| Livestock Research for Rural Development 31 (7) 2019 | Guide for preparation of papers | LRRD Newsletter | Citation of this paper |
Honeybee colony performance includes colony strength and productivity which are critical factors for maximizing honey production and pollination services by Apis mellifera. In order to gain some insights into honeybee colony performance in Ugandan honeybees, this study was conducted to identify environmental and management factors associated with colony performance. The performance of 175 and 195 honeybee colonies was evaluated during the dry and wet seasons respectively between December 2014 and September 2015. The findings indicate an association of honeybee colony performance with: altitude, season, dominant vegetation type, height of beehive placement and apiary management. Indicators for colony performance were significantly negatively correlated to altitude. Honeybee colonies in apiaries located in protected areas performed better than those located in farmlands and under Eucalyptus grandis plantations. Maintaining natural or semi-natural environments for beekeeping should be pursued for sustainability of the apiculture industry. Moreover, there is need to strengthen beekeeping programmes through adequate training and extension. Emphasis should be put on understanding the factors that affect colony performance and practical skills in proper colony management for better returns from beekeeping.
Keywords: apiary management, brood, colony strength, environmental factors, honey
Honeybee colony performance is an important factor in beekeeping. Colony performance includes colony strength and productivity which are critical factors for maximizing honey production in Apis mellifera (Jevtić et al 2009; Neupane et al 2012). In addition, the strength of a honeybee colony is important in selecting colonies for pollination (Delaney and Tarpy 2008; Abou-Shaara 2014). Colony strength can be estimated by considering the number of adult worker bees in the hive, the brood pattern and the flight activities at the hive entrance (Pokhrel et al 2006; Vaudo et al 2011; Ali 2011; Delaplane et al 2013). Colony productivity on the other hand is measured in terms of honey produced and pollen collected (Ali 2011).
In Uganda, the honeybee, A. mellifera is mostly kept for its products including: honey, propolis, beeswax and more recently honeybee venom (Chemurot 2017). The role of honeybees in pollination is gradually being understood and appreciated following studies (e.g. Amulen et al 2017; Munyuli 2011; 2012) and beekeeping awareness programs (e.g. Chemurot 2011). In light of this increasing appreciation of the role of honeybees in food security, farmers are currently using bees for pollination of fruits and vegetables (Munyuli 2013). However, the performance of honeybee colonies in Uganda remains to be explored. As the beekeeping sector develops, it is important to understand the performance of honeybee colonies and how this might be affected by environmental factors for informed planning.
In order to gain some insights into honeybee colony performance in Ugandan honeybees, this study was conducted to identify environmental and management factors associated with colony performance. It was hypothesized that honeybee colony performance would vary with environmental and management factors including: elevation, vegetation, season, distances to water sources, apiary location, height of beehive placement and apiary management.
This study was conducted in the eastern and western highlands agro-ecological zones (AEZ) of Uganda by evaluating the performance of 175 colonies during the dry season and 195 colonies during the wet season (December 2014 to September 2015) in the two AEZs. Apiaries sampled in this study were selected based on altitudinal gradients and land uses. Since honeybees can efficiently forage within 3km radius (Steffan-Dewenter and Tscharntke 2000), the distance between one sampled apiary and the next was at least 3.5 km to minimize sampling honeybees foraging within the same area. The geographical coordinates and elevation at each apiary site were taken using a global positioning system (GPS) receiver. Details of the description of the study sites are shown elsewhere (Chemurot et al 2016).
Observations were made on human activities, apiary characteristics and landscape features such as the distances to the nearest potential honeybee water source. For this study, a potential water source is defined as the nearest stream/pond to the apiary. In apiaries sampled, there were between 3 to 64 honeybee colonies. Therefore, at each apiary, at least six honeybee colonies were randomly selected and sampled whenever it was possible. In cases where fewer than six colonies were found in sampled apiaries, all available colonies were sampled. Since beehives were placed at varying heights from ground, the distance of each sampled beehive from the ground was measured using a tape measure. Beekeepers managed their apiaries in different ways. Based on this, apiaries were categorized into four: apiaries with no management; occasionally slashed; well slashed; slashed plus inspected regularly. Furthermore, land use type varied in sampled apiaries. Therefore, apiaries were categorized depending on the type of land use around them into three categories, namely: protected area, farmland and Eucalyptus grandis plantation. The number of colonies sampled under each land-use category varied during the dry and wet seasons respectively: protected area (67, 57), farmland (72, 114) and Eucalyptus grandis plantations (36, 24). This was because some study colonies that had been sampled during the dry season were not found in the wet season and were considered to have absconded.
