Assessment of Maize Yield Response to Agricultural Management Strategies Using the DSSAT–CERES-Maize Model in Trans Nzoia County in Kenya

Kipkulei, Harison Kiplagat; Bellingrath-Kimura, Sonoko Dorothea; Lana, Marcos; Ghazaryan, Gohar; Baatz, Roland; Boitt, Mark; Chisanga, Charles Bwalya; Rotich, Brian; Sieber, Stefan

doi:10.1007/s42106-022-00220-5

Cited by 8 publications

(5 citation statements)

References 96 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…More details of the experiments and cultivar information are described in Kipkulei, Bellingrath‐Kimura, Lana, Ghazaryan, Baatz et al. (2022), including Supporting Information.…”

Section: Methodsmentioning

confidence: 99%

“…The model's performance for the H614 and H612 cultivars has been evaluated by Kipkulei, Bellingrath‐Kimura, Lana, Ghazaryan, Baatz et al. (2022) and Mfwango et al. (2018), respectively.…”

Section: Methodsmentioning

confidence: 99%

“…The analyzed data were used to design the soil files using the SBUILD program in the DSSAT program. More details of the experiments and cultivar information are described in Kipkulei, Bellingrath-Kimura, Lana, Ghazaryan, Baatz et al (2022), including Supporting Information.…”

Section: On-farm Experimentsmentioning

confidence: 99%

“…In addition to the calibrated coefficients from the on-farm experiments, the study also assessed the H612 cultivar, for which the cultivar coefficients were calibrated and tested in the southern highlands of Tanzania, a region with comparable climatic and environmental conditions to those of our study area (Mfwango et al, 2018). The model's performance for the H614 and H612 cultivars has been evaluated by Kipkulei, Bellingrath-Kimura, Lana, Ghazaryan, Baatz et al (2022) and Mfwango et al (2018), respectively.…”

Section: Model Calibration and Evaluationmentioning

confidence: 99%

“…However, the increment is possible if the nutrient management aligns with the government-recommended nitrogen fertilization of 75 kg N ha −1 (Chebet et al, 2017). The H614 cultivar is one of the superior cultivars in the region and has been evaluated by Kipkulei, Bellingrath-Kimura, Lana, Ghazaryan, Baatz et al (2022). The modeling results showed that the least feasible yield for the cultivar in the study area was 4400 kg ha −1 , an estimate slightly higher than the government statistics of 3700 kg ha −1 in Uasin Gishu County and 4000 kg ha −1 in Trans Nzoia County (MoA&LD, 2020).…”

Section: Influence Of Agronomic Practicesmentioning

confidence: 99%

See 4 more Smart Citations

Agronomic management response in maize (Zea mays L.) production across three agroecological zones of Kenya

Kipkulei,

Bellingrath‐Kimura,

Lana

et al. 2024

Agrosystems Geosci & Env

View full text Add to dashboard Cite

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.

show abstract

“…More details of the experiments and cultivar information are described in Kipkulei, Bellingrath‐Kimura, Lana, Ghazaryan, Baatz et al. (2022), including Supporting Information.…”

Section: Methodsmentioning

confidence: 99%

“…The model's performance for the H614 and H612 cultivars has been evaluated by Kipkulei, Bellingrath‐Kimura, Lana, Ghazaryan, Baatz et al. (2022) and Mfwango et al. (2018), respectively.…”

Section: Methodsmentioning

confidence: 99%

Section: On-farm Experimentsmentioning

confidence: 99%

Section: Model Calibration and Evaluationmentioning

confidence: 99%

Section: Influence Of Agronomic Practicesmentioning

confidence: 99%

See 3 more Smart Citations

Agronomic management response in maize (Zea mays L.) production across three agroecological zones of Kenya

Kipkulei,

Bellingrath‐Kimura,

Lana

et al. 2024

Agrosystems Geosci & Env

View full text Add to dashboard Cite

show abstract

Evaluation of CERES-Maize model for simulating maize phenology, grain yield, soil–water, evapotranspiration, and water productivity under different nitrogen levels and rainfed, limited, and full irrigation conditions

Irmak,

Amiri,

Bazkiaee

et al. 2024

Irrig Sci

View full text Add to dashboard Cite

End-to-end 3D CNN for plot-scale soybean yield prediction using multitemporal UAV-based RGB images

Bhadra,

Sagan,

Skobalski

et al. 2023

Precision Agric

View full text Add to dashboard Cite

Crop yield prediction from UAV images has significant potential in accelerating and revolutionizing crop breeding pipelines. Although convolutional neural networks (CNN) provide easy, accurate and efficient solutions over traditional machine learning models in computer vision applications, a CNN training requires large number of ground truth data, which is often difficult to collect in the agricultural context. The major objective of this study was to develope an end-to-end 3D CNN model for plot-scale soybean yield prediction using multitemporal UAV-based RGB images with approximately 30,000 sample plots. A low-cost UAV-RGB system was utilized and multitemporal images from 13 different experimental fields were collected at Argentina in 2021. Three commonly used 2D CNN architectures (i.e., VGG, ResNet and DenseNet) were transformed into 3D variants to incorporate the temporal data as the third dimension. Additionally, multiple spatiotemporal resolutions were considered as data input and the CNN architectures were trained with different combinations of input shapes. The results reveal that: (a) DenseNet provided the most efficient result (R2 0.69) in terms of accuracy and model complexity, followed by VGG (R2 0.70) and ResNet (R2 0.65); (b) Finer spatiotemporal resolution did not necessarily improve the model performance but increased the model complexity, while the coarser resolution achieved comparable results; and (c) DenseNet showed lower clustering patterns in its prediction maps compared to the other models. This study clearly identifies that multitemporal observation with UAV-based RGB images provides enough information for the 3D CNN architectures to accurately estimate soybean yield non-destructively and efficiently.

show abstract

Assessment of Maize Yield Response to Agricultural Management Strategies Using the DSSAT–CERES-Maize Model in Trans Nzoia County in Kenya

Cited by 8 publications

References 96 publications

Agronomic management response in maize (Zea mays L.) production across three agroecological zones of Kenya

Agronomic management response in maize (Zea mays L.) production across three agroecological zones of Kenya

Evaluation of CERES-Maize model for simulating maize phenology, grain yield, soil–water, evapotranspiration, and water productivity under different nitrogen levels and rainfed, limited, and full irrigation conditions

End-to-end 3D CNN for plot-scale soybean yield prediction using multitemporal UAV-based RGB images

Contact Info

Product

Resources

About