Manual Harvesting Research Articles

Effects of Harvesting Method on Seed Yield and Seed Quality in Urochloa ruziziensis (cv. ‘OKI-1’ and cv. ‘Br-203’)

Two field trials were conducted in Northern Thailand from 2018 to 2020 to determine the best manual harvesting method for maximizing seed yield and seed quality of two Urochloa ruziziensis cultivars, cv. OKI-1 and cv. Br-203. Four manual seed harvesting methods were implicated in a randomized complete block design with four replicates. The four methods were as follows: knocking seeds from seedheads into a nylon net receptacle and collecting them once every day (T1); knocking seeds from seedheads into a nylon net receptacle and collecting them once every three days (T2); allowing ripe seeds to fall into a nylon net sheet that was stretched as a receptacle beneath the seedheads and collecting seeds once every five days (T3); and covering the tied seedheads with a nylon net bag and collecting seeds once every five days (T4). The highest pure seed yield was obtained from T3 in cv. Br-203 (333.11 kg/ha), as well as the highest trend in cv. OKI-1 (534.67 kg/ha). T3 produced 22–46% and 11–27% more pure germinated seed yields than the other methods in cv. Br-203 and cv. OKI-1, respectively. As a result, T3 was recommended for higher seed yield, convenient seed harvest, and reduced manual seed harvest time.

Economic Evaluation of Precise Intelligent Cauliflower Harvester: A Comparative Study with Manual Harvesting

Aim: The traditional harvesting of cauliflower has been done by hand using labor-intensive, expensive, and time-consuming techniques that frequently lead to significant yield losses because immature curds are harvested without selection. A rising number of people are interested in creating selective cauliflower harvesters that can precisely detect and gather healthy, mature heads while reducing damage, waste, and labor-intensive operations in response to these difficulties. The purpose of this research article is to present a thorough analysis of the ownership and operating costs related to this kind of selective harvesting technology.
 Methodology: Through accurate cost insights, farmers can make well-informed decisions on the purchase of new machinery, the maintenance, and upkeep of current equipment, or the investigation of alternative approaches to improve farm productivity and financial results. The economic assessment was conducted through the computation of operating costs utilizing the straight-line method, coupled with breakeven point analysis and determination of the payback period.
 Results: The developed selective harvester's ownership and running costs are calculated to be 58.41 and 75.5 rupees per hour, respectively. The selective harvester has several advantages over typical manual harvesting techniques, as demonstrated by a comparative analysis. In particular, the selective harvester shows a remarkable 24.6% cost reduction in addition to saving an astounding 60.6% of the necessary time.
 Conclusion: These results highlight the efficiency and financial gains that come with using selective cauliflower harvesting technologies. This analysis is further utilized by farmers for decision-making.

Design and experiment on mechanized batch harvesting of Shiitake mushrooms

Shiitake mushroom must be harvested in time or its commercial value will be reduced. At present, manual harvesting is time-consuming and labor-intensive, and the robotic selective harvesting is far from mature and marketable. Based on the analysis of manual harvesting, a mechanized batch-based harvesting method for the stick cultivation of shiitake mushrooms is proposed as an alternative harvest method in the scenario of large-scale factory production. The feasibility of the simplified picking method was verified by a data acquisition system with integrated force sensor and inertial measurement unit, and the picking force and picking action during manual picking were measured. A mechanized harvesting scheme by picking rings with forward forces to the mushroom stalk was proposed, and its finite element analysis concluded that the mushroom could be picked without damaging the mushroom by using a 5/10 size latex tube. A new mushroom Batch Harvesting Picker (BHP) was designed, including a pushing mechanism, a guiding mechanism, and a harvesting mechanism. By analyzing the causes of harvesting failure and mushroom damage, the stick surface profiling unit were improved by multi-body dynamics simulation. The simulation results show that when the preload force of the reset spring of the harvesting mechanism is set to 30 N, the least pressure on the mushroom stick is 8.97 N, and the picking ring working closer along the surface of stick. The experiment of BHP showed that the harvesting success rate is 98.93 %, the harvesting damage rate is 3.31 %, and the average harvesting time of a single mushroom stick is 9.8 s, which improved the harvesting efficiency of single stick by 2.2–6.6 times compared with the manual harvesting process, which proved the feasibility and efficiency of this batch harvesting scheme in large-scale factory production of shiitake mushrooms.

