Biosystems Engineering最新文献

{"title":"Effect of slurry separation and air-plasma treatment on NH3 and VOC emissions from field applied biogas digestate and pig slurry to grassland","authors":"Johanna Pedersen ,&nbsp;Rodrigo Labouriau ,&nbsp;Anders Feilberg","doi":"10.1016/j.biosystemseng.2024.09.014","DOIUrl":"10.1016/j.biosystemseng.2024.09.014","url":null,"abstract":"<div><div>Different technologies can be utilised to mitigate environmentally harmful ammonia (NH₃) emissions after field application of liquid animal manure (slurry). After a solid-liquid separation, air-plasma technology can acidify the liquid fraction and enrich its nutrient value by increasing the amount of inorganic nitrogen. The present work investigates the emissions of NH₃ and volatile organic compounds (VOC) after field application of the following fractions of pig slurry and slurry digestate: i) untreated slurry (UN), ii) liquid fraction of slurry (LF), iii) liquid fraction of slurry treated with air from the plasma treatment (LP). Emissions were measured with a system of wind tunnels and a cavity ring-down spectrometer for NH₃ concentration measurements and a proton-transfer-reaction mass-spectrometer for measurements of VOC. For both slurry types, the cumulative NH₃ emissions were in the following order UN &gt; LF &gt; LP. All the differences were significant (P &lt; 0.05), except between pig slurry LF and LP. The reduction in cumulative NH₃ emission obtained by the treatments compared to UN were 55–74% and 70–89% for LF and LP, respectively. The slurry separation decreased dry matter by 46–54% and resulted in a rapid decrease in slurry exposed surface area after application, presumably due to high infiltration. Several VOCs were measured after application of the slurry, but continuous emission was undetectable for all VOCs. The very low VOC emission was presumably due to high infiltration of the low dry matter slurry treatments and low concentration of VOC in the digestate.<span><div><span><div><strong>Science4Impact Statement</strong></div></span><div><div>This work demonstrates how treating slurry with plasma treated air can mitigate ammonia emissions after field application. The presented findings can be used for additional technology development and verification. Future research efforts should e.g. clarify what level of solid-liquid separation is needed before treating the liquid fraction with plasma treated air, to assess whether the additional ammonia reductions are profitable. Furthermore, the findings can be used by decision makers and advisory bodies to assess the compliancy of this slurry application technology with applicable environmental regulations. The work also highlights important remaining knowledge gaps that need to be investigated before the technology can be deemed fit for wider practical application.</div></div></div></span></div></div>","PeriodicalId":9173,"journal":{"name":"Biosystems Engineering","volume":"247 ","pages":"Pages 257-266"},"PeriodicalIF":4.4,"publicationDate":"2024-10-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142445858","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Nutrient flows in biofloc-Nile tilapia culture: A semi-physical modelling approach","authors":"Nurhayati Br Tarigan ,&nbsp;Marc Verdegem ,&nbsp;Julie Ekasari ,&nbsp;Karel J. Keesman","doi":"10.1016/j.biosystemseng.2024.09.021","DOIUrl":"10.1016/j.biosystemseng.2024.09.021","url":null,"abstract":"<div><div>Biofloc culture systems potentially reduce the nutrient losses in aquaculture. However, knowledge of the nutrient flows in the system is not yet well-developed. This study deployed experimental data to develop a semi-physical model to understand the dynamics and flows of carbon (C), nitrogen (N), and phosphorus (P) in a biofloc-Nile tilapia-rearing system. The model involved eight process variables, which are pelleted feed A, C, N, P, fish, biofloc, periphyton, and water volume. Model calibration and validation were done under a Control-diet and High-NSP-diet, respectively. The diets differed by the type of starch in which the latter contains three times higher fibrous starch, called non-starch polysaccharides, than the former. Except for biofloc, the behaviour of the process variables fit the observations with a root mean square error (RMSE) of less than 30% of the corresponding average observations. The biofloc biomass was predicted using exponential growth model and results in a RMSE of 49% and 56% for the Control and High-NSP-diet, respectively. Scenario analyses, using the validated model, showed that the biofloc system generates less waste when the stocking density is doubled, which means double fish production and less nutrient losses. In terms of different diets, the high-NSP-diet resulted in more organic waste than the Control-diet. However, the amount of loss and unutilised C and P were similar which was mainly caused by the ability of biofloc and periphyton to assimilate more waste, especially C, in the High-NSP-diet.