Dissertations / Theses on the topic 'Meteorology, Agricultural – Data processing'

A maioria das aplicações envolvendo a influência do clima na agricultura requer um grande volume de dados que, geralmente, não estão disponíveis. Desta forma, há necessidade de um aplicativo computacional para facilitar a organização dos dados necessários. O sistema computacional SIAGRO foi desenvolvido para dar suporte a uma plataforma de coleta de dados termo-pluviométricos e para atender à demanda dos usuários da informação agrometeorológica para agricultura. O sistema proposto permite, a partir de dados coletados a intervalos de 15 minutos, cadastrar outras estações, importar dados, calcular a evapotranspiração por diferentes modelos (Thornthwaite; Camargo; Thornthwaite modificado por Camargo e Hagreaves e Samani), utilizar a classificação climática de Thornthwaite e determinar médias para os parâmetros coletados em períodos distintos de tempo. Os resultados são apresentados em forma de gráficos e tabelas num computador pessoal ou via Internet, que podem ser exportados para uso em outros aplicativos computacionais ou comparados com os resultados de outras estações cadastradas no sistema. Disponibilizar o SIAGRO de informação que permita gerir de forma eficiente programas de irrigação para atender as carências de água nos cultivos, permitiu que se avaliasse o desempenho de três métodos de referência para estimar a evapotranspiração com dados obtidos em lisímetros de lençol freático constante. Os dados foram coletados diariamente e processados em escala mensal. O desempenho dos métodos foi analisado a partir do coeficiente de correlação r e do índice de concordância de Willmot d. Os resultados mostraram que a melhor estimativa foi obtida com o modelo de Thornthwaite modificado por Camargo, devido ao seu melhor ajuste aos dados lisimétricos, apresentando uma concordância ótima, com índice d de 0,91.
Since most of the applications involving the influence of climate in agriculture require a great amount of data that usually are unavailable, a computational tool is needed to help to organize the necessary data. The computational system SIAGRO was developed in an attempt to support such a demand of users of climate information in agriculture. The system makes it possible to register other stations, import climatic data, to calculate evapotranspiration by means of different methods (Thornthwaite; Camargo; Thornthwaite modified by Camargo and Hagreaves e Samani), to apply a climatic classification and to determine averages for different periods of time from daily data. The system presents its results in graphics and tables, which can be copied for use in other computer applications or used to be compared with results of other weather stations registered in this system. To supply SIAGRO with profitable information for irrigation scheduling and increase the efficiency in water use by crops, allowed the evaluation of three reference methods to estimating evapotranspiration through correlation with data obtained in constant water table lisimeter. The data were collected daily and processed in a monthly basis. The performance evaluations of the methods were based on the correlation coefficient r and Willmott agreement coefficient d. The results showed that the best estimate was obtained with the Thornthwaite modified by Camargo model, which shows the best adjustment to lysimeter data, with the index d equal to 0.91.

This thesis describes the use of a distrometer and two IBM-PC's to collect data in a large propagation experiment. The uses and methods of collecting drop size distribution are discussed as are the uses of IBM-PC's for both data collection and control. Methods of requiring the PC's to operate in real time are also included.
M.S.

Thesis (MTech (Information Technology))--Cape Peninsula University of Technology, 2017.
The objective of this study was to determine the current status of e-agriculture research in the South African context. A systematic literature review was used to gather and analyse data in alignment with the objective of the study. The researcher used keywords and combined search keywords on web search engines and digital databases to obtain pertinent research papers. The scope of the study was limited to the period 2000-2016. The books, theses, conference papers and journal articles identified as pertinent to conduct the study, amounted to 114 in number. The analysis of the study described the focus of research papers, research methods, research approaches, theoretical lenses, units of analysis and observation, levels of analysis, historical development, and major concepts and disciplines used by authors in their studies. The study also sought to discover the year of publication and assessment of searchability of the papers. The results indicate that 13 papers (11.4%) were published in the first five years (2000- 2004) and 51 papers (44.7%) in the last five years (2012-2016) of the delimited period for the study. The results of the study further indicate that the application of geographic information systems (GISs) towards improving agriculture was the most prominent eagriculture research area in South Africa (27 papers, 23.6%), followed by the use of satellite enhancing agriculture (26 papers, 22.8%). E-government direct services, mobile in agriculture, and agricultural information systems were the least prominent e-agriculture research areas in South Africa with a contribution of two papers (1.8%) each. The results of this study show that information mapping was the most used research method by researchers in their studies (57 papers, 50%), followed by the case study method with 31 papers (27.1%). The results further denote that the least used research method was industry reports with no mention of it in any of the pertinent papers, followed by grounded theory with two papers (1.7%). Interpretivism was the most used research approach by researchers (six papers, 5.2%) during the period 2000-2016. The findings of this study clearly show that researchers still need to address certain issues or problems regarding e-agriculture in South Africa in order to improve the agricultural sector. The contribution of the study is to understand the importance of enhancing research capability and socio-economic transformation of farmworkers and farmers through enhanced communication of agriculture research knowledge in the area of agricultural informatics. A foundation for further studies was created for continuous e-agriculture research in South Africa.

