Reduce Labor Requirements Research Articles

KOALA: A Modular Dual-Arm Robot for Automated Precision Pruning Equipped with Cross-Functionality Sensor Fusion

Landscape maintenance is essential for ensuring agricultural productivity, promoting sustainable land use, and preserving soil and ecosystem health. Pruning is a labor-intensive task among landscaping applications that often involves repetitive pruning operations. To address these limitations, this paper presents the development of a dual-arm holonomic robot (called the KOALA robot) for precision plant pruning. The robot utilizes a cross-functionality sensor fusion approach, combining light detection and ranging (LiDAR) sensor and depth camera data for plant recognition and isolating the data points that require pruning. The You Only Look Once v8 (YOLOv8) object detection model powers the plant detection algorithm, achieving a 98.5% pruning plant detection rate and a 95% pruning accuracy using camera, depth sensor, and LiDAR data. The fused data allows the robot to identify the target boxwood plants, assess the density of the pruning area, and optimize the pruning path. The robot operates at a pruning speed of 10–50 cm/s and has a maximum robot travel speed of 0.5 m/s, with the ability to perform up to 4 h of pruning. The robot’s base can lift 400 kg, ensuring stability and versatility for multiple applications. The findings demonstrate the robot’s potential to significantly enhance efficiency, reduce labor requirements, and improve landscape maintenance precision compared to those of traditional manual methods. This paves the way for further advancements in automating repetitive tasks within landscaping applications.

IoT- enabled crop waste mulching machine for sustainable farming: perspective of circular economy

Abstract A large amount of crop waste is generated after crop harvesting. A substantial quantity of waste on the farm is burnt to clear the field, contributing to environmental harm and global warming. The study aims to develop a technological solution for crop waste management (CWM) by designing and developing an IoT-enabled crop waste mulching machine. This initiative addresses the environmental and health issues caused by burning crop waste on farms. The research involves designing and developing an IoT-enabled mulching machine that can be attached to the back side of a tractor. The machine consists of power-transmitting elements, supports, and mechanisms to distribute mulching material evenly onto the plant bed. It incorporates advanced IoT load cell sensors, soil moisture sensors, and proximity sensors to optimize the mulching process based on soil moisture content and to prevent machinery blockages. The IoT-enabled mulching machine effectively manages crop waste by utilizing it in mulch, thus preventing the harmful practice of burning waste. This machine ensures a more efficient, safer, and environmentally friendly approach to mulching, offering significant improvements over traditional manual mulching methods.
The novelty of this research lies in integrating IoT technologies within mulching machine. The mulching machine uses sensors to automate and optimize the mulching process, representing a significant technological advancement over conventional methods. The study offers practical benefits by reducing labour requirements, minimizing the risk of injuries, and ensuring a more uniform mulch application. This technology facilitates more sustainable farming practices, aligning with global sustainability goals. The study can potentially revolutionize the agricultural equipment manufacturing industry (AEMI) by shifting the focus toward environmental sustainability. It promotes replacing plastic film mulching machines with more eco-friendly crop waste mulching solutions. This technology has the potential to reduce air pollution, carbon emissions, and mitigate climate. It also enhances soil health through better mulching practices.

Comparative Study on Cost Analysis of Mechanical and Manual Transplanting of Rice

In Telangana, rice cultivation predominantly relies on traditional manual transplanting, which requires a significant labor force. However, the migration of agricultural laborers to urban areas in search of better wages has led to a shortage of workers, particularly during the critical transplanting season. To address this labor shortage and reduce cultivation costs, an efficient and cost-effective method of rice transplanting is essential, without compromising grain yield. To explore such a solution, Krishi Vigyan Kendra in Jammikunta, Karimnagar district, conducted 30 frontline demonstrations between 2018-19 and 2020-21, showcasing rice cultivation using a self-propelled, walk-behind six-row mechanical transplanter. The performance of this mechanical method was evaluated by collecting data on various growth parameters, including plant height, number of productive tillers per hill, panicle length, grains per panicle, and grain yield, and comparing it with traditional manual transplanting. Economic analysis revealed that the gross returns from mechanical transplanting were Rs. 135,956, compared to Rs. 128,629 for manual transplanting. Similarly, the net returns for mechanical and manual methods were Rs. 95,106 and Rs. 82,204, respectively. The benefit-cost ratio was found to be 3.32 for mechanical transplanting and 2.77 for manual transplanting. These findings indicate that mechanical rice transplanting offers a viable, cost-effective alternative, reducing labor requirements and drudgery while delivering higher yields.

