Developing Methods to Evaluate Phenotypic Variability in Biological Nitrification Inhibition (BNI) Capacity of \u3cem\u3eBrachiaria\u3c/em\u3e Grasses

As part of the nitrogen (N) cycle in the soil, nitrification is an oxidation process mediated by microorganisms that transform the relatively immobile ammonium (NH4+)to the water soluble nitrate (NO3-), enabling the production of nitrous oxide (N2O, a potent greenhouse gas) by denitrification as a by-product (Canfield et al. 2010). Researchers at CIAT-Colombia in collaboration with JIRCAS-Japan, reported that Brachiaria humidicola forage grasses have the ability to inhibit the nitrification process by exuding chemical compounds from its roots to the soil. A major hydrophobic compound was discovered and named brachial-actone (Subbarao et al. 2009). This capacity of Brachiaria grasses is known as biological nitrification inhibition (BNI) and it could contribute to better N use efficiency in crop-livestock systems by improving recovery of applied N while reducing NO3- leaching and N2O emission. The current methodologies for quantifying the BNI trait need further improvement to facilitate high throughput evaluation to quantify genotypic differences. In this paper, we aim to develop new (or improve the existing) phenotyping methods for this trait. Preliminary results were obtained using three different methods to quantify BNI: (1) a mass spectrometry method to quantify brachialactone; (2) a static chamber method to quantify N2O emission from soils under greenhouse conditions; and (3) an improved molecular method to quantify microbial populations by Real-Time PCR. Using these three methods we expect to score a bi-parental hybrid population (n=134) of two B. humidicola accessions differing in their BNI capacity CIAT26146 (medium to low BNI) x CIAT16888 (high BNI), in an attempt to identify QTLs associated with the BNI trait

Biological Nitrification Inhibition (BNI) in \u3cem\u3eBrachiaria\u3c/em\u3e Pastures: A Novel Strategy to Improve Eco-Efficiency of Crop-Livestock Systems and to Mitigate Climate Change

Up to 70% of the nitrogen (N) fertilizers applied to agricultural systems are lost due to nitrification and denitrification. Nitrification is a microbiological process that generates nitrate (NO3-) and promotes the losses of N fertilizers by leaching and denitrification. Nitrification and denitrification are the only known biological processes that generate nitrous oxide (N2O), a powerful greenhouse gas contributing to global warming. There is an urgent need to suppress nitrification process in soil to improve N-recovery and N use efficiency (NUE) of agricultural systems and to mitigate climate change (Subbarao et al. 2012). Certain Brachiaria grasses (B. humidicola) can suppress soil-nitrification by releasing biological nitrification inhibitors (BNIs) from roots, thereby reducing N2O emissions. This phenomenon, termed biological nitrification inhibition (BNI), has been the subject of recent research to characterize and validate the concept under field conditions (Subbarao et al. 2009). Advances on three aspects of BNI research are reported here: (1) gene quantification of soil nitrifying microorganisms to determine BNI activity in B. humidicola; (2) screening of B. humidicola breeding materials to identify hybrids with contrasting levels of BNI: and (3) quantification of the BNI-residual effect from B. humidicola on N-recovery and agronomic-NUE of the subsequent maize crop

Regulation of Nitrification in Soil: Advances in Integration of \u3cem\u3eBrachiaria\u3c/em\u3e Hybrids to Intensify Agriculture and to Mitigate Climate Change

Higher rates of nitrification in soil facilitate nitrogen (N) losses from agricultural systems through nitrate-leaching and denitrification. Plants’ ability to produce and release nitrification inhibitors from roots and suppress soil-nitrifier activity is termed ‘biological nitrification inhibition’ (BNI) (Subbarao et al., 2015). Up to 70% of applied N-fertilizer is lost (via NO3−leaching and gaseous-N emissions) from agricultural systems and the annual economic loss from lost N-fertilizer is estimated at 90 US$ billion. Previous research has indicated that Brachiaria humidicola (Bh), a tropical forage grass that is well adapted to infertile and waterlogged soils, has high capacity to inhibit nitrification in soil and reduce emissions of a highly potent greenhouse gas, nitrous oxide (N2O) (Subbarao et al., 2009). CIAT has an on-going Brachiaria breeding program that generates interspecific (B. decumbens, B. brizantha, B. ruziziensis) and intraspecific (Bh) hybrids that combine several desirable attributes. An interinstitutional and multidisciplinary project was initiated in 2012 to integrate Brachiaria hybrids into crop-livestock systems of smallholders to improve livestock productivity and mitigate climate change by reducing nitrification in soil (Rao et al., 2014). Here we report the major advances from the last three years of work from this project

