Journal of the ASABE最新文献

{"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":null,"pages":null},"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}

{"title":"FARnet: Farming Action Recognition From Videos Based on Coordinate Attention and YOLOv7-tiny Network in Aquaculture","authors":"Xinting Yang, Liang Pan, Dinghong Wang, Yuhao Zeng, Wentao Zhu, Dongxiang Jiao, Zhenlong Sun, Chuanheng Sun, Chao Zhou","doi":"10.13031/ja.15362","DOIUrl":"https://doi.org/10.13031/ja.15362","url":null,"abstract":"Highlights The automatic detection and recognition of farming action in video are realized. The YOLOv7-tiny was enhanced by incorporating Coordinate Attention (CA). The performance indices mAP@.5 and mAP@.5:.95 improved by 0.1% and 6.6%, respectively. An intelligent method for detecting \"inspection\" and \"applying pesticides\" is provided. Abstract. In aquaculture, regular \"inspection\" and \"applying pesticides\" are essential to improving production efficiency and fish disease treatment, but the current aquaculture system does not effectively support these strategies. Therefore, this paper proposes a farming action recognition network (FARnet), which can accurately locate the farmers in the video and detect the actions of “applying pesticides” and “inspection.” The dataset was captured and produced by multi-angle cameras, which were consulted with relevant experts. In this network, Coordinate Attention (CA) was used to improve the Efficient Layer Aggregation Networks-tiny (ELAN-tiny) and Spatial Pyramid Pooling (SPP) structures in the YOLOv7-tiny network. The precise implementation methods are as follows: (1) The convolution in ELAN-tiny was replaced with the CA module, and a shortcut was added. (2) A CA module was added to the final layer of the Spatial Pyramid Pooling (SPP) module. (3) The improved Efficient Layer Aggregation Networks-Coordinate Attention (ELAN-CA) and Spatial Pyramid Pooling-Coordinate Attention (SPP-CA) were used to extract action features and perform feature correction by ADD (Feature fusion by feature map summation) in the backbone. The results demonstrated that the FARnet achieved significantly better detection results than the YOLOv7-tiny network, where mAP@.5 improved by 0.1% from 99.4% to 99.5%, and the mAP@.5:.95 improved by 6.6% from 78.2% to 84.8%. Therefore, the FARnet can effectively detect and identify the “inspection” and “applying pesticides” actions of farmers and provide useful input information for the intelligent management system. Keywords: Action detection, Applying pesticides, Coordinate attention, FARnet, Inspection.","PeriodicalId":29714,"journal":{"name":"Journal of the ASABE","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"79931100","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":null,"pages":null},"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":"Estimating WEPP Cropland Erodibility Values From Soil Properties","authors":"W. Elliot, D. Flanagan","doi":"10.13031/ja.15218","DOIUrl":"https://doi.org/10.13031/ja.15218","url":null,"abstract":"","PeriodicalId":29714,"journal":{"name":"Journal of the ASABE","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"77674925","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":"Evaluating Draft EPA Emissions Models for Laying Hen Facilities","authors":"Y. Xiong, Guoming Li, B. Ramirez, R. Burns, R. Gates","doi":"10.13031/ja.15237","DOIUrl":"https://doi.org/10.13031/ja.15237","url":null,"abstract":"Highlights Draft EPA emission models for laying hen facilities were systematically evaluated. The models performed poorly on predicting the air pollutants when input variables were out of the NAEMS data range. A key finding was the unanticipated sensitivity of the draft model outputs to bird inventory and climate zones. Further revision and improvement may be necessary for draft models before they can be adopted by the egg industry. Abstract. In August 2021, the U.S. Environmental Protection Agency (EPA) released draft models to estimate daily NH3, H2S, PM10, PM2.5, and TSP emissions from egg-layer houses (high-rise and manure-belt) and manure storage using inputs of daily mean ambient temperature, relative humidity (RH), and hen inventory. These models were developed from refined datasets generated by the National Air Emissions Monitoring Study fieldwork completed in 2009. Notably, they do not include data for cage-free housing. Currently, 66% of U.S. laying hens are housed in cages; thus, these models, if adopted, will have a