Resumo em Inglês:

ABSTRACT Physical classification is the official standard method for determining grain quality for commercialization. However, it is a time-consuming, subjective operation, susceptible to errors, and requires skilled labor. Optical methods of indirect measurement emerge as a promising evaluation alternative, offering economic advantages, standardization in the assessment of grain nutritional quality, and greater accuracy. Therefore, this study aimed to evaluate the use of near-infrared (NIR) spectroscopy and multivariate statistical analyses to determine the physicochemical quality of parboiled rice grains. Parboiled rice samples were classified according to the Technical Regulation for Rice (Type 1 to Type 5 and Off-Type). Each type was analyzed by NIR to determine the proximate composition (crude protein, moisture, lipids, crude fiber, ash, and starch). The data obtained were subjected to analysis of variance, Tukey's test, Pearson correlation, and principal component analysis. Regarding starch, the main constituent of rice grains, Types 1 and 2 had the highest concentrations (70.11% and 70.16%, respectively), while the lowest concentrations (66.52% and 66.73%) were found in Types 3 and 5, respectively. The results indicated that NIR, combined with multivariate statistical analyses, can be an efficient alternative for characterizing the physicochemical quality of parboiled rice, highlighting clear patterns, especially in starch and fiber content.

Resumo em Inglês:

ABSTRACT Sugarcane is pivotal in the global bioeconomy, providing a renewable resource for products such as ethanol, sugar, bioenergy, animal feed, and bioplastics. Its versatility makes it an essential crop for industries seeking sustainable alternatives to fossil fuels. This study presents an advanced approach that uses artificial neural networks (ANNs), specifically a multilayer perceptron model, to accurately estimate sugarcane productivity and biomass. The model incorporates the effects of micronutrient applications, both in the planting furrow and on the leaves, effectively capturing the complex interactions that influence crop yield. During training, the ANN demonstrated high precision, achieving a mean squared error (MSE) of 0.000097 and an R2 of 0.98, closely aligning the predicted outputs with experimental results. In the validation phase, using previously unseen data, it maintained strong performance, with an MSE of 0.0008796. This performance supports the model's ability to generalize beyond the training set, reliably estimating sugarcane yield and biomass under varying conditions. These findings highlight the potential of ANN-based approaches to enhance agricultural management, offering a robust tool to optimize crop performance and improve resource allocation in real-world farming scenarios.

Resumo em Inglês:

ABSTRACT Land use and land cover (LULC) changes, essential to understanding environmental dynamics under human influence, are significantly intensified by the agricultural sector. This study aims to characterize and analyze spatiotemporal changes in an area primarily influenced by agricultural activities, using geoprocessing and remote sensing. The methodology involved acquiring satellite images from the wet and dry seasons of 1990, 2000, 2010, 2020, and 2023, then delineating and analyzing LULC classes. Among the analyzed periods, the most significant change was the shift from temporary crops to exposed soil, attributed to the fallow period. In contrast, forest areas, buildings, pastures, lagoons, transmission lines, motorways, and access roads underwent only minor alterations. Since 1990, pastures, forest cover, and temporary crops have been the most prevalent classes, totaling 735, 686, and 533 hectares, respectively, during the 2023 rainy season. In conclusion, the methods used here can be adapted to any region, and the resulting products provide essential tools for managers to devise strategies that enhance agricultural and environmental management.

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ABSTRACT This article aimed to develop and analyze a linear programming application to maximize crop yield in a small farm in Serranópolis do Iguaçu-PR, Brazil. Linear programming is a crucial tool in agriculture for optimizing technical, economic, and financial outcomes. The study was carried out between May and August 2023 and involved administering a questionnaire to the production unit manager and developing a mathematical model using LINGO 18.0 software. Our results showed that the model can generate an annual revenue of R$ 676.478,70, exceeding the farm’s current revenue by 12.50%. Comparative simulations showed that this Annual Economic Result (REA) maximization model had superior financial performance to the observed situation. Our findings highlight the effectiveness of linear programming in optimizing agricultural production, enabling more efficient and sustainable management.