Deep learning approach for detecting tomato flowers and buds in greenhouses on 3P2R gantry robot

In recent years, significant advancements have been made in the field of smart greenhouses, particularly in the application of computer vision and robotics for pollinating flowers. Robotic pollination offers several benefits, including reduced labor requirements and preservation of costly pollen through artificial tomato pollination. However, previous studies have primarily focused on the labeling and detection of tomato flowers alone. Therefore, the objective of this study was to develop a comprehensive methodology for simultaneously labeling, training, and detecting tomato flowers specifically tailored for robotic pollination. To achieve this, transfer learning techniques were employed using well-known models, namely YOLOv5 and the recently introduced YOLOv8, for tomato flower detection. The performance of both models was evaluated using the same image dataset, and a comparison was made based on their Average Precision (AP) scores to determine the superior model. The results indicated that YOLOv8 achieved a higher mean AP (mAP) of 92.6% in tomato flower and bud detection, outperforming YOLOv5 with 91.2%. Notably, YOLOv8 also demonstrated an inference speed of 0.7 ms when considering an image size of 1920×1080\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$1920 \ imes 1080$$\\end{document} pixels resized to 640×640\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$640 \ imes 640$$\\end{document} pixels during detection. The image dataset was acquired during both morning and evening periods to minimize the impact of lighting conditions on the detection model. These findings highlight the potential of YOLOv8 for real-time detection of tomato flowers and buds, enabling further estimation of flower blooming peaks and facilitating robotic pollination. In the context of robotic pollination, the study also focuses on the deployment of the proposed detection model on the 3P2R gantry robot. The study introduces a kinematic model and a modified circuit for the gantry robot. The position-based visual servoing method is employed to approach the detected flower during the pollination process. The effectiveness of the proposed visual servoing approach is validated in both un-clustered and clustered plant environments in the laboratory setting. Additionally, this study provides valuable theoretical and practical insights for specialists in the field of greenhouse systems, particularly in the design of flower detection algorithms using computer vision and its deployment in robotic systems used in greenhouses.

Macroeconomic determinants of labour costs in the EU: a comprehensive panel and cluster analysis

Abstract Labour costs are a fundamental component of production expenses, significantly impacting both the quantity and quality of output. This study explores the determinants of labour costs within EU member states that have implemented minimum wage policies over the past two decades. The research technique includes a comprehensive panel analysis of EU member states to identify significant variables influencing labour costs, as well as cluster analysis to discover underlying patterns across the nations under examination. Our findings reveal that higher minimum wage levels, higher employment rates, increased labour productivity, and greater trade openness are positively correlated with higher labour costs. Specifically, increases in these variables lead to higher wages and a broader tax base, while greater trade openness results in elevated labour costs due to expanded market opportunities. Conversely, gross fixed capital formation negatively affects labour costs, as investments in production assets tend to reduce labour requirements or hours worked. The cluster analysis led to the identification of three distinct groups. The first cluster consists of well-developed economies with modest labour cost increases and average minimum wages. The second cluster includes countries with substantial labour cost increases, low minimum wages, and significant productivity gains. The third cluster features nations with high minimum wages and high employment rates. This paper contributes to the field by highlighting the complex interplay between labour costs and economic factors, offering insights for decision-makers to tailor macroeconomic and company-level strategies to specific local conditions. The findings emphasise the importance of balancing wage policies with sustainable economic development to enhance competitiveness while ensuring fair labour conditions.

