Reduce Yield Losses Research Articles

A review on the applicability of adsorption techniques for remediation of recalcitrant pesticides

Pesticide has revolutionised the agricultural industry by reducing yield losses and by enhancing productivity. But indiscriminate usage of such chemicals can negatively impact human health and ecosystem balance as certain pesticides can be recalcitrant in nature. Out of some of the suggested sustainable techniques to remove the pesticide load from the environment, adsorption is found to be highly efficient and can also be implemented on a large scale. It has been observed that natural adsorption that takes place after the application of the pesticide is not enough to reduce the pesticide load, hence, adsorbents like activated carbon, plant-based adsorbents, agricultural by-products, silica materials, polymeric adsorbents, metal organic framework etc are being experimented upon. It is becoming increasingly important to choose adsorbents which will not leave any secondary pollutant after treatment and the cost of production of such adsorbent should be feasible. In this review paper, it has been established that certain adsorbent like biochar, hydrochar, resin, metal organic framework etc can efficiently remove pesticides namely chlorpyrifos, diazinon, 2,4-Dichlorophenoxyacetic Acid, atrazine, fipronil, imidacloprid etc. The mechanism of adsorption, thermodynamics and kinetic part have been discussed in detail with respect to the pesticide and adsorbent under discussion. The reason behind choosing an adsorbent for the removal of a particular pesticide have also been explained. It is further highly recommended to carry out a cost analysis before implementing an absorbent because inspite of its efficacy, it might not be cost effective to use it for a particular type of pesticide or contaminant.

Systematic Analysis of BELL Family Genes in Zizania latifolia and Functional Identification of ZlqSH1a/b in Rice Seed Shattering.

The loss of seed shattering is an important event in crop domestication, and elucidating the genetic mechanisms underlying seed shattering can help reduce yield loss during crop production. This study is the first to systematically identify and analyse the BELL family of transcription factor-encoding genes in Chinese wild rice (Zizania latifolia). ZlqSH1a (Zla04G033720) and ZlqSH1b (Zla02G027130) were identified as key candidate genes involved in seed shattering in Z. latifolia. These genes were involved in regulating the development of the abscission layer (AL) and were located in the nucleus of the cell. Over-expression of ZlqSH1a and ZlqSH1b resulted in a complete AL between the grain and pedicel and significantly enhanced seed shattering after grain maturation in rice. Transcriptome sequencing revealed that 172 genes were differentially expressed between the wild type (WT) and the two transgenic (ZlqSH1a and ZlqSH1b over-expressing) plants. Three of the differentially expressed genes related to seed shattering were validated using qRT-PCR analysis. These results indicate that ZlqSH1a and ZlqSH1b are involved in AL development in rice grains, thereby regulating seed shattering. Our results could facilitate the genetic improvement of seed-shattering behaviour in Z. latifolia and other cereal crops.

ECry1Gb.1Ig, A Novel Chimeric Cry Protein with High Efficacy against Multiple Fall Armyworm (Spodoptera frugiperda) Strains Resistant to Different GM Traits.

Spodoptera frugiperda (fall armyworm, FAW) is one of the most devastating insect pests to corn and soybean production in the Americas and is rapidly expanding its range worldwide. It is known to be hard to control either by chemical insecticide applications or by GM. Although the use of GM traits can be an effective way to control this pest, it is very rare to find native insecticidal proteins that provide the necessary level of FAW control in crop fields where FAW pressure and damage are high. Insecticidal Cry proteins sourced from Bacillus thuringiensis have been heavily utilized in the development of crops with GM traits; however, it is increasingly difficult to identify Cry proteins with unique modes of action. Protein engineering via a phylogenetically guided Cry protein domain swapping approach enabled us to discover novel chimeric Cry proteins engineered from inactive parent sequences. Some of these chimeras show excellent efficacy against key biotypes of FAW from Brazil and North America. In this study, we characterized a Cry-based chimera eCry1Gb.1Ig that is a very potent FAW toxin. eCry1Gb.1Ig showed high efficacy against multiple FAW strains that are resistant to various traits, including Cry1Fa, Vip3Aa and Cry1A.105/Cry2Ab. These results clearly indicate that the FAW strains resistant to Cry1Fa, Vip3Aa or Cry1A.105/Cry2Ab demonstrate no cross-resistance to eCry1Gb.1Ig and strongly suggest that eCry1Gb.1Ig acts through a novel mode of action compared to the existing traits. In addition to its FAW activity, eCry1Gb.1Ig has also been shown to control Chrysodeixis includens (soybean looper, SBL) and Anticarsia gemmatalis (velvetbean caterpillar, VBC), which are significant pests of soybean. When eCry1Gb.1Ig was introduced into corn and soybean crops, transgenic events showed strong efficacy against FAW, SBL and VBC, but no adverse plant phenotypes. This suggests that the in planta expression of the eCry1Gb.1Ig protein does not compromise plant growth or reproduction and can protect plants from FAW-related damage. Therefore, this valuable discovery will provide a differentiating FAW control trait that will give growers another tool to help them reduce yield loss due to FAW.

