Nearly half of global anthropogenic N2O emissions are accounted for by nitrogen fertiliser application. Therefore, accurate prediction of fertiliser-induced N2O fluxes is crucial for optimising fertiliser strategies and mitigating climate change. In this work, we introduce DA-Mamba: a data augmentation-enhanced state space model that can capture long-range N2O flux dynamics and their interactions with agri-environmental variables, even when data is limited. Using a publicly available dataset of fertiliser-induced N2O emissions, DA-Mamba achieves state-of-the-art performance, outperforming six baseline models. Additionally, we have integrated DA-Mamba as a containerised microservice within ParallelFarm, our cloud-based precision fertilisation and farm management system. The service uses real-time weather, soil and management data to generate optimised fertiliser plans and field-level N2O–yield predictions, thereby supporting sustainable agricultural decision-making.

The prediction of fertilization rates is a critical area of research in the agricultural field and is essential for ensuring global food security. With the ongoing expansion of the global population and the escalating repercussions of climate change, precise crop fertilization rate predictions have become paramount. This is because accurate predictions can optimize resource allocation and improve agricultural productivity. Moreover, they can provide scientific support for policy-making and agricultural input management, thereby promoting sustainable agricultural development. Despite its importance, the complexity of agricultural systems, which is influenced by multiple factors including climate, geography, soil conditions, and management practices, poses significant challenges to prediction accuracy. In this paper, we propose a deep learning framework based on Graph Neural Networks (GNNs) that effectively incorporates geographical knowledge and multi-dimensional feature information. By modeling spatial relationships through graph structures (nodes and edges), our framework enhances fertilization rate prediction accuracy. We validate the model using two datasets of different scales. The results demonstrate excellent predictive performance across all datasets and strong scalability, highlighting its potential for agricultural fertilization rate prediction.

Promoting sustainable agriculture plays a crucial role in reducing greenhouse gas (GHG) emissions, lowering the carbon footprint, and improving farm resilience. Three challenges must be overcome to achieve sustainable agriculture management. Firstly, there is a lack of reliable and sustainable fertiliser solutions to improve fertiliser use efficiency, reduce GHG emissions while maintaining crop production. In addition, how to cost-effectively quantify the response of soil carbon and GHG fluxes to different fertilisation practices. Thirdly, there is a requirement to integrate multi-source farming data and AI models into a farm management information system (FMIS) to support intelligent decisions for farmers. To address these challenges, we developed the ParellelFarm, an AI-enabled sustainable farming management system that integrates multi-source farming data and AI-driven fertiliser and soil carbon models into a multi-tenant cloud platform, to support sustainable farming. It also provides remote field visualisation and management as well as instant messaging via web and mobile clients, supporting fast and accurate labour allocation with fewer resources. It is a potential solution for a cost-effective, highly productive and sustainable modern net-zero farm.

In order to control the first wave of COVID-19 pandemic in 2020, many models have shown effectiveness in predicting the spread of new coronary pneumonia and the different interventions. However, few models can collect large amounts of high-quality real-time data faster under the premise of protecting privacy, considering the impact of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) variant and the mass vaccination program as a new intervention. Therefore, we developed a mobile intelligent application that can collect a large amount of real-time data while protecting privacy and conducted a feasibility study by defining a new COVID-19 mathematical model SEMCVRD. By simulating different intervention measures, the prediction model of the mobile intelligent application used in this article simulates the epidemic situation in the U.K. as an example. The findings are as below: the optimal intervention strategy is to suppress the intervention at$P=3$(intervention intensity: the average number of contacts per person per day) before the end of March 2021, then gradually release the intervention intensity at a rate of$P+2$, and finally release the intensity to$P=9$in June 2021. The COVID-19 pandemic will end at the end of June 2021, when the total number of deaths will reach 128772. This strategy will be able to balance the tradeoff between loss of life and economic loss. Compared with the official statistics released by the U.K. government on May 31, 2021, our model can accurately predict the relative error rate of the total number of cases is less than 6.9%, and the relative error rate of the total number of deaths is less than 1%. Furthermore, the model is also suitable for collecting data from countries/regions around the world.