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Article published In: Australian Review of Applied Linguistics: Online-First Articles

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Development of word lists for agriculture and forestry students

 | Kasetsart UniversityBangkok
ORCiD logo with link | Kasetsart UniversityBangkok
Published online: 2 June 2025
DOI logo with link
https://doi.org/10.1075/aral.24104.bun
Abstract
It is undeniable that vocabulary plays a vital role in second-language learning since it facilitates comprehension and production of the target language. Similarly, students in English for Specific Purposes (ESP) classes are required to know both academic and field-specific vocabulary to master academic competence in their disciplines. In this corpus-based research, three field-specific word lists were developed from 1,569 academic research articles in the areas of agriculture and forestry, including the Agriculture Word List (AGWL), the Forestry Word List (FRWL), and the Agriculture-Forestry Word List (AFWL). The corpora in this research comprised 6,785,011 running words for the agriculture corpus, 2,402,045 running words for the forestry corpus, and 9,187,056 running words for the agriculture-forestry corpus. To analyze the data, the Key BNC Program and the AntWordProfiler Program were used to develop the three word lists. After 2,000 word families in the General Service List (GSL) and 570 word families in the Academic Word List (AWL) were eliminated, there were 1,149 word families for the Agriculture Word List, 348 word families for the Forestry Word List, and 1,726 word families for the Agriculture-Forestry Word List, with the word lists accounting for 9.10%, 6.63%, and 10.42% of the three corpora, respectively. These word lists can familiarize agriculture and forestry students with the academic words in their disciplines. They could also be beneficial for teachers and material designers to prepare instructional activities and develop useful educational materials as well as vocabulary tests for ESP classrooms.
Article outline
  • 1.Introduction
  • 2.Literature review
    • 2.1Development of word lists
    • 2.2Lexical coverage
  • 3.Methodology
    • 3.1Corpus compilation
    • 3.2Data analysis
  • 4.Results
    • 4.1Key lexical items in the areas of agriculture and forestry
    • 4.2Coverage of the key lexical items in the word lists
  • 5.Discussion
  • 6.Conclusion and implications
  • Generative AI statement
  • References
References (62)
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