Management and Breeding Strategies for the improvement of grain and Oil Quality

LUIS ADOLFO NAZARENO AGUIRREZABAL; MARTRE, P.; PEREYRA IRUJO, G.; IZQUIERDO, N.; ALLARD, V.

Crop Physiology: Applications for Genetic Improvement and Agronomy.

Elsevier

Lugar: No Informado; Año: 2009; p. 389 - 423

The quality of a harvested organ can most simply be defi ned as its suitability to the intended market or processing and product manufacture. The term quality may therefore encompass many criteria, as the compositional (chemical) and textural (biomechanical) requirements may vary from one product to another. For example, for oilseed sunfl ower it includes the potential industrial yield, the nutritional value of the oil and its stability ( Box 1 ). For bread wheat, it includes the milling performance, the dough rheology, the baking quality, the nutritional value for humans or animals and its suitability for storage. Moreover, for a given end product, the relative importance of quality attributes changes along the market chain from grower to consumer. For example, elevated oil or protein concentration is of economic importance for farmers where a premium is paid for these attributes, whereas high baking quality in wheat or oxidative stability in oilseeds are of economic importance to food manufacturers. Also for a given crop species, quality criteria vary depending on the product end use ( Box 1 ). For example, high-protein fl ours are required for leavened bread or pasta, while their low-protein counterparts are desirable for biscuits, crackers, cakes or oriental noodles. An effi cient agro-industrial production system, therefore, needs to know the year-to-year variation in composition of raw materials that can be obtained in different regions or under different management practices. The identifi cation of novel genes or loci with major effects on quality traits has resulted in new cultivars with improved quality ( Velasco and Fernandez-Martinez, 2002 ; DePauw et al., 2007 , Chapter 14). Nonetheless, dealing with genotype-by-environment (G  E) interactions, and with pleiotropic effects (i.e. trait-bytrait interactions) remains a major diffi culty in plant breeding, especially for grain quality ( de la Vega and Chapman, 2001 ). A physiological perspective provides useful insights into G  E, as shown in this and other chapters of this book (Section 3.1 in Chapter 11; Sections 3 and 4 in Chapter 10). Further in this chapter, we argue that grain oil and protein concentration and composition are primarily determined at the crop level, and cannot be correctly understood or predicted by extrapolating from the individual plant to the population. We will show how physiological concepts and methods classically applied to yield analysis can be used to investigate and model the genetic and environmental determinants of grain oil and protein concentration and composition, for example, identifi cation of critical periods (Section 5 in Chapter 12; Section 3.2.2 in Chapter 15), kinetics of biomass and nitrogen accumulation and partitioning (Chapters 7 and 8). Models can range from detailed mechanistic descriptions to simple response curves to environmental variables, which are ‘meta-mechanisms’ at the plant or crop level ( Tardieu, 2003 ). If models are robust enough, one set of parameters represents one genotype ( Hammer et al., 2006 ; Chapter 10), and thus they can be used to analyse complex traits with G  E and pleitropic effects. chapters of this book (Section 3.1 in Chapter 11; Sections 3 and 4 in Chapter 10). Further in this chapter, we argue that grain oil and protein concentration and composition are primarily determined at the crop level, and cannot be correctly understood or predicted by extrapolating from the individual plant to the population. We will show how physiological concepts and methods classically applied to yield analysis can be used to investigate and model the genetic and environmental determinants of grain oil and protein concentration and composition, for example, identifi cation of critical periods (Section 5 in Chapter 12; Section 3.2.2 in Chapter 15), kinetics of biomass and nitrogen accumulation and partitioning (Chapters 7 and 8). Models can range from detailed mechanistic descriptions to simple response curves to environmental variables, which are ‘meta-mechanisms’ at the plant or crop level ( Tardieu, 2003 ). If models are robust enough, one set of parameters represents one genotype ( Hammer et al., 2006 ; Chapter 10), and thus they can be used to analyse complex traits with G  E and pleitropic effects. interactions) remains a major diffi culty in plant breeding, especially for grain quality ( de la Vega and Chapman, 2001 ). A physiological perspective provides useful insights into G  E, as shown in this and other chapters of this book (Section 3.1 in Chapter 11; Sections 3 and 4 in Chapter 10). Further in this chapter, we argue that grain oil and protein concentration and composition are primarily determined at the crop level, and cannot be correctly understood or predicted by extrapolating from the individual plant to the population. We will show how physiological concepts and methods classically applied to yield analysis can be used to investigate and model the genetic and environmental determinants of grain oil and protein concentration and composition, for example, identifi