Research Article | Open Access
Bin Mushambanyi Théodore Munyuli, "Is Cut-Flower Industry Promotion by the Government Negatively Affecting Pollinator Biodiversity and Environmental/Human Health in Uganda?", International Scholarly Research Notices, vol. 2014, Article ID 368953, 52 pages, 2014. https://doi.org/10.1155/2014/368953
Is Cut-Flower Industry Promotion by the Government Negatively Affecting Pollinator Biodiversity and Environmental/Human Health in Uganda?
A study was conducted from 2010 to 2012 around the flower growing areas in central Uganda to generate baseline information on the status of pollinators. Primary data were gathered using a questionnaire that aimed at determining farmers and flower farm officials’ perceptions on the impact of activities carried out inside greenhouses on pollinators, human health, and on crop production in the surroundings. Results indicated that the quantity of pesticides and fertilizers applied daily varied among the different flower farms visited. Bee species richness and abundance varied significantly () according to flower farm location, to the landscape vegetation type, and to field types found in the surrounding of flower farms. Bee richness found around flower farms varied in number from 20 to 40 species in total across seasons and years. Bee density increased significantly with the increase in flower density. Small-scale farmers were aware of the value and importance of pollination services in their farming business. There was no clear evidence of a direct effect of agrochemicals application on bee communities living in the surrounding habitats. There is a need for further research to be conducted on human health risks and for toxicological studies on soils, plants, flowers, and bees in the farm landscape.
Due to government policy of enhancing crop productivity in response to population growth, agricultural modernization in many forms is increasing at high speed in Uganda. Uganda produces approximately 11.1 million tonnes of flowers and is the second largest in South Saharan Africa after Nigeria. Uganda is among the top 10 producing flowers in the world. The first rose farms in Uganda were planted in 1992 and since then, the country flower industry has grown gradually. The average exports of flowers increased from US$9.72 million in 1998 to US$29.45 million in 2009. About 95% of the production is exported and 5% is sold on local market or thrown away . Uganda’s floricultural sector has over the last 16 years emerged as an important nontraditional export earner, contributing over US$35 million in foreign exchange earnings in 2012 and directly employing over 6500–9000 people. The current government of Uganda’s objective for the flower industry is to stimulate its rapid development because of its contribution to the diversification of the export base and rural development.
Practically, cut-flower industry is growing in Uganda. Currently, there are many flower firms established in the country. However, various stakeholders from government departments/agencies, the academia, research organization, community based organizationss, and nongovernmental organizations are of the view that this industry is impacting the health of the environment and the health of human beings . Biodiversity (such as bee biodiversity) is suspected to be affected (disappearing) in the surrounding of flower farms . Therefore, there was a need to conduct a baseline study to gather information about the potential effect of flower farms on pollinators (bees).
A pollinator is the biotic agent (vector) that moves pollen from the male (anthers) of a flower to the female (stigma) of a flower . Pollinators are a key component of global biodiversity that play an important functional role in most terrestrial ecosystems . They represent a key ecosystem service that is vital to the maintenance of both wild plant communities and agricultural productivity [3–6]. Pollinators are critically important for the maintenance of the human agricultural enterprise, since they provide vital ecosystem services to crops and wild plants through their pollinating activities [7–9]. The most important pollinators in the world are bees. Bees are essential for healthy and diverse ecosystems through their pollinating activities. Approximately 80% of flowering plants depend on pollinators, mainly bees. However, bees constitute a fragile link to food production chains due to their vulnerability to various factors, mainly anthropogenic factors. Without pollinators (bees), ecosystem functioning, trophic cascades, and the survival and maintenance of genetic diversity of many wild plant populations would be at risk and economic yields of crops may suffer a drastic reduction [2, 10]. Hence, pollination is an essential step in the production of fruits and many vegetables . An estimated 70% of world crops experience increases in size, quality, or stability because of pollinator services, benefiting 35% of the global food supply [7–10, 12, 13]. Animal pollination also contributes to the stability of food prices, food security, food diversity, and human nutrition [14–16].
The value of pollination to agricultural production worldwide is currently estimated to be worth €153 billion per year or approximately 39% of the world crop production value (€675 billion) from the total value of 46 insect pollinated direct crop species [14–16]. This value (€153 billion) is of more than €600 billion when added the economic benefit received from beekeeping products (sale of honey, propolis, etc.).
Though crop pollinators include a wide array of insects (e.g., beetles, butterflies, flies, etc.), bees are the most important and effective of these pollinators [14, 15]. As the world’s primary pollinators, bees are a critically important functional group because, roughly 90% of the world’s plant species are pollinated by animals and the main animal pollinators in most ecosystems are bees [15–17]. Although other taxa including butterflies, flies, beetles, wasps, bats, birds, lizards, and mammals can be important pollinators in certain habitats and for particular plant species, none achieve the numerical dominance as flower visitors worldwide as bees . The likely reason for this is that unlike other taxa, bees are obligate florivores throughout their life cycle, with both adults and larvae depending on floral products, primarily pollen and nectar. Bees (Hymenoptera: Apoidea) constitute an extremely species rich fauna, with an estimated 20,000–30,000 species worldwide [19–21] but approximately 3000 afrotropical species and only 700–1100 species recorded in Uganda so far [21–28].
The tropics are home to immense faunal and floral diversity and encompass much of the world’s biodiversity hotspots including bees . Much of the tropics exist as a mosaic of agricultural lands and forest patches, and these human-altered landscapes can have strong impacts on local bee biodiversity [21–28]. Tropical Sub-Saharan Africa is also characterized by strong ecological and agricultural dependencies on pollination [24, 25]. Hence, pollination shortage/decline is of great concern for food security in a continent where scientists are just beginning to understand how anthropogenic land-use impacts wild and managed pollinators. Since bees are very important in agricultural production, their status is therefore of great concern not only to the farmers but to any responsible government as well, as it has a direct impact on people’s livelihoods and the economy. This concern can therefore be translated into developing management techniques for the conservation of effective native bee species.
Although bees provide enormous ecological and economic benefits to flowering plants, wildlife, and humans, they are, however, under increasing threat from anthropogenic factors. There is considerable evidence for the negative impacts of habitat alteration on pollinators in highly disturbed regions of the world [28, 29], particularly in Europe and North America . Pollinator crisis exists even in tropical and subtropical areas, where natural habitat is well represented . Studies indicate that native pollinator populations face many threats, and evidence of a global pollination crisis is steadily growing [28, 29]. Currently, there are sufficient scientific evidence of a sizeable decrease in the population and range of many pollinators such as bees, butterflies, moths, hummingbirds, and bats from most biomes of the globe [29, 30].
Pollinators are at risk from numerous threats and this, in turn, threatens the many benefits people and ecosystems derive from pollination services. Drivers (disturbance types) of pollinator loss/decline [31, 32] include seminatural and natural habitats loss/degradation (destruction) and fragmentation through intensive land-use, misuse/over-use of toxic pesticides in agriculture (agricultural chemicals), pathogens [31, 32], alien species, toxic effects of secondary compounds produced by genetically engineered plants , and climate change and the interactions between them [34–36]. The current challenge for the conservation of pollination services in rural landscapes is to better quantify the relative importance of a range of drivers (and pressures) and in particular their simultaneous synergistic effects in order to understand the magnitude of their impact, particularly if these are coupled with the clear ecological and economic risks associated with pollinator loss and crop yield failures [35, 36] such as agriculture intensification activities.
Agricultural intensification has got around 13 components of intensification [20–24]. However, the use of insecticides and fungicides is the component known to have consistent negative effects on biodiversity and ecosystem services delivery [35, 37]. Agriculture modernization or modern agricultural practices (agrochemical applications), landscape fragmentation, and habitat degradation have been identified as key drivers that negatively affect bee populations in rural landscapes by the elimination of resources needed for successful reproduction such as nesting sites and pollen and nectar sources . In Uganda, agricultural intensification is taking place. An example of agricultural modernization in Uganda includes the upsurge of the floriculture industry. Flower farms with high agrochemical inputs are clear evidence of agricultural modernization. The negative effects of agrochemicals on biodiversity in farmlands are well documented [35–39]. As mentioned above, pesticides are considered a threat to pollinators [40, 41] although little is known about the potential impacts of their widespread use on pollination services in flower growing regions in Uganda.
