Systematic monitoring of pasture quantity and quality is important to match the herd forage demand (pasture removal by grazing or harvest) to the supply of forage with adequate nutritive value. The ...aim of this research was to monitor, assess and manage changes in pasture growth, morphology and digestibility by integrating information from an Unmanned Aerial Vehicle (UAV) and two process-based models. The first model, Systems Approach to Land Use Sustainability (SALUS), is a process-based crop growth model used to predict pasture regrowth based on soil, climate, and management data. The second model, Morphogenetic and Digestibility of Pasture (MDP), uses paddock-scale values of herbage mass as input to predict leaf morphogenesis and forage nutritive value. Two field experiments were carried out on tall fescue- and ryegrass-based pastures under rotational grazing with lactating dairy cattle. The first experiment was conducted at plot scale and was used to calibrate the UAV and to test models. The second experiment was conducted at field scale and was used to test the UAV's ability to predict pasture biomass under grazing rotation. The Normalized Difference Vegetation Index (NDVI) calculated from the UAV's multispectral reflectance (n = 72) was strongly correlated (p < 0.001) to plot measurements of pasture biomass (R2 = 0.80) within the range of ~226 and 4208 kg DM ha-1. Moreover, there was no difference (root mean square error, RMSE < 500 kg DM ha-1) between biomass estimations by the UAV (1971±350 kg ha-1) and two conventional methods used as control, the C-Dax proximal sensor (2073±636 kg ha-1) and ruler (2017±530 kg ha-1). The UAV approach was capable of mapping at high resolution (6 cm) the spatial variability of pasture (16 ha). The integrated UAV-modeling approach properly predicted spatial and temporal changes in pasture biomass (RMSE = 509 kg DM ha-1, CCC = 0.94), leaf length (RMSE = 6.2 cm, CCC = 0.62), leaf stage (RMSE = 0.7 leaves, CCC = 0.65), neutral detergent fiber (RMSE = 3%, CCC = 0.71), digestibility of neutral detergent fiber (RMSE = 8%, CCC = 0.92) and digestibility of dry matter (RMSE = 5%, CCC = 0.93) with reasonable precision and accuracy. These findings therefore suggest potential for the present UAV-modeling approach for use as decision support tool to allocate animals based on spatially and temporally explicit predictions of pasture biomass and nutritive value.
The world's rangelands and drylands are undergoing rapid change, and consequently are becoming more difficult to manage. Big data and digital technologies (digital tools) provide land managers with a ...means to understand and adaptively manage change. An assortment of tools—including standardized field ecosystem monitoring databases; web‐accessible maps of vegetation change, production forecasts, and climate risk; sensor networks and virtual fencing; mobile applications to collect and access a variety of data; and new models, interpretive tools, and tool libraries—together provide unprecedented opportunities to detect and direct rangeland change. Accessibility to and manager trust in and knowledge of these tools, however, have failed to keep pace with technological advances. Collaborative adaptive management that involves multiple stakeholders and scientists who learn from management actions is ideally suited to capitalize on an integrated suite of digital tools. Embedding science professionals and experienced technology users in social networks can enhance peer‐to‐peer learning about digital tools and fulfill their considerable promise.
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•An online method is proposed to analyze the foraging behavior of free grazing cattle.•Its low computational cost allows real-time execution on a low-cost embedded system.•A bottom-up ...approach is adopted based on a prior jaw movement recognition.•Recognition of grazing and rumination bouts is assessed on acoustic signals of several hours in length.•The algorithm highly improves rumination time estimation compared to a commercial system.
The growth of the world population expected for the next decade will increase the demand for products derived from cattle (i.e., milk and meat). In this sense, precision livestock farming proposes to optimize livestock production using information and communication technologies for monitoring animals. Although there are several methodologies for monitoring foraging behavior, the acoustic method has shown to be successful in previous studies. However, there is no online acoustic method for the recognition of rumination and grazing bouts that can be implemented in a low-cost device. In this study, an online algorithm called bottom-up foraging activity recognizer (BUFAR) is proposed. The method is based on the recognition of jaw movements from sound, which are then analyzed by groups to recognize rumination and grazing bouts. Two variants of the activity recognizer were explored, which were based on a multilayer perceptron (BUFAR-MLP) and a decision tree (BUFAR-DT). These variants were evaluated and compared under the same conditions with a known method for offline analysis. Compared to the former method, the proposed method showed superior results in the estimation of grazing and rumination bouts. The MLP-variant showed the best results, reaching F1-scores higher than 0.75 for both activities. In addition, the MLP-variant outperformed a commercial rumination time estimation system. A great advantage of BUFAR is the low computational cost, which is about 50 times lower than that corresponding to the former method. The good performance and low computational cost makes BUFAR a highly feasible method for real-time execution in a low-cost embedded monitoring system. The advantages provided by this system will allow the development of a portable device for online monitoring of the foraging behavior of ruminants.
