In this breeding program we compared three breeding lines in the same yard (Elifelet). Queens from each line were let to naturally capulate in three different locations:

  • Ayelet > Elifelet
  • Anna > Philbox
  • Doron > Elbaz-Ramot

The aim of the breeding program was to get 5-6 excellent hives that will be used as source for queen rearing in the following fall (Sep-Oct 2024).

The hives were placed in Elifelet yard on 10/1/2024, 16 hives from each line, and recived regular treatments (sugar syrop feeding as needed, no moving of frames was done between hives).

Summer harvest:
On the 16/6/2024 (Ayelet and Anna), hives were harvested and individualy weighted (in Doron line, only the overall weight was noted, on the 24/6/2024).

After summer harvest (10/7/2024) we evaluated the following parameters:

  • colony streingth: honey storage weight, brood storage weight, general scoring (all scored 0-5)

  • queen condition: original/replaced/marked

  • brood: # of brood frames

  • honey weight: summer harvest net weight (kg)

  • Hygeanic behaviour
    We tested for the trait of hygieanic behaviour for each hive using the pin test.
    only excellnt hives in summer harvest (above 21 kg net) were tested
    after 24 hours, we chekced the percentage of cells opened and cleaned.

Summarizing table for aech line:

Line surviving colonies average summer harvest (kg) # hives for hygienic behaviour test
Ayelet Hashchar 14/16 (88%) 19.8 kg 5 hives
Anna 12/16 (75%) 19.4 kg 2 hives
Doron 15/16 (94%) 31.0 kg 6 hives

In summary, 13 hives were tested for hygienic behaviour, ____

library(tidyverse)
library(dplyr)
library(ggplot2)
library(ggpubr)
#library(reshape2)
library(hrbrthemes)
library("gridExtra")
#library(cowplot)
library(plotly)
library(scales) # to calculate percentages, and put into dataframe
library(ggrepel)
knitr::opts_chunk$set(echo = TRUE)
data = read.csv("/Users/nuriteliash/Documents/GitHub/Ayelet_lines_2024/data/lines_ayelet.csv") %>%
    dplyr::mutate(hive = as.character(hive))

#df = data %>% 
  #dplyr::mutate(date = as.Date(date, format = "%d/%m/%Y")) %>% # Convert to Date object 
 # dplyr::mutate(hive = as.character(hive))

colony strength

data %>%
  mutate(hive = factor(hive, levels = sort(unique(as.numeric(as.character(hive)))))) %>%
  ggplot(aes(x=hive, y=spring_harvest_net)) +
  geom_col() +
  geom_hline(yintercept = mean(data$spring_harvest_net, na.rm = TRUE), linetype = "dashed", color = "red") +
  ggtitle("Spring harvest (net kg)") +    
  theme_ipsum() +
  facet_wrap(~line, scales = "free_x")

data %>%
  mutate(hive = factor(hive, levels = sort(unique(as.numeric(as.character(hive)))))) %>%
  ggplot(aes(x=hive, y=honey_store_score)) +
  geom_col() +
  geom_hline(yintercept = mean(data$honey_store_score, na.rm = TRUE), linetype = "dashed", color = "red") +
  ggtitle("Honey store score (0-5 score)") +    
  theme_ipsum() +
  facet_wrap(~line, scales = "free_x")

data %>%
  mutate(hive = factor(hive, levels = sort(unique(as.numeric(as.character(hive)))))) %>%
  ggplot(aes(x=hive, y=population_score)) +
  geom_col() +
  geom_hline(yintercept = mean(data$population_score, na.rm = TRUE), linetype = "dashed", color = "red") +
  ggtitle("Population score (0-5 score)") +    
  theme_ipsum() +
  facet_wrap(~line, scales = "free_x")

data %>%
  mutate(hive = factor(hive, levels = sort(unique(as.numeric(as.character(hive)))))) %>%
  ggplot(aes(x=hive, y=brood_store_score)) +
  geom_col() +
  geom_hline(yintercept = mean(data$brood_store_score, na.rm = TRUE), linetype = "dashed", color = "red") +
  ggtitle("Brood store score (0-5 score)") +    
  theme_ipsum() +
  facet_wrap(~line, scales = "free_x")

data %>%
  mutate(hive = factor(hive, levels = sort(unique(as.numeric(as.character(hive)))))) %>%
  ggplot(aes(x=hive, y=brood_combs)) +
  geom_col() +
  geom_hline(yintercept = mean(data$brood_combs, na.rm = TRUE), linetype = "dashed", color = "red") +
  ggtitle("Brood combs (# of brood frames)") +    
  theme_ipsum() +
  facet_wrap(~line, scales = "free_x")

data %>%
  mutate(hive = factor(hive, levels = sort(unique(as.numeric(as.character(hive)))))) %>%
  ggplot(aes(x=hive, y=general_score)) +
  geom_col() +
  geom_hline(yintercept = mean(data$general_score, na.rm = TRUE), linetype = "dashed", color = "red") +
  ggtitle("General hive score (0-5)") +    
  theme_ipsum() +
  facet_wrap(~line, scales = "free_x")

data %>%
  mutate(hive = factor(hive, levels = sort(unique(as.numeric(as.character(hive)))))) %>%
  ggplot(aes(x=hive, y=percentage_cleaned_cells)) +
  geom_col() +
  geom_hline(yintercept = mean(data$percentage_cleaned_cells, na.rm = TRUE), linetype = "dashed", color = "red") +
  ggtitle("Hygeanic behaviour (0-100%)") +    
  theme_ipsum() +
  facet_wrap(~line, scales = "free_x")

varroa infestation

update data

varroa resistance

update data