The negative effects of agrochemicals (synthetic insecticides, botanical insecticides, miticides, acaricides, biologicals and natural enemies, fungicides, herbicides, seed dressing, adjuvant, nematicides, horticultural detergents, flower preservatives, plant growth regulators, foliar fertilizers, soil amendments, chelates, specialty fertilizers, grain storage insecticides, termiticides, rodenticides, etc.) on biodiversity in farmlands are well documented [35, 38, 39]. As mentioned above, pesticides are considered as a threat to pollinators [40, 41] although little is known about the potential impact of their widespread use on pollination services in habitats surrounding flower growing regions in Uganda. Since it is well established that agricultural chemicals can pose negative effects to biodiversity and to the environment of the areas where they have been applied [40, 41], loss of biodiversity around flowers is therefore expected in Uganda. However, assessing negative effects of agrochemicals applied by flower farms on biodiversity in the surrounding environment is very challenging since these agrochemicals are applied inside greenhouses and therefore expected to have almost no effect on the surrounding environment.
To our knowledge, there exists no study on the impact of inorganic fertilizer (NPK) application in greenhouses of flower farms on pollinators living in the surrounding habitats. Even when studies of the impact of fertilizers on biodiversity exist, they generally conclude the overall range of effects of inorganic fertilizers on species richness and abundance being arguably negligible or of little impact . Contrastingly, Le Féon et al.  showed that increased nitrogen (and related inorganic fertilizer) input can cause a decline in floral resource diversity and abundance (nectariferous native plants) in European farmland habitats.
Overall, there is a need to determine the trends in response of biotic organisms (including bees) to inorganic fertilizations in and outside flower farms since they are agrochemicals and they cannot be without disturbance on surrounding local ecosystems. The best way to detect negative effects of pesticides application by the flower farm on biodiversity is to study responses of most sensitive biota to pesticides application regimes. Pollinators (bees) are the best candidates for such studies. They are good bioindicators of environmental health [42–44]. Since it is well established that agricultural chemicals can have negative effects on biodiversity and on the environment of the areas where they have been applied, loss of biodiversity around flowers is expected . However, assessing negative effects of agrochemicals applied by flower farms on biodiversity in the surrounding environment is very challenging since these agrochemicals are applied inside greenhouses and therefore expected to have almost no effect on the surrounding environment.
Local people’s experiences and perceptions of the effects of rural development projects (e.g., flower farm industry) are often not reported or taken into account by decision-makers despite strong arguments that local opinions can help in building a policy enabling achieving a win-win conservation/development scenario to meet development targets, and that a community’s willingness to become involved in decision-making for the establishment of a project in their village is closely linked to their past experiences and to their perception of the benefit. Much as gender-based differences exist in perceptions of problems, integrating understanding of people’s perceptions with field observations of the functioning of environmental systems is critical for developing sustainable resource management activities. Previous work examining farmers’ perceptions about importance of pollinators in crop production has shown that farmers often have acute and accurate awareness of problems, and they can propose effective interventions, even if they appear unable or unwilling to tackle them .
In the absence of relevant information, decision-makers may be obliged to formulate their policies based on perceptions and views of farmers that appear very relevant. Since historical data collections do not exist in Uganda, it is difficult to know which bee species has declined. Thus, farmers’ surveys remain the only reliable source of information that can help to provide researchers with an idea or an indication of what might have happened in the environment few years ago. Using farmers’ knowledge and perceptions about changes in bee populations over the last 5 to 50 years can help to understand what happened in the area several years ago and potential causes that led to such a situation.
Even though pollinator declines are a global biodiversity threat, drivers of pollination decline/loss in natural and in agricultural ecosystems of Uganda have not been taken into account by policy-makers, conservationists, and researchers, although Ugandan agriculture owe much of its production to services delivered by locally available diverse pollinator species [20–25]. The real magnitude of pollinator decline is not easy to determine, particularly in countries like Uganda where there has been no historical data collections.
Based on the above background, there was a need to assess bee activities in the surrounding environment where different flower farms have been established. Accurate measurements of population densities (visitations) and species richness of bees are essential for any meaningful assessment of decline [23–25]. For nonsocial bees, this can be done with direct counts of individuals and classical abundance measures. For social bees, however, the number of colonies rather than the number of individuals is the crucial parameter for conservation  and assessment of decline. Density estimates derived from direct counts of bees are tedious and can be unreliable because natural nests are hard to detect particularly when there is little time in the field. For rapid surveys, the number of individuals can be used to give an indication on the potential richness of colonies in the landscape because the number of colonies in the wild can be sometimes very difficult to assess. The census of managed hives and detection of wild colonies of honeybees within a radius of 0.1–3 km from a given sampling location can exhaustively be surveyed but it needs personnel with knowledge of local beekeeping operations. Therefore, for a study of short period (<3 years) like the one presented here, the spatiotemporal assessment of visitations of different bee species to flowering plants in a given habitat can be a useful approach to detect historical or previous changes (decline, increase) in bee species and populations in relationship to farm management and local and landscape drivers such as pesticide application intensity, availability of floral resources, and so forth.
Currently, there exist no studies from Uganda addressing effects of multiple drivers on bee abundance and species richness. Information on how pollinators respond to different drivers may improve the understanding of the nature, causes, and consequences of declines in pollinator services at a local and national scale, as well as providing light on how to invest for the development of mitigation options to slow the decline of pollinators in Uganda.
As previously highlighted, in Uganda, there have been various protestations and complaints of farmers and various stakeholders from government agencies, academia, research organizations, civil society organisations, and nongovernmental organisations about the boom of flower farms . There were strong views and suspicion regarding the negative unknown impact of flower farms on health of humans, environment, and biodiversity in areas where the flower farms have been established. Consequently, there was a need to check whether these claims/complaints had a scientific foundation, when regarding effects on sensitive taxa such as bees. A preliminary field visit was conducted by the researcher and from that visit, it was pointed out that pollinators (specifically bees) inhabiting the surrounding of flower growing areas were likely to be at risk of disappearing. To verify this suspicion, an in-depth field study was therefore needed to be conducted. This was found to be necessary to ascertain community complaints.
The general objective of this study was therefore to conduct a rapid assessment on the status of pollinators (bees) around flower farms in central Uganda and provide guidelines on the preparation of a monitoring plan for the pollinators in the flower growing areas around Lake Victoria.
The specific objectives were (i) to gather information on the agrochemicals used by flower farms and their potential impacts on pollinator bees inhabiting the surroundings of the flower farms; (ii) to measure pollinator activities and assess the richness, abundance, and diversity of bees in relationship to landscape and habitat types found in the surroundings of flower farms; (iii) to assess the level of knowledge of pollination service importance in crop production by small-scale farmers living in the surroundings of flower farms; (iv) to document perceptions of local people with regards to cause of pollinator decline in their villages, (v) to collect views and perceptions of farmers about benefits and negative effects of the flower farms established in their villages; (vi) to collect views of farmers on the ways to promote flower industry in a sustainable manner (floriculture industry that matches people’s needs and desires, requirements for living in a clean nonpolluted environment); (vii) to outline bee monitoring and conservation strategies in the flower farm growing regions.
