GPS Tracking to Monitor the Spatiotemporal Dynamics of Cattle Behavior and Their Relationship with Feces Distribution

The sustainability of agrosilvopastoral systems, e.g., dehesas, is threatened. It is necessary to deepen the knowledge of grazing and its environmental impact. Precision livestock farming (PLF) technologies pose an opportunity to monitor production practices and their effects, improving decision-making to avoid or reduce environmental damage. The objective of this study was to evaluate the potential of the data provided by commercial GPS collars, together with information about farm characteristics and weather conditions, to characterize the distribution of cattle dung in paddocks, paying special attention to the identification of hotspots with an excessive nutrient load. Seven animals were monitored with smart collars on a dehesa farm located in Cordoba, Spain. Dung deposition was recorded weekly in 90 sampling plots (78.5 m2) distributed throughout the paddock. Grazing behavior and animal distribution were analyzed in relation to several factors, such as terrain slope, insolation or distance to water. Animal presence in sampling plots, expressed as fix, trajectory segment or time counting, was regressed with dung distribution. Cattle showed a preference for flat terrain and areas close to water, with selection indices of 0.30 and 0.46, respectively. The accumulated animal presence during the experimental period explained between 51.9 and 55.4% of the variance of dung distribution, depending on the indicator used, but other factors, such as distance to water, canopy cover or ambient temperature, also had a significant effect on the spatiotemporal dynamics of dung deposition. Regression models, including GPS data, showed determination coefficients up to 82.8% and were able to detect hotspots of dung deposition. These results are the first step in developing a decision support tool aimed at managing the distribution of dung in pastures and its environmental effects

Multistage and adaptive sampling protocols combined with near-infrared spectral sensors for automated monitoring of raw materials in bulk

A near-infrared (NIR) spectroscopy-based real-time monitoring system is proposed to sample and analyse agro-industrial raw materials transported in bulk in a single stage, easing and optimising the evaluation process of incoming lots at reception of agri-food plants. NIR analysis allows rapid and cost-effective analytical results to be obtained, and hence to rethink current sampling protocols. For this purpose, multistage and adaptive sampling designs were tested in this paper, which have been reported (in soil science and ecology) to be more flexible and efficient than conventional strategies to study patterns of clustering or patchiness, which can be the result of natural phenomena. The additional spatial information provided by NIR has also been exploited, using geostatistical analysis to model the spatial pattern of key analytical constituents in Processed Animal Proteins (PAPs). This study addresses the assessment of two kinds of quality/safety issues in PAP lots – moisture accumulation and cross-contamination. After a simulation study, qualitative and quantitative analyses were carried out to make a performance comparison between sampling designs. Results show that sampling densities below 10–15% demonstrated higher estimation errors, failing to represent the actual spatial patterns, while a stratified adaptive cluster sampling design achieved the best performance

Performance comparison of sampling designs for quality and safety control of raw materials in bulk: a simulation study based on NIR spectral data and geostatistical analysis

This study exploits the potential of near infrared (NIR) spectroscopy to deliver a measurement for each sampling point. Furthermore, it provides a protocol for the modelling of the spatial pattern of analytical constituents. On the basis of these two aspects, the methodology proposed in this work offers an opportunity to provide a real-time monitoring system to evaluate raw materials, easing and optimising the existing procedures for sampling and analysing products transported in bulk. In this paper, Processed Animal Proteins (PAPs) were selected as case study, and two types of quality/safety issues were tested in PAP lots —induced by moisture and cross-contamination. A simulation study, based on geostatistical analysis and the use of a set of sampling protocols, made a qualitative analysis possible to compare the representation of the spatial surfaces produced by each design. Moreover, the Root Mean Square Error of Prediction (RMSEP), calculated from the differences between the analytical values and the geostatistical predictions at unsampled locations, was used to measure the performance in each case. Results show the high sensitivity of the process to the sampling plan used — understood as the sampling design plus the sampling intensity. In general, a gradual decrease in the performance can be observed as the sampling intensity decreases, so that unlike for higher intensities, the too low ones resulted in oversmoothed surfaces which did not manage to represent the actual distribution. Overall, Stratified and Simple Random samplings achieved the best results in most cases. This indicated that an optimal balance between the design and the intensity of the sampling plan is imperative to perform this methodology

Control individualizado de cerdos ibéricos "in vivo" en campo y sobre la canal en matadero mediante tecnología NIRS

El objetivo de este trabajo es la puesta a punto y optimización de la tecnología NIRS para el control del cerdo Ibérico tanto en campo sobre el animal vivo, ya que es una técnica completamente inocua para el animal, como sobre la canal en el matadero, lo cual permitirá consolidar un sistema de trazabilidad basado en sensores no destructivos y rápidos

Predicting Acorn-Grass Weight Gain Index using non-destructive Near Infrared Spectroscopy in order to classify Iberian pig carcasses according to feeding regime

