Temporal and spatial variability in photosynthetic activity of Vitellaria paradoxa in agroforestry parklands of Burkina Faso

Temporal and spatial variability in photosynthetic activity of Vitellaria paradoxa in agroforestry parklands of Burkina Faso | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Temporal and spatial variability in photosynthetic activity of Vitellaria paradoxa in agroforestry parklands of Burkina Faso Flavien G. B. Sawadogo, Hugues R. Bazié, Paulin Bazié, Martin Karlson, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7939299/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 24 Feb, 2026 Read the published version in Agroforestry Systems → Version 1 posted 7 You are reading this latest preprint version Abstract The Vitellaria paradoxa) is a key woody species in sub-Saharan Africa, supporting the livelihood of over 18.4 million people - particularly women. However, its survival and productivity are increasingly threatened by climate change, characterized by raising temperatures and reduced water availability. The species’ future resilience will depend on its physiological adaptability to shifting climatic conditions. To assess this adaptability, we studied photosynthetic performance of 24 shea trees in situ from April to December 2023 the most active period of leaf phenology across two contrasting climatic zones in Burkina Faso. We evaluated how photosynthetic efficiency responded to climatic variability over this nine-month period. Unregulated energy dissipation (φNO) and regulated energy dissipation in the form of heat (φNPQ) were significantly different (P < 0.001) between phytogeographic zones during monitoring period. The chlorophyll content was significantly higher (P < 0.001) in leaves from humid southern Sudanian phytogeographic zone (42.9 ± 0.4 SPAD) than in drier northern Sudanian zone (40.8 ± 0.37 SPAD). Linear regression showed a significant increase in protective energy dissipation (NPQt) in response to instantaneous photosynthetically active radiation in shea leaves from dryer northern site (R²=0.4). Additionally, leaf temperature was strongly correlated with ambient temperature, explaining 78–84% of variations (P < 0.001). Overall, V paradoxa from the more humid southern site zone exhibited better photosynthetic performance. These findings highlight spatial differences in photosynthetic responses and provide valuable insights about which photosynthetic parameters are affected by climate change. This can pave the way for management options to cope with climate change effects. Shea tree chlorophyll fluorescence effective quantum yield of photosystem II PAR VPD Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Figure 9 Figure 10 Figure 11 1. Introduction Climate change is causing major environmental impacts such as droughts, the recurrence of which is accelerating the decline of forests in the West African Sahel (Belem et al., 2017 ; Ouédraogo and Thiombiano, 2012 ), reducing vegetation cover and agricultural yields, and promoting the expansion of bare land (Bambara et al., 2013 ; Dosso Jnr 2014; Mesele et al. 2025 ). It is a threat to ecosystems. Terrestrial ecosystems play an extremely important role in mitigating Climate change (Conradi et al. 2024 ; Hölzel et al. 2016 ). Persistent climate variability can contribute to changes in precipitation patterns and temperatures, forcing ecosystems to either adapt or disappear (Raza et al., 2019 ). The increase in air temperature at the heart of climate change projections has the potential to alter the function and structure of forest ecosystems by exceeding optimal temperatures for carbon accumulation. Such changes are likely to threaten the survival of sensitive species, leading to local extinctions, range shifts and changes in forest composition (Gunderson et al. 2010). Climate change also has other severe adverse effects, including desiccation and mortality of woody plants, reduced fruit production, premature drying of water reservoirs, and degradation of vegetation cover (Gonzalez et al. 2012 ; Lu et al. 2024 ). These factors are likely to reduce vegetation productivity and may even shift forest ecosystems from functioning as carbon sinks to becoming sources of carbon emissions (Reichstein et al., 2013 ; Verbesselt et al., 2016 ). The yield of woody plants depends on the carbon sequestration capacity of their leaves (Mndela et al. 2022 ). According to Benbrahim et al. ( 2004 ), the combined effects of increasing human pressure on natural resources and climate change are causing ecosystem dysfunction. The degree of dysfunction may vary from one phytogeographic zone to another. Woody plants are a key component of ecosystems because they help mitigate the negative effects of complex climate change, such as increased carbon dioxide, high temperatures and drought (Matyssek et al. 2017; Mndela et al. 2022 ). Woody plants provide essential ecosystem services, and their selection in agroecosystems is guided by specific ecological and functional criteria (Dimobe et al. 2018 ; Case et al. 2020 ). However, the selective us of certain species has led to the transformation of many wooded savannahs into shrub or even grasslands, often characterized by a few large, widely sparse trees (Dimobe et al. 2018 ; Lohbeck et al. 2020 ). One of the tree species found in these arid and semi-arid zones is the shea tree ( Vitellaria paradoxa Gaertn F.C.) (Hall et al. 1996 ; Fischer et al. 2011 ). This species is a member of the Sapotaceae family and is the most common species in the savannah systems in Burkina Faso and other semi-arid West African countries. Shea provides numerous non-timber forest products (Lovett and Haq, 2013 ), the most valuable of which is the butter extracted from its kernels. According to Naughton et al. ( 2015 ), shea kernels is collected, processed, and marketed by an estimated 18.4 million people, particularly women, across a 3.4 million km 2 belt of sub-Saharan Africa. The butter extracted from shea kernels is the primary source of edible fat across the species' natural range (Hall et al., 1996 ; Lamien et al., 1996 ; Pouliot, 2012 ). Butter is also used in the cosmetics and food industries worldwide (Elias and Carney, 2007 ; Wardell et al. 2021 ). However, the highest export demand (⁓90%) for shea is related to the extraction of edible stearin, which is used in the formulation of cocoa butter equivalents (CBE) for chocolate confectionery (Rousseau et al., 2015 ). Despite its ecological and economic importance, the population of the shea tree has declined sharply in agroforestry parklands, dropping from an estimated 230 trees per hectare in the 1940s to fewer than 11 trees per hectare by 2011 (Wardell et Zida, 2021). The importance of this species in the region has prompted many studies, most of which have focused on its distribution (Dimobe et al. 2020 ), phenology (Bazié et al., 2019; Okullo et al., 2004 ), regeneration (Aleza et al., 2015 ; Kelly et al., 2007 ; Okullo et al., 2004 ), production (Bayala et al., 2008 ; Lamien et al., 2006 Nasare et al. 2022 ), carbon sequestration (Dimobe et al. 2023; Sanogo et al. 2016 , 2021 ), genetics (Hale et al., 2021 ; Nguekeng et al. 2021) and physiology (Bayala et al. 2009 , 2018 ; Bazié et al., 2018 ). According to Bondé et al. ( 2019 ) and Nasare et al. ( 2022 ), fruit production varied considerably from one year to the next within each climate zone. In contrast, the physiological responses of this species in the natural environment have been less studied (Awessou et al. 2017 ; Bayala et al. 2002 ; Bazié et al., 2018 ), although these are key aspects for understanding the mechanisms of resilience to the semi-arid environment, as well as the impact of fluctuating environmental conditions in phytogeographical zones on photosynthetic performance. How shea trees respond to environmental stimuli, especially temperature and light fluctuations, remains largely unexplored in a context of climate change characterised by declining productivity and regression of agroforestry patches. The objective of this study was to understand the responses of photosynthetic activities of shea trees in two different phytogeographical zones with contrasting climates. Specifically, the objectives were (i) to analyse the temporal variation in photosynthesis of shea tree leaves during the most period of this organ (April to December), (ii) to evaluate the effect of shea tree morphology on photosynthesis across two phytogeographical zones, and (iii) to understand the influence of climatic variables on the photosynthetic performance of shea. 2. Material and methods 2.1. Plant species and study site The study was carried from April to December 2023 in agroforestry parklands of the commune of Saponé (12°04'46'"N, 1°34'04"'W) and the commune of Cassou (11°34'50'"N, 2°02'57"'W), located in the North Sudanian (NSZ) and South Sudanian phytogeographic zones (SSZ), respectively (Fig. 1 ). The study period runs from leaf budding to leaf abscission in shea trees, following the annual leaf phenology cycle in shea trees (Bazié et al. 2019). Annual rainfall at North Sudanian zone ranges from 700 to 900 mm, whereas in the South Sudanian phytogeographic zones, it ranges from 900 to 1200 mm. In Cassou the rainy season is unimodal and usually lasts from May through September (Etongo et al., 2015). Sapone has a drier climate with a mean annual rainfall of 730 mm, but the inter-annual variability is high (Bayala et al., 2008 ). The rainy season in Sapone generally starts in June and ends in October. The parkland at Saponé is dominated by V. paradoxa (Bayala et al., 2002 ). The tree cover in Cassou is generally denser and more diverse in terms of species composition, with key tree species including Combretum sp., Deuterium microcarpum and Vitellaria paradoxa (Oliveira et al., 2025 ). The main soil types in both Cassou and Saponé are silt-clay cambisols, sandy lixisols, and loamy ferric luvisols (Bayala et al., 2002 ; Bazié et al., 2012) 2.2. Plant Material A total of 24 V. paradoxa trees from two phytogeographical zones (NSZ and SSZ), with 12 trees sampled in each zone were considered (Fig. 1 , Table 1 ). The trees were grouped into three stem diameter class and used to study the in situ photosynthetic performance of shea trees. All shea trees were selected in the fields. The dendrometric characteristics of the trees are presented in Table 1 . Table 1 Dendrometric characteristics of the sampled shea trees ( Vitellaria paradoxa ) in Saponé and Cassou, Burkina Faso Phytogeographical zones Tree Diameter Class Code Number of trees Average Diameter at Breast Height (DBH) Average plant height (H) Average crown estimates circumference (CC) North Sudanian (NSZ) [15–30] DC1 4 25.00 ± 2.03 6.93 ± 0.70 35.85 ± 2.00 [30–45] DC2 4 37.18 ± 1.93 10.53 ± 0.43 78.25 ± 3.84 > 45 DC3 4 56.13 ± 2.86 11.50 ± 0.47 142.88 ± 8.28 South Sudanian (SSZ) [15–30] DC1 4 24.68 ± 1.39 10.95 ± 0.46 37.36 ± 11.60 [30–45] DC2 4 35.60 ± 1.64 11.48 ± 0.44 83.29 ± 25.27 > 45 DC3 4 56.55 ± 4.42 13.38 ± 0.40 127.41 ± 19.60 Value (average ± standard deviation) 2.3. In situ environmental conditions during the measurement period Instantaneous environmental conditions were recorded using the MultispeQ 2.0, PhotosynQ, USA. During the various measurements, the PAR sensor was positioned parallel to the leaf surface, allowing the estimation of the actual PAR intercepted by the leaves in situ , which depended on the prevailing weather conditions. Leaf surface temperature was measured by the device as the difference between ambient temperature and the temperature at the leaf surface. This variable reflects the influence of temperature on physiological processes more accurately than ambient temperature alone. Automatic Tinytags Plus 2 data loggers (TGP-4017, Gemini Data Loggers, UK) were used to monitoring air temperature and relative humidity during the photosynthetically measurement to monitor changes in these two climatic parameters throughout the experiment. The vapour pressure deficit (VPD) was calculated according to the formula proposed by Allen et al. ( 1998 ), using air temperature and humidity data collected by Tinytags Plus 2-TGP-4017 sensors installed on each shea tree canopy. 2.4. In situ photosynthetic measurements of Shea trees The monitoring of photosynthetic activity involved all the 24 shea trees. For each tree, eight healthy leaves were selected and tagged at the beginning of budburst. Leaf selection was carried out according to the cardinal directions (East, West, North, South) on two alternating levels of the tree crown. Thus, four leaves were exposed to direct sunlight, while the other four were shaded by other leaves. Optical measurements were performed using a portable device, the MultispeQ 2.0 ( https://photosynq.com ), based on the model described by Kuhlgert et al. ( 2016 ). The measurement involved inserting the leaf into the measuring chamber of the MultispeQ. Once the leaf was detected, a series of measurements was taken, estimating the environmental parameters at the time of measurement. These parameters included: instantaneous Photosynthetically Active Radiation (PAR instant ), ambient temperature and humidity, and leaf surface temperature (T_ leaf ). These measurements were taken simultaneously with the physiological measurements. Optical measurements of chlorophyll fluorescence changes were carried out and allowed the estimation of several parameters: effective quantum yield of photosystem II (φII), quantum yield of non-regulated non-photochemical energy loss in photosystem II (φNO), quantum yield of regulated heat dissipation in photosystem II (φNPQ), the linear electron flow (LEF), and non-photochemical quenching (NPQt). The device also enabled measurement of leaf thickness and relative chlorophyll content (SPAD). Measurements were taken each month between 11:30 am and 12:30 pm using a 3.5-metre-high ladder Stomatal conductance was measured using a portable leaf porometer (SC-1 Leaf Porometer, Decagon Devices Inc., Pullman, USA). Measurements were conducted on the same leaves at the same time as photosynthetic parameters assessments, from April to December 2023, until leaf abscission. 