Geospatial analysis of meteorological drought impact on Southern Africa biomes

Within Southern African biomes, droughts are recurrent with devastating impacts on ecological, economic, and human wellbeing. In this context, understanding the drought impact on vegetation is of extreme importance. However, information on drought impact on natural vegetation at the biome level is scanty and remains poorly understood. Most studies of drought impact on vegetation have largely focussed on crops. The few existing studies on natural vegetation are based on experiments and field measurements at individual tree level which are not representative of biomes. In this study, we mapped the spatial extent and severity of drought using the Standardized Precipitation Evapotranspiration Index (SPEI) and then quantified the drought impact on Southern African biomes using the Vegetation Condition Index (VCI) for the period 1998 to 2017. To compare drought impact across the biomes, we computed the percentage area of the biome with seasonal VCI <30. The drought trend for each biome was computed for each pixel using a linear regression model in R software using the seasonal VCI images from 1998 to 2017. Our result showed that extreme drought impact on vegetation was mainly confined to the southwestern biomes (i.e. the Nama karoo and desert biomes) with most drought occurring during the first half of the season. We also observed an increasing trend of VCI (1998 to 2017) across all biomes and this increasing VCI trend might be explained by woody encroachment which is prevalent in the Savannah and Grassland biomes. The results of this study provide baseline information on drought hotspots.The University of Pretoria Postgraduate Doctoral Bursaryhttp://www.tandfonline.com/loi/tres202021-12-30hj2021Forestry and Agricultural Biotechnology Institute (FABI)Plant Production and Soil Scienc

Review of the current models and approaches used for maize crop yield forecasting in sub-Saharan Africa, and their potential use in early warning systems

Agriculture is the mainstay of many developing economies, and successful production is intricately linked to food security, economic development, and regional stability. Estimates of crop yield for strategic grain crops, such as maize (Zea mays L.) have been used in national food security planning to develop response strategies in years of shortfalls and secure markets in years of surplus. Past studies have shown that despite the potential of models in maize crop yield assessment, they have not been effectively used in understanding seasonal and annual production dynamics. Thus, stakeholders require the availability of accurate and timely data on maize production potential and hence the development and application of crop yield models for maize yield estimation. However, current methods of assessing maize crop yields are based on field assessments, which are expensive, laborious and inaccurate. This mixed methods paper, therefore, aimed to; (i) review information sources for maize crop yield assessments, looking at their strengths, limitations, and potential for application in sub-Saharan Africa, (ii) perform trend and distribution analyses of publications in maize crop yield simulation, and (iii) discuss the challenges in the application of models in agriculture planning in the African agriculture systems

Estimating maize grain yield from crop growth stages using remote sensing and GIS in the Free State Province, South Africa

Early yield prediction of a maize crop is important for planning and policy decisions. Many countries, including South Africa use the conventional techniques of data collection for maize crop monitoring and yield estimation which are based on ground-based visits and reports. These methods are subjective, very costly and time consuming. Empirical models have been developed using weather data. These are also associated with a number of problems due to the limited spatial distribution of weather stations. Efforts are being made to improve the accuracy and timeliness of yield prediction methods. With the launching of satellites, satellite data are being used for maize crop monitoring and yield prediction. Many studies have revealed that there is a correlation between remotely sensed data (vegetation indices) and crop yields. The satellite based approaches are less expensive, save time, data acquisition covers large areas and can be used to estimate maize grain yields before harvest. This study applied Landsat 8 satellite based vegetation indices, Normalized Difference Vegetation Index (NDVI), Soil Adjusted Vegetation Index (SAVI) and Moisture Stress Index (MSI) to predict maize crop yield. These vegetation indices were derived at different growth stages. The investigation was carried out in the Kopanong Local Municipality of the Free State Province, South Africa. Ground-based data (actual harvested maize yields) was collected from Department of Agriculture, Forestry and Fisheries (DAFF). Satellite images were acquired from Geoterra Image (Pty) Ltd and weather data was from the South African Weather Service (SAWS). Multilinear regression approaches were used to relate yields to the remotely sensed indices and meteorological data was used during the development of yield estimation models. The results showed that there are significant correlations between remotely sensed vegetation indices and maize grain yield; up to 63 percent maize yield was predicted from vegetation indices. The study also revealed that NDVI and SAVI are better yield predictors at reproductive growth stages of maize and MSI is a better index to estimate maize yield at both vegetative and reproductive growth stages. The results obtained in this study indicated that maize grain yields can be estimated using satellite indices at different maize growth stages