2. Materials and Methods
2.1. Study Area Description, Visits to Flower Farms, and Dialogue with Production Managers
This study was conducted in the banana-coffee system of Lake Victoria Arc covering several districts of central Uganda. The study zone (latitude: 0.5°31′22′′; longitude: 31°11′71′′; altitude: 1080–1325 m) is characterized by ferris oils with high to medium fertility level and receives on average 1000–1800 mm of rainfall per annum on a bimodal pattern (rainy seasons: March–May, September–November; semidry to dry seasons: June–August, December–February) with 28.7 ± 2.77°C and 68.65 ± 8.91% of mean annual temperature and relative humidity, respectively [22–26]. But the rainfall amounts and patterns are unpredictable. The study zone belongs to the Lake Victoria phytochorion [23–25] with shrubs of Acacia spp., legume trees, melliferous plant species, Papyrus, and palms ranging from 2 to 15 m high dominating the remnant secondary vegetation [20–22]. In this study region, coffee (Coffea canephora, Pierre ex Froehner) is the main cash crop and banana the main staple food crop. Several pollinator-dependent food and cash crops are grown in small-scale monoculture and/or polyculture fields that are integrated into this coffee-banana agroforestry system including home-gardens. There were no standard crops per study sites but most crops were found grown in almost all study sites. Crops grown as sole or in association with coffee and or banana include cassava (Manihot esculentum L.), sweetpotato (Ipomoea batatus L.), maize (Zea mays L.), beans (Phaseolus vulgaris L.), groundnut (Arachis hypogea L.), tomato (Lycopersicon esculentum L.), watermelon (Citrullus lanatus L.), pumpkin (Cucurbita moschata L.), cucumber (Cucumis sativus L.), melon (Cucumis melo L.), chilies (Capsicum spp.), and several other fruits, vegetables, and horticultural crops (cabbage, onion, etc., egg plants, sim-sim, etc.). The majority of these crops are grown in small-scale monoculture and or polyculture fields that are integrated into the coffee-banana agroforest production systems. The agroforestry system is also dominated by several native/indigenous fruit and agroforestry tree species. Banana-coffee agroforests and small-scale farms cover about 60% of the land, whereas mixed mosaic seminatural habitats cover approximately 40% of the farm-landscape studied. There exist in this study region some large monoculture plantations (sugar cane plantations, coffee plantations, tea plantations, etc.) and some flower farms companies.
Rural central Uganda is a mosaic landscape where “islands” of patches of natural habitats (forest fragments, forest reserves, wetlands, woodlands) and linear (eg., hedgerows) and nonlinear (fallow fields, grasslands, woodlots, cattle pastures, or rangelands) features of seminatural habitats  that serve as “field boundaries” of the variety of small-scale fields are found scattered within agricultural matrices. Compared to other districts of the country, the study area (central Uganda) is also characterized by high demographic pressure, limited access to arable lands, continuous cultivation, and over-exploited lands under unrevised land policies . All study sites had also some forest remnant tree species retained within them, ranging from 1 to 175 trees/ha found both in crop fields as well as inside remnant natural vegetations scattered inside the forest. Flower farms are located in four major zones of central Uganda: the first zone is Entebbe airport zone located at 60 km from Kampala city (example: Wagagai flower farm and Rosebud-II. Wagagai and Rosebud II are separated by a distance of 30 km). The second zone is in Mukono district located at about 250 km from Kampala city (example: Mairye estates), the third zone is Ntungamo zone located at about 500 km far away from Kampala city (example: Pear flower farm). In addition, mantel test () showed that there was no evidence of significant spatial autocorrelation between pollinator counts on transects within a landscape surrounding a flower farm. The distance between greenhouses and people’s homes varied from a flower farm to another one: >20 m–100 m. In many cases, small-scale gardens are established closer (0–20 m) to greenhouses. Thus, field visits were conducted to make a rapid survey on the status of pollinators (bees) around the flower farms in the Lake Victoria shores. To be able to understand the actual activities and operations within the flower farm, four flower farms were visited out of a total of 12 existing and operating flower farms in central Uganda, given the budgetary and time constraints. The 4 selected flower farms included Fiduga (located at 15 km far from Kampala, Wakiso district), Pearl flower farm (located at about 400 km far from Kampala in Ntungamo district), Mairye estates (located at 30 km far from Kampala in Mukono district) and Wagagai flower famr (located at about 40 km far away from Kampala along Entebbe airport road). Overall, commercial floriculture is still a new industry in Uganda, dating back to only 1993. Cut-flowers, cut foliage and, to a lesser degree, pot plant cuttings are the main outputs. Cut-flowers include a variety of roses, chrysanthemum cuttings, carnations, and summer flowers. The four flowers grow different types of flowers and varieties (Table 1) and apply different levels of pesticides. The major flower varieties grown and exported from Kenya are roses, carnation, alstroemeria, lisianthus, statice, and cut foliage. Rose flower dominates.
Flower farm visits were conducted for two years (2010 to 2012). During a visit of each flower farm, discussions with production manager were engaged by the researchers. The discussion focused on collection of information likely to enlighten the potential negative effect of activities conducted at the flower farm on pollinators as well as collecting information likely to help generate information that is more rewarding to policy-makers. During the course of discussions, various types of information were collected, mainly, information about the type of agrochemicals (pesticides, fertilizers) used by the flower farms, the type of varieties grown, the type of production conducted (e.g., exporting flowers cuts or stems or roses), the total number of people employed (including the proportion of females employed), the total size of the flower farm (including the number of hectares in production), the number of years since the flower farm was established, the monthly total production exported, the gross income from sale of flower products, the costs of labor, the costs of other general inputs, costs of pesticides/fertilizers purchases, and information about measures taken to control runoff of chemicals into the surrounding environment. The researcher visited stores of agrochemicals to confirm trade names reported by the flower farm production managers. In addition, production managers were asked whether they understood pollination and if pollination by bees was an important factor in their production business.
2.2. Landscape/Habitat, Bee Biodiversity, and Floral Resources Surveys
2.2.1. Landscape/Habitat Surveys
After farmers’ interviews, surveys of bees were conducted in different landscape/habitats in the immediate surroundings of the flower farms. For each flower farm, surveys were conducted in all directions considering the flower farm to be at the center. Two types of fields were found in the surroundings of flower farms: cultivated fields and noncultivated fields. The type of dominant landscape vegetations characterizing these cultivated and uncultivated fields were cropland and grassland vegetations. Cropland vegetation types that dominated cultivated fields were composed of a mixture of crops grown under various cropping/agroforestry systems. The grassland vegetation types that dominated the uncultivated fields were composed by a mosaic of seminatural habitats. The dominant habitat types in the croplands were different land-use types or crop associations (e.g., maize + beans + banana), whereas the dominant seminatural habitats in grasslands were pastures, fallows, woodlots, hedgerows, field margins, and so forth.
2.2.2. Transect Counts for Bee Surveys
Different beneficial insect taxa respond to agricultural practices at different spatial scales and often at multiple spatial scales . Therefore, it was found important to assess bee diversity and visitations at different spatial scales by placing sampling transects at different distances from the flower farm. Placing sampling transects at different distances was sought to help in determining at which spatial scale chemical applications in the flower farms could affect significantly activities of bees in cultivated and noncultivated fields in the surrounding landscape habitats. The collection on bees was conducted for five consecutive rounds across dry and rainy seasons during two years (2010–2012) at regular periods (dates) of visits of the flowers farms. Hence, five transects were set from the edge of the flower farm into either croplands or grasslands or both. Transects were established, extending from the flower farm boundary into farmlands. Transects were separated by a distance of 3–5 km. They were set in north, east, west, and southern part of each flower farm. With this distance, bee samples were independents since the normal foraging distance of most bees is 2 km. Each transect measured a total length of 2.2 km. Bees were sampled on these transects (20 m-large × 2.2 km-long) at 0–10 m, 500 m, 1500 m, and 2000 m from the edge of the flower farm. Transects were set parallel to flower farms at the above mentioned distances. Plots were set mainly at different flower patches alongside transects. In this study, flower patches were isolated groups of blooming plants; the group was composed of various plants of same or various species associated in a given site. Flowers were composed of either natural vegetation or crop plants and in some cases both.
While walking along transect, at each flower patch, data was collected on bee populations using field observations, hand-nets, and transect-count methods. Observations were also made on other pollinators, such as butterflies, moth, and hover flies; although detailed data is not presented since the focus was on bees.
Within each plot, bee species within the plots were counted and their visitation intensity was measured and recorded on datasheets. Parallel to bee surveys, habitat/land-use variables were also recorded such as the percentage cover and number of seminatural habitats. When needed (in some few cases), nests of solitary bees were recorded using subjective (focused) searches in particular habitats (nesting habitats). The different nests were also located either by chance during random searches or by inspecting tree-holes/termite mounds located alongside established belt transects of width 20 m and length 1.1 km per transect.