Resumo em Inglês:

ABSTRACT Minas Gerais is the leading state in groundwater usage for rural areas, particularly for agricultural practices, making effective monitoring and management of these water resources essential. This study analyzes the hydrogeological context of two Regional Environmental Offices (REOs) in Minas Gerais: Jequitinhonha (JQ) and Leste Mineiro (LM). The research cross-referenced data on groundwater availability with information on soil requirements for fertilizer and soil corrective applications, utilizing a robust dataset of over 1,400 licensed wells in both regions. The results reveal a concerning scenario: 66% of wells in areas of high agricultural demand exhibit static water levels of less than 9 meters, indicating low groundwater availability. This situation underscores the urgent need for effective monitoring and resource management. The intersection of high agricultural water demand and limited groundwater availability highlights the necessity of policies promoting water-use sustainability. Therefore, implementing management strategies that account for groundwater vulnerability is crucial to minimizing aquifer contamination risks.

Resumo em Inglês:

ABSTRACT Soil compaction configures a major threat to soil structural quality. Mitigating compaction induced by agricultural machinery involves knowing the state of stress transmission in the soil which in turn is affected by tire inflation pressure. Hence, this study evaluated the impact of three tire inflation pressure levels from a sugarcane harvesting truck on soil stress propagation. Using the 'PredComp' computational model and machine and tire parameters, simulations were performed for 70, 100 and 130% of the recommended inflation pressure which corresponded, respectively, to pressures of 350, 500, and 650 kPa. Size of contact area decreased as the inflation pressure increased, yielding values of 0.13 m2, 0.12 m2 and 0.11 m2; whereas the maximum stress levels in the center of the tires increased by 465, 623 and 784 kPa to 70, 100 and 130% of inflation pressure, respectively. The propagated stress levels at layers 10 or 30 cm deep were −5% to 70% of the inflation pressure and +5% to 130% of the inflation pressure in relation to the reference (100%). Our simulations indicate that the increase in inflation pressure can increase the stress levels at the tire-soil interface thereby increasing the mechanical stresses propagated is the topsoil and subsoil by approximately 5%, which increases the risk of compaction if the stress levels exceed soil load-bearing capacity.

Resumo em Inglês:

ABSTRACT Storage can reduce seed germination potential and viability, especially in high-lipid species, such as peanuts. This study evaluated the physiological quality of stored peanut seeds from different lineages under long-term storage conditions. The experiment followed a completely randomized design, including ten peanut lines, comprising four commercial cultivars and six genotypes from the Peanut Improvement Program (PMA) of EMBRAPA, with five replications. Seeds were evaluated at two time points: before storage (time zero) and after 12 months of storage. Assessments included moisture content, seed germination percentage, seedling emergence, and seedling development. Data were analyzed using analysis of variance (ANOVA), and means were compared using Tukey's test (p < 0.05). Results indicated that storage time reduced the physiological potential of peanut seeds. However, seeds from lines 5, 10, 4, and 9 maintained viabilities over 12 months, whereas lineage 8 exhibited low germination and seedling emergence after storage. The substrate emergence test proved to be a more sensitive indicator of physiological seed quality among peanut lines.

Resumo em Inglês:

ABSTRACT The grain classification process, essential in the industry, has traditionally relied on manual methods that are prone to inaccuracies and delays. The introduction of computer vision has revolutionized this scenario, enabling faster and more precise analyses. This study evaluated a digital classification device for identifying defects in rice grains, aiming to assess its reliability and associated benefits. Processed rice samples were manually classified by trained classifiers and subsequently analyzed using the Machvision Rice Analyzer equipment. A comparison of the results revealed remarkable consistency, validated by statistical analyses (including principal component analysis). The equipment achieved an average efficiency of 93.13% compared to the classifiers, with particular emphasis on the identification of defects, such as “chopped + stained” and “white belly”. Furthermore, multivariate analysis highlighted the significance of the “chalky” and “white belly” components in classification. The study concluded that adopting computer vision provides a reliable advantage, enhancing the standardization and efficiency of the grain classification process.

