Applied Engineering in Agriculture最新文献

{"title":"Determination of the Field Performance of a Prototype Combined Poppy Harvester","authors":"T. Saracoglu, Cengiz Ozarslan, A. F. Hacıyusufoğlu","doi":"10.13031/aea.15373","DOIUrl":"https://doi.org/10.13031/aea.15373","url":null,"abstract":"HighlightsA poppy harvester can be used as an alternative to the manual harvesting.Effective field capacity of a poppy harvester is 34 to 53 times higher than the manual harvest.Depending on the forward speed, field losses increase.Cleaning efficiency can be increased by a more aggressive sieving application.Abstract. The operations of collecting the poppy from the field by hand and then breaking it apart require intensive labor and time consumption, which increases the cost significantly. A mechanical harvester to be used for poppy harvesting will save human labor and reduce time consumption. Hence, a poppy harvester was designed to harvest and crush poppy capsules, and separate the stalks, seeds, and capsule parts from the shredded material with this study. The prototype harvester consists of a harvest unit, conveying unit, threshing unit, separating and cleaning unit, bagging unit, and power transmission unit. The machine is pulled by the tractor and its moving units are driven by the PTO and hydraulic system. In field experiments with the prototype poppy harvester, the material capacity of the machine (seed, capsule pieces, and total product), cleaning efficiency, and harvest losses were determined. The experiments were conducted in a randomized complete block design with three replicates. The prototype machine was operated at two forward speeds of 1.24 km h-1 and 1.95 km h-1. The effective field capacity of the harvester was determined to be 34 to 53 times higher than the manual harvest, and increasing forward speed, increased machine capacity by approximately 50%. The cleaning efficiency was determined to be approximately 84% for both forward speeds. Depending on the forward speed, field losses increased and varied between 13% and 21%. Keywords: Keywords., Cleaning capacity, Field losses, Harvesting capacity, Poppy harvester.","PeriodicalId":55501,"journal":{"name":"Applied Engineering in Agriculture","volume":"1 1","pages":""},"PeriodicalIF":0.9,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"67052808","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 Power Matching Control Strategy for Sugarcane Combine Harvesters","authors":"Ke Liang, Yuzhen Feng, Bowei Yao, Huasheng Chen, Mingzhang Pan, Yongzhi Tang, Wei Guan","doi":"10.13031/aea.15560","DOIUrl":"https://doi.org/10.13031/aea.15560","url":null,"abstract":"Highlights The optimal speed of each mechanism of the sugarcane combine harvester in the medium harvesting working condition is different. A power intelligent matching strategy for sugarcane combine harvester is proposed. Precisely matched performance indicators of target harvesting conditions. A number of suggestions are provided to optimize the power matching of the entire powertrain. Abstract. Due to the complex topography, small plot size, rainy climate, and different crop sparsity in western China, sugarcane harvesting operations suffer from poor harvesting performance and unstable harvesting effect. Therefore, the power matching strategy of sugarcane combine harvester needs to be optimized to solve these serial problems. In this article, the whole power system of sugarcane combine harvester is designed, and the load-sensitive system control is used to improve the efficiency of the hydraulic system and optimize the power distribution. Meanwhile, this article studies the power control system and proposes a power intelligent matching strategy for sugarcane combine harvester to adjust the power output of the power system. The power intelligent matching strategy considers the harvesting conditions and system structure of the sugarcane harvester, optimizes the engine output power and operating point distribution by using filters and fuzzy control algorithms, uses an accumulator to store excess energy and replenish system power to precisely match performance targets for target harvesting conditions and improve fuel economy. The experimental results show that the sugarcane combine harvester with a power intelligent matching strategy can freely switch the working mode in upslope, downslope, sunny, rainy and various crop density fields, and meet the demanded power of each device by dynamically adjusting the working point of the engine according to the operating conditions, enabling the system to work better. The research method in this article can provide a theoretical basis for small sugarcane combine harvesters to harvest sugarcane in hilly areas, rainy seasons, and different crop densities. Keywords: Harvest conditions, Power matching, Smooth power following control strategy, Sugarcane combine harvester.","PeriodicalId":55501,"journal":{"name":"Applied Engineering in Agriculture","volume":"30 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":"135910272","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":"Low-cost Calibration Method for the Infrared Camera","authors":"Derek Koji Uemura, Sanjay B. Shah, Prafulla