The Influence of tree shade on soil attributes, water storage and microclimate in Robusta coffee grown in a rainfall gradient of the mid-altitude zone of Central Uganda

The Influence of tree shade on soil attributes, water storage and microclimate in Robusta coffee grown in a rainfall gradient of the mid-altitude zone of Central Uganda | 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 The Influence of tree shade on soil attributes, water storage and microclimate in Robusta coffee grown in a rainfall gradient of the mid-altitude zone of Central Uganda Winfred Nabiteeko Nakyagaba, Herbert Talwana, Samuel Kyamanywa, and 11 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8558650/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Robusta coffee ( Coffea canephora ) is a key economic crop in the mid-altitude zone of central Uganda whose cultivation is expanding to marginal areas with variable rainfall, high temperatures, humidity variation, reduced soil moisture, and low soil physical and chemical attributes, which are increasingly threatening its sustainable production. This study investigated the influence of tree shade on soil attributes, microclimate regulation, and soil water storage in Robusta coffee plots across rainfall gradients within the mid-altitude zone of central Uganda. A nested field experiment was conducted in 20 by 20-meter de-alienated coffee plots across three categorised thresholds (low ( 1200 mm year − 1 ) in central Uganda. The treatments included six shaded and unshaded coffee plots per rainfall threshold. Results indicated that tree shade significantly improved soil physical and chemical attributes, particularly organic matter and cation exchange capacity, resulting in enhanced soil health. Tree shade moderated microclimate conditions by reducing the maximum air temperature by 0.78°C and increasing the maximum and minimum relative humidity by 1.02% and 0.56%, respectively. Shade increased the average volumetric soil moisture content at 10cm by 0.28mm and 1.47mm at 50cm profile levels. The benefits of shade were pronounced in high rainfall and shaded plots. The study, therefore, concludes that integrating shade in Robusta coffee improves soil attributes, buffers microclimate extremes, and enhances soil moisture storage in marginal areas. The Results indicate the potential of tree shade as a climate adaptation strategy in sustainable Robusta coffee production amidst rainfall variability. Robusta coffee microclimate soil moisture and nutrients rainfall thresholds Figures Figure 1 Figure 2 Figure 3 1. Introduction Robusta coffee ( Coffea canephora ) is native to Uganda, where it is a traditional income-generating crop, grown in over 85% of the districts by over 1.8 million households (UCDA, 2024 ). Uganda is a major Robusta coffee producer in the world, ranked in seventh position, and second in Africa (ICO, 2023 ). Robusta coffee is mainly grown in the lowlands of central Uganda, where the majority are smallholder farmers with an average of 0.18 hectares (0.45acres) (UCDA, 2024 ). Robusta coffee plays a critical role in Uganda’s culture and livelihoods. The mid-altitude zone of Uganda accounts for over 80% of Robusta coffee production and export (Kyalo et al., 2024 ). Specifically, greater Luweero, one of the regions in central Uganda, with 10% of Robusta coffee production. However, the coffee crop is sensitive to increased air temperature, variation in relative humidity, soil moisture dynamics, and soil physical-chemical nutrient attributes, which influence its growth and yield. Climate variability is mainly described by rainfall, temperature, and relative humidity, which play physiological roles in coffee growth (Alvarez-Clare & Mack, 2011 ; Sachs et al., 2019 ). Although Robusta coffee was indicated to be more climate resilient due to its tolerance to warmer temperatures and robust nature, recent studies indicate that it is equally affected by climate variability and predicted to lose suitability due to its sensitivity to high temperatures (Bunn et al., 2015; Kath et al., 2020; Sachs et al., 2019 ). High temperatures are indicated to adversely impact coffee production and productivity (Melorose et al., 2015 ). Variation in climate variables reduces suitable production areas, coffee growth and quality (Sachs et al., 2019 ; Ghini et al., 2011). Further, research indicates that moderate to severe water stress can lead to small and damaged coffee beans (DaMatta et al. 2018). Robusta coffee is therefore sensitive to variation in rainfall, temperature, relative humidity, and drought incidences. For example, Kath et al. ( 2021 ) associated increased rainfall and temperature with higher total bean defects. The increasing temperature variability and erratic rainfall are critical to sustainable Robusta production in the mid-altitude zones of central Uganda. Robusta coffee requires adequate soil moisture of 1200 to 2000 mm year − 1 for proper growth (UCDA, 2019 a). Nevertheless, Bunn et al. ( 2019 ) noted that rainfall has become variable, affecting soil moisture levels. In addition, poor soil fertility and extreme rainfall variability are threats to productivity in most coffee-growing regions. Though the variation in soil parameters under rainfall thresholds was indicated by Nakyagaba et al. ( 2024), the decline in soil fertility (Nyombi, 2013 ; van Asten et al., 2012) and extremes of soil pH are associated with climate variability, low coffee productivity, poor yield, and increased yield gaps (Nakyagaba et al., 2024 ). The pH extremes and unbalanced soil nutrients may affect cation levels, their availability, and plant uptake (Bhattarai et al., 2017 ; Núñez et al., 2011 ). In addition, erratic rains and increased temperature may create an environment that indirectly impacts soil fertility, soil moisture, relative humidity, and temperature variables. On the other hand, Robusta coffee requires an optimal temperature range between 24°C and 30°C for proper growth (UCDA, 2019 ; Barnes et al., 2015 ). The rising temperatures are harmful to coffee productivity (Craparo, 2017 ), cause loss of yield, and may alter the suitability of production areas to higher elevations (Jaleta, 2021 ). Besides, about 7.3 million hectares (equivalent to approximately 12%) of the global Robusta coffee growing area may be lost as a result of temperature increase (Sachs et al., 2019 ). Drought can also cause up to 80% yield loss (DaMatta & Ramalho, 2006). Moreover, the impact of climate variability is worse in marginal areas associated with high temperatures, drought, water shortage and low nutrients; noted as key constraints. Nevertheless, certain management practices may reduce the effects associated with climate variability, increase coffee resilience and sustain production (Le et al., 2023 ; Nakyagaba et al., 2025 ). However, a limited number of coffee farmers can adapt to climate variability because the majority rely on natural conditions for production (Nakyagaba et al., 2025 ). Adapting climate-smart practices like tree shade is essential in compensating for reduced soil fertility and soil moisture dynamics through plant water relations and nutrient uptake, especially in marginal areas (Caldwell et al., 1998 ; Caldwell & Richards, 1989 ). Research indicates that coffee, an understorey of sub-Saharan African rainforests, grows better under tree shade, which has major ecological benefits (Piato et al., 2020 ; Tscharntke et al., 2011 ). For instance, shade moderates extreme temperatures, thereby reducing heat stress on coffee plants. Coffee also utilises the water released from the roots of tree shades through hydraulic redistribution (Caldwell et al., 1998 ). In addition, the tree shade associated with low temperature and high relative humidity reduces water loss from the soil surface through transpiration (Barnes et al., 2015 ). The high relative humidity close to saturation levels favours vegetative growth (DaMatta & Ramalho, 2006). Thus, the favourable microclimate factors may improve coffee productivity and reduce yield gaps (Tscharntke et al., 2011 ). Fortunately, 85% of Robusta farmers in the mid-altitude zone of central Uganda grow coffee under a shade system (Bukomeko et al., 2015 ; Kalanzi & Nansereko, 2014 ; van Asten et al., 2012). The major sources of shade on these smallholder coffee farms are traditionally trees and bananas (Bukomeko et al., 2017; Kalanzi & Nansereko, 2014 ; Melorose et al., 2015 ; UCDA, 2019 ; van Asten et al., 2012). Several species of tree shades are grown in these coffee gardens (Bukomeko, 2017 ; Bukomeko et al., 2019 ). For example, Bukomeko et al. ( 2015 ) associated a high yield benefit in low precipitation zones with trees. Similarly, cases of hydraulic lift in arid lands was documented (Caldwell et al., 1998 ). Unfortunately, McMahon ( 2012 ) indicated that certain trees reduce soil pH, compete with crops for soil nutrients, water, and other resources. Further, Beer ( 1988 ) indicated that trees may sequester potassium in the stem of the tree, affecting its availability for coffee uptake. Other shade trees can transpire more water and reduce the available water for coffee uptake. Besides, coffee is evergreen with continuous transpiration and nutrient requirements. Shade is therefore a critical component offering ecological and agronomic benefits associated with improved soil quality, microclimate modification, and enhanced water retention in the mid-altitude zone of Central Uganda, where climate and rainfall variability are increasing. Despite the numerous studies on tree shades, there is limited data on the influence of tree shade on soil physical-chemical parameters and microclimate in a rainfall gradient, particularly in marginal areas of central Uganda. This gap is significant, given that Robusta coffee production is expanding into these marginal areas. A sustainable strategy is necessary to adapt coffee to the risks of a changing climate associated with marginal areas. Furthermore, worldwide, there are fewer shade tree studies conducted in Robusta compared to Arabica coffee (Piato et al., 2020 ), particularly in Uganda. This led to a coffee tree shade research in marginal areas of central Uganda, where soil fertility and moisture are generally low with unfavourable microclimate. This research, therefore, determined the influence of growing Robusta coffee with or without tree shade along a rainfall gradient (thresholds) on: (i) microclimates (temperature and humidity) and (ii) soil chemical and physical parameters (moderating soil pH, soil water content, nutrient recycling and cation exchange capacity (CEC)). It was hypothesised that tree shade influences soil nutrient content, microclimate and soil water storage along a rainfall gradient. Therefore, the findings on the influence of tree shade on microclimate, soil fertility and moisture may improve coffee management decisions and may help in sustainable Robusta coffee management amidst climate variability. This may increase Robusta coffee productivity and narrow the yield gap, especially in marginal areas of central Uganda. 2. Materials and Methods 2.1 Study Site The study was conducted in the mid-altitude zone of central Uganda, where selected sub-counties were classified as marginal and others as traditional coffee growers. Central Uganda, specifically the Lave Victoria crescent is a centre of origin for Robusta coffee (Kyalo et al., 2024), with over 80% of Robusta coffee production area and volume. The smallholders in the study area traditionally grow coffee under tree shade (Bukomeko et al., 2017; UCDA, 2019a; van Asten et al., 2012). The three classified rainfall thresholds were established in two districts of central Uganda, Luweero and Nakasongola, with three sub-counties in Luweero and one sub-county in Nakasongola district. The highest rainfall threshold was represented by Luweero and Katikamu sub-counties (>1200 mm year -1 ), Zirobwe represented the moderate threshold (1100 – 1200 mm year -1 ), and Katuugo represented the low rainfall threshold (<1100 mm year -1 in Nakasongola district (Table 1). These study areas, therefore, differed in the amount of rainfall (Nakyagaba et al., 2024) and the vegetation cover (Bukomeko, 2017). Table 1: Study area characteristics for the rainfall thresholds Site characteristics High Moderate Low District Luweero Luweero Nakasongola Sub-county Luweero (3 farms) Zirobwe (6 farms) Katuugo (6 farms) Katikamu (3 farms) Altitude (masl) 1100 m (3600ft) 1100 m (3600ft) 1167m (3828 ft) Longitude 32°32'59"E 32°42'04"E 32°40'0"E Latitude 0°49'0"N 00°40'59"N 0°15'0"N Average annual temperature (°C) 24.7°C (76.48°F) 22.1°C (71.8°F) 25°C (77°F) Average annual rainfall (mm) >1200 mm 1100 - 1200 mm < 1100 mm Soil type Red Sandy Clay Loam (Ferralsols) Red Sandy Clay Loam (Ferralsols) Red or brown Sandy Loam (Ferralsols & Plinthosols) Estimated coffee age (years) 24 20 16 Greater Luweero is a high Robusta coffee growing region with about 10% of the total country production. However, certain areas in greater Luweero are semi-arid with high climate and rainfall variability. Nevertheless, Robusta coffee production is expanding to these semi-arid areas. This further exposes Robusta coffee to the impacts of climate variability and associated constraints of increased temperatures, humidity variation, reduced soil moisture and low soil physical - chemical attributes. These constraints negatively impact the yield of Robusta coffee and increase yield gaps (Nakyagaba et al., 2024). This directly affects the government's coffee plan and Vision 2030, which aims for 20 million bags (1.2 million metric tons) worth US$1.5 billion per year. 2.2 Experimental Units, Study Design, Plots and Measurements The selected coffee farms were taken to have similar management practices and were each considered dependent data points and replicates for the rainfall thresholds. The experimental units were the 150 coffee farms used in the biophysical study and farm survey by Nakyagaba et al. (2024, 2025). The treatments and factor levels were the three rainfall thresholds of low, moderate and high, the two canopy closures of shade and no shade, and the individual farmer fields (Fig. 1). The rainfall thresholds (Nakyagaba et al., 2024) were the main and fixed factors in which the two management practices (treatments) of canopy closure (shade) were nested. The selected farms were considered random sampling units (for random effects) with each farm hosting two experimental plots (shaded and unshaded) from which component observations were obtained according to Brown (2021). A two-stage nested experimental design was therefore adopted for the study. The design was hierarchically structured and nested with repeated/longitudinal measures within the individual plots (cross-over trial) (Schielzeth & Nakagawa, 2013). Coffee plot sizes of 20 m x 20 m (400 m 2 ) were de-lineated from farmers' coffee fields. Five coffee bushes per plot were considered for data collection. The study, therefore, involved 180 coffee bushes from 36 coffee plots on 18 coffee farms under the rainfall thresholds in the two districts of Luweero and Nakasongola (Table 1). Therefore, the study had both clustered and longitudinal data. The plot-level experiment was useful to study microclimate, soil moisture, and soil attributes along rainfall thresholds and shade levels. The microclimate and moisture data variables were corrected several times from each experimental plot for at least 20 and 15 months, respectively. The measurements were air temperature, relative humidity, soil moisture storage, and soil physical-chemical attributes from the Robusta coffee experimental plots under shaded and unshaded conditions along a rainfall gradient. 2.3 Percentage Canopy Cover The study plots per farm were either located in shade or without shade trees. Research indicates that percentage shade levels are prone to change throughout the year, depending on weather, rainfall, drought, tree species, management practices, especially the pruning cycles, and the extent of tree leaf drops (Mouen Bedimo et al., 2008). The ideal shade for coffee is indicated as 30 to 50% (Beer et al., 1998; Staver et al., 2001). The changes in percentage canopy closure during the data collection period were considered by taking readings every two to three months to get the average percentage and to explain the variation during the data collection period. Canopy cover quantified the shade levels and their influence on microclimate. The canopy closure from each experimental plot was determined using a spherical densiometer Model A, which measured the light penetrating through the tree's shade canopy. Using at least five random points on top of each of the marked study coffee bushes and a ladder, the canopy closure for the shaded plots was measured by recording light intercepted at the top of the coffee bushes. On the other hand, the canopy closure from unshaded plots was randomly taken from at least five points in each coffee plot. Using the densitometer results, the average area of shade cast in each plot was then calculated. The shaded plots had a significantly (p<0.001) higher estimated canopy cover (mean = 60.58%) than unshaded plots (mean = 17.54%). However, the shade level of 60% was slightly above the recommended levels of 30 to 50% for coffee (Beer et al., 1998; Staver et al., 2001). The obtained average percentage shade level per plot was the basis in determining the variation in microclimate, soil moisture storage and soil attributes along the rainfall thresholds. 2.4 Soil Sampling and Analysis Each of the 36 delineated shaded and unshaded coffee plots was used to evaluate the soil physical-chemical attributes. Compound samples were randomly collected once from each of the 36 plots at the time of setting up the experiment. Within each plot, a soil auger was used to take soil samples from the agricultural layer at 30 cm. Efforts were taken to ensure a distance of at least three feet away from the coffee stem/bush. The collected samples were handled according to Okalebo et al. (2002). Analysis for major soil fertility physical-chemical parameters was done on pH, SOM, total N, available K, P, exchangeable Ca and Mg (Kyalo et al., 2024; Nakyagaba et al., 2024). Additionally, micro nutrients: Zinc, copper, manganese, iron and sand, silt, and clay were analysed following the methodology outlined in Okalebo et al. (2002). Extraction of soil nutrients: calcium (Ca), potassium (K), phosphorus (P), magnesium (Mg), iron (Fe), Zinc (Zn), manganese (Mn) and copper (Cu) using the Mehlich-3 method and an atomic absorption spectrophotometer and a flame photometer used to take readings. Using a pH meter and a 1:2.5 soil water suspension, the soil pH was measured. For nitrogen, the micro-Kjeldahl was used, while the Walkley and Black method was used for organic matter determination. The exchangeable acidity was measured using the sum of exchangeable bases, CEC (S (K + +Ca 2+ +Mg 2+ )). To classify the textural classes of the plots, the hydrometer method was used to determine the granulometric composition (particle size) as percentage sand, silt, and clay content of the soil samples. 2.5 Volumetric Soil Moisture Data Measurements The soil moisture data were collected using a soil moisture sensor, the Sentek soil moisture probe (Diviner 2000 equipment). Each coffee plot was installed with two data collection points (access tubes) for volumetric water content (mm cm -3 ) according to Sarmiento-Soler et al. (2019). Each data collection point used a two-inch-sized access plastic pipe inserted in the soil profile up to 60 cm deep. However, the Diviner readings were taken up to 50 cm deep. Taking data from the two data collection points per plot for moisture determination, there were at least 72 data collection points from the 18 farms. The pipe was inserted into the soil profile, leaving at least 4 cm above the soil to avoid likely changes in moisture content around the pipe. In order to avoid direct water entry into the pipe, the pipes were covered below and above because water affects the functionality of the probe. Using the same data collection points and at each data collection time (2-week interval), the Diviner was inserted in each of these data collection points and it recorded volumetric water content at every 10-cm interval up to 50 cm deep. The default Sentek calibration equation used (Sentek Pty Ltd (2009)) converted the reading to volumetric moisture content (scaled frequency = 0.2746*(volumetric water content <0.3314) + 0). The soil water content obtained per profile (cm) was used and expressed in millimetres (mm) (Sarmiento-Soler et al., 2019). The cumulative sum of the volumetric water content in the profiles was also calculated. The soil moisture data were collected for 15 months, from which 30 observations were made from each of the 72 data collection points/pipes (2160 observations). The moisture data were used to determine the influence of shade on soil moisture along rainfall thresholds. 2.6 Air Temperature and Relative Humidity The daily microclimate data (relative humidity (%), mean, minimum and maximum temperatures (ºC)) were recorded using the Hygrochron TM iButtons (DS1923) F5 data loggers. These were installed within the delineated study plots with one iButton per plot, two iButtons per coffee farm, and 36 iButton loggers for the entire study plots. The iButtons provided precise actual and current point meteorological data (humidity and temperature) despite the plot's surrounding environment. Each iButton had a unique registration number, which allowed data traceability. The iButtons measured the valid and accurate temperature and relative humidity (RH) of thermal comfort (Shin et al., 2017). The iButton missions were set with a resolution of ±0.6% for relative humidity and ±0.5ºC for temperature. Small plastic cups were used to fix iButtons and covered with silver paper and sole tape to protect against failures and data loss, as well as reduce the environmental influence on readings. The iButtons were fixed on a coffee stem/branch at approximately 1.5 m above the ground (Moreira et al., 2018) within the coffee canopy, with the eye facing down. Brabyn et al. (2014) indicated that surface land temperature is a determinant of microclimate and a better measure of environmental temperature. The iButtons recorded data hourly and this data was collected for 20 months (approximately 14,400 hours) and downloaded at least every two to three months to avoid data loss with a faulty device. The values were later converted to daily and monthly averages for data analysis. 