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Planting Geometry May Be Used to Optimize Plant Density and Yields without Changing Yield Potential per Plant in Sweet Corn
Planting geometry is one of the most important management practices that determine plant growth and yield of corn. The effects of eight planting geometries (35 × 23 cm, 40 × 21 cm, 45 × 19 cm, 50 × 18 cm, 55 × 17 cm, 60 × 16 cm, 65 × 15 cm, 70 × 15 cm) on plant growth and yields of three sweet corn hybrids (Argos F1, Challenger F1, Khan F1) were investigated under Erzurum, Türkiye conditions in 2022 and 2023 years. Variance analysis of the main factors shows a highly significant effect on whole traits but in two-way interactions some of the traits were significant and in the three-way interactions, it was insignificant. As an average of years, the number of plants per hectare at the harvest varied between 92,307 (35 × 23 cm) and 120,444 (70 × 15 cm) according to the planting geometries. The highest marketable ear number per hectare (107,456), marketable ear yield (24,887 kg ha−1), and fresh kernel yield (19,493 kg ha−1) were obtained from the 40 × 21 cm planting geometry. The results showed that the variety Khan F1 grown at 40 × 21 cm planting geometry obtained the highest marketable ear number (112,472), marketable ear yield (29,788 kg ha−1), and fresh kernel yield (22,432 kg ha−1). The plant density was positively correlated with marketable ear number (r = 0.904 **), marketable ear yield (r = 0.853 **), and fresh kernel yield (r = 0.801 **). The differences among the varieties were significant for the studied traits, except for plant density and kernel number per ear. In conclusion, the variety Khan F1 should be grown at the 40 × 21 cm planting geometry to maximize yields under study area conditions without water and nutrient limitations.
Genotype-Trait (GT) Biplot Analysis for Yield and Quality Stability in Some Sweet Corn (Zea mays L. saccharata Sturt.) Genotypes
A strong statistical method for investigating the correlations between traits, assessing genotypes based on numerous traits, and finding individuals who excel in particular traits is genotype–trait (GT) biplot analysis. The current study was applied to evaluate 11 sweet corn (Zea mays L. saccharata) genotypes and correlate them based on genotype–trait (GT) biplot analysis for two cropping seasons in Erzurum, Türkiye using the RCBD experimental design with three reputations. The results showed that the genotypes were significantly different for the majority of the examined variables according to the combined analysis of variance findings at 0.01 probability level. An ecological analysis was performed to evaluate sweet corn varieties and environmental conditions and interactions between them (genotype × environmental conditions). Our results showed that the summation of the first two and second main components was responsible for 73.51% of the combined cropping years of the sweet corn growth and development variance, demonstrating the biplot graph’s optimum relative validity, which was obtained. In this study, the Khan F1 (G6) genotype was found to be the stablest genotype, and the Kompozit Seker (G7) genotype was the non-stable genotype, moreover based on the first cropping year, second cropping year, and the average mean of the two cropping years. As a conclusion, the Khan F1 (G6) genotype is the highest-yielding genotype, and the Kompozit Seker (G7) is the lowest. Based on the heat map dendrogram, the context of the differential extent of trait association of all genotypes into two clusters is indicated. The highest genetic distance was shown between the BATEM Tatlı (G3) and Febris (G5) genotypes. Our results provide helpful information about the sweet corn genotypes and environments for future breeding programs.