Thesis (MSc)--Stellenbosch University, 2003.
ENGLISH ABSTRACT: With the inception of precision farming techniques during the last decade, agricultural efficiency has improved, leading to greater productivity and enhanced economic benefits associated with agriculture. The awareness of health risks associated with food borne diseases has also increased. Systems such as Hazard Analysis and Critical Control Points (RACCP) in the USA and Good Agricultural Practices (GAP) in Europe are trying to ensure that no food showing signs of microbial contamination associated with production techniques are allowed onto the export market. Growers participating in exporting are thus being forced to conform to the requirements set by international customers. The aim of this study was to compile a computerized record keeping system that would aid farmers with the implementation of GAP on farms, by making use of GIS capabilities. A database, consisting of GAP-specific data was developed. ArcView GIS was used to implement the database, while customized analyses procedures through the use of Avenue assisted in GAP-specific farming related decisions. An agricultural area focusing on the export market was needed for this study, and the nut producing Levubu district was identified as ideal. By making use of ArcView GIS, distinct relationships between different data sets were portrayed in tabular, graphical, geographical and report format. GAP requirements state that growers must base decisions on timely, relevant information. With information available in the above-mentioned formats, decisions regarding actions taken can be justified. By analysing the complex interaction between datasets, the influences that agronomical inputs have on production were portrayed, moving beyond the standard requirements of GAP. Agricultural activities produce enormous quantities of data, and GIS proved to be an indispensable tool because of the ability to analyse and manipulate data with a spatial component. The implementation of good agricultural practices lends itself to the use of GIS. With the correct information available at the right time, better decisions can promote optimal croppmg, whilst rmmrrnzmg the negative effects on the consumer and environment.
AFRIKAANSE OPSOMMING: Gedurende die afgelope dekade het die gebruik van presisie boerderytegnieke tot verbeterde gewasverbouing gelei, wat verhoogde produktiwiteit en ekonomiese welvarendheid tot gevolg gehad het. 'n Wêreldwye bewustheid ten opsigte van die oordrag van siektekieme geasosieer met varsprodukte het ontstaan. Met die implementering van Hazard Analysis and Critical Control Points (HACCP) en Good Agricultural Practices (GAP), poog die VSA en Europa om voedsel wat tekens van besmetting toon van die invoermark te weerhou. Buitelandse produsente en uitvoerders word dus hierdeur gedwing om by internasionale voedselstandaarde aan te pas. Hierdie navorsing het ten doel gehad om 'n gerekenariseerde rekordhouding stelsel daar te stel wat produsente sal bystaan tydens die implementering van GAP, deur gebruik te maak van GIS. 'n Databasis gerig op die implementering van GAP is ontwerp. ArcView GIS is gebruik word om die databasis te implementeer, waarna spesifieke navrae die data ontleed het om sodoende die besluitnemingsproses te vergemaklik. 'n Landbou-area wat aktief in die uitvoermark deelneem was benodig vir dié studie, en die Levubu distrik was ideaal. Verwantskappe tussen datastelle is bepaal en uitgebeeld in tabel-, grafiek- en verslag vorm. Die suksesvolle implementering van GAP vereis dat alle besluite op relevante inligting gebaseer word, en met inligting beskikbaar in die bogenoemde formaat kan alle besluite geregverdig word. Deur die komplekse interaksie tussen insette en produksie te analiseer, was dit moontlik om verwantskappe uit te beeld wat verder strek as wat GAP vereistes stipuleer. Deur die gebruikerskoppelvlak in ArcView te verpersoonlik is die gebruiker nie belaai met onnodige berekeninge nie. Aktiwiteite soos landbou produseer groot datastelle, en die vermoë van GIS om die ruimtelike verwantskappe te analiseer en uit te beeld, het getoon dat GIS 'n instrumentele rol in die besluitnemingsproses speel. Deur middel van beter besluitneming kan optimale gewasverbouing verseker word, terwyl die negatiewe impak op die verbruiker en omgewing tot 'n minimum beperk word.

