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  • 1
    In: Agriculture, MDPI AG, Vol. 10, No. 11 ( 2020-10-30), p. 515-
    Abstract: Understanding the detailed timing of crop phenology and their variability enhances grain yield and quality by providing precise scheduling of irrigation, fertilization, and crop protection mechanisms. Advances in information and communication technology (ICT) provide a unique opportunity to develop agriculture-related tools that enhance wall-to-wall upscaling of data outputs from point-location data to wide-area spatial scales. Because of the heterogeneity of the worldwide agro-ecological zones where crops are cultivated, it is unproductive to perform plant phenology research without providing means to upscale results to landscape-level while safeguarding field-scale relevance. This paper presents an advanced, reproducible, and open-source software for plant phenology prediction and mapping (PPMaP) that inputs data obtained from multi-location field experiments to derive models for any crop variety. This information can then be applied consecutively at a localized grid within a spatial framework to produce plant phenology predictions at the landscape level. This software runs on the ‘Windows’ platform and supports the development of process-oriented and temperature-driven plant phenology models by intuitively and interactively leading the user through a step-by-step progression to the production of spatial maps for any region of interest in sub-Saharan Africa. Maize (Zea mays L.) was used to demonstrate the robustness, versatility, and high computing efficiency of the resulting modeling outputs of the PPMaP. The framework was implemented in R, providing a flexible and easy-to-use GUI interface. Since this allows for appropriate scaling to the larger spatial domain, the software can effectively be used to determine the spatially explicit length of growing period (LGP) of any variety.
    Type of Medium: Online Resource
    ISSN: 2077-0472
    Language: English
    Publisher: MDPI AG
    Publication Date: 2020
    detail.hit.zdb_id: 2651678-0
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  • 2
    In: Agronomy, MDPI AG, Vol. 13, No. 1 ( 2023-01-09), p. 203-
    Abstract: The fall armyworm (FAW), Spodoptera frugiperda, a pest of maize native to the Americas first reported in West and Central Africa in 2016, severely threatens maize production and food security in Sub-Saharan Africa. Native genetic resistance is one of the best methods of control of insect pests as it is contained in the seed making it more amenable for use by farmers compared to other interventions and it is also compatible with other integrated pest management (IPM) options. An intensive screening against FAW was carried out by artificial infestation in greenhouse conditions in Kenya between 2017 and 2018 on about 3000 inbred lines available in the germplasm collection of the International Maize and Wheat Improvement Center (CIMMYT). Among these lines, only four showed to be resistant to FAW, but the mechanisms of resistance are not yet known. The objective of this study was to determine the resistance mechanisms specifically non-preference and antibiosis to S. frugiperda in these four selected resistant inbred lines. The studies were conducted under laboratory and net house conditions in Kenya from April 2020 to November 2021. Non-preference was assessed estimating the feeding preference by counting the number of FAW neonates found on each leaf portion, silk portion and grain using binary and multiple choice methods under laboratory conditions, while antibiosis was assessed through the relative growth rate (RGR) and developmental time of FAW larvae on leaves, silks and grains under both laboratory and net house conditions. Among the four resistant maize inbred lines tested, two, namely CML71 and CKSBL10008, exhibited the highest level of antibiosis resistance on leaves. Under laboratory conditions, the larval RGR reduced from 13 mg/d on the most susceptible line to 8 mg/d on CML71. CML71 also showed a good non-preference on leaves compared to other tested lines. Only 6% of neonates choose to feed on CML71 whereas more than 10% choose to feed on the other lines (and 15% on the most susceptible) in multiple choice tests. The non-preference for feeding and lower RGR of larvae on CML71 suggest a biochemical involvement resistance to FAW. Through this study, CML71 is revealed as a highly promising line for use in breeding for native genetic resistance to FAW in tropical maize.
    Type of Medium: Online Resource
    ISSN: 2073-4395
    Language: English
    Publisher: MDPI AG
    Publication Date: 2023
    detail.hit.zdb_id: 2607043-1
    SSG: 23
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  • 3
    In: Genes, MDPI AG, Vol. 13, No. 2 ( 2022-01-28), p. 251-
    Abstract: The recent invasion, rapid spread, and widescale destruction of the maize crop by the fall armyworm (FAW; Spodoptera frugiperda (J.E. Smith)) is likely to worsen the food insecurity situation in Africa. In the present study, a set of 424 maize lines were screened for their responses to FAW under artificial infestation to dissect the genetic basis of resistance. All lines were evaluated for two seasons under screen houses and genotyped with the DArTseq platform. Foliar damage was rated on a scale of 1 (highly resistant) to 9 (highly susceptible) and scored at 7, 14, and 21 days after artificial infestation. Analyses of variance revealed significant genotypic and genotype by environment interaction variances for all traits. Heritability estimates for leaf damage scores were moderately high and ranged from 0.38 to 0.58. Grain yield was negatively correlated with a high magnitude to foliar damage scores, ear rot, and ear damage traits. The genome-wide association study (GWAS) revealed 56 significant marker–trait associations and the predicted functions of the putative candidate genes varied from a defense response to several genes of unknown function. Overall, the study revealed that native genetic resistance to FAW is quantitative in nature and is controlled by many loci with minor effects.
    Type of Medium: Online Resource
    ISSN: 2073-4425
    Language: English
    Publisher: MDPI AG
    Publication Date: 2022
    detail.hit.zdb_id: 2527218-4
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