Advancing agricultural monitoring for improved yield estimations using SPOT-VGT and PROBA-V type remote sensing data

Accurate and timely crop condition monitoring is crucial for food management and the economic development of any nation. However, accurately estimating crop yield from the field to global scales is a challenge. According to the global strategy of the World Bank, in order to improve national agricultural statistics, crop area, crop production, and crop yield are key variables that all countries should be able to provide. Crop yield assessment requires that both an estimation of the quantity of a product and the area provided for that product should be available. The definition seems simple; however, these measurements are time consuming and subject to error in many circumstances. Remote sensing is one of several methods used for crop yield estimation. The yield results from a combination of environmental factors, such as soil, weather, and farm management, which are responsible for the unique spectral signature of a crop captured by satellite images. Additionally, yield is an expression of the state, structure, and composition of the plant. Various indices, crop masks, and land observation sensors have been developed to remotely observe and control crops in different regions. This thesis focuses on how much low spatial resolution satellites, such as Project for On Board Autonomy Vegetation (PROBA V), can contribute to global crop monitoring by aiding the search for improved methods and datasets for better crop yield estimation. This thesis contains three chapters. The first chapter explores how an existing product, Dry Matter Productivity (DMP), that has been developed for Satellites Pour l’Observation de la Terre or Earth observing Satellites VeGeTation (SPOT VGT), and transferred to PROBA V, can be improved to more closely relate to yield anomalies across selected regions. This chapter also covers the testing of the contribution of stress factors to improve wheat and maize yield estimations. According to Monteith’s theory, crop biomass linearly correlates with the amount of Absorbed Photosynthetically Active Radiation (APAR) and constant Radiation Use Efficiency (RUE) downregulated by stress factors such as CO2, fertilization, temperature, and water stress. The objective of this chapter is to investigate the relative importance of these stress factors in relation to the regional biomass production and yield. The production efficiency model Copernicus Global Land Service Dry Matter Productivity (CGLS DMP), which follows Monteith’s theory, is modified and evaluated for common wheat and silage maize in France, Belgium, and Morocco using SPOT VGT for the 1999–2012 period. The correlations between the crop yield data and the cumulative modified DMP, CGLS DMP, Fraction of APAR (fAPAR), and Normalized Difference Vegetation Index (NDVI) values are analyzed for different crop growth stages. The best results are obtained when combinations of the most appropriate stress factors are included for each selected region, and the modified DMP during the reproductive stage is accumulated. Though no single solution can demonstrate an improvement of the global product, the findings support an extension of the methodology to other regions of the world. The second chapter demonstrates how PROBA V can be used effectively for crop identification mapping by utilizing spectral matching techniques and phenological characteristics of different crop types. The study sites are agricultural areas spread across the globe, located in Flanders (Belgium), Sria (Russia), Kyiv (Ukraine), and Sao Paulo (Brazil). The data are collected for the 2014–2015 season. For each pure pixel within a field, the NDVI profile of the crop type for its growing season is matched with the reference NDVI profile. Three temporal windows are tested within the growing season: green up to senescence, green up to dormancy, and minimum NDVI at the beginning of the growing season to minimum NDVI at the end of the growing season. In order of importance, the crop phenological development period, parcel size, shorter time window, number of ground truth parcels, and crop calendar similarity are the main reasons behind the differences between the results. The methodology described in this chapter demonstrates the potentials and limitations of using 100 m PROBA V with revisiting frequency every 5 days in crop identification across different regions of the world. The final chapter explores the trade off between the different spatial resolutions provided by PROBA V products versus the temporal frequency and, additionally, explores the use of thermal time to improve statistical yield estimations. The ground data are winter wheat yields at the field level for 39 fields across Northern France during one growing season 2014–2015. An asymmetric double sigmoid function is fitted, and the NDVI values are integrated over thermal time and over calendar time for the central pixel of the field, exploring different thresholds to mark the start and end of the cropping season. The integrated NDVI values with different NDVI thresholds are used as a proxy for yield. In addition, a pixel purity analysis is performed for different purity thresholds at the 100 m, 300 m, and 1 km resolutions. The findings demonstrate that while estimating winter wheat yields at the field level with pure pixels from PROBA V products, the best correlation is obtained with a 100 m resolution product. However, several fields must be omitted due to the lack of observations throughout the growing season with the 100 m resolution dataset, as this product has a lower temporal resolution compared to 300 m and 1 km. This thesis is a modest contribution to the remote sensing and data analysis field with its own merits, in particular with respect to PROBA V. The experiments provide interesting insight into the PROBA V dataset at 1 km, 300 m, and 100 m resolutions. Specifically, the results show that 100 m spatial resolution imagery could be used effectively and advantageously in agricultural crop monitoring and crop identification at local – field level – and regional – the administrative regions defined by the national governments – levels. Furthermore, this thesis discusses the limitations of using a low resolution satellite, such as the PROBA V 100 m dataset, in crop monitoring and identification. Also, several recommendations are made for space agencies that can be used when designing the new generation of satellites

