Enhancing Phenotyping Accuracy for Selection of Urochloa spp. Tolerant Genotypes to Red Spider Mite (Oligonychus trichardti)

Publication Type

Journal Article

Journal Name

Grass and Forage Science

Publication Date

7-1-2025

Abstract

The red spider mites are a major biotic limitation to productivity for Brachiaria (Urochloa spp.) grasses in tropical Eastern Africa, causing severe economic losses mainly in the dry seasons. However, due to the lack of reports of integrated pest management strategies, genetic resistance can play a crucial role in Brachiaria-based livestock systems, minimising the impact of this pest. The objective of this study was to develop and validate a methodology for accurately categorising resistance responses in Urochloa genotypes to identify potential sources of resistance to the Western Kenya red spider mite Oligonychus trichardti (Meyer 1974). Accordingly, 25 genotypes of Urochloa spp. were evaluated under the infestation of O. trichardti. Response variables included mite count and percentage of plant damage 14 days after infestation. Plant damage quantification involved visual assessment using a severity scale (AVR) and two high-throughput digital image analysis methodologies: one based on the threshold establishment in the RGB colour space (DTR) and another one based on image quantisation using three colour subsets (DQU). A linear regression of these methodologies demonstrated that the DTR methodology is more accurate and has higher concordance with the visual assessments, hence chosen for selecting tolerant genotypes. The genotypes evaluated did not exhibit an antibiotic effect; however, varying levels of tolerance were observed. Ultimately, six tolerant genotypes were identified. CIAT 16125 (cv. Piata) and CIAT 26646 (cv. La Libertad) exhibited the highest levels of tolerance, while CIAT 6426 and CIAT 664 were the most susceptible and are recommended as control candidates for future trials.

Keywords

high-throughput, host–plant resistance, image analysis, plant breeding, plant damage quantification

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