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  • 1
    Online Resource
    Online Resource
    Relationship between Grain Yield and Quality Traits under Optimum and Low-Nitrogen Stress Environments in Tropical Maize
    MDPI AG ; 2022
    In:  Agronomy Vol. 12, No. 2 ( 2022-02-10), p. 438-
    Details
    In: Agronomy, MDPI AG, Vol. 12, No. 2 ( 2022-02-10), p. 438-
    Abstract: Breeding for nitrogen use efficiency (NUE) is important to deal with food insecurity and its effect on grain quality, particularly protein. A total of 1679 hybrids were evaluated in 16 different trials for grain yield (GY), grain quality traits (protein, starch and oil content) and kernel weight (KW) under optimum and managed low soil nitrogen fields in Kiboko, Kenya, from 2011 to 2014. The objectives of our study were to understand (i) the effect of low soil N stress on GY and quality traits, (ii) the relationship between GY and quality traits under each soil management condition and (iii) the relationship of traits with low-N versus optimum conditions. From the results, we observed the negative effects of low N on GY, KW and the percentage of protein content, and a positive effect on the percentage of starch content. The correlation between GY and all quality traits was very weak under both soil N conditions. GY had a strong relationship with KW under both optimum and low soil N conditions. Protein and starch content was significantly negative under both optimum and low-N conditions. There was no clear relationship among quality traits under optimum and low N, except for oil content. Therefore, it seems feasible to simultaneously improve GY along with quality traits under both optimum and low-N conditions, except for oil content. However, the negative trend observed between GY (starch) and protein content suggests the need for the regular monitoring of protein and starch content to identify varieties that combine both high GY and acceptable quality. Finally, we recommend further research with a few tropical maize genotypes contrasting for NUE to understand the relationship between the change in grain quality and NUE under low-N conditions.
    Type of Medium: Online Resource
    ISSN: 2073-4395
    URL: Article
    DOI: 10.3390/agronomy12020438
    Language: English
    Publisher: MDPI AG
    Publication Date: 2022
    detail.hit.zdb_id: 2607043-1
    SSG: 23
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  • 2
    Online Resource
    Online Resource
    Identifying Quality Protein Maize Inbred Lines for Improved Nutritional Value of Maize in Southern Africa
    MDPI AG ; 2022
    In:  Foods Vol. 11, No. 7 ( 2022-03-22), p. 898-
    Details
    In: Foods, MDPI AG, Vol. 11, No. 7 ( 2022-03-22), p. 898-
    Abstract: Malnutrition, as a result of deficiency in essential nutrients in cereal food products and consumption of a poorly balanced diet, is a major challenge facing millions of people in developing countries. However, developing maize inbred lines that are high yielding with enhanced nutritional traits for hybrid development remains a challenge. This study evaluated 40 inbred lines: 26 quality protein maize (QPM) lines, nine non-QPM lines, and five checks (three QPM lines and two non-QPM lines) in four optimum environments in Zimbabwe and South Africa. The objective of the study was to identify good-quality QPM inbred lines for future hybrid breeding efforts in order to increase the nutritional value of maize. The QPM lines had a lower protein content (7% lower) than that of the non-QPM lines but had 1.9 times more tryptophan and double the quality index. The lysine- and tryptophan-poor α-zein protein fraction was 41% lower in QPM than in non-QPM, with a subsequent increase in γ-zein. There was significant variation within the QPM inbred lines for all measured quality characteristics, indicating that the best lines can be selected from this material without a yield penalty. QPM lines that had both high protein and tryptophan levels, which can be used as parents for highly nutritious hybrids, were identified.
