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HighlightsAnnual average PM10 (daily) and PM2.5 (hourly) concentrations of 509 ±203 and 43.3 ±36.1 µg m-3, respectively.Significant seasonal and diurnal variations in PM concentrations; higher in colder seasons and during daytime.Annual average PM10 (daily) and PM2.5 (hourly) emission rates of 17.9 ±9.6 and 1.41 ±1.53 mg d-1 hen-1, respectively.Significant diurnal variations in PM emission rates but inconsistent seasonal variations; higher during daytime.Abstract. Particulate matter (PM) concentration and emission rate information is needed to assess air quality concerns and environmental impacts related to egg production. Many egg layer buildings were retrofitted from high-rise to manure-belt houses over the past few decades. However, no studies have reported these PM data for the retrofitted layer houses. In this study, concentrations and emission rates of PM10 and PM2.5 were continuously monitored for 12 months at two commercial retrofitted manure-belt layer houses in the Midwestern U.S. The average daily mean PM10 and average hourly mean PM2.5 concentrations in the houses were 509 ±203 µg m-3 and 43.3 ±36.1 µg m-3, respectively. Significant seasonal and diurnal variations in PM concentrations were observed, with higher concentrations in colder seasons and during daytime. The average daily mean PM10 and average hourly mean PM2.5 emission rates from the two houses were 17.9 ±9.6 mg d-1 hen-1 and 1.41 ±1.53 mg d-1 hen-1, respectively. There were no consistent patterns in seasonal variations. Significant diurnal variations were also observed for PM emission rates, with higher PM emission rates observed during daytime. The PM concentrations were significantly correlated with ambient temperature, ventilation rate, bird age, egg production, daily mortality, and feed conversion ratio, while PM emission rates were significantly correlated with ambient temperature, bird age, bird mass, egg mass, and ventilation rate. Keywords: Diurnal variation, Dust, Egg production, PM, Poultry, Seasonal variation.

HighlightsAnnual average NH3 concentrations in two retrofitted manure-belt layer houses were 4.0 ±3.3 and 5.2 ±3.0 ppmv.Seasonal and diurnal variations were observed for NH3 concentrations (higher in colder seasons and early morning).Annual average NH3 emission rates from the two layer houses were 0.081 ±0.004 and 0.099 ±0.004 g d-1 hen-1.No consistent pattern was found for either seasonal or diurnal variations in NH3 emission rates.Abstract. Ammonia emission measurements at poultry houses are necessary to assess air quality and emission factors associated with poultry operations, but no data have been reported for manure-belt layer houses retrofitted from high-rise layer houses. Two commercial retrofitted manure-belt layer houses (both 121.9 m long, 19.5 m wide, and 7.7 m high; 170,000 bird nominal capacity each) in Ohio with mixed usage of tunnel and cross ventilation systems were continuously monitored for one year. The daily averages of the exhaust NH3 concentrations varied from 0.03 to 17.7 ppmv in house 1 and 0.37 to 14.4 ppmv in house 2 with annual means (±SD) of 4.0 ±3.3 and 5.2 ±3.0 ppmv, respectively. The NH3 emission factors based on the full year of data for houses 1 and 2 were 0.081 ±0.004 and 0.099 ±0.004 g d-1 hen-1 (12.5 ±10.1 and 15.2 ±10.6 kg d-1 house-1 or 24.9 ±20.0 and 31.1 ±23.4 g d-1 AU-1), respectively. Seasonal variations were observed for NH3 concentrations, with higher concentrations in winter and lower concentrations in summer. Within a day, NH3 concentrations were highest from 4:00 to 8:00 and lowest from 16:00 to 20:00. No consistent pattern was observed for seasonal or diurnal variations of NH3 emission rates. Higher NH3 concentrations and emissions were observed at the east and west exhaust air streams of the houses compared to the north and south exhaust air streams due to the unique configuration of the ventilation systems. NH3 emission was correlated with exhaust absolute humidity, hen caloric intake, feed consumption, and protein percentage of feed. Keywords: Ammonia emission factor, Diurnal variation, High-rise, Retrofitted poultry house, Seasonal variation, Spatial variation.

