Journal of the ASABE最新文献

{"title":"Simplified Modeling of High-Solids Anaerobic Digestion of Dairy Manure in a Pilot-Scale Stirred Tank Anaerobic Digester","authors":"I. D. Kariyama, Weixiang Li, Shaoqi Yu, Long Chen, R. Qi, Hao Zhang, Xiaxia Li, Xin Deng, Jiansen Lin, Binxin Wu","doi":"10.13031/ja.15203","DOIUrl":"https://doi.org/10.13031/ja.15203","url":null,"abstract":"Highlights HSAD is a cost-effective approach for managing high-solids manure. Batch digestion of HSAD at a low inoculum ratio is unsuitable. Mixing once a day was enough to maintain a stable digestion process. The stoichiometric method with an appropriate biodegradability factor provided perfect prediction. Simplified biokinetics can predict methane productivity at steady-state conditions. Abstract. Anaerobic digestion (AD) is considered one of the most effective methods of managing dairy manure. To effectively and economically treat the huge volumes of manure produced by commercial dairy farms, high-solids anaerobic digestion (HSAD) is to be encouraged. In this manuscript, batch and semi-continuous anaerobic digestion experiments of dairy manure with a high volatile solid (VS) content were conducted in a pilot-scale stirred digester with an effective volume of 1.63 m3, operated under mesophilic temperature conditions. Three intermittent mixing treatments (50, 100, and 150 rpm) were mixed once a day during feeding with a constant mixing duration of 5 minutes, including a non-mixed experiment, operating at a 30-day hydraulic retention time. The objectives were to determine an optimum mixing intensity to enhance HSAD efficiency and economy and to apply simplified models to model the digestion process. The simplified kinetic models were modified to accurately predict methane growth, yield, and production rates. The modified Gompertz growth model predicted the methane growth at the batch experiment perfectly. The first-order kinetic model predictions of the biodegradability factor, the specific methane yield, and the specific methane production rate were consistent with the batch experimental results. The stoichiometric method and the Karim model were modified to accurately model the effect of mixing intensity on the methane yield and the specific methane production rate. Three linear equations were successfully developed to predict the methane production rate. Optimized mixing intensity and organic loading rate are critical for high methane production rates. This study contributes to the ongoing research to improve the efficiency of HSAD. Keywords: Dairy manure, High-solids anaerobic digestion, Methane productivity, Mixing intensity.","PeriodicalId":29714,"journal":{"name":"Journal of the ASABE","volume":"4 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"90325834","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Optimized Chassis Stability Relative to Dynamic Terrain Profiles in a Self-Propelled Sprayer Multibody Dynamics Model","authors":"Bailey Adams, M. Darr, Aditya Shah","doi":"10.13031/ja.15230","DOIUrl":"https://doi.org/10.13031/ja.15230","url":null,"abstract":"Highlights This study presented a new optimization methodology using a prismatic joint with high stiffness and damping. The virtual suspension model contained the main bodies, an optimization subsystem, and a free-floating cylinder. Under aggressive terrain, an optimized chassis platform resulted in a 19.5% increase in boom height stability. Abstract. Multibody dynamics (MBD) models are continuing to be valuable for engineering design and product development, especially regarding subsystem optimization. Most MBD optimization processes begin with a sensitivity analysis of treatment factors and levels to understand how uncertainty in model inputs can be attributed to different sources of uncertainty within model outputs; however, this study developed a new MBD methodology to automatically determine the optimized dynamic chassis suspension responses on each corner of the vehicle from a single simulation for a self-propelled sprayer model as the chosen application use-case. This technique leveraged a prismatic joint (with a high spring stiffness and damping coefficient) connected between the chassis mainframe and the simplified optimization tire to create a distance constraint that held the chassis body at a near-consistent height above the ground. Then the solver optimized the response of the chassis suspension system to maintain a stable chassis platform relative to the terrain beneath it as the vehicle traversed across dynamic terrain conditions. This optimization response was also accomplished by replacing the baseline chassis suspension components with a free-floating cylinder, which permitted the