Revista Brasileira de Engenharia de Biossistemas

Gypsum-based composites reinforced with bamboo particles

Thu, 06 Jun 2024 00:00:00 +0000

This study aimed to evaluate the quality of gypsum-based mineral composites reinforced with bamboo particles. The particles size was 1.68 mm and 0.841 mm. The density adopted for the composites was 0.80 g/cm³. The following weight replacement ratios were adopted: 0; 2.5; 5.0; 7.5 and 10.0%. The water/solid mass factor of the composite remained constant. For each treatment two slabs of composites were produced. Physical and mechanical properties were determined: humidity, apparent density, water absorption (2 and 24 hours), modulus of rupture, modulus of elasticity and compression. The results showed that the apparent density and moisture content of the composites were not influenced by the insertion of the bamboo particles, while the water absorption was significantly reduced. The addition of the bamboo reinforcement particles did not cause improvements in the MOR and MOE properties, but all the MOR values of the treatments reached the value established by EN 13279-2 (EN, 2004). Although all treatments have reached the minimum values stipulated by the standard for compressive strength, all values were reduced with the insertion of bamboo particles. In general, the Dendrocalamus giganteus can be used as reinforcement in gypsum composites, however new parameters should be tested, such as: particle size; increasing the proportions of particles, pre-treatment of particles, addition of other additives, such as superplasticizer to water to improve workability and even use more than one reinforcement to obtain composites with improved properties.

Genetic resistance and silicon in the control of stem rot in Capsicum spp.

Bruno Arcanjo Silva, Lorenna Leal Pires, José Evando Aguiar Beserra Jr — Wed, 05 Jun 2024 00:00:00 +0000

Pepper stem rot is a disease caused by Sclerotium delphinii, a necrotrophic pathogen and a natural soil inhabitant. Identifying genotypes of Capsicum resistant to the pathogen and applying silicon (Si) can be effective management measures. The objective of the study was to identify sources of resistance in 24 accessions of Capsicum spp. against S. delphinii, and to evaluate the potential of sodium silicate (Si) to induce resistance. Two experiments were conducted: In Experiment I, the resistance reaction of Capsicum in a greenhouse was evaluated. The experiment was conducted in two periods of the year (July and November 2019). In Experiment II, the effect of Si on Capsicum resistance was evaluated. The experimental design used in Experiment I employed randomized blocks in a factorial design of 2 (isolates) x 24 (accessions), with five replications. For Experiment II, six accessions were selected with contrasting resistance responses observed in Experiment I, in a factorial design of 1 (isolate) x 6 (accessions) x 4 (doses: 0.0, 0.025, 0.05, and 0.1 mL per vase). Accessions BGH 71 and BAGC 134 showed greater resistance to the pathogen. Accession BAGC 134 demonstrated high resistance stability in both periods and against the two isolates tested. Si doses had no significant effect on the resistance reaction. Therefore, the genotypes BGH 71 and BAGC 134 have the potential to be used in breeding programs for Capsicum for resistance to S. delphinii for control of stem rot.

Cotton responses to potassium fertilization in Northeastern Brazil

Thu, 06 Jun 2024 00:00:00 +0000

Carrying out research evaluating the responses of cotton plants to potassium fertilization in different regions is of great importance, as it allows the development of recommendations for fertilization of this nutrient for the crop based on results from localized studies. Therefore, the aim of this work was to evaluate the responses of cotton plants to potassium fertilization in the climate and soil conditions of the Cariri region, located in the northeastern State of Ceará, Brazil. The experiment was conducted in an experimental area at the School of Technology (FATEC - Cariri campus), located in the city of Juazeiro do Norte, Ceará State. A completely randomized design (CRD) was adopted for statistics. The treatments were composed of a combination of five doses of potassium (0, 25, 50, 75, and 100 kg ha^-1 of K₂O, equivalent to 0, 50, 100, 150, and 200% of the K recommended for cotton cultivation) with four replications. At 70 days after sowing, the plants were collected. Measurements were taken of stem diameter, number of leaves, root dry matter, shoot dry matter, total dry matter, number of floral buds, number of cotton balls, and cotton ball weight. Except for the shoot dry matter and the cotton ball weight, the remaining analysed variables were significantly influenced by potassium doses. Potassium doses between 65 and 100 kg ha^-1 of K₂O maximized the growth, dry matter, and production components of the cotton plant in the soil and climate conditions of the Cariri region, northeastern Brazil.