During fieldwork, colony performance was estimated by making observations and or measurements on the strength and productivity of each sampled colony. At each apiary, each colony sampled was gently smoked and opened before observations and measurements (counts on the number of top-bars/frames with honey (comb honey), brood, pollen, covered with bees and comb honey yield in kilograms that was harvested) were made since the colonies were very defensive.
Data were analyzed using SPSS statistical program (version 21). Mann-Whitney test was used to compare the colony performance in the two AEZs of Uganda. Three linear regression models were built modeling colony performance as a function of landscape and human factors that were thought to potentially impact on honeybee colony strength and performance. The goal was to build models indicating the performance of colonies. To fulfill all underlying assumptions of the models, square root transformation of the outcome variables were done. Forward stepwise linear regression was conducted to predict honeybee colony performance. A Bonferroni correction of all critical values was done to correct for type I errors.
Overall, the performance of 175 colonies (in hives placed between 0 – 4.5m from ground) during the dry season in the two AEZs was assessed. By the wet season, 38 and 45 % of sampled colonies had absconded in the western and eastern AEZs, respectively. Overall, honeybee colonies recorded better performance during the dry season (Table 1).
| Table 1. Seasonal variation in colony performance indicators estimated | |||
| S/N |
Colony performance indicator |
Dry season | Wet season |
| 1 | Bees | 12.00±0.50 | 10.20±0.50 |
| 2 | Honey comb | 2.20±0.23 | 1.43±0.31 |
| 3 | Brood | 1.60±0.14 | 0.87±0.30 |
| 4 | Pollen | 0.4.±0.03 | 0.30±0.20 |
| 5 | Kg of honey | 5.60±0.80 | 0.00 |
The results show that except for number of combs with bees, other colony performance indicators assessed did not vary significantly between the two AEZs (Table 2). Honeybee colonies in the western highlands were significantly stronger than colonies in the eastern AEZ.
| Table 2. Comparison of honeybee colony performance (mean ranks) in the two AEZs of Ugand | ||||
| S/N |
Colony performance indicator |
Eastern AEZ |
Western highlands AEZ |
p |
| 1 | Brood | 84.58 | 90.47 | >0.05 |
| 2 | Bees | 118.72 | 165.63 | <0.05 |
| 3 | Honey harvested (kg) | 19.50 | 19.50 | >0.05 |
| Rows with colony performance indicators in italics represent those with p < 0.05 | ||||
The linear regression model developed explains 32.4 % of the variance in honeybee colony strength (number of top bars with bees). The factors included in the model were: altitude, height of hive placement, distance to water sources, season, agro-ecological zone, apiary location, farming intensity and dominant vegetation around the apiary. All together, these factors significantly explained the number of top bars with bees (F21, 260 = 5.946, p<0.01). However, only elevation, agro-ecological zone, season, apiary location, farming intensity, apiary management and dominant vegetation (coffee) significantly predicted colony strength levels (Table 3).
