Carbon sequestration effects of agricultural high-quality development: evidence from China’s high-standard farmland construction DOI Creative Commons
Yufei Zhou,

Qiuguang Hu,

Shuqin Li

et al.

Environmental Research Communications, Journal Year: 2024, Volume and Issue: 6(12), P. 125030 - 125030

Published: Dec. 1, 2024

Abstract Based on the perspective of carbon sequestration, this paper selects county-level panel data from China 2000 to 2017 and uses a Difference-in-Differences (DID) Model examine impact high-standard basic farmland construction (HSFC) enhancement regional sequestration capacity. The results indicate that HSFC policy enhances capacity in respective areas. Heterogeneity analysis reveals counties with more significant promotion implementation exhibit substantially better local compared those were early pilot Non-grain-producing smaller agricultural production scales effects when implementing grain-producing counties. also shows varying different regions cropping systems. Further improvement mechanization reduction fertilizer application can effectively enhance policy. Moreover, has spillover levels neighboring regions.

Language: Английский

Knowledge-guided machine learning can improve carbon cycle quantification in agroecosystems DOI Creative Commons
Licheng Liu, Wang Zhou, Kaiyu Guan

et al.

Nature Communications, Journal Year: 2024, Volume and Issue: 15(1)

Published: Jan. 8, 2024

Accurate and cost-effective quantification of the carbon cycle for agroecosystems at decision-relevant scales is critical to mitigating climate change ensuring sustainable food production. However, conventional process-based or data-driven modeling approaches alone have large prediction uncertainties due complex biogeochemical processes model lack observations constrain many key state flux variables. Here we propose a Knowledge-Guided Machine Learning (KGML) framework that addresses above challenges by integrating knowledge embedded in model, high-resolution remote sensing observations, machine learning (ML) techniques. Using U.S. Corn Belt as testbed, demonstrate KGML can outperform black-box ML models quantifying dynamics. Our approach quantitatively reveals 86% more spatial detail soil organic changes than coarse-resolution approaches. Moreover, outline protocol improving via various paths, which be generalized develop hybrid better predict earth system

Language: Английский

Citations

40
Livestock manure: From waste to resource in a circular economy DOI Creative Commons
Amir Sadeghpour,

Reza Keshavarz Afshar

Journal of Agriculture and Food Research, Journal Year: 2024, Volume and Issue: 17, P. 101255 - 101255

Published: June 6, 2024

This article provides an overview of the pivotal role played by manure in animal agriculture and circular economy. We delve into technological advancements policy frameworks that harness as a valuable resource for generating both fertilizer energy. Additionally, we explore future considerations integrating ecosystem marketplace, emphasizing its potential environmental sustainability. The discussion emphasizes critical need robust interdisciplinary collaboration involving researchers, policymakers, outreach specialists, stakeholders throughout value chain. Such collaborative efforts are indispensable economy context, ultimately seeking to enhance overall sustainability agriculture.

Language: Английский

Citations

13
A flexible and efficient knowledge-guided machine learning data assimilation (KGML-DA) framework for agroecosystem prediction in the US Midwest DOI Creative Commons
Qi Yang, Licheng Liu, Junxiong Zhou

et al.

Remote Sensing of Environment, Journal Year: 2023, Volume and Issue: 299, P. 113880 - 113880

Published: Oct. 25, 2023

Language: Английский

Citations

17
Satellite-enabled enviromics to enhance crop improvement DOI Creative Commons

Rafael T Resende,

Lee T. Hickey, Cibele Hummel do Amaral

et al.

Molecular Plant, Journal Year: 2024, Volume and Issue: 17(6), P. 848 - 866

Published: April 17, 2024

Enviromics refers to the characterization of micro- and macroenvironments based on large-scale environmental datasets. By providing genotypic recommendations with predictive extrapolation at a site-specific level, enviromics could inform plant breeding decisions across varying conditions anticipate productivity in changing climate. Enviromics-based integration statistics, envirotyping (i.e., determining factors), remote sensing help unravel complex interplay genetics, environment, management. To support this goal, exhaustive generate precise profiles would significantly improve predictions genotype performance genetic gain crops. Already, informatics management platforms aggregate diverse datasets obtained using optical, thermal, radar, light detection ranging (LiDAR)sensors that capture detailed information about vegetation, surface structure, terrain. This wealth information, coupled freely available climate data, fuels innovative research. While holds immense potential for breeding, few obstacles remain, such as need (1) integrative methodologies systematically collect field data scale expand observations landscape satellite data; (2) state-of-the-art AI models integration, simulation, prediction; (3) cyberinfrastructure processing big scales seamless interfaces deliver forecasts stakeholders; (4) collaboration sharing among farmers, breeders, physiologists, geoinformatics experts, programmers research institutions. Overcoming these challenges is essential leveraging full captured by satellites transform 21st century agriculture crop improvement through enviromics.

