J. L. Foster

The future of warm-season, tropical and subtropical forage legumes in sustainable pastures and rangelands

Forage legumes have the potential to contribute substantially to warm-season, subtropical and tropical pastures and rangelands. Compared to grasses, they have advantages in accessing subsoil nutrients and moisture; legumes typically concentrate protein in forage, even in infertile soils, and they can also provide ruminants with plant proteins and soluble carbohydrates that increase digestibility of grasses when consumed in legume–grass mixtures. Yet their inclusion in warm-season, subtropical or tropical pasture seed mixes or rangeland rehabilitation is minimal considering the percentage of grasslands coverage in these regions. Why have past diligent attempts failed to develop the germplasm, agronomic techniques, dissemination and ultimate widespread acceptance by land managers in regions where these legumes are widely adapted? Successful forage legume reports indicate that farmers’ participation in technology development, persistence with minimal management, adequate seed supply following release of new varieties, meeting recognised needs, delivery of clear benefits and profits, and communication among researchers, extension and stakeholders are crucial. Current and future research and development programs based on limited past successes and widespread failures should enhance successful commercial use of warm-season, subtropical and tropical forage legumes.

Sustainable intensification of cultivated pastures using multiple herbivore species

Demand for animal products is growing faster than for any other agricultural product. As a result, pressure for greater output from cultivated pastures is expected to increase. Assuming cultivated pasture area will decrease with land degradation, conversion to grain crops or urban expansion, the only alternative is to increase productivity per area. We suggest an underutilised solution: increase herbivore diversity on cultivated pastures. We review multiple herbivore species (MHS) ecology in natural ecosystems (rangeland and wildlife parks) for guidelines to implementing this approach in cultivated pasture. In rangeland or natural grassland systems, sequential or simultaneous introduction of MHS results in greater productivity, diversity and resilience of plant as well as animal populations. Replacing historical mono-ruminant systems with MHS or classes on cultivated pasture is currently beyond landowner experience and will stretch cultivated pasture science. This approach becomes more feasible, however, as cultivated pastures increase in plant biodiversity and canopy complexity. We enumerate research and demonstration topics that might promulgate MHS in cultivated pastures.

Automatic detection and counting of cattle in UAV imagery based on machine vision technology (Conference Presentation)

Beef production is the main agricultural industry in Texas, and livestock are managed in pasture and rangeland which are usually huge in size, and are not easily accessible by vehicles. The current research method for livestock location identification and counting is visual observation which is very time consuming and costly. For animals on large tracts of land, manned aircraft may be necessary to count animals which is noisy and disturbs the animals, and may introduce a source of error in counts. Such manual approaches are expensive, slow and labor intensive. In this paper we study the combination of small unmanned aerial vehicle (sUAV) and machine vision technology as a valuable solution to manual animal surveying. A fixed-wing UAV fitted with GPS and digital RGB camera for photogrammetry was flown at the Welder Wildlife Foundation in Sinton, TX. Over 600 acres were flown with four UAS flights and individual photographs used to develop orthomosaic imagery. To detect animals in UAV imagery, a fully automatic technique was developed based on spatial and spectral characteristics of objects. This automatic technique can even detect small animals that are partially occluded by bushes. Experimental results in comparison to ground-truth show the effectiveness of our algorithm.