| Table 3. Linear regression model predicting the number of top bars with bees | |||
| Factor | Beta | t | p |
| Elevation | -0.27 | -2.59 | 0.01 |
| Height of beehive placement | -0.01 | -0.05 | 0.96 |
| Distance to water source | 0.08 | 0.77 | 0.44 |
| Agro-ecological zone (Eastern) | -0.26 | -3.19 | 0.00 |
| Season (dry) | 0.25 | 4.41 | 0.00 |
| Apiary location (Eucalyptus grandis) | 0.178 | 1.25 | 0.21 |
| Apiary location (farmland) | 0.46 | 2.05 | 0.04 |
| Farming intensity (protected area) | 0.35 | 1.66 | 0.10 |
| Farming intensity (new farm land) | 0.04 | 0.41 | 0.68 |
| Farming intensity (old farm land) | -0.16 | -2.03 | 0.04 |
| Apiary management (no management) | 0.01 | 0.03 | 0.97 |
| Apiary management (some slashing) | -0.03 | -0.45 | 0.66 |
| Apiary management (well slashed) | -0.24 | -3.04 | 0.00 |
| Dominant vegetation (Accacia spp.) | -0.06 | -0.71 | 0.48 |
| Dominant vegetation (Coffea arabica) | -0.34 | -4.87 | 0.00 |
| Dominant vegetation (Combretum spp.) | 0.10 | 1.43 | 0.16 |
| Dominant vegetation (Eucalyptus grandis) | -0.12 | -1.69 | 0.09 |
| Dominant vegetation (Lantana spp.) | -0.13 | -1.71 | 0.09 |
| Dominant vegetation (Mangifera indica) | 0.06 | 0.91 | 0.36 |
| Dominant vegetation (Pinus spp.) | -0.09 | -1.00 | 0.32 |
| Dominant vegetation (Tea) | -0.03 | -0.54 | 0.59 |
Specifically, at higher altitudes, colonies were weaker than at lower altitudes. The eastern AEZ had significantly weaker colonies than the Western highlands. Honeybee colonies were significantly stronger during the dry season compared to the wet season. Colonies in old farmlands with tree plantations were significantly stronger than those in new farmlands. Apiaries that were well slashed had significantly stronger colonies. Apiaries which had coffee as the dominant vegetation recorded colonies that were significantly weaker.
The model developed explains 30 % of the variance in the number of top bars with brood. Factors entered in the model included: altitude, height of hive placement, distance to water sources, season, agro-ecological zone, apiary location, farming intensity and dominant vegetation around the apiary. All together these factors significantly explained the number of combs with bees (F20, 153 = 3.278, p<0.01). In the model; agro-ecological zone, season, farming intensity, apiary management and dominant vegetation (Lantana spp.) significantly predicted the number of top bars with brood (Table 4).
| Table 4. Linear regression model predicting the number of top bars with brood | |||
| Factor | Beta | t | p |
| Elevation | -0.29 | -1.80 | 0.07 |
| Height of beehive placement | 0.12 | 0.97 | 0.34 |
| Distance to water source | -0.07 | -0.39 | 0.69 |
| Agro-ecological zone (Eastern) | -0.39 | -3.41 | 0.00 |
| Season (dry) | 0.20 | 2.58 | 0.01 |
| Apiary location (Eucalyptus grandis) | 0.06 | 0.24 | 0.81 |
| Apiary location (farmland) | -0.47 | -1.28 | 0.20 |
| Farming intensity (protected area) | -0.43 | -1.24 | 0.22 |
| Farming intensity (new farm land) | -0.18 | -1.52 | 0.13 |
| Farming intensity (old farm land) | -0.50 | -4.78 | 0.00 |
| Apiary management (no management) | 0.62 | 3.68 | 0.00 |
| Apiary management (some slashing) | -0.12 | -1.07 | 0.28 |
| Apiary management (well slashed) | -0.03 | -0.37 | 0.71 |
| Dominant vegetation (Accacia spp.) | 0.07 | 0.51 | 0.61 |
| Dominant vegetation (Coffea arabica) | -0.10 | -1.02 | 0.31 |
| Dominant vegetation (Combretum spp.) | -0.01 | -0.06 | 0.95 |
| Dominant vegetation (Eucalyptus grandis) | -0.08 | -0.71 | 0.48 |
| Dominant vegetation (Lantana spp.) | -0.26 | -2.25 | 0.03 |
| Dominant vegetation (Pinus spp.) | -0.17 | -1.47 | 0.14 |
| Dominant vegetation (Tea) | -0.16 | -2.01 | 0.05 |
The Eastern AEZ had significantly weaker colonies (with fewer top bars with brood) than the Western Highlands. Colonies were significantly stronger (had more top bars with brood) during the dry season compared to the wet season. Colonies in old farmlands were significantly weaker than those in new farmlands and protected areas. Apiaries that were well managed had significantly stronger colonies. Apiaries with Lantana spp. as the dominant vegetation had colonies that were significantly weaker than those with other vegetation types being dominant.
