[ad_1]
There is a complex relationship between leaf area index and yield, which is influenced by an intricate interplay of genetic, environmental, and management factors.
Time is running out to find sustainable solutions to boost crop yields in the face of resource scarcity, climate change, and a growing global population. New research will help guide breeders to more quickly identify and select plant genotypes that can boost crop yields, ensuring food security for the future.
Developing new high-yielding crop cultivars relies on the practice of genotypic selection. This involves growing a genetically diverse population of plants to identify those that confer desirable traits (phenotypes) after extensive measurement and evaluation. However, this process is complicated by the fact that plant phenotypes are influenced not only by genetic makeup, but also by the management practices and environmental conditions to which the plant is exposed.
The sheer number of possible combinations of genetic, environmental, and management variables that can influence plant phenotypes makes it inherently difficult to determine the optimal cultivars for a location that has not yet been tested.
Iowa State University Graduate Student Mariana Chiozza led a research study that sought to overcome this limitation by using computer models to simulate the phenotypes of a wide range of genotypes grown under different management practices and environmental conditions. This study, published in in silico Plants, could help breeders and farmers determine the best genotypes and management practices for a given location.
Advances in image-based phenotyping have enabled researchers to rapidly capture large volumes of canopy-based phenotypic trait data orders of magnitude faster than manual methods (Figure 1). This allows them to efficiently measure the traits, such as leaf area index, of thousands of genotypes grown under specific management practices and environmental conditions.
Leaf area index is a measure of the amount of leaf material in a plant canopy (see Figure 2). This metric provides a useful proxy for the plant’s ability to capture sunlight and perform photosynthesis and is closely linked to seed yield. However, the exact relationship between leaf area index and yield is difficult to understand, as it is influenced by a complex interplay of genetic, environmental, and management factors.
Chiozza and colleagues turned to computer modeling to simulate how various genetic, environmental, and management variables interact to shape the leaf area index-yield relationship. “A significant part of the soybean breeding community utilizes linear approaches to relate canopy traits and seed yield. However, this relationship can vary significantly when factors such as genetics, environment, and management are taken into consideration” explains Chiozza (see Figure 3).
To address this problem the authors leveraged an existing model, Agricultural Production Systems sIMulator (APSIM), which simulates the biophysical processes involved in soybean growth and production. Within the APSIM framework, the researchers created 216 unique soybean genotypes by systematically varying parameter values for traits related to crop development (phenology and photoperiod) and biomass allocation (harvest index).
They then simulated the growth of all 216 genotypes under 24 different management approaches, such as variations in planting date, row spacing, and plant density, at 3 locations. This analysis was conducted over a 20-year period, resulting in a total of 311,040 individual simulation runs. This approach allowed the researchers to efficiently evaluate the performance and yield potential of a wide range of soybean genotypes under different environmental and management conditions, without the need for extensive field trials.
It is not surprising that that the researchers found that leaf area index values corresponding with the highest yield varied with location, genotype, density, row spacing, planting date, and other factors. This underscores the inherent complexity involved in identifying the optimal leaf area index values that breeders should target through genotypic selection to achieve maximum crop yields.
With this work, the authors demonstrated that linear approaches to relate canopy traits and yield are not always appropriate. In many cases, higher LAI had no effect or correlated with reduced yield. This could be attributed to self-shading or that the cost in constructing leaf tissue is detrimental towards seed production (Figure 4).
By evaluating the various factors that influence the relationship between a crop’s canopy traits and its seed yield in a more comprehensive manner, research like this can help breeders better leverage the utility of high-throughput phenotyping technologies. This deeper understanding of the complex interactions between plant characteristics and productivity can aid breeding programs in their efforts to develop improved crop varieties.
READ THE ARTICLE:
Mariana V Chiozza, Kyle Parmley, William T Schapaugh, Antonio R Asebedo, Asheesh K Singh, Fernando E Miguez, Changes in the leaf area-seed yield relationship in soybean driven by genetic, management and environments: Implications for High-Throughput Phenotyping, in silico Plants, 2024;, diae012, https://doi.org/10.1093/insilicoplants/diae012
[ad_2]
Source link
