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Crop and Pasture Science Crop and Pasture Science Society
Plant sciences, sustainable farming systems and food quality
RESEARCH ARTICLE (Open Access)

Identification of environment similarities using a crop model to assist the cultivation and breeding of a new crop in a new region

Yashvir S. Chauhan https://orcid.org/0000-0002-0135-6362 A * , Doug Sands B , Steve Krosch A , Peter Agius B , Troy Frederiks C , Karine Chenu https://orcid.org/0000-0001-7273-2057 D and Rex Williams E
+ Author Affiliations
- Author Affiliations

A Department of Agriculture and Fisheries (DAF), Kingaroy, Qld 4610, Australia.

B DAF, 99 Hospital Road, Emerald, Qld 4720, Australia.

C DAF, Leslie Research Centre, 13 Holberton Street, Toowoomba, Qld 4350, Australia.

D The University of Queensland, Queensland Alliance for Agriculture and Food Innovation, Gatton, Qld 4343, Australia.

E DAF, 203 Tor Street, Toowoomba, Qld 4350, Australia.

* Correspondence to: yash.chauhan@daf.qld.gov.au

Handling Editor: Jairo Palta

Crop & Pasture Science 75, CP23177 https://doi.org/10.1071/CP23177
Submitted: 27 June 2023  Accepted: 14 October 2023  Published: 26 October 2023

© 2024 The Author(s) (or their employer(s)). Published by CSIRO Publishing. This is an open access article distributed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License (CC BY-NC-ND)

Abstract

Context

Rainfed crop-growing environments are known for their high yield variability, especially in the subtropics and tropics. Improving the resilience of crops to such environments could be enhanced with breeding and agronomy research focusing on groups of similar environments.

Aim

This study presents a framework for developing these groups using the Agricultural Production Systems Simulator (APSIM, ver. 7.10) model.

Methods

As a case study, the framework was applied for pigeonpea (Cajanus cajan L. Millsp.) as a potential new pulse crop for the Australian northern grains region. The model was first validated and then used to simulate yield, compute heat and drought stress events and analyse their frequencies for 45 locations over 62 seasons from 1960 to 2021.

Key results

The model performed satisfactorily compared to field trial data for several sowing dates and locations. The simulated yield varied greatly across locations and seasons, with heat-stress events (maximum temperature ≥35°C) and rainfall showing highly significant associations with this variability. The study identified seven groups of locations after converting the simulated yield into percentiles, followed by clustering. Drought-and-heat stress patterns varied across these groups but less so within each group. Yield percentiles significantly declined over the seasons in three of the seven groups, likely due to changing climate.

Conclusions

The framework helped identify pigeonpea’s key production agroecological regions and the drought and heat constraints within each region.

Implications

The framework can be applied to other crops and regions to determine environmental similarity.

Keywords: APSIM, Cajanus cajan L. Millsp, environmental characterisation, envirotyping, high temperature, pigeonpea, water deficit, water stress.

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