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Read-me This dataset was part of the PhD work of Bessibié Bazongo from Institute of Rural Development, Nazi BONI University, 01 BP 1091 Bobo-Dioulasso, Burkina Faso. The work was carried out in 2021 and 2022 and focused on “Influence of Shrub Proximity, Species, and Management on Sorghum Productivity and Soil Biology in Low-Input Sudanian Agroforestry Systems.” Main advisors/contributors: Bessibié BAZONGO1,2, Mipro HIEN1, Barthélémy YELEMOU2, Karel Van Den MEERSCHE3, Cathy CLERMONT-DAUPHIN4* 1: Institute of Rural Development, Nazi BONI University, 01 BP 1091 Bobo-Dioulasso, Burkina Faso. 2: National Center for Scientific and Technological Research / Institute for Environment and Agricultural Research (CNRST/INERA), BP: Ouagadougou, Burkina Faso. 3: UMR Eco&Sols, CIRAD, Univ Montpellier, INRAE, Institut Agro, CIRAD, IRD, 2 place Viala, Montpellier 34060, France 4: UMR Eco&Sols, IRD, Univ Montpellier, INRAE, Institut Agro, CIRAD, IRD, 2 place Viala, Montpellier 34060, France Related article: A paper was recently submitted to the journal Agroforestry Systems Presentation of the field work: The study was carried out in the northern Sudanian zone of Burkina Faso, in the villages of Nassoulou, Nandiala, Saria and Poa-Yaoguin. It investigated the impact of common agroforestry systems found in central-west Burkina Faso on four key sustainability indicators: sorghum yield, soil labile carbon, soil macrofauna density and their ecological indices. Our research was designed to determine how shrub proximity, species, and management influence these indicators. A two-year study (2021-2022) was conducted on 252 sorghum plots across a 300 km² area to compare five management strategies. The plots were classified as a control group with no adjacent trees (T0), or were managed by either pruning P. reticulatum (T1) or pruning G. senegalensis (T2), by allowing P. reticulatum to regenerate naturally (T3), or by mulching the soil with tree residues (T4). Sorghum yield and soil labile carbon were measured in all plots, while soil macrofauna characteristics were quantified in a random subset. Description of the data base: It consists in an excel file with three sheets:
The sheet named database indicate in successive columns the values collected for each indicator per village (Nassoulou, Nandiala, Saria and Poa-Yaoguin), treatment (T0 to T4) and year (2021 and 2022). DM means that the data is missing. The sheet named Stats1, Tables+ indicated the results of the statiscal analysis with R. The sheet named Scripts report the code used in R to compare the correlations between the measured indicators.