</div></div>","PeriodicalId":9173,"journal":{"name":"Biosystems Engineering","volume":"248 ","pages":"Pages 108-129"},"PeriodicalIF":4.4,"publicationDate":"2024-10-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142442742","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"LSANNet: A lightweight convolutional neural network for maize leaf disease identification","authors":"Fu Zhang ,&nbsp;Ruofei Bao ,&nbsp;Baoping Yan ,&nbsp;Mengyao Wang ,&nbsp;Yakun Zhang ,&nbsp;Sanling Fu","doi":"10.1016/j.biosystemseng.2024.09.023","DOIUrl":"10.1016/j.biosystemseng.2024.09.023","url":null,"abstract":"<div><div>Maize (<em>Zea Mays</em>) is a major food crop and is of great importance to ensure national food security. However, maize leaf diseases occur from time to time, which poses a serious threat to grain yield and quality, so methods for the quick identification of maize leaf diseases are particularly important. In this paper, a long-short attention neural network (LSANNet) is proposed for maize leaf diseases identification. The main component of the LSANNet is the long-short attention block (LSAB). The long-short connection method enables the fusion of multi-scale features, which enhances the model generalisation capability. The attention mechanism is applied in the block, which aims to enhance the extraction of maize leaf features. The effectiveness of separable convolution and attention modules is demonstrated by ablation studies. Experimental results on 124 unseen images show that the accuracy of the proposed model on the test sets reaches 94.35%, which is better than the accuracy of existing models, such as VGG16, ResNet50, DenseNet201, MobileNetV3S, and Xception. The practical performance of the proposed network model is verified by deploying the model on a mobile device, demonstrating strong compatibility and high recognition. In this paper, a lightweight convolutional neural work is proposed for maize leaf disease identification, and the performance of the network on the test sets meets the required requirements. This research will provide an idea for the identification of maize leaf diseases and disease prevention schemes for agricultural production.</div></div>","PeriodicalId":9173,"journal":{"name":"Biosystems Engineering","volume":"248 ","pages":"Pages 97-107"},"PeriodicalIF":4.4,"publicationDate":"2024-10-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142420968","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Covering reduces emissions of ammonia, methane, and nitrous oxide from stockpiled broiler litter","authors":"Jesper N. Kamp,&nbsp;Anders Feilberg","doi":"10.1016/j.biosystemseng.2024.10.002","DOIUrl":"10.1016/j.biosystemseng.2024.10.002","url":null,"abstract":"<div><div>Poultry litter, a mix of excreta, bedding material, and discarded feed, is extracted from poultry houses, and used as fertiliser. The litter is often stored in stockpiles outside before field application thereby posing a risk for negative environmental and climatic impact from emissions of ammonia (NH₃) and greenhouse gases (GHG). This study investigated the emissions of methane (CH₄), NH₃, and nitrous oxide (N₂O) from a 22 tonnes broiler litter stockpile over 44 days. The emissions were measured on a farm-scale stockpile with and without coverage using the backward Lagrangian Stochastic method. The results showed distinct emission patterns for each gas during the measurement periods. For all compounds, the emissions during the covered period were significantly lower than during the two uncovered periods. The reduction due to coverage was 92–95% for NH₃, 25–40% for CH₄, and 82–89% for N₂O. NH₃ emissions were highest immediately after coverage removal and during stockpile removal. CH₄ emissions were highest during stockpile removal and lowest during coverage. N₂O emissions were lowest during coverage but a notable increase after coverage removal was observed. The temperature within the stockpile showed variations at different heights, with the highest temperatures recorded in the middle of the stockpile. GHG emissions, based on global warming potential, indicate substantial contributions from N₂O, accounting for 55–72% of emissions in CO₂-equivalents during uncovered periods and 27% during coverage. Furthermore, GHG emissions were reduced 63–72% during coverage compared to the uncovered periods highlighting the importance for immediate coverage of stockpiles to minimise NH₃ and GHG emissions.