The grain yield monitor is the most common evaluation tool for determining the productivity of grain cropping systems. Most evaluations of grain yield monitors have focused on lab scale tests of the sensor performance and its ability to be calibrated in field trials. This study focused on the performance of the impact-based grain yield monitor during a full corn harvest season with observations and conclusions drawn on the load-to-load variation, field level, and full season accuracy from data collected over five seasons and encompassing over 2,000 evaluation loads. The load variance expectation of the impact-based yield monitor was characterized and the yield difference requirements for statistical significance were developed to aid in yield monitor based evaluations of agronomic strip trials. Following manufacturer recommended calibrations, a single cropping season calibration in corn produced field mean errors of ±5% and a season mean error of 1%. Results showed statistically significant shift in the yield monitor accuracy for grain moisture content greater than 22.5% and a load accuracy dependency on the mean mass flow rate during the full harvest season calibration evaluation. Keywords: Combine harvesters, Mass flow, Precision agriculture, Real-time sensor, Yield map, Yield monitor.

Agricultural spray drift is affected by many factors including current weather conditions, topography of the surrounding area, fluid properties at the nozzle, and the height at which the spray is released. During the late spring/summer spray seasons of 2014 and 2015, wind direction, speed, and solar radiation (2014 only) were measured at 10 Hz, 1 m above the ground to investigate conditions that are typically encountered by a droplet when released from a nozzle on an agricultural sprayer. Measurements of wind velocity as the wind passed from an upwind sensor to a downwind sensor were used to evaluate what conditions wind may be most likely to have a significant direction or speed change which affects droplet trajectory. For two individual datasets in which the average wind speed was 3.6 and 1.5 m/s (8.0 and 3.4 mi/h), there exists little linear correlation of wind speed or wind direction between an upwind and downwind anemometer separated by 30.5 m (100 ft). The highest observed correlation, resulting from a 12-s lag between the upwind and downwind datasets, was 0.29 when the average wind speed was 3.6 m/s (8.0 mi/h). Correlations greater than 0.1 were only found for wind speeds exceeding 3 m/s. Using this lag time, it was observed that the wind direction 30 s into the future had a 30% chance to be different by more than 20° from current conditions. A wind speed difference of more than 1 m/s (2.2 mi/h) from current conditions [mean wind speed was 3.6 m/s (8.0 mi/h)] was observed about 50% of the time. Analyzing 36 days of the 2014 and 2015 spray season wind velocity data showed that the most variability in wind direction occurred with wind speeds below 2 m/s (4.5 mi/h). Greater wind direction variability occurred in the mid-afternoon with higher solar radiation. Keywords: Sprayers, Spray drift, Spray droplets, Turbulence, Wind effects.

Corn stover is the primary feedstock choice for most of the near-term first-generation cellulosic biorefineries in the Midwest with rated capacities between 76 and 114 million liters per year (MLPY). These biorefineries will require an annual feedstock supply of around 250–375 metric ton (t) to meet their feedstock demand. However, there is a dearth of studies based on sustainable ways to supply a vast quantity of feedstock to the biorefinery gate, thus this study focuses on stochastically evaluating the life-cycle energy use and greenhouse gas emissions (GHGE) of the different components of corn stover based feedstock supply system of a 114 MLPY cellulosic biorefinery in the Midwest by using production-scale experimental field data collected in Iowa. Different components of the supply chain considered includes corn stover harvesting in a large rectangular bale form, field collection, storage, handling and transportation. This study also evaluates the energy use and GHGE of nutrients removed from the field with collected stover. Average energy use, energy-use ratio (EUR) and GHGE for this feedstock supply system of a Midwest-based 114 MLPY biorefinery plant are estimated to be 1,502 MJ std. t−1, 21%, and 95.2 kg-CO2e std. t−1, respectively. Nutrients removed during stover harvest shares 38 to 47% of the overall life-cycle energy use and GHGE for the supply chain, followed by harvesting (~24–28%) and transportation (~23–27%). Life-cycle GHGE and energy use are identified to be the most sensitive to the quantity of nitrogen removed, bale density, bale length, harvest rate, baler field efficiency and dry matter loss. © 2017 Society of Chemical Industry and John Wiley & Sons, Ltd