2.7 Statistical Analysis A nested design was adopted for data collection, analysis and interpretation of the model (Schielzeth & Nakagawa, 2013). The nesting defined the response variation that was attributed to the nested factors without estimating interaction variance. It also allowed covariance structures. The collected data were analysed based on shade levels and rainfall gradients (thresholds), using R version 4.1.1 (Li, 2021) and Stata 14. The data were checked for normality using the Shapiro-Wilk test (skewness and kurtosis) results. Descriptive statistics (minimum, maximum, mean, frequency, median, SE, and SD) were used for quantitative variables. Simple correlation, general linear model (ANOVA) (Bono et al., 2021) and simple regression analysis were used to determine the relationships, variation, and level of significance between thresholds and shade levels. The t-test was used to determine variations between shade levels. For non-normal parameters, a non-parametric alternative, Kruskal-Wallis’s rank sum test, was used. For pairs of means that were significant between thresholds and tree shade levels, Tukey (HSD) and Sidak post hoc significant tests (p<0.05) were performed. The observations between farms were independent with dependence-correlated errors related to nesting (Serenini et al., 2019), hence, a mixed effect model. The Generalised Linear Mixed Model (GLMM) for nested, longitudinal repeated measures (soil moisture and microclimate) between and within a subject or group was used to analyse the repeated data measurements with fixed and random effects (Bono et al., 2021; Brown, 2021; Laird & Ware, 2007). The GLMM method is suitable for unbalanced designs (Liu et al.; 2012). Therefore, the generalised linear mixed effects model (GLMM / glmer) (also called multilevel, hierarchical, or random effects modelling) was used (Hair & Fávero, 2019). The random effects feature allowed for the clustering of data in groups (Schielzeth & Nakagawa, 2013). Model analysis allowed for diversity of general and flexible correlation patterns. The model allowed the intercept to vary between shade levels and thresholds but not the predictor coefficients. The model accounted for differences in several inherent, management, climatic and agronomic practices, tree shade species, age, coffee lines, or varieties and coffee age, which were kept constant since they varied between shade levels and rainfall thresholds. 3. Results 3.1 Variation in Soil Fertility Parameters across Rainfall Gradient The influence of rainfall on soil parameters was analysed using ANOVA and the Kruskal-Wallis rank sum test. The soil texture was classified as sandy clay loam in high rainfall threshold and sandy clay in moderate and low rainfall thresholds. Despite the differences in soil texture, most of the chemical properties were almost alike but the average physical-chemical parameters varied significantly (p<0.001) across thresholds (Table 2). Table 2 : Variation in soil physical-chemical parameters across rainfall thresholds (n=36) in Greater Luweero Soil Parameter High Moderate Low CNL Skew se p-value Mean SD Mean SD Mean SD Soil pH (1:2.5 water) 6.32 a 0.21 5.9 b 0.33 5.94 c 0.63 5.5 0.24 0 0.001 Organic Matter (%) 4.89 a 0.53 3.71 b 0.34 3.77 c 0.58 3 0.8 0.01 0.001 Total Nitrogen (%) 0.24 a 0.17 0.2 b 0.02 0.19 c 0.02 0.2 0.47 0 0.001 Phosphorus (mg kg -1 ) 27.4 a 5.49 17.24 b 2.63 20.48 c 5.13 15 0.53 0.02 0.001 Calcium (cmol (+) kg -1 ) 7.74 a 1.91 4.51 b 1.66 4.73 c 2.59 0.3 0.53 0.02 0.001 Magnesium (cmol (+) kg -1 ) 1.31 a 0.18 0.91 b 0.23 0.89 c 0.23 2 -0.19 0 0.001 Potassium (cmol (+) kg -1 ) 0.69 a 0.33 0.57 b 0.16 0.57 cb 0.19 2 1.36 0 0.001 CEC (cmol kg -1 ) 12.7 a 2.59 9.26 b 1.73 10.07 c 2.39 5-15 0.84 0.02 0.001 Aluminium (mg kg -1 ) 0.003 a 0.01 0.12 b 0.15 1.3 c 0.19 1.0 1.65 0 0.001 Iron (mg kg -1 ) 0.36 a 0.08 0.42 b 0.03 0.41 c 0.05 4.5 -0.93 0.47 0.001 Manganese (mg kg -1 ) 1.73 a 0.29 1.06 b 0.39 0.92 c 0.47 20 -0.18 4.1 0.001 Copper (mg kg -1 ) 3.31 a 1.91 1.9 bc 0.33 1.83 cb 0.27 5 3.24 0.01 0.001 Zinc (mg kg -1 ) 1.45 a 0.93 0.46 b 0.35 0.42 c 0.17 2 2.52 0.01 0.001 Soil texture Sandy Clay Loam Sandy Clay Sandy Clay Means followed by the same letters in the rows indicate that there are no significant differences (p> 0.05), while different letters in the row indicate significant differences (p <0.05) according to LSD (0.05). The soil parameters were within the critical levels for coffee productivity, except for cations: potassium, magnesium and calcium, which were low (Table 2). For instance there was adequate pH and nutrients within the critical range for high threshold, an indicator of good soil quality for coffee growth. However, K, Mg and Ca were below the critical levels required for proper growth and productivity. Equally, the cation exchange capacity (CEC) values varied significantly between rainfall thresholds with higher CEC at the high rainfall (12.65±2.59). The average CEC ranged from 9.26 in moderate to 12.65 in high threshold. Additionally, levels of micro nutrients iron, Zinc, Copper, aluminium and manganese were generally low for all the thresholds. However, the micronutrients were slightly higher in low and moderate than in high threshold (Table 2). 3.2 Influence of Tree Shade on Soil Fertility Parameters along the Rainfall Gradient The main shade trees were: Ficus natalensis (Mutuba), Ficus ovata ( F. brachypoda ) (Kookowe, mukookowe, nserere), Albizia coriaria (Mugavu- Omugavu Omuganda), Persea Americana (avocado), Maesopsis eminii (umbrella tree), Artocarpus heterophyllus (Jackfruit) and bananas. The soil parameter results under the shade levels were almost similar to those in unshaded plots. Still, shade had a significant (p<0.001) influence on soil parameters like soil potassium and pH with higher levels of pH and K in shaded than unshaded plots (Table 3). Besides, majority of soil parameters in the shade plots were within the critical levels required for Robusta coffee production except calcium, potassium, phosphorus and magnesium which were inadequate. Micro nutrients like iron and aluminium were lower in shaded than in unshaded plots. Further, the CEC varied significantly (p<0.05) between shade levels, with higher CEC in shaded (10.95) than in unshaded (10.4) plots. Nevertheless, all cation levels were generally low (Table 3). Table 3 : Variation in soil physical-chemical parameters across shade levels (n=36) in greater Luweero Soil parameter Shaded (60.58%) Unshaded (17.54%) CNL p-value (p<0.001) Soil pH (1:2.5 water) 6.17 5.95 5.5 0.001 Organic Matter (%) 4.17 4.09 3 0.001 Total Nitrogen (%) 0.212 0.21 0.25 0.001 Phosphorus (mg kg -1 ) 22.12 21.36 36 0.001 Calcium (cmol (+) kg -1 ) 5.97 5.38 10 0.001 Magnesium (cmol (+) kg -1 ) 1.08 1.00 3 0.001 Potassium (cmol (+) kg -1 ) 0.65 0.57 2 0.001 CEC (cmol kg -1 ) 10.95 10.40 5 ->15 0.001 Aluminium (mg kg -1 ) 0.05 0.114 1.0 0.001 Iron (mg kg -1 ) 0.395 0.403 4.5 0.001 Manganese (mg kg -1 ) 1.307 1.167 20 0.001 Copper (mg kg -1 ) 2.38 2.31 5 0.001 Zinc (mg kg -1 ) 0.796 0.760 2 0.002 3.3 The Variation in Soil Moisture (mm) in the Profile along a Rainfall Gradient There was significant (p<0.05) difference in soil water content (mm) across the soil profiles and the rainfall gradients. There was generally higher soil moisture in the profile under high rainfall than the moderate and low rainfall thresholds. Moreover, soil moisture increased with profile depth, except for the low rainfall threshold. For example, it was generally high for high and moderate rainfall gradients at 50 cm profile. Contrastingly, there was decreased soil moisture levels at 20 cm, 30 cm and 40 cm at the low rainfall threshold, which slightly increased at 50 cm. However, a clear increasing trend was observed for high and moderate rainfall thresholds between 20 cm and 50 cm, with a steadily increasing trend in moderate gradient. Largely, the total soil moisture for the profiles decreased with the rainfall gradient, with high moisture at high thresholds and lower at the low threshold (Table 4). Table 4 : Variation in soil moisture levels (in millimetres) across the soil profile along rainfall thresholds in greater Luweero Profile (cm) High Moderate Low p-value Mean SD Mean SD Mean SD 10 14.26 ab 7.06 13.59 a 6.55 14.93 b 6.23 0.0028 20 20.18 a 6.2 17.7 b 6.5 18.48 cb 5.35 0.001 30 20.67 a 7.78 18.46 b 6.64 15.13 c 6.82 0.001 40 22.15 a 8.44 19.42 b 6.47 13.17 c 7.14 0.001 50 22.38 a 8.89 19.98 b 6.08 14.01 c 6.52 0.001 Total (mm) 99.64 89.15 75.72 Means followed by different letters in rows indicate significant differences (p> 0.05), while the same letters in the row indicate that there are no significant differences (p <0.05) according to LSD (0.005). 3.4 Variation in Soil Moisture across the Profile and the shade levels Shade level had a significant (p=0.001) effect on soil moisture from profile depth level 30 cm to 50 cm. The soil moisture was higher down the profile depth (50cm) than at 10 and 20 cm. All moisture data collection points in the shaded plots had both lower minimum and higher maximum values than non-shade plots. However, the trend for maximum moisture levels was clear under unshaded plots, where it increased with the profile depth. Still, shade increased the mean and maximum soil moisture levels in all profile levels measured. Besides, total soil moisture (mm) was higher in shaded plots than in unshaded plots (Table 5). 3.5 Variation in Microclimate across Rainfall Gradient and Tree Shade Levels The microclimate data varied significantly (p<0.001) across rainfall gradients. The high rainfall threshold indicated a lower mean temperature (22.63±1.07). However, results showed higher mean (23.14±1.22) and a lower minimum temperature (19.42°C) in the moderate rainfall threshold. Instead, higher minimum (20.44°C) and maximum (26.33°C) temperatures were indicated in the low than in other rainfall thresholds. Table 5 : Variation in soil moisture levels (mm) across shade levels in greater Luweero Profile (cm) Shaded Unshaded p-value Mean Min Max Mean Min Max 10 14.4(7.38) 0 34.28 14.12 (6.72) 0.004 30.48 0.375 20 19.14(6.51) 0 36.51 18.72(5.8) 3.97 35.27 0.128 30 19.37(7.09) 0 38.97 17.03(7.67) 1.59 35.78 0.001 40 19.16(7.95) 0 37.55 17.66(8.63) 1.93 36.69 0.001 50 19.69(7.79 0 39.34 18.2(8.4) 1.35 39.27 0.001 Total (mm) 91.76 85.73 Correspondingly, the lowest mean (80.75±9.78), minimum (54.17%), and maximum (93.44%) RH were observed in the low than other thresholds (Table 6). Nevertheless, differences in mean temperature between the thresholds was about 0.6°C, and not greater than 1.8% for RH (Table 6). Table 6 : Variation in microclimate (Temp. and RH) across a rainfall gradient in greater Luweero Variable High Moderate Low p-value Temp℃ R.H (%) Temp. ℃ R.H. (%) Temp. ℃ R.H (%) Mean 22.63(1.07) 81.97 (6.9) 23.14(1.22) 81.23(7.6) 22.96 (1.2) 80.75(9.78) 0.001 Range 5.52 33.09 6.49 39.77 6.09 39.27 Var. 1.15 45.90 1.48 57.73 1.53 95.60 Min. 19.58 62.43 19.42 54.73 20.24 54.17 Max. 25.1 95.52 25.91 94.5 26.33 93.44 Similarly, tree shade had a significant (p<0.001) effect on microclimate. There were lower temperature in the shade than non-shade plots with a difference below 0.6°C. The minimum and maximum microclimate were lower in shade than in unshaded plots, but lower than 0.8°C between shade levels. Shading reduced the mean, minimum, and maximum temperatures but increased the minimum and maximum relative humidity by approximately 7.6% than non-shade plots (Table 7). Nevertheless, the maximum temperature limits were not above the photosynthetic requirements for Robusta coffee of about 30°C. While the minimum and maximum temperatures seemed similar for the shade levels, they were significantly (p<0.001) different (Figs. 2 and 3) and lower in shaded plots than in unshaded. Table 7: Variation in microclimate (Temp. and RH) across Shade levels Variable Shaded Unshaded p-value Temp. ℃ R.H. Temp. ℃ R.H. Mean 22.64 81.19 23.23 81.69 0.001 SD 1.19 7.98 1.14 7.35 Range 6.13 40.79 6.24 40.33 Variance 1.41 63.68 1.29 53.98 Minimum 19.42 54.73 20.09 54.17 Maximum 25.55 95.52 26.33 94.5 3.6 The Generalised Linear Mixed (GLM) Model The microclimate and soil moisture data were further analysed using the Generalised Linear Mixed (GLM) model for the longitudinal nested experiment. The results indicate significant (p<0.05) differences between thresholds and shade levels. Results show high soil moisture levels under shaded plots and high rainfall threshold. The interaction between thresholds and shade was positive and significant (p<0.05) for all models except model III and IV, where the interaction was negative but significant. The best soil moisture model was taken to be Model V at 50cm. Model V had the lowest Akaike's Information Criterion (AIC) and Bayesian Information Criterion (BIC). The variance was also lower in models V and IV (Table 8). Table 8 : Results of the Generalised Linear Mixed Model for soil moisture levels Variable Soil Moisture levels Model I: At 10cm Model II: At 20cm Model III: At 30cm Model IV: At 40cm Model V: At 50cm Moderate -3.88(.03) *** -0.62(.03) *** 6.85(.03) *** 10.1(.02) *** 7.83(.02) *** High -5.10(.03) *** 0.42(.03) *** 7.44(.03) *** 10.8(.03) *** 6.28(.02) *** Shaded -6.67(.04) *** -2.37(.05) *** 4.98(.05) *** 7.44(.04) *** -3.1(.04) *** Moderate*Shaded 7.31(.13) *** 2.36(.13) *** -6.2(.14) *** -9.2(.13) *** 1.34(.11) *** High*Shaded 6.53(.13) *** 1.98(.13) *** -5.3(.14) *** -7.7(013) *** 2.83(.11) *** _cons 15.87(.02) *** 18.78(.02) *** 12.8(.02) *** 10.8(.02) *** 14.6(.02) *** Profile levels (Var.) 39.09(.09) 40.72(.09) 45.1(.10) 36.9 28.1 AIC 2611274 2627710 2668504 2587894 2479117 BIC 2611350 2627787 2668581 2587970 2479193 For Relative humidity and temperature, these increased with increase in rainfall threshold with a significant (p<0.05) interactions between shade levels (Table 9). There was positive significant (P<0.05) relationship for humidity across the rainfall thresholds and between shade levels. Though, for temperature, a negative significant relationship was observed between low and high thresholds. Likewise, there was negative and significant interaction between shade levels and rainfall thresholds, for both temperature and humidity data (Table 9). Table 9 : Results of the Generalised Linear Mixed Model for relative humidity & temperature Variable Microclimate levels Relative Humidity (%) Temperature (℃) Moderate 1.13(.05) *** 0.21(.00) *** High 1.79(.05) *** -0.31(.01) *** Shaded 1.38(.07) *** 0.05(.01) *** Moderate*Shaded -1.07(.18) *** -0.04(.02) *** High*Shaded -1.58(.17) *** -0.07(.02) *** _cons 80.10(.05) *** 22.94(.00) *** RH and Temp (Var.) 58.02(.15) 1.39(.00) AIC 2173993 1246145 BIC 2174068 1246221 4 Discussion The results from the study indicate that shade influenced soil physical-chemical parameters, soil moisture, and modified the microclimate across thresholds. In addition, there was generally adequate soil organic matter, pH, and nitrogen levels for Robusta production across thresholds and shaded plots (Tables 2 and 3 ), contradicting Kyalo et al. ( 2024 ) findings. The soil parameters were higher in shaded plots and high rainfall thresholds than in low and moderate rainfall thresholds and unshaded plots. The higher soil parameter values at the high threshold may be linked to the sandy clay loam soil texture observed (Table 2 ), the presumed higher vegetative growth, and the higher organic matter from organic inputs, and lower temperatures (Table 6 ). The high rainfall and associated low temperatures (Table 6 ) may be associated with reduced OM breakdown there by increasing its content in the soil and adequate soil macro nutrients. Alvarez-Clare & Mack ( 2011 ) and Astera ( 2018 ) made similar observations of a significant relationship between rainfall and soil nutrients. The high OM in high rainfall and shaded plots may also be due to high organic inputs and biomass accumulation from shade tree leaves associated with higher soil organic carbon and reduced decomposition rates. Hence, the higher nutrient levels from nutrient recycling under shaded plots with more litter and biomass (McMahon, 2012 ; Nadaf & Bora, 2021 ). The recycled nutrients from deeper soil layers results from the nutrient pump effect which also increased soil chemical parameters under shade (Nadaf & Bora, 2021 ) and nutrient availability (Table 3 ). Similarly, the observed pH under shaded plots is related to high soil OM content (Sonon et al., 2002; van Asten et al., 2012). Soil OM buffers pH and increases soil cation exchange capacity and microbial activities, which contribute to soil fertility. On the other hand, the low soil OM content in the low rainfall threshold may be associated with low soil moisture (Tables 1 and 4 ) and high temperature (Table 6 ). The observed high temperature may raise the rate of OM decomposition, resulting in less accumulation in the soil. Hence, a reduction in OM content at the time of sample collection and analysis. As well, the low soil pH in the low and moderate thresholds may be an indicator for mild soil acidity (Table 2 ), which may affect the availability of basic cations, Mg, Ca, and phosphorus (Table 2 ). The low soil pH in the low rainfall threshold may also be related to increased micronutrients like iron, aluminium, and manganese than other thresholds (Table 2 ), whose higher uptake may be toxic to coffee and reduce productivity. Similarly, results indicated that there were lower CEC and critical levels for exchangeable magnesium, calcium, potassium and phosphorus for coffee productivity (Tables 2 ). However, the results indicate higher Ca than Mg and potassium for all rainfall thresholds and shade levels. The observed averagely lower CEC levels in moderate threshold (10) may be due to the sandy soil texture (Table 2 ). Similar observations of low CEC was made by Kyalo et al. ( 2024 ). The sandy texture is also related to low potassium levels associated with potassium deficiency (Agronomy Tech Note 76, 2009 ). Instead, the more than 10 CEC in high thresholds is due to the loamy texture. The low potassium levels in the soils (McMahon, 2012 ) are further compounded with reduced recycling of coffee husks that are high in potassium (Kasongo et al., 2011 ). Moreover, the nutrients continuously extracted through coffee harvesting are not recycled in coffee gardens where external potassium and other nutrient application is low (Kyalo et al., 2024 ) and poor management practices exist (Kyalo et al., 2024 ; Nakyagaba et al., 2025 ). However, the high moisture (Table 5 ), loamy texture (Table 2 ) and high OM content (Tables 2 and 3 ) are related to better soil porosity and aggregate stability, reduced leaching and increased potassium availability in high threshold and shaded plots. The release and availability of cations; Ca, K and Mg are also linked to soil moisture availability. Hence, the higher marginal differences in soil parameters between shaded and high rainfall thresholds than in low rainfall thresholds. The observed results of low soil parameters, especially cation levels, below critical levels call for continuous use of external sources of basic inorganic fertilisers since the low CEC and sandy texture affect the storage of un absorbed applied nutrients (Kyalo et al., 2024 ). The results are similar to Nakyagaba et al. ( 2024 ) and Van Asten et al. (2012) findings of low and limiting levels of nitrogen, phosphorus, calcium and potassium in the study area. However, the soil in shaded plots (Table 3 ) can moderately supply the required nutrients for better coffee production. Besides, the results differed from Asare (2015), who did not observe significant (P > 0.05) shade effect on soil nutrients when he worked with shaded cocoa in Ghana. Moreover, it is beneficial to include shade trees in Robusta coffee plots to improve soil nutrient content through hydraulic redistribution (Caldwell et al., 1998 ; Ke et al., 2022 ). As well, the soil micro nutrients were low across rainfall thresholds and shades levels (Tables 2 and 3 ). However, the low soil pH observed under the low rainfall threshold may be an indicator of the aluminium generating acid in the soil. Otherwise, the low micronutrients such as manganese, Zinc and copper in shaded plots and high rainfall threshold may be a result of higher OM and pH which are associated with immobilisation of these elements (Kasongo et al., 2011 ). Hence, the low micro nutrient levels pose low toxicity risks to coffee productivity. Differing from Le et al. ( 2023 ), the soil moisture results obtained and their interactions were consistently higher in high rainfall and shaded than in unshaded plots (Tables 4 and 5 ). Gomes et al. ( 2016 ) and Lin ( 2007 ) observed similar results. These indicate that shade trees contribute to higher soil water retention, stabilises soil moisture levels, reduce evaporation rates, and improve infiltration through litter and root systems, which is critical during drought scenarios. The shade trees may have facilitated the lifting of nutrients and water from deeper soil layers that are absorbed by the Robusta coffee fine fibrous roots (Caldwell et al., 1998 ). The moisture levels in the profile is also connected with the observed coffee wilting point of 20cm in the low threshold and unshaded plots compared to 30cm in the high threshold. The high OM content, litter fall and the loamy soil texture observed in high threshold and shaded plots further explain the higher moisture content, especially in the upper 30cm of the profile (Tables 4 and 5 ). The higher OM observed may have led to better soil structure, improved the soil water holding capacity and water infiltration in shaded plots. Moreover, soil OM is the major source of nutrients and fertility in many smallholder coffee farms that depend on nature for production. Though deeper soil profiles layers had more moisture, this could be related with the sandy nature of the soils in the study area (Table 1 ) that allows soil water to drain to deeper layers which may have higher clay particles (Moreira et al., 2018 ). The higher moisture down the profile, especially in high and moderate thresholds justifies the shade tree function of hydraulic lift. The results may also be due to reduced evaporation rates especially in the low rainfall thresholds during drought scenarios. In particular, shade trees demonstrate better soil moisture retention in low rainfall thresholds thereby mitigating the drought situations experienced in marginal areas. There may have been more positive effects of shade on maintaining better soil water retention which leads to higher drought tolerance. Likewise, the RH and temperature data (Tables 6 and 7 ) indicate that shade trees had an influence on microclimate with higher RH and lower temperature in shade than unshaded plots across rainfall thresholds. Shade trees significantly (p < 0.05) reduced air temperature and increased RH creating a stable and favourable microclimate. Shade trees, therefore, may protect coffee from the extreme effects of low RH and high temperatures thereby enhancing coffee resilience to heat stress. The results are in conformity with previous scholars who noted that shade trees improve microclimatic conditions (Carvalho Carelli et al., 2006 ; Moreira et al., 2018 ; Muschler & Bonnemann, 1997 ; Souza et al., 2012 ). The observed higher humidity in shaded plots may promote healthier coffee leaf development, reduce transpiration levels and less stress on the coffee plants. Besides, the lower temperatures observed, shade reduced the coffee water requirements since temperature and moisture have significant physiological effects on coffee growth and yield. Moreover, the micro climate results obtained were within and/or below the optimal Robusta requirements of 24 to 30°C. Besides, Moreira et al. ( 2018 ) obtained higher maximum and minimum temperatures under unshaded conditions. The results are key in adapting Robusta coffee production to climate variability using shade trees, especially in marginal/semi-arid areas where it enhances better microclimate stability. 