Harvesting Of Products Research Articles

Time consumption models for predicting harvester productivity when selection cutting, thinning from below, and clearcutting Scots pine-dominated stands in Finnish Lapland

ABSTRACT In this study, time consumption models to predict harvester productivity when selection cutting, thinning from below, and clearcutting Scots pine-dominated (Pinus sylvestris) stands in Finnish Lapland were constructed because no up-to-date or comparable studies were available. A conventional single-grip harvester was used to harvest industrial roundwood from time study stands in winter and fall conditions. The compiled models and parameters can be used to evaluate the feasibility of alternative forest management practices, e.g. the conversion of even-aged stands into continuous cover ones in northern Fennoscandia. Continuous cover forestry and especially selection cutting are considered as a complementary alternative to the dominant even-aged forest management and can facilitate the integration of wood production with multiple uses of boreal forests, e.g. in Finnish Lapland, where reindeer herding, nature-based tourism, and forestry are important livelihoods. The results indicated that harvester time consumption was strongly affected by the stem volume of the harvested trees and the number of harvested stems per hectare. The productivities of selection cutting and thinning from below were at the same level when the volume of harvested stems was equal, and this productivity was significantly lower than in clearcutting.

Arbuscular mycorrhizal fungi travel the world with harvested underground crops.

In recent years, the dispersal of potentially invasive plants, animals, and pathogens via international trading routes for fresh agricultural goods has been the subject of intensive research and risk assessment. Comparatively little is known about the potential impact of global food trade on the spreading of symbiotic soil microorganisms, such as arbuscular mycorrhizal (AM) fungi. The present study thus assessed whether internationally traded underground crop harvest products carry AM fungal propagules. Twenty batches of tubers, corms or bulbs originating from eight different countries were sampled and used to inoculate Sorghum bicolor (L.) Moench plants grown in a heat-sterilized, sandy dune soil from the United Arab Emirates (UAE). Results revealed that most of the underground crop harvest products contained AM fungal propagules able to establish AM symbioses in a pot experiment under greenhouse conditions. Though it is likely that most AM fungal propagules attached to harvest products will ultimately be eliminated in the waste or sewage stream, it is well possible that a certain portion would find its way into agricultural or natural ecosystems, e.g., via organic waste disposal or use of kitchen greywater for irrigation. Given the large volumes of underground crops traded worldwide, their impact on AM fungal dispersal and distribution deserves further investigation and assessment of associated risks of adulteration of soil microbial communities.

Agroclimatic and spectral regionalization for soybean in different agricultural settings in the state of Paraná, Brazil

Abstract Information related to the climate, sowing time, harvest, and crop development is essential for defining appropriate strategies for agricultural activities, which helps both producers and responsible bodies. Paraná, the second largest soybean producer in Brazil, has high climatic variability, which greatly influences planting, harvesting, and crop productivity periods. Therefore, the objective of this study was to regionalize the state of Paraná, considering decennial metrics associated with climate variables and the enhanced vegetation index (EVI) during the soybean cycle. Individual and global analyses of these metrics were conducted performed using multivariate techniques. These analyses were carried out in agricultural scenarios with low, medium, and high precipitation, corresponding to harvest years 2011/2012, 2013/2014, and 2015/2016, respectively. The results obtained from the scores of the retained factors and the cluster analysis were the profile of the groups, with Group 1 presenting more favourable climatic and agronomic conditions for the development of soybean crops for the three harvest years. The opposite occurred for Groups 2 (2011/2012 and 2013/2014) and Group 3 (2015/2016). During the soybean reproductive phases (R2 – R5), precipitation values were inadequate, especially for Group 2 (2011/2012 and 2013/2014) with high water deficit, resulting in a drop in soybean productivity. The climatic and agronomic regionalization of Paraná made it possible to identify the regions most suitable for growing soybeans, the effect of climatic conditions on phenological stages, and the variability of soybean productivity in the three harvest years.

