So far in this course we have dealt entirely with the evolution of characters that are controlled by simple Mendelian inheritance at a single locus. There are notes on the course website about gametic disequilibrium and how allele frequencies change at two loci simultaneously, but we didn’t discuss them. In every example we’ve considered we’ve imagined that we could understand something about evolution by examining the evolution of a single gene. That’s the domain of classical population genetics. For the next few weeks we’re going to be exploring a field that’s actually older than classical population genetics, although the approach we’ll be taking to it involves the use of population genetic machinery. If you know a little about the history of evolutionary biology, you may know that after the rediscovery of Mendel’s work in 1900 there was a heated debate between the “biometricians” (e.g., Galton and Pearson) and the “Mendelians” (e.g., de Vries, Correns, Bateson, and Morgan). Biometricians asserted that the really important variation in evolution didn’t follow Mendelian rules. Height, weight, skin color, and similar traits seemed to

Human biochemical genetics

Impatti, adattamento e vulnerabilita Le cause e le responsabilita dei cambiamenti climatici sono state trattate sul numero di ottobre della rivista Cda. Approfondiamo l’argomento presentando il documento: “Cambiamenti climatici 2007: impatti, adattamento e vulnerabilita” votato ad aprile 2007 dal secondo gruppo di lavoro del Comitato Intergovernativo sui Cambiamenti Climatici (Intergovernmental Panel on Climate Change). Si tratta del secondo di tre documenti che compongono il quarto rapporto sui cambiamenti climatici.

/pdf/climate-change-2007-impacts-adaptation-and-vulnerability-31xc1cf4d3.pdf

Climate Change 2007: Impacts, Adaptation and Vulnerability.

OUTPU T 29 OUTPU T 30 OUTPU T 31 OUTPU T 32 OUTPU T 25 OUTPU T 26 OUTPU T 27 OUTPU T 28 OUTPU T 21 OUTPU T 22 OUTPU T 23 OUTPU T 24 OUTPU T 17 OUTPU T 18 OUTPU T 19 OUTPU T 20 OUTPU T 13 OUTPU T 14 OUTPU T 15 OUTPU T 16 OUTPU T 9 OUTPU T 10 OUTPU T 11 OUTPU T 12 OUTPU T 5 OUTPU T 6 OUTPU T 7 OUTPU T 8 OUTPU T 1 OUTPU T 2 OUTPU T 3 OUTPU T 4 29 30 31 32 25 26 27 28 21 22 23 24 17 18 19 20 13 14 15 16 9

A User’s Guide

There is a pressing need to improve the water-use efficiency of rain-fed and irrigated crop production. Breeding crop varieties with higher water-use efficiency is seen as providing part of the solution. Three key processes can be exploited in breeding for high water-use efficiency: (i) moving more of the available water through the crop rather than it being wasted as evaporation from the soil surface or drainage beyond the root zone or being left behind in the root zone at harvest; (ii) acquiring more carbon (biomass) in exchange for the water transpired by the crop, i.e. improving crop transpiration efficiency; (iii) partitioning more of the achieved biomass into the harvested product. The relative importance of any one of these processes will vary depending on how water availability varies during the crop cycle. However, these three processes are not independent. Targeting specific traits to improve one process may have detrimental effects on the other two, but there may also be positive interactions. Progress in breeding for improved water-use efficiency of rain-fed wheat is reviewed to illustrate the nature of some of these interactions and to highlight opportunities that may be exploited in other crops as well as potential pitfalls. For C3 species, measuring carbon isotope discrimination provides a powerful means of improving water-use efficiency of leaf gas exchange, but experience has shown that improvements in leaf-level water-use efficiency may not always translate into higher crop water-use efficiency or yield. In fact, the reverse has frequently been observed. Reasons for this are explored in some detail. Crop simulation modelling can be used to assess the likely impact on water-use efficiency and yield of changing the expression of traits of interest. Results of such simulations indicate that greater progress may be achieved by pyramiding traits so that potential negative effects of individual traits are neutralized. DNA-based selection techniques may assist in such a strategy.

Breeding for high water-use efficiency.

