Agronomic performance and cooking quality characteristics for slow-darkening pinto beans

Slow-darkening (SD) pinto beans (Phaseolus vulgaris L.) possess a desirable new trait, conditioned by the recessive sd gene, that slows seed coat darkening under delayed harvest and under storage. The effect sd may have on performance needs investigation. We examined agronomic performance and cooking quality of SD pinto beans. There were 30 (15 SD and 15 regular darkening [RD]) recombinant inbred lines (RILs) from each of two biparental inbred populations. The 60 RILs were tested across three locations in North Dakota andWashington. In addition, advanced SD and RD pinto breeding lines were tested in trials from 2010 to 2012 and in 2018. Across 2010–2012 trials, the “early generation bred” SD pintos, as a group, had significantly lower emergence, increased lodging, less seed yield, and smaller seed size than the RD group. Conversely, in the 2018 trial, “recently bred” SD pinto breeding lines had competitive agronomic performance to RD lines for seed yield, reduced lodging, and increased emergence. Further research on cooking time is warranted given that SD RILs cooked 20% faster than the RD RILs in one population. Overall, SD pintos exhibited slightly better canning quality than RD pintos. Whether raw or cooked, SD pintos were much lighter in color than RD pintos, emphasizing the need to keep them separated as distinct market classes. Breeders should continue to focus on improving agronomic performance for emergence, lodging, seed yield, seed size, and canning quality of SD pinto beans

SNP Assay Development for Linkage Map Construction, Anchoring Whole-Genome Sequence, and Other Genetic and Genomic Applications in Common Bean.

A total of 992,682 single-nucleotide polymorphisms (SNPs) was identified as ideal for Illumina Infinium II BeadChip design after sequencing a diverse set of 17 common bean (Phaseolus vulgaris L) varieties with the aid of next-generation sequencing technology. From these, two BeadChips each with >5000 SNPs were designed. The BARCBean6K_1 BeadChip was selected for the purpose of optimizing polymorphism among market classes and, when possible, SNPs were targeted to sequence scaffolds in the Phaseolus vulgaris 14× genome assembly with sequence lengths >10 kb. The BARCBean6K_2 BeadChip was designed with the objective of anchoring additional scaffolds and to facilitate orientation of large scaffolds. Analysis of 267 F2 plants from a cross of varieties Stampede × Red Hawk with the two BeadChips resulted in linkage maps with a total of 7040 markers including 7015 SNPs. With the linkage map, a total of 432.3 Mb of sequence from 2766 scaffolds was anchored to create the Phaseolus vulgaris v1.0 assembly, which accounted for approximately 89% of the 487 Mb of available sequence scaffolds of the Phaseolus vulgaris v0.9 assembly. A core set of 6000 SNPs (BARCBean6K_3 BeadChip) with high genotyping quality and polymorphism was selected based on the genotyping of 365 dry bean and 134 snap bean accessions with the BARCBean6K_1 and BARCBean6K_2 BeadChips. The BARCBean6K_3 BeadChip is a useful tool for genetics and genomics research and it is widely used by breeders and geneticists in the United States and abroad

Genetic Associations in Four Decades of Multienvironment Trials Reveal Agronomic Trait Evolution in Common Bean

Multienvironment trials (METs) are widely used to assess the performance of promising crop germplasm. Though seldom designed to elucidate genetic mechanisms, MET data sets are often much larger than could be duplicated for genetic research and, given proper interpretation, may offer valuable insights into the genetics of adaptation across time and space. The Cooperative Dry Bean Nursery (CDBN) is a MET for common bean (Phaseolus vulgaris) grown for . 70 years in the United States and Canada, consisting of 20–50 entries each year at 10–20 locations. The CDBN provides a rich source of phenotypic data across entries, years, and locations that is amenable to genetic analysis. To study stable genetic effects segregating in this MET, we conducted genome-wide association studies (GWAS) using best linear unbiased predictions derived across years and locations for 21 CDBN phenotypes and genotypic data (1.2 million SNPs) for 327 CDBN genotypes. The value of this approach was confirmed by the discovery of three candidate genes and genomic regions previously identified in balanced GWAS. Multivariate adaptive shrinkage (mash) analysis, which increased our power to detect significant correlated effects, found significant effects for all phenotypes. Mash found two large genomic regions with effects on multiple phenotypes, supporting a hypothesis of pleiotropic or linked effects that were likely selected on in pursuit of a crop ideotype. Overall, our results demonstrate that statistical genomics approaches can be used on MET phenotypic data to discover significant genetic effects and to define genomic regions associated with crop improvement

Dry Bean: A Protein-Rich Superfood With Carbohydrate Characteristics That Can Close the Dietary Fiber Gap

Consumer food choices are often focused on protein intake, but the chosen sources are frequently either animal-based protein that has high fat content or plant-based protein that is low in other nutrients. In either case, these protein sources often lack dietary fiber, which is a nutrient of concern in the 2020–2025 Dietary Guide for Americans. Pulse crops, such as dry edible beans (Phaseolus vulgaris L.), are a rich source of dietary protein and contain approximately equal amounts of dietary fiber per 100 kcal edible portion; yet the consumer's attention has not been directed to this important fact. If product labeling were used to draw attention to the similar ratio of dietary protein to dietary fiber in dry bean and other pulses, measures of carbohydrate quality could also be highlighted. Dietary fiber is categorized into three fractions, namely, soluble (SDF), insoluble (IDF), and oligosaccharides (OLIGO), yet nutrient composition databases, as well as food labels, usually report only crude fiber. The objectives of this research were to measure the content of SDF, IDF, and OLIGO in a large genetically diverse panel of bean cultivars and improved germplasm (n = 275) and determine the impact of growing environment on the content of DF. Dietary fiber was evaluated using the American Association of Analytical Chemist 2011.25 method on bean seed grown at two locations. Dry bean cultivars differed for all DF components (P ≤ 0.05). Insoluble dietary fiber constituted the highest portion of total DF (54.0%), followed by SDF (29.1%) and OLIGO (16.8%). Mean total DF and all components did not differ among genotypes grown in two field environments. These results indicate that value could be added to dry bean by cultivar-specific food labeling for protein and components of dietary fiber

