Pastor.i: a smartphone application to facilitate grazing management

Grazing in extensive beef farming systems is often manage in an empirical way based on past experience and on the visual appreciation of animal behavior and forage potential. Records of entrances and exits of the animals in the paddocks are rare. However, knowing the occupation period and the animal density, when coupled with biomass defines the grazing pressure. This knowledge is essential for planning and making informed decisions, that influence the profitability of the farm. Moreover, adequate grazing pressure is crucial for the sustainability of many SSPs where system maintenance is dependent on the balance between grazing pressure and regeneration or maintenance of trees and shrubs. Pastor.i is a smartphone application (APP) designed to allow pasture data logging to be very simple. The application is synchronized with the website and allows the producer to have in his pocket all the farm, being possible to identify the paddock, calculate the area, record the movements of the animals and consult the occupation history of the paddock. The application calculates the actual stocking rate, that can be associated with the location of the animals, obtained if the animals are using collars with GPS, which allows to know the areas of the paddock that are most grazed, visualized through heat maps. The information enables localized actions, such as fertilizing or sowing, to improve areas that are not grazed. The application also allows you to save photos of the sward. This temporal photographic record provides information on the condition of trees, the botanical composition and on the tendency of grazing to improve or to worsen coverage. The APP is available for download, is compatible with Android and is being tested with focus groups

Overgrazing in the Montado? The need for monitoring grazing pressure at paddock scale

Montados are presently facing the threat of either abandonment or intensification, and livestock overgrazing has been suspected of contributing to reduced natural regeneration and biodiversity. However, reliable data are to our knowledge, lacking. To avoid potential risks of overgrazing, an adaptive and efficient management is essential. In the present paper we review the main sources of complexity for grazing management linked with interactions among pasture, livestock and human decisions. We describe the overgrazing risk in montados and favour grazing pressure over stocking rate, as a key indicator for monitoring changes and support management decisions. We suggest the use of presently available imaging and communication technologies for assessing pasture dynamics and livestock spatial location. This simple and effective tools used for monitoring the grazing pressure, could provide an efficient day-to-day aid for farm managers’ operational use and also for rangeland research through data collection and analysis

Pastor.i _ Uma aplicação de smartphone para facilitar a gestão do pastoreio

Grazing in extensive beef farming systems is often manage in an empirical way based on past experience and on the visual appreciation of animal behavior and forage potential. Records of entrances and exits of the animals in the paddocks in a regular basis are rare. However, knowing the occupation period and the animal density, when coupled with biomass defines the grazing pressure and carry capacity. This knowledge is essential for planning and making informed decisions, that influence the profitability of the farm. Moreover, adequate grazing pressure is crucial for the sustainability of many SSPs where system maintenance is dependent on the balance between grazing pressure and regeneration or maintenance of trees and shrubs. Pastor.i is a smartphone application (APP) designed to allow pasture data logging to be very simple. The application is synchronized with the website and allows the producer to have in his pocket all the farm, being possible to identify the paddock, calculate the area, record the movements of the animals and consult the occupation history of the paddock. The application calculates the actual stocking rate, that can be associated with the location of the animals, obtained if the animals are using collars with GPS, which allows to know the areas of the paddock that are most grazed, visualized through heat maps. The information enables localized actions, such as fertilizing or sowing, to improve areas that are not grazed. The application also allows you to save photos of the sward. This temporal photographic record provides information on the condition of trees, the botanical composition and on the tendency of grazing to improve or to worsen coverage. The APP is available for download, is compatible with Android and is being tested with focus groups

Can digital camera images provide useful information for pasture management?

Introduction The assessment of availability and nutritional quality of Mediterranean native pastures is a major challenge. Being multi-species they also present plant communities of high heterogeneity both in the vertical and horizontal planes. Pasture structure also changes along season as different plant species with different phenology mature. Therefore, frequent data collection with non-destructive methods, such as ground-based images (Inoue et al, 2014) should facilitate repeated measures across time on the same spot enabling the evaluation of spatial distribution of biomass along with insights on the evolution of pastures. The aim of this study was to determine the potential of the visible spectrum from digital images as a surrogate for biomass availability and quality of native pastures, compared with traditional clipping methods and other reflectance methods (NDVI). Material and methods Sampling was conducted in a native pasture (2.3 ha), located at University of Évora, Portugal (38° 32.2ʹ N; 8° 01.1ʹ W). The site was grazed by 15 adult, non-lactating Black Merino ewes, equipped with GNSS sensors. From April to mid-June pasture samples were collected on a weekly basis on 3 patches (400 m2 each) identified as the preferential grazing sites on the previous 24 hours. Percentage time spent grazing per hour was the criteria used to select preferential grazing sites. Inside each patch, 3 sampling points were randomly assigned using a 0.25m2 frame. Before pasture sample clipping in each sampling point, multispectral bands of the area surrounding the frame were acquired (proximity sensor OptRx® AOS, Ag Leader, Iowa, USA) and a set of two nadir images captured at 0.8 m above the ground and centred with the frame (commercially available “action” camera mounted on a pole, GoPro, Inc., San Mateo, CA, USA). Vegetation within the frame were then clipped, stored in plastic bags for dry matter, crude protein and NDF determination. Image analysis was performed with spatial analysis tools from the software ADI (version 1.3.7) (dew.globalsystemsscience.org) and red, green and blue profile extracted and used for calculating several visible spectrum indices (Greenness Index (GI), Green Leaf Algorithm(GLA), RGB Greenness (RGBG) and Green-Red Vegetation Index(GRVI). Results & Discussion The indices used to determine plant greenness (Fig.1) obtained both from the proximal sensor and from digital imagery, have shown similar temporal trends. For instance RGBG obtained from digital images is highly correlated with NDVI from the proximal sensor (r2 = 0.94). All the imagery indices provided poor estimations of green biomass, as evidenced by the correlation for GRVI (Fig.2). However RGBG index relates significantly with NDF content (r2=0.57; P< 0.001; Fig 3), and with Crude Protein (r2=0.46; P<0.001) evidencing the effect of phenological state of the pasture. Conclusion Pasture ground-based imagery is an easy-to-perform and useful methodology for long-term in situ observations, and is a promising tool to estimate pasture quality parameters. Further developments include coupling with other sensors so that it may also be useful for estimation of biomass availability