This paper addresses the issue of the control of activity and growth of the bee colonies (brooding) in the early spring period. The bees are brooding up already in the late winter, and the intensity of brooding in this period is determined by daytime temperatures and sunshine hours that increase the temperature of the inner hive space. The objective is to design and verify a technology that would ensure the conditions for the optimal brooding in the early spring period and thus the numerically strong bee colonies. The experimental part was based on the temperature regulation in the inner hive space. A preset temperature was maintained inside the hive by an electric heating system with regulation. A differential thermal regulation which enabled the optimal growth of the bee colonies in accordance with the phenophases was chosen for verification. To verify the proposed method, two groups of the bee colonies were assembled. One group of the bee colonies had a heating system with regulation installed inside the hive. The second group of the bee colonies was in the hives without the heating system installed. The dependence of the brood area on time was monitored for the evaluation of both groups of the bee colonies. It was proven that the differential thermal regulation enables the optimal growth of the bee colonies in the early spring period in accordance with the phenophases. The brood area increased evenly for the group of the bee colonies with a regulated temperature of the hive space, reaching a larger area.

This paper addresses the issue of the control of activity and growth of the bee colonies (brooding) in the early spring period. The bees are brooding up already in the late winter, and the intensity of brooding in this period is determined by daytime tempe ratures and sunshine hours that increase the temperature of the inner hive space. The objective is to design and verify a technology that would ensure the conditions for the optimal brooding in the early spring period and thus the numerically strong bee co lonies. The experimental part was based on the temperature regulation in the inner hive space. A preset temperature was maintained inside the hive by an electric heating system with regulation. A differential thermal regulation which enabled the optimal gr owth of the bee colonies in accordance with the phenophases was chosen for verification. To verify the proposed method, two groups of the bee colonies were assembled. One group of the bee colonies had a heating system with regulation installed inside the h ive. The second group of the bee colonies was in the hives without the heating system installed. The dependence of the brood area on time was monitored for the evaluation of both groups of the bee colonies. It was proven that the differential thermal regul ation enables the optimal growth of the bee colonies in the early spring period in accordance with the phenophases. The brood area increased evenly for the group of the bee colonies with a regulated temperature of the hive space, reaching a larger area.

The experiment was conducted at the research farm of Sher-e-Bangla Agricultural University, Dhaka-1207 during the period from November 2016 to February 2017. The experiment consisted of three different ways of insect visitors foraging for pollination of mustard flowers viz., T1= Open field (Control), i.e., the mustard field was fully open for free movement by the insect pollinators, T2= Netting with honey bee, i.e., the mustard plots was caged with muslin net and the bee hive was placed inside the cage and T3= Netting without honey bee, i.e., the mustard plots was caged with muslin net but the bee hive was not placed inside the cage. BARI Sharisha-8 [Brassica juncea (L.) Czernajew] was used as planting material. Randomized Complete Block Design was selected to lay out the present experiment with 7 replicates. Study showed that, honey bee was the most abundant hymenopterans in the mustard field as a pollinator. The yield and yield contributing traits were significantly influenced by different ways of insect visitors foraging for pollination. The maximum (3.50 g) 1000-seed weight was recorded from treatment T2 followed by T3 and the lowest 1000- seed weight (2.68 g) was recorded from T3. The highest seed yield (2.45 t ha-1) was exhibited from treatment T2 followed by T1 whereas the lowest seed yield (1.67 t ha-1) was recorded from T3. Finally, it can be concluded that providing honey bee colonies to the flowering mustard field can substantially contribute to the yield.

Big geospatial raster data pose a grand challenge to data management technologies for effective big data query and processing. To address these challenges, various big data container solutions have been developed or enhanced to facilitate data storage, retrieval, and analysis. Data containers were also developed or enhanced to handle geospatial data. For example, Rasdaman was developed to handle raster data and GeoSpark/SpatialHadoop were enhanced from Spark/Hadoop to handle vector data. However, there are few studies to systematically compare and evaluate the features and performances of these popular data containers. This paper provides a comprehensive evaluation of six popular data containers (i.e., Rasdaman, SciDB, Spark, ClimateSpark, Hive, and MongoDB) for handling multi-dimensional, array-based geospatial raster datasets. Their architectures, technologies, capabilities, and performance are compared and evaluated from two perspectives: (a) system design and architecture (distributed architecture, logical data model, physical data model, and data operations); and (b) practical use experience and performance (data preprocessing, data uploading, query speed, and resource consumption). Four major conclusions are offered: (1) no data containers, except ClimateSpark, have good support for the HDF data format used in this paper, requiring time- and resource-consuming data preprocessing to load data; (2) SciDB, Rasdaman, and MongoDB handle small/mediate volumes of data query well, whereas Spark and ClimateSpark can handle large volumes of data with stable resource consumption; (3) SciDB and Rasdaman provide mature array-based data operation and analytical functions, while the others lack these functions for users; and (4) SciDB, Spark, and Hive have better support of user defined functions (UDFs) to extend the system capability.

The cultivated area of pollinator-dependent crops is increasing globally, and thus many natural habitats are being replaced by cropland. This change in land use is one of the main causes of biodiversity losses, of which include wild pollinators. As a consequence, many bee species are increasingly being reared and sold specifically for crop pollination services. However, the spatial arrangement of colonies of managed bee species in crops, as well as the spatial design of plants within plantations to maximize pollen flows is not standardized and growers are still experimenting. Here, we propose a spatially explicit simulation model to test which spatial arrangement of hives and plants maximizes pollination services. We used apple orchards pollinated by honey bees as a case study. We found that the spatial arrangement of plants within plantations affects both the mean level and homogeneity of the pollination in apple orchards. Bees’ hive locations, on the other hand, only affected the mean levels of pollination. Our results showed that simulation models are powerful tools to provide management recommendations to farmers.