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Susan Barati Mohammad Reza Vahabi Mohammad Reza Mosaddeghi Mehdi Bassiri 《Archives of Agronomy and Soil Science》2016,62(1):69-91
Soil water availability is very crucial for pasture plants because their growth solely depends on the soil water storage. While plant-available water (PAW) is successfully related to plant growth, it is the energy required per unit mass of water, integrated over the PAW range, named the integral energy (EI) that determines how easily plants can take up water from the soil. The soil water retention function was integrated over the PAW range to calculate the EI. PAW and EI were determined for Medicago sativa (alfalfa, a legume) and Bromus tomentellus (a grass) species in five texturally different soils of semi-steppe rangeland in central Zagros, western Iran. The PAW was calculated as the difference between field capacity and permanent wilting point (nominal h of 15,000 hPa or actual h obtained from PWP value determined in greenhouse). EI values were calculated for the nominal and actual PAW values. M. sativa PAW and EI values were more than those from B. tomentellus, indicating that M. sativa was able to tolerate higher soil matric suctions at similar conditions. Results showed predicting EI only from basic soil properties is not accurate. PAW and EI are dependent on plant species and soil type interactions, and environmental compatibility. 相似文献
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Khabat KHOSRAVI Ali GOLKARIAN Rahim BARZEGAR Mohammad T. AALAMI Salim HEDDAM Ebrahim OMIDVAR Saskia D. KEESSTRA Manuel L&#;PEZ-VICENTE 《土壤圈》2023,33(3):479-495
Direct soil temperature (ST) measurement is time-consuming and costly;thus,the use of simple and cost-effective machine learning (ML) tools is helpful.In this study,ML approaches,including KStar,instance-based K-nearest learning (IBK),and locally weighted learning (LWL),coupled with resampling algorithms of bagging (BA) and dagging (DA)(BA-IBK,BA-KStar,BA-LWL,DA-IBK,DA-KStar,and DA-LWL) were developed and tested for multi-step ahead (3,6,and 9 d ahead) ST forecasting.In addition,a linear regress... 相似文献
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Khabat KHOSRAVI Phuong T. T. NGO Rahim BARZEGAR John QUILTY Mohammad T. AALAMI Dieu T. BUI 《土壤圈》2022,32(5):718-732
Water infiltration into soil is an important process in hydrologic cycle; however, its measurement is difficult, time-consuming and costly. Empirical and physical models have been developed to predict cumulative infiltration (CI), but are often inaccurate. In this study, several novel standalone machine learning algorithms (M5Prime (M5P), decision stump (DS), and sequential minimal optimization (SMO)) and hybrid algorithms based on additive regression (AR) (i.e., AR-M5P, AR-DS, and AR-SMO) and weighted instance handler wrapper (WIHW) (i.e., WIHW-M5P, WIHW-DS, and WIHW-SMO) were developed for CI prediction. The Soil Conservation Service (SCS) model developed by the United States Department of Agriculture (USDA), one of the most popular empirical models to predict CI, was considered as a benchmark. Overall, 154 measurements of CI (explanatory/input variables) were taken from 16 sites in a semi-arid region of Iran (Illam and Lorestan provinces). Six input variable combinations were considered based on Pearson correlations between candidate model inputs (time of measuring and soil bulk density, moisture content, and sand, clay, and silt percentages) and CI. The dataset was divided into two subgroups at random:70% of the data were used for model building (training dataset) and the remaining 30% were used for model validation (testing dataset). The various models were evaluated using different graphical approaches (bar charts, scatter plots, violin plots, and Taylor diagrams) and quantitative measures (root mean square error (RMSE), mean absolute error (MAE), Nash-Sutcliffe efficiency (NSE), and percent bias (PBIAS)). Time of measuring had the highest correlation with CI in the study area. The best input combinations were different for different algorithms. The results showed that all hybrid algorithms enhanced the CI prediction accuracy compared to the standalone models. The AR-M5P model provided the most accurate CI predictions (RMSE=0.75 cm, MAE=0.59 cm, NSE=0.98), while the SCS model had the lowest performance (RMSE=4.77 cm, MAE=2.64 cm, NSE=0.23). The differences in RMSE between the best model (AR-M5P) and the second-best (WIHW-M5P) and worst (SCS) were 40% and 84%, respectively. 相似文献
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Khabat Kheirabadi Amir Rashidi Sadegh Alijani Ikhide Imumorin 《Animal Science Journal》2014,85(11):925-934
We compared the goodness of fit of three mathematical functions (including: Legendre polynomials, Lidauer‐Mäntysaari function and Wilmink function) for describing the lactation curve of primiparous Iranian Holstein cows by using multiple‐trait random regression models (MT‐RRM). Lactational submodels provided the largest daily additive genetic (AG) and permanent environmental (PE) variance estimates at the end and at the onset of lactation, respectively, as well as low genetic correlations between peripheral test‐day records. For all models, heritability estimates were highest at the end of lactation (245 to 305 days) and ranged from 0.05 to 0.26, 0.03 to 0.12 and 0.04 to 0.24 for milk, fat and protein yields, respectively. Generally, the genetic correlations between traits depend on how far apart they are or whether they are on the same day in any two traits. On average, genetic correlations between milk and fat were the lowest and those between fat and protein were intermediate, while those between milk and protein were the highest. Results from all criteria (Akaike's and Schwarz's Bayesian information criterion, and ?2*logarithm of the likelihood function) suggested that a model with 2 and 5 coefficients of Legendre polynomials for AG and PE effects, respectively, was the most adequate for fitting the data. 相似文献
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Zamani Z Arjmand M Tafazzoli M Gholizadeh A Pourfallah F Sadeghi S Mirzazadeh R Mirkhani F Taheri S Iravani A Bayat P Vahabi F 《Pakistan journal of biological sciences: PJBS》2011,14(3):195-203
Vaccines require a period of at least three months for clinical trials, hence a method that can identify elicitation of immune response a few days after the first dose is a necessity. Evolutionary variable selections are modeling approaches for proper manipulation of available data which were used to set up an animal model for classification of time dependent 1HNMR metabolomic profiles and pattern recognition of fluctuations of metabolites in two groups of male rabbits. One group of rabbits was immunized with human red blood cells and the other used as control. Blood was obtained every 48 h from each rabbit for a period of six weeks and the serum monitored for antibodies and metabolites by 1HNMR spectra. Evaluation of data was carried out using orthogonal signal correction followed by principal component analysis and partial least square. A neural network was also set up to predict immunization profiles. A distinct separation in patterns of significant metabolites was obtained between the two groups, just a few days after the first and the second dose. These metabolites were used as targets of neural networks where each sample was used as test, validation and training and their quantitative influence predicted by regression. This model could be used for prediction of immunization in rabbits a few days after the first dose with 96% accuracy. Similar animals and human vaccine trials would assist greatly in reaching early conclusions in advance of the usual two month immunization schedule; resulting in an appreciable saving of cost and time. 相似文献
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