Abstract
To address challenges in food security, a better understanding of crop performance under varying and changing environmental conditions is required. Plant Time Warping (PTW) is a deep learning model that integrates high-throughput field phenotyping data with genomic and environmental information to predict wheat yield. PTW leverages image time series, genetic markers, and environmental covariates to learn genotype-specific physiological responses to temperature and vapor pressure deficit. Compared to mere genomic prediction models, PTW demonstrates superior performance when predicting yield in unseen environments across 48 year-locations in Europe. The PTW model captures non-linear growth responses varying with phenological stages and identifies distinct patterns associated with yield performance and stability. Specifically, varieties with higher yield stability exhibit reduced sensitivity to vapor pressure deficit around 1.5 kPa and distinctive temperature responses during emergence and senescence. The learned response pattern enable retrospective and prospective yield predictions, providing a foundation for location-specific variety recommendations and targeted breeding strategies. The integration of phenomic, genomic, and enviromic data has the potential to substantially advance research in climate adaptation strategies for crop production by addressing generalization challenges of predictions to novel environmental conditions.
Abstract from the paper’s OpenAlex record; open copy licensed cc-by. Read at the source ↗
The text the model read, with the source record’s inline markup (italics, sub- and superscripts) rendered and its character entities decoded; quote offsets refer to the raw text.
Claims 6
Claim · model-written summary
PTW predicts yield in unseen environments with superior performance compared with genomic prediction models
- Taxon
- wheat
Triticum aestivume5 - Outcome
- performance when predicting yield in unseen environmentse1
- Direction
- superiore1
- Comparator
- genomic prediction modelse1
- Setting
- 48 year-locations in Europee1
Organisms pictured · Representative pictures of the matched species, not figures from the paper.
Verbatim
e1“Compared to mere genomic prediction models, PTW demonstrates superior performance when predicting yield in unseen environments across 48 year-locations in Europe.”
e5“to predict wheat yield”
Claim · model-written summary
PTW captures non-linear growth responses that vary with phenological stages
- Taxon
- wheatTriticum aestivume5
- Organ / stage
- phenological stagese2
- Outcome
- non-linear growth responsese2
- Qualifier
- varying with phenological stagese2
Organisms pictured · Representative pictures of the matched species, not figures from the paper.
Verbatim
e2“The PTW model captures non-linear growth responses varying with phenological stages and identifies distinct patterns associated with yield performance and stability.”
e5“to predict wheat yield”
Claim · model-written summary
PTW identifies distinct response patterns associated with yield performance and stability
- Taxon
- wheatTriticum aestivume5
- Outcome
- distinct patternse2
- Qualifier
- associated with yield performance and stabilitye2
Organisms pictured · Representative pictures of the matched species, not figures from the paper.
Verbatim
e2“The PTW model captures non-linear growth responses varying with phenological stages and identifies distinct patterns associated with yield performance and stability.”
e5“to predict wheat yield”
Claim · model-written summary
Varieties with higher yield stability show reduced sensitivity to vapor pressure deficit around 1.5 kPa
- Taxon
- wheatTriticum aestivume5
- Material
- varieties with higher yield stabilitye3
- Treatment
- vapor pressure deficit around 1.5 kPae3
- Outcome
- sensitivity to vapor pressure deficite3
- Direction
- reducede3
Organisms pictured · Representative pictures of the matched species, not figures from the paper.
Verbatim
e3“Specifically, varieties with higher yield stability exhibit reduced sensitivity to vapor pressure deficit around 1.5 kPa and distinctive temperature responses during emergence and senescence.”
e5“to predict wheat yield”
Claim · model-written summary
Varieties with higher yield stability show distinctive temperature responses during emergence and senescence
- Taxon
- wheatTriticum aestivume5
- Material
- varieties with higher yield stabilitye3
- Organ / stage
- emergence and senescencee3
- Outcome
- temperature responsese3
- Qualifier
- distinctivee3
Not reportedDirection
Organisms pictured · Representative pictures of the matched species, not figures from the paper.
Verbatim
e3“Specifically, varieties with higher yield stability exhibit reduced sensitivity to vapor pressure deficit around 1.5 kPa and distinctive temperature responses during emergence and senescence.”
e5“to predict wheat yield”
Claim · model-written summary
The learned response patterns enable retrospective and prospective yield predictions
- Taxon
- wheatTriticum aestivume5
- Outcome
- retrospective and prospective yield predictionse4
Organisms pictured · Representative pictures of the matched species, not figures from the paper.
Verbatim
e4“The learned response pattern enable retrospective and prospective yield predictions”
e5“to predict wheat yield”