READ ME File For 'Dataset of tree, soil, crop and climate variables for the Chagga Homegardens, Tanzania' Dataset DOI: 10.5258/SOTON/D2464 ReadMe Author: Martin Watts, University of Southampton This dataset supports the publication: The potential impact of future climate change on the production of a major food and cash crop in tropical (sub)montane homegardens ------------------- GENERAL INFORMATION ------------------- This dataset contains data recorded for different soil, tree and climate parameters measures in a vertical transect covering a total of 26 plots which are used in the study 'The potential impact of future climate change on the production of a major food and cash crop in tropical (sub)montane homegardens'. Specifically, this study assesses how changes in climate conditions along a vertical elevation gradient in SE Kilimanjaro Region in Tanzania effects tree-soil-crop interactions and banana yield in the Chagga Homegardens. Date of data collection for tree and soil data: 01/04/2013 - 01/04/2013 Dates of data collection for banana yield data: 01/03/2021 - 01/04/2021 The climate data was extracted from high resolution climate maps in Appelhans et al (2016) which averaged annual rainfall, humidity and temperature data over a longer time period. As such, there is no specific date representative for the climate data. Geographic location of data collection: Chagga Homegardens, The Moshi Rural District, Kilimanjaro, Tanzania. Coordinates of the vertical transect are -3.38478, 37.45, -3.337097, 37.475037, -3.284045, 37.455122, -3.287, 37.437703, -3.334967, 37.45437, -3.37483, 37.437102. -------------------------- SHARING/ACCESS INFORMATION -------------------------- Licenses/restrictions placed on the data, or limitations of reuse: none This dataset supports the publication: The potential impact of future climate change on the production of a major food and cash crop in tropical (sub)montane homegardens AUTHORS: Watts, Martin. Dr TITLE: The potential impact of future climate change on the production of a major food and cash crop in tropical (sub)montane homegardens JOURNAL: Total Science of the Environment PAPER DOI IF KNOWN: Links to other publicly accessible locations of the data: none Links/relationships to ancillary or related data sets: none -------------------- DATA & FILE OVERVIEW -------------------- This dataset contains measures of 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) as soil parameters. For the climate parameters, mean annual precipitation (mm/yr), air temperature (°C), and relative humidity (%) for 2013. For the tree parameters tree above-ground carbon (kg/ha), species diversity, legume composition (%) and Shannon Diversity Index. For crop yield, annual banana yield in 2013 (kg/ha). -------------------------- METHODOLOGICAL INFORMATION -------------------------- Description of methods used for collection/generation of data: The soil dataset was also derived from Mpanda et al. (2016) who collected composite and cumulative soil samples (litter was removed) at the farm plot level across depths of 0-20 cm and 20-50 cm during April 2013. Samples were gathered using an inverted Y-shaped sampling design under the AfSIS protocol (UNEP 2012). In this design, three subplots were laid out radiating at an angle of 120o and distance of 12.2 m from the centre subplot. Composite soil samples from topsoil and subsoil were collected from each of the four subplots, mixed thoroughly and 500 g of each sample was packed in zip-lock bag, and labelled. Cumulative soil mass samples from topsoil and subsoil were collected separately at the centre subplot, packed in zip-lock bags, and labelled. Composite and cumulative soil samples were processed and analysed in soil laboratory to determine physical and chemical properties. The following is described in Mpanda et al (2016) regarding the collection of on-farm tree data. Within each plot, all trees (except for coffee shrubs and climbers) ≥5 cm diameter at breast height (dbh) were identified by a botanist and had their dbh measured. One hundred trees ranging from 5 to 90.7 cm dbh were randomly sampled for height-dbh measurement for establishing the equation (expression (I)) used in estimating height of the rest of trees. For trees outside this range, it was assumed that their height could not be higher than 45 m which was the height of maximum range of 90.7 cm dbh. Only 2.16% of all trees were later found to be larger than dbh of 90.7 cm. LnHt = 0.553 + 0.6817 × Ln(D); (R2 = 0.7741, SE = 0.037, n = 100) (I) where Ln = natural logarithm, ht = height (m), D = diameter at breast height (cm), R2 = coefficient of determination and SE = standard error. Above-ground tree biomass (AGB) was computed using allometric equation (expression(II)) developed by Chave et