in their distribution of ecosystem services at a subwatershed level three and six and the distribution of endangered and threatened species is more concentrated in the central part of the basin and most of these species occur mainly inside protected areas. Poaching and sustained civil wars were the biggest threat to elephant conservation in the region. Improvement in law enforcement, monitoring, and increasing household incomes for communities living adjacent to protected areas would help to reduce the impact of poaching on elephant population dynamics. Climate change did not show immediate direct impacts on the elephant population, but thermal and latent heat effects could be occurring. A change in habitat, however, showed resulted in an upward trend in elephant population for all age classes. The local communities heavily depend on these ecosystem services for their livelihoods. It is therefore recommended that reasonable financing to strengthen law enforcement and monitoring is done, multiple policy options should be considered in order to maximize ecosystem benefits. Creation of partnerships and research networks need to be promoted. Existing transboundary collaboration initiatives should continue and be supported by the donor community in order to build peace and dram support for wildlife and wild habitat protection