Resumo em Inglês:

ABSTRACT Brazilian farmers widely use the no-tillage (NT) system as a soil and water conservation practice. However, NT alone can have limitations in controlling runoff, requiring complementary practices such as agricultural terracing. This study evaluated the impact of agricultural terraces on runoff control, water conservation, and derivation of hydraulic and hydrological parameters on slopes cultivated with grains under NT conditions. The experiment was conducted in a Red-Yellow Latosol (Oxisol) located in the Central-Eastern mesoregion of Paraná, Brazil, on two macroplots: one with terraces (TP, 1.60 ha) and one without terraces (NTP, 1.48 ha). Soybeans and corn were grown in the summer, while wheat and black oats were grown in the winter. The total runoff volume (TRV) and peak discharge in an H-type flume were measured, while the runoff coefficient (RC) and curve number (CN) were calculated. Results showed that terraces reduced TRV by 85% compared to the non-terraced area. In an extreme rainfall-runoff event, the RC and CN values for TP were 2.35% and 47.8, respectively, while for NTP they were 12.69% and 60.6. Agricultural terracing thus proved effective in conserving water on slopes cultivated under no-tillage.

Resumo em Inglês:

ABSTRACT Soil organic matter is an indicator of soil functionality in semi-arid regions, and the study of organic components, along with biological and structural attributes, reflects soil management techniques and aids in agroecosystem management. In this scenario, this study aimed to evaluate the interrelations between soil organic matter fractions, microbial biomass carbon (MBC), and glomalin (GEF) with structural attributes in different land uses in the Chapada do Apodi plateau, semi-arid Brazil. The study was conducted in banana, papaya, pasture, and native forest areas. Disturbed and undisturbed soil samples were collected at depths of 0.00–0.10 m and 0.10–0.20 m to determine organic, biological, and structural attributes. The results were analyzed using multivariate statistical analysis, which identified three factors with a total accumulated variance of 90.19%. Papaya and banana cultivation areas were distinguished by total organic carbon, organic fractions, and labile and non-labile carbon. The native forest was characterized by high GEF (10.21 and 8.91 mg/g) and MBC (280.00 and 225.45 µg/g soil C). The pasture area showed signs of structural and biological degradation, indicated by the carbon management index (79.52 and 33.31%), GEF (2.32 and 2.20 mg/g), and MBC (123.64 and 61.82 µg/g soil C). Land uses with organic matter input, MBC, and GEF contributed to the maintenance of agroecosystems, whereas pastureland was more susceptible to degradation.

Resumo em Inglês:

ABSTRACT Yerba-mate (Ilex paraguariensis) is a native species native from subtropical and temperate regions of South America. Its production process may influence its physicochemical composition and the final quality. In this way, this study aims to evaluate the major macro- and micro minerals present in the yerba mate, both native and cultivated, after the processing. This experiment was conducted in an industry, where samples of dried yerba mate were collected. In total, samples were collected from 31 production areas, with 25 areas of cultivated yerba mate and 6 areas of native yerba mate. The determination of Ca, K, Mg, Na, S, Cu, Fe, Mn, and Zn was released by using inductively coupled plasma optical emission spectroscopy. The highest levels of macronutrients (Ca, K, Mg and S) and micronutrients (Cu, Fe and Mn) were detected, due to the management of the yerba mate plantations, which mostly receive organic or chemical fertilization, as well as the age of the branches and leaves, reflecting on the composition. In contrast, native yerba mate depends on the natural fertilization of the soil and the characteristics of the environment in which it is found.