cation of critical periods (Section 5 in Chapter 12; Section 3.2.2 in Chapter 15), kinetics of biomass and nitrogen accumulation and partitioning (Chapters 7 and 8). Models can range from detailed mechanistic descriptions to simple response curves to environmental variables, which are ‘meta-mechanisms’ at the plant or crop level ( Tardieu, 2003 ). If models are robust enough, one set of parameters represents one genotype ( Hammer et al., 2006 ; Chapter 10), and thus they can be used to analyse complex traits with G  E and pleitropic effects. chapters of this book (Section 3.1 in Chapter 11; Sections 3 and 4 in Chapter 10). Further in this chapter, we argue that grain oil and protein concentration and composition are primarily determined at the crop level, and cannot be correctly understood or predicted by extrapolating from the individual plant to the population. We will show how physiological concepts and methods classically applied to yield analysis can be used to investigate and model the genetic and environmental determinants of grain oil and protein concentration and composition, for example, identifi cation of critical periods (Section 5 in Chapter 12; Section 3.2.2 in Chapter 15), kinetics of biomass and nitrogen accumulation and partitioning (Chapters 7 and 8). Models can range from detailed mechanistic descriptions to simple response curves to environmental variables, which are ‘meta-mechanisms’ at the plant or crop level ( Tardieu, 2003 ). If models are robust enough, one set of parameters represents one genotype ( Hammer et al., 2006 ; Chapter 10), and thus they can be used to analyse complex traits with G  E and pleitropic effects.  E) interactions, and with pleiotropic effects (i.e. trait-bytrait interactions) remains a major diffi culty in plant breeding, especially for grain quality ( de la Vega and Chapman, 2001 ). A physiological perspective provides useful insights into G  E, as shown in this and other chapters of this book (Section 3.1 in Chapter 11; Sections 3 and 4 in Chapter 10). Further in this chapter, we argue that grain oil and protein concentration and composition are primarily determined at the crop level, and cannot be correctly understood or predicted by extrapolating from the individual plant to the population. We will show how physiological concepts and methods classically applied to yield analysis can be used to investigate and model the genetic and environmental determinants of grain oil and protein concentration and composition, for example, identifi cation of critical periods (Section 5 in Chapter 12; Section 3.2.2 in Chapter 15), kinetics of biomass and nitrogen accumulation and partitioning (Chapters 7 and 8). Models can range from detailed mechanistic descriptions to simple response curves to environmental variables, which are ‘meta-mechanisms’ at the plant or crop level ( Tardieu, 2003 ). If models are robust enough, one set of parameters represents one genotype ( Hammer et al., 2006 ; Chapter 10), and thus they can be used to analyse complex traits with G  E and pleitropic effects. chapters of this book (Section 3.1 in Chapter 11; Sections 3 and 4 in Chapter 10). Further in this chapter, we argue that grain oil and protein concentration and composition are primarily determined at the crop level, and cannot be correctly understood or predicted by extrapolating from the individual plant to the population. We will show how physiological concepts and methods classically applied to yield analysis can be used to investigate and model the genetic and environmental determinants of grain oil and protein concentration and composition, for example, identifi cation of critical periods (Section 5 in Chapter 12; Section 3.2.2 in Chapter 15), kinetics of biomass and nitrogen accumulation and partitioning (Chapters 7 and 8). Models can range from detailed mechanistic descriptions to simple response curves to environmental variables, which are ‘meta-mechanisms’ at the plant or crop level ( Tardieu, 2003 ). If models are robust enough, one set of parameters represents one genotype ( Hammer et al., 2006 ; Chapter 10), and thus they can be used to analyse complex traits with G  E and pleitropic effects.  E, as shown in this and other chapters of this book (Section 3.1 in Chapter 11; Sections 3 and 4 in Chapter 10). Further in this chapter, we argue that grain oil and protein concentration and composition are primarily determined at the crop level, and cannot be correctly understood or predicted by extrapolating from the individual plant to the population. We will show how physiological concepts and methods classically applied to yield analysis can be used to investigate and model the genetic and environmental determinants of grain oil and protein concentration and composition, for example, identifi cation of critical periods (Section 5 in Chapter 12; Section 3.2.2 in Chapter 15), kinetics of biomass and nitrogen accumulation and partitioning (Chapters 7 and 8). Models can range from detailed mechanistic descriptions to simple response curves to environmental variables, which are ‘meta-mechanisms’ at the plant or crop level ( Tardieu, 2003 ). If models are robust enough, one set of parameters represents one genotype ( Hammer et al., 2006 ; Chapter 10), and thus they can be used to analyse complex traits with G  E and pleitropic effects. E and pleitropic effects.