Table with sorghum leaf area index (LAI) time series (primary/raw) from central field experiment plots in the Vea catchment (one of main research sites in the WASCAL Core Research Program), Ghana, 2013.

These are data collected on the biomass production of five multi-purpose tree species (Moringa oleifera Lam., Jatropha curcas L., Leucaena leucocephala Lam., Anacardium occidentale L., and Parkia biglobosa Jacq.) from an afforestation trial conducted in the semi-arid zone of Northern Benin during 2014 and 2015. Seedlings were planted in July 2014 and the data were collected at the end of the first rainy season (October-November 2014), the start (May 2015) and end of the second rainy season (September-October 2015).

Table with sorghum plant height time series (primary/raw) from central field experiment plots in the Vea catchment (one of main research sites in the WASCAL Core Research Program), Ghana, 2013.

Table with sorghum phenology observations (primary/raw) from central field experiment plots in the Vea catchment (one of main research sites in the WASCAL Core Research Program), Ghana, 2013.

Table with maize biomass and yield (primary/raw) from central field experiment plots in the Vea, Dano and Dassari catchments (main research sites in the WASCAL Core Research Program), Ghana, Burkina Faso and Benin, 2012

Table with maize biomass and yield (primary/raw) from central field experiment plots in the Vea, Dano and Dassari catchments (main research sites in the WASCAL Core Research Program), Ghana, Burkina Faso and Benin, 2014

Growth of maize, sorghum, and cotton on researcher-managed fields.

This dataset is the seventh of a series of datasets addressing farmers' adaptation to climate change in West-Africa, specifically in Dano, Burkina Faso and Dassari, Benin. This data was obtained by interviews with 118 people from communities in Dano. It provides information on the household level in regard to biophisical and socio-economic data for agroforestry systems.

This dataset is the seventh of a series of datasets addressing farmers' adaptation to climate change in West-Africa, specifically in Dano, Burkina Faso and Dassari, Benin. This data was obtained by interviews with 137 people from 17 communities in Dassari. It provides information on the household level in regard to biophysical and socio-economic data for agroforestry systems.

These models enable the user to estimate forage quantity (green biomass, gBM) and forage quality (metabolisable energy, ME) from hyperspectral reflectance data of vegetation canopies measured using an ASD FieldSpec 3 (or higher) Hi-Res Portable Spectroradiometer. The models were calibrated in R statistical software and were provided as RData-Files. The models were calibrated using spectral reflectance as well as forage provision data from several sites within the study area, comprising a steep south-north gradient of climatic aridity reaching from northern Ghana to central Burkina Faso between 9.0°N and 13.5°N latitude and 0.1°W and 2.0°W longitude. It is located in the southern and northern Sudanian zone of West Africa’s savanna belt, capturing a precipitation range of 600 mm to 1200 mm corresponding to UNEP aridity indices of 0.31 (semi-arid) to 0.69 (humid). Data collection took place at 21 sites during the rainy season 2012 (June-September). Our sampling design intended to cover diverse vegetation types and a wide range of land-use intensities (including protected and degraded areas). We stratified sampling at sites by topographic position (upslope, footslope and lowland). Vegetation samples were oven-dried (60°C, > 48 h) to obtain gBM. Dried samples were ground for analysis of in vitro gas production (GP) using the Hohenheim gas test (HGT). Crude protein (CP) content was determined by LUFA NRW using Kjeldahl´s method (method 4.1.1). The ME was calculated using the following equation: ME (MJ kg-1 dry matter, DM) = 2.20 + 0.1357 GP + 0.0057 CP + 0.0002859 CP², where GP is expressed as ml 200 mg-1 DM and CP is expressed as g kg-1 DM. A partial least-squares regression was used to model the relations between spectral data and target variables (ME and gBM). We used the PLSR implementation in the R package 'autopls'. During PLSR, we also performed multiplicative scatter correction and brightness normalization of reflectance spectra. Before the models can be applied, spectra have to be smoothed using Savitzky-Golay smoothing filter and noisy regions (bands 940:1170, 1350:1700, 2001:5000) have to be excluded from model application. Further information: Ferner, J., Linstädter, A., Südekum, K. H., & Schmidtlein, S. (2015). Spectral indicators of forage quality in West Africa’s tropical savannas. International Journal of Applied Earth Observation and Geoinformation, 41, 99-106.