Maize Genomics and Genetics 2025, Vol.16, No.1, 45-59 http://cropscipublisher.com/index.php/mgg 55 enough, and more advanced analysis methods are required. Although the technology is advanced now, it is not easy to be accurate. In the final analysis, you still have to accumulate enough reliable data to slowly figure out the tricks. There is a very realistic problem in corn research now - genotyping analysis is cheap and fast, but phenotyping analysis is ridiculously expensive (Wallace et al., 2016). It may only cost a few hundred yuan to test a DNA, but the cost of accurately measuring field traits can be several times higher. What's more troublesome is that phenotypic data is particularly "delicate" and may be completely different in a different environment. A study in 2016 pointed out that this difference has led to a large amount of genotypic data in many projects, but the supporting phenotypic data cannot keep up (Wallace et al., 2016). Although everyone is trying to develop cheaper and faster phenotyping technology, this problem is really difficult to solve in the short term. After all, the performance of plants is greatly affected by the environment, and a lot of manpower and material resources must be invested to ensure the accuracy of the data. 9.2 Impact of emerging technologies on phenotypic and genotypic research in maize In recent years, the technology of corn research has really advanced by leaps and bounds. Take GBS for example, this technology can find thousands of SNP sites in the genome at once (Romay et al., 2013). It used to be very difficult to find these genetic markers, but now it is much easier. Interestingly, these technological advances have made GWAS analysis more accurate, even those rare alleles can be found. Although there are still some problems in actual operation, it does save a lot of trouble for breeding projects (Zhou and Hong, 2024). You see, it is now much more efficient to locate genetic markers related to important agronomic traits than before. But then again, technology is technology, and in the end it still depends on how it performs in the field. Phenotyping is very different now than it was before. New technologies such as digital imaging and remote sensing are really useful, especially when dealing with large quantities of germplasm resources (Nguyen and Norton, 2020). Although the initial investment is not small, in the long run it is much more time-saving and labor-saving than manual measurement. Interestingly, when these phenotypic data are analyzed together with the genotyping results, some unexpected results can often be found. Of course, no matter how advanced the technology is, it depends on the actual application effect. However, a study in 2020 has shown that this combination can indeed speed up the breeding process of stress-resistant varieties (Nguyen and Norton, 2020). Although there are still some limitations now, these new methods do bring new hope to corn research. 9.3 Research prospects for future maize breeding and production When it comes to the future direction of corn breeding, genetic diversity is gaining more and more attention. Al-Naggar and his colleagues found in their 2020 study that the genetic differences between different hybrids are actually quite large (Al-Naggar et al., 2020). Although the mainstream is still using those backbone inbred lines, making use of these diversities may be able to breed new varieties that are more resistant to stress. Abdel-Ghani did relevant research in the early years, proving that a diverse gene pool is particularly helpful in dealing with stresses such as drought and low nitrogen (Abdel-Ghani et al., 2012). Of course, it is not that simple in practice, after all, the challenges of climate change are getting bigger and bigger. But then again, if you want to ensure stable corn yields, this path really needs to be explored. After all, no one knows what new planting problems will be encountered in the future. Now there is a new idea for breeding corn hybrids - using multi-trait models for comprehensive evaluation. The AMMI model and the MGIDI model are quite practical (Azrai et al., 2023). Although single trait analysis can also explain some problems, integrating these models can give a more comprehensive understanding of the advantages and disadvantages of hybrids. In actual operation, it is found that some varieties perform well in specific environments, but not in other places. At this time, the advantages of multi-trait evaluation are revealed. Research in 2023 showed that using these models in breeding projects can indeed help us select more stable and higher-yielding hybrids (Azrai et al., 2023). Of course, the model is the model, and the field performance must be considered in the end, but at least it provides a more scientific screening basis for breeding work.
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