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The push toward precision agriculture is increasingly driven by environmental considerations alongside economic pressures, fueling progress in variable-rate fertilization (VRF) technology. Although centrifugal fertilizer spreaders offer benefits such as mechanical simplicity, low cost, and high throughput, non-homogeneous spread patterns are becoming a primary obstacle to their broader use. An urgent investigation is therefore required into how particles behave and what variability patterns emerge when centrifugal VRF spreaders operate under multi-pass overlapped application, to boost both evenness of coverage and field accuracy. This paper examined and assessed the operational characteristics of a custom-built centrifugal VRF spreader that uses real-time crop status data for rice and wheat, applying the collection tray protocols detailed in ISO 5690 and ASAE S341.2 (1999). Distribution uniformity was evaluated by calculating the coefficient of variation (CV) from weighed fertilizer captured in standardized trays, thereby gauging the evenness of the applied spread profile. The obtained data indicated effective swath widths of 21.05, 22.58, and 23.67 m corresponding to target application dosages of 225, 300, and 375 kg/ha, respectively. In practice, multi-pass, overlapped spreading tended to deliver application amounts exceeding the intended rates, with within-swath CVs of 11.51, 9.25, and 11.28% for each respective dosage level. Field-based assessments of multi-pass, overlapped operations recorded a mean discrepancy of 4.54% between delivered and target rates, along with a mean lateral distribution CV of 11.94% across the implement’s working span, results that align with the performance criteria for transverse particle placement using centrifugal broadcasters. The insights generated by this work provide a conceptual reference point for the design refinement and technological evolution of centrifugal VRF equipment, carrying considerable applied and societal value in hastening their integration into everyday precision farming practice.

The contemporary food system generates data at nearly every point where biological production, industrial processing, logistics, retail exchange, and consumer behaviour intersect. Farms produce sensor, satellite, machinery, weather, input, yield, and management data, while processing plants generate quality, safety, traceability, energy, water, and waste records. Downstream actors produce further streams of information through cold-chain monitoring, inventory systems, point-of-sale records, household consumption patterns, and nutrition-related choices. Despite this expanding data environment, the food system remains poor in system-wide intelligence because most data are captured, governed, and analysed within isolated organisational and technical domains. Production analytics, manufacturing intelligence, supply chain visibility, and consumer analytics have developed unevenly and are rarely connected into a coherent information architecture. As a result, decisions continue to be optimised locally rather than across the whole food system. This article proposes a conceptual framework for integrating production, processing, distribution, and consumption analytics into a unified food system intelligence capability. Food system intelligence is defined as the capacity to connect data across food system stages, convert them into actionable knowledge, and support decisions that improve productivity, sustainability, resilience, quality, and consumer value simultaneously. The article does not present new empirical data but synthesises existing literature to construct an integrative framework. The framework identifies four core data layers corresponding to production, processing, distribution, and consumption, joined through an integration layer based on standards, shared semantics, interoperable platforms, data-sharing agreements, and governance mechanisms. It also emphasises feedback flows, especially the use of downstream consumption, waste, and demand data to inform upstream production and processing decisions. Conversely, upstream sustainability, provenance, and production-practice data can enrich downstream retail, labelling, and consumer choice. The central argument is that food system intelligence is not primarily constrained by the absence of data but by the absence of integration and governance. Building this capability requires technical interoperability, institutional trust, reciprocal value creation, and a shift from stage-specific analytics to whole-system reasoning. The proposed framework offers a roadmap for connecting food system data to decisions from farm to fork and from fork back to farm.