2.5. Statistical analyses All statistical analyses were performed using R Core Team software (version 4.4.2). We tested the normality of our data using the Shapiro-Wilk test and the homogeneity of variances using Levene’s test. As our data did not meet the assumptions of normality or homogeneity, non-parametric methods were applied. Specifically, a Kruskal-Wallis test was used at a 5% significance level to assess the effects of the phytogeographical zone, diameter class and the sampling monthly period on plant physiological parameters. To identify which group pairs differed significantly, we performed a post hoc Dunn’s test with Bonferroni adjustment for multiple comparisons, using the FSA package (Ogle et al., 2025 ). In addition, regression analyses were performed to explore relationships between photosynthetic parameters and environmental variables. All figures describing the results (2,3, 4a, 7, 8, and 9) were generated using the ggplot2 (Wickham et al., 2025 ) and ggpubr packages (Kassambara, 2025 ). The plots use the square root of PAR to better resolve the results at lower PAR intant , and to partially linearize the responses. 3. Results 3.1 Variation in ambient climatic parameters Ambient temperature and photosynthetically active radiation (PAR) varied significantly by month and phytogeographical zone (P < 0.05) (Fig. 2 a). The highest temperatures (41.48°C ± 0.22) and instantaneous PAR values (369.10 ± 70.84 µmol photons m- 2 s −1 ) were recorded during the dry months (April, May October, November, and December) in both sites (Fig. 2 ). In contrast, from June to September, ambient temperature dropped and PAR showed considerable variability. The lowest temperature (32.63°C ± 0.21) was recorded in July, while the lowest PAR (96.28 ± 26.37 µmol photons m − 2 s − 1 ) occurred in August, coinciding with peak of the rainy season. Cloud cover during this period strongly influenced PAR levels. A significant interaction between months and phytogeographic zone was also observed (P < 0.001), indicating that the temporal patterns of temperature and PAR differed between sites. Statistical analysis revealed a significant difference in VPD according to month, phytogeographical zone and the interaction between month and phytogeographical zone (P < 0.05) (Fig. 2 b). From June to September, the presence of moisture in the air and the drop in temperatures led to a decrease in the VPD. The VPD remained low from June to September (Fig. 2 b), indicating a very low capacity of the air to absorb water during this period. After September, the VPD increased and reached 5.7 ± 0.07 kPa in December in both phytogeographical zones. 3.2. Variation of Shea leaf temperature and thickness Leaf temperature values revealed a significant difference depending on the month and phytogeographic zone (P < 0.001) (Fig. 3 a). However, no significant differences were found depending on tree diameter class (P = 0.15). Conversely, significant interactions were revealed between diameter class and month (P < 0.001), and between diameter class and phytogeographic zone (P < 0.001). The highest leaf temperatures were observed in both phytogeographic zones in April, and the lowest temperatures in July, namely 41.45 ± 0.27°C and 32.73 ± 0.15°C in the NSZ, and 41.10 ± 0.25°C and 31.55 ± 0.23°C in the SSZ, respectively. Low leaf temperatures were recorded in both phytogeographical zones during the months of June, July, August and September, while high temperatures were recorded in April (Fig. 3 a). The [15;30[ diameter class recorded the lowest leaf temperature (35.78 ± 0.23°C) in the South Sudanese phytogeographical zone, compared to the highest leaf temperature (37.27 ± 0.24°C) recorded in the North Sudanese phytogeographical zone. Leaf thickness differed significantly according to phytogeographical zone (P < 0.001) and month of the year (P < 0.001). However, no significant difference in leaf thickness was observed according to tree diameter class (P = 0.06). Conversely, significant interactions of diameter class*month of the year and phytogeographical zone, as well as month of the year*diameter class*phytogeographical zone were revealed (P < 0.001). Leaves from the northern Sudan zone were thicker (0.652 mm ± 0.015) than those from the southern Sudan zone (0.600 mm ± 0.015). A difference in leaf thickness was observed at the start of leaf expansion in early April; however, this difference was no longer evident from August until leaf fall in December (Fig. 3 b). A decrease in leaf thickness was observed from June to October in both phytogeographical zones. Similarly, the diameter class [30; 45] recorded the lowest values of leaf thickness (0.53mm ± 0.028) in the southern Sudan phytogeographic zone, while the highest(0.68 mm ± 0.027) was recorded in the northern Sudan phytogeographic zone in the diameter class ≥ 45. 3.3. Variation of shea photosynthetic parameters Values of relative chlorophyll content differed significantly for month and phytogeographical zone (P < 0.001), but not for diameter class (P = 0.98). Furthermore, significant interactions were found between diameter class and month, and between month, diameter class and phytogeographical zone (P < 0.001). At the beginning of bud break in April, the relative chlorophyll content was similar in the two phytogeographical zones. Over time, however, a difference emerged. Specifically, the mean chlorophyll content of shea leaves from the SSZ was higher (42.9 ± 0.4 SPAD) than that of leaves from the NSZ (40.8 ± 0.37 SPAD). The maximum chlorophyll content was recorded in July and August with values 47.28 ± 0.47 and 48.46 ± 0.47 SPAD, respectively. Relatively low chlorophyll content was recorded in April (33.53 ± 0.77 SPAD) and December (34.54 ± 0.94 SPAD). In both phytogeographic zones, maximum chlorophyll content was recorded in August: 47.17 ± 0.68 SPAD in the Northern Sudan phytogeographic zone, and 49.78 ± 0.56 SPAD in the Southern Sudan (Fig. 4 a). The lowest relative chlorophyll content (39.07 ± 0.70 SPAD) was recorded in the North Sudanian phytogeographic zone, while the highest value (44.89 ± 0.75 SPAD) in the South Sudanian phytogeographic zone both for the ≥ 45 diameter class (Fig. 4 b). There was a significant difference in photosystem II yield components as a function of month (P < 0.05). Similarly, there was a significant difference between phytogeographic zones for φNO (P = 0.04) and φNPQ (P = 0.001), but not for φII (P = 0.20) (Fig. 5 ). Tree diameter class did not show significant effect for any of these components (P > 0.05). Significant interaction between diameter class and photogeographic zone was observed for only φII (P = 0.002) as well as between month, diameter class and photogeographic zone for all components (P < 0.001). The φ II varied from 0.57 ± 0.02 to 0.65 ± 0.01 and from 0.54 ± 0.01 to 0.65 ± 0.001, respectively, between April and December in the SSZ and NSZ (Fig. 5 a). Conversely, the non-photochemical quenching efficiency (φNPQ) increased in the opposite direction to the photosystem II effective quantum yield (φII) (Fig. 5 b). The lowest values of (φNO) were recorded in April in both phytogeographic zones (Fig. 5 c). The φNO and φNPQ were 0.25 ± 0.002 and 0.13 ± 0.005 respectively in the southern Sudanian and northern Sudanian phytogeographic zones, compared with 0.24 ± 0.006 and 0.13 ± 0.005 respectively in the southern Sudanian and northern Sudanian phytogeographic zones. Significant monthly differences were revealed in non-photochemical quenching (NPQt) and linear electron flux (LEF) (P < 0.001). Similarly, the test revealed significant differences in NPQt between phytogeographic zone (P < 0.001), while no significant differences were observed for LEF (P = 0.42) as well as in linear electron flow (P = 0.91) or NPQt (P = 0.31) according to Shea tree diameter class.. No significant interactions were found between diameter class and phytogeographic zone for LEF (P = 0.11) and for NPQt (P = 0.68). However, significant interactions were revealed between diameter class and month, and between month, diameter class and phytogeographic zone (P < 0.001) for NPQt and LEF. Non-photochemical quenching was higher in the North Sudanese phytogeographic zone (0.78 ± 0.08) than in the South Sudanese zone (0.73 ± 0.06). The highest NPQt value was recorded in April (1.42 ± 0.20), compared to 0.35 ± 0.03 in July. Low NPQt values were recorded in June (0.37 ± 0.04) and July (0.031 ± 0.03), while high values were recorded in December (1.68 ± 0.48) and April (1.70 ± 0.35) in the South and North Sudanian phytogeographic zones respectively (Fig. 6 a). Linear electron flow followed the same trend, with the highest value observed in April (70.11 ± 6.42 µmol electrons m⁻² s⁻¹) compared to July (28.26 ± 3.73 µmol electrons m⁻² s⁻¹). Similarly, low LEF values were recorded in August (23.23 ± 4.10 µmol electrons m⁻² s⁻¹) and July (27.23 ± 4.65 µmol electrons m⁻² s⁻¹), while high values were recorded in April (66.83 ± 7.71 µmol electrons m⁻² s⁻¹) and (73.46 ± 10.27 µmol electrons m⁻² s⁻¹) for the southern and northern Sudanian phytogeographic zones, respectively (Fig. 6 b) 3.4. Relationship between climatic variables and shea photosynthetic parameters The regression between leaf temperature and PAR was significant (P < 0.001) and positive across phytogeographic zones. Regression coefficients remained low, with values of 0.08 and 0.06 in the northern and southern Sudanian phytogeographic zones, respectively . A significant (P < 0.001) and positive regression was observed in the relationship between ambient temperature and leaf temperature, depending on the phytogeographic zone. The coefficient of determination for leaf temperature and ambient temperature indicates that these two parameters are closely related. Indeed, 84% of leaf temperature is related to variations in ambient temperature in the northern Sudanese phytogeographic zone and 78% in the southern Sudanese phytogeographic zone (Fig. 7 a). An increase in ambient temperature leads to an increase in leaf temperature (Fig. 7 a). The linear regression between leaf temperature and vapour pressure deficit was significant (P < 0.001) and positive depending on the phytogeographic zone. The regression coefficients were 0.75 and 0.59 respectively in the North Sudanese and South Sudanese phytogeographic zones. The coefficient of determination of the linear regression was 0.89 between LEF and PAR in NSZ and 0,86 in SSZ (Fig. 8 ). Although the coefficient of determination of LEF was identical in the two phytogeographical zones, the half saturation point was different. The half-saturation point was 137.54 µmol photons m − 2 s − 1 and 175.60 µmol photons m − 2 s − 1 for PAR in the southern Sudanian and northern Sudanian phytogeographical zones, respectively. A significant (P < 0.01) and positive regression was observed between LEF and VPD on the one hand, and between LEF and ambient temperature according to phytogeographic zone on the other hand. However, the regression coefficients remained low and varied between 0.02 and 0.05 depending on the phytogeographic zone. The regression was positive and significant (P < 0.001) between NPQt and PAR according to phytogeographic zone. The regression coefficient was 0.25 for NPQt in the Southern Sudanian phytogeographical zone and 0.45 in the Northern Sudan phytogeographical zone (Fig. 9 ). A significant (P < 0.001) and positive regression was observed between NPQt and VPD, as well as between NPQt and ambient temperature, depending on the phytogeographic zone. However, regression coefficients remained low, varying between 0.04 and 0.06 depending on the phytogeographic zone The regression coefficients of the photosystem II yield components show that the photochemical efficiency of photosystem II (φII ) and the quantum yield of unregulated non-photochemical energy loss in photosystem II (φNO) decrease with increasing photosynthetically active radiation (PAR) (Fig. 10 ). The linear regression was negative and significant (P < 0.001) for PAR, φII and φNO, depending on the phytogeographic zone (Fig. 10 a, 10 c). The respective regression coefficients are 0.49 and 0.57 for φII and 0.31 and 0.07 for φNO in the northern and southern phytogeographic zones of Sudan. The quantum yield of regulated thermal dissipation in photosystem II (φNPQ) showed a positive, significant regression (P < 0.001) with PAR: φNPQ increased