Analysis of collision damage of Fritillaria ussuriensis Maxim during drum screening

AbstractDuring the drum screening of Fritillaria ussuriensis Maxim (FUM), collisions between FUM bulbs are the main cause of mechanical damage. The effects of the mass relationship and the position, direction, and height of the collision on the absorbed energy and mass–loss ratio of FUM were analyzed through collision tests. The results of the constructed regression models showed that the collision direction has no significant effect on the mass–loss ratio, whereas the mass relationship and the position, direction, and height of the collision showed significance. Under different mass relationships, the collision positions and direction, and the collision height are positively correlated with the absorbed energy and mass–loss ratio, and the absorbed energy is also positively correlated with the mass–loss ratio. In addition, the influence of the mass relationship and collision height, position, and direction on the absorbed energy are highly significant. The research findings serve as a valuable reference for designing and developing low‐loss drum screening processes.Practical applicationsFritillaria ussuriensis Maxim (FUM) is a traditional Chinese medicine. The planting area is wide and the economic benefit is high. The annual economic benefit for farmers is about 1.8 billion yuan. However, the existing production method has a low degree of mechanization, especially the harvest stage, which mainly relies on manual harvesting, is limited by the number of labor force and the harvest time is in the rainy season (FUM is easily spoiled in water), which is easy to cause economic losses. To determine better working parameters, the damage factors of FUM during screening were analyzed by developing and testing the drum‐type FUM harvester. It is helpful to improve the production efficiency of FUM, and reduce the labor demand and the damage of screening to FUM.

Investigating musculoskeletal risks in manual mushroom harvesting: An ergonomic field study in canadian farms

Canada's annual production of 132,000 metric tonnes of mushrooms relies on manual harvesting, posing risks of work-related musculoskeletal disorders (WMSDs). This study employs ergonomic evaluation techniques, including Rapid Upper Limb Assessment (RULA) and Cumulative Trauma Disorder (CTD) index, along with a discomfort assessment questionnaire, to analyze workers' postural loads and self-reported discomfort. Results indicate that immediate alterations are needed in 85% of picking postures to reduce WMSD risks (RULA score: 6–7). The overall process shows a CTD index of 1.16, indicating potential repetitive strain injuries. Pickers report discomfort, particularly in the hand and shoulder. The study underscores the need for alternatives like redesigned growing beds to enhance worker safety in mushroom harvesting.

夹持-拉断式苹果采摘机器人设计与实验

For the standardized apple orchards in China, which are mainly dwarfed and densely planted, firstly, according to the spatial distribution characteristics of fruits within the tree canopy, a clamping-pull-off apple picking robot was developed by analyzing the parameters of apple cultivation and picking methods, in order to replace the manual harvesting operation. Then, the D-H method was used to establish the kinematic Equations of the apple-picking robot, the forward and inverse kinematic calculations were carried out, and the Monte Carlo method was used to analyze the workspace of the robot. Through the robot picking task planning and the simulation of the trajectory of the robotic arm, the scheme of the robot's picking strategy was given, and MATLAB software was applied to simulate the motion trajectory as well as to verify the feasibility of the trajectory planning scheme and the picking strategy. Finally, an apple-picking test bed was set up, the corresponding picking control system program was designed, and 45 apples were selected for picking tests. The results showed that during the robot's picking process, the average time for picking each fruit was 7.59 seconds, the fruit recognition success rate was 86.67%, and the picking damage rate was 5.13%.