Principles of Plant Breeding

Recent review articles in this journal have compared the relative merits of two prominent statistical models for analyzing yield-trial data: Additive main effects and multiplicative interaction (AMMI) and genotype main effects and genotype × environment interaction (GGE). This review addresses more than 20 issues that require clarification after controversial statements and contrasting conclusions have appeared in those recent reviews. The AMMI2 mega-environment display incorporates more of the genotype main effect and captures more of the genotype × environment (GE) interaction than does GGE2, thereby displaying the which-won-where pattern more accurately for complex datasets. When the GE interaction is captured well by one principal component, the AMMI1 display of genotype nominal yields describes winning genotypes and adaptive responses more simply and clearly than the GGE2 biplot. For genotype evaluation within a single mega-environment, a simple scatterplot of mean and stability is more straightforward than the mean vs. stability view of a GGE2 biplot. Diagnosing the most predictively accurate member of a model family is vital for either AMMI or GGE, both for gaining accuracy and delineating mega-environments.

Statistical Analysis of Yield Trials by AMMI and GGE: Further Considerations

The expected move towards more sustainable crop-livestock systems implies wider cultivation of perennial forage legumes. Alfalfa (Medicago sativa subsp. sativa) is the main perennial legume in most temperate regions, especially where farm systems rely largely on forage conservation. White clover (Trifolium repens) and red clover (Trifolium pratense) are dominant in specific regions and farm systems. Although breeding progress for disease and insect resistance has been achieved, these crops have shown lower rates of genetic gain for yield than major grain crops, owing to lower breeding investment, longer selection cycles, impossibility to capitalize on harvest index, outbreeding mating systems associated with severe inbreeding depression, and high interaction of genotypes with cropping conditions and crop utilizations. Increasing yield, persistence, adaptation to stressful conditions (drought; salinity; grazing) and compatibility with companion grasses are major breeding targets. We expect genetic gain for...

Achievements and Challenges in Improving Temperate Perennial Forage Legumes

Cultivar adaptation and recommendation from alfalfa trials in Northern Italy

Europe has become heavily dependent on soya bean imports, entailing trade agreements and quality standards that do not satisfy the European citizen’s expectations. White, yellow and narrow-leafed lupins are native European legumes that can become true alternatives to soya bean, given their elevated and high-quality protein content, potential health benefits, suitability for sustainable production, and acceptability to consumers. Nevertheless, lupin cultivation in Europe remains largely insufficient to guarantee a steady supply to the food industry, which in turn must innovate to produce attractive lupin-based protein-rich foods. Here, we address different aspects of the food supply chain that should be considered for lupin exploitation as a high-value protein source. Advanced breeding techniques are needed to provide new lupin varieties for socio-economically and environmentally sustainable cultivation. Novel processes should be optimized to obtain high-quality, safe lupin protein ingredients, and marketable foods need to be developed and offered to consumers. With such an integrated strategy, lupins can be established as an alternative protein crop, capable of promoting socio-economic growth and environmental benefits in Europe.

http://digital.csic.es/bitstream/10261/124456/1/The_future_of_lupin.pdf

The future of lupin as a protein crop in Europe

Joint regression and Additive Main effects and Multiplicative Interaction (AMMI) models were compared for i) capacity of describing genotype-location (GL) and genotype-environment (GE) interaction effects (environments = location-season combinations), assessed in terms of estimated variance of heterogeneity of genotype regressions and of the sum of the variances of significant interaction principal component (PC) axes, and ii) repeatability between cropping seasons of measures of genotype stability across locations. These measures were Finlay and Wilkinson's regression coefficient for joint regression, and the Euclidean distance from the origin of significant interaction PC axes (D) and the absolute value of PC 1 score (| PC 1 |) for AMMI. Shukla's stability variance (σsup2;) was considered in addition. The study included three data sets for durum wheat, two for maize and one each for bread wheat and oat. Relationships between climatic variables and GL interaction occurrence were also assessed. AMMI proved distinctly more valuable in six data sets for description of GE effects and in four for description of GL effects over seasons. Its superiority was not crop-specific and tended to occur when more, distinct environmental constraints affected genotype responses. When both methods were appropriate, they provided a similar ordination of sites and genotypes for GL effects. The models that adequately described GL interaction over seasons generally provided also stability measures that were moderately repeatable between seasons. D was better repeatable than | PC 1 | and σ& 2; in a few cases. Ordination of locations on GL interaction PC 1 tended to be consistent both between wheat and between maize data sets having either no seasons or no genotypes in common.

Paolo Annicchiarico

Papers

Statistical Analysis of Yield Trials by AMMI and GGE: Further Considerations

Achievements and Challenges in Improving Temperate Perennial Forage Legumes

Cultivar adaptation and recommendation from alfalfa trials in Northern Italy

The future of lupin as a protein crop in Europe

Joint regression vs AMMI analysis of genotype-environment interactions for cereals in Italy