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

Elucidating the Obligate Nature and Biological Capacity of an Invasive Fungal Corn Pathogen

Tar spot is a devasting corn disease caused by the obligate fungal pathogen Phyllachora maydis. Since its initial identification in the United States in 2015, P. maydis has become an increasing threat to corn production. Despite this, P. maydis has remained largely understudied at the molecular level, due to difficulties surrounding its obligate lifestyle. Here, we generated a significantly improved P. maydis nuclear and mitochondrial genome, using a combination of long- and short-read technologies, and also provide the first transcriptomic analysis of primary tar spot lesions. Our results show that P. maydis is deficient in inorganic nitrogen utilization, is likely heterothallic, and encodes for significantly more protein-coding genes, including secreted enzymes and effectors, than previous determined. Furthermore, our expression analysis suggests that, following primary tar spot lesion formation, P. maydis might reroute carbon flux away from DNA replication and cell division pathways and towards pathways previously implicated in having significant roles in pathogenicity, such as autophagy and secretion. Together, our results identified several highly expressed unique secreted factors that likely contribute to host recognition and subsequent infection, greatly increasing our knowledge of the biological capacity of P. maydis, which have much broader implications for mitigating tar spot of corn. [Formula: see text] Copyright © 2023 The Author(s). This is an open access article distributed under the CC BY-NC-ND 4.0 International license.

Assessing the impact of biochar and nitrogen application on yield, water-nitrogen use efficiency and quality of intercropped maize and soybean

Introduction

Biochar (BC) and nitrogen (N) application have the potential to increase grain yield and resource use efficiency in intercropping systems. However, the effects of different levels of BC and N application in these systems remain unclear. To address this gap, the study is intended to ascertain the impact of various combinations of BC and N fertilizer on the performance of maize-soybean intercropping and determine the optimum application of BC and N for maximizing the effect of the intercropping system.

Methods

A two-year (2021-2022) field experiment was conducted in Northeast China to assess the impact of BC (0, 15, and 30 t ha^-1) and N application (135, 180, and 225 kg ha^-1) on plant growth, yield, water use efficiency (WUE), N recovery efficiency (NRE) and quality in an intercropping system. Maize and soybean were selected as materials in the experiment, where every 2 rows of maize were intercropped with 2 rows of soybean.

Results and discussion

The results showed that the combination of BC and N significantly affected the yield, WUE, NRE and quality of intercropped maize and soybean. The treatment of 15 t ha^-1 BC and 180 kg ha^-1 N increased grain yield and WUE, while that of 15 t ha^-1 BC and 135 kg ha^-1 N enhanced NRE in both years. Nitrogen promoted the protein and oil content of intercropped maize, but decreased the protein and oil content of intercropped soybean. BC did not enhance the protein and oil content of intercropped maize, especially in the first year, but increased maize starch content. BC was found to have no positive impact on soybean protein, but it unexpectedly increased soybean oil content. The TOPSIS method revealed that the comprehensive assessment value first increased and then declined with increasing BC and N application. BC improved the performance of maize-soybean intercropping system in terms of yield, WUE, NRE, and quality while N fertilizer input was reduced. The highest grain yield in two years was achieved for BC of 17.1-23.0 t ha^-1 and N of 156-213 kg ha^-1 in 2021, and 12.0-18.8 t ha^-1 BC and 161-202 kg ha^-1 N in 2022. These findings provide a comprehensive understanding of the growth of maize-soybean intercropping system and its potential to enhance the production in northeast China.

Responses of tea plants (Camellia sinensis) with different low-nitrogen tolerances during recovery from nitrogen deficiency

BACKGROUND

Tea plants have high nitrogen (N) consumptions, whereas molecular and physiological responses of tea plants to N recovery are still unclear.

RESULTS

By using non-invasive micro-test technology (NMT), ¹⁵ N tracer technique, ultra-performance liquid chromatography (UPLC), and transcriptome sequencing technology, we investigated the N recovery-induced changes in N absorptions, N tissue distributions, contents of free amino acids (FAAs), and global transcription of the low-N tolerant and intolerant tea genotypes [i.e. Wuniuzao (W) and Longjing43 (L)]. The results showed that the phenotype of Wuniuzao was better than that of Longjing43 under low-N condition. The N absorption and utilization of Wuniuzao were superior to Longjing43 under N recovery. The γ-aminobutyric acid (GABA) ratio (N recovery/N deficiency) in the root of Wuniuzao was significantly higher than that of Longjing43, while the glutamic acid ratio in the root of Wuniuzao was significantly lower than that of Longjing43. This findings suggested that Wuniuzao tended to enhance the GABA synthesis, while Longjing43 tended to inhibit the GABA synthesis under N recovery. The key genes in response to N recovery in Wuniuzao included N transport (AMT and NRT), N transformation (NR, NirA, and GAD), and amino acid transport (GAT) genes. In addition, some ribosome and flavonoid biosynthesis genes might help to maintain proteome homeostasis.

CONCLUSION

The N absorption and transport, and the conversion abilities of key amino acids (Glu and GABA) might improve the adaptability of tea plants to N recovery, which provided a basis for the breeding of N efficient tea varieties. © 2021 Society of Chemical Industry.

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

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.

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

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.