Publications

Moving toward the Biophysical Characterization of the Mangrove Swamp Rice Production System in Guinea Bissau: Exploring Tools to Improve Soil- and Water-Use Efficiencies

Garbanzo, Gabriel; Céspedes, Jesus; Sandoval, Joseph; Temudo, Marina; Paredes, Paula; do Rosário Cameira, Maria

Summary

The mangrove swamp rice production system (MSRPS) in West Africa faces significant challenges in soil, water, and salinity management, making rice production highly vulnerable to variations in the spatio-temporal distribution patterns of rainfall, which are exacerbated by climate change. This study’s results can provide the initial basis for co-developing strategies with farmers aiming to contribute to the biophysical characterization of the MSRPS, in particular: (i) estimate the water-harvesting efficiency (WLef) of the plots in the north and south of Guinea Bissau (GB); (ii) characterize the unevenness of the bottom of the plots, which leads to salinization spots; and (iii) create soil consistency maps to provide farmers with a tool to prioritize sites with optimal conditions for tillage. The research was conducted between 2021 and 2023 in the study site of Cafine-Cafal in the south and Elalab in the north of GB. Systematic soil sampling in a grid was designed to quantify the soil consistency and plot/ridge areas were determined. Linear models were developed to predict biophysical parameters (e.g., effective planting areas and water-logging depths) and geostatistics were used to create soil consistency maps for each study site. The results show precipitation water-harvesting efficiencies of 15% and 16% for the southern and northern regions, respectively. Furthermore, the plasticity limits of 18.6% for Elalab and 35.5% for Cafine-Cafal show the most appropriate times to start tillage in specific areas of the paddies. This study provides information on the efficient management of tillage and freshwater conservation, providing MSRPS farmers with useful tools to counteract the effects caused by salinity and rainfall variability.