Agronomic management response in maize (Zea mays L.) production across three agroecological zones of Kenya
Abstract 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...
| Main Authors: | , , , , , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Wiley
2024-03-01
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| Series: | Agrosystems, Geosciences & Environment |
| Online Access: | https://doi.org/10.1002/agg2.20478 |
| _version_ | 1797258577429135360 |
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| author | Harison Kiplagat Kipkulei Sonoko Dorothea Bellingrath‐Kimura Marcos Lana Gohar Ghazaryan Roland Baatz Custodio Matavel Mark Boitt Charles B. Chisanga Brian Rotich Rodrigo Martins Moreira Stefan Sieber |
| author_facet | Harison Kiplagat Kipkulei Sonoko Dorothea Bellingrath‐Kimura Marcos Lana Gohar Ghazaryan Roland Baatz Custodio Matavel Mark Boitt Charles B. Chisanga Brian Rotich Rodrigo Martins Moreira Stefan Sieber |
| author_sort | Harison Kiplagat Kipkulei |
| collection | DOAJ |
| description | Abstract 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. |
| first_indexed | 2024-04-24T22:55:45Z |
| format | Article |
| id | doaj.art-252df7a0f27a4719928e82dc4186a29d |
| institution | Directory Open Access Journal |
| issn | 2639-6696 |
| language | English |
| last_indexed | 2024-04-24T22:55:45Z |
| publishDate | 2024-03-01 |
| publisher | Wiley |
| record_format | Article |
| series | Agrosystems, Geosciences & Environment |
| spelling | doaj.art-252df7a0f27a4719928e82dc4186a29d2024-03-18T05:28:33ZengWileyAgrosystems, Geosciences & Environment2639-66962024-03-0171n/an/a10.1002/agg2.20478Agronomic management response in maize (Zea mays L.) production across three agroecological zones of KenyaHarison Kiplagat Kipkulei0Sonoko Dorothea Bellingrath‐Kimura1Marcos Lana2Gohar Ghazaryan3Roland Baatz4Custodio Matavel5Mark Boitt6Charles B. Chisanga7Brian Rotich8Rodrigo Martins Moreira9Stefan Sieber10Leibniz Centre for Agricultural Landscape Research (ZALF) Müncheberg GermanyLeibniz Centre for Agricultural Landscape Research (ZALF) Müncheberg GermanyDepartment of Crop Production Ecology Swedish University of Agricultural Sciences Uppsala SwedenLeibniz Centre for Agricultural Landscape Research (ZALF) Müncheberg GermanyLeibniz Centre for Agricultural Landscape Research (ZALF) Müncheberg GermanyLeibniz Institute for Agricultural Engineering and Bioeconomy (ATB) Potsdam GermanyInstitute of Geomatics, GIS & Remote Sensing (IGGReS) Dedan Kimathi University of Technology Nyeri KenyaDepartment of Plant and Environmental Sciences, School of Natural Resources Copperbelt University Kitwe ZambiaInstitute of Environmental Sciences Hungarian University of Agriculture and Life Sciences Gödöllő HungaryUniversidade Federal de Rondônia Porto Velho Rondônia BrazilLeibniz Centre for Agricultural Landscape Research (ZALF) Müncheberg GermanyAbstract 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.https://doi.org/10.1002/agg2.20478 |
| spellingShingle | Harison Kiplagat Kipkulei Sonoko Dorothea Bellingrath‐Kimura Marcos Lana Gohar Ghazaryan Roland Baatz Custodio Matavel Mark Boitt Charles B. Chisanga Brian Rotich Rodrigo Martins Moreira Stefan Sieber Agronomic management response in maize (Zea mays L.) production across three agroecological zones of Kenya Agrosystems, Geosciences & Environment |
| title | Agronomic management response in maize (Zea mays L.) production across three agroecological zones of Kenya |
| title_full | Agronomic management response in maize (Zea mays L.) production across three agroecological zones of Kenya |
| title_fullStr | Agronomic management response in maize (Zea mays L.) production across three agroecological zones of Kenya |
| title_full_unstemmed | Agronomic management response in maize (Zea mays L.) production across three agroecological zones of Kenya |
| title_short | Agronomic management response in maize (Zea mays L.) production across three agroecological zones of Kenya |
| title_sort | agronomic management response in maize zea mays l production across three agroecological zones of kenya |
| url | https://doi.org/10.1002/agg2.20478 |
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