Particulate matter from a tropical city in southeast Brazil: Impact of biomass burning on polycyclic aromatic compounds levels, health risks, and in vitro toxicity

In the context of a rising global temperature, biomass burning represents an increasing risk to human health, due to emissions of highly toxic substances such as polycyclic aromatic hydrocarbon (PAHs). Size-segregated particulate matter (PM) was collected in a region within the sugarcane belt of São Paulo state (Brazil), where biomass burning is still frequent, despite the phasing out of manual harvesting preceded by fire. The median of the total concentration of the 15 PAHs determined was 2.3 ± 1.8 ng m−3 (n = 19), where 63% of this content was in PM1.0. Concentrations of OPAHs and NPAHs were about an order of magnitude lower. PM2.5 collected in the dry season, when most of the fires occur, presented PAHs and OPAHs total concentrations three times higher than in the wet season, showing positive correlations with fire foci number and levoglucosan (a biomass burning marker). These results, added to the fact that biomass burning explained 65% of the data variance (PCA analysis), evidenced the importance of this practice as a source of PAHs and OPAHs to the regional atmosphere. Conversely, NPAHs appeared to be mainly derived from diesel-powered vehicles. The B[a]P equivalent concentration was estimated to be 4 times higher in the dry season than in the wet season, and was greatly increased during a local fire event. Cytotoxicity and genotoxicity of PM1.0 organic extracts were assessed using in vitro tests with human liver HepG2 cells. For both types of tests, significant toxicity was only observed for samples collected during the dry season. Persistent DNA damage that may have impaired the DNA repair system was also observed. The results indicated that there was a health risk associated with the air particulate mixture, mainly related to biomass burning, demonstrating the urgent need for better remediation actions to prevent the occurrence of burning events.

Performance Comparison of Harvesting Performance of Different Harvesting Methods in Oil Palm Plantation

Mechanisation in oil palm industry has long been promoted in order to increase the efficiency and productivity of the industry but its adoption is low. One of the reasons is the skeptical view of high cost in mechanisation adoption and the lack of productivity data to compare the performance of manual versus several mechanised operations. This paper attempts to analyse the performance of different harvesting methods available in oil palm industry and assess the efficiency of each method. This study compared the fresh fruit bunch (FFB) harvesting performance of manual, mechanised cutter, i.e. CANTAS and an excavator based harvesting machine. The price and productivity data of each method were taken from literature. The capital (CAPEX) and operation (OPEX) expenditure in terms of cost per tonne were calculated based on the collected data. The result of each method’s performance was then projected in the form of labour to land ratio to determine the efficiency of each method. The CAPEX of harvesting machine is the highest which was RM 280,155.55, followed by CANTAS with RM 3806.33 and manual harvesting for RM 601.00. This reflected to the slightly higher OPEX which was RM 46.45/tonne, compared to CANTAS of RM 28.72/tonne and manual harvesting with RM 39.35/tonne. The machine was shown to cover more plantation area for harvesting with labour to land ratio of 1:86 ha, compare to 1:35 ha for CANTAS and 1:21 ha for manual harvesting. In conclusion, mechanised harvesting machineries increased harvesting efficiency by covering more plantation area. Although mechanisation adoption is unfavourable due to initial capital cost, the difference in OPEX between manual and harvesting machine is shown to be small, and even lower if compared with CANTAS in the long term.

Does the Harvest Type Affect Olive Health? Influence of the Harvesting System and Storage Time on the Chemical, Volatile and Sensory Qualities of Extra Virgin Olive Oils.

With the aim of investigating the effect of bruising and its development during the postharvest time, olive fruits (Frantoio and Moraiolo), manually and mechanically harvested, were stored in climatic chambers at two different temperatures (5 °C and 18 °C) for five days. Visual observations highlighted changes in the olive peel with discoloration in the damaged areas and tissue bruising. Olive fruit polyphenols, volatile organic compounds (VOCs) and other oil quality parameters (phenolic content, free acidity and peroxide index) and sensory assessment were evaluated. Analyses were carried out on fruits and experimental extra virgin oils at harvesting and after 5 days of fruit storage. The results highlight that low-temperature storage (5 °C for 5 days) may contribute to the maintenance of high olive oil quality, and the quality of olives stored at room temperature drastically decreases after 5 days of storage. Moreover, mechanical harvesting, compared to manual harvesting, does not seem to affect the final oil quality, at least at harvesting, but seems to determine differences in the long-term storage period. Finally, the samples stored at 18 °C showed a quality deterioration with the development of sensorial defects.

