da Silva Souza Jardel, Martins Adriana Ferreira, de Oliveira Flávio Pereira, Unêda-Trevisoli Sandra Helena
Faculty of Agricultural and Veterinary Sciences, São Paulo State University, Jaboticabal, SP, Brasil.
Center for Agricultural Sciences, Federal University of Paraíba, Areia, PB, Brasil.
World J Microbiol Biotechnol. 2025 Jul 28;41(8):274. doi: 10.1007/s11274-025-04491-8.
This study explores the potential of machine learning to predict nitrogen fixation efficiency in rhizobia strains associated with cowpea (Vigna unguiculata), aiming to optimize bioinoculant selection for sustainable agriculture. Eight native strains were isolated from soils in the Brejo Paraibano region (Brazil), characterized morphologically on Yeast Mannitol Agar YMA medium, and evaluated in greenhouse bioassays for nitrogen accumulation and Relative Index of Nitrogen Fixation Efficiency (IRF%). A Ridge Regression model was then developed using phenotypic colony traits as predictors to estimate Total Nitrogen and IRF%. The results demonstrated strong correlations between predicted and actual values (r = 0.95-0.96), suggesting that visible colony characteristics can serve as reliable proxies for strain efficiency. This approach has the potential to offer a cost-effective alternative to traditional greenhouse trials, with indications of reduced time and resource demands. However, these results are theoretical and require validation through larger datasets and field conditions before broad application in sustainable agriculture can be considered.
本研究探讨了机器学习预测与豇豆（Vigna unguiculata）相关的根瘤菌菌株固氮效率的潜力，旨在优化生物肥料的选择以实现可持续农业。从巴西伯南布哥州的土壤中分离出8株本地菌株，在酵母甘露醇琼脂（YMA）培养基上进行形态学特征鉴定，并在温室生物测定中评估其氮积累和固氮效率相对指数（IRF%）。然后使用表型菌落特征作为预测因子建立岭回归模型，以估计总氮和IRF%。结果表明预测值与实际值之间存在强相关性（r = 0.95 - 0.96），这表明可见的菌落特征可以作为菌株效率的可靠指标。这种方法有可能为传统温室试验提供一种经济高效的替代方案，显示出减少时间和资源需求的迹象。然而，这些结果是理论性的，在考虑将其广泛应用于可持续农业之前，需要通过更大的数据集和田间条件进行验证。
