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<jats:p>Extensive research has focused on exploring the range of genome sizes in eukaryotes, with a particular emphasis on land plants, where significant variability has been observed. Accurate estimation of genome size is essential for various research purposes, but existing sequence-based methods have limitations, particularly for low-coverage datasets. In this study, we introduce LocoGSE, a novel genome size estimator designed specifically for low-coverage datasets generated by genome skimming approaches. LocoGSE relies on mapping the reads on single copy consensus proteins without the need for a reference genome assembly. We calibrated LocoGSE using 430 low-coverage Angiosperm genome skimming datasets and compared its performance against other estimators. Our results demonstrate that LocoGSE accurately predicts monoploid genome size even at very low depth of coverage (&amp;lt;1X) and on highly heterozygous samples. Additionally, LocoGSE provides stable estimates across individuals with varying ploidy levels. LocoGSE fills a gap in sequence-based plant genome size estimation by offering a user-friendly and reliable tool that does not rely on high coverage or reference assemblies. We anticipate that LocoGSE will facilitate plant genome size analysis and contribute to evolutionary and ecological studies in the field. Furthermore, at the cost of an initial calibration, LocoGSE can be used in other lineages.</jats:p>

<jats:p>Precise and timely leaf area index (LAI) estimation for winter wheat is crucial for precision agriculture. The emergence of high-resolution unmanned aerial vehicle (UAV) data and machine learning techniques offers a revolutionary approach for fine-scale estimation of wheat LAI at the low cost. While machine learning has proven valuable for LAI estimation, there are still model limitations and variations that impede accurate and efficient LAI inversion. This study explores the potential of classical machine learning models and deep learning model for estimating winter wheat LAI using multispectral images acquired by drones. Initially, the texture features and vegetation indices served as inputs for the partial least squares regression (PLSR) model and random forest (RF) model. Then, the ground-measured LAI data were combined to invert winter wheat LAI. In contrast, this study also employed a convolutional neural network (CNN) model that solely utilizes the cropped original image for LAI estimation. The results show that vegetation indices outperform the texture features in terms of correlation analysis with LAI and estimation accuracy. However, the highest accuracy is achieved by combining both vegetation indices and texture features to invert LAI in both conventional machine learning methods. Among the three models, the CNN approach yielded the highest LAI estimation accuracy (<jats:italic>R</jats:italic><jats:sup>2</jats:sup> = 0.83), followed by the RF model (<jats:italic>R</jats:italic><jats:sup>2</jats:sup> = 0.82), with the PLSR model exhibited the lowest accuracy (<jats:italic>R</jats:italic><jats:sup>2</jats:sup> = 0.78). The spatial distribution and values of the estimated results for the RF and CNN models are similar, whereas the PLSR model differs significantly from the first two models. This study achieves rapid and accurate winter wheat LAI estimation using classical machine learning and deep learning methods. The findings can serve as a reference for real-time wheat growth monitoring and field management practices.</jats:p>

<jats:p>The search for ecofriendly products to reduce crop dependence on synthetic chemical fertilizers presents a new challenge. The present study aims to isolate and select efficient native PGPB that can reduce reliance on synthetic NPK fertilizers. A total of 41 bacteria were isolated from the sediment and roots of mangrove trees (<jats:italic>Avicennia marina</jats:italic>) and assessed for their PGP traits under <jats:italic>in vitro</jats:italic> conditions. Of them, only two compatible strains of <jats:italic>Bacillus sp</jats:italic>ecies were selected to be used individually and in a mix to promote tomato seedling growth. The efficiency of three inoculants applied to the soil was assessed in a pot experiment at varying rates of synthetic NPK fertilization (0, 50, and 100% NPK). The experiment was set up in a completely randomized design with three replications. Results showed that the different inoculants significantly increased almost all the studied parameters. However, their effectiveness is strongly linked to the applied rate of synthetic fertilization. Applying bacterial inoculant with only 50% NPK significantly increased the plant height (44-51%), digital biomass (60-86%), leaf area (77-87%), greenness average (29-36%), normalized difference vegetation index (29%), shoot dry weight (82-92%) and root dry weight (160-205%) compared to control plants. Concerning the photosynthetic activity, this treatment showed a positive impact on the concentrations of chlorophyll a (25-31%), chlorophyll b (34-39%), and carotenoid (45-49%). Interestingly, these increases ensured the highest values significantly similar to or higher than those of control plants given 100% NPK. Furthermore, the highest accumulation of N, P, K, Cu, Fe, Zn, and Ca in tomato shoots was recorded in plants inoculated with the bacterial mix at 50% NPK. It was proven for the first time that the native PGP bacteria derived from mangrove plant species <jats:italic>A. marina</jats:italic> positively affects the quality of tomato seedlings while reducing 50% NPK.</jats:p>

