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apsim nextgen  (NextGen Sciences)

 
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    NextGen Sciences apsim nextgen
    Apsim Nextgen, supplied by NextGen Sciences, used in various techniques. Bioz Stars score: 90/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
    https://www.bioz.com/product/simulated+next-generation+sequencing+data/apsim+nextgen/pm39307368-56-4-12
    Average 90 stars, based on 1 article reviews
    apsim nextgen - by Bioz Stars, 2026-11
    90/100 stars

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    other:

    Article Title: Evaluating the impact of weather forecasts on productivity and environmental footprint of irrigated maize production systems.
    Article Snippet: The Next Generation of Agricultural Production Systems sIMulator (usually dubbed as APSIM NextGen; www.apsim.info) is an internationally recognised framework for modelling and simulating agricultural systems (Holzworth et al., 2018, 2014; Keating et al., 2003).

    Article Title: Achieving stable and sustainable Mungbean yields in Australia via optimal sowing dates
    Article Snippet: APSIM NextGen is commonly used to expand the scope of field experiments and explore the mechanisms driving crop response to climate, management, and genetics in the field.

    Article Title: Achieving stable and sustainable Mungbean yields in Australia via optimal sowing dates
    Article Snippet: Moreover, little is known about how to manage the crop well enough to achieve higher yields consistently and thereby improve grower and Abbreviations: WUE, Water Use Efficiency; VPD, Vapor Pressure Deficit; PAW, Plant Available Water; APSIM NextGen, Agricultural Production Systems sIMulator Next Generation.

    Article Title: Achieving stable and sustainable Mungbean yields in Australia via optimal sowing dates
    Article Snippet: While the APSIM NextGen Mungbean model is robust and accurate, its simulation capacity is limited by the data that was available for its parameterization.

    Article Title: Achieving stable and sustainable Mungbean yields in Australia via optimal sowing dates
    Article Snippet: Before this can be done effectively, however, it is important to address the current limitations in the APSIM NextGen Mungbean model. More heat-stress and waterlogging data should be collected for model improvement and in general, there needs to be more focus on incorporating biota and disease factors into crop models.

    Article Title: Achieving stable and sustainable Mungbean yields in Australia via optimal sowing dates
    Article Snippet: Methods: We simulated 60 years of growing mungbean at 180 different sowing times, four different initial soil water levels and at seven sites across Queensland and New South Wales using the new Agricultural Production Systems sIMulator Next Generation (APSIM NextGen) Mungbean model. We then calculated the impact of sowing date on mungbean water use efficiency and on the soil water deficit for the subsequent crop in the rotation.

    Article Title: Evaluating the impact of weather forecasts on productivity and environmental footprint of irrigated maize production systems.
    Article Snippet: It employs the Next Generation of Agricultural Production Systems sIMulator (APSIM NextGen) to simulate maize growth at eleven locations representing diverse climates globally.



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    Data preprocessing workflow. When analyzing NGS datasets with KPop, one can optionally pre-process sequencing reads in order to eliminate biases and/or have the method focus on specific parts of the genome. For instance, one might align reads to a (pan-)genome and separate them into reads that align (likely to originate from the organism being studied) and reads that do not (likely to come from contaminations). Furthermore, reads that do map to the pan-genome might be separated into groups specific to different genomic features; for instance, one might align them to a set of MLST genes or AMR genes. Full k -mer spectra would then be separately obtained from each group of reads (contaminations, pan-genomic, MLST genes, AMR genes) and given as input to downstream/classification methods. The choice of the group of reads from which spectra are computed determines the set of sequences seen by the method, and hence the scope of the classification

    Journal: Genome Biology

    Article Title: KPop: accurate and scalable comparative analysis of microbial genomes by sequence embeddings

    doi: 10.1186/s13059-025-03585-8

    Figure Lengend Snippet: Data preprocessing workflow. When analyzing NGS datasets with KPop, one can optionally pre-process sequencing reads in order to eliminate biases and/or have the method focus on specific parts of the genome. For instance, one might align reads to a (pan-)genome and separate them into reads that align (likely to originate from the organism being studied) and reads that do not (likely to come from contaminations). Furthermore, reads that do map to the pan-genome might be separated into groups specific to different genomic features; for instance, one might align them to a set of MLST genes or AMR genes. Full k -mer spectra would then be separately obtained from each group of reads (contaminations, pan-genomic, MLST genes, AMR genes) and given as input to downstream/classification methods. The choice of the group of reads from which spectra are computed determines the set of sequences seen by the method, and hence the scope of the classification

    Article Snippet: In order to do so, simulated next-generation sequencing (NGS) data was generated for each genome using ART [ ], emulating Illumina HiSeq 2500 paired-end reads of length 150 bp with an average coverage of 20-fold.

    Techniques: Sequencing