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

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.

Hyperspectral Estimation of Winter Wheat Leaf Area Index Based on Continuous Wavelet Transform and Fractional Order Differentiation

Leaf area index (LAI) is highly related to crop growth, and the traditional LAI measurement methods are field destructive and unable to be acquired by large-scale, continuous, and real-time means. In this study, fractional order differential and continuous wavelet transform were used to process the canopy hyperspectral reflectance data of winter wheat, the fractional order differential spectral bands and wavelet energy coefficients with more sensitive to LAI changes were screened by correlation analysis, and the optimal subset regression and support vector machine were used to construct the LAI estimation models for different growth stages. The precision evaluation results showed that the LAI estimation models constructed by using wavelet energy coefficients combined with a support vector machine at the jointing stage, fractional order differential combined with support vector machine at the booting stage, and wavelet energy coefficients combined with optimal subset regression at the flowering and filling stages had the best prediction performance. Among these, both flowering and filling stages could be used as the best growth stages for LAI estimation with modeling and validation R2 of 0.87 and 0.71, 0.84 and 0.77, respectively. This study can provide technical reference for LAI estimation of crops based on remote sensing technology.

STATISTICAL INFERENCE WITH F-STATISTICS WHEN FITTING SIMPLE MODELS TO HIGH-DIMENSIONAL DATA

We study linear subset regression in the context of the high-dimensional overall model $y = \vartheta +\theta ' z + \epsilon $ with univariate response y and a d-vector of random regressors z, independent of $\epsilon $ . Here, “high-dimensional” means that the number d of available explanatory variables is much larger than the number n of observations. We consider simple linear submodels where y is regressed on a set of p regressors given by $x = M'z$ , for some $d \times p$ matrix M of full rank $p < n$ . The corresponding simple model, that is, $y=\alpha +\beta ' x + e$ , is usually justified by imposing appropriate restrictions on the unknown parameter $\theta $ in the overall model; otherwise, this simple model can be grossly misspecified in the sense that relevant variables may have been omitted. In this paper, we establish asymptotic validity of the standard F-test on the surrogate parameter $\beta $ , in an appropriate sense, even when the simple model is misspecified, that is, without any restrictions on $\theta $ whatsoever and without assuming Gaussian data.

Using a GIS-based order weight Average (OWA) Methods to Predict Suitable Location for Artificial Recharge of Groundwater

The purpose of this study is to use the GIS-based analytic hierarchy process (AHP) and order weight average (OWA) to determine suitable locations for the artificial recharge of groundwater (ARG). Therefore, after preparing the fuzzy maps for each parameter, AHP method is used to pari comparison and determine the weight of each parameter. Then, using the OWA-AHP method based on different levels of confidence (different α values ​​), the weighting is done for each parameter to prepare the final land suitability maps with different risk levels. Also, the adaptive network-based fuzzy inference system (ANFIS) method is used to predict land suitability classes using input parameters. Then, using the Best subset regression method, the most important effective parameters for ARG are identified. The results of the Fuzzy-AHP method show that 27% of the area (in different parts) has good and very good conditions for ARG. The results of the combined OWA-AHP method show that, in case of low-level risk and no trade-off, more area is in very low class (80 %) while in case of the high level of risk and average trade-off, the highest area is in the very low class (27 %). The results of the ANFIS method show that fuzzy c–means (FCM) and sub-clustering methods have high accuracy to predict suitable places for ARG. The results of the best subset regression method show that slope, lithology, land use, and altitude with the lowest Cp values ​ (5.2) are effective parameters to determine ARG.

APPLICATION OF MAJOR ION CONCENTRATIONS FOR THE PREDICTION OF STABLE ISOTOPIC COMPOSITION IN SURFACE WATER

The δ2H and δ18O values in water bodies are essential to the management of water resources because of the ability to insight into hydrological processes. In this study, we have measured and analyzed the major ions (Na+, K+, Ca2+, Mg2+, Cl–, SO24– and HCO–3 ) and stable H-O isotopes (δ2H and δ18O) for fifteen surface water samples collected from the Xinbian River in Suzhou, northern Anhui Province, China. The results show that all of the water samples are classified to be Na-HCO3 type, and the mean values of δ2H and δ18O are –42.93‰ and –5.36‰, respectively. Gibbs diagram and the relationship between δ2H and δ18O indicate that both water chemistry and stable isotopes in river water are mainly controlled by evaporation. Correlation analysis reveals that a significant correlation between major ions and δ18O. Predictors (K+, SO24– and HCO–3 ) have been selected by optimal subset regression analysis were used to model the δ18O values in the river water. Moreover, the residuals of the model were normally distributed and values between –0.2‰ to 0.2‰ for most water samples, suggesting a strong relationship between the observed and predicted δ18O values.