A demanda mundial por alimentos cresce a cada ano, tornando necessário o desenvolvimento de novas tecnologias que aumentem a produção de grãos sem aumentar as áreas destinadas ao para plantio. A cultura de milho é uma das principais commodities do mundo, sendo utilizada na alimentação humana e como ração de outros animais, além de possuir outros fins industriais. O milho é sensível ao arranjo espacial de plantas e qualquer variação no padrão de distribuição pode levar à redução na produção do milho. Atualmente, o processo de verificação da uniformidade dos espaçamentos entre plantas é realizado de forma manual por agrônomos e produtores, a fim de prever possíveis perdas de produção. Nesse contexto, este trabalho propõe uma abordagem automática para a análise do arranjo espacial de plantas por meio da mensuração dos espaçamentos entre plantas de milho em fases inicias de crescimento. A partir dessa mensuração são extraídas informações relevantes como densidade populacional, uniformidade do plantio e estimativas de perdas. A abordagem proposta utiliza técnicas de visão computacional de baixo custo computacional para identificar as plantas de milho e mensurar os espaçamentos entre plantas, permitindo seu uso em dispositivos com baixo poder computacional como smartphones e tablets. Um conjunto de imagens foi construído como uma contribuição adicional do trabalho contento 222 imagens panorâmicas da linha de plantio de milho em três condições de plantio: direto, convencional e direto após aplicação de herbicidas. Os resultados dos experimentos alcançaram uma taxa de 90% de precisão e 87% de sensibilidade na identificação das plantas de milhos presentes na base. Uma comparação entre as medidas dos espaçamentos entre plantas realizadas de forma manual e por visão computacional, não apresentou diferenças significativas entre as medições, indicando a eficácia da abordagem proposta no trabalho.
Global demand for food is growing every year, requiring the development of new technologies that increase grain production without increasing the areas destined for planting. The corn crop is a major commodity in the world and is used as food, feed for other animals, in addition to having other industrial purposes. Corn is sensitive to the spatial arrangement of plants and any variation in distribution pattern can lead to reduction in the production of corn. Currently, the process of checking the uniformity of spacing between plants is done manually by agronomists and producers in order to predict possible production losses. In this context, this paper proposes an automatic approach to the analysis of the spatial arrangement of plants by measuring the spacing between corn plants in early stages of growth. From this measurement are extracted relevant information such as population density, uniformity of planting and loss estimates. The proposed approach uses computer vision techniques of low computational cost to identify corn plants and measure the spacing between plants, allowing its use in devices with low computational power such as smartphones and tablets. A set of images was built as an additional contribution of work, containing 222 panoramic images of corn planting in three conditions of planting: direct, conventional and direct after applying herbicides. The experimental results achieve 90% of rate accuracy and 87% sensitivity in identification of corn plants present on the base. A comparison of the measurements of the distances between plants made of manual and computer vision way, no presented significant differences between the measurements, indicating the effectiveness of the proposed approach at work.

Thesis (Ph. D.)--Ohio State University, 2005.
Title from first page of PDF file. Document formatted into pages; contains xxii, 271 p.; also includes graphics (some col.) Includes bibliographical references (p. 206-214).

In the forecast data post-processing at the Swedish Meteorological and Hydrological Institute (SMHI) a regular Kalman filter is used to debias the two meter air temperature forecast of the physical models by controlling towards air temperature observations. The Kalman filter however diverges when encountering greater nonlinearities in shifting weather patterns, and can only be manually reset when a new air mass has stabilized itself within its operating region. This project aimed to automate this process by means of a machine learning approach. The methodology was at its base supervised learning, by first algorithmically labelling the air mass shift occurrences in the data, followed by training a logistic regression model. Observational data from the latest twenty years of the Uppsala automatic meteorological station was used for the analysis. A simple pipeline for loading, labelling, training on and visualizing the data was built. As a work in progress the operating regime was more of a semi-supervised one - which also in the long run could be a necessary and fruitful strategy. Conclusively the logistic regression appeared to be quite able to handle and infer from the dynamics of air temperatures - albeit non-robustly tested - being able to correctly classify 77% of the labelled data. This work was presented at Uppsala University in June 1st of 2018, and later in June 20th at SMHI.

