Fine-Tuning Florigen Increases Field Yield Through Improving Photosynthesis in Soybean

Crop yield has been maintaining its attraction for researchers because of the demand of global population growth. Mutation of flowering activators, such as florigen, increases plant biomass at the expense of later flowering, which prevents crop maturity in the field. As a result, it is difficult to apply flowering activators in agriculture production. Here, we developed a strategy to utilize florigen to significantly improve soybean yield in the field. Through the screening of transgenic lines of RNAi-silenced florigen homologs in soybean (Glycine-max-Flowering Locus T Like, GmFTL), we identified a line, GmFTL-RNAi#1, with minor changes in both GmFTL expression and flowering time but with notable increase in soybean yield. As expected, GmFTL-RNAi#1 matured normally in the field and exhibited markedly high yield over multiple locations and years, indicating that it is possible to reach a trade-off between flowering time and high yield through the fine-tuning expression of flowering activators. Further studies uncovered an unknown mechanism by which GmFTL negatively regulates photosynthesis, a substantial source of crop yield, demonstrating a novel function of florigen. Thus, because of the highly conserved functions of florigen in plants and the classical RNAi approach, the findings provide a promising strategy to harness early flowering genes to improve crop yield.

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Optimization of Tomato Productivity Using Flowering Time Variants

Agronomy ◽

10.3390/agronomy11020285 ◽

2021 ◽

Vol 11 (2) ◽

pp. 285

Author(s):

Sujeevan Rajendran ◽

Jung Heo ◽

Yong Jun Kim ◽

Dae Heon Kim ◽

Kisung Ko ◽

...

Keyword(s):

Flowering Time ◽

Crop Yield ◽

Fruit Yield ◽

Fine Tuning ◽

Shoot Meristem ◽

Yield Model ◽

Axillary Shoots ◽

Floral Organs ◽

Late Flowering ◽

Time Continuum

The control of flowering time is a major contributing factor to the improvement of crop yield by optimizing plant growth in a crop cycle. Genetic variants that determine flowering time can provide insights into optimizing flowering time for higher yields and other beneficial traits in tomato crops. Here, we examined a collection of flowering time variants to assess their effects on biomass and total tomato yields. Five late flowering (lf), thirteen large plant (lp), and seven floral homeotic (fh) mutants were identified as flowering time variants that could be rearranged according to leaf production in the primary shoot meristem (PSM). A flowering time continuum of mutants was translated into a positive continuum of biomass yield with more leaves, branches, and floral organs. The flowering time continuum showed an optimal curve of fruit yield, indicating a certain late flowering time as optimal for fruit yield, with the yield gradually decreasing in both directions with earlier or later flowering times. We isolated lf1, lf10, lp22, and fh13 as high-yielding genotypes with optimal flowering time, showing a new balance between the vegetative and flowering phases of tomato. Additionally, lp8, fh8, and fh15 produced extremely high biomass in leaves, axillary shoots, and floral organs due to late flowering in shoot apices with additional production of floral organs and lateral shoot. Our new late-flowering variants provide new genetic resources that can be used to optimize crop yield by fine-tuning flowering time, and future molecular studies could be conducted by revisiting our yield model.

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FT/FD-GRF5 repression loop directs growth to increase soybean yield

10.1101/437053 ◽

2018 ◽

Author(s):

Kun Xu ◽

Xiao-Mei Zhang ◽

Guolong Yu ◽

Mingyang Lu ◽

Chunyan Liu ◽

...