<jats:p>Weeding is a key link in agricultural production. Intelligent mechanical weeding is recognized as environmentally friendly, and it profoundly alleviates labor intensity compared with manual hand weeding. While intelligent mechanical weeding can be implemented only when a large number of disciplines are intersected and integrated. This article reviewed two important aspects of intelligent mechanical weeding. The first one was detection technology for crops and weeds. The contact sensors, non-contact sensors and machine vision play pivotal roles in supporting crop detection, which are used for guiding the movements of mechanical weeding executive parts. The second one was mechanical weeding executive part, which include hoes, spring teeth, fingers, brushes, swing and rotational executive parts, these parts were created to adapt to different soil conditions and crop agronomy. It is a fact that intelligent mechanical weeding is not widely applied yet, this review also analyzed the related reasons. We found that compared with the biochemical sprayer, intelligent mechanical weeding has two inevitable limitations: The higher technology cost and lower working efficiency. And some conclusions were commented objectively in the end.</jats:p>

<jats:sec><jats:title>Introduction</jats:title><jats:p><jats:italic>Dendrobium</jats:italic> is an epiphytic herb plant with neuroprotective, gastroprotective, anti-inflammatory, and immunomodulatory effects. It is often found attached to tree trunks or rocks. With the development of the dendrobium industry, numerous epiphytic patterns exist, such as crushed stone, stump, and sawdust. The study of metabolites and endophytes of <jats:italic>D. nobile</jats:italic> under different epiphytic patterns, which revealed the effects of epiphytic patterns on <jats:italic>D. nobile</jats:italic> from the perspectives of metabolomics and microbiology, is of great significance for the healthy development of <jats:italic>D. nobile</jats:italic>.</jats:p></jats:sec><jats:sec><jats:title>Methods</jats:title><jats:p>In the study, the <jats:italic>D. nobile</jats:italic> under five epiphytic patterns grown in the same environment were selected. The metabolites were investigated by widely targeted metabolomics, and the endophytes were sequenced using high-throughput sequencing methods. Then, a correlation analysis between the different metabolites and endophytes was performed.</jats:p></jats:sec><jats:sec><jats:title>Results</jats:title><jats:p>A total of 1,032 metabolites were annotated in <jats:italic>D. nobile</jats:italic>. There are more flavonoids and phenolic acids accumulated on the epiphytic pattern of Danxia stone, whereas the accumulation of lipids on the other epiphytic patterns and 16 differential metabolites was screened out. The endophyte composition of <jats:italic>D. nobile</jats:italic> was dominated by <jats:italic>Proteobacteria</jats:italic>, <jats:italic>Actinomycetes</jats:italic>, <jats:italic>unidentified bacteria, Firmicutes</jats:italic>, and <jats:italic>Cyanobacteria</jats:italic>. For endophytic fungi, <jats:italic>Basidiomycota</jats:italic> and <jats:italic>Ascomycota</jats:italic> were the dominant phyla of <jats:italic>D. nobile</jats:italic>. The relative abundance of <jats:italic>Spirosoma</jats:italic>, <jats:italic>Nocardioides</jats:italic>, and <jats:italic>Arrhenia</jats:italic> in the Danxia stone was significantly higher than that of other epiphytic patterns. According to correlation analysis, we found a significant correlation between differential metabolites and <jats:italic>Spirosoma</jats:italic>, <jats:italic>Nocardioides</jats:italic>, and <jats:italic>Arrheni</jats:italic>.</jats:p></jats:sec><jats:sec><jats:title>Discussion</jats:title><jats:p>This study confirmed that Dendrobium quality was affected by its epiphytic patterns and revealed its possible causes from a microbiological point of view.</jats:p></jats:sec>

