A DATA-DRIVEN APPROACH TO INVESTIGATE THE YIELD PATTERNS OF BARI RELEASED POTATO VARIETIES

Istiak Ahmed (), Al Emran, Mohammad Rasel, Jamila Khatun Prioty and Md. Shakil Hossain
Additional contact information
Istiak Ahmed: Agricultural Statistics & ICT Division, Bangladesh Agricultural Research Institute (BARI), Gazipur, Dhaka, Bangladesh.
Al Emran: Department of Statistics, Jahanginagar University, Savar, Bangladesh
Mohammad Rasel: Agricultural Statistics & ICT Division, Bangladesh Agricultural Research Institute (BARI), Gazipur, Dhaka, Bangladesh.
Jamila Khatun Prioty: Agricultural Statistics & ICT Division, Bangladesh Agricultural Research Institute (BARI), Gazipur, Dhaka, Bangladesh.
Md. Shakil Hossain: Agricultural Statistics & ICT Division, Bangladesh Agricultural Research Institute (BARI), Gazipur, Dhaka, Bangladesh.

Reviews in Food and Agriculture (RFNA), 2024, vol. 5, issue 2, 86-90

Abstract: Data Science and Analytic systems in agriculture offer policymakers a wealth of data and information, aiding in informed decision-making and potentially increasing crop yields through analysis of environmental conditions. However, this study faced limitations due to insufficient data for clustering analysis. It focused on a potato variety developed by the Bangladesh Agricultural Research Institute (BARI), revealing that most varieties were released after 2011, peaking in 2014. Yield-wise, varieties released in the same year performed similarly. Years with only one variety were excluded. Pre-2012 varieties had low yields, while high-yielding ones began in 2012 but were released irregularly. BARI Alu-74, released in 2017, has been among the low-yielding varieties since 2011. To enhance crop yields, a reliable system leveraging historical data for analysis and delivering more precise outcomes must be established. Such a system could compare and analyze data and parameters like seed quantity, watering methods, and seed type through clustering.

Keywords: Data science; statistics; EDA; k-means (search for similar items in EconPapers)
Date: 2024
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Persistent link: https://EconPapers.repec.org/RePEc:zib:zbrfna:v:5:y:2024:i:2:p:86-90

DOI: 10.26480/rfna.02.2024.86.90

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