Regmi, Jesse Grimes, Lingjuan Wang-Li","doi":"10.13031/aea.15546","DOIUrl":"https://doi.org/10.13031/aea.15546","url":null,"abstract":"Highlights Simple, low-cost infrared camera calibration method proposed. Calibration equation can improve accuracy for a narrower range of surface temperature. Infrared camera moderately sensitive to both emissivity and reflected air temperature. Abstract. Infrared (IR) or thermal cameras are being increasingly used in livestock research and management. An IR camera’s accuracy is specified over its entire surface temperature measurement range, whereas in livestock research and management, a narrower range suffices. A camera’s accuracy could be higher in a narrower range of temperatures. Hence, a novel low-cost method was used to calculate the FLIR E8 camera’s accuracy in a range of 24°C to 37°C, representative of surface temperature of poultry birds. Sensitivity analyses were also performed to evaluate the impact of three user specified parameters, namely, emissivity (e), distance between camera and surface (d), and reflected air temperature (tair). A linear regression model was used to correct the camera’s absolute error of 2.8°C (greater than its published error). However, the camera possessed precision and hence, repeatability. The IR camera was moderately sensitive to e, and slightly sensitive to tair and d, but its error could increase with the difference between the measured and assumed tair values. Attention is required to accurately characterize e and tair. This simple calibration method can reduce cost and could improve accuracy in a narrower temperature range than the IR camera’s published range, which could be useful for applied research. Keywords: Absolute error, Accuracy, Emissivity, Heat stress, IR, Precision, Reflected air temperature, Sensitivity analysis.","PeriodicalId":55501,"journal":{"name":"Applied Engineering in Agriculture","volume":"4 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":"135559322","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":"Field Evaluation of Conventional and Downhole TDR Soil Water Sensors for Irrigation Scheduling in a Clay Loam Soil","authors":"Gary W. Marek, Steve Evett, Thomas Henry Marek, Dana Porter, Robert C. Schwartz","doi":"10.13031/aea.15574","DOIUrl":"https://doi.org/10.13031/aea.15574","url":null,"abstract":"Highlights Soil profile water content derived from Acclima TDR-315™ sensors approximated those from NMM measurements. Soil profile water content from Campbell Scientific SoilVUE™10 sensors grossly underestimated those from the NMM. VWC values from SoilVUE10 sensors were consistently less than those reported by the TDR-315 sensors at all depths. These findings do not support SoilVUE10 use for irrigation scheduling in clay loam soils. Abstract. A field study was performed to evaluate the efficacy of two commercially available time domain reflectometry (TDR) soil water sensors for irrigation scheduling in a clay loam soil near Bushland, Texas. SoilVUE10 (Campbell Scientific Inc., Logan, Utah) and TDR-315 (Acclima Inc., Meridian, Idaho) sensors were installed within 30 cm of neutron moisture meter (NMM) access tubes in a research field planted to corn (Zea mays L) in 2020 and irrigated by a center pivot sprinkler system. Irrigation treatments included 50%, 75%, and 100% of evapotranspiration (ET) replacement with two access tubes installed in each plot, totaling six sensor evaluation sites. Semiweekly measurements with a field-calibrated NMM were used to monitor soil water status and schedule irrigation throughout the growing season. Soil profile water content values integrated over the surface to 1.1-m depth range were derived from SoilVUE10 and vertically distributed arrays of Acclima TDR-315 sensors installed at equivalent depths and were compared with those from NMM data. Average profile soil water contents from the TDR-315 sensors trended well with those from the NMM having mean bias difference (MBD) values of -9.8, -3.1, and 8.4 mm for the 50%, 75%, and 100% treatments, respectively. In contrast, soil profile water content values from the SoilVUE10 sensors grossly underestimated those from the NMM for all irrigation treatments with MBD values of -54.4, -70.5, and -89.8 mm for the 50%, 75%, and 100% treatments, respectively. Comparisons of volumetric water content (VWC) at each of the nine depths common to both electromagnetic sensor types revealed that values from the SoilVUE10 sensors were consistently less than TDR-315 values for all irrigation treatments. Underestimation at the near surface (5 and 10 cm depths) was attributed to loss of soil to electrode contact possibly associated with clay shrinkage during periodic drying following irrigation. Although soil to electrode contact can be problematic at greater depths, the explanation for chronic underestimation of VWC was less obvious except to note that underestimation occurred immediately after installation, which indicated poor electrode-soil contact after installation despite use of manufacturer guidelines and tools. Other possible reasons include challenges for accurate estimation of soil permittivity for a measured permittivity that includes the plastic