How AI and Digital Technologies Can Enhance Sustainable Livestock Manure Management
An Overview From Treatment to Distribution
Bibliographic Data
| ID | 12306552 |
|---|---|
| Authors | Zhan Shi (0000-0002-1265-0356, Department of Land, Environment, Agriculture and Forestry University of Padova Legnaro Italy, corresponding author), Feihong Liang (0000-0003-1573-7622, College of Life Science Yulin University Yulin People's Republic of China), Francesco Marinello (0000-0002-3283-5665, Department of Land, Environment, Agriculture and Forestry University of Padova Legnaro Italy), Andrea Pezzuolo (0000-0002-9955-9896, Department of Land, Environment, Agriculture and Forestry University of Padova Legnaro Italy) |
| Year | 2026 |
| Publication date | 2026-02-06 |
| Open Access | Yes |
| Type | ARTICLE |
| Venue | Sustainable Development (JOURNAL) |
| Journal identifiers | ISSN: 0968-0802 • E-ISSN: 1099-1719 |
| Publisher | Wiley (PUBLISHER • GB) |
| DOI | 10.1002/sd.70728 |
| OpenAlex | W7128294421 |
| Language | EN |
| References cited | 76 |
Sustainable livestock manure management sits at the nexus of climate, nutrient circularity and water quality. This review explores how artificial intelligence (AI) and digital platforms are used across four management stages, that is, treatment, storage, valorisation and distribution, and figures out where integration fails to deliver auditable outcomes. A PRISMA‐guided search (2000–2025) identified 461 records, screened 91 articles; a 68‐study core was deeply analysed by multi‐dimensional coding matrix. Ninety‐three tags were regrouped into 44 explainable variables. A symmetric correlation matrix and complete‐linkage clustering mapped co‐occurrence structure and ‘Method—Stage—Outcome’ chains. Fifteen strong ties revealed mature systems, for example, AI with policy tools ( R 2 = 0.49), sensors with decision support (0.34), valorisation with waste‐to‐energy (WtE) outcomes (0.32). Seven modules emerged, including a policy–geospatial information systems (GIS) block and a slurry‐and‐digestate control block. Two chains were complete, that is, valorisation to WtE and policy–GIS tooling for greenhouse‐gas reporting, while slurry/digestate showed robust technical links (anaerobic digestion to control, R 2 = 0.23; slurry inputs to ammonia outcomes, 0.24) yet weak alignment with policy endpoints. Storage monitoring and water‐reuse outcomes lacked ≥ 0.20 bridges to functions or platforms. The structure explains why progress concentrates in energy valorisation and policy dashboards and why storage and reuse lag. Actionable routes follow: embed explainable control and monitoring at treatment, storage and distribution; stream plant and field data to GIS‐enabled monitoring–reporting–verification (MRV) and life‐cycle assessment (LCA) dashboards; and frame nutrient compliance as optimisation‐plus‐placement tied to digital permitting. These moves align with SDG 7/12/13/6 and EU climate‐neutrality and circular‐bioeconomy goals
Control (management · Manure · Manure management · Nexus (standard · Reuse · Sustainability · Sustainable Management · Valorisation · Agriculture Sustainability and Environmental Impact · Innovations in Aquaponics and Hydroponics Systems · Water-Energy-Food Nexus Studies
| Highly cited | No |
|---|