Improving production and fruit quality of tomato under abiotic stress: Genes for the future of tomato breeding for a sustainable agriculture

Climate change and a rising world population make generation of crops with enhanced abiotic stress tolerance and higher nutritive value a priority objective in agriculture. In this review we have summarized recent advances on tomato ( Solanum lycopersicum ), the most important horticultural species regarding production and nutritional quality. Firstly, the different and complex processes evolved in abiotic stress tolerance are summarized, with special attention on responses to osmotic, oxidative and ionic and nutritional stress, as well as the anatomical changes involved in plant adaptation to abiotic stress. In order to avoid or reduce production loss and improve fruit quality under abiotic stress, it is of utmost importance the identification of tomato key genes involved in abiotic stress long-term tolerance, that is, genes which simple variation determines a significant change not only in vegetative but also in reproductive growth. The functions of the tomato genes known to date affecting fruit yield under abiotic stress are described, including those involved in signal sensing, perception and transduction of abiotic stress, transcription factors and abiotic stress responsive genes. Together with production, fruit quality is crucial in tomato given its so high worldwide consumption per capita . Nutritional compounds as carotenoids and vitamin E are well known antioxidants and abiotic stress induces a secondary oxidative stress to which plants have to react. Although the increased metabolites production under abiotic stress may be due to physiological adaptation processes, advances in this field will go in parallel to the identification of genes improving fruit quality when the plant is grown subjected to stressful conditions. The key genes involved in improvement of nutritional quality of tomato fruit under abiotic stress are reviewed, in particular the main ones involved in accumulation of crucial metabolites in this aspect, such as carotenoids and tocopherols. One of the main bottlenecks in breeding programs nowadays is the strong reduction of phenotypic and genetic diversity in modern germplasm. Therefore, searching of new genetic resources for improvement of abiotic stress tolerance and fruit quality is critical, including sources of natural variation, like wild relatives and traditional varieties or landraces, as well as spontaneous and induced mutants with beneficial effects on these traits. All of them will expand our knowledge on the processes involved in abiotic stress tolerance and fruit quality in tomato. • Adaptation strategies essential to uphold growth through plant life cycle in stress • Long-term stress tolerance genes are critical to reduce yield losses • Increasing fruit quality when growing tomato plants under abiotic stress conditions • Finding new genetic resources for improvement of tomato fruit yield and quality

Transgenic Improvement for Biotic Resistance of Crops.

Biotic constraints, including pathogenic fungi, viruses and bacteria, herbivory insects, as well as parasitic nematodes, cause significant yield loss and quality deterioration of crops. The effect of conventional management of these biotic constraints is limited. The advances in transgenic technologies provide a direct and directional approach to improve crops for biotic resistance. More than a hundred transgenic events and hundreds of cultivars resistant to herbivory insects, pathogenic viruses, and fungi have been developed by the heterologous expression of exogenous genes and RNAi, authorized for cultivation and market, and resulted in a significant reduction in yield loss and quality deterioration. However, the exploration of transgenic improvement for resistance to bacteria and nematodes by overexpression of endogenous genes and RNAi remains at the testing stage. Recent advances in RNAi and CRISPR/Cas technologies open up possibilities to improve the resistance of crops to pathogenic bacteria and plant parasitic nematodes, as well as other biotic constraints.

Deep learning system for paddy plant disease detection and classification.

