Revista Brasileira de Engenharia de Biossistemas <table style="height: 190px;" width="800"> <tbody> <tr> <td width="141"> <h2 class="title"> </h2> <h2 class="title"> <img src="" /></h2> </td> <td width="425">The <em>Brazilian Journal of Biosystems Engineering</em> (BIOENG) publishes original articles that present theoretical, experimental, computational advances and innovations in the areas of agricultural and environmental systems, bringing applications for the sustainable development of agricultural and animal biosystem productions. BIOENG journal publishes interdisciplinary scientific articles and prioritizes issues related to Sustainable Development Goals (SDGs) of the United Nations (UN).</td> </tr> </tbody> </table> UNESP, Campus de Tupã en-US Revista Brasileira de Engenharia de Biossistemas 1981-7061 <p>Authors who publish in this journal agree to the following terms:</p> <p>a) Authors retain the copyright and grant the journal the right of first publication, with the work simultaneously licensed under the Creative Commons Attribution License that allows the sharing of the work with recognition of authorship and initial publication in this journal.</p> <p>b) Authors are authorized to assume additional contracts separately, for non-exclusive distribution of the version of the work published in this journal (eg, publish in an institutional repository or as a book chapter), with recognition of authorship and initial publication in this journal.</p> Parsley production using organic sources of phosphorus <p>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<sup>®</sup> (TY) and bone meal (BM)), two doses (recommended (180 kg.ha<sup>-1</sup> of P<sub>2</sub>O<sub>5</sub>, 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<sup>-3</sup>), 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.</p> Guilherme Gonçalves Machado Débora Cristina Mastroleo Luis Irene Santos Slusarz da Silva Lucas Daniel Pimenta Emanuele Possas de Souza Antonio Ismael Inácio Cardoso Copyright (c) 2024 Revista Brasileira de Engenharia de Biossistemas 2024-04-04 2024-04-04 18 10.18011/bioeng.2024.v18.1213 Artificial intelligence applied to estimate soybean yield <p>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<sup>-5</sup>, 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<sup>-3</sup>. These results indicate that our approach is adequate for optimizing soybean yield estimates in the area studied.</p> Wesley Prado Leão dos Santos Mariana Bonini Silva Alfredo Bonini Neto Carolina dos Santos Batista Bonini Adônis Moreira Copyright (c) 2024 Revista Brasileira de Engenharia de Biossistemas 2024-03-14 2024-03-14 18 10.18011/bioeng.2024.v18.1211