Enhancing Acclimatization of Micropropagated Pistachio Through Optimization of Light Spectrum and Vapor Pressure Deficit
The light spectrum and vapor pressure deficit (VPD) are key environmental factors that significantly influence the morphophysiological development and survival of micropropagated woody plants during acclimatization. However, few studies have focused on their interactive effects under ex vitro conditions. This study examined the combined effects of four light spectra (white, blue, red, and red–blue) and two VPD levels (low: 0.2 kPa; high: 1.0 kPa) on growth, photosynthesis pigments, biochemical indices, and leaf temperature of Pistacia spp. ‘UCB1’ plantlets over a 30-day acclimatization period. The results demonstrated that red–blue light under low VPD significantly enhanced plantlet performance across multiple parameters, resulting in the highest leaflet number (79.25 pieces), stem diameter (2.13 mm), leaf dry weight (0.048 g), leaf fresh weight (0.15 g), root length (1.48 cm), and leaf area (103.3 cm2). Furthermore, this treatment markedly increased anthocyanin, total soluble carbohydrate content, and photosynthetic pigments (chlorophyll a, chlorophyll b, and carotenoids). Principal component and correlation analyses identified that red–blue light under low VPD was strongly associated with traits linked to growth and photosynthetic ability, whereas blue and white light under high VPD showed the weakest responses. Entropy-weighted TOPSIS ranked red–blue light under low VPD as the most effective treatment for balanced morpho-physiological functions during acclimatization. These findings highlight the importance of optimizing spectral quality and VPD to enhance autotrophic transition and ex vitro establishment in pistachio plantlets. These findings are important for improving ex vitro survival and large-scale propagation efficiency of micropropagated pistachio plantlets.
Genetic Diversity and Population Structure in Bread Wheat Germplasm from Türkiye Using iPBS-Retrotransposons-Based Markers
This study investigated the genetic diversity and population structure of 63 genotypes from Turkish bread wheat germplasm using iPBS-retrotransposons primers. The thirty-four iPBS primers produced a total of 1231 polymorphic bands, ranging from 8 (iPBS-2375) to 60 (iPBS-2381) alleles per marker, with an average number of 36.00 alleles. The polymorphism information content (PIC) per marker varied between 0.048 (iPBS 2087) and 0.303 (iPBS 2382), with an average of 0.175. The numbers of effective alleles (ne), genetic diversity of Nei (h), and Shannon’s information index (I) value were calculated as 1.157, 0.95, and 0.144, respectively. The greatest genetic distance (0.164) was between Eastern Anatolia Agricultural Research Institute genotypes and Çukurova Agricultural Research Institute genotypes. The unweighted pair-group method with arithmetic mean (UPGMA) dendrogram placed the 63 wheat genotypes into three clusters. The percentage of genetic diversity explained by each of the three main coordinates of the basic coordinate analysis was determined to be 44.58, 12.08, and 3.44, respectively. AMOVA (Analysis of Molecular Variance) showed that the variation within populations was 99% and that between populations was 1%. The result of genetic structure analysis suggests that the greatest value of K was calculated as 3. The F-statistic (Fst) value was determined as 0.4005, 0.2374, and 0.3773 in the first to third subpopulations, respectively. Likewise, the expected heterozygosity values (He) were determined as 0.2203, 0.2599, and 0.2155 in the first, second, and third subpopulations, respectively. According to the information obtained in the study, the most genetically distant genotypes were the G1 (Aksel 2000) and G63 (Karasu 90) genotypes. This study provided a deep insight into genetic variations in Turkish bread wheat germplasm using the iPBS-retrotransposons marker system.
Absorpcja wielopierścieniowych węglowodorów aromatycznych (WWA) przez produkty spożywcze podczas wędzenia
Correction: Helal et al. Improving Yield Components and Desirable Eating Quality of Two Wheat Genotypes Using Si and NanoSi Particles under Heat Stress. Plants 2022, 11, 1819
In the original publication [...]
Effect of Selenium Enriched Yeast Culture Saccharomyces Cerevisiae Supplementation in TMR of Pregnant Heifers and Cows on the Colostrum Quality
Prediction of Potato (Solanum tuberosum L.) Yield Based on Machine Learning Methods
This research delves into the application of machine learning methods for predicting the yield of potato varieties used for French fries in Poland. By integrating a comprehensive dataset comprising agronomical, climatic, soil, and satellite-based vegetation data from 36 commercial potato fields over five growing seasons (2018–2022), we developed three distinct models: non-satellite, satellite, and hybrid. The non-satellite model, relying on 85 features, excludes vegetation indices, whereas the satellite model includes these indices within its 128 features. The hybrid model, combining all available features, encompasses a total of 165 features, presenting the most-comprehensive approach. Our findings revealed that the hybrid model, particularly when enhanced with SVM outlier detection, exhibited superior performance with the lowest Mean Absolute Percentage Error (MAPE) of 5.85%, underscoring the effectiveness of integrating diverse data sources into agricultural yield prediction. In contrast, the non-satellite and satellite models displayed higher MAPE values, indicating less accuracy compared to the hybrid model. Advanced data-processing techniques such as PCA and outlier detection methods (LOF and One-Class SVM) played a pivotal role in model performance, optimising feature selection and dataset refinement. The study concluded that machine learning methods, particularly when leveraging a multifaceted approach involving a wide array of data sources and advanced processing techniques, can significantly enhance the accuracy of agricultural yield predictions. These insights pave the way for more-efficient and -informed agricultural practices, emphasising the potential of machine learning in revolutionising yield prediction and crop management.
