ABSTRACT The present study was conducted at the Federal Rural University of Rio de Janeiro, in the years 2024 and 2025, to evaluate the performance of three supervised classifiers - maximum likelihood, random forest, and support vector machine - for spectral discrimination between maize (Zea mays L.) and the weed (Cyperus rotundus), considering different phenological stages and two cropping seasons (main and second crop), using multispectral images acquired by remotely piloted aircraft. Overall accuracy, as well as class metrics of precision, recall, and F1-score, were used to evaluate the performance of the algorithms in classifying four targets: maize, weed, soil, and shadow. The performance of the classifiers improved as the phenological stage progressed. In the first year (second crop), the V8 phenological stage showed the greatest differentiation between the plants, and the highest-performing classifier was maximum likelihood, with an overall accuracy of 89%. For the maize class, precision was 0.88, recall was 0.81, and F1-score was 0.85, and for the weed class, precision was 0.68, recall was 0.89, and F1-score was 0.77. In the second year (main crop), the best differentiation occurred at the V6 stage, and the maximum likelihood classifier again presented the best performance, with an overall accuracy of 88%, precision of 1.00, recall of 0.78, and F1-score of 0.88 for the maize class, and precision of 0.52, recall of 1.00, and F1-score of 0.68 for the weed class. Thus, across the years analyzed, the maximum likelihood classifier performed best.
ABSTRACT Limited chemical control options and high onion sensitivity to post-emergence herbicides hinder efficient weed management, especially under direct-seeding systems in semi-arid regions, increasing production costs. Reduced herbicide rates and split applications may improve crop selectivity while maintaining weed control. This study aimed to evaluate the selectivity of post-emergence herbicides applied at different doses and phenological stages in onion grown under Brazilian semi -arid conditions. Four field experiments were conducted in a randomized block design with three replications, each evaluating a single herbicide (oxyfluorfen, flumioxazin, pendimethalin, or oxadiazon). Fifteen treatments were evaluated, combining herbicide doses with three application timings (1-2, 3, and 5 fully expanded leaves), plus an untreated control. Crop injury was visually assessed at 7, 14, 21, and 28 days after application (DAA). Mild phytotoxicity symptoms (< 10%) were observed mainly at early growth stages, particularly when applications were performed at the 1- or 2-leaf stage, while treatments applied at more advanced stages (3 to 5 leaves) caused little or no injury. Symptoms were transient, with recovery observed by 21 DAA. Sequential applications occasionally prolonged low -intensity injury but did not exceed acceptable levels. No significant differences in commercial yield were detected among treatments. Bulb classification was predominantly concentrated in Class 3 (50-70 mm), with values generally above 60%, indicating high commercial quality and uniformity. The results demonstrate that the evaluated herbicides were selective for onion under semi-arid conditions when applied at appropriate doses and growth stages, with early stages being more sensitive and later stages more tolerant, without compromising yield or bulb quality.
ABSTRACT The reuse of domestic effluent in rural areas is an emerging social technology, especially in family farming systems. In this context, the adoption of these systems requires attention to the quality of the water used, since its physicochemical characteristics may influence soil and agricultural crops. Thus, this study aimed to evaluate, through multivariate analysis, the physicochemical quality of water sources from three domestic effluent reuse systems intended for irrigation in rural communities of the semi-arid region, aiming at their suitability for agricultural use and the identification of potential risks of soil salinization and sodification. Sampling was carried out in four periods throughout the year, encompassing water from the supply source, domestic effluent entering the treatment system, and effluent treated by the decanter-digester system. Physicochemical analyses focused on pH, electrical conductivity (EC), calcium, magnesium, sodium, potassium, chlorides, carbonates, and bicarbonates, assessing their suitability for agricultural use. The correlation matrix showed strong associations among electrical conductivity, sodium, chloride, and sodium adsorption ratio, indicating these parameters as the main risk factors for agricultural use. The principal components discriminated the raw effluent with high sodification potential; source waters showed low ionic load; and the treated effluent exhibited a tendency toward salinity, indicating reduction in sodicity, but without reducing the concentration of dissolved salts. The results of the factor analysis confirmed these patterns, highlighting salinity as the main limitation to the agricultural reuse of treated effluent.