Reduction of pesticide dosage and off‐target drift with enhanced control efficacy in unmanned aerial vehicle‐based application using lecithin adjuvants

AbstractBACKGROUNDUnmanned aerial vehicles (UAVs) are increasingly used in precision agriculture, particularly for pesticide application in rice cultivation. One challenge is off‐target pesticide drift, which raises environmental concerns and reduces pesticide efficiency. Lecithin adjuvants have been suggested to enhance droplet stability, reduce drift, and improve control efficacy. This study aims to evaluate the efficiency of lecithin adjuvants in reducing pesticide drift and improving deposition during UAV‐based pesticide application under various paddy field conditions.RESULTSThe addition of 1% lecithin adjuvants in 75% dosage of tricyclazole and ferimzone dual active ingredient formulations reduced off‐target drift by 2.62 to 3.16 times compared to the 100% and 75% dosage of standard formulations, with deposition efficiency along the spray path increasing by up to 155%. Wind direction and speed were found to be the primary environmental factor affecting deposition efficiency and drift rate. The control efficacy against leaf blast disease was significantly improved, with a maximum efficacy of 73.7% observed in the adjuvant‐treated group. Initial pesticide residues on rice plants were the highest in treatments with adjuvants, but their harvest products, brown rice and dried straw, were still within safe limits for human consumption.CONCLUSIONLecithin adjuvants significantly reduce off‐target drift and enhance pesticide deposition during UAV‐based application. This method allowed for lower pesticide dosages without compromising control efficacy, contributing to more sustainable agricultural practices. These findings highlight the potential of adjuvants to improve UAV pesticide application and reduce the environmental impact of pesticide use. © 2024 Society of Chemical Industry.

A super-hygroscopic SA-MXene@LiCl composite membrane with fast ab/desorption kinetics for efficient sorption-based atmospheric water harvesting

The daily water productivity of sorption-based atmospheric water harvesting (SAWH) materials is significantly limited by their low moisture sorption capacity and sluggish ab/desorption kinetics. Here, a thin, porous, mechanically stable and high LiCl-loading content super-hygroscopic SA-MXene@LiCl membrane (SM@LiCl) was fabricated via vacuum-assisted filtration and freeze-drying methods. The thin and porous characteristics of the cross-linked SA-MXene network significantly reduce moisture transport resistance, while the wrapped hygroscopic LiCl effectively enhances its moisture capture capacity. When applied to SAWH, this nanocomposite membrane demonstrates an impressive water uptake performance of 1.69 g g−1 within 1 h at 45 % RH and releases over 65 % of absorbed water after only 0.5 h of sunlight irradiation (1 kW/m2). With its high moisture sorption capacity and rapid ab/desorption kinetics, it can operate for up to 16 cycles per day in indoor environment, resulting in a record daily water yield of 19.46 L kg−1 at 45 % RH. The present study presents a straightforward approach for the development of high-quality sorbents with improved ab/desorption kinetics for SAWH.

A trajectory tracking control method for the discharge arm of the self-propelled forage harvester

The cooperative operation between the self-propelled forage harvester and the trailer aims to achieve precise and automatic forage unloading. The discharge arm serves as the core structure for conveying forage, and the precision and speed of its motion control are crucial factors influencing the efficiency of forage harvesting. In this context, we proposed a trajectory tracking control method for the discharge arm based on an improved particle swarm optimization (IPSO)-PID controller. Firstly, we utilized the Denavit-Hartenberg (D-H) model of the discharge arm for a positive kinematics solution and the geometric resolution and linear fitting method for the inverse kinematics solution. Secondly, we employed the polynomial interpolation method for trajectory planning on the joint space of the discharge arm, and the PSO algorithm for time-optimal trajectory planning. Then, we designed an IPSO-PID trajectory tracking control algorithm and built an Amesim-Simulink co-simulation model for multiple simulation experiments. Finally, we conducted several performance tests of the discharge arm automatic control system in the workstation and the field, respectively. The experiment results indicate that the performance of the IPSO-PID controller exceeds that of all other controllers, which can meet the discharge arm’s motion control accuracy and speed requirements. Our research results are of great significance for improving the productivity and automation process of the self-propelled forage harvester and provide valuable references for research on automatic and precise control of material loading in other agricultural cooperative harvesting.

