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Summary Future increases in drought severity and frequency are predicted to have substantial impacts on plant function and survival. However, there is considerable uncertainty concerning what drought adjustment is and whether plants can adjust to sustained drought. This review focuses on woody plants and synthesises the evidence for drought adjustment in a selection of key above‐ground and below‐ground plant traits. We assess whether evaluating the drought adjustment of single traits, or selections of traits that operate on the same plant functional axis (e.g. photosynthetic traits) is sufficient, or whether a multi‐trait approach, integrating across multiple axes, is required. We conclude that studies on drought adjustments in woody plants might overestimate the capacity for adjustment to drier environments if spatial studies along gradients are used, without complementary experimental approaches. We provide evidence that drought adjustment is common in above‐ground and below‐ground traits; however, whether this is adaptive and sufficient to respond to future droughts remains uncertain for most species. To address this uncertainty, we must move towards studying trait integration within and across multiple axes of plant function (e.g. above‐ground and below‐ground) to gain a holistic view of drought adjustments at the whole‐plant scale and how these influence plant survival.
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Leveraging soil mapping and machine learning to improve spatial adjustments in plant breeding trials
Abstract Spatial adjustments are used to improve the estimate of plot seed yield across crops and geographies. Moving means (MM) and P‐Spline are examples of spatial adjustment methods used in plant breeding trials to deal with field heterogeneity. Within the trial, spatial variability primarily comes from soil feature gradients, such as nutrients, but a study of the importance of various soil factors including nutrients is lacking. We analyzed plant breeding progeny row (PR) and preliminary yield trial (PYT) data of a public soybean breeding program across 3 years consisting of 43,545 plots. We compared several spatial adjustment methods: unadjusted (as a control), MM adjustment, P‐spline adjustment, and a machine learning‐based method called XGBoost. XGBoost modeled soil features at: (a) the local field scale for each generation and per year, and (b) all inclusive field scale spanning all generations and years. We report the usefulness of spatial adjustments at both PR and PYT stages of field testing and additionally provide ways to utilize interpretability insights of soil features in spatial adjustments. Our work shows that using soil features for spatial adjustments increased the relative efficiency by 81%, reduced the similarity of selection by 30%, and reduced the Moran's I from 0.13 to 0.01 on average across all experiments. These results empower breeders to further refine selection criteria to make more accurate selections and select for macro‐ and micro‐nutrients stress tolerance.
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- Award ID(s):
- 1954556
- PAR ID:
- 10579990
- Publisher / Repository:
- Wiley
- Date Published:
- Journal Name:
- Crop Science
- Volume:
- 64
- Issue:
- 6
- ISSN:
- 0011-183X
- Page Range / eLocation ID:
- 3135 to 3152
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.1002/csc2.21336