Automatic detection and analysis of rice crop diseases is widely required in the farming industry, which can be utilized to avoid squandering financial and other resources, reduce yield losses, and improve treatment efficiency, resulting in healthier crop output. An automated approach was proposed for accurately detecting and classifying diseases from a supplied photograph. The proposed system for the recognition of rice plant diseases adopts a computer vision-based approach that employs the techniques of image processing, machine learning, and deep learning, reducing the reliance on conventional methods to protect paddy crops from diseases like bacterial leaf blight, false smut, brown leaf spot, rice blast, and sheath rot, the five primary diseases that frequently plague the Indian rice fields. Following image pre-processing, image segmentation is employed to determine the diseased section of the paddy plant, with the diseases listed above being identified purely on the basis of their visual contents. An integration of a support vector machine classifier and convolutional neural networks are used to recognize and classify specific varieties of paddy plant diseases. With ReLU and softmax functions, the suggested deep learning-based strategy attained the highest validation accuracy of 0.9145. Following recognition, a predictive remedy is recommended, which can assist agriculture-related individuals and organizations in taking suitable measures to combat these diseases.

ANALISIS KINERJA MESIN AMG CNC PLATE CUTTING MENGGUNAKAN METODE OEE (OVERALL EQUIPMENT EFFECTIVENESS)

In the industrial world, technology is an important thing that can help improve the quality of the industry. Every company that uses technology definitely needs an adequate maintenance system for tools and machines. The purpose of maintenance is to prevent unexpected machine breakdowns that can result in the production process stopping, endangering worker safety and increasing machine repair costs. One of the machines that are often used in the production process at PT XYZ is the AMG CNC plate cutting machine. Therefore, one method to determine the effectiveness of a cutting machine is Overall Equipment Effectiveness (OEE). Overall Equipment Effectiveness is a measurement made on performance related to the availability of process productivity and quality. As a result, by applying the OEE (Overall Equipment Effectiveness) method for performance analysis of the AMG CNC plate cutting machine, we can find out the value for availability of 91%, performance efficiency of 96%, and rate of quality product of 99%, the measurement results of the AMG CNC plate machine cutting in January 2021 using the OEE method of 86% which has passed the value of the OEE world standard of 85%. the value of the six big losses obtained is the breakdown loss value of 1.8%, the set up and adjustment value of 10%, the idle and minor stoppage value of 3%, the value of reduce speed losses of 5.3%, the value of process defect losses of 1 ,1%, the value of reduce yield losses is 0.06%.

Engineering effector-triggered immunity in rice: Obstacles and perspectives.

Improving rice immunity is one of the most effective approaches to reduce yield loss by biotic factors, with the aim of increasing rice production by 2050 amidst limited natural resources. Triggering a fast and strong immune response to pathogens, effector-triggered immunity (ETI) has intrigued scientists to intensively study and utilize the mechanisms for engineering highly resistant plants. The conservation of ETI components and mechanisms across species enables the use of ETI components to generate broad-spectrum resistance in plants. Numerous efforts have been made to introduce new resistance (R) genes, widen the effector recognition spectrumand generate on-demand R genes. Although engineering ETI across plant species is still associated with multiple challenges, previous attempts have provided an enhanced understanding of ETI mechanisms. Here, we provide a survey of recent reports in the engineering of rice R genes. In addition, we suggest a framework for future studies of R gene-effector interactions, including genome-scale investigations in both rice and pathogens, followed by structural studies of R proteins and effectors, and potential strategies to use important ETI components to improve rice immunity.

UAV-Based Multi-Temporal Thermal Imaging to Evaluate Wheat Drought Resistance in Different Deficit Irrigation Regimes