Portable dual-functional hygroscopic device for efficient atmospheric water harvesting and hydrogen production in diverse environments

Developing integrated functional atmospheric water harvesting and hydrogen production devices based on cost-effective porous carbon materials is crucial for advancing their implementation and addressing multiple environmental challenges. Here, we fabricate a dual-functional hygroscopic composite device (HCD) under mild conditions, capable of obtaining clean water and hydrogen using sunlight and atmospheric moisture. The hierarchical HCD is designed as an ideal solar absorber by incorporating polyvinyl alcohol hydrogel into macroporous carbon and hosting dispersed LiCl and NiCoP10 catalyst. The HCD features hygroscopic and dual-functional photothermal interfaces with kinetic differentiation, exhibiting excellent machinability, high light absorptivity, optimized hygroscopic components, and favorable water evaporation enthalpy. This design, combined with a customized strategy, ensures consistently efficient performance of HCD, achieving a notable daytime water production performance of 3.89 kg·m−2·d−1 at a low total cost of 27.68 $·kg−1·d. The HCD can catalyze ammonia borane hydrolysis to generate hydrogen in various environments including cold and water-scarce conditions. Furthermore, the HCD shows potential for hydrogen energy system, achieving an open circuit voltage output ranging from 0.41 to 0.493 V. The miniaturized HCD flexibly provides a comprehensive solution for water and energy applications in complex environments, with significant potential for widespread implementation.

One-Step Strategy Streamlining Microalgae Biofuel Production: Integration of Microalgae Harvesting and Methane Production by Dual-Functional Magnetic Nanoparticles

One-Step Strategy Streamlining Microalgae Biofuel Production: Integration of Microalgae Harvesting and Methane Production by Dual-Functional Magnetic Nanoparticles

Evaluation Of Preferences And Willingness To Pay Of Aglaonema Consumers In Sleman District, Yogyakarta

Indonesia has natural conditions that are very supportive of horticultural growth, especially in the ornamental and consumer crops subsectors such as vegetables and fruits. Yogyakarta is one of the largest producers of ornamental plants in Indonesia. There has been a significant increase in the harvest area and production of several types of ornamental plants, such as aglaonema, which shows great potential for further development. The Covid-19 pandemic had a positive impact on the demand for ornamental plants, including aglaonema. People tend to seek new hobbies such as gardening or collecting ornamental plants to cope with stress and maintain mental health during periods of social distancing. Consumer preference for post-pandemic aglaonema in Sleman Regency is determined by attributes such as color, shape, size, motif, leaf texture, number of leaves, and plant appearance. This research uses descriptive and analytical methods with Chi-Square, Multiatribut Fishbein, and linear regression analysis techniques to identify consumer preferences and factors that influence willingness to pay for aglaonema plants. The results showed that the majority of buyers in Sleman Regency are women aged 30- 40 years with an undergraduate education level (S1). The majority of respondents' income ranged from Rp 3,000,000 to Rp 4,000,000 per month with the average respondent having 2 Aglaonema plants. Consumers tend to like Aglaonema with a number of 5-10 leaves, batik motifs, red leaf color, oval leaf shape, thick leaf texture, and healthy plant appearance with many leaves with the most considered attributes in purchasing Aglaonema in Sleman Regency are leaf color, plant appearance, leaf shape, leaf motif, leaf texture, leaf size, and price. Aglaonema consumers' willingness to pay is between Rp 100,000 to Rp 500,000. Factors such as price perception, age, trends, and physical attributes of Aglaonema have a significant influence on consumers' willingness to pay more for Aglaonema plants.

IMPLEMENTASI FUZZY TSUKAMOTO DALAM SISTEM PREDIKSI PANEN PADI DI KABUPATEN INDRAMAYU

Strategic efforts to maintain rice production, which is the staple food of the Indonesian people, must continue to be carried out, one of which is maintaining production in rice-producing areas such as Indramayu Regency is a step that must be prioritized. Knowing the prediction of the upcoming rice harvest is the right effort to make. Fuzzy Tsukamoto logic is a method that can be used as a calculation to make predictions, the selection of fuzzy Tsukamoto is due to its ability to predict with factors that have uncertainty such as rainfall, in addition, land area and harvest yields can also be used as variables to obtain predictions through fuzzy Tsukamoto. This study was conducted to implement fuzzy Tsukamoto for predicting rice harvest yields, where the results of the fuzzy Tsukamoto logic process that have been carried out produce six rules at the inference stage in the category of increasing or decreasing harvests, which are used as the basis for the defuzzification process to find the output value of the harvest. The calculation results are then applied in a website-based system using the Laravel framework and MySql DBMS so that they can be used to predict rice harvest yields. Based on the prediction process carried out through a system based on the fuzzy Tsukamoto method, out of 31 sub-districts, 16 sub-districts have the potential to experience a decline, and 15 sub-districts have experienced an increase in rice harvest production in Indramayu Regency.

