Artificial intelligence empowers sustainable agricultural nitrogen management

The alteration of the global nitrogen (N) cycle represents one of the most pressing environmental challenges of the 21^st century^[1,2]. While synthetic N fertilizers derived from the Haber-Bosch process have helped sustain the global population, the cascading effects of reactive N losses have caused severe ecological consequences, including air pollution, soil acidification, groundwater nitrate contamination, and the exacerbation of climate change through nitrous oxide (N₂O) emissions^[3]. The modern agricultural N crisis is driven by the spatial and systemic decoupling of crop and livestock production across multiple scales. At the field scale, soil biogeochemists and animal scientists operate in silos, using disconnected models with incompatible data protocols. At the farm scale, manure is treated as waste while croplands simultaneously purchase synthetic N. At the regional scale, concentrated animal feeding operations (CAFOs) create massive, localized N surpluses through manure accumulation, while distant croplands remain highly dependent on synthetic inputs^[4]. Achieving true sustainability requires re-establishing the nutrient loop, a paradigm known as crop-livestock integration (CLI) or circular agriculture^[4].

The rapid maturation of artificial intelligence (AI) offers an unprecedented opportunity to bridge these scales^[5]. AI possesses a strong capacity to uncover hidden patterns within massive, heterogeneous datasets, ranging from multi-spectral satellite imagery to high-frequency in-situ sensor networks. More importantly, the emergence of domain-specific Foundation Models and Large Language Models (LLMs) is revolutionizing the translation of these complex scientific models into actionable, whole-farm management practices^[6]. Yet over 90% of existing AI-enabled N management studies focus on cropland or livestock single systems, ignoring this core root cause.

This perspective aims to synthesize the current advancements and articulate a future roadmap for AI in comprehensive agricultural N management. We structure our discussion from perception to process modeling, exploring: (1) AI-driven perception across croplands and livestock facilities; (2) the critical coupling of AI with process-based biogeochemical models; and (3) the application of agricultural LLMs in translating these coupled mechanisms into sustainable, precision management decisions. Building on this framework, we illustrate how AI-empowered agriculture could bridge the broken nitrogen loop across spatial scales (Fig. 1). Through this lens, we aim to inspire interdisciplinary collaboration to redefine the future of sustainable nitrogen management.

Figure 1. 

AI-empowered roadmap for sustainable agricultural nitrogen management. By integrating AI-driven perception, process-based modeling (PINNs/KGML), and actionable intelligence, the framework enables precision nitrogen management across scale, and transition from a decoupled crop-livestock system to a sustainable, circular N economy.

The inherent spatiotemporal heterogeneity of the agricultural N cycle presents a substantial challenge for traditional monitoring. The emergence of AI, integrated with advanced observation technologies, is fundamentally reshaping our capabilities to perceive both cropland and livestock systems. In cropland systems, AI enhances N perception at multiple scales. At the macro scale, multi-modal remote sensing coupled with deep learning architectures enables regional mapping of crop N status. Foundation models tailored for agricultural remote sensing, such as EarthGPT and AgroMind, can directly interpret satellite imagery to identify N-deficiency patterns and estimate canopy N concentration with unprecedented accuracy (Table 1)^[7,8]. At the field scale, machine learning serves as a 'smart filter' for high-frequency in-situ sensors, processing noisy data streams from soil probes to continuously monitor NO₃⁻ leaching and episodic N₂O fluxes while isolating confounding environmental variables^[9−11].

In livestock systems, AI is establishing high-resolution monitoring of 'animal-end' N flows. Computer vision and wearable Internet of Things (IoT) sensors enable real-time tracking of individual animal health, feed intake, and metabolic efficiency, which directly govern N excretion rates^[12]. Within livestock housing, AI-driven sensor networks continuously monitor the microclimate, employing recurrent neural networks to predict and detect NH₃ and greenhouse gas emissions^[13].

