Can improved canopy light transmission ameliorate loss of photosynthetic efficiency in the shade An investigation of natural variation in Sorghum bicolor

Previous studies have found that maximum quantum yield of CO2 assimilation (φCO2,max,app) declines in lower canopies of maize and miscanthus, a maladaptive response to self-shading. These observations were limited to single genotypes, leaving it unclear whether the maladaptive shade response is a general property of this C4 grass tribe, the Andropogoneae. We explored the generality of this maladaptation by testing the hypothesis that erect leaf forms (erectophiles), which allow more light into the lower canopy, suffer less of a decline in photosynthetic efficiency than drooping leaf (planophile) forms. On average, φCO2,max,app declined 27% in lower canopy leaves across 35 accessions, but the decline was over twice as great in planophiles than in erectophiles. The loss of photosynthetic efficiency involved a decoupling between electron transport and assimilation. This was not associated with increased bundle sheath leakage, based on 13C measurements. In both planophiles and erectophiles, shaded leaves had greater leaf absorptivity and lower activities of key C4 enzymes than sun leaves. The erectophile form is considered more productive because it allows a more effective distribution of light through the canopy to support photosynthesis. We show that in sorghum, it provides a second benefit, maintenance of higher φCO2,max,app to support efficient use of that light resource

Understanding growth dynamics and yield prediction of sorghum using high temporal resolution UAV imagery time series and machine learning

Unmanned aerial vehicles (UAV) carrying multispectral cameras are increasingly being used for high-throughput phenotyping (HTP) of above-ground traits of crops to study genetic diversity, resource use efficiency and responses to abiotic or biotic stresses. There is significant unexplored potential for repeated data collection through a field season to reveal information on the rates of growth and provide predictions of the final yield. Generating such information early in the season would create opportunities for more efficient in-depth phenotyping and germplasm selection. This study tested the use of high-resolution time-series imagery (5 or 10 sampling dates) to understand the relationships between growth dynamics, temporal resolution and end-of-season above-ground biomass (AGB) in 869 diverse accessions of highly productive (mean AGB = 23.4 Mg/Ha), photoperiod sensitive sorghum. Canopy surface height (CSM), ground cover (GC), and five common spectral indices were considered as features of the crop phenotype. Spline curve fitting was used to integrate data from single flights into continuous time courses. Random Forest was used to predict end-ofseason AGB from aerial imagery, and to identify the most informative variables driving predictions. Improved prediction of end-of-season AGB (RMSE reduction of 0.24 Mg/Ha) was achieved earlier in the growing season (10 to 20 days) by leveraging early- and mid-season measurement of the rate of change of geometric and spectral features. Early in the season, dynamic traits describing the rates of change of CSM and GC predicted end-of-season AGB best. Late in the season, CSM on a given date was the most influential predictor of end-of-season AGB. The power to predict end-of-season AGB was greatest at 50 days after planting, accounting for 63% of variance across this very diverse germplasm collection with modest error (RMSE 1.8 Mg/ha). End-of-season AGB could be predicted equally well when spline fitting was performed on data collected from five flights versus 10 flights over the growing season. This demonstrates a more valuable and efficient approach to using UAVs for HTP, while also proposing strategies to add further value

Photosynthesis, Productivity, and Yield of Maize Are Not Affected by Open-Air Elevation of CO(2) Concentration in the Absence of Drought

While increasing temperatures and altered soil moisture arising from climate change in the next 50 years are projected to decrease yield of food crops, elevated CO(2) concentration ([CO(2)]) is predicted to enhance yield and offset these detrimental factors. However, C(4) photosynthesis is usually saturated at current [CO(2)] and theoretically should not be stimulated under elevated [CO(2)]. Nevertheless, some controlled environment studies have reported direct stimulation of C(4) photosynthesis and productivity, as well as physiological acclimation, under elevated [CO(2)]. To test if these effects occur in the open air and within the Corn Belt, maize (Zea mays) was grown in ambient [CO(2)] (376 μmol mol(−1)) and elevated [CO(2)] (550 μmol mol(−1)) using Free-Air Concentration Enrichment technology. The 2004 season had ideal growing conditions in which the crop did not experience water stress. In the absence of water stress, growth at elevated [CO(2)] did not stimulate photosynthesis, biomass, or yield. Nor was there any CO(2) effect on the activity of key photosynthetic enzymes, or metabolic markers of carbon and nitrogen status. Stomatal conductance was lower (−34%) and soil moisture was higher (up to 31%), consistent with reduced crop water use. The results provide unique field evidence that photosynthesis and production of maize may be unaffected by rising [CO(2)] in the absence of drought. This suggests that rising [CO(2)] may not provide the full dividend to North American maize production anticipated in projections of future global food supply

