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El objetivo de este trabajo fue evaluar la Riqueza Animal de un largo perí­odo en crecimiento-acabado a través de parámetros productivos y comportamientos positivos, en los sistemas productivos: Deep Bed (CP) y Free Aire (AL) en las estaciones del año. El lugar de realización fue el Módulo de Producción Porcina - Facultad de Ciencias Agrarias - UNR (Zavalla, Santa Fe- Argentina). Los animales de la etapa de crí­a con un peso vivo promedio (invierno = 28 ± 4 kg y verano = 24 ± 4 kg), se distribuyeron aleatoriamente entre los sistemas hijos: AL y CP (invierno -n: 36 AL, n: 36 CP- y verano -n: 65 AL, n: 65 CP). Para evaluar el bienestar animal se utilizaron indicadores recomendados por Welfare Quality ©, agrupados en parámetros de: Comportamiento social positivo (juzgar, investigar, reposo y otros -comiendo la bebida-) y parámetros productivos (Peso Final –PF- y Espesor de Grasa Dorsal - EDG). Estadí­sticamente, se utilizaron Análisis de Componentes Principales (PCA), Procedimientos de Permutación de Respuestas Múltiples (MRPP), estadí­stico χ2 y prueba t de Student. Entre los comportamientos de ambos sistemas, se observó a partir de la prueba χ2 que las diferencias entre los componentes fueron altamente significativas para verano (χ2 = 127.62; gl = 3; p = 2.2x10-16) y para invierno (χ2 = 19.037; gl = 3; p = 0,0003). A partir del test PCA y MANOVA se encontrarán diferencias significativas entre las estaciones del año (p

Estimating forage intake and monitoring behavior of grazing livestock are difficult tasks. Real-time detection and classification of events like chew, bite and chew-bite are necessary to estimate that information. It is well-known that acoustic monitoring is one of the best ways to characterize feeding behavior in ruminants. Although several methods have been developed to detect and classify events, their implementation is restricted to desktop computers, fact that confines their application to off-line analysis of a reduced number of animals. In this work, we present the design and implementation of an electronic system specifically developed for real-time monitoring of feeding patterns in dairy cows. The system is based on an embedded circuit to process the sound produced by the animal in order to detect, classify and quantify events of ruminant feeding behavior. The system implements an algorithm recently developed, which was adapted to be executed on a microcontroller-based electronic system. Only the results of sound analysis are stored in flash memory units. In addition to sound information, data from a GPS receiver is also stored, thus building a package of information. A microcontroller with power management technology, combined with a high-efficiency harvesting power supply and power management firmware, enables long operational time (more than five days of continuous operation). The system was evaluated using audio signals derived from the feeding activity of dairy cows that were acquired under normal operational conditions. The system correctly detected 92% of the events (i.e. considering them as possible events without making a classification). When the three types of events (i.e. chew, bite and chew-bite) were considered for classification, the recognition rate was about 78%. These results were obtained using reference labels provided by experts in ruminant ingestive behavior. The technology presented within this publication is protected under the international patent application PCT/IB2015/053721. Fil: Deniz, Nestor Nahuel. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Santa Fe. Instituto de Investigación en Señales, Sistemas e Inteligencia Computacional. Universidad Nacional del Litoral. Facultad de Ingeniería y Ciencias Hídricas. Instituto de Investigación en Señales, Sistemas e Inteligencia Computacional; Argentina Fil: Chelotti, Jose Omar. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Santa Fe. Instituto de Investigación en Señales, Sistemas e Inteligencia Computacional. Universidad Nacional del Litoral. Facultad de Ingeniería y Ciencias Hídricas. Instituto de Investigación en Señales, Sistemas e Inteligencia Computacional; Argentina Fil: Galli, Julio Ricardo. Universidad Nacional de Rosario; Argentina Fil: Planisich, Alejandra M.. Universidad Nacional de Rosario; Argentina Fil: Larripa, Marcelo J.. Universidad Nacional de Rosario; Argentina Fil: Rufiner, Hugo Leonardo. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Santa Fe. Instituto de Investigación en Señales, Sistemas e Inteligencia Computacional. Universidad Nacional del Litoral. Facultad de Ingeniería y Ciencias Hídricas. Instituto de Investigación en Señales, Sistemas e Inteligencia Computacional; Argentina. Universidad Nacional de Entre Ríos; Argentina Fil: Giovanini, Leonardo Luis. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Santa Fe. Instituto de Investigación en Señales, Sistemas e Inteligencia Computacional. Universidad Nacional del Litoral. Facultad de Ingeniería y Ciencias Hídricas. Instituto de Investigación en Señales, Sistemas e Inteligencia Computacional; Argentina

