Seasonal grassland productivity forecast for the U.S. Great Plains using Grass‐Cast

Abstract

Every spring, ranchers in the drought-prone U.S. Great Plains face the same difficult challenge-trying to estimate how much forage will be available for livestock to graze during the upcoming summer grazing season. To reduce this uncertainty in predicting forage availability, we developed an innovative new grassland productivity forecast system, named Grass-Cast, to provide science-informed estimates of growing season aboveground net primary production (ANPP). Grass-Cast uses over 30 yr of historical data including weather and the satellite-derived normalized vegetation difference index (NDVI)-combined with ecosystem modeling and seasonal precipitation forecasts-to predict if rangelands in individual counties are likely to produce below-normal, near-normal, or above-normal amounts of grass biomass (lbs/ac). Grass-Cast also provides a view of rangeland productivity in the broader region, to assist in largerscale decision-making-such as where forage resources for grazing might be more plentiful if a rancher's own region is at risk of drought. Grass-Cast is updated approximately every two weeks from April through July. Each Grass-Cast forecast provides three scenarios of ANPP for the upcoming growing season based on different precipitation outlooks. Near real-time 8-d NDVI can be used to supplement Grass-Cast in predicting cumulative growing season NDVI and ANPP starting in mid-April for the Southern Great Plains and mid-May to early June for the Central and Northern Great Plains. Here, we present the scientific basis and methods for Grass-Cast along with the county-level production forecasts from 2017 and 2018 for ten states in the U.S. Great Plains. The correlation between early growing season forecasts and the end-ofgrowing season ANPP estimate is >50% by late May or early June. In a retrospective evaluation, we compared Grass-Cast end-of-growing season ANPP results to an independent dataset and found that the two agreed 69% of the time over a 20-yr period. Although some predictive tools exist for forecasting upcoming growing season conditions, none predict actual productivity for the entire Great Plains. The Grass-Cast system could be adapted to predict grassland ANPP outside of the Great Plains or to predict perennial biofuel grass production.

Monitoring Climate Impacts on Annual Forage Production across U.S. Semi-Arid Grasslands

“…To validate our EEP model together with the conversion to biomass using SSURGO data, we compared the modeled biomass data with long-term grass clipping data from various locations in the study area. The R 2 value of the observed versus modeled biomass indicated a moderately strong relation and was comparable to values observed in other studies [3,26,69]. The ground-observed clipping data were in most cases obtained from multiple small rectangles (0.25-m 2 ) and scaled to the entire pasture, while the modeled data captured much larger areas (62,500-m 2 ) averaged over a few pixels covering the entire pasture.…”

“…The RT model was designed and run using Cubist ® software [49]. The target (dependent) variable in the RT model was the Growing Season NDVI (hereon called GSN), a proxy of annual vegetation growth, e.g., [26,50,51]. The NDVI is one of the most widely used vegetation indices capturing changes in vegetation greenness.…”

Global climate-related predictors at kilometre resolution for the past and future

“…Time-series data on climate-related variables are indispensable to understand the drivers of the many important Earth system processes that vary with time. Resolving how the primary weather patterns unfold, for example, allows for a much deeper understanding of the control of spatiotemporal patterns of ecosystem productivity (Hartman et al, 2020). Similarly, time-series of pet and cmi can be used to understand the country-wide temporal dynamics in crop yield (Zhang et al, 2015;Santini et al, 2022).…”

Spatial replication can best advance our understanding of population responses to climate

“…Understanding the responses of biodiversity to climate drivers is necessary to mitigate and adapt to climate change (Urban et al, 2016). In recent years, there are several examples of successful and directly applicable forecasts that predict the effects of climatic drivers on ecological variables (Grevstad et al, 2022;Harris et al, 2018;Hartman et al, 2020). There has been slower progress in predicting the effects of climate on populations and their demography, which is necessary to assess species extinction risk (Mace et al, 2008) and predict range shifts (Schurr et al, 2012).…”