Dataset of tree, soil, crop, climate variables for the Chagga Homegardens, supporting a publication 'The impact of different climate conditions on the production of a major food and cash crop in tropical (sub)montane homegardens'.

This data corresponds to the manuscript currently in review entitled 'the impact of different climate conditions on the production of a major food and cash crop in tropical (sub)montane homegardens'. The dataset comprises empirical measurements of physical and chemical soil properties taken at top soil (0-20cm) and subsoil (20-50cm) depths, on-farm trees, annual banana yield and climatic variables in Mt Kilimanjaro's Chagga Homegardens across 26 plots located along a vertical elevation transect spanning the area's midland and highland agro-ecological zones. Tree and soil data were collected in 2013 as part of Dr Mathew Mpanda's PhD research at Sokoine University of Agriculture in Tanzania. Tree above-ground carbon, species richness, Shannon diversity index and composition values are the tree variables calculated and included in this dataset. These tree variables were calculated from measurements of tree height, diameter at breast height and identification of trees at the species level for each tree within each plot (excluding coffee shrubs) at 5 cm diameter at breast height and above. The above-ground biomass (kg/ha) estimations for each study site are based on Chave et al's (2014) allometric equation for measuring trees in the tropics. Above-ground biomass was halved to gain the tree above-ground carbon (AGC) stock. Tree AGC was also used to compute a species composition variables (% of tree biomass per plot) for legume trees species. The soil data was collected using composite and cumulative soil sampling. Samples were gathered using an inverted Y-shaped sampling design under the AfSIS protocol (UNEP 2012). Further details on the soil data collection process can be found in Mpanda et al (2016). The soil parameters included in the dataset are gravimetric soil moisture content (%), bulk density (g/cm3), Exchangeable Calcium (ExCa) (mg/kg), Exchangeable Potassium (K) (mg/kg), Exchangeable Sodium (mg/kg), Exchangeable Magnesium (Mg) (mg/kg), Exchangeable Actinium (Ac) (mg/kg), Exchangeable bases (Bas) (mg/kg), Iron concentration (Fe) (mgkg-1), Aluminium concentration (Al) (mgkg-1), Boron concentration (B) (mgkg-1), Copper concentration (Cu) (mgkg-1), Manganese concentration (Mn) (mgkg-1), Zinc concentration (Zn) (mgkg-1), Phosphorus concentration (P) (mgkg-1), Sulfur concentration (S) (mgkg-1), soil pH, ECd (acidity), ESP (alkalinity), total carbon (C) content (g/kg) and total nitrogen (N) content (g/kg). This dataset also includes the estimated mean annual precipitation (mm/yr), air temperature (°C), and relative humidity (%) for 2013 for each of the 26 plots. Climate data was derived from Appelhans et al (2016) high resolution climate maps on Mt Kilimanjaro's southern slopes, which used data from Hemp's (2006) rain gauge network. Temperature and relative humidity values were extracted from the relevant maps for each of the 26 plots. For the mean annual precipitation, values were estimated for each plot based on precipitation's strong linear relationship (R2 = 0.98) with elevation and data from two local rainfall stations. The main food and cash crop grown in the Chagga Homegardens is banana. In estimating the banana yield data (kg/ha) included in the attached dataset, a household survey was employed in the district in 2020 which gathered recalled estimates of household's 2013 annual yield. Next, based on a statistically significant quadratic relationship between banana yield and elevation (P <0.001), the associated equation was applied with elevation data to predict 2013's banana yield for each of the 26 plots. In the dataset (sheet 2), the banana yield values used to establish the equation are refined to those households in villages which are 1) located closest to the 26 plot transect and 2), do not use irrigation. The banana yield data for all households surveyed in 2020 can be made available upon request. Geo-references were removed from the dataset to protect the identity of the households which took part in the data collection process. If required, these values can be made available upon request to Martin Watts. References: Appelhans, T., Mwangomo, E., Otte, I., Detsch, F., Nauss, T. and Hemp, A., 2016. Eco-meteorological characteristics of the southern slopes of Kilimanjaro, Tanzania. International Journal of Climatology, 36 (9), 3245–3258. Chave, J., Réjou-Méchain, M., Búrquez, A., Chidumayo, E., Colgan, M. S., Delitti, W. B. C., Duque, A., Eid, T., Fearnside, P. M., Goodman, R. C., Henry, M., Martínez-Yrízar, A., Mugasha, W. A., Muller-Landau, H. C., Mencuccini, M., Nelson, B. W., Ngomanda, A., Nogueira, E. M., Ortiz-Malavassi, E., Pélissier, R., Ploton, P., Ryan, C. M., Saldarriaga, J. G. and Vieilledent, G., 2014. Improved allometric models to estimate the aboveground biomass of tropical trees. Global Change Biology, 20 (10), 3177–3190. Hemp, A., 2006. Continuum or zonation? Altitudinal gradients in the forest vegetation of Mt. Kilimanjaro. Plant Ecology [online], 184 (1), 27–42. Mpanda, M., Majule, A. E., Sinclair, F. and Marchant, R., 2016. Relationships between on-farm tree stocks and soil organic carbon along an altitudinal gradient, Mount Kilimanjaro, Tanzania. Forests Trees and Livelihoods [online], 25 (4), 255–266