Resumo em Inglês:

ABSTRACT Soybean stands out as one of the most important crops for the Brazilian economy, and adopting techniques to increase yield has become essential. Controlled traffic farming (CTF) can help optimize soybean production and improve crop performance. This study evaluated soybean grain yield in areas with and without implementing controlled agricultural machinery traffic. The experiment was conducted at the Mato Grosso do Sul State University – Cassilândia University Unit (UEMS/UUC) in Cassilândia, Mato Grosso do Sul, Brazil. The trial was arranged in strips and split-plot with six replications. Treatments were applied in areas with and without controlled traffic, combined with different soil cover strategies: Urochloa (brachiaria), pearl millet (Pennisetum glaucum), a cover crop mix (brachiaria + pearl millet), and spontaneous vegetation. Soybean was sown and cultivated to assess grain yield responses under the different treatments. Vegetative cover with pearl millet and the mixed cover crop led to an increase in yield. Similarly, areas with controlled traffic exhibited the highest grain yield, resulting in a yield gain of 390 kg ha-1. It is concluded that controlled agricultural machinery traffic contributes to higher soybean yields.

Resumo em Inglês:

ABSTRACT In this study, we developed a mathematical model to maximize economic returns in small-scale agricultural production units. The study was conducted on a rural property in Medianeira, Paraná, Brazil, dedicated to producing animal protein, grains, and silage. Production dynamics were analyzed through interviews and cost estimates, gathering technical, economic, and financial coefficients for model formulation. Using LINGO software, we performed economic calculations and defined technical, financial, and environmental constraints. The model proved effective in maximizing biennial economic returns and efficiently utilizing the available area and generated waste. The results showed that mathematical modeling is a valuable tool for developing and managing agricultural activities, enhancing sustainability, and enabling informed decision-making in high-impact environmental contexts.

Resumo em Inglês:

ABSTRACT The ripeness level of bananas is a key factor for both producers and consumers, affecting the entire logistics chain of the fruit and its final price. Artificial intelligence techniques can be applied to enhance the efficiency of these processes. This article presents BananaRipe, a web application with mobile-friendly computer vision features designed to determine the ripeness level of bananas using a YOLO-v8-based classification model. In particular, a web application was developed in Python using the Flask microframework, integrated with a classification model trained on a dataset containing eight banana-ripeness classes. Test results showed that BananaRipe achieved 95.96% accuracy and 97.59% recall, demonstrating highly satisfactory compared to other models in the literature.

Resumo em Inglês:

ABSTRACT The purpose of this research was to evaluate the ingestive behavior of 24 adult male sheep from the Santa Inês, Morada Nova, Soinga, and without defined racial standard breeds, kept in a controlled environment (climatic chamber), at thermoneutral temperatures (TNT: 20.0; 24.0 and 28 .0 °C) and thermal stress (TSH: 32.0 and 36.0 °C), with its behavior being analyzed at each temperature over a 24-hour period. The design used was randomized with a 4x5 factorial scheme, with four breeds at 5 temperatures. The thermal stress index at temperatures of 20.0, 24.0 and 28.0°C were classified as comfortable, at a temperature of 32.0°C defined as mild to moderate discomfort and at a temperature of 36.0°C classified as alarming. Behavioral changes were similar between breeds, with feeding time being similar among breeds, with an increase in idle time and drinking water and a reduction in rumination time with the elevation of temperature. The animals kept the ability to maintain their ingestive behavior, even in situations of heat stress.

