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  • 1
    UID:
    edochu_18452_29363
    Format: 1 Online-Ressource (21 Seiten)
    ISSN: 2639-6696 , 2639-6696 , 2639-6696 , 2639-6696
    Content: Maize (Zea mays L.) productivity in Kenya has witnessed a decline attributed to the effects of climate change and biophysical constraints. The assessment of agronomic practices across agroecological zones (AEZs) is limited by inadequate data quality, hindering a precise evaluation of maize yield on a large scale. In this study, we employed the DSSAT-CERES-Maize crop model (where CERES is Crop Environment Resource Synthesis and DSSAT is Decision Support System for Agrotechnology Transfer) to investigate the impacts of different agronomic practices on maize yield across different AEZs in two counties of Kenya. The model was calibrated and evaluated with observed grain yield, biomass, leaf area index, phenology, and soil water content from 2-year experiments. Remote sensing (RS) images derived from the Sentinel-2 satellite were integrated to delineate maize areas, and the resulting information was merged with DSSAT-CERES-Maize yield simulations. This facilitated a comprehensive quantification of various agronomic measures at pixel scales. Evaluation of agronomic measures revealed that sowing dates and cultivar types significantly influenced maize yield across the AEZs. Notably, AEZ II and AEZ III exhibited elevated yields when implementing combined practices of early sowing and cultivar H614. The impacts of optimal management practices varied across the AEZs, resulting in yield increases of 81, 115, and 202 kg ha−1 in AEZ I, AEZ II, and AEZ III, respectively. This study underscores the potential of the CERES-Maize model and high-resolution RS data in estimating production at larger scales. Furthermore, this integrated approach holds promise for supporting agricultural decision-making and designing optimal strategies to enhance productivity while accounting for site-specific conditions.
    Content: Peer Reviewed
    In: Hoboken, NJ : Wiley, 7,1, 2639-6696
    In: 2639-6696
    Language: English
    URL: Volltext  (kostenfrei)
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  • 2
    UID:
    edochu_18452_27133
    Format: 1 Online-Ressource (48 Seiten)
    Content: Remote sensing (RS) enables a cost-effective, extensive, continuous and standardized monitoring of traits and trait variations of geomorphology and its processes, from the local to the continental scale. To implement and better understand RS techniques and the spectral indicators derived from them in the monitoring of geomorphology, this paper presents a new perspective for the definition and recording of five characteristics of geomorphodiversity with RS, namely: geomorphic genesis diversity, geomorphic trait diversity, geomorphic structural diversity, geomorphic taxonomic diversity, and geomorphic functional diversity. In this respect, geomorphic trait diversity is the cornerstone and is essential for recording the other four characteristics using RS technologies. All five characteristics are discussed in detail in this paper and reinforced with numerous examples from various RS technologies. Methods for classifying the five characteristics of geomorphodiversity using RS, as well as the constraints of monitoring the diversity of geomorphology using RS, are discussed. RS-aided techniques that can be used for monitoring geomorphodiversity in regimes with changing land-use intensity are presented. Further, new approaches of geomorphic traits that enable the monitoring of geomorphodiversity through the valorisation of RS data from multiple missions are discussed as well as the ecosystem integrity approach. Likewise, the approach of monitoring the five characteristics of geomorphodiversity recording with RS is discussed, as are existing approaches for recording spectral geomorhic traits/ trait variation approach and indicators, along with approaches for assessing geomorphodiversity. It is shown that there is no comparable approach with which to define and record the five characteristics of geomorphodiversity using only RS data in the literature. Finally, the importance of the digitization process and the use of data science for research in the field of geomorphology in the 21st century is elucidated and discussed.
    Content: Peer Reviewed
    In: Basel : MDPI, 14,9
    Language: English
    URL: Volltext  (kostenfrei)
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