unrestricted, optimized motion needed to keep the chassis body at a near-level position with respect to the roll and pitch profiles of the terrain. For a simulation with an aggressive terrain configuration, the analysis showed that an optimized suspension system resulted in a 46% decrease in operator comfort and a 19.5% increase in overall boom height stability as the boom height control system better maintained a dynamic position closer to the specified target height. Keywords: Boom height, Chassis suspension, Multibody dynamics (MBD), Optimization, Prismatic joint, Simulation, Terrain.","PeriodicalId":29714,"journal":{"name":"Journal of the ASABE","volume":"12 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"89617301","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Impacts on Teamwork Performance for an Engineering Capstone in Emergency Remote Teaching","authors":"Brandi Brown, Miguel Fudolig, T. Brown-Brandl, Deepak R. Keshwani","doi":"10.13031/ja.15265","DOIUrl":"https://doi.org/10.13031/ja.15265","url":null,"abstract":"Highlights Teamwork data from engineering capstone courses were analyzed to detect impacts of emergency remote teaching. The Comprehensive Assessment for Team-Member Effectiveness (CATME) data was analyzed via statistical modeling. Qualitative data attained from student responses were analyzed for patterns. Students found the lack of team camaraderie even more challenging than limitations on testing designs. This study offers avenues for developing engineering students’ teamwork skills in remote settings. Abstract. The onset of the global pandemic forced universities to rapidly shift to emergency remote teaching (ERT), which could cause even more perturbations for engineering courses with a hands-on, project-oriented focus. Thus, the purpose of this project was to gain a data-driven appreciation of how teamwork performance was impacted for engineering students in this environment and recommend focus areas for instructional designers. The Comprehensive Assessment for Team-Member Effectiveness (CATME) tool was used to assess different aspects of teamwork performance for 108 students in an undergraduate engineering capstone course during an in-person course offered in 2019-2020 (pre-pandemic) and an ERT course offered in 2020-2021 at a major Midwestern university. The classes were divided into teams for their capstone projects using the CATME Team-Maker tool. Students were asked to rate their teammates at the beginning, middle, and end of the course across five CATME dimensions: (1) Contribution to Team’s Work, (2) Interacting with Teammates, (3) Keeping the Team on Track, (4) Expecting Quality, and (5) Having Relevant Knowledge, Skills, and Abilities (KSAs). Statistical modeling was implemented to decipher how ratings differed throughout the year in each course as well as to identify specific CATME areas that varied between the in-person and ERT courses. A qualitative assessment was also implemented for the ERT course based on student responses to a prompt that asked them to comment on how the pandemic impacted their personal and team performance. Results revealed that engineering students showed a significant reduction in three categories in the ERT course compared to in-person: Contributing to Team’s Work, Expecting Quality, and Having Relevant KSAs. Interestingly, these three categories deal largely with student motivation toward team efforts, which was echoed in the qualitative assessment. The majority of alarming comments made by students were regarding not being able to build camaraderie with their teammates in the ERT environment. It was surprising to find that engineering students found this lack of team camaraderie even more challenging than the limitations on testing their designs. Thus, more data-driven analyses are necessary to examine which methods and technologies are ideal for teleworking project-based courses in terms of facilitating team bonding, helping teams brainstorm, and fostering more en","PeriodicalId":29714,"journal":{"name":"Journal of the ASABE","volume":"31 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"88154606","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Assessment of Wood Chips and Agricultural Residues as Denitrifying Bioreactor Feedstocks for Use in the Canadian Prairies","authors":"J. Kohn, Gregory S. Piorkowski, Nicole E. Seitz Vermeer, Janelle F. Villeneuve","doi":"10.13031/ja.15412","DOIUrl":"https://doi.org/10.13031/ja.15412","url":null,"abstract":"Highlights Performance of denitrifying bioreactors in Alberta was evaluated. Barley straw was more effective in reducing nitrate compared to wood chips. Hydraulic retention time, feedstock, and season are the primary