ABSTRACT The coconut palm (Cocos nucifera L.) is a perennial fruit palm of great global socioeconomic importance. To increase production and mitigate water deficit during the less rainy season, irrigation systems have been expanded across various fruit species, especially coconut palms, which are sensitive to water deficits. This study aimed to evaluate the influence of irrigation depths, based on crop evapotranspiration, on the physical variables of the fruits, fruit yield, and water productivity (in terms of fruits and coconut water) in a commercial coconut plantation in Santa Izabel do Pará, in the state of Pará, Brazil. The experiment was conducted in the municipality of Santa Izabel do Pará (PA), using a randomized block design with six blocks and four treatments corresponding to irrigation depths of 0 (rainfed), 50, 100, and 125% of crop evapotranspiration, with 24 experimental plots. The evaluation was carried out in relation to production variables: fruit mass and fruit water volume. Fruit yield and water productivity were assessed in terms of fruit number and coconut water volume. Statistical analyses were performed at a significance level equivalent to p ≤ 0.05. The variables of fruit production and yield were influenced by irrigation. Irrigation had a positive effect on water productivity in terms of fruit production; however, no differences were observed among treatments for water volume. It is concluded that irrigation is essential to maximize the production and yield of dwarf green coconut, especially in regions with prolonged dry periods.
ABSTRACT Climate change affects the productive performance of tomato plants. Limited information is available on the use of dwarfing genes in tomato breeding to address these challenges. This study evaluated the photosynthetic efficiency and agronomic performance of tomato plants with different genetic backgrounds (dwarf, wild, and domesticated). The study was conducted using 25 genotypes arranged in a randomized block design, with three replicates and six plants per plot. Hybrids showed higher acylsugar content compared to the cultivar Santa Clara, except for hybrids 1, 2, 6, and 8. According to the dendrogram, Group III comprised hybrids 1, 2, 3, 5, 6, 7, 8, 9, 10, 13, 14, and 15, which exerted a strong influence on yield and fruit number per plant. Meanwhile, according to the Kohonen Self-Organizing Map, Group V included hybrids 11 and 12, which showed genetic similarity, highlighting their close relationship in agronomic characteristics. Hybrid 12 stood out for variables related to yield, average fruit weight, and number of fruits per plant demonstrating photosynthetic efficiency and agronomic potential. The dwarf donor parent, UFU MC TOM 1, showed photosynthetic efficiency similar to that of the wild accession, Solanum pennellii. Therefore, it can be concluded that the use of dwarf male parents to develop hybrids with standard architecture provides advances in tomato cultivation.
ABSTRACT Indiscriminate collection of native Cerrado orchids highlights the need for methodologies for conservation and sustainable production. Thus, this study evaluated the in vitro growth and ex vitro establishment of Cattleya nobilior Rchb.f., Cattleya walkeriana Gardner, and Schomburgkia crispa Lindl. cultivated in media supplemented with organic compounds in a micropropagation system with gas exchange, aiming to contribute to conservation and sustainable production strategies for these species. A completely randomized design with six treatments and five replicates was used. Seedlings were cultivated in vitro in Murashige and Skoog (MS) medium; MS + banana pulp (BP); MS + coconut water (CW); MS + banana flour (BF); MS + BP + CW or MS + BF + CW. After evaluation, the plants were transferred to an ex vitro environment and, after 180 days, evaluated for survival and the same initial characteristics. For C. nobilior, the MS + BP + CW medium promoted in vitro growth and favored the acclimatization phase. For C. walkeriana, the MS + BF + CW medium produced the best in vitro biometric performance; however, only the MS + CW medium ensured ex vitro survival (54.54%), making it strictly required for the complete propagation protocol. For S. crispa, in vitro cultivation in MS + BP medium favored both in vitro growth and plant acclimatization. It is concluded that the use of organic supplements and micropropagation systems with gas exchange favor growth and acclimatization, being recommended for the in vitro and ex vitro cultivation of these species.
