What limits designing ideal crops from their genotypes?

Predict crop traits such as yield from genotype across environments well enough to design ideal varieties before they are planted.

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State the question, from 100 essential questions for the future of agriculture

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	1	+# What limits designing ideal crops from their genotypes?
	2	+
	3	+If breeders could predict how a plant will grow and yield from its DNA, in any field and season, they could design varieties on a computer before planting them. Today's predictions work well within breeding programmes but falter when the environment changes.
	4	+
	5	+## Question
	6	+
	7	+What are the challenges of designing crops with ideal phenotypes from their genotypes? This concerns predicting complex traits such as yield from genomic data across environments (genotype × environment interaction), integrating phenotyping, weather, soil and management data, and using such predictions to design new varieties.
	8	+
	9	+## Background
	10	+
	11	+The question is in the theme "exploring resources and technologies for breeding" of the 100 essential questions for the future of agriculture, compiled from 419 questions submitted worldwide in 2022. The authors note that combining genetics with high-throughput sequencing gives a theoretical basis for customised varieties, and that digitalisation and artificial intelligence have led to breakthroughs in molecular design breeding [1].
	12	+
	13	+## What is known
	14	+
	15	+- Genomic selection, which predicts breeding values from genome-wide markers, speeds up the breeding cycle and has delivered tangible genetic gains in maize; genotype × environment interaction is one of the main statistical complications of such models [2].
	16	+- The Genomes to Fields initiative made public the phenotypic, genotypic, soil, weather and management data for maize hybrids grown at 45 locations from 2014 to 2022, and used them for an open yield-prediction competition [3].
	17	+- In that competition the winning approach combined an environment-focused machine-learning model with a genetic one, while teams using quantitative genetics, deep learning, mechanistic crop models and ensembles also delivered satisfactory yield estimates [4].
	18	+- A 2024 round of the competition used data from 2014–2023 to predict grain yield in the 2024 field trials [5].
	19	+
	20	+## What would count as progress
	21	+
	22	+No single result settles a question like this; contributions like these would move it forward:
	23	+
	24	+- Predictions submitted before harvest for new trials and scored afterwards, as in the open maize competitions, extended to other crops and regions.
	25	+- Models that combine genomic data with crop growth models and remote-sensing phenotypes, validated on environments absent from training.
	26	+- Demonstrations that a variety designed by prediction for a target environment performs as predicted in independent field trials.
	27	+
	28	+## References
	29	+
	30	+1. Hu, Y. et al. (2023). [100 essential questions for the future of agriculture](https://doi.org/10.1002/moda.5). Modern Agriculture 1(1), 4–12.
	31	+2. Crossa, J. et al. (2017). [Genomic selection in plant breeding: methods, models, and perspectives](https://doi.org/10.1016/j.tplants.2017.08.011). Trends in Plant Science 22(11), 961–975.
	32	+3. Lima, D. C. et al. (2023). [Genomes to Fields 2022 maize genotype by environment prediction competition](https://doi.org/10.1186/s13104-023-06421-z). BMC Research Notes 16, 148.
	33	+4. Washburn, J. D. et al. (2025). [Global genotype by environment prediction competition reveals that diverse modeling strategies can deliver satisfactory maize yield estimates](https://doi.org/10.1093/genetics/iyae195). Genetics 229(2), iyae195.
	34	+5. Chen, Q. et al. (2026). [Genomes to fields 2024 maize genotype by environment prediction competition](https://doi.org/10.1186/s13104-026-07629-5). BMC Research Notes 19, 113.
	35	+
	36	+## Source
	37	+
	38	+Imported from [100 essential questions for the future of agriculture, question 24](https://doi.org/10.1002/moda.5), curated by Hu et al. Listed as open in the source (2023) and still an active research question as of 2026-10-20. The statement above is written in our own words; see the source for the original wording, history and discussion.