Deficit irrigation is a common approach in water-scarce regions to balance productivity and water use, whereas drought stress still occurs to various extents, leading to reduced physiological performance and a decrease in yield. Therefore, seeking a rapid and reliable method to identify wheat varieties with drought resistance can help reduce yield loss under water deficit. In this study, we compared ten wheat varieties under three deficit irrigation systems (W0, no irrigation during the growing season; W1, irrigation at jointing; W2, irrigation at jointing and anthesis). UAV thermal imagery, plant physiological traits [leaf area index (LAI), SPAD, photosynthesis (Pn), transpiration (Tr), stomatal conductance (Cn)], biomass and yield were acquired at different growth stages. Wheat drought resistance performance was evaluated through using the canopy temperature extracted from UAV thermal imagery (CT-UAV), in combination with hierarchical cluster analysis (HCA). The CT-UAV of W0 and W1 treatments was significantly higher than in the W2 treatment, with the ranges of 24.8–33.3 °C, 24.3–31.6 °C, and 24.1–28.9 °C in W0, W1 and W2, respectively. We found negative correlations between CT-UAV and LAI, SPAD, Pn, Tr, Cn and biomass under the W0 (R2 = 0.41–0.79) and W1 treatments (R2 = 0.22–0.72), but little relevance for W2 treatment. Under the deficit irrigation treatments (W0 and W1), UAV thermal imagery was less effective before the grain-filling stage in evaluating drought resistance. This study demonstrates the potential of ensuring yield and saving irrigation water by identifying suitable wheat varieties for different water-scarce irrigation scenarios.

Soybean disease identification using original field images and transfer learning with convolutional neural networks

Meeting the growing demand for soybeans will require increased production. One approach would be to reduce yield loss from plant diseases. In the U.S., soybean diseases account for approximately 8–25% of average annual yield loss. Early and accurate detection of pathogens is key for effective disease management strategies and can help to minimize pesticide usage and thus boost overall productivity. Recent advancements in computer vision could move us towards that goal by making disease diagnostics expertise more readily accessible to every-one. To that end, we developed an automated classifier of digital images of soybean diseases, based on convolutional neural networks (CNN). For model training and validation, we acquired more than 9,500 original soybean images, representing eight distinct disease and deficiency classes: (1) healthy/asymptomatic, (2) bacterial blight, (3) Cercospora leaf blight, (4) downy mildew, (5) frogeye leaf spot, (6) soybean rust, (7) target spot, and (8) potassium deficiency. To make training more efficient we experimented with a variety of approaches to transfer learning, data engineering, and data augmentation. Our best performing model was based on the DenseNet201 architecture. After training from scratch, it achieved an overall testing accuracy of 96.8%. Experimenting with full or partial freezing of core DenseNet201 model weights did not improve performance. Neither did a deliberate effort to increase the diversity of subject backgrounds in the digital images. Models performed best when trained on datasets composed exclusively of images of soybean leaves still attached to the plant in the field; conversely, mixing in images of detached leaves on simple backgrounds reduced performance. On the other hand, data augmentation to increase representational parity across disease classes provided a substantial performance boost. Our development experience may provide useful insights for researchers considering how to best build and analyze datasets for similar applications.

Dry Matter Yield Stability Analysis of Maize Genotypes Grown in Al Toxic and Optimum Controlled Environments

Breeding for Al tolerance is the most sustainable strategy to reduce yield losses caused by Al toxicity in plants. The use of rapid, cheap and reliable testing methods and environments enables breeders to make quick selection decisions. The objectives of this study were to (i) identify high dry matter yielding and stable quality protein maize (QPM) lines grown under Al toxic and optimum conditions and (ii) compare the discriminating power of laboratory- and greenhouse-based testing environments. A total of 75 tropical QPM inbred lines were tested at seedling stage for dry matter yield and stability under optimum and Al toxic growing conditions across six laboratory- and greenhouse-based environments. The nutrient solution method was used for the laboratory trials, while the soil bioassay method was used for the greenhouse trials. A yield loss of 55% due to Al toxicity was observed, confirming the adverse effects of Al toxicity on maize productivity. The ANOVA revealed the presence of genetic variation among the set of genotypes used in this study, which can be exploited through plant breeding. Seventeen stable and high-yielding lines were identified and recommended. Greenhouse-based environments were more discriminating than laboratory environments. Therefore, we concluded that greenhouse environments are more informative than laboratory environments when testing genotypes for Al tolerance.