substantial impact on the U.S. egg industry. This study evaluated the EPA draft models’ robustness and assessed model outputs for egg production systems under differing climate scenarios. The EPA draft models distort emission factors for bird inventories to be lower or higher than those used to develop the models. With inventory held constant, the marginal influence of ambient temperature and RH on daily emissions varied substantially, with some values falling below the measurement detection threshold while others exceeding literature findings. For twelve representative U.S. locations representing differing climates, substantial differences in emission factors were found for bird inventories outside the range in the database. Annual emissions estimated from inventories used to develop the EPA models also varied by location. We conclude that the current draft EPA emission models cannot be used to the degree of precision that is suitable to apply to a wide range of layer facilities, particularly cage-free systems. Revisions are suggested to accommodate a greater range of climates, laying hen facility types, and inventories for practical emission estimations. Keywords: Air quality, Ammonia, Egg production, Emission model, Hydrogen sulfide, Particulate matter, Poultry.","PeriodicalId":29714,"journal":{"name":"Journal of the ASABE","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"72830023","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":"GREENBOX Technology III - Financial Feasibility for Crop Production in Urban Settings","authors":"Ankit Kumar Singh, Boris Bravo-Ureta, Richard McAvoy, Xiusheng Yang","doi":"10.13031/ja.15345","DOIUrl":"https://doi.org/10.13031/ja.15345","url":null,"abstract":"Highlights We proposed to use GREENBOX technology for urban crop production in warehouse settings. We assessed the profitability of the application of GREENBOX technology using Benefit Cost Analysis (BCA) to evaluate the Net Present Value (NPV), Internal Rate of Return (IRR), and Payback Period (PP). We conducted sensitivity analyses on NPV, IRR, and PP over different scenarios. GREENBOX was found financially feasible for all the hypothetical scenarios in major cities in the USA. Abstract . Food security pressure, especially in urban areas, continues to rise due to surging demand for food resulting from a growing population and declining resources. It has been critical to improve crop production and make food readily available to consumers without traveling long distances in an economically sustainable manner. The novel GREENBOX technology uses Controlled Environment Agriculture (CEA) principles for leafy green crop production in urban structures. A GREENBOX is an individual thermally insulated chamber with an artificial lighting source and a soilless cultivation system (hydroponics) in an environment that is controlled at the grower's discretion. This study performed a financial feasibility study of GREENBOX technology for urban crop production in various scenarios to evaluate the system's profitability from an individual business's perspective and used market prices of the goods and services paid for or received by a project. The representative GREENBOX unit in the base case scenario had dimensions of a standard shipping pallet (1.0 x 1.2 x 0.9 m, or 40 x 48 x 36 in) and included thermally insulated walls, an LED artificial lighting source, a camera for monitoring growth, a Nutrient Film Technique (NFT) hydroponic growth platform, and an environmental monitoring and control system. A warehouse can host numerous GREENBOX units for mass production. We carried out a benefit-cost analysis by assessing the Net Present Value (NPV), Internal Rate of Return (IRR), and Payback Period (PP). These parameters were evaluated for a base case scenario from data collected or estimated for a representative GREENBOX unit. We also applied the base case scenario to investigate the financial performance of the GREENBOX setup in selected urban areas in the United States; New York City (New York), Miami (Florida), Los Angeles (California), Dallas (Texas), Atlanta (Georgia), Chicago (Illinois), Boston (Massachusetts), and Philadelphia (Pennsylvania). We then carried out a sensitivity analysis on NPV, IRR, and PP by keeping all the parameters in the base case scenario invariant except for one at a time. We obtained a summary equation to understand the variation of the financial parameters with changing lettuce sale price, electricity cost, rental cost, labor cost, and the