Effect of different seeding methods on green manure biomass, soil properties and rice yield in rice-based cropping systems

The cultivation of green manure crops plays an important role in soil quality and the sustainability of agricultural systems. Field experiments were conducted during one season (2007/2008) to evaluate the effects of different seeding methods on the biomass and N production of hairy vetch (Vicia villosa) and barley (Hordeum vuglare). The effects of treatments on rice yield and its components were also investigated. Specifically, the following four treatments were evaluated: broadcasting before rice harvesting (BBRH), partial tillage seeding (PTS), group seeding (GS) and drill seeding (DS). The overall results showed the following ranking of biomass and nitrogen production of hairy vetch by different seeding methods: BBRH > PTS > DS > GS. Additionally, biomass and nitrogen production of barley was lower than those of hairy vetch at all tested seeding treatments. BBRH and PTS treatments led to an increase in soil bulk density, porosity and soil organic content. The application of green manure with BBRH and PTS treatments led to a significant increase in rice yield (5,330 and 5,320 kg ha-1) when compared to conventional fertilization. Based on the results, BBRH and PTS are good practices for production of green manure in paddy soil. Chemical fertilizers can be replaced with hairy vetch as green manure in rice-based cropping systems.Key words: Green manure, rice, biomass, soil property, seeding method

Performance Validation of a Multi-Channel LiDAR Sensor: Assessing the Effects of Target Height and Sensor Velocity on Measurement Error

The objective of this study was to determine the effects of sensor velocity and target height above ground level on height measurement error when using a multi-channel LiDAR sensor. A linear motion system was developed to precisely control the dynamics of the LiDAR sensor in an effort to remove uncertainty in the LiDAR position and velocity while under motion. The linear motion system allowed the LiDAR to translate forward and backward in one direction parallel to the ground. A user control interface was developed to operate the system under different velocity profiles and to log LiDAR data synchronous to the motion of the system. The performance of the linear motion system was validated with a tracking total station, and the results showed that the position and velocity control errors were negligible as compared to the LiDAR accuracy. The LiDAR was then validated using 25 test targets at varying heights above ground level (0.1, 0.3, 0.5, 0.6, and 0.8 m) with five different velocity profiles (0.1, 0.5, 1.0, 1.5, and 2.2 m s-1) and six replications to determine the effects of sensor velocity and target height on measurement error. The targets were painted white on one side and black on the other to determine the effect of relative intensity on LiDAR height measurement error. Generalized linear mixed models were fitted with the measurement error and the standard deviation of the measurement error as the responses. Sensor velocity, target height, and their interaction were considered as fixed effects to determine if there were significant differences in average error and standard deviation of error for different sensor velocities and target heights. The results indicated that the velocity of the LiDAR was a significant factor affecting the average error and standard deviation of error in height measurements. However, higher velocities tended to result in only slightly larger average errors. A three-fold increase in the standard deviation was observed when increasing the velocity from 0.1 to 2.2 m s-1. Height of the target was either a weakly significant or insignificant factor in average error and a weakly significant factor affecting the standard deviation of the LiDAR measurements, representing mixed results. The average error and standard deviation were less than 10 and 30 mm, respectively, for all replications. Relative intensities of the LiDAR measurements were 88.2% and 5.4% for white and black targets, respectively, and the different target colors exhibited a 4.7 mm shift in average estimated height error. These uncertainties may not be substantial for agricultural applications, where other sources of error, such as moving crop canopies or error in resolving the position of the sensor, are more likely to dominate overall measurement error

Recalibration Methodology to Compensate for Changing Fluid Properties in an Individual Nozzle Direct Injection Systems

Limited advancement of direct injection pesticide application systems has been made in recent years, which has hindered further commercialization of this technology. One approach to solving the lag and mixing issues typically associated with injection-based systems is high-pressure individual nozzle injection. However, accurate monitoring of the chemical concentrate flow rate can pose a challenge due to the high pressure, low flow, and changing viscosities of the fluid. A methodology was developed for recalibrating high-pressure chemical concentrate injectors to compensate for fluid property variations and evaluate the performance of this technique for operating injectors in an open-loop configuration. Specific objectives were to (1) develop a method for continuous recalibration of the chemical concentrate injectors to ensure accurate metering of chemicals of varying viscosities and (2) evaluate the recalibration method for estimating individual injector flow rates from a system of multiple injectors to assess potential errors. Test results indicated that the recalibration method was able to compensate for changes in fluid kinematic viscosity (e.g., from temperature changes and/or product variation). Errors were less than 3.4% for the minimum injector duty cycle (DCi) (at 10%) and dropped 0.2% for the maximum DCi (at 90%) for temperature changes of up to 20°C. While larger temperature changes may be expected, these test results showed that the proposed method could be successfully implemented to meet desired injection rates. Because multiple injectors would be used in commercial deployment of this technology, a method was developed to calculate the desired injector flow rate using initial injector calibration factors. Using this multi-injector recalibration method, errors ranged from 0.23% to 0.66% between predicted and actual flow rates for all three injectors

RESISTING STATISTICAL HITS IN OPEN NETS

Many works were suggested in several types of threat to achieve various benefits for search for example single keyword search, multi-keyword rated search, and so forth.  Of individuals works, multi-keyword kinds of rated search is becoming more importance because of its realistic effectiveness.  We submit a great search method which pulls round the tree above encoded cloud information, and additionally it manages multi-keyword search additionally to dynamic process on selection of documents.  For acquiring of high search effectiveness, we produce a tree-based index structure and propose an formula while using the index tree. The forecasted plan's referred to as to provide multi-keyword query additionally to specific result ranking, furthermore dynamic update above document collections. Due to important structure of tree-based index, forecasted search system will effectively get sub-straight line search a serious amounts of manage the operation of deletion additionally to insertion of documents

