Introduction The identification and advancement of superior maize hybrids under the All India Coordinated Research Project (AICRP) on Maize rely on multi-environment evaluation integrating grain yield, maturity, and agronomic performance. Interpretation of large multi-environment datasets is often complex, time-consuming, and susceptible to subjectivity, highlighting the need for objective and reproducible decision-support tools. This study evaluated the effectiveness of REMATTOOL-R (Relative Maturity Adjustment Tool in R) in validating the existing hybrid advancement framework adopted under the AICRP on Maize. Methods Multi-environment trial data from the National Initial Varietal Trial (NIVT)-Late conducted during Kharif 2020–21 across five locations representing the Central West Zone (CWZ) of India were analysed. The dataset comprised 45 entries, including 40 experimental hybrids, four commercial checks, and one filler entry. REMATTOOL-R integrated grain yield with days to 50% anthesis, grain moisture at harvest, and harvested plant stand to facilitate simultaneous evaluation of grain yield, maturity, and adaptation-related traits. Least-square means generated from mixed-model analysis were used to identify superior hybrids based on a predefined grain yield superiority threshold (≥5%) over the standard check while maintaining comparable maturity and agronomic performance. Results REMATTOOL-R enabled rapid visualization and integrated assessment of multiple agronomic traits, allowing objective identification of superior hybrids. Five experimental hybrids—PM 21109L (Entry 30), R8050 (Entry 35), PM 21111L (Entry 32), BIO 978 (Entry 4), and DKC 9226 (Entry 9)—recorded ≥5% higher grain yield than the standard check Bio 9682 while maintaining statistically comparable days to 50% anthesis, grain moisture at harvest, and harvested plant stand. All five hybrids identified by REMATTOOL-R corresponded with the official AICRP decisions for advancement from NIVT to Advanced Varietal Trial-I (AVT-I), while three hybrids (R8050, PM 21111L, and DKC 9226) progressed further to AVT-II during subsequent testing cycles, confirming the reliability of the analytical framework. Discussion The findings demonstrate that REMATTOOL-R provides an efficient, transparent, and reproducible framework for the simultaneous evaluation of grain yield, maturity, and adaptation-related traits in maize multi-environment trials. By complementing the existing AICRP hybrid evaluation procedure, the tool facilitates objective advancement decisions and reduces subjectivity associated with manual interpretation of complex datasets. REMATTOOL-R therefore represents a valuable decision-support approach for coordinated maize breeding programmes and has considerable potential for application in large-scale hybrid evaluation systems.
Climate change will substantially impact agriculture, requiring the development of new crop cultivars adapted for resilience to future stresses. Developing a new crop cultivar is a decades-long process. Therefore climate change considerations must already be incorporated into breeding programs. National breeding programs have typically involved field trials in a single country, which has functioned satisfactorily during periods of stable climate variability. However, it is widely acknowledged that when field trial information across borders is made available, it can significantly aid plant breeding programs. This paper presents a pan-Nordic multi-environment dataset that consolidates national trial data and data from VCU (Value for Cultivation and Use) trials for spring barley, red clover, and potato, during 1970–2024. The dataset harmonizes information on yield across diverse environments, enabling comparative analyses and cross-regional assessments. By providing a unified resource for the Nordic region, this dataset supports the development of more resilient crop varieties and facilitates data-driven breeding strategies under changing climatic conditions.
Nora R. Aasen, Sybil A. Herrera-Foessel, S. M. Vandeskog et al.· Scientific Data· 0 citations
Maize is crucial a cereal, widely grown globally, and has significant potential to improve the livelihoods of millions of people in developing countries. High-yielder and well adapted maize hybrids are a valuable approach to improve productivity. The study was proposed with the objectives of (i) evaluating and selecting the high-performing hybrids in terms of yield and yield-related traits for the highland agroecology of Ethiopia and (ii) identifying representative and/or discriminative highland maize hybrids' testing locations and stable hybrids across environments. During the 2023 cropping season, twenty-seven genotypes and three hybrid checks were evaluated in a row-column design with three replications across six locations. Analysis of variance across locations revealed significant differences in genotype, environment, and genotype-by-environment interaction for all measured traits, except for silking date, plant height, and ear aspect. Hybrids such as 3XH2000090 (10.2 t ha-1) and 3XH2000110 (10.1 t ha-1) were high-yielding with a yield advantage of 16.5 % over the best hybrid checks. The GGE biplot analysis identified hybrids such as 3XH2000051, 3XH2000058, 3XH2000091, and 3XH2000097, which exhibited comparable yield performance across environments. The AMMI analysis also supported this finding, identifying 3XH2000051, 3XH2000091, 3XH2000090, 3XH2000035, and SXH1800174 hybrids with high yield and stability across environments. Considering mean grain yield and stability, these hybrids could be verified and released for wider environments. Adet, Jimma, and Holeta were the discriminating and representative testing environments used to identify hybrids that excel in particular locations. Ambo, Kulumsa, and Haramaya University were highly representative mega-environments. When resources are limited, these representative environments could be used interchangeably to evaluate maize hybrids for the highland agroecologies of the country.
