High-throughput, image-based phenotyping reveals nutrient-dependent growth facilitation in a grass-legume mixture

Abstract

This study used high throughput, image-based phenotyping (HTP) to distinguish growth patterns, detect facilitation and interpret variations to nutrient uptake in a model mixed-pasture system in response to factorial low and high nitrogen (N) and phosphorus (P) application. HTP has not previously been used to examine pasture species in mixture. We used redgreen-blue (RGB) imaging to obtain smoothed projected shoot area (sPSA) to predict absolute growth (AG) up to 70 days after planting (sPSA, DAP 70), to identify variation in relative growth rates (RGR, DAP 35-70) and detect overyielding (an increase in yield in mixture compared with monoculture, indicating facilitation) in a grass-legume model pasture. Finally, using principal components analysis we interpreted between species changes to HTPderived temporal growth dynamics and nutrient uptake in mixtures and monocultures. Overyielding was detected in all treatments and was driven by both grass and legume. Our data supported expectations of more rapid grass growth and augmented nutrient uptake in the presence of a legume. Legumes grew more slowly in mixture and where growth became more reliant on soil P. Relative growth rate in grass was strongly associated with shoot N concentration, whereas legume RGR was not strongly associated with shoot nutrients. High throughput, image-based phenotyping was a useful tool to quantify growth trait variation between contrasting species and to this end is highly useful in understanding nutrient-yield relationships in mixed pasture cultivations.

Assessing the competitive ability of the invader Senna obtusifolia with coexisting natives species under different water stress regimes

“…Similar studies on legume and non-legume mixtures had attributed high values of relative yields total through the use of different nitrogen sources in addition to differences in root and aboveground characteristics (Zand and Beckie 2002;Ball et al 2020). Contrary to their study, the results of this study showed a lower value of relative yield totals through the use of water stress as the main factor in addition to above-ground biomass.…”

Integrating Ecological Stoichiometry to Understand Nutrient Limitation and Potential for Competition in Mixed Pasture Assemblages

“…As anticipated, the faster-growing, more nutrient-acquisitive grass species responded positively to N addition, and when cultivated with a legume; the slower-growing, more nutrient-homeostatic legume species responded positively to P addition and growth in mixture. Moreover, the grass had a faster relative growth rate than the legume, and this effect was augmented in mixed cultivation (Ball et al, 2020). It is important to note that enhanced growth of grass under N addition in mixture may be attributed to a greater availability of N for two, versus four, grass plants and thus may not be due to facilitation.…”

“…Our experiment was conducted from the 28 th of October 2018 in a greenhouse maintaining natural average temperatures of 27°C/16°C on a day/night cycle and an average day length between 07:00 -19:00 under natural light, for 70 days (Ball et al, 2020). The 70-day growth cycle was chosen to capture the ideal temperature range for germinating and growing both species while avoiding potential for summer dormancy with increasing temperatures (Brar et al, 1991;Watson et al, 2000).…”

“…The experimental design was obtained using CycDesigN (Whitaker et al, 2002) and randomized using the dae package in R (Brien, 2018; R Core Team, 2020) (Fig. S1), (Ball et al, 2020).…”

Direct and higher-order interactions in plant communities under increasing weather persistence

“…They can be facilitative (positive) or competitive (negative). In grasslands, legumes often facilitate grasses through N-fixation, which increases ecosystem productivity (Ball et al, 2020), in return for improved microclimatic conditions such as wind protection (Walker et al, 2003). Conversely, fast-growing dominant grasses often compete with one another for limited resources, which can result in productivity loss (Corbin & D'Antonio, 2004;Fay et al, 2003).…”