Crop development models are commonly used in research. However, their use as crop management tools for growers is rare. Decision support systems (DSS), which combine crop models with expert systems, are being developed to provide management assistance to growers. Researchers at Oregon State University are in the process of developing a DSS. Research was conducted to develop a computer program to provide current and generated weather data for use by the DSS. The objectives of this research were to obtain a weather station, develop a set of quality control procedures to check data from the station, obtain a weather generator program, and create a weather data manager program to implement the above objectives. A weather station was obtained and was placed near two existing weather stations for ten months. Data from the weather station was compared with the other two stations for values of monthly average maximum temperature, minimum temperature, and daily total solar radiation and monthly total precipitation. The weather station performed well. Only measurements of total daily solar radiation were consistently different from the other stations. Based on a comparison of the weather station with an Eppley pyranometer, a factor was calculated to correct the solar radiation readings. The quality control procedures used on the weather data were adapted from automated procedures given in the literature. When tested, the procedures performed as desired. When used on actual data from the weather station, values that failed the procedures were apparently legitimate values. Options were added to the data manager program that allow the user to quickly decide what to do with failed values. For a weather data generator, WGEN was chosen from the generators presented in the literature. An input parameter file was created for the Corvallis, Oregon area and thirty years of data were generated. Monthly means from this data were compared with thirty-year historical monthly means for Corvallis. Precipitation data from WGEN compared well with the historical data. The generated data for maximum and minimum temperature and daily total solar radiation had great differences from the historical data. It is believed that the input parameters for the Corvallis area suggested by the authors of WGEN are not appropriate. The weather data manager program was written in the C programming language, and occupies approximately 98 kilobytes of disk space, not including the eleven files created directly and indirectly by the program. The main functions of the program are: 1) retrieving data from the weather station and performing quality control procedures on the data (allowing the user to decide what to do with values that failed QC); 2) viewing and editing of files by the user; 3) weather data generation (creating a file of only generated data or appending generated data to the file of current data from the weather station to create a file containing a full year of weather data); and 4) miscellaneous functions (monitoring the weather station, setting the calendar in the station's datalogger, and changing information used by the data manager program). It is hoped that this program will be a significant contribution towards the development of a decision support system.
Graduation date: 1992

Lau, Chi Shing = 一個應用於航空的高分辨率、快速更新的氣象數據分析系統 / 柳巳丞.
Thesis (M.Phil.)--Chinese University of Hong Kong, 2008.
Includes bibliographical references (leaves 76-78).
Abstracts in English and Chinese.
Lau, Chi Shing = Yi ge ying yong yu hang kong de gao fen bian lu, kuai su geng xin de qi xiang shu ju fen xi xi tong / Liu Sicheng.
Abstract --- p.i
Acknowledgement --- p.iii
Chapter 1 --- Introduction --- p.1
Chapter 1.1 --- Overview --- p.1
Chapter 1.2 --- Review on Windshear --- p.2
Chapter 2 --- Review of the Weather Radar System --- p.5
Chapter 2.1 --- Introduction --- p.5
Chapter 2.2 --- Reflectivity Measurement --- p.8
Chapter 2.3 --- Velocity Measurement --- p.11
Chapter 2.4 --- The Doppler Dilemma --- p.14
Chapter 2.5 --- TDWR and LIDAR used in Hong Kong --- p.16
Chapter 3 --- Design of the System --- p.19
Chapter 3.1 --- The Wind Analysis --- p.19
Chapter 3.2 --- The Cloud Analysis --- p.25
Chapter 3.3 --- Settings of the Domain --- p.26
Chapter 4 --- Data Preparation --- p.31
Chapter 4.1 --- Background Field --- p.31
Chapter 4.2 --- Non-radar Observation Data --- p.33
Chapter 4.3 --- The Radar Data --- p.33
Chapter 5 --- A Study on Sea Breeze --- p.37
Chapter 5.1 --- The Physical origin of Sea Breeze --- p.37
Chapter 5.2 --- Case Study on 10 March 2006 --- p.41
Chapter 6 --- A Study on Tropical Cyclone --- p.46
Chapter 6.1 --- The Physics of Tropical Cyclone --- p.46
Chapter 6.2 --- Case Study on 3 Aug 2006 --- p.51
Chapter 7 --- A Study on Microburst --- p.57
Chapter 7.1 --- The Physical origin of Microburst --- p.57
Chapter 7.2 --- Case Study on 8 June 2007 --- p.60
Chapter 8 --- Discussions and Conclusions --- p.67
Chapter 8.1 --- Discussions --- p.67
Chapter 8.2 --- Conclusions --- p.69
Chapter A --- Derivation of Radar Equation --- p.70
Chapter A.1 --- Radar Equation for Point Target --- p.70
Chapter A.2 --- Radar Equation for Distributed Targets --- p.71
Chapter B --- Technical Details --- p.73
Chapter B.1 --- Hardware and Timing --- p.73
Chapter B.2 --- Programming issues --- p.75
Bibliography --- p.76