Citizen science and remote sensing for crop yield gap analysis

The world population is anticipated to be around 9.1 billion in 2050 and the challenge is how to feed this huge number of people without affecting natural ecosystems. Different approaches have been proposed and closing the ‘yield gap’ on currently available agricultural lands is one of them. The concept of ‘yield gap’ is based on production ecological principles and can be estimated as the difference between a benchmark (e.g. climatic potential or water-limited yield) and the actual yield. Yield gap analysis can be performed at different scales: from field to global level. Of particular importance is estimating the yield gap and revealing the underlying explanatory factors contributing to it. As decisions are made by farmers, farm level yield gap analysis specifically contributes to better understanding, and provides entry points to increased production levels in specific farming systems. A major challenge for this type of analysis is the high data standards required which typically refer to (a) large sample size, (b) fine resolution and (c) great level of detail. Clearly, obtaining information about biophysical characteristics and crop and farm management for individual agricultural activities within a farm, as well as farm and farmer’s characteristics and socio-economic conditions for a large number of farms is costly and time-consuming. Nowadays, the proliferation of different types of mobile phones (e.g., smartphones) equipped with sensors (e.g., GPS, camera) makes it possible to implement effective and low-cost “bottom-up” data collection approaches such as citizen science. Using these innovative methodologies facilitate the collection of relatively large amounts of information directly from local communities. Moreover, other data collection methods such as remote sensing can provide data (e.g., on actual crop yield) for yield gap analysis. The main objective of this thesis, therefore, was to investigate the applicability of innovative data collection approaches such as crowdsourcing and remote sensing to support the assessment and monitoring of crop yield gaps. To address the main objective, the following research questions were formulated: 1) What are the main factors causing the yield gaps at the global, regional and crop level? 2) How could data for yield gap explaining factors be collected with innovative “bottom-up” approaches? 3) What are motivations of farmers to participate in agricultural citizen science? 4) What determines smallholder farmers to use technologies (e.g., mobile SMS) for agricultural data collection? 5) How can synergy of crowdsourced data and remote sensing improve the estimation and explanation of yield variability? Chapter 2 assesses data availability and data collection approaches for yield gap analysis and provides a summary of yield gap explaining factors at the global, regional and crop level, identified by previous studies. For this purpose, a review of yield gap studies (50 agronomic-based peer-reviewed articles) was performed to identify the most commonly considered and explaining factors of the yield gap. Using the review, we show that management and edaphic factors are more often considered to explain the yield gap compared to farm(er) characteristics and socio-economic factors. However, when considered, both farm(er) characteristics and socio-economic factors often explain the yield gap. Furthermore, within group comparison shows that fertilization and soil fertility factors are the most often considered management and edaphic groups. In the fertilization group, factors related to quantity (e.g., N fertilizer quantity) are more often considered compared to factors related to timing (e.g., N fertilizer timing). However, when considered, timing explained the yield gap more often. Finally, from the results at regional and crop level, it was evident that the relevance of factors depends on the location and crop, and that generalizations should not be made. Although the data included in yield gap analysis also depends on the objective, knowledge of explaining factors, and methods applied, data availability is a major limiting factor. Therefore, bottom-up data collection approaches (e.g., crowdsourcing) involving agricultural communities can provide alternatives to overcome this limitation and improve yield gap analysis. Chapter 3 explores the motivations of farmers to participate in citizen science. Building on motivational factors identified from previous citizen science studies, a questionnaire based methodology was developed which allowed the analysis of motivational factors and their relation to farmers’ characteristics. Using the developed questionnaire, semi-structured interviews were conducted with smallholder farmers in three countries (Ethiopia, Honduras and India). The results show that for Indian farmers a collectivistic type of motivation (i.e., contribute