Climate-smart optimization of sowing dates for double-cropping rice by combining crop model and machine learning
Abstract
Published as:
J. Zhang,
M. Huang,
I. N. Athanasiadis,
L. Liu,
B. Liu,
L. Xiao,
Y. Zhu,
W. Cao,
L. Tang,
Climate-smart optimization of sowing dates for double-cropping rice by combining crop model and machine learning,
Agricultural Systems, 239:104942,
2026, Elsevier BV, doi:10.1016/j.agsy.2026.104942.
layout: publication date: 2026-09-03 09:07 permalink: /publications/Zhang2026_b/
categories: [publications, articles] tags: [MyPapers]
authors: [J. Zhang, B. Liu, I. N. Athanasiadis, L. Liu, Y. Zhu, W. Cao, L. Xiao, L. Tang] title: “The role of observations in calibrating crop models: A multi-model analysis of rice phenology” bibtexkey: Zhang2026_b year: 2026 month: July
journal: Journal of Integrative Agriculture
publisher: Elsevier BV doi: 10.1016/j.jia.2026.07.028 issn: 2095-3119,2352-3425
layout: publication date: 2026-09-03 09:07 permalink: /publications/Zhang2026_a/
categories: [publications, articles] tags: [MyPapers]
authors: [J. Zhang, W. Tang, H. Ma, W. Yan, I. N. Athanasiadis, S. Zhang, L. Liu, B. Liu, L. Xiao, Y. Zhu, W. Cao, Y. Zhang, L. Tang] title: “Genomic prediction vs. gene-based crop models: a case study on rice trait prediction” bibtexkey: Zhang2026_a year: 2026 month: 24~August
journal: Theoretical and Applied Genetics volume: 139 number: 9
pages: 242
publisher: Springer Science and Business Media LLC doi: 10.1007/s00122-026-05342-2 issn: 0040-5752,1432-2242
abstract: “Conventional breeding for ideotypes in target environments remains challenging due to genotype-by-environment interactions and the genetic complexity of key agronomic traits. Traditional multi-environment field trials are costly and time-consuming, limiting rapid genetic gain. These challenges highlight the need for digital tools to support rice breeding. However, two major approaches, genomic prediction (GP) and gene-based crop models (GBCMs), have distinct advantages. In this study, a dataset derived from a natural rice population comprising 210 genotypes, genotyped with about 650,000 markers, rice dry matter, and yield across three environments, was used to develop two genomic prediction models, genomic best linear unbiased prediction (GBLUP) and a convolutional neural network (CNN), together with a gene-based crop modeling framework. The effectiveness of these models in predicting rice traits and assisting in breeding selection was subsequently evaluated. Prediction results indicated that biomass and yield could be effectively predicted by all models, with Normalized Root Mean Square Error (NRMSE) values ranging from 10.60% to 18.59% and 9.93% to 18.19 respectively. In terms of predictive accuracy, parameter-based crop models achieved the highest predictive accuracy, although it was confined to theoretical simulations. This was followed by the GBCM and CNN, whereas the GBLUP exhibited the lowest performance. Furthermore, GGE biplot analysis revealed the predictions of the GBCM aligned more closely with field observations than those of the CNN, emphasizing the potential of GBCM as a practical surrogate for digital breeding. These results provide valuable insights into modeling genotype-by-environment interactions and support the development of data-informed breeding strategies for future rice improvement.”
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