Posts Tagged ‘crop science’

The scientific grand challenges of the 21st century for the crop science society of America

Posted by Carelia Juarez on , in Journal Articles

Published in Crop Science 52(3): 1003-1010, 2012

Joseph G. Lauer, Caron Gala Bijl, Michael A. Grusak, P. Stephen Baenziger, Ken Boote, Sarah Lingle, Thomas Carter, Shawn Kaeppler, Roger Boerma, Georgia Eizenga, Paul Carter, Major Goodman, Emerson Nafziger, Kimberlee Kidwell, Rob Mitchell, Michael D. Edgerton, Ken Quesenberry, and Martha C. Willcox

Crop science is a highly integrative science employing expertise from multiple disciplines to broaden our understanding of agronomic, turf, and forage crops. A major goal of crop science is to ensure an adequate and sustainable production of food, feed, fuel, and fiber for our world’s growing population. The Crop Science Society of America (CSSA) identified key Grand Challenges which, when addressed, will provide the tools, technologies, and solutions required to meet these challenges. The Grand Challenges are: (i) Crop adaptation to climate change: Increase the speed with which agriculture can adapt to climate change by using crop science to address abiotic stresses such as drought and heat. (ii) Resistance to biotic stresses: Increase durability of resistance to biotic stresses that threaten yield and quality of major crops. (iii) Management for resource limited systems: Create novel crop cultivars and management approaches designed for problem soils and low-input farming to increase economic prosperity for farmers and overcome world hunger. (iv) Crop management systems: Create novel crop management systems that are resilient in the face of changes in climate and rural demographics. (v) Biofuels: Develop sustainable biofuel feedstock cropping systems that require minimal land area, optimize production, and improve the environment. (vi) Bioresources: Genotyping the major crop germplasm collections to facilitate identification of gene treasures for breeding and genetics research and deployment of superior genes into adapted germplasm around the globe. These challenges are intended to be dynamic and change as societal needs evolve. Available funding and national prioritization will determine the rate that they will be addressed.

Geostatistical analysis of quality protein maize outcrossing with pollen from adjacent normal endosperm maize varieties

Posted by Carelia Juarez on , in Journal Articles

Published in Crop Science  52 (3): 1235-1245, 2012

Lewis Machida, John Derera, Pangirayi Tongoona, Onisimo Mutanga and John MacRobert

Nutritional advantages of quality protein maize (QPM) (Zea mays L.) over normal endosperm maize (NM) were previously demonstrated by several researchers. However, QPM grain quality loss occurs when a QPM crop receives pollen from NM. This is because the opaque-2 gene allele that confers the QPM trait is recessive. The objective was to estimate outcrossing levels and patterns in QPM growing adjacent to NM. White grain QPM crops were grown on nine blocks of 0.21 ha each surrounded by at least a 10-m band of yellow NM at two sites in Zimbabwe. At maturity 160 samples of five QPM ears each were randomly selected to determine outcrossing. Outcrossing was estimated as percentage of yellow kernels on each ear. Ordinary kriging was used to estimate outcrossing levels in areas that were not sampled. Both prediction and error surfaces were produced for each block using the best ordinary kriging model out of the available 11 in ArcMAP 9.2 computer package. Results indicated that five models (exponential, stable, pentaspherical, rational quadratic, and J-Bessel) predicted outcrossing patterns of the nine experiments. Outcrossing levels were high (63 to 83%) in the peripheral areas of the QPM crops, but less than 20% outcrossing was observed on at least 60% of each of the crop areas with no significant compromise of QPM quality based on a QPM quality index of 0.8. In conclusion, QPM and NM can coexist, and ordinary kriging could be used in visualizing spatial distribution of outcrossing in a QPM crop.