with increasing PAR (Fig. 10 b), with R² values of 0.78 and 0.55 for the northern and southern Sudan phytogeographic zones, respectively. Almost 50% of the variation in φII and φNPQ can be explained by PAR (Fig. 10 b). Significant negative relationships (P < 0.001) were observed between φII and VPD, and between φNO and VPD, depending on the phytogeographic zone. However, regression coefficients remained low, ranging from 0.04 to 0.13 depending on the phytogeographic zone. As for φNPQ, the regression was positive and significant (P < 0.001) with respect to vapour pressure deficit (VPD), depending on the phytogeographic zone. Similarly, regression coefficients were low, at 0.06 for the NSZ and 0.05 for the SSZ. Significant negative relationships (P < 0.001) were observed between φII and ambient temperature, and between φNO and ambient temperature, depending on the phytogeographic zone. However, regression coefficients remained low, ranging from 0.01 to 0.18 depending on the phytogeographic zone. The regression of ambient temperature and φNO had R² values of 0.09 and 0.18 in the NSZ and SSZ, respectively. As for φNPQ, the regression was positive and significant (P < 0.001) with respect to vapour pressure deficit (VPD), depending on the phytogeographic zone. Similarly, the R² regression coefficients were low, at 0.08 for the NSZ and 0.07 for the SSZ. The regression was positive and significant (P < 0.001) between stomatal conductance and VPD according to phytogeographic zone. The coefficient of determination R² of the linear regression of stomatal conductance and VPD shows that 26% of stomatal conductance is explained by VPD in the northern Sudanian phytogeographical zone, whereas it is explained by 44% in the southern Sudanian phytogeographical zone. A higher sensitivity of stomatal conductance to VPD was observed in the southern Sudanian phytogeographical zone (slope of the equation = 190 mol m − 2 s − 1 ) compared to the northern Sudanian phytogeographical zone (slope of the equation = 140 mol m − 2 s − 1 ) (Fig. 11 ). The linear regression between PAR and stomatal conductance was positive and significant (P = 0.015) in the North Sudanese phytogeographic zone, but not significant in the South Sudanese phytogeographic zone. (R² = 0.03). The regression was positive and significant (P < 0.01) between ambient temperature and stomatal conductance according to phytogeographic zone with low regression coefficient (R²=0.03) in both zones. 4. Discussion Leaves chlorophyll increased with time, reaching maximum values in August at the height of the rainy season and decreased and reached minimum values before leaf abscission (Fig. 4 a). This variation is probably due to morphological and biochemical adjustments that leaves undergo during their life cycle. A peak in chlorophyll content was observed in both phytogeographic zones during the month of August, suggesting that physiological adjustments and environmental interactions that increase chlorophyll levels were significant during this period. Indeed, in the more humid South Sudanic zone, water availability in the rhizosphere could facilitate the mobilisation of minerals necessary for chlorophyll synthesis. Ishfaq et al. ( 2022 ) showed that magnesium (Mg) is an essential element for chlorophyll synthesis and that approximately 15–35% of absorbed Mg is fixed in chlorophyll pigments. These variations highlight the substantial impact of environmental gradients (light, temperature and soil moisture) in conjunction with the ontogenetic development of leaves on leaf characteristics. In tropical forests, the age and stage of development of leaves play a central role in the seasonal dynamics of photosynthetic capacity and functional characteristics (Kearsley et al., 2024 ; Wu et al., 2016 ). In addition, variations in water availability, temperature and changes in VPD may interact with leaf maturation to regulate nitrogen distribution within the photosynthetic apparatus. This reallocation of nitrogen promotes chloroplast development and helps maintain photosynthetic efficiency under changing conditions (Li et al., 2024 ; Liu et al., 2024 ; Wu et al., 2016 ). Prior to abscission of the shea leaves, a decrease in relative chlorophyll content was observed. This decrease could be explained by a remobilisation of nitrogen and minerals towards the sink organs, the apical buds in shea, to provide reserves to survive difficult periods. The remobilisation of nitrogen leads to a loss of green colour and the leaves take on a yellow colour, as well as a lignification of the leaves, which could be explained by the increase in leaf thickness. Croft et al. ( 2017 ) found a better correlation of leaf chlorophyll content to photosynthetic parameters over a growing season for four broadleaf tree species, indicating that chlorophyll content could be a better proxy for leaf photosynthetic capacity compared to leaf nitrogen content. Monthly variation in photosystem II yield in the two phytogeographical zones was related to ontogenetic leaf development and natural variation in environmental conditions, especially temperature and light intensity. The quantum yield of photosystem II of Shea was highest in the southern Sudanian phytogeographical zone between April and August, before decreasing to be almost identical to the φII of photosystem II for shea in the northern Sudanian phytogeographical zone. In contrast, the yields of φNO and φNPQ increased. High values of φNPQ were recorded at the beginning of leaf flowering (April) and in the post abscission period (December). These results could explain the contribution of climatic conditions and leaf ontogeny to the photosynthetic performance of V. paradoxa plants. The period from April to August in the shea tree is characterised by leaf proliferation and maturation, which requires the synthesis of organic matter to support growth and floral phenology in adult shea trees, as well as the accumulation of reserves to support difficult periods. The performance of photosystem II implies that it is during this period (April to August) that the shea tree optimises the synthesis of photoassimilates. Indeed, φII is a good indicator of the percentage of photons absorbed by chlorophyll for photosynthesis (Yu et al., 2024 ). In this study, φII was higher in shea from the southern Sudanian phytogeographical zone, and the relative chlorophyll content of the leaves was higher under milder climatic conditions. According to Sun et al. ( 2020 )and Yu et al. ( 2022 ), φII is sensitive to abiotic stresses, especially light intensity, temperature and water availability. The decrease in photosystem II quantum yield could be explained by leaf senescence. In both phytogeographical zones, signs of leaf senescence appeared from September onwards, characterised by a decrease in chlorophyll content and an increase in leaf thickness. The decrease in relative chlorophyll content and the increase in φNO and φNPQ indicate that the leaves are gradually losing their photosynthetic performance, characterised by a decrease in their capacity to convert light energy. Increase ambient temperature affected leaf surface temperature of the shea trees. -Linear regression showed that leaf temperature was closely related to ambient temperature. It explained 78–84% of leaf temperature. Difference in temperature could affect the physiology of the plants and the configuration of the cell membranes, given their phospholipid nature. According to Chen et al. ( 2012 ), when the ambient temperature is higher than the optimal plant temperature, the chloroplast structure is damaged, photosynthetic performance is reduced, and the relevant photosynthetic parameters are modified. Indeed, the month April and May were those characterised by high temperatures (Fig. 2 , 3 a). These months were also characterised by low humidity in both the northern and southern phytogeographical zones. Linear regression showed that stomatal conductance was sensitive to VPD, demonstrating the link between climatic conditions and the regulation of stomatal aperture. The sensitivity of stomatal conductance was more pronounced in the northern Sudanese phytogeographical zone. With the decrease in VPD between June and October, stomatal opening would be at an optimum, which could increase carbon assimilation by V. paradoxa tree in this phytogeographical zone. According to Cheng et al. ( 2022 ) and Green et al. ( 2020 ), VPD promotes photosynthesis in forest regions with high rainfall. The availability of water both on the ground and in the atmosphere due to rainfall during this period could maintain stomatal aperture at an optimal level over time, thereby enhancing carbon assimilation. VPD is an important factor in atmospheric water demand for plants and a critical variable in determining plant transpiration and photosynthesis (Novick et al., 2016 ; Yuan et al., 2019 ). Increase in VPD during dry periods is not without consequences for plant physiology. Indeed, the increase in VPD leads to a significant demand for leaf water, given the water limitation at the soil level, and the plant reduces stomatal opening, which has consequences for plant physiology. Reduced stomatal aperture limits the availability of carbon dioxide. VPD affects plant metabolism by limiting the carbon available to plants and can lead to plant mortality due to carbon starvation (Grossiord et al., 2020 ; Yuan et al., 2019 ). The linear regression between PAR and LEF shows that electron production by the thylakoid membrane is photosensitive. Although the regression coefficient R² is identical for the two phytogeographical zones (R²=0.8), light saturation of photosystem II occurs more rapidly for shea in the southern Sudanian phytogeographical zone. This is because the increase in PAR leads to a significant production of electrons through photolysis of water. These electrons are involved in the production of photochemical energy (ATP/NADPH), and when the energy produced exceeds the reduction capacity of the Calvin-Benson-Bassham (CBB) cycle, plant protection mechanisms such as non-photochemical quenching (NPQt) are triggered to diffuse the excess energy to prevent photodamage. Our results show that the mechanisms for diffusing excess energy occur more rapidly in shea from the southern Sudanese phytogeographical zone. For Kromdijk et al. ( 2016 ), the protective dissipation of energy is at the origin of the reduction in crop yield. The sensitivity of NPQt (R²=0.40) to instantaneous PAR in the northern Sudanian phytogeographical zone could reduce shea yields. this study shows high temperatures associated with low instantaneous PAR. An increase in temperature, even at low instantaneous PAR, could lead to reconfiguration or even destruction of the thylakoid membrane and thus reduce photosynthetic performance in shea. According to Hemantaranjan et al., 2014 , high temperature leads to various physiological changes in plants such as scorched leaves and stems, leaf abscission and senescence, inhibition of stem and root growth or reduction in number, pollen tube growth and pollen sterility, and fruit damage leading to catastrophic loss of crop yield resulting in significant loss of productivity. 5. Conclusion The findings of this study showed that the difference in temperature affected the physiological behaviour of V. paradoxa . Photosynthetic performance was better in the southern Sudanese phytogeographical zone. In the northern Sudanese phytogeographical zone, stomatal conductance was less sensitive to VPD, but NPQt was sensitive to PAR, which could lead to yield losses. The results show a positive correlation between leaf and ambient temperatures, suggesting that an increase in temperature will lead to an increase in leaf temperature. In addition, the study revealed the thermal and photosynthetic adjustment capacity of shea trees and reinforces the functional resilience of shea trees in parklands ecosystems. The results obtained provide a better understanding of the resilience of shea trees in order to optimise ecosystem services (carbon storage, water and microclimate management) and adapt agroforestry management to climate change. It would be necessary to determine optimal temperature for biochemical control factors of photosynthesis in shea leaves in field. This work could provide a basis for understanding variations in environmental factors on photosynthetic performance of woody plants in agroforestry parks in the Sudano-Sahelian zone and yield loss of shea tree in the context of climate change. Declarations Funding statement This research work was supported by the Swedish Research Council 2018-03722 and Formas 2018-00570 Acknowledgments We would also like to express our gratitude to the farmers of Cassou and Saponé for letting us use their fields and for the fruitful collaboration. Author contributions: Flavien G. B. Sawadogo : Writing-review and editing-original draft, visualization, Methodology, Investigation, Data curation, Formal analysis. Hugues R. Bazie: Writing-review and editing, Methodology, Supervision. Paulin Bazié: Writing-review and editing, Methodology. Martin Karlson: Writing-review and editing. Madelene Oswald: Writing-review and editing. Jules Bayala: Writing-review, editing and Supervision. 