Data envelopment analysis for identifying the most suitable cassava cultivar: a case study of various cultivated areas in Thailand

Naraphorn Paoprasert, Witsarooth Paisaltanakij, Piya Kittipadakul and Papis Wongchaisuwat

International Journal of Innovation and Learning, 2023, vol. 34, issue 4, 368-379

Abstract: This study analysed routine cassava plantation data to investigate the insights for suitable cultivars for various plantation areas based mainly on their efficiency. Data were classified into three sets at different collection periods and locations. Data envelopment analysis (DEA), a non-parametric method, was employed to evaluate the efficiency of each cultivar in each location. The effect of uncertainty was also captured using the Monte Carlo simulation approach. Various inputs such as soil pH value, soil nutrients, and rainfall were considered, whereas the outputs measured diverse perspectives of efficiencies. Although different datasets were analysed, HB80 was identified as the most stable cultivar in Thailand's north eastern region. However, in some areas, where geological factors were varied, HB80 was not the most stable cultivar. Different inputs and outputs with the DEA yielded distinct insights, leading to diverse conclusions. Hence, identifying appropriate input and output measures for each use case is unavoidably important.

Keywords: breeding; cassava; cassava production efficiency; data envelopment analysis; DEA; Monte Carlo simulation; innovation; Thailand. (search for similar items in EconPapers)
Date: 2023
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