    Type of Medium: Online Resource
    ISSN: 2304-8158
    URL: Article
    DOI: 10.3390/foods11070898
    Language: English
    Publisher: MDPI AG
    Publication Date: 2022
    detail.hit.zdb_id: 2704223-6
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  • 3
    Online Resource
    Online Resource
    Yield Limiting Factors to Food Barley Production in Ethiopia
    Informa UK Limited ; 1997
    In:  Journal of Sustainable Agriculture Vol. 10, No. 2-3 ( 1997-04-22), p. 97-113
    Details
    In: Journal of Sustainable Agriculture, Informa UK Limited, Vol. 10, No. 2-3 ( 1997-04-22), p. 97-113
    Type of Medium: Online Resource
    ISSN: 1044-0046 , 1540-7578
    URL: Article
    DOI: 10.1300/J064v10n02_09
    Language: English
    Publisher: Informa UK Limited
    Publication Date: 1997
    detail.hit.zdb_id: 2701490-3
    detail.hit.zdb_id: 2060629-1
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  • 4
    Online Resource
    Online Resource
    Genetic architecture of maize chlorotic mottle virus and maize lethal necrosis through GWAS, linkage analysis and genomic prediction in tropical maize germplasm
    Springer Science and Business Media LLC ; 2019
    In:  Theoretical and Applied Genetics Vol. 132, No. 8 ( 2019-8), p. 2381-2399
    Details
    In: Theoretical and Applied Genetics, Springer Science and Business Media LLC, Vol. 132, No. 8 ( 2019-8), p. 2381-2399
    Type of Medium: Online Resource
    ISSN: 0040-5752 , 1432-2242
    URL: Article
    DOI: 10.1007/s00122-019-03360-x
    RVK:
    WA 15000
    Language: English
    Publisher: Springer Science and Business Media LLC
    Publication Date: 2019
    detail.hit.zdb_id: 1478966-8
    SSG: 12
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  • 5
    Online Resource
    Online Resource
    Effectiveness of Genomic Prediction of Maize Hybrid Performance in Different Breeding Populations and Environments
    Oxford University Press (OUP) ; 2012
    In:  G3 Genes|Genomes|Genetics Vol. 2, No. 11 ( 2012-11-01), p. 1427-1436
    Details
    In: G3 Genes|Genomes|Genetics, Oxford University Press (OUP), Vol. 2, No. 11 ( 2012-11-01), p. 1427-1436
    Abstract: Genomic prediction is expected to considerably increase genetic gains by increasing selection intensity and accelerating the breeding cycle. In this study, marker effects estimated in 255 diverse maize (Zea mays L.) hybrids were used to predict grain yield, anthesis date, and anthesis-silking interval within the diversity panel and testcross progenies of 30 F2-derived lines from each of five populations. Although up to 25% of the genetic variance could be explained by cross validation within the diversity panel, the prediction of testcross performance of F2-derived lines using marker effects estimated in the diversity panel was on average zero. Hybrids in the diversity panel could be grouped into eight breeding populations differing in mean performance. When performance was predicted separately for each breeding population on the basis of marker effects estimated in the other populations, predictive ability was low (i.e., 0.12 for grain yield). These results suggest that prediction resulted mostly from differences in mean performance of the breeding populations and less from the relationship between the training and validation sets or linkage disequilibrium with causal variants underlying the predicted traits. Potential uses for genomic prediction in maize hybrid breeding are discussed emphasizing the need of (1) a clear definition of the breeding scenario in which genomic prediction should be applied (i.e., prediction among or within populations), (2) a detailed analysis of the population structure before performing cross validation, and (3) larger training sets with strong genetic relationship to the validation set.
    Type of Medium: Online Resource
    ISSN: 2160-1836
    URL: Article
    DOI: 10.1534/g3.112.003699
    Language: English
    Publisher: Oxford University Press (OUP)
    Publication Date: 2012
    detail.hit.zdb_id: 2629978-1
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  • 6
    Online Resource
    Online Resource
    Heritability and Associations among Grain Yield and Quality Traits in Quality Protein Maize (QPM) and Non-QPM Hybrids
    MDPI AG ; 2022
    In:  Plants Vol. 11, No. 6 ( 2022-03-08), p. 713-
    Details
    In: Plants, MDPI AG, Vol. 11, No. 6 ( 2022-03-08), p. 713-
    Abstract: Maize (Zea mays L.) is the main staple cereal food crop cultivated in southern Africa. Interactions between grain yield and biochemical traits can be useful to plant breeders in making informed decisions on the traits to be considered in breeding programs for high grain yield and enhanced quality. The objectives of this study were to estimate the heritability of grain yield and its related traits, as well as quality traits, and determine the association between quality protein maize (QPM) with non-QPM crosses. Grain yield, and agronomic and quality trait data were obtained from 13 field trials in two countries, for two consecutive seasons. Significant genotypic and phenotypic correlations were recorded for grain yield with protein content (rG = 0.38; rP = 0.25), and tryptophan with oil content (rG = 0.58; rP = 0.25), and negative rG and rP correlations were found for protein with tryptophan content and grain yield with tryptophan content. Path analysis identified ear aspect, ears per plant, and starch as the major traits contributing to grain yield. It is recommended that ear aspect should be considered a key secondary trait in breeding for QPM hybrids. The negative association between grain yield and tryptophan, and between protein and tryptophan, will make it difficult to develop hybrids with high grain yield and high tryptophan content. Hence, it is recommended that gene pyramiding should be considered for these traits.
