Diverging climate response of corn yield and carbon use efficiency across the U.S.

Abstract

In this paper, we describe constructing an algorithm and providing an open-source package to analyze the overall trend and responses of both carbon use efficiency (CUE) and corn yield to climate factors at the continental scale. Our algorithm enables automatic retrieval of remote sensing data through the Google Earth Engine and USDA agricultural production data at the county level across the United States through Application Programming Interface (API). We (1) integrated satellite images of MODIS-based net primary productivity (NPP) and gross primary productivity (GPP), and ECMWF-based climatic variables, (2) calculated CUE and commonly used climate metrics, and then (3) investigated the spatial heterogeneity of the variables. We applied a random forest algorithm to identify the key climate drivers of CUE and crop yield, and estimated the responses of CUE and yield to climate variability using time series data in each county across the United States. Our results show that growing degree days (GDD) has the highest predictive power for both CUE and yield, while extreme degree days (EDD) is the least important explanatory variable. We also found that yield increases with greater GDD and precipitation in most areas of the United States, but with more mixed and fragmented interactions in the southern regions—and that CUE decreases with GDD in the northern regions but increases with GDD in the southern ones. As global warming increases, both carbon sequestration and yield will increase in the south, though CUE will decrease in the north.

Regional Inversion Shows Promise in Capturing Extreme‐Event‐Driven CO2 Flux Anomalies but Is Limited by Atmospheric CO2 Observational Coverage

“…However, in reality the CUR parameter varies across vegetation type, age, and management practices (Campioli et al., 2015; DeLucia et al., 2007; Y. He et al., 2018; Manzoni et al., 2018; S. Yu et al., 2023). Second, we assumed that the influence of ΔHR on the ΔNEE was negligible.…”

“…The factor of 0.60 is an estimate of the carbon use efficiency (CUE), and is a relatively high estimate (Y. He et al., 2018; Manzoni et al., 2018), though may be representative of corn (Campioli et al., 2015; S. Yu et al., 2023). We assume an error of ±0.1 in CUE, and perform error analysis using factors of 0.5 and 0.7.…”

Regional inversion shows promise in capturing extreme-event-driven CO2 flux anomalies but is limited by atmospheric CO2 observational coverage

“…The factor of 0.60 is an estimate of the carbon use efficiency (CUE), and is a relatively high estimate Y. He et al, 2018), though may be representative of corn (S. Yu et al, 2023;. We assume an error of ±0.1 in CUE, and perform error analysis using factors of 0.5 and 0.7.…”

“…First, to estimate AR, we assumed that ∆GPP and ∆NPP can be related through a constant carbon use efficiency (CUE) parameter that varies across vegetation type, age, and management practices Y. He et al, 2018;S. Yu et al, 2023).…”