# The Effect of Land Cover on the Nectar Collection by Honeybee Colonies in Urban and Rural Areas
## 4 Discussion
This finding underscores the potential significance of qualitative factors that were not incorporated in the analysis, such as floral species composition, water availability, and exposure to agrochemicals. Honeybees have a wide foraging range, up to 13 km, according to [PERSON] [1], which enables them to compensate for local shortages by accessing distant foraging patches. Furthermore, studies by [PERSON] and [PERSON] [2] and [PERSON] et al. [7] emphasise that bees optimise their foraging routes based on the energetic value of available resources. This suggests that even in landscapes that appear to be poor in resources, bees may efficiently exploit the most productive areas within their reach. The absence of correlation between nectar yield and landscape type may, therefore, be indicative not only of the high mobility and adaptive foraging behaviour of honeybees but also of certain methodological limitations. For instance, the relatively low spatial resolution of remote sensing data utilised in this study (pixel size 10 m) may fail to capture subtle qualitative variations in vegetation that are pertinent to foraging. In future research, the use of UAV (Unmanned Aerial Vehicle) technologies and multispectral imaging for more precise identification of nectar-producing plants, as suggested by [PERSON] et al. [19] and [PERSON] et al. [21], may prove beneficial. The results of this study have significant practical implications. Firstly, the findings indicate that urban bekeeping can achieve comparable levels of productivity to that of rural bekeeping, contingent on the presence of suitable urban green structures. The promotion of diverse flowering plantings in urban green spaces, including parks, community gardens, and wildflower meadows, has the potential to support honeybees. In contrast, rural areas require landscape diversification, encompassing the preservation of shrublands, the establishment of flower strips, the reduction in monocultures, and the promotion of nectar-rich cover crops [36,37]. From a spatial planning and environmental management perspective, it is essential to integrate pollinator needs into green space design, both in urban and agricultural settings. The findings of this study lend support to the notion that the continuity and diversity of floral resources are of greater importance than the landscape type alone, thus making this a central consideration for sustainable land use strategies and pollinator conservation. The findings of this study highlight the need for a more in-depth analysis of the relationship between landscape structure and honeybee productivity, considering factors that go beyond basic land cover classification. In particular, expanding the number of study locations and observation years would allow for a better understanding of interannual variability, as well as a more accurate assessment of the influence of meteorological conditions and dominant nectar sources in each season. As shown in [42], factors such as flower opening time and environmental conditions directly affect nectar secretion in winter oilseed rape varieties, which can significantly modulate forager behaviour and nectar collection dynamics at the colony level. Future research should incorporate more detailed indicators of bee forage, including vegetation classified by species, flowering seasonality, and nectar production potential. Remote sensing technologies such as multispectral drone imagery [19] and other UAV-based floral resource detection systems [21] offer promising tools for mapping the actual foraging base available to bees. It would also be valuable to integrate hive weight data with other sources of information, such as weather conditions, the presence of agrochemicals near apiaries, or colony health indicators. This systems-based approach would allow researchers to assess not only the impact of forage availability on honey production but also on overall colony condition and resilience. Finally, the use of artificial intelligence and predictive models represents a promising avenue for future work.