Agriculture faces the dual challenge of reducing greenhouse-gas emissions while maintaining food production under increasing climatic stress. This review examines fertilizer and crop management strategies to reducing production-related emissions and restoring carbon stocks in agroecosystems. It links fertilizer-related emissions, plant carbon inputs, and nutrient cycling to assess soil carbon sequestration in relation to both climate mitigation and soil fertility. Mineral nitrogen fertilizers often account for the largest share of the carbon footprint of crop production, but an adequate nitrogen supply is also required for photosynthesis, net primary production, root growth, crop yield, and plant-derived carbon inputs to soil. Carbon management should therefore optimize fertilizer production and use rather than simply restrict nitrogen inputs. Relevant measures include lower-emission fertilizer manufacture, improved nutrient-use efficiency, mineral–organic fertilization, manure and residue recycling, increased use of legumes and cover crops, and regulation of carbon–nitrogen interactions during organic-matter decomposition. Mineral fertilizers generally produce modest increases in soil organic carbon (SOC), whereas organic amendments produce larger gains. However, finite carbon-storage capacity, declining sequestration rates, reversibility, limited biomass and nutrient resources, and measurement uncertainty substantially restrict the contribution of soil sequestration to global climate-change mitigation. The most realistic objective of agroecosystem recarbonization is therefore to restore soil fertility, support stable food production, and increase resilience to climatic extremes.
Nano-enabled fertilizers and precision agriculture are increasingly framed as complementary strategies to enhance nutrient-use efficiency and reduce environmental footprints. However, their operational integration remains unclear because precision denotes either spatial field management or targeted nano-enabled release. This scoping review evaluated whether nanofertilizers have been operationally integrated into spatially explicit nutrient-management systems. Systematic searches in Scopus and Web of Science, complemented by citation tracing, identified 60 reports representing 59 unique evidence units. An operational framework disaggregated integration into spatial decision-making (S), differential nano-enabled deployment (N), and spatially resolved outcome validation (O), with validation maturity coded from V0 to V6. Of the 59 evidence units, 52 (88.1%) represented conceptual convergence, five (8.5%) technological proximity without operational coupling, and only two (3.4%) advanced partial integration. The review did not identify a fully assessed study that demonstrated the complete S–N–O sequence. The two most advanced cases followed complementary pathways: S-N without spatial outcome validation, and S-O with uniform nano-input application. Citation tracing also revealed that secondary claims often overstate operational maturity relative to primary evidence. Closing this gap requires establishing field-scale S–N–O workflows that explicitly link spatial diagnosis, differential nano-actuation, and multi-zone agronomic and environmental validation.
As a new wave of scientific and technological revolution
The central question facing agriculture today is no longer whether it will be possible to produce more. It is whether this production can be done sustainably, more efficiently and at a sufficient scale, while ensuring that scientific progress is implemented as solutions that work on farm. In this sense, the future of agriculture will not be determined by discovery alone. It will be determined by how well agricultural science and technology is engineered into integrated systems that are field ready, scalable and capable of delivering both productivity gains and environmental benefits
Agrophotovoltaics (APV) integrates solar photovoltaic systems with agricultural production, offering a sustainable solution to meet growing energy and food demands. This review explores the transformative role of artificial intelligence (AI) and machine learning (ML) in optimizing APV performance. Key applications include energy yield forecasting, predictive maintenance, fault detection and crop-energy balancing strategies. Advanced algorithms, such as neural networks, decision trees and reinforcement learning, deliver high prediction accuracy and significant operational improvements. Reported studies show forecasting accuracies up to R 2 of 0.96 for simulated irradiance datasets, representing model prediction performance rather than actual plant energy conversion efficiency. Likewise, AI-driven co-optimization of light distribution and irrigation improved leafy-vegetable yields by 10%–18% in experimental APV plots in India and France. The integration of Internet of Things sensors with ML models enables real-time environmental monitoring and dynamic resource management. This review presents the integrated synthesis of AI/ML applications specific to APV systems. It proposes a taxonomy of AI/ML use-cases, a research roadmap, and a quantitative synthesis linking model performance metrics with agronomic and energy outcomes. The study also identifies research gaps in data interoperability, environmental variability, model interpretability, and socioeconomic adoption. Addressing these challenges through interdisciplinary research and policy frameworks can accelerate intelligent APV deployment, advancing renewable energy, sustainable agriculture and climate resilience.