Critically, our framework goes beyond isolated applications in either domain. By establishing a perception continuum across croplands and livestock facilities, it creates the foundational data infrastructure to trace N from fertilizer and feed inputs through to ultimate environmental fates, directly addressing the longstanding inability to track dynamic, 'invisible' N flows across disparate agricultural environments (Fig. 1).

While data-driven AI models excel at pattern recognition, they lack physical and biological constraints. Our framework uniquely addresses this by advocating for the deep coupling of AI with mechanistic models across soil biogeochemistry, animal metabolism, and manure management, a paradigm known as Knowledge-Guided Machine Learning (KGML) or Physics-Informed Neural Networks (PINNs)^[5]. This integration bridges three critical gaps in the fragmented N cycle: (1) field-scale discontinuity between crop and livestock science; (2) farm-scale asynchrony between manure generation and crop uptake; and (3) regional-scale spatial mismatch between CAFO nutrient surpluses and cropland deficits. In croplands, hybrid architectures are replacing highly uncertain empirical sub-modules (e.g., microbial kinetics governing N₂O production) within traditional models like DeNitrification-Decomposition (DNDC) with trained neural networks, while retaining overarching mass-balance frameworks^[14]. This significantly enhances the robustness of N-loss predictions under climate extremes. In livestock systems, AI is being coupled with animal nutrition models to dynamically optimize dietary amino acid profiles, maximizing N retention (meat/milk protein) and minimizing urinary N excretion^[15]. Additionally, machine learning models are deployed to optimize manure composting processes, predicting and mitigating N volatilization by adjusting aeration and carbon-to-nitrogen (C : N) ratios in real-time^[16].

The ultimate systemic challenge is resolving the spatial mismatch between manure nutrient supply and crop demand across scales. At the regional level, utilizing multi-objective optimization algorithms and reinforcement learning, AI can process regional GIS data, transport logistics, and localized soil N deficits to formulate optimal manure redistribution networks^[17]. By matching CAFO waste streams with the specific nutrient requirements of surrounding croplands, AI provides the mathematical blueprint for restoring the circular N economy, effectively substituting synthetic fertilizers with recycled organic N^[18]. This bridges the regional-scale decoupling that has historically severed livestock nutrient concentration from crop nutrient demand.

The translation of integrated crop-livestock modeling into farm-level decision-making has historically been hindered by complexity. The rapid expansion of agricultural LLMs is bridging this divide, offering natural language interfaces for systemic N management (Table 1). For cropland management, global models like AgroLLM and AgroGPT interactively diagnose N deficiencies and formulate precise '4R' (Right source, rate, time, place) prescriptions. Within the rapidly advancing Chinese AI ecosystem, comprehensive platforms such as the IMAP planting model and the Shennong (神农) Large Model 3.0 provide sophisticated production reasoning to optimize fertilizer efficiency (https://shennong.cau.edu.cn/). Open-source models like Sinong (司农)^[19] further democratize access to agronomic knowledge (Fig. 2). Pilot applications of the AI-driven 'Nutrient Expert' model have demonstrated an 18%–22% reduction in N fertilizer usage while simultaneously achieving a 6%–14% increase in crop yield.

Figure 2. 

Framework of the AI-Driven N management agent. The bottom layer continuously harvests multimodal, cross-scale data from crop, livestock, and waste nodes via in-situ sensors, UAVs, computer vision, and IoT (Internet of Things) networks. The middle layer serves as the mechanistic core, coupling traditional biogeochemical models with KGML to rapidly simulate N fluxes while strictly enforcing physical mass-balance constraints. Finally, the top layer acts as the cognitive engine. A master reasoning engine utilizes retrieval-augmented generation (RAG) and verified literature databases to synthesize model outputs and mitigate AI hallucinations.

