Enset and Climbing bean intercropping dataset
Description
This dataset contains extracted enset biomass, enset leaf length, c bean yield data, and the R scripts used for crop model simulation in the manuscript entitled Scenario-based Simulation of Enset–Climbing Bean Intercropping Productivity under Alternative Spacing and Pruning Regimes in Sidama, Ethiopia. The dataset was generated from field experiments and simulation analyses conducted to evaluate the productivity and management options of the enset–climbing bean intercropping system under different agronomic scenarios. Measurements were collected from enset plants and climbing bean crops grown under different spacing arrangements and pruning scenarios. The dataset includes observations on enset biomass production, leaf length, and climbing bean yield, together with supporting variables required for crop simulation analysis. Data were subjected to quality control procedures before analysis to ensure accuracy, consistency, and completeness. The accompanying R scripts provide the complete analytical workflow used to process the experimental data, perform statistical analyses, simulate crop productivity under alternative management scenarios, and generate the tables and figures presented in the associated manuscript. These scripts facilitate transparency, reproducibility, and verification of the study results. Users can execute the scripts with the provided datasets to reproduce the reported analyses or adapt them for similar studies in other enset-based production systems. The simulation component evaluates the effects of different plant spacing configurations and pruning practices on enset biomass production and climbing bean yield. These scenarios were developed to identify management practices that improve system productivity while maintaining sustainable resource use in smallholder farming systems. The dataset therefore provides valuable information for understanding crop interactions, optimizing intercropping management, and assessing the potential impacts of alternative agronomic practices. The files included in this repository consist of raw and processed experimental data, simulation input datasets, output files, metadata describing variable names and units, and annotated R scripts. Variables are clearly labeled and organized to facilitate reuse by researchers, students, agronomists, crop modeler, and development practitioner. This dataset is intended to support reproducible research and encourage further investigations into sustainable enset-based intercropping systems. It may be reused for comparative studies, crop model development and evaluation, agronomic analyses, and meta-analyses, provided that appropriate citation is given to the dataset and the associated research article. The dataset contribute to improving knowledge of climate-resilient and resource-efficient agricultural systems in Ethiopia and other regions where enset-based farming is practiced, while promoting open science, data sharing, and transparent scientific research.
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Steps to reproduce
This simulation study was developed using data generated from a preliminary field experiment conducted during the 2021 cropping seasons. The preliminary experiment was established to evaluate the performance of an enset (Ensete ventricosum)–climbing bean (Phaseolus vulgaris L.) production system under local growing conditions. During the field study, agronomic data were collected from both enset and climbing bean throughout the growing period. Measurements included growth parameters, yield, and other relevant crop performance indicators required to characterize the productivity of the intercropping system. Data related to enset growth and biomass accumulation were extracted from the original dataset and prepared for simulation analysis. Prior to analysis, the dataset was carefully examined for completeness, consistency, and accuracy and processed dataset served as the primary input for the crop simulation. Crop simulations were performed using R crop modeling software within the R statistical computing environment. R was selected because of its flexibility, reproducibility, and extensive range of packages for agricultural modeling, data management, and statistical analysis. The software provides an efficient framework for simulating crop growth processes, evaluating biomass production, and analyzing crop responses under different management scenarios. The use of an open-source platform also enhances transparency and facilitates the replication of simulation procedures by other researchers. The extracted enset biomass yield data was imported into the R environment and formatted according to the requirements of the crop modeling workflow. Appropriate scripts were developed to organize the input variables, execute the simulation, and generate model outputs. Biomass yield was considered the principal response variable because it represents one of the most important indicators of enset productivity and provides a reliable measure for assessing crop performance under different production conditions. The simulation procedure not involved validation using observed field data collected during the preliminary experiment. Model inputs were based on measured growth characteristics and biomass production rather than hypothetical values, thereby improving the biological relevance and credibility of the simulation results. The model generated estimates of biomass production, which was subsequently compared with the observed data to evaluate model performance. Descriptive statistical summaries and graphical outputs were produced to facilitate the interpretation of simulated and observed responses. The use of experimentally measured data for model parameterization reduced uncertainty associated with model inputs and improved the reliability of simulation outputs. Furthermore, the simulation framework provides a practical tool for exploring crop growth patterns without need to conduct repeated field experiments, thereby saving time, labor and financial resources.