{"title":"Assessment of Pull- and Push Technologies in Managing Spodoptera frugiperda in Maize and Multivariate Analysis of Associated Variables","authors":"Munsanda Walubita, Nchimunya Bbebe, L. Tembo","doi":"10.9734/ajrcs/2022/v7i230138","DOIUrl":null,"url":null,"abstract":"Aims: The effect of push and pull technology in controlling Spodoptera frugiperda in maize may vary depending on environment understudy and the cereal- legume combination treatment employed. The objectives of this study were therefore, to; i) assess the effectiveness of push-pull technologies in controlling Spodoptera frugiperda infestation in maize ii) cluster the technologies into distinct sets, and iii) identify the variables with high discriminating influence among clustered push-pull technology sets. \nPlace and Duration of Study: The research was undertaken in Chilanga district, Zambia during the 2021/ 22 cropping season. \nMethodology: The experiment was laid as a randomised complete block design (RCBD) with three replications and 6 treatments. Four push –pull combinations and the two controls (Negative and Positive). Maize was used as a test crop. Data on Spodoptera frugiperda incidence was collected at weekly intervals for a period of 5 weeks and at harvest from maize crop. Analysis on measured variables was computed using analysis of variance (ANOVA) and principle component analysis (PCA), a multivariate tool. \nResults: Significant differences were obtained on all measured variables except harvest index with regards to push- pull treatments main effects (P =0.05). The evaluation of treatments using principal component analysis showed that push - pull treatments clustered into four sets, arising from a phenotypic variation explained of 89.1%. \nConclusion: This study revealed Pearl millet/ Marigold push-pull treatment as the best performing treatment with a mean maize test yield value of 7.2 tons per hectare. For variables: number of damaged leaves, injury score leaves, egg batch, biomass with cobs, shelling %, plant height and grain yield were identified as important at differentiating the performance of push pull technologies.","PeriodicalId":415976,"journal":{"name":"Asian Journal of Research in Crop Science","volume":"3 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2022-07-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Asian Journal of Research in Crop Science","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.9734/ajrcs/2022/v7i230138","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 1
Abstract
Aims: The effect of push and pull technology in controlling Spodoptera frugiperda in maize may vary depending on environment understudy and the cereal- legume combination treatment employed. The objectives of this study were therefore, to; i) assess the effectiveness of push-pull technologies in controlling Spodoptera frugiperda infestation in maize ii) cluster the technologies into distinct sets, and iii) identify the variables with high discriminating influence among clustered push-pull technology sets.
Place and Duration of Study: The research was undertaken in Chilanga district, Zambia during the 2021/ 22 cropping season.
Methodology: The experiment was laid as a randomised complete block design (RCBD) with three replications and 6 treatments. Four push –pull combinations and the two controls (Negative and Positive). Maize was used as a test crop. Data on Spodoptera frugiperda incidence was collected at weekly intervals for a period of 5 weeks and at harvest from maize crop. Analysis on measured variables was computed using analysis of variance (ANOVA) and principle component analysis (PCA), a multivariate tool.
Results: Significant differences were obtained on all measured variables except harvest index with regards to push- pull treatments main effects (P =0.05). The evaluation of treatments using principal component analysis showed that push - pull treatments clustered into four sets, arising from a phenotypic variation explained of 89.1%.
Conclusion: This study revealed Pearl millet/ Marigold push-pull treatment as the best performing treatment with a mean maize test yield value of 7.2 tons per hectare. For variables: number of damaged leaves, injury score leaves, egg batch, biomass with cobs, shelling %, plant height and grain yield were identified as important at differentiating the performance of push pull technologies.