Mass flow estimation is of great importance to several industries, and it can be quite challenging to obtain accurate estimates due to limitation in expense or general infeasibility. In the context of agricultural applications, yield monitoring is a key component to precision agriculture and mass flow is the critical factor to measure. Measuring mass flow allows for field productivity analysis, cost minimization, and adjustments to machine efficiency. Methods such as volume or force-impact have been used to measure mass flow; however, these methods are limited in application and accuracy. In this work, we use deep learning to develop and test a vision system that can accurately estimate the mass of sugarcane while running in real-time on a sugarcane harvester during operation. The deep learning algorithm that is used to estimate mass flow is trained using very sparsely annotated images (semi-supervised) using only final load weights (aggregated weights over a certain period of time). The deep neural network (DNN) succeeds in capturing the mass of sugarcane accurately and surpasses older volumetric-based methods, despite highly varying lighting and material colors in the images. The deep neural network is initially trained to predict mass on laboratory data (bamboo) and then transfer learning is utilized to apply the same methods to estimate mass of sugarcane. Using a vision system with a relatively lightweight deep neural network we are able to estimate mass of bamboo with an average error of 4.5% and 5.9% for a select season of sugarcane., Comment: 22 pages, 21 figures, Computers and Electronics in Agriculture. arXiv admin note: text overlap with arXiv:1908.04387

Supervised learning is the workhorse for regression and classification tasks, but the standard approach presumes ground truth for every measurement. In real world applications, limitations due to expense or general in-feasibility due to the specific application are common. In the context of agriculture applications, yield monitoring is one such example where simple-physics based measurements such as volume or force-impact have been used to quantify mass flow, which incur error due to sensor calibration. By utilizing semi-supervised deep learning with gradient aggregation and a sequence of images, in this work we can accurately estimate a physical quantity (mass) with complex data structures and sparse ground truth. Using a vision system capturing images of a sugarcane elevator and running bamboo under controlled testing as a surrogate material to harvesting sugarcane, mass is accurately predicted from images by training a DNN using only final load weights. The DNN succeeds in capturing the complex density physics of random stacking of slender rods internally as part of the mass prediction model, and surpasses older volumetric-based methods for mass prediction. Furthermore, by incorporating knowledge about the system physics through the DNN architecture and penalty terms, improvements in prediction accuracy and stability, as well as faster learning are obtained. It is shown that the classic nonlinear regression optimization can be reformulated with an aggregation term with some independence assumptions to achieve this feat. Since the number of images for any given run are too large to fit on typical GPU vRAM, an implementation is shown that compensates for the limited memory but still achieve fast training times. The same approach presented herein could be applied to other applications like yield monitoring on grain combines or other harvesters using vision or other instrumentation., 9 pages, 19 figures, pre-print NIPS2019

Feedstock degradation is a major source of financial risk for cellulosic biorefineries. To assess the impact of the feedstock loss and quality during storage, this study explored interactions of moisture content and temperature on the degradation of biomass. Specifically, this study evaluated the dry matter loss and compositional change of corn stover during storage conditions common at industrial biomass storage facilities. This lab-scale study allowed for increased measurement precision associated with quantifying how biomass degradation changes the quality of stored biomass. Results of this work define stable biomass storage moisture content of less than 20% in which there is low risk for degradation over time. At moisture levels above 20%, biomass degradation will be prevalent with hemicellulose being the most degraded fraction of the feedstock. Additionally, results demonstrate that temperature has a minimal impact on biomass degradation in non-frozen storage conditions.

Composting facilities are essential parts of most manure-belt (MB) poultry houses in the U.S., but their NH3 concentrations and emission are not well understood. This may affect farm operation safety and limit the development of NH3 mitigation and management strategies. The study aimed to quantify NH3 concentrations and hen-specific emission rates (ER) at a commercial poultry manure composting facility and to understand their diurnal and seasonal variations. Two large tunnel-ventilated composting buildings with twelve 122-cm exhaust fans were chosen as the study site, which received manure from four on-site manure-belt layer barns. The inlet and exhaust NH3 concentrations at the compost building were monitored quasi-continuously for one month per season for two years. Ammonia ERs were calculated based on the NH3 concentrations and building ventilation rates. The average daily mean ± SD of the NH3 concentrations in spring, summer, fall, and winter were 114 ± 20, 144 ± 35, 115 ± 13, and 141 ± 25 ppmv, respectively. Seasonal and diurnal variations existed in both NH3 concentrations and ERs. The daytime NH3 ER was significantly higher than that of night time. These results showed that NH3 emissions from composting facilities are considerably high, and thus mitigation strategies are needed to further reduce NH3 from the whole MB layer facility system.