The classification of Iberian pig carcasses into different commercial categories according to feeding regime was evaluated by means of a non-destructive analysis of the subcutaneous adipose tissue using Near Infrared Spectroscopy (NIRS). A quantitative approach was used to predict the Acorn-Grass Weight Gain Index (AGWGI), and a set of criteria was established for commercial classification purposes. A total of 719 animals belonging to various batches, reflecting a wide range of feeding regimes, production systems and years, were analyzed with a view to developing and evaluating quantitative NIRS models. Results for the external validation of these models indicate that NIRS made clear differentiation of batches as a function of three feeding regimes possible with high accuracy (<i>Acorn, Recebo</i> and <i>Feed</i>), on the basis of the mean representative spectra of each batch. Moreover, individual analysis of the animals showed a broad consensus between field inspection information and the classification based on the AGWGI NIRS prediction, especially for extreme categories (<i>Acorn</i> and <i>Feed</i>).<br><br>La clasificación en distintas categorías comerciales según régimen alimenticio de canales de cerdo Ibérico fue evaluada mediante el análisis no destructivo de muestras de tejido adiposo subcutáneo por Espectroscopía del Infrarrojo Cercano (NIRS). Partiendo de una aproximación cuantitativa para predecir el Índice de Reposición en Montanera (IRM) se establecieron una serie de criterios para proceder a su clasificación comercial. Se analizaron un total de 719 animales pertenecientes a diversas partidas, que recogen una amplia variabilidad de muestras de distintos regímenes alimenticios, campañas y sistemas productivos, para el desarrollo y evaluación de los modelos NIRS cuantitativos. Los resultados de validación externa de los modelos indicaron que es posible discriminar con una gran exactitud entre partidas de distintos categorías (<i>Bellota, Recebo</i> y <i>Cebo</i>), en base al espectro medio representativo de cada partida. Además, el análisis individualizado de los animales mostró un amplio consenso entre la información recibida de campo y la clasificación en base a la predicción del parámetro IRM por NIRS, sobre todo para categorías con características extremas (<i>Bellota</i> y <i>Cebo</i>)

Near-infrared spectroscopy and geostatistical analysis for modeling spatial distribution of analytical constituents in bulk animal by-product protein meals

Control and inspection operations within the context of safety and quality assessment of bulk foods and feeds are not only of particular importance, they are also demanding challenges, given the complexity of food/feed production systems and the variability of product properties. Existing methodologies have a variety of limitations, such as high costs of implementation per sample or shortcomings in early detection of potential threats for human/animal health or quality deviations. Therefore, new proposals are required for the analysis of raw materials in situ in a more efficient and cost-effective manner. For this purpose, a pilot laboratory study was performed on a set of bulk lots of animal by-product protein meals to introduce and test an approach based on near-infrared (NIR) spectroscopy and geostatistical analysis. Spectral data, provided by a fiber optic probe connected to a Fourier transform (FT) NIR spectrometer, were used to predict moisture and crude protein content at each sampling point. Variographic analysis was carried out for spatial structure characterization, while ordinary Kriging achieved continuous maps for those parameters. The results indicated that the methodology could be a first approximation to an approach that, properly complemented with the Theory of Sampling and supported by experimental validation in real-life conditions, would enhance efficiency and the decision-making process regarding safety and adulteration issues

Predicción del Índice de Reposición en Montanera para la clasificación de canales de cerdo Ibérico según régimen alimenticio mediante el análisis no destructivo por Espectroscopía del Infrarrojo Cercano

The classification of Iberian pig carcasses into different commercial categories according to feeding regime was evaluated by means of a non-destructive analysis of the subcutaneous adipose tissue using Near Infrared Spectroscopy (NIRS). A quantitative approach was used to predict the Acorn-Grass Weight Gain Index (AGWGI), and a set of criteria was established for commercial classification purposes. A total of 719 animals belonging to various batches, reflecting a wide range of feeding regimes, production systems and years, were analyzed with a view to developing and evaluating quantitative NIRS models. Results for the external validation of these models indicate that NIRS made clear differentiation of batches as a function of three feeding regimes possible with high accuracy (Acorn, Recebo and Feed), on the basis of the mean representative spectra of each batch. Moreover, individual analysis of the animals showed a broad consensus between field inspection information and the classification based on the AGWGI NIRS prediction, especially for extreme categories (Acorn and Feed).La clasificación en distintas categorías comerciales según régimen alimenticio de canales de cerdo Ibérico fue evaluada mediante el análisis no destructivo de muestras de tejido adiposo subcutáneo por Espectroscopía del Infrarrojo Cercano (NIRS). Partiendo de una aproximación cuantitativa para predecir el Índice de Reposición en Montanera (IRM) se establecieron una serie de criterios para proceder a su clasificación comercial. Se analizaron un total de 719 animales pertenecientes a diversas partidas, que recogen una amplia variabilidad de muestras de distintos regímenes alimenticios, campañas y sistemas productivos, para el desarrollo y evaluación de los modelos NIRS cuantitativos. Los resultados de validación externa de los modelos indicaron que es posible discriminar con una gran exactitud entre partidas de distintos categorías (Bellota, Recebo y Cebo), en base al espectro medio representativo de cada partida. Además, el análisis individualizado de los animales mostró un amplio consenso entre la información recibida de campo y la clasificación en base a la predicción del parámetro IRM por NIRS, sobre todo para categorías con características extremas (Bellota y Cebo)