ABSTRACT The phyllochron is a key developmental parameter used to predict leaf appearance in crop simulation models. This study estimated the phyllochron of field-grown cut sunflower genotypes across tropical, subtropical, and temperate environments using single-linear and bilinear models, and tested the hypothesis that a distinct breakpoint reflects a change in phyllochron during ontogeny. Leaf appearance was monitored in twelve genotypes across multiple trials conducted between 2020 and 2023 in Brazil and Italy. The phyllochron was estimated using linear and bilinear regressions relating leaf number to accumulated thermal time and expressed in °C day per leaf. Field observations revealed a breakpoint in leaf appearance, with a longer phyllochron up to stage V6 (34.87 °C day per leaf) and a shorter phyllochron from stage V7 onward (21.82 °C day per leaf). This shift was associated with changes in leaf phyllotaxy and the onset of stem elongation. Phyllochron phases were consistent across genotypes and influenced by environmental factors, particularly air temperature, whereas planting method showed no significant effect. These findings improve understanding of phyllochron dynamics and provide an ecophysiological basis for incorporating a bilinear chronological response function into process-based models for cut sunflower.
ABSTRACT This study evaluated the effects of mineral and organic fertilization systems on copper (Cu) and zinc (Zn) concentrations in soil, leaves, and orange juice of Citrus sinensis orchards. Treatments consisted of mineral fertilization (MF), mineral fertilization combined with poultry litter (MF+PL), mineral fertilization combined with poultry litter and swine liquid manure (MF+PL+SLM), and poultry litter alone (PL). Mineral fertilization was applied using urea and 5-20-20 and 10-20-10 NPK formulations at 250 g per plant. Poultry litter was applied at rates of 6 to 10 t ha⁻1 over three years, and swine liquid manure at 25 m3 ha⁻1 in two applications. The highest pseudo-total Cu and Zn concentrations in soil were observed under MF, reaching average values of 300 mg kg⁻1 for Cu and 160 mg kg⁻1 for Zn. Cu concentrations in leaves ranged from 7.9 to 12.4 mg kg⁻1, while Zn concentrations in orange juice varied from 0.8 to 1.1 mg L⁻1 among treatments. Higher Cu concentrations in leaves were observed under MF+PL, whereas higher Zn concentrations in juice occurred under PL. Bioaccumulation factor values for Cu in leaves ranged from 0.12 to 0.38, and for Zn in juice from 0.09 to 0.27, both remaining below 1. These results indicate low uptake efficiency and restricted transfer of Cu and Zn from soil to plant organs and derived products, without excessive accumulation in edible products.
ABSTRACT The objective of this study was to assess the variability of annual and monthly precipitation under current and future climate conditions, and to characterize its effects on soybean grain yield in the municipality of Tangará da Serra, Mato Grosso state, Brazil, using future climate projections from General Circulation Models (GCMs) and the Decision Support System for Agrotechnology Transfer (DSSAT), employing the CROPGRO-Soybean module. Model calibration was based on climate, soil, and management data, as well as genetic coefficients and field observations from the 2015/2016 growing season, considering cultivars with different maturity groups. Simulations were performed for six different sowing dates. Results indicated both positive and negative monthly fluctuations in precipitation under the current scenario. Among these, significant reductions were observed in September, October, and December, along with a general decreasing trend in annual precipitation for the region. Future scenarios (RCPs 4.5 and 8.5) showed both positive and negative variations, though not statistically significant. Earlyand intermediate-maturing cultivars exhibited lower yields and greater variability, whereas the late-maturing cultivar showed superior yield across all scenarios and sowing dates. Early sowings (September 20 to October 20) resulted in the lowest yields, while late sowings (November 1 and 10) were more favorable. Annual average precipitation was 1711 mm (current), 1494 mm (RCP 4.5), and 1515 mm (RCP 8.5). It is concluded that climate variability significantly affects soybean grain yield, with late-maturing cultivars and delayed sowings being recommended to enhance adaptation under future climate conditions.
ABSTRACT Photovoltaic solar energy plays an important role in renewable energy, but its performance can be affected by operational issues such as partial shading and soiling accumulation on the modules. In this context, intelligent monitoring strategies are essential for identifying faults and assessing performance. This study aimed to develop and implement a cloud-enabled intelligent fault inference system for a photovoltaic installation, using locally acquired electrical and environmental data, preprocessed prior to cloud-based inference. The model was trained using machine learning and artificial neural network techniques with real experimental data collected at the photovoltaic plant of the Laboratory of Physics and Renewable Energy at the University of Pernambuco, Petrolina Campus. The neural network predictions were integrated into a web-based platform for data visualization, parameter analysis, and automated operational alerts. The system showed high precision in identifying the evaluated conditions, with Mean Square Error on the order of 10⁻2, Mean Absolute Error between 5.33 and 5.40%, and coefficients of determination (R2) ranging from 99.74 to 99.76%. These results indicate the potential of the proposed approach to support monitoring and predictive maintenance of photovoltaic systems.