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Cite Share Download PDF Status: Published Journal Publication published 24 Feb, 2026 Read the published version in Agroforestry Systems → Version 1 posted Reviews received at journal 13 Nov, 2025 Reviewers agreed at journal 07 Nov, 2025 Reviewers agreed at journal 07 Nov, 2025 Reviewers invited by journal 07 Nov, 2025 Editor assigned by journal 24 Oct, 2025 Submission checks completed at journal 24 Oct, 2025 First submitted to journal 24 Oct, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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1","display":"","copyAsset":false,"role":"figure","size":233905,"visible":true,"origin":"","legend":"\u003cp\u003eLocation of study sites of Saponé and Cassou, located respectively in the North Sudanian (NSZ) and South Sudanian phytogeographic zones (SSZ) of Burkina Faso\u003c/p\u003e","description":"","filename":"1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7939299/v1/4f3d47de9342046cfe0960d9.jpg"},{"id":96202398,"identity":"53f9e6bb-c260-4a68-8eaa-3741cbbd0f9e","added_by":"auto","created_at":"2025-11-18 16:41:30","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":109794,"visible":true,"origin":"","legend":"\u003cp\u003eMonthly and spatial variation of a) instantaneous photosynthetically active radiation, ambient temperature and b) vapour pressure deficit (VPD) \u0026nbsp;in the study sites of Saponé and Cassou in Burkina Faso\u003c/p\u003e","description":"","filename":"2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7939299/v1/d02c412d4c32d328ae62f97b.jpg"},{"id":96202385,"identity":"d8af7430-d556-4e08-8f97-0875f0b30e20","added_by":"auto","created_at":"2025-11-18 16:41:29","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":84588,"visible":true,"origin":"","legend":"\u003cp\u003eMonthly and spatial variation of a) Shea leaf temperature, b) shea tree leaf thickness\u003c/p\u003e","description":"","filename":"3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7939299/v1/9c4738fdbf7022d7a0a4f9cf.jpg"},{"id":96202393,"identity":"3ec0d462-8244-4dd8-8206-6be3118b7fbc","added_by":"auto","created_at":"2025-11-18 16:41:30","extension":"jpg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":110172,"visible":true,"origin":"","legend":"\u003cp\u003e(a) Monthly and spatial variation in relative chlorophyll content, (b) effect of diameter class on chlorophyll content of shea \u0026nbsp;leaves\u003c/p\u003e","description":"","filename":"4.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7939299/v1/c217b6437c937062bb0959b6.jpg"},{"id":96252356,"identity":"0a077a89-0798-4a33-bd2f-bfda80549ecc","added_by":"auto","created_at":"2025-11-19 07:40:51","extension":"jpg","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":136556,"visible":true,"origin":"","legend":"\u003cp\u003e(a) Monthly variation of effective quantum yield of photosystem II (φII), (b) quantum yield of regulated heat dissipation in photosystem II (φNPQ), (c) quantum yield of non-regulated non-photochemical energy loss in photosystem II (φNO)\u003c/p\u003e","description":"","filename":"5.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7939299/v1/03ee739c81727deefbdd9e8d.jpg"},{"id":96202404,"identity":"88c1d3c6-68bd-43cc-849b-7b7a8f159272","added_by":"auto","created_at":"2025-11-18 16:41:30","extension":"jpg","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":129427,"visible":true,"origin":"","legend":"\u003cp\u003eMonthly and spatial variation on non-photochemical quenching (NPQt) (a) and linear electron flux (LEF) (b) of shea leaves in two phytogeographical zones\u003c/p\u003e","description":"","filename":"6.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7939299/v1/34a23e76d85786d3e7a00355.jpg"},{"id":96202403,"identity":"64bd810e-dfa3-4fde-98fa-7e72080d3aec","added_by":"auto","created_at":"2025-11-18 16:41:30","extension":"jpg","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":148499,"visible":true,"origin":"","legend":"\u003cp\u003eRelationship between leaf temperature of shea (\u003cem\u003eVitellaria paradoxa\u003c/em\u003e) and (a) ambient temperature, (b) vapour pressure deficit (VPD) as a function of phytogeographic zone in Burkina Faso\u003c/p\u003e","description":"","filename":"7.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7939299/v1/0175f9319ee5b4c07be58202.jpg"},{"id":96202405,"identity":"6e0e8c6a-f458-4935-85c6-a9d4a37b52c8","added_by":"auto","created_at":"2025-11-18 16:41:30","extension":"jpg","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":64742,"visible":true,"origin":"","legend":"\u003cp\u003eThe relationship between linear electron flow (LEF) of shea and instantaneous photosynthetically active radiation (PARinstant) as a function of phytogeographic zone\u003c/p\u003e","description":"","filename":"8.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7939299/v1/c58b57839d3ed685a4e7dd62.jpg"},{"id":96202399,"identity":"4e69993d-bdcb-4bd9-8136-68ea37c5a510","added_by":"auto","created_at":"2025-11-18 16:41:30","extension":"jpg","order_by":9,"title":"Figure 9","display":"","copyAsset":false,"role":"figure","size":64277,"visible":true,"origin":"","legend":"\u003cp\u003eThe relationship between Non‐photochemical quenching\u003cem\u003e \u003c/em\u003e(NPQt) of shea (\u003cem\u003eVitellaria paradoxa\u003c/em\u003e) and instantaneous photosynthetically active radiation (PARinstant)as a function of phytogeographic zone in Burkina Faso\u003c/p\u003e","description":"","filename":"9.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7939299/v1/fe8db1b02f8dd74c864b4d1e.jpg"},{"id":96202392,"identity":"df33dc72-e84d-44c5-92d9-c26a1d60caca","added_by":"auto","created_at":"2025-11-18 16:41:29","extension":"jpg","order_by":10,"title":"Figure 10","display":"","copyAsset":false,"role":"figure","size":171746,"visible":true,"origin":"","legend":"\u003cp\u003eThe relationship between the yield components of photosystem II (φII, φNPQ, φNO) of shea (\u003cem\u003eVitellaria paradoxa\u003c/em\u003e) and instantaneous photosynthetically active radiation (PARinstant)as a function of phytogeographic zone in Burkina Faso\u003c/p\u003e","description":"","filename":"10.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7939299/v1/5d87ba6e49b9f591275c34a2.jpg"},{"id":96202396,"identity":"fec91b06-3bd2-41d8-99d7-4aa36a70d41d","added_by":"auto","created_at":"2025-11-18 16:41:30","extension":"jpg","order_by":11,"title":"Figure 11","display":"","copyAsset":false,"role":"figure","size":65493,"visible":true,"origin":"","legend":"\u003cp\u003eRelationship between stomatal conductance of shea (\u003cem\u003eVitellaria paradoxa\u003c/em\u003e) and vapour pressure deficit (VPD) as a function of phytogeographic zone in Burkina Faso\u003c/p\u003e","description":"","filename":"11.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7939299/v1/babbe050ccb173d2c2ba8157.jpg"},{"id":103766292,"identity":"d3c22957-5a3a-44dd-9baa-011ad92a1d58","added_by":"auto","created_at":"2026-03-02 16:13:36","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2255168,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7939299/v1/5e9a6f45-bb7f-4476-98b1-0babab4c82c3.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"\u003cp\u003eTemporal and spatial variability in photosynthetic activity of Vitellaria paradoxa in agroforestry parklands of Burkina Faso\u003c/p\u003e","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eClimate change is causing major environmental impacts such as droughts, the recurrence of which is accelerating the decline of forests in the West African Sahel (Belem et al., \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Ou\u0026eacute;draogo and Thiombiano, \u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e2012\u003c/span\u003e), reducing vegetation cover and agricultural yields, and promoting the expansion of bare land (Bambara et al., \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; Dosso Jnr 2014; Mesele et al. \u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). It is a threat to ecosystems. Terrestrial ecosystems play an extremely important role in mitigating Climate change (Conradi et al. \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; H\u0026ouml;lzel et al. \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). Persistent climate variability can contribute to changes in precipitation patterns and temperatures, forcing ecosystems to either adapt or disappear (Raza et al., \u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e2019\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eThe increase in air temperature at the heart of climate change projections has the potential to alter the function and structure of forest ecosystems by exceeding optimal temperatures for carbon accumulation. Such changes are likely to threaten the survival of sensitive species, leading to local extinctions, range shifts and changes in forest composition (Gunderson et al. 2010). Climate change also has other severe adverse effects, including desiccation and mortality of woody plants, reduced fruit production, premature drying of water reservoirs, and degradation of vegetation cover (Gonzalez et al. \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; Lu et al. \u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). These factors are likely to reduce vegetation productivity and may even shift forest ecosystems from functioning as carbon sinks to becoming sources of carbon emissions (Reichstein et al., \u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; Verbesselt et al., \u003cspan citationid=\"CR72\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). The yield of woody plants depends on the carbon sequestration capacity of their leaves (Mndela et al. \u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). According to Benbrahim et al. (\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2004\u003c/span\u003e), the combined effects of increasing human pressure on natural resources and climate change are causing ecosystem dysfunction. The degree of dysfunction may vary from one phytogeographic zone to another. Woody plants are a key component of ecosystems because they help mitigate the negative effects of complex climate change, such as increased carbon dioxide, high temperatures and drought (Matyssek et al. 2017; Mndela et al. \u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Woody plants provide essential ecosystem services, and their selection in agroecosystems is guided by specific ecological and functional criteria (Dimobe et al. \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Case et al. \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). However, the selective us of certain species has led to the transformation of many wooded savannahs into shrub or even grasslands, often characterized by a few large, widely sparse trees (Dimobe et al. \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Lohbeck et al. \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). One of the tree species found in these arid and semi-arid zones is the shea tree (\u003cem\u003eVitellaria paradoxa\u003c/em\u003e Gaertn F.C.) (Hall et al. \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e1996\u003c/span\u003e; Fischer et al. \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2011\u003c/span\u003e). This species is a member of the \u003cem\u003eSapotaceae\u003c/em\u003e family and is the most common species in the savannah systems in Burkina Faso and other semi-arid West African countries. Shea provides numerous non-timber forest products (Lovett and Haq, \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e2013\u003c/span\u003e), the most valuable of which is the butter extracted from its kernels. According to Naughton et al. (\u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e2015\u003c/span\u003e), shea kernels is collected, processed, and marketed by an estimated 18.4\u0026nbsp;million people, particularly women, across a 3.4\u0026nbsp;million km\u003csup\u003e2\u003c/sup\u003e belt of sub-Saharan Africa. The butter extracted from shea kernels is the primary source of edible fat across the species' natural range (Hall et al., \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e1996\u003c/span\u003e; Lamien et al., \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e1996\u003c/span\u003e; Pouliot, \u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). Butter is also used in the cosmetics and food industries worldwide (Elias and Carney, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2007\u003c/span\u003e; Wardell et al. \u003cspan citationid=\"CR75\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). However, the highest export demand (⁓90%) for shea is related to the extraction of edible stearin, which is used in the formulation of cocoa butter equivalents (CBE) for chocolate confectionery (Rousseau et al., \u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e2015\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eDespite its ecological and economic importance, the population of the shea tree has declined sharply in agroforestry parklands, dropping from an estimated 230 trees per hectare in the 1940s to fewer than 11 trees per hectare by 2011 (Wardell et Zida, 2021). The importance of this species in the region has prompted many studies, most