José Luis Araus

Breeding for Yield Potential and Stress Adaptation in Cereals

The need to accelerate breeding for increased yield potential and better adaptation to drought and other abiotic stresses is an issue of increasing urgency. As the population continues to grow rapidly, the pressure on resources (mainly untouched land and water) is also increasing, and potential climate change poses further challenges. We discuss ways to improve the efficiency of crop breeding through a better physiological understanding by both conventional and molecular methods. Thus the review highlights the physiological basis of crop yield and its response to stresses, with special emphasis on drought. This is not just because physiology forms the basis of proper phenotyping, one of the pillars of breeding, but because a full understanding of physiology is also needed, for example, to design the traits targeted by molecular breeding approaches such as marker-assisted selection (MAS) or plant transformation or the way these traits are evaluated. Most of the information in this review deals with cereals, since they include the worlds main crops, however, examples from other crops are also included. Topics covered by the review include the conceptual framework for identifying secondary traits associated with yield potential and stress adaptation, and how to measure these secondary traits in practice. The second part of the review deals with the real role of molecular breeding for complex traits from a physiological perspective. This part examines current developments in MAS and quantitative trait loci (QTL) detection as well as plant transformation. Emphasis is placed on the current limitations of these molecular approaches to improving stress adaptation and yield potential. The essay ends by presenting some ideas regarding future avenues for crop breeding given the current and possible future challenges, and on a multidisciplinary approach where physiological knowledge and proper phenotyping play a major role.

Prediction of genetic values of quantitative traits in plant breeding using pedigree and molecular markers

The availability of dense molecular markers has made possible the use of genomic selection (GS) for plant breeding. However, the evaluation of models for GS in real plant populations is very limited. This article evaluates the performance of parametric and semiparametric models for GS using wheat (Triticum aestivum L.) and maize (Zea mays) data in which different traits were measured in several environmental conditions. The findings, based on extensive cross-validations, indicate that models including marker information had higher predictive ability than pedigree-based models. In the wheat data set, and relative to a pedigree model, gains in predictive ability due to inclusion of markers ranged from 7.7 to 35.7%. Correlation between observed and predictive values in the maize data set achieved values up to 0.79. Estimates of marker effects were different across environmental conditions, indicating that genotype × environment interaction is an important component of genetic variability. These results indicate that GS in plant breeding can be an effective strategy for selecting among lines whose phenotypes have yet to be observed.

Water and nitrogen conditions affect the relationships of Δ13C and Δ18O to gas exchange and growth in durum wheat

Whereas the effects of water and nitrogen (N) on plant Δ13C have been reported previously, these factors have scarcely been studied for Δ18O. Here the combined effect of different water and N regimes on Δ13C, Δ18O, gas exchange, water-use efficiency (WUE), and growth of four genotypes of durum wheat [Triticum turgidum L. ssp. durum (Desf.) Husn.] cultured in pots was studied. Water and N supply significantly increased plant growth. However, a reduction in water supply did not lead to a significant decrease in gas exchange parameters, and consequently Δ13C was only slightly modified by water input. Conversely, N fertilizer significantly decreased Δ13C. On the other hand, water supply decreased Δ18O values, whereas N did not affect this parameter. Δ18O variation was mainly determined by the amount of transpired water throughout plant growth (Tcum), whereas Δ13C variation was explained in part by a combination of leaf N and stomatal conductance (gs). Even though the four genotypes showed significant differences in cumulative transpiration rates and biomass, this was not translated into significant differences in Δ18Os. However, genotypic differences in Δ13C were observed. Moreover, ∼80% of the variation in biomass across growing conditions and genotypes was explained by a combination of both isotopes, with Δ18O alone accounting for ∼50%. This illustrates the usefulness of combining Δ18O and Δ13C in order to assess differences in plant growth and total transpiration, and also to provide a time-integrated record of the photosynthetic and evaporative performance of the plant during the course of crop growth.

Oxygen isotope enrichment (Δ18O) reflects yield potential and drought resistance in maize.

