ArticleScientific reports2025
Linear and non-linear impact of key agricultural components on greenhouse gas emissions.
Article in Scientific reports, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
4 citing papers in PubMed.
- Unlocking Barley's Phosphorus Efficiency: The Emerging Role of RNA Processing in Low-Phosphorus Adaptation.Plants (Basel, Switzerland) · 2026Review
- Predicting energy prices and renewable energy adoption through an optimized tree-based learning framework with explainable artificial intelligence.Scientific reports · 2026Article
- Cattle production drives agricultural energy intensity and greenhouse gas emissions in Latin America and the Caribbean.Scientific reports · 2026Article
- Sustainable elements of the ongoing growth in the demand for halal products in OBOR regional markets.Frontiers in nutrition · 2025Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
Abstract
Agriculture significantly impacts the global environment, contributing to greenhouse gas (GHG) emissions, air and water pollution, and biodiversity loss. As the global population grows and demands higher agricultural output, these environmental impacts are expected to intensify. Among global contributors, China, with its vast population and prominent agricultural sector, plays a leading role in GHG emissions. Understanding and mitigating these impacts in China is crucial for addressing broader global environmental challenges. To address these key issues, we conducted a study on the dynamic impact of agricultural key variables (agricultural land, fertilizer consumption, energy use for agriculture, agricultural value-added, forest land, livestock, fisheries, and crop production) on GHG emissions by utilizing the data from 1990 to 2020, and employed linear and non-linear linear autoregressive distributed lag (ARDL and NARDL) models. In the study, co-integration analysis confirms the long-run relationship between variables, and the long-term findings from the ARDL model reveal important insights, increased agricultural land use, fertilizer consumption, agricultural energy use, crop production, livestock production, and fishery production increases GHG emissions in China and GHG emissions can be reduced by increasing forest land in the long term. Furthermore, with the asymmetric NARDL regression applied to three key variables, the positive shock analysis results confirm that agricultural land use (AGL+), fertilizer consumption (FC+), and agricultural energy use (EUA+) can significantly contribute to long-term GHG emissions. However, adverse shocks to (AGL-), (FC-), and (EUA-) could significantly compress GHG emissions. These findings offer valuable implications for Chinese authorities' focus on expanding forest land, using more renewable energy, and minimizing the usage of chemicals in agriculture. These measures can help to mitigate emissions while promoting sustainable agricultural practices.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.