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(multcompView) # to correct for multiple comparisons after Tukey HSD
knitr::opts_chunk$set(echo = TRUE)

Here we followed the effect of Oxalic acid (OA) treatment on varroa mite infestation level.
The mite infestation level was estimated by counting the mites falling under a net-floor every 24/48/76/96 hours.
The observation was taken place in two yards in Harduf apiary:

  • Haklaut (חקלאות, גן קהילה), 24 hives.
  • Refet (רפת קש), 34 hives

The aim was to measure the efficacy of OA cardboards treatemnt in crontroing varroa, and to follow the extent to which the cardboards are active in controling the mites.

The number of free falling mites was counted before OA applied (day -1), and then every 24 hours for one week, and 48, 72 and 96 hours till day 24 post treatment application.
At the end of the treatment, the cardboards were taken out, and another count was made, in the absence of OA in the hives * NEEDS TO BE COMPLTED*.
Each hive received cardboards soaked with Oxalic acid dissolved in glycerin (40:60 by weight, about 10 gr OA per card). One cardboard per 2.5 populated frames, 4 cardboard per hive, a total of 40gr OA per hive.

Haklaut yard

data <- read.csv("/Users/nuriteliash/Documents/GitHub/OA_Biogaz/data/Haklaut_aug2024.csv")

df = data %>% 
  unite(Hive, c("hive_name", "hive_number"),sep = ".") %>%
      dplyr::mutate(Hive = as.character(Hive)) %>%
dplyr::mutate(mites_per_day = as.numeric(mites_per_day))

# order the hive number:
df$Hive <- factor(df$Hive, level=c("husein_1.1","husein_3.3","husein_4.4","husein_5.5","misgeret_1.6","misgeret_2.7","misgeret_3.8","polit_1.9","polit_2.10","polit_3.11","polit_4.12","shishit_1.13","shishit_2.14", "hamishit_1.15", "hamishit_2.16", "hamishit_3.17", "arbait_2.18","arbait_4.19", "ziva.20","lifia.21","iltit.22","arbit.23","shearit.24","shsekit.25"))

mite infestation level - free fall

df %>% 
  ggplot(aes(x = days_post_treatment, y = mites_per_day, group = Hive, color = Hive)) +         
  geom_line() + 
  geom_point() +
  ylab("Mite count (falling/day)") +
  labs(title = "Varroa infestation level (Harduf apiary, Haklaut yard)",
       subtitle = "free falling under net floor") +
  theme_classic() +
  theme(legend.position = "bottom")

 df %>% 
  ggplot(aes(x = days_post_treatment, y = mites_per_day, group = Hive, color = Hive)) +         
  geom_line() + 
  geom_point() +
  ylab("Mite count (falling/day)") +
  labs(title = "Varroa infestation level (Harduf apiary, Haklaut yard)",
       subtitle = "free falling under net floor") +
  theme_classic() +
  theme(legend.position = "none")+
   facet_wrap(~ Hive, scales = "free_y")+
  theme(axis.text.x = element_text(angle = 60, vjust = 0.5, hjust=0.5))

How many hives were left with high infestation (>40 mites per day), 10 days post treatment?

 df %>% 
  dplyr::filter(days_post_treatment > 10) %>%
     dplyr::filter(mites_per_day > 40) %>%
   ggplot(aes(x = days_post_treatment, y = mites_per_day, group = Hive, color = Hive)) +         
  geom_line() + 
  geom_point() +
  ylab("Mite count (falling/day)") +
  labs(title = "Varroa infestation level (Harduf apiary, Haklaut yard)",
       subtitle = "only hives with high infestation (>40) in the last 10 days post treatment") +
  theme_classic() +
  theme(legend.position = "bottom")

Refet yard

data <- read.csv("/Users/nuriteliash/Documents/GitHub/OA_Biogaz/data/Refet_aug2024.csv")

df = data %>% 
  unite(Hive, c("hive_name", "hive_number"),sep = ".") %>%
      dplyr::mutate(Hive = as.character(Hive)) %>%
dplyr::mutate(mites_per_day = as.numeric(mites_per_day))


# order the hive number:
df$Hive <- factor(df$Hive, level=c("kash_1.1","kash_2.2","kash_3.3","kash_5.4","kash_6.5","kash_7.6","kash_10.7","kash_11.8","kash_12.9","kash_13.10", "kash_14.11","kash_15.12","kash_16.13","kash_17.14","kash_18.15","kash_20.16","pina_1.17","pina_2.18","pina_3.19","pina_4.20", "pina_5.21","pina_6.22","pina_7.23","havit_1.24","havit_2.25","havit_3.26","blokit_1.27","blokit_2.28","galgalit_2.29","igul_emza.30", "igul_maarav.31",  "gan_habait_1.32","gan_habait_2.33", "gan_habait_3.34"))

mite infestation level - free fall

df %>% 
  ggplot(aes(x = days_post_treatment, y = mites_per_day, group = Hive, color = Hive)) +         
  geom_line() + 
  geom_point() +
  ylab("Mite count (falling/day)") +
  labs(title = "Varroa infestation level (Harduf apiary, Refet yard)",
       subtitle = "free falling under net floor") +
  theme_classic() +
  theme(legend.position = "bottom")

 df %>% 
  ggplot(aes(x = days_post_treatment, y = mites_per_day, group = Hive, color = Hive)) +         
  geom_line() + 
  geom_point() +
  ylab("Mite count (falling/day)") +
  labs(title = "Varroa infestation level (Harduf apiary, Refet yard)",
       subtitle = "free falling under net floor") +
  theme_classic() +
  theme(legend.position = "none")+
   facet_wrap(~ Hive, scales = "free_y")+
  theme(axis.text.x = element_text(angle = 60, vjust = 0.5, hjust=0.5))

How many hives were left with high infestation (>40 mites per day), 10 days post treatment?

 df %>% 
  dplyr::filter(days_post_treatment > 10) %>%
     dplyr::filter(mites_per_day > 40) %>%
   ggplot(aes(x = days_post_treatment, y = mites_per_day, group = Hive, color = Hive)) +         
  geom_line() + 
  geom_point() +
  ylab("Mite count (falling/day)") +
  labs(title = "Varroa infestation level (Harduf apiary, Refet yard)",
       subtitle = "only hives with high infestation (>40) in the last 10 days post treatment") +
  theme_classic() +
  theme(legend.position = "bottom")