https://www.alice.cnptia.embrapa.br/alice/handle/item/20 Embrapa Instrumentação (CNPDIA) Thu, 21 May 2026 15:39:20 GMT 2026-05-21T15:39:20Z https://www.alice.cnptia.embrapa.br/alice/handle/doc/1186929 Título: Removal of Inorganic Contaminants Using Sodalite Synthesized From Alum Sludge. Autoria: MACHADO, R. C.; MAJARON, V. F.; MARTINS JUNIOR, A. A. F.; BARRIOS, N. L.; CINTRA JUNIOR, W. de A.; RIBEIRO, C. Conteúdo: The growing concern over water contamination by potentially toxic metals underscores the need for sustainable and cost-effective remediation strategies. In this study, sodalite (SOD-Na) was synthesized from alum sludge (AS), a by-product of water treatment plants rich in Si and Al, through a hydrothermal process in an autoclave. Zeolitic concentrate was characterized by x-ray diffraction (XRD), scanning electron microscopy (SEM), x-ray fluorescence (XRF), Brunauer Emmet Teller (BET) method, and zeta potential analysis, confirming the formation of sodalite with mesoporous structure and surface charge favorable to cation adsorption. Adsorption experiments demonstrated high affinity for Pb2 + (189 mg g− 1 ), Cd2 + (121 mg g− 1 ), and Ni2 + (58.4 mg g− 1 ), whereas limited adsorption was observed for Cr6 + (Cr2 O7 2 − ) (8.2 mg g− 1 ), consistent with the material’s negative surface charge and ion-exchange mechanism. Tests with real wastewater confirmed the material’s applicability, showing Ni2 + removal capacity of 26.5 mg g− 1 despite competitive interactions with coexisting ions. These findings highlight the potential of sodalite synthesized from alum sludge as an effective and sustainable adsorbent for wastewater treatment, contributing to circular economy strategies and reducing environmental liabilities. Thu, 01 Jan 2026 00:00:00 GMT https://www.alice.cnptia.embrapa.br/alice/handle/doc/1186929 2026-01-01T00:00:00Z https://www.alice.cnptia.embrapa.br/alice/handle/doc/1186887 Título: Cotton yield map prediction using Sentinel-2 satellite imagery in the Brazilian Cerrado production system. Autoria: VAZ, C. M. P.; FERREIRA, E. J.; SPERANZA, E. A.; FRANCHINI, J. C.; NAIME, J. de M.; INAMASU, R. Y.; LOPES, I. de O. N.; CHAGAS, S. das; SCHELP, M. X.; VECCHI, L.; GALBIERI, R. Conteúdo: Yield maps from combine harvesters are essential in precision agriculture for capturing within-field variability and guiding variable-rate input management. However, in largescale systems such as those in the Brazilian Cerrado, these maps are often inconsistent due to calibration errors, use of multiple harvesters, and complex post-processing. Orbital remote sensing offers an alternative by providing consistent vegetation index (VI) data for crop monitoring and yield estimation. This study developed regression models relating Sentinel-2 VIs (EVI, TVI, NDVI, and NDRE) to cotton yield data obtained from combine harvesters across 30 commercial plots in Mato Grosso, Brazil, over six cropping seasons (2019–2024), totaling 76 plot-season datasets. Vegetation indices were grouped into 15-day intervals based on days after sowing, and a logistic growth function was applied in the regression modeling. Model performance evaluated using 15 independent plot-seasons showed good pixel-level accuracy, with RMSE of 0.695 t ha−1 and R2 of 0.78, with EVI performing slightly better. At the plot scale, mean yield predictions across all datasets achieved an RMSE of 0.41 t ha−1, reflecting the higher reliability of module-based yield measurements. These results demonstrate the potential of Sentinel-2 VIs combined with logistic regression to predict cotton yields in the Cerrado, complementing or replacing harvester-based monitoring. Wed, 01 Jan 2025 00:00:00 GMT https://www.alice.cnptia.embrapa.br/alice/handle/doc/1186887 2025-01-01T00:00:00Z https://www.alice.cnptia.embrapa.br/alice/handle/doc/1186876 Título: Optimizing maize planting density for enhanced economic returns. Autoria: NAIME, J. de M.; SPERANZA, E. A.; LOPES, I. de O. N.; VAZ, C. M. P.; INAMASU, R. Y.; NOGARA NETO, F.; FRANCHINI, J. C. Conteúdo: Abstract: Variable rate seeding (VRS) can enhance profitability, but its adoption by small and medium-sized producers is limited. This work presents a practical on-farm methodology to determine economically optimal corn seeding rates, designed for direct use by farm managers. The methodology was applied in two commercial fields in Paraná State, Brazil. In Field A, four seeding rates were tested on two corn hybrids using a planter with a mechanical drive. In Field B, four management zones (MZs) were delineated using multiple data layers, and variable seeding rates were applied to assess spatially-differentiated responses. Results showed distinct responses between hybrids and MZs. In Field A, increasing the seeding rate by approximately 10% boosted yield by 7.9% for one hybrid but was detrimental to the other. In Field B, an economic analysis revealed that the profit-maximizing seeding rate varied by MZ: a 20% rate increase was optimal in MZ2 (+2.44% profit), while a 10% increase was optimal in MZ4 (+2.57% profit). The study demonstrates that this accessible, on-farm experimental approach allows growers to effectively customize seeding rates, optimizing resource use and profitability without requiring significant investment in specialized technology or expertise. Wed, 01 Jan 2025 00:00:00 GMT https://www.alice.cnptia.embrapa.br/alice/handle/doc/1186876 2025-01-01T00:00:00Z https://www.alice.cnptia.embrapa.br/alice/handle/doc/1181494 Título: A milk-probe to identify the stability condition of the raw milk. Autoria: MELO, W. L. de B.; ZANELA, M. B.; FOSCHINI, M. M.; PERES, R. P. Conteúdo: Abstract: This work concerns an application of the milk probe equipment with the purpose of identifying the stability conditions, whether stable (normal) or unstable (acidic) milk. The milk probe is an instrument composed basically of a light source, LEDs, with 12 different wavelengths. The equivalent absorbance spectrum is obtained through the application of the Kubelka-Munk function. Approximately 537 samples of herd milk were used. The statistical methods were Random Forest, Support Vector Machine (Polynomial SVM) and Logistic Regression in 4095 models of arrangement of wavelengths. The results indicated excellent agreement between the predicted values and those of the classifications. However, these are agreement values, and their precision or accuracy cannot be affirmed. Resumo: Este trabalho trata de uma aplicação do equipamento sondaleite com a finalidade de identificar as condições de estabilidade do leite, se normal ou ácido. O Sondaleite é um instrumento composto basicamente por um fonte de luz, LEDs, com 12 comprimentos de onda distintos. O espectro de absorbância equivalente se obtém através da função de Kubelka-Munk. Cerca de 537 amostras de leites de rebanho foram usadas. O método estatístico foram Random Forest, Support Vector Machine (Polynomial SVM) and Logistic Regression em 4095 modelos de arranjos dos comprimentos de onda. Os resultados indicaram ótimas concordâncias entre os valores previstos e aqueles das classificações. Contudo, trata-se de valor de concordância, não podendo afirmar sua precisão ou acurácia. Wed, 01 Jan 2025 00:00:00 GMT https://www.alice.cnptia.embrapa.br/alice/handle/doc/1181494 2025-01-01T00:00:00Z