Dataset on potato phenology, growth, and yield for DSSAT/APSIM crop model calibration at central region of Bangladesh

TL;DR

This paper presents a dataset on how different planting schedules affect potato growth and yield in Bangladesh, helping optimize farming practices.

Contribution

The study provides a detailed dataset for calibrating crop models like DSSAT/APSIM under subtropical conditions in Bangladesh.

Findings

Data shows how planting schedules influence potato phenology and yield in the central region of Bangladesh.

The dataset identifies optimal variety-schedule pairings for maximizing potato production.

The study captures microclimate effects on growth patterns and yield under different planting times.

Abstract

Understanding potato phenology under varying planting schedules is essential for optimizing planting practices, predicting harvest time, improving pest and disease management, and ensuring climate resilience. In Bangladesh, where potato is a priority crop in the winter season, determining the optimum planting schedules is critical to maximize yield potentiality. However, limited information exists on how shifts in planting schedules influence growth dynamics, phenology, and thermal indices of potato varieties under central region conditions. Respond to this problem, a field experiment was conducted during winter season in the central region of Bangladesh to evaluate the response of two potato varieties (BARI Alu-25 and BARI Alu-41) to four planting schedules at 10-day intervals (20 November, 30 November, 10 December, and 20 December 2023–2024). The trial was laid out in a Randomized Complete Block Design (RCBD) with three replications. The data compiled for this article such as crop Phenology (seedling emergence, stolon initiation, tuber initiation, and bulking), biological and economic yield, total dry matter (TDM), Leaf area index (LAI), leaf dry weight, leaf growth rate, leaf area expansion rate, tuber dry weight, potential tuber growth rate (PTGR) traits were collected as per schedule following the established methods and protocols. All collected data were analyzed using a computer-based software R. This dataset explored the impact of varying planting schedules on the local microclimate and how these changes affect potato development, growth patterns, and yield. It further determines the best pairing of potato varieties and planting schedules to achieve maximum production. This dataset captures potato phenology, growth, and yield under varying planting schedules in subtropical Bangladesh. These data will help researchers study potato growth and genotype × environment effects, and guide policymakers to set best planting schedules for sustainable production in South Asia.