ISOLATION AND IDENTIFICATION OF PLANT PARASITIC NEMATODES AFFECTING TOMATO (SOLANUM LYCOPERSICUM, LINN.) IN GIWA LOCAL GOVERNMENT AREA, KADUNA, NIGERIA
DOI:
https://doi.org/10.33003/fjs-2024-0806-3076Keywords:
Nematode, Meloidogyne, Giwa, Uprooted, Identification, ExtractionAbstract
Plant-parasitic nematodes are the major biotic stressor in crop cultivation. They are recognized as one of the greatest threats to crops worldwide. The study evaluated nematodes that affect tomato in Giwa Local Government area, Kaduna State, Nigeria. Samples were collected from two selected farms each from seven locations. The tomato samples were classified as diseased and healthy based on the appearance of the plants. In each farm, four samples were taken during the growing season; two from diseased plants and two from apparently healthy looking plants. Similarly, soil samples from diseased and apparently healthy soil were collected. The soil and tomato samples were extracted using Cobb-Sieving and Decanting method. Descriptive statistics, students t-test and species diversity were used to analyze the data. Nineteen (19) genera of plant parasitic nematodes were isolated and identified, with 18 genera each in diseased soil and root samples, 12 and 9 genera from apparently healthy soil and roots respectively. Scutellonema spp. (1121) had the highest number of nematodes genera while Tetylenchus (20) had the lowest, in diseased soil samples. In diseased root samples, Meloidogyne (415) had the highest nematodes while Tetylenchus (10) had the lowest number of nematodes. In apparently healthy soil samples, Scutellonema (522) had the highest number of collection, while Tylenchorynchus (20) had the least. In apparently healthy root samples, Pratylenchus (415) had the highest and Hoplolaimus (10) had the lowest number of collection across all the locations. There was no significant difference (p > 0.05) in the presence of nematodes in the...
References
Algamal, Z. Y. (2015). Penalized poisson regression model using adaptive modified elastic net penalty. Electronic Journal of Applied Statistical Analysis, 8(2), 236-245.
Anbari, M. E., & Mkhadri, A. (2014). Penalized regression combining the L 1 norm and a correlation based penalty. Sankhya B, 76, 82-102.
Babarinsa, O., Sofi, A. Z. M., Mohd, A. H., Eluwole, A., Sunday, I., Adamu, W., Daniel, L. (2022). Note on the history of (square) matrix and determinant. FUDMA JOURNAL OF SCIENCES, 6(3), 177-190.
Beck, A., & Teboulle, M. (2009). A fast iterative shrinkage-thresholding algorithm for linear inverse problems. SIAM journal on imaging sciences, 2(1), 183-202.
Biecek, P., & Burzykowski, T. (2021). Explanatory model analysis: explore, explain, and examine predictive models: Chapman and Hall/CRC.
Bondell, H. D., & Reich, B. J. (2006). Simultaneous regression shrinkage, variable selection and clustering of predictors with OSCAR. Retrieved from
Breiman, L. (1996). Heuristics of instability and stabilization in model selection. The annals of statistics, 24(6), 2350-2383.
Efron, B., & Tibshirani, R. (1997). Improvements on cross-validation: the 632+ bootstrap method. Journal of the American statistical Association, 92(438), 548-560.
Efron, B., & Tibshirani, R. J. (1994). An introduction to the bootstrap: Chapman and Hall/CRC.
Fan, J., & Li, R. (2001). Variable selection via nonconcave penalized likelihood and its oracle properties. Journal of the American statistical Association, 96(456), 1348-1360.
Fan, J., & Li, R. (2006). Statistical challenges with high dimensionality: Feature selection in knowledge discovery. arXiv preprint math/0602133.
Fu, W. J. (1998). Penalized regressions: the bridge versus the lasso. Journal of computational and graphical statistics, 7(3), 397-416.
Garba, W., Yahya, G., & Aremu, M. (2016). Multiclass Sequential Feature Selection and Classification Method for Genomic Data. Blood, 7(10).
Grandvalet, Y., Chiquet, J., & Ambroise, C. (2012). Sparsity by Worst-Case Penalties. arXiv preprint arXiv:1210.2077.
Hanke, M., Dijkstra, L., Foraita, R., & Didelez, V. (2024). Variable selection in linear regression models: Choosing the best subset is not always the best choice. Biometrical Journal, 66(1), 2200209.
Hapfelmeier, A., Babatunde, W., Yahya, R. R., & Ulm, K. (2012). 26 Predictive modeling of gene expression data. Handb Stat Clin Oncol, 4, 71.
Hoerl, A., & Kennard, R. (1970). Ridge regression: Biased estimation for nonorthogonal problems. Technometrics, 12(1), 55-67.
Kohavi, R. (1995). A study of cross-validation and bootstrap for accuracy estimation and model selection. Paper presented at the International Joint Conference on Artificial Intelligence.
Ryan, T. (2008). Modern regression methods (Vol. 655): John Wiley & Sons.
Scheetz, T. E., Kim, K.-Y. A., Swiderski, R. E., Philp, A. R., Braun, T. A., Knudtson, K. L., . . . Casavant, T. L. (2006). Regulation of gene expression in the mammalian eye and its relevance to eye disease. Proceedings of the National Academy of Sciences, 103(39), 14429-14434.
Stamey, T. A., Warrington, J. A., Caldwell, M. C., Chen, Z., Fan, Z., Mahadevappa, M., . . . Zhang, Z. (2001). Molecular genetic profiling of Gleason grade 4/5 prostate cancers compared to benign prostatic hyperplasia. The Journal of urology, 166(6), 2171-2177.
Tan, Q. E. A. (2012). Correlation adjusted penalization in regression analysis. (Ph.D.), University of Manitoba Canada.
Tibshirani, R. (1996). Regression shrinkage and selection via the lasso. Journal of the Royal Statistical Society Series B: Statistical Methodology, 58(1), 267-288.
Tutz, G., & Ulbricht, J. (2009). Penalized regression with correlation-based penalty. Statistics and Computing, 19, 239-253.
Wang, X., Dunson, D., & Leng, C. (2016). No penalty no tears: Least squares in high-dimensional linear models. Paper presented at the International Conference on Machine Learning.
Zhang, C.-H. (2010). Nearly unbiased variable selection under minimax concave penalty. Annals of Statistics, 101
-1429.
Zou, H., & Hastie, T. (2005). Regularization and variable selection via the elastic net. Journal of the Royal Statistical Society Series B: Statistical Methodology, 67(2), 301-320.
Published
How to Cite
Issue
Section
FUDMA Journal of Sciences