Data prep

The Impact of Drought on Corn Production and Prices in Iowa.

Drought is a major concern for agricultural production, especially in the Midwest where corn is a significant crop. Iowa is the leading producer of corn in the United States, making it an important state to study the impact of drought on corn production and prices. This research proposal aims to investigate the following:

Ø  The historical impact of drought on corn production and prices in Iowa: This study will use historical data on drought and corn production in Iowa to analyze how drought has affected corn production and prices in the past. The analysis will also identify the key factors that contributed to the impact of drought on corn production and prices.

Ø  The potential impact of future droughts on corn production in Iowa and prices  Iowa: This study will use climate change projections and models to forecast the potential impact of future droughts on corn production and prices in Iowa. The analysis will consider the different scenarios of drought severity and their impact on corn production and prices.

Methodology

  1. Data Collection: Collect data on corn production, corn prices, and drought conditions (temp, rainfall, drought index) in Iowa for the study period.

  2. Data Preparation: Clean the data by removing any missing or inconsistent values, merging datasets, and transforming variables as needed. Not much data cleanign was required

  3. Descriptive Statistics: Conduct descriptive statistics to summarize the data, including mean, median, standard deviation, and range for each variable.

  4. Correlation Analysis: Use correlation analysis to examine the relationship between corn production, corn prices, and drought conditions.

  5. Regression Analysis: Perform regression analysis to estimate the impact of drought on corn production and corn prices. You can use linear regression, multiple regression, or time series regression, depending on the nature of your data.

  6. Visualization: Visualize the results of the analysis using plots, graphs, and charts. This can help to better understand the patterns and relationships in the data.

  7. Interpretation: Interpret the results of the analysis and draw conclusions about the impact of drought on corn production and corn prices in Iowa.

library(tidyverse) 
── Attaching packages ─────────────────────────────────────── tidyverse 1.3.2 ──
✔ ggplot2 3.4.0     ✔ purrr   1.0.1
✔ tibble  3.1.8     ✔ dplyr   1.1.0
✔ tidyr   1.3.0     ✔ stringr 1.5.0
✔ readr   2.1.3     ✔ forcats 1.0.0
── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
✖ dplyr::filter() masks stats::filter()
✖ dplyr::lag()    masks stats::lag()
library(readxl)
library(tidyr)
library(dplyr)
Corn_Prod_Data <- read_csv("C:/Users/aishu/OneDrive/Desktop/Corn Production Data ( Final).csv", skip = 1)
Rows: 33 Columns: 7
── Column specification ────────────────────────────────────────────────────────
Delimiter: ","
dbl (7): Year, Average Temperature (Deg F), Precipitation (Inches), Average ...

ℹ Use `spec()` to retrieve the full column specification for this data.
ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.
print(Corn_Prod_Data)
# A tibble: 33 × 7
    Year `Average Temperature (Deg F)` Precipi…¹ Avera…² Yeild…³ Produ…⁴ Corn …⁵
   <dbl>                         <dbl>     <dbl>   <dbl>   <dbl>   <dbl>   <dbl>
 1  1990                          47.6      29.6  -1.41      126  1.56e9    2.52
 2  1991                          46.7      32.7   0.519     117  1.43e9    2.47
 3  1992                          46.1      34.5   2.83      147  1.90e9    2.39
 4  1993                          43.2      39.4   5.87       80  8.8 e8    2.39
 5  1994                          45.4      28.0   3.63      152  1.92e9    2.50
 6  1995                          45.4      32.2   3.12      123  1.43e9    2.81
 7  1996                          43        29.9   2.52      138  1.71e9    3.67
 8  1997                          45.3      23.4   0.931     138  1.64e9    2.75
 9  1998                          49.1      31.4   0.383     145  1.77e9    2.38
10  1999                          48.1      24.8  -0.318     149  1.76e9    2.12
# … with 23 more rows, and abbreviated variable names
#   ¹​`Precipitation (Inches)`, ²​`Average Palmer Drought Severity Index (PDSI)`,
#   ³​`Yeild (BU/Acre)`, ⁴​`Production (BU)`, ⁵​`Corn Price (USD)`

The echo: false option disables the printing of code (only output is displayed).