Forecasting Pasture Productivity from Satellite Imagery for Use in Adaptive Grazing Management

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Commodities

Practices

• Animal Production: grazing management, pasture fertility

Grazing lands are essential for global food security and delivery of ecosystem services, and they represent a predominant land use in the southeastern United States, especially in Florida. The multi-functionality of these landscapes, as well as the spatiotemporal variability therein, create complex management problems, which are best solved adaptively and with a process-based understanding. Adaptive multi-paddock (AMP) grazing management has been shown to enhance economic returns for farmers, as well as the delivery of ecosystem services. Grazing land managers rely on frequent pasture monitoring to implement AMP systems, such as pasture regrowth periods, stocking densities, and livestock rotations. However, traditional tools for on-farm forage measurement – visual identification, rising plate meter, etc. – provide limited information and do not readily scale. By contrast, satellite-borne remote sensing platforms capture a broad spectrum of data over large extents and have been shown to provide near real-time estimates of pasture quantity and quality. Moreover, these estimates can be inserted into pasture productivity models and developed into a user-friendly format, providing forecast of pasture characteristics valuable to grazing land managers at a variety of scales. Therefore, the objectives of this study are (1) to assimilate high quality land surface data, including satellite imagery and weather models, into a pasture productivity model for the forecasting of pasture characteristics most useful to land managers (e.g., biomass, crude protein, digestibility) and (2) to evaluate the integration of this technology into AMP grazing systems.

The objectives of this study are:

1. to assimilate high quality land surface data, including satellite imagery and weather models, into a pasture productivity model for the forecasting of pasture characteristics most useful to land managers (e.g., biomass, crude protein, digestibility)
2. to evaluate the integration of this technology into AMP grazing systems.

Objective 1

The process-based pasture growth model will assimilate key pasture characteristics – biomass, LAI, foliar nitrogen content – estimated from Sentinel-2 data. The empirical models to estimate these variables are currently in development, in our large ground truthing experiment. Preliminary models from Landsat-8 imagery and gridded climate data show strong capability to predict out-of-sample biomass (R² = 0.72) and crude protein (R² = 0.88). The pasture growth model will require calibration of parameters specific to subtropical pasture, such as canopy light extinction, radiation use efficiency (RUE), and rate of senescence. To accomplish this calibration, a time series of pasture growth and development data will be developed, and values for each parameter will be empirically established. With all components in place, the model will be developed to receive near real-time updates of Sentinel-2 imagery and gridded climate data via the Google Earth Engine (GEE) cloud-computing platform. State variable outputs will be validated on experimental and operational pastures.

Objective 2

To evaluate the potential for integration of the developed model into a decision support framework for AMP management, a historical dataset of pasture growth on Deseret Cattle and Timber pastures will be developed using the pasture growth model and historical Sentinel-2 images. We will pair this dataset with a record of grazing management where available from each pasture, in collaboration with our partners at Deseret. From these datasets, we will retroactively forecast pasture production and evaluate the corresponding management decisions to determine if forage quantity and quality were optimally utilized based on our model.