Stressful environments such as salinity, drought, and high temperature (heat) cause alterations in a wide range of physiological, biochemical, and molecular processes in plants. Photosynthesis, the most fundamental and intricate physiological process in all green plants, is also severely affected in all its phases by such stresses. Since the mechanism of photosynthesis involves various components, including photosynthetic pigments and photosystems, the electron transport system, and CO2 reduction pathways, any damage at any level caused by a stress may reduce the overall photosynthetic capacity of a green plant. Details of the stress-induced damage and adverse effects on different types of pigments, photosystems, components of electron transport system, alterations in the activities of enzymes involved in the mechanism of photosynthesis, and changes in various gas exchange characteristics, particularly of agricultural plants, are considered in this review. In addition, we discussed also progress made during the last two decades in producing transgenic lines of different C3 crops with enhanced photosynthetic performance, which was reached by either the overexpression of C3 enzymes or transcription factors or the incorporation of genes encoding C4 enzymes into C3 plants. We also discussed critically a current, worldwide effort to identify signaling components, such as transcription factors and protein kinases, particularly mitogen-activated protein kinases (MAPKs) involved in stress adaptation in agricultural plants.

Photosynthesis under stressful environments: An overview

A reviewer for the Journal of the Royal Statistical Society of England comments \"This is the first book covering in one volume all important topics in genetical statistics.\" Written to provide basic probability ideas in terms of genetic situations, since the theory of genetics is a probability theory; to give a definitive treatment of applications of these ideas to genetic theory; and to describe statistical methods appropriate to the data models that are developed.

An Introduction to Genetic Statistics

Nitrogen is the most limiting nutrient for crop production in many of the world's agricultural areas and its efficient use is important for the economic sustainability of cropping systems Furthermore, the dynamic nature of N and its propensity for loss from soil‐plant systems creates a unique and challenging environment for its efficient management Crop response to applied N and use efficiency are important criteria for evaluating crop N requirements for maximum economic yield Recovery of N in crop plants is usually less than 50% worldwide Low recovery of N in annual crop is associated with its loss by volatilization, leaching, surface runoff, denitrification, and plant canopy Low recovery of N is not only responsible for higher cost of crop production, but also for environmental pollution Hence, improving N use efficiency (NUE) is desirable to improve crop yields, reducing cost of production, and maintaining environmental quality To improve N efficiency in agriculture, integrated N management strategies that take into consideration improved fertilizer along with soil and crop management practices are necessary Including livestock production with cropping offers one of the best opportunities to improve NUE Synchrony of N supply with crop demand is essential in order to ensure adequate quantity of uptake and utilization and optimum yield This paper discusses N dynamics in soil‐plant systems, and outlines management options for enhancing N use by annual crops

Enhancing nitrogen use efficiency in crop plants

Some important physiological selection criteria for salt tolerance in plants

Heritability (H) on a progeny mean basis is frequently estimated in recurrent selection experiments for the purpose of estimating the expected progress from family selection; however, appropriate measures of precision have been developed for only a few heritability estimators. The objective of this research was to develop a measure of precision for H for certain balanced linear models. Exact confidence intervals for H were derived and are not restricted to a specific experimental design. The confidence intervals were applied to sorghum [Sorghum bicolor (L.) Moench] half-sib family data.

An exact confidence interval for heritability on a progeny mean basis

Heterosis (F₁ — MP), useful heterosis (F₁ — ‘Deltapine 16’), and gene action estimates were obtained at four locations from the six groups of populations involving ‘Deltapine 16’ (Gossypium hirsutum L.) crossed with six cultivars (‘Q1,’ ‘Del Cerro 183,’ ‘FTA 263-20,’ ‘Acala 3080,’ ‘Mo277-396,’ and ‘Stoneville 603’). Heterosis for lint yield was detected in each cross. F₁ yields ranged from 92 to 115% of that of Deltapine 16, while F₂ yields ranged from 86 to 107%. Increases in boll size were partially responsible for the increased productivity of the hybrids. Parental lines originating from outside the Delta in general produced hybrid-by-location mean squares greater than those from Delta-derived parents. These interactions were related to the higher relative performance of Deltapine 16 at the locations with higher yield. The potential benefits from utilizing heterosis were especially evident when all traits were considered simultaneous. Four of the six F₁'s produced yields equal to or greater than Deltapine 16 while also having longer, stronger, and finer fiber. Heterosis for each of the nine traits studied was determined primarily by dominance gene action. Of the 54 possible tests additive-by-additive epistasis was significant in only four instances, and even in those four cases the mean squares for epistasis were much smaller than were those for additive or dominance effects. Additive effects predominated for lint percentage, seed index, fiber strength, fiber elongation, and fiber fineness. However, dominance was greater for boll size. Both were involved for lint yield and fiber length. In every case there were some crosses that did not fit the general trend. The interaction of additive effects with locations was usually of greater magnitude than the dotninance-bylocation interaction. There were 28, 13, and 1 instances of significant additive, dominance, and additive by additive epistatic by location interactions, respectively. Interactions with locations were the greatest practical limitation for lint yield selection and they were less important for the other traits. The interactions with locations were particularly large for the cross involving Acala 3080. Breeding implications are discussed.