Bee collections/censuses were conducted from morning to evening hours (07:00 h to 17:00 h, local time). Pesticides applications inside greenhouses are commonly conducted at different interval of times: 5:00 h–7:30 h, 7:30 h–10:30 h, 15:30 h–18:30 h. Therefore, some bee collections coincided with periods of pesticides application.
2.2.3. Censuses of Bee-Visitations
Visitations are part of four measures of pollination services delivery by pollinators to plants/crops: (a) pollination rate (number of pollinators visiting flowers/min), (b) proportion of visited flowers (number of flowers visited by pollinators/total number of flowers in a plot), (c) flower handling time (seconds) or the mean time pollinators spent on individual flowers in a plot, and (d) pollination efficiency (% fruit set after single visit by a p). These four parameters of pollination services can be used to forecast high reproductive success of plants/crops after visitation of crops/plants as compared to those that were not visited. Thus, pollinator visitations (number of bee individuals landing on flower reproductive parts and moving/collecting floral resources such as pollen/nectar for at least 0.1–1 minute before flying away) to plants/crops species in flower patches were conducted from morning to evening hours each sampling day.
All floral visitors, pollinators and nonpollinators to male and female flowers and inflorescences of all flowering plant species were collected at the time of their greatest activity. The pollinator censuses were conducted for 2 full years (2010–2012 and for each year, data was collected during four consecutive sampling rounds (R1: December–February, R2: March–May, R3: June–August, R4: September–November)). The pollinator censuses were conducted within permanent plots (20 m × 100 m) that were placed in a systematic way (along line transects of 20 m × 1100 m) and separated by 10 m. Pollination information was basically collected on transects. Overall, data recorded was based on an observable number of bees per flower patch per 25 m2 (5 m × 5 m) from each sampling unit (plot). The data recorded and stored in datasheets was expressed as number of bee-visits per bee species per 100–500 flowers/15–20 min observations per flower patch of 25 m2 (5 m × 5 m).
While walking along transect, censuses of bees were performed during 15–30 min at each flowering patch met along the transect. In each census, several floral stems with 100–5000 flowers were observed. A total of 10 to 50 flower patches were met and censured each study visit. Minimum observation distance from the flowers was 2 m. In each census, the number and identity of visitors were recorded, including the number of flowers visited, the visitation intensity of each bee species, and the behavior of these visitors on the flowers. The abundance, diversity, and composition of the flower visitor assemblage of the focal populations were determined by counting the number of insects visiting the flowers by means of point-centered 30 min surveys. Sampling took place during full bloom of plants. Thus, fully blooming inflorescences/flowers were monitored during the entire foraging period of the day including the period of peak insect visitation activity (9:00 h–15:00 h).
Flower-visiting insects were identified, and buzz-pollinating ability (based on observation of sonication and pollen release by some bees such as Xylocopa) was recorded for each visit whenever possible. During bee counts, local temperature and wind speed were recorded at the start of each transect as these can affect pollinator behavior, although counting was conducted only at temperatures above 18°C to eliminate the impact of low temperatures on counts. Censuses were conducted along the day from 6:30 h to 17:30 h local time. Each study visit, surveys were carried out across the five transect.
Duration of flower visits to individual inflorescences/flowers of each plant species was recorded for several individual bees and rounded up to full minutes. A flower visit was defined as the period between the first landing on the inflorescence and final departure (irrespective of short hovering flights for scent transfer). More specifically, a visit was defined to have occurred when the visitor’s body contacted the reproductive organs (stigma or anther) of any available fresh flower. The bee observations were conducted each day that weather conditions allowed pollinator activities. The order of observation of plots alongside transect was random, each plot being observed only once per day. During each observation period on flowering patches in sampling plots, the number and identity of flower visitors to flowers or inflorescences (depending on the species) of all species blooming in the plots were noted.
The identification of some flower visitors was done in the field with the experience of the researcher. For species that could not be identified in the field during foraging observations, the visitor was given a morpho-species name and immediately after finishing foraging observation data collection on datasheets, a handnet was used to collect specimens. Voucher specimens of visiting bees were collected from flowering plants on separate moments to avoid disturbance to pollinator activity. The specimens were saved in alcohol 70% and later the identification confirmed in the laboratory using available collection of bees of Uganda. The bees were identified using the author’s reference collection of bees that is located at Makerere University zoology museum.
2.2.4. Assessment of Floral Resources
To account for the floral abundance of the plant species, after each observation period, each species’ number of open attraction units to bees which could be flowers or inflorescences was counted. The data recorded was expressed as number of fresh flowers (all plant species combined) per flower patch per 25 m2. In addition, the number of flowering plant species was recorded per sampling plot alongside transect walks.
2.3. Field Surveys and Farmer’s Interviews on Pollination Knowledge and on Causes of Pollinators Decline in Relationship to Activities Carried Out at Flower Farms
In the surrounding of each flower farm, four directions were followed. In each direction, 2 villages were selected in the north, south, east, and western directions of the flower farm. In each direction, the first village was selected between 0 m and 1 km far from the edge of the flower farm; the second village was selected between 2 and 3 km far from the flower farm. These villages were selected to cover the range of space at which a farmer could smell and could not smell the scent of the chemicals from the flower farms. In each village 20 people (10 men and 10 women) were randomly selected while walking alongside main trucks in the village. In total, 160 people were interviewed from 4 villages from the four directions in the surrounding of the 4 flower farms, making them 640 people interviewed in total from the surroundings of the four flower farms. Farmers interviewed were those met in their gardens busy farming. During interviews, after finishing the dialogue with farmers, the researcher conducted field inspection under their guidance. Field visit and inspection were conducted to enable scientists/researchers to verify whatever farmers were reporting when interested in gathering information about their ecological knowledge of pollination process.
Primary data were collected by administering a questionnaire to production managers of different flower farms and to small-scale farmers living in the surroundings of the flower farms. In addition, a separate datasheet was used to gather data on bee species diversity and populations. The questionnaire captured information on agrochemicals used, bee diversity, landscape/terrain, weather conditions, especially at the time of bee surveys, farmers, and flower official perceptions and the impact of their activities on pollinators and crops.
More specifically, the effects of farm management practices (agrochemical pesticides utilization intensity), landscape vegetation types, distance from the flower farm, and the flower farm location on bee communities foraging in environments surrounding flower farms were investigated. The investigation aimed at identifying factors that could help in understanding the potential role of agrochemical activities carried out inside flower farms on bee biodiversity living in the adjacent habitats. In addition, farmers’ surveys were conducted in order to get an idea of the potential causes of decline of bees in the villages including the impact of flower farm activities (agrochemical applications).
The questionnaire was also administered to assess if farmers understood the meaning of pollinators, pollination, and pollination service and its importance in crop production activity. Villages where flower farm activities were not expected to impact agricultural production were identified (villages located at more than 5 km far away from the edge of the flower farms) and sampled to ascertain the differences in bee populations and species richness. Only farmers found doing some activities in their gardens were interviewed. Efforts were made to be gender sensitive during the course of interview. However, women were frequently found in the gardens than men.
The questionnaire consisted of a mixture of open and closed ended questions. The questionnaire was pretested by the researcher a few weeks prior to field surveys. Semistructured interviews that had a number of predefined starting questions were used. Thus, farmers were interviewed using a pretested structured questionnaire. The questionnaire was piloted by the researcher one week prior to the actual survey and the necessary corrections were made. Interviews were largely conducted in the local languages (Luganda, Runyakore) with translation into English whenever necessary.
Data collection related to interviews at the different sites took place from 9:00 am onwards, because most women started their agricultural activities by 6:00 am and were completed by midday. Prior to data collection, the researchers solicited informed consent from participants. The questionnaires took approximately 20–30 minutes to administer to each individual respondent. The questionnaire was filled using face to face interviews. Interviews were conducted either at the farmer’s home or in the field, where such fields were within 0.5–1 km from a farmer’s homestead and the farmer was willing to be interviewed on site (field or garden). Once conversation on a topic was initiated, it was allowed to roam freely until exhausted at which point a new topic was begun. It was ensured that interviewees had the opportunity to ask the researchers questions at the end.