factors affecting nitrate removal. Abstract. This study evaluated the performance of pilot-scale denitrifying bioreactors (LWD: 6 × 0.6 × 1m) filled with different carbon substrates, including barley straw, hemp straw, and woodchips, for removing dissolved nitrogen from simulated subsurface drainage at two representative geographic locations in Alberta. In this study, the bioreactors were tested under varying hydraulic retention times (4, 8, and 12 h) in the spring, summer, and fall of one year. Tracer studies were conducted to evaluate flow and dispersion characteristics. The mean of nitrate removal efficiency ranged from 19% to 87% during the spring, 44% to 95% during the summer, and 21% to 68% during the fall. We found that barley straw was more effective in reducing nitrate (45% to 95%) compared to wood chips (19% to 54%). This study is the first testing of the effect of different biomass types and hydraulic residence times on bioreactor performance in the Canadian prairies (Alberta) and will allow agricultural producers and regulators to assess the suitability of these systems within the region. Keywords: Bioreactor, Denitrification, Water quality, Wood chips, Agricultural residues, Subsurface Drainage.","PeriodicalId":29714,"journal":{"name":"Journal of the ASABE","volume":"17 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"91144126","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Denitrifying Bioreactor In Situ Woodchip Bulk Density","authors":"L. Christianson, R. Christianson, C. Díaz-García, G. Johnson, B. Maxwell, R. Cooke, N. Wickramarathne, L. Gentry","doi":"10.13031/ja.15364","DOIUrl":"https://doi.org/10.13031/ja.15364","url":null,"abstract":"Highlights The bulk density of woodchips in denitrifying bioreactors in the field is unknown. In situ bulk density estimation methods were developed for use during construction or excavation. Dry bulk densities of aged woodchips at bioreactor bottoms were lower than previous literature values. Moisture and particle size and density explained some, but not all, of the variation in in situ bulk densities. Abstract. Woodchip bulk density in a denitrifying bioreactor governs system hydraulics, but this prime physical attribute has never been estimated in situ. The objectives were twofold: (1) to establish estimates of in situ woodchip bulk density at bioreactors in the field, and (2) evaluate causal factors for and resulting impacts of these estimates. Proof-of-concept bulk density methods were developed at a pilot-scale bioreactor using three ways to estimate volume: surveying the excavated area, pumping the excavation full through a flow meter, and using iPhone Light Detection and Ranging (LiDAR). These methods were then further tested at two new and three old full-size bioreactors. Additional ex situ (off-site) testing with the associated woodchips included analysis of bulk density along a moisture gradient and particle size, particle density, wood composition, and hydraulic property testing. In situ dry bulk densities based on the entire volume of the new bioreactors (206-224 kg/m3) were similar to values from previous lab-scale studies. In situ estimates for woodchips at the bottom of aged bioreactors (22-mo. to 6-y) were unexpectedly low (120-166 kg/m3), given that these woodchips would presumably be the most compacted. These low moisture-content corrected dry bulk densities were influenced by high moisture contents in situ (>70% wet basis). The impacts of particle size and particle density on bulk density were somewhat mixed across the dataset, but in general, smaller woodchips had higher dry bulk densities than larger, and several woodchips sourced from the bottom of bioreactors had low particle densities. Although dry bulk densities in the zone of flow in bioreactors in the field were shown to be relatively low, the resulting permeability coefficients under those packing conditions did not differ from those of the original woodchips. The LiDAR-based volume estimation method was the most practical for large-scale, full-size evaluations and allowed high precision with small features (e.g., vertical reactor edges, drainage fittings). Keywords: Compaction, Cone penetrometer, Drainable porosity, LiDAR, Moisture content, Survey.","PeriodicalId":29714,"journal":{"name":"Journal of the ASABE","volume":"68 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"91166711","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Design of Non-Thermal Plasma Alfalfa Seed Vigor Enhancement Device and Study of Treatment Effect","authors":"Yunting Hui, Yangyang Liao, Sibiao Li, Changyong Shao, Decheng Wang, Yong You","doi":"10.13031/ja.15309","DOIUrl":"https://doi.org/10.13031/ja.15309","url":null,"abstract":"Highlights