Measurement of stable isotopes in plant dry matter is a useful phenotypic tool for speeding up breeding advance in C(3) crops exposed to different water regimes. However, the situation in C(4) crops is far from resolved, since their photosynthetic metabolism precludes (at least in maize) the use of carbon isotope discrimination. This paper investigates the use of oxygen isotope enrichment (Delta(18)O) as a new secondary trait for yield potential and drought resistance in maize (Zea mays L). A set of tropical maize hybrids developed by the International Maize and Wheat Improvement Center was grown under three contrasting water regimes in field conditions. Water regimes clearly affected plant growth and yield. In accordance with the current theory, a decrease in water input was translated into large decreases in stomatal conductance and increases in leaf temperature together with concomitant (18)O enrichment of plant matter (leaves and kernels). In addition, kernel Delta(18)O correlated negatively with grain yield under well-watered and intermediate water stress conditions, while it correlated positively under severe water stress conditions. Therefore, genotypes showing lower kernel Delta(18)O under well-watered and intermediate water stress had higher yields in these environments, while the opposite trend was found under severe water stress conditions. This illustrates the usefulness of Delta(18)O for selecting the genotypes best suited to differing water conditions.

Shoot δ15N gives a better indication than ion concentration or Δ13C of genotypic differences in the response of durum wheat to salinity

We compared the performance of different physiological traits that reveal genotypic variations in tolerance to salinity in durum wheat. A set of 114 genotypes was grown in hydroponics for over 3 months. Three conditions: control, moderate (12u2009dSu2009m-1) and severe (17u2009dSu2009m-1) salinity, were maintained for nearly 8 weeks before harvest. The genotype biomass in control conditions correlated with the biomass at the two salinity levels. Subsequently, two subsets of 10 genotypes each were selected on the basis of extreme differences in biomass at the two salinity levels while showing relatively similar biomass in control conditions. Carbon isotope discrimination (Δ13C), nitrogen isotope composition (δ15N), and the concentration of nitrogen, phosphorus and several ions (K+, Na+, Ca2+, Mg2+) were analysed in the two subsets for the three treatments. At 12u2009dSu2009m-1, K+ concentration, K+/Na+ ratio, Δ13C and δ15N correlated positively and Na+ correlated negatively with shoot biomass. Under control conditions and at 17u2009dSu2009m-1 no correlation was observed. However, the trait that correlated best with genotypic differences in biomass was δ15N at 12u2009dSu2009m-1. This trait was the first variable chosen at each of the two salinity levels in a stepwise analysis. We consider the possible mechanisms relating δ15N to biomass and the use of this isotopic signature as a selection trait.

How yield relates to ash content, Δ13C and Δ18O in maize grown under different water regimes

BACKGROUND AND AIMSnStable isotopes have proved a valuable phenotyping tool when breeding for yield potential and drought adaptation; however, the cost and technical skills involved in isotope analysis limit its large-scale application in breeding programmes. This is particularly so for Delta(18)O despite the potential relevance of this trait in C(4) crops. The accumulation of minerals (measured as ash content) has been proposed as an inexpensive way to evaluate drought adaptation and yield in C(3) cereals, but little is known of the usefulness of this measure in C(4) cereals such as maize (Zea mays). The present study investigates how yield relates to ash content, Delta(13)C and Delta(18)O, and evaluates the use of ash content as an alternative or complementary criterion to stable isotopes in assessing yield potential and drought resistance in maize.nnnMETHODSnA set of tropical maize hybrids developed by CIMMYT were subjected to different water availabilities, in order to induce water stress during the reproductive stages under field conditions. Ash content and Delta(13)C were determined in leaves and kernels. In addition, Delta(18)O was measured in kernels.nnnKEY RESULTSnWater regime significantly affected yield, ash content and stable isotopes. The results revealed a close relationship between ash content in leaves and the traits informing about plant water status. Ash content in kernels appeared to reflect differences in sink-source balance. Genotypic variation in grain yield was mainly explained by the combination of ash content and Delta(18)O, whilst Delta(13)C did not explain a significant percentage of such variation.nnnCONCLUSIONSnAsh content in leaves and kernels proved a useful alternative or complementary criterion to Delta(18)O in kernels for assessing yield performance in maize grown under drought conditions.