number of GREENBOX units. A GREENBOX unit would require an initial investment of $398 to assemble and an annual outflow of $157 to cover operating expenses. GREENBOX cultivation was financially viable","PeriodicalId":29714,"journal":{"name":"Journal of the ASABE","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135318290","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":"GREENBOX Technology I - Technical Feasibility and Performance in Warehouse Environment","authors":"Ankit Kumar Singh, Richard McAvoy, Boris Bravo-Ureta, Xiusheng Yang","doi":"10.13031/ja.15343","DOIUrl":"https://doi.org/10.13031/ja.15343","url":null,"abstract":"Highlights There are pressures on food security due to increasing population, demand, and urbanization. GREENBOX uses controlled environment agriculture for urban crop production in warehouse settings. GREENBOX provided the required environmental conditions and comparable biomass output year-round. GREENBOX is technically feasible for urban crop production. Abstract. The surging worldwide population and urbanization have increased food security and safety pressures. Therefore, there is a need to increase food production capacity in urban areas to feed this growing population. We have developed the GREENBOX technology to grow vegetables in individual climate-controlled boxes in urban warehouse environments. A GREENBOX is a thermally insulated modular structure of standard size with an artificial lighting source, a hydroponic nutrient supply system, and environmental controls. GREENBOX units can be used together in various numbers to form different configurations and production capacities. This study was conducted to evaluate the technical feasibility and performance of the GREENBOX technology for urban crop production in warehouse settings commonly found in urban areas. Two model GREENBOX units, constructed with commercially available parts, were located in a high-ceiling headhouse of a laboratory greenhouse complex at Storrs, Connecticut, USA, for the study. Forty-eight (48) heads of Butterhead Rex lettuce (Lactuca sativa) were grown in the model GREENBOX units (24 in each) over a 30-day growing cycle for four seasons. Environmental data, including light, temperature, relative humidity, and carbon dioxide, were collected using iPonic sensors at a frequency of every minute and processed to 15-minute averages. Crop growth was quantified with biomass data, which were wet weight, dry weight, total leaf area, and lettuce head area, using destructive and non-destructive methods every three days. A lysimeter was used to determine the water consumption rate by plants every fifteen minutes. We derived the Daily Light Integral (DLI), Leaf Area Index (LAI), Specific Leaf Area (SLA), productivity, and water consumed per lettuce head, per unit wet weight, and per unit dry weight. Descriptive statistics were used to describe and analyze the results. The DLI in the GREENBOX ranged between 32.48-37.23 mol/m2.d at the lettuce heads' height, higher than the recommended minimum DLI of 6.5-9.7 mol/m2.d. GREENBOX does not rely on external light but solely on the artificial lighting source, regulated at the grower's discretion. The mean temperatures inside were 24.5-26.9°C, falling within the optimal range of 17-29°C for lettuce. The artificial lighting source was a heat source to sustain cultivation. All year, the average relative humidity was 35.53%-58.54%, mostly within the ideal range of 40%-60%. The CO2 concentration inside the boxes fell slightly below the ambient concentration of 350 ppm, between 301.39 and 311.34 ppm over different seasons. Measured growth par","PeriodicalId":29714,"journal":{"name":"Journal of the ASABE","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135601265","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":"Emissions of Greenhouse Gases and Ammonia From a Wheat Site Under Intensive Management Affected by Different Fertilization Practices","authors":"Zhong-kai Zhou, Siyu Zhang, Naisen Jiang, Hai-fang Zhang, Li-li Wang, W. Xiu, Jian-ning Zhao, Dian-lin Yang","doi":"10.13031/ja.14852","DOIUrl":"https://doi.org/10.13031/ja.14852","url":null,"abstract":"Highlights The recommended mineral fertilizers plus organic fertilizer treatments increased the soil total carbon (TC) and nitrogen (TN) levels. The application of organic fertilizer markedly reduced the loss of NH3-N compared to the application of mineral nitrogen alone. CO2 and N2O emissions from the application of organic fertilizer were