LABORATORY SCALE CONCEPT VALIDATION AND EVALUATION OF COMPROMISING PLANT NODAL INTEGRITY AS A MEANS TO INCREASE BALE DENSITY

Transportation costs represent a significant role in the economics of packaged hay and biomass crops. The material’s low bulk density limits transportation efficiency. Density is currently limited by the ability of the baling twine to withstand the expansion forces generated by the baled material shortly after it is ejected from the bale chamber. It was hypothesized that compromising the structure of the plant, particularly the plant nodes could reduce the amount of energy stored in the material as it is compressed and thereby reduce the material’s elastic response to compression. Literature pertinent to the biomass material’s behavior in compression was reviewed. Bulk samples of switchgrass and miscanthus were subject to uniaxial compression, and the required pressure needed to obtain a target density of 256 kg/m3 was compared on a wet and dry density basis. Both switchgrass and miscanthus showed a statistically significant decrease in the required compression pressure, and the interaction between the moisture level and required pressure was also significant. Existing models for the pressure density relationship of compressed bulk material were evaluated for suitability. Individual nodes and internode sections were subject to radial compression and the apparent modulus of elasticity and maximum contact stress were determined

DEVELOPMENT OF A DECISION SUPPORT SYSTEM FOR CAPACITY PLANNING FROM GRAIN HARVEST TO STORAGE

This dissertation investigated issues surrounding grain harvest and transportation logistics. A discrete event simulation model of grain transportation from the field to an on-farm storage facility was developed to evaluate how truck and driver resource constraints impact material flow efficiency, resource utilization, and system throughput. Harvest rate and in-field transportation were represented as a stochastic entity generation process, and service times associated with various material handling steps were represented by a combination of deterministic times and statistical distributions. The model was applied to data collected for three distinct harvest scenarios (18 total days). The observed number of deliveries was within ± 2 standard deviations of the simulation mean for 15 of the 18 input conditions examined, and on a daily basis, the median error between the simulated and observed deliveries was -4.1%. The model was expanded to simulate the whole harvest season and include temporary wet storage capacity and grain drying. Moisture content changes due to field dry down was modeled using weather data and grain equilibrium moisture content relationships and resulted in an RMSE of 0.73 pts. Dryer capacity and performance were accounted for by adjusting the specified dryer performance to the observed level of moisture removal and drying temperature. Dryer capacity was generally underpredicted, and large variations were found in the observed data. The expanded model matched the observed cumulative mass of grain delivered well and estimated the harvest would take one partial day longer than was observed. Usefulness of the model to evaluate both costs and system performance was demonstrated by conducting a sensitivity analysis and examining system changes for a hypothetical operation. A dry year and a slow drying crop had the largest impact on the system’s operating and drying costs (12.7% decrease and 10.8% increase, respectively). The impact of reducing the drying temperature to maintain quality in drying white corn had no impact on the combined drying and operating cost, but harvest took six days longer. The reduced drying capacity at lower temperatures resulted in more field drying which counteracted the reduced drying efficiency and increased field time. The sensitivity analysis demonstrated varied benefits of increased drying and transportation capacity based on how often these systems created a bottleneck in the operation. For some combinations of longer transportation times and higher harvest rates, increasing hauling and drying capacity could shorten the harvest window by a week or more at an increase in costs of less than $12 ha-1. An additional field study was conducted to examine corn harvest losses in Kentucky. Total losses for cooperator combines were found to be between 0.8%-2.4% of total yield (86 to 222 kg ha-1). On average, the combine head accounted for 66% of the measured losses, and the total losses were highly variable, with coefficients of variation ranging from 21.7% to 77.2%. Yield and harvest losses were monitored in a single field as the grain dried from 33.9% to 14.6%. There was no significant difference in the potential yield at any moisture level, and the observed yield and losses displayed little variation for moisture levels from 33.9% to 19.8%, with total losses less than 1% (82 to 130 kg dry matter ha-1). Large amounts of lodging occurred while the grain dried from 19.8% to 14.6%, which resulted in an 18.9% reduction in yield, and harvest losses in excess of 9%. Allowing the grain to field dry generally improved test weight and reduced mechanical damage, however, there was a trend of increased mold and other damage in prolonged field drying

A Method for Reflectance Index Wavelength Selection from Moisture-Controlled Soil and Crop Residue Samples