Patlıcanda Kurşuni Küf Hastalığı (Botrytis cinerea) Üzerine Bazı Bakteriyel İzolatların in vitro ve in vivo Koşullarda Biyolojik Mücadele Etkinliklerinin Araştırılması

Gray mold agent Botrytis cinerea (teleomorph: Botryotinia fuckliana (de Barry) Whetzel) causes significant yield losses in many economically important vegetables. Chemicals are used in the control to reduce yield loss. As a result of the intensive use of chemicals in the control of plant diseases, human health and the environment are adversely affected. Therefore, interest in environmentally friendly control practices has increased recently. One such application is the use of benefical bacteria in the biological control of diseases. In this study; biocontrol potentials of 12 bioagent bacteria strains (1 Bacillus cereus, 2 Bacillus megaterium, 2 Bacillus pumilus, 2 Bacillus subtilis, 1 Bacillus thuringiensis subsp. kurstaki, 1 Paenibacillus polymyxa, 2 Pantoea agglomerans and 1 Pseudomonas fluorescens) have been determined against gray mold disease agent B. cinerea on eggplant in vitro and in vivo. Pseudomonas chlororaphis supsp. aurofaciens and Bacillus amyloliquefaciens, which were the most efficient strains found in vitro conditions, were also effective against B. cinerea in vivo. In conclusion, it was determined that two most effective bacterial strains could be used as a biocontrol agent in the biological control of B. cinerea in eggplant growing.

Comparative transcriptomic analysis of the haustoria of Gymnosporangium yamadae and G. asiaticum

To provide a theoretical basis for controlling the spread of rust disease, cultivating disease-resistant varieties and reducing yield losses, we investigated the transcriptome differences between Gymnosporangium yamadae and Gymnosporangium asiaticum at the haustorial stage and revealed a specialized selection mechanism for Gymnosporangium species to infect host plants. We sequenced the transcriptomes of the haustoria in rust-infected leaves when basidiospores of G. yamadae and G. asiaticum infected their hosts, and obtained 21 213 and 13 015 unigenes, respectively. Real-time fluorescence quantitative PCR validation of five genes selected from G. yamadae and G. asiaticum, respectively, showed that their expression profiles were generally consistent with the results of transcriptome analysis, demonstrating the reliability of the transcriptome data. We used seven databases such as Nr, GO, KEGG, and KOG to perform gene function annotation and enrichment analysis, and found that the genes from both rusts were mainly enriched in cellular processes, translation, and metabolism-related pathways. Moreover, we used SignalP, TMHMM online website and other software such as dbCAN, BLSAT, HMMER to show that there were 343 (2.51%) and 175 (2.79%) candidate effector proteins containing 14 and 5 proteases and 10 and 3 lipases in the haustoria of G. yamadae and G. asiaticum, respectively. Furthermore, we used OrthoFinder, BLAST and KaKs Calculator software to analyze the evolutionary relationship of the two fungi. Among one-to-one homologous genes, gene pairs with > 82% alignment were considered to be under conservative selection, and 12.37% under positive selection. Five effectors of G. asiaticum were under positive selection, and one of which was a lipase. No significant differences were found in the enrichment of expressed genes between G. yamadae and G. asiaticum, indicating the biological processes involved in haustoria were relatively conserved, despite the typical host selectivity between species. The low protein similarity between the two species suggested that they were under greater host selective pressure and there was significant evolutionary divergence, which might be related to the host-specific selection mechanism. In the haustorial, the main purpose of the effectors might be to regulate physiological processes in the plants rather than attacking the host directly, and G. yamadae and G. asiaticum might use plant lipids as energy sources.

Phytosociological survey of weeds in irrigated maize fields in a Southern Guinea Savanna of Nigeria

Maize is a food crop for millions of people in sub-Saharan Africa. However, severe weed infestation might cause significant yield loss. This study investigated weed composition, abundance and distribution in maize-based cropping systems in the Southern Guinea Savanna of Nigeria. Fields were surveyed between February and March 2022. The Importance Value Index (IVI) of each weed species was determined using relative frequency, relative density, and relative abundance. A total of 29 weed species from 15 families were identified. Poaceae (34.9 %) was the most prevalent weed family, followed by Cyperaceae (26.9 %) and Portulacaceae (12.8 %). The IVI showed that Cyperus rotundus (38.6), Portulaca oleraceae (29.4), Digitaria horizontalis (25.5), Brachiaria deflexa (24.2), Senna obtusifolia (17.6), Ageratum conyzoides (16.0), Cynodon dactylon (12.6), Phyllanthus niruri (11.1) and Eragrostis sp. (10.6) were the most dominant. Principal Component Analysis (PCA) ordination biplot revealed that Setaria pumila was strongly associated with sole maize cultivation, as was Desmodium scorpiurus with maize/rice intercropping, C. dactylon with maize/pepper, Euphorbia hirta with maize/cassava, and Cleome viscosa with maize/amaranthus intercropping. Special attention to these weed species is required when making an informed decision on the choice of weed control measures.to reduce yield losses in endemic areas.

