Multivariate characterisation of a blackberry–alder agroforestry system in South Africa: hydrological, pedological, dendrological and meteorological measurements

Abstract

Trees established in linear formations can be utilised as windbreak structures on farms as a form of agroforestry system. We present an extensive data package collected from an active berry farm located near Stellenbosch, South Africa, considering hydrological, pedological, dendrological and meteorological measurements centred around an Italian alder (Alnus cordata (Loisel.) Duby) windbreak and a blackberry (Rubus fruticosus L. Var. “Waldo”) crop. Data were collected between September 2019 and June 2021. The data are available from Hassler et al. (2024) and include the following measurements (i) meteorological variables – solar radiation, precipitation characteristics, vapour pressure deficit, air temperature, humidity, atmospheric pressure, wind speed and direction, gust speed, and lightning strikes and distance recorded at 10 min intervals; (ii) hydrological measurements – soil moisture and matric potential in two profiles at 15 min intervals alongside soil samples at various depths describing soil texture, hydraulic conductivity, and water retention parameters; (iii) soil characteristics – a soil profile description accompanied by 60 topsoil samples describing carbon, nitrogen, and exchangeable base cation concentrations, as well as potential cation exchange capacity and descriptions of soil texture; and (iv) dendrological measurements – point cloud data for the studied windbreak trees and surrounding features, cylinder models of the windbreak trees with volume and biomass data, and foliage data as a product of an existing leaf creation algorithm. The described dataset provides a multidisciplinary approach to assess the impact and interaction of windbreaks and tree structures in agroforestry landscapes, aiding future work concerning water fluxes, nutrient distribution, microclimate and carbon sequestration. The dataset, including high-resolution time series and point cloud data, offers valuable insights for managing the windbreak's influence and serves as a unique training dataset for spatial analysis (https://doi.org/10.5880/fidgeo.2023.028, Hassler et al., 2024).