By Brittany Bunce and Maurice Beseng

Figure: Pastoralists near the Sudanese village of Gallabat, close to the Metema border crossing with Ethiopia. (Photo credit: Brittany Bunce)

In the last two decades, there has been a growing appeal to use data derived from Earth Observation (EO) to support sustainable development policies in Africa, especially in the agricultural sector where there is a lack of reliable and timely information. EO data such as satellite remote sensing is seen as a powerful tool to modernise the monitoring and improvement of agriculture on the continent. Globally, there are many  Earth Observation for Development (EO4D) initiatives, and an increasing number focusing on Africa that draw on remote sensing data to support decision-making in agricultural practices such as land cover/ land use mapping, crop/vegetation monitoring and famine early warning. An example is the “Enhancing food security in African AgriCultural systems with the support of REmote Sensing (AfriCultuReS)- a European Union-funded Horizon 2020 Project, which is developing an integrated agricultural monitoring and early warning system that brings remote sensing data together with crop and climate models to inform decision-making, with the ultimate aim of improving food security in Africa. The platform will provide seven bundled services related to climate, drought, land, livestock, crops, water and weather. It is hoped that a diverse set of end-users will be able to make use of the final platform, including small-scale farmers and pastoralists as the primary producers, as well as actors in the agribusiness, public, financial and academic sectors, across eight African partner countries.

While projects such as AfriCultuReS indicate the immense interest and ambition to leverage EO4D, the path from satellite images to sustainable agricultural policies or services in Africa is not straightforward. There are increasing concerns that current efforts to create EO4D applications for African agricultural development are largely built on foreign engineering designs rooted in the North, are financially unsustainable and fail to grapple with the key constraints faced by end users in Africa. Hence, despite the palpable excitement around the potential of using remote sensing data for agricultural development, there remains reason to be cautious and to take stock of some of the shortcomings and limitations of technical fixes to Africa’s complex agrarian system.

Emerging voices on EO4D for African agriculture advocate partnering with end-users in the co-creation of EO products through all stages of their design, implementation and evaluation. In this context, ‘capacity development’ approaches are being reconceptualized to accommodate the need to improve the ability of technical developers to create data and services that more closely serve the needs of end users. This is a welcomed move away from the paternalistic and rather simplistic focus on ‘building’ the capacity of end users to understand EO data, that may or may not be asking the right questions and hence might be unable to solve the key challenges end users face on the ground.

Figure: Smallholder goat and mango farm, Tzaneen, Limpopo Province, South Africa (Photo Credit: Brittany Bunce)

Clearly, solving food security challenges across various African socio-political contexts requires a methodology that allows remote sensing data to be embedded more closely within the complexity of diverse African agrarian systems. The concept of crowdsensing is being promoted to allow for the gathering of important georeferenced data by farmers and other end users, which can be processed through apps and shared in a collective manner through mobile devices. Importantly, the resulting platforms should integrate indigenous knowledge systems and be able to respond to the varied specificities of smallholder production. Remote sensing platforms should be able to take account of this world of complex social and ecological relations, otherwise purely top-down interventions are unlikely to be effective and worse, are at risk of endangering livelihoods or reinforcing local inequalities.

A question foremost in our mind is how end users without technical expertise in remote sensing and GIS can practically make use of a remote sensing platform to inform decision-making processes that improve the resilience and productivity of their farming systems. This concern has been echoed by partners and potential end users in engagements hosted in AfriCultuReS partner countries. It was suggested that in order to achieve impacts on food security it would be necessary to develop more accessible and user-friendly ways to package the data and to target a diversified set of communication methods to address some of the systemic technology barriers in different contexts e.g. the high cost of data in many African countries. Hence the challenge for developers and for capacity development approaches, is to now devise ways in which this data can be rendered practical and participatory and literally put to work in the field to aid farmers and pastoralists.

A lot of progress has already been made to develop off-line, open-source apps targeted at smallholders and pastoralists (m-Agri Services). However, smartphone access is patchy, and so this approach risks excluding some stakeholders. For this reason, current lessons emerging from AfriCultuReS partners suggest the need to make use of existing communication networks, such as agricultural extension systems, farmer associations or other context specific social networks, or making use of more broadly available communication networks such as local radio, SMS or social media. However, even if a user-friendly interface for an app can be produced and other accessible ways to package the data can be devised, some caveats still remain. Developing capacity among data developers to create the right resources and among end users to understand the data, does not in itself ensure effective decision-making that promotes food security. In other words, what needs to be done to ensure end users are able to interpret the data provided, and then use it to make very practical production and investment decisions on farms, rangelands, in markets and policy spaces?

To this end, participatory learning and decision-making processes can be integrated to bridge the gap between the platform that is developed and decisions that are made on the farm. There are a number of initiatives that are developing innovative ways to do this, for example, the Participatory Integrated Climate Services for Agriculture (PICSA) approach, which has been used across 20 countries. PICSA provides a step-by-step participatory decision-making framework that allows farmers to work together with trained facilitators (e.g. extension officers) to analyze climate data, together with farm-level data and links them to practical management approaches for specific farms or rangelands. Conceivably a similar approach could be implemented to ensure the seven bundled services provided by AfriCultuReS can be of practical use to farmers and pastoralists, although a precise approach is still being designed.

Figure: Smallholder cabbage plot, Vryheid, KwaZulu-Natal Province, South Africa (Photo Credit: Joyce Chitja)

The strength of a participatory approach like PICSA, is that farmers and pastoralists are in the driving seat and are being supported to make informed decisions, rather than being told what to do by extension workers, a radio announcement or a ‘SMS push notification’, that wouldn’t be able to understand the specificities of their individual farms and livelihood systems. This kind of participatory approach embraces the farm as an integrated social system, rather than just a composite of crops and livestock. The remedy posed to the complexity of EO data, however, is usually to simplify it through something like a ‘SMS push notification’. However, in the case of EO4D, simplicity may be a misfit for a complex challenge like food security and so grappling with complexity may be well worth the effort to ensure we find effective ways to support smallholders and pastoralists.

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