Phenotypic and genotypic characterization of white maize inbreds, hybrids and synthetics under stress and non-stress environments

Maize is susceptible to biotic and abiotic stresses. The most important abiotic stresses in Africa are drought and low soil fertility. Aflatoxin contamination is a potential problem in areas facing drought and low soil fertility. Three studies were conducted to evaluate maize germplasm for tolerance to stress. In the first study, fifteen maize inbred lines crossed in a diallel were evaluated under drought, low N stress, and well-watered conditions at six locations in three countries to estimate general (GCA) and specific combining ability (SCA), investigate genotype x environment interaction, and estimate genetic diversity and its relationship with grain yield and heterosis. GCA effects were not significant for grain yield across environments. Lines with good GCA effect for grain yield were P501 and CML258 across stresses. Lines CML339, CML341, and SPLC7-F had good GCA effects for anthesis silking interval across stresses. Additive genetic effects were more important for grain yield under drought and well-watered conditions. Heterosis estimates were highest in stress environments. Clustering based on genetic distance calculated using marker data from AFLP, RFLP, and SSRs grouped lines according to origin. Genetic distance was positively correlated with grain yield and specific combining ability. In the second study, synthetic hybrids were evaluated at seven locations in three countries to estimate GCA and SCA effects under low N stress and optimal conditions and investigate genotype x environment interaction. GCA effects were significant for all traits across low N stress and optimal conditions. The highest yielding synthetic hybrids involved synthetics developed from stress tolerant lines. Synthetics 99SADVIA-# and SYNA00F2 had good GCA for grain yield across low N stress conditions. Heterosis was highly correlated with grain yield. Optimal environments explained more variation than stress environments. The third study evaluated the agronomic performance and aflatoxin accumulation of single and three-way cross white maize hybrids at five locations in Texas. Inbreds CML343, Tx601W, and Tx110 showed positive GCA effects for grain yield. Significant GCA effects for reduced aflatoxin concentration were observed in lines CML269, CML270, and CML78 across locations. Differences in performance between single and three-way crosses hybrids were dependent mostly on the inbred lines

Investigating the nature of GxE interaction under different management systems and yield levels using linear-bilinear models: The case of CIMMYT maize hybrids trials in Eastern Africa

The International Center for Maize and Wheat Improvement(CIMMYT) conducts selection of stress-tolerant genotypes under managed stress conditions. Data sets for this study were from Intermediate to Late Hybrid Trials (ILHT) conducted in five Eastern and Central Africa (ECA) countries from 2008 to 2011. Several trials, which were categorized into four management systems and two yield levels were used for this study. Variance Components, broad sense heritability (H), Site Regression (SREG), Genotypic Regression (GREG) and Factor Analytic (FA) models were fitted. We argue that it is preferable to first fit the fixed effect models before proceeding to the mixed effect model, as the former shows the level of complexity of the GE component and the number of axes required to explain it. The fixed effect model, SREG2, is preferable for trials targeting comparison of hybrids with checks. From the GGE biplots it was noted that the first two principale components (PC) did not account for sufficient percentage of variation for all years. Nevertheless, since PC1 accounted for large percentage of variation than PC2, the plot gives some idea of which hybrids won where. Most importantly, location of genotypes along PC1 can serve for judging yielding potential of the genotypes to guide in selection decision. Equivalence between Finlay - Wilkinson and GREG was established. The few environmental covariables obtained for 2009 were used to fit Partial Least Square (PLS) regression. The result indicated complexity in the GE component, as PLS latent factors accounted for small percentage of variation. It was recommended to use information from SREG2, GREG2 and FA(1) models in order to identify stable genotypes.Keywords: AMMI, Biplot, Factor Analytic Model, GREG, Mixed Effect Model, SREG, Stability

REMATTOOL-R

REMATTOOL-R is a tool that helps breeders to visualize and assess the relationship between yield and several agronomic traits. Users can download the application, an abstract of the paper describing the application, and a sample dataset. The sample dataset includes maize field trial result means of an experiment from the Water Efficient Maize for Africa (WEMA) Project funded by the Bill and Melinda Gates Foundation (BMGF, Grant # OPP1019943) and the Howard Buffet Foundation. The tabulated means are from data obtained from five locations across Kenya

New crop and fodder genotypes for sustainable intensification in semi-arid agro-ecologies of Tanzania

United States Agency for International Developmen

Methodological approach for predicting and mapping the phenological adaptation of tropical maize (Zea mays L.) using multi‑environment trials

Open Access Journal; Published online: 7 Dec 2018Background The phenological development of the maize crop from emergence through flowering to maturity, usually expressed as a rate (i.e. 1/duration), is largely controlled by temperature in the tropics. Maize plant phenological responses vary between varieties and quantifying these responses can help in predicting the timing and duration of critical periods for crop growth that affect the quality and quantity of seed. We used routine multi-environment trials data of diverse tropical maize varieties to: (1) fit 82 temperature dependent phenology models and select the best model for an individual variety, (2) develop a spatial framework that uses the phenology model to predict at landscape level the length of the vegetative and reproductive phases of diverse varieties of maize in different agro-ecologies. Multi-environment trial data of 22 maize varieties from 16 trials in Kenya, Ethiopia, and Sudan was analyzed and the Levenberg–Marquardt algorithm combined with statistical criteria was applied to determine the best temperature-dependent model. Results The Briere model, which is not often used in plant phenology, provided the best fit, with observed and predicted days to flowering showing good agreement. Linking the model with temperature and scaling out through mapping gave the duration from emergence to maturity of different maize varieties in areas where maize could potentially be grown. Conclusion The methodology and framework used in the study provides an opportunity to develop tools that enhance farmers’ ability to predict stages of maize development for efficient crop management decisions and assessment of climate change impacts. This methodology could contribute to increase maize production if used to identify varieties with desired maturity for a specific agro-ecology in in the targeted regions

REMATTOOL-R: a smart tool for identifying superior maize genotypes from multi-environment yield trials

Breeders routinely evaluate many experimental hybrids that may be of different maturities. In maize (Zea mays L.), days to 50% anthesis and percent grain moisture content are used as proxies for relative maturity. The lack of an easy-to-use statistical tool that gives yield potential of all entries in a trial while classifying them into different relative maturity categories in a single visualization makes it difficult to quickly assess superior genotypes. We report on a tool called REMATTOOL-R to aid breeders in visualizing and assessing the relationship between yield and certain agronomic traits, viz., days to anthesis, percent harvest grain moisture content, and number of harvested plants, and help them in advancing experimental hybrids to the next stage. REMATTOOL-R uses either Best Linear Unbiased Estimators (BLUEs) or Best Linear Unbiased Predictors (BLUPs) of yield and agronomic traits from multilocation trials to perform various computations. The various computations produce graphical and tabular visualizations of the relationship between grain yield and days to anthesis, moisture content, and number of harvested plants that can be used to support selection decisions by the breeder. REMATTOOL-R output tables show entries with at least 5% higher yield than the check varieties in the trial. REMATTOOL-R is a robust, simple, user-friendly, and easily comprehensible tool, convenient for identifying superior genotypes during all the trial stages of a maize breeding program. REMATTOOL-R will be useful to breeders and researchers in related disciplines

Africa RISING genetic intensification in Central Tanzania and Zambia

United States Agency for International Developmen