Soil compaction in progressive agricultural tractor treads

Wed, 05 Jun 2024 00:00:00 +0000

Soil compaction is one of the main problems in world agriculture. It is known that, even in soil conservation management, such as in no-till, the transit of agricultural machinery damages the soil structure, therefore, it is essential to better understand the compaction processes and ways to alleviate the problem. In soils that have traditional tillage management, just one machine pass can damage the physical structure. This research aimed to evaluate the levels of compaction as a function of different passages of an agricultural tractor, considering the hypothesis that, during agricultural operations, a machine transits several times through the same place in the crop. The experiment was carried out on plowed and harrowed agricultural soil in the state of São Paulo. Resistance to soil penetration at different depths was evaluated, and the averages were correlated as a function of the number of steps taken by the tractor. Results showed that approximately 60% of the total soil compaction occurs in the first passes of the agricultural tractor, and above five passes the increase in compaction is minimal. At depths of 20 to 30 cm, the largest RSPs were found. It is concluded that a good planning of machinery traffic is essential, because in the case of a motor-mechanized set moving out of its predestined route, the soil structure is permanently affected.

Bean yield estimation using unmanned aerial vehicle imagery

Diane Gomes Campos, Rodrigo Nogueira Martins — Thu, 06 Jun 2024 00:00:00 +0000

The common bean is a crop of substantial socioeconomic importance that is cultivated throughout the Brazilian territory. Despite that, studies conducted so far have shown limitations in the methodologies used for yield estimation. In this sense, emerging technologies such as unmanned aerial vehicles (UAVs) can help both in crop monitoring and in assessing crop yield. Therefore, this study aimed: (1) to estimate the bean yield using spectral variables derived from UAV imagery and (2) to define the best vegetative stage for yield estimation. For this, data from a field experiment were used. The beans were planted in a conventional system in an area of 600 m² (20 x 30 m). During the crop cycle, six flights were carried out using a UAV equipped with a five-band multispectral camera (Red, Green, Blue, Red Edge, and Near-infrared). After that, 10 spectral variables composed of the bands and five vegetation indices (VIs) were obtained. At the end of the season, the area was harvested, and the yield (kg ha^-1) was determined. Then, the data was submitted to correlation (r), and regression analysis. Overall, all developed models showed moderate performance, but in accordance with the literature, with R² and RMSE values ranging from 0.52 to 0.57 and from 252.79 to 208.84 kg ha^-1, respectively. Regarding the best vegetative stage for yield estimation, the selected models used data from the second flight (52 days after planting) at the beginning of pod formation and filling (between stages R7 and R8).

Parsley production using organic sources of phosphorus

Thu, 04 Apr 2024 00:00:00 +0000

Parsley is a condiment produced mainly by small producers, often in the organic system. Organic fertilizers make nutrients slowly available to plants when compared to inorganic fertilizers, an important quality for phosphorus (P), which is a nutrient that tends to fixate and adsorption. Thus, the objective of this work was to evaluate the production of parsley with the use of organic sources of phosphorus in different proportions. Fourteen treatments were evaluated, resulting from the factorial 6 x 2 + 2: six proportions of two phosphate fertilizers (thermophosphate Yoorin^® (TY) and bone meal (BM)), two doses (recommended (180 kg.ha^-1 of P₂O₅, and double this) + two controls (without phosphate fertilizer; and with inorganic triple superphosphate fertilizer (recommended dose)). The proportions were: 100% P with TY; 80% P with TY + 20% with BM; 60% P with TY + 40% with BM; 40% P with TY + 60% with BM; 20% P with TY + 80% with BM; 100% P with BM. Shoot height, number of leaves, fresh and dry weight of leaves in two harvests and the total of these two harvests were evaluated. No significant differences were obtained in the two harvests. The lack of effect to phosphate fertilization may be related to the high initial P content in the soil (123 mg.dm^-3), which shows that in this case, fertilization with this nutrient is not necessary to produce parsley, despite the official recommendation to fertilize with phosphorus in a soil with a high P content.

Artificial intelligence applied to estimate soybean yield

Thu, 14 Mar 2024 00:00:00 +0000

The application of mathematical models using biotic and abiotic factors for the efficient use of fertilizers to obtain maximum economic productivity can be an important tool to minimize the cost of soybean (Glycine max (L.) Merr.) grain yield. In this sense, using Artificial Neural Networks (ANN) is an important tool in studies involving optimization. This study aimed to estimate soybean yield in Luiziana, Paraná state, Brazil, by considering two growing seasons and an Artificial Neural Network (ANN) as a function of the morphological and nutritional parameters of the plants. Results reveal a well-trained network, with a margin of error of approximately 10^-5, thus acting as a tool to estimate soybean data. For the phases, model validation and network test, i.e., data that were not part of the training (validation), the errors averaged 10^-3. These results indicate that our approach is adequate for optimizing soybean yield estimates in the area studied.