Quality control of lifetime drift effects

Electrical parameters of semiconductor devices are specified in their product data sheets. These parameters have to stay within their specified ranges over lifetime. The lifetimes of semiconductor devices are simulated by accelerated stress tests. In the course of these stress tests, electrical parameters are checked for any change in their values. This change can occur at semiconductor devices and is called lifetime drift. In order to avoid that electrical parameters drift out of their specified limits, the devices are tested with tighter limits at the end of the production line. These limits are referred to as test limits. The differences between test limits and specification limits are called guard bands. Guard bands are optimized to reduce yield losses and ensure compliance with the product data sheets over lifetime.In this paper, a simplified lifetime drift model for continuous electrical parameters is introduced. The drift calculation is faster compared to existing models, requires less memory resources, and is suitable for edge devices. It is tailored to the needs of semiconductor manufacturers and their development and production flows. It considers additional information on the stress test data. With this model, one can detect and quantify lifetime drift, calculate typical drift patterns, and optimize guard bands based on quality targets. Thus, this model enables fast, efficient, and accurate calculations of test limits.

Spatio-Temporal Anomaly Detection for Substrate Strip Bin Map in Semiconductor Assembly Process

With the development of deep learning, data-driven approaches have shown great success in the semiconductor manufacturing. For example, wafer bin maps (WBM) recognition is a critical application to identify failure modes and finding root-cause to reduce yield loss. However, the WBM studies provide static classification results without tracing the temporal patterns. This study develops a spatio-temporal strip map prediction system for the flip-chip bonding process in the assembly house. The proposed strip bin map (SBM) prognostic system including prediction model and recognition model can provide pre-alarm of defect and predict the defect mode for the final process. In practice, not all processes are followed by a functional test (FT) and thus this proposed system can simulate the defect generation based on the Bayes' theorem for tracking the changes of the spatio-temporal patterns. An empirical study of the semiconductor assembly manufacturer in Taiwan was conducted to validate the proposed framework, and the results show that the proposed SBM system successfully predict the SBM and defect mode for the final test and provide an effective pre-alarm.

Yield reduction due to diseases and lodging and impact of input intensity on yield in variety trials in five cereal crops

Breeding of multi-resistant varieties to reduce yield loss due to disease damage and lodging, and reduction of input intensity are of high importance for a more sustainable cereal production. The aim of this study was to evaluate (i) yield reduction caused by diseases and lodging and (ii) impact of input intensity and soil fertility in cereal variety trials grown under two intensities. Intensity 2 was treated with and intensity 1 without fungicides and growth regulators. We applied multiple regression approaches based on mixed linear models. First, we estimated relative yield reduction in intensity 1 compared to intensity 2 as a function of severity scores of diseases and lodging. High yield reductions occurred in winter wheat and winter triticale, moderate in winter rye and winter barley and low in spring barley. The damage potential was highest for yellow rust, followed by brown rust, lodging and Septoria tritici blotch. Medium damage potential was identified for dwarf leaf rust and low for powdery mildew, Septoria nodorum blotch, Rhynchosporium as well as for stem and ear buckling. Second, differences in input intensity did not affect yield in intensity 2 across the range of nitrogen and fungicide application rates while higher yield occurred at higher growth regulator rates and soil fertility. Growth regulator was strongly related with higher yield in winter rye and winter barley, however in spring barley, a negative relation was found. Soil fertility showed the strongest yield impact in all crops.

Maize stomatal responses against the climate change.