Notes were taken on individuals’ responses. The researcher visited every respondent’s crop field in order to verify some of their responses. The questionnaire submitted to small-scale farmers focused mainly on crop production and the relevance of pollinators in crop production. The survey questionnaire comprised two main parts. The first section sought general sociodemographic information about respondents, including age, gender, household income, gender labour in crop production, marital status, number of children, and formal education levels. The second section gathered information relating to respondents’ knowledge of crop pollination, pollinator types, perception of the importance of pollinators to crop yield, potential causes of decline of pollinators in the village, and potential role played by activities (application of agrochemicals) carried out by the flower farm located in the village. Specifically, farmers were asked to (i) describe/define their understanding of pollination, (ii) to name, identify, and differentiate between wild bees and honeybees and other insect pollinators they knew and indicate the area where they sleep (nesting site), (iii) mention the role of bees and other pollinators in crop fruit/seed set, (iv) to explain the importance of pollinators in their farming business, (v) to comment on the effects of pesticides application on wild bees and other pollinators, (vi) to list (and justify why they think so) the potential causes of decline/loss of pollinators in their village including describing the role played by activities carried out at the flower farm such as intensive application of agrochemicals, (vii) to comment on the linkage between crop yields reduction and bee decline in the village, (viii) to list advantages (benefits) they get by living near the flower farms, (ix) to propose sustainable solutions to resolve their conflicts with flower farms. Photographs of different bee species and different other pollinator species were presented to respondents to help in identification of different species of bees visiting crop flowers.
2.4. Data Analysis
2.4.1. Cumulative Analysis of Agrochemicals Application
From the raw data obtained during discussions with the flower farm managers, the total amount of agrochemicals applied on a daily basis was used to calculate the amount of agrochemicals used per annum in relationship to total amount of water used. Later on, the annual amount of pesticides used was cumulated based on the number of years since the flower has been in production. The cumulative analysis was undertaken using Microsoft Excel 2007.
2.4.2. Bee Visitation and Floral Resources Raw Data Files Pooling and Organizations
Data on bee counts and visitations that were recorded per each sampling plot were summed to obtain the total number of bee-visits and bee species per transect each study round. Similarly, the total number of fresh flowers and number of flowering plant species per transect was obtained by summing values obtained per sampling plot (20 m × 100 m).
2.4.3. Variation in Bee Abundance, Visitation, Species Richness, and in Flower Density between Surrounding Habitats of the Different Flower Farms
Cross-tabulations with selected variables (number of species and individuals, visitation frequency) were undertaken using pivot table in Microsoft Excel 2007, to verify anomalies and correct errors in raw data files before data analysis.
En ce qui concerne the diversity and numbers of bees, the abundance of flower visitors was estimated by standardizing the number of visits per time unit (expressed as visits per population per hour/flower patch). Flower visitor diversity was assessed by calculating species richness and evenness. Richness was calculated as the number of flower-visiting species found visiting flowers in flowering patches. Diversity of bee communities between the four flower farms was estimated using the Shannon-Wiener’s diversity index () and the similarity in bee communities among the four flower farms was estimated by the Sorensen similarity index according to Magurran  using raw data collected across four rounds of data collection over two years (2010–2012).
2.4.4. Effects of Landscape/Habitat Types on Bee Abundance and Species Richness
All variables were tested for normality and the strongly skewed variables were transformed prior to analyses if necessary to meet the assumption of normality and homogeneity of variances. Therefore, the percentage cover of flowering plants was arcsine-square-root (+0.5) transformed and number of species or counts of bee individuals and bee-visitations counts were transformed. The differences in number of bee individuals and species and in number of fresh flowers between the 4 flower farms were tested with general linear model (GLM) analysis of variance (ANOVA) in Minitab release version 165. GLM analyses were fitted with pesticides application intensity (very high: 4, high: 3, medium: 2, and low: 1) landscape vegetation types, flower farms, and number of transects as treatment factors (predictors) and the abundance, visitation frequency, species richness of bees, and number of fresh flowers as the response variables. Where GLM test indicated significant differences, posthoc Tukey’s test was used for means separations.
2.4.5. Farmers’ Surveys
The survey data were entered into a spreadsheet and checked prior to analysis. Cross-tabulation with selected variables, percentages, and means were undertaken using pivot table in Microsoft Excel 2007. Percentages were based on either the total number of respondents or total responses, details of which are provided in the respective text or tables. Chi-square test was used to determine association between variables such as to determine the effects of farmers’ sociodemographic profiles on their knowledge of pollinators, pollinator unfriendly farming practices, farmers’ perceptions of crop yield reduction, and causes of decline of pollinators in their villages. During interviews of farmers about potential negative effects of agrochemicals applied by the flower farms on crop yields, decline of bees, and environmental pollution, most often a farmer could give more than one justification/statement (opinion). Therefore, both the summary of the statements made by farmers and the full statements were presented in separate tables/figures. The frequency of occurrence of the statements from the entire population interviewed was calculated and presented in respective tables.
3.1. Agronomic and Socioeconomic Characteristics of Flower Farms Studied
The different farms studied were established with clear production objectives. For example, Mairye and Pearl flower farms were found to be specialized in the production of cut flowers and roses whereas Fiduga flower farm was found to be specialized in the production and export of Crysantemum cuttings (stems). Hence, different companies grow different types of flower varieties (see Table 1). The land under production played a big role in the production potential for each farm.
The number of employed people was 800, 485, and 255, respectively, at Mairye, Fiduga, and Pearl flower farms. Across flower farms, the proportion of employed females oscillated between 55 and 65%. The monthly total production for export varied from one flower farm to another: 4 million cut-flowers for Mairye, 36 million cuttings for Fiduga, and 2 million cut flowers (including roses) for Pearl. Consequently, the declared total annual income obtained from sales of flower cuttings was US$90,000, US$47,000, and US$7000 for Mairye, Fiduga, and Pearl flowers farms, respectively. The monthly cost for pesticides/fertilizers purchase oscillated between US$10,000 and US$53,000 across flower farms.
There was a positive correlation between the monthly total income obtained per flower farm after sales of flower cuttings and (i) the cost of general inputs (, , ), (ii) cost of labor (; , ), (iii) the number of employed females by the flower farm (; ; ), and (iv) total size of the flower farm in production (, , ). On the other hand, there was no significant () correlation between the monthly total income obtained per flower farm after sales of flower cuttings and (i) the number of employed males (; , ), (ii) the total number of workers (; , ), (iii) the total farm size land (, , ), and (iv) the cost pesticides/fertilizers (; , ). This last result indicated that it may not be necessarily rational for a flower farm to over spend on agrochemicals (pesticides/fertilizers) that are dangerous to the health of human beings and to the environment. In other words, it is still possible to have a flower company reaching high profitability while spending little on toxic pesticides. Different options of buying and using nontoxic and effective pesticides can still be adopted by a flower company and obtain good profitability.
3.2. Variation among Flower Farms in the Application of Agrochemicals (Pesticides, Fertilizers) inside Greenhouses
During field visits, information on agrochemicals used by the different flower farms was collected. A checklist of different fertilizers and pesticides used and dosages are given in Tables 3 and 4, respectively. Long-term use of fertilizers for agricultural purposes has been an issue of concern to researchers. The presence of metals in some agricultural fertilizers raised fears that continued application of fertilizers may lead to accumulation of these metals to toxic levels in the soil for living organisms including plants. Prolonged fertilizers use is also a concern in sites where cut-flower industries are established in that it may affect neighboring small-scale lands.
In these flower farms, spraying of pesticide is done manually by male workers provided with almost no means of protection apart from gumboot. There was a high variability in quantity of pesticides and fertilizers applied daily among the different flower farms visited (Tables 3 and 4). In the pesticide class, fungicides, miticide-nematicide, insecticides, and herbicides were applied by the different flower farms at different levels.
