A canopy architectural model to study the competitive ability of chickpea with sowthistle

Abstract

Background and aims: Improving the competitive ability of crops is a sustainable method of weed management. This paper shows how a virtual plant model of competition between chickpea (Cicer arietinum) and sowthistle (Sonchus oleraceus) can be used as a framework for discovering and/or developing more competitive chickpea cultivars.

Methods: The virtual plant models were developed using the L-systems formalism, parameterized according to measurements taken on plants at intervals during their development. A quasi-Monte Carlo light-environment model was used to model the effect of chickpea canopy on the development of sowthistle. The chickpea-light environment-sowthistle model (CLES model) captured the hypothesis that the architecture of chickpea plants modifies the light environment inside the canopy and determines sowthistle growth and development pattern. The resulting CLES model was parameterized for different chickpea cultivars (viz. 'Macarena', 'Bumper', 'Jimbour' and '99071-1001') to compare their competitive ability with sowthistle. To validate the CLES model, an experiment was conducted using the same four chickpea cultivars as different treatments with a sowthistle growing under their canopy.

Results and conclusions: The growth of sowthistle, both in silico and in glasshouse experiments, was reduced most by '99071-1001', a cultivar with a short phyllochron. The second rank of competitive ability belonged to 'Macarena' and 'Bumper', while 'Jimbour' was the least competitive cultivar. The architecture of virtual chickpea plants modified the light inside the canopy, which influenced the growth and development of the sowthistle plants in response to different cultivars. This is the first time that a virtual plant model of a crop-weed interaction has been developed. This virtual plant model can serve as a platform for a broad range of applications in the study of chickpea-weed interactions and their environment.

Fig. 1.

Side view of a virtual chickpea plant 15 and 60 d after germination, with the in silico thermal time set to 20 °C per day.

Fig. 2.

The communication of the two virtual plant models with the QMC light-environment model. Information on polygon positions (chickpea leaflets and sowthistle sensor) is passed to the QMC light-environment model (1), which calculates light availability for a virtual sensor at the sowthistle location (2); information is transmitted to the sowthistle through the virtual sensor (3).

Fig. 3.

Visualizations taken from the CLES model, showing the effect of the different chickpea cultivars (green) or full light on sowthistle (red) over time. The plants on the left are at 40 d after germination and those on the right are at 70 d. The average in silico temperature was set to 20 °C per day.

Fig. 4.

Observed and simulated sowthistle bolting time (growing degree-days, GGD) as affected by different chickpea cultivars and full light. Values are means ± s.e.

Fig. 5.

Observed and simulated sowthistle growth index (SGI) as affected by different chickpea cultivars and full light. Values are means ± s.e.

Fig. 6.

The simulated height (70 d after germination) of sowthistle when growing with different types of chickpea architecture in the CLES model. R, reference line; treatment numbers indicate different simulated architectures as shown in Table 2.