A Procedure to Characterize Wood Pile Inventories at Roadside

Tracking roadside wood inventories is necessary for wood procurement. However, this operation is increasingly problematic due to the costs associated with reaching remote sites, labour shortage, and the methods providing limited information on the characteristics of the logs required for transport planning. To overcome these problems, an automated procedure has been developed in Arcmap to characterize individual wood pile inventories at roadside by using GPS points of forwarders, harvester production files, and the road network shape files. An inventory of the logs in the harvest area followed by a wood pile inventory at the roadside were made to evaluate how the procedure could trace logs from machine operating trail to predicted wood pile locations. The study was done at six harvest blocks in the Saguenay-Lac-Saint-Jean region in the province of Quebec, Canada. The procedure was not able to differentiate individual wood piles and aggregated several piles into predicted unloading areas. Results indicated a similarity index of 72% to manually inventoried wood piles. The similarity index could be explained by the low percentage of inventoried unpredicted wood pile lengths (3%) and a high percentage of overpredicted wood pile lengths (27%). The positive allocation rate could not be assessed at the level of the individual piles. On the other hand, the procedure properly allocated 96% of the logs to unloading areas. With the level of precision obtained, the developed procedure could be beneficial for managing the transportation of wood at the level of the road segment since it provides all the dendrometric data of the logs available for transport without requiring human intervention in the forest.

A 3D multiobjective multi-item eco-routing problem for refrigerated fresh products delivery using NSGA-II with hybrid chromosome

In a developing countries, say India, about 40 percent fresh foods are wasted during transportation and $14 billion of post harvest products are lost due to inefficiency of cold supply chain such as using non-refrigerated vehicle during last mile transport. Hence, temperature control through refrigerated transportation is very essential. But, the by-product of this process i.e., carbon emission (CE) due to transportation and refrigeration is detrimental for the world. Only logistic transportation contributes about 25 percent of global CE. Due to the world wide infrastructural development, there are several available routes for the road transportation among different cities. For the maintenance of these routes, there are some toll plazas for collection of money. Some routes also cross the rail lines. For maximum profit, a supplier distributes the products at the earliest as the retailers fix the product’s price with respect to its freshness. Again, this process generates more CE. With these realities, a 3D multiobjective multi-item eco-routing problems for refrigerated fresh products (3DMOMIERPsfRFPs) are developed, where a refrigerated vehicle starts from a depot (refrigerated) and comes back after distributing the fresh products to retailers as per their demands. The optimal routing plan, vehicle’s velocity and preservation rate are determined so that total profit and CE are respectively maximized and minimized simultaneously. For solution, an NSGA-II with different modified operators (NSGA-IIwDOs) is developed with hybrid chromosomes having discrete and continuous variables together, probabilistic selection, problem-specific crossover and generation-dependent mutation. Some performance metrics are presented through NSGA-IIwDOs on the standard TSPLIB problems. Results of 3DMOMIERPsfRFPs without and with specific delivery time slots, road-dependent vehicle velocity and time constraints are evaluated. Pareto front for multiobjective are depicted. As a particular case, results of a previous investigation are obtained. Some of the managerial decisions are observed.

Post-Harvest Handling Training for Red Chilies as an Effort to Optimize Harvest Quality in Farmer Group Samarasa

Red chilies are a horticultural commodity that is susceptible to damage, resulting in a decrease in product quality. The decline in the quality of chilies after harvest occurs due to the ongoing respiration process, so that the chilies wilt (dry) or rot. Other damage to chilies can also occur due to rotting caused by microbes. Therefore, the red chilies produced by the Samarasa farmer group have been directly sold or used by themselves. If the harvest is large in quantity, it will be sold at a cheaper price. This is done because it is feared that red chili harvest products that are stored for a long time will rot and become unsellable. This service activity aims to provide training and assistance regarding post-harvest handling of red chilies so that they can improve their quality and make their shelf life longer. Several stages of service activities carried out include coordination with partners, post-harvest handling training, discussion and evaluation. This activity went well and smoothly. Evaluation results from questionnaires filled out by participants showed an increase in farmers' knowledge and skills regarding how to handle post-harvest red chilies with an increase of 24% and 12% respectively after attending the training.

