Clothing the Emperor: Dynamic Root-Shoot Allocation Trajectories in Relation to Whole-Plant Growth Rate and in Response to Temperature

OPT-ing out: Root-shoot dynamics are caused by local resource capture and biomass allocation, not optimal partitioning

Combining plant growth analysis with a simple model of local resource capture and biomass allocation applied to exemplary experimental data, showed that dynamic changes in allocation to roots when nutrients are scarce is caused by disparities in growth rates between roots and shoots. Whole-plant growth rates also change but are not caused by an adaptive allocation response. Allocation and whole-plant growth rate are interdependent, not independent, traits. Following a switch in nutrient availability or partial biomass removal, convergence of allocation and growth rate trajectories does not reflect goal-seeking behaviour, but the constraints imposed by finite resource availability. Optimal root-shoot allocations are unnecessary to maximise whole-plant growth rate. Similar growth rates are attainable with different allocations. Changes in allocation cannot maintain or restore a superior whole-plant growth rate. Roots and shoots do not have to be tightly coordinated but can operate semiautonomously, as their modular construction permits. These findings question the plausibility of the prevailing general explanation of plants' root-shoot allocation responses, 'optimal partitioning theory' (OPT). Local allocation models, not OPT, explain the origins of variability in root-shoot trade-offs in individuals, the allocation of biomass at global and ecosystem scales and inform selection for allocation plasticity in crop breeding.

On the Possible Trade-Off between Shoot and Root Biomass in Wheat

Numerous studies have shown that under a limited water supply, a larger root biomass is associated with an increased above-ground biomass. Root biomass, while genetically controlled, is also greatly affected by the environment with varying plasticity levels. In this context, understanding the relationship between the biomass of shoots and roots appears prudent. In this study, we analyze this relationship in a large dataset collected from multiple experiments conducted up to different growth stages in bread wheat (Triticum aestivum L.) and its wild relatives. Four bread wheat mapping populations as well as wild and domesticated members of the Triticeae tribe were evaluated for the root and shoot biomass allocation patterns. In the analyzed dataset the root and shoot biomasses were directly related to each other, and to the heading date, and the correlation values increased in proportion to the length of an experiment. On average, 84.1% of the observed variation was explained by a positive correlation between shoot and root biomass. Scatter plots generated from 6353 data points from numerous experiments with different wheats suggest that at some point, further increases in root biomass negatively impact the shoot biomass. Based on these results, a preliminary study with different water availability scenarios and growth conditions was designed with two cultivars, Pavon 76 and Yecora Rojo. The duration of drought and water level significantly affected the root/shoot biomass allocation patterns. However, the responses of the two cultivars were quite different, suggesting that the point of diminishing returns in increasing root biomass may be different for different wheats, reinforcing the need to breed wheats for specific environmental challenges.

Deciphering the Genetic Basis of Root and Biomass Traits in Rapeseed (Brassica napus L.) through the Integration of GWAS and RNA-Seq under Nitrogen Stress

An excellent root system is responsible for crops with high nitrogen-use efficiency (NUE). The current study evaluated the natural variations in 13 root- and biomass-related traits under a low nitrogen (LN) treatment in a rapeseed association panel. The studied traits exhibited significant phenotypic differences with heritabilities ranging from 0.53 to 0.66, and most of the traits showed significant correlations with each other. The genome-wide association study (GWAS) found 51 significant and 30 suggestive trait–SNP associations that integrated into 14 valid quantitative trait loci (QTL) clusters and explained 5.7–21.2% phenotypic variance. In addition, RNA sequencing was performed at two time points to examine the differential expression of genes (DEGs) between high and low NUE lines. In total, 245, 540, and 399 DEGs were identified as LN stress-specific, high nitrogen (HN) condition-specific, and HNLN common DEGs, respectively. An integrated analysis of GWAS, weighted gene co-expression network, and DEGs revealed 16 genes involved in rapeseed root development under LN stress. Previous studies have reported that the homologs of seven out of sixteen potential genes control root growth and NUE. These findings revealed the genetic basis underlying nitrogen stress and provided worthwhile SNPs/genes information for the genetic improvement of NUE in rapeseed.