5 Conclusions and Recommendations The study results generally indicate that shade trees in coffee production play critical roles by enhancing soil physical and chemical attributes through improving soil moisture retention by buffering against excessive soil moisture and maintains adequate moisture in the marginal areas there by boosting long time soil health and reducing the effect of drought on Robusta coffee. Shade trees also modify and stabilise microclimate by increasing relative humidity and reduce evaporation. Further, shade reduces temperature within the mid altitude zone of central Uganda. Hence, the study reveals the significance of shade trees as a mitigation measure to climate variability, especially in marginal areas of greater Luweero with low to moderate rainfall. In these areas shade significantly benefits coffee microclimate and soil water dynamics there by mitigating drought stress and stabilize soil moisture. The higher soil parameters, moisture levels and lower temperature under shaded plots and a high rainfall threshold may favour coffee growth, development, productivity and may close the high Robusta coffee yield gap in the mid altitude zone of central Uganda. The identified variations in soil parameters and microclimate can improve sustainability, guide coffee management and increase resilience of coffee farming systems to achieve higher productivity amidst climate variability. However, the effect of shade may vary with the rainfall threshold indicating specific shade management practices depending on the rainfall threshold. Shade tree management is therefore important in improving coffee quality, yield and favourable microclimate conditions. The results are beneficial to small-scale farmers, most of whom depend on nature to produce coffee and cannot afford fertilisers and irrigation. Integration of shade trees as part of intercrops support Robusta coffee farmers to increase resilience systems, improve yields and coffee quality amidst climate variability. The results are also important in making rational management decisions amidst unpredictable climate, declining fertility, increasing temperatures and reduced carbon storage. However, the study was conducted within two years but extended research may be required to allow more inter-annual climatic variations in all the studied parameters, especially nutrient acquisition and recycling (nutrient pump), hydraulic lift and moisture relations and other associated factors. This study, therefore justifies integration of shade trees in Robusta coffee fields as an adaptation and mitigation measure. Nevertheless, there is need for appropriate selection of shade tree species and optimal density, with emphasis on optimising smallholder farmer’s resilience for social, environmental and economic benefits across rainfall thresholds. There is also need to observe the improvement in growth parameters especially plant height and leaf size in shaded plots. It is also necessary to improve this research over a long period by relating the age of the shade tree with diversity along rainfall thresholds. The study provided important, realistic, and urgent adaptive strategies to policy makers and farmers to consider integrating tree shade in coffee production for sustainable management of coffee cultivation practices aimed at reducing the effects of climate variability and improve adaptation in the mid altitude zone of central Uganda, where there is increased rainfall variability and drought associated with marginal areas. However, shade tree selection and management should be tailored to rainfall thresholds to maximize benefits in the mid-altitude Robusta coffee growing zones of central Uganda. Therefore, shade trees are the easiest, fastest and most sustainable adaptation and mitigation strategy to unpredictable climate, especially the impact on soil parameters under declining soil fertility, soil moisture retention and microclimate, which may affect the projected country’s 1.2 metric tons year − 1 . Declarations Conflict of Interest The author declares no conflicts of interest with respect to the publication of this article. ORCID Nakyagaba Winfred http://orcid.org/0000-0002-5134-8571 Funding This research work was supported by the United States Agency for International Development (USAID) [grant number BFS-G-11-00002], through the CGIAR Research Program on Climate Change, Agriculture and Food Security (CCAFS) implemented at IITA Uganda, NARO Uganda, and the Makerere University Center for Climate Change Research and Innovations (MUCCRI)/ERICCA through FHI360 [cooperative agreement number AID-617-A-1300008]. Author Contribution Author A. Nakyagaba N. Winfred: Conceptualization, validation, Data Collection, investigation, formal analysis, writing original draft, writing - review and editing, visualizationAuthor B. Herbert Talwana and C. Samuel Kyamanywa: Supervision, Writing-Review and editing, visualizationAuthor D. Godfrey H. Kagezi: Conceptualization, Writing- review and editing, supervision, project administration, fundingAuthor E. David Mfitumukiza, F. Yazidhi Bamutaze and H. Revocatus Twinomuhangi: Supervision, Writing- review and editing, project administration, fundingAuthor G. Catherine Mulinde and J. Benard Fungo: Conceptualization, generating Figure 1, writing review and editingAuthor I. David Mukasa: Methodology, investigation, data collection, review, and editingAuthor K. David Amwonya: Methodology, verification, data analysis, generating data for tables, figure production and interpretationAuthor L. Judith Asiimwe: Data collection tools, acquisition of gadgets, project administration and fund managementAuthor M. van Asten Piet and N. Laurence Jassogne: Conceptualization, Methodology, data collection tools, verification, acquisition of gadgets, Writing- review and editing, supervision, project administration, funding Acknowledgement The authors thank the farmers who provided the study plots on their farms for the two years of data collection. 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Cumagum (Ed.), Plant Pathology (pp. 241–273). www.intechopen.com. https://doi.org/10.5772/32490 Melorose, J., Perroy, R., & Careas, S. (2015). Coffee Sector Profile. Statewide Agricultural Land Use Baseline , 1456 , 453–603. www.ugandainvest.go.ug(coffee-sector-profile) Moreira, S. L. S., Pires, C. V., Marcatti, G. E., Santos, R. H. S., Imbuzeiro, H. M. A., & Fernandes, R. B. A. (2018). Intercropping of coffee with the palm tree, macauba, can mitigate climate change effects. Agricultural and Forest Meteorology , 256 – 257 (June 2017), 379–390. https://doi.org/10.1016/j.agrformet.2018.03.026 Mouen Bedimo, J. A., Njiayouom, I., Bieysse, D., Ndoumbè Nkeng, M., Cilas, C., & Nottéghem, J. L. (2008). Effect of shade on arabica coffee berry disease development: Toward an agroforestry system to reduce disease impact. Phytopathology , 98 (12), 1320–1325. https://doi.org/10.1094/PHYTO-98-12-1320 Muschler, R. G., & Bonnemann, A. (1997). Potentials and limitations of agroforestry for changing land-use in the tropics: Experiences from Central America. Forest Ecology and Management , 91 (1), 61–73. https://doi.org/10.1016/S0378-1127(96)03887-X Nadaf, S. A., & Bora, A. R. (2021). Nutrients Status in Arabica Coffee (Coffea Arabica L) Soils of Non-Traditional Area (NTA). In D. H. H. Ho (Ed.), International Journal of Plant & Soil Science (Vol. 33, Issue 13, pp. 17–22). https://doi.org/10.9734/ijpss/2021/v33i1330490 Nakyagaba, N. W., Talwana, H., Kyamanywa, S., Kagezi, G. H., Bamutaze, Y., Mfitumukiza, D., Twinomuhangi, R., Mukasa, D., Mulinde, C., & Fungo, B. (2025). Smallholder Farmers ’ Management Practices and Perceived Constraints to Robusta Coffee Production in Mid-Altitude Zones of Central Uganda. East African Journal of Agriculture and Biotechnology , 8 (2), 449–464. https://doi.org/10.37284/eajab.8.2.4063.IEEE Nakyagaba, W. N., Talwana, H., Kyamanywa, S., Kagezi, G. H., Bamutaze, Y., Mftumukiza, D., Twinomuhangi, R., Bukomeko, H., Mukasa, D., Fungo, B., Piet, V. A., & Jassogne, L. (2024). Biophysical Constraints to Robusta Coffee Productivity in Low , Moderate , and High Rainfall Areas. International Journal of Agronomy , 2024 , 14. https://doi.org/10.1155/ioa/4683226 Núñez, P. A., Pimentel, A., Almonte, I., Sotomayor-Ramírez, D., Martínez, N., Pérez, A., & Céspedes, C. M. (2011). Soil fertility evaluation of coffee (coffea spp.) production systems and management recommendations for the Barahona Province, Dominican Republic. Journal of Soil Science and Plant Nutrition , 11 (1), 127–140. https://doi.org/10.4067/S0718-95162011000100010 Nyombi, K. (2013). Towards sustainable highland banana production in Uganda: Opportunities and challenges. African Journal of Food, Agriculture, Nutrition and Development , 13 (57), 7544–7561. https://doi.org/10.18697/ajfand.57.11080 Okalebo, J. R., Gathua, K. W., & Paul, L. W. (2002). Laboratory Methods of Soil and Plant Analysis: A Working Manual The Second Edition. In SACRED Africa, Kenya Any: Vol. SECOND EDI . Piato, K., Lefort, F., Subía, C., Caicedo, C., Calderón, D., Pico, J., & Norgrove, L. (2020). Effects of shade trees on robusta coffee growth, yield and quality. A meta-analysis. Agronomy for Sustainable Development , 40 (6), 1–13. https://doi.org/10.1007/s13593-020-00642-3 Richards, J. H., & Caldwell, M. M. (1987). Hydraulic lift: Substantial nocturnal water transport between soil layers by Artemisia tridentata roots. Oecologia , 73 (4), 486–489. https://doi.org/10.1007/BF00379405 Sachs, J. D., Cordes, K. Y., Rising, J., Toledano, P., & Maennling, N. (2019). Ensuring Economic Viability and Sustainability of Coffee Production. In SSRN Electronic Journal . https://doi.org/10.2139/ssrn.3660936 Sarmiento-Soler, A., Vaast, P., Hoffmann, M. P., Rötter, R. P., Jassogne, L., van Asten, P. J. A., & Graefe, S. (2019). Water use of Coffea arabica in open versus shaded systems under smallholder’s farm conditions in Eastern Uganda. Agricultural and Forest Meteorology , 266 – 267 (December 2018), 231–242. https://doi.org/10.1016/j.agrformet.2018.12.006 Schielzeth, H., & Nakagawa, S. (2013). Nested by design: Model fitting and interpretation in a mixed model era. Methods in Ecology and Evolution , 4 (1), 14–24. https://doi.org/10.1111/j.2041-210x.2012.00251.x Serenini, J. P., Guedes, T. A., Roberto, J. G. F., & De Carvalho Nunes, W. M. (2019). Generalized mixed models-an application to longitudinal data of citrus canker. Acta Scientiarum - Technology , 41 (1), 1–10. https://doi.org/10.4025/actascitechnol.v41i1.41646 Shin, M., Patton, R., Mahar, T., Ireland, A., Swan, P., & Chow, C. M. (2017). Calibration and validation processes for relative humidity measurement by a Hygrochron iButton. Physiology and Behavior , 179 , 208–212. https://doi.org/10.1016/j.physbeh.2017.06.019 Sonon, L. S., Kissel, D. E., & Saha, U. (2014). Cation Exchange Capacity and Base Saturation-UGA Cooperative Extension Circular 1040 . UGA Cooperative Extension; University of Georgia EXTENSION. https://secure.caes.uga.edu/extension/publications/files/pdf/C 1040_2.PDF Souza, H. N. de, de Goede, R. G. M., Brussaard, L., Cardoso, I. M., Duarte, E. M. G., Fernandes, R. B. A., Gomes, L. C., & Pulleman, M. M. (2012). Protective shade, tree diversity and soil properties in coffee agroforestry systems in the Atlantic Rainforest biome. Agriculture, Ecosystems and Environment , 146 (1), 179–196. https://doi.org/10.1016/j.agee.2011.11.007 Staver, C., Guharay, F., Monterroso, D., & Muschler, R. G. (2001). Designing pest-suppressive multistrata perennial crop systems: Shade-grown coffee in central america. Agroforestry Systems , 53 (2), 151–170. https://doi.org/10.1023/A:1013372403359 Tscharntke, T., Clough, Y., Bhagwat, S. A., Buchori, D., Faust, H., Hertel, D., Hölscher, D., Juhrbandt, J., Kessler, M., Perfecto, I., Scherber, C., Schroth, G., Veldkamp, E., & Wanger, T. C. (2011). Multifunctional shade-tree management in tropical agroforestry landscapes - A review. Journal of Applied Ecology , 48 (3), 619–629. https://doi.org/10.1111/j.1365-2664.2010.01939.x UCDA. (2019). Robusta Coffee Handbook. In Uganda Coffee Development Authority (UCDA) (A sustaina, Issue Icc 124-8 7). THE MINISTRY OF AGRICULTURE, ANIMAL INDUSTRY & FISHARIES. https://ugandacoffee.go.ug/sites/default/files/Resource_center/Robusta Coffee Handbook.pdf UCDA. (2024). UCDA Fact sheet (pp. 0–4). Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted 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. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-8558650","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":588093040,"identity":"8c862bf4-0265-46f4-8610-2b89c843c13f","order_by":0,"name":"Winfred Nabiteeko Nakyagaba","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA90lEQVRIiWNgGAWjYDACZhBxAETwMDB8AFJs7KRoYZwB0sJMlFVQLcw8cEPwAIPjvMce/DhzWF7e/ezBzza/tsnzMTMwfviYg0fLYb50w54bhw03nslLls7tu23YxszALDlzG24tZod5zCR4Phxm3NiQYyCd23ObEaiFjZmXgBbJPx8O22/sf2P827Lntj1RWqR5bhxOnC+RYybN8ON2IkEt9iAtMmfSkzdIvEuz7G24ndzGzNiM1y+S/WfMJN8cs7ad3597+MaPP7dt57c3H/zwEY8WKGhmMDgApBjbQBzGBoLqgaCOQR6s7g8xikfBKBgFo2CkAQDyRVRMQSvFSwAAAABJRU5ErkJggg==","orcid":"","institution":"National Agricultural Research Organisation","correspondingAuthor":true,"prefix":"","firstName":"Winfred","middleName":"Nabiteeko","lastName":"Nakyagaba","suffix":""},{"id":588093041,"identity":"1d7e07b1-b0c4-492d-aab3-1480930654cf","order_by":1,"name":"Herbert Talwana","email":"","orcid":"","institution":"Makerere University","correspondingAuthor":false,"prefix":"","firstName":"Herbert","middleName":"","lastName":"Talwana","suffix":""},{"id":588093042,"identity":"ecf62d97-cbbf-42f0-a884-a615b562d765","order_by":2,"name":"Samuel Kyamanywa","email":"","orcid":"","institution":"Makerere University","correspondingAuthor":false,"prefix":"","firstName":"Samuel","middleName":"","lastName":"Kyamanywa","suffix":""},{"id":588093043,"identity":"08eba913-664d-4cd1-8702-936b3f4d8a42","order_by":3,"name":"Godfrey H. 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Agriculture","correspondingAuthor":false,"prefix":"","firstName":"van","middleName":"Asten","lastName":"Piet","suffix":""},{"id":588093054,"identity":"7d918648-419a-4efb-a7a1-67da7cfa1e59","order_by":13,"name":"Laurence Jassogne","email":"","orcid":"","institution":"International Institute of Tropical Agriculture","correspondingAuthor":false,"prefix":"","firstName":"Laurence","middleName":"","lastName":"Jassogne","suffix":""}],"badges":[],"createdAt":"2026-01-09 08:38:16","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8558650/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8558650/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":102408601,"identity":"a8bcf703-717c-4fbc-94a8-9c9cd5d787c1","added_by":"auto","created_at":"2026-02-11 11:30:04","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":192947,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eThe Nested Experimental Design\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-8558650/v1/fb25ffd6889f1118789ce493.png"},{"id":102408600,"identity":"6ed0b38a-02c4-414f-ad9c-8507343144f1","added_by":"auto","created_at":"2026-02-11 11:30:04","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":137355,"visible":true,"origin":"","legend":"\u003cp\u003eVariation in temperature (\u003csup\u003eo\u003c/sup\u003eC) across rainfall gradients and shade levels\u003c/p\u003e","description":"","filename":"22.png","url":"https://assets-eu.researchsquare.com/files/rs-8558650/v1/dcc51c605984de9102dadf28.png"},{"id":102408602,"identity":"d9eea9f2-e0c5-4b7d-a5ca-27f91c7b5c13","added_by":"auto","created_at":"2026-02-11 11:30:04","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":161553,"visible":true,"origin":"","legend":"\u003cp\u003eVariation in relative humidity (%) across rainfall gradients and shade levels\u003c/p\u003e","description":"","filename":"33.png","url":"https://assets-eu.researchsquare.com/files/rs-8558650/v1/27660a92078d2795b15034b0.png"},{"id":106107579,"identity":"281c99b7-eb01-4245-9823-b938ce4aab0e","added_by":"auto","created_at":"2026-04-03 14:11:30","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2150904,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8558650/v1/27e4bee3-1a1f-47cd-8fc9-b6837623b021.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"The Influence of tree shade on soil attributes, water storage and microclimate in Robusta coffee grown in a rainfall gradient of the mid-altitude zone of Central Uganda","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eRobusta coffee (\u003cem\u003eCoffea canephora\u003c/em\u003e) is native to Uganda, where it is a traditional income-generating crop, grown in over 85% of the districts by over 1.8\u0026nbsp;million households (UCDA, \u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Uganda is a major Robusta coffee producer in the world, ranked in seventh position, and second in Africa (ICO, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Robusta coffee is mainly grown in the lowlands of central Uganda, where the majority are smallholder farmers with an average of 0.18 hectares (0.45acres) (UCDA, \u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Robusta coffee plays a critical role in Uganda\u0026rsquo;s culture and livelihoods. The mid-altitude zone of Uganda accounts for over 80% of Robusta coffee production and export (Kyalo et al., \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Specifically, greater Luweero, one of the regions in central Uganda, with 10% of Robusta coffee production. However, the coffee crop is sensitive to increased air temperature, variation in relative humidity, soil moisture dynamics, and soil physical-chemical nutrient attributes, which influence its growth and yield.\u003c/p\u003e \u003cp\u003eClimate variability is mainly described by rainfall, temperature, and relative humidity, which play physiological roles in coffee growth (Alvarez-Clare \u0026amp; Mack, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2011\u003c/span\u003e; Sachs et al., \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Although Robusta coffee was indicated to be more climate resilient due to its tolerance to warmer temperatures and robust nature, recent studies indicate that it is equally affected by climate variability and predicted to lose suitability due to its sensitivity to high temperatures (Bunn et al., 2015; Kath et al., 2020; Sachs et al., \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). High temperatures are indicated to adversely impact coffee production and productivity (Melorose et al., \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). Variation in climate variables reduces suitable production areas, coffee growth and quality (Sachs et al., \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Ghini et al., 2011). Further, research indicates that moderate to severe water stress can lead to small and damaged coffee beans (DaMatta et al. 2018). Robusta coffee is therefore sensitive to variation in rainfall, temperature, relative humidity, and drought incidences. For example, Kath et al. (\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) associated increased rainfall and temperature with higher total bean defects. The increasing temperature variability and erratic rainfall are critical to sustainable Robusta production in the mid-altitude zones of central Uganda.