scholarly journals Statistical models expressing relations between soil moisture, aggregate speed, and tillage depth at plowing and cultivation

2022 ◽  
Vol 1216 (1) ◽  
pp. 012006
Author(s):  
P M Veleva ◽  
G M Hristova
Keyword(s):  
Soil Moisture ◽  
Field Experiment ◽  
Control Systems ◽  
Statistical Models ◽  
Exponential Model ◽  
Quadratic Model ◽  
Coefficient Of Determination ◽  
Agricultural Machinery ◽  
Tillage Depth ◽  
One Year

Abstract The study is based on a one-year field experiment (2019) in the land of the Chirpan region located in central Bulgaria. The agrotechnical operations of plowing and cultivation, applied in technology for the production of sunflower, are studied. Four models (Linear, Exponential, Logarithmic, and Quadratic) were compared at p < 0.05, defining the relation between soil moisture, aggregate speed, and the uniformity of the soil index Tillage depth during plowing and cultivation. It was found that in plowing at a speed of 4 km/h the Quadratic model described the relation between soil moisture and tillage depth with the highest coefficient of determination (R2 = 0.682). Relating to plowing at a speed of 4.5 km/h the most suitable is the Exponential model (R2 = 0.729), i.e. about 68.2% and 72.9% of the variations in tillage depth are due to the influence of the moisture of the soil. The coefficients of determination, calculated when cultivating at speeds of 8 km/h (R2 = 0.526) and 9 km/h (R2 = 0.557), show that the Quadratic model most strongly (52.6% and 55.7%) determines the relation between soil moisture and tillage depth. The developed models could be used to optimize the control systems of agricultural machinery.

LIME AND DAIRY PRODUCTION

1982 ◽  
pp. 93-103
Author(s):  
N.A. Thomson
Keyword(s):  
Soil Moisture ◽  
Dairy Cows ◽  
Milk Production ◽  
Soil Ph ◽  
Dairy Production ◽  
Pasture Production ◽  
The Third ◽  
One Year ◽  
Plasma Magnesium

In a four year grazing trial with dairy cows the application of 5000 kg lime/ ha (applied in two applications of 2500 kg/ha in winter of the first two years) significantly increased annual pasture production in two of the four years and dairy production in one year. In three of the four years lime significantly increased pasture growth over summer/autumn with concurrent increases in milk production. In the last year of the trial lime had little effect on pasture growth but a relatively large increase in milkfat production resulted. A higher incidence of grass staggers was recorded on the limed farmlets in spring for each of the four years. In the second spring immediately following the second application of lime significant depressions in both pasture and plasma magnesium levels were recorded. By the third spring differences in plasma magnesium levels were negligible but small depressions in herbage magnesium resulting from lime continued to the end of the trial. Lime significantly raised soil pH, Ca and Mg levels but had no effect on either soil K or P. As pH levels of the unlimed paddocks were low (5.2-5.4) in each autumn and soil moisture levels were increased by liming, these factors may suggest possible causes for the seasonality of the pasture response to lime

Decolorization of Reactive Black B from wastewater by electro-coagulation: optimization using multivariate RSM and ANN

2020 ◽  
Vol 0 (0) ◽  
Author(s):  
Kajal Gautam ◽  
Rishi K. Verma ◽  
Suantak Kamsonlian ◽  
Sushil Kumar
Keyword(s):  
Current Density ◽  
Operating Cost ◽  
Quadratic Model ◽  
Coefficient Of Determination ◽  
Process Time ◽  
Mathematical Tool ◽  
Electrode Gap ◽  
Artificial Neural Network Ann ◽  
Electro Coagulation ◽  
Optimized Conditions