Resumo em Inglês:

ABSTRACT Cover crops have proven effective in restoring ecosystem services in areas degraded by hydroelectric plant operations. This study evaluated the residual effects of cover crops and soil amendments on soil organic carbon (SOC) content and soil organic carbon stock (SOCS) in a degraded Oxisol. The experiment was established in 1992 in a randomized complete block design, with different cover crop species combined with lime and lime + gypsum applications. Soil samples were collected at 0–0.05, 0.05–0.10, and 0.10–0.20 m soil depths to analyze SOC content, SOC stock, pH, and bulk density. Results demonstrated that, nearly 30 years later, cover crops and soil amendments significantly influenced SOC—particularly at the 0.05–0.10 m and 0.10–0.20 m depths—with treatments T2 and T7 showing the greatest effects. SOC stock increased in treatments T2, T6, and T7 at 0.10–0.20 m depth. Overall, SOC content and stock were influenced using cover crops and lime application; however, these effects varied depending on the cover crop species, particularly in terms of carbon distribution throughout the soil profile.

Resumo em Inglês:

ABSTRACT Cowpea (Vigna unguiculata L.) is a key crop in Brazil's Northeast due to its adaptability to semi-arid conditions. This study evaluated the effects of different water regimes and irrigation frequencies on cowpea cultivation under a semi-arid climate. The experiment was conducted at the experimental area of IFCE – Campus Iguatu-CE, using a randomized block design with 20 treatments and four replications, arranged in a 5 × 4 factorial scheme. The treatments consisted of five water regimes (R1 – 50%, R2 – 75%, R3 – 100%, R4 – 125%, and R5 – 150% of crop evapotranspiration, ETc) and four irrigation frequencies (F1 – daily, F2 – every two days, F3 – every three days, and F4 – every four days). Irrigation was applied via a drip system. The variables evaluated included the number of pods per plant, number of grains per pod, pod length, pod mass, grain yield, and water use efficiency. Results indicated that daily irrigation led to the highest grain yield (381.94 kg ha⁻1) and water use efficiency (1.43 kg ha⁻1 mm⁻1). The most efficient water regime was 407.11 mm (113.7% of ETc), which had a significant isolated effect on pod number, pod mass, and the number of grains per pod.

Resumo em Inglês:

ABSTRACT Potassium, due to its physiological functions, can enhance plant tolerance to salinity. Based on this premise, the present study aimed to evaluate fruit production and biomass partitioning in Gália melon (McLaren hybrid) fertigated with saline nutrient solutions supplemented with additional potassium. The experiment was conducted in a completely randomized design with four treatments: S1 – standard nutrient solution (2.5 dS·m⁻1); S2 – salinized nutrient solution with NaCl (5.0 dS·m⁻1); S3 – salinized solution with NaCl + 50% additional K (6.5 dS·m⁻1); and S4 – salinized solution with NaCl + 100% additional K (7.5 dS·m⁻1). The following variables were evaluated: fruit production, stem dry mass, root dry mass, fruit dry mass, total dry mass, and biomass partitioning among plant organs. Salinity stress significantly reduced fruit production and shoot dry matter accumulation in melon plants. Potassium supplementation did not mitigate the negative effects of salinity on biomass accumulation. High potassium doses led to a reduction in fruit dry mass.

Resumo em Inglês:

ABSTRACT Agriculture plays an essential role in Brazil, especially in the production of beans (Phaseolus vulgaris), an important source of plant protein. In this study, a convolutional neural network (CNN) model was developed to classify the nutritional status of the bean plant focusing on nitrogen (N) content, using RGB images. The experiment was conducted at USP, in Pirassununga, with five nitrogen fertilization treatments and 30 bean plant pots. Weekly images of the leaves were captured starting from 30 days after emergence (DAE). The images were processed and used to train and test different CNN configurations. The results indicated that larger sets of images and smaller blocks (10x10 pixels) increased accuracy, especially at 37 DAE. It is concluded that the proposed model is effective for nutritional monitoring, providing an efficient alternative to traditional leaf analysis.