Comparative analysis of high-throughput RNA extraction kits in Naïve Non-Human Primate (NHP) tissues for downstream applications utilizing Xeno Internal Positive Control (IPC)

Ribonucleic acid (RNA) extraction and purification play pivotal roles in molecular biology and cell and gene therapy, where the quality and integrity of RNA are critical for downstream applications. Automated high-throughput systems have gained interest due to their potential for scalability and reduced labor requirements compared to manual methods. However, ensuring high-throughput capabilities, reproducibility, and reliability while maintaining RNA yield and purity remains challenging.This study evaluated and compared the performance of four commercially available high-throughput magnetic bead-based RNA extraction kits across six types of naïve non-human primate (NHP) tissue matrices: brain, heart, kidney, liver, lung, and spleen. The assessment focused on RNA purity, yield, and extraction efficiency (EE) using Xeno Internal Positive Control (IPC) spiking.Samples (∼50 mg) were homogenized via bead-beating and processed according to the manufacturer's protocol on the KingFisher Flex platform in eight replicates. RNA purity and yield were measured using a NanoDrop® spectrophotometer, while EE was evaluated via real-time reverse transcription-quantitative polymerase chain reaction (RT-qPCR).The findings indicate consistent high RNA purity across all tested extraction kits, yet substantial variation in RNA yield. Extraction efficiency exhibited variations across tissue types, with decreasing trends observed from brain to lung tissues. These results underscore the importance of careful kit selection and method optimization for achieving reliable downstream applications. The MagMAX™ mirVana™ Total RNA Isolation Kit stands out as the most accurate and reproducible, making it the preferred choice for applications requiring high RNA quality and consistency. Other kits, such as the Maxwell® HT simplyRNA Kit, offer a good balance between cost and performance, though with some trade-offs in precision. These findings highlight the importance of selecting the appropriate RNA isolation method based on the specific needs of the research, underscoring the critical role of accurate nucleic acid extraction in gene and cell therapy research.In conclusion, this study highlights the critical factors influencing RNA extraction performance, emphasizing the need for researchers and practitioners to consider both kit performance and tissue characteristics when designing experimental protocols. These insights contribute to the ongoing efforts to enhance the reproducibility and reliability of RNA extraction methods in molecular biology and cell/gene therapy applications.

Current Status of Patent Research on Picking End Effector

Background: Fruit and vegetable picking, as the most important part of fruit and vegetable production, is a labor-intensive industry. Fruit and vegetable picking robots can effectively improve the efficiency of picking and reduce labor requirements. As the most important part of the picking robot, the picking end-effector effectively plays a key role in improving the picking efficiency and ensuring the wholeness of the fruit. Therefore, the development trend of picking end effectors has received more and more attention. Objective: The study aimed to solve the problems of high labor consumption and low picking efficiency of manual picking; thus, the grasping method of the picking end effector has been continuously improved. Methods: This article has presented various representative patents related to the picking end effector, such as the suction end effector, multi-finger end effector, and bionic end effector. Results: Through the investigation of a large number of picking end effector patents, the current problems, such as low accuracy and large clamping damage, are summarized and analyzed. In addition, the future trends of fruit and vegetable picking end effectors are discussed. Conclusion: The optimization of the picking end effector is conducive to reducing manual labor and improving the picking rate of fruits and vegetables. More relevant patents will be invented in the future.

Trait development and genetic parameters of resilience indicators based on variability in milk consumption recorded by automated milk feeders in North American Holstein calves