 

Genetic yield gains and changes in associated traits of CIMMYT spring bread wheat in a ‘Historic’ set representing 30 years of breeding

Posted by Carelia Juarez on , in Journal Articles

Published in Crop Science  52 (3): 1123-1131, 2012

M. S. Lopes, M. P. Reynolds, Y. Manes, R. P. Singh, J. Crossa and H. J. Braun

The genetic yield progress of 26 spring wheat (Triticum aestivum L.) advanced lines released by the International Maize and Wheat Improvement Centre (CIMMYT) in the period from 1977 to 2008 was evaluated in the selection environment in Mexico as well as at a set of target environments in Asia and Africa. In Mexico, grain yield progress was significantly linear and about 0.7% yr−1 and yield was associated with fewer days to heading, cooler canopy temperature at grain filling, and increased stay-green and thousand kernel weight. These results suggested that changes in the former traits at least partially explain the yield progress. When genetic yield progress was measured in subsets of sites in Asia and Africa grouped into high, intermediate, and low yielding, the genetic yield progress was 0.9, 0.7, and 0.5% yr−1, respectively. We conclude that there is no evidence that genetic gains to increase yield have slowed down in wheat lines released by CIMMYT.

 

Efficiency of managed-stress screening of elite maize hybrids under drought and low-N for yield under rainfed conditions in Southern Africa

Posted by Carelia Juarez on , in Journal Articles

Published in Crop Science  52 (3): 1011-1020, 2012

Vanessa S. Weber, Albrecht E. Melchinger, Cosmos Magorokosho, Dan Makumbi, Marianne Bänziger and Gary N. Atlin

Maize (Zea mays L.) yields in southern Africa are low, due largely to drought and low-N stress. Selection of stress-tolerant genotypes by CIMMYT is conducted indirectly under managed stress conditions, although the selection efficiency of this approach is not known. A retrospective analysis of 704 elite hybrid trials conducted from 2001 to 2009 was used to determine the relative ability of optimal, low-N, and managed drought trials to predict performance under the conditions of random abiotic stress and low-N fertility usually faced by African farmers. Well-fertilized trials in the rainy season were categorized as having experienced random abiotic stress if mean yield was <3 t ha−1 and the yield–anthesis date correlation was <0.1; otherwise they were classed as optimal. High genetic correlations were estimated between random abiotic stress and low-N or optimal conditions. Heritability was highest under optimal conditions and lowest under random abiotic stress. Indirect selection under low-N and optimal conditions was more efficient than direct selection under random abiotic stress or indirect selection under managed drought, especially for early maturing genotypes, but direct selection was most efficient for predicting performance under low N. Elite maize hybrids tolerant to random abiotic stress can be most efficiently selected under optimal and/or low-N conditions while low-N tolerant genotypes should be selected directly under low N.

Drought Adaptive Traits and Wide Adaptation in Elite Lines Derived from Resynthesized Hexaploid Wheat

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Published in Crop Science 51(4): 1617-1626, 2011

Drought Adaptive Traits and Wide Adaptation in Elite Lines Derived from Resynthesized Hexaploid Wheat

 Marta S. Lopes and Matthew P. Reynolds

It has been shown previously that under drought, synthetic hexaploid derived wheat (Triticum aestivum L.) lines outperformed recurrent parents in part due to increased root mass at depth and better water extraction capacity. A group of four elite synthetic derived (SYN-DER) lines and parents was grown under full irrigation and drought conditions to dissect some of the physiological features conferring tolerance to drought. Synthetic derived wheat lines showed on average a 26% yield increase as compared to the parental hexaploid wheats under terminal drought. Different strategies for drought tolerance were observed, including earliness to flowering, greater root mass at depth, greater water extraction capacity, and increased water use efficiency (WUE) at anthesis. Some degree of independence was identified between these traits when comparing SYN-DER lines suggesting that these traits are regulated by different genes. The elite SYN-DER line ‘Vorobey’ was an important source of improved root mass at depth under drought. We conclude that the use of wild species of wheat has the potential to improve a range of stress-adaptive traits and may permit modern bread wheat to become adapted to a wider range of environments including climate change scenarios.