Keyword(s):

Glycine Max ◽

Crop Yield ◽

Fine Tuning ◽

Reproductive Growth ◽

Soybean Yield ◽

Auxin Synthesis ◽

Agricultural Yield ◽

Vegetative And Reproductive Growth ◽

Reproductive Phases ◽

Increase Crop Yield

AbstractMajor advances in crop yield are eternally needed to cope with population growth. To balance vegetative and reproductive growth plays an important role in agricultural yield. To extend vegetative phase can increase crop yield, however, this strategy risks loss of yield in the field as crops may not mature in time before winter come. Here, we identified a repression feedback loop between GmFTL/GmFDL and GmGRF5-1 (Glycine-max-Flowering-Locus-T/Glycine-max-FDL and Glycine-max-GROWTH-REGULATING-FACTOR5-1), which functions as a pivotal regulator in balancing vegetative and reproductive phases in soybean. GmFTL/GmFDL and GmGRF5-1 directly repress gene expression each other. Additionally, GmGRF5-1 enhances vegetative growth by directly enhancing expression of photosynthesis- and auxin synthesis-related genes. To modulate the loop, such as fine-tuning GmFTL expression to trade-off vegetative and reproductive growth, increases substantially soybean yield in the field. Our findings not only uncover the mechanism balancing vegetative and reproductive growth, but open a new window to improve crop yield.

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Effect of Row Spacing and Seeding Rate on Russian Thistle (Salsola tragus) in Spring Barley and Spring Wheat

Plants ◽

10.3390/plants10010126 ◽

2021 ◽

Vol 10 (1) ◽

pp. 126

Author(s):

Judit Barroso ◽

Nicholas G. Genna

Keyword(s):

Seed Production ◽

Crop Yield ◽

Spring Wheat ◽

Plant Biomass ◽

Integrated Management ◽

Spring Barley ◽

Management Strategies ◽

Crop Productivity ◽

Row Spacing ◽

Seeding Rate

Russian thistle (Salsola tragus L.) is a persistent post-harvest issue in the Pacific Northwest (PNW). Farmers need more integrated management strategies to control it. Russian thistle emergence, mortality, plant biomass, seed production, and crop yield were evaluated in spring wheat and spring barley planted in 18- or 36-cm row spacing and seeded at 73 or 140 kg ha−1 in Pendleton and Moro, Oregon, during 2018 and 2019. Russian thistle emergence was lower and mortality was higher in spring barley than in spring wheat. However, little to no effect of row spacing or seeding rate was observed on Russian thistle emergence or mortality. Russian thistle seed production and plant biomass followed crop productivity; higher crop yield produced higher Russian thistle biomass and seed production and lower crop yield produced lower weed biomass and seed production. Crop yield with Russian thistle pressure was improved in 2018 with 18-cm rows or by seeding at 140 kg ha−1 while no effect was observed in 2019. Increasing seeding rates or planting spring crops in narrow rows may be effective at increasing yield in low rainfall years of the PNW, such as in 2018. No effect may be observed in years with higher rainfall than normal, such as in 2019.

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Fine tuning the glycolytic flux ratio of EP-bifido pathway for mevalonate production by enhancing glucose-6-phosphate dehydrogenase (Zwf) and CRISPRi suppressing 6-phosphofructose kinase (PfkA) in Escherichia coli

Microbial Cell Factories ◽

10.1186/s12934-021-01526-1 ◽

2021 ◽

Vol 20 (1) ◽

Author(s):

Ying Li ◽

He Xian ◽

Ya Xu ◽

Yuan Zhu ◽

Zhijie Sun ◽

...

Keyword(s):