Heterosis and Gene Action in Cotton, Gossypium hirsutum L.1

Chromosome identities were assigned to 15 linkage groups of the RFLP joinmap developed from four intraspecific cotton (Gossypium hirsutum L.) populations with different genetic backgrounds (Acala, Delta, and Texas Plains). The linkage groups were assigned to chromosomes by deficiency analysis of probes in the previously published joinmap, based on genomic DNA from hypoaneuploid chromosome substitution lines. These findings were integrated with QTL identification for multiple fiber and yield traits. Overall results revealed the presence of 63 QTLs on five different chromosomes of the A subgenome (chromosomes-03, � 07, � 09, � 10, and � 12) and 29 QTLs on the three different D subgenome (chromosomes-14 Lo, � 20, and the long arm of � 26). Linkage group-1 (chromosome-03) harbored 26 QTLs, covering 117 cM with 54 RFLP loci. Linkage group-2, (the long arm of chromosome-26) harbored 19 QTLs, covering 77.6 cM with 27 RFLP loci. Approximately 49% of the putative 92 QTLs for agronomic and fiber quality traits were placed on the above two major joinmap linkage groups, which correspond to just two different chromosomes, indicating that cotton chromosomes may have islands of high and low meiotic recombination like some other eukaryotic organisms. In addition, it reveals highly recombined and putative gene abundant regions in the cotton genome. QTLs for fiber quality traits in certain regions are located between two RFLP markers with an average of less than one cM (0.4;0.6 Mb) and possibly represent targets for map-based cloning. Identification of chromosomal location of RFLP markers common to different intra- and interspecificpopulations will facilitate development of portable framework markers, as well as genetic and physical mapping of the cotton genome.

Chromosomal Assignment of RFLP Linkage Groups Harboring Important QTLs on an Intraspecific Cotton (Gossypium hirsutum L.) Joinmap

An RFLP genetic linkage joinmap was constructed from four different mapping populations of cotton ( Gossypium hirsutum L.). Genetic maps from two of the four populations have been previously reported. The third genetic map was constructed from 199 bulk-sampled plots of an F(2.3) (HQ95-6x'MD51ne') population. The map comprises 83 loci mapped to 24 linkage groups with an average distance between markers of 10.0 centiMorgan (cM), covering 830.1 cM or approximately 18% of the genome. The fourth genetic map was developed from 155 bulk-sampled plots of an F(2.3) (119- 5 sub-okrax'MD51ne') population. This map comprises 56 loci mapped to 16 linkage groups with an average distance between markers of 9.3 cM, covering 520.4 cM or approximately 11% of the cotton genome. A core of 104 cDNA probes was shared between populations, yielding 111 RFLP loci. The constructed genetic linkage joinmap from the above four populations comprises 284 loci mapped to 47 linkage groups with the average distance between markers of 5.3 cM, covering 1,502.6 cM or approximately 31% of the total recombinational length of the cotton genome. The linkage groups contained from 2 to 54 loci each and ranged in distance from 1.0 to 142.6 cM. The joinmap provided further knowledge of competitive chromosome arrangement, parental relationships, gene order, and increased the potential to map genes for the improvement of the cotton crop. This is the first genetic linkage joinmap assembled in G. hirsutum with a core of RFLP markers assayed on different genetic backgrounds of cotton populations (Acala, Delta, and Texas plain). Research is ongoing for the identification of quantitative trait loci for agronomic, physiological and fiber quality traits on these maps, and the identification of RFLP loci lineage for G. hirsutum from its diploid progenitors (the A and D genomes).

/pdf/rflp-genetic-linkage-maps-from-four-f-2-3-populations-and-a-5d6mdfcxcs.pdf

RFLP genetic linkage maps from four F(2.3) populations and a joinmap of Gossypium hirsutum L.

Breakup of Linkage Blocks in Cotton, Gossypium hirsutum L.1

Insufficient potassium (K) nutrition produces detrimental effects on cotton (Gossypium hirsutum L.) lint yield and fiber quality. To further understand the deleterious effects caused by K deficiency, a 2‐yr (1991 and 1992) field study was conducted to determine how dry matter partitioning and nutrient concentrations of various plant tissues for the cotton genotypes, ‘DES 119’ and ‘MD 51 ne’, were altered by varying the application rate of fertilizer K and nitrogen (N). All plots received a preplant application of 112 kg N ha‐1, and half of the plots were later sidedressed with an additional 38 kg N ha‐1. Within each N treatment, half the plots received 112 kg K ha‐1, preplant incorporated, with the remaining plots not receiving any fertilizer K. Dry matter harvests were taken three times in 1991 and two times in 1992. At cutout (slowing of vegetative growth and flowering), plants that received K fertilization had a 14% more leaf area index (LAI), a 3% increase in the number of main stem nodes, an...

William R. Meredith

Papers

Heterosis and Gene Action in Cotton, Gossypium hirsutum L.1

Chromosomal Assignment of RFLP Linkage Groups Harboring Important QTLs on an Intraspecific Cotton (Gossypium hirsutum L.) Joinmap

RFLP genetic linkage maps from four F(2.3) populations and a joinmap of Gossypium hirsutum L.

Breakup of Linkage Blocks in Cotton, Gossypium hirsutum L.1

Dry matter production, nutrient uptake, and growth of cotton as affected by potassium fertilization