Language: Английский

Citations

8
Feasibility of Formulating Ecosystem Biogeochemical Models From Established Physical Rules DOI Creative Commons
Jinyun Tang, W. J. Riley, Stefano Manzoni

et al.

Journal of Geophysical Research Biogeosciences, Journal Year: 2024, Volume and Issue: 129(6)

Published: June 1, 2024

Abstract To improve the predictive capability of ecosystem biogeochemical models (EBMs), we discuss feasibility formulating processes using physical rules that have underpinned many successes in computational physics and chemistry. We argue currently popular empirically based approaches, such as multiplicative empirical response functions law minimum, will not lead to EBM formulations can be continuously refined incorporate improved mechanistic understanding observations processes. Instead, propose parameterizations, a lossy data compression problem, better formulated established widely used chemistry, different more robustly integrated within reactive‐transport framework. Through several examples, demonstrate how mathematical representations derived from relevant enable effective communication between modelers, observationalists, experimentalists regarding essential questions, what measurements are needed meaningfully inform generate new process‐level hypotheses test studies. Finally, while with parameters often less robust, rules‐based robust show lower equifinality, stemming their enhanced consistency processes, interactions spatial scaling.

Language: Английский

Citations

4
Knowledge-guided machine learning for improving crop yield projections of waterlogging effects under climate change DOI Creative Commons
Linchao Li, Qinsi He, Matthew Tom Harrison

et al.

Resources Environment and Sustainability, Journal Year: 2024, Volume and Issue: 19, P. 100185 - 100185

Published: Dec. 10, 2024

Language: Английский

Citations

4
A model-data fusion approach for quantifying the carbon budget in cotton agroecosystems across the United States DOI Creative Commons
Rongzhu Qin, Kaiyu Guan, Bin Peng

et al.

Agricultural and Forest Meteorology, Journal Year: 2025, Volume and Issue: 363, P. 110407 - 110407

Published: Jan. 20, 2025

Language: Английский

Citations

0
Near-surface remote sensing applications for a robust, climate-smart measurement, monitoring, and information system (MMIS) DOI Creative Commons
Benjamin R. K. Runkle, Mallory L. Barnes, Matthew P. Dannenberg

et al.

Carbon Management, Journal Year: 2025, Volume and Issue: 16(1)

Published: Feb. 20, 2025

To reduce its greenhouse gas (GHG) impact, the United States government plans GHG Measurement, Monitoring, and Information System (MMIS) activities to better quantify sources sinks in natural, forested, agricultural ecosystems. The national strategy discusses several areas where a robust MMIS can be strengthened by near-surface remote sensing (RS) technology—i.e. non-contact measurement of electromagnetic signals sensors mounted near ground, on towers, or drones. Here, we outline specific applications RS for an MMIS, using tools presently available offering guidance improvements needed expansion their applications. Near-surface help carbon stocks assessing vegetation structure function, it inform cross-scale understanding ecosystem processes properties. integration into will overcome some limitations uncertainties current cycle accounting methods project implementation. Development robust, standardized systems accomplished through transdisciplinary partnerships among agencies, academics, land managers, private sector. result hasten achievement objectives improved bottom-up top-down estimation accessibility standardization data measurements.

Language: Английский

Citations

0
Market approaches to sequester soil organic carbon on farms: justifications and suggested transformations from embedded market actors DOI
Ashley Colby, McKenzie F. Johnson, Courtney Hammond Wagner

et al.

Agriculture and Human Values, Journal Year: 2025, Volume and Issue: unknown

Published: Feb. 25, 2025

Language: Английский

Citations

0
Soil Organic Carbon Assessment for Carbon Farming: A Review DOI Creative Commons
Theodoros Petropoulos, Lefteris Benos, Patrizia Busato

et al.

Agriculture, Journal Year: 2025, Volume and Issue: 15(5), P. 567 - 567

Published: March 6, 2025

This review is motivated by the urgent need to improve soil organic carbon (SOC) assessment methods, which are vital for enhancing health, addressing climate change, and promoting farming. By employing a structured approach that involves systematic literature search, data extraction, analysis, 86 relevant studies were identified. These evaluated address following specific research questions: (a) What state-of-the-art approaches in sampling, modeling, acquisition? (b) key challenges, open issues, potential advancements, future directions needed enhance effectiveness of farming practices? The findings indicate while traditional SOC techniques remain foundational, there significant shift towards incorporating model-based machine learning models, proximal spectroscopy, remote sensing technologies. emerging primarily serve as complementary laboratory analyses, overall accuracy reliability assessments. Despite these challenges such spatial temporal variability, high financial costs, limitations measurement continue hinder progress. also highlights necessity scalable, cost-effective, precise tools, alongside supportive policies incentives encourage farmer adoption. Finally, development “System-of-Systems” integrates sensing, modeling offers promising pathway balancing cost accuracy, ultimately supporting practices.

Language: Английский

Citations

0