Sang et al. (2026) Observation-constrained assessment of long-term cropland productivity trends and drivers at the global scale

Identification

• Journal: Agricultural and Forest Meteorology
• Year: 2026
• Date: 2026-10-15
• Authors: Yuxing Sang, Chenzhi Wang, Liangliang Zhang, Mingzhu He, Yean‐Uk Kim, Shiyu Qu, Quanbo Zhao, Yue He, Xuhui Wang
• DOI: 10.1016/j.agrformet.2026.111508

Research Groups

• Institute of Carbon Neutrality, Sino-French Institute for Earth System Sciences, College of Urban and Environmental Sciences, Peking University, Beijing, China
• Leibniz Centre for Agricultural Landscape Research (ZALF), Germany
• Joint International Research Laboratory of Catastrophe Simulation and Systemic Risk Governance, Beijing Normal University, Zhuhai, China
• School of National Safety and Emergency Management, Beijing Normal University, Zhuhai, China
• Department of Landscape Architecture and Rural Systems Engineering, Seoul National University, Seoul, Republic of Korea
• Interdisciplinary Program in Agricultural and Forest Meteorology, Seoul National University, Seoul, Republic of Korea
• Research Institute of Agriculture and Life Sciences, Seoul National University, Seoul, Republic of Korea

Short Summary

This study provides a global, observation-constrained assessment of cropland gross primary productivity (GPP) trends and their drivers from 1982 to 2016. It reveals a significant increase in cropland GPP, primarily driven by vegetation structural changes and a residual term combining CO₂ fertilization and agricultural management, with climate variability imposing a negative effect.

Objective

• To systematically assess the long-term trends and underlying drivers of cropland GPP at the global scale over the period 1982–2016, relying on multi-source observations.
• To disentangle the relative contributions of vegetation structural changes, climate change, and a combined group of atmospheric CO₂ and other anthropogenic factors to observed GPP trends.
• To compare these attributions between croplands and other natural vegetation types.
• To explore how observation-constrained cropland GPP can inform emerging land-based carbon dioxide removal strategies, specifically by quantifying the plant-assisted weathering effect (PAWE).

Study Configuration

• Spatial Scale: Global, with data aggregated and resampled to a 0.5° × 0.5° spatial resolution.
• Temporal Scale: 1982–2016 (35 years), with monthly data used for model training and prediction.

Methodology and Data

• Models used:
  • Machine Learning: Random Forest (RF), Boosted Regression Trees (BRT), Artificial Neural Networks (ANN).
  • Land Surface Models (TRENDY ensemble for comparison): CLASS, CLM4.5, ISAM, JSBACH, LPJ, LPX, ORCHIDEE, VEGAS, VISIT.
• Data sources:
  • Eddy Covariance Flux Data: FLUXNET 2015 database (GPPNTVUT_MEAN, meteorological observations).
  • Cropland Distribution: Potapov et al. (2022) dataset (0.025° × 0.025° original, aggregated to 0.5° × 0.5°).
  • Climate Data: CRU NCEP V8 (temperature, precipitation, radiation, vapor pressure deficit), National Tibetan Plateau Data Center (global shortwave radiation), GLEAM v3.3 (vapor pressure deficit, soil water content).
  • Vegetation Indices: AVHRR GIMMS3g NDVI, MODIS13C2 V6 NDVI, MODIS13C2 V6 EVI.
  • Land Cover Type: MODIS MCD12Q1 V5.
  • Satellite-derived GPP products (GPPPROXY): GPPSIF (Li and Xiao, 2019), GPPNIRv (Wang et al., 2021).
  • Other GPP products: GPPFLUXCOM (Tramontana et al., 2016), GPPVPM (Zhang et al., 2017), GPPWEC (Cheng et al., 2017), GPPPM (Keenan et al., 2016).
  • Carbon Use Efficiency (CUE): He et al. (2018).
  • Crop Distribution: SPAM 2000 (Spatial Production Allocation Model) harvested area dataset.

Main Results

• Global cropland GPP averaged 10.4 ± 0.1 Pg C yr⁻¹ from 2001 to 2016, representing 8.6% of the global total GPP.
• Croplands contributed 18.8% to the overall global GPP increase during 1982–2016, ranking second only to evergreen broadleaf forests.
• Since 2000, croplands became the largest contributor to global GPP gains, accounting for 22.5% of the total increment.
• Cropland GPP exhibited a significant increase between 1982 and 2016, with an average growth rate of 4.4 ± 2.7% per year⁻¹ decade⁻¹.
• Attribution analysis revealed that vegetation structural changes (canopy expansion) explained approximately 43% of the cropland GPP increase.
• Climate variability imposed an overall negative effect of −9% on cropland GPP trends.
• A residual term, combining rising atmospheric CO₂, intensified management, crop breeding, and differences among GPP products, emerged as the dominant driver, explaining 66% of the total increase.
• Spatially, approximately 80% of global croplands showed increased productivity since 2000, with statistically significant increases in 52% of these areas, particularly in China, India, and the United States (up to 30% increase).
• Negative climate impacts were primarily associated with increases in temperature and vapor pressure deficit (VPD), suppressing productivity in about 13% of cropland regions, notably in Western Europe and the southeastern United States.
• Plant-assisted weathering efficiency (PAWE) showed a nonlinear increase with productivity, yielding a 2.5–4.5-fold biological amplification in major breadbasket regions (e.g., North America, Western Europe, southern China). Highest PAWE values (1 to 2 times higher than other regions) were observed in parts of South America and East Africa.

Contributions

• Provides a global, observation-constrained assessment of long-term cropland GPP trends and their underlying drivers, reducing reliance on single products or model configurations.
• Quantifies the disproportionate and increasing contribution of croplands to global GPP increases, particularly since 2000, where they surpassed other vegetation types.
• Disentangles the relative contributions of canopy structural changes, climate variability, and a combined residual term (CO₂ fertilization, management, breeding) to cropland GPP trends.
• Highlights the net negative impact of climate change (warming and increased VPD) on cropland GPP, suggesting it offsets part of potential productivity gains.
• Introduces and quantifies the plant-assisted weathering efficiency (PAWE) as an observation-based metric for the biological enhancement of enhanced rock weathering (ERW) in agro-ecosystems, providing a novel constraint for carbon dioxide removal assessments.
• Identifies spatial patterns of GPP trends and drivers, showing where specific factors dominate and where biological enhancement of weathering is most pronounced.

Funding

• National Natural Science Foundation of China (grant nos. 42361144876, 42471114, and 42303107)
• Horizon Europe Research and Innovation Programme (Marie Skłodowska-Curie grant agreement no. 101154967)
• Leibniz Female Professorship Award (application no. P102/2020)
• Open access funding enabled and organized by Projekt DEAL.

Citation

@article{Sang2026Observationconstrained,
  author = {Sang, Yuxing and Wang, Chenzhi and Zhang, Liangliang and He, Mingzhu and Kim, Yean‐Uk and Qu, Shiyu and Zhao, Quanbo and He, Yue and Wang, Xuhui},
  title = {Observation-constrained assessment of long-term cropland productivity trends and drivers at the global scale},
  journal = {Agricultural and Forest Meteorology},
  year = {2026},
  doi = {10.1016/j.agrformet.2026.111508},
  url = {https://doi.org/10.1016/j.agrformet.2026.111508}
}