<jats:p>Under-canopy afforestation using different tree species is a key approach in close-to-nature management to improve the structural and functional stability of plantation forests. However, current research on understory afforestation mainly focuses on the seedling stage, with limited attention to saplings or young trees. In this study, we evaluated the growth characteristics and leaf traits of 14-year-old <jats:italic>Pinus sylvestris</jats:italic> var. <jats:italic>Mongolica</jats:italic> trees under four different upper forest density (UFD) treatments: 0 trees/hm<jats:sup>2</jats:sup> (canopy openness 100%, CK), 150 trees/hm<jats:sup>2</jats:sup> (canopy openness 51.9%, T1), 225 trees/hm<jats:sup>2</jats:sup> (canopy openness 43.2%, T2), and 300 trees/hm<jats:sup>2</jats:sup> (canopy openness 28.4%, T3). We found that the survival rate of <jats:italic>P. sylvestris</jats:italic> in the T3 was significantly lower than in the other treatments, with a decrease of 30.2%, 18.3%, and 19.5% compared to CK, T1, and T2, respectively. The growth of <jats:italic>P. sylvestris</jats:italic> in the T1 treatment exhibited superior performance. Specifically, T1 showed a significant increase of 18.8%, 5.5%, and 24.1% in tree height, diameter at breast height, and crown width, respectively, compared to the CK. The mean trunk biomass ratio in the understory was significantly higher than that in full light by 15.4%, whereas the mean leaf biomass ratio was significantly lower by 12.3%. Understory <jats:italic>P. sylvestris</jats:italic> trees tended to allocate more biomass to the trunk at the expense of decreasing leaf biomass, which would facilitate height growth to escape the shading environment, although the promotion was relatively limited. Leaf length, leaf width, leaf area, leaf thickness, mesophyll tissue thickness, epidermis thickness, and leaf carbon content were the highest in the CK and tended to decrease with increasing UFD, indicating that a high-light environment favored leaf growth and enhanced carbon accumulation. In summary, young <jats:italic>P. sylvestris</jats:italic> trees adapted to moderate shading conditions created by the upper canopy, and the T1 treatment was optimal for the growth of understory <jats:italic>P. sylvestris</jats:italic>. This study provides insights into different adaptive strategies of young <jats:italic>P. sylvestris</jats:italic> trees to changes in light environment, providing practical evidence for under-canopy afforestation using light-demanding trees during pure plantation transformation.</jats:p>

<jats:p>Climate change poses a major threat to global food security, significantly reducing crop yields as cause of abiotic stresses, and for boosting the spread of new and old pathogens and pests. Sustainable crop management as a route to mitigation poses the challenge of recruiting an array of solutions and tools for the new aims. Among these, the deployment of positive interactions between the micro-biotic components of agroecosystems and plants can play a highly significant role, as part of the agro-ecological revolution. Endophytic microorganisms have emerged as a promising solution to tackle this challenge. Among these, Arbuscular Mycorrhizal Fungi (AMF) and endophytic bacteria and fungi have demonstrated their potential to alleviate abiotic stresses such as drought and heat stress, as well as the impacts of biotic stresses. They can enhance crop yields in a sustainable way also by other mechanisms, such as improving the nutrient uptake, or by direct effects on plant physiology. In this review we summarize and update on the main types of endophytes, we highlight several studies that demonstrate their efficacy in improving sustainable yields and explore possible avenues for implementing crop-microbiota interactions. The mechanisms underlying these interactions are highly complex and require a comprehensive understanding. For this reason, omic technologies such as genomics, transcriptomics, proteomics, and metabolomics have been employed to unravel, by a higher level of information, the complex network of interactions between plants and microorganisms. Therefore, we also discuss the various omic approaches and techniques that have been used so far to study plant-endophyte interactions.</jats:p>

