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

Towards the Development of a Gene-based Eco-Physiology Model for Common Bean: Genotype by Environment Interactions

Video summarizing Ph.D. dissertation for a non-specialist audience.National Science FoundationNortharvest Bean Growers AssociationPlant SciencesPlant SciencesCollege of Agriculture, Food Systems and Natural Resource

Rendimiento de materia seca y composición química de asociaciones de sorgo forrajero con leguminosas anuales

Forage sorghum [Sorghum bicolor L. Moench 'Brown midrib' (BMR)] and annual legumes lablab [Lablab purpureus L. 'Rongai' (L)] and mucuna [Mucuna pruriens L. 'Vine 90 d' (M)] are suitable fodder for the dairy industry in Puerto Rico, but BMR harvested at 90 days, usually has low crude protein (CP, 6%) content, which limits its usefulness. There is the possibility of increasing the CP by intercropping legumes with BMR, but this option has been only slightly investigated. This study proposes to compare BMR in monoculture and in intercropping with 'Rongai' (SL) and 'Vine 90 d' (SM), in addition to L and M in monoculture, in terms of total leaf mass (FM) and botanical components (sorghum, legumes and weeds) and chemical (CP and fiber fractions) at two harvest times. The experimental plots (25 m2) were sown in February and August 2008 in a randomized complete block design with five replications. At each harvest time, 2 m2 MF area was sampled 90 days after sowing. Data were analyzed by using the MIXED procedure of SAS and mean separation tests were performed by using F-protected LSD at 95% level of confidence. No significant differences (combining fodder botanical component and weeds) were detected for the total MF between BMR and BMR associated with legumes in monoculture, with mean values of 8.94, 8.81, and 8.42 Mg/ha for BMR-'Rongai', BMR-'Vine 90 d', and BMR, respectively. Overall, there was no significant difference in MF between May and August (8.1 vs. 7.5 Mg/ha). 'Rongai' yield (Mg/ha) did not change between May and August (3.6 vs. 3.0) but was lower in the intercrop (2.74) than in monoculture (3.94). 'Vine 90 d' had the lowest yield of 2.8 and 1.3 Mg/ ha in May and August, respectively. No differences (P > 0.05) in NDF and ADF content between BMR and BMR-'Rongai'-'Vine 90 d', whose values were 60.1 and 63.9% and 40.1 and 46.5%, respectively. However, differences (P < 0.05) were found in CP content between monoculture and intercropping, with values of 14.1, 11.1, and 6.0% for 'Rongai', 'Vine 90 d', and sole BMR, respectively, whereas the BMR-'Rongai' and BMR- 'Vine 90 d' CP were 9.8 and 9.1%, but these last values exceed by three units the percentage in BMR. In conclusion, BMR intercropped with 'Rongai' and 'Vine 90 d' improved the forage chemistry and helps to control weeds while favoring the performance of desirable botanical components.El sorgo forrajero (Sorghum bicolor L. Moench 'Nervadura marrón' o 'Brown midrib', BMR) y las leguminosas anuales Lablab (Lablab purpureus L. 'Rongai', L) y Mucuna (Mucuna pruriens L. 'Vine 90 d', M) son forrajes aptos para uso en la industria lechera de Puerto Rico, pero BMR cosechada a los 90 días suele tener bajos contenidos de proteína bruta (PB, 6%), lo cual limita su utilidad. Existe la posibilidad de incrementar la PB mediante la asociación de leguminosas con BMR, pero esta opción ha sido poco investigada. El estudio presente se realizó para comparar BMR en monocultivo y en cultivos asociados con 'Rongai' (SL) y 'Vine 90 d' (SM), además de L y M en monocultivos, en cuanto a la masa foliar total (MF), y en la composición botánica (sorgo, leguminosa y maleza) y química (PB y fracciones de fibras) a dos épocas de corte. Las parcelas experimentales (25 2m ) se sembraron en febrero y agosto del 2008 en bloques completos al azar con cinco repeticiones. A cada corte se estimó la MF en áreas de muestreo 2 de 2 m . Los datos se analizaron usando el PROC Mixed del programa SAS, y para aquellas variables que fueron significativas (P < 0.05) se separaron las medias con la prueba DMS (0.05) de Fisher. No se encontró diferencias (abarcando componente botánico deseado más malezas) en MF total entre BMR asociado con leguminosas y BMR en monocultivo, siendo las medias 8.94, 8.81, y 8.42 Mg/ha, para BMR-'Rongai', BMR-'Vine 90 d', y BMR, respectivamente. No se verificó una diferencia global en MF entre mayo y agosto (8.1 contra 7.5 Mg/ha). El rendimiento de la leguminosa 'Rongai' (Mg/ ha) no varió entre mayo y agosto (3.6 contra 3.0), pero fue menor en el cultivo asociado (2.74) que en monocultivo (3.94). La leguminosa 'Vine 90 d' tuvo el menor rendimiento de 2.8 y 1.3 Mg/ha, en mayo y agosto, respectivamente. No hubo diferencias en contenidos de FDN y FDA entre BMR-'Rongai' y BMR-'Vine 90 d' cuyos valores fueron de 60.1 y 63.9%; y 40.1 y 46.5%, respectivamente. En cambio, sí se encontraron diferencias en contenido de PB entre monocultivos y cultivos asociados, con valores para 'Rongai', 'Vine 90 d', y BMR en monocultivos de 14.1, 11.1, y 6.0%, respectivamente, mientras la PB de BMR-'Rongai' y BMR-'Vine 90 d' fue 9.8 y 9.1%, pero estos últimos valores superan por tres unidades de porciento al de BMR. En conclusión, la asociación de BMR con 'Rongai' y 'Vine 90 d' mejoró la composición química del forraje y ayudó a combatir las malezas mientras favorecieron el rendimiento de los componentes botánicos deseables