of which have focused on its distribution (Dimobe et al. \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2020\u003c/span\u003e), phenology (Bazi\u0026eacute; et al., 2019; Okullo et al., \u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e2004\u003c/span\u003e), regeneration (Aleza et al., \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Kelly et al., \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2007\u003c/span\u003e; Okullo et al., \u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e2004\u003c/span\u003e), production (Bayala et al., \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2008\u003c/span\u003e; Lamien et al., \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2006\u003c/span\u003e Nasare et al. \u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e2022\u003c/span\u003e), carbon sequestration (Dimobe et al. 2023; Sanogo et al. \u003cspan citationid=\"CR70\" class=\"CitationRef\"\u003e2016\u003c/span\u003e, \u003cspan citationid=\"CR69\" class=\"CitationRef\"\u003e2021\u003c/span\u003e), genetics (Hale et al., \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Nguekeng et al. 2021) and physiology (Bayala et al. \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2009\u003c/span\u003e, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Bazi\u0026eacute; et al., \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). According to Bond\u0026eacute; et al. (\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2019\u003c/span\u003e) and Nasare et al. (\u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e2022\u003c/span\u003e), fruit production varied considerably from one year to the next within each climate zone. In contrast, the physiological responses of this species in the natural environment have been less studied (Awessou et al. \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Bayala et al. \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2002\u003c/span\u003e; Bazi\u0026eacute; et al., \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2018\u003c/span\u003e), although these are key aspects for understanding the mechanisms of resilience to the semi-arid environment, as well as the impact of fluctuating environmental conditions in phytogeographical zones on photosynthetic performance. How shea trees respond to environmental stimuli, especially temperature and light fluctuations, remains largely unexplored in a context of climate change characterised by declining productivity and regression of agroforestry patches.\u003c/p\u003e\u003cp\u003eThe objective of this study was to understand the responses of photosynthetic activities of shea trees in two different phytogeographical zones with contrasting climates. Specifically, the objectives were (i) to analyse the temporal variation in photosynthesis of shea tree leaves during the most period of this organ (April to December), (ii) to evaluate the effect of shea tree morphology on photosynthesis across two phytogeographical zones, and (iii) to understand the influence of climatic variables on the photosynthetic performance of shea.\u003c/p\u003e"},{"header":"2. Material and methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003e2.1. Plant species and study site\u003c/h2\u003e\u003cp\u003eThe study was carried from April to December 2023 in agroforestry parklands of the commune of Sapon\u0026eacute; (12\u0026deg;04'46'\"N, 1\u0026deg;34'04\"'W) and the commune of Cassou (11\u0026deg;34'50'\"N, 2\u0026deg;02'57\"'W), located in the North Sudanian (NSZ) and South Sudanian phytogeographic zones (SSZ), respectively (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). The study period runs from leaf budding to leaf abscission in shea trees, following the annual leaf phenology cycle in shea trees (Bazi\u0026eacute; et al. 2019). Annual rainfall at North Sudanian zone ranges from 700 to 900 mm, whereas in the South Sudanian phytogeographic zones, it ranges from 900 to 1200 mm. In Cassou the rainy season is unimodal and usually lasts from May through September (Etongo et al., 2015). Sapone has a drier climate with a mean annual rainfall of 730 mm, but the inter-annual variability is high (Bayala et al., \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2008\u003c/span\u003e). The rainy season in Sapone generally starts in June and ends in October. The parkland at Sapon\u0026eacute; is dominated by \u003cem\u003eV. paradoxa\u003c/em\u003e (Bayala et al., \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2002\u003c/span\u003e). The tree cover in Cassou is generally denser and more diverse in terms of species composition, with key tree species including \u003cem\u003eCombretum\u003c/em\u003e sp., \u003cem\u003eDeuterium microcarpum\u003c/em\u003e and \u003cem\u003eVitellaria paradoxa\u003c/em\u003e (Oliveira et al., \u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). The main soil types in both Cassou and Sapon\u0026eacute; are silt-clay cambisols, sandy lixisols, and loamy ferric luvisols (Bayala et al., \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2002\u003c/span\u003e; Bazi\u0026eacute; et al., 2012)\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e\u003ch2\u003e2.2. Plant Material\u003c/h2\u003e\u003cp\u003eA total of 24 \u003cem\u003eV. paradoxa\u003c/em\u003e trees from two phytogeographical zones (NSZ and SSZ), with 12 trees sampled in each zone were considered (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e, Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e ). The trees were grouped into three stem diameter class and used to study the in situ photosynthetic performance of shea trees. All shea trees were selected in the fields. The dendrometric characteristics of the trees are presented in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eDendrometric characteristics of the sampled shea trees (\u003cem\u003eVitellaria paradoxa\u003c/em\u003e) in Sapon\u0026eacute; and Cassou, Burkina Faso\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"7\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\"\u0026plusmn;\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\"\u0026plusmn;\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\"\u0026plusmn;\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePhytogeographical zones\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eTree Diameter Class\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eCode\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eNumber of trees\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eAverage Diameter at Breast Height (DBH)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eAverage plant height (H)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003eAverage crown estimates circumference (CC)\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e\u003cp\u003eNorth Sudanian (NSZ)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e[15\u0026ndash;30]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eDC1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c5\"\u003e\u003cp\u003e25.00\u0026thinsp;\u0026plusmn;\u0026thinsp;2.03\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c6\"\u003e\u003cp\u003e6.93\u0026thinsp;\u0026plusmn;\u0026thinsp;0.70\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c7\"\u003e\u003cp\u003e35.85\u0026thinsp;\u0026plusmn;\u0026thinsp;2.00\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e[30\u0026ndash;45]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eDC2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c5\"\u003e\u003cp\u003e37.18\u0026thinsp;\u0026plusmn;\u0026thinsp;1.93\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c6\"\u003e\u003cp\u003e10.53\u0026thinsp;\u0026plusmn;\u0026thinsp;0.43\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c7\"\u003e\u003cp\u003e78.25\u0026thinsp;\u0026plusmn;\u0026thinsp;3.84\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u0026gt;\u0026thinsp;45\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eDC3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c5\"\u003e\u003cp\u003e56.13\u0026thinsp;\u0026plusmn;\u0026thinsp;2.86\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c6\"\u003e\u003cp\u003e11.50\u0026thinsp;\u0026plusmn;\u0026thinsp;0.47\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c7\"\u003e\u003cp\u003e142.88\u0026thinsp;\u0026plusmn;\u0026thinsp;8.28\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e\u003cp\u003eSouth Sudanian (SSZ)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e[15\u0026ndash;30]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eDC1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c5\"\u003e\u003cp\u003e24.68\u0026thinsp;\u0026plusmn;\u0026thinsp;1.39\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c6\"\u003e\u003cp\u003e10.95\u0026thinsp;\u0026plusmn;\u0026thinsp;0.46\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c7\"\u003e\u003cp\u003e37.36\u0026thinsp;\u0026plusmn;\u0026thinsp;11.60\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e[30\u0026ndash;45]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eDC2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c5\"\u003e\u003cp\u003e35.60\u0026thinsp;\u0026plusmn;\u0026thinsp;1.64\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c6\"\u003e\u003cp\u003e11.48\u0026thinsp;\u0026plusmn;\u0026thinsp;0.44\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c7\"\u003e\u003cp\u003e83.29\u0026thinsp;\u0026plusmn;\u0026thinsp;25.27\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u0026gt;\u0026thinsp;45\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eDC3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c5\"\u003e\u003cp\u003e56.55\u0026thinsp;\u0026plusmn;\u0026thinsp;4.42\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c6\"\u003e\u003cp\u003e13.38\u0026thinsp;\u0026plusmn;\u0026thinsp;0.40\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c7\"\u003e\u003cp\u003e127.41\u0026thinsp;\u0026plusmn;\u0026thinsp;19.60\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"7\"\u003eValue (average\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviation)\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec5\" class=\"Section2\"\u003e\u003ch2\u003e2.3. In situ environmental conditions during the measurement period\u003c/h2\u003e\u003cp\u003eInstantaneous environmental conditions were recorded using the MultispeQ 2.0, PhotosynQ, USA. During the various measurements, the PAR sensor was positioned parallel to the leaf surface, allowing the estimation of the actual PAR intercepted by the leaves \u003cem\u003ein situ\u003c/em\u003e, which depended on the prevailing weather conditions. Leaf surface temperature was measured by the device as the difference between ambient temperature and the temperature at the leaf surface. This variable reflects the influence of temperature on physiological processes more accurately than ambient temperature alone. Automatic Tinytags Plus 2 data loggers (TGP-4017, Gemini Data Loggers, UK) were used to monitoring air temperature and relative humidity during the photosynthetically measurement to monitor changes in these two climatic parameters throughout the experiment. The vapour pressure deficit (VPD) was calculated according to the formula proposed by Allen et al. (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e1998\u003c/span\u003e), using air temperature and humidity data collected by Tinytags Plus 2-TGP-4017 sensors installed on each shea tree canopy.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec6\" class=\"Section2\"\u003e\u003ch2\u003e2.4. \u003cem\u003eIn situ\u003c/em\u003e photosynthetic measurements of Shea trees\u003c/h2\u003e\u003cp\u003eThe monitoring of photosynthetic activity involved all the 24 shea trees. For each tree, eight healthy leaves were selected and tagged at the beginning of budburst. Leaf selection was carried out according to the cardinal directions (East, West, North, South) on two alternating levels of the tree crown. Thus, four leaves were exposed to direct sunlight, while the other four were shaded by other leaves.\u003c/p\u003e\u003cp\u003eOptical measurements were performed using a portable device, the MultispeQ 2.0 (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://photosynq.com\u003c/span\u003e\u003cspan address=\"https://photosynq.com\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e), based on the model described by Kuhlgert et al. (\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). The measurement involved inserting the leaf into the measuring chamber of the MultispeQ. Once the leaf was detected, a series of measurements was taken, estimating the environmental parameters at the time of measurement. These parameters included: instantaneous Photosynthetically Active Radiation (PAR\u003csub\u003einstant\u003c/sub\u003e), ambient temperature and humidity, and leaf surface temperature (T_\u003csub\u003eleaf\u003c/sub\u003e). These measurements were taken simultaneously with the physiological measurements.