Coffee yield prediction using high-resolution satellite imagery and crop nutritional status in Southeast Brazil

Timely and accurate prediction of arabica coffee yield is pivotal to support harvest planning and to increase the profitability in the coffee industry. Currently, yield estimates are made by harvesting coffee fruits in a few plants on the field, whose actual yield is then extrapolated to the entire production area. These estimates are time-consuming and considered subjective, demonstrating the need for faster and more accurate methodologies to predict the coffee yield. Therefore, this study aimed to (1) compare the effectiveness of using spectral bands and VIs versus foliar nutrient content for yield prediction, as well as to (2) evaluate the feasibility of combining these variables as input for developing more robust prediction models. For that, an experiment was set up in a 22-ha commercial field of arabica coffee located in Southern Minas Gerais State, Brazil. Data collection was performed using a georeferenced regular grid with 64 sampling points (57 × 57 m). The spectral variables were composed of five bands (RGB, redEdge, and NIR) and six VIs derived from the RapidEye satellite imagery. Images from 10 dates were acquired throughout the 2011–2012 and 2012–2013 crop seasons. The foliar nutrient analyses were composed of nitrogen, phosphorus, potassium, calcium, and magnesium. Lastly, the coffee yield (60 kg-bag ha−1) was determined by manual harvesting of the coffee fruits in the five plants within each sampled point. The average value of the five plants was considered as the yield of the sampled point. Then, prediction models based on simple and multiple linear regression were developed using as input the results of a principal component analysis, which was carried out considering the dataset from each image acquisition date. Overall, the regression models based only on the spectral data enabled the prediction of the crop yield up to 9 months before the harvest date with R2 and RMSE values ranging from 0.04 to 0.69 and from 36.45 to 18.43 bags ha−1 for both seasons. In addition, when the foliar nutrient contents were combined with the spectral bands and VIs, the accuracy was improved with R2 and RMSE varying from 0.57 to 0.72 and from 23.78 to 19.39 bags ha−1. Finally, the study demonstrated that the time-consuming methodology can be replaced or supported by alternative approaches, especially, when using high-resolution satellite imagery.

Fruit growing direction recognition and nesting grasping strategies for tomato harvesting robots

AbstractIn recent years, the potential of robotic harvesting in greenhouse tomato production has garnered significant attention within the tomato industry. However, there is a lack of sufficient research on the complete replacement of manual harvesting with this technology. In this paper, we propose a tomato harvesting method that utilizes a nesting approach to simplify the process and minimize damage. The paper describes the tomato harvesting robot prototype, the visual system equipped with three vision‐based tomato detectors: YOLOv5_CBAM, which incorporates a convolutional block attention module; YOLOv5_SE, enhanced with a squeeze‐and‐excitation block; and a standard YOLOv5s model. Additionally, a novel shear gripping method for fruit bunches is presented, utilizing a bottom‐up snapping technique during harvesting. Point cloud data are utilized to determine the position of the tomato's main stem and bunch. The paper includes field tests and experimental findings, which indicate that the YOLOv5_CBAM model achieves the highest precision (82.62%) and recall (82.57%), outperforming YOLOv5_SE and standard YOLOv5s. Field experiments demonstrate that the improved end‐effector and vision system have significantly enhanced the robot's performance, achieving a 57.5% harvesting success rate in just 14.9 s.

A comb-brushing-type green soybean pod harvesting equipment: Design and experiment.