to scientific research) was more important than egoistic and altruistic motivations. For Ethiopian and Honduran farmers an egoistic intrinsic type of motivation (i.e., interest in sharing information) was most important. Moreover, the majority of the farmers in the three countries indicated that they would like to receive agronomic advice, capacity building and seed innovation as the main returns from the citizen science process. Country and education level were the two most important farmers’ characteristics that explained around 20% of the variation in farmers’ motivations. The results also show that motivations to participate in citizen science are different for smallholders in agriculture compared to other sectors. For example fun has appeared to be an important egoistic intrinsic factor to participate in other citizen science projects, the smallholder farmers involved in this research valued ‘passing free time’ the lowest. Chapter 4 investigates the factors that determine farmers to adopt mobile technology for agricultural data collection. To identify the factors, the unified theory of acceptance and use of technology (UTAUT2) model was employed and extended with additional constructs of trust, mastery-approach goals and personal innovativeness in information technology. As part of the research, we setup data collection platforms using open source applications (Frontline SMS and Ushahidi) and farmers provided their farm related information using SMS for two growing seasons. The sample for this research consisted of group of farmers involved in a mobile SMS experiment (n=110) and another group of farmers which was not involved in a mobile SMS experiment (n=110), in three regions of Ethiopia. The results from the structural equation modelling showed that performance expectancy, effort expectancy, price value and trust were the main factors that influence farmers to adopt mobile SMS technology for agricultural data collection. Among these factors, trust is the strongest predictor of farmer’s intention to adopt mobile SMS. This clearly indicates that in order to use the citizen science approach in the agricultural domain, establishing a trusted relationship with the smallholder farming community is crucial. Given that performance expectancy significantly predicted farmer’s behavioural intention to adopt mobile SMS, managers of agricultural citizen science projects need to ensure that using mobile SMS for agricultural data collection offers utilitarian benefits to the farmers. The importance of effort expectancy on farmer’s intention to adopt mobile SMS clearly indicates that mobile phone software developers need to develop easy to use mobile applications. Chapter 5 demonstrates the results of synergetic use of remote sensing and crowdsourcing for estimating and explaining crop yields at the field level. Sesame production on medium and large farms in Ethiopia was used as a case study. To evaluate the added value of the crowdsourcing approach to improve the prediction of sesame yield using remote sensing, two independent models based on the relationship between vegetation indices (VIs) and farmers reported yield were developed and compared. The first model was based on VI values extracted from all available remote sensing imagery acquired during the optimum growing period (hereafter optimum growing period VI). The second model was based on VI values extracted from remote sensing imagery acquired after sowing and before harvest dates per field (hereafter phenologically adjusted VI). To select the images acquired between sowing and harvesting dates per field, farmers crowdsourced crop phenology information was used. Results showed that vegetation indices derived based on farmers crowdsourced crop phenology information had a stronger relationship with sesame yield compared to vegetation indices derived based on the optimum growing period. This implies that using crowdsourced information related to crop phenology per field used to adjust the VIs, improved the performance of the model to predict sesame yield. Crowdsourcing was further used to identify the factors causing the yield variability within a field. According to the perception of farmers, overall soil fertility was the most important factor explaining the yield variability within a field, followed by high presence of weeds. Chapter 6 discusses the main findings of this thesis. It draws conclusions about the main research findings in each of the research questions addressed in the four main chapters. Finally, it discusses the necessary additional steps (e.g., data quality, sustainability) in a broader context that need to be considered to utilize the full potential of innovative data collection approaches for agricultural citizen science.</p

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

UAV and field spectrometer based remote sensing for maize phenotyping, varietal discrimination and yield forecasting.