Ph.D. (Geography)
Skillful management and planning of the earth's natural resources and of agricultural production necessitates a great deal of Information regarding the resources and relevant soclo-economlc factors, as well as better Information on crop yield expectations throughout the year. These Intricate processes can often be simplified Into models. Most of Nature's systems (for example climatic systems) are, however, too complex to allow realistic models to be evaluated numerically and are therefore examined by means of simulation models through which the Interaction with time between physical processes Is established. The purpose Is to understand these processes In order to forecast the results of these changes and Interactions. The aim of this study Is to construct a composite climate model that (1) computes missing climate values, and (2) extrapolates climate values until the expected date of harvesting, by simulation using the random sampling of values from reference ("look-up") tables, In order that (3) the climate files, with simulated climate values, could be used with the parameter files as Input files for the CERES-Maize model. The CERES-Maize model uses dally values to simulate the growth, development and yield of the maize plant. The respective crop forecasting results obtained for actual and simulated climate values are then to be evaluated. Climate files, with four variables, were obtained on magnetic computer tape from the South African Weather Bureau for the study area In the Eastern Transvaal. The preliminary processing was done by the use of SA5-programmes and these files were then exported from the mainframe computer to a personal computer and stored on floppy disks. Climate reference flies were compiled from the original climate flies by sorting the climate data according to the Julian date. The missing values In the climate reference flies and the original climate files were restored from the files of neighbouring weather stations, as calculated orestimated values by.means of a suitable method of computation. Some of the methods used, were derived after comparing the graphs of the time-series of a number of climate files. Aclimate simulation model was compiled In which climatic elements were simulated by sampling values a set number of times randomly from the climate reference files. The mean of these sampled values were adjusted by multiplying It with a factor representing the climatic change over time. A climate file, also containing simulated values, and a theoretical parameter Input file were then used as the Input flies for a revised edition of the CERES-Maize model. A comparison of the results obtained for the 1986/87 growing season when the climate files, with actual and simulated values respectively, were used as Inputs for the CERES-Maize model, Indicated very promising results. The values predicted for two climate flies (1962-1987) differed by about 18%, whereas a difference of only about 8% between those predicted for two smaller climate files (actual and simulated values respectively), representing only the 1986/87 season, was recorded. The difference between values predicted for the climate file, mentioned last, and consisting only of simulated climate values, and those forecasted for the original and complete climate data file, was only 5%. As Indicated by the arithmetic mean, there is again a tendency towards the mean values.