higher than those from the application of mineral nitrogen under long-term fertilization. Abstract. Greenhouse gas (GHG) and ammonia (NH3) emissions from wheat fields have been a serious challenge to agriculture and the environment. The integration of the use of inorganic N fertilizer, organic fertilizer, and crop residues and their environmental effects is needed under conventional tillage. In situ field experiments were established to evaluate the impact of different fertilization practices on soil greenhouse gas and ammonia emissions from a winter wheat field. A fertilizer experiment was performed from 24th October 2019 to 11th June 2020 in a winter wheat (Triticum aestivum L.) field in China with six fertilization treatments: (1) unfertilized control (UC); (2) recommended mineral fertilizer application of 200 kg ha-1 N (RF); (3) RF plus 15 t ha-1 of organic fertilizer (RFLO); (4) RF plus 30 t ha-1 of organic fertilizer (RFMO); (5) RF plus 45 t ha-1 of organic fertilizer (RFHO); and (6) traditional mineral fertilizer application of 300 kg ha-1 N (TF). The results showed that the RF plus organic fertilizer treatments increased the soil organic total carbon (TC) and nitrogen (TN) levels. Under long-term fertilization, the CO2 emissions from the RFLO, RFMO, and RFHO treatments were 18.3, 19.9, and 20.0 t ha-1, respectively, compared with those from the RF and TF treatments (13.2 and 16.0 t ha-1, respectively). In addition, the N2O emissions from the organic-inorganic fertilizer treatment were 7.6 kg ha-1 for the RFLO treatment, 12.4 kg ha-1 for the RFMO treatment, and 8.1 kg ha-1 for the RFHO treatment, which were higher than those from the RF and TF treatments (3.1 and 5.6 kg ha-1, respectively). The NH3 emissions from the RFLO, RFMO, and RFHO treatments (17.3, 26.2, and 22.4 kg ha-1, respectively) were lower than those from the RF (31.2 kg ha-1) and TF (49.7 kg ha-1) treatments under long-term fertilization. The methane emission potential of organic-inorganic fertilizer applications was 27.0% to 98.5% higher than a single application of inorganic fertilizer. Keywords: Ammonia, Carbon dioxide, Fertilization management, Nitrous oxide, Organic fertilizers, Winter wheat.","PeriodicalId":29714,"journal":{"name":"Journal of the ASABE","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"85007226","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":"Agriculture and Agricultural Water Management: Historical Perspectives and Integration of Research and Extension/Outreach for Large-Scale Technology Adoption in Production Fields","authors":"S. Irmak","doi":"10.13031/ja.15272","DOIUrl":"https://doi.org/10.13031/ja.15272","url":null,"abstract":"Highlights AWMN had a significant impact on enhancing agricultural production efficiency. Irrigated land area represented by the Network partners reached 1.20 million ha. On a 16-yr average, the reduction in water withdrawal was 144 mm/ha per growing season. AWMN reduced irrigation water withdrawal by 5 billion m3 from 2005 to 2020. $304 million was saved due to consuming less diesel fuel for pumping irrigation water. A total reduction of 900,000 tons in CO2 emissions was achieved from 2005 to 2020. Abstract. To achieve impact for water resources conservation enhancement and agricultural crop water productivity (CWP) per unit of input for meeting the food, fiber, feed, fuel, finance, and farmstead (6Fs) needs of the rapidly increasing global population, societies must find innovative ways to enable the transfer of research- and science-based data, knowledge, information, technology, and strategies for adoption in agricultural production fields. The objective of this study is to present historical perspectives on the evolution of agriculture and agricultural water management in different parts of the world and present a modern-era agricultural water management network, its objectives, and functions in achieving large-scale impacts to enhance water resource management. The Agricultural Water Management Network (AWMN) was established in 2005 to integrate science, research, and education/outreach principles into producers’ practices to help them make better-informed decisions, conserve water and energy resources, reduce CO2 emissions, and enhance CWP. Through coordinated research, demonstration, and education programs, the AWMN significantly enhanced water resource management and the protection of the environment. It contributed to the sustainability of natural resources and the agricultural economy through the