Reflectance indices are a method for reducing the dimensionality of spectral measurements used to quantify material properties. Choosing the optimal wavelengths for developing an index based on a given material and property of interest is made difficult by the large number of wavelengths typically available to choose from and the lack of homogeneity when remotely sensing agricultural materials. This study aimed to determine the feasibility of using a low-cost method for sensing the moisture content of background materials in traditional crop remote sensing. Moisture-controlled soil and wheat stalk residue samples were measured at varying heights using a reflectance probe connected to visible and near-infrared spectrometers. A program was written that used reflectance data to determine the optimal pair of narrowband wavelengths to calculate a normalized difference water index (NDWI). Wavelengths were selected to maximize the slope of the linear index function (i.e., sensitivity to moisture) and either maximize the coefficient of determination (R2) or minimize the root mean squared error (RMSE) of the index. Results showed that wavelengths centered near 1300 nm and 1500 nm, within the range of 400 to 1700 nm, produced the best index for individual samples. Probe height above samples and moisture content were examined for statistical significance using the selected wavelengths. The effect of moisture was significant for both bare soil and wheat stalks, but probe height was only significant for wheat stalk samples. The index, when applied to all samples, performed well for soil samples but poorly for wheat stalk samples. Index calculations from soil reflectance measurements were highly linear (R2 \u3e 0.95) and exhibited small variability between samples at a given moisture content, regardless of probe height. Index calculations from wheat stalk reflectance measurements were highly variable, which limited the usefulness of the index for this material. Based on these results, it is expected that crop residues, such as wheat stalks, will reduce the accuracy of remotely sensed soil surface moisture measurements

As-Applied Estimation of Volumetric Flow Rate from a Single Sprayer Nozzle Series Using Water-Sensitive Spray Cards

The objective of this study was to test the feasibility of using coverage measurements from water-sensitive spray cards to estimate the volumetric flow rate at an individual sprayer nozzle. TeeJet VisiFlow Even Flat Spray Tips were selected due to their uniform distribution of coverage. Spray distribution for each nozzle was validated using a spray patternator table with 2.5 cm sampling widths. A rotary test fixture translated water-sensitive spray cards through the spray dispersion (water at ambient conditions) at a constant angular velocity and a radius of 1.2 m. The test fixture measured volumetric flow and pressure at the nozzle and recorded data at a rate of 10 Hz. A helical gear pump and a piston-type pressure regulating valve were used to provide constant pressure. The first experiment fixed the test fixture speed at 3.14 rad s-1 and used varying pressures from 70 to 552 kPa (10 to 80 psi) in 70 kPa (10 psi) increments. First-order and second-order regression models were developed for the nozzle series, and validation data were collected at intermediate pressures to test the ability of the model to predict volumetric flow rates. The second experiment fixed the system pressure at 310 kPa (45 psi) and varied the speed of the test fixture at seven increments between 2.0 and 3.8 rad s-1. Spray cards were digitized using a scanner and processed for coverage using the MATLAB image processing toolbox. Results showed that the accuracy of the spray card method was within 1% full-scale of a commercial impeller flowmeter for a single series of nozzles moving at constant speed. Varying speed could be accounted for but required knowledge of the individual nozzle model. The method demonstrated in this study may be useful for field validation of variable-rate control systems on agricultural sprayers

Countermovement jump and pull-up performance before and after a swimming race in preparatory and competitive phases of a swimming season

Purpose: Monitoring performance athletes’ training responses can be efficiently completed at competitive events. This study aimed to explore the changes in swimming, countermovement jump (CMJ) and pull-up (PU) performance following training across a competitive phase, as well as immediately before (PRE) and after (POST) each race. Methods: Fourteen well-trained male sprint/middle-distance swimmers (height 179 ± 7 cm; mass 70 ± 8 kg; age: 18 ± 2 years), from 3 regional training groups, completed CMJ and PU tests PRE and POST national competitions in October (PREP phase) and May (COMP), when race performance was also assessed. Results: Swimming race performance was significantly improved from PREP to COMP (1.8 ± 3.2 %, p = 0.044, d = 0.60, moderate effect). Although there were no significant changes in PU velocity, CMJ performance significantly improved from PREP to COMP (Mean difference 2.29 cm, p = 0.004, d = 3.52) and showed PRE to POST race decreases (Mean difference -1.64 cm, p = 0.04, d = 2.28). Conclusion: Swimming performance and CMJ performance improved as the season progressed, although these improvements were not directly correlated. PU performance did not appear to be sensitive to training or race-induced fatigue, in contrast to CMJ, in this group of male swimmers

Statewide Dissemination and Implementation of Physical Activity Standards in Afterschool Programs: Two-Year Results

