[1]

Fowler D, Steadman CE, Stevenson D, Coyle M, Rees RM, et al. 2015. Effects of global change during the 21st century on the nitrogen cycle. Atmospheric Chemistry and Physics 15:13849−13893

doi: 10.5194/acp-15-13849-2015
[2]

Galloway JN, Townsend AR, Erisman JW, Bekunda M, Cai Z, et al. 2008. Transformation of the nitrogen cycle: recent trends, questions, and potential solutions. Science 320:889−892

doi: 10.1126/science.1136674
[3]

Sutton MA, Oenema O, Erisman JW, Leip A, van Grinsven H, et al. 2011. Too much of a good thing. Nature 472:159−161

doi: 10.1038/472159a
[4]

Jin S, Zhang B, Wu B, Han D, Hu Y, et al. 2021. Decoupling livestock and crop production at the household level in China. Nature Sustainability 4:48−55

doi: 10.1038/s41893-020-00596-0
[5]

Aderele MO, Srivastava AK, Butterbach-Bahl K, Rahimi J. 2025. Integrating machine learning with agroecosystem modelling: current state and future challenges. European Journal of Agronomy 168:127610

doi: 10.1016/j.eja.2025.127610
[6]

Yin S, Xi Y, Zhang X, Sun C, Mao Q. 2025. Foundation models in agriculture: a comprehensive review. Agriculture 15:847

doi: 10.3390/agriculture15080847
[7]

Zhang W, Cai M, Zhang T, Zhuang Y, Mao X. 2024. EarthGPT: a universal multimodal large language model for multi-sensor image comprehension in remote sensing domain. arXiv 00:2401.16822v3

doi: 10.48550/arXiv.2401.16822
[8]

Li Q, Zhang Y, Mai Z, Chen Y, Lou S, et al. 2025. Can large multimodal models understand agricultural scenes? Benchmarking with AgroMind. arXiv 00:2505.12207

doi: 10.48550/arXiv.2505.12207
[9]

Haq SIU, Wang G, Khan SN, Song C, Ma C, et al. 2025. Multi-dimensional optical remote sensing in agriculture: spectral, angular, and spatial scaling for crop stress monitoring. Smart Agricultural Technology 12:101583

doi: 10.1016/j.atech.2025.101583
[10]

Fei S, Hassan MA, Xiao Y, Su X, Chen Z, et al. 2023. UAV-based multi-sensor data fusion and machine learning algorithm for yield prediction in wheat. Precision Agriculture 24:187−212

doi: 10.1007/s11119-022-09938-8
[11]

Dhaliwal JK, Panday D, Robertson GP, Saha D. 2025. Machine learning reveals dynamic controls of soil nitrous oxide emissions from diverse long-term cropping systems. Journal of Environmental Quality 54:132−146

doi: 10.1002/jeq2.20637
[12]

Tedeschi LO, Lopez PG, Menendez HM III, Seo S. 2025. Advancing precision livestock farming: integrating artificial intelligence and emerging technologies for sustainable livestock management. Animal Bioscience 39(4):250289

doi: 10.5713/ab.25.0289
[13]

Bordignon F, Provolo G, Riva E, Caria M, Todde G, et al. 2025. Smart technologies to improve the management and resilience to climate change of livestock housing: a systematic and critical review. Italian Journal of Animal Science 24:376−392

doi: 10.1080/1828051X.2025.2455500
[14]

Liu L, Xu S, Tang J, Guan K, Griffis TJ, et al. 2022. KGML-ag: a modeling framework of knowledge-guided machine learning to simulate agroecosystems: a case study of estimating N2O emission using data from mesocosm experiments. Geoscientific Model Development 15:2839−2858

doi: 10.5194/gmd-15-2839-2022
[15]

Condotta ICFS, Tedeschi LO. 2026. ASAS-NANP Symposium: mathematical modeling in animal nutrition: revolutionizing animal farming with artificial intelligence: trends, challenges, and opportunities. Journal of Animal Science 104:skaf441

doi: 10.1093/jas/skaf441
[16]

Aydın Temel F, Cagcag Yolcu O, Turan NG. 2023. Artificial intelligence and machine learning approaches in composting process: a review. Bioresource Technology 370:128539

doi: 10.1016/j.biortech.2022.128539
[17]

Yan J, Yan B, Shi W, Feng Y. 2025. Selecting suitable sites for livestock manure composting via the integration of machine learning, Median center and geographic information system. Agricultural Systems 226:104300

doi: 10.1016/j.agsy.2025.104300
[18]

Saha S, Bhardwaj A. 2025. A narrative review of artificial intelligence to optimize the use of fertilizers: a game changing opportunity. Crop, Forage & Turfgrass Management 11:e70027

doi: 10.1002/cft2.70027
[19]

Nanjing Agricultural University & Nanjing University of Science and Technology. 2016. Sinong. https://github.com/njauzzx/Sinong
[20]

Durrant A, Markovic M, Matthews D, May D, Enright J, et al. 2022. The role of cross-silo federated learning in facilitating data sharing in the agri-food sector. Computers and Electronics in Agriculture 193:106648

doi: 10.1016/j.compag.2021.106648
[21]

Zhang X, Davidson EA, Mauzerall DL, Searchinger TD, Dumas P, et al. 2015. Managing nitrogen for sustainable development. Nature 528:51−59

doi: 10.1038/nature15743
[22]

Basso B, Antle J. 2020. Digital agriculture to design sustainable agricultural systems. Nature Sustainability 3:254−256

doi: 10.1038/s41893-020-0510-0
[23]

Ouyang Z, Ma Y, Kou Y, Liu X. 2025. Data governance, core technologies, and application challenges of China's agricultural large models. Transactions of the Chinese Society of Agricultural Engineering 41:153−162 (in Chinese)

doi: 10.11975/j.issn.1002-6819.202505226
[24]

Mehrabi Z, McDowell MJ, Ricciardi V, Levers C, Martinez JD, et al. 2021. The global divide in data-driven farming. Nature Sustainability 4:154−160

doi: 10.1038/s41893-020-00631-0