Knowledge-Based Translation: A Study on the English Translation of Agricultural Science and Technology Teaching Texts
1. School of Foreign Languages, Central South University of Forestry and Technology, Changsha 410001, China
2. Bangor College, Central South University of Forestry and Technology, Changsha 410001, China
| Abstract: | The English translation of agricultural science and technology teaching materials for foreign-aid training programs is constrained by multiple factors, including media form, audience characteristics, and textual features. Traditional full translation, which emphasizes strict fidelity to the original text in both content and form, often fails to meet the conciseness by PPT-based curricular and the practical application of overseas farmer trainees to “understand, learn, and apply” the knowledge effectively, thereby resulting in information redundancy and excessive cognitive load. Against this background, this study takes the teaching materials of the “Hybrid Pepper Breeding” foreign-aid training project developed by Yuan Longping High-Tech Agriculture Co., Ltd. as the research object. Guided by Huang Zhonglian’s Translation Variation Theory, the study introduces the knowledge dimension framework of the revised Bloom’s Taxonomy to construct a “knowledge-based –translation variation strategy” analytical model. The source text is first categorized into three types of knowledge: factual knowledge, conceptual knowledge, and procedural knowledge. Based on the instructional functions and cognitive requirements of each knowledge type, corresponding adaptive translation variation strategies are explored. The findings indicate that factual knowledge, aiming at accurate information transmission, mainly adopts such strategies as addition, deletion, condensation, and adaptation to highlight key information; conceptual knowledge, oriented toward comprehension enhancement, primarily employs narration and combination to simplify abstract principles and reorganize knowledge structures; procedural knowledge, focusing on operational application, mainly utilizes adaptation, combination, and edition to optimize and visualize operational procedures. The study demonstrates that knowledge classification can provide an objective basis for the selection of translation variation strategies, enabling the English translation of agricultural science and technology teaching texts to shift from experience-driven practice to knowledge-type-oriented analytical practice. The proposed knowledge-classification-based translation variation model not only expands the application scope of Translation Variation Theory in agricultural science and technology teaching translation, but also provides practical insights for the English translation of agricultural foreign-aid training materials. |
| Keywords: | Agricultural Science and Technology Texts; Translation Variation Theory; Knowledge Classification; Teaching Translation |
| DOI: | 10.57237/j.ha.2026.02.001 |
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Received: 7 May 2026
Accepted: 25 May 2026
Published Online: 28 May 2026
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