</div></div>","PeriodicalId":9173,"journal":{"name":"Biosystems Engineering","volume":"248 ","pages":"Pages 73-81"},"PeriodicalIF":4.4,"publicationDate":"2024-10-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142420967","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Spatial LiDAR odometry and mapping for complex agricultural environments - Spatial FieldLOAM","authors":"Jurij Rakun ,&nbsp;František Duchoň ,&nbsp;Peter Lepej","doi":"10.1016/j.biosystemseng.2024.09.020","DOIUrl":"10.1016/j.biosystemseng.2024.09.020","url":null,"abstract":"<div><div>The challenge of autonomous driving in natural environments, without the use of GNSS devices is addressed. It utilises the readings from a multichannel LiDAR, supported by IMU, and enhances the capabilities of the FieldSLAM algorithm to establish an independent localisation and mapping system. This system is designed for performing specific tasks in predefined agricultural areas, employing incremental LOAM techniques. By comparing the outcomes of the novel Spatial FieldLOAM algorithm with the assistance of a precise Inertial Measurement Unit (IMU) and using the state-of-the-art RTK-GPS system as the ground truth, it is concluded that the Spatial FieldLOAM achieves an error rate of 5.5%, whereas the Xsens IMU yields an error rate of 5.7%. In terms of Euclidean distances to the final RTK GPS supported localisation on a 68.7 m test run, the error rates are 3.78 m and 3.92 m, respectively, or 0.0038 m per epoch for the Spatial FieldLOAM algorithm during non-vegetation season. The tests were also conducted during the vegetation season in a total length of 210 m, revealing a difference of 3.07 m distance between the final position calculated by the Spatial FieldLOAM and Xsens IMU.</div></div>","PeriodicalId":9173,"journal":{"name":"Biosystems Engineering","volume":"248 ","pages":"Pages 58-72"},"PeriodicalIF":4.4,"publicationDate":"2024-10-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142421060","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Evaluating a hybrid process of anaerobic digestion, aerobic degradation, and electrochemical separation for swine wastewater treatment with methane and nutrient recovery","authors":"Run-Feng Chen ,&nbsp;Chun-Hai Wei ,&nbsp;Hai-Tao Zhong ,&nbsp;Xiu-Feng Ye ,&nbsp;Jun-Jie Ye ,&nbsp;Kai Liu ,&nbsp;Quan-Bao Zhao ,&nbsp;Huu Hao Ngo","doi":"10.1016/j.biosystemseng.2024.09.022","DOIUrl":"10.1016/j.biosystemseng.2024.09.022","url":null,"abstract":"<div><div>A hybrid process of anaerobic digestion (AD), aerobic degradation, and electrochemical separation was evaluated for treating real swine wastewater that is rich in organic and nutrient to achieve methane and nutrient recovery and industry standard discharge quality. Fe anode electrocoagulation and Mg anode struvite electrochemical precipitation (SEP) were evaluated as AD pretreatments. Both removed partial chemical oxygen demand (COD) from raw swine wastewater, but only SEP slightly enhanced the methane yield of pretreated swine wastewater. The SEP efficiency of the AD effluent was significantly better than raw swine wastewater. A further coupled micro/ultra-filtration produced high-purity (96%) struvite. SEP and struvite chemical precipitation (SCP) were evaluated for AD effluent treatment. This showed that compared with SCP following first-order reaction kinetics (reaction rate constant of 0.791 and 0.854 h⁻¹ for NH₄⁺-N and PO₄^3--P), SEP not only achieved better removal of COD, NH₄⁺-N and PO₄^3--P, but was also shown to follow zero-order reaction kinetics (reaction rate constant of 5.72 and 5.78 mmol L⁻¹ h⁻¹ for NH₄⁺-N and PO₄^3--P). The SEP and SCP treated AD effluent was evaluated by conventional activated sludge (CAS), showing faster COD removal (first-order reaction rate constant of 0. 213 and 0.163 h⁻¹) and lower residual COD (150 and 248 mg L⁻¹) from SEP than SCP treated AD effluent, making the final effluent well below Chinese livestock wastewater discharge standards. Therefore, an emerging hybrid anaerobic membrane bioreactor (AnMBR)-SEP-CAS is proposed for swine wastewater treatment and proved to be more economically viable than the conventional hybrid AD-SCP-CAS process via cost-benefit analysis.