Statistical modeling for analyzing grain yield of durum wheat under rainfed conditions in Azad Jammu Kashmir, Pakistan / Modelagem estatística para analisar o rendimento de grãos de trigo durum sob condições de chuva em Azad Jammu Kashmir, Paquistão

One of the most important traits that plant breeders aim to improve is grain yield which is a highly quantitative trait controlled by various agro-morphological traits. Twelve morphological traits such as Germination Percentage, Days to Spike Emergence, Plant Height, Spike Length, Awn Length, Tillers/Plant, Leaf Angle, Seeds/Spike, Plant Thickness, 1000-Grain Weight, Harvest Index and Days to Maturity have been considered as independent factors. Correlation ,regression, and principal component analysis (PCA) are used to identify the different durum wheat traits, which significantly contribute to the yield. The necessary assumptions required for applying regression modeling have been tested and all the assumptions are satisfied by the observed data. The outliers are detected in the observations of fixed traits and Grain Yield. Some observations are detected as outliers but the outlying observations did not show any influence on the regression fit. For selecting a parsimonious regression model for durum wheat, best subset regression, and stepwise regression techniques have been applied. The best subset regression analysis revealed that Germination Percentage, Tillers/Plant, and Seeds/Spike have a marked increasing effect whereas Plant thickness has a negative effect on durum wheat yield. While stepwise regression analysis identified that the traits, Germination Percentage, Tillers/Plant, and Seeds/Spike significantly contribute to increasing the durum wheat yield. The simple correlation coefficient specified the significant positive correlation of Grain Yield with Germination Percentage, Number of Tillers/Plant, Seeds/Spike, and Harvest Index. These results of correlation analysis directed the importance of morphological characters and their significant positive impact on Grain Yield. [...].

Uma das características mais importantes que os produtores de plantas visam melhorar é o rendimento de grãos, que é uma particularidade altamente quantitativa e controlada por várias características agromorfológicas. Foram considerados 12 traços morfológicos como fatores independentes, como Porcentagem de Germinação, Dias para Emergência da Espiga, Altura da Planta, Comprimento da Espiga, Comprimento da Aresta, Perfilhos /Planta, Ângulo da Folha, Sementes /Espiga, Espessura da Planta, Peso de 1000 Grãos, Índice de Colheita e Dias até a Maturidade. A correlação, regressão e análise de componentes principais (em inglês Principal Component Analysis (PCA)) são usadas para identificar as diferentes características do trigo duro, que contribuem significativamente para o rendimento. As suposições necessárias exigidas para a aplicação da modelagem de regressão foram testadas e todas as suposições são adequadas de acordo com os dados observados. Os outliers são detectados nas observações de características fixas e rendimento de grãos. Algumas observações são detectadas como outliers, mas as observações outliers não mostraram qualquer influência no ajuste da regressão. Para selecionar um modelo de regressão parcimonioso para o trigo duro, foram aplicadas tanto a melhor regressão de subconjunto quanto as técnicas de regressão stepwise. A melhor análise de regressão de subconjunto revelou que a porcentagem de germinação, perfilhos /planta e sementes /espiga tem um efeito de aumento acentuado, enquanto a espessura da planta tem um efeito negativo sobre o rendimento do trigo duro. Enquanto a análise de regressão passo a passo identificou que as características, porcentagem de germinação, perfilhos/planta e sementes /espiga contribuem significativamente para aumentar a produtividade do trigo duro. O coeficiente de correlação simples especificou a correlação positiva significativa do [...].

Statistical modeling for analyzing grain yield of durum wheat under rainfed conditions in Azad Jammu Kashmir, Pakistan

Abstract One of the most important traits that plant breeders aim to improve is grain yield which is a highly quantitative trait controlled by various agro-morphological traits. Twelve morphological traits such as Germination Percentage, Days to Spike Emergence, Plant Height, Spike Length, Awn Length, Tillers/Plant, Leaf Angle, Seeds/Spike, Plant Thickness, 1000-Grain Weight, Harvest Index and Days to Maturity have been considered as independent factors. Correlation, regression, and principal component analysis (PCA) are used to identify the different durum wheat traits, which significantly contribute to the yield. The necessary assumptions required for applying regression modeling have been tested and all the assumptions are satisfied by the observed data. The outliers are detected in the observations of fixed traits and Grain Yield. Some observations are detected as outliers but the outlying observations did not show any influence on the regression fit. For selecting a parsimonious regression model for durum wheat, best subset regression, and stepwise regression techniques have been applied. The best subset regression analysis revealed that Germination Percentage, Tillers/Plant, and Seeds/Spike have a marked increasing effect whereas Plant thickness has a negative effect on durum wheat yield. While stepwise regression analysis identified that the traits, Germination Percentage, Tillers/Plant, and Seeds/Spike significantly contribute to increasing the durum wheat yield. The simple correlation coefficient specified the significant positive correlation of Grain Yield with Germination Percentage, Number of Tillers/Plant, Seeds/Spike, and Harvest Index. These results of correlation analysis directed the importance of morphological characters and their significant positive impact on Grain Yield. The results of PCA showed that most variation (70%) among data set can be explained by the first five components. It also identified that Seeds/Spike; 1000-Grain Weight and Harvest Index have a higher influence in contributing to the durum wheat yield. Based on the results it is recommended that these important parameters might be considered and focused in future durum wheat breeding programs to develop high yield varieties.