To solve the problem of low efficiency of manual harvesting of green soybeans and lack of adaptable harvesters, in this study, a brushing-type green soybean harvester was designed. The comb-brushing type green soybean pod harvesting equipment is composed of a front-mounted separation drum, a full-width material delivery mechanism, a negative pressure cleaning system, and a stalk-pod separation system. Based on the operation requirements of the front-mounted brushing-type detachment drum, the drum parameters, parameters of comb arrangement, and structural parameters of the comb, the force analysis in detachment was performed. By taking the pod detachment rate and damage rate as the response indexes, the rotational speed of the drum, the travel speed of the device, and teeth distance as influencing factors, a three-factor five-level orthogonal rotary combination test was carried out by the software Design-Expert. By establishing mathematical regression models for various influencing factors and evaluation indicators, conducting variance analysis and significance analysis on the response indicators of each factor, the optimal parameters were obtained at a rotational speed of teeth of 397.36 rpm/min, minimum axial teeth distance of 4.8 mm and travel speed of the device of 0.5 m/s. Field test results showed that, under the optimal parameter combination, the pod detachment rate was 94%, the damage rate was 3.04%, the harvesting efficiency was greater than 0.187 hm2/h, and impurity content was less than 7.8%, all of which met the design and usage requirements. The research results can provide a reference for the design of soybean harvesters.

Innovative Selective Harvesting Technology for Cauliflower: A Design Approach using Plant Characteristics

Traditional manual harvesting methods for cauliflower are labor-intensive, time-consuming, and costly, and non-selective harvesting often leads to significant yield losses due to immature curds being harvested. To address these challenges, there is growing interest in developing selective cauliflower harvesters that can accurately identify and harvest mature and healthy cauliflower heads while minimizing damage and waste. This research paper focuses on the determination of design parameters for an efficient and adaptive selective cauliflower harvester based on a comprehensive understanding of the physical and mechanical properties of cauliflower plants. Two cauliflower varieties, Pusa Meghna, and Pusa Sharad, were studied to measure various physical properties such as plant width, height, curd diameter, curd depth, stalk diameter, stalk length, and stalk moisture content. Additionally, cutting force was measured to design the cutting unit of the harvester. The results revealed that design parameters, including frame height, width, length, cutter length, and cutter height, should be tailored to the specific physical properties of cauliflower plants to optimize the selective harvesting process. This research contributes valuable insights to the advancement of agricultural robotics and engineering, laying the groundwork for the development of sophisticated selective cauliflower harvesters.

Harvesting and Threshing Methods for Paddy-I: A Review

Harvesting methods adopted for paddy crop are largely dependent on the land size of the farmers. Mechanized methods are completely implemented by the farmers of medium and large categories. On the other hand, the marginal and small farmers are struggling between manual and mechanized methods because of unavailability of implements that can suit to their land size an economic level. Cutting and threshing operations are performed separately which are responsible for more input cost and grain loss. Various methods of harvesting were studied and the loss of grain associated with the methods is emphasized. In manual method, before threshing, the cutting, collecting, bundling and transportation operation requires which consumes time, energy and cost and increase the grain loss significantly. In mechanized methods, the time, energy and input cost can be minimized. The manual crop cutting requires about 8 to 12 and 25 to 45 times, respectively, more man-hours per hectare compared to rotary blade cutter and vertical conveyor reaper. The field capacity of manual harvesting methods is 4 to 10 times less than the mechanized methods.

Digital Image Processing On Kaffir Orange Peel With Canny Edge Detection Algorithm

Object tracking is a form of application of computer vision. To be able to track an object, a stage is needed in the image processing process. Image Processing is a field related to the process of image transformation (image). The image processing process is carried out to obtain better image quality. The harvesting system in kaffir lime is done manually, by choosing fruits whose skin color is green, and not yellowish. Due to the small and asymmetrical size and shape of kaffir lime, manual harvesting systems are still widely used to maintain the quality and quantity of the harvest. In addition, the manual harvesting system can also avoid damage to kaffir lime trees and obtain optimally ripe kaffir limes. Kaffir lime also has genders like humans, namely males and females. In male kaffir lime there is a circle that is more prominent in size underneath, while female kaffir lime has a flat shape. However, for consumption and medicinal purposes both male and female kaffir lime can be used without affecting the taste or quality of the fruit. With the image processing to determine the level of wrinkles on quality kaffir lime peel, kaffir lime will be selected which is usually used for herbal medicines. In this case, the Canny Edge Detection algorithm can be used to identify density edges in kaffir lime peels. Thus, the degree of wrinkles in kaffir lime peel can be calculated and measured to be more accurate. And can be separated quality or non-quality kaffir lime with the image of kaffir lime that has been seen through the image. The results obtained in designing and analyzing the quality of kaffir lime are clearer and more accurate with an image resolution value of 248 x 216 that the orange is included in the female kaffir lime type.The results tested that the right edge detection method in carrying out the edge detection process in the image of kaffir lime peel is the Canny Edge Detection Algorithm. By using the image on the Canny Edge Detection Algorithm, more dense and quality kaffir lime results are obtained so that it can be used for herbal medicine.