    Type of Medium: Online Resource
    ISSN: 2223-7747
    URL: Article
    DOI: 10.3390/plants11060713
    Language: English
    Publisher: MDPI AG
    Publication Date: 2022
    detail.hit.zdb_id: 2704341-1
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  • 7
    Online Resource
    Online Resource
    Evaluating Maize Genotype Performance under Low Nitrogen Conditions Using RGB UAV Phenotyping Techniques
    MDPI AG ; 2019
    In:  Sensors Vol. 19, No. 8 ( 2019-04-16), p. 1815-
    Details
    In: Sensors, MDPI AG, Vol. 19, No. 8 ( 2019-04-16), p. 1815-
    Abstract: Maize is the most cultivated cereal in Africa in terms of land area and production, but low soil nitrogen availability often constrains yields. Developing new maize varieties with high and reliable yields using traditional crop breeding techniques in field conditions can be slow and costly. Remote sensing has become an important tool in the modernization of field-based high-throughput plant phenotyping (HTPP), providing faster gains towards the improvement of yield potential and adaptation to abiotic and biotic limiting conditions. We evaluated the performance of a set of remote sensing indices derived from red–green–blue (RGB) images along with field-based multispectral normalized difference vegetation index (NDVI) and leaf chlorophyll content (SPAD values) as phenotypic traits for assessing maize performance under managed low-nitrogen conditions. HTPP measurements were conducted from the ground and from an unmanned aerial vehicle (UAV). For the ground-level RGB indices, the strongest correlations to yield were observed with hue, greener green area (GGA), and a newly developed RGB HTPP index, NDLab (normalized difference Commission Internationale de I´Edairage (CIE)Lab index), while GGA and crop senescence index (CSI) correlated better with grain yield from the UAV. Regarding ground sensors, SPAD exhibited the closest correlation with grain yield, notably increasing in its correlation when measured in the vegetative stage. Additionally, we evaluated how different HTPP indices contributed to the explanation of yield in combination with agronomic data, such as anthesis silking interval (ASI), anthesis date (AD), and plant height (PH). Multivariate regression models, including RGB indices (R2 〉 0.60), outperformed other models using only agronomic parameters or field sensors (R2 〉 0.50), reinforcing RGB HTPP’s potential to improve yield assessments. Finally, we compared the low-N results to the same panel of 64 maize genotypes grown under optimal conditions, noting that only 11% of the total genotypes appeared in the highest yield producing quartile for both trials. Furthermore, we calculated the grain yield loss index (GYLI) for each genotype, which showed a large range of variability, suggesting that low-N performance is not necessarily exclusive of high productivity in optimal conditions.
    Type of Medium: Online Resource
    ISSN: 1424-8220
    URL: Article
    DOI: 10.3390/s19081815
    Language: English
    Publisher: MDPI AG
    Publication Date: 2019
    detail.hit.zdb_id: 2052857-7
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  • 8
    Online Resource
    Online Resource
    Genetic Gains in Grain Yield Through Genomic Selection in Eight Bi‐parental Maize Populations under Drought Stress
    Wiley ; 2015
    In:  Crop Science Vol. 55, No. 1 ( 2015-01), p. 154-163
    Details
    In: Crop Science, Wiley, Vol. 55, No. 1 ( 2015-01), p. 154-163
    Abstract: Genomic selection incorporates all the available marker information into a model to predict genetic values of breeding progenies for selection. The objective of this study was to estimate genetic gains in grain yield from genomic selection (GS) in eight bi‐parental maize populations under managed drought stress environments. In each population, 148 to 300 F 2:3 (C 0 ) progenies were derived and crossed to a single‐cross tester from a complementary heterotic group. The resulting testcrosses of each population were evaluated under two to four managed drought stress and three to four well‐watered conditions in different locations and genotyped with 191 to 286 single nucleotide polymorphism (SNP) markers. The top 10% families were selected from C 0 using a phenotypic selection index and were intermated to form C 1 . Selections both at C 1 and C 2 were based on genomic estimated breeding values (GEBVs). The best lines from C 0 were also advanced using a pedigree selection scheme. For genetic gain studies, a total of 55 entries representing the eight populations were crossed to a single‐cross tester, and evaluated in four managed drought stress environments. Each population was represented by bulk seed containing equal amounts of seed of C 0 , C 1 , C 2 , C 3 , parents, F 1 s, and lines developed via pedigree selection. Five commercial checks were included for comparison. The average gain from genomic selection per cycle across eight populations was 0.086 Mg ha –1 . The average grain yield of C 3 –derived hybrids was significantly higher than that of hybrids derived from C 0 . Hybrids derived from C 3 produced 7.3% (0.176 Mg ha –1 ) higher grain yield than those developed through the conventional pedigree breeding method. The study demonstrated that genomic selection is more effective than pedigree‐based conventional phenotypic selection for increasing genetic gains in grain yield under drought stress in tropical maize.