AbstractThe present study is aimed to model and optimize the electrocoagulation (EC) process with five important parameters for the decolorization of Reactive Black B (RBB) from simulated wastewater. A multivariate approach, response surface methodology (RSM) together with central composite design (CCD) is used to optimize process parameters such as pH (5–9), electrode gap (0.5–2.5 cm), current density (2.08–10.41 mA/cm2), process time (10–30 min), and initial dye concentration (100–500 mg/l). The predicted percentage decolorization of dye is obtained as 97.21% at optimized conditions: pH (6.8), gapping (1.3 cm), current density (8.32 mA/cm2), time (23 min), and initial dye concentration (200 mg/L), which is very close to experimental percent decolorization (98.41%). The statistical analysis of variance (ANOVA) is performed to evaluate the quadratic model (RSM), and shows good fit of experimental data with coefficient of determination R2 >0.93. An Artificial Neural Network (ANN) is also used to predict the percentage decolorization and gives overall 94.96% which shows performance accuracy between the predicted and actual value of decolorization. The additional considerations of operating cost and current efficiency are also taken care to show the efficacy of EC process with mathematical tool. The sludge characteristics are determined by FE-SEM/EDX.

scholarly journals Soil moisture depletion and ground water use by bed planted barley as influenced by cultivars, crop geometry and moisture regimes

2017 ◽  
Vol 9 (3) ◽  
pp. 1465-1468 ◽  
Author(s):  
Naveen Kumar ◽  
Suresh Kumar ◽  
Parveen Kumar ◽  
Meena Sewhag
Keyword(s):  
Soil Moisture ◽  
Field Experiment ◽  
Ground Water ◽  
Water Use ◽  
Moisture Regime ◽  
Rabi Season ◽  
Shallow Water Table ◽  
Moisture Regimes ◽  
Crop Geometry ◽  
Plot Design

A field experiment was conducted during rabi season 2011-2012 at Research Farm, CCS Haryana Agri-cultural University, Hisar, Haryana (India) to study the periodic soil moisture depletion and ground water use by bed planted barley as influenced by cultivars, crop geometry and moisture regimes under shallow water table conditions. The experiment was laid out in split plot design with three replications keeping combinations of three cultivars viz., BH 393, BH 902 and BH 885 and two crop geometries viz 2 rows per bed and 3 rows per bed (70 cm wide with 40 cm top and 30 cm furrow) in main plots and three moisture regimes (irrigation at IW/CPE 0.3, 0.4 & 0.5) in sub plots. The results revealed that maximum soil moisture depletion (105 mm) and ground water contribution (62 mm) were recorded in BH 902, followed by BH 393 and BH 885. Among crop geometries, soil moisture depletion (96.6 mm) and ground water contribution (61 mm) were recorded higher in 3 rows per bed than 2 rows per bed. Among three moisture regimes, the soil moisture depletion (108 mm) and ground water contribution (65 mm) decreased with increase in moisture regime from irrigation at IW/CPE 0.3 to irrigation at IW/CPE 0.4 or 0.5.

scholarly journals Response of Native White Sorghum to Irrigation under Different Nitrogen-Fertility Levels and Seeding Rates in Lajas Valley, Puerto Rico

1969 ◽  
Vol 50 (2) ◽  
pp. 92-112
Author(s):  
R. Vázquez ◽  
A. Eschenwald-Hess ◽  
M. J. Martínez-Luciano
Keyword(s):  
Puerto Rico ◽  
Soil Moisture ◽  
Field Experiment ◽  
Dry Matter ◽  
Root Zone ◽  
High Moisture ◽  
Nitrogen Levels ◽  
Nitrogen Fertility ◽  
Native White ◽  
Active Root

A field experiment was conducted at Lajas Substation in order to study the effects of four irrigation and three nitrogen levels under three different seeding rates on dry-matter yields of White Native sorghum. The following irrigation treatments were tried: High moisture, plots irrigated when the average soil-moisture suction in the active root-zone reached 0.7 atm.; medium moisture, irrigated when the average soil-moisture suction reached 2.0 atm.; low moisture, irrigated when the average soil-moisture suction reached 5.0 atm., and nonirrigated plots were used as check. The nitrogen levels tested were 40, 80, and 120 pounds per acre per harvest. The seeding rates used were 10, 20, and 30 pounds per acre.

