Mid-Season Leaf Glutamine Predicts End-Season Maize Grain Yield and Nitrogen Content in Response to Nitrogen Fertilization under Field Conditions

Goron, Travis L.; Nederend, Jacob; Stewart, G. A.; Deen, Bill; Raizada, Manish N.

doi:10.3390/agronomy7020041

Cited by 13 publications

(6 citation statements)

References 85 publications

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“…Advantages of an auxotroph compared to expression-based reporter fusions are that there is an absolute requirement for the metabolite (i.e., Gln) and significantly fewer artifacts caused by microbial or plant metabolism or signaling. We showed that GlnLux cells exposed to extracts of leaf punches taken across the veins of growing leaves can accurately report N availability in maize ( Tessaro et al, 2012 ; Goron and Raizada, 2016 ; Goron et al, 2017 ).…”

Section: Introductionmentioning

confidence: 95%

A Biosensor-Based Leaf Punch Assay for Glutamine Correlates to Symbiotic Nitrogen Fixation Measurements in Legumes to Permit Rapid Screening of Rhizobia Inoculants under Controlled Conditions

2017

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Legumes are protein sources for billions of humans and livestock. These traits are enabled by symbiotic nitrogen fixation (SNF), whereby root nodule-inhabiting rhizobia bacteria convert atmospheric nitrogen (N) into usable N. Unfortunately, SNF rates in legume crops suffer from undiagnosed incompatible/suboptimal interactions between crop varieties and rhizobia strains. There are opportunities to test much large numbers of rhizobia strains if cost/labor-effective diagnostic tests become available which may especially benefit researchers in developing countries. Inside root nodules, fixed N from rhizobia is assimilated into amino acids including glutamine (Gln) for export to shoots as the major fraction (amide-exporting legumes) or as the minor fraction (ureide-exporting legumes). Here, we have developed a new leaf punch based technique to screen rhizobia inoculants for SNF activity following inoculation of both amide exporting and ureide exporting legumes. The assay is based on measuring Gln output using the GlnLux biosensor, which consists of Escherichia coli cells auxotrophic for Gln and expressing a constitutive lux operon. Subsistence farmer varieties of an amide exporter (lentil) and two ureide exporters (cowpea and soybean) were inoculated with different strains of rhizobia under controlled conditions, then extracts of single leaf punches were incubated with GlnLux cells, and light-output was measured using a 96-well luminometer. In the absence of external N and under controlled conditions, the results from the leaf punch assay correlated with 15N-based measurements, shoot N percentage, and shoot total fixed N in all three crops. The technology is rapid, inexpensive, high-throughput, requires minimum technical expertise and very little tissue, and hence is relatively non-destructive. We compared and contrasted the benefits and limitations of this novel diagnostic assay to methods.

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Section: Introductionmentioning

confidence: 95%

A Biosensor-Based Leaf Punch Assay for Glutamine Correlates to Symbiotic Nitrogen Fixation Measurements in Legumes to Permit Rapid Screening of Rhizobia Inoculants under Controlled Conditions

2017

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“…Generally, the crop yield mission always spans from the in-season to the end-of-season [43,44,45,46,47]. In the U.S., USDA provides a crop yield forecasting service, namely, Objective Yield (OY) surveys, which can provide monthly forecasts of crop yield by state-level.…”

Section: Introductionmentioning

confidence: 99%

County-Level Soybean Yield Prediction Using Deep CNN-LSTM Model

Sun

et al. 2019

Sensors

202

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Yield prediction is of great significance for yield mapping, crop market planning, crop insurance, and harvest management. Remote sensing is becoming increasingly important in crop yield prediction. Based on remote sensing data, great progress has been made in this field by using machine learning, especially the Deep Learning (DL) method, including Convolutional Neural Network (CNN) or Long Short-Term Memory (LSTM). Recent experiments in this area suggested that CNN can explore more spatial features and LSTM has the ability to reveal phenological characteristics, which both play an important role in crop yield prediction. However, very few experiments combining these two models for crop yield prediction have been reported. In this paper, we propose a deep CNN-LSTM model for both end-of-season and in-season soybean yield prediction in CONUS at the county-level. The model was trained by crop growth variables and environment variables, which include weather data, MODIS Land Surface Temperature (LST) data, and MODIS Surface Reflectance (SR) data; historical soybean yield data were employed as labels. Based on the Google Earth Engine (GEE), all these training data were combined and transformed into histogram-based tensors for deep learning. The results of the experiment indicate that the prediction performance of the proposed CNN-LSTM model can outperform the pure CNN or LSTM model in both end-of-season and in-season. The proposed method shows great potential in improving the accuracy of yield prediction for other crops like corn, wheat, and potatoes at fine scales in the future.

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“…About the N concentration in the corn grains, some studies have shown that N fertilization can increase the protein content of the grains (Goron et al, 2017;Martins et al, 2017). These authors also reported that N fertilization had more impact on the nutritional quality of the grains, while the inoculation of A. brasilense had a more pronounced effect on the increase of grain yield.…”

Section: Resultsmentioning

confidence: 93%

Application methods of Azospirillum brasilense in first- and second-crop corn

Alvarez

Benetão

Barzotto

et al. 2019

Rev. bras. eng. agríc. ambient.

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This study investigated whether inoculation of Azospirillum brasilense in the seeds or sowing furrow could reduce the need for nitrogen fertilizer application in the corn grown in the first and second crop harvest. The experiments were conducted, in the municipality of Chapadão do Sul, MS, Brazil, in the first crop harvest (2014/2015) and in the second crop harvest (2016). The early maturity hybrids CD 384 HX and P3132H were used in the first and in the second harvest corn, respectively. In both experiments, the experimental design was the randomized blocks with eight treatments and three repetitions. At the silking (R1 stage), the leaf N concentration, relative chlorophyll index and nitrate reductase activity were measured, at the physiological maturity (R6 stage), the plant height, first ear insertion height, one hundred grain mass, grain yield and grain N concentration were measured. In the first crop harvest, the application of Azospirillum brasilense at the sowing furrow resulted in higher N concentration and relative chlorophyll index in corn leaves, while the inoculation of A. brasilense in the seeds provided higher N concentration in the grains. The nitrate reductase activity in corn leaves was lower when inoculation was performed. In the second crop harvest, the inoculation did not affected the N concentration in the leaves and in the grains, the relative chlorophyll index and the growth of corn plants. Inoculation in the sowing furrow associated with the application of 20 kg N ha-1 at sowing and 120 kg N ha-1 at topdressing resulted in lower nitrate reductase activity.

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Mid-Season Leaf Glutamine Predicts End-Season Maize Grain Yield and Nitrogen Content in Response to Nitrogen Fertilization under Field Conditions

Cited by 13 publications

References 85 publications

A Biosensor-Based Leaf Punch Assay for Glutamine Correlates to Symbiotic Nitrogen Fixation Measurements in Legumes to Permit Rapid Screening of Rhizobia Inoculants under Controlled Conditions

A Biosensor-Based Leaf Punch Assay for Glutamine Correlates to Symbiotic Nitrogen Fixation Measurements in Legumes to Permit Rapid Screening of Rhizobia Inoculants under Controlled Conditions

County-Level Soybean Yield Prediction Using Deep CNN-LSTM Model

Application methods of Azospirillum brasilense in first- and second-crop corn

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