\u003c/p\u003e \u003cp\u003eRobusta coffee requires adequate soil moisture of 1200 to 2000 mm year\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e for proper growth (UCDA, \u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e2019\u003c/span\u003ea). Nevertheless, Bunn et al. (\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2019\u003c/span\u003e) noted that rainfall has become variable, affecting soil moisture levels. In addition, poor soil fertility and extreme rainfall variability are threats to productivity in most coffee-growing regions. Though the variation in soil parameters under rainfall thresholds was indicated by Nakyagaba et al. ( 2024), the decline in soil fertility (Nyombi, \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; van Asten et al., 2012) and extremes of soil pH are associated with climate variability, low coffee productivity, poor yield, and increased yield gaps (Nakyagaba et al., \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). The pH extremes and unbalanced soil nutrients may affect cation levels, their availability, and plant uptake (Bhattarai et al., \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; N\u0026uacute;\u0026ntilde;ez et al., \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2011\u003c/span\u003e). In addition, erratic rains and increased temperature may create an environment that indirectly impacts soil fertility, soil moisture, relative humidity, and temperature variables.\u003c/p\u003e \u003cp\u003eOn the other hand, Robusta coffee requires an optimal temperature range between 24\u0026deg;C and 30\u0026deg;C for proper growth (UCDA, \u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Barnes et al., \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). The rising temperatures are harmful to coffee productivity (Craparo, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2017\u003c/span\u003e), cause loss of yield, and may alter the suitability of production areas to higher elevations (Jaleta, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Besides, about 7.3\u0026nbsp;million hectares (equivalent to approximately 12%) of the global Robusta coffee growing area may be lost as a result of temperature increase (Sachs et al., \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Drought can also cause up to 80% yield loss (DaMatta \u0026amp; Ramalho, 2006). Moreover, the impact of climate variability is worse in marginal areas associated with high temperatures, drought, water shortage and low nutrients; noted as key constraints. Nevertheless, certain management practices may reduce the effects associated with climate variability, increase coffee resilience and sustain production (Le et al., \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Nakyagaba et al., \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). However, a limited number of coffee farmers can adapt to climate variability because the majority rely on natural conditions for production (Nakyagaba et al., \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2025\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eAdapting climate-smart practices like tree shade is essential in compensating for reduced soil fertility and soil moisture dynamics through plant water relations and nutrient uptake, especially in marginal areas (Caldwell et al., \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e1998\u003c/span\u003e; Caldwell \u0026amp; Richards, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e1989\u003c/span\u003e). Research indicates that coffee, an understorey of sub-Saharan African rainforests, grows better under tree shade, which has major ecological benefits (Piato et al., \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Tscharntke et al., \u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e2011\u003c/span\u003e). For instance, shade moderates extreme temperatures, thereby reducing heat stress on coffee plants. Coffee also utilises the water released from the roots of tree shades through hydraulic redistribution (Caldwell et al., \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e1998\u003c/span\u003e). In addition, the tree shade associated with low temperature and high relative humidity reduces water loss from the soil surface through transpiration (Barnes et al., \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). The high relative humidity close to saturation levels favours vegetative growth (DaMatta \u0026amp; Ramalho, 2006). Thus, the favourable microclimate factors may improve coffee productivity and reduce yield gaps (Tscharntke et al., \u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e2011\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eFortunately, 85% of Robusta farmers in the mid-altitude zone of central Uganda grow coffee under a shade system (Bukomeko et al., \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Kalanzi \u0026amp; Nansereko, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; van Asten et al., 2012). The major sources of shade on these smallholder coffee farms are traditionally trees and bananas (Bukomeko et al., 2017; Kalanzi \u0026amp; Nansereko, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Melorose et al., \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; UCDA, \u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; van Asten et al., 2012). Several species of tree shades are grown in these coffee gardens (Bukomeko, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Bukomeko et al., \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). For example, Bukomeko et al. (\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2015\u003c/span\u003e) associated a high yield benefit in low precipitation zones with trees. Similarly, cases of hydraulic lift in arid lands was documented (Caldwell et al., \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e1998\u003c/span\u003e). Unfortunately, McMahon (\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2012\u003c/span\u003e) indicated that certain trees reduce soil pH, compete with crops for soil nutrients, water, and other resources. Further, Beer (\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e1988\u003c/span\u003e) indicated that trees may sequester potassium in the stem of the tree, affecting its availability for coffee uptake. Other shade trees can transpire more water and reduce the available water for coffee uptake. Besides, coffee is evergreen with continuous transpiration and nutrient requirements. Shade is therefore a critical component offering ecological and agronomic benefits associated with improved soil quality, microclimate modification, and enhanced water retention in the mid-altitude zone of Central Uganda, where climate and rainfall variability are increasing.\u003c/p\u003e \u003cp\u003eDespite the numerous studies on tree shades, there is limited data on the influence of tree shade on soil physical-chemical parameters and microclimate in a rainfall gradient, particularly in marginal areas of central Uganda. This gap is significant, given that Robusta coffee production is expanding into these marginal areas. A sustainable strategy is necessary to adapt coffee to the risks of a changing climate associated with marginal areas. Furthermore, worldwide, there are fewer shade tree studies conducted in Robusta compared to Arabica coffee (Piato et al., \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2020\u003c/span\u003e), particularly in Uganda. This led to a coffee tree shade research in marginal areas of central Uganda, where soil fertility and moisture are generally low with unfavourable microclimate. This research, therefore, determined the influence of growing Robusta coffee with or without tree shade along a rainfall gradient (thresholds) on: (i) microclimates (temperature and humidity) and (ii) soil chemical and physical parameters (moderating soil pH, soil water content, nutrient recycling and cation exchange capacity (CEC)). It was hypothesised that tree shade influences soil nutrient content, microclimate and soil water storage along a rainfall gradient. Therefore, the findings on the influence of tree shade on microclimate, soil fertility and moisture may improve coffee management decisions and may help in sustainable Robusta coffee management amidst climate variability. This may increase Robusta coffee productivity and narrow the yield gap, especially in marginal areas of central Uganda.\u003c/p\u003e"},{"header":"2. Materials and Methods","content":"\u003cp\u003e\u003cstrong\u003e2.1 \u0026nbsp; \u0026nbsp; \u0026nbsp; Study Site\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe study was conducted in the mid-altitude zone of central Uganda, where selected sub-counties were classified as marginal and others as traditional coffee growers. Central Uganda, specifically the Lave Victoria crescent is a centre of origin for Robusta coffee (Kyalo et al., 2024), with over 80% of Robusta coffee production area and volume. The smallholders in the study area traditionally grow coffee under tree shade (Bukomeko et al., 2017; UCDA, 2019a;\u0026nbsp;van Asten et al., 2012). The three classified rainfall thresholds were established in two districts of central Uganda, Luweero and Nakasongola, with three sub-counties in Luweero and one sub-county in Nakasongola district. The highest rainfall threshold was represented by Luweero and Katikamu sub-counties (\u0026gt;1200 mm year\u003csup\u003e-1\u003c/sup\u003e), Zirobwe represented the moderate threshold (1100 \u0026ndash; 1200 mm year\u003csup\u003e-1\u003c/sup\u003e), and Katuugo represented the low rainfall threshold (\u0026lt;1100 mm year\u003csup\u003e-1\u003c/sup\u003e in Nakasongola district (Table 1). These study areas, therefore, differed in the amount of rainfall (Nakyagaba et al., 2024) and the vegetation cover (Bukomeko, 2017).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 1: Study area characteristics for the rainfall thresholds\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"654\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 228px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSite characteristics\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 144px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eHigh\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 138px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eModerate\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 144px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eLow\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 228px;\"\u003e\n \u003cp\u003eDistrict\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 144px;\"\u003e\n \u003cp\u003eLuweero\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 138px;\"\u003e\n \u003cp\u003eLuweero\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 144px;\"\u003e\n \u003cp\u003eNakasongola\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 228px;\"\u003e\n \u003cp\u003eSub-county\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 144px;\"\u003e\n \u003cp\u003eLuweero \u0026nbsp;(3 farms)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 138px;\"\u003e\n \u003cp\u003eZirobwe (6 farms)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 144px;\"\u003e\n \u003cp\u003eKatuugo (6 farms)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 144px;\"\u003e\n \u003cp\u003eKatikamu (3 farms)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 138px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 144px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 228px;\"\u003e\n \u003cp\u003eAltitude (masl)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 144px;\"\u003e\n \u003cp\u003e1100 m (3600ft)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 138px;\"\u003e\n \u003cp\u003e1100 m (3600ft)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 144px;\"\u003e\n \u003cp\u003e1167m (3828 ft)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 228px;\"\u003e\n \u003cp\u003eLongitude\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 144px;\"\u003e\n \u003cp\u003e32\u0026deg;32\u0026apos;59\u0026quot;E\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 138px;\"\u003e\n \u003cp\u003e32\u0026deg;42\u0026apos;04\u0026quot;E\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 144px;\"\u003e\n \u003cp\u003e32\u0026deg;40\u0026apos;0\u0026quot;E\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 228px;\"\u003e\n \u003cp\u003eLatitude\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 144px;\"\u003e\n \u003cp\u003e0\u0026deg;49\u0026apos;0\u0026quot;N\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 138px;\"\u003e\n \u003cp\u003e00\u0026deg;40\u0026apos;59\u0026quot;N\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 144px;\"\u003e\n \u003cp\u003e0\u0026deg;15\u0026apos;0\u0026quot;N\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 228px;\"\u003e\n \u003cp\u003eAverage annual temperature (\u0026deg;C)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 144px;\"\u003e\n \u003cp\u003e24.7\u0026deg;C (76.48\u0026deg;F)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 138px;\"\u003e\n \u003cp\u003e22.1\u0026deg;C (71.8\u0026deg;F)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 144px;\"\u003e\n \u003cp\u003e25\u0026deg;C (77\u0026deg;F)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 228px;\"\u003e\n \u003cp\u003eAverage annual rainfall (mm)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 144px;\"\u003e\n \u003cp\u003e\u0026gt;1200 mm\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 138px;\"\u003e\n \u003cp\u003e1100 - 1200 \u0026nbsp; \u0026nbsp; mm\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 144px;\"\u003e\n \u003cp\u003e\u0026lt; 1100 mm\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 228px;\"\u003e\n \u003cp\u003eSoil type\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 144px;\"\u003e\n \u003cp\u003eRed Sandy Clay Loam (Ferralsols)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 138px;\"\u003e\n \u003cp\u003eRed Sandy Clay Loam (Ferralsols)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 144px;\"\u003e\n \u003cp\u003eRed or brown Sandy Loam (Ferralsols \u0026amp; Plinthosols)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 228px;\"\u003e\n \u003cp\u003eEstimated coffee age (years)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 144px;\"\u003e\n \u003cp\u003e24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 138px;\"\u003e\n \u003cp\u003e20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 144px;\"\u003e\n \u003cp\u003e16\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eGreater Luweero is a high Robusta coffee growing region with about 10% of the total country production. However, certain areas in greater Luweero are semi-arid with high climate and rainfall variability. Nevertheless, Robusta coffee production is expanding to these semi-arid areas. This further exposes Robusta coffee to the impacts of climate variability and associated constraints of increased temperatures, humidity variation, reduced soil moisture and low soil physical - chemical attributes. These constraints negatively impact the yield of Robusta coffee and increase yield gaps (Nakyagaba et al., 2024). This directly affects the government\u0026apos;s coffee plan and Vision 2030, which aims for 20 million bags (1.2 million metric tons) worth US$1.5 billion per year.\u0026nbsp;\u003c/p\u003e\n\u003cp id=\"_Toc205208669\"\u003e\u003cstrong\u003e2.2 \u0026nbsp; \u0026nbsp; \u0026nbsp; Experimental Units, Study Design, Plots and Measurements\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe selected coffee farms were taken to have similar management practices and were each considered dependent data points and replicates for the rainfall thresholds. The experimental units were the 150 coffee farms used in the biophysical study and farm survey by Nakyagaba et al. (2024, 2025). The treatments and factor levels were the three rainfall thresholds of low, moderate and high, the two canopy closures of shade and no shade, and the individual farmer fields (Fig. 1). \u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe rainfall thresholds (Nakyagaba et al., 2024) were the main and fixed factors in which the two management practices (treatments) of canopy closure (shade) were nested. The selected farms were considered random sampling units (for random effects) with each farm hosting two experimental plots (shaded and unshaded) from which component observations were obtained according to Brown (2021). A two-stage nested experimental design was therefore adopted for the study. The design was hierarchically structured and nested with repeated/longitudinal measures within the individual plots (cross-over trial) (Schielzeth \u0026amp; Nakagawa, 2013). Coffee plot sizes of 20 m x 20 m (400 m\u003csup\u003e2\u003c/sup\u003e) were de-lineated from farmers\u0026apos; coffee fields. Five coffee bushes per plot were considered for data collection. The study, therefore, involved 180 coffee bushes from 36 coffee plots on 18 coffee farms under the rainfall thresholds in the two districts of Luweero and Nakasongola (Table 1). Therefore, the study had both clustered and longitudinal data.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe plot-level experiment was useful to study microclimate, soil moisture, and soil attributes along rainfall thresholds and shade levels. The microclimate and moisture data variables were corrected several times from each experimental plot for at least 20 and 15 months, respectively. The measurements were air temperature, relative humidity, soil moisture storage, and soil physical-chemical attributes from the Robusta coffee experimental plots under shaded and unshaded conditions along a rainfall gradient. \u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cspan id=\"_Toc205208670\"\u003e\u003cstrong\u003e2.3 \u0026nbsp; \u0026nbsp; \u0026nbsp; Percentage Canopy Cover\u003c/strong\u003e\u003c/span\u003e\u003c/p\u003e\n\u003cp\u003eThe study plots per farm were either located in shade or without shade trees. Research indicates that percentage shade levels are prone to change throughout the year, depending on weather, rainfall, drought, tree species, management practices, especially the pruning cycles, and the extent of tree leaf drops (Mouen Bedimo et al., 2008). The ideal shade for coffee is indicated as 30 to 50% (Beer et al., 1998; Staver et al., 2001). The changes in percentage canopy closure during the data collection period were considered by taking readings every two to three months to get the average percentage and to explain the variation during the data collection period. Canopy cover quantified the shade levels and their influence on microclimate.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe canopy closure from each experimental plot was determined using a spherical densiometer Model A, which measured the light penetrating through the tree\u0026apos;s shade canopy. Using at least five random points on top of each of the marked study coffee bushes and a ladder, the canopy closure for the shaded plots was measured by recording light intercepted at the top of the coffee bushes. On the other hand, the canopy closure from unshaded plots was randomly taken from at least five points in each coffee plot. Using the densitometer results, the average area of shade cast in each plot was then calculated. The shaded plots had a significantly (p\u0026lt;0.001) higher estimated canopy cover (mean = 60.58%) than unshaded plots (mean = 17.54%). However, the shade level of 60% was slightly above the recommended levels of 30 to 50% for coffee (Beer et al., 1998; Staver et al., 2001). The obtained average percentage shade level per plot was the basis in determining the variation in microclimate, soil moisture storage and soil attributes along the rainfall thresholds. \u0026nbsp;\u0026nbsp;\u003c/p\u003e\n\u003cp id=\"_Toc205208671\"\u003e\u003cstrong\u003e2.4 \u0026nbsp; \u0026nbsp; \u0026nbsp; Soil Sampling\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;and Analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eEach of the 36 delineated shaded and unshaded coffee plots was used to evaluate the soil physical-chemical attributes. Compound samples were randomly collected once from each of the 36 plots at the time of setting up the experiment. Within each plot, a soil auger was used to take soil samples from the agricultural layer at 30 cm. Efforts were taken to ensure a distance of at least three feet away from the coffee stem/bush. The collected samples were handled according to Okalebo et al. (2002). Analysis for major soil fertility physical-chemical parameters was done on pH, SOM, total N, available K, P, exchangeable Ca and Mg (Kyalo et al., 2024; Nakyagaba et al., 2024). Additionally, micro nutrients: Zinc, copper, manganese, iron and sand, silt, and clay were analysed following the methodology outlined in Okalebo et al. (2002). Extraction of soil nutrients: calcium (Ca), potassium (K), phosphorus (P), magnesium (Mg), iron (Fe), Zinc (Zn), manganese (Mn) and copper (Cu) using the Mehlich-3 method and an atomic absorption spectrophotometer and a flame photometer used to take readings. Using a pH meter and a 1:2.5 soil water suspension, the soil pH was measured. For nitrogen, the micro-Kjeldahl was used, while the Walkley and Black method was used for organic matter determination. The exchangeable acidity was measured using the sum of exchangeable bases, CEC (S (K\u003csup\u003e+\u003c/sup\u003e+Ca\u003csup\u003e2+\u003c/sup\u003e+Mg\u003csup\u003e2+\u003c/sup\u003e)). To classify the textural classes of the plots, the hydrometer method was used to determine the granulometric composition (particle size) as percentage sand, silt, and clay content of the soil samples. \u0026nbsp;\u003c/p\u003e\n\u003cp id=\"_Toc205208672\"\u003e\u003cstrong\u003e2.5 \u0026nbsp; \u0026nbsp; \u0026nbsp; Volumetric Soil Moisture Data\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;Measurements\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe soil moisture data were collected using a soil moisture sensor, the Sentek soil moisture probe (Diviner 2000 equipment). Each coffee plot was installed with two data collection points (access tubes) for volumetric water content (mm cm\u003csup\u003e-3\u003c/sup\u003e) according to Sarmiento-Soler et al. (2019). Each data collection point used a two-inch-sized access plastic pipe inserted in the soil profile up to 60 cm deep. However, the Diviner readings were taken up to 50 cm deep. Taking data from the two data collection points per plot for moisture determination, there were at least 72 data collection points from the 18 farms. The pipe was inserted into the soil profile, leaving at least 4 cm above the soil to avoid likely changes in moisture content around the pipe. In order to avoid direct water entry into the pipe, the pipes were covered below and above because water affects the functionality of the probe. Using the same data collection points and at each data collection time (2-week interval), the Diviner was inserted in each of these data collection points and it recorded volumetric water content at every 10-cm interval up to 50 cm deep. The default Sentek calibration equation used (Sentek Pty Ltd (2009)) converted the reading to volumetric moisture content (scaled frequency = 0.2746*(volumetric water content \u0026lt;0.3314) + 0). The soil water content obtained per profile (cm) was used and expressed in millimetres (mm) (Sarmiento-Soler et al., 2019). The cumulative sum of the volumetric water content in the profiles was also calculated. The soil moisture data were collected for 15 months, from which 30 observations were made from each of the 72 data collection points/pipes (2160 observations). The moisture data were used to determine the influence of shade on soil moisture along rainfall thresholds.