Landscape Enhancements in Apple Orchards: Higher Bumble Bee Queen Species Richness, but No Effect on Apple Quality

Bumble bees are among the most effective pollinators in orchards during the blooming period, yet they are often threatened by the high levels of pesticide use in apple production. This study aimed to evaluate the influence of landscape enhancements (e.g., hedgerows, flower strips) on bumble bee queens in apple orchards. Bumble bee queens from 12 orchards in southern Québec (Canada) were marked, released, and recaptured in the springs and falls of 2017 to 2019. Half of the 12 orchards had landscape enhancements. Apples were harvested in 2018 and 2019 to compare their quality (weight, diameter, sugar level, and seed number) in sites with and without landscape enhancements. Species richness, as well as the occurrence of three species out of eight, was higher in orchards with landscape enhancements than in orchards without such structures. The occurrence of Bombus ternarius was lower in orchards with high levels of pesticide use. Apples had fewer seeds when collected in orchards with landscape enhancements and were heavier in orchards that used more pesticides. Our work provides additional evidence that landscape enhancements improve bumble bee presence in apple orchards and should therefore be considered as a means to enhance pollination within farms.

Bee for mining (B4M) – A novel rule discovery method using the Bees algorithm with quality-weight and coverage-weight

This paper proposes a novel tool known as Bee for Mining (B4M) for classification tasks, which enables the Bees Algorithm (BA) to discover rules automatically. In the proposed B4M, two parameters namely quality-weight and coverage-weight have been added to the BA to avoid any ambiguous situations during the prediction phase. The contributions of the proposed B4M algorithm are two-fold: the first novel contribution is in the field of swarm intelligence, using a new version of BA for automatic rule discovery, and the second novel contribution is the formulation of a weight metric based on quailty and coverage of the rules discovered from the dataset to carry out Meta-Pruning and making it suitable for any classification problem in the real world. The proposed algorithm was implemented and tested using five different datasets from University of California, at Irvine (UCI Machine Learning Repository) and was compared with other well-known classification algorithms. The results obtained using the proposed B4M show that it was capable of achieving better classification accuracy and at the same time reduce the number of rules in four out of five UCI datasets. Furthermore, the results show that it was not only effective and more robust, but also more efficient, making it at least as good as other methods such as C5.0, C4.5, Jrip and other evolutionary algorithms, and in some cases even better.

PROPAGATION OF Lachenalia CULTIVARS FROM LEAF CUTTINGS

Lachenalia is a poorly known genus of ornamental bulbous plants from South Africa with a huge floricultural potential. The paper discusses in vivo multiplication with leaf cuttings of Lachenalia cv. ‘Namakwa’, ‘Romaud’, ‘Ronina’ and ‘Rupert’. Mature leaves were collected from non-flowering donor plants. Each leaf was halved to make proximal and distal leaf cuttings. ‘Ronina’ distal leaf cuttings produced the lowest number of bulblets (5.7 per cutting), while ‘Namakwa’ proximal leaf cuttings turned out the most productive (10.5 bulblets per cutting). The bulblets produced by cv. ‘Namakwa’ distal leaf cuttings were the lightest (0.3 g per bulblet), and those formed on cv. ‘Ronina’ proximal leaf cuttings were the heaviest (0.9 g per bulblet). In general, the weight of individual bulblets ranged from 0.1 to 1.2 g. Irrespective of the cutting type, the leaves of cv. ‘Namakwa’ produced the greatest number of bulblets, and the leaves of ‘Ronina’ yielded the heaviest bulblets. With no dependence on their genotype, proximal leaf cuttings were advantageous in terms of total number of generated bulblets and their quality (weight, diameter).