Magnesium doped biochar for simultaneous adsorption of phosphate and nitrogen ions from aqueous solution

Phosphorus (P) and Ammonium Nitrogen (N) are essential nutrients for plants and environmental stability. However, their excess in water causes eutrophication, damaging aquatic ecosystems. While adsorption is a promising solution, finding affordable and efficient adsorbents remains a challenge. In this study, magnesium (Mg), iron (Fe), and Mg/Fe doped biochars (BC) adsorbents were synthesized, and evaluated for adsorption of individual P and N and a P + N mixture from a solution and wastewater from a wastewater treatment plant. Compared to other adsorbents, Mg/BC showed excellent performance in adsorbing phosphorus (P) and ammonium nitrogen (N) from aqueous solutions. It demonstrated a large adsorption capacity of 64.65 mg/g and 62.50 mg/g from individual P and N solutions, and 30.3 mg/g and 27.67 mg/g from the P and N mixture solution, respectively. In addition, Mg/BC efficiently removed P and N from real-life wastewater. In the real wastewater, P and N removal efficiencies reached 88.30% and 59.36%, respectively. Kinetics analysis revealed that the pseudo-second-order model accurately described the adsorption of phosphorus (P) and ammonium nitrogen (N) in all solutions. The adsorbent followed the monolayer-Langmuir isotherm for N ions and the multilayer-Freundlich isotherm for P, indicating efficient adsorption processes. Thermodynamic experiments indicated that the adsorption of P and N was not only feasible but also occurred spontaneously in a natural manner. This study revealed that the strategic modification of biochar plays a crucial role in advancing effective wastewater treatment technologies designed for nutrient removal.

Research Note: Effect of stocking density on crop fill progression in broilers grown to 14 d

Crop fill rates are measured as an indirect means of assessing management during the brooding phase. Primary breeder guidelines indicate that 95% of the chicks assessed should present a crop that is full, soft, and rounded after 24 h, which indicates chicks have successfully located feed and water. Crop fill progression has received little attention in the scientific literature and is primarily discussed in trade literature, and thus, the dynamic nature of crop fill progression has not been previously characterized. This study examined the role of 2 market weight stocking density treatments (29.3 kg/m2 and 43.9 kg/m2) on performance and crop fill of broilers grown to 14 d. Crop fill progression was observed at 2, 4, 8, 12, 24, and 48 h after placement and tracked BW of birds that presented empty crops at 24 h; chicks with empty crops were identified to track postplacement BW. Stocking density had no significant effect on bird performance or crop fill. At 24 h, 86% of birds in this study had full, soft, and rounded crops, while only 3% of birds had crops that were devoid of food or water at 24 h. BW for birds with empty crops was significantly lower at 7 d (P = 0.006) but not at 14 d (P = 0.535). The data herein indicate that crop fill rates of 95% or higher at 24 h may be difficult to achieve in typical commercial broiler settings. In addition, assessing crop fill may be a useful tool to diagnose conspicuous management problems during brooding, but it does not appear to be a direct predictor of early performance.

Practices and Applications of Convolutional Neural Network-Based Computer Vision Systems in Animal Farming: A Review

Convolutional neural network (CNN)-based computer vision systems have been increasingly applied in animal farming to improve animal management, but current knowledge, practices, limitations, and solutions of the applications remain to be expanded and explored. The objective of this study is to systematically review applications of CNN-based computer vision systems on animal farming in terms of the five deep learning computer vision tasks: image classification, object detection, semantic/instance segmentation, pose estimation, and tracking. Cattle, sheep/goats, pigs, and poultry were the major farm animal species of concern. In this research, preparations for system development, including camera settings, inclusion of variations for data recordings, choices of graphics processing units, image preprocessing, and data labeling were summarized. CNN architectures were reviewed based on the computer vision tasks in animal farming. Strategies of algorithm development included distribution of development data, data augmentation, hyperparameter tuning, and selection of evaluation metrics. Judgment of model performance and performance based on architectures were discussed. Besides practices in optimizing CNN-based computer vision systems, system applications were also organized based on year, country, animal species, and purposes. Finally, recommendations on future research were provided to develop and improve CNN-based computer vision systems for improved welfare, environment, engineering, genetics, and management of farm animals.