Genetic Characterization and Agronomic Evaluation of Drought Tolerance in Ten Egyptian Wheat (Triticum aestivum L.) Cultivars
This investigation was carried out for genetic characterization and determination of drought tolerance of ten Egyptian cultivars of wheat (Triticum aestivum L.), namely Misr 1, Misr 2, Gemmiza 9, Gemmiza 10, Gemmiza 11, Gemmiza 12, Shandawel 1, Giza 168, Giza 171, and Sids 14. These cultivars were grown in two winter seasons: 2018/2019 and 2019/2020 at the experimental farm Fac. of Agric., Suez Canal Univ., Ismailia, Egypt, under two watering regimes: normal (100%) and stress (50% FC) conditions. Six agronomic traits and five tolerance indices, namely stress tolerance (TOL), mean productivity (MP), geometric mean productivity (GMP), yield stability index (YSI), and drought susceptibility index (DSI), were used to evaluate the impact of drought stress. The results reflected Giza 171, Misr 2, and Giza 168 as precious germplasm for breeding of high-yielding drought-tolerant wheat. A highly significant positive correlation was recorded between yield under normal and stress conditions on the one hand and each of MP and GMP on the other hand. In addition, YSI appeared engaged in a highly significant positive correlation with yield under drought conditions only. TOL and DSI appeared insignificantly correlated with yield. Therefore, MP and GMP were reflected as the first runners among indices suitable to distinguish the high-yielding cultivars under drought conditions. At the molecular level, five primers of Start Codon Targeted (SCoT) markers were able to resolve and characterize the studied cultivars, which reflected SCoT as a potent gene-targeting molecular marker, able to characterize and resolve genetic diversity in wheat at the cultivar level using few primers. Therefore, SCoT is a time-efficient molecular marker, and it can efficiently replace indices in characterization of drought-tolerant genotypes with a high confidence level and reasonable cost.
New Developments in Smart Farming Applied in Sustainable Agriculture
Sustainable agriculture aims to increase agricultural productivity while minimising negative environmental impacts [...]
Retrieval of Cu2+ and Cd2+ ions from aqueous solutions using sustainable guar gum/PVA/montmorillonite nanocomposite films: effect of temperature and adsorption isotherms
Uncontrolled or improperly managed wastewater is considered toxic and dangerous to plants, animals, and people, as well as negatively impacting the ecosystem. In this research, the use of we aimed to prepare polymer nanocomposites (guar gum/polyvinyl alcohol, and nano-montmorillonite clay) for eliminating heavy metals from water-based systems, especially Cu2+ and Cd2+ ions. The synthesis of nanocomposites was done by the green method with different ratios of guar gum to PVA (50/50), (60/40), and (80/20) wt%, in addition to glycerol that acts as a cross-linker. Fourier-transform infrared spectroscopy (FT-IR) analysis of the prepared (guar gum/PVA/MMT) polymeric nano-composites’ structure and morphology revealed the presence of both guar gum and PVA’s functional groups in the polymeric network matrix. Transmission electron microscopy (TEM) analysis was also performed, which verified the creation of a nanocomposite. Furthermore, theromgravimetric analysis (TGA) demonstrated the biocomposites’ excellent thermal properties. For those metal ions, the extreme uptake was found at pH 6.0 in each instance. The Equilibrium uptake capacities of the three prepared nanocomposites were achieved within 240 min. The maximal capacities were found to be 95, 89 and 84 mg/g for Cu2+, and for Cd2+ were found to be 100, 91, 87 mg/g for guar gum (80/20, 60/40 and 50/50), respectively. The pseudo-2nd-order model with R2 > 0.98 was demonstrated to be followed by the adsorption reaction, according to the presented results. In less than 4 hours, the adsorption equilibrium was reached. Furthermore, a 1% EDTA solution could be used to revitalize the metal-ion-loaded nanocomposites for several cycles. The most promising nanocomposite with efficiency above 90% for the removal of Cu2+ and Cd2+ ions from wastewater was found to have a guar (80/20) weight percentage, according to the results obtained.