Background: In 2015, YMCA afterschool programs (ASPs) across South Carolina, USA pledged to achieve the YMCA physical activity standard calling for all children to accumulate 30 min of moderate-to-vigorous physical activity (MVPA) while attending their ASPs. This study presents the final two-year outcomes from the dissemination and implementation efforts associated with achieving this MVPA standard. Methods: Twenty ASPs were sampled from all South Carolina YMCA-operated ASPs (N = 97) and visited at baseline (2015) and first (2016) and second year (2017) follow-up. All ASPs were provided training to increase MVPA during the program by extending the scheduled time for activity opportunities and modifying commonly played games to increase MVPA. The RE-AIM framework was used to evaluate the statewide intervention. Accelerometer-derived MVPA was the primary outcome. Intent-to-treat (ITT) models were conducted summer 2017. Programs were also classified, based on changes in MVPA from 2015 to 2016 and 2016–2017, into one of three categories: gain, maintain, or lost. Implementation, within the three groups, was evaluated via direct observation and document review. Results: Adoption during the first year was 45% of staff attending training, with this increasing to 67% of staff during the second year. ITT models indicated no increase in the odds of accumulating 30 min of MVPA after the first year for either boys (odds ratio [OR] 1.06, 95CI 0.86–1.31) or girls (OR 1.14, 95CI 0.87–1.50), whereas an increase in the odds was observed during the second year for boys (OR 1.31, 95CI 1.04–1.64) and girls (OR 1.50 95CI 1.01–1.80). Programs that lost MVPA (avg. − 5 to − 7.5 min/d MVPA) elected to modify their program in a greater number of non-supportive ways (e.g., reduce time for activity opportunities, less time spent outdoors), whereas ASPs that gained MVPA (avg. + 5.5 to + 10.1 min MVPA) elected to modify their program in more supportive ways. Conclusions: The statewide study demonstrated minimal improvements in overall MVPA. However, child MVPA was dramatically influenced by ASPs who elected to modify their daily program in more supportive than non-supportive ways, with no one program modifying their program consistently across the multi-year initiative. These findings have important implications for organizations seeking to achieve the MVPA standard

Influence of Kernel Shape and Size on the Packing Ratio and Compressibility of Hard Red Winter Wheat

Grain compaction occurs during bin storage, and its determination is important for the grain mass estimation needed for inventory and auditing. The degree of compaction is dependent on grain type, bin type, moisture content, amount of grain, initial grain bulk density, coefficients of friction, lateral-to-vertical pressure coefficient, and variation in kernel size. Previous studies have correlated several of these parameters, such as bulk density and grain packing, with moisture content. This study investigated the influence of wheat kernel shape and size distribution on packing ratio and compressibility. Two dockage-free hard red winter (HRW) wheat samples, with no shrunken or broken kernels, were sieved using U.S. Tyler sieves #6, #7, #8, and #10, and the kernels retained on the sieves were used in the experiments. The kernel dimensional parameters and bulk sample parameters were measured, and additional derived parameters were calculated for each size fraction and variety. Packing ratio and compressibility of the size fractions and of binary and ternary mixtures of the size fractions were also determined for each variety. Packing ratio increased with larger kernel size, while compressibility decreased. Sphericity and flatness shape factor had strong positive linear relationships with packing ratio and strong negative relationships with compressibility, while elongation shape factor behaved the opposite way with packing ratio and compressibility. The higher the percentage mass of the larger kernel fraction in a mixture, the higher was its packing ratio and the lower its compressibility. The two tested varieties of wheat did not significantly differ in packing ratio and compressibility. These findings can be used in developing models for more accurate estimation of grain pack factor and to determine the mass of grain inside bins and other storage structures

Modeling the Compressibility Behavior of Hard Red Wheat Varieties

Citation: Turner A., M. Montross, S.G. McNeill, M.P. Sama, M.E. Casada, J.M. Boac, R. Bhadra, R.G. Maghirang, and S.A. Thompson. Modeling the compressibility behavior of hard red wheat varieties. 2016. Transaction of ASABE 59(3): 1029-10385.The bulk density of grain in a storage structure varies vertically and horizontally due to the overburden pressure created by the cumulative weight of the overlying material. As the overburden pressure increases, the stored material compacts. This compaction is believed to be caused by rearrangement of kernels along with higher intergranular stress between particles, leading to kernel deformation. This compaction is of primary concern when estimating the amount of grain in a storage structure. In this comprehensive study, confined uniaxial compression tests were conducted on 27 different samples of hard red winter wheat, at three moisture levels, over the range of pressures typically encountered in storage structures (0 to 138 kPa). Mathematical models using the prior, modified, and new forms of the bulk density equation were evaluated to describe the resulting pressure-density relationship as a function of moisture content. With the new data set, the modified version of the Page equation had the lowest root mean square error (RMSE) of 4.7 kg m-3, while the other equations, including the original polynomial equation used in the WPACKING program, had RMSEs between 6.0 and 7.1 kg m-3. The models were validated using previously published compressibility data and the root mean square prediction error was determined to vary from 8.1 to 13.4 kg m-3. Four of the best performing models were subsequently applied to field measurements from 35 concrete and 16 steel bins. When applied to the field data a slight bias was observed in steel and concrete bins, but several of the models, including the modified Page and polynomial models, produced an average error of less than 2% from the measured grain mass