sensor body. Results from this study suggest vertically distributed arrays of TDR-315 sensors can provide profile water content values ","PeriodicalId":55501,"journal":{"name":"Applied Engineering in Agriculture","volume":"183 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":"135560338","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":"Asynchronous Overlapping: An Image Segmentation Method for Key Feature Regions of Plant Phenotyping","authors":"Lingyan Hu, Wei Xu, Zhanjun Guo, Shaohang Qiu, Y. Pei, Zu-Min Wang","doi":"10.13031/aea.15083","DOIUrl":"https://doi.org/10.13031/aea.15083","url":null,"abstract":"Highlights Asynchronous overlapping—an automatic image acquisition method for key feature regions of plant phenotypes. The distance from the plant to the camera can be characterized by the brightness in the grayscale image. Asynchronously acquire daytime RGB and nighttime grayscale images of the plant to use the proposed algorithm. In the test of the plant images, the IoU is 0.8497, reaching a similar level of interactive algorithms. Abstract. Acquiring and describing plant phenotyping is an important proposition in botany and agronomy research. In this study, a computer vision-based asynchronous overlapping segmentation algorithm is proposed for automatic image acquisition of key feature regions of plant phenotyping. Firstly, day-time RGB and night-time grayscale images of infrared light filling the crop body at the same angle are asynchronously obtained using a common closed-circuit television surveillance camera. Then, thresholding and morphological filtering of grayscale images are conducted to extract the initial region contours. With this as a precondition, the algorithm adaptively finds edge paths of key feature regions in daytime RGB images. In the test of the cherry plant image, the intersection over union (IoU) of the algorithm to segment the key feature regions is 0.8497, reaching a similar level of interactive algorithms that require human involvement. The proposed method has low cost, high segmentation accuracy, and strong applicability. The proposed method can independently realize the acquisition of the key feature regions of plant image phenotypes and can be applied to large-scale agricultural production.","PeriodicalId":55501,"journal":{"name":"Applied Engineering in Agriculture","volume":"1 1","pages":""},"PeriodicalIF":0.9,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"67051127","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":"Using Nonstationary Depth-Frequency Curves to Characterize Local Precipitation Trends","authors":"Kalra Marali, R. Cibin","doi":"10.13031/aea.15247","DOIUrl":"https://doi.org/10.13031/aea.15247","url":null,"abstract":"Highlights Design storms should incorporate nonstationarity under changing climate scenarios. Three generalized extreme value distributions were fitted to represent nonstationarity for local precipitation analysis. The nonstationary models proposed in this study perform well at sites with strong precipitation trends. Abstract. As climate change advances, the stationarity assumption that governs traditional precipitation analysis is becoming untenable. Studies that incorporate nonstationarity typically use global circulation model (GCM) projections to determine the magnitude and direction of expected precipitation changes. However, the high computational costs and the coarse spatial resolution of GCMs make this method unsuitable for local precipitation analysis. In this study, nonstationarity is represented by a precipitation probability distribution with time-varying parameters. Three generalized extreme value (GEV) distributions are fitted: (1) the shift model, where the GEV location parameter varies linearly with time, (2) the stretch model, where the GEV location and scale parameters both vary linearly with time, and (3) the stationary model, a time-invariant distribution provided for the purpose of comparison. This procedure is applied to 24-h annual maximum precipitation records for ninety years (1900-1989) at five long-term measuring sites in Pennsylvania. Results varied among the five sites, suggesting that localized climate effects can cause precipitation differences at a small spatial scale. No significant nonstationarity was detected in two of the five locations. In three locations, however, increases in GEV location and scale combined to create a substantial, though not always significant, rise in the frequency of extreme precipitation. These trends were extrapolated forward over 30 years (1990-2019) and compared with an observed distribution for that year. The nonstationary models appeared to perform better at sites with stronger precipitation trends, which suggests a simple procedure for selecting sites where nonstationary analysis is most needed. Keywords: Climate change, Design storm, Generalized extreme value, Nonstationarity.","PeriodicalId":55501,"journal":{"name":"Applied Engineering in Agriculture","volume":"1 1","pages":""},"PeriodicalIF":0.9,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"67051566","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 Irrigation Methods and Scheduling on Water and Onion Productivity