Maize Genomics and Genetics 2025, Vol.16, No.3, 139-148 http://cropscipublisher.com/index.php/mgg 143 For example, we can use data from past droughts or simulate some droughts that may be encountered in the future. This can give us a more comprehensive view of whether the model is reliable (Rahmati et al., 2020; Fooladi et al., 2021; Ahmad et al., 2024). In addition, we need to see if the model can detect changes in the time and location of droughts. This is also critical. Most importantly, it must be able to predict core indicators such as yields relatively accurately under these different drought conditions (Fooladi et al., 2021; Prodhan et al., 2021; Zhang and Xu, 2024). 5.3 Robustness and generalization in diverse environments Robustness means that the model can still maintain good results in different data, environments or populations. The best way to evaluate its generalization ability is to combine internal and external cross-validation, external data testing, and sensitivity analysis (Takada et al., 2021). Research has shown that if the data used for training and testing comes from different regions and backgrounds, such a model is more likely to adapt to the new environment and will not be "boxed" by a certain type of data. Therefore, it is very important to train with multiple types of data (Ho et al., 2020; Adkinson et al., 2024). A good evaluation method should not only look at whether the model is accurate, but also consider whether it is stable and has no deviations, and have clear evaluation criteria, such as the similarity and difference between data (Cabitza et al., 2021). 6 Case Studies 6.1 Application in CIMMYT’s drought-resilient maize breeding The International Maize and Wheat Improvement Center (CIMMYT) has done a lot of research on drought-resistant maize. They have used two methods, marker-assisted recurrent selection (MARS) and genomic selection (GS), to breed a number of drought-resistant maize varieties in sub-Saharan Africa (Figure 2). These new methods are more effective than traditional breeding methods and can select more stress-resistant varieties more quickly. To make breeding more efficient, CIMMYT combines QTL mapping (finding gene loci associated with important traits), high-throughput phenotyping, and some molecular tools. In this way, not only drought resistance is improved, but also nitrogen use efficiency and disease resistance are improved (Masuka et al., 2017; Prasanna, 2023). In the past 15 years, more than 300 climate-resistant maize varieties have been bred in sub-Saharan Africa and South Asia. Seeds of these varieties have been widely promoted, helping millions of small farmers (Semagn et al., 2015; Prasanna et al., 2021; Bm, 2022). 6.2 Model deployment in Chinese hybrid maize lines Corn is grown in many parts of China. Areas such as the northern plains and the Loess Plateau often encounter droughts, which affects corn yields. In order to solve this problem, some agricultural universities in China and local breeding units have cooperated in research. They used machine learning methods to build a prediction model and evaluated more than 300 corn hybrids. These varieties were tested under water and without water. The researchers used two models: support vector regression (SVR) and deep neural network (DNN). They analyzed the genetic data of SNPs and soil moisture conditions together to see which varieties were more drought-resistant. In the end, it was found that this method can more accurately select good varieties and provide a lot of useful information for breeding. In this way, those high-quality drought-resistant corn varieties can also be promoted to drought-resistant areas more quickly (Prasanna et al., 2021; Bm, 2022). 6.3 Regional application in sub-Saharan Africa In sub-Saharan Africa, CIMMYT and partner institutions have developed and tested hundreds of drought-tolerant maize varieties using GS, MARS and multi-environment testing methods. These varieties include hybrids and open-pollinated varieties. Studies have found that these newly developed varieties perform well under drought conditions, and have higher yields than old varieties both in controlled trials and in natural environments. In particular, the performance of new varieties is more obvious in some low-yield areas. With the joint efforts of governments, enterprises and seed systems, these drought-resistant maize varieties have been widely promoted, covering millions of hectares of land. This has greatly helped to improve the food security and risk resistance of small farmers (Worku et al., 2016; Manigben et al., 2024).
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