Climate-Smart Crop-Livestock Systems for Smallholders in the Tropics: Integration of New Forage Hybrids to Intensify Agriculture and to Mitigate Climate Change through Regulation of Nitrification in Soil

It is widely recognized that less than 50% of applied nitrogen (N) fertilizer is recovered by crops, and based on current fertilizer prices the economic value of this “wasted N” globally is currently estimated as US$81 billion annually. Worse still, this wasted N has major effects on the environment (Subbarao et al. 2012). CIAT researchers and their collaborators in Japan reported a major breakthrough in managing N to benefit both agriculture and the environment (Subbarao et al. 2009). Termed Biological Nitrification Inhibition (BNI), this natural phenomenon has been the subject of long-term collaborative research that revealed the mechanism by which certain plants (and in particular the tropical pasture grass B. humidicola) naturally inhibit the conversion of N in the soil from a stable form to forms subject to leaching loss (NO3) or to the potent greenhouse gas N2O (Subbarao et al. 2012). Brachiaria humidicola which is well adapted to the low-nitrogen soils of South American savannas has shown high BNI-capacity among the tropical grasses tested (Subbarao et al. 2007). The major nitrification inhibitor in Brachiaria forage grasses is brachialactone, a cyclic diterpene (Subbarao et al. 2009). Reduction of N loss from the soil under a B. humidicola pasture has a direct and beneficial environmental effect. We hypothesize that this conservation of soil N will have an additional positive impact on a subsequent crop (e.g. maize). At present, recovery of fertilizer N and the impact on crop yield is not known. The main purpose of our inter-institutional and multi-disciplinary project, targeting small-scale farmers, is to develop the innovative approach of BNI using B. humidicola forage grass hybrids to realize sustainable economic and environmental benefits from integrated crop-livestock production systems

Evaluación de sustratos inorgánicos para la exportación de inoculante de micorriza vesículo-arbuscular

60 p.Moreta Mejía, Danilo. 2002. Evaluación de sustratos inorgánicos para la exportación de inoculante de micorriza vesículo-arbuscular. Proyecto Especial de Ingeniero Agrónomo, Zamorano, Honduras. 45 p. La habilidad de las micorrizas vesículo-arbusculares (VAM) para promover el desarrollo de las plantas ha sido bien investigada; sin embargo, la aplicación e implementación de tecnologías para la exportación de inoculantes no ha sido muy explorada. Esto es una limitante que reduce la distribución comercial del producto a otros países. El objetivo de este estudio fue evaluar sustratos inorgánicos para la exportación de inoculante de VAM, y determinar su factibilidad técnica y económica. La investigación se llevó a cabo en la Escuela Agrícola Panamericana, Honduras; entre febrero y agosto de 2002. Los sustratos evaluados fueron el sustrato convencional utilizado por Zamorano para la producción de MYCORAL® (suelo agrícola y arena en proporción 2:1), perlita, vermiculita, suelo rojo, suelo blanco, piedra pómez, piedra de cantera, ladrillo y carbón vegetal. El material experimental fue Brachiaria decumbens. A los sustratos inorgánicos se les aplicó una solución nutritiva baja en P, basada en los requerimientos para el desarrollo del pasto hospedero. Se utilizó un diseño experimental de bloques completos al azar (BCA) con seis repeticiones. Se realizaron dos muestreos, a las 6 y 11 semanas después de sembrado el B. decumbens. La vermiculita fue superior a los demás sustratos en el volumen y peso seco de las raíces, y en el peso seco del follaje; esto pudo deberse a la densidad aparente relativamente baja que posee este sustrato. La mayor infección de raíces se observó en la piedra de cantera, aunque este sustrato no fue estadísticamente diferente a la mayoría; esta respuesta probablemente se debió a que este sustrato presentó una de las menores concentraciones de P y/o al efecto de la aplicación de la solución nutritiva. El número de esporas en el testigo fue notablemente superior a los demás sustratos, debido su baja concentración de P; en este sustrato no se aplicó la solución nutritiva. El análisis económico reflejó que la producción a gran escala de VAM en sustratos inorgánicos no es factible, debido a que sus costos de implementación son relativamente altos en comparación con el sustrato convencional. Sin embargo, a pesar del alto costo, para fines de introducción de cepas puras a otro país, su empleo es económicamente justificado. Los sustratos inorgánicos y la aplicación de la solución nutritiva favorecieron la infección de raíces y al peso seco de las raíces y del follaje.1. Indice de cuadros 2. Indice de figuras 3. Indice de anexos 4. Introducción 5. Revisión de literatura 6. Materiales y métodos 7. Resultados y discusión 8. Conclusiones 9. Recomendaciones 10. Referencias bibliográficas 11. Anexo