<jats:p>In the last century, breeding programs have traditionally favoured yield-related traits, grown under high-input conditions, resulting in a loss of genetic diversity and an increased susceptibility to stresses in crops. Thus, exploiting understudied genetic resources, that potentially harbour tolerance genes, is vital for sustainable agriculture. Northern European barley germplasm has been relatively understudied despite its key role within the malting industry. The European Heritage Barley collection (ExHIBiT) was assembled to explore the genetic diversity in European barley focusing on Northern European accessions and further address environmental pressures. ExHIBiT consists of 363 spring-barley accessions, focusing on two-row type. The collection consists of landraces (~14%), old cultivars (~18%), elite cultivars (~67%) and accessions with unknown breeding history (~1%), with 70% of the collection from Northern Europe. The population structure of the ExHIBiT collection was subdivided into three main clusters primarily based on the accession’s year of release using 26,585 informative SNPs based on 50k iSelect single nucleotide polymorphism (SNP) array data. Power analysis established a representative core collection of 230 genotypically and phenotypically diverse accessions. The effectiveness of this core collection for conducting statistical and association analysis was explored by undertaking genome-wide association studies (GWAS) using 24,876 SNPs for nine phenotypic traits, four of which were associated with SNPs. Genomic regions overlapping with previously characterised flowering genes (HvZTLb) were identified, demonstrating the utility of the ExHIBiT core collection for locating genetic regions that determine important traits. Overall, the ExHIBiT core collection represents the high level of untapped diversity within Northern European barley, providing a powerful resource for researchers and breeders to address future climate scenarios.</jats:p>

<jats:p>Apple (<jats:italic>Malus pumila Mill.</jats:italic>) is one of the important economic crops in the arid areas of Xinjiang, China. For a long time, there has been a problem of high consumption but low yield in water and fertilizer management, prevent improvements in apple quality and yield. In this study, 5-year-old ‘Royal Gala’ apple trees in extremely arid areas of Xinjiang were used as experimental materials to carry out field experiments. considering 5 irrigation levels (W1, 30 mm; W2, 425 mm; W3, 550 mm; W4, 675 mm; W5, 800 mm) and 5 fertilization levels (F1, 280 kg·ha<jats:sup>-1</jats:sup>; F2, 360 kg·ha<jats:sup>-1</jats:sup>; F3, 440 kg·ha<jats:sup>-1</jats:sup>; F4, 520 kg·ha<jats:sup>-1</jats:sup>; F5, 600 kg·ha<jats:sup>-1</jats:sup>) under magnetoelectric water irrigation conditions. The results demonstrated that magnetoelectric water combined with the application of 675 mm irrigation amount and 520 kg·ha<jats:sup>-1</jats:sup> fertilization amount was the most effective combination. These results occurred by increasing net photosynthetic rate of apple leaves, improved the quality of apples, increased apple yield, and promoted the improvement of water and fertilizer use efficiency. Additionally, the quadratic regression model was used to fit the response process of yield, IWUE and PFP to irrigation amount and fertilization amount, and the accuracy was greater than 0.8, indicating good fitting effects. The synergistic effect of water and fertilizer has a positive effect on optimizing apple water and fertilizer management. Principal component analysis showed that the magnetoelectric treatment combined water and fertilizer mainly affected apple yield, water and fertilizer use efficiency and vitamin C content related to quality. This study provides valuable guidance for improving water and fertilizer productivity, crop yield and quality in extreme arid areas of Xinjiang by using Magnetoelectric water irrigation.</jats:p>