\u003c/p\u003e\u003cp\u003eOptical measurements of chlorophyll fluorescence changes were carried out and allowed the estimation of several parameters: effective quantum yield of photosystem II (φII), quantum yield of non-regulated non-photochemical energy loss in photosystem II (φNO), quantum yield of regulated heat dissipation in photosystem II (φNPQ), the linear electron flow (LEF), and non-photochemical quenching (NPQt). The device also enabled measurement of leaf thickness and relative chlorophyll content (SPAD). Measurements were taken each month between 11:30 am and 12:30 pm using a 3.5-metre-high ladder\u003c/p\u003e\u003cp\u003eStomatal conductance was measured using a portable leaf porometer (SC-1 Leaf Porometer, Decagon Devices Inc., Pullman, USA). Measurements were conducted on the same leaves at the same time as photosynthetic parameters assessments, from April to December 2023, until leaf abscission.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e\u003ch2\u003e\u003cb\u003e2.5. Statistical analyses\u003c/b\u003e\u003c/h2\u003e\u003cp\u003eAll statistical analyses were performed using R Core Team software (version 4.4.2). We tested the normality of our data using the Shapiro-Wilk test and the homogeneity of variances using Levene\u0026rsquo;s test. As our data did not meet the assumptions of normality or homogeneity, non-parametric methods were applied. Specifically, a Kruskal-Wallis test was used at a 5% significance level to assess the effects of the phytogeographical zone, diameter class and the sampling monthly period on plant physiological parameters. To identify which group pairs differed significantly, we performed a post hoc Dunn\u0026rsquo;s test with Bonferroni adjustment for multiple comparisons, using the FSA package (Ogle et al., \u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). In addition, regression analyses were performed to explore relationships between photosynthetic parameters and environmental variables. All figures describing the results (2,3, 4a, 7, 8, and 9) were generated using the ggplot2 (Wickham et al., \u003cspan citationid=\"CR76\" class=\"CitationRef\"\u003e2025\u003c/span\u003e) and ggpubr packages (Kassambara, \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). The plots use the square root of PAR to better resolve the results at lower PAR\u003csub\u003eintant\u003c/sub\u003e, and to partially linearize the responses.\u003c/p\u003e\u003c/div\u003e"},{"header":"3. Results","content":"\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e\u003ch2\u003e3.1 Variation in ambient climatic parameters\u003c/h2\u003e\u003cp\u003eAmbient temperature and photosynthetically active radiation (PAR) varied significantly by month and phytogeographical zone (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05) (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ea). The highest temperatures (41.48\u0026deg;C\u0026thinsp;\u0026plusmn;\u0026thinsp;0.22) and instantaneous PAR values (369.10\u0026thinsp;\u0026plusmn;\u0026thinsp;70.84 \u0026micro;mol photons m-\u003csup\u003e2\u003c/sup\u003es\u003csup\u003e\u0026minus;1\u003c/sup\u003e) were recorded during the dry months (April, May October, November, and December) in both sites (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). In contrast, from June to September, ambient temperature dropped and PAR showed considerable variability. The lowest temperature (32.63\u0026deg;C\u0026thinsp;\u0026plusmn;\u0026thinsp;0.21) was recorded in July, while the lowest PAR (96.28\u0026thinsp;\u0026plusmn;\u0026thinsp;26.37 \u0026micro;mol photons m\u003csup\u003e\u0026minus;\u0026thinsp;2\u003c/sup\u003es\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e) occurred in August, coinciding with peak of the rainy season. Cloud cover during this period strongly influenced PAR levels. A significant interaction between months and phytogeographic zone was also observed (P\u0026thinsp;\u0026lt;\u0026thinsp;0.001), indicating that the temporal patterns of temperature and PAR differed between sites.\u003c/p\u003e\u003cp\u003eStatistical analysis revealed a significant difference in VPD according to month, phytogeographical zone and the interaction between month and phytogeographical zone (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05) (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eb). From June to September, the presence of moisture in the air and the drop in temperatures led to a decrease in the VPD. The VPD remained low from June to September (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eb), indicating a very low capacity of the air to absorb water during this period. After September, the VPD increased and reached 5.7\u0026thinsp;\u0026plusmn;\u0026thinsp;0.07 kPa in December in both phytogeographical zones.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e\u003ch2\u003e3.2. Variation of Shea leaf temperature and thickness\u003c/h2\u003e\u003cp\u003eLeaf temperature values revealed a significant difference depending on the month and phytogeographic zone (P\u0026thinsp;\u0026lt;\u0026thinsp;0.001) (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ea). However, no significant differences were found depending on tree diameter class (P\u0026thinsp;=\u0026thinsp;0.15). Conversely, significant interactions were revealed between diameter class and month (P\u0026thinsp;\u0026lt;\u0026thinsp;0.001), and between diameter class and phytogeographic zone (P\u0026thinsp;\u0026lt;\u0026thinsp;0.001). The highest leaf temperatures were observed in both phytogeographic zones in April, and the lowest temperatures in July, namely 41.45\u0026thinsp;\u0026plusmn;\u0026thinsp;0.27\u0026deg;C and 32.73\u0026thinsp;\u0026plusmn;\u0026thinsp;0.15\u0026deg;C in the NSZ, and 41.10\u0026thinsp;\u0026plusmn;\u0026thinsp;0.25\u0026deg;C and 31.55\u0026thinsp;\u0026plusmn;\u0026thinsp;0.23\u0026deg;C in the SSZ, respectively. Low leaf temperatures were recorded in both phytogeographical zones during the months of June, July, August and September, while high temperatures were recorded in April (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ea). The [15;30[ diameter class recorded the lowest leaf temperature (35.78\u0026thinsp;\u0026plusmn;\u0026thinsp;0.23\u0026deg;C) in the South Sudanese phytogeographical zone, compared to the highest leaf temperature (37.27\u0026thinsp;\u0026plusmn;\u0026thinsp;0.24\u0026deg;C) recorded in the North Sudanese phytogeographical zone.\u003c/p\u003e\u003cp\u003eLeaf thickness differed significantly according to phytogeographical zone (P\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and month of the year (P\u0026thinsp;\u0026lt;\u0026thinsp;0.001). However, no significant difference in leaf thickness was observed according to tree diameter class (P\u0026thinsp;=\u0026thinsp;0.06). Conversely, significant interactions of diameter class*month of the year and phytogeographical zone, as well as month of the year*diameter class*phytogeographical zone were revealed (P\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Leaves from the northern Sudan zone were thicker (0.652 mm\u0026thinsp;\u0026plusmn;\u0026thinsp;0.015) than those from the southern Sudan zone (0.600 mm\u0026thinsp;\u0026plusmn;\u0026thinsp;0.015). A difference in leaf thickness was observed at the start of leaf expansion in early April; however, this difference was no longer evident from August until leaf fall in December (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eb). A decrease in leaf thickness was observed from June to October in both phytogeographical zones. Similarly, the diameter class [30; 45] recorded the lowest values of leaf thickness (0.53mm\u0026thinsp;\u0026plusmn;\u0026thinsp;0.028) in the southern Sudan phytogeographic zone, while the highest(0.68 mm\u0026thinsp;\u0026plusmn;\u0026thinsp;0.027) was recorded in the northern Sudan phytogeographic zone in the diameter class\u0026thinsp;\u0026ge;\u0026thinsp;45.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\u003ch2\u003e3.3. Variation of shea photosynthetic parameters\u003c/h2\u003e\u003cp\u003eValues of relative chlorophyll content differed significantly for month and phytogeographical zone (P\u0026thinsp;\u0026lt;\u0026thinsp;0.001), but not for diameter class (P\u0026thinsp;=\u0026thinsp;0.98). Furthermore, significant interactions were found between diameter class and month, and between month, diameter class and phytogeographical zone (P\u0026thinsp;\u0026lt;\u0026thinsp;0.001). At the beginning of bud break in April, the relative chlorophyll content was similar in the two phytogeographical zones. Over time, however, a difference emerged. Specifically, the mean chlorophyll content of shea leaves from the SSZ was higher (42.9\u0026thinsp;\u0026plusmn;\u0026thinsp;0.4 SPAD) than that of leaves from the NSZ (40.8\u0026thinsp;\u0026plusmn;\u0026thinsp;0.37 SPAD). The maximum chlorophyll content was recorded in July and August with values 47.28\u0026thinsp;\u0026plusmn;\u0026thinsp;0.47 and 48.46\u0026thinsp;\u0026plusmn;\u0026thinsp;0.47 SPAD, respectively. Relatively low chlorophyll content was recorded in April (33.53\u0026thinsp;\u0026plusmn;\u0026thinsp;0.77 SPAD) and December (34.54\u0026thinsp;\u0026plusmn;\u0026thinsp;0.94 SPAD). In both phytogeographic zones, maximum chlorophyll content was recorded in August: 47.17\u0026thinsp;\u0026plusmn;\u0026thinsp;0.68 SPAD in the Northern Sudan phytogeographic zone, and 49.78\u0026thinsp;\u0026plusmn;\u0026thinsp;0.56 SPAD in the Southern Sudan (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003ea). The lowest relative chlorophyll content (39.07\u0026thinsp;\u0026plusmn;\u0026thinsp;0.70 SPAD) was recorded in the North Sudanian phytogeographic zone, while the highest value (44.89\u0026thinsp;\u0026plusmn;\u0026thinsp;0.75 SPAD) in the South Sudanian phytogeographic zone both for the \u0026ge;\u0026thinsp;45 diameter class (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eb).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eThere was a significant difference in photosystem II yield components as a function of month (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05). Similarly, there was a significant difference between phytogeographic zones for φNO (P\u0026thinsp;=\u0026thinsp;0.04) and φNPQ (P\u0026thinsp;=\u0026thinsp;0.001), but not for φII (P\u0026thinsp;=\u0026thinsp;0.20) (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e). Tree diameter class did not show significant effect for any of these components (P\u0026thinsp;\u0026gt;\u0026thinsp;0.05). Significant interaction between diameter class and photogeographic zone was observed for only φII (P\u0026thinsp;=\u0026thinsp;0.002) as well as between month, diameter class and photogeographic zone for all components (P\u0026thinsp;\u0026lt;\u0026thinsp;0.001).\u003c/p\u003e\u003cp\u003eThe φ II varied from 0.57\u0026thinsp;\u0026plusmn;\u0026thinsp;0.02 to 0.65\u0026thinsp;\u0026plusmn;\u0026thinsp;0.01 and from 0.54\u0026thinsp;\u0026plusmn;\u0026thinsp;0.01 to 0.65\u0026thinsp;\u0026plusmn;\u0026thinsp;0.001, respectively, between April and December in the SSZ and NSZ (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003ea). Conversely, the non-photochemical quenching efficiency (φNPQ) increased in the opposite direction to the photosystem II effective quantum yield (φII) (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eb). The lowest values of (φNO) were recorded in April in both phytogeographic zones (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003ec). The φNO and φNPQ were 0.25\u0026thinsp;\u0026plusmn;\u0026thinsp;0.002 and 0.13\u0026thinsp;\u0026plusmn;\u0026thinsp;0.005 respectively in the southern Sudanian and northern Sudanian phytogeographic zones, compared with 0.24\u0026thinsp;\u0026plusmn;\u0026thinsp;0.006 and 0.13\u0026thinsp;\u0026plusmn;\u0026thinsp;0.005 respectively in the southern Sudanian and northern Sudanian phytogeographic zones.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eSignificant monthly differences were revealed in non-photochemical quenching (NPQt) and linear electron flux (LEF) (P\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Similarly, the test revealed significant differences in NPQt between phytogeographic zone (P\u0026thinsp;\u0026lt;\u0026thinsp;0.001), while no significant differences were observed for LEF (P\u0026thinsp;=\u0026thinsp;0.42) as well as in linear electron flow (P\u0026thinsp;=\u0026thinsp;0.91) or NPQt (P\u0026thinsp;=\u0026thinsp;0.31) according to Shea tree diameter class.. No significant interactions were found between diameter class and phytogeographic zone for LEF (P\u0026thinsp;=\u0026thinsp;0.11) and for NPQt (P\u0026thinsp;=\u0026thinsp;0.68). However, significant interactions were revealed between diameter class and month, and between month, diameter class and phytogeographic zone (P\u0026thinsp;\u0026lt;\u0026thinsp;0.001) for NPQt and LEF.