Thesis (DTech. degree in Business Informatics)--Tshwane University of Technology, 2009.
South Africa’s post-apartheid Black farmers commonly referred to nowadays as “emerging farmers” need knowledge support to be successful in agricultural productions. Web-based learning enables knowledge users and knowledge providers to actively engage interactively, and provides a dynamic platform of knowledge support with unparallel flexibility and convenience. However, this technology is yet to be adequately harnessed to the benefit of South Africa Black emerging farmers, whourgently requires knowledge support that could enable their transformation into market oriented farming. In this study we present a model to solve this problem. Qualitative grounded theory approach was utilized as the research methodology. Theresearch revealed that weakly on-demand knowledge support, lack of requisite formal agricultural education, management skill and knowledge for market oriented farming will remain a major setback to the transformation of Black emerging farmers. The researcher proposed an agricultural knowledge support portal-model. In order to cover the entire spectrum of agricultural knowledge support process of the emerging farmers, this study has modelled a number of fundamental components such as synchronous and asynchronous knowledge support systems, Interactive services for personalised knowledge support; Virtual laboratory for researchers and expert viii networking; knowledge bureau for problems presentation and linkage with expert through expert directory; knowledge repository for resource re-use and knowledge sharing; and e-learning for formal agricultural education of farmers and their family. The benefit of this research and knowledge support model will enables large groups of dispersed knowledge providers to directly support individuals, thereby creating a link between agricultural knowledge systems, educational institution and other research organization to pool resources and provide a coherent platform for action. In addition, it will provide a platform of interaction, collaboration and enhance access to knowledge based on requirement on the web. Emerging farmers will no longer seek solutions, rather personalized solutions comes to the farmers. Knowledge support portal holds a promise to enhance agricultural knowledge acquisition and utilization, for the transformation of Black emerging farmers into market oriented or commercial farming.

Computer decision aids can help integrate and apply diverse sources of information and expertise to problems of integrated pest management (IPM) in agriculture and forestry. AppleMgr combines a rule-based expert system with databases and spreadsheets in a prototype decision aid intended to be expanded and modified for use by extension workers in the Northwest U.S. The program requires an IBMcompatible microcomputer with hard disk. AppleMgr concentrates on the two most important insect pests on apple in the Northwest--codling moth, Cydia, pomonella (L.), and San Jose scale, Quadraspidiotus perniciosus Comstock, and on phytophagous mites, whose control largely depends on predators. The primary goal of AppleMgr is to demonstrate an improved process of decision making in apple IPM. AppleMgr is composed of modules for diagnosis of pest injury, identification of pest and natural enemy specimens, and management. The first two modules arrive at conclusions through backward-chaining inference from user observations. The management module uses backward chaining supplemented with external calculation programs to find the net benefit of a pesticide application. A method is included to predict yield and fruit size from crop samples. Cullage from codling moth and San Jose scale, mite effect on fruit size, probability of biological mite control and pesticide efficacy are predicted from researchers' data and estimates. Selected relative beneficial and adverse side effects of pesticides are presented in spreadsheets. An analysis of packing house records for apple crops from eight orchards at three yields using 1987 and 1988 prices and packing charges showed that net crop value varied by up to $8000 per acre. The variability in crop value and the importance of adverse side effects of pesticides suggest that the commonly-used "action thresholds" for treatment are seriously inadequate. AppleMgr may point the way toward more dynamic and realistic methods of IPM decision making.
Graduation date: 1990

Indiana University-Purdue University Indianapolis (IUPUI)
The primary objective of this research involves mapping out and validating the existence of sub surface drainage tiles in a given cropland using Remote Sensing and GIS methodologies. The process is dependent on soil edge differentiation found in lighter versus darker IR reflectance values from tiled vs. untiled soils patches. Data is collected from various sources and a primary classifier is created using secondary field variables such as soil type, topography and land Use and land cover (LULC). The classifier mask reduces computational time and allows application of various filtering algorithms for detection of edges. The filtered image allows an efficient feature recognition platform allowing the tile drains to be better identified. User defined methods and natural vision based methodologies are also developed or adopted as novel techniques for edge detection. The generated results are validated with field data sets which were established using Ground Penetration Radar (GPR) studies. Overlay efficiency is calculated for each methodology along with omission and commission errors. This comparison yields adaptable and efficient edge detection techniques which can be used for similar areas allowing further development of the tile detection process.

