Household Survey and Bioeconomy data of rice producers in Ecuador.
Description
The Data was recollected by CIAT Group. This Data is for the article Rice (Oryza Sativa L.) Bioeconomy: A DEA approach (VRS, CRS & Bootstrapping). 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 2019-2020 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 Before this process is the recollection Data, CIAT group had the organization. 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.