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cspan id=\"_Toc205208673\"\u003e\u003cstrong\u003e2.6 \u0026nbsp; \u0026nbsp; \u0026nbsp; Air Temperature and Relative Humidity\u003c/strong\u003e\u003c/span\u003e\u003c/p\u003e\n\u003cp\u003eThe daily microclimate data (relative humidity (%), mean, minimum and maximum temperatures (\u0026ordm;C)) were recorded using the Hygrochron\u003csup\u003eTM\u003c/sup\u003e iButtons (DS1923) F5 data loggers. These were installed within the delineated study plots with one iButton per plot, two iButtons per coffee farm, and 36 iButton loggers for the entire study plots. The iButtons provided precise actual and current point meteorological data (humidity and temperature) despite the plot\u0026apos;s surrounding environment. Each iButton had a unique registration number, which allowed data traceability. The iButtons measured the valid and accurate temperature and relative humidity (RH) of thermal comfort (Shin et al., 2017). The iButton missions were set with a resolution of \u0026plusmn;0.6% for relative humidity and \u0026plusmn;0.5\u0026ordm;C for temperature. Small plastic cups were used to fix iButtons and covered with silver paper and sole tape to protect against failures and data loss, as well as reduce the environmental influence on readings. The iButtons were fixed on a coffee stem/branch at approximately 1.5 m above the ground (Moreira et al., 2018) within the coffee canopy, with the eye facing down. Brabyn et al. (2014) indicated that surface land temperature is a determinant of microclimate and a better measure of environmental temperature. The iButtons recorded data hourly and this data was collected for 20 months (approximately 14,400 hours) and downloaded at least every two to three months to avoid data loss with a faulty device. The values were later converted to daily and monthly averages for data analysis. \u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cspan id=\"_Toc205208674\"\u003e\u003cstrong\u003e2.7 \u0026nbsp; \u0026nbsp; \u0026nbsp; Statistical Analysis\u003c/strong\u003e\u003c/span\u003e\u003c/p\u003e\n\u003cp\u003eA nested design was adopted for data collection, analysis and interpretation of the model (Schielzeth \u0026amp; Nakagawa, 2013). The nesting defined the response variation that was attributed to the nested factors without estimating interaction variance. It also allowed covariance structures. The collected data were analysed based on shade levels and rainfall gradients (thresholds), using R version 4.1.1 (Li, 2021) and Stata 14. The data were checked for normality using the Shapiro-Wilk test (skewness and kurtosis) results. Descriptive statistics (minimum, maximum, mean, frequency, median, SE, and SD) were used for quantitative variables. Simple correlation, \u0026nbsp;general linear model (ANOVA) (Bono et al., 2021) and simple regression analysis were used to determine the relationships, variation, and level of significance between thresholds and shade levels. The t-test was used to determine variations between shade levels. For non-normal parameters, a non-parametric alternative, Kruskal-Wallis\u0026rsquo;s rank sum test, was used. For pairs of means that were significant between thresholds and tree shade levels, Tukey (HSD) and Sidak post hoc significant tests (p\u0026lt;0.05) were performed. \u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe observations between farms were independent with dependence-correlated errors related to nesting (Serenini et al., 2019), hence, a mixed effect model. The Generalised Linear Mixed Model (GLMM) for nested, longitudinal repeated measures (soil moisture and microclimate) between and within a subject or group was used to analyse the repeated data measurements with fixed and random effects (Bono et al., 2021; Brown, 2021; Laird \u0026amp; Ware, 2007). The GLMM method is suitable for unbalanced designs (Liu et al.; 2012). Therefore, the generalised linear mixed effects model (GLMM / glmer) (also called multilevel, hierarchical, or random effects modelling) was used (Hair \u0026amp; F\u0026aacute;vero, 2019). The random effects feature allowed for the clustering of data in groups (Schielzeth \u0026amp; Nakagawa, 2013). Model analysis allowed for diversity of general and flexible correlation patterns. The model allowed the intercept to vary between shade levels and thresholds but not the predictor coefficients. The model accounted for differences in several inherent, management, climatic and agronomic practices, tree shade species, age, coffee lines, or varieties and coffee age, which were kept constant since they varied between shade levels and rainfall thresholds.\u003c/p\u003e"},{"header":"3. Results","content":"\u003cp\u003e\u003cstrong\u003e3.1 \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003eVariation in Soil Fertility Parameters across Rainfall Gradient\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe influence of rainfall on soil parameters was analysed using ANOVA and the Kruskal-Wallis rank sum test. The soil texture was classified as sandy clay loam in high rainfall threshold and sandy clay in moderate and low rainfall thresholds. Despite the differences in soil texture, most of the chemical properties were almost alike but the average physical-chemical parameters varied significantly (p\u0026lt;0.001) across thresholds (Table 2). \u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 2\u003c/strong\u003e\u003cstrong\u003e:\u0026nbsp;\u003c/strong\u003eVariation in soil physical-chemical parameters across rainfall thresholds (n=36) in Greater Luweero\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"111%\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" style=\"width: 25px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSoil Parameter\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 14px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eHigh\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 14px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eModerate\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 15px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eLow\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 6px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCNL\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 7px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSkew\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 7px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ese\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 7px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ep-value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 8px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMean\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSD\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMean\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSD\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMean\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSD\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 25px;\"\u003e\n \u003cp\u003eSoil pH (1:2.5 water)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 8px;\"\u003e\n \u003cp\u003e6.32\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 6px;\"\u003e\n \u003cp\u003e0.21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 7px;\"\u003e\n \u003cp\u003e5.9\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 7px;\"\u003e\n \u003cp\u003e0.33\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 7px;\"\u003e\n \u003cp\u003e5.94\u003csup\u003ec\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 7px;\"\u003e\n \u003cp\u003e0.63\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 6px;\"\u003e\n \u003cp\u003e5.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 7px;\"\u003e\n \u003cp\u003e0.24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 7px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 7px;\"\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 25px;\"\u003e\n \u003cp\u003eOrganic Matter (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 8px;\"\u003e\n \u003cp\u003e4.89\u003csup\u003ea\u003c/sup\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 6px;\"\u003e\n \u003cp\u003e0.53\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 7px;\"\u003e\n \u003cp\u003e3.71\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 7px;\"\u003e\n \u003cp\u003e0.34\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 7px;\"\u003e\n \u003cp\u003e3.77\u003csup\u003ec\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 7px;\"\u003e\n \u003cp\u003e0.58\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 6px;\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 7px;\"\u003e\n \u003cp\u003e0.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 7px;\"\u003e\n \u003cp\u003e0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 7px;\"\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 25px;\"\u003e\n \u003cp\u003eTotal Nitrogen (%)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 8px;\"\u003e\n \u003cp\u003e0.24\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 6px;\"\u003e\n \u003cp\u003e0.17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 7px;\"\u003e\n \u003cp\u003e0.2\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 7px;\"\u003e\n \u003cp\u003e0.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 7px;\"\u003e\n \u003cp\u003e0.19\u003csup\u003ec\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 7px;\"\u003e\n \u003cp\u003e0.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 6px;\"\u003e\n \u003cp\u003e0.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 7px;\"\u003e\n \u003cp\u003e0.47\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 7px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 7px;\"\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 25px;\"\u003e\n \u003cp\u003ePhosphorus (mg kg\u003csup\u003e-1\u003c/sup\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 8px;\"\u003e\n \u003cp\u003e27.4\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 6px;\"\u003e\n \u003cp\u003e5.49\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 7px;\"\u003e\n \u003cp\u003e17.24\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 7px;\"\u003e\n \u003cp\u003e2.63\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 7px;\"\u003e\n \u003cp\u003e20.48\u003csup\u003ec\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 7px;\"\u003e\n \u003cp\u003e5.13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 6px;\"\u003e\n \u003cp\u003e15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 7px;\"\u003e\n \u003cp\u003e0.53\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 7px;\"\u003e\n \u003cp\u003e0.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 7px;\"\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 25px;\"\u003e\n \u003cp\u003eCalcium (cmol (+) kg\u003csup\u003e-1\u003c/sup\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 8px;\"\u003e\n \u003cp\u003e7.74\u003csup\u003ea\u003c/sup\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 6px;\"\u003e\n \u003cp\u003e1.91\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 7px;\"\u003e\n \u003cp\u003e4.51\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 7px;\"\u003e\n \u003cp\u003e1.66\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 7px;\"\u003e\n \u003cp\u003e4.73\u003csup\u003ec\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 7px;\"\u003e\n \u003cp\u003e2.59\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 6px;\"\u003e\n \u003cp\u003e0.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 7px;\"\u003e\n \u003cp\u003e0.53\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 7px;\"\u003e\n \u003cp\u003e0.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 7px;\"\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 25px;\"\u003e\n \u003cp\u003eMagnesium (cmol (+) kg\u003csup\u003e-1\u003c/sup\u003e)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 8px;\"\u003e\n \u003cp\u003e1.31\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 6px;\"\u003e\n \u003cp\u003e0.18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 7px;\"\u003e\n \u003cp\u003e0.91\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 7px;\"\u003e\n \u003cp\u003e0.23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 7px;\"\u003e\n \u003cp\u003e0.89\u003csup\u003ec\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 7px;\"\u003e\n \u003cp\u003e0.23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 6px;\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 7px;\"\u003e\n \u003cp\u003e-0.19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 7px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 7px;\"\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 25px;\"\u003e\n \u003cp\u003ePotassium (cmol (+) kg\u003csup\u003e-1\u003c/sup\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 8px;\"\u003e\n \u003cp\u003e0.69\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 6px;\"\u003e\n \u003cp\u003e0.33\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 7px;\"\u003e\n \u003cp\u003e0.57\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 7px;\"\u003e\n \u003cp\u003e0.16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 7px;\"\u003e\n \u003cp\u003e0.57\u003csup\u003ecb\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 7px;\"\u003e\n \u003cp\u003e0.19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 6px;\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 7px;\"\u003e\n \u003cp\u003e1.36\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 7px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 7px;\"\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 25px;\"\u003e\n \u003cp\u003eCEC (cmol kg\u003csup\u003e-1\u003c/sup\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 8px;\"\u003e\n \u003cp\u003e12.7\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 6px;\"\u003e\n \u003cp\u003e2.59\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 7px;\"\u003e\n \u003cp\u003e9.26\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 7px;\"\u003e\n \u003cp\u003e1.73\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 7px;\"\u003e\n \u003cp\u003e10.07\u003csup\u003ec\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 7px;\"\u003e\n \u003cp\u003e2.39\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 6px;\"\u003e\n \u003cp\u003e5-15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 7px;\"\u003e\n \u003cp\u003e0.84\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 7px;\"\u003e\n \u003cp\u003e0.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 7px;\"\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 25px;\"\u003e\n \u003cp\u003eAluminium (mg kg\u003csup\u003e-1\u003c/sup\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 8px;\"\u003e\n \u003cp\u003e0.003\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 6px;\"\u003e\n \u003cp\u003e0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 7px;\"\u003e\n \u003cp\u003e0.12\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 7px;\"\u003e\n \u003cp\u003e0.15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 7px;\"\u003e\n \u003cp\u003e1.3\u003csup\u003ec\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 7px;\"\u003e\n \u003cp\u003e0.19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 6px;\"\u003e\n \u003cp\u003e1.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 7px;\"\u003e\n \u003cp\u003e1.65\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 7px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 7px;\"\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 25px;\"\u003e\n \u003cp\u003eIron (mg kg\u003csup\u003e-1\u003c/sup\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 8px;\"\u003e\n \u003cp\u003e0.36\u003csup\u003ea\u003c/sup\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 6px;\"\u003e\n \u003cp\u003e0.08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 7px;\"\u003e\n \u003cp\u003e0.42\u003csup\u003eb\u003c/sup\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 7px;\"\u003e\n \u003cp\u003e0.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 7px;\"\u003e\n \u003cp\u003e0.41\u003csup\u003ec\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 7px;\"\u003e\n \u003cp\u003e0.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 6px;\"\u003e\n \u003cp\u003e4.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 7px;\"\u003e\n \u003cp\u003e-0.93\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 7px;\"\u003e\n \u003cp\u003e0.47\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 7px;\"\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 25px;\"\u003e\n \u003cp\u003eManganese (mg kg\u003csup\u003e-1\u003c/sup\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 8px;\"\u003e\n \u003cp\u003e1.73\u003csup\u003ea\u003c/sup\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 6px;\"\u003e\n \u003cp\u003e0.29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 7px;\"\u003e\n \u003cp\u003e1.06\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 7px;\"\u003e\n \u003cp\u003e0.39\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 7px;\"\u003e\n \u003cp\u003e0.92\u003csup\u003ec\u003c/sup\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 7px;\"\u003e\n \u003cp\u003e0.47\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 6px;\"\u003e\n \u003cp\u003e20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 7px;\"\u003e\n \u003cp\u003e-0.18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 7px;\"\u003e\n \u003cp\u003e4.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 7px;\"\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 25px;\"\u003e\n \u003cp\u003eCopper (mg kg\u003csup\u003e-1\u003c/sup\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 8px;\"\u003e\n \u003cp\u003e3.31\u003csup\u003ea\u0026nbsp;\u003c/sup\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 6px;\"\u003e\n \u003cp\u003e1.91\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 7px;\"\u003e\n \u003cp\u003e1.9\u003csup\u003ebc\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 7px;\"\u003e\n \u003cp\u003e0.33\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 7px;\"\u003e\n \u003cp\u003e1.83\u003csup\u003ecb\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 7px;\"\u003e\n \u003cp\u003e0.27\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 6px;\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 7px;\"\u003e\n \u003cp\u003e3.24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 7px;\"\u003e\n \u003cp\u003e0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 7px;\"\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 25px;\"\u003e\n \u003cp\u003eZinc (mg kg\u003csup\u003e-1\u003c/sup\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 8px;\"\u003e\n \u003cp\u003e1.45\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 6px;\"\u003e\n \u003cp\u003e0.93\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 7px;\"\u003e\n \u003cp\u003e0.46\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 7px;\"\u003e\n \u003cp\u003e0.35\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 7px;\"\u003e\n \u003cp\u003e0.42\u003csup\u003ec\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 7px;\"\u003e\n \u003cp\u003e0.17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 6px;\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 7px;\"\u003e\n \u003cp\u003e2.52\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 7px;\"\u003e\n \u003cp\u003e0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 7px;\"\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 25px;\"\u003e\n \u003cp\u003eSoil texture\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"bottom\" style=\"width: 14px;\"\u003e\n \u003cp\u003eSandy Clay Loam\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 14px;\"\u003e\n \u003cp\u003eSandy Clay\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 15px;\"\u003e\n \u003cp\u003eSandy Clay\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 6px;\"\u003e\u0026nbsp;\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 7px;\"\u003e\u0026nbsp;\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 7px;\"\u003e\u0026nbsp;\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 7px;\"\u003e\u0026nbsp;\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003eMeans followed by the same letters in the rows indicate that there are no significant differences (p\u0026gt; 0.05), while different letters in the row indicate significant differences (p \u0026lt;0.05) according to LSD (0.05).