Key message Genome-wide association study (GWAS) on 923 maize lines and validation in bi-parental populations identified significant genomic regions for kernel-Zinc and-Iron in maize. Abstract Bio-fortification of maize with elevated Zinc (Zn) and Iron (Fe) holds considerable promise for alleviating under-nutrition among the world’s poor. Bio-fortification through molecular breeding could be an economical strategy for developing nutritious maize, and hence in this study, we adopted GWAS to identify markers associated with high kernel-Zn and Fe in maize and subsequently validated marker-trait associations in independent bi-parental populations. For GWAS, we evaluated a diverse maize association mapping panel of 923 inbred lines across three environments and detected trait associations using high-density Single nucleotide polymorphism (SNPs) obtained through genotyping-by-sequencing. Phenotyping trials of the GWAS panel showed high heritability and moderate correlation between kernel-Zn and Fe concentrations. GWAS revealed a total of 46 SNPs (Zn-20 and Fe-26) significantly associated (P ≤ 5.03 × 10⁻⁰⁵) with kernel-Zn and Fe concentrations with some of these associated SNPs located within previously reported QTL intervals for these traits. Three double-haploid (DH) populations were developed using lines identified from the panel that were contrasting for these micronutrients. The DH populations were phenotyped at two environments and were used for validating significant SNPs (P ≤ 1 × 10⁻⁰³) based on single marker QTL analysis. Based on this analysis, 11 (Zn) and 11 (Fe) SNPs were found to have significant effect on the trait variance (P ≤ 0.01, R² ≥ 0.05) in at least one bi-parental population. These findings are being pursued in the kernel-Zn and Fe breeding program, and could hold great value in functional analysis and possible cloning of high-value genes for these traits in maize.

Maize (Zea mays L.) production in tropical equatorial regions faces significant challenges due to agroclimatic and soil fertility variability, necessitating the evaluation of maize hybrid adaptability and phenotypic stability across diverse agroecosystems. This study compares the effectiveness of the additive main effects and multiplicative interaction (AMMI) and multi-trait genotype-ideotype distance (MGIDI) models for identifying superior maize hybrids well-suited to the equatorial climate. Fifteen genotypes, including 13 hybrid candidates and two popular commercial varieties (BISI 2 and NASA 29), were analyzed in 10 distinct environments in Indonesia over three consecutive years (2018–2020). The ANOVA method used in the AMMI model analyzed variance into three major components, with PCA analysis indicating that environments (E), genotypes (G), and their interaction (G × E) had a highly significant effect on yield (p < 0.001). Two hybrids, HM04 (CI301032/G102612) and HM02 (CI272022/G102612), displayed high adaptability and stability across various environments, with significantly higher yields than the grand mean by AMMI analysis. Additionally, HM10 (MAL03/CLYN231) and HM09 (G102612/CLYN231) were narrowly adapted to the ME-1 and ME-2 mega-environments, indicating they are best suited for these specific environments. Similar to AMMI, the MGIDI model suggested HM04 (MGIDI index = 1.74) and HM02 (MGIDI index = 1.76) as the two highest-performing hybrids, determined by their yield and nine other traits. Using the multiple trait combination index as a tool to assess the performance of these hybrids enabled researchers to determine the most effective traits for each genotype. The two models are recommended and may be integrated for comprehensive data interaction analysis, which simplifies the process of delineating genotypes with the environment and enables stakeholders to select desired traits while considering their strengths and weaknesses.

Unpredictable rainfall in the tropics often increases the risk of waterlogging or even flooding in agricultural lands, hindering the efforts to fulfill maize demands. Breeding maize for waterlogging tolerance is necessary yet challenging since performing varietal testing on a set of hybrids might be biased toward the presence of genotype and environment interaction (GEI). This study aimed to elucidate the GEI effects on yield and related agronomic traits of tropical maize hybrids under normal irrigation and waterlogging conditions and to assess the adaptability of these hybrids in such conditions using several stability models. Ten hybrids including two commercial checks were evaluated across 14 environments under normal and waterlogging conditions in Indonesia from 2018 to 2020. Waterlogging imposed at the V6 stage for ten consecutive days significantly hampered the plant height and ear height, slightly delayed flowering dates, and reduced yield and yield components. The genotype, location, and genotype by location effects were significant on yield, but the genotype by waterlogging effect was not. Stress tolerance index is highly significantly correlated (p < 0.01) with yield in both normal (r = 0.90) and waterlogging (r = 0.96) conditions. The GGE biplot analysis on yield revealed five sectors, two mega-environments, and five vertex genotypes. This study indicated the possibility of breeding maize hybrids tolerant to waterlogging (G05), as well as high-yielding hybrids under both conditions (G07).

