A data revolution for agricultural production statistics in sub-Saharan Africa

January, 2019
by Channing Arndt, Amy Dale, Patrick Hatzenbuehler, Jordan Kyle, Innocent Matshe, Adam Schlosser, and Emily Schmidt

Abstract

We argue that vastly improved agricultural production forecasts and estimates in SSA fit within an emerging view of information as a key input into the development process. We assess the current quality and timeliness of agricultural production data, finding both quality and timeliness to be inadequate. These inadequacies persist despite substantial benefits of improved agricultural production projections and estimates, notably benefits to market participants, not least farmers. New technologies such as satellite remote sensing provide scope to vastly improve agricultural production estimation methods at substantially lower cost than traditional farm surveys. Research efforts in this area are required to identify the most robust and effective applications of new technologies to agricultural statistics. Consideration of institutional challenges and frameworks is also necessary to benefit from these technologies. We conclude that accurate and timely agricultural production projections and estimates are possible at lower cost than at any time in recent history. And, these relatively small investments in improving SSA agricultural production data systems have the potential to deliver real progress towards attaining key Sustainable Development Goals.

Download SA-TIED Working Paper #55

This working paper has subsequently been published in the Ghanaian Journal of Economics and can be found here.

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