scholarly journals Flow Prediction Using Remotely Sensed Soil Moisture in Irish Catchments

Water ◽  
2020 ◽  
Vol 12 (8) ◽  
pp. 2202
Author(s):  
Chanyu Yang ◽  
Fiachra E. O’Loughlin
Keyword(s):  
Soil Moisture ◽  
Remote Sensing Data ◽  
Remotely Sensed ◽  
Coefficient Of Determination ◽  
Energy Budgets ◽  
Care Needs ◽  
Soil Moisture Accounting ◽  
Model Soil ◽  
Ungauged Basin ◽  
Behavioural Parameter

Owing to a scarcity of in situ streamflow data in ungauged or poorly gauged basins, remote sensing data is an ideal alternative. It offers a valuable perspective into the dynamic patterns that can be difficult to examine in detail with point measurements. For hydrology, soil moisture is one of the pivotal variables which dominates the partitioning of the water and energy budgets. In this study, nine Irish catchments were used to demonstrate the feasibility of using remotely sensed soil moisture for discharge prediction in ungagged basins. Using the conceptual hydrological model “Soil Moisture Accounting and Routing for Transport” (SMART), behavioural parameter sets (BPS) were selected using two different objective functions: the Nash Sutcliffe Efficiency (NSE) and Coefficient of Determination (R2) for the calibration period. Good NSE scores were obtained from hydrographs produced using the satellite soil moisture BPS. While the mean performance shows the feasibility of using remotely sensed soil moisture, some outliers result in negative NSE scores. This highlights that care needs to be taken with parameterization of hydrological models using remotely sensed soil moisture for ungauged basin.

scholarly journals Predicting Rainfall and Runoff Through Satellite Soil Moisture Data and SWAT Modelling for a Poorly Gauged Basin in Iran

Water ◽  
2019 ◽  
Vol 11 (3) ◽  
pp. 594 ◽  
Author(s):  
Majid Fereidoon ◽  
Manfred Koch ◽  
Luca Brocca
Keyword(s):  
Soil Moisture ◽  
Hydrological Model ◽  
Assessment Tool ◽  
Swat Model ◽  
Rainfall Data ◽  
Coefficient Of Determination ◽  
Hydrological Models ◽  
Streamflow Prediction ◽  
Soil Moisture Data ◽  
Monthly Runoff

Hydrological models are widely used for many purposes in water sector projects, including streamflow prediction and flood risk assessment. Among the input data used in such hydrological models, the spatial-temporal variability of rainfall datasets has a significant role on the final discharge estimation. Therefore, accurate measurements of rainfall are vital. On the other hand, ground-based measurement networks, mainly in developing countries, are either nonexistent or too sparse to capture rainfall accurately. In addition to in-situ rainfall datasets, satellite-derived rainfall products are currently available globally with high spatial and temporal resolution. An innovative approach called SM2RAIN that estimates rainfall from soil moisture data has been applied successfully to various regions. In this study, first, soil moisture content derived from the Advanced Microwave Scanning Radiometer for the Earth observing system (AMSR-E) is used as input into the SM2RAIN algorithm to estimate daily rainfall (SM2R-AMSRE) at different sites in the Karkheh river basin (KRB), southwest Iran. Second, the SWAT (Soil and Water Assessment Tool) hydrological model was applied to simulate runoff using both ground-based observed rainfall and SM2R-AMSRE rainfall as input. The results reveal that the SM2R-AMSRE rainfall data are, in most cases, in good agreement with ground-based rainfall, with correlations R ranging between 0.58 and 0.88, though there is some underestimation of the observed rainfall due to soil moisture saturation not accounted for in the SM2RAIN equation. The subsequent SWAT-simulated monthly runoff from SM2R-AMSRE rainfall data (SWAT-SM2R-AMSRE) reproduces the observations at the six gauging stations (with coefficient of determination, R² > 0.71 and NSE > 0.56), though with slightly worse performances in terms of bias (Bias) and root-mean-square error (RMSE) and, again, some systematic flow underestimation compared to the SWAT model with ground-based rainfall input. Additionally, rainfall estimates of two satellite products of the Tropical Rainfall Measuring Mission (TRMM), 3B42 and 3B42RT, are used in the calibrated SWAT- model after bias correction. The monthly runoff predictions obtained with 3B42- rainfall have 0.42 < R2 < 0.72 and−0.06 < NSE < 0.74 which are slightly better than those obtained with 3B42RT- rainfall, but not as good as the SWAT-SM2R-AMSRE. Therefore, despite the aforementioned limitations, using SM2R-AMSRE rainfall data in a hydrological model like SWAT appears to be a viable approach in basins with limited ground-based rainfall data.

