Data Analysis on Honeybees and Neonic Pesticides

Posted on May 28, 2021
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INTRODUCTION

Having data studied the effects of invasive pest species on agriculture as a graduate student, I understand the increasing necessity for pesticide use. With the planet hosting a population expanding well over 7 billion, the need to maximize food production to feed all those people has become a high priority. Pests, causing financial losses in the hundreds of billions, cause farmers and corporations go to great lengths to protect their agricultural products from the 7 thousand approximate species that hamper their industries. (Govorushko 2014)

While there are a wide variety of pest control strategies in use globally, a class of pesticide, Neonicotinoids, saw use for almost 20 years in the United States starting in 1990’s, with a reduction in use, and sometimes a complete disuse altogether, only beginning in 2017. This class of pesticide works by attacking specific points in the nervous system, preventing it from functioning properly, and ultimately resulting in death. While very effective, they unfortunately are not selective in design, and are toxic to humans and pollinators alike (Blacquière et al, 2012).

One of the most crucial pollinators to our agricultural industry, the honeybee, was no exception. The scientific community has kept a close eye on the slowly declining population of honeybees around the world, and it is possible that with that decline, a drop in extant plant species may follow.

THE DATA

Due to the financial and scientific benefits that come from understanding this type of information, I chose a dataset that was aggregated from the National Agricultural Statistics Service (NASS), the USGS, the US Census, and the USDA, that covers honey production, pricing, value and yield, in conjunction with the neonic pesticide use in the corresponding part of the United States. The data spans from the early use of neonic pesticides in 1998, all the way to its decline in use in 2017.

In my analysis, I chose to focus on the top ten honey producing states from the dataset: North Dakota, South Dakota, California, Texas, Montana, Idaho, Florida, Minnesota, Washington, and Michigan. 

RStudio, ggplot2, dplyr and tidyr were used to refine, process and analyze the dataset, and a RShiny app was developed to interact with the original data in a user friendly way. The original dataset can be downloaded from (Kaggle), the Github repository (here), and the app can be accessed (here).

WHAT TO LOOK FOR...

Many arthropods, especially honeybees, are very sensitive to environmental factors, both biotic and abiotic in nature. A change in weather, chemical runoff, land development, disease and food availability, can have drastic effects on honey production and honeybee health. Honey production may vary dramatically year to year, due to these ever changing factors, so to start, one wants to look at the long term effects: Do we see any significant changes from when neonic pesticide use began, and do we see any lingering effects from after it ended?

Data Analysis on Honeybees and Neonic Pesticides
Total honey production over a 20 year period: 1998-2017 (Idaho).

Here we can see illustrated, an example of the total production (lbs) of honey over time, from 1998 to 2017 (Alabama).  Across the top ten honey producing states, overall, there was no significant difference in collective honey production between 1998 and 2017, however, outside of those, this changes from state to state. Some show increases in production, and many show decreases, but only a handful show any visually striking changes. The biomes of these states differ drastically, and many factors can be the cause of these changes specific to those locations. 

Data on POSSIBLE RED FLAGS 

Data Analysis on Honeybees and Neonic Pesticides
Honey yield per colony (lbs) over a 20 year period: 1998-2017 (North Dakota)

If we take a look at the difference in honey yield per colony over a 20 year period, statistical analysis shows that there was a significant change in yield per colony during the time neonic pesticides were used.  Production value across the top ten states overall, also, did not see a significant change in value, however, this once again varied state to state. 

Data Analysis on Honeybees and Neonic Pesticides
Value of honey (USD) vs Neonic pesticide Application (kgs): 1998-2017 (North Dakota)

WHERE TO GO FROM HERE

Aggregated data on pesticide application can be very valuable, not just in a monetary sense, but to the health of our ecosystems as well. By evaluating how pesticides are applied in different parts of the country, we can see where these treatments are most effective, and where they are doing more harm than good. It will also help companies limit their effect on the environment, especially in areas where treatments have little to no effect on their production value. If we are to continue using treatments in agriculture that harm our pollinators, it has to be done responsibly, and through calculated and educated decisions.

Given more time, I would like to assess the effects of the different types of neonic pesticides, and how they effect honeybee colonies, production and honey production value. It is possible that certain pesticides are more effective in certain states than others, and that the application of one or more, does more harm than good, both ecologically and financially.

REFERENCES

Blacquière, T., Smagghe, G., van Gestel, C.A.M. et al. Neonicotinoids in bees: a review on concentrations, side-effects and risk assessment. Ecotoxicology 21, 973–992 (2012). https://doi.org/10.1007/s10646-012-0863-x

Govorushko, S. M. "Mammals and birds as agricultural pests: a global situation." Сельскохозяйственная биология 6 (eng) (2014).

https://www.freepik.com/free-photo/selective-focus-shot-honeybee-collecting-pollen_14264427.htm#page=1&query=honeybee&position=6

About Author

David Green

Certified in data science, confident working in R, Python, Git and SQL development. Skilled in applying machine learning techniques in data analysis of large datasets, alongside traditional statistical analysis triage
View all posts by David Green >

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