Types (herbicides, insecticides nematicides, miticides, and fungicides) and number of pesticide applications on flowers were retained as a surrogate variable for agricultural practice intensity inside the flower farm. Indeed, this variable reflected both the amount of inputs in field greenhouse (to increase flower productivity) and disturbance of environment and human health caused by each spray session by farming pumps or spray machines. Overall, the intensity of pesticide application (that combines both the types and number of pesticides applied per month) was considered as a proxy of flower production practice intensity. This surrogate has a potential indirect impact on pollinator communities living in habitats surrounding flower farms. The intensity of pesticide application by the flower farm was measured at four levels (very high: 4, high: 3, medium: 2, and low: 1). Based on quantity reported by the production mangers and based on field experiences (observations, field impressions), Fiduga flower farm was classified as the flower farm with very high level of intensity of pesticide application; and Rosebud-II was classified as with high level of intensity of pesticide application whereas Mairye was classified as flower farm with medium level of intensity of pesticide applications. The flower farm that was classified as with low level of intensity of pesticide applications was Pearl flower farm.
It was not clear whether the quantity of pesticides had an effect on pollinators living in the surrounding habitats, since most of the agrochemicals were applied inside greenhouses of the flower farms. However, daily application of pesticides cannot be without effect on the surrounding environment with its living organisms. In the short term, the effects may not be visible or perceptible. However, in the long-term, the effects may be visible/perceptible given the fact that different types of pesticides have different characteristics of persistence in the environment. The more pesticides were applied from one focal point, the more they would accumulate in the environment with consequences of disturbing/disorganizing natural and ecological systems.
The results indicated that the amount of agrochemicals spent so far per flower varied with the number of years since the flower farm was established (Figure 2). Among the 3 flower farms for which data was available, Mairye is likely to have applied 250,000 kg (m.a) of fungicides; 1200000 kg (m.a.) of insecticides; 65000 kg of nematicide-maticide; 35000 kg of herbicides; and approximately 200,000,000 liters of water for a total land in production of 18 ha. This amount of pesticide application with high environmental persistence cannot be with any consequence to the local environment. There is a need to choose to apply less toxic pesticides by the flower firms.
3.3. Precautionary Measures Taken by Owners of Flower Farms for Containing Chemical Runoff from Flowers into the Surrounding Environment
When asked about the strategies/measures taken by the flower farm to reduce negative effects of chemicals runoff into the environment, all production managers said they recycled and controlled well the quality of water before releasing it into the environment. Across all flower farms, greenhouses were set in such a way as to be always open to the outside; and this was suspected to have a great influence on pollinators and movement of pesticides into farmland habitats.
Production managers interviewed said management of agrochemicals and effluent from flower farms were a major concern by their companies. They also said that they were aware that pesticides, fertilisers, and herbicides can pollute river, lake, and wetland systems as a result of poor management of effluent from the flower farms and this constitutes a threat to aquatic life like fish and human health. Hence, they had to take measures that minimise soil and water pollution, such as constructing lagoons and planting papyrus to perform water purification (artificial wetlands). For example, production managers said that the water recycling system has enabled them to reduce water needs from 50,000 liters of water/day/ha to 13–20 liters/day/ha. However, during office discussion with the production managers, a question was asked about their perceptions/views in response to farmers’ complaints of chemicals sprayed in the flower farms affecting their crops/livestock and their own health in the village nearby, some production managers said that they have never received complaints from the nearby communities, others said they control perfectly chemical runoff, and therefore accusations of farmers living nearby were not correct.
3.4. Effects of the Types of Habitats Found in the Surrounding Landscape of Flower Farms on Bee Abundance and Species Richness and on Availability of Floral Resources
Bee nests density, species richness, abundance, and visitation frequency to blooming plants in landscapes found in the villages surrounding the flower farms varied significantly (GLM test, ) across transects and locations of the flower farms. They also varied by the intensity of agrochemicals (pesticides, fertilizers) applications by the flower farms (Table 3) since flower farms that used more agrochemicals were also involved in regular (frequent) throwing (dumping) of agricultural wastes (agrochemical wastes) in the grasslands/croplands in the villages nearby the flower farms. However, the richness and the abundance of blooming plant species were not significantly () affected by the intensity of application of agrochemicals (pesticides) nor by the flower farm location. But they varied significantly (, GLM test) across transects and according to landscape types/vegetation type. This result indicated that agrochemicals application during flower production process did not affect the richness of blooming plants in the neighborhood; few plant species managed to get adapted to such environment. Adapted plants were seen to be abundantly in bloom around the neighborhood of the flower farm (Table 3) even when there were few bees visiting such blossoms. Also, there was a significant positive correlation (, ) between the intensity of pesticides application by the different flower farms and the monthly net income from sales of cuttings.
Farmers living in the surrounding of different flower farms are engaged in the cultivation of different types of crop species in association. The average number of pollinator-dependent crop species inventoried during the study survey was 5.1 ± 0.9 (Fiduga), 3.1 ± 0.6 (Mairye), 2.3 ± 0.9 (Pearl), and 4.6 ± 0.85 (Rosebud = Wagagai). The number of nonpollinator-dependent crop species grown was 3.3 ± 0.35 (Fiduga), 1.9 ± 0.7 (Mairye), 2.2 ± 0.45 (Pearl), and 2.3 ± 1.12 (Rosebud) (Table 4).
In the surrounding of each flowering farm, there were significant () differences in species richness, abundance, visitation frequency, and number of fresh flowers per transect at different distances (0 m, 10 m, 50 m, 500 m, 1500 m) far away from the flower farm into farmland habitats. For example, for Pearl flower farm, there were significant differences between the 5 distances (0 m, 10 m, 50 m, 500 m, 1500 m, 2000 m) in species richness (GLM: , ), abundance (GLM: , ), bee visitation frequency (GLM: , ), and abundance of fresh flowers (GLM: , ) (Figure 3). Similar trends in the results were observed at Mairye, Rosebud, and Fiduga flower farms (Figure 3).
In the surrounding of each flower farm and across sampling rounds (R1, R2, R3, R4), there were significant differences in the species richness and abundance of bees. In fact, for Pearl flower farm, there were significant differences in species richness of blooming plants in croplands (GLM: , ) as well as in farmland habitats (GLM: , ). Results of similar trends were observed for the percentage cover of mass blooming plants/transects in both croplands (GLM: , ) and grassland habitats (GLM: , ; Figure 4).
The abundance of bees varied significantly (, test) across the different land-use types and seminatural habitats encountered on transects during transect counts of bees across the surroundings of the four flower farms (Table 5). The most visited land-use types were the banana + bean + cassava, followed by young fallows, field margins, and coffee + banana + cassava + beans + fruit trees + agroforestry trees. The most visited seminatural habitat among those encountered during transect counts of bees in grassland and rangeland habitats was “shrubby fallow”, followed by unfenced grazing plot followed by pad-docking fenced grazing plot with live fence, unreclaimed papyrus swampy habitat, and hedgerow (Table 5). Different bee species made visits to blooming plants in these different habitats/land-uses at different periods of the day; most frequently, they made intense visits from 10:30 h to 15:30 h. The spray of chemicals in flower farms is generally conducted between 6:30 h and 8:30 h or between 15:30 h and 18:30 h.
|Chi-square test: , , , .|
|Chi-square test: , , , .|
Different bee species were recorded significantly ( 3 df = 7.87, ) in the different cropland and grassland habitats in the surrounding of the flower farms. They occurred with different abundance. In total: 37, 26, 45, and 33 bee species were recorded in the surrounding of respectively, Fiduga, Mairye, Pearl, and Wagagai (Rosebud-II) flower farms. Landscape habitats surrounding Pearl flower supported more diverse bee communities (mean ± SE of ) than Mairye (mean ± SE of ), Fiduga (mean ± SE of ), and Wagagai (mean ± SE of ) flower farms (GLM-ANOVA: , ). However, there were no significant differences (, , df = 3) among flower farms in the average similarity index values of shared bee species. In other words, bee communities from these flowers were statistically similar in species composition. Common bee species were frequently recorded to be abundant on flowers than specialist bees. In the tropics, the structure of most bee communities is not different from that of other insects. There is always 1–5% of dominant bee species and 90–95% of species that are rare or appear as singletons or doubletons.
