Identifying plant traits to increase wheat yield under irrigated conditions

Abstract

Crop models have frequently been used to identify desired plant traits for rainfed wheat (Triticum aestivum L.). However, efforts to apply these models to irrigated wheat grown under non-limiting water and nitrogen conditions have been rare. Using simulation models to identify plant traits that impact yield can facilitate more targeted cultivar improvement and reduce time and cost. In this study, the SSM-iCrop model was employed to identify effective plant traits for increasing the yield of irrigated wheat in four distinct environments in Iran. A comprehensive range of traits related to phenology, leaf area development, dry matter production, and yield formation, which exhibited reported genetic variation, were tested. The impact of these traits on yield showed slight variation across different environmental zones due to genetic × environment interaction. However, across all environments, modifying current cultivars to increase radiation use efficiency (RUE) resulted in a 19 % increase in yield, accelerating leaf area development led to a 10 %-15 % increase, lengthening the grain filling period resulted in a 14 % increase, and extending the vegetative period led to a 6 % increase. These improvements were all statistically significant. Considering that longer duration cultivars may disrupt cropping systems and the need to develop simple methods for targeting and phenotyping RUE, faster leaf area development was found as the most promising option to increase irrigated wheat yield under optimal water and nitrogen management within a short time frame. It should be noted that cultivars with modified traits needed higher water and nitrogen inputs to support increased yields. These findings can be applied to select desirable key traits for targeted breeding and expedite the production of high-yielding cultivars of irrigated wheat in various environmental zones. The potential for further improvement through combined traits requires further investigation.

Keywords: Crop modelling; Nitrogen; Traits; Water; Wheat improvement; Yield.

Fig. 1

The weather stations (Ahvaz, Gorgan, Quchan and Karaj) that were used in the current study.

Fig. 2

Monthly solar radiation (–––––), maximum (- - - -) and minimum (… …) temperature and rainfall (bars) for weather stations: Ahvaz (a), Gorgan (b), Quchan (c) and Karaj (d) based on weather records from 2000 to 2015.

Fig. 3

Simulated relative change (%) in grain yield by changing physiological traits (see Table 1 for definition) for each weather station: Ahvaz (a), Gorgan (b), Quchan (c) and Karaj (d). Simulated long-term average control grain yields were 680 g m⁻² for Ahvaz, 738 g m⁻² for Gorgan, 914 g m⁻² for Quchan and 863 g m⁻² for Karaj. Box boundaries indicate the 25th and 75th percentiles, the line within the box marks the median and the boundaries of the lower and upper whiskers are the minimum and maximum values of the data set, respectively.

Fig. 4

Percentage of crop yield change in modified cultivars. Letters compare differences of the cultivars over the selected environments; cultivars with the same letter have no significant difference at P = 0.05 with respect to the percentage change in crop yield.

Fig. 5

Grain yield versus (a) accumulated nitrogen in the above-ground crop organs and (b) total plant transpiration. Each point is the 15-year average yield of the standard cultivar or a modified cultivar in the study zones.