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Maize (Zea mays L.) is one of the most widely cultivated grain crops globally. In sub-Saharan Africa (SSA), it plays an important role in ensuring both food and income security for smallholder farmers. This study was conducted to (i) assess the performances of testcross hybrids constituted from maize lethal necrosis (MLN) tolerant doubled haploid (DH) lines under various management conditions; (ii) estimate the combining ability effects and determine the nature of gene action in the DH lines; and (iii) identify DH lines and testcross hybrids for resistance to MLN, high grain yield, and other important traits. Eleven DH lines were crossed with 11 single-cross testers using the line-by-tester mating design, and 115 successful testcross hybrids were generated. These hybrids, along with five commercial check hybrids, were evaluated across four optimum management conditions, two MLN artificial inoculations, and one managed drought environment in Kenya. Under each management condition, the effects of genotypes, environments, and genotype-by-environment interactions were significant for grain yield (GY) and most other traits. Hybrids T1/L3, T10/L3, and T11/L3 exhibited higher grain yields under at least two management conditions. A combining ability analysis revealed that additive gene effects were more important than non-additive effects for GY and most other traits, except for leaf senescence (SEN) and MLN disease severity score. DH line L3 exhibited a desirable general combining ability (GCA) effect for GY, while L5 was the best general combiner for anthesis date (AD) and plant height (PH) across all management conditions. DH lines L2, L6, and L7 showed negative GCA effects for MLN disease severity. Single-cross testers T11 and T10 were good general combiners for GY under all management conditions. Hybrids T2/L11, T9/L10, and T2/L10 demonstrated high specific combining ability (SCA) effects for GY under all conditions. This study identified DH lines and testers with favorable GCA effects for grain yield, MLN resistance, and other agronomic traits that can be used in breeding programs to develop high-yielding and MLN-resistant maize varieties. Better-performing testcross hybrids identified in the current study could be verified through on-farm testing and released for commercial production to replace MLN-susceptible, low-yield hybrids grown in the target ecologies. [ABSTRACT FROM AUTHOR]

Increasing the whole-kernel methionine [MET; 2-amino-4-(methylthio)-butyric acid] level of maize (Zea mays L.) would improve the quality of the grain for poultry feed. Our objective was to increase the MET levels of A632, B73, Mo17, A632JMo17, B73/A632, and B73/Mo17, with the high-MET inbred BSSS53 as a donor parent. Backcross-derived lines were evaluated per se in trials during 3 yr, and hybrids among subsets of these lines were evaluated in three replicates during 1998. Methionine levels of the best A632 recoveries were increased by 17% after three backcrosses and 11% after four backcrosses. Methionine levels of the best B73 recoveries were increased by 20% after three backcrosses and 17% after four backcrosses. In the Mo17 genetic background, MET levels of the best recoveries were increased by 55% after three backcrosses and 31% after four backcrosses. Crosses among the highest MET backcross-derived lines produced hybrids with significant 23 to 43% increases in whole-kernel MET compared with corresponding control hybrids. Significant increases in the MET levels of experimental hybrids indicate that BSSS53 is a useful donor source of whole-kernel MET and that selection within normal dent maize germplasm can be effectively utilized to produce high-MET hybrids., MAIZE IS THE primary energy-supplying grain for animal feed in the USA. The nutritional quality of maize protein is poor because of deficiencies of the essential amino acids lysine, tryptophan, [...]

As one of the important concepts in conventional quantitative genetics and breeding, genetic gain can be defined as the amount of increase in performance that is achieved annually through artificial selection. To develop products that meet the increasing demand of mankind, especially for food and feed, in addition to various industrial uses, breeders are challenged to enhance the potential of genetic gain continuously, at ever higher rates, while they close the gaps that remain between the yield potential in breeders' demonstration trials and the actual yield in farmers' fields. Factors affecting genetic gain include genetic variation available in breeding materials, heritability for traits of interest, selection intensity, and the time required to complete a breeding cycle. Genetic gain can be improved through enhancing the potential and closing the gaps, which has been evolving and complemented with modern breeding techniques and platforms, mainly driven by molecular and genomic tools, combined with improved agronomic practice. Several key strategies are reviewed in this article. Favorable genetic variation can be unlocked and created through molecular and genomic approaches including mutation, gene mapping and discovery, and transgene and genome editing. Estimation of heritability can be improved by refining field experiments through well-controlled and precisely assayed environmental factors or envirotyping, particularly for understanding and controlling spatial heterogeneity at the field level. Selection intensity can be significantly heightened through improvements in the scale and precision of genotyping and phenotyping. The breeding cycle time can be shortened by accelerating breeding procedures through integrated breeding approaches such as marker-assisted selection and doubled haploid development. All the strategies can be integrated with other widely used conventional approaches in breeding programs to enhance genetic gain. More transdisciplinary approaches, team breeding, will be required to address the challenge of maintaining a plentiful and safe food supply for future generations. New opportunities for enhancing genetic gain, a high efficiency breeding pipeline, and broad-sense genetic gain are also discussed prospectively. [ABSTRACT FROM AUTHOR]