Pentose Phosphate Pathway ◽

Conversion Rate ◽

Metabolic Flux ◽

Flux Ratio ◽

Reducing Power ◽

Pentose Phosphate ◽

Fine Tuning ◽

High Yield ◽

Glycolytic Flux ◽

Acetyl Coa

Abstract Background Natural glycolysis encounters the decarboxylation of glucose partial oxidation product pyruvate into acetyl-CoA, where one-third of the carbon is lost at CO2. We previously constructed a carbon saving pathway, EP-bifido pathway by combining Embden-Meyerhof-Parnas Pathway, Pentose Phosphate Pathway and “bifid shunt”, to generate high yield acetyl-CoA from glucose. However, the carbon conversion rate and reducing power of this pathway was not optimal, the flux ratio of EMP pathway and pentose phosphate pathway (PPP) needs to be precisely and dynamically adjusted to improve the production of mevalonate (MVA). Result Here, we finely tuned the glycolytic flux ratio in two ways. First, we enhanced PPP flux for NADPH supply by replacing the promoter of zwf on the genome with a set of different strength promoters. Compared with the previous EP-bifido strains, the zwf-modified strains showed obvious differences in NADPH, NADH, and ATP synthesis levels. Among them, strain BP10BF accumulated 11.2 g/L of MVA after 72 h of fermentation and the molar conversion rate from glucose reached 62.2%. Second, pfkA was finely down-regulated by the clustered regularly interspaced short palindromic repeats interference (CRISPRi) system. The MVA yield of the regulated strain BiB1F was 8.53 g/L, and the conversion rate from glucose reached 68.7%. Conclusion This is the highest MVA conversion rate reported in shaken flask fermentation. The CRISPRi and promoter fine-tuning provided an effective strategy for metabolic flux redistribution in many metabolic pathways and promotes the chemicals production.

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Agro-Nanotechnology as an Emerging Field: A Novel Sustainable Approach for Improving Plant Growth by Reducing Biotic Stress

Applied Sciences ◽

10.3390/app11052282 ◽

2021 ◽

Vol 11 (5) ◽

pp. 2282

Author(s):

Masudulla Khan ◽

Azhar U. Khan ◽

Mohd Abul Hasan ◽

Krishna Kumar Yadav ◽

Marina M. C. Pinto ◽

...

Keyword(s):

Plant Growth ◽

Crop Yield ◽

Biotic Stress ◽

Crop Production ◽

Plant Disease ◽

Yield Loss ◽

Growth Parameters ◽

Plant Diseases ◽

High Yield ◽

Plant Disease Management

In the present era, the global need for food is increasing rapidly; nanomaterials are a useful tool for improving crop production and yield. The application of nanomaterials can improve plant growth parameters. Biotic stress is induced by many microbes in crops and causes disease and high yield loss. Every year, approximately 20–40% of crop yield is lost due to plant diseases caused by various pests and pathogens. Current plant disease or biotic stress management mainly relies on toxic fungicides and pesticides that are potentially harmful to the environment. Nanotechnology emerged as an alternative for the sustainable and eco-friendly management of biotic stress induced by pests and pathogens on crops. In this review article, we assess the role and impact of different nanoparticles in plant disease management, and this review explores the direction in which nanoparticles can be utilized for improving plant growth and crop yield.

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Responses of Branch Number and Yield Component of Soybean Cultivars Tested in Different Planting Densities

Agriculture ◽

10.3390/agriculture11010069 ◽

2021 ◽

Vol 11 (1) ◽

pp. 69

Author(s):

Cailong Xu ◽

Ruidong Li ◽

Wenwen Song ◽

Tingting Wu ◽

Shi Sun ◽

...

Keyword(s):

Key Management ◽

Management Practices ◽

Yield Component ◽

Planting Density ◽

Yield Potential ◽

High Yield ◽

Branch Number ◽

Soybean Yield ◽

Soybean Cultivars ◽

Pod Number

Increasing planting density is one of the key management practices to enhance soybean yield. A 2-yr field experiment was conducted in 2018 and 2019 including six planting densities and two soybean cultivars to determine the effects of planting density on branch number and yield, and analyze the contribution of branches to yield. The yield of ZZXA12938 was 4389 kg ha−1, which was significantly higher than that of ZH13 (+22.4%). In combination with planting year and cultivar, the soybean yield increased significantly by 16.2%, 31.4%, 41.4%, and 46.7% for every increase in density of 45,000 plants ha−1. Yield will not increase when planting density exceeds 315,000 plants ha−1. A correlation analysis showed that pod number per plant increased with the increased branch number, while pod number per unit area decreased; thus, soybean yield decreased. With the increase of branch number, the branch contribution to yield increased first, and then plateaued. ZH13 could produce a high yield under a lower planting density due to more branches, while ZZXA12938 had a higher yield potential under a higher planting density due to the smaller branch number and higher tolerance to close planting. Therefore, seed yield can be increased by selecting cultivars with a little branching capacity under moderately close planting.