Resumo Uma das características mais importantes que os produtores de plantas visam melhorar é o rendimento de grãos, que é uma particularidade altamente quantitativa e controlada por várias características agromorfológicas. Foram considerados 12 traços morfológicos como fatores independentes, como Porcentagem de Germinação, Dias para Emergência da Espiga, Altura da Planta, Comprimento da Espiga, Comprimento da Aresta, Perfilhos /Planta, Ângulo da Folha, Sementes /Espiga, Espessura da Planta, Peso de 1000 Grãos, Índice de Colheita e Dias até a Maturidade,. A correlação, regressão e análise de componentes principais (em inglês Principal Component Analysis (PCA)) são usadas para identificar as diferentes características do trigo duro, que contribuem significativamente para o rendimento. As suposições necessárias exigidas para a aplicação da modelagem de regressão foram testadas e todas as suposições são adequadas de acordo com os dados observados. Os outliers são detectados nas observações de características fixas e rendimento de grãos. Algumas observações são detectadas como outliers, mas as observações outliers não mostraram qualquer influência no ajuste da regressão. Para selecionar um modelo de regressão parcimonioso para o trigo duro, foram aplicadas tanto a melhor regressão de subconjunto quanto as técnicas de regressão stepwise. A melhor análise de regressão de subconjunto revelou que a porcentagem de germinação, perfilhos /planta e sementes /espiga tem um efeito de aumento acentuado, enquanto a espessura da planta tem um efeito negativo sobre o rendimento do trigo duro. Enquanto a análise de regressão passo a passo identificou que as características, porcentagem de germinação, perfilhos/planta e sementes /espiga contribuem significativamente para aumentar a produtividade do trigo duro. O coeficiente de correlação simples especificou a correlação positiva significativa do rendimento de grãos com a porcentagem de germinação, número de perfilhos/planta, sementes / espiga e índice de colheita. Esses resultados da análise de correlação direcionaram a importância dos caracteres morfológicos e seu impacto positivo e significativo no rendimento de grãos. Os resultados da PCA mostraram que a maior parte da variação (70%) entre o conjunto de dados pôde ser explicada pelos cinco primeiros componentes. Também identificou que Sementes / Espiga, Peso de 1000 Grãos e Índice de Colheita têm uma maior influência na contribuição para o rendimento do trigo duro. Com base nos resultados, recomenda-se que esses importantes parâmetros possam ser considerados e focados em futuros programas de melhoramento de trigo duro para desenvolver variedades de alto rendimento.

Statistical modeling for analyzing grain yield of durum wheat under rainfed conditions in Azad Jammu Kashmir, Pakistan / Modelagem estatística para analisar o rendimento de grãos de trigo durum sob condições de chuva em Azad Jammu Kashmir, Paquistão

One of the most important traits that plant breeders aim to improve is grain yield which is a highly quantitative trait controlled by various agro-morphological traits. Twelve morphological traits such as Germination Percentage, Days to Spike Emergence, Plant Height, Spike Length, Awn Length, Tillers/Plant, Leaf Angle, Seeds/Spike, Plant Thickness, 1000-Grain Weight, Harvest Index and Days to Maturity have been considered as independent factors. Correlation, regression, and principal component analysis (PCA) are used to identify the different durum wheat traits, which significantly contribute to the yield. The necessary assumptions required for applying regression modeling have been tested and all the assumptions are satisfied by the observed data. The outliers are detected in the observations of fixed traits and Grain Yield. Some observations are detected as outliers but the outlying observations did not show any influence on the regression fit. For selecting a parsimonious regression model for durum wheat, best subset regression, and stepwise regression techniques have been applied. The best subset regression analysis revealed that Germination Percentage, Tillers/Plant, and Seeds/Spike have a marked increasing effect whereas Plant thickness has a negative effect on durum wheat yield. While stepwise regression analysis identified that the traits, Germination Percentage, Tillers/Plant, and Seeds/Spike significantly contribute to increasing the durum wheat yield. The simple correlation coefficient specified the significant positive correlation of Grain Yield with Germination Percentage, Number of Tillers/Plant, Seeds/Spike, and Harvest Index. These results of correlation analysis directed the importance of morphological characters and their significant positive impact on Grain Yield. The results of PCA showed that most variation (70%) among data set can be explained by the first five components. It also identified that Seeds/Spike; 1000-Grain Weight and Harvest Index have a higher influence in contributing to the durum wheat yield. Based on the results it is recommended that these important parameters might be considered and focused in future durum wheat breeding programs to develop high yield varieties.

