An intelligent pratacultural framework for sustainable grassland management

Weikang Zhao, Yi SUN, Adilbek Nogayev, Shuhua Yi, Fujiang Hou

Farming System, 2026

Full text Open access: Gold Metadata (CSL-JSON)
DOI 10.1016/j.farsys.2026.100265
Data confidence 70 medium
How this score is calculated

It is built from checkable properties of the record itself: whether the article has a DOI, how many independent sources confirmed its data, whether the required fields are filled in, and how recent the latest confirmation is. Disagreement between sources and a missing identifier lower it. A value of 80 or above means the record was published without further checking; 60 to 79 means it is published at reduced confidence, with a manual check advisable. A record verified by an editor by hand is never pushed below 90 by a recalculation. This scores our data about the article, not the article itself.

Abstract

: Traditional prataculture management models are increasingly inadequate under the dual pressures of global climate change and land degradation, limiting the sustainable development of grassland resources and the realization of China's "big food" concept. Grounded in systems holism and socio-ecological coupling theory, we propose an intelligent pratacultural management framework integrating artificial intelligence (AI), Internet of Things (IoT), remote sensing, and multi-source data fusion. The framework was developed through a structured synthesis of peer-reviewed literature on socio-ecological systems, digital agriculture, and grassland management, systematically compared against existing frameworks to identify structural and functional gaps. The framework organizes management around three factor groups (abiotic, biological, and social), three interfaces (plant–land, pasture–livestock, and pasture/animal–market), and four production layers (pre-plant, plant, animal, and post-biological), operationalized through five sequential stages: data collection, modeling, analysis, decision-making, and implementation. The framework enables real-time ecosystem monitoring, dynamic simulation, optimized resource allocation, and predictive early-warning across the full forage–livestock–market chain. Key challenges to AI adoption in prataculture, including data quality, algorithm adaptability, talent shortages, and economic barriers, are identified, alongside future development opportunities. Unlike general socio-ecological system frameworks or crop-focused smart-farming models, this framework provides a grassland-specific, hierarchically operationalized system with real-time dynamic coupling capabilities, offering a practical blueprint for the digital and sustainable transformation of grassland management, with China's grassland systems serving as the primary empirical basis for framework development and illustrated application, while the underlying architecture is intended to be transferable, with region-specific calibration, to other extensive pastoral systems.