Molecular Characterization of a Diverse Maize Inbred Line Collection and its Potential Utilization for Stress Tolerance Improvement

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Published in Crop Science 51(6): 2569-2581, 2011

Molecular Characterization of a Diverse Maize Inbred Line Collection and its Potential Utilization for Stress Tolerance Improvement

Weiwei Wen, Jose Luis Araus, Trushar Shah, Jill Cairns, George Mahuku, Marianne Bänziger, Jose Luis Torres, Ciro Sánchez and Jianbing Yan

A diverse collection of 359 advanced maize (Zea mays L.) inbred lines from the International Maize and Wheat Improvement Center (CIMMYT) and International Institute for Tropical Agriculture (IITA) breeding programs for drought, low N, soil acidity (SA), and pest and disease resistance was genotyped using 1260 single nucleotide polymorphism (SNP) markers. Model-based population partition, neighbor-joining (NJ) clustering, and principal component analysis (PCA) based on the genotypic data were employed to classify the lines into subgroups. A subgroup largely consisting of lines developed from La Posta Sequía (LPS) consistently separated from other lines when using different methods based on both SNP and SNP haplotype data. Lines related by pedigree tended to cluster together. Nine main subsets of lines were determined based on pedigree information, environmental adaptation, and breeding scheme. Analysis of molecular variance (AMOVA) revealed that variation within these subsets was much higher than that among subsets. Genetic diversity and linkage disequilibrium (LD) level were tested in the whole panel and within each subset. The potential of the panel for association mapping was tested using 999 SNP markers with minor allelic frequency (MAF) ≥ 0.05 and phenotypic data (grain yield [GY], ears per plant [EPP], and anthesis to silking interval [ASI]). Results show the panel is ideal for association mapping where type I error can be controlled using a mixed linear model (Q + K). Use of pedigree, heterotic group, and ecological adaptation information together with molecular characterization of this panel presents a valuable genetic resource for stress tolerance breeding in maize.

Prediction Assessment of Linear Mixed Models for Multienvironment Trials

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Published in Crop Science 51(3), 2011

Prediction Assessment of Linear Mixed Models for Multienvironment Trials

Juan Burgueño, José Crossa, José Miguel Cotes, Felix San Vicente and Biswanath Das

Fixed linear models have been used for describing genotype × environment interaction (GE). Previous attempts have been made to assess the predictive ability of some linear mixed models when GE components are treated as random effects and modeled by the factor analytic (FA) model. This study compares the predictive ability of linear mixed models when the GE is modeled by the FA model with that of simple linear mixed models when the GE is not modeled. A cross-validation scheme is used that randomly deletes some genotypes from sites; the values for these genotypes are then predicted by the different models and correlated with their observed values to assess model accuracy. A total of six multienvironment trials (one potato [Solanum tuberosum L.] trial, three maize [Zea mays L.] trials, and two wheat [Triticum aestivum L.] trials) with GE of varying complexity were used in the evaluation. Results show that for data sets with complex GE, modeling GE using the FA model improved the predictability of the model up to 6%. When GE is not complex, most models (with and without FA) gave high predictability, and models with FA did not seem to lose much predictive ability. Therefore, we concluded that modeling GE with the FA model is a good thing.

https://www.crops.org/publications/cs/abstracts/51/3/944

Association mapping for enhancing maize (Zea mays L.) genetic improvement

Posted by on , in Journal Articles

Published in Crop Science 51(2); 17 p, 2011

Association mapping for enhancing maize (Zea mays L.) genetic improvement

Yan, J.; Warburton, M.; Crouch, J.

