دورية أكاديمية
Maize yield prediction and condition monitoring at the sub-county scale in Kenya: synthesis of remote sensing information and crop modeling.
| العنوان: | Maize yield prediction and condition monitoring at the sub-county scale in Kenya: synthesis of remote sensing information and crop modeling. |
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| المؤلفون: | Kipkulei HK; Leibniz Centre for Agricultural Landscape Research (ZALF), Eberswalder Straße 84, 15374, Müncheberg, Germany. harison.kipkulei@zalf.de.; Humboldt Universität zu Berlin, Faculty of Life Sciences, Invalidenstraße 42, 10115, Berlin, Germany. harison.kipkulei@zalf.de.; Department of Geomatic Engineering and Geospatial Information Systems, Jomo Kenyatta University of Agriculture and Technology, P.O. Box, 62000, Nairobi, 00200, Kenya. harison.kipkulei@zalf.de.; Faculty of Applied Computer Sciences, Institute of Geography, University of Augsburg, Alter Postweg 118, 86159, Augsburg, Germany. harison.kipkulei@zalf.de., Bellingrath-Kimura SD; Leibniz Centre for Agricultural Landscape Research (ZALF), Eberswalder Straße 84, 15374, Müncheberg, Germany.; Humboldt Universität zu Berlin, Faculty of Life Sciences, Invalidenstraße 42, 10115, Berlin, Germany., Lana M; Department of Crop Production Ecology, Swedish University of Agricultural Sciences, Box 7043, 75007, Uppsala, Sweden., Ghazaryan G; Leibniz Centre for Agricultural Landscape Research (ZALF), Eberswalder Straße 84, 15374, Müncheberg, Germany.; Geography Department, Humboldt-Universität zu Berlin, Unter Den Linden 6, 10099, Berlin, Germany., Baatz R; Leibniz Centre for Agricultural Landscape Research (ZALF), Eberswalder Straße 84, 15374, Müncheberg, Germany., Matavel C; Leibniz Institute for Agricultural Engineering and Bioeconomy (ATB), Max-Eyth-Allee 100, 14469, Potsdam, Germany., Boitt MK; Institute of Geomatics, GIS and Remote Sensing (IGGReS), Dedan Kimathi University of Technology, P.O. Box 657-10100, Nyeri, Kenya., Chisanga CB; Department of Plant and Environmental Sciences, School of Natural Resources, Copperbelt University, Off Jambo Drive, Box 21692, 10101, Kitwe, Zambia., Rotich B; Institute of Environmental Sciences, Hungarian University of Agriculture and Life Sciences, Gödöllő, 2100, Hungary., Moreira RM; Universidade Federal de Rondônia, Porto Velho, RO, Brazil., Sieber S; Leibniz Centre for Agricultural Landscape Research (ZALF), Eberswalder Straße 84, 15374, Müncheberg, Germany.; Humboldt Universität zu Berlin, Faculty of Life Sciences, Invalidenstraße 42, 10115, Berlin, Germany. |
| المصدر: | Scientific reports [Sci Rep] 2024 Jun 20; Vol. 14 (1), pp. 14227. Date of Electronic Publication: 2024 Jun 20. |
| نوع المنشور: | Journal Article |
| اللغة: | English |
| بيانات الدورية: | Publisher: Nature Publishing Group Country of Publication: England NLM ID: 101563288 Publication Model: Electronic Cited Medium: Internet ISSN: 2045-2322 (Electronic) Linking ISSN: 20452322 NLM ISO Abbreviation: Sci Rep Subsets: MEDLINE |
| أسماء مطبوعة: | Original Publication: London : Nature Publishing Group, copyright 2011- |
| مواضيع طبية MeSH: | Remote Sensing Technology*/methods , Zea mays*/growth & development , Crops, Agricultural*/growth & development, Kenya ; Crop Production/methods ; Agriculture/methods ; Models, Theoretical ; Seasons |
| مستخلص: | Agricultural production assessments are crucial for formulating strategies for closing yield gaps and enhancing production efficiencies. While in situ crop yield measurements can provide valuable and accurate information, such approaches are costly and lack scalability for large-scale assessments. Therefore, crop modeling and remote sensing (RS) technologies are essential for assessing crop conditions and predicting yields at larger scales. In this study, we combined RS and a crop growth model to assess phenology, evapotranspiration (ET), and yield dynamics at grid and sub-county scales in Kenya. We synthesized RS information from the Food and Agriculture Organization (FAO) Water Productivity Open-access portal (WaPOR) to retrieve sowing date information for driving the model simulations. The findings showed that grid-scale management information and progressive crop growth could be accurately derived, reducing the model output uncertainties. Performance assessment of the modeled phenology yielded satisfactory accuracies at the sub-county scale during two representative seasons. The agreement between the simulated ET and yield was improved with the combined RS-crop model approach relative to the crop model only, demonstrating the value of additional large-scale RS information. The proposed approach supports crop yield estimation in data-scarce environments and provides valuable insights for agricultural resource management enabling countermeasures, especially when shortages are perceived in advance, thus enhancing agricultural production. (© 2024. The Author(s).) |
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| فهرسة مساهمة: | Keywords: CERES-Maize; DSSAT; Evapotranspiration; Phenology; Remote sensing; WaPOR |
| تواريخ الأحداث: | Date Created: 20240620 Date Completed: 20240620 Latest Revision: 20240623 |
| رمز التحديث: | 20240623 |
| مُعرف محوري في PubMed: | PMC11190209 |
| DOI: | 10.1038/s41598-024-62623-w |
| PMID: | 38902311 |
| قاعدة البيانات: | MEDLINE |
Find this article in full text from Springer Verlag. 
Full Text Finder | تدمد: | 2045-2322 |
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| DOI: | 10.1038/s41598-024-62623-w |