Resumo em Inglês:

ABSTRACT Soil organic matter (SOM) varies significantly along soil profiles, directly influencing soil fertility and structure. The aim of this study was to estimate SOM levels at various soil depths using VisNIR-SWIR spectroscopy combined with partial least squares regression (PLSR). The experiment was conducted on a Dystroferric Red Latosol under different crop rotation systems maintained since 1985. Soil samples were collected in March 2019 and stratified into eight layers (0–40 cm), totaling 384 samples. Spectral readings were obtained using a FieldSpec 3 Jr spectroradiometer, and SOM content was determined using a colorimetric method. The PLSR models demonstrated strong predictive capacity, especially for the 10-cm layer (R2 = 0.96, RMSE = 0.74 g dm3) and for the full dataset (R2 = 0.82, RMSE = 3.20 g dm3), with RPD values above 2, indicating excellent performance. The most relevant spectral bands were found in the ranges 580–590 nm, 870–930 nm, and 2400 nm. It was concluded that VisNIR-SWIR spectroscopy combined with PLSR is a promising and sustainable tool for estimating SOM, and is applicable across various soil layers and agricultural management contexts.

Resumo em Inglês:

ABSTRACT Water scarcity in semiarid regions requires the efficient management of groundwater for population growth and regional development. Evaluating the flow rate variability and electrical conductivity (EC) of deep wells is crucial because these parameters indicate the availability and quality of water. This study aimed to map the spatial distribution of these data in the state of Ceará via information from the Geological Survey of Brazil and the inverse distance weighting (IDW) method. A total of 6,018 flow data points and 9,434 EC data points were used for interpolation in SAGA software. The mean flow was 5.45 m3/h, and the EC was 2.21 dS/m. An exponential semi-variogram was the best model for spatial interpolation. Geomorphological factors, aquifer types and environmental conditions influence the distribution of parameters. Regions with more weathered soils presented greater water storage potential. The Salgado, Baixo Jaguaribe and Coreaú Basins had the highest quantitative groundwater potentials. It is concluded that the methodology used is effective in guiding public policies and water management strategies.

Resumo em Inglês:

ABSTRACT Root development contributes to the performance of soybean crops and the achievement of high yields, and the use of techniques that promote this development is essential, such as controlled traffic farming, which confines soil compaction to permanent traffic lanes. This study evaluated areas with and without controlled agricultural machinery traffic and its effect on soybean root development. The experiment was conducted at the Mato Grosso do Sul State University, in Cassilândia, MS, Brazil. The split-plot design with six replications was used. The treatments included areas with controlled traffic (CT) and without controlled traffic (WCT) of machinery, as well as the use of Urochloa (signal grass), pearl millet, a cover crop mix (Urochloa + pearl millet), and spontaneous species for cover formation. Soybean was sown, and at full flowering, the root system was evaluated by collecting monoliths, which were later scanned and processed using SAFIRA software for each treatment. The areas with controlled traffic and Urochloa cover presented greater soybean root length, reaching 130 cm in the planting row and 175 cm between rows. The greatest root development was observed in the controlled traffic areas.

Resumo em Inglês:

ABSTRACT The benefits of drip irrigation for sugarcane are widely evidenced and discussed in the literature. To improve the efficiency of this irrigation system for sugarcane, this study aimed to evaluate the effect of pulse drip irrigation combined with the application of different nitrogen doses via fertigation on sugarcane productivity. The experiment was conducted at EECAC (Carpina Sugarcane Experimental Station). A randomized block experimental design with a 2 x 5 factorial arrangement and four replications was used, totaling 40 experimental plots. Two types of drip were tested: pulse subsurface (IP) and continuous subsurface (IC), in combination with five nitrogen doses (80, 120, 160, 240 and 320 kg-N ha-1). In pulse irrigation, four pulses were applied with 40-min intervals between applications. Irrigation was based on daily water balance. Subsurface pulse drip irrigation provided greater sugarcane productivity at a dose of 160 kg-N ha-1. The use of subsurface pulse drip irrigation and a dose of 222.7 kg-N ha-1 provided greater water use efficiency for sugarcane (11.3 kg-cane/m3). Plants under pulse drip irrigation combined with a dose of 160 kg-N ha-1 showed greater N use efficiency compared to those irrigated continuously.