ABSTRACT Melon cultivation in northeastern Brazil faces water challenges due to low rainfall. Technologies such as infrared imaging show promise for monitoring water demand and heat stress. The objective of this study was to evaluate the vegetative, physiological, and yield responses of melon plants subjected to different irrigation depths, as well as monitoring the thermal index as a water stress indicator. The study was conducted using a randomized block design, with four treatments related to irrigation depth (50, 75, 100, and 125% of crop evapotranspiration) and five replicates. Drip irrigation and the class A pan method were used to determine evapotranspiration. Stem diameter showed the highest value at 75% ETc at the beginning of the cycle, and the length of the main branch increased linearly with irrigation depth. At 62 days after transplanting (DAT), transpiration and stomatal conductance showed quadratic behavior, with peaks close to the 100% ETc irrigation depth, while the photosynthetic rate and thermal index showed significant linear regression, positive and negative, respectively, with the applied irrigation depth. Fruit mass, circumference, and diameter increased linearly with the irrigation depth, but total soluble solids decreased and pH showed no significant difference. Yield increased with the increase in irrigation depth, while water use efficiency was reduced. As the thermal index showed a negative significant linear regression, it can be inferred that the greater the volume of water available to the plant, the lower the thermal index, which can be used as an indicator of water stress from 62 DAT onwards.
ABSTRACT Climatic variability significantly affects seed quality in tropical forage systems; however, comprehensive studies on Urochloa spp. seed production fields remain scarce. This study aimed to evaluate the impact of environmental conditions on Urochloa spp. production fields by analyzing their physical, physiological, and sanitary attributes. Climatic variables and seed quality were monitored in U. brizantha cv. Marandú and Piatã, U. humidicola cv. Humidicola, and U. ruziziensis cv. Ruziziensis. Seeds were collected by sweeping from 16 production fields in Mato Grosso do Sul, Brazil. Physiological quality was assessed through purity analysis, thousand-seed weight, viability using the tetrazolium test, germination tests, and sanitary evaluation using the blotter method. After confirming normality (Shapiro-Wilk test) and homogeneity of variances (Bartlett test), data were analyzed using ANOVA, Tukey’s test, and principal component analysis (PCA). Seed physical quality showed partial variation, as seed purity differed among fields within cultivars, while thousand-seed weight remained unchanged. However, a high incidence of fungal contamination was observed. Fusarium sp. and Rhizoctonia solani were more prevalent in fields with lower precipitation, negatively affecting physiological quality. In contrast, elevated temperatures combined with intense rainfall during maturation and harvesting favored a higher occurrence of phytopathogens. These findings indicate that regional climatic patterns in Mato Grosso do Sul necessitate adjustments in management practices to ensure the maintenance of seed viability standards.
ABSTRACT Undergraduate programs in agricultural engineering face challenges in attracting and retaining students in Brazil. A dynamic curriculum should incorporate didactic tools that make it easier to teach specific subjects. This study aimed to develop a workbench to simulate the mechanisms of a row-crop planter in a classroom setting. Its mechanical design was created on Inventor software, with components designed using dimensions and shapes optimized for 3D printing. Main workbench parts included an electric motor set, a seed reservoir, a seed-metering disk, gear transmissions, a seed delivery tube, and a conveyor belt to receive the metered seeds. Device evaluation considered varying speed conditions. Results showed that 60% of the samples had an average seed spacing within quality limits (8 to 10.5 cm). Across a speed range from 0.3 to 1.7 m s⁻1 (based on the conveyor belt), the dosing rate remained at an average of 10.5 seeds m-1. These results evince the device’s potential for educational purposes by enabling interaction with realistic simulations applying changes to the operating mode. In conclusion, the seed-metering workbench can serve as an active learning instrument that provides students with hands-on experience.
