Study area and community
The study was conducted in Nakpalli (8°58'52.1"N, 0°19'13.4"E) in the North-Eastern part of Ghana (Fig. 1a,b) and included two months of fieldwork from January 20 to March 2, 2022. Data collection took place in the dry season and no rainfall occurred during the stay.
Nakpalli is a medium-sized village situated in the Northern Region of Ghana, around 180 km South-West from Tamale and less than 30 km from the border to Togo. The village has a population of over 4000 people, living in around 318 households. The population is mainly constituted by the Dagomba ethnic group, with a minority of Fulani, a few Kokomba and Ewe. The main activities in the village include farming, small businesses, petty trading and shea nuts collection. Crop cultivation is carried out by farmers who manage and decide over particular land plots, following a customary land tenure system. The chief is vested with the authority to allocate land for cultivation to people who ask for it (Amanor 2009).
Characteristics of the Land and Climate
Nakpalli is located in the ecoregion of Sudanian Savanna (Liu et al. 2017). Farming seasons are defined by a mono-modal rainfall system. Only one rainy season occurs between July and September, which constitutes the major farming season. A minor farming season occurs in the following drier months. The average annual rainfall is 1,000 mm (Ministry of Food and Agriculture in Ghana 2016). Analysis of soil types at the sampling sites was not possible. According to the ISRIC World Soil Information (ISRIC 2023), soils in this region are ferric luvisols, which typically are characterized by an argic horizon overlaid by loamy sand.
The vegetation is characterized by trees and bush with a herbaceous layer of forbs and annual and perennial grasses (Sawadogo et al. 2010). Agriculture is predominated by rainfed smallholder farming, and most farm holdings do not exceed 2 hectares in size (ibid.). Most farmers in Nakpalli managed agroforestry parkland systems by practicing a seasonal crop rotation with sequential cultivation and fallow periods. Farmers kept their land fallow for an average duration of 2.8 years and cultivated the lands for 3 years on average (Stoppini and Jepsen 2022). Almost every household had one or more farmland plots situated around the village, where they mainly cultivated yam (Dioscorea rotundata (Poir.) J. Miége), maize (Zea mays L.), sorghum (Sorghum bicolor (L.) Moench), cassava (Manihot esculenta Crantz) and millet (Pennisetum glaucum (L.) R. Br). The parklands were dominated by tree species of anthropogenic interest, such as shea, locust bean (Parkia biglobosa (Jacq.) R.Br. ex G. Don), mango (Mangifera indica L.), neem (Azadirachta indica A. Juss) and teak (Tectona grandis L.f.) trees.
Size distributions and densities of shea trees in fields, fallows and bushlands
Shea tree circumferences and numbers of minor-sized shea trees and seedlings were registered in fields, fallows and bushlands. These three land types are shown in Fig. 2.
The registration of trees was done to obtain knowledge on shea tree population structures and to evaluate the size-distributions, densities and regeneration of tree populations in the sequential agroforestry system (fields and fallows) and in lands that were minimally used for human activities (bushlands) for comparison. A total of 10 plots of fields, 10 plots of fallow lands and 10 plots of bushlands, each containing 20 shea trees as a minimum, were selected. Plot sizes were measured using the perimeter function on a Garmin eTrex(R) 10 Global Positioning System (GPS) (www.garmin.com).
In the defined plots, circumferences of the trunks at breast height (130 cm) of all shea trees > 130 cm height were measured using measuring tape and registered. Circumferences at breast height were converted into the widely used unit, diameter at breast height (dbh). Five subplots of 5 × 5 m were placed in corners and centres of the main plots. All shea trees < 130 cm in height, including seedlings and coppices within the sub-plots, were counted and used for estimating the total number for the whole plot by extrapolation. A graphic overview of the sampling setup is presented in Fig. S1.
The average durations of land-use in the registered lands were 3.2 ± 0.57 (n = 10) years of fallow and 3.1 ± 0.57 (n = 10) years of cultivation. The spatial distribution of locations of measurements of densities and size-distributions of shea tree populations according to land-use type are shown in Fig. 1b.
To describe and compare the shea tree size distributions in fields, fallows and bushlands, the continuous datasets of shea tree diameters at breast height in the three land-use types respectively were fitted to a Weibull distribution and shape and scale parameters were extracted. Weibull functions are described as follows:
Where f(d) is the probability density, d is the tree diameter at breast height, λ is the shape parameter determining the shape of the distribution, and α is the scale parameter determining the width and the height of the distribution.
To detect whether the shape and scale parameters provided by the functions for the Weibull distribution curves were different for the three land-use types, a one-way ANOVA with the land-use type as the single factor was applied. Dependent data were the scale and shape parameters respectively for each individual land plot (Appendix A). To detect the effects that land-use may have on shea tree densities of larger shea trees > 130 cm in height and of minor shea trees and seedlings < 130 cm in height respectively a one-way ANOVA test was applied with the land-use type as the single factor and the shea tree (> or < 130 cm) density (expressed in trees/ha) for each individual land plot as the dependent factor (Appendix B).
Below-canopy microclimate
Between January 30 and February 3, 2022, three different climate variables were measured underneath and outside shea tree canopies in the fields to evaluate the microclimate under shea trees. Soil surface temperatures and air temperatures were measured using a Laserliner CondenseSpot Plus (082.046A) (www.laserliner.com). This device is a combined thermometer for measuring ambient temperatures and an IR thermometer that detects infrared radiation (heat) emitted from surfaces. Emissivity level of the IR thermometer was set to 0.94 as suitable for soil surface temperature measurement (Manual, Laserliner CondenseSpot Plus). A LI-COR Quantum sensor (LI-250A Light Meter) (www.licor.com) was used for measuring the penetration of Photosynthetic Active Radiation (PAR) through the shea tree canopies. In accordance with Agena (2014) the data collection took place at midday, in this case at 12.05–12.25 pm, when the sun was at its highest point.