Equally critical is the emergence of highly specialized livestock LLMs that optimize the animal N-footprint (Fig. 2). Models such as the AI4DLLM (孺子牛) focus on the dairy sector, optimizing feed efficiency and milk protein yield (https://news.cau.edu.cn/mtndnew/ff81332b045b49fbb623b5eb0cb0d725.htm). The Suwu (苏武) Smart Sheep Raising Model provides systemic herd management through the integration of satellite remote sensing-derived grassland nitrogen status with precision supplementary feeding algorithms. In the swine industry, the PigGPT platform, deployed in collaboration with the National Pig Big Data Center, integrates multi-source data from over 3,000 pig farms to provide real-time feed nitrogen formulation optimization. Early operational results indicate a 10%–15% improvement in whole-herd nitrogen use efficiency, with corresponding reductions in manure nitrogen loading per unit of pork produced. Furthermore, whole-farm crop-livestock integration is being realized through systems like the Smart Layer Chicken Model S1, which optimizes the orchard-chicken N cycle to achieve a 12%–18% reduction in feed N alongside a 20%–25% reduction in N fertilizer application (Table 1).

Our framework points toward integrating these domain-specific agents via APIs into a unified Whole-Farm Intelligent Agent, capable of co-optimizing crop fertilizer prescriptions, animal feed formulations, and manure recycling logistics simultaneously. This resolves the historical barrier of model complexity, translating highly technical, siloed biogeochemical data into natural language so that everyday farmers and decision-makers can actually execute sustainable N management.

Despite the immense potential, integrating AI into global N management faces critical challenges. First, data silos and heterogeneity remain primary bottlenecks^[20]. High-resolution data on soil nitrogen fluxes and animal metabolism are held and managed by different institutions and protocols^[21]. Building a unified, standardized agricultural nitrogen data platform, potentially via federated learning to protect commercial farm privacy, is imperative^[22].

Second, the 'black-box' nature of deep learning hinders policy adoption. Explainable AI (XAI) is essential, particularly when AI outputs are used to guide environmental regulations or allocate subsidies for manure applications. Integrating causal inference with machine learning ensures that AI explicitly elucidates the underlying biogeochemical and metabolic drivers of agricultural N cycling processes^[23].

Third, the ethical implications of AI-driven agriculture must be addressed to prevent widening the socio-economic divide^[24]. High-resolution perception tools, such as Unmanned Aerial Vehicles (UAVs) and wearable IoT sensors, require significant capital investment, risking a scenario where AI-optimized N management exclusively benefits large-scale farmers. To ensure equitable access, three architectural innovations address smallholder constraints (limited connectivity, heterogeneous plots, and minimal capital): (1) Federated learning frameworks (e.g., the Sinong platform) enable collective model training without raw data sharing; (2) Edge-computing (e.g., Ruzi Niu Model) shifts inference to low-cost local devices; and (3) Physics-informed models reduce data dependence via embedded biogeochemical constraints of N cycling. For small mixed farms in sub-Saharan Africa and South Asia, where synthetic fertilizers are cost-prohibitive, these systems offer immediate agronomic returns, extending the framework's benefits to diverse global contexts.

Looking ahead, AI will serve as the central nervous system of circular agriculture. Leveraging edge-computing architectures scalable from industrial CAFOs to remote smallholder plots, next-generation systems will autonomously coordinate smart barns and field robotics to continuously match nutrient outputs with localized N demands.

The profound disruption of the global nitrogen cycle necessitates a paradigm shift away from decoupled agricultural practices toward systemic, sustainable management. This work establishes a three-tier AI-enabled framework for closed-loop agricultural nitrogen management, unifying a full-chain perception continuum, physics-informed coupled modeling, and inclusive agricultural LLM decision-making. This framework addresses long-standing barriers of data silos, model black-box limitations, and inequitable technology access. Embracing this AI-empowered framework is essential to navigate the delicate trade-off between securing global food systems and restoring planetary nitrogen balance. Yet, realizing this vision will require unprecedented interdisciplinary collaboration among biogeochemists, animal scientists, agronomists, and computer scientists.