Recalibration Methodology to Compensate for Changing Fluid Properties in an Individual Nozzle Direct Injection Systems

Limited advancement of direct injection pesticide application systems has been made in recent years, which has hindered further commercialization of this technology. One approach to solving the lag and mixing issues typically associated with injection-based systems is high-pressure individual nozzle injection. However, accurate monitoring of the chemical concentrate flow rate can pose a challenge due to the high pressure, low flow, and changing viscosities of the fluid. A methodology was developed for recalibrating high-pressure chemical concentrate injectors to compensate for fluid property variations and evaluate the performance of this technique for operating injectors in an open-loop configuration. Specific objectives were to (1) develop a method for continuous recalibration of the chemical concentrate injectors to ensure accurate metering of chemicals of varying viscosities and (2) evaluate the recalibration method for estimating individual injector flow rates from a system of multiple injectors to assess potential errors. Test results indicated that the recalibration method was able to compensate for changes in fluid kinematic viscosity (e.g., from temperature changes and/or product variation). Errors were less than 3.4% for the minimum injector duty cycle (DCi) (at 10%) and dropped 0.2% for the maximum DCi (at 90%) for temperature changes of up to 20°C. While larger temperature changes may be expected, these test results showed that the proposed method could be successfully implemented to meet desired injection rates. Because multiple injectors would be used in commercial deployment of this technology, a method was developed to calculate the desired injector flow rate using initial injector calibration factors. Using this multi-injector recalibration method, errors ranged from 0.23% to 0.66% between predicted and actual flow rates for all three injectors

Fourth-Generation Fan Assessment Numeration System (FANS) Design and Performance Specifications

The Fan Assessment Numeration System (FANS) is a measurement device for generating ventilation fan performance curves. Three different-sized FANS currently exist for assessing ventilation fans commonly used in poultry and livestock housing systems. All FANS consist of an array of anemometers inside an aluminum shroud that traverse the inlet or outlet of a ventilation fan. The FANS design has been updated several times since its inception and is currently in its fourth-generation (G4). The current design iteration (FANS-G4) is reported in this article with an emphasis on the hardware and software control, data acquisition systems, and operational reliability. Six FANS-G4 units were fabricated at the University of Kentucky (UK) Agricultural Machinery Research Laboratory and calibrated at the University of Illinois Urbana-Champaign (UIUC) Bioenvironmental and Structural Systems (BESS) Laboratory. Results demonstrated that the FANS-G4 was capable of measuring volumetric airflow to within 0.6% of full-scale (FS), which ranged from 15,000 to 56,000 m3 h-1

Automated Calibration of Electrochemical Oxygen Sensors for Use in Compost Bedded Pack Barns

The objective of this study was to develop an automated calibration process for a galvanic cell type oxygen sensor. The manufacturer recommended a two-point calibration at room temperature; however, testing revealed that the response was not linear when both the temperature and oxygen concentrations varied. Thus, additional points were needed to generate a representative calibration equation and to reduce the sensor prediction interval. The calibration process needed to be capable of automatically recording sensor response (voltage) at an array of temperatures and oxygen concentrations. Calibration gases were used to precisely control the oxygen concentration inside a small manifold, and an electronically controlled water bath was used to regulate the sensor and gas temperature. A custom computer program controlled the sampling order and the data collection process. The responses for three sensors were recorded at six temperature (10°C, 20°C, 30°C, 40°C, 50°C, and 60°C) and five oxygen concentration (0%, 5%, 10%, 15%, and 20% O2 absolute) combinations, for a total of 30 measurements per calibration. Calibration data were used to create a second-degree polynomial model with oxygen sensor voltage and temperature as input parameters, which reduced the prediction interval by over 1% O2 for each of the three sensors tested. The resulting prediction intervals ranged between 0.75% and 0.95% O2. Three sensors were mounted in a prototype oxygen probe and tested under controlled conditions to demonstrate the ability to measure oxygen concentration versus depth in a composting environment

Recalibration Methodology to Compensate for Changing Fluid Properties in an Individual Nozzle Direct Injection Systems

Limited advancement of direct injection pesticide application systems has been made in recent years, which has hindered further commercialization of this technology. One approach to solving the lag and mixing issues typically associated with injection-based systems is high-pressure individual nozzle injection. However, accurate monitoring of the chemical concentrate flow rate can pose a challenge due to the high pressure, low flow, and changing viscosities of the fluid. A methodology was developed for recalibrating high-pressure chemical concentrate injectors to compensate for fluid property variations and evaluate the performance of this technique for operating injectors in an open-loop configuration. Specific objectives were to (1) develop a method for continuous recalibration of the chemical concentrate injectors to ensure accurate metering of chemicals of varying viscosities and (2) evaluate the recalibration method for estimating individual injector flow rates from a system of multiple injectors to assess potential errors. Test results indicated that the recalibration method was able to compensate for changes in fluid kinematic viscosity (e.g., from temperature changes and/or product variation). Errors were less than 3.4% for the minimum injector duty cycle (DCi) (at 10%) and dropped 0.2% for the maximum DCi (at 90%) for temperature changes of up to 20°C. While larger temperature changes may be expected, these test results showed that the proposed method could be successfully implemented to meet desired injection rates. Because multiple injectors would be used in commercial deployment of this technology, a method was developed to calculate the desired injector flow rate using initial injector calibration factors. Using this multi-injector recalibration method, errors ranged from 0.23% to 0.66% between predicted and actual flow rates for all three injectors