scholarly journals Assessing the Wet Deposition Mechanism of Benzo(a)pyrene in the Atmosphere by MF-DCCA

Atmosphere ◽  
2019 ◽  
Vol 10 (6) ◽  
pp. 331 ◽  
Author(s):  
Chunqiong Liu ◽  
Kai Shi ◽  
Jian Liang ◽  
Hongliang Huang
Keyword(s):  
Time Scale ◽  
Wet Deposition ◽  
Cross Correlation ◽  
Coefficient Of Determination ◽  
Scaling Properties ◽  
Temporal Scaling ◽  
Cross Correlation Analysis ◽  
Cross Correlations ◽  
One Year

Based on the 19 year observation from 1998 to 2016 at the Tsuan Wan and Central/Western District monitoring stations in Hong Kong, the aim of this paper was to assess the wet deposition pathway of Benzo(a)pyrene (BaP) on a large time-scale. In order to achieve this goal, multi-fractal detrended cross-correlation analysis (MF-DCCA) was used to characterize the long-term cross-correlations behaviors and multi-fractal temporal scaling properties between BaP (or PM2.5) and precipitation. The results showed that the relationships between BaP and precipitation (or PM2.5) displayed long-term cross-correlation at the time-scale ranging from one month to one year; no cross-correlation between each other was observed in longer temporal scaling regimes (greater than one year). These results correspond to the atmospheric circulation of the Asian monsoon system and are explained in detail. Similar dynamic processes of the wet deposition of BaP and PM2.5 suggested that the main removal process of atmospheric BaP was rainfall deposits of PM2.5-bound BaP. Furthermore, cross-correlations between BaP (or PM2.5) and precipitation at the long time-scale have a multi-fractal nature and long-term persistent power-law decaying behavior. The temporal evolutions of the multi-fractality were investigated by the approach of a sliding window. Based on the evolution curves of multi-fractal parameters, the wet deposition pathway of PM2.5-bound BaP is discussed. Finally, the contribution degree of wet deposition to PM2.5-bound BaP was derived from the coefficient of determination. It was demonstrated that about 45% and 60% of atmospheric BaP removal can be attributed to the wet deposition pathway of PM2.5-bound BaP for the Tsuan Wan and Central/Western District areas, respectively. The findings in this paper are of great significance for further study on the removal mechanism of atmospheric BaP in the future. The MF-DCCA method provides a novel approach to assessing the geochemical cycle dynamics of BaP.

scholarly journals Soil Moisture Estimation Using Citizen Observatory Data, Microwave Satellite Imagery, and Environmental Covariates

Water ◽  
2020 ◽  
Vol 12 (8) ◽  
pp. 2160
Author(s):  
Daniel Kibirige ◽  
Endre Dobos
Keyword(s):  
Soil Moisture ◽  
Expert Knowledge ◽  
Coefficient Of Determination ◽  
Landsat 8 ◽  
Regression Kriging ◽  
Environmental Covariates ◽  
Observatory Data ◽  
Digital Elevation ◽  
Elevation Model