An effective seed treatment method is provided. Three generations of field growth trials were conducted. We Investigated the effects of low-temperature plasma treatment on the biological characters and yield components. Abstract. An atmospheric pressure, low-temperature dielectric barrier discharge (DBD) plasma seed treatment device was developed for plasma seed treatment. The device worked continuously on alfalfa seeds and evenly distributed the seeds in a plasma discharge range. The processing time, voltage amplitude, and frequency were adjustable. The device was used to study the effect of DBD plasma treatment at different voltages and times on alfalfa seed germination using untreated alfalfa seeds as the control (CK). The results showed that the DBD plasma treatment of alfalfa seeds promoted seed germination and seedling growth, and the optimal discharge conditions were a discharge voltage of 11 kV and a discharge time of 40 s. Compared with CK, the germination potential and germination rate increased by 12.49% and 18.08%, respectively. After treatment using the optimal discharge time, the germination potential, germination rate, dry weight, and seedling height increased by 9.9%, 16.1%, 15%, and 32.9%, respectively, compared with CK. The Scanning Electron Microscope images of the seed epidermis treated with 11 kV and 40 s plasma showed that the surface of alfalfa seeds was etched. Different doses of discharge radiation had different effects on physiological processes in seeds, and their sensitivity to plasma discharge was different. In a certain range, the germination rate, germination potential, fresh weight, dry weight, root length, and seedling height of alfalfa seeds improved to different degrees under different discharge voltages and times. Plasma has a good application prospect for improving the growth of alfalfa seeds. Keywords: Alfalfa, Dielectric barrier discharge plasma, Germination, Seed treatment device, Seedling growth.","PeriodicalId":29714,"journal":{"name":"Journal of the ASABE","volume":"120 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"77419558","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Air-Pinch PWM Valve to Regulate Flow Rate of Hollow-Cone Nozzles for Variable-Rate Sprayers","authors":"Javier Campos, Heping Zhu, Hongyoung Jeon, Ramón Salcedo, Erdal Ozkan, Carla Roman, Emilio Gil","doi":"10.13031/ja.15601","DOIUrl":"https://doi.org/10.13031/ja.15601","url":null,"abstract":"Highlights Air-pinch PWM valve was investigated as an alternative to electric PWM valves to manipulate hollow-cone nozzles. Air-pinch and electric PWM valves performed comparable accuracy in flow rate modulations. Droplet sizes from hollow-cone nozzles with both PWM valves were comparable across DUCs ranging from 20% to 100%. Air-pinch PWM valve had great potential of use due to its capacity to isolate the internal parts of the valve from chemicals. Abstract. Electric pulse width modulation (PWM) solenoid valves are commonly used to regulate nozzle flow rates to achieve precision variable-rate spray applications. However, some pesticide formulations, such as wettable powders and adhesive additives, can potentially cause a malfunction such that the valve cannot completely shut off during flow rate modulation if spray lines are not cleaned thoroughly after spray applications. An air-pinch PWM valve was evaluated as a potential alternative to conventional PWM valves to modulate the flow rates of hollow-cone nozzles used on air-assisted orchard sprayers. With the air-pinch valve, spray mixtures only passed through a flexible tube to avoid chemicals directly contacting the moving components inside the valve chamber. The flow rate modulation was performed by pinching and releasing the tube back and forth with air-pilot PWM actions. Evaluations included the flow rate modulation capability along with droplet size distributions from three disc-core hollow-cone nozzles coupled with the PWM pinch valve and compared with a conventional electric PWM valve. Both air-pinch and electric PWM valves performed comparably in the flow rate modulation accuracy and droplet size distribution for hollow-cone nozzles operated at 414 and 827 kPa pressures across the duty cycles (DUCs) ranging from 10% to 100%, except for the air-pinch valve that could not activate at 10% DUC. The flow rates of nozzles modulated with both PWM valves at all DUCs were 5.3% greater on average than the target flow rates, while the flow rates were similar at 90% and 100% DUCs. Droplet size classifications based on ASABE Standard S-572.3 were generally consistent across DUCs ranging from 20% to 100% for the same nozzle and pressure with the