Influence of Kernel Shape and Size on the Packing Ratio and Compressibility of Hard Red Winter Wheat

Grain compaction occurs during bin storage, and its determination is important for the grain mass estimation needed for inventory and auditing. The degree of compaction is dependent on grain type, bin type, moisture content, amount of grain, initial grain bulk density, coefficients of friction, lateral-to-vertical pressure coefficient, and variation in kernel size. Previous studies have correlated several of these parameters, such as bulk density and grain packing, with moisture content. This study investigated the influence of wheat kernel shape and size distribution on packing ratio and compressibility. Two dockage-free hard red winter (HRW) wheat samples, with no shrunken or broken kernels, were sieved using U.S. Tyler sieves #6, #7, #8, and #10, and the kernels retained on the sieves were used in the experiments. The kernel dimensional parameters and bulk sample parameters were measured, and additional derived parameters were calculated for each size fraction and variety. Packing ratio and compressibility of the size fractions and of binary and ternary mixtures of the size fractions were also determined for each variety. Packing ratio increased with larger kernel size, while compressibility decreased. Sphericity and flatness shape factor had strong positive linear relationships with packing ratio and strong negative relationships with compressibility, while elongation shape factor behaved the opposite way with packing ratio and compressibility. The higher the percentage mass of the larger kernel fraction in a mixture, the higher was its packing ratio and the lower its compressibility. The two tested varieties of wheat did not significantly differ in packing ratio and compressibility. These findings can be used in developing models for more accurate estimation of grain pack factor and to determine the mass of grain inside bins and other storage structures

Stored Grain Pack Factors for Wheat: Comparison of Three Methods to Field Measurements

Storing grain in bulk storage units results in grain packing from overbearing pressure, which increases grain bulk density and storage unit capacity. This study compared pack factors of hard red winter (HRW) wheat in vertical storage bins using different methods: the existing packing model (WPACKING), the USDA Risk Management Agency (RMA) method, and the USDA Farm Service Agency Warehouse Licensing and Examination Division (FSA-W) method. Grain bins containing HRW wheat were measured in Kansas, Oklahoma, and Texas. Packing was measured in corrugated steel bins and reinforced concrete bins with diameters ranging from 4.6 to 31.9 m (15.0 to 104.6 ft) and equivalent level grain heights ranging from 4.1 to 41.6 m (13.4 to 136.6 ft). The predicted masses of compacted stored wheat based on WPACKING, RMA, and FSA-W were compared to the reported mass from scale tickets. Pack factors predicted by WPACKING ranged from 0.929 to 1.073 for steel bins and from 0.986 to 1.077 for concrete bins. Pack factors predicted by the RMA method ranged from 0.991 to 1.157 for steel bins and from 0.993 to 1.099 for concrete bins. Pack factors predicted by the FSA-W method ranged from 0.985 to 1.126 for steel bins and from 1.012 to 1.101 for concrete bins. The average absolute and median differences between the WPACKING-predicted mass and reported mass were 1.64% and -1.26%, respectively, for corrugated steel bins and 3.75% and 2.16%, respectively, for concrete bins. In most cases, WPACKING underpredicted the mass in corrugated steel bins and overpredicted the mass in concrete bins. Comparison of the RMA-predicted mass and reported mass showed an average absolute difference of 4.41% with a median difference of 1.91% for HRW wheat in steel bins and an average absolute difference of 3.25% with a median difference of 1.03% for concrete bins. For the FSA-W-predicted mass versus reported mass, the average absolute and median differences were 3.40% and 3.86%, respectively, for steel bins and 4.34% and 3.50%, respectively, for concrete bins. Most of the mass values were overpredicted by both the RMA and FSA-W methods. Some of the large differences observed for concrete bins can be attributed to the unique geometry of these bins and the difficulty in describing these bin shapes mathematically. Overall, compared to the reported mass, WPACKING predicted the mass of grain in the bins with less error than the current RMA and FSA-W methods. Some of the differences may be because the RMA and FSA-W methods do not include the effects of grain moisture content, bin wall type, and grain height on pack factors