</div></div>","PeriodicalId":9173,"journal":{"name":"Biosystems Engineering","volume":"248 ","pages":"Pages 47-57"},"PeriodicalIF":4.4,"publicationDate":"2024-10-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142421059","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"PointStack based 3D automatic body measurement for goat phenotypic information acquisition","authors":"Bo Jin ,&nbsp;Guorui Wang ,&nbsp;Jingze Feng ,&nbsp;Yongliang Qiao ,&nbsp;Zhifeng Yao ,&nbsp;Mei Li ,&nbsp;Meili Wang","doi":"10.1016/j.biosystemseng.2024.09.008","DOIUrl":"10.1016/j.biosystemseng.2024.09.008","url":null,"abstract":"<div><div>The body size of livestock is an essential phenotypic trait in genetic breeding, gene improvement, health screening, and animal welfare. To develop a non-contact automatic system for measuring goat body traits, we propose a point-cloud segmentation model based on an improved PointStack, which segments the automatically acquired three-dimensional (3D) point-cloud data of goats into different parts, including the head, front legs, hind legs, chest, abdomen, hip, and tail. The segmented point cloud, along with the physiological features of the goat, is then used to locate the corresponding key points for body size measurement. A novel method for key point localisation is proposed that includes coordinate normalisation, retrieval of key clusters, key point adjustment, optimisation of the traveling salesman problem, and edge detection. These methods were designed to reduce discrepancies at crucial points of body features, thereby facilitating the precise computation of the body size parameter in goats. In this work, 326 point clouds representing the upright posture of 55 goats were used for segmentation and body size measurement testing. The proposed segmentation model achieved a mean intersection over union of 89.21% and accuracy of 94.54%, outperforming comparative models. In the body traits measurement experiment, mean absolute percentage errors for body length, body height, chest width, chest girth, hip height, and hip width were recorded as 3.24%, 2.54%, 5.43%, 3.08%, 2.16%, and 4.59%, respectively. In summary, the proposed automated measurement method demonstrates high accuracy, strong robustness, and holds significant potential for widespread application.</div></div>","PeriodicalId":9173,"journal":{"name":"Biosystems Engineering","volume":"248 ","pages":"Pages 32-46"},"PeriodicalIF":4.4,"publicationDate":"2024-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142357508","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Semantics-aware next-best-view planning for efficient search and detection of task-relevant plant parts","authors":"Akshay K. Burusa,&nbsp;Joost Scholten,&nbsp;Xin Wang,&nbsp;David Rapado-Rincón,&nbsp;Eldert J. van Henten,&nbsp;Gert Kootstra","doi":"10.1016/j.biosystemseng.2024.09.018","DOIUrl":"10.1016/j.biosystemseng.2024.09.018","url":null,"abstract":"<div><div>Searching and detecting the task-relevant parts of plants is important to automate harvesting and de-leafing of tomato plants using robots. This is challenging due to high levels of occlusion in tomato plants. Active vision is a promising approach in which the robot strategically plans its camera viewpoints to overcome occlusion and improve perception accuracy. However, current active-vision algorithms cannot differentiate between relevant and irrelevant plant parts and spend time on perceiving irrelevant plant parts. This work proposed a semantics-aware active-vision strategy that uses semantic information to identify the relevant plant parts and prioritise them during view planning. The proposed strategy was evaluated on the task of searching and detecting the relevant plant parts using simulation and real-world experiments. In simulation experiments, the semantics-aware strategy proposed could search and detect 81.8% of the relevant plant parts using nine viewpoints. It was significantly faster and detected more plant parts than predefined, random, and volumetric active-vision strategies that do not use semantic information. The strategy proposed was also robust to uncertainty in plant and plant-part positions, plant complexity, and different viewpoint-sampling strategies. In real-world experiments, the semantics-aware strategy could search and detect 82.7% of the relevant plant parts using seven viewpoints, under complex greenhouse conditions with natural variation and occlusion, natural illumination, sensor noise, and uncertainty in camera poses. The results of this work clearly indicate the advantage of using semantics-aware active vision for targeted perception of plant parts and its applicability in the real world. It can significantly improve the efficiency of automated harvesting and de-leafing in tomato crop production.