Journal of Energy Bioscience 2025, Vol.16, No.4, 182-192 http://bioscipublisher.com/index.php/jeb 187 Figure 2 Comparison between simulated and measured leaf area index (A) and aboveground biomass yield (B) of melkasa-2 maize cultivar during model calibration at Harbu wereda, Ethiopia (Adopted from Chekole and Ahmed, 2022) 6.3 Carbon sequestration and ecosystem services Corn is not only an energy crop, but also plays a role in the ecosystem, such as helping to fix carbon and protect soil. Climate change will affect corn yields and change its carbon emissions and net energy output (Olasogba and Duckers, 2020). If we can optimize field management, such as less tillage and reasonable fertilization, we can reduce carbon emissions while maintaining yields. This approach can make corn output more energy than input, which helps to improve the sustainability of biomass energy (Falconnier et al., 2020; Olasogba and Duckers, 2020). Corn cultivation can also help the soil store more carbon and increase soil organic matter. This is good for the ecosystem and is in line with the goals of low-carbon agriculture and sustainable development (Olasogba and Duckers, 2020). 7 Technological Innovations and Phenotyping Tools 7.1 High-throughput phenotyping In recent years, unmanned aerial vehicles (UAVs) combined with multiple sensors, such as hyperspectral, thermal imaging, and LiDAR, have become important tools for studying corn phenotyping. These devices can quickly collect a lot of field data during corn growth without damaging the plants. By integrating different types of data together and combining them with deep learning methods, researchers can more accurately predict important traits such as dry matter, ear weight, and nitrogen fertilizer use efficiency. Studies have found that combining hyperspectral and LiDAR data can solve the problem of "saturation" of a single remote sensing method, that is, it can better identify complex traits such as biomass. Moreover, when using multi-task deep learning models, multiple traits can be predicted at the same time, and the effect is also good. These technologies provide practical methods and theoretical basis for breeding and field management (Nguyen et al., 2023). Some crop-safe remote sensing methods, such as spectral reflectance or infrared thermal imaging, are also often used to check corn's resistance to adverse environments such as drought or low nitrogen (Masuka et al., 2012). 7.2 Omics approaches Integrating different types of “omics” data (such as transcriptome, proteome and metabolome) can help us better understand the genetic causes that affect maize biomass. By combining genetic information and trait data of different maize varieties or hybrids, researchers can find out which genes or regions are related to biomass,
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