air-pinch PWM valve and from 10% to 100% with the conventional electric PWM valve. The consistency of droplet sizes across DUCs and accuracy of flow rate modulations demonstrated the potential advantage of using the air-pinch PWM solenoid valve as an alternative for precision variable-rate sprayers to accurately apply different chemicals. Keywords: Droplet size, Flow rate control, Pesticide, Pinch valve, Precision farming, Pulse width modulation.","PeriodicalId":29714,"journal":{"name":"Journal of the ASABE","volume":"6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135214085","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A Multimodal Optical Sensing System for Automated and Intelligent Food Safety Inspection","authors":"J. Qin, Jeehwa Hong, Hyunjeong Cho, J. V. Van Kessel, I. Baek, K. Chao, M. Kim","doi":"10.13031/ja.15526","DOIUrl":"https://doi.org/10.13031/ja.15526","url":null,"abstract":"Highlights A multimodal optical sensing system was developed for food safety applications. The prototype system can conduct dual-band Raman spectroscopy at 785 and 1064 nm. The system can automatically measure samples in Petri dishes or well plates. The system with AI software is promising for identifying species of foodborne bacteria. Abstract. A novel multimodal optical sensing system was developed for automated and intelligent food safety inspection. The system uses two pairs of compact point lasers and dispersive spectrometers at 785 and 1064 nm to realize dual-band Raman spectroscopy and imaging, which is suitable to measure samples generating low- and high-fluorescence interference signals, respectively. Automated spectral acquisition can be performed using a direct-drive XY moving stage for solid, powder, and liquid samples placed in customized well plates or randomly scattered in standard Petri dishes (e.g., bacterial colonies). Three LED lights (white backlight, UV ring light, and white ring light) and two miniature color cameras are used for machine vision measurements of samples in the Petri dishes using different combinations of illuminations and imaging modalities (e.g., transmission, fluorescence, and color). Real-time image processing and motion control techniques are used to implement automated sample counting, positioning, sampling, and synchronization functions. System software was developed using LabVIEW with integrated artificial intelligence functions able to identify and label interesting targets instantly. The system capability was demonstrated by an example application for rapid identification of five common foodborne bacteria, including Bacillus cereus, E. coli, Listeria monocytogenes, Staphylococcus aureus, and Salmonella spp.. Using a machine learning model based on a linear support vector machine, a classification accuracy of 98.6% was achieved using Raman spectra automatically collected from 222 bacterial colonies of the five species grown on nutrient nonselective agar in 90 mm Petri dishes. The entire system was built on a 30×45 cm2 breadboard, enabling it compact and portable and its use for field and on-site biological and chemical food safety inspection in regulatory and industrial applications. Keywords: Artificial intelligence, Automated sampling, Bacteria, Food safety, Machine learning, Machine vision, Raman, Sensing.","PeriodicalId":29714,"journal":{"name":"Journal of the ASABE","volume":"6 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"74394392","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Calibration and Validation of RZWQM2-P Model to Simulate Phosphorus Loss in a Clay Loam Soil in Michigan","authors":"Md Sami Bin Shokrana, E. Ghane, Z. Qi","doi":"10.13031/ja.15283","DOIUrl":"https://doi.org/10.13031/ja.15283","url":null,"abstract":"Highlights RZWQM2-P was tested and validated for clay loam soil using daily discharge and load data. The model performed satisfactorily in predicting hydrology and TP load, but DRP prediction was unsatisfactory. Inability of the model to simulate P loss in subsurface drainage discharge after fertilization event was one of the reasons for the unsatisfactory model performance. Abstract. Phosphorus (P) loss and transport through subsurface drainage systems is a primary focus for addressing harmful algal blooms in freshwater systems. The recent development of the phosphorus (P) routine of the Root Zone Water Quality Model (RZWQM2-P) has the potential to enhance our understanding of the fate and transport of P from subsurface-drained fields to surface water. However, there is a need to test the model under different fertilization, soil, climate, and cropping conditions. The objective of this study was to test the model's performance with daily drainage discharge, dissolved