Project Number: 6022-63000-005-14-S
Project Type: Non-Assistance Cooperative Agreement
Start Date: Apr 1, 2019
End Date: Jun 30, 2021
Objective 1- Determining how spatially variable landscape attributes impact preferential grazing in tree-livestock (Silvopasture) systems. Objective 2- Linking precision agricultural and digital soil mapping technologies to improve on-farm profitability and sustainability.
Approach corresponding to objectives: Objective 1. a) evaluate causative factors of preferential grazing in silvopasture systems by assessing soil nutrient, carbon, and water availabilities by tracking grazing patterns of cattle using GPS collars across the landscape. b) assess appropriate drought and shade tolerant alley crops depending on landscape position, soil moisture, and canopy dynamics, thereby allowing producers increased flexibility to respond to variations in biomass, forage, and forest product markets. c) match crop needs to spatially variable landscape attributes to manage yield potential and soil moisture. d) compare native warm-season grasses, as both grazable and energy crops. e) quantify how grazing pressure, soil moisture availability, fertilization, and forage species affect root decomposition, rhizosphere carbon content, and subsequent soil health. f) correlate grazing pressure and forage availability to soil properties based on digital soil mapping of soil physical and chemical properties (such as texture, depth, water-holding capacity, plant available water, and nutrient dynamics) in silvopasture systems. Objective 2. a) identify actual reductions in irrigation, seed, fertilizer (organic and inorganic), and chemical inputs when automated steering and digital soil mapping technologies are integrated based on terrain attributes. b) use data obtained in objective i to identify efficiency gains and feasibility of adoption from this technology by determining break-even prices based on farming operation type, farm size, and capital investment requirements. c) quantify subsequent soil health and water quality impacts from reductions in agricultural inputs based on in-field data from crop farms and cattle ranches.