The implementation of automated milk feeders (AMF) on precision dairy farms has enabled efficient management of large numbers of group-housed replacement calves with reduced labor requirements and improved calf welfare. In this study, we investigated the feasibility of deriving calf resilience indicators based on variability in milk consumption using data from 10,076 North American Holstein calves collected between 2015 and 2021. We modeled and evaluated deviations in observed and predicted daily milk consumption trajectories as indicators of resilience to environmental perturbations. We also analyzed average milk intake and the number of treatments for bovine respiratory disease (BRD) and their genetic correlations with the derived resilience parameters. Milk consumption was recorded using the Förster-Technik AMF. Deviations in cumulative milk intake were modeled using various methods, including quantile regression and the Gompertz function. Ten resilience indicators were derived to quantify the degree and duration of perturbations, including amplitude, perturbation time, recovery time, and deviation velocities. After data editing, genomic data from 9,273 calves and pedigree information from 10,076 calves with 321,388 phenotypic records were used to estimate genetic parameters for 12 traits, including 10 calf resilience indicators as well as average milk intake and treatments for bovine respiratory disease. Substantial phenotypic variability was observed for all calf resilience indicators derived and genetic parameters related to these novel resilience indicators were estimated. The heritability estimates for the resilience traits are as follows: amplitude of the deviation (in L) 0.047 (0.032, 0.064) (HPD interval), perturbation time of deviation (in d) 0.011 (0.0056, 0.016), recovery time of the deviation (in d) 0.025 (0.016, 0.035), maximum velocity of perturbation (L/d) 0.039 (0.024, 0.053), average velocity of perturbation (L/d) 0.038 (0.022, 0.050), area between the curves (L x d) 0.039 (0.027, 0.054), recovery ratio 0.053 (0.036, 0.072), deviation variance 0.049 (0.32, 0.068), log-deviation variance 0.027 (0.016, 0.044), deviation auto-correlation 0.010 (0.0042, 0.017) and number of deviation occurrences 0.023 (0.0094, 0.036). Some of the highlighted genetic correlations observed with average milk consumption include amplitude: 0.569 (0.474, 0.666), perturbation time: -0.534 (-0.73, -0.342), and average velocity: 0.554 (0.432, 0.672). Similarly, the genetic correlations between the number of times treated for BRD with perturbation time was 0.494 (0.251, 0.723), -0.294 (-0.52, -0.095) with number of deviations, and 0.348 (0.131, 0.578) with deviation autocorrelation. This study highlights the genetic influence on various resilience traits in calves, including amplitude, perturbation time, recovery time, and velocity measures of the perturbation. Our findings suggest the need for prioritizing genetic selection based on traits like recovery time, which exhibits higher heritability and a moderate genetic correlation with the number of times a calf is treated for BRD. The combination of AMF data, mathematical modeling, and genomic evaluation provides a comprehensive framework for assessing and breeding more resilient dairy calves in the face of environmental and health challenges.

Development of an unmanned ground vehicle for seed planting

As global population growth intensifies the demand for sustainable food production, the application of robotics to agriculture emerges as a promising solution. This research focuses on the design, development, and deployment of an unmanned ground vehicle for seed planting, also known as a robotic seed planter. The robotic seed planter automates seed planting processes, offering advantages such as increased accuracy, reduced labour requirements, and optimal resource usage. Parametric Technology Corporation (PTC) Creo was used for the structural design, Proteus 8.14 for the circuitry design, and Arduino IDE 2.0 with Visual Studio Code for the programming. The design incorporates seed metering and drilling mechanisms guided by intelligent systems. Results show exceptional accuracy in seed placement (94%), operational efficiency, and adaptability to diverse conditions, with energy consumption relatively low. The planter is equipped with a web application for remote monitoring and control. The application is hosted on one of the microcontrollers and WebSockets protocol is utilized for inter-microcontroller communication. It offers an auto mode for automated planting and Manual mode for easier manoeuvrability. The findings of this study demonstrate the robotic seed planter’s transformative impact on precision agriculture, providing a glimpse into the future of efficient and sustainable farming operations.

Flow-through imaging and automated analysis of oil-exposed early stage Atlantic cod (Gadus morhua)

Toxicology studies in early fish life stages serve an important function in measuring the impact of potentially harmful substances, such as crude oil, on marine life. Morphometric analysis of larvae can reveal the effects of such substances in retarding growth and development. These studies are labor intensive and time consuming, typically resulting in only a small number of samples being considered. An automated system for imaging and measurement of experimental animals, using flow-through imaging and an artificial neural network to allow faster sampling of more individuals, has been described previously and used in toxicity experiments. This study compares the performance of the automated imaging and analysis system with traditional microscopy techniques in measuring biologically relevant endpoints using two oil treatments as positive controls. We demonstrate that while the automated system typically underestimates morphometric measurements relative to analysis of manual microscopy images, it shows similar statistical results to the manual method when comparing treatments across most endpoints. It allows for many more individual specimens to be sampled in a shorter time period, reducing labor requirements and improving statistical power in such studies, and is noninvasive allowing for repeated sampling of the same population.