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Role of PIF4 and FLC in controlling flowering time under daily variable temperature profile

10.1101/2020.10.22.348540 ◽

2020 ◽

Author(s):

Jutapak Jenkitkonchai ◽

Poppy Marriott ◽

Weibing Yang ◽

Napaporn Sriden ◽

Jae-Hoon Jung ◽

...

Keyword(s):

Flowering Time ◽

Molecular Mechanisms ◽

Transcriptional Regulatory Network ◽

Variable Temperature ◽

Loss Of Function ◽

Environmental Signals ◽

Regulatory Interactions ◽

Flowering Genes ◽

Gene Regulatory ◽

Flowering Locus

ABSTRACTInitiation of flowering is a crucial developmental event that requires both internal and environmental signals to determine when floral transition should occur to maximize reproductive success. Ambient temperature is one of the key environmental signals that highly influence flowering time, not only seasonally but also in the context of drastic temperature fluctuation due to global warming. Molecular mechanisms of how high or low constant temperatures affect the flowering time have been largely characterized in the model plant Arabidopsis thaliana; however, the effect of natural daily variable temperature outside laboratories is only partly explored. Several groups of flowering genes have been shown to play important roles in temperature responses, including two temperature-responsive transcription factors (TFs), namely PHYTOCHROME INTERACTING FACTOR 4 (PIF4) and FLOWERING LOCUS C (FLC), that act antagonistically to regulate flowering time by activating or repressing floral integrator FLOWERING LOCUS T (FT). In this study, we have demonstrated that the daily variable temperature (VAR) causes early flowering in both natural accessions Col-0, C24 and their late flowering hybrid C24xCol, which carries both functional floral repressor FLC and its activator FRIGIDA (FRI), as compared to a constant temperature (CON). The loss-of-function mutation of PIF4 exhibits later flowering in VAR, suggesting that PIF4 at least in part, contributes to acceleration of flowering in response to the daily variable temperature. We find that VAR increases PIF4 transcription at the end of the day when temperature peaks at 32 °C. The FT transcription is also elevated in VAR, as compared to CON, in agreement with earlier flowering observed in VAR. In addition, VAR causes a decrease in FLC transcription in 4-week-old plants, and we further show that overexpression of PIF4 can reduce FLC transcription, suggesting that PIF4 might also regulate FT indirectly through the repression of FLC. To further conceptualize an overall model of gene regulatory mechanisms involving PIF4 and FLC in controlling flowering in response to temperature changes, we construct a co-expression – transcriptional regulatory network by combining publicly available transcriptomic data and gene regulatory interactions of our flowering genes of interest and their partners. The network model reveals the conserved and tissue-specific regulatory functions of 62 flowering-time-relating genes, namely PIF4, PIF5, FLC, ELF3 and their immediate neighboring genes, which can be useful for confirming and predicting the functions and regulatory interactions between the key flowering genes.

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An interaction regression model for crop yield prediction

Scientific Reports ◽

10.1038/s41598-021-97221-7 ◽

2021 ◽

Vol 11 (1) ◽

Author(s):

Javad Ansarifar ◽

Lizhi Wang ◽

Sotirios V. Archontoulis

Keyword(s):

Machine Learning ◽

Regression Model ◽

Crop Yield ◽

Machine Learning Algorithms ◽

Training Data ◽

Yield Prediction ◽

Soybean Yield ◽

Global Food Security ◽

Management Scenario ◽

Complex Interactions

AbstractCrop yield prediction is crucial for global food security yet notoriously challenging due to multitudinous factors that jointly determine the yield, including genotype, environment, management, and their complex interactions. Integrating the power of optimization, machine learning, and agronomic insight, we present a new predictive model (referred to as the interaction regression model) for crop yield prediction, which has three salient properties. First, it achieved a relative root mean square error of 8% or less in three Midwest states (Illinois, Indiana, and Iowa) in the US for both corn and soybean yield prediction, outperforming state-of-the-art machine learning algorithms. Second, it identified about a dozen environment by management interactions for corn and soybean yield, some of which are consistent with conventional agronomic knowledge whereas some others interactions require additional analysis or experiment to prove or disprove. Third, it quantitatively dissected crop yield into contributions from weather, soil, management, and their interactions, allowing agronomists to pinpoint the factors that favorably or unfavorably affect the yield of a given location under a given weather and management scenario. The most significant contribution of the new prediction model is its capability to produce accurate prediction and explainable insights simultaneously. This was achieved by training the algorithm to select features and interactions that are spatially and temporally robust to balance prediction accuracy for the training data and generalizability to the test data.

