Data for Analysis of rice (oryza sativa l.) bioeconomy: a DEA approach to economy efficiency
Description
This dataset comprises efficiency assessment data from 610 rice (Oryza sativa L.) production units in Ecuador during the 2019 agricultural cycle. The data was collected through a nationally representative survey and includes key production and economic variables such as total income, total costs, land area, irrigation, seed use, input prices, farmer age, education level, and farming experience. The study employs Data Envelopment Analysis (DEA) models under both Variable Returns to Scale (VRS) and Constant Returns to Scale (CRS) frameworks in an input-oriented approach. The Bootstrap method was applied to estimate confidence intervals, providing robust efficiency scores. Efficiency measurements include Technical Efficiency (TE), Allocative Efficiency (AE), and Cost Efficiency (CE), with scores ranging from 0.218 to 1.185. The dataset offers valuable insights into the efficiency levels of Ecuadorian rice producers and serves as a foundation for further research on agricultural optimization, bioeconomic performance, and policy recommendations.
Files
Steps to reproduce
Steps for Conducting the Research: From Data Collection to DEA Analysis in R Studio 1. Data Collection Conduct a nationally representative survey of 612 rice (Oryza sativa L.) producers in Ecuador. Gather data on key production and economic variables, including: Output: Rice sale revenue Inputs: Area (Ha) Wages for Fertilization (Input 1) Price for Fertilization (Input 1) Wages for Weed Control (Input 2) Price for Weed Control (Input 2) Wages for Pest and Disease Control (Input 3) Price for Pest and Disease Control (Input 3) Wages for Planting (Input 4) Price for Planting (Input 4) 2. Data Preparation Structure the dataset in CSV format for easy import into R Studio. Load the dataset into R Studio: data <- read.csv("rice_data.csv") # Load dataset 3. Conducting DEA in R Studio Install and load necessary packages: 3.1. Calculate Technical Efficiency (TE) Define inputs and outputs based on the collected data: 3.2. Calculate Allocative Efficiency (AE) Use input price data to determine the optimal input mix: r Copy Edit # Define input prices input_prices <- as.matrix(data[, c("Price_Fertilization", "Price_WeedControl", "Price_PestDiseaseControl", "Price_Planting")]) # Allocative Efficiency (AE) ae_results <- costdeafrontier(X = inputs, Y = outputs, W = input_prices, RTS = "vrs", ORIENTATION = "in") data$AE <- ae_results$eff 3.3. Calculate Cost Efficiency (CE) Cost Efficiency (CE) = TE × AE r Copy Edit data$CE <- data$TE_VRS * data$AE 4. Statistical Analysis and Correlation Matrix Analyze correlations between efficiency measures: r Copy Edit install.packages("corrplot") library(corrplot) efficiency_matrix <- cor(data[, c("TE_VRS", "AE", "CE")]) corrplot(efficiency_matrix, method = "color", title = "Efficiency Correlation Matrix") 5. Output Tables and Visualization Summary statistics of efficiency scores: r Copy Edit summary(data[, c("TE_VRS", "AE", "CE")]) Efficiency by region: r Copy Edit aggregate(data[, c("TE_VRS", "AE", "CE")], by = list(data$Region), FUN = mean) Export results: r Copy Edit write.csv(data, "DEA_results.csv", row.names = FALSE) 6. Interpretation of Results and Policy Implications Analyze efficiency distributions and identify key patterns. Compare efficiencies across different regions and farm sizes. Suggest policy recommendations based on inefficiencies and potential improvements. Prepare figures and tables for inclusion in the research article.
Institutions
- Universidad Nacional Agraria La Molina
- Universidad Nacional Autonoma de Nicaragua Leon
- Centro de Investigacion Agricola Tropical