Application of Performance Improvement Methods to Improve Timeliness of Lung Cancer Care

Background/Aims: Timeliness, an essential element of high-quality care as defined by the Institute of Medicine, is critical in cancer care delivery. Timeliness of diagnosis and treatment is associated with improved outcomes for some cancers. A Kaiser Permanente Southern California (KPSC) organizational goal is to achieve optimal timeliness of cancer care to ensure best possible outcomes for KPSC members. Methods: A working group at the KPSC Los Angeles Medical Center (LAMC), led by the physician director of the cancer program, the director of performance improvement and the director of oncology services, convened a series of multidisciplinary meetings focused on improving timeliness of lung cancer care. Participants included LAMC physicians, nurses and administrators as well as KPSC researchers. Alignment of physician education time and education credits was facilitated by administrative leaders. Performance improvement activities included review of current state of LAMC lung cancer care, definition of key metrics, iterative development of specialty-based process maps, chart abstraction and identification of potential interventions. Physicians participated in all activities, facilitated by group leaders. Data sources included medical records and tumor registry data. Results: Review of LAMC baseline data for patients newly diagnosed with lung cancer in 2012 (N=60) showed that the median number of days from initial imaging to date of diagnosis is 21 days (range: 0–112). Median days from postdiagnosis physician consultation to initial treatment varied by physician specialty: 4 (radiology), 12 (radiation oncology), 24 (medical oncology) and 27 days (surgery). Chart review revealed that patients diagnosed in the inpatient setting had shorter time to receipt of services. Root cause analysis revealed several target areas for improvement interventions including: development of a lung cancer protocol for radiology, a closed-loop system with pulmonology, improved integration of palliative and hospice care, a rapid results system for genetic studies, and increased frequency of lung tumor board. A new position of lung cancer coordinator was proposed to oversee activities, and standardized “smart phrases” were created for documentation of encounters. Discussion: Multidisciplinary groups with clinician, performance improvement and research participation can achieve significant progress in identifying areas for improvement using robust methods. Next steps will include implementation and evaluation of the proposed improvements

Yield prediction through integration of genetic, environment, and management data through deep learning

Accurate prediction of the phenotypic outcomes produced by different combinations of genotypes, environments, and management interventions remains a key goal in biology with direct applications to agriculture, research, and conservation. The past decades have seen an expansion of new methods applied toward this goal. Here we predict maize yield using deep neural networks, compare the efficacy of 2 model development methods, and contextualize model performance using conventional linear and machine learning models. We examine the usefulness of incorporating interactions between disparate data types. We find deep learning and best linear unbiased predictor (BLUP) models with interactions had the best overall performance. BLUP models achieved the lowest average error, but deep learning models performed more consistently with similar average error. Optimizing deep neural network submodules for each data type improved model performance relative to optimizing the whole model for all data types at once. Examining the effect of interactions in the best-performing model revealed that including interactions altered the model’s sensitivity to weather and management features, including a reduction of the importance scores for timepoints expected to have a limited physiological basis for influencing yield—those at the extreme end of the season, nearly 200 days post planting. Based on these results, deep learning provides a promising avenue for the phenotypic prediction of complex traits in complex environments and a potential mechanism to better understand the influence of environmental and genetic factors