Sodium Azide as a Chemical Mutagen in Wheat (Triticum aestivum L.): Patterns of the Genetic and Epigenetic Effects with iPBS and CRED-iPBS Techniques
Wheat, which is scientifically known as Triticum aestivum L., is a very nutritious grain that serves as a key component of the human diet. The use of mutation breeding as a tool for crop improvement is a reasonably rapid procedure, and it generates a variety that may be used in selective breeding programs as well as functional gene investigations. The present experiment was used to evaluate the potential application of a conventional chemical mutagenesis technique via sodium azide (NaN3) for the germination and seedling growth stage in wheat. Experiments with NaN3 mutagenesis were conducted using four different treatment periods (0, 1, 2, and 3 h) and five different concentrations (0, 0.5, 1, 1.5, and 2 mM). The genomic instability and cytosine methylation of wheat using its seeds were investigated after they were treated. In order to evaluate the genomic instability and cytosine methylation in wheat that had been treated, interprimer binding site (iPBS) markers were used. The mutagenic effects of NaN3 treatments had considerable polymorphism on a variety of impacts on the cytosine methylation and genomic instability of wheat plants. The results of the experiment showed considerable changes in the iPBS profiles produced by the administration of the same treatments at different dosages and at different times. Coupled restriction enzyme digestion interprimer binding site (CRED-iPBS) assays identified changes in gDNA cytosine methylation. The highest polymorphism value was obtained during 1 h + 2 mM NaN3, while the lowest (20.7%) was obtained during 1 h + 1.5 mM NaN3. Results showed that treatments with NaN3 had an effect on the level of cytosine methylation and the stability of the genomic template in wheat plants in the germination stage. Additionally, an integrated method can be used to for mutation-assisted breeding using a molecular marker system in wheat followed by the selection of desired mutants.
Impact of Smoking Technology on the Quality of Food Products: Absorption of Polycyclic Aromatic Hydrocarbons (PAHs) by Food Products during Smoking
The food industry is striving for a sustainable development of thermal food processing. Smoking is an example of a process that has grown in popularity in recent years. There is a lack of systematic knowledge in the literature regarding this undervalued process, so the purpose of this review is to analyze the state of knowledge about the methods and technologies of smoking food products and their impact on changing the quality of essential food products. Therefore, a comprehensive review of the literature on smoking processes from the past two decades was conducted. The most essential components absorbed from smoke during smoking are polycyclic aromatic hydrocarbons (PAHs). In the present work, 24 PAHs are summarized, and the capability of 12 food products to absorb them is described. Analysis of the principal components of absorbed PAHs showed that some products from different groups exhibit a similar ability to absorb these compounds, mainly influenced by their physical properties. The pre-treatment practices of raw materials before smoking, the smoking raw materials used, and their quality parameters were characterized (along with the effects of smoking methods on selected product groups: fish, meats, and cheeses). In addition, the gap in research concerning the absorption of other components of smoke, e.g., phenols, alcohols, ketones, and aldehydes, which directly impact food quality, is indicated.