Association mapping through linkage disequilibrium (LD) analysis is a powerful tool for the dissection of complex agronomic traits and for the identification of alleles that can contribute to the enhancement of a target trait. With the developments of high throughput genotyping techniques and advanced statistical approaches as well as the assembling and characterization of multiple association mapping panels, maize has become the model crop for association analysis. In this paper, we summarize progress in maize association mapping and the impacts of genetic diversity, rate of LD decay, population size, and population structure. We also review the use of candidate genes and gene-based markers in maize association mapping studies that has generated particularly promising results. In addition, we examine recent developments in genome-wide genotyping techniques that promise to improve the power of association mapping and significantly refine our understanding of the genetic architecture of complex quantitative traits. The new challenges and opportunities associated with genome-wide analysis studies are discussed. In conclusion, we review the current and future impacts of association mapping on maize improvement along with the potential benefits for poor people in developing countries who are dependent on this crop for their food security and livelihoods.

Hierarchical Multiple-Factor Analysis for Classifying Genotypes Based on Phenotypic and Genetic Data

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Published in Crop Science 50(1):105-117, 2010

Hierarchical Multiple-Factor Analysis for Classifying Genotypes Based on Phenotypic and Genetic Data

Jorge Franco, José Crossa and Santosh Desphande

A numerical classification problem encountered by breeders and gene-bank curators is how to partition the original heterogeneous population of genotypes into non-overlapping homogeneous subpopulations. The measure of distance that may be defined depends on the type of variables measured (i.e., continuous and/or discrete). The key points are whether and how a distance may be defined using all types of variables to achieve effective classification. The objective of this research was to propose an approach that combines the use of hierarchical multiple-factor analysis (HMFA) and the two-stage Ward Modified Location Model (Ward-MLM) classification strategy that allows (i) combining different types of phenotypic and genetic data simultaneously; (ii) balancing out the effects of the different phenotypic, genetic, continuous, and discrete variables; and (iii) measuring the contribution of each original variable to the new principal axes (PAs). Of the two strategies applied for developing PA scores to be used for clustering genotypes, the strategy that used the first few PA scores to which phenotypic and genetic variables each contributed 50% (i.e., a balanced contribution) formed better groups than those formed by the strategy that used a large number of PA scores explaining 95% of total variability. Phenotypic variables account for much variability in the initial PA; then their contributions decrease. The importance of genetic variables increases in later PAs. Results showed that various phenotypic and genetic variables made important contributions to the new PA. The HMFA uses all phenotypic and genetic variables simultaneously and, in conjunction with the Ward-MLM method, it offers an effective unifying approach for the classification of breeding genotypes into homogeneous groups and for the formation of core subsets for genetic resource conservation.

Generalizing the sites regression model to three-way interaction including multi-attributes

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Published in Crop Science 49(6): 2043-2057

Generalizing the Sites Regression Model to Three-Way Interaction Including Multi-Attributes

Mario Varela, Jose Crossa, Arun Kumar Joshi, Paul L. Cornelius and Yann Manes

When a multienvironment trial (MET) is established across several locations and years, the interaction is referred to as a three-way array. Three-way interaction can be studied by means of three-way principal components analysis. In this study, the three-way principal components analysis is adapted to the sites regression model (three-way SREG). The three-way SREG with location and year combines the effects of genotype, genotype x location, genotype x year, and genotype x location x year. The objective of this study is to show how the three-way SREG can be put to practical use in agriculture and breeding. We utilized two wheat (Triticum aestivum L) data sets that have already been used for fitting a three-way additive main effects and multiplicative interaction model. One data set had genotype (25) x location (4) x sowing times (4) and eight attributes, and the other data set included genotype (20) x irrigation level x year on grain yield. The three-way SREG applied simultaneously to eight attributes facilitates the interpretation of genotypic performance for all traits in specific locations and across locations for a selected sowing time. Results of the three-way SREG for both data sets show the different response patterns of genotypes for locations and sowing dates (Data Set 1), as well as genotypic responses across irrigation levels in different years (Data Set 2). Using Data Set 1, we show that fitting a three-way data structure to a three-way SREG model is more effective for detecting important interaction patterns than using the two-way SREG.