Conventional agriculture is specializing rapidly into the management of few monoculture crops, threatening crop diversity and questioning the sustainability of extensive cropping systems. The grazing of cover crops in integrated crop-livestock systems could be a feasible biologically based technology to restore crop diversity and mitigate ecological issues in cropping systems. However, there is limited evidence on plausible synergies or trade-offs for the practice, and about how grazing plans could affect the herbage production and services from cover crops. This work assessed the effects of cattle grazing on the primary and secondary production of annual ryegrass (Lolium multiflorum) in an integrated ryegrass–soybean rotation system. Specifically, the prediction for synergistic effects of cattle grazing on the ryegrass herbage production, residual crop cover and animal performance were tested in a 2-year (2014 and 2015) study comprising a randomized complete block design of four grazing intensity treatments, replicated three times. A no-cattle grazing treatment (NG), used as control, or continuous grazing with Holstein heifers (~220kg live weight) at targeted sward heights of 5, 10, 15 and 20cm (hereafter referred as G5, G10, G15 and G20, respectively) was applied to ryegrass plots. The herbage production and residual herbage cover of ryegrass, and the average daily gain (ADG, kg/day) and live weight gain per hectare (LWG, kg/ha) of heifers were analyzed by ANOVA (P

Accurate measurement of herbage intake rate is critical to advance knowledge of the ecology of grazing ruminants. This experiment tested the integration of behavioral and acoustic measurements of chewing and biting to estimate herbage dry matter intake (DMI) in dairy cows offered micro-swards of contrasting plant structure. Micro-swards constructed with plastic pots were offered to three lactating Holstein cows (608±24.9 kg of BW) in individual grazing sessions (n=48). Treatments were a factorial combination of two forage species (alfalfa and fescue) and two plant heights (tall=25±3.8 cm and short=12±1.9 cm) and were offered on a gradient of increasing herbage mass (10 to 30 pots) and number of bites (~10 to 40 bites). During each grazing session, sounds of biting and chewing were recorded with a wireless microphone placed on the cows? foreheads and a digital video camera to allow synchronized audio and video recordings. Dry matter intake rate was higher in tall alfalfa than in the other three treatments (32±1.6 v. 19±1.2 g/min). A high proportion of jaw movements in every grazing session (23 to 36%) were compound jaw movements (chew-bites) that appeared to be a key component of chewing and biting efficiency and of the ability of cows to regulate intake rate. Dry matter intake was accurately predicted based on easily observable behavioral and acoustic variables. Chewing sound energy measured as energy flux density (EFD) was linearly related to DMI, with 74% of EFD variation explained by DMI. Total chewing EFD, number of chew-bites and plant height (tall v. short) were the most important predictors of DMI. The best model explained 91% of the variation in DMI with a coefficient of variation of 17%. Ingestive sounds integrate valuable information to remotely monitor feeding behavior and predict DMI in grazing cows. Fil: Galli, Julio Ricardo. Universidad Nacional de Rosario; Argentina Fil: Cangiano, Carlos Alberto. Instituto Nacional de Tecnología Agropecuaria. Centro Regional Buenos Aires; Argentina Fil: Pece, M. A.. Universidad Nacional de Rosario; Argentina Fil: Larripa, M. J.. Universidad Nacional de Rosario; Argentina Fil: Milone, Diego Humberto. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Santa Fe. Instituto de Investigación en Señales, Sistemas e Inteligencia Computacional. Universidad Nacional del Litoral. Facultad de Ingeniería y Ciencias Hídricas. Instituto de Investigación en Señales, Sistemas e Inteligencia Computacional; Argentina Fil: Utsumi, S. A.. Michigan State University; Estados Unidos Fil: Laca, E. A.. University of California at Davis; Estados Unidos