Application of machine learning to predict of energy use efficiency and damage assessment of almond and walnut production

A study was conducted in Shahrekord city, Iran, focusing on improving the production of almond and walnut crops on rural agricultural lands. The gardeners selected for the study shared similar characteristics and production histories. One of the major challenges in producing these crops was the manual harvesting process, which required a significant amount of human labor in the region. To collect data, questionnaires and face-to-face interviews were conducted. The study used machine learning models, specifically artificial neural network (ANN) and adaptive neuro-fuzzy inference system (ANFIS) models, to predict energy use efficiency and environmental impacts in almond and walnut production. Among the models used, the ANFIS model with a three-level topology was found to be the most accurate in predicting output energy generation and environmental impacts in both almond and walnut production. The R2 values for the testing stage ranged from 0.969 to 0.996 for output energy generation and 0.994 to 0.997 for environmental impacts. The study demonstrated the effectiveness of using machine learning models like ANN and ANFIS in predicting energy use efficiency and environmental impacts in almond and walnut production, which can aid in planning and managing these crops more sustainably and efficiently in the future.

Simulation Analysis of Working Circuit Performance of Mountain Pepper Harvester Based on Improved Load-Sensitive System

China’s Guizhou is a typical karst landscape province with high production of chili pepper, but it is mostly planted in mountainous areas, while manual harvesting of chili pepper has the deficiencies of high labor intensity, low efficiency, and high labor cost; in addition, there is no harvesting machinery applicable to the dense planting pattern of the chili pepper in mountainous areas in China. The fully hydraulic mountain track-based self-propelled pepper harvester 4JZ-1.0A is designed to solve the above problems. The pepper harvester spiral comb picking head is an important part of the whole machine design, the design of the hydraulic system of the working circuit of the picking head is the key to realizing the hydraulic control part of the whole system. In this paper, the working principle diagram of the improved load-sensitive hydraulic system is designed and analyzed for the study of whether the working circuit of the pepper picking head of the pepper machine can meet the requirements of mountain operation, taking the working circuit of the mountain pepper harvester as the research object. In addition, the load-sensitive pump model and the simulation model of the whole working circuit are established by the AMESim platform 2019.2 (Siemens simcenter amesim). The operating performance of the system under variable flow conditions, variable load conditions, and an improved sensitive system is analyzed. The simulation results show that the improved load-sensitive system can effectively reduce the oscillation and cavitation during cylinder operation and improve the system efficiency and the performance and service life of the components. The performance of the hydraulic system of the working circuit of the mountain pepper harvester was verified in the test, meeting the requirements of working use. This provides a theoretical basis for the improvement and optimal design of a mountain pepper harvester hydraulic system.

Investigation of muscle synergies and their consistency among fresh fruit bunches manual harvesters in a real-life oil palm industry

Oil palm industry still relies on manual harvesting, a physicality-reliant task that was found related to the occurrence of musculoskeletal disorders (MSDs) among the harvesters. Through experience, common muscle synergies could be found among healthy subjects, which could be useful for screening any possible musculoskeletal disorders. This study investigated and confirmed the consistency of the muscle synergies used by healthy, well-trained harvesters during a real-life fresh fruit bunches (FFB) manual harvesting task with eight enrolled subjects. The subjects' muscle synergies during the manual harvesting were investigated from seven upper limb muscles in an outdoor environment. Our results demonstrated that the harvesters consistently used three muscle synergies to perform the manual harvesting task, dedicated to different upper body parts (neck, upper arm, and lower back). The consistent number of synergies was accompanied by high intra- and inter-subject similarities (similarity index >0.7) found in the contribution of muscle in the synergies (weighting coefficient). On the other hand, the alteration in activation timing (SI < 0.6) in the harvesting movement was presumed to be related to the adaptation to environmental constraints, which were also observed in other relevant studies. The result, therefore, confirmed consistent muscle synergies among the oil palm harvesters even in an uncontrolled outdoor environment, providing a reference that brings benefits to the knowledge in the screening of musculoskeletal disorders among the harvesters. Relevance to industryThe paper presents an investigation of muscle synergies and their consistency among healthy, well-trained manual fresh fruit bunches (FFB) oil palm harvesters in an outdoor environment. The result, together with the presented methodologies, provide a reference that can be used in screening musculoskeletal disorders among harvesters in the industry.