Release of tepary bean cultivar ‘USDA Fortuna’ with improved disease and insect resistance, seed size, and culinary quality

Tepary bean (Phaseolus acutifolius A. Gray) is a viable and nutritious alternative to common bean (P. vulgaris L.) in areas with excessively high temperatures and/or chronic drought. Tepary bean is a traditional crop of the Tohono O'odham Indians of the Sonoran Desert in the Southwest United States and Mexico, as well as other Indigenous peoples of the United States, Mexico, and Central America. Despite its potential for broad applications for reduced water-input agriculture or for hot, semi-arid, marginal production zones, tepary bean remains an orphan crop. ‘USDA Fortuna’ (Reg. no. CV-352, PI 698459) is an improved tepary bean cultivar with enhanced seed size, seed quality, tolerance to Bean golden yellow mosaic virus, and resistance to local strains of rust in Puerto Rico. It has leafhopper pest resistance, common bacterial blight resistance, and moderate resistance to powdery mildew. USDA Fortuna is a high-yielding tepary bean with an attractive black speckled seed color and a quick cooking time. This cultivar was developed cooperatively by the USDA-ARS, the University of Puerto Rico, Zamorano University, the Instituto Dominicano de Investigaciones Agropecuarias y Forestales (IDIAF) of the Dominican Republic, Quisqueya University of Haiti, the National Seed Service of Haiti, Instituto Nacional de Innovación y Transferencia en Tecnología Agropecuaria (INTA) of Costa Rica, and Iowa State University.This article is published as Porch, T. G., Rosas, J. C., Cichy, K., Lutz, G. G., Rodriguez, I., Colbert, R. W., Demosthene, G., Hernández, J. C., Winham, D. M., & Beaver, J. S. (2023). Release of tepary bean cultivar ‘USDA Fortuna’ with improved disease and insect resistance, seed size, and culinary quality. Journal of Plant Registrations, 00, 1–10. https://doi.org/10.1002/plr2.20322. Posted with permission. © 2023 American Society of Agronomy, Crop Science Society of America, and Soil Science Society of America. © 2023 The Authors. Journal of Plant Registrations published by Wiley Periodicals LLC on behalf of Crop Science Society of America. This article has been contributed to by U.S. Government employees and their work is in the public domain in the USA. This is an open access article under the terms of the Creative Commons Attribution-NonCommercial License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited and is not used for commercial purposes

A Predictive Model for Time-to-Flowering in the Common Bean Based on QTL and Environmental Variables

The common bean is a tropical facultative short day legume that is now grown in tropical and temperate zones. This observation underscores how domestication and modern breeding can change the adaptive phenology of a species. A key adaptive trait is the optimal timing of the transition from the vegetative to the reproductive stage. This trait is responsive to genetically controlled signal transduction pathways and local climatic cues. A comprehensive characterization of this trait can be started by assessing the quantitative contribution of the genetic and environmental factors, and their interactions. This study aimed to locate significant QTL (G) and environmental (E) factors controlling time-to-flower in the common bean, and to identify and measure G x E interactions. Phenotypic data were collected from a bi-parental [Andean x Mesoamerican] recombinant inbred population (F11:14, 188 genotypes) grown at five environmentally distinct sites. QTL analysis using a dense linkage map revealed 12 QTL, five of which showed significant interactions with the environment. Dissection of G x E interactions using a linear mixed-effect model revealed that temperature, solar radiation, and photoperiod play major roles in controlling common bean flowering time directly, and indirectly by modifying the effect of certain QTLs. The model predicts flowering time across five sites with an adjusted r-square of 0.89 and root-mean square error of 2.52 days. The model provides the means to disentangle the environmental dependencies of complex traits, and presents an opportunity to identify in-silico QTL allele combinations that could yield desired phenotypes under different climatic conditions

Cognitive and neural models of threat appraisal in psychosis: A theoretical integration

© 2016 The Authors. Cognitive models of psychosis propose that maladaptive appraisals of anomalous experiences contribute to distress and disability in psychosis. Attentional, attributional and reasoning biases are hypothesised to drive these threat-based appraisals. Experimental and self-report data have provided support for the presence of these biases in psychosis populations, but recently there have been calls for neurobiological data to be integrated into these findings. Currently, little investigation has been conducted into the neural correlates of maladaptive appraisals. Experimental and neuroimaging research in social cognition employing threatening stimuli provide the closest equivalent of maladaptive appraisal in psychosis. Consequently, a rapprochement of these two literatures was attempted in order to identify neural networks relevant to threat appraisal in psychosis. This revealed overlapping models of aberrant emotion processing in anxiety and schizophrenia, encompassing the amygdala, insula, hippocampus, anterior cingulate cortex, and prefrontal cortex. These models posit that aberrant activity in these systems relates to altered emotional significance detection and affect regulation, providing a conceptual overlap with threat appraisal in psychosis, specifically attentional and attributional biases towards threat. It remains to be seen if direct examination of these biases using neuroimaging paradigms supports the theoretical integration of extant models of emotion processing and maladaptive appraisals in psychosis.Medical Research Counci