An integrated, multivariate characterisation of water and photothermal regimes for faba bean in Australia

• Single-variable environment types (ETs) covary in time and space. • Multivariate ETs identified a syndrome of ‘dry, hot and high evaporative demand’. • The syndrome reduced Australian faba bean yield by 62 %. • Realised climate change increased the syndrome's frequency by 8 %. • Early sowing and phenology reduced the syndrome across Australia. Environmental characterisation provides a useful summary of a major source of variation in grain yield. Environments consist of water and photothermal regimes that covary in time and space, but previous characterisations have focussed on single-variable regimes such as drought, or downplayed the temporal pattern of multivariate regimes. Season-long, multivariate characterisations are needed to more realistically represent the complex growing conditions that crops encounter. We conducted two studies on faba bean, an important source of plant protein, in Australia, its largest exporter. From a database of yield with 299 variety trials, Study 1 tested the timing and strength of the association of seed yield with maximum and minimum temperature, heat stress, frost, solar radiation, vapour pressure deficit and simulated water supply:demand. Study 2 used cluster analysis of 30,096 simulated crops (1957–2022, three sowing dates, two varieties, 76 locations) to determine environment types for these variables, individually and combined. We tested the real-world relevance of the environment types with the seed yield data of Study 1. Water supply:demand, maximum temperature and vapour pressure deficit had the strongest links to grain yield in both studies. We identified four multivariate environment types that ranged from syndromes of ‘wet, cool and low evaporative demand’ to ‘dry, hot and high evaporative demand’. From least to most stressful environment type, median seed yield reduced by 62 %. Frequency of environment types varied with location, sowing date and variety, highlighting the potential value of earlier sowing and phenology for a large part of the country. From 1963–1992 compared with 1993–2022, the frequency of stressful environment types increased by 4 to 9 %, highlighting the need to adapt to a challenging future climate. Our findings can inform breeding, management and research of faba bean in Australia and beyond, and our multivariate method can be applied to other crops and environments.

Authors

Institutions

Publication Details

Journal
Agricultural and Forest Meteorology
Published
2025-02-03
DOI
https://doi.org/10.1016/j.agrformet.2025.110426
Citations
3
Primary Topic
Genetic and Environmental Crop Studies
Type
article
Field-Weighted Citation Impact
5.25

Funders

Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

An integrated, multivariate characterisation of water and photothermal regimes for faba bean in Australia

James B. Manson, Víctor O. Sadras, Lachlan Lake, Matthew D. Denton
3 citations
Agricultural and Forest Meteorology
Genetic and Environmental Crop Studies
5.25
article

An integrated, multivariate characterisation of water and photothermal regimes for faba bean in Australia

James B. Manson, Víctor O. Sadras, Lachlan Lake, Matthew D. Denton
article en
3 citations

Abstract

• Single-variable environment types (ETs) covary in time and space. • Multivariate ETs identified a syndrome of ‘dry, hot and high evaporative demand’. • The syndrome reduced Australian faba bean yield by 62 %. • Realised climate change increased the syndrome's frequency by 8 %. • Early sowing and phenology reduced the syndrome across Australia. Environmental characterisation provides a useful summary of a major source of variation in grain yield. Environments consist of water and photothermal regimes that covary in time and space, but previous characterisations have focussed on single-variable regimes such as drought, or downplayed the temporal pattern of multivariate regimes. Season-long, multivariate characterisations are needed to more realistically represent the complex growing conditions that crops encounter. We conducted two studies on faba bean, an important source of plant protein, in Australia, its largest exporter. From a database of yield with 299 variety trials, Study 1 tested the timing and strength of the association of seed yield with maximum and minimum temperature, heat stress, frost, solar radiation, vapour pressure deficit and simulated water supply:demand. Study 2 used cluster analysis of 30,096 simulated crops (1957–2022, three sowing dates, two varieties, 76 locations) to determine environment types for these variables, individually and combined. We tested the real-world relevance of the environment types with the seed yield data of Study 1. Water supply:demand, maximum temperature and vapour pressure deficit had the strongest links to grain yield in both studies. We identified four multivariate environment types that ranged from syndromes of ‘wet, cool and low evaporative demand’ to ‘dry, hot and high evaporative demand’. From least to most stressful environment type, median seed yield reduced by 62 %. Frequency of environment types varied with location, sowing date and variety, highlighting the potential value of earlier sowing and phenology for a large part of the country. From 1963–1992 compared with 1993–2022, the frequency of stressful environment types increased by 4 to 9 %, highlighting the need to adapt to a challenging future climate. Our findings can inform breeding, management and research of faba bean in Australia and beyond, and our multivariate method can be applied to other crops and environments.

Agricultural and Forest MeteorologyVol. 363
Flinders University (AU), South Australian Research and Development Institute (AU), The University of Adelaide (AU)
Australian Government, Grains Research and Development Corporation
Clean water and sanitation
Openalex Percentile: Top 6%
Genetic and Environmental Crop Studies
5.25
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.