    Type of Medium: Online Resource
    ISSN: 0011-183X , 1435-0653
    URL: Article
    DOI: 10.2135/cropsci2014.07.0460
    Language: English
    Publisher: Wiley
    Publication Date: 2015
    detail.hit.zdb_id: 1480918-7
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  • 9
    Online Resource
    Online Resource
    Gains in Maize Genetic Improvement in Eastern and Southern Africa: I. CIMMYT Hybrid Breeding Pipeline
    Wiley ; 2017
    In:  Crop Science Vol. 57, No. 1 ( 2017-01), p. 168-179
    Details
    In: Crop Science, Wiley, Vol. 57, No. 1 ( 2017-01), p. 168-179
    Abstract: Monitoring of genetic gain in crop genetic improvement programs is necessary to measure the efficiency of the program. Periodic measurement of genetic gain also allows the efficiency of new technologies incorporated into a program to be quantified. Genetic gain within the International Maize and Wheat Improvement Centre (CIMMYT) breeding program for eastern and southern Africa were estimated using time series of maize ( Zea mays L.) hybrids. A total of 67 of the best‐performing hybrids from regional trials from 2000 to 2010 were selected to form an era panel and evaluated in 32 trials in eight locations across six countries in eastern and southern Africa. Treatments included optimal management, managed and random drought stress, low‐nitrogen (N) stress and maize streak virus (MSV) infestation. Genetic gain was estimated as the slope of the regression of grain yield on the year of hybrid release. Genetic gain under optimal conditions, managed drought, random drought, low N, and MSV were estimated to have increased by 109.4, 32.5, 22.7, 20.9 and 141.3 kg ha −1 yr −1 , respectively. These results are comparable with genetic gain in maize yields in other regions of the world. New technologies to further increase the rate of genetic gain in maize breeding for eastern and southern Africa are also discussed.
    Type of Medium: Online Resource
    ISSN: 0011-183X , 1435-0653
    URL: Article
    DOI: 10.2135/cropsci2016.05.0343
    Language: English
    Publisher: Wiley
    Publication Date: 2017
    detail.hit.zdb_id: 1480918-7
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  • 10
    Online Resource
    Online Resource
    Genotype × Environment Interaction of Quality Protein Maize Hybrids under Contrasting Management Conditions in Eastern and Southern Africa
    Wiley ; 2019
    In:  Crop Science Vol. 59, No. 4 ( 2019-07), p. 1576-1589
    Details
    In: Crop Science, Wiley, Vol. 59, No. 4 ( 2019-07), p. 1576-1589
    Abstract: Drought and low soil fertility are major abiotic stresses limiting yield of maize ( Zea mays L.) in eastern and southern Africa. The present study was undertaken to determine genotype by environment interaction (GEI) and grain yield stability of quality protein maize (QPM) experimental hybrids. A total of 108 hybrids, including two commercial checks, were tested across 13 environments under drought, low N, and optimal environments in Ethiopia, Zambia, and Zimbabwe in 2015 and 2016. Environment, hybrid, and hybrid × environment interaction effects were significant ( P 〈 0.01) across environments and within management conditions. The highest yielding hybrids were H40, H41, H56, and H58 under optimum management; H2, H9, H40, and H87 under low N; H3, H10, H11, and H94 under drought; and H9, H10, H40, H56, and H94 across environments. The GEI and grain yield stability analysis using different models indicated that additive main effects and multiplicative interaction (AMMI), and genotypic main effects plus GEI (GGE) models were more efficient and precise compared to the linear regression stability model in identifying high‐yielding hybrids with stable performance. Based on the AMMI and GGE biplots, the most promising QPM hybrids were identified under different management conditions. Hybrid H40 was the most outstanding genotype under various management conditions and could be used in breeding programs or commercialized in target areas. Gwebi optimum and Bako low N were identified as the most discriminating and representative environments under the contrasting management conditions. In general, results of the present study depicted the possibility of developing high‐yielding and stable QPM hybrids for stress and nonstress conditions.
    Type of Medium: Online Resource
    ISSN: 0011-183X , 1435-0653
    URL: Article
    DOI: 10.2135/cropsci2018.12.0722
    Language: English
    Publisher: Wiley
    Publication Date: 2019
    detail.hit.zdb_id: 1480918-7
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