Corn stover is the primary feedstock choice of the first generation of cellulosic biorefineries in the Midwestern US due to its abundance in the region. The technically sound, economic and environment-friendly supply system of this feedstock is inevitable for the commercial success and proliferation of these biorefineries. Such supply systems involve different aspects of stover production, harvesting, collection, handling, transportation, storage and preprocessing. This review discusses the environmental concerns of stover collection for biofuels production, and provides an update on the technological status of the corn stover feedstock supply system. The major environmental concerns of corn stover collection discussed here include its impact on soil nitrogen availability and crop requirements, changes in crop productivity, soil erosion and change in soil organic carbon, soil fertility, carbon sequestration, nutrient emissions and disease pressures. The cellulosic biorefinery feedstock quality req...

The primary feedstock choice for most first-generation commercial cellulosic biorefineries in the Midwest is corn stover. Their rated capacities are between 76 and 114 million liters per year (MLPY). For uninterrupted operation of these plants, a year-round supply of corn stover needs to be secured, requiring a robust, efficient, and cost-effective feedstock supply system. Thus, the main objective of this work is to stochastically analyze the techno-economics of corn stover supply system for a 114 MLPY cellulosic biorefinery in the Midwest using production-scale experimental field data collected in Iowa. This study analyzes the resources requirements (equipment, labor, fuel, and consumables) and costs of different components of the supply chain that include harvesting (windrowing and baling), in-field bale collection and stacking at the field-edge, handling and transportation of bales from the field-edge to the distributed storage facilities and then to the biorefinery plant, storage, and finally the audit of nutrients removed during stover collection from the field. Additionally, this study identifies and ranks different supply chain parameters based on their relative influence on the overall resources and costs requirements. A 114 MLPY cellulosic biorefinery annually requires around 0.95 million corn stover bales supply, 250 000 hours equivalent of labor and 4.3 million L of diesel fuel. The average cost of biorefinery gate delivered corn stover is estimated to be 121.9 $-std. t−1. This cost is identified to be the most sensitive to bale density followed by harvest rate, bale length, baler field efficiency, and annual harvest days. © 2016 Society of Chemical Industry and John Wiley & Sons, Ltd

Crop yield maps have become the standard tool for building agricultural production plans for future crop years. Yield monitoring systems must be accurate to ensure validity of producer decisions. The objective of this research was to propose a yield monitoring system that maintains accuracy with limited calibration requirements. Maintaining accuracy of the system requires moving to a fundamental system of yield measurement. Impact-based yield monitoring relies upon regression of a calibration curve to fit the mass flow sensor response signal to the actual mass flow rate of grain. The system is built upon a foundation of factor dependent analysis and error is induced when conditions are experienced that do not directly fit into the model. Volumetric grain flow was selected as the measurement method for determining crop yield. A camera was selected as a sensor to measure velocity of grain as it traveled through the fountain auger and into the grain tank on a commercial combine. Velocity was obtained through measurement of grain displacement between image frames. The particle flow yield monitor was used to quantify the volumetric flow rate of grain passing through the combine and the accuracy of this system in different harvesting conditions. Yield estimation and sensor response had positive, linear relationships with load size and flow rate intake of the combine. The initial performance of the particle flow yield monitor showed great potential for a future yield monitoring solution, although future designs should reflect the issues observed with velocity profile of the grain and maintenance of a grain cavity area.

Ammonia (NH 3 ) emissions from poultry animal feeding operations (AFOs) have caused health and environmental concerns. Current NH 3 emission measurement methods are accurate and reliable but also time-consuming, expensive, and impractical for most animal facilities. In this study, an alternative mass balance method was developed to effectively predict NH 3 emissions from manure belt (MB) poultry layer facilities. This method can eliminate the need for tracking manure flow rates in traditional mass balance analyses for estimation of ammonia nitrogen (NH 3 -N) emissions. It was applied to three MB layer poultry houses in Ohio, with approximately 160,000 hens in each house, and validated using continuous NH 3 emission measurement data. Feed, manure, and egg samples were collected from the three houses in different months over a year to evaluate possible seasonal variation in NH 3 emissions from the poultry houses. The estimated NH 3 -N emissions from houses 1, 2, and 3 were 0.394 ±0.143, 0.293 ±0.1, and 0.284 ±0.129 g NH 3 -N hen -1 d -1 , respectively, and the measured NH 3 -N emission rates were 0.200 ±0.067, 0.220 ±0.036, and 0.237 ±0.211 g NH 3 -N hen -1 d -1 , respectively. These results are comparable with NH 3 -N emission rates published in the literature (0.024 to 0.592 g NH 3 -N hen -1 d -1 ). A statistical comparison of the measured and estimated NH 3 -N emissions showed that the root mean square error (RMSE), normalized mean square error (NMSE), and fractional bias (FB) were 0.179 g NH 3 -N hen -1 d -1 , 0.426, and 0.457, respectively. These statistical parameters indicated that the estimations were acceptable according to the criteria of NMSE 3 -N emissions from MB poultry layer houses. However, the method estimates total nitrogen gas emissions, which is an upper limit of NH 3 -N emissions. A minimum of 22 sampling and modeling events is suggested for reliable estimation of NH 3 -N emission factors for MB poultry layer houses using the alternative mass balance method with a 90% confidence level (I± = 0.1) and a maximum error of 15%.