Prediction of Pea (Pisum sativum L.) Seeds Yield Using Artificial Neural Networks
A sufficiently early and accurate prediction can help to steer crop yields more consciously, resulting in food security, especially with an expanding world population. Additionally, prediction related to the possibility of reducing agricultural chemistry is very important in an era of climate change. This study analyzes the performance of pea (Pisum sativum L.) seed yield prediction by a linear (MLR) and non-linear (ANN) model. The study used meteorological, agronomic and phytophysical data from 2016–2020. The neural model (N2) generated highly accurate predictions of pea seed yield—the correlation coefficient was 0.936, and the RMS and MAPE errors were 0.443 and 7.976, respectively. The model significantly outperformed the multiple linear regression model (RS2), which had an RMS error of 6.401 and an MAPE error of 148.585. The sensitivity analysis carried out for the neural network showed that the characteristics with the greatest influence on the yield of pea seeds were the date of onset of maturity, the date of harvest, the total amount of rainfall and the mean air temperature.
Comparative Evaluation of CNN and Transformer Architectures for Flowering Phase Classification of Tilia cordata Mill. with Automated Image Quality Filtering
Understanding and monitoring the phenological phases of trees is essential for ecological research and climate change studies. In this work, we present a comprehensive evaluation of state-of-the-art convolutional neural networks (CNNs) and transformer architectures for the automated classification of the flowering phase of Tilia cordata Mill. (small-leaved lime) based on a large set of real-world images acquired under natural field conditions. The study introduces a novel, automated image quality filtering approach using an XGBoost classifier trained on diverse exposure and sharpness features to ensure robust input data for subsequent deep learning models. Seven modern neural network architectures, including VGG16, ResNet50, EfficientNetB3, MobileNetV3 Large, ConvNeXt Tiny, Vision Transformer (ViT-B/16), and Swin Transformer Tiny, were fine-tuned and evaluated under a rigorous cross-validation protocol. All models achieved excellent performance, with cross-validated F1-scores exceeding 0.97 and balanced accuracy up to 0.993. The best results were obtained for ResNet50 and ConvNeXt Tiny (F1-score: 0.9879 ± 0.0077 and 0.9860 ± 0.0073, balanced accuracy: 0.9922 ± 0.0054 and 0.9927 ± 0.0042, respectively), indicating outstanding sensitivity and specificity for both flowering and non-flowering classes. Classical CNNs (VGG16, ResNet50, and ConvNeXt Tiny) demonstrated slightly superior robustness compared to transformer-based models, though all architectures maintained high generalization and minimal variance across folds. The integrated quality assessment and classification pipeline enables scalable, high-throughput monitoring of flowering phases in natural environments. The proposed methodology is adaptable to other plant species and locations, supporting future ecological monitoring and climate studies. Our key contributions are as follows: (i) introducing an automated exposure-quality filtering stage for field imagery; (ii) publishing a curated, season-long dataset of Tilia cordata images; and (iii) providing the first systematic cross-validated benchmark that contrasts classical CNNs with transformer architectures for phenological phase recognition.
Energy deprivation to financial prosperity: Unveiling multidimensional energy Poverty's influence
Agronomic Performance of Rainfed Barley Genotypes under Different Tillage Systems in Highland Areas of Dryland Conditions
Conservation agriculture (CA) is becoming increasingly attractive to farmers due to advantages such as lower production costs and less destruction of soil structures compared to the conventional tillage. The cultivars introduced for the conventional systems may not be suitable under CA environments, and newly adapted cultivars need to be developed. Accordingly, four separate field experiments were conducted over two cropping seasons (2018–2019 and 2019–2020) to study the agronomic performance of seven barley genotypes under three tillage systems: conventional tillage (full tillage with residue removed), reduced tillage (chisel plowing with residue retained) and CA system (no tillage with residue retained on soil surface). The genotypes were grown under rainfed conditions in two different agro-ecological regions (Kamyaran and Hosseinabad locations) in the west of Iran. Significant genotypic differences were observed for grain yield and yield components except 1000-kernel weight. The results of this study showed that rainfed barley genotypes under a CA system produced yields equal to, or better (0.7%) than, the conventional tillage; while reduced tillage system decreased their performance by 4.9%. Regarding genotype × tillage interaction, the barley genotypes Catalhuyuk 2001 and Bulbule positively interacted with conventional tillage and showed higher performance than other genotypes, whereas genotypes Çumra 2001, Ansar and Abidar expressed highest performance under CA system. Consequently, genotypes Bulbule, Catalhuyuk 2001 and Gumharriyet 50 outperformed the domestic performance and the amount of grain yield and showed the highest adaptation to the tested environments. The results of the present study could be useful to improve the efficiency of a CA system in rainfed cultivation of barley and open new windows for the cereal production in arid and semi-arid regions with food security concerns.