The impact on Cd bioavailability and accumulation in rice (Oryza sativa L.) induced by dry direct-seeding cultivation method in field-scale experiments

Dry direct-seeded rice cultivation has gained popularity and expanded its cultivated area due to reduced labor requirements and water consumption. However, the impact of this cultivation method on cadmium (Cd) bioavailability in soil and the accumulation levels in grains remains uncertain. Field experiments were conducted in acidic soils at two locations in southern China to compare rice varieties and evaluate the dry direct-seeding method alongside the wet direct-seeding and traditional transplanting methods. Dry direct-seeded rice reached significantly higher Cd concentrations in its tissues starting from the heading stage than transplanted rice. Cd accumulation levels by the maturation stage in the brown rice of dry direct-seeded rice were 18.33 %–150.69 % higher than those of wet direct-seeded and transplanted rice, with a considerable ability to translocate Cd into brown rice. Furthermore, dry direct seeding decreased iron plaque formation, particularly in the amorphous Fe form; it resulted in high soil temperature and low moisture content during tillering, elevating Cd availability in the soil. Additionally, the proportion of ions and more labile forms of Cd in the soil solution was high. Moreover, the soil under dry direct seeding had high urease and acid phosphatase enzyme activities. However, low richness and diversity in the bacterial community were characterized by a significant increase in the relative abundance of Actinobacteria and Gammaproteobacteria at the class level, while exhibiting decreased relative abundances of Alphaproteobacteria, Bacilli, and KD4-96, along with fewer biomarkers. Nonetheless, these differences are gradually reduced during the maturation stage. Overall, although dry direct seeding offers several advantages, it is crucial to implement additional measures to mitigate the increased health risks linked to rice cultivation through this approach in Cd-contaminated areas.

Design and Development of Agri-bot for Autonomous Seed Sowing and Harvesting

Abstract: This research delves into the development of an Autonomous Agriculture Robot (AAR) capable of performing seed sowing and harvesting operations. The AAR operates in two distinct modes: seed sowing and harvesting, leveraging advanced sensors and a control system to automate these critical agricultural processes. The AAR is engineered with dual operational modes, facilitated by an encoder system for precise autonomous navigation, servo actuation for operational control, and a Battery Management System (BMS) for efficient power usage. This paper details the design and development of a hardware prototype that presents the integration of these technologies. Validation results demonstrate the AAR’s capability to autonomously navigate and perform the defined tasks. The implementation of the AAR prototype signifies a pivotal step towards automating manual agricultural processes, potentially transforming the agricultural landscape by reducing labour requirements and increasing efficiency.

DESIGN AND DEVELOPMENT OF FERTILISER SPRAYING AGRICULTURAL ROBOT

Modern agriculture is ensured by the development and dissemination of agricultural automation and intelligence. The current focus of agricultural robot research is autonomous navigation, which can significantly improve labor productivity while effectively reducing labor requirements. It is the most successful robot in the agricultural field. The main functions of the robot are chassis control, navigation control, and mechanical design for fertilization and spreading. The main theme of robots is to reduce work, workload, burden and stress. Agriculture is an extremely resource- and labor-intensive industry. Therefore, farmers are increasingly turning to technology and automation to address this problem. Future recommendations include making agricultural robots fully autonomous. Key Words: Fertilization, agricultural robots modern agriculture, autonomous navigation.

IoT-Based Smart Vertical Hydroponic System for Chili Plant

Agriculture serves as the primary source of food for the global population, playing a critical role in meeting the nutritional needs of people worldwide. With the projected population reaching 9.7 billion by 2050, ensuring an adequate and sustainable food supply becomes increasingly challenging. Hydroponics, a technique that cultivates plants without soil by delivering mineral nutrients through water, offers a potential solution. Hydroponics requires only 10% of the space and 90% less water compared to conventional farming, enabling year-round production of nearly organic food. Vertical hydroponic systems, which arrange plants in stacked or suspended layers, optimize the use of vertical space and are particularly suitable for indoor and urban agriculture where land is limited. However, these systems require regular maintenance and monitoring, including managing nutrient solution balance, pH levels, water quality, and lighting conditions. This complexity can pose challenges for growers lacking time, resources, or technical expertise. To address these issues and ensure sustainability, an automated monitoring method is proposed for vertical hydroponic farming. This project aims to develop a system that automates the checking and maintenance of nutrient levels in the vertical farming process, monitoring factors such as Electrical Conductivity (EC), pH, liquid level, and water temperature. The integration of automation technologies in vertical hydroponics enhances precision, efficiency, and productivity by reducing labour requirements, optimizing resource utilization, and granting growers greater control over the growing environment. These advancements result in healthier plants, higher yields, and more sustainable agricultural practices.