Web demo available at: https://sinc.unl.edu.ar/web-demo/bufar/.
•A novel algorithm for monitoring the livestock grazing behavior is proposed.•The three basic grazing events are detected and classified using acoustic signals.•The algorithm shows robustness to ...different operational conditions.•It has linear computational complexity and works fully automatically in real-time.
Assessment of both grazing behavior and herbage intake are two very difficult tasks that can be concurrently accomplished by means of accurate detection, classification and measurement of grazing events such as chews, bites and chew-bites. It is well known that acoustic monitoring is among the best methods to automatically quantify and classify ingestive and rumination events in grazing animals. However, most existing methods of signal analysis appear to be computationally complex and costly, and are therefore difficult to implement. In this work, we present and test a novel analysis system called Chew-Bite Real-Time Algorithm (CBRTA) that works fully automatically in real-time to detect and classify ingestive events of grazing cattle. The system employs a directional wide-frequency microphone facing inwards on the forehead of animals, and a coupled signal analysis and decision logic algorithm that measures shape, amplitude, duration and energy of sound signals to iteratively detect and classify ingestive events. Performance and validation of the CBRTA was determined using two databases of grazing signals. Signals were recorded on dairy cows offered either, natural pasture (N=25), or experimental micro-swards in indoor controlled environment (N=50). The CBRTA exhibited a simple linear complexity capable to execute 50 times faster than real-time and without undermining overall recognition rate and accuracy when signals were processed at 4kHz sampling frequency and 8bits quantization. Furthermore, CBRTA was capable to detect ingestive events with a 97.4% success rate, while achieving up to 84.0% success for their classification as exclusive chews, bites or composite chew-bites. The methodology proposed with CBRTA has promising application in embedded microcomputer systems that necessarily depend on fast real-time execution to minimize computational load, power source and storage memory. Such a system can readily facilitate the transmission of processed data through wireless network or the storage in an onboard device.
•A novel algorithm for monitoring the livestock grazing behavior i s proposed.•The three basic grazing events are detected and classified using acoustic signals.•Chew events related to grazing or to ...rumination are differentiated from each other•The algorithm shows robustness to different operational conditions.•It has l inear computational complexity and works fully automatically in real-time.
Monitoring behaviour of the grazing livestock is a difficult task because of its demanding requirements (continuous operation, large amount of information, computational efficiency, device portability, precision and accuracy) under harsh environmental conditions. Detection and classification of jaw movements (JM) events are essential for estimating information related with foraging behaviour. Acoustic monitoring is the best way to classify and quantify ruminant events related with its foraging behaviour. Although existing acoustic methods are computationally efficient, a common failure for broad applications is the deal with interference associated with environmental noises. In this work, the acoustic method, called Chew-Bite Energy Based Algorithm (CBEBA), is proposed to automatically detect and classify masticatory events of grazing cattle. The system incorporates computations of instantaneous power signal for JM-events classification associated with chews, bites and composite chew-bites, and additionally between two classes of chew events: i) low energy chews that are associated with rumination and ii) high energy chews that are associated with grazing. The results demonstrate that CBEBA achieve a recognition rate of 91.9% and 91.6% in noiseless and noisy conditions, respectively, with a high classification precision and a marginal increment of computational cost compared to previous algorithms, suggesting feasibility for implementation in low-cost embedded systems.
Monitoring livestock feeding behavior may help assess animal welfare and nutritional status, and to optimize pasture management. The need for continuous and sustained monitoring requires the use of ...automatic techniques based on the acquisition and analysis of sensor data. This work describes an open dataset of acoustic recordings of the foraging behavior of dairy cows. The dataset includes 708 h of daily records obtained using unobtrusive and non-invasive instrumentation mounted on five lactating multiparous Holstein cows continuously monitored for six non-consecutive days in pasture and barn. Labeled recordings precisely delimiting grazing and rumination bouts are provided for a total of 392 h and for over 6,200 ingestive and rumination jaw movements. Companion information on the audio recording quality and expert-generated labels is also provided to facilitate data interpretation and analysis. This comprehensive dataset is a useful resource for studies aimed at exploring new tools and solutions for precision livestock farming.
Body condition score (BCS) has been a useful tool in estimating the health of cattle for many years now. This categorical metric requires experienced observers to visually inspect cows and assess ...body fat deposits regularly via a time consuming, subjective process. Low cost RGB+depth cameras have been used alongside machine learning algorithms in the past and have shown great promise, however, more advanced techniques are projected to yield better performance. In this work, a vision transformer (ViT) is pretrained using a recently developed self-supervised pretraining method, masked image modeling, and then fine-tuned on RGB+depth BCS data with the objective of improving performance. Model accuracy was found to be highly dependent on dataset curation, ranging from 64% to 92% accuracy. These discrepancies are attributed to non-unique data in the training and test splits and an inherently unbalanced dataset, both of which are discussed in detail. It is recommended that engineers and animal scientists collaborate more closely, as certain details related to dataset curation are critical to thoroughly assess performance and robustness of automated methods for BCS determination.