Maximization of Micractinium sp., biomass and use of Dual-Purpose reduced graphene supported vanadium oxide nanoparticles for harvesting and subsequent biodiesel production

A sustainable media composition comprising of nanourea (NU), groundnut de-oiled cake (GDOC), and seaweed Extract (SE) was formulated using a mixture design for the cultivation of Micractinium sp. Maximum biomass yield and productivity of 5.52 ± 0.09 g L−1 and 0.72 ± 0.03 g L−1 d−1 were observed at 1.67 mM NU, 0.134 g L−1 GDOC and 0.250 mL L−1 SE, respectively. The highest lipid yield of 2.73 ± 0.24 g L−1 was also observed, respectively. The reduced graphene-supported vanadium oxide nanoparticles (RGO-VNPs) with a net surface charge of + 34.10 mV were developed, which acted as a flocculant as well as a catalyst for transesterification. A maximum flocculation efficiency of 98 % was observed with 200 ppm of RGO-VNPs. The Fatty Acid Methyl Ester (FAME) yield of 96.81 ± 1.61 % was observed. Thus, the present study could provide a plausible solution for one-pot harvesting and synthesis of biodiesel utilizing microalgal lipids.

Penerapan Metode K-Medoid Dalam Pengelompokkan Data Pertanian Di Dinas Tanaman Pangan Hortikultura Dan Perkebunan Provinsi Bengkulu

The Department of Agriculture, Food Crops, Horticulture and Plantations of Bengkulu Province has collected data on agricultural commodities in each Regency/city in Bengkulu Province which are recapitulated annually, but at the Department of Agriculture, Food Crops and Horticulture and Plantations of Bengkulu Province there is no system that can be used to classify agricultural yield data for each of these various commodities so that it is difficult to obtain information on potential harvest-producing agricultural areas in Bengkulu Province for each commodity. Therefore, a system is needed that can help provide information on the results of grouping agricultural data for each commodity so that it can be known which regency/ cities are categorized as potential crop producers in Bengkulu Province based on harvest area (Ha), production (Ton) and productivity (Ku / Ha). The implementation of K-Medoid Method in classifying agricultural data at the Department of Food Crops, Horticulture and Plantation of Bengkulu Province can provide information on the results of grouping data on agricultural areas that have potential crop yields for the last 1 year in Bengkulu Province, and can be used as a reference in providing assistance from the Department of Food Crops, Horticulture and Plantation of Bengkulu Province. From the results of clustering using the K-Medoid Method, it was found that Rejang Lebong Regency area had high yield potential (cluster 1) in all three commodities (rice, corn, and peanuts), while the Mukomuko Regency, Kepahiang, Central Bengkulu, and Bengkulu City areas had low yield potential (cluster 2) in all three commodities (rice, corn, and peanuts).

Implementation of hybrid tomatoes as an effort to increase tomato harvest production

Tomatoes are a horticultural commodity that is widely cultivated, especially in Bandung Regency. Tomato plants are widely cultivated because they have a fairly high economic value and are in demand by the community because of their many health benefits, among them are vitamin and antioxidant content. Currently, tomato production is declining, so education and assistance are needed to increase the potential for results. The target of this activity is tomato farmers in four sub-districts in Bandung Regency. The purpose of this activity is to increase farmers' knowledge in cultivating hybrid tomatoes at various altitudes. The activity started with counseling, field practice, and evaluation of the results of the activity. As a result of this activity, farmers have additional knowledge in appropriate technology for tomato plants in their respective locations. After the evaluation process, tomato production increased significantly, both in terms of quantity and quality, so it had a positive impact on the economic welfare of farmers.

In-field performance evaluation of robotic arm developed for harvesting cotton bolls

Robotic harvesting of cotton bolls combines the advantages of manual picking and mechanical harvesting, including selective picking and reduced labor dependency. Thus, we designed, developed, and tested a cotton-picking robotic arm equipped with a depth camera, a 4-DoF manipulator, and a vacuum-type end-effector. Convolutional neural networks were implemented for cotton boll recognition, while an artificial neural network-assisted particle swarm optimization-based model was utilized for solving inverse kinematics. Linear actuators, controlled by a microcontroller and relays, positioned the end-effector for cotton boll harvesting. Laboratory testing resulted in picking 44–49 cotton bolls per hour, taking approximately 73.0 s per boll. During field testing, the robotic arm picked 40–42 bolls per hour with an average time of 86.0 s per boll. Picking efficiency ranged from 76.19% to 85.71% in the lab and 66.32% to 72.04% in the field. The complex structure of cotton plants sometimes hindered the end-effector’s path, resulting in lower picking efficiency during field testing. As observed in present study, manual pickers achieved a significantly higher picking rate, harvesting 1.381 kg of cotton per hour, nearly 4.7 times more than the robotic arm. Manual pickers achieved 98.04% picking efficiency, significantly higher than the robotic arm’s 68.25%. This significant difference in efficiency is due to the agility, adaptability, and decision-making abilities of human pickers. The developed robotic arm demonstrates competitive efficiency in the domain of harvesting robots, contributing to advancements in robotic agriculture. Future research and development efforts in this field hold the potential to enhance the efficiency and productivity of robotic cotton harvesters.