ABSTRACT The Vietnamese Mekong Delta (VMD), a crucial region for rice cultivation, faces challenges from climate variability, including altered rainfall patterns, rising temperatures, and droughts, which pose risks of rain-fed water deficits. This study investigated the impacts of global climate change on irrigation water potential and irrigation water demand for rice paddies in the Plain of Reeds. The study applied the FAO-AquaCrop model to simulate irrigation water potential and irrigation water demand for the winter-spring, summer-fall, and fall-winter seasons under current climatic conditions (baseline) and future Representative Concentration Pathways - RCP4.5 and RCP8.5 scenarios for 2011-2040 (2025s), 2041-2070 (2055s), and 2071-2099 (2085s). The model was calibrated and validated against observed yield data from 2002-2023. The results indicate a general increase in irrigation water potential across seasons and sub-areas, with the winter-spring crop showing the most substantial rise (up to 65.7% by 2071-2099 under RCP8.5). Irrigation water demand also generally increased, particularly for the winter-spring crop, though RCP8.5 did not always result in higher demand than RCP4.5, suggesting complex climate-irrigation interactions. These findings highlight the need for adaptive water management and the potential of adjusting sowing schedules to mitigate increased irrigation water demand.
ABSTRACT Advances in artificial intelligence and natural language processing are transforming decision-support systems in agriculture, enabling real-time interaction between humans and machines for smarter and more accessible crop management. This study presents the development and evaluation of an intelligent monitoring system for hydroponic cultivation, integrating a chatbot based on large language models (LLMs) and a retrieval-augmented generation (RAG) architecture. Designed to support smallholders, the system provides real-time technical assistance in natural language by integrating environmental sensor data, such as pH, temperature, and electrical conductivity (EC), with a semantic vector database indexed by four embedding models: text-embedding-ada-002, multilingual-v2, all-MiniLM-L6-v2, and Word2Vec. Twenty structured and unstructured agronomic questions were used to assess the system’s ability to generate relevant and factually accurate responses through ChatGPT and Gemini. Real-time data from lettuce cultivation trials were formatted in JSON, which were used to simulate decision scenarios. These included both historical queries (e.g., pH trends) and conceptual questions (e.g., ideal EC range). Results showed that the text-embedding-ada-002 model consistently achieved the highest semantic relevance (mean = 0.88) and factual faithfulness (mean = 0.72 with Gemini), outperforming the other embeddings. The multilingual-v2 showed moderate results, while the MiniLM and Word2Vec performed poorly in complex or context-dependent tasks. Gemini slightly outperformed ChatGPT in producing faithful responses. The proposed system is effective as a low-cost decision-support tool for hydroponics and can be scaled to other domains, such as irrigation and greenhouse management. This study highlights the potential of embedding enhanced LLMs and RAG architectures to democratize access to precision agriculture technologies.
ABSTRACT Soil bioanalysis and vegetation indices such as NDVI and EVI help to monitor soil fertility and the vigor of oil palm. This contributes to optimized and sustainable crop management in the Amazon. However, the integration of these data to elucidate spatial patterns and optimize soil management remains limited. The objective of this study was to evaluate the relationship between Soil Bioanalysis (BioAS) and vegetation spectral indices (NDVI and EVI) within a conventional oil palm (Elaeis guineensis Jacq.) plantation in northeastern Pará, Brazil. The following attributes were analyzed: Arylsulfatase, β-glucosidase, SQI Biological, SQI Chemical, SQI FertBio, and cycling, storing, and supplying nutrients functions. Geostatistical analysis revealed strong spatial dependence for most of the attributes, allowing interpolation by ordinary kriging to generate predictive maps. Principal Component Analysis (PCA) indicated an inverse relationship between the spectral indices and the soil’s biological attributes, suggesting that areas with higher vegetative vigor may be associated with changes in nutrient dynamics and other soil-related factors, such as physical properties and water availability, under the studied conditions. In addition, orthogonality was observed between NDVI, EVI, and some soil attributes, such as F3 - Supplying Nutrients, SQI Chemical, SQI FertBio, and arylsulfatase, indicating a lack of direct correlation under the studied conditions. These results highlight the importance of integrating soil bioanalysis, remote sensing, and geostatistical techniques to improve the understanding and monitoring of soil conditions and oil palm productivity.