CONCIENTIZACIÓN AMBIENTAL SOBRE LOS GEI Y EL CAMBIO CLIMÁTICO EN EL INSTITUTO TECNOLÓGICO METROPOLITANO, INSTITUCIÓN UNIVERSITARIA (ITM)

La preocupación actual por las notables evidencias del cambio climático global está llevando a los países a mitigar sus principales causas: los gases efecto invernadero (GEI), a través de diferentes estrategias, entre ellas y haciendo un gran énfasis esta la educación ambiental.  La huella de carbono es un indicador del impacto ambiental sobre la atmósfera, que cuantifica las diferentes emisiones de GEI, que se pueden estimar de manera aproximada haciendo uso de los aplicativos disponibles en la web. Se presentan en el presente artículo las principales campañas de educación ambiental y experiencias desarrolladas por el semillero de cultura, gestión e investigación ambiental “Cuida Tu Huella”, con relación a la Huella de  Carbono en el Instituto Tecnológico Metropolitano (ITM). La estimación de la huella de carbono personal arrojó resultados de 4.22 y 3.87 ton CO2/año para los años 2011 y 2012 respectivamente, siendo aún más importante el impacto que se ha podido lograr en la comunidad académica, que trasciende el espacio geográfico de la Institución, ya que ha permitido lograr la concientización en cuanto a identificar las diferentes actividades que más producen GEI, transformando así los hábitos de vida por otros más ambientalmente sostenibles.

Development of a QTL-environment-based predictive model for node addition rate in common bean

To select a plant genotype that will thrive in targeted environments it is critical to understand the genotype by environment interaction (GEI). In this study, multi-environment QTL analysis was used to characterize node addition rate (NAR, node day− 1) on the main stem of the common bean (Phaseolus vulgaris L). This analysis was carried out with field data of 171 recombinant inbred lines that were grown at five sites (Florida, Puerto Rico, 2 sites in Colombia, and North Dakota). Four QTLs (Nar1, Nar2, Nar3 and Nar4) were identified, one of which had significant QTL by environment interactions (QEI), that is, Nar2 with temperature. Temperature was identified as the main environmental factor affecting NAR while day length and solar radiation played a minor role. Integration of sites as covariates into a QTL mixed site-effect model, and further replacing the site component with explanatory environmental covariates (i.e., temperature, day length and solar radiation) yielded a model that explained 73% of the phenotypic variation for NAR with root mean square error of 16.25% of the mean. The QTL consistency and stability was examined through a tenfold cross validation with different sets of genotypes and these four QTLs were always detected with 50–90% probability. The final model was evaluated using leave-one-site-out method to assess the influence of site on node addition rate. These analyses provided a quantitative measure of the effects on NAR of common beans exerted by the genetic makeup, the environment and their interactions

Agronomic performance and cooking quality characteristics for slow-darkening pinto beans

Slow-darkening (SD) pinto beans (Phaseolus vulgaris L.) possess a desirable new trait, conditioned by the recessive sd gene, that slows seed coat darkening under delayed harvest and under storage. The effect sd may have on performance needs investigation. We examined agronomic performance and cooking quality of SD pinto beans. There were 30 (15 SD and 15 regular darkening [RD]) recombinant inbred lines (RILs) from each of two biparental inbred populations. The 60 RILs were tested across three locations in North Dakota andWashington. In addition, advanced SD and RD pinto breeding lines were tested in trials from 2010 to 2012 and in 2018. Across 2010–2012 trials, the “early generation bred” SD pintos, as a group, had significantly lower emergence, increased lodging, less seed yield, and smaller seed size than the RD group. Conversely, in the 2018 trial, “recently bred” SD pinto breeding lines had competitive agronomic performance to RD lines for seed yield, reduced lodging, and increased emergence. Further research on cooking time is warranted given that SD RILs cooked 20% faster than the RD RILs in one population. Overall, SD pintos exhibited slightly better canning quality than RD pintos. Whether raw or cooked, SD pintos were much lighter in color than RD pintos, emphasizing the need to keep them separated as distinct market classes. Breeders should continue to focus on improving agronomic performance for emergence, lodging, seed yield, seed size, and canning quality of SD pinto beans

YIELD GAINS IN DRY BEANS IN THE U.S.

INTRODUCTION Yield Gains can be estimated by comparing On-Farm yields vs. Potential yield (a.k.a., realized yield) or measured in common trials. The difference between On-Farm yield and Potential yield is known as the Yield Gap. Plant breeders primarily focus on increasing Potential yield while also attempting to optimize the interaction between genotype, environment, and agronomic practices to increase On-Farm yield and reduce the Yield Gap. In recent years, scientists in developing countries and the U.S. have made major advances in dry bean disease resistance, stress tolerance, and increased yield (Kelly, 2004). Agronomic and biotechnological tools have contributed to these achievements. The objective is to estimate Yield Gains in dry beans in the U.S