Advances in maize genomics and their value for enhancing genetic gains from breeding

Maize is an important crop for food, feed, forage, and fuel across tropical and temperate areas of the world. Diversity studies at genetic, molecular, and functional levels have revealed that, tropical maize germplasm, landraces, and wild relatives harbor a significantly wider range of genetic variation. Among all types of markers, SNP markers are increasingly the marker-of-choice for all genomics applications in maize breeding. Genetic mapping has been developed through conventional linkage mapping and more recently through linkage disequilibrium-based association analyses. Maize genome sequencing, initially focused on gene-rich regions, now aims for the availability of complete genome sequence. Conventional insertion mutation-based cloning has been complemented recently by EST- and map-based cloning. Transgenics and nutritional genomics are rapidly advancing fields targeting important agronomic traits including pest resistance and grain quality. Substantial advances have been made in methodologies for genomics-assisted breeding, enhancing progress in yield as well as abiotic and biotic stress resistances. Various genomic databases and informatics tools have been developed, among which MaizeGDB is the most developed and widely used by the maize research community. In the future, more emphasis should be given to the development of tools and strategic germplasm resources for more effective molecular breeding of tropical maize products.

Seasonal changes in the photosynthetic capacity and leaf structure of Fatsia japonica leaves grown in a shadehouse

SummaryThe ornamental plant Fatsia japonica Decne & Planch is a cold-hardy species widely utilized in outdoor gardening in temperate regions. Seasonal changes in photosynthetic capacity, leaf anato...

Crop stress management and global climate change.

Part 1: Looking at the past 1. Global change and the origins of agriculture Part 2: Present and future challenges in different agricultural systems 2. Climate change in drylands: from assessment methods to adaptation strategies 3. Agronomic avenues to maximize the benefits of rising atmospheric CO2 concentration in the Asian irrigated rice systems 4. Recent changes in Pampean agriculture: possible new avenues to cope global change challenges 5. Global change challenges for horticultural systems Part 3: Coping with Climate Change 6. The impact of high CO2 on plant abiotic stress tolerance 7. Breeding to improve grain-yield in water-limited environments: The CSIRO experience with wheat 8. Molecular breeding for a changing climate: Bridging ecophysiology and molecular biology 9. Crop management to cope with global change: a systems perspective aided by information technologies Part 4: Integrating efforts in the future 10. The way ahead: from science to policy coordinating efforts in a global world.

Comparative canopy cover estimation using RGB images from UAV and ground

Canopy cover is an important agronomical component for determining grain yield in cereals. Estimates of the canopy cover area of crops may contribute to improving the efficiency of crop management practices and breeding programs. Conventional high resolution RGB cameras can be used to acquire zenithal images taken at ground level or from a UAV (Unmanned Aerial Vehicle). Canopy-image segmentation is complicated in field conditions by numerous factors, including soil, shadows and unexpected objects. Spatial resolution is a key factor for estimating canopy cover area because low spatial resolution may introduce artifacts in the digital image. We propose a comparison of canopy cover segmentation using different spatial resolutions to test the scalability potential of these different techniques. Field trials were carried out during the 2015/2016 crop season in the Arazuri experimental station of INTIA in Navarra, Spain. Three barley genotypes, 10 different N fertilization regimens and three replicates were used in this study. This work uses zenithal RGB images taken from 1 m above the crop and images from the UAV were taken at the intervals of 2 s the during of the flight at distances of 25, 50 and 100 m. Images from the ground were taken at 1 m above the canopy. The CerealScanner plugin for FIJI (Fiji is Just ImageJ) was used to calculate the BreedPix RGB vegetation indices. The comparative results demonstrate the algorithm’s effectiveness in scaling through high correlation values between images with different spatial resolutions taken from the UAV and images taken from the ground.