\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe soil parameters were within the critical levels for coffee productivity, except for cations: potassium, magnesium and calcium, which were low (Table 2). For instance there was adequate pH and nutrients within the critical range for high threshold, an indicator of good soil quality for coffee growth. However, K, Mg and Ca were below the critical levels required for proper growth and productivity. Equally, the cation exchange capacity (CEC) values varied significantly between rainfall thresholds with higher CEC at the high rainfall (12.65\u0026plusmn;2.59). The average CEC ranged from 9.26 in moderate to 12.65 in high threshold. Additionally, levels of micro nutrients iron, Zinc, Copper, aluminium and manganese were generally low for all the thresholds. However, the micronutrients were slightly higher in low and moderate than in high threshold (Table 2). \u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cspan id=\"_Toc163662561\"\u003e\u003cstrong\u003e3.2 \u0026nbsp; \u0026nbsp; \u0026nbsp; Influence of Tree Shade on Soil Fertility Parameters\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;along the Rainfall Gradient\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/span\u003e\u003c/p\u003e\n\u003cp\u003eThe main shade trees were: \u003cem\u003eFicus natalensis\u003c/em\u003e (Mutuba), Ficus ovata (\u003cem\u003eF. brachypoda\u003c/em\u003e) (Kookowe, mukookowe, nserere), \u003cem\u003eAlbizia coriaria\u003c/em\u003e (Mugavu- Omugavu Omuganda), \u003cem\u003ePersea Americana\u003c/em\u003e (avocado), \u003cem\u003eMaesopsis eminii\u003c/em\u003e (umbrella tree),\u003cem\u003e\u0026nbsp;Artocarpus heterophyllus\u003c/em\u003e (Jackfruit) and bananas. The soil parameter results under the shade levels were almost similar to those in unshaded plots. Still, shade had a significant (p\u0026lt;0.001) influence on soil parameters like soil \u0026nbsp; potassium and pH with higher levels of pH and K in shaded than unshaded plots (Table 3). Besides, majority of soil parameters in the shade plots were within the critical levels required for Robusta coffee production except calcium, potassium, phosphorus and magnesium which were inadequate. Micro nutrients like iron and aluminium were lower in shaded than in unshaded plots. Further, the CEC varied significantly (p\u0026lt;0.05) between shade levels, with higher CEC in shaded (10.95) than in unshaded (10.4) plots. Nevertheless, all cation levels were generally low (Table 3). \u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cspan id=\"_Toc205246228\"\u003e\u003cstrong\u003eTable 3\u003c/strong\u003e\u003cstrong\u003e:\u0026nbsp;\u003c/strong\u003eVariation in soil physical-chemical parameters across shade levels (n=36) in greater Luweero\u003c/span\u003e\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"624\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 185px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSoil parameter\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 121px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eShaded (60.58%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 138px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eUnshaded (17.54%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 78px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCNL\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 102px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ep-value (p\u0026lt;0.001)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 185px;\"\u003e\n \u003cp\u003eSoil pH (1:2.5 water)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 121px;\"\u003e\n \u003cp\u003e6.17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 138px;\"\u003e\n \u003cp\u003e5.95\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 78px;\"\u003e\n \u003cp\u003e5.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 102px;\"\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 185px;\"\u003e\n \u003cp\u003eOrganic Matter (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 121px;\"\u003e\n \u003cp\u003e4.17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 138px;\"\u003e\n \u003cp\u003e4.09\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 78px;\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 102px;\"\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 185px;\"\u003e\n \u003cp\u003eTotal Nitrogen (%)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 121px;\"\u003e\n \u003cp\u003e0.212\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 138px;\"\u003e\n \u003cp\u003e0.21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 78px;\"\u003e\n \u003cp\u003e0.25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 102px;\"\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 185px;\"\u003e\n \u003cp\u003ePhosphorus (mg kg\u003csup\u003e-1\u003c/sup\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 121px;\"\u003e\n \u003cp\u003e22.12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 138px;\"\u003e\n \u003cp\u003e21.36\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 78px;\"\u003e\n \u003cp\u003e36\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 102px;\"\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 185px;\"\u003e\n \u003cp\u003eCalcium (cmol (+) kg\u003csup\u003e-1\u003c/sup\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 121px;\"\u003e\n \u003cp\u003e5.97\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 138px;\"\u003e\n \u003cp\u003e5.38\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 78px;\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 102px;\"\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 185px;\"\u003e\n \u003cp\u003eMagnesium (cmol (+) kg\u003csup\u003e-1\u003c/sup\u003e)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 121px;\"\u003e\n \u003cp\u003e1.08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 138px;\"\u003e\n \u003cp\u003e1.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 78px;\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 102px;\"\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 185px;\"\u003e\n \u003cp\u003ePotassium (cmol (+) kg\u003csup\u003e-1\u003c/sup\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 121px;\"\u003e\n \u003cp\u003e0.65\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 138px;\"\u003e\n \u003cp\u003e0.57\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 78px;\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 102px;\"\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 185px;\"\u003e\n \u003cp\u003eCEC (cmol kg\u003csup\u003e-1\u003c/sup\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 121px;\"\u003e\n \u003cp\u003e10.95\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 138px;\"\u003e\n \u003cp\u003e10.40\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 78px;\"\u003e\n \u003cp\u003e5 -\u0026gt;15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 102px;\"\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 185px;\"\u003e\n \u003cp\u003eAluminium (mg kg\u003csup\u003e-1\u003c/sup\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 121px;\"\u003e\n \u003cp\u003e0.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 138px;\"\u003e\n \u003cp\u003e0.114\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 78px;\"\u003e\n \u003cp\u003e1.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 102px;\"\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 185px;\"\u003e\n \u003cp\u003eIron (mg kg\u003csup\u003e-1\u003c/sup\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 121px;\"\u003e\n \u003cp\u003e0.395\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 138px;\"\u003e\n \u003cp\u003e0.403\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 78px;\"\u003e\n \u003cp\u003e4.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 102px;\"\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 185px;\"\u003e\n \u003cp\u003eManganese (mg kg\u003csup\u003e-1\u003c/sup\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 121px;\"\u003e\n \u003cp\u003e1.307\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 138px;\"\u003e\n \u003cp\u003e1.167\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 78px;\"\u003e\n \u003cp\u003e20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 102px;\"\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 185px;\"\u003e\n \u003cp\u003eCopper (mg kg\u003csup\u003e-1\u003c/sup\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 121px;\"\u003e\n \u003cp\u003e2.38\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 138px;\"\u003e\n \u003cp\u003e2.31\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 78px;\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 102px;\"\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 185px;\"\u003e\n \u003cp\u003eZinc (mg kg\u003csup\u003e-1\u003c/sup\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 121px;\"\u003e\n \u003cp\u003e0.796\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 138px;\"\u003e\n \u003cp\u003e0.760\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 78px;\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 102px;\"\u003e\n \u003cp\u003e0.002\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cspan id=\"_Toc205208679\"\u003e\u0026nbsp;3.3 \u0026nbsp; \u0026nbsp; \u0026nbsp;The Variation in Soil Moisture (mm) in the Profile along a Rainfall Gradient\u0026nbsp;\u003c/span\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThere was significant (p\u0026lt;0.05) difference in soil water content (mm) across the soil profiles and the rainfall gradients. There was generally higher soil moisture in the profile under high rainfall than the moderate and low rainfall thresholds. Moreover, soil moisture increased with profile depth, except for the low rainfall threshold. For example, it was generally high for high and moderate rainfall gradients at 50 cm profile. Contrastingly, there was decreased soil moisture levels at 20 cm, 30 cm and 40 cm at the low rainfall threshold, which slightly increased at 50 cm. However, a clear increasing trend was observed for high and moderate rainfall thresholds between 20 cm and 50 cm, with a steadily increasing trend in moderate gradient. Largely, the total soil moisture for the profiles decreased with the rainfall gradient, with high moisture at high thresholds and lower at the low threshold (Table 4).\u003c/p\u003e\n\u003cp id=\"_Toc205246229\"\u003e\u003cstrong\u003eTable 4\u003c/strong\u003e\u003cstrong\u003e:\u0026nbsp;\u003c/strong\u003eVariation in soil moisture levels (in millimetres) across the soil profile along rainfall thresholds in greater Luweero\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"100%\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" style=\"width: 16px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eProfile (cm)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 23px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eHigh\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 23px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eModerate\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 23px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eLow\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ep-value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMean\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSD\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMean\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSD\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMean\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSD\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e14.26\u003csup\u003eab\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e7.06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e13.59\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e6.55\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e14.93\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e6.23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e0.0028\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003e20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e20.18\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e6.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e17.7\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e6.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e18.48\u003csup\u003ecb\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e5.35\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003e30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e20.67\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e7.78\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e18.46\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e6.64\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e15.13\u003csup\u003ec\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e6.82\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003e40\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e22.15\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e8.44\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e19.42\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e6.47\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e13.17\u003csup\u003ec\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e7.14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003e50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e22.38\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e8.89\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e19.98\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e6.08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e14.01\u003csup\u003ec\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e6.52\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003eTotal (mm)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e99.64\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e89.15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e75.72\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003eMeans followed by different letters in rows indicate significant differences (p\u0026gt; 0.05), while the same letters in the row indicate that there are no significant differences (p \u0026lt;0.05) according to LSD (0.005).\u003c/strong\u003e\u003c/p\u003e\n\u003cp id=\"_Toc205208680\"\u003e\u003cstrong\u003e3.4 \u0026nbsp; \u0026nbsp; \u0026nbsp; Variation in Soil Moisture across the Profile and the shade levels\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eShade level had a significant (p=0.001) effect on soil moisture from profile depth level 30 cm to 50 cm. The soil moisture was higher down the profile depth (50cm) than at 10 and 20 cm. All moisture data collection points in the shaded plots had both lower minimum and higher maximum values than non-shade plots. However, the trend for maximum moisture levels was clear under unshaded plots, where it increased with the profile depth. Still, shade increased the mean and maximum soil moisture levels in all profile levels measured. Besides, total soil moisture (mm) was higher in shaded plots than in unshaded plots (Table 5).\u003c/p\u003e\n\u003cp id=\"_Toc205208681\"\u003e\u003cstrong\u003e3.5 \u0026nbsp;Variation in Microclimate across Rainfall Gradient and Tree Shade Levels\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe microclimate data varied significantly (p\u0026lt;0.001) across rainfall gradients. The high rainfall threshold indicated a lower mean temperature (22.63\u0026plusmn;1.07). However, results showed higher mean (23.14\u0026plusmn;1.22) and a lower minimum temperature (19.42\u0026deg;C) in the moderate rainfall threshold. Instead, higher minimum (20.44\u0026deg;C) and maximum (26.33\u0026deg;C) temperatures were indicated in the low than in other rainfall thresholds.\u0026nbsp;\u003c/p\u003e\n\u003cp id=\"_Toc205246230\"\u003e\u003cstrong\u003eTable 5\u003c/strong\u003e\u003cstrong\u003e:\u0026nbsp;\u003c/strong\u003eVariation in soil moisture levels (mm) across shade levels in greater Luweero\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"624\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" style=\"width: 114px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eProfile (cm)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" style=\"width: 228px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eShaded\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" style=\"width: 210px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eUnshaded\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 72px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ep-value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 108px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMean\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 54px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMin\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMax\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 96px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMean\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 54px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMin\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 60px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMax\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 114px;\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 108px;\"\u003e\n \u003cp\u003e14.4(7.38)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 54px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e34.28\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 96px;\"\u003e\n \u003cp\u003e14.12 (6.72)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 54px;\"\u003e\n \u003cp\u003e0.004\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 60px;\"\u003e\n \u003cp\u003e30.48\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e0.375\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 114px;\"\u003e\n \u003cp\u003e20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 108px;\"\u003e\n \u003cp\u003e19.14(6.51)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 54px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e36.51\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 96px;\"\u003e\n \u003cp\u003e18.72(5.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 54px;\"\u003e\n \u003cp\u003e3.97\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 60px;\"\u003e\n \u003cp\u003e35.27\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e0.128\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 114px;\"\u003e\n \u003cp\u003e30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 108px;\"\u003e\n \u003cp\u003e19.37(7.09)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 54px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e38.97\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 96px;\"\u003e\n \u003cp\u003e17.03(7.67)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 54px;\"\u003e\n \u003cp\u003e1.59\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 60px;\"\u003e\n \u003cp\u003e35.78\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 114px;\"\u003e\n \u003cp\u003e40\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 108px;\"\u003e\n \u003cp\u003e19.16(7.95)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 54px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e37.55\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 96px;\"\u003e\n \u003cp\u003e17.66(8.63)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 54px;\"\u003e\n \u003cp\u003e1.93\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 60px;\"\u003e\n \u003cp\u003e36.69\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 114px;\"\u003e\n \u003cp\u003e50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 108px;\"\u003e\n \u003cp\u003e19.69(7.79\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 54px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e39.34\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 96px;\"\u003e\n \u003cp\u003e18.2(8.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 54px;\"\u003e\n \u003cp\u003e1.35\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 60px;\"\u003e\n \u003cp\u003e39.27\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 114px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eTotal (mm)\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 108px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e91.76\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 54px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 96px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e85.73\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 54px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 60px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eCorrespondingly, the lowest mean (80.75\u0026plusmn;9.78), minimum (54.17%), and maximum (93.44%) RH were observed in the low than other thresholds (Table 6). Nevertheless, differences in mean temperature between the thresholds was about 0.6\u0026deg;C, and not greater than 1.8% for RH (Table 6).