To ensure that measurements of the climate variables occurred as close to midday as possible, the data collection took place on five different, but consecutive days. Days of clear sky were chosen for the data collection, ensuring that cloud cover would not interfere with the measurements.
The climate variables were measured under the shea trees in the following zones: next to the trunk and at 1/3, 2/3 and 3/3 of the canopy radius. Five shea trees with similar characteristics were chosen for the microclimate data collection (Table 1). This was done in four different angles, resulting in 16 points of measurement per tree and 80 measurements in total per parameter (Fig. S2). For control, PAR and soil surface temperatures were measured in full sunlight outside the canopy, and air temperatures were measured in shaded conditions outside the canopy. Control measurements were done in the same field as the tree, at four points as far away from any trees as possible.
Table 1
Characteristics of the trees used for measuring below canopy microclimate.
|
Average
|
Diameter breast height (cm)
|
20.0 ± 5.7 (n = 5)
|
Tree height (m)
|
6.1 ± 1.2 (n = 5)
|
Lower canopy height (m)
|
1.9 ± 0.4 (n = 5)
|
Canopy width (m)
|
2.6 ± 0.6 (n = 20)
|
Weather data were registered at midday for the five days of data collection using a portable climate station (GMR Strumenti, Italy). The average values of PAR, air temperature and air humidity are shown in Table 2.
Table 2
Mean PAR, air temperature and air humidity at midday on the days for microclimate data collection (January 1 to February 3, 2021). The values are recorded using a climate station.
Time for measurement
|
Mean Photosynthetically Active Radiation
(µmol/s)
|
Mean Air Temperature
(°C)
|
Mean Air Humidity
(%)
|
12:21:01
|
1691.2 ± 103.7 (n = 5)
|
36.2 ± 0.8 (n = 5)
|
9.2 ± 0.6 (n = 5)
|
One-way ANOVA tests with random effects were applied to detect the effects that shea trees may have on soil temperature, air temperature and photosynthetically active radiation at different distances to the trunk, as compared to outside shea tree canopies (Appendix C).
Yields of yams cultivated underneath and outside shea tree canopies
Yam is an essential subsistence and cash crop in the village, which is widely cultivated in the agroforestry systems. Yams were planted in approximately 50 cm tall manually prepared mounds 1.5 meters apart as can be seen on Fig. 2a. To evaluate whether yields of yams were affected by shea trees, weights of yams cultivated in mounds underneath and outside shea tree canopies were determined. Yams were weighed in two different farms from 25 mounds located outside shea tree canopies and from 25 mounds underneath 9 different shea trees using a SALTER Hanging Scale Model 235 6S. The mounds were located in the following zones under shea trees: at 1/3 canopy radius (n = 8), 2/3 canopy radius (n = 8), 3/3 canopy radius (n = 9) and on open land (n = 25).
Each yam mound contained between 1 and 3 yam tubers. All yam tubers from each mound were harvested and weighed separately and locations, and site, tree, mound and the zone from where the yams were harvested were registered. In order to obtain information on yam yields per hectare, the number of mounds per hectare was estimated by extrapolating the number of yam mounds per 5 × 5 m2 plots (n = 5). To detect possible effects of shea trees on yam yields at different distances to the trunk (expressed in zones) compared to yam yields outside shea tree canopies, a linear mixed model i.e., a one-way ANOVA test with random effects was applied to the data (Appendix D).
Pre-dawn water potentials of shea tree leaves in fields, fallows and bushlands
To evaluate the water status of shea trees during the dry season and determine whether the this differs between shea trees located in fields, fallows or bushlands, the predawn leaf water potentials (ψp) were measured in all three land-use types. Only mature trees were assessed. ψp was measured from 2 leaves from the lower canopies of 10 shea trees per land-use type, resulting in 20 measurements pr. land-use type and 60 measurements in total. Measurements were made using a Pump-up Pressure Chamber (PMS Instrument Co., OR) before dawn (6.15 AM), i.e., between 4.28 am at earliest and 5.42 am at latest. Three consecutive days (February 24 to 26, 2022) without any major changes in weather were chosen for the data collection.
The precautions for data collection stated by Turner (1988) were considered. Thus, right before harvesting a leaf, it was sealed loosely by a zip-lock aluminium bag to avoid evaporative water loss. The leaf stem was cut with a razor blade, ensuring that the length of the petiole external to the sealing was as short as possible. The leaf was placed in the pressure chamber within 30 seconds. The pressure chamber was filled slowly, to avoid heating and to capture the exact endpoints.
Locations for measurements of ψp are shown in Fig. S3. The fallow land was located 166 metres above sea level (MASL), the field was located at 147 MASL and the bushland was located at 176 MASL. To detect the effects that land-use may have on shea tree predawn leaf water potentials, a linear mixed model namely a one-way ANOVA with random effects was applied (Appendix E).
Statistical analyses
The statistical analyses were performed using the software RStudio (version 4.2.0). Figures were generated using the R package ggplot2 (https://www.R-project.org/). Weibull curves for distribution of tree size classes was estimated in the R-software using the package fitdistrplus. Weibull curves were fitted to size distribution histograms (trees/ha) by overlay in Microsoft Excel version 16.59.