in Semi-Arid Areas of Ethiopia","authors":"Tesfay Gebretsadkan Gebremicael, G. Haile, Mulubrhan Kifle, Teferi Gebremedhin, Matthew J. Deitch, K. Reda, A. Reda","doi":"10.13031/aea.15256","DOIUrl":"https://doi.org/10.13031/aea.15256","url":null,"abstract":"Highlights Irrigation experiments on onion productivity were conducted at two sites for two consecutive years in Ethiopia. Factors from irrigation interval and irrigation method analyzed independently and factorially. Basin irrigation and fixed irrigation produced higher bulb yields and water productivity. Farmers’ income can be enhanced using improved irrigation practices for increased onion productivity. Abstract. How to meet the crop water demand and improve crop productivity is a particular concern for small-scale farmers, where the availability of water resources is limited. This study evaluated three different irrigation methods (furrow, basin and border) and two types of irrigation scheduling (CROPWAT schedule and farmers’ practices) with three replications for two consecutive years (2016-2017) at two sites using onion crops. The results showed that the CROPWAT schedule, basin irrigation method, and their interactions showed better performances and produced higher yield and water productivity. An average of 26 and 27 metric tons/hectare of onion were obtained under the basin irrigation method and basin irrigation with CROPWAT schedule combined, respectively. The water productivity (WP) and irrigation water productivity (IWP) also showed higher results under the basin irrigation method compared to other treatment combinations. The basin irrigation method produced higher marketable onion bulbs with firm medium bulb sizes that are essential for onion producers to earn maximum profit. The findings of this study also indicate that focusing on enhanced irrigation scheduling techniques and irrigation methods is paramount for better onion productivity in irrigation water-limited areas. Keywords: Irrigation interval, Irrigation methods, Irrigation practices, Onion productivity, Water management.","PeriodicalId":55501,"journal":{"name":"Applied Engineering in Agriculture","volume":"1 1","pages":""},"PeriodicalIF":0.9,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"67051760","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":"An On-Site Feces Image Classifier System for Chicken Health Assessment: A Proof of Concept","authors":"Guoming Li, Richard S Gates, Brett C. Ramirez","doi":"10.13031/aea.15607","DOIUrl":"https://doi.org/10.13031/aea.15607","url":null,"abstract":"Highlights A mobile application embedded onto smart mobile devices was developed for on-site chicken health assessment based on fecal images. A trained deep learning image classification model was programmed into the application for classifying healthy birds or unhealthy birds infected with Coccidiosis , Salmonella , and Newcastle disease . Animal caretakers can capture fecal images on farms, upload them to the developed application on their mobile devices, and receive health assessment results during daily flock inspection. The study demonstrates a successful proof-of-concept system but requires further work for consolidating system performance. Abstract. Rapid and accurate chicken health assessment can assist producers in making timely decisions, reducing disease transmission, improving animal welfare, and decreasing economic loss. The objective of this research was to develop and evaluate a proof-of-concept mobile application system to assist caretakers in assessing chicken health during their daily flock inspections. A computer server was built to assign users with different usage credentials and receive uploaded fecal images. A dataset containing fecal images from healthy and unhealthy birds (infected with Coccidiosis, Salmonella, and Newcastle disease) was used for classification model development. The modified MobileNetV2 model with additional layers of artificial neural networks was selected after a comparative evaluation of six models. The developed model was embedded into a local server for image classification. An application was developed and deployed, allowing a user with the application on a mobile device to upload a fecal image to a website hosted on the server and receive results processed by the model. Health status is transferred back to the user and can be shared with production managers. The system achieved over 90% accuracy for identifying diseases, and the whole operational procedure took less than one second. This proof-of-concept demonstrates the feasibility of a potential framework for mobile poultry health assessment based on fecal images. However, further development is needed to expand applicability to different production systems through the collection of fecal images from various genetic lines, ages, feed components, housing backgrounds, and flooring types in the poultry industry and improve system performance. Keywords: Artificial intelligence, Coccidiosis, Newcastle disease, Salmonella, Software development.","PeriodicalId":55501,"journal":{"name":"Applied Engineering in Agriculture","volume":"40 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":"135910584","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":"Research