The bulk density of raw corn stover is a major limitation to its large-scale viability as a biomass feedstock. Raw corn stover has a bulk density of 50 kg/m 3 , which creates significant transportation costs and limits the optimization of transport logistics. Producing a densified corn stover product during harvest would reduce harvest and transportation costs, resulting in viable pathways for the use of corn stover as a biomass feedstock. This research investigated the effect of different process variables (compression pressure, moisture content, particle size, and material composition) on a densification method that produces briquettes from raw corn stover. A customized bench-scale densification system was designed to evaluate different corn stover inputs. Quality briquette production was possible using non-reduced particle sizes and low compression pressures achievable in a continuous in-field production system. At optimized bench settings, corn stover was densified to a dry bulk density of 190 kg/m 3 . Corn stover with a moisture content above 25% wb was not suitable for this method of bulk densification, and greater cob content had a positive effect on product quality.

Crop yield data and maps from previous years are a primary source of information from which crop management recommendations and decisions are based upon. Yield data is a useful tool for making crop management decisions, but becomes irrelevant when it is not accurate or reliable. The objectives of this research were to benchmark commercial yield monitoring systems to better understand performance and to assess limitations of measurement methods from mechanical and environmental influences. Two commercial yield monitors that measured mass and volumetric flow for yield estimation were selected for benchmarking. Each system was calibrated using manufacturer procedures and evaluated in a yield monitor test stand compliant with standards. Clean grain elevator paddle type and machine orientation were selected as treatment factors to evaluate accumulated load accuracy at different grain flow rates. There was no significant difference in mean estimation error for different paddle types for the impact-based mass flow yield monitor. There were significant differences in mean estimation error for different paddle types for the volumetric flow yield monitor. This was attributed to presentation of grain to the sensor between flat and misshapen paddles. Rolled and pitched machine orientations were shown to have significant influence on estimation accuracy for the volumetric flow yield monitor. However, the volumetric flow system maintained lower variability across flow ranges than the impact-based mass flow yield monitor because of a fundamental measurement system that does not rely entirely upon calibration. A fundamental measurement system and known machine properties may be able to overcome the challenges of a harvesting environment. Maintenance of yield monitor accuracy with less calibration will contribute to increased uptime and better basis for crop management decisions.

Tractors and agricultural machinery have been designed specifically for land preparation, tillage, and other agricultural operation's tasks. Tractors are the primary source of power in farms and fields. Thus, to obtain the optimum output from them, proper management and utilization is needed. Agricultural machinery performance has been studied over the past three decades and optimum results have been obtained for different kinds of agricultural machinery. In general, the evaluation of agricultural machinery using traditional methods is problematic as they are time consuming and labor intensive. Moreover, by using the common evaluation methods it is typically difficult to obtain accurate and instant results. Accurate measurements of field performance parameters are required for monitoring machinery performance and management decisions. Recently, the improvement in the electronics technology has made field operational management easier to monitor. Controller Area Network (CAN) Bus technology is being used as a communication system in tractors and allows connections between Electrical Control Units (ECU). CAN Bus technology broadcast unique electronic messages which contain continuously updated information about the engine, power train, equipment, power take off, hydraulic system, and other parts of the machines. To evaluate the performance of agricultural machinery, there is no longer a need for myriad measurement instruments producing widely varying output to individually measuring fuel consumption for each speed, gear shift and the whole operation. As a result, this study was conducted to evaluate tractor performance by CAN Bus technology as a simple to use, easy to install, high speed data collection, and convenient to retrieve the stored data. These techniques allow for substantial saving of money and time, reducing our workload and eliminating training necessary for specialized measurement tools.