Productivity of 283 clinical dietitians employed in 40-bed to 1,200-bed medical centers and community and general hospitals nationwide was measured from a survey Stepwise multiple linear regression statistical analyses were used to analyze data. Five measures of productivity were developed; hours in non-patient care divided by total hours worked and the activity level in activities nonproductive in relation to the job description activities produced the better models for measuring productivity of the clinical dietitian. Activity levels measured in the study showed high-level task activities (consuming a large amount of work time) were conducting individual diet instructions, conducting nutrition assessments, reviewing and recording in medical records, and supervising support personnel. Taking anthropometric measurements, participating in medical rounds, attending professional conferences, and purchasing and dispensing specialized products were found to be low-level task activities (consuming little work time). The average amount of time spent on the job per week was 42.7 hours with a mean of 20.4 hours spent in direct patient care, 13.6 hours in indirect patient care, and 7.3 hours in nonpatient care. J Am Diet Assoc 89:490-493, 1989., As a result of the federal government's 1983 enactment of the prospective payment system based on Diagnostic Related Groups, much interest has been directed toward improved management of costs throughout [...]

Maize lethal necrosis (MLN), caused by co-infection of maize chlorotic mottle virus and sugarcane mosaic virus, can lead up to 100% yield loss. Identification and validation of genomic regions can facilitate marker assisted breeding for resistance to MLN. Our objectives were to identify marker-trait associations using genome wide association study and assess the potential of genomic prediction for MLN resistance in a large panel of diverse maize lines. A set of 1400 diverse maize tropical inbred lines were evaluated for their response to MLN under artificial inoculation by measuring disease severity or incidence and area under disease progress curve (AUDPC). All lines were genotyped with genotyping by sequencing (GBS) SNPs. The phenotypic variation was significant for all traits and the heritability estimates were moderate to high. GWAS revealed 32 significantly associated SNPs for MLN resistance (at p < 1.0 × 10−6). For disease severity, these significantly associated SNPs individually explained 3–5% of the total phenotypic variance, whereas for AUDPC they explained 3–12% of the total proportion of phenotypic variance. Most of significant SNPs were consistent with the previous studies and assists to validate and fine map the big quantitative trait locus (QTL) regions into few markers' specific regions. A set of putative candidate genes associated with the significant markers were identified and their functions revealed to be directly or indirectly involved in plant defense responses. Genomic prediction revealed reasonable prediction accuracies. The prediction accuracies significantly increased with increasing marker densities and training population size. These results support that MLN is a complex trait controlled by few major and many minor effect genes. [ABSTRACT FROM AUTHOR]

Although long-term genetic gain has been achieved through increasing use of modern breeding methods and technologies, the rate of genetic gain needs to be accelerated to meet humanity's demand for agricultural products. In this regard, genomic selection (GS) has been considered most promising for genetic improvement of the complex traits controlled by many genes each with minor effects. Livestock scientists pioneered GS application largely due to livestock's significantly higher individual values and the greater reduction in generation interval that can be achieved in GS. Large-scale application of GS in plants can be achieved by refining field management to improve heritability estimation and prediction accuracy and developing optimum GS models with the consideration of genotype-by-environment interaction and non-additive effects, along with significant cost reduction. Moreover, it would be more effective to integrate GS with other breeding tools and platforms for accelerating the breeding process and thereby further enhancing genetic gain. In addition, establishing an open-source breeding network and developing transdisciplinary approaches would be essential in enhancing breeding efficiency for small- and medium-sized enterprises and agricultural research systems in developing countries. New strategies centered on GS for enhancing genetic gain need to be developed., (© 2019 The Authors.)