</div></div>","PeriodicalId":9173,"journal":{"name":"Biosystems Engineering","volume":"248 ","pages":"Pages 1-14"},"PeriodicalIF":4.4,"publicationDate":"2024-09-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142357509","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Predicting wheat scab levels based on rotation detector and Swin classifier","authors":"Dongyan Zhang ,&nbsp;Zhipeng Chen ,&nbsp;Hansen Luo ,&nbsp;Gensheng Hu ,&nbsp;Xin-Gen Zhou ,&nbsp;Chunyan Gu ,&nbsp;Liping Li ,&nbsp;Wei Guo","doi":"10.1016/j.biosystemseng.2024.09.016","DOIUrl":"10.1016/j.biosystemseng.2024.09.016","url":null,"abstract":"<div><div>Wheat scab is a highly destructive disease that adversely impact wheat crops throughout their growth cycle. It is crucial to promptly evaluate the levels of wheat scab in the field to prevent its spread. Manual observation, however, is inefficient and time-consuming. Recent research has indicated that computer vision-based methods can enhance efficiency in this regard. This study proposed a method for predicting wheat scab levels using a rotation detector and Swin classifier.</div><div>To minimise background interference, the study incorporated the rotation wheat detector (RWD) network for detecting wheat heads. The RWD network employed the Kalman filter Intersection over Union (KFIoU) to predict the angle, thereby improving accuracy. The Swin wheat classifier (SWC) network was employed to classify healthy and diseased wheat heads. The SWC network benefited from the shifted window self-attention module (SW-MSA), which enhanced feature extraction by establishing connections with other windows. The proposed method was evaluated using wheat field images collected over 3 years. The results demonstrate promising performance, achieving a 96% accuracy in predicting wheat scab levels. Furthermore, the <em>R</em>^<em>2</em> and RMSE values for diseased wheat count were 97.62% and 3.61, respectively. This method offers an accurate means of predicting wheat scab levels through the analysis of wheat field images. Additionally, the introduction of the rotation detector presents a novel contribution to research on wheat scab detection.</div></div>","PeriodicalId":9173,"journal":{"name":"Biosystems Engineering","volume":"248 ","pages":"Pages 15-31"},"PeriodicalIF":4.4,"publicationDate":"2024-09-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142357510","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"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 of crate design, number of heat lamps and lying posture on the occurrence of shoulder lesions in sows","authors":"Shubham Bery ,&nbsp;Tami M. Brown-Brandl ,&nbsp;Gary A. Rohrer ,&nbsp;Sudhendu Raj Sharma ,&nbsp;Suzanne M. Leonard","doi":"10.1016/j.biosystemseng.2024.09.017","DOIUrl":"10.1016/j.biosystemseng.2024.09.017","url":null,"abstract":"<div><div>This study investigated the interaction of sow and engineering factors on shoulder lesion formation. Sows were randomly assigned to three farrowing crate designs: Traditional Stall Layout, Expanded Creep Stall Layout, and Expanded Sow &amp; Creep Stall Layout. Each crate configuration was further differentiated by the inclusion of either one (1HL) or two (2HL) heat lamps. Digital and depth images were collected from an overhead time of flight depth camera (Kinect V2) every 5 s. Computer vision techniques were employed to analyze top-down digital images from the 21st to the 24th day of farrowing to detect and estimate lesion size. Additionally, the study incorporated an analysis of sow lying behaviors on the occurrence and size of lesions using depth images. Sow's environmental and phenotypic data - weight, parity, body condition score, total lying time and number of lying transitions in a day were investigated for impact on shoulder lesion. The results indicated that the interaction of smaller crate sizes and increased heat lamp usage significantly impacted lesion occurrence (p &lt; 0.05). Also, higher parity and lighter weight sows showed higher lesion occurrence (p &lt; 0.05). However, other factors, such as the number of heat lamps alone and detailed metrics of lying postures, did not show a significant impact on lesion occurrence. In contrast, none of the studied factors showed a significant impact on the size of shoulder lesions. This highlights the importance of allocating crate space with respect to heat lamp placement to the sows.</div><div>Science4Impact Statement (S4IS): This manuscript evaluates shoulder lesions' presence and size in lactating sows housed within farrowing stalls. Shoulder lesions are one of the main causes of premature culling in sows and are a major concern for animal well-being. Understanding the impact of crate design and the number of heat lamps is important for the engineering design of the farrowing environment.</div></div>","PeriodicalId":9173,"journal":{"name":"Biosystems Engineering","volume":"247 ","pages":"Pages 249-256"},"PeriodicalIF":4.4,"publicationDate":"2024-09-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142314111","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}