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.

Revista Brasileira de Engenharia de Biossistemas

Parsley production using organic sources of phosphorus

Artificial intelligence applied to estimate soybean yield