征稿启事丨AI驱动下气候智慧型农业系统重构

AI-Driven Reconfiguration of Climate-Smart Agricultural Systems

Submission Type: Invitation-Only Special Issue

Please note that this Special Issue is invitation-only. Manuscripts will be considered only if submitted upon invitation by the Guest Editors. Unsolicited submissions will not be considered for this collection.

Background

Agricultural and extensive livestock farming systems are under increasing pressure from climate change, land degradation, resource scarcity, and the urgent need to reduce greenhouse gas emissions while maintaining productivity and resilience. In response, climate-smart and regenerative agriculture has emerged as a promising pathway to support sustainable food production, improve soil and ecosystem health, and strengthen the adaptive capacity of farming systems.

At the same time, rapid advances in artificial intelligence (AI), machine learning, remote sensing, sensor networks, process-based modelling, and digital agriculture are creating new opportunities to redesign farming systems in more integrated, predictive, and adaptive ways. These technologies can improve monitoring, diagnosis, forecasting, and management decision-making across soil, crop, livestock grazing, water, nutrient, and carbon management. They also offer the potential to connect experimental knowledge, field observations, and regional-scale applications, thereby accelerating the transition from fragmented technological innovations to scalable and resilient farming system solutions.

This Special Issue aims to bring together high-quality invited contributions that explore how AI-enabled, data-driven, and systems-based approaches can support the reconstruction of climate-smart and regenerative agricultural systems under a changing climate.

Aims of the Special Issue

This Special Issue seeks to advance the scientific and practical understanding of how AI and data-driven approaches can contribute to the redesign of agricultural systems toward greater sustainability, resilience, and low-carbon performance. Specifically, the issue aims to:

● highlight recent advances in AI-enabled monitoring, modelling, and decision support for climate-smart and regenerative agriculture.

● promote interdisciplinary research linking soil, crops, livestock grazing, water, nutrients, carbon, engineering, and digital technologies.

● showcase field-validated and scalable solutions that move from proof-of-concept to real-world farming implementation.

● examine how technological innovation can be integrated with governance, adoption, and policy support for sustainable transition; and 

● provide new conceptual, methodological, and applied insights for resilient and low-carbon agricultural system redesign.

Scope

Invited manuscripts may address, but are not limited to, the following themes:

1. AI for soil carbon, soil health, and belowground processes

This topic covers AI applications in soil carbon monitoring, prediction, and accounting, soil health assessment, microbial communities, and rhizosphere processes, as well as the use of AI and machine learning to identify and interpret soil ecological processes.

土壤碳监测、预测与核算，土壤健康评价，微生物群落与根际过程解析，以及 AI/机器学习在土壤生态过程识别中的应用。

2. Climate-smart and regenerative management of farming systems

This area focuses on regenerative agricultural practices, including conservation tillage, cover cropping, organic amendments, crop rotation, and integrated crop–livestock systems, and their combined effects on productivity improvement, emission reduction, carbon sequestration, and resilience enhancement.

再生农业管理措施、保护性耕作、覆盖作物、有机培肥、轮作与种养结合系统，以及这些措施对增产、减排、固碳、韧性提升的综合影响。

3. Data-driven nutrient, water, and carbon management

This topic addresses nutrient cycling, water management, coupled carbon–nitrogen processes, resource-use efficiency improvement, and precision management and decision support under multi-objective optimization frameworks.

养分循环、水分管理、碳氮耦合过程、资源利用效率提升，以及多目标优化下的精准管理与决策支持。

4. Intelligent sensing, modelling, and decision support in agriculture and extensive livestock farming

Relevant contributions include remote sensing, sensor networks, digital twins, integration of process-based models with machine learning, early warning systems, diagnostic monitoring, and intelligent decision-support systems for practical farming applications.

遥感、传感器网络、数字孪生、过程模型与机器学习融合、早期预警、诊断监测和面向生产实践的智能决策系统。

5. System redesign for resilient and low-carbon agriculture

This theme welcomes studies on the redesign and optimization of cropping systems, crop–livestock integration systems, regional agricultural systems, and landscape-scale systems, with particular attention to climate adaptation, resilience, recovery capacity, and low-carbon transition.