Agronomic and Starch Content Studies of Local Adlay Accessions (Coix lacrima jobi - L.) from West Sumatra, Indonesia

Indonesia has a high potential for diverse local food resources, one of which is the plant of adlay. Exploration activities aimed at collecting genetic resources of adlay have been successfully carried out in the province of West Sumatra, Indonesia, resulting in the collection of six local genotypes. The purpose of this research is to obtain information about the agronomic potential and starch content of several local adlay accessions. The study was conducted from April to September 2023 in Limau Manis, Padang City, West Sumatra. The six adlay accessions used in the study are PTA-1, KKD-3, PH-4, BTA-2, GT-2, and TJR-2. The experimental design employed was a completely randomized design with three replications. Based on the research findings, significant differences were observed in both agronomic aspects and starch content among the tested accessions. Adlay with the accession code PH-4 exhibited advantages in terms of harvest maturity and production. The starch content of adlay ranged from 56.64% to 61.26%, with the PH-4 accession having the highest amylopectin content (96.60%), while the highest amylose content was found in the GT-2, TJR-2, and KKD-3 accession (19.90%, 20.03%, and 20.48%). Accession PH-4 can be proposed as a candidate for Adlay variety assembly. Keywords: agronomic, starch content, adlay accessions, Coix lacrima jobi

Fresh water production via atmospheric water harvesting using solar energy for green hydrogen production

Abstract Solar energy is used to drive an atmospheric water harvesting unit to produce fresh water needed for the electrolysis process to produce hydrogen, as well as to drive the electrolyzer. So, the system can be considered as a solar powered hybrid system for fresh water harvesting and green hydrogen production implementing the electrolysis concept. Implementation of the system specially in arid regions with high levels of water scarcity and high relative humidity levels helps in achieving SDGs 3, 7, 9, and 13 progressively, improving health and wellbeing, reducing water scarcity, using clean renewable energy, and limiting climate change. The system is proposed and investigated for the production of both fresh and clean water and green hydrogen gas. To improve the productivity of the overall system, performance enhancement of the atmospheric water harvesting subsystem is experimentally investigated to assure the reliability and continuous operation of the overall green hydrogen hybrid system. It was investigated in summer and winter using different number of porous sheet metals with the silica gel bed. The results show that the productivity of the atmospheric water harvesting system can be increased by a minimum of 6% with one mesh metal sheet and 30% with two meshes, and increases by a maxinmum of 60% with one mech and 260% with two meches.

Harvesters’ productivity prediction in Brazilian Eucalyptus plantations: development of a model from machine learning

ABSTRACT Productivity analysis in mechanized harvesting has traditionally relied on statistical expertise and mathematical modeling. However, machine learning tools have emerged as a viable alternative, as they serve the same purpose, utilizing a combination of varied attributes (quantitative and qualitative) and handling large datasets. This study aimed to determine whether the inherent attributes of mechanized timber harvesting of Eucalyptus spp. plantations enable the creation of a high-performance model that can accurately predict productivity from machine learning. For the modeling, we considered five attributes concerning forest inventory, in addition to working hours and the operator experience level. We considered the productivity, timber harvested per working hour, as the target attribute of the modeling. We subjected the database to 17 common algorithms in default mode and compared them according to error metrics and accuracy. We also determined the relative importance of each attribute in the predictive model. The inherent attributes concerning mechanized timber harvesting of Eucalyptus spp. plantations evaluated in this study enable the creation of a high-performance model that can accurately predict productivity from machine learning. The Gradient boosting model in ensemble mode can predict the productivity of harvesters in Eucalyptus spp. plantations with an R2 of 0.81. The attributes that have greater relative importance are operator experience level, average individual tree volume, and stand density with 100%, 76.3%, and 65.8%, respectively.