There was a significance ( test, ) in relative abundance of different bee species in the surrounding of different flower farms. The most abundant bee species in the surrounding habitats (croplands, grasslands) were Apis mellifera adansonii (14.08%), followed by Meliponula ferruginea (10.90%) and Nomia brevipes (9.03%). The most abundant species in the farmland around Mairye were Apis mellifera adansonii (23.3%), followed by Meliponula ferruginea (19.84%) and Apis mellifera scutellata (8.67%). The most abundant species in the surrounding of Pearl flower farm were Apis mellifera adansonii (23.67%), followed by Apis mellifera scutellata (10.64%) and Ceratina tanganyicensis (8.58%). The most abundant species in the surrounding habitats of Wagagai (Rosebud-II) were Apis mellifera adnasonii (37.25%), followed by Apis mellifera scutelatta (15.7%) and Halictus orientalis (9.59%) (Table 6).
Overall, high bee species richness was associated with different habitats (land-uses) found around Pearl flower farm, probably because the flower farm was young (recently established). There may be little accumulation in the environment of chemicals applied at Pearl flower farm to affect bee populations. The majority of bee species recorded were characterized by different ecological requirements. They belonged to different functional groups, but on overall most species recorded were solitary, polylectic, multivoltine, and ground nesting bees. However, high population density was observed in the less rich functional groups of species: social bees (Apini, Meliponini). In addition, a high number of nesting sites and nests was counted for various solitary bee species in the landscapes. On average, nest density (10.98 to 183.91 nests/transect) was high in rangelands/pasturelands than in agroforestry landscapes around Pearl flower farm. Few (2.5 to 5.89/transect) stingless bee nests were counted in croplands, indicating the fact that most managed and wild bee species found in the surrounding of flower farms used natural and seminatural habitats as preferential nesting sites (reservoirs). However, the different bee species used different foraging habitat types. While walking in croplands, “banana + beans-cassava” and “young fallows” were found to harbor a high number of bee foragers, whereas frequency of visitations by individual bees belonging to different bee species was intense in old and bushy fallows and in hedgerows. This indicated that the conservation of bees in the flower producing zones has to involve the conservation of seminatural habitats (hedgerows, fallows) in the surrounding of flower farms. It may be relevant to say here that both flower farm managers and small-scale farms should be sensitized about the value of conserving seminatural habitats for the maintenance of pollinators in the habitats surrounding their flower farms.
3.5. Farmers’ Surveys Results
3.5.1. Characteristics of Interviewed Farmers
Several farmers from Baganda, Bakiga, and Banyankore tribes were interviewed. Across flower farm location, the majority of respondents were females (62%), aged between 35–60 years. The main lucrative activity of these farmers was crop production, although in Ntungamo, farmers interviewed were cattle keepers (cattle keeping being the main lucrative activity and crop production being the secondary subsistence activity). The majority of small-scale farmers interviewed had a total land allocated to crop production of 0.1 to 10 ha maximum. The majority of these farmers hired or paid 1 to 2 workers and this result indicated that they had almost no labour cost. Interviewed farmers did grow various crop species in association. Most frequently, it was common to find a mixture of pollinator-dependent crops with non-pollinator-dependent crops (Table 7).
|Chi-square test for difference in knowledge of what bees are after on crop flowers: , , , .|
3.5.2. Farmers’ Knowledge of Pollination, Pollinator Groups, Pollination Processes, and Value of Pollinating Services to Their Crops
The percentage of farmers understanding the word pollination (those knowing different pollinators of their crops) increased and was significantly () positively related to (a) the education level of the farmer (number of years schooling), (b) the age of the respondent, (c) the number of years the farmer has been growing pollinator-dependent crops among those interviewed, (e) the total land allocated for crop production by a farmer, and (f) the proportion of rich farmers in the village (community) among those interviewed (Figure 5). Surprisingly, there was a negative relationship between the number of rich farmers and knowledge about pollination (Figure 5(f)).
When asked the question, do you know or understand what we mean by pollination?, the percentage of farmers saying they understand what pollination means was of 80% against 20% who said they did not understand or know what pollination means ( 1 df = 36.56, ). When asked to name 1 to 6 species of pollinators they knew and saw visiting the flower of their crops, approximately 5%, 37%, 38%, 14%, 4%, and 2% of farmers interviewed declared knowing (were able to name), respectively, at least 0, 1, 2, 3, 4, and 6 bee species/groups ( 1 df = 85.129, ). But, when asked to describe the types of pollinator/bee groups (species) they see visiting flowers of their crops, interviewed farmers had significantly ( test, ) correct knowledge of more than 2 pollinator groups. Farmers (14.1–19.3%) knew honeybees, Xylocopa (“Civuvumira” in local language: Luganda) and stingless bees (“Kadoma” in local language: Luganda) as the frequent flower bee species/groups of their crops (Figure 6).
There were significantly (, test) different farmers’ perceptions on roles played by bees in crop flowers. When asked about what they think bees are doing on flowers of their crops, farmers provided different responses. Most frequent statement (14.6%) from farmers was that they believed that if “bees fall on flowers on their crops, they will see fruits/seeds coming in 1 to 3 weeks.” Other farmers (11.5%) believed that “bees come to drink water and collect other foods on their crops.” In 0.8% of frequency of statements, some farmers believed that “bees come to facilitate marriage of their crops.” However, some farmers believed that bees were just playing with flowers of their crops and doing nothing valuable for their crops (Table 7). When asked the question: “is crop pollination by bees important in your crop production?”, 73.9%, 9.9%, and 16.2% ( 2 df = 74.81, ) of farmers declared, respectively, that they believe (i) bees are important, (ii) they are not important, and (iii) they are not sure if bees are important in their crop production activities. More frequently, farmers reported that they “think crop pollination by bees is important in their farming business because they frequently believed (8.15%) that “with bees, they will get honey and they are convinced that if no bee-visits, there is no yield from their crops” (Table 8). Farmers who grow vegetables and fruits had higher understanding of pollination than those who grow legume, cereals, root, and tubers. Other farmers said bees contribute little and for them they are aware that bees that visit their crops come from community hives and or from surrounding forests and lake edges/wetlands; but for them they are convinced that “if no bees visiting crop flowers, wind & other insects will still pollinate and they will still harvest something.”
|Chi-square test for difference in knowledge of what bees are after on crop flowers: , , , .|
When asked “how much do you think bee-visitations to flowers of your crops contribute to crop yields?”, approximately 23 to 28% of farmers perceived significantly ( test, ) that bees contributed, respectively, to half (41–50%) or to third (26–40%) of yield increase of crop yields in their villages (Figure 7). In fact, there were significant differences ( test, ) between the average pollination experimental data [21, 22] and the farmers’ perceived contribution of bees to yield of different crops such as beans, citrus, coffee, cowpea, mangoes, passion fruit, and pepper (Table 10). In most cases, farmers guessed little value as compared to the pollination experimentally derived data [21–23]. For other crops (avocado, egg plant, watermelon, tomato, etc.) farmers perceived the value of the contribution of bees to yield that was statistically ( test, ) similar to the one that was derived empirically after conducting field pollination experiments (Table 9) by the author.
YNFBV: When no/few; YNFBV: yields when very few or no bees visit my crop flowers (bad yield).
YHBV: When I receive high bee-visits to crops; YHBV: yields if bees came with high visitation frequency to flowers of my crop.
A: (Fruit/seed set in %) = (mean YHBV − mean YNFBV)/mean YHBV * 100.