Analysis of farmers satisfaction with the use of combine harvester for rice harvesting in farming in Ujong Tanoh Village, Setia District, Aceh Barat Daya Regency

The development of agricultural machinery has a big role in agriculture to increase farmers’ income. Harvest handling with machine aid is significantly more productive than manual harvesting in a sustainable agricultural system. The combine harvester is an example of a piece of agricultural equipment utilized in contemporary agriculture. The purpose of this study was to see how the level of farming community satisfaction with the use of the Combine Harvester machine in Ujong Tanah Village, Setia District, Aceh Barat Daya Regency. The method used in this study is the Customer Satisfaction Index (CSI) to obtain valid (direct) data so that the level of satisfaction of the farming community with the use of the Combine Harvester can be determined. Based on the results of the study, it was found that the CSI for the confirmation of expectation indicators is 0.6126 or 61.26%. The CSI of the indicator of interest in reuse is 0.6477 or 64.78%. The CSI of the indicator of willingness to recommend is 0.6429 or 64.29%. The requirements for Quite Satisfied are met by the average CSI for agricultural community satisfaction with the usage of a combine harvester, which is 0.6344 or 63.44%.

Sustainable scale-up of Irish seaweed production: Quantifying potential environmental, economic, and social impacts of wild harvesting and cultivation pathways

Seaweed is a versatile bioresource which can be used as a source of food, feed, fertilizer, and higher-value products. Countries with extensive sea areas such as Ireland have the potential to produce significant volumes domestically, but limitations and consequences to this potential should be considered. This study aims to capture the environmental, economic, and social consequences of different pathways for scaling up Irish seaweed production using a life cycle sustainability assessment (LCSA) framework. Six pathways are considered: manual wild harvesting by foot, manual wild harvesting by boat, mechanical wild harvesting by trawler, and three longline cultivation systems. Environmental, economic, and social impacts are considered through quantifying exergy extraction (MJex), global warming potential (kg CO2-eq), minimum selling price (MSP), and improvements in human wellbeing (HP). Finally, limitations to scale-up are assessed. The results demonstrate that manual wild harvesting has a relatively low climate impact (0.03–0.04 kg CO2-eq/kg fresh seaweed), resource intensity (1.75–2.00 MJex/kg fresh seaweed), and MSP (0.10–0.12 €/kg fresh seaweed), but a low increase in wellbeing (5.01–5.45 HP/kg fresh seaweed), while mechanical wild harvesting has a worse performance than manual harvesting in every dimension (0.14 kg CO2-eq/kg fresh seaweed, 3.50 MJex/kg fresh seaweed, 0.16 €/kg fresh seaweed, 0.60 HP/kg fresh seaweed). The impacts of cultivation pathways vary significantly, but generally perform better than wild harvesting for social impacts (18.53–20.59 HP/kg fresh seaweed) and worse for environmental and economic (MSP) impacts (0.12–0.35 kg CO2-eq/kg fresh seaweed; 2.30–5.95 MJex/kg fresh seaweed; 1.05–1.80 €/kg fresh seaweed). Nonetheless, limitations to upscaling manual wild harvesting (max 8.4 % of a future production target of 900,000 t fresh seaweed) determine that both mechanical wild harvesting and cultivation pathways will be needed to achieve future targets. It is therefore important that steps be taken to optimize each of these pathways based on the overall priorities of society.