Uma das características mais importantes que os produtores de plantas visam melhorar é o rendimento de grãos, que é uma particularidade altamente quantitativa e controlada por várias características agromorfológicas. Foram considerados 12 traços morfológicos como fatores independentes, como Porcentagem de Germinação, Dias para Emergência da Espiga, Altura da Planta, Comprimento da Espiga, Comprimento da Aresta, Perfilhos /Planta, Ângulo da Folha, Sementes /Espiga, Espessura da Planta, Peso de 1000 Grãos, Índice de Colheita e Dias até a Maturidade,. A correlação, regressão e análise de componentes principais (em inglês Principal Component Analysis (PCA)) são usadas para identificar as diferentes características do trigo duro, que contribuem significativamente para o rendimento. As suposições necessárias exigidas para a aplicação da modelagem de regressão foram testadas e todas as suposições são adequadas de acordo com os dados observados. Os outliers são detectados nas observações de características fixas e rendimento de grãos. Algumas observações são detectadas como outliers, mas as observações outliers não mostraram qualquer influência no ajuste da regressão. Para selecionar um modelo de regressão parcimonioso para o trigo duro, foram aplicadas tanto a melhor regressão de subconjunto quanto as técnicas de regressão stepwise. A melhor análise de regressão de subconjunto revelou que a porcentagem de germinação, perfilhos /planta e sementes /espiga tem um efeito de aumento acentuado, enquanto a espessura da planta tem um efeito negativo sobre o rendimento do trigo duro. Enquanto a análise de regressão passo a passo identificou que as características, porcentagem de germinação, perfilhos/planta e sementes /espiga contribuem significativamente para aumentar a produtividade do trigo duro. O coeficiente de correlação simples especificou a correlação positiva significativa do rendimento de grãos com a porcentagem de germinação, número de perfilhos/planta, sementes / espiga e índice de colheita. Esses resultados da análise de correlação direcionaram a importância dos caracteres morfológicos e seu impacto positivo e significativo no rendimento de grãos. Os resultados da PCA mostraram que a maior parte da variação (70%) entre o conjunto de dados pôde ser explicada pelos cinco primeiros componentes. Também identificou que Sementes / Espiga, Peso de 1000 Grãos e Índice de Colheita têm uma maior influência na contribuição para o rendimento do trigo duro. Com base nos resultados, recomenda-se que esses importantes parâmetros possam ser considerados e focados em futuros programas de melhoramento de trigo duro para desenvolver variedades de alto rendimento.

Is a simple model based on two mixing reservoirs able to reproduce the intra-annual dynamics of DOC and NO3 stream concentrations in an agricultural headwater catchment?

Agriculture disturbs the biogeochemical cycles of major elements, which alters the elemental stoichiometry of surface stream waters, with potential impacts on their ecosystems. However, models of catchment hydrology and water quality remain relatively disconnected, even though the observation that dissolved organic carbon (DOC) and nitrate (NO3-) have opposite spatial and temporal patterns seems relevant for improving our representation of hydrological transport pathways within catchments. We tested the ability of a parsimonious model to simultaneously reproduce intra-annual dynamics of stream flow, DOC and NO3- concentrations using 15 years of daily data from a small headwater agricultural catchment (AgrHyS observatory). The model consists of an unsaturated reservoir, a slow reservoir representing the groundwater and a fast reservoir representing the riparian zone and preferential flow paths. The sources of DOC and NO3- are assumed to behave as infinite pools with a fixed concentration in each reservoir that contributes to the stream. Stream concentrations thus result from simple mixing of slow and fast reservoir contributions. The model simultaneously reproduced annual and storm-event dynamics of discharge, DOC and NO3- concentrations in the stream, with calibration KGE scores of 0.77, 0.64 and 0.58 respectively, and validation KGE scores of 0.72, 0.58 and 0.43 respectively. These results suggest that the dynamics of these concentrations can be explained by hydrological transport processes and thus by temporally variable contributions from slow (NO3- rich and DOC poor) and fast reservoirs (DOC rich and NO3- poor), with a poor representation of the biogeochemical transformations. Unexpectedly, using the concentration time series to calibrate the model increased uncertainty in the parameters that control hydrological fluxes of the model. The legacy storage of NO3- resulting from agricultural history in the studied catchment supports the assumption that the main DOC and NO3- sources behave as infinite pools at the scale of several years. Nevertheless, reproducing the long-term trends in solute concentration would require additional information about DOC and NO3- trends within the reservoirs.