Agricultural spray drift is affected by many factors including current weather conditions, topography of the surrounding area, fluid properties at the nozzle, and the height at which the spray is released. During the late spring/summer spray seasons of 2014 and 2015, wind direction, speed, and solar radiation (2014 only) were measured at 10 Hz, one meter above the ground to simulate conditions that are present for a droplet. Measurements of wind velocity as the wind passed from an upwind sensor to a downwind sensor were used to evaluate under what conditions wind may be most likely to have a significant direction or speed change which affects droplet trajectory. For two individual datasets in which the average wind speed was 3.6 m/s and 1.5 m/s, it was found that there existed little linear correlation of wind speed or wind direction between an upwind and downwind anemometer separated by 30.5 meters (100 ft). The highest observed correlation, resulting from a 12 second lag between the upwind and downwind datasets, was 0.29 where the average wind speed was 3.6 m/s. Correlations were only found for wind speeds exceeding 3 m/s. Using this lag time, it was observed that the wind direction 30 seconds into the future had a 30% chance to be different by more than 20 degrees from current conditions. While a wind speed difference of more than 1 m/s from current conditions (mean wind speed was 3.6 m/s) happened about 50% of the time. Looking at 2014 and 2015 spray season data, it was found that the most variability occurred with wind speeds below 2 m/s.

The notion that wind speed and direction can be approximated by adding a random fluctuation to the previous value was investigated. The data were recorded at one meter above a field to simulate conditions that are present at a ground sprayer‘s boom. Variance ratio tests were carried out to test the null hypothesis that wind possesses similar properties to a random walk versus the alternative that wind does not. More specifically, are the random fluctuations auto correlated with one another in time? This process was done to a 10Hz sample and averages of the measured wind data at 0.5, 1, 5, 10, 30, 60, 300, and 600 seconds. It was found that for all tests, except for the 300 and 600 second data samples, the null hypothesis was rejected at greater than 99.9% certainty. This indicates that there is evidence of autocorrelation (rather than randomness) in the measurements of wind speed and direction, associated with each other in time.

Storage of raw biomass feedstocks is a critical component in a viable biofuels supply chain. This article focuses on the current knowledge and existing challenges to establishing a sufficient biomass storage infrastructure to meet the long-term needs of the biofuels industry. Effects of storage on feedstock stability and biomass quality, with regards to dry-matter loss and self-heating characteristics of stored biomass, have been thoroughly discussed. Features, suitability and costs of different commercial-scale biomass storage infrastructures for open, tarped, permanent structure and anaerobic storage systems are discussed in detail. Additionally, discussions include supply-chain characteristics, including feedstock handling, bale density and quality, and identification and traceability of feedstock, which impact storage and transportation interaction.

Large scale biofuel production will be possible only if significant quantities of biomass feedstock can be stored, transported, and processed in an economic and sustainable manner. Torrefaction has the potential to significantly reduce the cost of transportation, storage, and downstream processing through the improvement of physical and chemical characteristics of biomass. The main objective of this study was to investigate the effects of particle size, plant components, and gas residence time on the production of torrefied corn (Zea mays) stover. Different particle sizes included 0.85 mm and 20 mm. Different stover components included ground corn stover, whole corn stalk, stalk shell and pith, and corn cob shell. Three different purge gas residence times were employed to assess the effects of interaction of volatiles and torrefied biomass. Elemental analyses were performed on all of the samples, and the data obtained was used to estimate the energy contents and energy yields of different torrefied biomass samples. Particle density, elemental composition, and fiber composition of raw biomass fractions were also determined. Stalk pith torrefied at 280 °C and stalk shell torrefied at 250 °C had highest and lowest dry matter loss, of about 44% and 13%, respectively. Stalk pith torrefied at 250 °C had lowest energy density of about 18–18.5 MJ/kg, while cob shell torrefied at 280 °C had the highest energy density of about 21.5 MJ/kg. The lowest energy yield, at 59%, was recorded for stalk pith torrefied at 280 °C, whereas cob and stalk shell torrefied at 250 °C had highest energy yield at 85%. These differences were a consequence of the differences in particle densities, hemicellulose quantities, and chemical properties of the original biomass samples. Gas residence time did not have a significant effect on the aforementioned parameters.