Policy makers, water managers, farmers and many other sectors of the society in southern Africa are confronting increasingly complex decisions as a result of the marked day-to-day, intra-seasonal and inter-annual variability of climate. Hence, forecasts of hydro-climatic variables with lead times of days to seasons ahead are becoming increasingly important to them in making more informed risk-based management decisions. With improved representations of atmospheric processes and advances in computer technology, a major improvement has been made by institutions such as the South African Weather Service, the University of Pretoria and the University of Cape Town in forecasting southern Africa’s weather at short lead times and its various climatic statistics for longer time ranges. In spite of these improvements, the operational utility of weather and climate forecasts, especially in agricultural and water management decision making, is still limited. This is so mainly because of a lack of reliability in their accuracy and the fact that they are not suited directly to the requirements of agrohydrological models with respect to their spatial and temporal scales and formats. As a result, the need has arisen to develop a GIS based framework in which the “translation” of weather and climate forecasts into more tangible agrohydrological forecasts such as streamflows, reservoir levels or crop yields is facilitated for enhanced economic, environmental and societal decision making over southern Africa in general, and in selected catchments in particular. This study focuses on the development of such a framework. As a precursor to describing and evaluating this framework, however, one important objective was to review the potential impacts of climate variability on water resources and agriculture, as well as assessing current approaches to managing climate variability and minimising risks from a hydrological perspective. With the aim of understanding the broad range of forecasting systems, the review was extended to the current state of hydro-climatic forecasting techniques and their potential applications in order to reduce vulnerability in the management of water resources and agricultural systems. This was followed by a brief review of some challenges and approaches to maximising benefits from these hydro-climatic forecasts. A GIS based framework has been developed to serve as an aid to process all the computations required to translate near real time rainfall fields estimated by remotely sensed tools, as well as daily rainfall forecasts with a range of lead times provided by Numerical Weather Prediction (NWP) models into daily quantitative values which are suitable for application with hydrological or crop models. Another major component of the framework was the development of two methodologies, viz. the Historical Sequence Method and the Ensemble Re-ordering Based Method for the translation of a triplet of categorical monthly and seasonal rainfall forecasts (i.e. Above, Near and Below Normal) into daily quantitative values, as such a triplet of probabilities cannot be applied in its original published form into hydrological/crop models which operate on a daily time step. The outputs of various near real time observations, of weather and climate models, as well as of downscaling methodologies were evaluated against observations in the Mgeni catchment in KwaZulu-Natal, South Africa, both in terms of rainfall characteristics as well as of streamflows simulated with the daily time step ACRU model. A comparative study of rainfall derived from daily reporting raingauges, ground based radars, satellites and merged fields indicated that the raingauge and merged rainfall fields displayed relatively realistic results and they may be used to simulate the “now state” of a catchment at the beginning of a forecast period. The performance of three NWP models, viz. the C-CAM, UM and NCEP-MRF, were found to vary from one event to another. However, the C-CAM model showed a general tendency of under-estimation whereas the UM and NCEP-MRF models suffered from significant over-estimation of the summer rainfall over the Mgeni catchment. Ensembles of simulated streamflows with the ACRU model using ensembles of rainfalls derived from both the Historical Sequence Method and the Ensemble Re-ordering Based Method showed reasonably good results for most of the selected months and seasons for which they were tested, which indicates that the two methods of transforming categorical seasonal forecasts into ensembles of daily quantitative rainfall values are useful for various agrohydrological applications in South Africa and possibly elsewhere. The use of the Ensemble Re-ordering Based Method was also found to be quite effective in generating the transitional probabilities of rain days and dry days as well as the persistence of dry and wet spells within forecast cycles, all of which are important in the evaluation and forecasting of streamflows and crop yields, as well as droughts and floods. Finally, future areas of research which could facilitate the practical implementation of the framework were identified.
Thesis (Ph.D.)-University of KwaZulu-Natal, Pietermaritzburg, 2007.

The importance of agricultural knowledge systems (AKS) especially in rural communities cannot be overemphasized. AKS are important for creation, sharing and enhancing access and usage of agricultural knowledge. They link agricultural research and farms; increase adoption of good agricultural practices; improve the performance of agricultural marketing systems; and enhance effective post-harvest management. Despite the importance of agriculture to the economy and livelihoods of majority of Tanzanians, there is a consensus from scholars that the sector has been performing poorly. This is partially due to limited access to agricultural knowledge resulting into irrational decisions on agricultural activities thus dwarfing the sector. The modified Knowledge Management Processes Model guided the study in investigating how AKS can be strengthened to enhance access and usage of agricultural knowledge among stakeholders. The study adopted a pragmatic paradigm and used mixed method research by applying a survey, key informant interviews, focus group discussions (FGDs) and document reviews. Structured questionnaires were administered to 314 farmers while key informant interviews involved 57 respondents among village executives, councillors, input-suppliers, information providers, buyers, agricultural extension officers and researchers. Moreover, three FGDs involving 24 farmers were conducted. Qualitative data were analysed through classical content and constant comparison analysis, while SPSS software was used to analyse quantitative data. Quantitative and qualitative data were mixed during analysis, interpretation and discussion of results. The study identified farmers, the private sector and the government as major actors, but not working in unison. Most actors used human based systems while few used ICT and paper based systems. Actors needed agricultural knowledge on weather, farm preparation, seeds, crop maintenance, post-harvest practices, agricultural marketing and credits. Most actors shared agricultural knowledge through face-to-face interactions and mobile phones, few through internet. It was concluded that poor linkage among actors limited accessibility of agricultural knowledge. To improve accessibility to agricultural knowledge, a model for strengthening AKS usage is proposed. It is recommended that actors should be linked together and involved in enhancing access and usage of agricultural knowledge. Moreover, the proposed model should be validated before applying it.
Information Science
D. Litt. et Phil (Information Science)