Seeding detection and distribution evaluation using the developed automatic maize seeding machine

Seeding is a time-consuming and labour-intensive process in the detection of maize seeds germination rate. Automatic seeding can reduce labour requirements and improve efficiency. The quality of automatic seeding plays an important role in collecting accurate germination rates. In this research, a newly developed automatic maize seeding machine (AMSM) was used to detect the maize seeds and evaluate distribution by advanced computer vision and analysis methodologies. Three methods were utilized to detect maize seeds after they were loaded into the holes of the seed plate of AMSM: colour segmentation + connected component analysis, support vector machine (SVM) + sliding window, and YOLOV5. The results showed that compared with colour + connected component analysis and sliding window + SVM, YOLOV5 exhibited higher accuracy and improved computational efficiency. When the input image pixel size was 960 × 960, the precision of YOLOV5 + merge-NMS was 97.3 %, the recall was 0.918, the F1-score was 0.945, and the processing time of a single image was 0.38 ms, which is the optimal performance. YOLOV5 + merge-NMS demonstrated robust detection maize seeds in images with different illumination equalization processing. Based on maize seeds detection results, seeding distribution evaluation was realized with an average accuracy of 91.14 %. In addition, the normal seeding of the AMSM was 82.3 %. Utilization of YOLOV5 + merge-NMS enables rapid and precise detection of maize seeding while establishing a basis for accurately calculating seed distribution. The AMSM can meet the requirements of automatic maize seeds detection and distribution evaluation and the developed methodologies have the potential for automatic germination detection.

NGHIÊN CỨU XÂY DỰNG HỆ THỐNG PHA CHẾ ĐỒ UỐNG TỰ ĐỘNG ỨNG DỤNG CÔNG NGHỆ IOT

This paper focuses on the research and development of an automated beverage mixing system. By applying modern Internet of Things (IoT) technology and information technology, the system incorporates intelligent functions such as touch sensing, internet connectivity, and beverage dispenser control software. It can provide a wide variety of high-quality beverages, including coffee, tea, juices, alcoholic drinks, and drinks made with syrups, sugar and milk. The advantages of this beverage dispenser include cost-effectiveness in installation, reduced labor requirements, compact size for versatile placement, and the ability to meet customer demands conveniently and flexibly. Results from practical testing demonstrate the stable operation of the machine, accurately following the pre-programmed beverage mixing instructions. The product can be used in real-world settings such as cafes, households, or office spaces.

Sensor-Based System for Mechanised Oil Palm Herbicide Spraying

The application of an electronic control system embedded in machinery for oil palm plantation operation is a relatively new concept. The application is currently being pursued to overcome several issues and concerns, such as reducing workforce requirements, increasing productivity, and improving effective chemical utilisation. Technologies are revolving, and IR4.0 components are cost-effective to be embraced in the field. A study was carried out to evaluate the performance of a sensor-spraying system attached to a three-wheeler prime mover. A Lidar sensor was used for palm circle spraying activity. An average 25% chemical reduction was obtained by embracing the sensor-spraying compared to a manually triggered system. The technology's effective cost is about RM 3 per ha with almost 30 ha per day coverage area. Integrating the system with IoT provides traceability of the activity on a web-based application. Thus, the system could enhance the standard operating procedure of chemical spraying in the field. The Lidar spraying system could provide better operational cost savings, reduce chemical consumption, increase worker productivity, provide a better monitoring system, and reduce labour requirements for oil palm fields' general upkeep activity.

