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Open AccessArticle

Mapping Flood-Based Farming Systems with Bayesian Networks

World Agroforestry Centre (ICRAF), United Nations Avenue, Gigiri, Nairobi 30677, Kenya
Department of Environmental Sciences, Kenyatta University, Kenya Drive, Nairobi City 43844, Kenya
University of Bonn, Department of Horticultural Sciences, Auf dem Hügel 6, 53121 Bonn, Germany
Center for Development Research (ZEF), University of Bonn, Genscherallee 3, 53113 Bonn, Germany
Author to whom correspondence should be addressed.
Land 2020, 9(10), 369;
Received: 23 August 2020 / Revised: 25 September 2020 / Accepted: 29 September 2020 / Published: 2 October 2020
Many actors in agricultural research, development, and policy arenas require accurate information on the spatial extents of cropping and farming practices. While remote sensing provides ways for obtaining such information, it is often difficult to distinguish between different types of agricultural practices or identify particular farming systems. Stochastic system behavior or similarity in the spectral signatures of different system components can lead to misclassification. We addressed this challenge by using a probabilistic reasoning engine informed by expert knowledge and remote sensing data to map flood-based farming systems (FBFS) across Kisumu County in Kenya and the Tigray region in Ethiopia. Flood-based farming is an important form of agricultural production employed in regions with seasonal water surplus, which can be harvested and used to irrigate crops. Geographic settings for FBFS vary widely in terms of hydrology, vegetation, and local practices of agronomic flooding. Agronomic success is often difficult to anticipate, because the timing and amount of flooding usually cannot be precisely predicted. We generated a Bayesian network model to describe the FBFS settings of the study regions. We acquired three years (2014–2016) of Moderate Resolution Imaging Spectroradiometer (MODIS) Terra spectral data as eight-day composite time series and elevation data from the Shuttle Radar Topography Mission (SRTM) to compute 10 spatial data metrics corresponding to 10 of the 17 Bayesian network nodes. We used the spatial data metrics in a fully probabilistic framework to generate the 10 spatial data nodes. We then used these as inputs for the probabilistic model to generate prior and posterior spatial estimates for specific metrics along with their spatially explicit uncertainties. We show how such an approach can be used to predict plausible areas for FBFS based on several scenarios. We demonstrate how spatially explicit information can be derived from remote sensing data as fuzzy quantifiers for incorporating uncertainties when mapping complex systems. The approach achieved a remarkably accurate result in both study areas, where 84–90% of various FBFS fields sampled were correctly mapped as having a high chance of being suitable for the practice. View Full-Text
Keywords: flood irrigation; Bayesian network; remote sensing; uncertainty estimates; MODIS data; SRTM DEM flood irrigation; Bayesian network; remote sensing; uncertainty estimates; MODIS data; SRTM DEM
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MDPI and ACS Style

Liman Harou, I.; Whitney, C.; Kung’u, J.; Luedeling, E. Mapping Flood-Based Farming Systems with Bayesian Networks. Land 2020, 9, 369.

AMA Style

Liman Harou I, Whitney C, Kung’u J, Luedeling E. Mapping Flood-Based Farming Systems with Bayesian Networks. Land. 2020; 9(10):369.

Chicago/Turabian Style

Liman Harou, Issoufou; Whitney, Cory; Kung’u, James; Luedeling, Eike. 2020. "Mapping Flood-Based Farming Systems with Bayesian Networks" Land 9, no. 10: 369.

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