Doctoral Degree. University of KwaZulu-Natal, Pietermaritzburg.Maize is the major staple food crop in the majority of Sub-Saharan African (SSA) countries. However, production statistics (croplands and yields) are rarely measured, and where they are recorded, accuracy is poor because the statistics are updated through the farm survey method, which is error-prone and is time-consuming, and expensive. There is an urgent need to use affordable, accurate, timely, and readily accessible data collection and spatial analysis tools, including robust data extraction and processing techniques for precise yield forecasting for decision support and early warning systems. Meeting Africa’s rising food demand, which is driven by population growth and low productivity requires doubling the current production of major grain crops like maize by 2050. This requires innovative approaches and mechanisms that support accurate yield forecasting for early warning systems coupled with accelerated crop genetic improvement. Recent advances in remote sensing and geographical information system (GIS) have enabled detailed cropland mapping, spatial analysis of land suitability, crop type, and varietal discrimination, and ultimately grain yield forecasting in the developed world. However, although remote sensing and spatial analysis afforded us unprecedented opportunities for detailed data collection, their application in maize in Africa is still limited. In Africa, the challenge of crop yield forecasting using remote sensing is a daunting task because agriculture is highly fragmented, cropland is spatially heterogeneous, and cropping systems are highly diverse and mosaic. The dearth of data on the application of remote sensing and GIS in crop yield forecasting and land suitability analysis is not only worrying but catastrophic to food security monitoring and early warning systems in a continent burdened with chronic food shortages. Furthermore, accelerated crop genetic improvement to increase yield and achieve better adaptation to climate change is an issue of increasing urgency in order to satisfy the ever-increasing food demand. Recently, crop improvement programs are exploring the use of remotely sensed data that can be used cost-effectively for varietal evaluation and analysis in crop phenotyping, which currently remains a major bottleneck in crop genetic improvement. Yet studies on evaluation of maize varietal response to abiotic and biotic stresses found in the target production environments are limited. Therefore, the aim of this study was to model spatial land suitability for maize production using GIS and explore the potential use of field spectrometer and unmanned aerial vehicles (UAV) based remotely sensed data in maize varietal discrimination, high-throughput phenotyping, and yield prediction. Firstly, an overview of major remote-sensing platforms and their applicability to estimating maize grain yield in the African agricultural context, including research challenges was provided. Secondly, maize land suitability analysis using GIS and analytical hierarchical process (AHP) was performed in Zimbabwe. Finally, the utility of proximal and UAV-based remotely sensed data for maize phenotyping, varietal discrimination, and yield forecasting were explored. The results showed that the use of remote sensing data in estimating maize yield in the African agricultural systems is still limited and obtaining accurate and reliable maize yield estimates using remotely sensed data remains a challenge due to the highly fragmented and spatially heterogeneous nature of the cropping systems. Our results underscored the urgent need to use sensors with high spatial, temporal and spectral resolution, coupled with appropriate classification techniques and accurate ground truth data in estimating maize yield and its spatiotemporal dynamics in heterogeneous African agricultural landscapes for designing appropriate food security interventions. In addition, using modern spatial analysis tools is effective in assessing land suitability for targeting location-specific interventions and can serve as a decision support tool for policymakers and land-use planners regarding maize production and varietal placement. Discriminating maize varieties using remotely sensed data is crucial for crop monitoring, high throughput phenotyping, and yield forecasting. Using proximal sensing, our study showed that maize varietal discrimination is possible at certain phenological growth stages at the field level, which is crucial for yield forecasting and varietal phenotyping in crop improvement. In addition, the use of proximal remote sensing data with appropriate pre-processing algorithms such as auto scaling and generalized least squares weighting significantly improved the discrimination ability of partial least square discriminant analysis, and identify optimal spectral bands for maize varietal discrimination. Using proximal sensing was not only able to discriminate maize varieties but also identified the ideal phenological stage for varietal discrimination. Flowering and onset of senescence appeared to be the most ideal stages for accurate varietal discrimination using our data. In this study, we also demonstrated the potential use of UAV-based remotely sensed data in maize varietal phenotyping in crop improvement. Using multi-temporal UAV-derived multispectral data and Random Forest (RF) algorithm, our study identified not only the optimal bands and indices but also the ideal growth stage for accurate varietal phenotyping under maize streak virus (MSV) infection. The RF classifier selected green normalized difference vegetation index (GNDVI), green Chlorophyll Index (CIgreen), Red-edge Chlorophyll Index (CIred-edge), and the Red band as the most important variables for classification. The results demonstrated that spectral bands and vegetation indices measured at the vegetative stage are the most important for the classification of maize varietal response to MSV. Further analysis to predict MSV disease and grain yield using UAV-derived multispectral imaging data using multiple models showed that Red and NIR bands were frequently selected in most of the models that gave the highest prediction precision for grain yield. Combining the NIR band with Red band improved the explanatory power of the prediction models. This was also true with the selected indices. Thus, not all indices or bands measure the same aspect of biophysical parameters or crop productivity, and combining them increased the joint predictive power, consequently increased complementarity. Overall, the study has demonstrated the potential use of spatial analysis tools in land suitability analysis for maize production and the utility of remotely sensed data in maize varietal discrimination, phenotyping, and yield prediction. These results are useful for targeting location-specific interventions for varietal placement and integrating UAV-based high-throughput phenotyping systems in crop genetic improvement to address continental food security, especially as climate change accelerates