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High-yield hydrogen production from biomass by in vitro metabolic engineering: Mixed sugars coutilization and kinetic modeling

Proceedings of the National Academy of Sciences ◽

10.1073/pnas.1417719112 ◽

2015 ◽

Vol 112 (16) ◽

pp. 4964-4969 ◽

Cited By ~ 137

Author(s):

Joseph A. Rollin ◽

Julia Martin del Campo ◽

Suwan Myung ◽

Fangfang Sun ◽

Chun You ◽

...

Keyword(s):

New Technologies ◽

Plant Biomass ◽

Low Cost ◽

Energy Conversion Efficiency ◽

Green Energy ◽

High Yield ◽

Yield Parameters ◽

Production Of Hydrogen ◽

Nonlinear Kinetic

The use of hydrogen (H2) as a fuel offers enhanced energy conversion efficiency and tremendous potential to decrease greenhouse gas emissions, but producing it in a distributed, carbon-neutral, low-cost manner requires new technologies. Herein we demonstrate the complete conversion of glucose and xylose from plant biomass to H2 and CO2 based on an in vitro synthetic enzymatic pathway. Glucose and xylose were simultaneously converted to H2 with a yield of two H2 per carbon, the maximum possible yield. Parameters of a nonlinear kinetic model were fitted with experimental data using a genetic algorithm, and a global sensitivity analysis was used to identify the enzymes that have the greatest impact on reaction rate and yield. After optimizing enzyme loadings using this model, volumetric H2 productivity was increased 3-fold to 32 mmol H2⋅L−1⋅h−1. The productivity was further enhanced to 54 mmol H2⋅L−1⋅h−1 by increasing reaction temperature, substrate, and enzyme concentrations—an increase of 67-fold compared with the initial studies using this method. The production of hydrogen from locally produced biomass is a promising means to achieve global green energy production.

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Synthetic glycolate metabolism pathways stimulate crop growth and productivity in the field

Science ◽

10.1126/science.aat9077 ◽

2019 ◽

Vol 363 (6422) ◽

pp. eaat9077 ◽

Cited By ~ 156

Author(s):

Paul F. South ◽

Amanda P. Cavanagh ◽

Helen W. Liu ◽

Donald R. Ort

Keyword(s):

Quantum Yield ◽

Crop Yield ◽

Metabolic Pathways ◽

Biomass Productivity ◽

Field Trials ◽

Agricultural Field ◽

Bisphosphate Carboxylase ◽

Transgenic Lines ◽

Glycolate Metabolism ◽

Pathway Flux

Photorespiration is required in C3 plants to metabolize toxic glycolate formed when ribulose-1,5-bisphosphate carboxylase-oxygenase oxygenates rather than carboxylates ribulose-1,5-bisphosphate. Depending on growing temperatures, photorespiration can reduce yields by 20 to 50% in C3 crops. Inspired by earlier work, we installed into tobacco chloroplasts synthetic glycolate metabolic pathways that are thought to be more efficient than the native pathway. Flux through the synthetic pathways was maximized by inhibiting glycolate export from the chloroplast. The synthetic pathways tested improved photosynthetic quantum yield by 20%. Numerous homozygous transgenic lines increased biomass productivity between 19 and 37% in replicated field trials. These results show that engineering alternative glycolate metabolic pathways into crop chloroplasts while inhibiting glycolate export into the native pathway can drive increases in C3 crop yield under agricultural field conditions.

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