\u003c/p\u003e\u003cp\u003eNon-photochemical quenching was higher in the North Sudanese phytogeographic zone (0.78\u0026thinsp;\u0026plusmn;\u0026thinsp;0.08) than in the South Sudanese zone (0.73\u0026thinsp;\u0026plusmn;\u0026thinsp;0.06). The highest NPQt value was recorded in April (1.42\u0026thinsp;\u0026plusmn;\u0026thinsp;0.20), compared to 0.35\u0026thinsp;\u0026plusmn;\u0026thinsp;0.03 in July. Low NPQt values were recorded in June (0.37\u0026thinsp;\u0026plusmn;\u0026thinsp;0.04) and July (0.031\u0026thinsp;\u0026plusmn;\u0026thinsp;0.03), while high values were recorded in December (1.68\u0026thinsp;\u0026plusmn;\u0026thinsp;0.48) and April (1.70\u0026thinsp;\u0026plusmn;\u0026thinsp;0.35) in the South and North Sudanian phytogeographic zones respectively (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003ea). Linear electron flow followed the same trend, with the highest value observed in April (70.11\u0026thinsp;\u0026plusmn;\u0026thinsp;6.42 \u0026micro;mol electrons m⁻\u0026sup2; s⁻\u0026sup1;) compared to July (28.26\u0026thinsp;\u0026plusmn;\u0026thinsp;3.73 \u0026micro;mol electrons m⁻\u0026sup2; s⁻\u0026sup1;). Similarly, low LEF values were recorded in August (23.23\u0026thinsp;\u0026plusmn;\u0026thinsp;4.10 \u0026micro;mol electrons m⁻\u0026sup2; s⁻\u0026sup1;) and July (27.23\u0026thinsp;\u0026plusmn;\u0026thinsp;4.65 \u0026micro;mol electrons m⁻\u0026sup2; s⁻\u0026sup1;), while high values were recorded in April (66.83\u0026thinsp;\u0026plusmn;\u0026thinsp;7.71 \u0026micro;mol electrons m⁻\u0026sup2; s⁻\u0026sup1;) and (73.46\u0026thinsp;\u0026plusmn;\u0026thinsp;10.27 \u0026micro;mol electrons m⁻\u0026sup2; s⁻\u0026sup1;) for the southern and northern Sudanian phytogeographic zones, respectively (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eb)\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e\u003ch2\u003e3.4. Relationship between climatic variables and shea photosynthetic parameters\u003c/h2\u003e\u003cp\u003eThe regression between leaf temperature and PAR was significant (P\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and positive across phytogeographic zones. Regression coefficients remained low, with values of 0.08 and 0.06 in the northern and southern Sudanian phytogeographic zones, respectively .\u003c/p\u003e\u003cp\u003eA significant (P\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and positive regression was observed in the relationship between ambient temperature and leaf temperature, depending on the phytogeographic zone. The coefficient of determination for leaf temperature and ambient temperature indicates that these two parameters are closely related. Indeed, 84% of leaf temperature is related to variations in ambient temperature in the northern Sudanese phytogeographic zone and 78% in the southern Sudanese phytogeographic zone (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003ea). An increase in ambient temperature leads to an increase in leaf temperature (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003ea).\u003c/p\u003e\u003cp\u003eThe linear regression between leaf temperature and vapour pressure deficit was significant (P\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and positive depending on the phytogeographic zone. The regression coefficients were 0.75 and 0.59 respectively in the North Sudanese and South Sudanese phytogeographic zones.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eThe coefficient of determination of the linear regression was 0.89 between LEF and PAR in NSZ and 0,86 in SSZ (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003e). Although the coefficient of determination of LEF was identical in the two phytogeographical zones, the half saturation point was different. The half-saturation point was 137.54 \u0026micro;mol photons m\u003csup\u003e\u0026minus;\u0026thinsp;2\u003c/sup\u003es\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e and 175.60 \u0026micro;mol photons m\u003csup\u003e\u0026minus;\u0026thinsp;2\u003c/sup\u003es\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e for PAR in the southern Sudanian and northern Sudanian phytogeographical zones, respectively. A significant (P\u0026thinsp;\u0026lt;\u0026thinsp;0.01) and positive regression was observed between LEF and VPD on the one hand, and between LEF and ambient temperature according to phytogeographic zone on the other hand. However, the regression coefficients remained low and varied between 0.02 and 0.05 depending on the phytogeographic zone.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eThe regression was positive and significant (P\u0026thinsp;\u0026lt;\u0026thinsp;0.001) between NPQt and PAR according to phytogeographic zone. The regression coefficient was 0.25 for NPQt in the Southern Sudanian phytogeographical zone and 0.45 in the Northern Sudan phytogeographical zone (Fig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e9\u003c/span\u003e). A significant (P\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and positive regression was observed between NPQt and VPD, as well as between NPQt and ambient temperature, depending on the phytogeographic zone. However, regression coefficients remained low, varying between 0.04 and 0.06 depending on the phytogeographic zone\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eThe regression coefficients of the photosystem II yield components show that the photochemical efficiency of photosystem II (φII ) and the quantum yield of unregulated non-photochemical energy loss in photosystem II (φNO) decrease with increasing photosynthetically active radiation (PAR) (Fig.\u0026nbsp;\u003cspan refid=\"Fig10\" class=\"InternalRef\"\u003e10\u003c/span\u003e). The linear regression was negative and significant (P\u0026thinsp;\u0026lt;\u0026thinsp;0.001) for PAR, φII and φNO, depending on the phytogeographic zone (Fig.\u0026nbsp;\u003cspan refid=\"Fig10\" class=\"InternalRef\"\u003e10\u003c/span\u003ea, \u003cspan refid=\"Fig10\" class=\"InternalRef\"\u003e10\u003c/span\u003ec). The respective regression coefficients are 0.49 and 0.57 for φII and 0.31 and 0.07 for φNO in the northern and southern phytogeographic zones of Sudan. The quantum yield of regulated thermal dissipation in photosystem II (φNPQ) showed a positive, significant regression (P\u0026thinsp;\u0026lt;\u0026thinsp;0.001) with PAR: φNPQ increased with increasing PAR (Fig.\u0026nbsp;\u003cspan refid=\"Fig10\" class=\"InternalRef\"\u003e10\u003c/span\u003eb), with R\u0026sup2; values of 0.78 and 0.55 for the northern and southern Sudan phytogeographic zones, respectively. Almost 50% of the variation in φII and φNPQ can be explained by PAR (Fig.\u0026nbsp;\u003cspan refid=\"Fig10\" class=\"InternalRef\"\u003e10\u003c/span\u003eb).\u003c/p\u003e\u003cp\u003eSignificant negative relationships (P\u0026thinsp;\u0026lt;\u0026thinsp;0.001) were observed between φII and VPD, and between φNO and VPD, depending on the phytogeographic zone. However, regression coefficients remained low, ranging from 0.04 to 0.13 depending on the phytogeographic zone. As for φNPQ, the regression was positive and significant (P\u0026thinsp;\u0026lt;\u0026thinsp;0.001) with respect to vapour pressure deficit (VPD), depending on the phytogeographic zone. Similarly, regression coefficients were low, at 0.06 for the NSZ and 0.05 for the SSZ.\u003c/p\u003e\u003cp\u003eSignificant negative relationships (P\u0026thinsp;\u0026lt;\u0026thinsp;0.001) were observed between φII and ambient temperature, and between φNO and ambient temperature, depending on the phytogeographic zone. However, regression coefficients remained low, ranging from 0.01 to 0.18 depending on the phytogeographic zone. The regression of ambient temperature and φNO had R\u0026sup2; values of 0.09 and 0.18 in the NSZ and SSZ, respectively. As for φNPQ, the regression was positive and significant (P\u0026thinsp;\u0026lt;\u0026thinsp;0.001) with respect to vapour pressure deficit (VPD), depending on the phytogeographic zone. Similarly, the R\u0026sup2; regression coefficients were low, at 0.08 for the NSZ and 0.07 for the SSZ.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eThe regression was positive and significant (P\u0026thinsp;\u0026lt;\u0026thinsp;0.001) between stomatal conductance and VPD according to phytogeographic zone. The coefficient of determination R\u0026sup2; of the linear regression of stomatal conductance and VPD shows that 26% of stomatal conductance is explained by VPD in the northern Sudanian phytogeographical zone, whereas it is explained by 44% in the southern Sudanian phytogeographical zone. A higher sensitivity of stomatal conductance to VPD was observed in the southern Sudanian phytogeographical zone (slope of the equation\u0026thinsp;=\u0026thinsp;190 mol m\u003csup\u003e\u0026minus;\u0026thinsp;2\u003c/sup\u003es\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e) compared to the northern Sudanian phytogeographical zone (slope of the equation\u0026thinsp;=\u0026thinsp;140 mol m\u003csup\u003e\u0026minus;\u0026thinsp;2\u003c/sup\u003es\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e) (Fig.\u0026nbsp;\u003cspan refid=\"Fig11\" class=\"InternalRef\"\u003e11\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eThe linear regression between PAR and stomatal conductance was positive and significant (P\u0026thinsp;=\u0026thinsp;0.015) in the North Sudanese phytogeographic zone, but not significant in the South Sudanese phytogeographic zone. (R\u0026sup2; = 0.03).\u003c/p\u003e\u003cp\u003eThe regression was positive and significant (P\u0026thinsp;\u0026lt;\u0026thinsp;0.01) between ambient temperature and stomatal conductance according to phytogeographic zone with low regression coefficient (R\u0026sup2;=0.03) in both zones.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e"},{"header":"4. Discussion","content":"\u003cp\u003eLeaves chlorophyll increased with time, reaching maximum values in August at the height of the rainy season and decreased and reached minimum values before leaf abscission (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003ea). This variation is probably due to morphological and biochemical adjustments that leaves undergo during their life cycle. A peak in chlorophyll content was observed in both phytogeographic zones during the month of August, suggesting that physiological adjustments and environmental interactions that increase chlorophyll levels were significant during this period. Indeed, in the more humid South Sudanic zone, water availability in the rhizosphere could facilitate the mobilisation of minerals necessary for chlorophyll synthesis. Ishfaq et al. (\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) showed that magnesium (Mg) is an essential element for chlorophyll synthesis and that approximately 15\u0026ndash;35% of absorbed Mg is fixed in chlorophyll pigments. These variations highlight the substantial impact of environmental gradients (light, temperature and soil moisture) in conjunction with the ontogenetic development of leaves on leaf characteristics. In tropical forests, the age and stage of development of leaves play a central role in the seasonal dynamics of photosynthetic capacity and functional characteristics (Kearsley et al., \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Wu et al., \u003cspan citationid=\"CR77\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). In addition, variations in water availability, temperature and changes in VPD may interact with leaf maturation to regulate nitrogen distribution within the photosynthetic apparatus. This reallocation of nitrogen promotes chloroplast development and helps maintain photosynthetic efficiency under changing conditions (Li et al., \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Liu et al., \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Wu et al., \u003cspan citationid=\"CR77\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). Prior to abscission of the shea leaves, a decrease in relative chlorophyll content was observed. This decrease could be explained by a remobilisation of nitrogen and minerals towards the sink organs, the apical buds in shea, to provide reserves to survive difficult periods. The remobilisation of nitrogen leads to a loss of green colour and the leaves take on a yellow colour, as well as a lignification of the leaves, which could be explained by the increase in leaf thickness. Croft et al. (\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2017\u003c/span\u003e) found a better correlation of leaf chlorophyll content to photosynthetic parameters over a growing season for four broadleaf tree species, indicating that chlorophyll content could be a better proxy for leaf photosynthetic capacity compared to leaf nitrogen content. Monthly variation in photosystem II yield in the two phytogeographical zones was related to ontogenetic leaf development and natural variation in environmental conditions, especially temperature and light intensity. The quantum yield of photosystem II of Shea was highest in the southern Sudanian phytogeographical zone between April and August, before decreasing to be almost identical to the φII of photosystem II for shea in the northern Sudanian phytogeographical zone. In contrast, the yields of φNO and φNPQ increased. High values of φNPQ were recorded at the beginning of leaf flowering (April) and in the post abscission period (December). These results could explain the contribution of climatic conditions and leaf ontogeny to the photosynthetic performance of \u003cem\u003eV. paradoxa\u003c/em\u003e plants. The period from April to August in the shea tree is characterised by leaf proliferation and maturation, which requires the synthesis of organic matter to support growth and floral phenology in adult shea trees, as well as the accumulation of reserves to support difficult periods. The performance of photosystem II implies that it is during this period (April to August) that the shea tree optimises the synthesis of photoassimilates. Indeed, φII is a good indicator of the percentage of photons absorbed by chlorophyll for photosynthesis (Yu et al., \u003cspan citationid=\"CR79\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). In this study, φII was higher in shea from the southern Sudanian phytogeographical zone, and the relative chlorophyll content of the leaves was higher under milder climatic conditions. According to Sun et al. (\u003cspan citationid=\"CR71\" class=\"CitationRef\"\u003e2020\u003c/span\u003e)and Yu et al. (\u003cspan citationid=\"CR78\" class=\"CitationRef\"\u003e2022\u003c/span\u003e), φII is sensitive to abiotic stresses, especially light intensity, temperature and water availability. The decrease in photosystem II quantum yield could be explained by leaf senescence. In both phytogeographical zones, signs of leaf senescence appeared from September onwards, characterised by a decrease in chlorophyll content and an increase in leaf thickness. The decrease in relative chlorophyll content and the increase in φNO and φNPQ indicate that the leaves are gradually losing their photosynthetic performance, characterised by a decrease in their capacity to convert light energy.