reactive phosphorus (DRP), and total phosphorus (TP) load collected from a subsurface-drained field with clay loam soil. We calibrated RZWQM2-P using two years of measured data. Subsequently, we validated RZWQM2-P using a year and nine months of measured data. We used the Nash-Sutcliffe model efficiency (NSE) and percentage bias (PBIAS) statistics for the RZWQM2-P model evaluation. The results showed that the model performance was “good” (daily NSE = 0.66 and PBIAS = -7.16) in predicting hydrology for the calibration period. For the validation period, the hydrology prediction of the model was “very good” (daily NSE = 0.76), but it had a “satisfactory” underestimation bias (PBIAS = 23.57). The model’s performance was “unsatisfactory” in simulating DRP for both calibration (daily NSE = 0.31 and PBIAS = -61.50) and validation (daily NSE = 0.32 and PBIAS = 43.68) periods. The P model showed “satisfactory” performance in predicting TP load for both calibration (daily NSE = 0.46 and PBIAS = -32.41) and validation (daily NSE = 0.39 and PBIAS = 42.90) periods, although both periods showed “unsatisfactory” percent bias. The underperformance may have been due to the model’s inability to partition fertilizer P into different P pools under high water tables or ponding conditions when using daily data. In conclusion, the RZWQM2-P model performed well for drainage discharge with daily data, but further investigation is needed to improve the P component of the model. Keywords: Field-scale modeling, Nutrient load, Phosphorus modeling, Subsurface drainage, Tile drainage, Water Quality.","PeriodicalId":29714,"journal":{"name":"Journal of the ASABE","volume":"176 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"73169360","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Predictor Selection and Machine Learning Regression Methods to Predict Saturated Hydraulic Conductivity From a Large Public Soil Database","authors":"Toby A. Adjuik, S. Nokes, M. Montross, M. Sama, O. Wendroth","doi":"10.13031/ja.15068","DOIUrl":"https://doi.org/10.13031/ja.15068","url":null,"abstract":"Highlights In this study, six machine learning (ML) models were developed using a large database of soils to predict saturated hydraulic conductivity of these soils using easily measured soil characteristics. Tree-based regression models outperformed all other ML models tested. Neural networks were not suitable for predicting saturated hydraulic conductivity. Clay content, followed by bulk density, explained the highest amount of variation in the data of the predictors examined. Abstract. One of the most important soil hydraulic properties for modeling water transport in the vadose zone is saturated hydraulic conductivity. However, it is challenging to measure it in the field. Pedotransfer Functions (PTFs) are mathematical models that can predict saturated hydraulic conductivity (Ks) from easily measured soil characteristics. Though the development of PTFs for predicting Ks is not new, the tools and methods used to predict Ks are continuously evolving. Model performance depends on choosing soil features that explain the largest amount of Ks variance with the fewest input variables. In addition, the lack of interpretability in most “black box” machine learning models makes it difficult to extract practical knowledge as the machine learning process obfuscates the relationship between inputs and outputs in the PTF models. The objective of this study was to develop a set of new PTFs for predicting Ks using machine learning algorithms and a large database of over 8000 soil samples (the Florida Soil Characterization Database) while incorporating statistical methods to inform predictor selection for the model inputs. Of the machine learning (ML) models tested, random forest regression (RF) and gradient-boosted regression (GB) gave the best performances, with R2 = 0.71 and RMSE = 0.47 cm h-1 on the test data for both. Using the permutation feature importance technique, the GB and RF regression models showed similar results, where clay content described the most variation in the data, followed by bulk density. The implication of this study is that, when predicting Ks using the Florida Soil Characterization Database, priority should be given to obtaining quality data on clay content and bulk density as they are the most influential predictors for estimating Ks. Keywords: Deep learning, Gradient boosted regression, Pedotransfer functions, Random forest regression, Soil database, Soil properties.","PeriodicalId":29714,"journal":{"name":"Journal of the ASABE","volume":"49 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"79780815","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}