K. Sadessa, Abenezer Abebe, Misgana Merga et al.· Turkish Journal of Agricultu...· 0 citations
The development of high-yielding and well-adapted maize hybrids is essential for ensuring the sustainability of maize production in Indonesia. This study aimed to evaluate yield performance, adaptability, stability and downy mildew resistance of the hybrid candidate BAY233 across different agroecological environments. Multilocation trials were conducted during 2023-2024 planting season at ten testing locations representing major maize-production regions in Indonesia. The experiment was arranged in a randomized complete block design with six replications. BAY233 evaluated alongside with BAY234 and two commercial check varieties, BISI959 and NK7328. Yield performance was analyzed using analysis of variance (ANOVA), while genotype x environment (GxE) interaction and stability were assessed using the Additive Main Effects and Multiplicative Interaction (AMMI) model. Resistance to downy mildew was evaluated through artificial inoculation using Peronosclerospora philippinensis and P. maydis. BAY233 produced grain yields ranging from 8.43 to 12.40 t ha⁻¹ across test locations. Although its stability was slightly lower than that of BAY234 but still categorized as promising hybrid due to its performance. BAY233 demonstrated superior yield performance and broad adaptability. Furthermore, BAY233 exhibited strong resistance to downy mildew, as indicated by its low incidence under artificial inoculation test. These findings suggest that BAY233 is a promising hybrid candidate for commercial release and cultivation in Indonesia owning to its high yield potential, broad adaptability, and disease resistance.
Eko Budi Andriyanto, Muhammad Azrai· Gunung Djati Conference Seri...· 0 citations
In Nepal, hybrid maize is increasingly popular in the Terai region due to rising demand for maize grains, higher yield potential, and superior agronomic performance. The purpose of this study was to assess the agronomic performance, yield stability, and adaptability of maize hybrids. A total of 23 single cross maize hybrids was evaluated at four locations in the Terai region of Nepal: Tarahara, Parwanipur, Rampur, and Khajura. The trial was laid-out in randomized complete block design with three replicates per location during the winter season of 2023-24. Flowering traits, growth, and yield-related traits were recorded following the protocols developed by CIMMYT. Analysis of variance was performed using ADEL-R, correlation analysis with PBTools, and stability analysis with GEA-R software. Results indicated significant genotype, environment, and genotype × environment interactions for all traits, with heritability estimates ranging from 0.78 to 0.96. The grain yield varied across locations, with Rampur recorded the highest average of 8.05 t ha-1. RML108/RL2118, CML161/RML96, and RML36/RML2244 were the top performing hybrids with grain yield of 8.13, 7.93, and 7.85 t ha-1 respectively. RML108/RL2118 identified as high yielding and stable (Pi=1.10) hybrid, RL143/RML96 as moderate in yield but excellent stability (bi=-0.10), while Sultan (bi=1.98) and RML62/RML2 (bi=1.69) showed poor stability despite high mean yield. The positive genetic correlations observed between days to anthesis with grain yield (r=0.31), ear position with grain yield (r=0.57), and kernel weight with grain yield (r=0.92). This study highlighted the value of multi-location testing to identify region-specific hybrids that are resilient to local environmental conditions. The results indicate that the identified hybrids possess high yield potential and can be promoted as candidate hybrids for winter season in the Terai region.
Mahendra Prasad Tripathi, B. Adhikari, Jiban Shrestha et al.· Nepal Agriculture Research J...· 0 citations
Bread wheat is a vital crop for Ethiopia’s food security, with expanding cultivation driven by variety development efforts and strategic national initiatives. This study presents a multi-environment trial (MET) analysis to evaluate the genetic performance of 140 unique bread wheat genotypes, using data from 20 trials conducted across Ethiopia’s major wheat-growing regions. Each trial was designed using a row-column design (RCD) arranged in a rectangular array of plots. Four traits —grain yield (GYLD), hectoliter weight (HLW), days to heading (DTH), and thousand kernel weight (TKW) were considered in the analysis. We fitted a factor analytic linear mixed model (FAMM) based on a one-stage approach. Spatial models were fitted to account for field trends, while factor analytic (FA) model was used to model genotype-by-environment (G × E) effects. Model comparisons showed that the FA model consistently outperformed the diagonal (DIAG) model across all traits, offering greater flexibility capturing the variation in G × E effects. Heritability estimates were generally high, especially for DTH, which also showed strong genetic correlations across environments highlighting its reliability as a selection trait. In contrast, yield-related traits exhibited complex G × E patterns and variable heritability, reflecting their polygenic nature and environmental sensitivity. While the highest-yielding check variety, Balcha, retained its lead in GYLD, three genotypes—EBW202104, EBW202110, and EBW193155—demonstrated stable and competitive performance across environments for multiple traits, particularly grain quality and maturity. This makes them strong candidates for varietal release and registration for commercial production in Ethiopia. Future research should integrate genomic and environmental data.
N. Geleta, Tarekegn Argaw, Bayisa Asefa et al.· Discover Agriculture· 0 citations
A timely and reliable system of maize yield forecasting well in advance is prime emphasis to farmers and other people who are dependent on cereal crop. The best model was generated using maize field experiment trial which was conducted at Dorbasta union of Gobindagonj upazila, Gaibandha during two consecutive Rabi crops growing season 2018-19 and 2019-20. Randomized Complete Block Design (RCBD) along with five treatments (or varieties) and three replications were considered for maize yield performance. The agronomical and weather parameters and also, satellite data (Landsat 8 OLI) were used for the required maize field experiment. We found that Normalized Difference Vegetation Index (NDVI) was strongly positively correlated with the weather variables in this study. Stepwise regression method was applied for generating best estimated model. Best estimated model (Backward elimination) showed that only five controlled variables which were variety 5 (BHM 13), 1000 grain weight, diameter of cob, plant height and NDVI that were factors to the yield of maize. The developed maize yield forecast model (ideal model) including agronomical, weather and satellite data give the better results of yield estimation at regional level on the basis of best model criterion. Therefore, the ideal model used in specific region including all types of data that gives more precise result on maize yield or production that should be more significant and reliable in national level. So, the researcher, policymaker can use this maize yield prediction model forty to fifty days earlier of harvesting time.
Bangladesh J. Agril. Res. 48(4): 433-449, December 2023
N. Mohammad, MA Islam, MM Rahman et al.· Bangladesh Journal of Agricu...· 0 citations
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