Christiano Santos Rocha Pitta

Nitrogen fertilizer use efficiency, recovery and leaching of an alexandergrass pasture

Nitrogen usually determines the productive potential of forage crops, although it is highly unstable in the environment. Studies on recovery rates and use efficiency are important for more reliable fertilizer recommendations to reduce costs and avoid environmental pollution. The purpose of this study was to evaluate N use efficiency and recovery rate of Alexandergrass pasture (Brachiaria - Syn. Urochloa plantaginea) as well as N-NO3- and N-NH4+ soil concentrations using different levels of N fertilization under two grazing intensities. The experiment was arranged in a randomized block design in a factorial scheme with three replications. Treatments consisted of three N rates (0, 200 and 400 kg ha-1 N) and two grazing intensities termed low mass (LM; forage mass of 2,000 kg ha-1 of DM) and high mass (HM; forage mass of 3,600 kg ha-1 of DM) under continuous stocking and variable stocking rates. Results of N fertilization with 200 kg ha-1 were better than with 400 kg ha-1 N. There was a significant effect of N rates on soil N-NO3-concentration with higher levels in the first layer of the soil profile in the treatment with 400 kg ha-1 N. Grazing intensity also affected soil N-NO3- concentration, by increasing the levels under the higher stocking rate (lower forage mass).

In Vivo Pattern Classification of Ingestive Behavior in Ruminants Using FBG Sensors and Machine Learning.

Pattern classification of ingestive behavior in grazing animals has extreme importance in studies related to animal nutrition, growth and health. In this paper, a system to classify chewing patterns of ruminants in in vivo experiments is developed. The proposal is based on data collected by optical fiber Bragg grating sensors (FBG) that are processed by machine learning techniques. The FBG sensors measure the biomechanical strain during jaw movements, and a decision tree is responsible for the classification of the associated chewing pattern. In this study, patterns associated with food intake of dietary supplement, hay and ryegrass were considered. Additionally, two other important events for ingestive behavior were monitored: rumination and idleness. Experimental results show that the proposed approach for pattern classification is capable of differentiating the five patterns involved in the chewing process with an overall accuracy of 94%.

Dynamic of a papuã pasture under two grazing intensities and two nitrogen levels

This work aimed to evaluate the effects of grazing intensities and nitrogen fertilization levels on the dynamics of a Brachiaria plantaginea pasture. The experiment was laid out as a random block design, in a 3 × 2 factorial scheme, composed of three nitrogen levels (0, 200 and 400 kg/ha nitrogen in urea form) and two grazing intensities termed low mass (LM; forage mass of 1500 kg of DM/ha) and high mass (HM; forage mass of 3000 kg of DM/ha) in a continuously stocked swards with two replications. Animals used were half-blood Boer goats averaging five months old and 21 kg body weight (BW) for adjustment of the target forage mass. Real forage mass, average height of the plants and animal stocking rates were of 2,567 and 3,862 kg DM/ha; 23.6 and 31.2 cm and 2,804 and 2,089 kg BW/ha respectively for low and high mass. Accumulation rate was of 100, 147 and 132 kg DM/ha/day and total production 13,659; 19,834 and 17,820 kg DM/ha, respectively to the 0, 200 and 400 kg N/ha. There were no differences on pasture yield among grazing intensities suggesting that higher stocking rates can be used. The level of 200 kg N/ha promotes the best response on papua grass pastures. Papua grass shows high yield, good production distribution during the plant cycle and excellent nutritional value.

In-vivo determination of chewing patterns using FBG and artificial neural networks

This paper reports the process of pattern classification of the chewing process of ruminants. We propose a simplified signal processing scheme for optical fiber Bragg grating (FBG) sensors based on machine learning techniques. The FBG sensors measure the biomechanical forces during jaw movements and an artificial neural network is responsible for the classification of the associated chewing pattern. In this study, three patterns associated to dietary supplement, hay and ryegrass were considered. Aditionally, two other important events for ingestive behavior studies were monitored, rumination and idle period. Experimental results show that the proposed approach for pattern classification has been capable of differentiating the materials involved in the chewing process with a small classification error.

Produção animal e de grãos de trigo duplo propósito em diferentes períodos de pastejo

The objective of this work was to evaluate the influence of different grazing periods on beef animal production and on wheat forage and grain yield. The experiment was carried out in Pato Branco, PR, Brazil. Six grazing periods were evaluated (0, 21, 42, 63, 84, and 105 days) on dual-purpose wheat cultivar BRS Taruma. Puruna steers, with average live weight of 162 kg and ten months of age, were kept under continuous grazing using a variable stocking rate, in order to maintain the established sward height of 25 cm. Greater increases in total animal gain (TAG) occurred with longer grazing periods. However, there was little increase after 63 days (490 kg ha-1), and TAG decreased from 552 to 448 kg ha-1 between 84 and 105 days. Grain yield decreased from 2,830 to 610 kg ha-1 when the grazing period increased from 0 to 105 days, but there was little change after 63 days (750 kg ha-1). Cultivar BRS Taruma shows excellent animal production potential, and the decision on how long wheat pastures should be grazed must be based on relative prices of grain and livestock.

Optical Fiber Bragg Grating Strain Sensor for Bone Stress Analysis in Bovine During Masticatory Movements

This paper describes an optical-fiber sensor development and implementation, based on Bragg gratings, to measure the mechanical deformation at the bone surface of a bovine mandible, caused by chewing different types of food and rumination. A suitable encapsulation was developed using a titanium mesh as a transducer and sensor calibration and characterization tests were performed. The proposed sensor was deployed in a living animal transmitting the detected bone deformation to the data collecting instrument. The acquired signal was applied to a pattern-classification algorithm based on decision trees for identifying the chewing process of different foods. The results demonstrate that the sensor is effective and sensitive, capable of capturing the force generated during the masticatory process.

In vivo analysis of bone strain using fiber Bragg grating sensor and decision tree algorithm in bovine during masticatory movements

This study focused on the development of a biosensor able to follow the deformations of the bone tissue and to allow for future studies in the areas of health sciences. The biosensor is designed with a titanium mesh, which is fixed in the jaws of the animal to be monitored. The animal received different kinds of foods to allow analysis of the signal acquired during feeding. The acquired signal was then subjected to a processing, which has been classified and capable of identifying the animal chewing process for each type of food. This technology has application in grazing, and can be useful in studies related to nutrition and animal health. The classification of the masticatory patterns is based on decision trees algorithm. The results demonstrate that the sensor is effective and is able to capture the differences in the deformation generated during the chewing process, generating a signal that can be identified by the algorithm presented.