The African Plant Breeders of Tomorrow

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Effect of leaf area on maize productivity

Maize (Zea mays L) leaves provide energy for growth and development. Increases in plant densities the past 75 years have contributed to increased maize grain yields. No recorded change has been observed in leaf area per plant during this period, but some change may have occurred. Plant density increases are associated with in¬creases in leaf area per-unit of land mass. Grain yield increases resulted from hybrids with improved tolerance to higher plant densities. Recently developed maize hybrids have upright leaves and smaller tassels allowing more light to penetrate the leaf canopy. Tolerance to increased plant density is directly related to intra and inter-plant shading plus changes in leaf area per plant may change leaf canopy structure. To evaluate the concept, maize leaf area affects grain yield, we developed high- and low leaf area hybrids. Objectives were to evaluate productivity of high and low leaf area maize hybrids at three high plant densities for two years. Averaged over three plant densities low leaf area hybrids produced significantly more grain than high leaf area hybrids. Low leaf area hybrids tolerated higher plant density better than high leaf area hybrids. Results indicate low leaf area hybrids are superior in several maize productivity traits

Acute mountain sickness.

Acute mountain sickness (AMS) is a clinical syndrome occurring in otherwise healthy normal individuals who ascend rapidly to high altitude. Symptoms develop over a period ofa few hours or days. The usual symptoms include headache, anorexia, nausea, vomiting, lethargy, unsteadiness of gait, undue dyspnoea on moderate exertion and interrupted sleep. AMS is unrelated to physical fitness, sex or age except that young children over two years of age are unduly susceptible. One of the striking features ofAMS is the wide variation in individual susceptibility which is to some extent consistent. Some subjects never experience symptoms at any altitude while others have repeated attacks on ascending to quite modest altitudes. Rapid ascent to altitudes of 2500 to 3000m will produce symptoms in some subjects while after ascent over 23 days to 5000m most subjects will be affected, some to a marked degree. In general, the more rapid the ascent, the higher the altitude reached and the greater the physical exertion involved, the more severe AMS will be. Ifthe subjects stay at the altitude reached there is a tendency for acclimatization to occur and symptoms to remit over 1-7 days

Expression of Nutritional Traits in Vegetable Cowpea Grown under Various South African Agro-Ecological Conditions

Cowpea (Vigna unguiculata L.), a traditional legume food crop indigenous to Africa, has potential as both a vegetable and grain crop in contributing to dietary diversity to support health and address malnutrition, especially for those relying heavily on wheat, maize, and rice. The expression of nutritional traits (protein content and concentrations of iron (Fe), zinc (Zn), and manganese (Mn)) in cowpea leaves was evaluated over diverse agro-ecologies of South Africa and typical agronomic practices of smallholder farmers. The genotypes evaluated displayed genetic variation for all four traits. The mean values of Fe, Zn, Mn and protein content varied from 33.11 to 69.03 mg.100.g−1; 4.00 to 4.70 mg.100.g−1; and 14.40 to 19.63 mg.100.g−1 and 27.98 to 31.98%, respectively. The correlation analysis revealed significant degree of positive association between protein and Zn (r = 0.20), while negative associations were observed between Mn and protein (−0.46) and between Mn and Fe (r = −0.27). Furthermore, the expression of these important nutrient traits was influenced by the climatic conditions represented by six environments (location by year combinations) as is typical of ‘quality’ traits. Additionally, genotype-by-environment interaction effects were detected, suggesting that local soil properties and soil health may play a role in nutritional content in plants, perhaps particularly for legume crops that rely on symbiotic relationships with soil bacterial populations to fix nitrogen, which is crucial to protein formation. Further studies are needed to understand how to coordinate and align agronomic and soil management practices in vegetable cowpea production, especially those workable for the smallholder farmer, to realize the full genetic potential and nutritional value of improved vegetable cowpea varieties

Nonparametric Method for Genomics-Based Prediction of Performance of Quantitative Traits Involving Epistasis in Plant Breeding

<div><p>Genomic selection (GS) procedures have proven useful in estimating breeding value and predicting phenotype with genome-wide molecular marker information. However, issues of high dimensionality, multicollinearity, and the inability to deal effectively with epistasis can jeopardize accuracy and predictive ability. We, therefore, propose a new nonparametric method, pRKHS, which combines the features of supervised principal component analysis (SPCA) and reproducing kernel Hilbert spaces (RKHS) regression, with versions for traits with no/low epistasis, pRKHS-NE, to high epistasis, pRKHS-E. Instead of assigning a specific relationship to represent the underlying epistasis, the method maps genotype to phenotype in a nonparametric way, thus requiring fewer genetic assumptions. SPCA decreases the number of markers needed for prediction by filtering out low-signal markers with the optimal marker set determined by cross-validation. Principal components are computed from reduced marker matrix (called supervised principal components, SPC) and included in the smoothing spline ANOVA model as independent variables to fit the data. The new method was evaluated in comparison with current popular methods for practicing GS, specifically RR-BLUP, BayesA, BayesB, as well as a newer method by Crossa <em>et al.,</em> RKHS-M, using both simulated and real data. Results demonstrate that pRKHS generally delivers greater predictive ability, particularly when epistasis impacts trait expression. Beyond prediction, the new method also facilitates inferences about the extent to which epistasis influences trait expression.</p> </div

Mean percentage of variation (across the 12 simulation scenarios) explained by the top 18 SPCs with pRHKS, which together explain 70% of the total variation.

<p>Mean percentage of variation (across the 12 simulation scenarios) explained by the top 18 SPCs with pRHKS, which together explain 70% of the total variation.</p

Applying pRKHS to real life scenarios, Pearson correlation coefficients between estimated breeding value (EBV) and phenotype obtained from ten-fold CV using genotypes and phenotypes of barley lines in year 2007 and prediction based on genotypes of different lines in year 2008 and 2009 implemented for grain yield (GYD) and plant height (PHT) for each of the 6 statistical methods.

<p>The optimal number of markers contributing to phenotypic variation and percent of variations explained by the included SPCs were shown for pRKHS methods; results were averaged across five repeated fittings. Optimal cosine value was 0.3 for pRKHS-E across all datasets.</p