Soil moisture (SM) is a key variable in the climate system and a key parameter in earth surface processes. This study aimed to test the citizen observatory (CO) data to develop a method to estimate surface SM distribution using Sentinel-1B C-band Synthetic Aperture Radar (SAR) and Landsat 8 data; acquired between January 2019 and June 2019. An agricultural region of Tard in western Hungary was chosen as the study area. In situ soil moisture measurements in the uppermost 10 cm were carried out in 36 test fields simultaneously with SAR data acquisition. The effects of environmental covariates and the backscattering coefficient on SM were analyzed to perform SM estimation procedures. Three approaches were developed and compared for a continuous four-month period, using multiple regression analysis, regression-kriging and cokriging with the digital elevation model (DEM), and Sentinel-1B C-band and Landsat 8 images. CO data were evaluated over the landscape by expert knowledge and found to be representative of the major SM distribution processes but also presenting some indifferent short-range variability that was difficult to explain at this scale. The proposed models were evaluated using statistical metrics: The coefficient of determination (R2) and root mean square error (RMSE). Multiple linear regression provides more realistic spatial patterns over the landscape, even in a data-poor environment. Regression kriging was found to be a potential tool to refine the results, while ordinary cokriging was found to be less effective. The obtained results showed that CO data complemented with Sentinel-1B SAR, Landsat 8, and terrain data has the potential to estimate and map soil moisture content.

scholarly journals Performance Evaluation of the Multiple Quantile Regression Model for Estimating Spatial Soil Moisture after Filtering Soil Moisture Outliers

2020 ◽  
Vol 12 (10) ◽  
pp. 1678
Author(s):  
Chunggil Jung ◽  
Yonggwan Lee ◽  
Jiwan Lee ◽  
Seongjoon Kim
Keyword(s):  
Soil Moisture ◽  
Quantile Regression ◽  
Outlier Detection ◽  
Land Surface ◽  
Removal Rate ◽  
Distribution Functions ◽  
Cumulative Distribution ◽  
Soil Types ◽  
Coefficient Of Determination ◽  
Random Errors

The spatial distribution of soil moisture (SM) was estimated by a multiple quantile regression (MQR) model with Terra Moderate Resolution Imaging Spectroradiometer (MODIS) and filtered SM data from 2013 to 2015 in South Korea. For input data, observed precipitation and SM data were collected from the Korea Meteorological Administration and various institutions monitoring SM. To improve the work of a previous study, prior to the estimation of SM, outlier detection using the isolation forest (IF) algorithm was applied to the observed SM data. The original observed SM data resulted in IF_SM data following outlier detection. This study obtained an average data removal rate of 20.1% at 58 stations. For various reasons, such as instrumentation, environment, and random errors, the original observed SM data contained approximately 20% uncertain data. After outlier detection, this study performed a regression analysis by estimating land surface temperature quantiles. The soil characteristics were considered through reclassification into four soil types (clay, loam, silt, and sand), and the five-day antecedent precipitation was considered in order to estimate the regression coefficient of the MQR model. For all soil types, the coefficient of determination (R2) and root mean square error (RMSE) values ranged from 0.25 to 0.77 and 1.86% to 12.21%, respectively. The MQR results showed a much better performance than that of the multiple linear regression (MLR) results, which yielded R2 and RMSE values of 0.20 to 0.66 and 1.08% to 7.23%, respectively. As a further illustration of improvement, the box plots of the MQR SM were closer to those of the observed SM than those of the MLR SM. This result indicates that the cumulative distribution functions (CDF) of MQR SM matched the CDF of the observed SM. Thus, the MQR algorithm with outlier detection can overcome the limitations of the MLR algorithm by reducing both the bias and variance.

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