Spatial Knowledge and Information Canada, 2019, 7(1), 1 Using WebGIS as a Tool for Agricultural Development with African Indigenous Vegetables COLIN MINIELLY1, DEREK PEAK2, WEIPING ZENG 3, SHUO YUAN3, SCOTT BELL4 colin.minielly@usask.ca, 1School of Environment and Sustainability, 2Department of Soil Science, derek.peak@usask.ca, 3The Spatial Initiative, wez948@mail.usask.ca, 4Department of Geography and Planning, scott.bell@usask.ca University of Saskatchewan, Canada cannot develop economically, resulting in ABSTRACT continued malnourishment. Food security is a high priority for much of The MicroVeg project seeks to address Africa. In the Benin Republic and Nigeria, economic and social issues within the Benin research teams are developing tools and Republic and Nigeria. The project is a strategies to alleviate stress caused by food collaboration involving the University of insecurity. An innovative approach to Parakou, the Benin Republic, Osun State, addressing food security is to use a Web- Obafemi Awolowo Universities in Nigeria, based GIS system, but this system requires a and the Universities of Saskatchewan and comprehensive collection of data. For the Manitoba, in Canada (Adebooye, study area, this data was previously Akponikpe, Oyedele, Peak, & Aluko, 2017). unavailable. Tools such as a map interface To address data access and availability and gross profit calculator were created to limitations and to enable the use of GIS data enhance the web-based system. Research an integrative, multidevice, multiplatform data was then integrated to provide GIS database was developed on a Web- recommendations to smallholder farmers of based system, known as WebGIS. This work the region. This approach can serve as a builds on the framework and goals outlined model for future development research by Li et al., (2017). WebGIS was designed around the world. using MicroVeg research data allowing farmers, researchers, and Non- Governmental Organizations (NGOs) to 1. Introduction access data. This system is managed by The Spatial Initiative (TSI), University of Saskatchewan, and can be viewed at Based on Gross National Income, 52 of the www.microveg.ca. world’s 124 developing nations are in Africa (United Nations, 2018). The majority of these countries face food insecurity. Food insecurity is defined as a lack of nutritious 2. Methods and Data food. Economic development is one potential solution to improving food Figure 1 shows the framework of the security. Uncertainties associated with WebGIS development process. The model is climate change and food insecurity make divided into three categories: data, WebGIS, this a developmental challenge. Without and agricultural (project) extension. improvement at the market level, countries 2 WebGIS and Food Security in Africa Figure 1 WebGIS Development Framework. WebGIS and Food Security in Africa 3 2.1 Data Management current, and poses potential data integration issues. WorldClim published a As described in Minielly et al. (2018) the climate dataset with a variety of variables at data came from various sources and in 1 km2 resolution, with complete global several formats. A range of research data coverage, suggesting it is an ideal dataset; was included to allow for extrapolation of unfortunately, the latest published data was site-specific characteristics from the for the 1970 - 2000 climate normal. database. Each dataset originated from a research institution and thus has multiple Variables such as soil classification, citations associated with its development. elevation, and administrative boundaries The WebGIS application helps farmers and serve as supplementary data to aid researchers access GIS data without MicroVeg researchers. Per the FAO soil extensive training. Data were grouped into classification system, the most six types: Precipitation, Temperature, comprehensive soil survey of the MicroVeg Elevation, Administrative boundaries, region was completed at a scale of Ecoregions, and Soil (Figure 1). Additional 1:3,000,000 (Jones et al., 2013). Elevation datasets including hydrology, infrastructure, data were at the same resolution as climate and municipalities, were not used for data. General relief can be noted, but little agronomic modelling. else (U.S. Department of the Interior, 1996). Administrative boundaries, including The ecoregion data required additional country boundaries and governmental processing to be used in WebGIS. A subdivisions, are used to approximate systematic approach to address locations for additional interventions inconsistencies in nomenclature was (Hijmans, Guarino, & Mathur, 2012). undertaken. As a project three regions were used to categorize the data; however, these The final, and arguably most novel regions did not contain any spatial reference information obtained for WebGIS, is the (Adebooye, Akponikpe, Oyedele, Peak, & incorporation of agronomic research data Aluko, 2017; Olson et al., 2001. In contrast, from the project. The MicroVeg project with the help of the Food and Agriculture focused on four Under-utilized Indigenous Organization of the United Nations (FAO), Vegetables (UIV). For each UIV the optimal Olson et al. (2001) created a dataset that yield, fertilizer rates, and water contained 25 ecoregions within the scope of requirements were obtained. Project data the project. To match the terminology of the also included the location of 102 research project, Olson et al. (2001) dataset were sites (Adebooye et al., 2017; Adebooye, reduced to 3 larger regions. This reduction Akponikpe, Oyedele, Peak, & Aluko, 2018). of ecoregions was achieved by consulting The UIVs are commonly accepted crops. both the data within the dataset and With additional research, the nutritional researchers. With the spatial attributes to value of these UIVs is increasing. Therefore, support the ecoregion terminology for the with increased adoption, these UIVs might project, a new standard for the project was be essential to