Drought and heat, in the context of climate change, are expected to increase in many agricultural areas across the globe. Among current abiotic stresses, they are the most limiting factors that influence crop growth and productivity. Maize is one of most widely produced crops of the world, being the first in grain production with a yield that exceeded 1.1 billion tons in 2021. Despite its wide distribution in semi-arid regions, it is highly vulnerable to climate change, which triggers important losses in its productivity. This article explores how maize yield may persevere through climate change by focusing on the stomatal regulation of gas exchange. The emerging picture unravels that maize copes with drought stress by reducing stomatal size and stomatal pore area, and increasing stomatal density, which, in turn, reduces transpiration and photosynthetic rate. When drought and heat co-occur, heat enhances stomatal response to drought stress. To avoid plant heat damage, the decline in stomatal aperture could trigger the expansion of the distance of action, from the longitudinal leaf veins, of ZmSHR1, which might act to positively regulate ZmSPCHs/ZmICE1 heterodimers, increasing the stomatal density. Only when drought is not very severe, elevated CO2 levels reduce yield losses. The knowledge of the upcoming climate changes together with the prediction of the developmental and physiological stomatal responses will allow not only to anticipate maize yield in the next years, but also to contribute to the correct decision-making in the management of this important crop.

Comparative transcriptome meta-analysis reveals a set of genes involved in the responses to multiple pathogens in maize.

Maize production is constantly threatened by the presence of different fungal pathogens worldwide. Genetic resistance is the most favorable approach to reducing yield losses resulted from fungal diseases. The molecular mechanism underlying disease resistance in maize remains largely unknown. The objective of this study was to identify key genes/pathways that are consistently associated with multiple fungal pathogen infections in maize. Here, we conducted a meta-analysis of gene expression profiles from seven publicly available RNA-seq datasets of different fungal pathogen infections in maize. We identified 267 common differentially expressed genes (co-DEGs) in the four maize leaf infection experiments and 115 co-DEGs in all the seven experiments. Functional enrichment analysis showed that the co-DEGs were mainly involved in the biosynthesis of diterpenoid and phenylpropanoid. Further investigation revealed a set of genes associated with terpenoid phytoalexin and lignin biosynthesis, as well as potential pattern recognition receptors and nutrient transporter genes, which were consistently up-regulated after inoculation with different pathogens. In addition, we constructed a weighted gene co-expression network and identified several hub genes encoding transcription factors and protein kinases. Our results provide valuable insights into the pathways and genes influenced by different fungal pathogens, which might facilitate mining multiple disease resistance genes in maize.

Differing responses of root morphology and physiology to nitrogen application rates and their relationships with grain yield in rice

Root morphology and physiology influence aboveground growth and yield formation in rice. However, root morphological and physiological differences among rice varieties with differing nitrogen (N) sensitivities and their relationship with grain yield are still unclear. In this study, rice varieties differing in N sensitivity over many years of experiments were used. A field experiment with multiple N rates (0, 90, 180, 270, and 360 kg ha−1) was conducted to elucidate the effects of N application on root morphology, root physiology, and grain yield. A pot experiment with root excision and exogenous application of 6-benzyladenine (6-BA) at heading stage was used to further verify the above effects. The findings revealed that (1) under the same N application rate, N-insensitive varieties (NIV) had relatively large root biomass (root dry weight, length, and number). Grain yield was associated with root biomass in NIV. The oxidation activity and zeatin (Z) + zeatin riboside (ZR) contents in roots obviously and positively correlated with grain yield in N-sensitive varieties (NSV), and accounted for its higher grain yield than that of NIV at lower N application rates (90 and 180 kg ha−1). (2) The root dry weight required for equal grain yield of NIV was greater than that of NSV. Excision of 1/10 and 1/8 of roots at heading stage had no discernible effect on the yield of Liangyoupeijiu (NIV), and it significantly reduced yield by 11.5% and 21.3% in Tianyouhuazhan (NSV), respectively, compared to the treatment without root excision. The decrease of filled kernels and grain weight after root excision was the primary cause for the yield reduction. Root excision and exogenous 6-BA application after root excision had little influence on the root activity of NIV. The oxidation activity and Z + ZR contents in roots of NSV decreased under root excision, and the increase in the proportion of excised roots aggravated these effects. The application of exogenous 6-BA increased the root activity of NSV and increased filled kernels and grain weight, thereby reducing yield loss after root excision. Thus, the root biomass of NIV was large, and there may be a phenomenon of “root growth redundancy.” Vigorous root activity was an essential feature of NSV. Selecting rice varieties with high root activity or increasing root activity by cultivation measures could lead to higher grain yield under lower N application rates.