An ICT strategy for sustainable tourism in Zimbabwe

This research sought to develop an ICT strategy for sustainable tourism in Zimbabwe. The Government of Zimbabwe has identified ICT and tourism both as strategic industries and pillars for economic revival. The tourism sector was identified as an essential source of foreign exchange earnings and as a means to drive the economy to greater heights and reduce poverty through direct employment in down-stream and up-stream industries. Despite being endowed with rich natural resources that include five World Natural Heritage sites, exquisite flora and fauna. Zimbabwe is not performing well in the world tourism sector. The Southern African country continues to be ranked low on World Economic Forum Travel and Tourism Competitiveness Index. Despite the country‘s strengths, in terms of endowments, the low Tourism and Travel Competitiveness Index suggests weaknesses in related areas. There is currently no clear strategy for the sustainable use of ICT in the tourism sector in Zimbabwe. Despite huge investments and advances in ICT, services in the tourism sector in Zimbabwe continue to be delivered manually and in traditional ways. Therefore, this research sought to propose a strategy for the sustainable use of ICT in the tourism sector in Zimbabwe. However, this strategy had to be informed and supported by an empirical study of the shortcomings in the existing situation in the tourism sector. In order to achieve this objective, this research, which is a comprehensive case study on the tourism sector in Zimbabwe, deployed semi-structured interviews, questionnaires, observations and netnography to collect data. The case study was carried out in accordance with the steps for conducting a case as outlined by Yin guided by an interpretive paradigm. The entities and organisations that formed part of the case study were purposefully chosen to provide a representative sample of the tourism role-players in Zimbabwe. The samples were based on sample variation and feasibility (taking into consideration factors such as geographical location, organisational thrust and size). Data collection involved semi-structured interviews with various role-players in the tourism sector. The role-players interviewed included officials from the government, the regulator and from the service providers. The interviews were held in Harare, Bulawayo, Gweru, Chinhoyi, Matopos, Hwange and Victoria Falls. A Zimbabwe annual premier tourism expo, Sanganai/Hlanganani World Travel and Tourism Africa Fair, was also attended in order to interact with various players and to conduct formal and informal interviews. The questionnaires, which were completed by tourists, were administered in Harare, Hwange, Victoria Falls, Matopos, and the Beitbridge and Kazungula border posts. An online version of the questionnaire was also administered. The questionnaire link was e-mailed to tourists, as some of them did not have enough time to complete them during their stay in Zimbabwe and agreed to do so via email. The data was analysed in two phases - structured coding and thematic analysis. . Key factors were identified through structural coding and thematic analysis. Country-specific, sector-specific and organisationalspecific factors were identified for tourists, the government, the regulator and service providers. After a further analysis of the results, those that pointed to the same factors were grouped together and a deduction was made as a diagnosis of the problem was identified. The identified problems were synthesised into seven diagnostics: (i) lack of infrastructure and enabling services; (ii) e-customer relationship management; (iii) lack of collaboration and poor systems integration, (iv) policy and regulation; (v) lack of financial resources; (vi) poor ICT governance and (vii) poor human resource development. A guiding policy was then identified for each of the diagnostics, leading to a set of coherent actions. The research also showed the implementation of this set of actions that consisting of three layers, namely, government, regulator and service providers. This research contributes to the existing body of knowledge by providing a clear strategy formulation model and showing how the implementation will be rolled out. The ICT-related challenges were diagnosed, guiding policies formulated to address the situation and required coherent actions suggested. This research is deemed to be significant for understanding the future of ICT use in developing countries like Zimbabwe