\u003c/p\u003e\u003cp\u003eIncrease ambient temperature affected leaf surface temperature of the shea trees. -Linear regression showed that leaf temperature was closely related to ambient temperature. It explained 78\u0026ndash;84% of leaf temperature. Difference in temperature could affect the physiology of the plants and the configuration of the cell membranes, given their phospholipid nature. According to Chen et al. (\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2012\u003c/span\u003e), when the ambient temperature is higher than the optimal plant temperature, the chloroplast structure is damaged, photosynthetic performance is reduced, and the relevant photosynthetic parameters are modified. Indeed, the month April and May were those characterised by high temperatures (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e, \u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ea). These months were also characterised by low humidity in both the northern and southern phytogeographical zones. Linear regression showed that stomatal conductance was sensitive to VPD, demonstrating the link between climatic conditions and the regulation of stomatal aperture. The sensitivity of stomatal conductance was more pronounced in the northern Sudanese phytogeographical zone. With the decrease in VPD between June and October, stomatal opening would be at an optimum, which could increase carbon assimilation by \u003cem\u003eV. paradoxa\u003c/em\u003e tree in this phytogeographical zone. According to Cheng et al. (\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) and Green et al. (\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2020\u003c/span\u003e), VPD promotes photosynthesis in forest regions with high rainfall. The availability of water both on the ground and in the atmosphere due to rainfall during this period could maintain stomatal aperture at an optimal level over time, thereby enhancing carbon assimilation. VPD is an important factor in atmospheric water demand for plants and a critical variable in determining plant transpiration and photosynthesis (Novick et al., \u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Yuan et al., \u003cspan citationid=\"CR80\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Increase in VPD during dry periods is not without consequences for plant physiology. Indeed, the increase in VPD leads to a significant demand for leaf water, given the water limitation at the soil level, and the plant reduces stomatal opening, which has consequences for plant physiology. Reduced stomatal aperture limits the availability of carbon dioxide. VPD affects plant metabolism by limiting the carbon available to plants and can lead to plant mortality due to carbon starvation (Grossiord et al., \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Yuan et al., \u003cspan citationid=\"CR80\" class=\"CitationRef\"\u003e2019\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eThe linear regression between PAR and LEF shows that electron production by the thylakoid membrane is photosensitive. Although the regression coefficient R\u0026sup2; is identical for the two phytogeographical zones (R\u0026sup2;=0.8), light saturation of photosystem II occurs more rapidly for shea in the southern Sudanian phytogeographical zone. This is because the increase in PAR leads to a significant production of electrons through photolysis of water. These electrons are involved in the production of photochemical energy (ATP/NADPH), and when the energy produced exceeds the reduction capacity of the Calvin-Benson-Bassham (CBB) cycle, plant protection mechanisms such as non-photochemical quenching (NPQt) are triggered to diffuse the excess energy to prevent photodamage. Our results show that the mechanisms for diffusing excess energy occur more rapidly in shea from the southern Sudanese phytogeographical zone. For Kromdijk et al. (\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2016\u003c/span\u003e), the protective dissipation of energy is at the origin of the reduction in crop yield. The sensitivity of NPQt (R\u0026sup2;=0.40) to instantaneous PAR in the northern Sudanian phytogeographical zone could reduce shea yields. this study shows high temperatures associated with low instantaneous PAR. An increase in temperature, even at low instantaneous PAR, could lead to reconfiguration or even destruction of the thylakoid membrane and thus reduce photosynthetic performance in shea. According to Hemantaranjan et al., \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2014\u003c/span\u003e, high temperature leads to various physiological changes in plants such as scorched leaves and stems, leaf abscission and senescence, inhibition of stem and root growth or reduction in number, pollen tube growth and pollen sterility, and fruit damage leading to catastrophic loss of crop yield resulting in significant loss of productivity.\u003c/p\u003e"},{"header":"5. Conclusion","content":"\u003cp\u003eThe findings of this study showed that the difference in temperature affected the physiological behaviour of \u003cem\u003eV. paradoxa\u003c/em\u003e. Photosynthetic performance was better in the southern Sudanese phytogeographical zone. In the northern Sudanese phytogeographical zone, stomatal conductance was less sensitive to VPD, but NPQt was sensitive to PAR, which could lead to yield losses. The results show a positive correlation between leaf and ambient temperatures, suggesting that an increase in temperature will lead to an increase in leaf temperature. In addition, the study revealed the thermal and photosynthetic adjustment capacity of shea trees and reinforces the functional resilience of shea trees in parklands ecosystems. The results obtained provide a better understanding of the resilience of shea trees in order to optimise ecosystem services (carbon storage, water and microclimate management) and adapt agroforestry management to climate change. It would be necessary to determine optimal temperature for biochemical control factors of photosynthesis in shea leaves in field. This work could provide a basis for understanding variations in environmental factors on photosynthetic performance of woody plants in agroforestry parks in the Sudano-Sahelian zone and yield loss of shea tree in the context of climate change.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eFunding statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis research work was supported by the Swedish Research Council 2018-03722 and Formas 2018-00570\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgments\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe would also like to express our gratitude to the farmers of Cassou and Sapon\u0026eacute; for letting us use their fields and for the fruitful collaboration.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor contributions:\u003c/strong\u003e Flavien G. B. Sawadogo : Writing-review and editing-original draft, visualization, Methodology, Investigation, Data curation, Formal analysis. Hugues R. Bazie: Writing-review and editing, Methodology, Supervision. Paulin Bazi\u0026eacute;: Writing-review and editing, Methodology. Martin Karlson: Writing-review and editing. Madelene Oswald: Writing-review and editing. Jules Bayala: Writing-review, editing and Supervision.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData Availability Statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe data that support the findings of this study are available on request from the corresponding author. The data are not publicly available due to privacy or ethical restrictions.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflicts of Interest\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no Conflicts of Interest.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eAleza, K., Wala, K., Bayala, J., Villamor, G. B., Dourma, M., Atakpama, W., \u0026amp; Akpagana, K. (2015). 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However, its survival and productivity are increasingly threatened by climate change, characterized by raising temperatures and reduced water availability. The species\u0026rsquo; future resilience will depend on its physiological adaptability to shifting climatic conditions. To assess this adaptability, we studied photosynthetic performance of 24 shea trees \u003cem\u003ein situ\u003c/em\u003e from April to December 2023 the most active period of leaf phenology across two contrasting climatic zones in Burkina Faso. We evaluated how photosynthetic efficiency responded to climatic variability over this nine-month period. Unregulated energy dissipation (φNO) and regulated energy dissipation in the form of heat (φNPQ) were significantly different (P\u0026thinsp;\u0026lt;\u0026thinsp;0.001) between phytogeographic zones during monitoring period. The chlorophyll content was significantly higher (P\u0026thinsp;\u0026lt;\u0026thinsp;0.001) in leaves from humid southern Sudanian phytogeographic zone (42.9\u0026thinsp;\u0026plusmn;\u0026thinsp;0.4 SPAD) than in drier northern Sudanian zone (40.8\u0026thinsp;\u0026plusmn;\u0026thinsp;0.37 SPAD). Linear regression showed a significant increase in protective energy dissipation (NPQt) in response to instantaneous photosynthetically active radiation in shea leaves from dryer northern site (R\u0026sup2;=0.4). Additionally, leaf temperature was strongly correlated with ambient temperature, explaining 78\u0026ndash;84% of variations (P\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Overall, \u003cem\u003eV paradoxa\u003c/em\u003e from the more humid southern site zone exhibited better photosynthetic performance. These findings highlight spatial differences in photosynthetic responses and provide valuable insights about which photosynthetic parameters are affected by climate change. This can pave the way for management options to cope with climate change effects.\u003c/p\u003e","manuscriptTitle":"Temporal and spatial variability in photosynthetic activity of Vitellaria paradoxa in agroforestry parklands of Burkina Faso","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-11-18 16:41:24","doi":"10.21203/rs.3.rs-7939299/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"editorInvitedReview","content":"","date":"2025-11-13T13:36:02+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"171204912731565178766250406421446537180","date":"2025-11-07T11:17:05+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"272466156638487244214608831887751167476","date":"2025-11-07T08:11:03+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-11-07T07:27:08+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-10-24T19:36:39+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-10-24T14:30:15+00:00","index":"","fulltext":""},{"type":"submitted","content":"Agroforestry Systems","date":"2025-10-24T09:38:53+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"agroforestry-systems","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"agfo","sideBox":"Learn more about [Agroforestry Systems](http://link.springer.com/journal/10457)","snPcode":"10457","submissionUrl":"https://submission.nature.com/new-submission/10457/3","title":"Agroforestry Systems","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"dec029e6-1abb-4b6a-b3d6-8db7579c44b0","owner":[],"postedDate":"November 18th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2026-03-02T16:10:36+00:00","versionOfRecord":{"articleIdentity":"rs-7939299","link":"https://doi.org/10.1007/s10457-026-01466-y","journal":{"identity":"agroforestry-systems","isVorOnly":false,"title":"Agroforestry Systems"},"publishedOn":"2026-02-24 15:58:10","publishedOnDateReadable":"February 24th, 2026"},"versionCreatedAt":"2025-11-18 16:41:24","video":"","vorDoi":"10.1007/s10457-026-01466-y","vorDoiUrl":"https://doi.org/10.1007/s10457-026-01466-y","workflowStages":[]},"version":"v1","identity":"rs-7939299","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7939299","identity":"rs-7939299","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}