B: (Fruit/seed set in %); this is data obtained after conducting pollination experiments.
|Chi-square test for differences in beliefs of farmers about area where bees are abundant: , , , .|
3.5.3. Farmers’ Knowledge and Perception of Drivers of Bees in Villages Immediately Surrounding Flower Companies
When farmers were asked to explain where do they think bees are abundant between the edge of the flower farm (0.01–0.2 km) and far away (>2 km) in the village, most (90%) farmers believed that bees should be abundant in their villages since the “fumes” or chemicals sprayed daily inside greenhouses of the flower farms will not reach at such distance (>2 km) (Table 10). Some villagers frequently stated that bees were many in the village (>2 km far away from flower farm) because “bees cannot survive where they spray daily toxic chemicals.” However, the answer “I do not know, I am not sure, I cannot tell” was frequently given by farmers (18.3% of frequency) and this indicated that some farmers were not good naturalists or had almost no interest in understanding the work of bees in their farming business. Different justifications (reasons) for getting higher yields far away from the flower farm were given, but must frequently, farmers believed that higher yields can only be due to difference in field management systems, fertility levels, types of varieties grown, and to difference in bee-visitations (Table 11) because bee-visitations are almost absent near the flower farm where they apply toxic chemicals.
|Chi-square test for difference in arguments (statements) frequency: , , , .|
When asked if they have ever observed any changes (reduction, increase) in crop yields in the village over the last 5 to 20 years, and if yes, this may be due to what farmers perceived as significant ( test, ) changes (reductions) in crop yields during the last 5–20 years for various reasons such as soil infertility of their lands (Table 11). The presence of the flower farm spraying chemical toxic pesticides nearby and environmental degradation were key reasons provided by farmers even when some farmers said that there has been little change (14.62%) while most farmers (20.10%) said they were not sure if change has ever happened or affected crop yields in their villages (Table 11).
When farmers were asked if they have seen any change in the population density (abundance) of bees in the village, most farmers were not sure if there has been change. Farmers who believed in changes attributed that to declining in beekeeping, “flower fumes”, bad farming practices, or forest bush clear-cutting (Table 12). When asked if they felt that bee populations have been stable or changing (increasing/decreasing) over the last 5 to 15 years in their village landscapes, there were various answers (perceptions) among farmers. Most (45%) of farmers perceived that bee populations have been reducing (declining) seriously in their villages in the last 5 to 15 years. Some farmers (32.1%) were not sure or had no idea (they did not know anything) whether changed has occurred or not. A few farmers (19.8%) said they perceived no changes while only 3.1% of these respondents felt that there has been an increase of bee populations in the village over the last 5 to 15 years. The difference in perceptions among the four categories of respondents was significant ( 3 df = 38.367, ).
|Chi-square test for differences in explanations about causes of changes in bee numbers: , , .|
Also, when asked if they felt that crop yields have been stable or changing (increasing/decreasing) over the last 5 to 15 years in their village landscapes, most (48.3%) of the farmers perceived that crop yield has been reduced by 10–50% in their villages in the last 5 to 15 years. Some farmers (17.8%) felt that crop yield has been stable. Another group of respondents (18%) perceived that they were not sure (they could not tell) if change has occurred or not. A small portion of respondents (6.1%) perceived that crop yield has been increasing slightly in the 10 to 35% proportions. Approximately 3.8% of respondents perceived that crop yield has increased in the 40–85% proportion while another small group of respondents (6%) perceived that crop has declined seriously by 50 to 85%. The difference in views/perceptions among the 6 groups of respondents interviewed was significant ( 5 df = 7.87, ). Overall, when asked if the currently observed changes (reductions) in crop yield are a consequence of reduction in bee populations in the villages, most farmers perceived that there has been a reduction in crop yield as a consequence of the decline in bee populations in the villages (Table 13).
|Chi-square test for difference in arguments (statements) frequency: , , , .|
When asked if they believed that if “crop yield reduction/loss was linked to decline in bee populations in the village”, there were different statements given by farmers (Table 14). Overall, 35.4% of respondents were of neutral views, 39.2% disagreed, and 25.45% agreed. The difference in views between the 3 categories of respondents (neutral, disagreeing, agreeing) was significant ( 2 df = 3.056, ). In fact, around 58% of farmers interviewed believed that growing crops far away (>2 km) from the flower farm was a better option to obtain higher yields even when some farmers (22.9%) believed that it should be similar yields. Only a few farmers (1.5%) believed that the one growing crops at (0.1–0.3 km) the edge of the flower farm could get better yield. A small proportion (17.6%) of farmers stated that they were not sure (they did not know). The difference in views/perceptions of the 4 groups of respondents was statistically significant ( 3 df = 68.032, ). Overall the majority of farmers doubted negative effects of the proximity to the flower farm on yields of their crops.
|Chi-square test for difference in statement frequency: , , , .|
When asked to guess the place where someone can get better yield between near the flower farm and far away >2 km) from the flower farm, most farmers were not sure (10.9%), while others stated that there should be no difference in yield (11.6%). A small proportion of farmers felt that yield will be higher in the villages (>2 km far away) because the fumes of the flower farms do not reach over there (Table 13).
When farmers were asked to say when they started observing changes (in bee populations/crop yields) in their villages the majority of farmers (22.3–44%) believed there has been significant ( test, ) changes (reductions) occurring in either crop yields or bee populations. However, some farmers (29-30%) believed that changes in bee populations started occurring some 5 to 10 years ago (Figure 8).
Several drivers of bees in farmlands have been identified worldwide. During discussion meetings, farmers were asked to name key factors likely leading to the current ongoing bee loss in their villages. These drivers were put together on a paper and farmers were again asked to rank them in terms of importance from the most important (dangerous) to the less important factors that may be associated directly/indirectly with bee loss in the village. There were variations in assessment (ranking) of drivers by farmers from the four study districts (where the 4 flower farms are located). However, on average, all farmers ranked as significant (, Kruskal-Wallis test) primary drivers the following drivers: (i) fragmentation of national habitats such as forests/wetlands (average rank: 1.90 ± 0.13), (ii) forests/bush clear-cutting in the village (average rank: 1.26 ± 0.002), (iii) logging, charcoal burning, timber/poles/firewood collection (average rank: 1.26 ± 0.02), (iv) fires burning intensification (1.69 ± 0.04), and (v) local climate change such as rainfall pattern changes (1.84 ± 0.002).
Some worldwide documented key drivers of bee biodiversity loss (road construction up-country, land-use change, global climate change, environmental/degradation, grazing intensification, and agriculture modernization plan by the government) were perceived by local farmers as factors playing a tertiary role or as factors playing no role in the decline process or as factors having nothing to do with bee decline in the villages. Most farmers said they were not sure if these factors could affect bees even when a few of them believed that these factors could be associated to some extent with bee disappearance in the villages (Table 16). There was a great variability in perception of farmers concerning the place (area) where to get better yield between the near and the far areas to the flower farm (Table 15).
|Ranks: 1: very important (or primary causal) agent; 2: important causal agent; 3: associate (secondary) important causal agent, 4: playing tertiary role; 5: play very little or no role in the decline process; 6: has nothing to do with bee decline (not sure this can affect bees).|
Farmers were requested to relate the rate of disappearance of bees due to degradation of natural/seminatural habitats; and it was observed that only old farmers (aged 70–80 years) think/perceive that forests and other good seminatural habitats disappeared 10–50 years ago. Old farmers perceived also that in areas covered by forests, it is possible to observe visits of bees to crop flowers at the rate of 500 bee-visits/10 min/50 m2 garden plot. Young people (20–35 years old) perceived that forests disappeared in the villages some 5–10 years ago and that bee-visits to crop do not exceed 20 bee-visits/10 min observations/50 m2 garden plots (Figure 9) even in regions covered by forests or plenty of seminatural habitats.
3.5.4. Farmers’ Attitudes towards Flower Industry Growth and Development in the Surrounding of Their Villages: Perceptions of the Negative Effects of Flower Industries on Environmental, Human, and Agricultural Health
When asked about the benefit of living near the flower farm, most (66%) respondents significantly ( test, ) declared (stressed) gaining no benefit (advantage) by having their homesteads established close to flower farms (Figure 10). However, the proportion of farmers declaring having no problem with the presence of the flower farm in their villages was of 86% against 14% of farmers who said they had a problem with the flower farm ( 1 df = 51.46, ). Those who had problems with the presence of the flower farm in their village raised several reasons for why they had problems with the presence of the flower farm in their village (Table 17).
|Chi-square test for difference in frequency of statements: , ,|