on Recognition Method of Chinese Cabbage Growth Periods Based on Swin Transformer and Transfer Learning","authors":"Xin Chen, Yuexin Shi, Xiang Li","doi":"10.13031/aea.15260","DOIUrl":"https://doi.org/10.13031/aea.15260","url":null,"abstract":"Highlights To the best of our knowledge, this study was the first intelligent recognition for Chinese cabbage growth period and proposed the Swin Transformer+1 model. If the four growth periods were considered, the recognition accuracy rate of the model on the test set was 96.15%. If the transition periods of Chinese cabbage growth were considered, the model recognition accuracy rate was 97.17%. Experiments showed that the Swin Transformer+1 model was robust and could be applied in real agricultural production. Abstract. In order to facilitate agricultural management and improve the quality and yield of Chinese cabbage, it is necessary to intelligently identify the growth periods of Chinese cabbage. In this study, a transfer learning-based recognition model for Chinese cabbage growth periods was proposed, which could identify four growth periods of Chinese cabbage: “germination and seedling period,” “rosette period,” “heading period,” and “dormant period.” The data set of Chinese cabbage growth periods was built. The recognition model was named Swin Transformer+1, using Swin Transformer as the backbone network to extract image features, and a fully connected layer as the classifier. To optimize the model, we used Letterbox instead of Stretching to resize the image, used Focal Loss instead of Cross Entropy Loss as the loss function, and used Stochastic Weight Averaging instead of Adam as the optimizer. Transfer learning was used for training, which could solve the problems of overfitting and underfitting when training deep network with a small data set. We verified the effectiveness of the above improved methods through ablation experiments. Experiments showed that the Swin Transformer+1 model had a high recognition accuracy rate. If only the four growth periods were considered, the recognition accuracy rate was 96.15%. If the transition periods between two growth periods of Chinese cabbage were considered, the recognition accuracy rate was 97.17%. The model had strong robustness. It maintained a high recognition accuracy rate when the images in the test set were augmented. In general, Swin Transformer+1 model has high application value in actual agricultural production scenarios. Keywords: Chinese cabbage growth period, Deep learning, Image recognition, Swin transformer, Transfer learning","PeriodicalId":55501,"journal":{"name":"Applied Engineering in Agriculture","volume":"38 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":"135910287","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 Technoeconomic Model for Estimating Costs of Harvesting and Debarking Shrub Willow","authors":"Azadwinder Chahal, Daniel E. Ciolkosz, Virendra Puri, Jude Liu, Michael Jacobson","doi":"10.13031/aea.15454","DOIUrl":"https://doi.org/10.13031/aea.15454","url":null,"abstract":"Highlights Technoeconomic analysis helps increase understanding of the potential of shrub willow debarking and provide assessment of economic opportunities. Harvest and transport costs are increased by debarking and account for more than 50% of total costs. The fraction of clean wood material is higher when shrub willow is harvested during the dormant season. Energy to break the wood-bark bond is almost five times higher in the dormant season than the growing season. High yield of willow biomass, high wood fraction, and high field capacity have a positive influence on profitability. Abstract. A technoeconomic model was developed for integrated debarking/harvesting of shrub willow that assesses the costs associated with debarking of willow and provides a platform for estimating the minimum selling price (MSP) for separated bark material. Harvest and transport costs are influenced by the addition of a debarking process and accounts for more than 50% of the total costs of willow production. The estimated MSP for bark material is $24.53 Mg -1 when the willow biomass is harvested in the dormant season and rises to $28.65 Mg -1 when harvested during growing season. The fraction of clean wood material recovered during the dormant season harvest averaged to 72% compared to 66% in the growing season (for shrub willow cultivars in this study). Increasing the field size from 10 to 50 ha decreases MSP of bark by 47%. High yield (~26 Mg ha -1 ) creates a condition in which a producer can be profitable by selling clean wood material only (with positive NPV). Likewise, sensitivity analysis shows that under the conditions modeled in this study, shrub willow varieties with high wood fraction have a lower MSP for bark material; MSP for bark material approaches zero when the fraction of wood rises to 86.6%. Keywords: Biomass, Debarking, Shrub willow, Techno-economic analysis.","PeriodicalId":55501,"journal":{"name":"Applied Engineering in Agriculture","volume":"34 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":"135910585","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}