The equilibrium moisture content (EMC) of biomass affects transportation, storage, downstream feedstock processing, and the overall economy of biorenewables production. Torrefaction is a thermochemical process conducted in the temperature regime between 200 and 300 °C under an inert atmosphere that, among other benefits, aims to reduce the innate hydrophilicity and susceptibility to microbial degradation of biomass. The objective of this study was to examine water sorption properties of torrefied corn stover. The EMC of raw corn stover, along with corn stover thermally pretreated at three temperatures, was measured using the static gravimetric method at equilibrium relative humidity (ERH) and temperatures ranging from 10 to 98% and from 10 to 40 °C, respectively. Five isotherms were fitted to the experimental data to obtain the prediction equation that best describes the relationship between the ERH and the EMC of lignocellulosic biomass. Microbial degradation of the samples was tested at 97% ERH and 30 °...

Agricultural energy usage is an important topic among agricultural industry producers, manufacturers, and regulators. The transfer of power between axle and drawbar is identified as one of the greatest inefficiencies in agricultural field operations. Understanding the stresses at the soil-tire interface would provide insight into the current state of tire traction development and data useful in developing future tire designs. This article presents a measurement system to quantify the normal stresses at the soil-tire interface of an agricultural tractor tire, thus making it possible to evaluate these stresses along the tire footprint. A normal stress measurement system was developed in the laboratory and tested in the field. Additionally, a dedicated data acquisition system was developed, tested, and validated in the field environment. Test results show the system capabilities as well as information on the challenges of drawing general, consistent conclusions concerning the stresses developed between a tire and the soil at field working speeds in agricultural soil.

Biomass is a primary source of renewable carbon that can be utilized as a feedstock for biofuels or biochemicals production in order to achieve energy independence. The low bulk density, high moisture content, degradation during storage and low energy density of raw lignocellulosic biomass are all significant challenges in supplying agricultural residues as a cellulosic feedstock. Torrefaction is a thermochemical process conducted in the temperature range between 200 and 300 °C under an inert atmosphere which is currently being considered as a biomass pretreatment. Competitiveness and quality of biofuels and biochemicals may be significantly increased by incorporating torrefaction early in the production chain while further optimization of the process might enable its autothermal operation. In this study, torrefaction process parameters were investigated in order to improve biomass energy density and reduce its moisture content. The biomass of choice (corn stover) was torrefied at three moisture content levels (30%, 45% and 50%), three different temperatures (200, 250 and 300 °C), and three unique reaction times (10, 20 and 30 min). Solid, gaseous, and liquid products were analyzed, and the mass and energy balance of the reaction was quantified. An overall increase in energy density (2–19%) and decrease in mass and energy yield (3–45% and 1–35% respectively) was observed with the increase in process temperature. Mass and energy losses also increased with an increase in the initial biomass moisture content.

Densification of agricultural residues is a key step in a cost-effective, large-scale, biomass feedstock supply system. Lab-scale densification systems exist which can produce large-scale densified corn stover briquettes that measure between 220 and 420 mm in length and with an average bulk density of 190 kg/m3 (dry particle density of 460 kg/m3). MOG (material other than grain ) and pure cob briquettes produced similar lengths and dry particle densities of 424 kg/m3 and 421 kg/m3, respectively. Durability testing, utilizing a modified form of ASABE S269.4 (ASABE Standards, 2007) was conducted to determine the overall product quality associated with these densified briquettes. This publication describes experiments which quantified the durability or briquettes produced with different material types (corn stover, MOG, the chaff from a conventional corn harvest, and pure cobs) and material particle sizes (produced from a combine chopper, MOG, and a hammer mill with a 19-mm screen opening size). The durability rating varied with each of the main effect test parameters and produced a maximum 46% durability rating using pure cobs directly from a combine without additional size reduction. The durability rating was quite low for corn stover and MOG briquettes, and it was improved for pure cob briquettes. Biomass preprocessing in a hammermill significantly reduced durability due to a lack of fiber interaction throughout the large briquette.

Increasing regulation of spray drift has led to the development of real-time drift monitoring systems that present drift potential to applicators so that drift reduction spraying techniques can be implemented on an as-needed basis. The central component in each of these state-of-the-art systems is a drift prediction model. A real-time drift monitoring system was developed using look-up tables produced from simulations of a random-walk model (FLUENT via DRIFTSIM). The predictive accuracy of this system, evaluated as the difference between predicted drift and in-field measured drift, was compared to alternative prediction models to determine the suitability of random-walk models for real-time drift prediction. DRIFTSIM was found to produce a significantly more accurate representation of real-time predicted drift when compared to four of the six alternative models tested. No significant difference in predictive accuracy was found when comparing DRIFTSIM to the two other models. When compared to alternative models at incremented distances downwind from the point of spraying, DRIFTSIM’s predictions were found to be overall more accurate up to 10 m from the boom edge; however, three alternative models provided more accurate predictions for long-distance drift (20 to 50 m from the boom). These results suggest the potential of using DRIFTSIM in future real-time drift monitoring for increased accuracy and performance. However, additional development is needed to improve far-field (>10 m downwind of an application) drift prediction accuracy.