FAKTOR-FAKTOR YANG MEMENGARUHI TINGKAT PREVALENSI KONTRASEPSI MODERN DI INDONESIA TAHUN 2017

Contraceptive Prevalence Rate (CPR) atau tingkat prevalensi kontrasepsi merupakan indikator yang digunakan untuk melihat seberapa besar pemakaian kontrasepsi di suatu wilayah, sedangkan CPR modern merupakan indikator yang dikhususkan pada pemakaian kontrasepsi dengan cara modern. Dari tahun 2015 hingga 2017, angka CPR baik untuk semua metode maupun modern terus mengalami penurunan dan masih belum mencapai target dari BKKBN meskipun target CPR telah diturunkan. Penelitian ini bertujuan untuk menganalisis faktor-faktor yang memengaruhi tingkat prevalensi kontrasepsi modern dengan menggunakan metode analisis regresi linear berganda, dimana model terbaik dipilih dengan metode best subset regression. Data yang digunakan merupakan data sekunder yang diperoleh dari Survei Demografi dan Kesehatan Indonesia (SDKI) 2017, Data dan Informasi Kemiskinan Kabupaten/Kota tahun 2017, serta Laporan Akuntabilitas Kinerja Instansi Pemerintah BKKBN 2017. Hasil analisis menunjukkan bahwa model terbaik yang terpilih adalah model dengan variabel bebas persentase penduduk miskin, umur kawin pertama dan pengetahuan pria kawin tentang kontrasepsi. Berdasarkan uji secara simultan diperoleh kesimpulan bahwa minimal terdapat satu variabel bebas yang signifikan terhadap tingkat prevalensi kontrasepsi modern. Sedangkan secara parsial hanya variabel umur kawin pertama dan pengetahuan pria kawin tentang kontrasepsi yang signifikan terhadap tingkat prevalensi kontrasepsi modern, dimana umur kawin pertama berpengaruh negatif dan pengetahuan pria kawin tentang kontrasepsi berpengaruh positif.

Using best subset regression to identify clinical characteristics and biomarkers associated with sepsis-associated acute kidney injury

Sepsis-associated acute kidney injury (AKI) is a complex clinical disorder associated with inflammation, endothelial dysfunction, and dysregulated coagulation. With standard regression methods, collinearity among biomarkers may lead to the exclusion of important biological pathways in a single final model. Best subset regression is an analytic technique that identifies statistically equivalent models, allowing for more robust evaluation of correlated variables. Our objective was to identify common clinical characteristics and biomarkers associated with sepsis-associated AKI. We enrolled 453 septic adults within 24 h of intensive care unit admission. Using best subset regression, we evaluated for associations using a range of models consisting of 1−38 predictors (composed of clinical risk factors and plasma and urine biomarkers) with AKI as the outcome [defined as a serum creatinine (SCr) increase of ≥0.3 mg/dL within 48 h or ≥1.5× baseline SCr within 7 days]. Two hundred ninety-seven patients had AKI. Five-variable models were found to be of optimal complexity, as the best subset of five- and six-variable models were statistically equivalent. Within the subset of five-variable models, 46 permutations of predictors were noted to be statistically equivalent. The most common predictors in this subset included diabetes, baseline SCr, angiopoetin-2, IL-8, soluble tumor necrosis factor receptor-1, and urine neutrophil gelatinase-associated lipocalin. The models had a c-statistic of ∼0.70 (95% confidence interval: 0.65–0.75). In conclusion, using best subset regression, we identified common clinical characteristics and biomarkers associated with sepsis-associated AKI. These variables may be especially relevant in the pathogenesis of sepsis-associated AKI.