Intermountain chronic disease risk score (ICHRON) validation for prediction of incident chronic disease diagnoses in an australian primary prevention population.

BACKGROUND: The Intermountain Chronic Disease Risk Score (ICHRON) is a primary prevention risk prediction tool that uses commonly ordered blood tests and is designed to be calculated by the electronic health record. ICHRON was highly predictive of 3-year chronic disease (ChrD) diagnosis in an internal validation; however, external validation is needed. METHODS: ICHRON was calculated among patients from a region of Australia using sex-specific weightings of age and components of the comprehensive metabolic panel and the complete blood count. Original ICHRON weightings and risk stratification thresholds from the US-based derivation at Intermountain Healthcare were used. ICHRON was evaluated as a predictor of an incident 3-year ChrD diagnosis (coronary artery disease, myocardial infarction, heart failure, atrial fibrillation, stroke, dementia, diabetes, renal failure, chronic obstructive pulmonary disease, and peripheral vascular disease). RESULTS: A total 5,512 females (49.6 ± 19.2) and 3,461 males (53.0 ± 18.6) were studied. Among females, 50.3%, 33.7% and 16.0% were low, moderate, and high-risk, respectively; and for males, 39.6%, 35.0%, and 25.4%. Frequency of a 3-year ChrD diagnosis among females was 2.2%, 8.5%, and 15.9% for low, moderate, and high-risk groups, respectively, and 4.7%, 9.7%, and 20.0% among males. C-statistics were 0.726 (0.700, 0.752) for females and 0.694 (0.665, 0.724) for males. CONCLUSION: ICHRON predicted ChrD diagnosis at 3-years among an external, geographically distant validation cohort. These findings show the value of ICHRON for primary care patients in distinct locales. Additionally, electronic calculation of ICHRON empowers the clinical use of this tool to identify and differentially manage and treat high-risk patients.

Hankel Matrix Rank as Indicator of Ghost in Bearing-only Tracking.

Usually, bearing angle measurements are employed in triangulation methods to display the position of targets. However, in multi-radar and multi-target scenarios, triangulation approaches bring out ghosts that operate like real targets. This article proposes a target/ghost classifier that relies on the fact that the trajectory of a ghost is actually a function of trajectories of at least two targets and therefore, the complexity of a ghost trajectory is "greater" than the complexity of targets' trajectories.

Hydro-climatic forcing of dissolved organic carbon in two boreal lakes of Canada.

The boreal forest of the northern hemisphere represents one of the world's largest ecozones and contains nearly one third of the world's intact forests and terrestrially stored carbon. Long-term variations in temperature and precipitation have been implied in altering carbon cycling in forest soils, including increased fluxes to receiving waters. In this study, we use a simple hydrologic model and a 40-year dataset (1971-2010) of dissolved organic carbon (DOC) from two pristine boreal lakes (ELA, Canada) to examine the interactions between precipitation and landscape-scale controls of DOC production and export from forest catchments to surface waters. Our results indicate that a simplified hydrologically-based conceptual model can enable the long-term temporal patterns of DOC fluxes to be captured within boreal landscapes. Reconstructed DOC exports from forested catchments in the period 1901-2012 follow largely a sinusoidal pattern, with a period of about 37years and are tightly linked to multi-decadal patterns of precipitation. By combining our model with long-term precipitation estimates, we found no evidence of increasing DOC transport or in-lake concentrations through the 20th century.

Flexible multivariate marginal models for analyzing multivariate longitudinal data, with applications in R.

Most of the available multivariate statistical models dictate on fitting different parameters for the covariate effects on each multiple responses. This might be unnecessary and inefficient for some cases. In this article, we propose a modelling framework for multivariate marginal models to analyze multivariate longitudinal data which provides flexible model building strategies. We show that the model handles several response families such as binomial, count and continuous. We illustrate the model on the Kenya Morbidity data set. A simulation study is conducted to examine the parameter estimates. An R package mmm2 is proposed to fit the model.