\u0026nbsp;\u003c/p\u003e\n\u003cp id=\"_Toc205246231\"\u003e\u003cstrong\u003eTable 6\u003c/strong\u003e\u003cstrong\u003e:\u0026nbsp;\u003c/strong\u003eVariation in microclimate (Temp. and RH) across a rainfall gradient in greater Luweero\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" align=\"\" width=\"648\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" style=\"width: 75px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eVariable\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 171px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eHigh\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 174px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eModerate\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 168px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eLow\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 60px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ep-value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eTemp℃\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eR.H (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 86px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eTemp. ℃\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 88px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eR.H. (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 82px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eTemp. ℃\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 86px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eR.H (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 75px;\"\u003e\n \u003cp\u003eMean\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e22.63(1.07)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e81.97 (6.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 86px;\"\u003e\n \u003cp\u003e23.14(1.22)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 88px;\"\u003e\n \u003cp\u003e81.23(7.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 82px;\"\u003e\n \u003cp\u003e22.96 (1.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 86px;\"\u003e\n \u003cp\u003e80.75(9.78)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"5\" valign=\"top\" style=\"width: 60px;\"\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 75px;\"\u003e\n \u003cp\u003eRange\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e5.52\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e33.09\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 86px;\"\u003e\n \u003cp\u003e6.49\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 88px;\"\u003e\n \u003cp\u003e39.77\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 82px;\"\u003e\n \u003cp\u003e6.09\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 86px;\"\u003e\n \u003cp\u003e39.27\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 75px;\"\u003e\n \u003cp\u003eVar.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e1.15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e45.90\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 86px;\"\u003e\n \u003cp\u003e1.48\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 88px;\"\u003e\n \u003cp\u003e57.73\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 82px;\"\u003e\n \u003cp\u003e1.53\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 86px;\"\u003e\n \u003cp\u003e95.60\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 75px;\"\u003e\n \u003cp\u003eMin.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e19.58\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e62.43\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 86px;\"\u003e\n \u003cp\u003e19.42\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 88px;\"\u003e\n \u003cp\u003e54.73\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 82px;\"\u003e\n \u003cp\u003e20.24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 86px;\"\u003e\n \u003cp\u003e54.17\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 75px;\"\u003e\n \u003cp\u003eMax.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e25.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e95.52\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 86px;\"\u003e\n \u003cp\u003e25.91\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 88px;\"\u003e\n \u003cp\u003e94.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 82px;\"\u003e\n \u003cp\u003e26.33\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 86px;\"\u003e\n \u003cp\u003e93.44\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eSimilarly, tree shade had a significant (p\u0026lt;0.001) effect on microclimate. There were lower temperature in the shade than non-shade plots with a difference below 0.6\u0026deg;C. The minimum and maximum microclimate were lower in shade than in unshaded plots, but lower than 0.8\u0026deg;C between shade levels. Shading reduced the mean, minimum, and maximum temperatures but increased the minimum and maximum relative humidity by approximately 7.6% than non-shade plots (Table 7). Nevertheless, the maximum temperature limits were not above the photosynthetic requirements for Robusta coffee of about 30\u0026deg;C. While the minimum and maximum temperatures seemed similar for the shade levels, they were significantly (p\u0026lt;0.001) different (Figs. 2 and 3) and lower in shaded plots than in unshaded.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 7:\u0026nbsp;\u003c/strong\u003eVariation in microclimate (Temp. and RH) across Shade levels\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"594\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 132px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eVariable\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 185px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eShaded\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 185px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eUnshaded\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 92px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ep-value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 132px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 92px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eTemp. ℃\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 92px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eR.H.\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 92px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eTemp. ℃\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 92px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eR.H.\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 92px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 132px;\"\u003e\n \u003cp\u003eMean\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 92px;\"\u003e\n \u003cp\u003e22.64\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 92px;\"\u003e\n \u003cp\u003e81.19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 92px;\"\u003e\n \u003cp\u003e23.23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 92px;\"\u003e\n \u003cp\u003e81.69\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 92px;\"\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 132px;\"\u003e\n \u003cp\u003eSD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 92px;\"\u003e\n \u003cp\u003e1.19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 92px;\"\u003e\n \u003cp\u003e7.98\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 92px;\"\u003e\n \u003cp\u003e1.14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 92px;\"\u003e\n \u003cp\u003e7.35\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 92px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 132px;\"\u003e\n \u003cp\u003eRange\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 92px;\"\u003e\n \u003cp\u003e6.13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 92px;\"\u003e\n \u003cp\u003e40.79\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 92px;\"\u003e\n \u003cp\u003e6.24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 92px;\"\u003e\n \u003cp\u003e40.33\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 92px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 132px;\"\u003e\n \u003cp\u003eVariance\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 92px;\"\u003e\n \u003cp\u003e1.41\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 92px;\"\u003e\n \u003cp\u003e63.68\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 92px;\"\u003e\n \u003cp\u003e1.29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 92px;\"\u003e\n \u003cp\u003e53.98\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 92px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 132px;\"\u003e\n \u003cp\u003eMinimum\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 92px;\"\u003e\n \u003cp\u003e19.42\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 92px;\"\u003e\n \u003cp\u003e54.73\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 92px;\"\u003e\n \u003cp\u003e20.09\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 92px;\"\u003e\n \u003cp\u003e54.17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 92px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 132px;\"\u003e\n \u003cp\u003eMaximum\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 92px;\"\u003e\n \u003cp\u003e25.55\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 92px;\"\u003e\n \u003cp\u003e95.52\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 92px;\"\u003e\n \u003cp\u003e26.33\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 92px;\"\u003e\n \u003cp\u003e94.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 92px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n\u003cp id=\"_Toc205208682\"\u003e\u003cstrong\u003e3.6 \u0026nbsp; \u0026nbsp; \u0026nbsp; The Generalised Linear Mixed (GLM) Model\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe microclimate and soil moisture data were further analysed using the Generalised Linear Mixed (GLM) model for the longitudinal nested experiment. The results indicate significant (p\u0026lt;0.05) differences between thresholds and shade levels. Results show high soil moisture levels under shaded plots and high rainfall threshold. The interaction between thresholds and shade was positive and significant (p\u0026lt;0.05) for all models except model III and IV, where the interaction was negative but significant. The best soil moisture model was taken to be Model V at 50cm. Model V had the lowest Akaike\u0026apos;s Information Criterion (AIC) and Bayesian Information Criterion (BIC). The variance was also lower in models V and IV (Table 8).\u0026nbsp;\u003c/p\u003e\n\u003cp id=\"_Toc205246232\"\u003e\u003cstrong\u003eTable\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003e8\u003c/strong\u003e\u003cstrong\u003e:\u0026nbsp;\u003c/strong\u003eResults of the Generalised Linear Mixed Model for soil moisture levels\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"654\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" style=\"width: 132px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eVariable\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"5\" style=\"width: 522px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSoil Moisture levels\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 106px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eModel I: At 10cm\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 106px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eModel II: At 20cm\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 106px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eModel III: At 30cm\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 106px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eModel IV: At 40cm\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 100px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eModel V: At 50cm\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 132px;\"\u003e\n \u003cp\u003eModerate\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 106px;\"\u003e\n \u003cp\u003e-3.88(.03) ***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 106px;\"\u003e\n \u003cp\u003e-0.62(.03) ***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 106px;\"\u003e\n \u003cp\u003e6.85(.03) ***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 106px;\"\u003e\n \u003cp\u003e10.1(.02) ***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 100px;\"\u003e\n \u003cp\u003e7.83(.02) ***\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 132px;\"\u003e\n \u003cp\u003eHigh\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 106px;\"\u003e\n \u003cp\u003e-5.10(.03) ***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 106px;\"\u003e\n \u003cp\u003e0.42(.03) ***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 106px;\"\u003e\n \u003cp\u003e7.44(.03) ***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 106px;\"\u003e\n \u003cp\u003e10.8(.03) ***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 100px;\"\u003e\n \u003cp\u003e6.28(.02) ***\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 132px;\"\u003e\n \u003cp\u003eShaded\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 106px;\"\u003e\n \u003cp\u003e-6.67(.04) ***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 106px;\"\u003e\n \u003cp\u003e-2.37(.05) ***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 106px;\"\u003e\n \u003cp\u003e4.98(.05) ***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 106px;\"\u003e\n \u003cp\u003e7.44(.04) ***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 100px;\"\u003e\n \u003cp\u003e-3.1(.04) ***\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 132px;\"\u003e\n \u003cp\u003eModerate*Shaded\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 106px;\"\u003e\n \u003cp\u003e7.31(.13) ***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 106px;\"\u003e\n \u003cp\u003e2.36(.13) ***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 106px;\"\u003e\n \u003cp\u003e-6.2(.14) ***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 106px;\"\u003e\n \u003cp\u003e-9.2(.13) ***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 100px;\"\u003e\n \u003cp\u003e1.34(.11) ***\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 132px;\"\u003e\n \u003cp\u003eHigh*Shaded\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 106px;\"\u003e\n \u003cp\u003e6.53(.13) ***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 106px;\"\u003e\n \u003cp\u003e1.98(.13) ***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 106px;\"\u003e\n \u003cp\u003e-5.3(.14) ***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 106px;\"\u003e\n \u003cp\u003e-7.7(013) ***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 100px;\"\u003e\n \u003cp\u003e2.83(.11) ***\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 132px;\"\u003e\n \u003cp\u003e_cons\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 106px;\"\u003e\n \u003cp\u003e15.87(.02) ***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 106px;\"\u003e\n \u003cp\u003e18.78(.02) ***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 106px;\"\u003e\n \u003cp\u003e12.8(.02) ***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 106px;\"\u003e\n \u003cp\u003e10.8(.02) ***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 100px;\"\u003e\n \u003cp\u003e14.6(.02) ***\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 132px;\"\u003e\n \u003cp\u003eProfile levels (Var.)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 106px;\"\u003e\n \u003cp\u003e39.09(.09)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 106px;\"\u003e\n \u003cp\u003e40.72(.09)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 106px;\"\u003e\n \u003cp\u003e45.1(.10)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 106px;\"\u003e\n \u003cp\u003e36.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 100px;\"\u003e\n \u003cp\u003e28.1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 132px;\"\u003e\n \u003cp\u003eAIC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 106px;\"\u003e\n \u003cp\u003e2611274\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 106px;\"\u003e\n \u003cp\u003e2627710\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 106px;\"\u003e\n \u003cp\u003e2668504\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 106px;\"\u003e\n \u003cp\u003e2587894\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 100px;\"\u003e\n \u003cp\u003e2479117\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 132px;\"\u003e\n \u003cp\u003eBIC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 106px;\"\u003e\n \u003cp\u003e2611350\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 106px;\"\u003e\n \u003cp\u003e2627787\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 106px;\"\u003e\n \u003cp\u003e2668581\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 106px;\"\u003e\n \u003cp\u003e2587970\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 100px;\"\u003e\n \u003cp\u003e2479193\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eFor Relative humidity and temperature, these increased with increase in rainfall threshold with a significant (p\u0026lt;0.05) interactions between shade levels (Table 9). There was positive significant (P\u0026lt;0.05) relationship for humidity across the rainfall thresholds and between shade levels. Though, for temperature, a negative significant relationship was observed between low and high thresholds. Likewise, there was negative and significant interaction between shade levels and rainfall thresholds, for both temperature and humidity data (Table 9).\u0026nbsp;\u003c/p\u003e\n\u003cp id=\"_Toc205246233\"\u003e\u003cstrong\u003eTable\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003e9\u003c/strong\u003e\u003cstrong\u003e:\u0026nbsp;\u003c/strong\u003eResults of the Generalised Linear Mixed Model for relative humidity \u0026amp; temperature\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"594\" class=\"fr-table-selection-hover\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" valign=\"bottom\" style=\"width: 198px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eVariable\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"bottom\" style=\"width: 396px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMicroclimate levels\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 198px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eRelative Humidity (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 198px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eTemperature (℃)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 198px;\"\u003e\n \u003cp\u003eModerate\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 198px;\"\u003e\n \u003cp\u003e1.13(.05) ***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 198px;\"\u003e\n \u003cp\u003e0.21(.00) ***\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 198px;\"\u003e\n \u003cp\u003eHigh\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 198px;\"\u003e\n \u003cp\u003e1.79(.05) ***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 198px;\"\u003e\n \u003cp\u003e-0.31(.01) ***\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 198px;\"\u003e\n \u003cp\u003eShaded\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 198px;\"\u003e\n \u003cp\u003e1.38(.07) ***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 198px;\"\u003e\n \u003cp\u003e0.05(.01) ***\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 198px;\"\u003e\n \u003cp\u003eModerate*Shaded\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 198px;\"\u003e\n \u003cp\u003e-1.07(.18) ***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 198px;\"\u003e\n \u003cp\u003e-0.04(.02) ***\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 198px;\"\u003e\n \u003cp\u003eHigh*Shaded\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 198px;\"\u003e\n \u003cp\u003e-1.58(.17) ***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 198px;\"\u003e\n \u003cp\u003e-0.07(.02) ***\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 198px;\"\u003e\n \u003cp\u003e_cons\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 198px;\"\u003e\n \u003cp\u003e80.10(.05) ***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 198px;\"\u003e\n \u003cp\u003e22.94(.00) ***\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 198px;\"\u003e\n \u003cp\u003eRH and Temp (Var.)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 198px;\"\u003e\n \u003cp\u003e58.02(.15)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 198px;\"\u003e\n \u003cp\u003e1.39(.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 198px;\"\u003e\n \u003cp\u003eAIC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 198px;\"\u003e\n \u003cp\u003e2173993\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 198px;\"\u003e\n \u003cp\u003e1246145\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 198px;\"\u003e\n \u003cp\u003eBIC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 198px;\"\u003e\n \u003cp\u003e2174068\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 198px;\"\u003e\n \u003cp\u003e1246221\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e"},{"header":"4 Discussion","content":"\u003cp\u003eThe results from the study indicate that shade influenced soil physical-chemical parameters, soil moisture, and modified the microclimate across thresholds. In addition, there was generally adequate soil organic matter, pH, and nitrogen levels for Robusta production across thresholds and shaded plots (Tables\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e and \u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e), contradicting Kyalo et al. (\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2024\u003c/span\u003e) findings. The soil parameters were higher in shaded plots and high rainfall thresholds than in low and moderate rainfall thresholds and unshaded plots. The higher soil parameter values at the high threshold may be linked to the sandy clay loam soil texture observed (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e), the presumed higher vegetative growth, and the higher organic matter from organic inputs, and lower temperatures (Table\u0026nbsp;\u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e6\u003c/span\u003e). The high rainfall and associated low temperatures (Table\u0026nbsp;\u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e6\u003c/span\u003e) may be associated with reduced OM breakdown there by increasing its content in the soil and adequate soil macro nutrients. Alvarez-Clare \u0026amp; Mack (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2011\u003c/span\u003e) and Astera (\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2018\u003c/span\u003e) made similar observations of a significant relationship between rainfall and soil nutrients. The high OM in high rainfall and shaded plots may also be due to high organic inputs and biomass accumulation from shade tree leaves associated with higher soil organic carbon and reduced decomposition rates. Hence, the higher nutrient levels from nutrient recycling under shaded plots with more litter and biomass (McMahon, \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; Nadaf \u0026amp; Bora, \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). The recycled nutrients from deeper soil layers results from the nutrient pump effect which also increased soil chemical parameters under shade (Nadaf \u0026amp; Bora, \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) and nutrient availability (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). Similarly, the observed pH under shaded plots is related to high soil OM content (Sonon et al., 2002; van Asten et al., 2012). Soil OM buffers pH and increases soil cation exchange capacity and microbial activities, which contribute to soil fertility.