Non Technical Summary
Animal Health Component
Research Effort Categories
Goals / Objectives
The first of four priority areas specified in the Long-Term Agroecosystem Research (LTAR) Shared Research Strategy document is ¿agro-ecosystem productivity¿. Unfortunately, the Walnut Gulch Experimental Watershed is not well suited to study productivity. There are 5 active ranches, each of which also contain grazed areas that are not on the watershed, and historically the ranchers have not shared animal numbers, weight gains or grazing regimes. In contrast, 80 kilometers to the west the Santa Rita Experimental Range (SRER), operated by the University of Arizona, is in the same Major Land Resource Area (with similar rangeland productivity) as Walnut Gulch Experimental Watershed (WGEW), but on land owned by the State of Arizona. Management is determined by SRER experimental needs, with the rancher leasing the land reporting the details of animal numbers, weight gains, and grazing schedule. Further, the SRER is a National Ecological Observatory Network (NEON) Core site, so it will benefit from the detailed NEON Airborne Observation Platform (AOP) imagery with coincident, 1 meter scale hyperspectral and the rapidly maturing technology of LiDAR. The objective of this project is to develop a common set of field monitoring, ground-based LiDAR, drone LiDAR and imagery protocols to be able to interpret NEON AOP products, when flown, to understand the agro-ecosystem productivity of the SRER and infer the plant and animal productivity of the Walnut Gulch LTAR site, and, with modification, to other rangeland LTAR sites.
We anticipate that both the Santa Rita Experimental Range and the Walnut Gulch Experimental Watershed will have an unprecedented opportunity to characterize the vegetation community and production across the landscape using data from the NEON AOP. However, there are five tasks to accomplish prior to applying the NEON AOP data: 1) Review LiDAR derived digital elevation models to ensure bare earth topography has been accurately separated from vegetation and determine which algorithms should be used to characterize various vegetation communities into life forms; 2) Map large woody species on SRER and WGEW using existing LiDAR data; 3) Develop a method to scale from individual perennial midgrass plants to NEON AOP data at 1 meter resolution using ground and UAV LiDAR and imagery. 4) Assess the ability of UAV flown LiDAR to quantify both forage production and utilization (consumption) at the pasture scale; 5) Contract for a consistent classification on both the SRER and Walnut Gulch into vegetative states appropriate for the MLRA 41 ecological site state and transition models. With this information in hand, using both NEON AOP data and UAV LiDAR, we should be able to quantify vegetation production and consumption, and infer animal weight gain by ecological sites and states within pastures across both study areas.