作物系统、种养循环系统、区域农业系统和景观尺度系统优化，重点关注气候变化背景下的适应能力、恢复力与低碳转型。

6. Field validation, engineering integration, and scalable deployment

This area emphasizes the pathway from experiment to application and from isolated technologies to integrated system solutions, with a focus on engineering integration, field validation, regional adaptability, scalability, and replicability.

从试验到应用、从技术点到系统方案的工程化集成，关注技术落地、区域适配、可推广性和可复制性。

7. Governance, adoption, and policy support for sustainable transition

This topic includes technology adoption, behavioral responses, institutional arrangements, policy instrument design, and the barriers and incentives for promoting AI-enabled and regenerative agricultural practices across different regions.

技术采用、行为响应、制度安排、政策工具设计，以及AI和再生农业实践在不同地区推广中的障碍与激励机制。

Manuscript Types

This Special Issue will primarily include 10–12 invited paper 

• ● Research Articles
• ● Review Articles

A limited number of invited short-format papers may be considered, including:

• ● Commentary
• ● Perspective
• ● Letter

Short-format contributions should normally be within 2,000 words and include no more than 10 references.

Manuscript eligibility requirements

To ensure robustness and practical relevance, studies based on field experimentation should meet at least one of the following conditions:

• ● field data covering at least two years or two full life cycles; or 
• ● one year of data collected across multiple experimental sites. 

Studies based only on a single site and a single life cycle will generally not be considered for this Special Issue.

Submission and Editorial Process

This Special Issue is designed as a curated, invitation-only collection. To maintain a high academic standard and thematic coherence, a preliminary list of invited authors and proposed topics will be developed by the Guest Editor Team.

Authors are expected to first prepare an initial draft and share it with the Guest Editors for internal review. The Guest Editors will provide comments on relevance, quality, structure, and potential improvements before the manuscript is formally submitted through the journal’s online submission system. Only manuscripts that align with the Special Issue scope and meet the quality expectations of the Guest Editors will proceed to formal submission and peer review.

All submitted manuscripts will undergo the journal’s standard peer-review process and must meet the journal’s editorial and ethical standards.

Timeline

Guest Editor Team

● Dr.Tong Li, School of Agriculture and Food Sustainability, The University of Queensland, St Lucia, Australia, tong.li1@uq.edu.au

●  Professor Dr.Apostolos Kyriazopoulos, Department of Forestry and Management of the Environment and Natural Resources, Democritus University of Thrace, Greece; apkyriaz@fmenr.duth.gr

● Professor Dr. Rui Jiang, Research Center for Cultural Landscape Protection and Ecological Restoration, China–Portugal Belt and Road Cooperation Laboratory of Cultural Heritage Conservation Science, Gold Mantis School of Architecture, Soochow University, Suzhou 215006, China; ruijiang@suda.edu.cn

● Dr. Sreeni Chadalavada，School of Science, Engineering and Digital Technologies, University of Southern Queensland, Sreeni. Chadalavada@unisq.edu.au

About ENGINEERING Agriculture:

ENGINEERING Agriculture (EA) is jointly sponsored by the Chinese Academy of Engineering, China Agricultural University, and Higher Education Press. The journal is led by Editor-in-Chief Prof. Fusuo Zhang, Member of the Chinese Academy of Engineering and Professor at China Agricultural University, and Executive Editor-in-Chief Prof. Wei Chen, Member of the Chinese Academy of Engineering and President of China Agricultural University.

EA is indexed in major international databases, including DOAJ, Scopus, ESCI, and CSCD. The journal has a 2024 Impact Factor of 2.8 and a five-year Impact Factor of 3.5, ranking in JCR Q1 in Agronomy. Its 2024 CiteScore is 5.6, placing it in Q1 in both the General Agricultural and Biological Sciences category and the General Veterinary category.

EA is a fully open-access journal and does not charge article processing fees. Dedicated to advancing global agricultural modernization through engineering-oriented agricultural science and technology, EA publishes work that explores scientific discovery and technological innovation in agriculture while emphasizing the translation of science and technology into practical engineering solutions that address real-world challenges and drive agricultural transformation and progress.

Journal website: https://journal.hep.com.cn/fase/EN/home