al. (2014), carbon content was computed as 50% of the dry tree biomass. AGB = 0.0673 × (pD2H)0.976 (II) where AGB = above-ground biomass, ρ = wood specific gravity (g cm−3), D = diameter at breast height cm) and H = height (m). Wood specific gravity of each species was determined from Global Wood Density Database(Chave et al. 2009; Zanne et al. 2009). Tree stocking parameters on per hectare basis for number of stems (N), basal area (G), AGB and above-ground tree biomass carbon (AGC) were computed. Climate data was derived from Appelhans et al (2016) high resolution climate maps on Mt Kilimanjaro's southern slopes. To generate their maps, Appelhans et al. (2016) used a long-term dataset from a network of about 70 rain gauges on Mt Kilimanjaro (Hemp 2006) , and air temperature and above-ground air humidity collected from 52 combined temperature and relative humidity sensors spatially distributed across Mt Kilimanjaro’s southern slopes. Kriging, considering elevation, aspect, slope, sky-view factor, and normalized difference vegetation index, was used to generate the monthly relative humidity and air temperature maps, whilst kriging and machine learning techniques developed the average annual precipitation map. Methods for processing the data: The soil data was not processed and was used in its raw form. Tree AGC was used to compute a species composition variables (% of tree biomass per plot) for legume trees species. A Shannon Diversity Index (SDI) was computed for each site using Equation 1 in the paper, where pi represents the proportion of each individual tree species in the farm and ln represents the natural log. To derive the mean annual temperature (MAT) and average relative humidity values for 2013, raster layers for each month of that year were averaged. MAT and average relative humidity values were then extracted for each of the 26 plots. Due to the different methods Appelhans et al. (2016) used to generate their long-term precipitation maps, it was not possible to extract 2013's mean annual precipitation (MAP) the same way. To estimate 2013's MAP, we firstly extracted the long-term averaged annual precipitation for 52 sites across Moshi Rural's elevation gradient (from lowland to montane), which included two rainfall stations located in Kilema Forest (1820m asl, N 9640472, E 329366) and Himo Sisal Estate (850m asl, N 9625000, E 338000). Next, the MAP for 2013 were obtained from the two rainfall stations to determine a correction factor which was then applied to all 50 plots to account for the difference in precipitation between 2013 and the long-term average. Software- or Instrument-specific information needed to interpret the data, including software and hardware version numbers: Microsoft Excel Standards and calibration information, if appropriate: none Describe any quality-assurance procedures performed on the data: none -------------------------- DATA-SPECIFIC INFORMATION -------------------------- Number of variables: Number of cases/rows: Variable list, defining any abbreviations, units of measure, codes or symbols used: *note, soil variables beginning with 'top' represent values taken from top soil layers (0-20cm depth). Those beginning with 'sub' represent those taken in the sub soil layer (20-50 cm depth). Altitude m asl (meters above sea level), Agroecological zone (midland = 900-1200 m asl, highland = 1200-1800 m asl), Relative_Humidity (%), Temperature (average annual air temperature), Rainfall (annual precipitation (mm)), (Estimated annual banana yield (kg/ha), Basal area/ha (tree basal area per hectare), volume/ha (volume of trees per hectare), Biomass/ha (Tree biomass per hectare), Carbon_kg/ha (Tree Carbon in kilograms per hectare), Carbon_t/ha (Tree Carbon in tonnes per hectare), Species_R (Tree Species Richness), SDO (Shannon Diversity Index), Legume Composition (%) (percentage of amount of tree carbon per plot which are legumes), soilSM (soil moisture content (%)), soilBD (bulk density (g/cm3)), ECd (Electric Conductivity), ESP (Exchangeable Sodium Percentage), ExAc (Exchangeable Actinium (mg/kg)), ExBas (Exchangeable bases (mg/kg), ExCa (Exchangeable Calcium (mg/kg), ExK (Exchangeable Potassium (mg/kg)), EXMg (Exchangeable Magnesium (mg/kg), ExNa (Exchangeable Sodium (mg/kg)), Al (Aluminium concentration (mgkg-1)), B (Boron concentration (mgkg-1)), Cu (Copper concentration (mgkg-1)), Fe (Iron concentration (mgkg-1)), Mn (Manganese concentration (mgkg-1)), P (Phosphorous concentration (mgkg-1)), S (Sulfur concentration (mgkg-1)), Zn (Zinc concentration (mgkg-1)), pH (acidity), PSI (soil compaction), Total Carbon Content (g/kg)) and Total Nitrogen Content (g/kg)). Missing data codes: none. Dataset available under a CC BY 4.0 licence Publisher: University of Southampton, U.K. Date: December 2022