This study aimed to evaluate the linear and spatial correlations of soil physical attributes in a soybean field under a no-tillage system in the 0.00-0.20 m and 0.20-0.40 m layers in Selviria, Mato Grosso do Sul, Brazil. Soil sampling was carried out at 60 georeferenced points arranged in a 5 & times; 5 m experimental grid, considering soil texture (sand, clay, silt), porosity variables (Ma, Mi, dTP and cTP), density attributes (SD and PD), penetration resistance (PRi and PRb), gravimetric moisture (GM), and total soil deoxyribonucleic acid (DNAt). Data were analyzed using statistical and geostatistical techniques, with experimental semivariogram fitting and kriging interpolation. The surface layer showed greater variability and 23 significant linear correlations, including strong to very strong magnitudes between attributes, in contrast to the subsurface layer. DNAt was positively correlated with silt and negatively correlated with clay, indicating a physical-biological integration between soil structure and microbial activity. Most dynamic attributes exhibited a pure nugget effect, suggesting structural homogenization typical of consolidated and irrigated Oxisols under long-term no-tillage system, with variability restricted to the microscale. These results indicate that site-specific management should be targeted only to attributes with proven spatial dependence (silt, clay, and PR), whereas the remaining properties can be managed uniformly due to their intrinsic stability within the system.
Watermelon cultivation is an important component of Brazilian agribusiness, with growing relevance in emerging states such as Esp & iacute;rito Santo, where agricultural diversification is expanding. In the Northwest region, high solar radiation and adverse climatic conditions pose challenges to fruit quality, making the use of management strategies, such as sunscreens, a potential solution. This study aimed to evaluate the effects of different sunscreens on the yield and postharvest quality of watermelon grown in this region. The experiment was conducted using Crimson Sweet watermelons in a randomized block design with five treatments and four replicates: T1 - no protection; T2 - paper protection; T3 - calcium carbonate (18.5%) + zinc oxide (0.5%) + adjuvants; T4 - 95% kaolin; and T5 - 97% calcium hydroxide with magnesium and 3% additives. The fruits were sprayed weekly. The variables evaluated included growth, firmness, peel thickness, total soluble solids, chlorophyll indices (a and b), normalized difference vegetation index (NDVI), fruit temperature, and scald index. It was verified that the protectants significantly reduced both temperature and sunscald damage compared to the control, with the paper treatment providing the greatest protection. The sunscreen treatments also increased total soluble solids and chlorophyll indices in the fruit. It is concluded that agricultural sunscreens are an effective strategy to improve watermelon quality under adverse climatic conditions.
Cultivating legumes in fallow areas can enhance soil fertility and agricultural sustainability, especially when combined with the presence of remaining sugarcane straw and chemical weed control. Thus, the objective of the study was to evaluate the agronomic and qualitative performance of common bean subjected to different amounts of residual sugarcane straw and Fomesafen application. The experimental design was a randomized block design in a 4 & times; 2 factorial scheme with four replications. The factors were the amounts of straw (0, 1, 5, and 10 t ha-1) and two herbicide application conditions (without and with application-1.0 L of commercial product per hectare). The soil is classified as Oxisol with a clayey texture. Cultivating the BRS FC104 bean cultivar under sugarcane straw increased grain yield by up to 50.5%, crude protein content by 3.6 percentage points, and leaf area by 33% compared to bare soil. Fomesafen application resulted in higher grain yields and crude protein content, shorter cooking times, and improved sieve yields. Therefore, it is concluded that the use of straw and Fomesafen in the BRS FC104 bean cultivar positively influences grain development, yield, and quality.
ABSTRACT Mechanized sugarcane harvesting can have negative implications, depending on soil moisture and climate conditions. Among the associated challenges is soil compaction, caused by heavy machinery traffic under inadequate moisture and speed conditions, which limits sugarcane yield and longevity. Therefore, the objective of this study was to evaluate sugarcane yield and agroindustrial performance as a function of harvesting speed during dry and rainy seasons. A two-year experiment was conducted in the eastern region of Paraíba state, Brazil, during the dry and rainy seasons, using a randomized block design. Harvester speeds (2, 3, 4, 5, and 6 km h-1) were used in two harvesting seasons (dry season – Site 1; and rainy season – Site 2), with four replicates. The results indicated that harvesting in the dry season resulted in a greater number of culms per meter, culm diameter, internode length, and leaf area at the end of the ratoon cycle. Conversely, the rainy season favored culm height and internode number. Harvester speed of 5 km h-1 increased leaf area and culm diameter in ratoon cane. It was concluded that sugarcane harvested in the dry season exhibited better vegetative performance throughout the ratoon cycle. Furthermore, different harvesting speeds did not significantly affect the industrial quality of the cane, regardless of the harvest season.
