Evidence map›Paper›PMID 38933002›Full record

ArticleFundamental research2023

Detect and attribute the extreme maize yield losses based on spatio-temporal deep learning.

Renhai Zhong, Yue Zhu, Xuhui Wang, Haifeng Li, Bin Wang, Fengqi You, Luis F Rodríguez, Jingfeng Huang, K C Ting, Yibin Ying and 1 more

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Fundamental research, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

2 citing papers in PubMed.

  1. Article
  2. Review
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

11 authors.

Renhai ZhongCollege of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou, Zhejiang 310058, China.
Yue ZhuCollege of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou, Zhejiang 310058, China.
Xuhui WangSino-French Institute for Earth System Science, College of Urban and Environmental Sciences, Peking University, Beijing 100871, China.
Haifeng LiSchool of Geosciences and Info-Physics, Central South University, South Lushan Road, Changsha 410000, China.
Bin WangNSW Department of Primary Industries, Wagga Wagga Agricultural Institute, Pine Gully Road Wagga Wagga, NSW 2650, Australia.
Fengqi YouRobert Frederick Smith School of Chemical and Biomolecular Engineering, Cornell University, Ithaca, NY 14853, USA.
Luis F RodríguezDepartment of Agricultural and Biological Engineering, University of Illinois at Urbana-Champaign, Urbana, IL 61801, USA.
Jingfeng HuangInstitute of Applied Remote Sensing and Information Technology, Zhejiang University, Hangzhou, Zhejiang 310058, China.
K C TingInternational Campus, Zhejiang University, Haining, Zhejiang 314400, China.
Yibin YingCollege of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou, Zhejiang 310058, China.
Tao LinCollege of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou, Zhejiang 310058, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Providing accurate crop yield estimations at large spatial scales and understanding yield losses under extreme climate stress is an urgent challenge for sustaining global food security. While the data-driven deep learning approach has shown great capacity in predicting yield patterns, its capacity to detect and attribute the impacts of climatic extremes on yields remains unknown. In this study, we developed a deep neural network based multi-task learning framework to estimate variations of maize yield at the county level over the US Corn Belt from 2006 to 2018, with a special focus on the extreme yield loss in 2012. We found that our deep learning model hindcasted the yield variations with good accuracy for 2006-2018 (R

Indexed as

Attribution analysisCrop yield estimationDeep LearningExtreme yield lossLong short-term memoryMulti-task learning

Identifiers

PMID38933002
PMCPMC11197588

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.