Data for Rice (Oryza Sativa L.) Bioeconomy: A DEA approach (VRS, CRS & Bootstrapping)
Description
The present dataset contains the original data from 612 rice farms in the five provinces of Ecuador. The dataset includes adjusted data for application in R for statistical analysis and the DEA methodology with BCC and CCR models adjusted with Bootstrap. Data were collected from 612 rice-producing farms in Ecuador during the 2022-2023 year or cycle. Details were gathered on Total Income [ti], Total Cost [tc], Total CO2 Emissions (kg CO2 eq/cycle) [te], Urea Used (kg/ha) [u], Farmer Age [age], Years of Study [Study_year], Years of Experience [experience], Land Area (ha) [area_ha], and Yield in Tons per Hectare [rend_ton_ha]. The provinces in Ecuador where the data were collected are: Guayas, El Oro, Manabi, Loja, and Los Rios.
Files
Steps to reproduce
Steps to Prepare Data for Applying DEA (BCC, CCR, FDH) and Bootstrap Step 1: Load and Clean the Data Import Data: Ensure your data is in a compatible format (e.g., CSV, Excel) and load it into R. Check Data Structure: Review the variables and their data types to ensure they are correctly formatted. Step 2: Select and Adjust Variables Select Variables: Choose the input and output variables for the analysis: Inputs: Total Cost (tc), Total CO2 Emissions (te), Urea Used (u) Outputs: Total Income (ti) Verify and Clean Data: Check for missing or anomalous values and clean the data as necessary. Step 3: Apply the DEA Model DEA with BCC and CCR Models: Apply the DEA method specifying: For the BCC model, use Variable Returns to Scale (VRS). For the CCR model, use Constant Returns to Scale (CRS). DEA with FDH Model: Apply the DEA method using the Free Disposal Hull (FDH) option. Step 4: Apply Bootstrap Implement Bootstrap: Use the bootstrap method to adjust the DEA scores, specifying the number of replications needed. Step 5: Analyze Results Obtain Technical Efficiencies: Extract and review the calculated technical efficiencies for each model. Interpret Results: Analyze the efficiencies and the confidence intervals obtained from the Bootstrap method to understand the variability and reliability of the efficiency scores. Step 6: Save Results Save Results: Export the results to a CSV or Excel file for further analysis and documentation.