adoption of innovative methodologies and strategies. Since the beginning of the AWMN, over 18,000 producers, crop consultants, state and federal agency personnel, irrigation district personnel, agricultural industry personnel, and other professionals have participated as learners and adopters in over 800 Extension, education, and/or outreach programs conducted by the AWMN team between 2005 and 2020. The irrigated land area represented by the Network partners and collaborators reached over 1.20 million ha in 2020. Water withdrawal for irrigation was reduced from 119 mm/ha per growing season in 2006 to 163 mm/ha per growing season in 2020, with a 16-year average of 144 mm/ha, due to the adoption of technologies and management strategies demonstrated and taught in the Network. Between 2005 and 2020, the AWMN is estimated to have reduced water withdrawal for irrigation by over 5 billion m3 (5 km3; 4.1 million acre-ft). Due to the reduction in irrigation water withdrawals, conservatively, over $304 million was saved by consuming less diesel fuel for pumping irrigation water. The AWMN has effectively reduc","PeriodicalId":29714,"journal":{"name":"Journal of the ASABE","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"86831327","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":"Effects of Rotary Tine Tool Velocity on Soil Reaction Forces, Power, and Energy Intensity","authors":"Safal Kshetri, B. Steward, M. Tekeste","doi":"10.13031/ja.15377","DOIUrl":"https://doi.org/10.13031/ja.15377","url":null,"abstract":"Highlights Draft force and torque on a vertical rotary tine tool were studied for various longitudinal velocities and speed ratios. Draft force increased with longitudinal velocity but decreased with speed ratio, and soil reaction torque increased with both longitudinal velocity and speed ratio. Total power required by the tool increased with speed ratio, and energy intensity increased with speed ratio and longitudinal velocity, with substantial changes observed at higher velocities. Abstract. Studying soil-tool interaction can provide valuable information on the actuation force and energy requirements of a weeding tool operating in soil. Soil-tine interaction was investigated for a vertical rotary tine tool that was intended to be used as a weeding tool for an automated mechanical intra-row weeder. The main objective of the research was to investigate the effects of linear and rotational velocities on soil reaction forces and power associated with actuation of the rotary tine tool in soil. A series of soil bin experiments were conducted in loam soil. Soil horizontal (draft) force and torque on the tool were measured at three longitudinal/travel velocities of 0.09 m s-1, 0.29 m s-1, and 0.5 m s-1 that were used to move the tool linearly across the soil bin length. The speed ratio, defined as the ratio of the longitudinal velocity to the peripheral velocity of the rotary tines, determined the rotational speeds required for the study. The draft force and torque were evaluated at four speed ratio levels (0, 1, 1.5, and 2). An Analysis of Variance (ANOVA) performed for statistical analysis using p < 0.05 showed that both longitudinal velocity and speed ratio had significant main and interaction effects on the draft force and torque. In most cases, the draft force decreased while torque increased with increasing speed ratios for the different longitudinal velocities used in the study. Power and energy intensity were also calculated using draft force and torque measurements for different experimental settings. For increases in speed ratios, the power requirements for tool draft force decreased, whereas the power requirements for rotating the tool increased for each longitudinal velocity. At the highest test travel speed of 0.5 m s-1, the power decreased from 66 W to 28 W for draft and increased from 0 W to 76 W for rotation of the tool at increasing speed ratios. The maximum total power calculated for the tool was 110 W at 0.5 m s-1 and a speed ratio of 2. The study shows the changes in power and energy requirements of a vertical rotary tine tool for different operating parameters for weed control. This information could be valuable for optimizing the physical weeding process. Keywords: Energy intensity, Power, Rotary tool, Soil-tine interaction, Weed control.","PeriodicalId":29714,"journal":{"name":"Journal of the ASABE","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"87037023","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}