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

I.  Objective 1

The study will take place at the Beef Research Unit in Gainesville, Florida, in fields managed by the University of Florida (UF) Agronomy department. The fields have been historically used as pasture and are currently predominantly Pensacola bahiagrass. The soil order is Spodosol with 0 to 2% slopes and moderately poor drainage. These characteristics are representative of a majority of Florida pasture. On-site weather variables are collected including precipitation, PAR, and temperature.

To deliver the RS-informed pasture productivity forecasting model, the following components are necessary:

1. Empirical models estimating pasture physiological state variables (LAI, biomass, %N) from RS imagery
2. Development of a process-based growth model appropriate to forecasting Florida pasture characteristics, followed by calibration of its parameters (canopy light extinction, RUE, and senescence) in a framework that can both generalize to regional pasture and capture critical site-site variations
3. Assimilation of RS imagery into the growth model
4. Validation of the model outputs

Component 1

In a funded collaboration with Deseret Cattle and Timber – the largest ranching outfit in Florida - we have already developed very promising preliminary models for estimating key pasture characteristics, and we are currently running a large ground truthing experiment to improve and validate these models. Thus, at the beginning of this SARE-funded project, component 1 will already be fulfilled or nearing completion.

Component 2

First, we have already developed a small suite of minimally complex growth models based on biophysical principles of light interception (driven by leaf area index, LAI), and production of pasture driven by light-use efficiency (LUE). Our model for LUE in turn can readily represent variations in pasture composition, as well as variability in weather (temperature, precipitation) and edaphic conditions. Moreover, through implicit representation of mean canopy age, and explicit representation of canopy N, our models are also well-suited to track critical parameters of pasture quality including crude protein, lignin levels and digestibility. To calibrate parameters of our LUE growth models based on empirical data, we will need to track pasture physiological development over time. This will be performed in ten small scale (2m x 2m) plots for the duration of the growing season (April-October). One plot will be manually clipped to 2-inch residual height each week in a 10-week rotation. This gradient design ensures the measurement of physiological development from an array of regrowth periods and for the range of climatic conditions typically exhibited in southeastern pasture subtropical pasture. The physiological measurements taken weekly in a subsample destructive harvest include forage biomass, LAI, light interception, nutritive evaluation (crude protein, %N, digestibility), and percentage of senescence.  

At the end the growing season, we will have a time series of bahiagrass pasture physiological development from which key parameters for the process-based model will be calibrated using empirical statistical methods.

Component 3

Using the GEE cloud-computing platform, RS imagery and gridded climate data will be acquired in near-real time. From the empirical models, the RS imagery will be converted into gridded distributions of pasture characteristics, which are used as inputs of the process-based pasture productivity model. With the addition of the calibrated parameters of Component 2, all components are ready to validate the model performance and allow cloud updates for continuous model improvement.

Component 4

To validate model performance, pixel-sized pasture plots (20m x 20m) will be sampled for the state variables of interest (biomass, LAI, crude protein). Some of these plots will be located inside experimental paddocks at BRU. Additional plots will be located in the operational pastures of Deseret. The pasture productivity model should allow for accurate forecasting of pasture development in between satellite image acquisitions. Validating data will continue to be taken until a statistically significant number of plots has been reached. Simulated model outputs will be evaluated using goodness-of-fit assessments.

II.  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.

Specifically, grazing rotations will be evaluated on regrowth period and stocking density for each management unit. For example, if livestock were rotated into paddock A, but our model reveals that this paddock had not yet achieved optimal levels of biomass and crude protein, then we can quantify the potential for forage production improvement. Furthermore, management units contain variation in production. We will evaluate the grazing rotation scheme of the ranch to determine if management units are being over- or under-utilized. Our evaluations will be guided by AMP principles to optimize forage quantity and quality. We will solicit feedback from Deseret on the utility and feasibility of the management recommendations. Findings will be shared with the scientific community through conferences and publications and with the producer community through extension events and further efforts to make the model publicly accessible.