The linear regression model developed explains 74.3 % of the variance in the amount of honey that was harvested. Factors included in the model were: altitude, height of hive placement, distance to water sources, season, agro-ecological zone, apiary location, farming intensity and dominant vegetation around the apiary. All together, these factors significantly explained the quantity of honey harvested (F13, 24 = 5.342, p <0.01). However, in the model only season significantly predicted the amount of honey harvested (Table 5). Honeybee colonies produced significantly more honey during the dry season compared to the wet season.
| Table 5. Linear regression model predicting amount of honey harvested | |||
| Factor | Beta | t | p |
| Elevation | 0.02 | 0.02 | 0.99 |
| Height of beehive placement | -0.07 | -0.24 | 0.81 |
| Distance to water source | -0.11 | -0.15 | 0.89 |
| Agro-ecological zone (Eastern) | -0.09 | -0.07 | 0.95 |
| Season (dry) | 1.05 | 5.12 | 0.00 |
| Apiary location (farmland) | 0.00 | 0.00 | 1.00 |
| Farming intensity (new farm land) | 0.10 | 0.11 | 0.91 |
| Farming intensity (old farm land) | 0.00 | 0.01 | 0.99 |
| Apiary management (no management) | 0.22 | 0.23 | 0.82 |
| Apiary management (some slashing) | -0.30 | -0.40 | 0.70 |
| Apiary management (well slashed) | -0.02 | -0.03 | 0.98 |
| Dominant vegetation (Combretum spp.) | -0.30 | -0.85 | 0.40 |
| Dominant vegetation (Eucalyptus grandis) | -0.34 | -0.68 | 0.50 |
Even though there is a drive to promote beekeeping in Uganda, there have not been enough studies on the performance of honeybee colonies under the prevailing environmental and management conditions. In this study, most beekeepers in the two highland agro-ecological zones of Uganda used inappropriate colony management practices that were characterized by high absconding and low honey yields despite the abundant potential bee forage. These findings are similar to reports from other parts of the country (Ayo 2017; Chemurot 2011) and indicate that actions are required to harness the honey production potential in Uganda.
Lack of proper apiary management affects production of beehive products and beekeepers’ income from the sale of these products (Ayo 2017). Appropriate apiary management includes hive inspection, pest control and provision of water which improve colony performance by reducing abscondment and swarming leading to better yields of beehive products (Kumsa and Takele 2014). Therefore, interventions in modern beekeeping should be focused on empowering beekeepers with skills including seasonal bee management practices and proper use of improved beekeeping technologies.
The findings show an association of honeybee colony performance with: altitude, season, dominant vegetation type, height of beehive placement and apiary management. Three indicators for colony performance were significantly negatively correlated to altitude. At higher altitudes, temperatures are cooler (Hemp 2005) which make worker honeybees spend much of their time warming the brood resulting in limited foraging. Consequently, this leads to poor colony performance at higher altitudes.
Colony performance indicators (the number of combs with bees, brood, and honey) were significantly better in colonies located in protected areas compared to colonies located in farmlands. In agricultural lands, resources for bees such as wild habitats and forage are destroyed during the process of land conversion (Vaudo et al 2012). Importantly, agriculture can affect plant communities, encourage the spread of diseases and use of pesticides (Matson et al 1997, Williams et al 2002). For example, infestation of Nosema ceranae parasite (Invernizzi et al 2011) and honeybee pests (Chemurot et al 2018) were shown to be higher under Eucalyptus grandis and in farmlands compared to protected areas. The results from this study support maintenance of natural or semi-natural environments for beekeeping to ensure sustainability of the apiculture industry.
Until now, relatively few studies have looked into the factors affecting honeybee colony performance in African honeybees. The present study bears traits of a case study, and some of the results may warrant replication before they can be generalized. However, the low honeybee colony performance under farmlands compared to protected areas is a strong evidence to support maintenance of natural or semi-natural environments for beekeeping. This finding indicates the need to strengthen beekeeping programmes through adequate training and extension with emphasis on understanding the factors that affect colony performance and practical skills in proper colony management for better returns from beekeeping. Finally, in light of the high deforestation rate in Uganda (Obua et al 2010), government should set up bee nature reserves specifically for better beehive products’ yields and for the conservation of other pollinator species.
This work was funded by Erasmus Mundus Caribu project. Authors contribution MC and DCdG designed the study. MC conducted the field work, analysed data and drafted the manuscript. All authors proofread and approved the manuscript.
We declare that we have no conflict of interest.
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Received 29 March 2019; Accepted 6 June 2019; Published 2 July 2019