alleviate regional food established allowing for a more insecurity. comprehensive understanding of the effects Equation 1. Irrigation requirement function. Values of the study. recorded as percentages. Much of West Africa, including the Benin Republic and Nigeria, have limited climate monitoring; thus, neither regional-scale nor country-scale climate data exist. Therefore, Optimum water use was obtained for each a global climate dataset was used. Often, UIV and each ecoregion. When the global data is either coarse in resolution, nor optimum water value for each UIV is 4 WebGIS and Food Security in Africa compared to the precipitation value, there is version of the database. All the data were a significant portion of the region that does projected in WGS 1984 Web Auxiliary not receive sufficient rainfall to produce Sphere. Using ArcGIS server and indigenous vegetables at optimal rates JavaScript API, the data were transformed without irrigation. Calculating the water into the online database. deficiency of each UIV was an essential step WebGIS includes a table of contents, zoom to establish a stronger relationship between controls, and an option to go to the current water use and food security. Equation 1 location. In the table of contents (TOC), describes how irrigation requirements were users have access to more features including calculated and used. The resulting data are resources, and legends, and they can modify presented in percentages, allowing for an layer opacity. illustration of where water is needed. Values represent water shortages. The values The gross profit calculator page was built suggest that if a farmer wants to grow a UIV using a mobile-friendly template. All the in a region where the value is 100 percent, calculations and formulas are stored in a then all the estimated water requirements JavaScript file. In a separate window, the would need to come from irrigation. user sees the results of the calculation, which can be exported in a text file for Values that range from 100 percent to zero further processing. GPS coordinates are indicate the amount of irrigation required. necessary to run the profit calculator. Thus, Any value lower than zero means that there by using JSON cookies, GPS information is enough precipitation and no irrigation is can be transferred among tools. needed. The lowest value reported by the model was - 2000 percent, in the southern portion of Nigeria. This region receives 3. Results more than 2500 mm of rain a year. The MicroVeg website and an online The water requirement is derived using WebGIS system were developed to provide a equation 1; its value represents the amount tool for farmers. This tool helps them of additional water required via irrigation understand information such as based on previous selections within the precipitation, temperature, soil type, and to calculator. The value is scaled to litres per calculate the gross profit of a spatial ha, matching yield data. location. Equation 1 is used multiple times within the WebGIS database. In the data viewer, the 3.1 Homepage resulting values of the equation are shown as the irrigation requirement for each of the Upon visiting www.microveg.ca, users can UIVs. Moreover, the results are used in the browse information and, resources of the profit calculator to calculate any associated MicroVeg project, as shown in Figure 2. costs and the volume of water to grow a UIV, if irrigation is required. In the profit calculator, the values are converted from percentages to litres per ha, which matches the inputted yield data. 2.2 WebGIS Framework The second tier of Figure 1 outlines the WebGIS development. Data were prepared for the WebGIS database using the desktop WebGIS and Food Security in Africa 5 3.2.2 Table of Contents The WebGIS TOC, shown in Figure 4 is designed for a user who might not have used GIS previously. This TOC contains six significant data groupings, resources, opacity functions, and legends. This TOC combines ecoregion, research data, and administrative boundaries into one category and adds additional layers; which were Figure 2 Screenshot of the homepage for MicroVeg. previously separate in Figure 1. 3.2 WebGIS Tools This WebGIS includes three tools for agriculture extension: a map layout, a data viewer, and a gross profit calculator. Zeng et al. (2017) describe the details of WebGIS and how each tool pertains to its respected interface. 3.2.1 Map Layout Before the development of this database, researchers were only able to access limited data relevant to climate and food security and to access such data independent of one another. In WebGIS, the map layout and data viewer relies on data being extracted from multiple data inputs. Figure 3 shows a sample view of the map layout. By using the map layout, users can Figure 4 Sample view of the Table of Contents with Annual Temperature selected. look at an attribute. Data from different areas can be examined at various scales; this is useful for reviewing drought-prone The legends are dynamic and will change regions, for example. depending on what layers are active. When multiple layers are active, the order in the TOC is the visual order, but users can modify the opacity or order of any layer. Resources also include a user manual, citations, and links to the homepage and gross profit calculator. 