The African Green Revolution Forum (AGRF) is an annual forum co-organised by the Alliance for a Green Revolution in Africa (AGRA) which pulls together various stakeholders to engage in discourse to better Africa's agricultural landscape (AGRF 2019). In 2019, the theme for the forum was Grow Digital: leveraging digital transformation to drive sustainable food systems in Africa. The forum focused a lot on agri-data and what these innovative technological solutions can do to better farming practices. Ghana's Esoko is a positive case study exemplifying how open data can be harnessed to bridge the price asymmetry between smallholder farmers and buyers that often disadvantages the former (Esoko 2019). Using open government data, Esoko found a way to increase the accessibility of this information to smallholder farmers using price alerts. It makes transparent the information that often becomes opaque in a large global value chain. This service helps farmers make more informed decisions regarding the full value of their product even if they sell their items at the farm gate than the district markets (which they often do) owing to the inconvenience of distance and transportation costs (Schalkwyk et al. 2017).
Apart from market information for farmers, data can also be used to assess the impact of climate change, hydrological and climatic systems - elements that have direct impact on farmers food production. In July 2019, Zimbabwe faced an acute drought and water shortage problem. Although the drought is an annual occurrence, the 2019 one was a more serious affair because it occurred earlier and caught them off guard (Gronewold 2019). Given the extreme climate change impacts and irregularity of its events, forecasting and accurate predictions have become even more important.
The need for more accurate forecasting of climatic impacts was echoed in the United Nation's 2019 State of Climate Services report:
"Climate information and associated services have demonstrably led to improved agricultural and good security outcomes and benefits for stakeholders in the sector. [...] The challenge is to strengthen the global-regional-national hydrometeorological system needed to operationalise and deliver these products and services at country level [...]" - United Nations State of Climate Services report (2019)
However, climate forecasts are difficult. For one, hydrological and climatic systems are inherently complex. We can attempt to reduce uncertainty in forecasts but it is prohibitively difficult, even impossible to include all variables and processes in our models to fully eradicate uncertainty (Kay et al. 2008).
Even if we could include all variables, the problem often stems from the lack of reliable datasets. For example, for any hydrological model, information regarding volume of precipitation (Kidd and Huffman 2011) and run-off would prove invaluable. Hughes' (2006) study in southern Africa lamented the "short, patchy and unreliable hydro-meteo-rological records that have poor spatial coverage)" (Hughes 2006: 74). Governments often do not place priority on investments into gauging systems and networks, exacerbating the problem. Schuol et al. (2007) study in West Africa was limited to the extent that it lacked monthly data from the gauging stations required for a simulation.
Sometimes the data exists but access is limited by politics. Historical climate data over Africa is valuable - monetarily and for research purposes. However, such data may not be shared that easily because governmental organisations holding these records would much rather sell this data for profits to commercial companies. There is a conflict of interest between the private sector and government sector seeking to commercialise its archives. For example, the South African Weather Service has rejected offers to digitise its records because providing unrestricted access of such information to another party will hinder its commercial obligations (Nordling 2019).
It's not just the input that matters. The output provided by climate change models can also be limited. The coarser spatial resolution provided by the common Global Climate Models (GCM) is limited in its ability to inform policy on a local scale (Gebrechorkos et al. 2019). Bias and uncertainties increase with the move from the global to the local (Knutti and Sedláček 2013). Regardless, to the average Joe, it is the quality of models and the accuracy of predictions that is of utmost importance. Science bolsters (and could likewise reduce) public trust and confidence (Lemonick 2011).
While data has so much potential to revolutionalise and better the water, food and climate nexus in Africa, this post also outlines that the quantity and quality of data are key to harnessing the power of data. An example of a positive development is the CGIAR Big Data Platform that launched in 2017 that manages open data while pulling together big data experts, modellers, statisticians and the like to store and crunch data to observe trends and anomalies. The Trans-African HydroMeteorological Observatory (TAHMO) another good example that aims to create a network of weather stations across Africa. While there was mention about how rain gauges will be affordable and how the project spans a large geographical area, one observation I made and am curious about is regarding the durability and maintenance of these gauges too. Without a maintenance that carries through the entire duration, in the worst case scenario, the gauges will just enter into decline like many of its previous failed predecessors (Walker et al. 2016). After 2003, Tourian et al. (2013) noted that only 40% of gauges were providing discharge data for the Global Runoff Data Center.
Models themselves are equally important. Working Group I for the IPCC's fifth assessment report The Physical Basis (2013) quantified and provided a nuanced argument that while models have generally improved over the years and there are some models that are better than others, no one model is perfect. Science is constantly evolving as researchers themselves find ways of including and quantifying uncertainty in their models (Hughes 2006) or finding methods to compare observations and models (Cowtan et al. 2015). Some argue that this obsessiveness over the uncertainty of climate models should not hinder its usefulness at present (Knutti and Sedláček 2013).
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