Torrefaction of biomass early in the supply chain is one method of enhancing or preservin biomass quality during storage; reducing storage and transportation costs; attaining uniformity among different feedstock types; and meeting biorefineries' quality and quantity demands for feedstock in a cost effective and sustainable manner. The main objectives of this study were to analyze the mass and energy balance, to assess the techno-economic feasibility of a production-scale torrefaction system, and to quantify the sensitivity of energy and cost results to changes in initial feedstock moisture content, torrefaction process temperature, plant operating window, and initial capital investment. The output from this analysis provides clear guidance on the opportunities and costs associated with production-scale torrefaction of biomass as a feedstock intermediate. Analysis of energy results indicated that the net external energy required for the torrefaction process increased and energy efficiency of the torrefaction system decreased with increasing moisture contents. However, both of these energy metrics exhibited a decreasing trend with increasing process temperatures. In addition to the energy results, unit torrefaction process cost exhibited a decreasing trend with decreasing initial moisture contents of feedstock and decreasing torrefaction process temperatures. For the typical moisture content of 30%wb, process temperature of 240°C, plant operating window of 6 mo.yr-1 and initial capital investment of $7.5 million for the system with rated capacity of 25 Tton.hr-1, unit torrefaction process cost was estimated to be 17.5 $-Tton-1. Additional system improvements through capital cost reduction and wider operating windows can yield a torrefaction product cost of ∼12 $-Tton-1. © 2011 Society of Chemical Industry and John Wiley & Sons, Ltd

In today's production agriculture industry, a renewed interest has been placed on input costs and energy efficiency. The fact that the soil-tire interface of a tractive vehicle is inherently inefficient is widely known but not widely understood. To support evaluation of the soil-tire interface a measurement system was created to sense the normal stress at the interface of the tire and soil. To acquire and log the data from this sensor array, a unique data acquisition system was developed and is presented in this paper. The system utilized a microcontroller to process and write data to a compact flash card from a total of 56 analog channels. It also used a two-dimensional accelerometer to determine the angular position of the vehicle tire and the attached sensors. Custom data acquisition software was developed to log the data to the compact flash card in an efficient, organized manner. Results confirmed that the data acquisition system was capable of operating at 887Hz per-channel analog sampling frequency. Data from field testing illustrated the capability of the accelerometer to determine wheel angular rotation and associate the applied normal stress with that angular rotation. Results also confirmed the capability of the data acquisition system to estimate the rotational angle of contact of the tire while engaging soil.

As an alternative to commercial GPS tracking collars, a low-cost GPS Herd Activity and Well-being Kit (GPS HAWK) was developed to monitor locomotion behavior of cattle at a high sampling frequency (20 s). The operational goal of the GPS HAWK was to collect GPS location data at a user-specified frequency and store the data in a secure format (compact flash media) for retrieval and optimize power consumption to extend the sampling period. The GPS HAWK uses a Garmin 12-channel low-power GPS receiver powered by a sealed-lead acid battery housed in an aluminum enclosure on a shoulder-mounted harness. Data gathered by the GPS HAWKs were used to determine individual daily travel distance (DTD) and the effect of sample interval (SI) on this measurement. Differences (P < 0.0001) were shown in DTD across four days using animals with available data. The Angus cows (Bos Tarus L.) averaged 4.05 ± 0.14 km/d (2.52 ± 0.09 mi/d) during the four days. Sample interval (SI) had an effect on DTD. Differences in DTD were detected for all SIs (P < 0.0001) except between the 60- and 120-s intervals. By changing the SI from 20 s to 20 min, the mean DTD decreased by 1.68 km or 44%. Significant errors in estimates of cattle energetics from GPS monitoring can be introduced by increasing sampling interval. Therefore, researchers must account for increasing error in DTD due to undersampling as SI is increased to save battery power and to increase the interval between animal handling periods. Holding the GPS system in place consistently with a shoulder mounted harness proved to be somewhat challenging.