Improving the tolerance of crop species to abiotic stresses that limit plant growth and productivity is essential for mitigating the emerging problems of global warming. In this context, imaged data analysis represents an effective method in the 4.0 technology era, where this method has the non-destructive and recursive characterization of plant phenotypic traits as selection criteria. So, the plant breeders are helped in the development of adapted and climate-resilient crop varieties. Although image-based phenotyping has recently resulted in remarkable improvements for identifying the crop status under a range of growing conditions, the topic of its application for assessing the plant behavioral responses to abiotic stressors has not yet been extensively reviewed. For such a purpose, bibliometric analysis is an ideal analytical concept to analyze the evolution and interplay of image-based phenotyping to abiotic stresses by objectively reviewing the literature in light of existing database. Bibliometricy, a bibliometric analysis was applied using a systematic methodology which involved data mining, mining data improvement and analysis, and manuscript construction. The obtained results indicate that there are 554 documents related to image-based phenotyping to abiotic stress until 5 January 2023. All document showed the future development trends of image-based phenotyping will be mainly centered in the United States, European continent and China. The keywords analysis major focus to the application of 4.0 technology and machine learning in plant breeding, especially to create the tolerant variety under abiotic stresses. Drought and saline become an abiotic stress often using image-based phenotyping. Besides that, the rice, wheat and maize as the main commodities in this topic. In conclusion, the present work provides information on resolutive interactions in developing image-based phenotyping to abiotic stress, especially optimizing high-throughput sensors in image-based phenotyping for the future development.

Multi-trait selection helps breeders identify genotypes that appeal to divergent groups of preferences. In this study, we performed simultaneous selection of sweet-waxy corn hybrids on several traits covering the perspectives of consumers (taller kernel depth, better eating quality), growers (early maturity, shorter plant stature, and high ear yield), and seed producers (high flowering synchrony, acceptable seed yield, and good plant architecture). Three supersweet corn lines and 8 waxy corn lines were intercrossed to generate 48 F1 hybrids according to North Carolina Design II, and these genotypes were laid out in a randomized complete block design with 3 replications across 2 seasons between 2017 and 2018. A sensory blind test on sweetness, stickiness, tenderness, and overall liking was conducted to assess the eating quality of steamed corn samples. Two methods of simultaneous selection, namely unweighted selection index and overall rank-sum index (ORSI), were applied to rank crosses, following all targeted groups of preferences. Genetic parameters and genetic gain were estimated to evaluate the effectiveness of those selection methods. Both approaches had similar patterns of preferable realized gain on each given trait and could identify similar top five crosses with only slight order changes, implying that these methods were effective to rank genotypes according to given selection criteria. One of the tested crosses, 101L/TSC-10 × KV/mon, consistently had the highest unweighted selection index in the dry (7.84) and the rainy (7.15) seasons and the lowest ORSI (310), becoming a promising candidate as synergistic sweet-waxy corn hybrid appealing to consumers, growers, and seed producers. The expected ideotypes of sweet-waxy corn hybrid are discussed.

Rice is a major world staple food crop, and therefore breeding improved varieties is of ultimate importance. Genotype-by-environment analyses are essential to understand the potential performance of the lines over environments. This study aimed to elucidate the stability, agronomic performance, and grain quality of elite new plant type (NPT) rice lines developed for tropical lowland areas. Elite NPT rice lines were evaluated in eight locations along with national varieties as checks. Combined analysis across environments and several stability analyses were performed. The results revealed the possibility of breeding high-yielding NPT rice varieties with different stability profiles, namely broad adaptation (IPB189-F-13-1-1 (G5), b_i = 0.91), suited for favorable environments (IPB187-F-37-1-2 (G1), b_i = 1.47), and adapted to marginal environments (IPB193-F-30-2-1 (G9), b_i = 0.54). G1 and G5 belonged to different groups of grain characteristics, each with low and high amylose content, respectively. The highest yielding line (IPB189-F-23-2-2 (G6), 8.68 ton ha⁻¹) had an advantage of 19.39% over the highest yielding check, i.e., Inpari 32 (7.27 ton ha⁻¹). This increase in yield was contributed by the success of breeding with a greater 1000-grain weight (11.91%) and number of filled grains per panicle (42.37%) than Inpari 32; meanwhile, the plant height also increased by 19 %. In addition, the rank correlations among three stability parameters, sdi2, Wi2, and σi2 were positive and highly significant. This study enlightens the prospect of breeding NPT rice varieties with different adaptations in tropical lowland areas.