Hyperspectral imaging has become one of the most popular technologies in plant phenotyping because it can efficiently and accurately predict numerous plant physiological features such as plant biomass, leaf moisture content, and chlorophyll content. Various hyperspectral imaging systems have been deployed in both greenhouse and field phenotyping activities. However, the hyperspectral imaging quality is severely affected by the continuously changing environmental conditions such as cloud cover, temperature and wind speed that induce noise in plant spectral data. Eliminating these environmental effects to improve imaging quality is critically important. In this thesis, two approaches were taken to address the imaging noise issue in greenhouse and field separately. First, a computational simulation model was built to simulate the greenhouse microclimate changes (such as the temperature and radiation distributions) through a 24-hour cycle in a research greenhouse. The simulated results were used to optimize the movement of an automated conveyor in the greenhouse: the plants were shuffled with the conveyor system with optimized frequency and distance to provide uniform growing conditions such as temperature and lighting intensity for each individual plant. The results showed the variance of the plants’ phenotyping feature measurements decreased significantly (i.e., by up to 83% in plant canopy size) in this conveyor greenhouse. Secondly, the environmental effects (i.e., sun radiation) on aerial hyperspectral images in field plant phenotyping were investigated and modeled. An artificial neural network (ANN) method was proposed to model the relationship between the image variation and environmental changes. Before the 2019 field test, a gantry system was designed and constructed to repeatedly collect time-series hyperspectral images with 2.5 minutes intervals of the corn plants under varying environmental conditions, which included sun radiation, solar zenith angle, diurnal time, humidity, temperature and wind speed. Over 8,000 hyperspectral images of corn (Zea mays L.) were collected with synchronized environmental data throughout the 2019 growing season. The models trained with the proposed ANN method were able to accurately predict the variations in imaging results (i.e., 82.3% for NDVI) caused by the changing environments. Thus, the ANN method can be used by remote sensing professionals to adjust or correct raw imaging data for changing environments to improve plant characterization.

Indiana University-Purdue University Indianapolis (IUPUI)
Over the past two decades, northern parts of the United States have experienced extreme heat conditions. Some of the notable heat wave impacts have occurred in Chicago in 1995 with over 600 reported deaths and in Philadelphia in 1993 with over 180 reported deaths. The distribution of extreme heat events in Indianapolis has varied since the year 2000. The Urban Heat Island effect has caused the temperatures to rise unusually high during the summer months. Although the number of reported deaths in Indianapolis is smaller when compared to Chicago and Philadelphia, the heat wave in the year 2010 affected primarily the vulnerable population comprised of the elderly and the lower socio-economic groups. Studying the spatial distribution of high temperatures in the vulnerable areas helps determine not only the extent of the heat affected areas, but also to devise strategies and methods to plan, mitigate, and tackle extreme heat. In addition, examining spatial patterns of vulnerability can aid in development of a heat warning system to alert the populations at risk during extreme heat events. This study focuses on the qualitative and quantitative methods used to measure extreme heat events. Land surface temperatures obtained from the Landsat TM images provide useful means by which the spatial distribution of temperatures can be studied in relation to the temporal changes and socioeconomic vulnerability. The percentile method used, helps to determine the vulnerable areas and their extents. The maximum temperatures measured using LST conversion of the original digital number values of the Landsat TM images is reliable in terms of identifying the heat-affected regions.