Field indicators of forage nutritive value could help farmers with rapid management decisions to optimize timing and intensity of grazing and meet objectives regarding animal nutrition. The objective ...of this research was to evaluate the likely relationships among leaf blade nutritive value, herbage mass and leaf stage of pasture regrowth under different growing seasons and residual sward heights. Experiments were performed on perennial ryegrass (Lolium perenne L.) and tall fescue (Festuca arundinacea Schreb.) pastures during spring and summer of 2016. In both pastures, three residual sward height treatments (3, 6 and 12 cm) were imposed on plots arranged in a split plot design, replicated in three blocks. Sward plots were harvested 5–6 times at intervals spaced 7–10 days apart to measure herbage mass, plant morphology, neutral detergent fibre (NDF), and the 24‐hr in vitro digestibility of NDF (NDFD) and dry matter (DMD) of leaf blades. Pastures showed strong (R2: .62 to .70), but variable, negative relationships between NDFD and herbage mass that varied with the rate at which pasture grew in each season of experimentation. Although there was a consistent NDFD decline as leaf stage of regrowth progressed (R2: .75 to .97), the NDFD also decreased as residual sward height increased, most notably in tall fescue. Additionally, findings indicate that the greater leaf length plasticity of tall fescue compared to residual sward heights may offer opportunities to manage both post‐ and pre‐grazing targets to achieve tall fescue forages with a similar high nutritive value as perennial ryegrass. However, the evaluation of this hypothesis at the farm level and its impacts on animal intake and performance warrants further careful investigations.
•An algorithm based on autocorrelation is proposed to analyze the foraging behavior of free-ranging cattle.•Regularity of foraging behavior is exploited for segmentation and classification.•Grazing ...and rumination are recognized from acoustic signals of several hours length.•Performance is extensively assessed with a set of short- and long-term time-scale metrics.•The algorithm highly improves rumination time estimation compared to a commercial system.
Continuous monitoring of cattle foraging behavior is a major requirement for precision livestock farming applications. Several strategies have been proposed for this task but monitoring of free-ranging cattle for a long period of time has not been fully achieved yet. In this study, an algorithm is proposed for long-term analysis of foraging behavior that uses the regularity of this behavior to recognize grazing and rumination bouts. Acoustic signals are analyzed offline in two main stages: segmentation and classification. In segmentation, a complete recording is analyzed to detect regular masticatory events and to define the time boundaries of foraging activity blocks. This stage also defines blocks that correspond to no foraging activity (resting bouts). The detection of event regularity is based on the autocorrelation of the sound envelope. For classification, the energy of sound signals within a block is analyzed to detect pauses and to characterize their regularity. Rumination blocks present regular pauses, whereas grazing blocks do not. The evaluation of the proposed algorithm showed very good results for the segmentation task and activity classification. Both tasks were extensively analyzed with a new set of multidimensional metrics. Frame-based F1-score was up to 0.962, 0.891 and 0.935 for segmentation, rumination classification, and grazing classification, respectively. The average time estimation error was below 0.5 min for classification of rumination and grazing on recordings of several hours in length. In addition, a comparison for rumination time estimation was done between the proposed system and a commercial one (Hi-Tag; SCR Engineers Ltd., Netanya, Israel). The proposed algorithm showed a narrower error distribution, with a median of −2.56 min compared to −13.55 min in the commercial system. These results suggest that the proposed system can be used in practical applications.
Web demo available at: http://sinc.unl.edu.ar/web-demo/rafar/.
Animal welfare monitoring relies on sensor accuracy for detecting changes in animal well-being. We compared the distance calculations based on global positioning system (GPS) data alone or combined ...with motion data from triaxial accelerometers. The assessment involved static trackers placed outdoors or indoors vs. trackers mounted on cows grazing on pasture. Trackers communicated motion data at 1 min intervals and GPS positions at 15 min intervals for seven days. Daily distance walked was determined using the following: (1) raw GPS data (RawDist), (2) data with erroneous GPS locations removed (CorrectedDist), or (3) data with erroneous GPS locations removed, combined with the exclusion of GPS data associated with no motion reading (CorrectedDist_Act). Distances were analyzed via one-way ANOVA to compare the effects of tracker placement (Indoor, Outdoor, or Animal). No difference was detected between the tracker placement for RawDist. The computation of CorrectedDist differed between the tracker placements. However, due to the random error of GPS measurements, CorrectedDist for Indoor static trackers differed from zero. The walking distance calculated by CorrectedDist_Act differed between the tracker placements, with distances for static trackers not differing from zero. The fusion of GPS and accelerometer data better detected animal welfare implications related to immobility in grazing cattle.
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