Monitoring agroecosystem productivity and phenology at a national scale: A metric assessment framework

Authors: Browning, Dawn; RUSSELL, ERIC - Washington State University; PONCE-CAMPOS, GUILLERMO - University Of Arizona; Kaplan, Nicole; RICHARDSON, ANDREW - Northern Arizona University; SEYEDNASROLLAH, BIJAN - Northern Arizona University; Spiegal, Sheri; Saliendra, Nicanor; Alfieri, Joseph; Baker, John; Bernacchi, Carl; Bestelmeyer, Brandon; Bosch, David - Dave; BOUGHTON, ELIZABETH - Archbold Biological Station; BOUGHTON, RAOL - Archbold Biological Station; Clark, Pat; Flerchinger, Gerald; GOMEZ-CASANOVAS, NURIA - University Of Illinois; Goslee, Sarah; HADDAD, NICK - Michigan State University; Hoover, David; Jaradat, Abdullah; MAURITZ, MARGUERITE - University Of Texas - El Paso; MILLER, GRETCHEN - Texas A&M University; McCarty, Gregory; SADLER, JOHN - University Of Missouri; SAHA, AMARTYA - Archbold Biological Station; Scott, Russell - Russ; SUYKER, ANDREW - University Of Nebraska; TWEEDIE, CRAIG - Texas A&M University; WOOD, JEFFREY - University Of Missouri; ZHANG, XUKAI - Archbold Biological Station; Taylor, Shawn

Submitted to: Ecological Indicators
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 8/23/2021
Publication Date: 8/27/2021
Citation: Browning, D.M., Russell, E.S., Ponce-Campos, G.E., Kaplan, N.E., Richardson, A.D., Seyednasrollah, B., Spiegal, S.A., Saliendra, N.Z., Alfieri, J.G., Baker, J.M., Bernacchi, C.J., Bestelmeyer, B.T., Bosch, D.D., Boughton, E.H., Boughton, R.K., Clark, P., Flerchinger, G.N., Gomez-Casanovas, N., Goslee, S.C., Haddad, N., Hoover, D.L., Jaradat, A.A., Mauritz, M., Miller, G.R., McCarty, G.W., Sadler, J., Saha, A., Scott, R.L., Suyker, A., Tweedie, C., Wood, J., Zhang, X., Taylor, S.D. 2021. Monitoring agroecosystem productivity and phenology at a national scale: A metric assessment framework. Ecological Indicators. 131. Article 108147. https://doi.org/10.1016/j.ecolind.2021.108147.
DOI: https://doi.org/10.1016/j.ecolind.2021.108147

Interpretive Summary: Technology offers many options for metrics used by agricultural agencies, managers, and individual producers. Yet, knowledge and information are necessary to guide the use of technology to meet management and production needs, specifically, which sensors should be used and when to use them. Capitalizing on the unprecedented opportunity provided by the LTAR network, we documented differences in metrics for growing season length and productivity in diverse agroecosystems. This approach can serve as a new path forward to integrate the data streams available to the producers and land managers for better monitoring and forecasting of primary production at short- and longer-term time scales. We present a novel “metric assessment framework” to optimize the selection of instruments used to monitor, model, and forecast ecosystem productivity. Researchers, land managers, policy-makers, and industry leaders working at multiple scales of agroecosystem management will benefit from this analysis and framework designed to streamline research design and provide information on the timing of optimal production potential with shifts in growing season due to weather and changes in climate.

Technical Abstract: Effective measurement of seasonal variations in the timing and amount of production are critical to managing spatially heterogeneous agroecosystems in a changing climate. Although numerous technologies for such measurements are available, their relationships to one another at a continental extent are unknown. Using data collected from across the Long-Term Agroecosystem Research (LTAR) network and other networks, we investigated correlations among key metrics representing primary production, phenology, and carbon fluxes in croplands, grazinglands, and crop-grazing integrated systems across the continental U.S. Metrics examined included gross primary productivity (GPP) estimated from eddy covariance (EC) towers and modelled from Landsat satellite, and vegetation greenness [Green Chromatic Coordinate (GCC)] from tower-mounted PhenoCams for 2017 and 2018. Overall, we analyzed production dynamics estimated from three independent ground and remote platforms using data for 34 agricultural sites constituting 51 site-years of co-located time series. We found that similar methods applied across the platforms yielded stronger correlation for end of season (EOS) dates (Pearson R ranged from 0.6 to 0.7) than start of season (SOS) dates (0.46 to 0.49) for pairwise sensor comparisons. Overall, moderate to high correlations between SOS and EOS metrics complemented one another with special consideration for lower productivity grazingland sites where estimating SOS can be challenging. Growing season length estimates derived from 16-day Landsat GPP were significantly longer than those from PhenoCam GCC (padj < 0.0001) and EC GPP (padj < 0.0001). Landscape heterogeneity did not explain differences in SOS and EOS estimates. Annual integration estimates of productivity from EC GPP and PhenoCam GCC diverge from those estimated by satellite GPP at sites where annual production exceeds 1000 gC m-2 yr-1. Based on our results, we developed a “metric assessment framework” that articulates where and how metrics from satellite, eddy covariance and PhenoCams complement, diverge from, or are redundant with one another. The framework is designed to optimize instrumentation selection for monitoring, modeling, and forecasting ecosystem functioning with the ultimate goal of informing decision-making by land managers, policy-makers, and industry leaders working at multiple scales.