Data from: High fidelity detection of crop biomass QTL from low-cost imaging in the field

Field-based, rapid, and non-destructive techniques for assessing plant productivity are needed to accelerate the discovery of genotype-to-phenotype relationships in next-generation biomass grass crops. The use of hemispherical imaging and light attenuation modeling was evaluated against destructive harvest measures with respect to their ability to accurately capture phenotypic and genotypic relationships in a field-grown grass crop. Plant area index (PAI) estimated from below-canopy hemispherical images, as well as a suite of thirteen traits assessed by manual destructive harvests, were measured in a Setaria recombinant inbred line mapping population segregating for aboveground productivity and architecture. A significant correlation was observed between PAI and biomass production across the population at maturity (r2 = 0.60), as well as for select diverse genotypes sampled repeatedly over the growing season (r2 = 0.79). Twenty-seven quantitative trait loci (QTL) were detected for manually collected traits associated with biomass production. Of these, twenty-one were found in four clusters of co-localized QTL. Analysis of image-based estimates of PAI successfully identified all four QTL hotspots for biomass production. QTL for PAI had greater overlap with those detected for traits associated with biomass production than with those for plant architecture and biomass partitioning. Hemispherical imaging is an affordable and scalable method, which demonstrates how high-throughput phenotyping can identify QTL related to biomass production of field trials in place of destructive harvests that are labor, time, and material intensive

Rising atmospheric carbon dioxide concentration and the future of C4 crops for food and fuel

Crops with the C4 photosynthetic pathway are vital to global food supply, particularly in the tropical regions where human well-being and agricultural productivity are most closely linked. While rising atmospheric [CO2] is the driving force behind the greater temperatures and water stress, which threaten to reduce future crop yields, it also has the potential to directly benefit crop physiology. The nature of C4 plant responses to elevated [CO2] has been controversial. Recent evidence from free-air CO2 enrichment (FACE) experiments suggests that elevated [CO2] does not directly stimulate C4 photosynthesis. Nonetheless, drought stress can be ameliorated at elevated [CO2] as a result of lower stomatal conductance and greater intercellular [CO2]. Therefore, unlike C3 crops for which there is a direct enhancement of photosynthesis by elevated [CO2], C4 crops will only benefit from elevated [CO2] in times and places of drought stress. Current projections of future crop yields have assumed that rising [CO2] will directly enhance photosynthesis in all situations and, therefore, are likely to be overly optimistic. Additional experiments are needed to evaluate the extent to which amelioration of drought stress by elevated [CO2] will improve C4 crop yields for food and fuel over the range of C4 crop growing conditions and genotypes

Novel Bayesian Networks for Genomic Prediction of Developmental Traits in Biomass Sorghum

The ability to connect genetic information between traits over time allow Bayesian networks to offer a powerful probabilistic framework to construct genomic prediction models. In this study, we phenotyped a diversity panel of 869 biomass sorghum (Sorghum bicolor (L.) Moench) lines, which had been genotyped with 100,435 SNP markers, for plant height (PH) with biweekly measurements from 30 to 120 days after planting (DAP) and for end-of-season dry biomass yield (DBY) in four environments. We evaluated five genomic prediction models: Bayesian network (BN), Pleiotropic Bayesian network (PBN), Dynamic Bayesian network (DBN), multi-trait GBLUP (MTr-GBLUP), and multi-time GBLUP (MTi-GBLUP) models. In fivefold cross-validation, prediction accuracies ranged from 0.46 (PBN) to 0.49 (MTr-GBLUP) for DBY and from 0.47 (DBN, DAP120) to 0.75 (MTi-GBLUP, DAP60) for PH. Forward-chaining cross-validation further improved prediction accuracies of the DBN, MTi-GBLUP and MTr-GBLUP models for PH (training slice: 30-45 DAP) by 36.4–52.4% relative to the BN and PBN models. Coincidence indices (target: biomass, secondary: PH) and a coincidence index based on lines (PH time series) showed that the ranking of lines by PH changed minimally after 45 DAP. These results suggest a two-level indirect selection method for PH at harvest (first-level target trait) and DBY (second-level target trait) could be conducted earlier in the season based on ranking of lines by PH at 45 DAP (secondary trait). With the advance of high-throughput phenotyping technologies, our proposed two-level indirect selection framework could be valuable for enhancing genetic gain per unit of time when selecting on developmental traits