Improving Yield Components and Desirable Eating Quality of Two Wheat Genotypes Using Si and NanoSi Particles under Heat Stress
Global climate change is a significant challenge that will significantly lower crop yield and staple grain quality. The present investigation was conducted to assess the effects of the foliar application of either Si (1.5 mM) or Si nanoparticles (1.66 mM) on the yield and grain quality attributes of two wheat genotypes (Triticum aestivum L.), cv. Shandweel 1 and cv. Gemmeiza 9, planted at normal sowing date and late sowing date (heat stress). Si and Si nanoparticles markedly mitigated the observed decline in yield and reduced the heat stress intensity index value at late sowing dates, and improved yield quality via the decreased level of protein, particularly glutenin, as well as the lowered activity of α-amylase in wheat grains, which is considered a step in improving grain quality. Moreover, Si and nanoSi significantly increased the oil absorption capacity (OAC) of the flour of stressed wheat grains. In addition, both silicon and nanosilicon provoked an increase in cellulose, pectin, total phenols, flavonoid, oxalic acid, total antioxidant power, starch and soluble protein contents, as well as Ca and K levels, in heat-stressed wheat straw, concomitant with a decrease in lignin and phytic acid contents. In conclusion, the pronounced positive effects associated with improving yield quantity and quality were observed in stressed Si-treated wheat compared with Si nanoparticle-treated ones, particularly in cv. Gemmeiza 9.
Approaches and Challenges in Machine Learning for Monitoring Agricultural Products and Predicting Plant Physiological Responses to Biotic and Abiotic Stresses
Machine Learning Analysis of the Impact of Silver Nitrate and Silver Nanoparticles on Wheat (Triticum aestivum L.): Callus Induction, Plant Regeneration, and DNA Methylation
The objective of this study was to comprehend the efficiency of wheat regeneration, callus induction, and DNA methylation through the application of mathematical frameworks and artificial intelligence (AI)-based models. This research aimed to explore the impact of treatments with AgNO3 and Ag-NPs on various parameters. The study specifically concentrated on analyzing RAPD profiles and modeling regeneration parameters. The treatments and molecular findings served as input variables in the modeling process. It included the use of AgNO3 and Ag-NPs at different concentrations (0, 2, 4, 6, and 8 mg L−1). The in vitro and epigenetic characteristics were analyzed using several machine learning (ML) methods, including support vector machine (SVM), random forest (RF), extreme gradient boosting (XGBoost), k-nearest neighbor classifier (KNN), and Gaussian processes classifier (GP) methods. This study’s results revealed that the highest values for callus induction (CI%) and embryogenic callus induction (EC%) occurred at a concentration of 2 mg L−1 of Ag-NPs. Additionally, the regeneration efficiency (RE) parameter reached its peak at a concentration of 8 mg L−1 of AgNO3. Taking an epigenetic approach, AgNO3 at a concentration of 2 mg L−1 demonstrated the highest levels of genomic template stability (GTS), at 79.3%. There was a positive correlation seen between increased levels of AgNO3 and DNA hypermethylation. Conversely, elevated levels of Ag-NPs were associated with DNA hypomethylation. The models were used to estimate the relationships between the input elements, including treatments, concentration, GTS rates, and Msp I and Hpa II polymorphism, and the in vitro output parameters. The findings suggested that the XGBoost model exhibited superior performance scores for callus induction (CI), as evidenced by an R2 score of 51.5%, which explained the variances. Additionally, the RF model explained 71.9% of the total variance and showed superior efficacy in terms of EC%. Furthermore, the GP model, which provided the most robust statistics for RE, yielded an R2 value of 52.5%, signifying its ability to account for a substantial portion of the total variance present in the data. This study exemplifies the application of various machine learning models in the cultivation of mature wheat embryos under the influence of treatments and concentrations involving AgNO3 and Ag-NPs.