Pasture growth curve impacts the economic merit of extended lactation: a simulation study

Voluntarily increasing the calving interval beyond 12-months (extended lactation; EL) has possible benefits in reducing labour requirements, herd replacement rate, and surplus calf numbers. With limited information on feed balance and economic effects with EL, reductions in profitability were hypothesised. A MS Excel spreadsheet model was developed to compare calving systems: spring calving annually (Base), two herds between spring and autumn calving with 18-month calving intervals (EL18), or two herds calving in alternating springs with 24-month intervals (EL24). Weekly feed balances using pasture growth curves for four regions (Northland, Waikato, Canterbury, Southland) and partial budget analysis informed estimations of supplement costs and net income (NI). The EL24 system had lower supplement costs (-$240 /ha) and higher NI (+$439 /ha) than the Base system for Northland only. The EL18 systemalways had the highest annual milk production and total revenue, but highest supplement costs and lowest NI in the four regions. Replacement, calving, and mating costs were lower for EL systems than Base; however, costs for milking and repairs and maintenance were higher. This preliminary study indicates there is merit in investigating EL systems, particularly for Northland. Further data from EL systems are needed to increase confidence in the system’s potential.

PSIII-2 Comparison of Continuous Monitoring and Scan Sampling for Monitoring Postural Changes in Sows Housed in Gestation Crates

Abstract Continuous monitoring of the behavior of sows is the most accurate approach of measurement but is laborious. Scan sampling, which involves monitoring behavior at fixed intervals, can reduce labor requirements compared with continuous monitoring. However, the accuracy of scan sampling compared with continuous monitoring needs to be established. The objective of this study was to compare 2 sampling methods (continuous monitoring vs. scan sampling) for monitoring postural changes in individually housed sows after weaning. The study was carried out as a within-subject longitudinal study using 99 multiparous sows that were monitored over an 11-day period following weaning. Sows were housed in individual stalls and were checked daily for standing reflex from day 3 to 7 post-weaning. Video recording of the sows was carried out throughout the study period. The postures recorded were lying, standing, sitting, and active (standing + sitting). All recording of postures was carried out by a single trained observer. For the continuous monitoring, video recordings were observed continuously and the exact time and duration that sows exhibited each posture over the study period was recorded. The scan sampling method involved pausing the video recordings at 5 min intervals and recording the posture of the sow at each time. Postures recorded from the scan sampling were reported as the number of observations of each posture as a percentage of the total number of observations. Postures recorded from the continuous monitoring were reported as the time spent in each posture as a percentage of the total time. A repeated-measures analysis was carried out using PROC GLIMMIX of SAS; the model included the fixed effects of study day, sampling method, and the interaction. There was no difference (P > 0.05) between sampling methods for any of the postures on any study day or for the overall study period (Table 1). In conclusion, these results suggest that scan sampling at 5-minute intervals gives similar results to continuous monitoring for measuring the posture of newly weaned sows housed in individual crates. Further research is needed to define the maximum interval for scan sampling that maintains the accuracy of monitoring sow posture.

Alternative slaughter procedures: on-farm slaughter and transport system for broilers

This paper focuses on "alternative methods for initial broiler processing" and exploration of alternative processing including slaughter at the farm immediately after catching. On-farm slaughter and transport (FSaT) is envisioned as a mobile unit that stuns, slaughters, and shackles the broiler carcasses at the farm. A separate trailer-unit then transports the shackled broiler carcasses to the processing plant. Once at the processing plant carcasses are mechanically transferred into plant shackle lines and moved into processing. The hypothesis is that the FSaT approach will dramatically improve overall bird welfare and well-being by reducing live handling and eliminating live transport from the farm to the processing plant. In addition, ancillary impacts could include: improving yield efficiencies by eliminating dead on arrivals, potentially reducing water and energy consumption, reducing labor requirements at the processing plant with the elimination of live rehang, and offering an economically sustainable alternative. The FSaT approach represents a radical change from traditional processing, and its effects on poultry processing need to be evaluated. This paper presents results of experiments conducted at a commercial poultry processor to evaluate feather picking efficiency, carcass bacteriological loading, and meat quality for delayed processed carcasses.