Evaluation of the root zone water quality model for predicting water and NO3–N movement in an Iowa soil

Evaluation of computer models with field data is required before they can be effectively used for predicting agricultural management systems. A study was conducted to evaluate tillage effects on the movement of water and nitrate–nitrogen (NO3–N) in the root zone under continuous corn (Zea mays L.) production. Four tillage treatments considered were: chisel plow (CP), moldboard plow (MP), no-tillage (NT), and ridge-tillage (RT). The root zone water quality model (RZWQM: V.3.25) was used to conduct these simulations. Three years (1990–1992) of field observed data on soil water contents and NO3–N concentrations in the soil profile were used to evaluate the performance of the model. The RZWQM usually predicted higher soil water contents compared with the observed soil water contents. The model predicted higher NO3–N concentrations in the soil profile for MP and NT treatments in comparison with CP and RT treatments, but the magnitude of simulated NO3–N peak concentrations in the soil profile were substantially different from those of the observed peaks. The average NO3–N concentrations for the entire soil profile predicted by the model were close to the observed concentrations except for ridge tillage (percent difference for CP=+5.1%, MP=+12.8%, NT=+18.4%, RT=−44.8%). Discrepancies between the simulated and observed water contents and NO3–N concentrations in the soil profile indicated a need for the calibration of plant growth component of the model further for different soil and climatic conditions to improve the N-uptake predictions of the RZWQM

Movement of NO3-N and atrazine through soil columns as affected by lime application

Lime (CaCO3) applied to the soil, to minimize or neutralize the soil pH, can influence the fate and transport of other chemicals in soil. This study was conducted to investigate the effect of lime application on the movement of NO3-N and atrazine through soil columns under saturated and unsaturated conditions

An Contemplated Approach for Criminality Data using Mining Algorithm

We propose an approach for the arrangement and execution of bad behavior area and criminal recognizing confirmation for Indian urban groups using data mining frameworks. Our approach is parceled into six modules, to be particular�information extraction (DE), information preprocessing (DP), grouping, Google outline, characterization and WEKA� execution. To begin with module, DE expels the unstructured wrongdoing dataset from various wrongdoing Web sources, in the midst of the season of 2000� 2018. Second module, DP cleans, facilitates and diminishes the removed wrongdoing data into sorted out 5,038 wrongdoing events. We address these events using 35 predefined wrongdoing attributes. Secure measures are taken for the wrongdoing database accessibility. Rest four modules are useful for bad behavior acknowledgment, criminal recognizing evidence and desire, and bad behavior affirmation, independently. Wrongdoing acknowledgment is explored using k-suggests gathering, which iteratively makes two wrongdoing bundles that rely upon equivalent wrongdoing properties. Google portray observation to k-infers. Criminal conspicuous verification and estimate is dismembered using KNN portrayal. Bad behavior check of our results is done using WEKA�. WEKA� checks an exactness of 93.62 and 93.99 % in the course of action of two bad behavior clusters using picked bad behavior attributes. Our approach contributes in the change of the overall population by helping the looking at workplaces in bad behavior area and guilty parties' recognizing confirmation, and in this way decreasing the bad behavior rates. Wrongdoings are a social unsettling influence and cost the overall population to an awesome degree from various perspectives. Any examination that can help in separating and comprehending wrongdoing speedier pays for itself. Crime data mining has the capacity of extricating helpful data and concealed examples from the substantial wrongdoing informational indexes. The crime data mining challenges are getting to be fortifying open doors for the coming years. Since the writing of crime information mining has expanded energetically as of late, it winds up obligatory to build up a diagram of the cutting edge. This orderly survey centers around crime data mining procedures and innovations utilized as a part of past investigations. The current work is grouped into various classifications and is introduced utilizing perceptions. This paper additionally demonstrates a few difficulties identified with crime data research

Calibration and Evaluation of Subsurface Drainage Component of RZWQM V.2.5

This study was designed to calibrate and evaluate the subsurface drain flow component of the Root Zone Water Quality Model (RZWQM; Version 2.5) for four tillage-systems: chisel plow (CP), moldboard plow (MB), no-tillage (NT), and ridge-tillage (RT). Measured subsurface drain flow data for 1990 was used for model calibration. Main parameters calibrated were lateral saturated hydraulic conductivity, and effective porosity. Subsurface drain flow predictions were made using calibrated parameters and compared with measured subsurface drain flows for 1991 and 1992. Measured subsurface drain flow data for all 3 yrs was obtained from the Nashua Water Quality Site in Iowa. The model, in general, showed a good agreement between measured and predicted subsurface drain flow values, although discrepancies existed for several days of a given year. Coefficients of determination calculated for predicted vs. measured daily subsurface drain flows ranged from 0.51 to 0.68 for 1990, 0.70 to 0.78 for 1991, and 0.54 to 0.69 for 1992. Simulated tillage effect on subsurface drain flows for 1991 and 1992 were consistent with those for calibrated year 1990 (maximum subsurface drain flow was observed under NT and minimum under MB). However, observed tillage effects varied from year to year, indicating a change in soil hydraulic properties, e.g., macroporosity. Other factors that could have caused the discrepancies between measured and simulated subsurface drain flows were: groundwater flux due to natural gradient, deep seepage, inaccuracies involved in the estimation of breakpoint rainfall data, and spatial variability in soil properties