A model-data fusion approach for quantifying the carbon budget in cotton agroecosystems across the United States

Authors: QIN, RONGZHU - University Of Illinois; GUAN, KAIYU - University Of Illinois; PENG, BIN - University Of Illinois; ZHANG, FENG - Lanzhou University; ZHOU, WANG - University Of Illinois; TANG, JINYUN - Lawrence Berkeley National Laboratory; HU, TONGXI - University Of Illinois; GRANT, ROBERT - University Of Alberta; RUNKLE, BENJAMIN - University Of Arkansas; Reba, Michele; WU, XIAOCUI - University Of Illinois

Submitted to: Agricultural and Forest Meteorology
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 1/17/2025
Publication Date: 1/20/2025
Citation: Qin, R., Guan, K., Peng, B., Zhang, F., Zhou, W., Tang, J., Hu, T., Grant, R., Runkle, B.R., Reba, M.L., Wu, X. 2025. Quantifying carbon budget in cotton agroecosystems across the United States. Agricultural and Forest Meteorology. 363(110407). https://doi.org/10.1016/j.agrformet.2025.110407.
DOI: https://doi.org/10.1016/j.agrformet.2025.110407

Interpretive Summary: The United States is one of the largest exporters of cotton in the world. Understanding how cotton production impacts the magnitude and patterns of carbon flux and cotton lint yield is important. An advanced process-based model and a deep learning based model-data fusion framework were used to determine carbon budgets at the county scale across cotton growing regions of the U.S. Field-measurements of carbon flux was used to determine model predictions at select locations. Atmospheric conditions, specifically vapor pressure deficit, were found to influence results, along with soil characteristics and crop management. This study established a framework to assess climate mitigation strategies for the U.S. cotton growing region important to modelers and land managers.

Technical Abstract: Cotton (Gossypium hirsutum L.) cultivation significantly contributes to economic development, particularly in the Cotton Belt of the Southern United States (U.S.). As one of the world's largest exporters of cotton, the U.S. cotton industry plays a pivotal role in both the domestic and international markets. Accurate quantification of carbon budgets and their responses to the environment is crucial for the sustainable production of cotton, but such quantification at the regional scale remains unclear. Here we use a framework that combines an advanced process-based model, ecosys, and a deep-learning based Model-Data Fusion (MDF) approach to quantify magnitude and patterns of carbon flux and cotton lint yield under both rainfed and irrigated conditions in the U.S. We first evaluate the process-based model’s performance in simulating carbon budgets of cotton agroecosystems using eddy-covariance (EC) measurements at production-scale farm sites. Then we apply MDF to use satellite-based gross primary production (GPP) and survey-based cotton lint yield data as constraints of the ecosys model to generate the holistic carbon budget of cotton cropland at county level across the U.S. from 2008 to 2019. Validation at the three EC sites indicates that the R2 achieves 0.9, 0.8 and 0.9 for daily ecosystem GPP, ecosystem respiration, and leaf area index, respectively. The R2 is 0.8 for both lint yield and GPP at county level from our MDF approach. The spatio-temporal pattern of simulated cotton lint yield, GPP, and their responses to climate factors are consistent with observations, indicating our MDF approach captures the underlying processes relating environmental conditions to cotton growth. Vapor pressure deficit (VPD) is a key climate factor for both observed and simulated cotton productivity (lint yield and GPP), especially under rainfed conditions. Our results also show that the carbon budget terms, including net primary productivity, lint yield, and soil heterotrophic respiration, declined significantly as VPD increased. Conversely, predicted soil organic carbon change was less influenced by climate, being more significantly impacted by soil properties. The variable impacts of crop management practices, climatic factors, and soil characteristics on carbon budgets highlight the intricate interactions among these factors that shape carbon dynamics in cotton agroecosystems, and further emphasize the necessity of accurately simulating the carbon budgets of cotton agroecosystems across temporal and spatial scales. This study has established a framework that utilizes advanced MDF to assess climate mitigation strategies for the U.S. cotton agroecosystems.