\u003c/p\u003e \u003cp\u003eOn the other hand, the low soil OM content in the low rainfall threshold may be associated with low soil moisture (Tables\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e and \u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e) and high temperature (Table\u0026nbsp;\u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e6\u003c/span\u003e). The observed high temperature may raise the rate of OM decomposition, resulting in less accumulation in the soil. Hence, a reduction in OM content at the time of sample collection and analysis. As well, the low soil pH in the low and moderate thresholds may be an indicator for mild soil acidity (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e), which may affect the availability of basic cations, Mg, Ca, and phosphorus (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). The low soil pH in the low rainfall threshold may also be related to increased micronutrients like iron, aluminium, and manganese than other thresholds (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e), whose higher uptake may be toxic to coffee and reduce productivity.\u003c/p\u003e \u003cp\u003eSimilarly, results indicated that there were lower CEC and critical levels for exchangeable magnesium, calcium, potassium and phosphorus for coffee productivity (Tables\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). However, the results indicate higher Ca than Mg and potassium for all rainfall thresholds and shade levels. The observed averagely lower CEC levels in moderate threshold (10) may be due to the sandy soil texture (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Similar observations of low CEC was made by Kyalo et al. (\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). The sandy texture is also related to low potassium levels associated with potassium deficiency (Agronomy Tech Note 76, \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2009\u003c/span\u003e). Instead, the more than 10 CEC in high thresholds is due to the loamy texture. The low potassium levels in the soils (McMahon, \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2012\u003c/span\u003e) are further compounded with reduced recycling of coffee husks that are high in potassium (Kasongo et al., \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2011\u003c/span\u003e). Moreover, the nutrients continuously extracted through coffee harvesting are not recycled in coffee gardens where external potassium and other nutrient application is low (Kyalo et al., \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2024\u003c/span\u003e) and poor management practices exist (Kyalo et al., \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Nakyagaba et al., \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). However, the high moisture (Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e), loamy texture (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e) and high OM content (Tables\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e and \u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e) are related to better soil porosity and aggregate stability, reduced leaching and increased potassium availability in high threshold and shaded plots. The release and availability of cations; Ca, K and Mg are also linked to soil moisture availability. Hence, the higher marginal differences in soil parameters between shaded and high rainfall thresholds than in low rainfall thresholds.\u003c/p\u003e \u003cp\u003eThe observed results of low soil parameters, especially cation levels, below critical levels call for continuous use of external sources of basic inorganic fertilisers since the low CEC and sandy texture affect the storage of un absorbed applied nutrients (Kyalo et al., \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). The results are similar to Nakyagaba et al. (\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2024\u003c/span\u003e) and Van Asten et al. (2012) findings of low and limiting levels of nitrogen, phosphorus, calcium and potassium in the study area. However, the soil in shaded plots (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e) can moderately supply the required nutrients for better coffee production. Besides, the results differed from Asare (2015), who did not observe significant (P\u0026thinsp;\u0026gt;\u0026thinsp;0.05) shade effect on soil nutrients when he worked with shaded cocoa in Ghana. Moreover, it is beneficial to include shade trees in Robusta coffee plots to improve soil nutrient content through hydraulic redistribution (Caldwell et al., \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e1998\u003c/span\u003e; Ke et al., \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). As well, the soil micro nutrients were low across rainfall thresholds and shades levels (Tables\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e and \u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). However, the low soil pH observed under the low rainfall threshold may be an indicator of the aluminium generating acid in the soil. Otherwise, the low micronutrients such as manganese, Zinc and copper in shaded plots and high rainfall threshold may be a result of higher OM and pH which are associated with immobilisation of these elements (Kasongo et al., \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2011\u003c/span\u003e). Hence, the low micro nutrient levels pose low toxicity risks to coffee productivity.\u003c/p\u003e \u003cp\u003eDiffering from Le et al. (\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2023\u003c/span\u003e), the soil moisture results obtained and their interactions were consistently higher in high rainfall and shaded than in unshaded plots (Tables\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e and \u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e). Gomes et al. (\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2016\u003c/span\u003e) and Lin (\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2007\u003c/span\u003e) observed similar results. These indicate that shade trees contribute to higher soil water retention, stabilises soil moisture levels, reduce evaporation rates, and improve infiltration through litter and root systems, which is critical during drought scenarios. The shade trees may have facilitated the lifting of nutrients and water from deeper soil layers that are absorbed by the Robusta coffee fine fibrous roots (Caldwell et al., \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e1998\u003c/span\u003e). The moisture levels in the profile is also connected with the observed coffee wilting point of 20cm in the low threshold and unshaded plots compared to 30cm in the high threshold. The high OM content, litter fall and the loamy soil texture observed in high threshold and shaded plots further explain the higher moisture content, especially in the upper 30cm of the profile (Tables\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e and \u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e). The higher OM observed may have led to better soil structure, improved the soil water holding capacity and water infiltration in shaded plots. Moreover, soil OM is the major source of nutrients and fertility in many smallholder coffee farms that depend on nature for production. Though deeper soil profiles layers had more moisture, this could be related with the sandy nature of the soils in the study area (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e) that allows soil water to drain to deeper layers which may have higher clay particles (Moreira et al., \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). The higher moisture down the profile, especially in high and moderate thresholds justifies the shade tree function of hydraulic lift. The results may also be due to reduced evaporation rates especially in the low rainfall thresholds during drought scenarios. In particular, shade trees demonstrate better soil moisture retention in low rainfall thresholds thereby mitigating the drought situations experienced in marginal areas. There may have been more positive effects of shade on maintaining better soil water retention which leads to higher drought tolerance.\u003c/p\u003e \u003cp\u003eLikewise, the RH and temperature data (Tables\u0026nbsp;\u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e6\u003c/span\u003e and \u003cspan refid=\"Tab7\" class=\"InternalRef\"\u003e7\u003c/span\u003e) indicate that shade trees had an influence on microclimate with higher RH and lower temperature in shade than unshaded plots across rainfall thresholds. Shade trees significantly (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05) reduced air temperature and increased RH creating a stable and favourable microclimate. Shade trees, therefore, may protect coffee from the extreme effects of low RH and high temperatures thereby enhancing coffee resilience to heat stress. The results are in conformity with previous scholars who noted that shade trees improve microclimatic conditions (Carvalho Carelli et al., \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2006\u003c/span\u003e; Moreira et al., \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Muschler \u0026amp; Bonnemann, \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e1997\u003c/span\u003e; Souza et al., \u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). The observed higher humidity in shaded plots may promote healthier coffee leaf development, reduce transpiration levels and less stress on the coffee plants. Besides, the lower temperatures observed, shade reduced the coffee water requirements since temperature and moisture have significant physiological effects on coffee growth and yield. Moreover, the micro climate results obtained were within and/or below the optimal Robusta requirements of 24 to 30\u0026deg;C. Besides, Moreira et al. (\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2018\u003c/span\u003e) obtained higher maximum and minimum temperatures under unshaded conditions. The results are key in adapting Robusta coffee production to climate variability using shade trees, especially in marginal/semi-arid areas where it enhances better microclimate stability.\u003c/p\u003e"},{"header":"5 Conclusions and Recommendations","content":"\u003cp\u003eThe study results generally indicate that shade trees in coffee production play critical roles by enhancing soil physical and chemical attributes through improving soil moisture retention by buffering against excessive soil moisture and maintains adequate moisture in the marginal areas there by boosting long time soil health and reducing the effect of drought on Robusta coffee. Shade trees also modify and stabilise microclimate by increasing relative humidity and reduce evaporation. Further, shade reduces temperature within the mid altitude zone of central Uganda. Hence, the study reveals the significance of shade trees as a mitigation measure to climate variability, especially in marginal areas of greater Luweero with low to moderate rainfall. In these areas shade significantly benefits coffee microclimate and soil water dynamics there by mitigating drought stress and stabilize soil moisture. The higher soil parameters, moisture levels and lower temperature under shaded plots and a high rainfall threshold may favour coffee growth, development, productivity and may close the high Robusta coffee yield gap in the mid altitude zone of central Uganda. The identified variations in soil parameters and microclimate can improve sustainability, guide coffee management and increase resilience of coffee farming systems to achieve higher productivity amidst climate variability. However, the effect of shade may vary with the rainfall threshold indicating specific shade management practices depending on the rainfall threshold. Shade tree management is therefore important in improving coffee quality, yield and favourable microclimate conditions.\u003c/p\u003e \u003cp\u003eThe results are beneficial to small-scale farmers, most of whom depend on nature to produce coffee and cannot afford fertilisers and irrigation. Integration of shade trees as part of intercrops support Robusta coffee farmers to increase resilience systems, improve yields and coffee quality amidst climate variability. The results are also important in making rational management decisions amidst unpredictable climate, declining fertility, increasing temperatures and reduced carbon storage. However, the study was conducted within two years but extended research may be required to allow more inter-annual climatic variations in all the studied parameters, especially nutrient acquisition and recycling (nutrient pump), hydraulic lift and moisture relations and other associated factors. This study, therefore justifies integration of shade trees in Robusta coffee fields as an adaptation and mitigation measure.\u003c/p\u003e \u003cp\u003eNevertheless, there is need for appropriate selection of shade tree species and optimal density, with emphasis on optimising smallholder farmer\u0026rsquo;s resilience for social, environmental and economic benefits across rainfall thresholds. There is also need to observe the improvement in growth parameters especially plant height and leaf size in shaded plots. It is also necessary to improve this research over a long period by relating the age of the shade tree with diversity along rainfall thresholds. The study provided important, realistic, and urgent adaptive strategies to policy makers and farmers to consider integrating tree shade in coffee production for sustainable management of coffee cultivation practices aimed at reducing the effects of climate variability and improve adaptation in the mid altitude zone of central Uganda, where there is increased rainfall variability and drought associated with marginal areas. However, shade tree selection and management should be tailored to rainfall thresholds to maximize benefits in the mid-altitude Robusta coffee growing zones of central Uganda. Therefore, shade trees are the easiest, fastest and most sustainable adaptation and mitigation strategy to unpredictable climate, especially the impact on soil parameters under declining soil fertility, soil moisture retention and microclimate, which may affect the projected country\u0026rsquo;s 1.2 metric tons year\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e \u003ch2\u003eConflict of Interest\u003c/h2\u003e \u003cp\u003eThe author declares no conflicts of interest with respect to the publication of this article.\u003c/p\u003e \u003c/p\u003e\u003cp\u003e \u003ch2\u003eORCID\u003c/h2\u003e \u003cp\u003eNakyagaba Winfred \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://orcid.org/0000-0002-5134-8571\u003c/span\u003e\u003cspan address=\"http://orcid.org/0000-0002-5134-8571\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e \u003c/p\u003e\u003ch2\u003eFunding\u003c/h2\u003e \u003cp\u003eThis research work was supported by the United States Agency for International Development (USAID) [grant number BFS-G-11-00002], through the CGIAR Research Program on Climate Change, Agriculture and Food Security (CCAFS) implemented at IITA Uganda, NARO Uganda, and the Makerere University Center for Climate Change Research and Innovations (MUCCRI)/ERICCA through FHI360 [cooperative agreement number AID-617-A-1300008].\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eAuthor A. Nakyagaba N. Winfred: Conceptualization, validation, Data Collection, investigation, formal analysis, writing original draft, writing - review and editing, visualizationAuthor B. Herbert Talwana and C. Samuel Kyamanywa: Supervision, Writing-Review and editing, visualizationAuthor D. Godfrey H. Kagezi: Conceptualization, Writing- review and editing, supervision, project administration, fundingAuthor E. David Mfitumukiza, F. Yazidhi Bamutaze and H. Revocatus Twinomuhangi: Supervision, Writing- review and editing, project administration, fundingAuthor G. Catherine Mulinde and J. Benard Fungo: Conceptualization, generating Figure 1, writing review and editingAuthor I. David Mukasa: Methodology, investigation, data collection, review, and editingAuthor K. David Amwonya: Methodology, verification, data analysis, generating data for tables, figure production and interpretationAuthor L. Judith Asiimwe: Data collection tools, acquisition of gadgets, project administration and fund managementAuthor M. van Asten Piet and N. Laurence Jassogne: Conceptualization, Methodology, data collection tools, verification, acquisition of gadgets, Writing- review and editing, supervision, project administration, funding\u003c/p\u003e\u003ch2\u003eAcknowledgement\u003c/h2\u003e\u003cp\u003eThe authors thank the farmers who provided the study plots on their farms for the two years of data collection. The research Assistants, Mr. Bosco Habib Ssengombe, Mr. Sam Ssemugga and Mr. Ssebulime Charles for their time and efforts during the two years of data collection.\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eAll data supporting the findings of this study are available within the paper\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eAgronomy Tech Note 76. 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THE MINISTRY OF AGRICULTURE, ANIMAL INDUSTRY \u0026amp; FISHARIES. https://ugandacoffee.go.ug/sites/default/files/Resource_center/Robusta Coffee Handbook.pdf\u003c/li\u003e\n\u003cli\u003eUCDA. (2024). \u003cem\u003eUCDA Fact sheet\u003c/em\u003e (pp. 0\u0026ndash;4).\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Robusta coffee, microclimate, soil moisture and nutrients, rainfall thresholds","lastPublishedDoi":"10.21203/rs.3.rs-8558650/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8558650/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eRobusta coffee (\u003cem\u003eCoffea canephora\u003c/em\u003e) is a key economic crop in the mid-altitude zone of central Uganda whose cultivation is expanding to marginal areas with variable rainfall, high temperatures, humidity variation, reduced soil moisture, and low soil physical and chemical attributes, which are increasingly threatening its sustainable production. This study investigated the influence of tree shade on soil attributes, microclimate regulation, and soil water storage in Robusta coffee plots across rainfall gradients within the mid-altitude zone of central Uganda. A nested field experiment was conducted in 20 by 20-meter de-alienated coffee plots across three categorised thresholds (low (\u0026lt;\u0026thinsp;1100 mm year-1), moderate (1100\u0026ndash;1200 mm year\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e), and high (\u0026gt;\u0026thinsp;1200 mm year\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e) in central Uganda. The treatments included six shaded and unshaded coffee plots per rainfall threshold. Results indicated that tree shade significantly improved soil physical and chemical attributes, particularly organic matter and cation exchange capacity, resulting in enhanced soil health. Tree shade moderated microclimate conditions by reducing the maximum air temperature by 0.78\u0026deg;C and increasing the maximum and minimum relative humidity by 1.02% and 0.56%, respectively. Shade increased the average volumetric soil moisture content at 10cm by 0.28mm and 1.47mm at 50cm profile levels. The benefits of shade were pronounced in high rainfall and shaded plots. The study, therefore, concludes that integrating shade in Robusta coffee improves soil attributes, buffers microclimate extremes, and enhances soil moisture storage in marginal areas. The Results indicate the potential of tree shade as a climate adaptation strategy in sustainable Robusta coffee production amidst rainfall variability.\u003c/p\u003e","manuscriptTitle":"The Influence of tree shade on soil attributes, water storage and microclimate in Robusta coffee grown in a rainfall gradient of the mid-altitude zone of Central Uganda","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-02-11 11:29:59","doi":"10.21203/rs.3.rs-8558650/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"99bf0f71-46f6-48a0-ae28-cc11117ae648","owner":[],"postedDate":"February 11th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2026-04-03T14:10:40+00:00","versionOfRecord":[],"versionCreatedAt":"2026-02-11 11:29:59","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8558650","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8558650","identity":"rs-8558650","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}