3.2.3 Data Viewer Popup Window Complementary to the map layout is the Figure 3 View of annual temperature using the Map data viewer, Figure 5. The data viewer gives Layout in WebGIS, microveg.ca. a complete list of attributes for the selected 6 WebGIS and Food Security in Africa spatial location. This tool provides all the optimum yields and water management are information stored in the database, which achieved. can be exported or used in the gross profit calculator. The data viewer contains relevant information to ensure the success of farmers and NGOs aiding farmers. As a resource, the data viewer gives multiple users the opportunity to review the same data. From this data, and communication, recommendations or management strategies can be discussed or implemented. Figure 6 Sample View of the Gross Profit Calculator. To use the profit calculator, it must be opened after a location has been selected and viewer in the data viewer. In this order, by using a JSON object a cookie with the pertinent information is sent from the data viewer to the gross profit calculator. Conversions into various currencies allow farmers to understand the exported data better. Included currencies are the Canadian Figure 5 Sample view of Data Viewer. dollar (CAN), the West African Franc, the Benin Republic (XOF), the Nigerian Naira (NGN), the U.S. dollar (USD), and the Euro 3.2.4 Gross Profit Calculator (EUR). The gross profit calculator utilizes data from A user can input plot size in hectares (ha), the data viewer including irrigation square meters (m2), or square feet (ft2). requirements, fertilizer recommendations, Other variables that have multiple options and expected yields. By using the research include the season and the UIV of interest. data, the resulting outputs help estimate the Users are asked to “confirm” responses to profitability of the suite of tools MicroVeg is ensure the correct information is pulled promoting to aide in alleviating food from the server. Once the confirmation insecurity. button has been pressed both back, and Some attributes within the profit calculator, front-end data are updated. such as expected yield and water requirements are geographical and thus not To obtain market information for a target editable, these are extracted from the region, a user can input estimations for an research data. All other attributes are expected sale price, fertilizer and water editable, thus making this a dynamic tool. usage, and labour costs into the gross profit calculator. Figure 6 shows a sample view of the gross profit calculator. The gross profit calculator Fertilizer application rates are predefined allows farmers or NGOs to see how much for the user. For users, further explanation more profitable one scenario is to another of the application rate, and the application or current farming practices assuming that technique can be obtained by talking to an extension agent or a researcher. WebGIS and Food Security in Africa 7 We thank the International Development Labour can be inputted via simple or Research Center (IDRC), Canada and Global advanced fields to the calculator, depending Affairs Canada for funding MicroVeg Project on available information. 07983. The authors thank researchers and technical staff in the Benin Republic and 3.2.5 Agricultural Extension Nigeria. The authors also thank the computer programmers and technical staff The final tier of Figure 1 is the agricultural at The Spatial Initiative (TSI), the Social extension. This tier is not an independent Sciences Research Laboratories (SSRL), at aspect of the WebGIS application, but an the University of Saskatchewan. outcome. By combining all the above elements, including irrigation requirements, References researchers now have new tools to promote agriculture. By designing tools for specific Adebooye, O. C., Akponikpe, P. B. I., groups, and incorporating other groups, Oyedele, D. J., Peak, D., & Aluko, E. R. dissemination of data and discussions can (2017). Synergizing fertilizer micro- occur. dosing and indigenous vegetable production to enhance food and economic security of West African 4. Conclusion farmers. Ottawa, Canada. Adebooye, O. C., Akponikpe, P. B. I., West Africa has a high rate of food Oyedele, D. J., Peak, D., & Aluko, E. R. insecurity and requires innovative tools to (2018). Synergizing fertilizer micro- address this challenge. The Benin Republic dosing and indigenous vegetable and Nigeria now have new tools to address production to enhance food and economic security of West African local and regional food insecurity through the research described in this manuscript. farmers. Ottawa, Canada. Climate and field-collected data were Hijmans, R. J., Guarino, L., & Mathur, P. (2012). DIVA-GIS. Retrieved from combined to create a comprehensive dataset for the MicroVeg project. With further http://www.geocities.com/SiliconValle y/Network/2114/ collaboration more data, concerning both types and volume, can be inputted into the Jones, A., Breuning-Madsen, H., Brossard, database. Thus, making this a robust and M., Dampha, A., Deckers, J., Dewitte, dynamic system for improving west African O., … Zougmoré, R. (2013). Soil Atlas food security. of Africa. Soil Atlas of Africa. Luxembourg: Publications Office of the The tools described in the WebGIS database European Union. will be extended in the future to include https://doi.org/10.2788/523191 updated climate data, climate modelling, Li, M., Minielly, C. M., Peak, D., Zeng, W., Lu, X., Lei, J., & Bell, S. (2017). An and a GIS-driven scaling approach. It is hoped that this will support policy changes Open Source WebGIS Framework for in the region. Climate change, economic Collaborative Research in West Africa. reforms, and food insecurity are now more In Spatial Knowledge Initiative integrated than ever before. Our MicroVeg Conference (pp. 1–9). Banff AB. WebGIS tools are a starting point for Minielly, C., Peak, D., Natcher, D., & Zeng, discussions and the alleviation of food W. W. (2018). Scaling Up Research insecurity. Using GIS and WebGIS Spatial Tools: Case Study of the MicroVeg project. Acta Horticulturae, In Press. 5. Acknowledgements U.S. Department of the Interior. (1996). GTOPO 30 DEM of the Globe. U.S. 8 WebGIS and Food Security in Africa Geological Survey. Retrieved from t.asp https://earthexplorer.usgs.gov Zeng, W. W., Yaun, S., Tang, F., Minielly, C., United Nations. (2018). World Economic & Peak, D. (2017). MicroVeg WebGIS Situation and Prospects 2018. New User Manual. Saskatoon, SK: York. Retrieved from University of Saskatchewan. http://www.escwa.un.org/main/contac